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title: 'The Priorities of End Users of Emergency Department Electronic Health Records:
Modified Delphi Study'
journal: JMIR Human Factors
year: 2023
pmcid: PMC10039402
doi: 10.2196/43103
license: CC BY 4.0
---
# The Priorities of End Users of Emergency Department Electronic Health Records: Modified Delphi Study
## Abstract
### Background
The needs of the emergency department (ED) pose unique challenges to modern electronic health record (EHR) systems. A diverse case load of high-acuity, high-complexity presentations, and ambulatory patients, all requiring multiple transitions of care, creates a rich environment through which to critically examine EHRs.
### Objective
This investigation aims to capture and analyze the perspective of end users of EHR about the strengths, limitations, and future priorities for EHR in the setting of the ED.
### Methods
In the first phase of this investigation, a literature search was conducted to identify 5 key usage categories of ED EHRs. Using key usage categories in the first phase, a modified Delphi study was conducted with a group of 12 panelists with expertise in both emergency medicine and health informatics. Across 3 rounds of surveys, panelists generated and refined a list of strengths, limitations, and key priorities.
### Results
The findings from this investigation highlighted the preference of panelists for features maximizing functionality of basic clinical features relative to features of disruptive innovation.
### Conclusions
By capturing the perspectives of end users in the ED, this investigation highlights areas for the improvement or development of future EHRs in acute care settings.
## Introduction
Modern electronic health record (EHR) systems face difficulties meeting the unique needs of the emergency department (ED) [1-3]. High volumes of patients through the ED drive documentation burden; high-acuity cases demand efficient deployment of care measures; diagnostic uncertainty increases the need for clinical decision support tools; and the interdisciplinary, collaborative environment drives a need for EHRs to support efficient transitions of care [4]. In addition to these challenges, changes to the field of emergency medicine over the last several decades increase the need for highly efficient and capable information systems. As the complexity of patient’s presentations to the ED increases, measures of departmental crowding rise [5]. Complexity and nuance to treatment plans further increase need to leverage digital health tools in the management of complex patients to improve clinical decision-making and patient outcomes, albeit with increasing complexity of our digital systems [6-8]. The current COVID-19–mediated health human resource crisis has only exacerbated these challenges.
The International Standards Organization defines usability as “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use” [9]. In the context of the ED, the specified goals of end users of an EHR may take on a variety of perspectives, given the different demands of this clinical space. A study evaluating the user-centered design principles of 11 EHR developers found that more than half of the developers had limited to inadequate interactions with clinicians in the development process of their products [10]. Despite the complexity of the unique needs of an ED EHR, there is a gap in the literature examining the perspective of the end user in an emergency medicine setting.
Delphi methods are a validated survey method to establish consensus opinion from a panel of experts [11]. The traditional Delphi process involves 3 rounds of information gathering: an initial round consisting of open-ended, qualitative questions followed by 2 rounds of Likert-scale rankings that allow for relative prioritization [11]. This process may be modified by introducing an initial set of parameters to narrow the scope of discussion [12-14]. A modified Delphi method offers the benefit of allowing focused discussion around specific attributes of a given problem. Delphi methods are unique in their ability to handle mixed types of information, both qualitative and quantitative in nature. They have previously been employed in the emergency medicine setting across several areas of investigation: investigating role definition of allied health team members, the development of violence screening criteria, the establishment of violence reduction strategies, and the selection of key performance indicators [15-18]. Delphi methods offer a validated method of synthesizing diverse perspectives about the current state and future improvements to ED EHRs.
To support hospital systems and practitioners develop future procurement criteria, and prioritize modifications, additions, or upgrades to their existing EHRs, we completed a systematic assessment of end user needs and priorities in the ED. This study aims to understand the nuances of perspective in physician end users regarding the ideal ED EHR.
## Identification of Key Usage Categories
In phase 1 of our study, 2 independent reviewers completed review of academic literature on MEDLINE to build a list of usage categories of EHR. The reviewers also searched gray literature through web-based hand searches for topics related to information systems in acute care settings. After an iterative review of literature relating to both emergency medicine settings and EHR, 5 usage categories were developed inductively by the 2 reviewers. The findings were discussed with a working group comprised of 4 investigators with expertise in emergency medicine, health systems, and health informatics. The working group came to an agreement about 5 proposed key usage categories that were inputted into phase 2 to narrow the focus of discussion.
## Establishing Group Consensus Through Delphi Methods
Phase 2 used Delphi methods that involved sequential rounds of survey and data dissemination to experts in both emergency medicine and information systems regarding their perspectives on each of the 5 usage categories. Recruitment of expert panelists was done through purposive sampling beginning with 4 investigators identifying candidates with expertise in both the clinical environment of the ED and health informatics at 6 tertiary- and quaternary-care centers across southwestern Ontario, including 3 level 1 trauma centers. Subsequently, the identified candidates were also invited to provide information on other potential informants. In total, 12 expert panelists were recruited across several hospital systems with extensive experience in both emergency medicine and health information systems. The panelists were spread across 3 separate disciplines (7 of 12 in emergency medicine, 3 of 12 in pediatrics emergency medicine, and 2 of 12 in general internal medicine). Several panelists held multiple leadership roles in their departments, with 4 of 12 acting as either chief or deputy chief, 7 of 12 acting as department lead across roles in quality and safety, virtual care, artificial intelligence and machine learning, and quality improvement. Several panelists also performed adjacent clinical duties with 4 of 12 serving as Trauma Team Leaders. Two panelists also fulfilled C-level positions at their respective hospital systems for roles in medical informatics. All panelists were associated with the University of Toronto in teaching and academic roles.
The Delphi study was conducted in 3 rounds of surveys [11]. Survey administration was conducted using the Research Electronic Data Capture (REDCap 12.0.29) tools hosted at the University of Toronto [19,20]. To reduce bias in both survey responses and response analysis, the identity of all panelists was kept anonymous through the Delphi rounds. Panelists and investigators were unaware of the identity of panelist’s responses and panelists were not aware of the identity of other members of the Delphi panel until the conclusion of the study. The analysis of outputs from each round was conducted by 2 independent reviewers and consensus was established before circulation of findings to panelists between each round.
The first-round survey involved qualitative information gathering through free-text responses. Free-text responses were analyzed using NVivo (NVivo Version 12). First, responses were coded deductively, using usage categories defined in Phase I of the study. Second, sentiment coding was performed by NVivo’s sentiment analysis with manual adjustment and necessary recoding based on consensus by the 2 independent reviewers. Outputs were circulated to panelists for review. The second-round survey gathered quantitative information on the perceived importance of first-round outputs using Likert scales and qualitative free-text responses about areas of disagreement from first-round responses. The quantitative outputs from the second-round survey were analyzed using Microsoft Excel (MSO Version 2205; Microsoft Inc) to generate descriptive statistics around measured variables and the qualitative outputs from the second-round survey were circulated to the panelists [21]. The third-round survey focused on establishing a ranked list of priorities based on the second-round outputs with the highest perceived importance resulting in a ranked list of priorities for each usage category.
## Ethical Considerations
Phase 2 received the approval of the Research Ethics Board through the University of Toronto (protocol #00040996).
## Results
In total, the perspectives captured by the expert panel spanned 6 separate hospital sites and 5 separate EHRs. Across all 3 rounds of survey, there was full retention of the original cohort of 12 expert panelists with no loss to follow-up between rounds. By using 5 key usage categories established by the working group members in phase 1 of the project (Table 1), the first round of surveys gathered free-form responses about the current needs of each category and generated a list of 10 features per usage category for a total of 50 features. Through the second-round survey, the panelists narrowed down the list to 25 features across key usage categories. Finally, in the third round of the survey, the panelists prioritized the top 5 features in each usage category relative to one another, for a total of 25 priorities (Textbox 1). Analysis of free-text responses produced statements of strengths and weaknesses for each category (Table 2). Several panelists raised ideas that may fall under the term of potential disruptive innovation, defined by Clayton Christensen as, “an innovation that makes things simpler and more affordable, and ‘technology’ is a way of combining inputs of materials, components, information, labor, and energy into outputs of greater value” [22]. Based on the priorities defined in Textbox 1 and the free-text responses by Table 2, possible features and innovations have been mapped to a typical journey through the ED, as a conceptualization of what an EHR may look like with these suggestions implemented (Figure 1).
## Category I: Information Input
Overall, it was found that panelists preferred that current EHRs improve on existing capabilities before trying to tackle potential disruptive innovations [22]. Panelists specifically listed and ranked digital ambient scribes, which process information from a patient–physician interview into a note in an attempt to reduce documentation burden, and auto-population of documentation from other sources of clinical information, lower than basic functionality such as multiauthor documentation and support for documentation of other forms of media. As strength, it was found that panelists thought that EHRs have streamlined the collation and standardization of information. A limitation of current information input capabilities of EHRs is the lack of support for multiauthor documentation, increasing the need to repeatedly gather, and document redundant information that has already been collected by other members of the patient’s care team. This drives documentation burden and creates inefficiencies.
## Category II: Digital Health Tools
It was largely believed by panelists that human factors limit the implementation of digital health tools such as machine learning algorithms that provide clinical decision support, as opposed to the technical capacities of the current EHRs. Furthermore, the priorities list shows that panelists prioritize tools supporting clinicians in acute care settings such as identifying high-risk patients as opposed to pulling previous information from other sources such as previous charts or clinical portals. Panelists mostly expressed that EHRs have streamlined the ability to conduct repetitive, previously tedious tasks. However, they state that innovation requires large amounts of coordination and health human resources, so while the potential may exist for implementation, there may not be the current appetite or means to sustain this change.
## Category III: Usability
The priorities of end users in this category saw 2 sentiments of thought, which first may seem conflicting. On the one hand, there was an interest in having increased customizability options within ED EHRs, such as the enablement of customization of quick picks and inbox management. However, there was also the argument for adaptation on the part of the end user to the features and limitations of the EHR. Overall, panelists believed that EHRs have increased standardization of care delivery through order sets that are vetted by central decision support teams, ensuring that orders are up to current care standards. However, in their current form, EHRs are limited in the customization options that they provide for their end users, even with respect to personal workflow features such as inbox task management, or “look and feel” customizations such as the layout of a given dashboard.
## Category IV: Clinical Workflow
Panelists again prioritized basic functionality (ie, discharge planning, interdisciplinary communication) as opposed to disruptive innovation. Although EHRs have increased ease of collaboration among teams in the ED through collation of documentation from triage, panelists still raised concerns around the limitations of interoperability between hospital systems and other systems such as primary care EHRs. Additionally, even within a single-hospital system, it was found to be difficult to communicate with other services that did not use the same EHR or charting method (ie, different clinical systems or paper charting).
## Category V: Research and Data Analytics
Overall, panelists express that there was limitation with fluid access and usability of information. An undeniable strength of the EHR is that it has augmented the ability to collect, store, and access structured data. However, panelists identified that the ability to access the data in a meaningful way is still limited due to the format of stored data. Although it is possible to access volumes of information, the standardization of information input is lacking, such that any information sought for research purposes will still require manual recoding. Suggestions in this realm included improving drop-down menus to provide standardization of documentation input.
## Principal Results
The key usage categories developed in our investigation and the panelists’ priorities determined by Delphi outputs span several steps of a patient’s journey through the ED (Figure 1). These priorities highlight the balancing act that must occur in each usage category with the development and deployment of ED EHRs. With respect to information input, support for multiauthor documentation helps to reduce redundancy of information gathering and input, and support for innovations helps reduce documentation burden. With respect to digital health tools, improved governance structures could support the development and deployment of innovations that may aid in decision-making. With respect to usability, an optimized EHR for the ED would have customizability options for workflow and maintain strong standardization for deployment of care, such as order sets. With respect to clinical workflow support for communication beyond the hospital helps to ensure efficient and safe patient discharges, while consolidated information systems ensure efficient access to conducted investigations. With respect to research and data analytics, improved accessibility allows for more contribution from end users with respect to the development of new knowledge and useful clinical insights.
In Gawande’s [46] article titled, “Why Doctors Hate Their Computers,” Gawande writes of EHRs: “I’ve come to feel that a system that promised to increase my mastery over my work has, instead, increased my work’s mastery over me.” His assertion mainly centers around the collection of large amounts of unused information from a patient–physician encounter, which drives documentation burden and decreases patient–physician interaction time. Previous studies have estimated that the ED physicians may spend as much as $25\%$ of the total time caring for a single patient on documentation [47]. Aligned with the previous literature and clinical experiences of documentation burden, Gawande highlights a key issue where EHRs can decrease efficiency and become a burden rather than a valuable tool.
These concerns are aligned with the findings from this investigation, with panelists broadly prioritizing functionality over disruptive innovation, and issues such as interoperability and the reduction of documentation burden being prioritized across several usage categories. For example, with respect to information input, support for multiauthor documentation and picture integration was prioritized over features such as digital ambient scribes or population from past documentation. Another example is seen in panelists’ priorities with respect to clinical workflow, where panelists prioritized discharge communication methods over auto-population of patient information from previous documentation. Panelists were sampled from a variety of care settings employing several different EHRs at each site, suggesting that no single EHR vendor comprehensively captures the priorities identified in this investigation.
By examining the discrepancies between the identified priorities of panelists and the qualitative responses of strengths and limitations, it is possible to identify areas for impactful improvements. For example, with respect to digital health tools, streamlined governance structures were identified as both a top priority (Textbox 1) and listed as a limitation (Table 2). Another usage category that demonstrated this was in research and data analytics, where panelists identified streamlined governance structures and increased role-based access as priorities (Textbox 1) and identified privacy as a limiting factor for gathering information (Table 2). Integrating this information identifies areas of high priority and can potentially inform prioritization of where system administrators can best optimize their own EHRs or build evidence-informed criteria in future acquisitions.
Compared to the deployment of Delphi methods in other emergency medicine clinical questions, the modifications to the process of this investigation optimized for depth of discussion in defined usage categories. The specific modification to the traditional process entailed defining the 5 usage categories through literature review which subsequently served as inputs to the Delphi model. Other investigations either integrate the literature review as one of the 3 traditional rounds or rely on free-text responses as a means to providing a focus of discussion [16,17]. A trade-off of the selected modification is that it prevents panelists from suggesting their own mental schema of usage categories of EHRs; however, this trade-off was made to achieve a deeper understanding of priorities within discrete categories. An additional benefit of a preliminary literature review is that focused discussion ensured concrete outputs from each round, which may have contributed to the complete retention of panelists across the 3 rounds of the Delphi process. Overall, through a preliminary literature review and a Delphi process with narrow targets based on prior inputs, the modified Delphi method strikes an appropriate balance between breadth and depth in the examination of ED EHRs.
## Limitations
One potential limitation to this study is the generalizability of findings. Panelists are familiar with both ED care settings and health informatics in tertiary-care hospitals in southern Ontario, all with enterprise-wide deployments of their hospital EHR. This may lead to panelist-specific prioritization of other clinically adjacent activities such as academic research or data organization. Subspecialty interests may introduce additional variance to captured perspectives. Furthermore, this investigation focused on capturing the perspectives of physicians as the end user, which does not capture the perspectives of other disciplines that engage with an ED EHR.
## Conclusions
Improving EHRs to effectively meet the unique priorities of the ED demands a thorough understanding of the priorities of end users. A modified Delphi approach allows an in-depth analysis of perspectives of expert panelists in discretely defined usage categories. Capturing the perspectives of an expert panel from tertiary and quaternary care centers across Southwestern Ontario and served by diverse EHR vendors, the findings of this study highlight end-user prioritization of functionality over disruptive innovation. At a provider level, these findings will lead to meaningful reflection and discussions with department leadership about how an EHR can fit local needs. At an institution level, these findings will have implications for choosing future EHRs and adaptation of existing systems. At a developer level, these findings will have further sensitized developers to the preferences of end users in high-acuity settings. The future steps in discussions around EHR improvement should involve gathering the perspectives of allied health professionals who also engage with EHRs and with patients as they are the beneficiaries of improvements to information systems. Furthermore, comparison of perspectives gathered in the ED to perspectives from other areas of the hospital would establish commonalities, common pain points, and enhance our understanding of the information system preferences of end users.
## Data Availability
The data sets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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|
---
title: 'Determinants Influencing the Adoption of Internet Health Care Technology Among
Chinese Health Care Professionals: Extension of the Value-Based Adoption Model With
Burnout Theory'
journal: Journal of Medical Internet Research
year: 2023
pmcid: PMC10039406
doi: 10.2196/37671
license: CC BY 4.0
---
# Determinants Influencing the Adoption of Internet Health Care Technology Among Chinese Health Care Professionals: Extension of the Value-Based Adoption Model With Burnout Theory
## Abstract
### Background
The global COVID-19 pandemic has been widely regarded as a catalyst for adopting internet health care technology (IHT) in China. IHT consists of new health care technologies that are shaping health services and medical consultations. Health care professionals play a substantial role in the adoption of any IHT, but the consequences of doing so can often be challenging, particularly when employee burnout is prevalent. Few studies have explored whether employee burnout influences the adoption intention of IHT in health care professionals.
### Objective
This study aims to explain the determinants influencing the adoption of IHT from the perspective of health care professionals. To do so, the study extends the value-based adoption model (VAM) with consideration for employee burnout as a determining factor.
### Methods
A cross-sectional web-based survey using a sample of 12,031 health care professionals selected through multistage cluster sampling from 3 provinces in mainland China was conducted. The hypotheses of our research model were developed based on the VAM and employee burnout theory. Structural equation modeling was then used to test the research hypotheses.
### Results
The results indicate that perceived usefulness, perceived enjoyment, and perceived complexity positively correlate with perceived value (β=.131, $$P \leq .01$$; β=.638, $P \leq .001$; β=.198, $P \leq .001$, respectively). Perceived value had a positive direct effect on adoption intention (β=.725, $P \leq .001$), perceived risk negatively correlated with perceived value (β=−.083, $P \leq .001$), and perceived value negatively correlated with employee burnout (β=−.308, $P \leq .001$). In addition, employee burnout was negatively related to adoption intention (β=−.170, $P \leq .001$) and mediated the relationship between perceived value and adoption intention (β=.052, $P \leq .001$).
### Conclusions
Perceived value, perceived enjoyment, and employee burnout were the most important determinants of IHT adoption intention by health care professionals. In addition, while employee burnout was negatively related to adoption intention, perceived value inhibited employee burnout. Therefore, this study finds that it is necessary to develop strategies to improve the perceived value and reduce employee burnout, which will benefit the promotion of the adoption intention of IHT in health care professionals. This study supports the use of the VAM and employee burnout in explaining health care professionals’ adoption intention regarding IHT.
## Introduction
During the past several years, the Chinese government has been committed to promoting the use of the internet to provide health care services. Internet health care has experienced several major development phases with the help of the “internet plus” national policy in 2015 [1]. The COVID-19 pandemic brought in a second phase by rapidly catapulting telemedicine into the forefront of health care delivery [2]. These measures have been promoted in optimizing the allocation of medical resources, alleviating the high cost of medical treatment, and promoting the independence and well-being of society. Internet hospitals are a new health care service facilitated by technology. Unlike traditional health services, such hospitals rely on the development of emerging technologies, including cloud computing, big data, and artificial intelligence [3]. These hospitals provide web-based diagnosis and treatment, web-based drug purchase, disease management, and other health care services. A raft of research has been undertaken regarding such developments, with patients’ attitudes and perceptions of internet health care well documented [4-6]. However, as providers of medical services, health care professionals’ understanding and attitudes toward internet technology have been underexplored.
Internet health care technology (IHT) is defined as “a form of new healthcare technologies in terms of professional health services and medical consultations” [7,8]. It represents “noncontact medicine” where instead of meeting in person, health care professionals and patients can exchange information on the internet through text, pictures, voice, and other means. IHT has developed rapidly in recent years with multiple modes of applications, such as telemedicine, teleconsultation, telerehabilitation, internet health care, digital health care, eHealth, and Internet of Medical Things [9-13]. The benefits of IHT include the promotion of resource optimization, the improvement of medical hardware and software, the humanization of medical service models, the proximity of medical costs, and the improvement of the efficiency and accuracy of doctors’ diagnoses. Besides, the IHT represents services provided by health care professionals in general, including doctors, nurses, pharmacists, and others [14].
A growing number of Chinese health care professionals are providing internet health care services via internet hospitals; among them, the majority are full-time employees of Chinese public hospitals [15]. It has been documented how the constant switch between online and offline channels has led to problems with increased workload [16]. For health care professionals, IHT requires them to learn and update new and additional technologies and practices. In addition, employee burnout (BUR) is a prevalent issue, with patients of burned-out health care professionals experiencing more errors and having lower satisfaction with their care [17,18]. Attention to such an area has great importance particularly as physician burnout, characterized by emotional exhaustion or depersonalization, is such a widespread issue [19]. Wen et al [20] reported that $76.9\%$ of Chinese physicians reported burnout symptoms. The COVID-19 pandemic has added new social and job-related factors that increase the risk of burnout in health care professionals worldwide [21-24]. Otherwise, Shah et al [25] found that although teledermatology has become more prevalent as a result of the pandemic, $37\%$ of physicians reported teledermatology as a contributor to their professional burnout. Gardner et al [26] reported that health information technology–related stress in physicians is common and associated with burnout symptoms. Bambe [27] found that increased use of technology could also lead to BUR. Shanafelt et al [28] reported that physicians who used electronic health records or computerized physician entry had higher rates of burnout. Kroth et al [29] showed that data entry requirements, inefficiently designed user interfaces, and information overload brought on by technology were associated with physician stress.
A variety of factors are associated with the adoption of new technology (see Multimedia Appendix 1 [7,8,14,30-78] for more details about literature review and hypotheses development). Further research is needed to determine if the development of IHT can improve health care services or if it will simply increase workloads and even aggravate burnout. Few studies have explored factors affecting the willingness to use IHT from the perspective of health care professionals, considering the impact of BUR. To achieve this goal, a measurement instrument based on the unified theory of the value-based adoption model (VAM) and BUR was used. The interrelationships between the BUR and adoption intention (AI) are investigated with the view to providing an enhanced understanding of factors that may influence the willingness to accept IHT from the perspective of health care professionals.
## Development of the Study Questionnaire
A survey questionnaire was developed for this study. The questionnaires contained a sociodemographic section (age, sex, education background, and professional title), BUR, and the VAM model (see Multimedia Appendix 1 for more details about literature review and hypotheses development).
## VAM Model
A survey questionnaire informed by the VAM was developed. This model seeks to measure the perceived usefulness (PU), perceived enjoyment (PE), perceived complexity (PC), perceived risk (PR), perceived value (PV), and AI of particular innovations. To ensure content validity, all items were adapted from previous research, and the wording was modified to fit internet health care services. All items of the VAM component of the model were covered by 16 questions. The questionnaire items were measured with a 5-point Likert scale ranging from “strongly disagree” [1] to “strongly agree” [5]. Further details of the questionnaire are provided in Textbox 1. The items in the questionnaire were tested for multicollinearity, and the results showed that there was no multicollinearity (Table S1 in Multimedia Appendix 1).
## Employee Burnout
Burnout among health care professionals was measured by the Maslach Burnout Inventory-General Survey (MBI-GS) [30]. The MBI-GS consists of 15 items over 3 metrics: emotional exhaustion, cynicism, and reduced professional efficacy. The items are scored on a Likert scale from “never” [0] to “every day” [7]. The MBI-GS does not compute a total score for burnout to reflect the burnout state. The higher the scores on the 3 metrics, the higher the level of burnout indicated. The Chinese version of the MBI-GS, developed by Li [79], also has good validity and reliability, obtaining a Cronbach α of.920. In addition, the factor loadings of the measured items were higher than 0.72, indicating a reasonable construct validity of the scale.
The preliminary design of the questionnaire was reviewed by experts familiar with the research topic to ensure that the questions successfully captured the topic and did not contain common errors, such as leading, confusing, or double-barreled questions. Prior to the survey, a pilot study was conducted among 20 health care professionals to ensure that there were no problems in reading the frame information and understanding and answering the questions. All participants said that the frames were easy to understand, and the length of the questionnaire was appropriate.
## Participants
This study was conducted using a multistage sampling method to ensure that the sample was representative. In the first stage, a stratified random sampling method was used to divide the 23 provinces and 5 autonomous regions of China according to geographical region (eastern, central, and western regions) and economic development, with 1 provincial administrative region randomly selected. In the second stage, 1 tertiary public hospital was randomly selected as a sample hospital within each rank of the provincial administrative regions according to the performance appraisal of tertiary public hospitals in 2019 (excellent, good, and general). The 3 representative hospitals were located in each of the provinces. All hospitals’ health care professionals were included, excluding workers not involved in health care, such as administration and logistics workers. The health care professionals were involved in clinician, nursing, public health, medical technology, pharmacy, etc. The sampling method is illustrated in Figure 1.
The questionnaire was accompanied by detailed instructions and informed consent. All questions were required to be completed before submission. In addition, the same IP could only be filled once to combat duplicate completion. We also set a time restriction on completing the questionnaire. All questionnaires that were answered in less than 1 minute were also excluded. Moreover, the same questions with the same answers for consecutive questions were excluded. Finally, 12,031 valid questionnaires were obtained.
**Figure 1:** *The sampling method of this study.*
## Ethics Approval
This study received ethical approval from the Ethics Committee of the School of Public Health, Shanghai Jiao Tong University School of Medicine, China (SJUPN-202008).
## Hypotheses Development and Control Variable
This study proposes 8 research hypotheses based on the VAM model and burnout theory (Figure 2).
**Figure 2:** *Theoretical model: research framework combining value-based adoption model and employee burnout to predict the adoption of internet health care technology from the perspective of health care professionals. H: hypothesis.*
Adding control variables will increase the explanatory power of this work [80]. Thus, we include the control variables of gender, age, education, professional title, geographical region, and the rank of the performance appraisal of tertiary public hospitals in 2019.
## Statistical Analysis
Descriptive analysis was performed to summarize participants’ sociodemographic characteristics after the data were exported to SPSS (version 23.0; IBM Corp). The extent of relationships among variables were evaluated by structural equation modeling using SPSS Amos version 23. Cronbach α was used to check the reliability and the verification standard was α>.7. Confirmatory factor analysis was used to discuss the structural validity and internal consistency of each construct; the average variance extracted (AVE) values were used to measure the correlation between different structures [81,82]. All hypotheses from the studies were also tested. Assumptions of multivariate normality, multicollinearity, sample size appropriateness, and positive definiteness were then checked.
## Demographic Characteristics
In this study, 9453 ($78.6\%$) respondents were female, 2792 were clinicians, 5200 ($43.2\%$) respondents were between the ages of 30-39 years, 8537 ($71\%$) respondents had received a college education, and half of the respondents were from eastern China. Table 1 provides further details. We also found that 6055 ($50.3\%$) health care professionals came from the eastern coastal provinces in this survey. This may be related to the economically developed and abundant health care resources in the eastern part of China.
**Table 1**
| Characteristic | Characteristic.1 | Respondents, n (%) |
| --- | --- | --- |
| Gender | Gender | |
| | Male | 2578 (21.4) |
| | Female | 9453 (78.6) |
| Age (years) | Age (years) | |
| | <30 | 4330 (36.0) |
| | 30-39 | 5200 (43.2) |
| | 40-49 | 1717 (14.3) |
| | ≥50 | 784 (6.5) |
| Professional background | Professional background | |
| | Clinician | 2792 (23.2) |
| | Nurse | 7220 (60.0) |
| | Public health | 47 (0.4) |
| | Medical technician | 1472 (12.2) |
| | Pharmacist | 500 (4.2) |
| Education level | Education level | |
| | Doctoral degree | 559 (4.6) |
| | Master’s degree | 1554 (12.9) |
| | Bachelor’s degree | 8537 (71.0) |
| | High school graduate | 1267 (10.5) |
| | Less than high school | 114 (0.9) |
| Location | Location | |
| | West region | 2263 (18.8) |
| | Central region | 3713 (30.9) |
| | East region | 6055 (50.3) |
| Professional title | Professional title | |
| | Senior | 445 (3.7) |
| | Vice senior | 1047 (8.7) |
| | Intermediate | 3988 (33.1) |
| | Junior | 5666 (47.1) |
| | | 885 (7.4) |
| Performance | Performance | |
| | Excellent | 4846 (40.3) |
| | Good | 5526 (45.9) |
| | General | 1659 (13.8) |
## Reliability and Validity Test
A confirmatory factor analysis was performed to test the measurement equation, including reliability and validity tests. As presented in Table 2, the reliability and convergent validity of items and constructs are assessed using Cronbach α, composite reliability, and AVE. Constructs with Cronbach α and composite reliability value >0.7 and AVE>0.5 are considered acceptable. Convergent validity is established by evaluating composite reliability and AVE. Accordingly, all constructs demonstrate an acceptable level of reliability and validity. Item loadings range from 0.716 to 0.916 and are all significant at the.01 level. Composite reliabilities range from 0.838 to 0.925, and AVE ranges from 0.642 to 0.806. Combined, these indices indicate a high degree of convergent validity. Cronbach α coefficients range from 0.843 to 0.926, which suggest a high level of reliability.
The discriminant validity was conducted to evaluate the range to which a provided study latent variable is distinct from others. Hence, when the average variance extracted of an individual latent construct is higher than the multiple squared correlations of that construct with other constructs, the discriminant validity will be at an acceptable level. The discriminant validity was tested using the Fornell-Larcker criterion. The square roots of the corresponding AVE are on the diagonal, where each construct’s AVE is higher than its highest correlation with any other construct. The results are shown in Table 3.
Fit measures of the structural model are presented in Table 4. The values of goodness-of-fit index, adjusted goodness-of-fit index, comparative fit index, and normed fit index are greater than 0.90. The fit indices of the study models are all higher than the normal average acceptance level, which indicates a good fit of the study models to the collected data.
## Structural Model Testing
Figure 3 shows that all 7 (H1-H8) research hypotheses are supported. Table 5 indicates the total, direct, and indirect effect of the model variables on attitude toward the AI. PE had the strongest positive relationship with PV (β=.638, $P \leq .001$). PU had a moderate positive relationship with PV (β=.131, $$P \leq .01$$). Furthermore, PR had a slightly negative direct relationship with PV (β=−.083, $P \leq .001$). Surprisingly, PC had a moderately positive relationship with PV (β=.198, $P \leq .001$). PV had a negative relationship with BUR (β=−.308, $P \leq .001$), BUR had a negative effect on AI (β=−.170, $P \leq .001$), and PV had a positive direct effect on AI (β=.725, $P \leq .001$). In addition, PV had an indirect effect on AI, and the effects were mediated by BUR (β=.052, $P \leq .001$).
**Figure 3:** *Results of the final research model. H: hypothesis.* TABLE_PLACEHOLDER:Table 5
## Principal Findings
China is now facing an aging population, which has resulted in an expansion of the chronic disease patient population and has substantially increased the demand for health care resources. However, the “siphon effect” of high-quality public hospitals and the inappropriate allocation of health care resources have led to a growing problem of “difficult and expensive access to health care,” as well as a serious imbalance between supply and demand for health care services. In this context, the integration of information technology and health care has accelerated, and the internet health care service model has emerged to satisfy the public demand for life cycle health care services. In addition, the COVID-19 pandemic has created significant development opportunities for internet hospitals since 2020. Based on the need for pandemic prevention and control in China, the National Health Commission, the National Health Insurance Bureau, and other health departments have established a series of policies to vigorously promote the development of internet hospitals [83-85]. Engaging more health care professionals in internet health care is necessary to realize the healthy and sustainable development of internet hospitals. However, Lo et al [86] found that the overall prevalence of burnout symptoms among doctors in China ranged from $66.5\%$ to $87.8\%$, with young doctors and doctors working in tertiary hospitals being more at risk of burnout. The deterioration of the doctor-patient relationship has become a major problem in China’s health care system, with one-third of doctors experiencing conflict and thousands being injured [87]. Burnout for health care professionals may influence their engagement in internet health care. This study first integrates VAM and burnout theory to explain the AI toward IHT. The 2 perspectives are then used to propose a research model for exploring the adoption of mobile health care services in a complementary manner. The primary findings are summarized as follows.
First, the results indicate that PU and PE have a strongly positive effect on PV. Moreover, PE has a much greater influence on PV than PU (β=.638 vs.131). PV has a strongly positive direct effect on AI toward IHT, and the findings are consistent with the work of Wu et al [31]. Most prior research focused on the factors of perceived ease of use, PU, and social support value, which could facilitate individuals’ IHT adoption [88]. In this study, PE was found to be particularly important in encouraging PV. The reason behind this may be due to health care professionals being more concerned with improving the doctor-patient relationship and increasing satisfaction and reputation among patients than facilitating service to patients [80]. This further reflects the tension between doctors and patients in China. Therefore, it would be useful for IHT providers to offer more practical benefits in order to attract more health care professionals and retain existing ones, including “reputation rewards,” “answer rating,” and other incentives, to help health care professionals build their reputation and customer satisfaction, which also contributes to enhancing the reputation of internet hospitals [32,80,89].
Second, according to our findings, PR can inhibit health care professionals’ AI. The PRs in this study included privacy risks concerning personal information, passwords, consultation records, and other information used in IHT. Other studies showed that privacy risk is an important factor in the decline of willingness to use IHT, and Guo et al [90] and Zhang et al [91] concluded that privacy risk could affect AI toward IHT. Although prior research has examined the factors that can inhibit individuals’ IHT adoption, most research focuses on the factors of privacy risk, performance risk, and legal concerns [33,90,91], ignoring workload and doctor-patient dispute problems. This study extends the results of prior studies by demonstrating that workload and doctor-patient disputes are significant inhibitors of IHT AIs. Hence, IHT providers should improve the overall quality and use security protection mechanisms to ensure the security of users’ private information [33]. Meanwhile, an appointment model can be adopted to reduce the workload and improve the adoption enthusiasm of health care professionals.
To our surprise, we found that PC positively affects PV (β=.198). *In* general, PC has a negative influence on PV. Currently, the recent health care environment is involved in a major shift in internet technology updates, with increasing technological complexity and the management of more patients with fewer resources, thus health care practitioners are faced with higher requirements [92]. Within the technological context, complexity explains how the new technology is perceived in terms of its use and comprehension by the users. In Malaysia, Ahmadi et al [93] surveyed the experts’ adoption of a hospital information system within public hospitals. They found that PC of hospital information system innovation leads to resistance due to the lack of skills and knowledge. However, we found that PC positively affects PV. In Saudi Arabia, Alamri [94] investigated students’ adoption of massive open online courses (MOOC) during the COVID-19 pandemic. They found PC was positively connected with perceived ease of use and PU. They thought that if the MOOC systems were able to satisfy students’ research needs, notwithstanding the PC, they will also consider the MOOC system to be helpful during the COVID-19 pandemic. Complicated operations in health care work are designed to provide greater protection for the privacy of doctors and patients and safeguard the security of their accounts, such as registration, name verification, and log-in processes. In addition, individuals are often more technologically literate than their peers 10 years ago and can easily adapt to modern mobile health services [34]. Moreover, 9530 ($79.2\%$) health care professionals in this study were younger than 40 years, which is similar to the percentage reported in the China Health Statistics Yearbook 2020. These health care professionals are more likely to adopt IHT. The younger generations of health care professionals are more accepting of the newest internet technologies. This also may be due to health care professionals considering the value gained from internet health care as greater than the effort expended. Even so, IHT providers should improve service quality and simplify processes for health care professionals to enhance adoption.
Third, this study confirmed that BUR is negatively related to AI (β=−.170), the result is consistent with the findings of other studies. Gardner et al [26] reported that $70\%$ of clinicians experienced health information technology–related stress. Physicians reporting moderately high or excessive time spent on electronic health records at home were 1.9 times more likely to experience burnout [90]. A qualitative study found that electronic consultations shifted some burden of specialty work to primary care clinicians. Electronic consultations can contribute to burnout through increased administrative burden and changing workflow for providers, especially in the immediate term [26,95]. In addition, we found that the PV inhibits BUR (β=−.308). PV also has indirect effects on AI, and the effects are mediated by BUR (β=.052). Hartzband et al [96] reported that autonomy, competence, and relatedness supported the restoration of intrinsic motivation and the reversal of burnout in health care professionals. Moreover, they deemed that doctors wanted to give patients the time and support they needed and wanted the system to value and recognize their efforts to provide this kind of health care. This perspective is consistent with the findings of our study. Furthermore, the tense doctor-patient relationship in China has resulted in low patient respect for health care professionals, resulting in burnout [97]. Nevertheless, the original motive that drives a doctor to endure hardship is to relieve the suffering of patients. Hence, the potential strategies for managing burnout in Chinese health care professionals should consider promoting the medical humanities and restoring moral respect for doctors [86]. IHT providers must also undertake measures to increase the PV of health care professionals so that they feel that the benefits of engagement in internet health care outweigh their efforts.
## Limitations
This study has several limitations. First, the data we collected were obtained from the health care professionals of tertiary public hospitals in China. From a perspective of data integrity, data from health care professionals in primary hospitals should also be included. Second, this study focused on the facilitators and the inhibitors of AI in health care professionals. There are other potential factors that may influence AI that should be explored in future research. Third, we did not analyze the internet technology, such as application scenarios and the functionality of use, only the AI to use the technology. Fourth, the ratio of doctors-nurses among the health care professionals surveyed in this research was 1:2.59, which may deviate from the actual situation. Therefore, the ratio of doctors to nurses to medical technicians should be fully considered in future studies. Fifth, it was found that burnout has a negative effect on the AI of IHT. It is hoped that further research will reveal the mechanisms to explain the negative relationship between burnout and AI of IHT.
## Conclusions
This research aimed to determine the key factors influencing the AI of IHT among health care professionals based on the VAM and BUR theory. The results indicated that PU, PC, and PE positively correlated with AI via PV. In contrast, PR negatively correlated with PV, and PV negatively correlated with BUR. BUR mediated the relationship between PV and AI. These findings provide valuable information to internet health care service system designers, governments, investors, and hospital administrators to promote the use of this technology by health care professionals.
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|
---
title: 'Implementing an Activity Tracker to Increase Motivation for Physical Activity
in Patients With Diabetes in Primary Care: Strengths, Weaknesses, Opportunities
and Threats (SWOT) Analysis'
journal: JMIR Formative Research
year: 2023
pmcid: PMC10039411
doi: 10.2196/44254
license: CC BY 4.0
---
# Implementing an Activity Tracker to Increase Motivation for Physical Activity in Patients With Diabetes in Primary Care: Strengths, Weaknesses, Opportunities and Threats (SWOT) Analysis
## Abstract
### Background
Many projects related to technology implementation in the context of chronic diseases have been developed over the years to better manage lifestyle medicine interventions and improve patient care. However, technology implementation in primary care settings remains challenging.
### Objective
The aim is to carry out a strengths, weaknesses, opportunities, and threats (SWOT) analysis [1] to assess satisfaction among patients with type 2 diabetes using an activity tracker to increase motivation for physical activity (PA) and [2] to explore the research and health care team’s perceptions of this technology’s implementation in a primary care setting.
### Methods
A 3-month hybrid type 1 study, which included 2 stages, was conducted in an academic primary health center in Quebec City, Quebec, Canada. In stage 1, a total of 30 patients with type 2 diabetes were randomized to the intervention (activity tracker) group or the control group. In stage 2, a SWOT analysis was performed on both patients and health care professionals to determine the components of successful technology implementation. Two questionnaires were used to gather feedback: a satisfaction and acceptability questionnaire concerning an activity tracker (15 patients in the intervention group) and a questionnaire based on the SWOT elements (15 patients in the intervention group and 7 health care professionals). Both questionnaires contained quantitative and qualitative questions. Qualitative variables from open questions were synthesized in a matrix and ranked according to apparition frequency and global importance. A thematic analysis was performed by the first author and validated by 2 coauthors separately. The information gathered was triangulated to propose recommendations that were then approved by the team. Both quantitative (randomized controlled trial participants) and qualitative (randomized controlled trial participants and team) results were combined for recommendations.
### Results
In total, $86\%$ ($\frac{12}{14}$) of the participants were satisfied with their activity tracker use and $75\%$ ($\frac{9}{12}$) felt that it incited them to stick to their PA program. The main strengths of the team members’ perspectives were the project initiation and involvement of a patient partner, the study design, the team, and the device. The weaknesses were the budgetary constraints, the turnover, and the technical issues. The opportunities were the primary care setting, the loan of equipment, and common technology. The threats were recruitment issues, administrative challenges, technological difficulties, and a single research site.
### Conclusions
Patients with type 2 diabetes were satisfied with their activity tracker used to improve motivation for PA. Health care team members agreed that implementation can be done in primary care, but some challenges remain in using this technological tool in clinical practice regularly.
### Trial Registration
ClinicalTrials.gov NCT03709966; https://clinicaltrials.gov/ct2/show/NCT03709966
## Background
Over the years, many research projects related to technology implementation in the context of chronic diseases have been developed in primary care. One of the main effects is to contribute to a favorable lifestyle medicine change, which may have positive repercussions on patient health and the management of chronic diseases [1-4].
Recently, a scoping review by Clarkson et al [5] identified the increased use of digital tools, in combination with human support, to help people with long-term conditions and to maintain physical activity (PA). This review shows that web-based digital tools continue to predominate with the more recent emergence of gamification, applications, and virtual environments. However, most participants were from younger age groups, the use and description of the theory in the development of the tools were limited, and most studies highlighted the need for human engagement to support their use [5]. The lack of digital tools for multimorbid long-term conditions, longer-term follow-ups, understanding participants’ experiences, and informing future questions about the effectiveness were the obvious gaps [5].
Another scoping review, conducted by Motahari-Nezhad et al [6], revealed that clinical evidence concerning digital biomarkers has been systematically reviewed across a wide range of study populations, interventions, digital devices, and sensor technologies with the dominance of PA and cardiac monitors. To understand the clinical value of digital biomarkers, the strength and quality of the evidence on their health consequences need to be systematically evaluated.
Indeed, the use of digital technology to help patients with chronic diseases such as type 2 diabetes is an emerging field of research. There is a variety of equipment available to patients, including consumer activity trackers, pedometers, smartphone apps, and blood glucose monitors. The advantages of these technologies are that they can allow health care professionals to remotely monitor patients and reduce the need for patients to regularly attend clinics [7]. Recently, a mixed methods study by Hodgson et al [4] explored the use of an activity tracker for 4 weeks to support an active lifestyle in adults diagnosed with type 2 diabetes. Overall, the results demonstrated that fitness trackers could support an active lifestyle in adults with type 2 diabetes, but more detailed discussions with health care professionals could identify methods of integrating activity trackers into patient care [4]. Additionally, a multicenter prospective observational study (set up for 7 weeks) has evaluated the feasibility of using a nonmedicalized device to monitor the lifestyle of elderly patients with type 2 diabetes in a primary care setting [8]. Researchers observed a high level of acceptance of portable devices based on the impressions of patients and health care professionals [8]. Nevertheless, the authors suggest further studies to assess devices’ acceptability for longer periods [8].
Additionally, it is important to point out that technical failures were faced in many studies, such as log-in difficulties [3], connectivity errors [3,9], a lack of reliability and validity [3,9,10], as well as some patients feeling overwhelmed by the technical complexities of the activity tracker and its software [4,10]. Patient motivation dropped when such complications occurred [3]. The limited resources, especially in terms of staff [9,11] and money [9], were also reported as barriers to implementation.
## Context of the Study
A hybrid type 1 trial aims to determine the effectiveness of clinical intervention and to better understand the implementation context [12]. It is useful to explore if a clinical intervention works in a specific context and to gather potential barriers or facilitators to an intervention’s widespread implementation. Based on this design, a randomized pilot trial was conducted to evaluate the impact of an activity tracker on PA and cardiometabolic variables in a real-life settings among patients with type 2 diabetes [13,14]. Alongside this randomized pilot trial, we tested its effects on relevant outcomes while observing and gathering information from patients and the health care team with a strengths, weaknesses, opportunities, and threats (SWOT) analysis [15]. SWOT analysis has been used to determine the components of successful technology implementation in primary care settings, and its objective is to use the knowledge an organization has about its environment to formulate its strategy accordingly [15]. To the best of our knowledge, no study has simultaneously studied the feasibility and implementation among patients with type 2 diabetes using an activity tracker to increase motivation for PA in primary care with a SWOT analysis.
## Objectives
The aim of this study is to carry out a SWOT analysis to assess the acceptance of this novel technology by [1] assessing satisfaction among patients with type 2 diabetes using an activity tracker to increase motivation for PA and [2] exploring the health care team’s perception of its implementation in a primary care setting.
## Hybrid Type 1 Study
We conducted a hybrid type 1 study for 3 months in an academic primary health center in Quebec City, Quebec, Canada. The study consisted of 2 stages. The first stage was a pilot randomized controlled trial to test a clinical intervention, and the second stage was to gather information on its implementation potential with patients’ and the team’s feedback using a SWOT analysis.
## First Stage: a Pilot Randomized Controlled Trial
The first component consisted of a pilot randomized controlled trial of 30 patients with 2 groups of 15 patients each to evaluate the impact of an activity tracker on PA and cardiometabolic variables in a real-life context among patients with type 2 diabetes. The complete study protocol was published on ClinicalTrials.gov (NCT03709966) [13]. The results of this study are published elsewhere [14]. The control group received routine follow-up, which included a PA promotion intervention supported by a kinesiologist. The intervention group is routinely followed up with the addition of an activity tracker (Fitbit Charge HR, Fitbit Inc) worn during the study. Participants in the intervention group were also provided with a tablet (iPad, Apple Inc) and with an application linked to the activity tracker (the Fitbit app). Cardiometabolic risk variables, PA motivation, and PA were assessed at the baseline and at the end of the trial.
## Second Stage: Gathering Information on Implementation Through a SWOT Analysis
The second component of the study consisted of gathering information on the delivery and potential of implementation. Feedback from patients and the health care team was gathered to perform a SWOT analysis. A SWOT analysis was particularly relevant in the context of this project in order to identify factors that could influence the replication of this intervention in a similar context. The SWOT analysis considers factors that are internal (strengths and weaknesses) and external (opportunities and threats) to the intervention. Thus, it can inform on both the design of an intervention and the strategic planning of its implementation [15].
## Satisfaction and Acceptability of the Activity Tracker: Patients’ Feedback
Satisfaction and acceptability of the activity tracker as well as the application were measured by a questionnaire inspired by the Technology Acceptance Model [16] and the System Usability Scale [17,18]. The 15 participants in the intervention group were invited at the end of the study to fill out the 10-question questionnaire on their satisfaction with the device and the support provided by the research team, their opinion about the information the device is providing, the impact on their lives, and their comments on the study. The questionnaire was in French and contained both quantitative and qualitative elements. The questionnaire is available in Multimedia Appendix 1.
## Implementation Questionnaire: Health Care Team’s Feedback
Research team members and health care professionals who were engaged in this project were also consulted. We used LimeSurvey, a web-based survey tool, to collect the team’s feedback [19]. In total, 12 participants and team members were invited to fill out an original questionnaire based on the SWOT analysis elements [15]. It contained 11 questions about their identification, the study’s strengths and weaknesses, the achievement of its objectives, the barriers faced, the facilitating factors, the integration of an activity tracker in primary care, and possible improvements. The questionnaire was in French and contained both quantitative and qualitative elements. A link to fill out the questionnaire was sent via email, and a reminder was also emailed a month later. The questionnaire is available in Multimedia Appendix 2.
## Ethics Approval
The authors are accountable for all aspects of the work. The trial was conducted in accordance with the Declaration of Helsinki (as revised in 2013) [20]. The study was approved by the institutional ethics board of the Centre intégré universitaire en santé et services sociaux de la Capitale-Nationale (No: 2017-2018-07), and informed consent was obtained from all the participants. Data were deidentified to preserve confidentiality. Furthermore, the data from the activity tracker are subject to the Fitbit privacy policy [21]. No financial compensation was given, but parking tickets for the clinic were provided.
## Data Analyses
Quantitative variables from selected answers (satisfaction and acceptability of the activity tracker’s questionnaire) were reported in frequency tables. Qualitative variables from open questions (the implementation questionnaire) were compiled in a file, and key concepts were identified by hand by the first author (CP). The first author then manually performed an inductive thematic analysis [22] to regroup key concepts as they emerged from data from both questionnaires, which were validated separately by 2 other coauthors (CR and MPG). The themes were then synthesized in a matrix and ranked according to apparition frequency and global importance. The information gathered was triangulated using the SWOT model (Opportunities-Strengths [O-S], Opportunities-Weaknesses [O-W], Threats-Strengths [T-S], and Threats-Weaknesses [T-W]) to propose recommendations that were then approved by the research team. For quantitative variables, normality was assessed using the Kolmogorov-Smirnov test. Variables following a normal distribution were expressed as mean (SD) values; otherwise, median (IQR) values were used. Missing data were excluded. The analyses were performed with the software SAS (version 9.4; SAS Institute).
## First Stage: Summary of Meaningful Results of the Randomized Pilot Trial Previously Published
The first stage of the study, the randomized pilot trial, showed that PA assessed by questionnaire increased in the group with a PA intervention supported by a kinesiologist (the control) and in the group with an activity tracker in addition to the PA intervention (the intervention). High-density lipoprotein cholesterol increased in the intervention group and decreased in the control group ($$P \leq .01$$). Glycated hemoglobin tended to decrease in both groups ($$P \leq .08$$). The full results are published elsewhere [14].
## Satisfaction and Acceptability Outcomes: Patients’ Feedback
The baseline demographics of participants in the intervention group are presented in Table 1. Since there was 1 dropout in the intervention group, 14 participants out of 15 completed the satisfaction and acceptability questionnaire. Satisfaction with the activity tracker use and technical support provided by the team are shown in Table 2. In total, $86\%$ ($\frac{12}{14}$) of the participants were satisfied using their activity tracker. Some of the participants ($\frac{11}{14}$, $79\%$) were satisfied with the technical support provided by the team. The perceived usefulness of the information displayed by both the activity tracker and the application is reported in Table 3. Approximately $79\%$ ($\frac{11}{14}$) of the participants found the information useful. The step count was perceived as the most useful parameter to track PA and for PA motivation (Table 4). More than half of the participants ($\frac{6}{11}$, $55\%$) were planning to buy an activity tracker after the study, $36\%$ ($\frac{4}{11}$) were not planning to buy an activity tracker, and $9\%$ ($\frac{1}{11}$) were undecided.
Participants were also asked to name the principal change they made to their lifestyle habits during the study. Many of them reported an increase in their PA, such as using the stairs, walking more, and doing more PA overall.
Participants were also asked if they continued to integrate PA into their daily routine once the study was over and why. Most of them said yes ($$n = 12$$) and mentioned reasons such as pleasure, habit, feeling good, and achieving goals. If they answered yes, participants were asked to what extent the activity tracker incited them in sticking to their PA program once the study was completed. The results are presented in Table 5. In total, $75\%$ ($\frac{9}{12}$) of the respondents thought the activity tracker had encouraged them to stick to their PA program upon completion of the study. One participant wrote: “It’s more realistic, I see that I can control my physical activity despite my schedule.”
## Health Care Team’s Feedback
The implementation questionnaire was completed by 7 health care team members. There were 2 men and 5 women, and the mean age was 44.7 (SD 18.6) years. The health care team’s responders were composed of a family physician, researchers, a graduate student (MD-MSc), a professional coordinator, a kinesiologist, and a layperson (patient partner). Team members’ perceptions regarding the strengths, weaknesses, opportunities, and threats related to the implementation of an activity tracker to increase motivation for PA among patients with type 2 diabetes as well as patients’ feedback are summarized in a SWOT matrix (Textbox 1).
## Study Design and Team
The project’s initiation by a patient partner who was involved throughout the study, from the beginning to the end, was considered a major strength. One team member mentioned: The collaboration of many health professionals from the scientific field also optimized knowledge transfer and allowed a better understanding of research in primary care, according to the team members. Some characteristics of the team members were mentioned, including, “The presence of a skilled research professional ensuring the participant’s follow-up, the leadership of the clinician-researcher in charge, the accessibility of the principal investigator” [Team member 2].
The follow-up with the kinesiologist was considered a positive aspect of the study design as it encouraged patients and “optimized the use of the activity tracker (goals and PA intensity)” [Team member 3].
## Device
The high level of participants’ satisfaction regarding the device was perceived as a major strength. The motivational aspect of the device was also mentioned: [1] “The use of a technological tool to track more objectively PA was appreciated by patients. Actually, there are few motivational tools used by physicians to track PA” [Team member 1], and [2] “The possibility with technology to motivate ourselves to initiate healthy lifestyle habits and maintain them” [Team member 4].
## Budgetary Constraints and Implications
One of the main weaknesses in the study was the small budget, which limited the number of pedometers and activity trackers and subsequently the sample size (30 patients). The part-time research coordinator, due to the limited budget, may have had an impact on the follow-up on measures and missing data:
## Turnover and Technical Issues
Staff turnover due to uncontrollable reasons had a major impact on the follow-up on data extraction, cardiometabolic risk measurements, and research trajectory and was therefore perceived as a weakness. Due to technical problems during data download, the data from a few patients’ devices could not be extracted: 4 participants had missing data, 1 had withdrawn, and there was a problem extracting data for 3 participants who forgot their password.
## Primary Care Setting
The primary care setting was seen by the team as the principal opportunity. The study took place in a primary care setting in an academic health center, which allowed research students to be trained and introduced primary care research to health professionals, clinicians, and patients. For instance, a medical student completed her master’s degree with this project. Results were presented at provincial, national, and interventional congresses, and an article was published in a scientific journal. Thus, this research optimized faster knowledge transfer from research to the medical setting.
## Loan of Equipment
The activity tracker and iPad were loaned by the researcher, who is specialized in IT (Canada Foundation for Innovation). This collaboration combined both expertise and resources. There was no donation from the manufacturer or sponsorship.
## Common Technology
The fact that an activity tracker is a common technology available to the public is also a factor contributing to the real-life setting. Fitness trackers are readily available at a relatively affordable cost and are not exclusive to research. It is an increasingly popular and growing technology. Given that it is a common technology, the outcomes of interdisciplinary team care for the patient can include using mobile apps that track lifestyle change progress and that prompt lifestyle intervention. Support with common digital technology (eg, apps, wearable devices) is a key construct for effective, sustainable patient care self-management.
## Recruitment Issues
The difficulty of recruiting participants from the medical clinic was perceived as the biggest threat by the research team:
## Administrative Challenges
The team felt it was a challenge to coordinate the necessary resources for recruitment and participants’ follow-up:
## Technological Difficulties
The difficulty synchronizing the activity tracker and app was mentioned by the team, as was the watch’s lack of batteries. The team also added, “The use of an activity tracker can be challenging for certain people, including the elderly” [Team member 3]. Some patients forgot their password, which caused problems with data extraction as well as watch battery running out and recording problems that influenced measure collection.
## Single Research Site
The study was limited to one research site and participants with diabetes. As well, there was only one family medicine unit included in the study. One member also mentioned equipment availability as a concern: “We have to find a way to make this technology available for all” [Team member 4]. Thus, the possibility of conducting a multicenter trial was limited.
## Improvements
The improvements suggested by the research team and the participants were classified according to the O-S, O-W, T-S, and T-W strategies (Textbox 1). The main points for O-S were [1] integration of activity tracker data in the electronic medical record, [2] organization of academic health centers, and [3] follow-up on a longer period. The main points for O-W were [1] a larger study with more participants; [2] extraction of all the activity tracker data; [3] collection of data in pre-post intervention, clarification of objectives; and [4] formation of groups of motivation. The main points for T-S were [1] full-time team dedicated and available to the clinical intervention, [2] addition of more academic health centers, and [3] limit the number of professionals involved. The main points for T-W were [1] full-time research coordinator, [2] scientific counselor, [3] training on how to use the device, [4] team training with the technological tool, and [5] logging access given by the team.
## Feasibility and Implementation
Overall, the team members felt that the implementation in primary care was feasible: Another team member mentioned that “it [activity tracker] brings objective data on PA and could improve physician-patient communication” [Team member 3]. One team member felt some people might benefit more from it: “I think that this device is interesting mostly in cases of instability or huge variability in results/medical analyses of patients. The device allows us to see fluctuations and potentially establish certain tendencies” [Team member 5].
## Principal Findings
This is a 3-month pilot study conducted in Quebec, Canada, that aimed to assess the satisfaction of patients with type 2 diabetes who use an activity tracker to increase motivation for PA, as well as to investigate the patients’ and team's perception of the implementation of this technology in a primary care facility. According to this research, patients with type 2 diabetes were satisfied with their activity tracker, which was used to increase PA motivation. Members of the health care and research teams agreed that this novel technology tool can be used in primary care, although there are still obstacles to its frequent use in clinical practice.
## Satisfaction and Acceptability: Patients’ Perspective
Most of the participants were satisfied with the use of their activity tracker and with the technical support provided by the research team. They found the information useful, with the step count being perceived as the most useful parameter to track PA and for PA motivation. Overall, participants reported an increase in their PA and all of them did stick to their PA program once the study was over, with the activity tracker playing a substantial role. One Canadian out of 4 ($24\%$) owns at least 1 connected device allowing health or wealth data capture, of which $88\%$ of them have a smart watch or bracelet [23]. Ware et al [24] explored Canadian older adults’ perceptions of the use of eHealth technologies. Their findings support the potential value they perceive in eHealth technologies, particularly in their ability to give access to personal health information and facilitate communication between providers and peers living with similar conditions. We consider that in our study this technology was well accepted by older individuals (the mean age of participants was 62 years), as demonstrated by the high satisfaction with the device used. Ummels et al [10] described the experience of commercially available activity trackers embedded in the physical therapy of patients with a chronic disease. Participants perceived the activity tracker as a motivation to be more physically active and reach their goals, similar to our findings [10]. However, participants experienced some technical failures too and found it complex [10].
## Health Care Team Members’ Perspective
According to the team members’ perceptions, the main strengths were the project initiation and involvement of a patient partner, the study design and team, as well as the device used. Patients’ opinions during the course of the study in a real-world context gave credibility to the study and increased the research quality. Patient engagement in health research is an emerging phenomenon and contributes, among others, to identify research questions and outcomes important to patients and clinicians, data collection processes, interpretation of results, and dissemination [25,26]. Indeed, patient engagement allows patients to become partners with academic researchers to create a meaningful and active collaboration in governance, priority setting, conducting research, and knowledge transfer [27,28]. Patient engagement helps transfer research findings into practice and can ultimately improve patients’ outcomes [29]. Furthermore, patient-centered design for digital health facilitates implementation and improves the relevance of research and its uptake into health care [30]. Another interesting point to discuss is that our study was designed as a mixed methods study, which could represent a strength. Both quantitative and qualitative data (from randomized controlled trial participants) were collected by using a questionnaire. While the qualitative data was obtained from the questionnaire in the intervention group, this design can dilute the strengths of both methods. However, the questionnaire for the team members and participants was based on the validated models analysis [15-18]. A suggested solution will be to extend the study with more patients and to recommend the qualitative design as the main method for the participants (eg, in-depth interviews). The hybrid type 1 design itself, combining dual testing such as a randomized pilot trial and SWOT analysis, allowed us to collect valuable information for use in subsequent implementation research trials (hybrid or not). All the information gathered in our matrix will help speed the translation of our research findings into routine practice, develop more effective implementation strategies, and provide more useful information for decision makers.
The principal weaknesses were the budgetary constraints, the turnover, and the technical issues. The budgetary constraints had an impact on sample size, duration and design of the study, cardiometabolic measurement choices, number of pedometers and activity trackers, human resources, and so on. The turnover compromised the follow-up and consequently had an impact on technological failure. The technical issues were missing data, recording problems, and a lack of data extraction, as previously reported in other studies, and were related to turnover of health professionals and the part-time research coordinator [3,4,9-11].
The opportunities were the real-life setting in a primary care setting and the common technology. Primary care and primary care academics have steadily contributed to many aspects of health research, but they have been particularly important in applied research at the structural and inspirational levels [31]. With the increasing use of digital solutions, there is a growing need to evaluate their impact in primary care, including risks and benefits, and to inform health policies that are both patient-centered and evidence-based [32]. The authors have proposed 5 wishes for the future of digital care, such as co-design with primary health care professionals and patients, better infrastructures, support and training, data sharing, clear regulations and best practice standards, and ensuring patient safety and privacy [32]. The COVID-19 pandemic has shown the need and relevance of collaboration as part of a global community to develop a shared agenda that supports collaboration in general practice, research, and policy, and facilitates the delivery of digital solutions that leave no one behind [32]. Support with common digital technology (eg, apps and wearable devices) is a key construct for effective, sustainable patient care self-management. Two studies in type 2 diabetes patients with activity trackers show maximum effects at the beginning of the study, within 2 months. [ 2,33] Thus, such renting or lending could be beneficial, and those who really enjoyed their experience could actually buy an activity tracker on their own afterward, just like these $55\%$ ($\frac{6}{11}$) planned to do after this study.
The main threats were the recruitment issues, the administrative challenges, the technological difficulties, and the single research site. The administrative challenges were the recruitment of patients lasting 1-year, the coordination or synchronization of too many professionals from different establishments, and less-than-optimal communication between professionals. The technological difficulties were: difficulty synchronizing the activity tracker and application, technology harder to use for certain people, extraction of the activity tracker data, forgotten login access, and so on. There is a need to develop patient recruitment strategies that minimize the efforts required by staff to recruit patients while meeting privacy and ethical responsibilities and minimizing the risk of selection bias, as studies have identified barriers to the recruitment of patients in a primary care cluster randomized trials [34]. Another exploratory study provides preliminary evidence of an internal structure to optimize recruitment in primary care [35]. In the fall and winter, the recruitment was more difficult as participants were less willing to practice PA outdoors due to the weather. Since participants in the study knew they were being observed, it was possible that they changed some of their habits.
## Challenges and Strategies of Implementation
Improvements included the integration of relevant activity tracker data in the electronic medical record, involving more family medicine units, repeating the study with more participants, and hiring a full-time research coordinator. Most of the team members perceived that an activity tracker could be integrated into the follow-up of patients in primary care. Overall, participants appreciated the activity tracker, as did the team, but as shown by the SWOT analysis, there are some challenges. The results obtained are consistent with the preliminary literature regarding the implementation of activity trackers in primary care. Reed et al [9] reported an increase in PA among inactive rural adults when implementing a 12-week Fitbit-based intervention and technological difficulties, while the nursing staff cited human resources and money as barriers. Similar work has been completed among both patients who had diabetes earlier [36] and those with diabetes and other cardiometabolic conditions reported in a recent systematic review and meta-analysis [37].
We believe that to facilitate digital health research in primary care, it is essential to provide a structure dedicated to supporting clinical research using digital technologies, which requires an adequate organization of academic health centers, substantive funding, and human resources. The clinical research coordinator supports and oversees the daily activities and plays a critical role in the conduct of the clinical trials. It is suggested that a research coordinator should be hired in academic health centers to optimize clinical research in the primary care setting.
The next step is to integrate the activity tracker into the electronic medical record, as suggested by our team. Shannahan et al [38] demonstrated that the patients’ activity tracker data can be embedded within visits with primary providers to personalize recommendations and that patient-physician information sharing is feasible. They conclude that activity trackers may foster patient-physician communication regarding PA, but infrastructure and resources are needed [38]. Bliudzius et al [39] stated that the data from physical monitoring systems and external medical devices should be integrated into the medical record system as these are essential in clinical work. Moreover, system integration is useful to make detailed analyses and have a global or clear picture of the patient’s health [39]. We strongly believe that patient PA data and other cardiometabolic parameters are essential in clinical work, especially in primary care settings. It helps health care practitioners to review and use patient data collected, understand how the patient feels in real-life situations, adhere to the physician’s or health professional team’s recommendations, and thus solve problems faster [39]. In order to implement the technology on a larger scale, it should be important to obtain consent and explore the patients’ stress over having their data shared with a third party. As reported by Hodgson et al [4], there is future scope for using Fitbit activity trackers to support active lifestyles in adults diagnosed with type 2 diabetes. More detailed discussion with health care professionals could identify methods of integrating activity trackers into the care of patients.
## Limitations of the Study Design
This study has a number of limitations. The first limitation is the small sample size, which could compromise the power of the study. However, the sample size was determined for the participant according to the availability of the equipment, and for the team members, it was determined according to their involvement in the pilot study. We believe that we have gathered sufficient helpful information to draw general recommendations to optimize the implementation of this technological tool in primary care. It is also important to report that some satisfaction may be tracker-dependent. The use of more sophisticated devices (eg, AppleWatch) may increase the satisfaction but also cause some other technical issues (eg, the download of the data). Therefore, some conclusions should be reformulated as “device-related,” since changing the used device may give different results. Another limitation is the relatively short-term follow-up period of 3 months, which was selected in the context of a pilot study to prevent an increased withdrawal rate in the control group and to align with our budget. However, we are aware that a longer duration may have been beneficial for incorporating lifestyle habits and also for a long-term comparison with the control group. Moreover, a longer duration could be interesting to observe the tendency of motivation over time because the highest step average was recorded after the follow-up phone call and the lowest weekly step average was recorded at week 12 at the end of the intervention. A long-term study will be interesting to observe the motivation tendency and find solutions to optimize and maintain PA motivation with an activity tracker over time. It is interesting to point out that most studies, even pilot studies, have recommended a follow-up duration of at least 6 months and preferably 12 months or longer. Similar studies had a follow-up period of 3 to 6 months [1,2,40]. A meta-analysis of activity trackers in adults with cardiometabolic conditions reports a median duration of 17 weeks and up to 18 months [37]. Another study on young adult cancer survivors found that a 12-week Fitbit and Facebook-based physical intervention was feasible for this population and had promising effects on reducing sedentary time [41].
## Conclusions
In conclusion, patients with type 2 diabetes were satisfied with the activity tracker and felt that it incited them to stick with their PA program once the study was over. According to the research team, its implementation is feasible in primary care, but some challenges remain to having this technological tool in clinical practice. From a clinical innovation perspective, it would be interesting to find a way to synchronize relevant activity tracker data to the electronic medical record to optimize the collaboration between patients and health professionals in a primary care facility.
Based on a patient partner’s idea and his continuous involvement, this project showed that laypersons have an important role in implementation research by informing the design of realistic interventions and optimizing their feasibility. Moreover, health researchers, clinicians, and other health care professionals need to clarify the opportunities to integrate digital technologies into public health to maximize their potential to improve public health outcomes and patient care.
## Data Availability
Questionnaires are available in the supplementary files. The data sets used and analyzed during this study are available from the corresponding author on reasonable request.
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|
---
title: 'Outcomes in Lower Pole Kidney Stone Management Using Mini-Percutaneous Nephrolithotomy
Compared With Retrograde Intra Renal Surgery: A Randomized Controlled Trial'
journal: Cureus
year: 2023
pmcid: PMC10039418
doi: 10.7759/cureus.35343
license: CC BY 3.0
---
# Outcomes in Lower Pole Kidney Stone Management Using Mini-Percutaneous Nephrolithotomy Compared With Retrograde Intra Renal Surgery: A Randomized Controlled Trial
## Abstract
Background Because of the anatomical properties of the inferior calyx, lower pole stones are difficult to remove through the ureter, even if the stones are fragmented. Retrograde intra-renal surgery (RIRS) is typically employed to treat the smaller lower pole stones (1.0-2.0 cm) while percutaneous nephrolithotomy (PCNL) is primarily used to treat the larger diameter stones or when RIRS has failed to clear the stones. This study was conducted to compare mini-PCNL and RIRS for the management of lower pole kidney stones in terms of stone clearance.
Material and methods This randomized control trial was conducted in the Department of Urology, Shaikh Zayed Hospital, Lahore from October 2020 to December 2022. A total of 150 patients between the ages of 18 and 80 years with a kidney stone size of 10-20 mm at the lower pole were included. Patients with positive urine culture, anatomical abnormalities, uncontrolled diabetes (hemoglobin{Hb}A1c >$9\%$), and undergone previous renal surgery were excluded. Group A patients were treated with mini-PCNL, while group B patients were managed with RIRS. Follow-up visits were planned four weeks postoperatively with CT KUB (computed tomography of kidneys, ureters, and bladder) plain to assess stone clearance.
Results The mean age in group A was 43.27 ± 13.86 years, while in group B was 45.32 ± 14.14 years. Out of 150 patients, 102 ($68.0\%$) were males and 48 ($32.0\%$) were females. Mean size of the stone was 15.30 ± 2.21 mm. Stone clearance after mini-PCNL was found in 69 ($92.0\%$) patients and after RIRS in 59 ($78.67\%$) patients (p-value = 0.021). Mean hospital stay after RIRS was 1.1 ± 0.09 days, while it was 2.3 ± 0.64 days after mini-PCNL (p-value < 0.001). Two ($2.67\%$) patients in the mini-PCNL group developed bleeding postoperatively. The stone clearance rate in older patients (51 to 80 years) was significantly higher in the mini-PCNL group than RIRS group. Similarly, the stone clearance rate in female patients and in patients with larger stones (16 to 20 mm) was found to be higher in mini-PCNL group as compared to the RIRS group.
Conclusion This study concluded that both mini-PCNL and RIRS are safe and efficient techniques for treating lower pole kidney stones with a size of 11-15 mm. However, mini-PCNL has a higher stone clearance rate compared to RIRS in the treatment of stones larger than 15 mm in size. This study further suggested that patients treated with mini-PCNL had a longer hospital stay compared to patients treated with RIRS.
## Introduction
Kidney stones are among the most common diseases in urology. It has a lifetime prevalence of $10\%$ around the world. Males are more at risk of urolithiasis than females, with a ratio of 1.5:1. Kidney stones usually develop in saturated urine, which in turn depends on solute concentration, urine pH, ionic gradient, and complexation. Urinary stones are classified into five predominant types: calcium oxalate stones, calcium phosphate stones, struvite stones, uric acid stones, and finally cystine stones. Stones are composed primarily of calcium oxalate or phosphate ($85\%$). Depending on the calcium content, kidney stones can be radiopaque or radiolucent. Calcium oxaltes, or phosphate stones, are radiopaque due to the increased amount of calcium in the stones. Uric acid and cystine stones are usually radiolucent due to decreased calcium composition. The clinical presentation includes acute, incessant colicky lumbar to groin pain, occurring in $50\%$ of cases requiring intervention. In addition, $50\%$ of affected patients will develop a relapse in their lifetime. The non-contrast CT scan is the most accurate imaging modality in diagnosing kidney stones [1,2].
Kidney stones usually arise in the lower pole of the kidney and account for $35\%$ of all kidney stones. Treatment of lower pole kidney stones (LPS) depends on the composition of the stone, anatomical location, size, patient preference, clinical expertise, and equipment availability. Different treatment modalities for LPS range from less invasive extracorporeal lithotripsy (ESWL) to minimally invasive options with higher stone-free rates such as retrograde intrarenal surgery (RIRS) and percutaneous nephrolithotomy (PCNL) [3].
Retrograde intrarenal surgery (RIRS) is a widely used treatment option for a variety of urological disorders. Small kidney stones, calyx stones, blind calyces, and urothelial tumors of the upper urinary tract can be treated by RIRS using flexible ureteroscopes and lasers. Since it was reported as a treatment option for stone disease in 2002, the major limiting factor for RIRS has been larger stone size. Recently, some centers and surgeons have advocated RIRS to treat large stones with fewer complications and improved morbidity. The European Association of Urology (EAU) also recommended RIRS as an effective treatment for stones that are relatively larger in their recent guidelines [4]. PCNL is highly recommended for the treatment of large stone loads because of its higher success rate. However, it has higher complication rates, up to $25\%$, reported in some studies. With recent advances in technology and techniques, this equation has improved. Miniaturized PCNL (mini-PCNL) by using smaller instruments (nephroscopes) clears kidney stones with a high stone clearance rate and fewer complications [5,6].
There are very few studies comparing mini-PCNL and RIRS for the treatment of lower pole kidney stones. Therefore, this study was conducted to evaluate the comparison of RIRS and mini-PCNL in kidney stones with lower pole stones ranging in size from 10 mm to 20 mm.
## Materials and methods
This randomized controlled trial was conducted in the Department of Urology, Shaikh Zayed Hospital, Lahore from October 2020 to December 2022. Approval was obtained from the Hospital Ethical Committee Institutional Review Board and the Higher Board of Studies Shaikh Zayed Hospital before commencing the recruitment (approval number IRB/SZH/$\frac{20}{45}$). After taking informed consent, patients satisfying the inclusion criteria were recruited through the outpatient and emergency departments.
A sample size of 150 patients was computed with expected percentage of stone-free rate of $85\%$ in patients treated with mini-PCNL and $97\%$ in patients operated with RIRS by taking a confidence level of $5\%$ and power of $80\%$ [7]. Patients between the ages of 18 and 80 years of both sexes with kidney stones at the lower pole of size 10 mm to 20 mm as evaluated by CT KUB (computed tomography of kidneys, ureters, and bladder) plain were included in this study. Kidney stone patients with positive urine culture, anatomical anomalies determined by ultrasonography, uncontrolled diabetes (hemoglobin{Hb}A1c >$9\%$), and undergone previous renal surgery were excluded from the study.
Patients were randomly segregated into groups A (mini-PCNL) and B (RIRS) using a random number table. Procedures were carried out under general anesthesia by consultant urologists who had at least three years of experience in the procedure. In group A (mini-PCNL) following procedure was done. A 6 Fr ureteral catheter was inserted into the tract using a cystoscope (KARL STORZ SE & Co., Tuttlingen, Germany), and a dye was instilled to opacify the pelvicalyceal system. After elaborating on the calyceal system through fluoroscopy, the affected calyceal was punctured and a 16 F sheath was used to dilate the tract. Mini-nephroscope 14 Fr was then introduced and stones were fragmented by holmium: YAG laser (Olympus, Beijing, China). Afterwards the collecting system was examined through a nephroscope (KARL STORZ SE & Co., Tuttlingen, Germany) and fluoroscopic confirmation was done to ensure stone fragmentation and clearance. In all cases, a 6 Fr 24 cm DJ stent (Bostone scientific, Marlborough, England) was inserted and the patient was discharged on postoperative day 2 with oral antibiotics.
In group B, RIRS was performed. In this procedure, a double J stent was placed to dilate the calyceal system two weeks prior to the surgery. During the procedure, a cystoscopy was performed, and a 0.035-inch guide wire was in the pelvi-calyceal system. A ureteric access sheath of 12 Fr was then positioned and with the help of a digital polyscope (Polydiagnost Gmbh, Hallbergmoos, Germany), the stone was fragmented using Holmium: YAG laser. DJ stent 6F 24 cm was placed in all the cases. In uneventful surgery, the patient was discharged on postoperative day 1 with oral antibiotics. Follow-up visits were planned four weeks postoperatively with CT KUB plain to assess stone clearance. Stone clearance was defined as no residual stone material found on the CT KUB plain in the lower pole of the kidney. Data was collected through a well-designed proforma.
SPSS version 23.0 (IBM Corp., Armonk, NY) was used for data analysis. Frequencies and percentages were computed for qualitative variables such as gender and stone clearance. Values were presented as mean ± standard deviation for quantitative variables such as age and hospital stay. Chi square test was used to evaluate categorical variables (stone clearance) and the t-test was done to compare continuous variables (hospital stay). Data was stratified for age, gender, and stone size to deal with effect modifiers. A value of p ≤0.05 was considered significant.
## Results
In this study, the mean age was 44.65 ± 14.09 years. The mean age in group A was 43.27 ± 13.86 years while in group B was 45.32 ± 14.14 years. Of 150 patients, 102 ($68.0\%$) patients were male and 48 ($32.0\%$) patients were female with a male to female ratio of 2.3:1. The mean stone size was 15.30 ± 2.21 mm (Table 1).
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Group A | Group B | Total |
| --- | --- | --- | --- | --- |
| Age (years) | | 43.27 ± 13.86 | 45.32 ± 14.14 | 44.65 ± 14.09 |
| Gender | Male | 52 (69.3% ) | 50 (66.7%) | 102 (68%) |
| | Female | 23 (30.7% ) | 25 (33.3% ) | 48 (32%) |
| Size of stones (mm) | | 15.29 ± 2.29 | 15.33 ± 2.18 | 15.30 ± 2.21 |
In this study, the stone clearance rate after mini-PCNL was found in 69 ($92.0\%$) patients, and after RIRS stone clearance rate was found in 59 ($78.67\%$) patients (p-value = 0.021). The mean hospital stay after RIRS was 1.1 ± 0.09 days while it was 2.3 ± 0.64 days after mini-PCNL (p-value < 0.001). Two ($2.67\%$) patients in the mini-PCNL group developed bleeding postoperatively (Table 2).
**Table 2**
| Unnamed: 0 | Group A | Group B | p-value |
| --- | --- | --- | --- |
| Stone clearance | 69 (92.0%) | 59 (78.67%) | 0.021 |
| Hospital stay (days) | 2.3 ± 0.64 | 1.1 ± 0.09 | < 0.001 |
| Complications | 2 (2.67%) | 0 (0%) | 0.310 |
The stone clearance rate in elderly patients (51 to 80 years) was significantly higher in the mini-PCNL group than in the RIRS group. Similarly, it was found that the stone clearance rate was higher in the mini-PCNL group than in the RIRS group in female patients and patients with larger stones (16-20 mm) (Table 3).
**Table 3**
| Unnamed: 0 | Unnamed: 1 | Stone clearance in group A | Stone clearance in group A.1 | Stone clearance in group B | Stone clearance in group B.1 | p-value |
| --- | --- | --- | --- | --- | --- | --- |
| | | Yes | No | Yes | No | p-value |
| Age (years) | 18-50 | 49 | 06 | 50 | 06 | 0.974 |
| Age (years) | 51-80 | 20 | 00 | 09 | 10 | 0.0001 |
| Gender | Male | 46 | 06 | 41 | 09 | 0.357 |
| Gender | Female | 23 | 00 | 18 | 07 | 0.006 |
| Size of stones (mm) | 11-15 | 36 | 05 | 39 | 03 | 0.436 |
| Size of stones (mm) | 16-20 | 33 | 01 | 20 | 13 | 0.0001 |
## Discussion
Because of the anatomical properties of the inferior calyx, lower pole stones are difficult to remove through the ureter, even if the stones are fragmented. RIRS is typically employed to treat the smaller lower pole stones (1.0-2.0 cm) while PCNL is primarily used to treat the larger diameter LPS or when RIRS has failed to clear the stones. With the increasing development of medical technology and technical expertise, the applicability of RIRS has increasingly extended to treat kidney stones larger than 2 cm or even larger than 3 cm [8]. On the other hand, many new PCNL technologies, including mini-PCNL and ultra-mini-PCNL (UMP), can not only treat larger kidney stones but also reduce the risk of kidney injury postoperatively. RIRS and UMP each have their own strengths and weaknesses, leading to controversy regarding the indication of these surgical procedures in the treatment of LPS. Compared to RIRS, PCNL can achieve a higher rate of stone fragmentation, although it carries greater surgical risk. A urologist named Janak Desai developed the ultra-mini percutaneous nephrolithotomy (UMP) in 2013 with a canal size of 11-14 F to lessen the risk of complications. As the percutaneous tract becomes smaller, the operation efficiency decreases and the intrarenal pressure may rise too high during the operation, causing damage to the kidney. Therefore, UMP is used to treat kidney stones smaller than 2 cm [9,10].
This study compared the min-PCNL and RIRS in terms of stone clearance rate, hospital stay, and postoperative complications. The results suggested that mini-PCNL had a better stone clearance rate as compared to RIRS however mini-PCNL was associated with longer hospital stay as compared to RIRS. Only two patients in the mini-PCNL group developed significant postoperatively bleeding requiring blood transfusion. Jiao et al. compared the results of mini-PCNL and RIRS in a meta-analysis of 8 randomized controlled trials (RCTs) in 725 patients with upper urinary calculi. They concluded that mini-PCNL had better efficacy in terms of stone clearance as compared to RIRS however mini-PCNL was associated with longer hospitalization time and a higher incidence of hematoma formation [11]. These results are in concordance with this study. Similarly another meta-analysis carried out by Gao et al. reported that mini-PCNL was more successful than RIRS for lower calyx stones in terms of stone clearance rate; however, RIRs involved shorter hospital stay and less hemoglobin fall [12]. Zheng et al. conducted a meta-analysis including two RCT and six controlled clinical trials and concluded that RIRS had similar results in terms of stone clearance as compared to mini-PCNL but RIRS was superior in terms of shorter hospital stay and lower complication rates [13]. Similar results were also quoted by Barone et al. in their meta-analysis [14] and Tsai et al. in their systemic review [15]. Although there is wide disparity in outcomes of mini-PCNL and RIRS in terms of stone removal, hospitalization, and complication rates for upper urinary tract kidney stones, given the existing literature, it is a generally accepted notion that RIRS is more effective in treating smaller stones (less than 2 cm) and PCNL is more effective on larger stones (more than 2 cm).
Of 75 patients in the mini-PCNL group, six patients had residual stones. Four of these had residual stones less than 4 mm in size and were treated conservatively, while two patients had stone sizes greater than 4 mm and were treated by extracorporeal shock lithotripsy. In the RIRS group, 16 patients had residual stones. Six patients had a stone size of less than 4 mm and were treated conservatively. Four patients opted for lithotripsy and the remaining patients underwent RIRS again. In the mini-PCNL group, two patients developed postoperative bleeding. Both patients were treated conservatively with blood transfusions and close monitoring.
This study has certain limitations. First, it is a single center study, which limits the generalization of our results to a broader population. Second, it has a smaller sample size. Multicenter studies with larger sample sizes will be beneficial in further evaluating the role of mini-PCNL and RIRS in the treatment of LPS.
## Conclusions
This study concluded that both mini-PCNL and RIRS are safe and efficient techniques for treating lower pole kidney stones with a size of 11-15 mm. However, mini-PCNL has a higher stone clearance rate compared to RIRS in the treatment of stones larger than 15 mm in size. This study further suggested that patients treated with mini-PCNL have a longer hospital stay compared to patients treated with RIRS. Therefore, the decision between the two procedures should be made based on anatomical parameters, particularly stone size, and patient preference.
## References
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|
---
title: The Psychological Impact of Rhino-Orbital Mucormycosis During the Second Wave
of COVID-19 Pandemic From South East Asian Country
journal: Cureus
year: 2023
pmcid: PMC10039460
doi: 10.7759/cureus.35349
license: CC BY 3.0
---
# The Psychological Impact of Rhino-Orbital Mucormycosis During the Second Wave of COVID-19 Pandemic From South East Asian Country
## Abstract
Aim: The present study addressed overcoming the lacunae in the literature of psychiatric manifestations associated with rhino-orbital mucormycosis. The current study aimed to assess the symptoms of depression, anxiety, stress, coping measures, suicidal intent, and visual disability in patients of rhino-orbital mucormycosis (ROM) during the epidemic of the disease at the nodal tertiary care center in North India.
Methods: Fifty-four inpatients of laboratory-proven rhino-orbital mucor-mycosis (ROM) were included for an observational, cross-sectional study at nodal, designated COVID-19, and mucormycosis treating tertiary care hospital. Patients with Hindi Mini-Mental State Examination score <24, prior psychiatric illness, and severely ill requiring ventilator support were excluded. The psychological variables were assessed using Depression, Anxiety, and Stress Scale 21 (DASS 21), Beck’s Suicide intent Scale, Coping Scale Questionnaire, and Visual disability scale (IND-VFQ33). Their socioeconomic status was assessed using the Modified Kuppuswamy Scale.
Results: Ninety percent of patients with ROM had diabetes mellitus. The majority ($44\%$) of patients belonged to lower socioeconomic strata. Higher frequencies of severe depression ($28\%$), extremely severe anxiety ($26\%$), and mild stress ($17\%$) were noted in the study participants. On the Tukey test, depression score was higher in patients of ROM compared to COVID (with ROM) (p-value= 0.016). On Tukey analysis, anxiety score was significantly higher in ROM patients compared to COVID (with ROM) patients (p-value = 0.018). Coping scores were significantly higher in COVID (with ROM) patients compared to ROM patients (p value = 0.035). Mild to moderate visual disability was noted in the study participants.
Conclusion: The current study reflects the association of higher depression and anxiety scores in cases with ROM that indicated higher mental health needs. Early assessment, early detection, and early intervention for psychological help, along with the multidisciplinary team, helped to improve the overall psychological outcome of the affected patients.
## Introduction
Mucormycosis, a ubiquitous fungus belonging to the taxonomic order of Mucorales, is known to cause angio-invasive fungal infection in immunocompromised hosts like patients of uncontrolled diabetes, leukemia, lymphopenia, and organ transplantation [1].
During the pre-COVID era, the annual prevalence of mucormycosis estimated by The Leading International Fungal Education (LIFE) portal was estimated to be around 10,000 cases, but with the inclusion of Indian data, the cases escalated to 910,000 [2]. During the COVID pandemic, the Indian global prevalence of mucormycosis was 70 times higher, with diabetes mellitus emerging as the major risk factor [3].
Due to the sudden surge in cases reaching epidemic levels, the Government of India declared mucormycosis to be a notifiable disease in May 2021. Guru Teg Bahadur hospital (GTBH), the largest tertiary care teaching hospital in East Delhi, was declared a nodal center for the treatment of both COVID-19 cases as well as mucormycosis.
Desai et al. found the following treatment measures, such as control of diabetes mellitus, intravenous infusion of intravenous amphotericin B, and surgical debridement of sinuses, useful for rhino-orbital mucormycosis (ROM) seen in post-COVID-19 [4]. In a recent study carried out on 104 participants in Ireland during the initial phase of the pandemic, rating scales such as Patient Health Questionnaire 9 (PHQ9) and General Anxiety Disorder 7 (GAD 7) were used for the assessment of psychological morbidity. They found that the prevalence of generalized anxiety disorder (GAD) was $20\%$, and depression was $22.8\%$ in that study. The younger age group, female gender, and low income were associated with the above-mentioned psychiatric morbidity [5].
During the second wave of the pandemic, a web-based online survey was conducted on 500 participants using GAD7, Centre for Epidemiology Scale of Depression. They reported $25.4\%$ of GAD and $18.8\%$ of depressive symptoms, respectively. The study also noted that the younger age group had a higher risk of developing depressive symptoms [6]. In a recent study carried out in 2021, $44.18\%$ presented with post-traumatic stress disorder-like symptoms, $48.8\%$ had significant depression, $65.56\%$ had anxiety, $22.09\%$ had stress symptoms, and $11.27\%$ had disturbed sleep. This study used the Depression, Anxiety, and Stress Scale 21 (DASS 21) for the assessment of psychiatric manifestations. Mental well-being was disturbed in $74.75\%$, and only $4.15\%$ had high resilience capacity [7].
As per the media reports, a large number of suicides were committed among students probable reason cited was the delay in examination (due to the lockdowns implemented by the authorities to contain the spread of COVID-19), resulting in their career-related uncertainty [8]. The mass movement of farmers, election rallies, religious processions, lack of coordination between center and state level resources, technical expertise, and inequitable distribution of health care facilities in rural and urban areas during the lockdown period probably led to mental health problems in the population [9].
The current study aimed to assess the symptoms of depression, anxiety, and stress along with coping capabilities, suicidal intent, and visual disability in patients of ROM during the epidemic of the disease at the nodal tertiary care center in North India. The early detection and assessment of the psychological manifestations can aid in early intervention.
## Materials and methods
Study design and population The present study was an observational cross-sectional study jointly conducted by the Department of Psychiatry and Ophthalmology on ROM patients recruited between July 2021 and September 2021 during the second wave of COVID-19 pandemic and the epidemic of mucormycosis reported from India. The study was approved by the institution’s ethics committee and adhered to tenets of the Declaration of Helsinki.
Sample size To the best of the knowledge of the authors, no study had been conducted before the start of the present study to assess the psychological impact on patients of mucormycosis. Hence, a convenient sample of a minimum of 50 patients with mucormycosis was decided for the present work.
During the study period from July 2021 to September 2021, 450 patients of ROM utilized emergency hospital services. Guru Teg Bahadur Hospital (GTBH), a tertiary care hospital affiliated with a medical school, was turned into a COVID designated facility completely in line with the Government of India guidelines. An additional healthcare facility for less severe cases of COVID-19 was made at Ramlila ground which was also being managed by the staff of GTBH.
One hundred seventy patients were transferred to an additional healthcare facility. Out of the remaining 280 patients, 64 were shifted to Intensive Care Unit as they required ventilatory support. Eighty-four patients were shifted to the internal medicine department due to severe systemic derangement of electrolytes, blood sugars, or blood pressure, and 46 patients were shifted under the care of the neurosurgery department as they had poor cognitive scores [Hindi Mini Mental Scale Scores, (HMMSE) <24]. Eighty-six patients were admitted under the Department of Ophthalmology and Ear, Nose, Throat (ENT), out of which eight were excluded as they had a prior psychiatric illness, and 24 patients did not give consent to participate in the study. Finally, a total of 54 patients were included for analysis in the study (Figure 1).
**Figure 1:** *Shows sample size selection of rhino-orbital mucormycosis cases (N=54)*
Data collection and evaluation A detailed history, including presenting complaints, past history, personal history, family history, socio-demographic history, and detailed physical, mental state, and ophthalmological examination, were carried out for all study participants.
Confirmed COVID-19 positive patients were defined as patients who tested positive for SARS-CoV-2 RNA by reverse transcriptase polymerase chain reaction (RT-PCR). COVID-19 suspects were defined as patients who had clinical symptoms suggestive of COVID-19 but whose RTPCR was negative or who were unable to get tested due to RT-PCR kit constraints.
A patient of mucormycosis was defined as a patient who tested positive by microbiological evidence from scrapings taken by direct nasal endoscopy or tissue biopsy and stained with potassium hydroxide (KOH) stain. All patients were staged according to the staging of Rhino-Orbital-*Cerebral mucormycosis* given by Honavar et al. [ 10], who staged the disease according to the site of involvement, signs, symptoms, and diagnostic measures needed for ROM.
Psychiatric questionnaires for assessment of depression, stress, anxiety symptom scale, coping scale, and visual disability scales were applied. The scales were applied within 24-48 hours of admission before the pharmacological intervention with amphotericin B. Cognitive functions were assessed for all participants, and informed consent was obtained.
Hindi version of the Depression, Anxiety, and Stress scale 21 (DASS 21) was used for the assessment of depression, anxiety, and stress symptom scores. Each of the three DASS 21 scales contained seven items divided into subscales with similar content. After the application of the scales, a final score was obtained, which helped to classify patients into normal, mild, moderate, severe, and extremely severe classes of depression, anxiety, and stress [11].
Beck’s suicide intent scale was used to assess suicide intent in patients in a psychometric/measurable way. It had 15 items; and each item was scored from 0 to 2. The final score calculated helped to classify the patients into low, medium, and high risk of suicide [12].
A coping scale questionnaire was used to assess cognitive, emotional, and behavioral methods of dealing with problems in patients with mucormycosis. It comprised 13 items, and each item was scored from 1 to 4. The final score was achieved by adding all the scores. A higher score indicated a higher coping capability [13].
Visual Disability scale (IND-VFQ33) had three scales: general functioning (21 items), psychosocial impact (five items), and visual symptoms (seven items). General functioning pertains to a person’s ability to move about, household chores, and activities of daily life on a 5-point scale. Visual symptoms pertained to the effect of quality of life due to visual loss on a 4-point scale. Psychosocial impact pertained to a person’s well-being as well as the quality of social and family interactions on a 4-point scale. A higher score meant poor functionality and, hence, poor vision-related quality of life. This scale was specially designed for the Indian population [14].
HMMSE had components of orientation (10 points) which includes temporal orientation (five points) and spatial orientation (five points); Memory (six points) which consisted of immediate recall (three points) and delayed recall (three points); language (eight points) which had naming (two points), verbal repetition (one point), verbal comprehension (three points), writing (one point) and reading a sentence (one point); Attention and calculation (five points) and design copying (one point). The final score was calculated by adding all the scores. A score of 23 or < was taken as a poor cognitive score and, hence, was excluded from the study [15]. All the study participants were classified into three groups. Group 1 had COVID-19 suspect and ROM, group 2 had only ROM, and group 3 had both confirmed COVID-19 and ROM.
Statistical tests *Qualitative data* were presented as frequency and percentage. Quantitative data were analyzed using ANOVA and Post hoc Tukey’s test. For statistical significance, a p-value of <0.05 was considered statistically significant. Data analysis was done using IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.
## Results
The study group comprised 54 patients of ROM, with mean age of 53.14 (range 35-70) years. Thirty-four ($62.95\%$) belonged to the male gender, and 20 ($37.04\%$) were females. All study participants ($$n = 54$$) had a cognitive score of >24 on the administration of the HMMSE scale. The mean cognitive score by the HMMSE scale for the study participants was 27.35±1.43.
Table 1 illustrates the socio-demographic details of the study patients ($$n = 54$$). The majority of patients belonged to the upper middle and lower middle classes, according to the modified Kuppuswamy Scale [16]. According to the staging of rhino-orbital-cerebral mucormycosis, 17 ($31.48\%$) patients had sinus with eye involvement (3a). One patient had a history of seizure disorder in the past. No patient in the study had a history of head injury.
**Table 1**
| Parameter | Frequency (N=54) | Percentage (%) |
| --- | --- | --- |
| Gender | | |
| Male | 34.0 | 62.95% |
| Female | 20.0 | 37.04% |
| Socioeconomic class | | |
| Upper middle | 8.0 | 14.81% |
| Lower middle | 22.0 | 40.74% |
| Upper lower | 24.0 | 44.44% |
| Occupation of the person | | |
| Professional | 5.0 | 9.25% |
| Clerks/Shop owner | 1.0 | 1.85% |
| Skilled worker | 7.0 | 12.96% |
| Semi-skilled worker | 12.0 | 22.22% |
| Unskilled worker | 5.0 | 9.25% |
| Unemployed | 24.0 | 44.44% |
| Qualification of the person | | |
| Graduate | 3.0 | 5.5% |
| Intermediate or diploma | 4.0 | 7.4% |
| High school certificate | 13.0 | 24.07% |
| Middle school certificate | 20.0 | 37.04% |
| Illiterate | 14.0 | 25.92% |
| Marital status | | |
| Married | 54.0 | 100% |
| Hypertension | 20.0 | 37.03% |
| Diabetes | 49.0 | 90.74% |
| Beck's suicide intent scale | | |
| Low risk | 53.0 | 98.14% |
| Medium | 1.0 | 1.85% |
| High risk | 0.0 | 0% |
| History of suicide in family | 2.0 | 3.70% |
| Staging of disease | | |
| 2c | 2.0 | 3.70% |
| 2d | 5.0 | 9.20% |
| 3a | 17.0 | 31.48% |
| 3b | 13.0 | 24.07% |
| 3c | 14.0 | 25.92% |
| 4b | 3.0 | 5.55% |
| Substance abuse | | |
| Alcohol | 3.0 | 5.55% |
| Smoking | 6.0 | 11.11% |
Figure 2 depicts the frequency of depression, anxiety, and stress symptom scores in the study participants ($$n = 54$$). Higher frequencies of severe depression ($28\%$), extremely severe anxiety ($26\%$), and mild stress ($17\%$) were noted in the study participants.
**Figure 2:** *Bar diagram showing the frequency distribution of depression, anxiety, and stress symptom scores in ROM (N=54)The numbers in the figure represent the percentages, ROM: Rhino-orbital mucormycosis*
On comparing depression between three groups; group 1 as COVID suspect & ROM; group 2 as ROM, and group 3 as COVID confirmed & ROM; depression emerged as the statistically significant factor between the three groups (p-value = 0.021) (Table 2).
**Table 2**
| Scales | Mean± Standard deviation | ANOVA (p-value) |
| --- | --- | --- |
| DASS-21 Anxiety Scores | | |
| CS+ROM | 16.40±4.33 | |
| ROM | 24.00±8.29 | 0.012* |
| CC+ROM | 18.21±6.36 | |
| DASS-21 Depression | | |
| CS+ ROM | 22.00±6.16 | |
| ROM | 23.90±5.46 | 0.021* |
| CC+ ROM | 19.07±6.02 | |
| DASS-21 Stress | | |
| CS+ ROM | 24.80±5.02 | |
| ROM | 26.19±8.17 | 0.940 |
| CC+ ROM | 25.93±8.13 | |
| Coping scale | | |
| CS+ ROM | 47.80±1.30 | |
| ROM | 46.57±1.99 | 0.040* |
| CC+ ROM | 47.93±1.78 | |
| VFQ-33 (GFS) | | |
| CS+ ROM | 26.20±5.49 | |
| ROM | 27.29±7.00 | 0.850 |
| CC+ ROM | 27.36±6.93 | |
| VFQ-33 (PIS) | | |
| CS+ ROM | 10.00±1.22 | |
| ROM | 8.86±1.39 | 0.400 |
| CC+ ROM | 8.93±1.99 | |
| VFQ-33 (VSS) | | |
| CS+ ROM | 10.40±1.67 | |
| ROM | 10.57±2.29 | 0.944 |
| CC+ ROM | 10.36±2.19 | |
| HMMSE score | | |
| CS+ ROM | 27.60±1.34 | |
| ROM | 27.29±1.15 | 0.910 |
| CC+ ROM | 27.36±1.67 | |
| Age | | |
| CS+ ROM | 60.20±7.15 | |
| ROM | 52.00±9.24 | 0.262 |
| CC+ ROM | 52.75±11.06 | |
On Tukey analysis, it was seen that the depression score was higher in group 2 (ROM) compared to group 3 (CC+ROM) (p-value= 0.016). There was no statistically significant difference between other CS+ROM vs. ROM and CS+ROM vs. CC+ROM pairs in relation to depression scores (Table 3).
**Table 3**
| Post Hoc Tukey’s Test p-values | Post Hoc Tukey’s Test p-values.1 | Post Hoc Tukey’s Test p-values.2 | Post Hoc Tukey’s Test p-values.3 |
| --- | --- | --- | --- |
| | CS+ROM vs ROM | CC+ROM vs ROM | CS+ROM vs CC+ROM |
| DASS-21-Anxiety | 0.087 | 0.018* | 0.857 |
| DASS-21-Depression | 0.789 | 0.016* | 0.558 |
| DASS-21-Stress | 0.934 | 0.993 | 0.954 |
| Coping scale | 0.377 | 0.035* | 0.989 |
| VFQ-33 (GFS) | 0.837 | 0.989 | 0.873 |
| VFQ-33 (PIS) | 0.386 | 0.989 | 0.415 |
| VFQ-33 (VSS) | 0.987 | 0.939 | 0.999 |
| HMSE score | 0.902 | 0.984 | 0.937 |
| Age | 0.242 | 0.964 | 0.291 |
Using ANOVA, anxiety was seen to be statistically significant between the three groups (p-value = 0.012). On further comparison between the groups using Tukey analysis, it was seen that the anxiety score was significantly higher in the MM group compared to the CC+ROM group (p-value = 0.018). There was no statistically significant difference between other CS+ROM vs. ROM and CS+ROM vs. CC+ROM pairs in relation to anxiety parameters (Tables 2, 3).
The coping parameter was seen to be statistically significant between the three groups (p-value = 0.04) using ANOVA. On Tukey analysis, it was seen that the coping scale score was significantly higher in the CC+ROM group compared to the ROM group (p-value = 0.035). There was no statistically significant difference between other CS+ROM vs. ROM and CS+ROM vs. CC+ROM pairs (Tables 2, 3) with respect to coping scale measures.
Between the three groups, the visual disability scale (IND-VFQ33), which included the general functioning scale, psychosocial scale, and visual symptom scale, was not statistically significant. The mean value for the general functioning scale (score range 21-105) was 26.20 for CS+ROM, 27.29 for ROM, and 27.36 for the CC+ROM group indicating that all patients had mild to moderate visual disability while pursuing their daily needs. The mean value for the psychosocial impact scale (score range 5-20) was 10.00 for the CS+ROM group, 8.86 for the ROM group, and 8.93 for the CC+ROM group indicating that mild to moderate disability due to vision affected their psychological and social well-being. The mean value for the visual symptom scale (score range 7-28) was 10.40 for CS+ROM, 10.57 for ROM, and 10.36 for the CC+ROM group indicating the satisfactory quality of life scale measures as measured on the visual disability scale.
## Discussion
The present study was carried out in a tertiary care teaching hospital, which was declared a dedicated COVID care and mucormycosis facility by the Government of India. The majority of the patients recruited in this study belonged to the age group of 35-70 years, with a mean age of 53 years. This is in accordance with the data established by a previous study that compared the epidemiology of mucormycosis in *India versus* the world. Muthu et al. observed that the mean age of presentation in India was 56 years in comparison with 52 years worldwide [17]. We also observed a male preponderance ($62.95\%$) in the patients who presented to us. This observation goes hand in hand with the existing epidemiological studies on mucormycosis in literature [3,17-20].
The majority of patients in this study belonged to the lower socio-economic strata and were unemployed ($44.44\%$), which is in agreement with a study that attributes the preponderance of mucormycosis cases in India to a large population belonging to the lower socioeconomic status [21].
Another observation in this study was that a large population of the study were high school graduates. To the best of our knowledge, this factor has not been studied earlier with respect to mucormycosis. All the participants of this study were married, as seen in another study by Ahuja A et al. in 2021 [22]. The risk factors associated with mucormycosis in our patients include diabetes mellitus ($90.74\%$) followed by hypertension ($37.03\%$). The association of these factors has largely been established in the existing literature [23,24].
Steenblock's study [25] reported the interface of COVID-19 with both diabetes and depression, where one condition can predispose to the other condition. One of the risk factors associated with COVID-19 survivors is the development of new-onset DM due to deranged blood sugar levels and viral infiltration of islets of the pancreas [25]. Steenblock's study [25] reported that both COVID-19 and diabetes mellitus are associated with insulin resistance and the role of inflammation. A recent study reported higher levels of inflammatory markers such as C reactive protein and Neutrophil-Lymphocyte ratio in the depressive-suicidal patient group [26]. All three conditions, namely COVID-19, diabetes, and depression, as per the previous studies mentioned above, are associated with inflammation, although the causative role is yet not established.
The majority of the participants were classified as Stage 3 and above. This is also in accordance with a multi-centric Indian study published in 2021 [18]. The sound methodology and standard rating scales, larger sample size, availability of amphotericin B injections during the pandemic, and dedicated multidisciplinary treating team were the strengths of this study.
Previous evidence, literature, and media highlighted the need for assessment of suicidal risk, as the pandemic overall had set in a gloomy picture [5-9]. The finding of $98\%$ cases of low suicide risk could be explained by the regular counseling services which were provided for all the patients after initial evaluation. One patient reported high suicidal risk (who had a history of suicide in the family) and was clinically diagnosed as suffering from grief reaction with a depressive episode (ICD F43). Selective serotonin reuptake inhibitor (SSRI), Fluoxetine 10 mg per day, was administered along with regular counseling sessions. Another study by Ahuja et al. [ 22] did not report any significant association of COVID-associated mucormycosis with suicidal ideation.
The study sample comprising ROM had stress symptoms in $100\%$ of patients and depressive and anxiety symptoms in $92\%$ and $96\%$, respectively. The depression (p-value = 0.021) and anxiety (0.012) symptoms were significantly associated with COVID suspect (with ROM), COVID (with ROM), and only ROM. Previous studies also report a higher association between depression and anxiety symptoms during the COVID-19 pandemic [6-7]. A previous study by Ahuja et al. [ 22] reported that $76.4\%$ of patients were experiencing anxiety or sadness before surgical intervention.
Our study was unique in highlighting the presence of Rhino-Orbital Mucormycosis ($$n = 54$$) with higher mean scores of both depression and anxiety symptoms which was like the previous study by Nair et al. [ 27] that consisted of $64\%$ of a sample of the cutaneous type of mucormycosis. The post hoc Tukey test also confirmed the higher scores of depression (p-value = 0.016) and anxiety (p value=0.018) in only ROM. The confirmed laboratory presence of ROM alerted the clinicians in collaborating with the departments of endocrinology as well as psychiatry in an inpatient setting at an early stage for providing comprehensive management.
Poorer coping methods were associated with ROM (p value= 0.035) in comparison with COVID cases in the study sample. This could be explained by the visual disability symptoms observed more in ROM cases that lead to impairment of day-to-day activities. One of the possible mechanisms for the higher association of depression and anxiety symptoms in ROM cases could be attributed to underlying physical ailments such as blindness in one eye, diabetes mellitus, hypertension, and higher stage of ROM 3 or above in the present study.
Psychiatry consultations were made a norm at the COVID and mucormycosis designated study center for all ROM admitted patients, in liaison with the other departments managing the disease as a part of the multi-disciplinary team. Appropriate interventions involving both pharmacological measures and counseling/psycho-educative support sessions were executed for all screened positive ROM cases (after the application of scales to assess their psychological state) by a trained senior psychiatrist who had expertise in dealing with COVID-19/mucormycosis cases.
Follow-up counseling sessions were exclusively conducted in association with the department of ophthalmology to deal with patients with mild to moderate visual disability. This is the first study in the literature to assess visual disability due to mucormycosis in terms of how it affects their general functioning, social life, and its effect on quality of life. Apart from the study cases, psychological support sessions were also conducted for their primary caregivers for educating them on how to handle the patients and help them deal with the post-discharge complications/interventions of both COVID and ROM. The limitation of this study is that the results cannot be extrapolated to the entire Asian region, as the data is obtained from a nodal tertiary center in North India dealing with the cases of COVID and ROM. Further research work requires pooled data from different regional centers involving different geographical locations.
## Conclusions
Nevertheless, the current study reflects the association of higher depression and anxiety scores in cases with ROM, which emphasizes the need for early intervention to improve the mental health as well as the overall well-being of the affected patients. Early assessments, regular counselling sessions may have contributed to the low suicide risk in ROM cases. The study enabled early detection of cases with higher depression, anxiety, stress symptoms, poor coping methods, and visually disabling symptoms resulting in poor psychosocial and general functioning. Early intervention by trained mental health professionals in liaison with a dedicated multidisciplinary team of medical experts improved the overall outcome of these ROM cases.
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|
---
title: Clustering of physical activity, sedentary behavior, and diet associated with
social isolation among brazilian adolescents
authors:
- Thiago Sousa Matias
- Julianne Fic Alves
- Gislaine Terezinha Amaral Nienov
- MarcusVinicius Veber Lopes
- Diego Itibere Cunha Vasconcellos
journal: BMC Public Health
year: 2023
pmcid: PMC10039485
doi: 10.1186/s12889-023-15444-x
license: CC BY 4.0
---
# Clustering of physical activity, sedentary behavior, and diet associated with social isolation among brazilian adolescents
## Abstract
### Backgound
Although obesogenic behaviors have been found to be related to social isolation, evidence-based person-centered approaches are lacking. This study investigated the association between clusters of obesogenic behavior – derived from a data-driven process – and social isolation among Brazilian adolescents.
### Methods
Data from the National Adolescent School-based Health Survey (PeNSE) 2015 were analyzed. A total of 100,794 9th-grade students ($51.3\%$ females; 14.3 ± 0.1 years old) enrolled in 3,040 public and private high schools participated in the study. Social isolation was assessed by two outcomes (i.e., perceived loneliness and lack of close friends). A two-step cluster analysis was conducted to identify patterns of obesogenic behaviors with the input of leisure-time physical activity (PA), sitting time as a proxy of sedentary behavior (SB), and the weekly consumption of healthy and unhealthy food. Crude and adjusted binary logistic regression models were applied to evaluate the associations between the clusters of obesogenic behaviors and social isolation variables in adolescents.
### Results
Three clusters were identified. Adolescents in the “Health-promoting SB and diet” ($32.6\%$; OR = 0.69; $95\%$ CI = 0.62–0.76) and “Health-promoting PA and diet” ($44.9\%$; OR = 0.73; $95\%$ CI = 0.67–0.79) clusters had lower odds of loneliness compared to those in the “Health-risk” cluster ($22.5\%$). Those belonging to the “Health-promoting PA and diet” cluster were more likely to report having close friends (OR = 1.19; $95\%$ CI = 1.00–1.41) than those in the “Health-risk” cluster.
### Conclusion
Adolescents in clusters where positive behaviors outweighed negative ones were less likely to perceive themselves as lonely and without close connections.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15444-x.
## Introduction
Regular practice of physical activity (PA) and reduced time in sedentary behaviors (SB) have been positively associated with mental health in adolescence [1, 2]. This benefit includes favoring socialization and avoiding social isolation [3, 4]. Thus, recent evidence has shown that both high PA and low SB are associated with lower loneliness [5]. Active adolescents can be more socially integrated [4], while having friends help them to overcome barriers associated with a less active lifestyle [6].
It is important to consider that PA and SB do not occur in isolation in adolescents’ lives. These behaviors carry synergies with other behaviors such as diet, for example, which can (synergically speaking) influence socialization [6]. This influence might depend on the extension of adolescents' lifestyle have more favorable than unfavorable behaviors coexisting [7, 8]. These behaviors can be more or less obesogenic depending on the different profiles observed [3].
The problem that arises is that: (a) being in an obesogenic cluster can make socialization difficult, increasing social isolation [9, 10]; and (b) clusters that combine positive and negative behaviors can still favor better socialization and psychological disposition in adolescents when compared to clusters that mostly combine risk behaviors [2, 11]. The pioneering study by Iannotti and Wang [2013] [11], which investigated clusters of obesogenic behaviors and their impact on physical and mental health in adolescents in the United States, found that the group with the highest sedentary behavior and the highest proportion of non-healthy diet, but who still moderately met physical activity criteria or consumed fruits and vegetables daily, had lower levels of body dissatisfaction. Similarly, Matias and colleagues reported that adolescents in healthier clusters were more likely to be satisfied with their body image [2]. More recently, a large survey of Brazilian adolescents found that obesogenic clusters with more positive behaviors than negative ones were associated with prosocial attitudes and reduced adolescent bullying [12].
Despite some evidence from cross-sectional studies, few investigations have explored the association between clusters of obesogenic behaviors and psychosocial factors, such as social isolation. It is important to note that adolescents do not possess isolated virtues in their lifestyle, and there is an interplay between essentially positive or negative behaviors that can have an impact on mental health in numerous ways. Therefore, countries with low- to middle-income, like Brazil, have undergone demographic and epidemiologic transitions characterized by economic and sociocultural disparities, significant increases in inequality [13], and a corresponding rise in diseases associated with changes in lifestyle. Thus, the aim of the present study was to investigate the association between clusters of obesogenic behaviors and social isolation in a population-based study of Brazilian adolescents.
## Study design and participants
A cross-sectional study using data from the National Adolescent School-based Health Survey (PeNSE – sample 1) was conducted in 2015 by the Brazilian Institute of Geographic and Statistics and the Ministry of Health of Brazil. PeNSE relates to World Health Organization recommendations for health surveys among students. The study investigates adolescents’ health and lifestyle behaviors among a nationally-representative sample of students enrolled in the 9th grade of elementary school from public and private schools. PeNSE sampling process was planned to represent all geographical areas of Brazil. A total of 102,301 students among 3,040 schools were initially assessed; 229 students declined to participate or did not report their age or sex. The sampling strategy included geographical stratification and the multi-stage selection that can be seen elsewhere (Oliveira et al., 2017). Ethical approval was obtained, and the participation of all subjects was approved by the National Committee of Ethics in Research (Comissão Nacional de Ética em Pesquisa [Conep]) number $\frac{1.006.467}{2015.}$ Methods were performed in accordance with the relevant guidelines and regulations.
The present survey is in its third edition. The questionnaire used for data collection is based on the Global School-Based Student Health Survey and Youth Risk Behavior Surveillance System and has been tested and adjusted [14].
## Clusters formation
To cluster formation, leisure-time physical activity (PA), sedentary behavior (SB), and diet were analyzed. Students’ PA was assessed using the question: In the past 7 days, without considering physical education class, how many days did you practice some physical activity like sports, dance, gym exercises, combat sports or other activity? The answers ranged from none to seven days in a week. SB was reasonable during the sitting time using the question: *In a* regular day, how much time do you spend watching television, playing video games, talking with friends or other sitting activities? The response options ranged from one to nine hour a day. Diet was assessed as continuous scores related to the weekly consumption of healthy (green salads or vegetables and fruits) and unhealthy food (deep-fried empanadas, candies, soda, fast foods, and ultra-processed food). Details on measurement, clusters formation procedure, and description have been provided elsewhere [8].
Briefly, a two-step cluster analysis was employed using the log-likelihood as the distance measure (to account for the congruency between clusters). Leisure-time PA, SB, and both healthy and unhealthy diet scores were independently included as continuous variables in the model. The low Schwarz’s Bayesian Criterion (BIC), the high ratio of distance measures, and the high ratio of BIC changes were used to determine the number of clusters. The theoretical assumption regarding the acceptability of the profiles was taken into account. The analysis was replicated among younger and older adolescents to check clusters’ acceptability [8]. Adolescents that have incomplete or missing data for PA, SB, or diet were not analyzed ($$n = 1$$,507, < $1.5\%$ of the total sample).
100,794 adolescent students were distributed into three different profiles: The “Health-promoting SB and diet,” comprising $32.6\%$ of the sample; the “Health-promoting PA and diet” ($44.9\%$ of the sample), the “Health-risk” cluster containing $22.5\%$ of the sample. The two health-promoting clusters are those where positive behaviors prevail over the negative ones, and the health-risk cluster combines a negative profile for all variables (see Table 1). The process of cluster labeling was arbitrary and aimed to give a more intuitive interpretation of the results. The assumptions of "healthy" or "risk" were based on the z-score of each variable imputed in the model. The cluster names were an overall idea of a possibly “more healthy” or “more unhealthy” pattern. Therefore, the labeling does not represent standardized cut-off points. A detailed description of the clusters can be seen elsewhere [8].Table 1Physical activity (PA), sedentary behavior (SB), unhealthy diet, and healthy diet in each of the three clusters. National School-Based Health Survey among ninth-grade students—PeNSE, Brazil 2015 (sample 1)Health-promoting SB and dietMean ± SDHealth-promoting PA and dietMean ± SDHealth-riskMean ± SDPhysical activity (day/week)0.68 ± 0.924.56 ± 2.050.86 ± 1.24Sedentary behavior (hour/day)2.59 ± 1.553.85 ± 2.317.78 ± 1.39Unhealthy diet (day/week)1.68 ± 0.972.87 ± 1.493.24 ± 1.45Healthy diet(day/week)2.68 ± 1.974.10 ± 2.072.58 ± 1.97
## Social isolation
Participants were asked about two aspects: (a) “In the past 12 months, how often have you felt alone”. A five-point Likert scale ranging from never to always was the response option. Those who responded always or most of the time were considered lonely; (b) “How many close friends do you have?”. The option of response was zero/one/two/three or more. Those who responded “zero” were considered to having no close friends.
## Covariates
The covariates were: sex, skin color, age, live with mother, live with father, mother's schooling, residents of the house, cigarette smoking, alcohol consumption, drugs consumption, physically aggression by an adult at home, involved in a fight with a firearm, involved in a fight with a melee weapon, suffered physical aggression, got involved in a fight, body satisfaction, health perception, type of school.
## Statistical analysis
The participants’ characteristics were described using absolute and relative frequency with $95\%$ confidence intervals ($95\%$CI) for nominal variables and means with standard deviations (SD) for numerical variables. Crude and adjusted binary logistic regression models were applied to evaluate the associations between exposure (the clusters) and social isolation variables (perceived loneliness and having close friends). For each model, a set of covariates was selected based on empirical and theoretical evidence [12] (see tables notes); The Rao Scott chi-square test were performed to evaluate the association of each covariate and both outcomes of social isolation. Significant predictors at p-value < 0.2 were retained and included in the adjusted models. Collinearity was examined using the Variance Inflation Factor (VIF). No evidence of multicollinearity was found as the VIF values for all covariates were small (< 5). The “Health-risk” cluster was the reference category in the regression models. The results were expressed in odds ratios (OR) and the respective ($95\%$ CI). All inferential procedures included the survey design and weighting. Data were analyzed using STATA 15 software (Stata Inc., College Station, TX, USA), except for cluster procedures. The significance level was defined as $p \leq 0.05.$
## Results
Table 2 shows the characteristics of the participants. A total of 100,794 students with a mean age of 14.28 years old (SD = 0.013) were observed. Regarding social isolation, $16.39\%$ of the adolescents experienced loneliness ($95\%$ CI = 15.93–16.85) and $4.29\%$ reported not having any close friends ($95\%$ CI = 4.05–4.54). Other individual aspects such as behavioral characteristics, victimization, and health outcomes, are presented in the supplemental material. Table 2Characteristics of the sample. PENSE, Brazil 2015 ($$n = 100$$,794)Variablesn%$95\%$ CISchool-level covariates Type of school Municipal2880.10.04—0.26 State49,46248.3545.05—51.66 Federal31,40437.0934.15—40.13 Private20,91814.4612.53—16.63 Full-time school Yes22,85422.0721.19—22.98 No78,71577.9377.02—78.81 Boarding school Yes3,8464.0853.748—4.451 No97,94295.9295.55—96.25Household-level covariates Computer at home Yes69,82269.5668.51—70.6 No32,14430.4429.4—31.49 Internet at home Yes78,39577.5576.69—78.38 No23,57222.4521.62—23.31Household residents (mean)–4.494.47—4,52Sociodemographic factors Sex Male49,29048.7248.09—49.34 Female52,78251.2850.66—51.91 Skin Color White33,77536.1535.12—37.18 Black12,84913.3912.88—13.92 Yellow4,5804.113.87—4.36 Pardo46,93543.0542.17—43.94 Indigenous3,8253.293.07—3.53 Age, mean–14.2914.26—14.31 Mother's schooling Did not studied5,5315.385.089—5.695 Incomplete elementary school18,21719.3818.8—19.97 Elementary School6,0246.4656.167—6.775 Incomplete high school6,2756.055.78—6.33 High school17,90318.0117.47—18.57 Incomplete college5,4564.544.27—4.83 College17,23213.2612.34—14.23 Do not know25,18326.926.25—27.56 Live with mother Yes90,45889.9489.57—90.3 No11,54310.069.7—10.43 Live with father Yes63,60063.7163.01—64.41 No38,34136.2935.59—36.99 *Has a* cell phone Yes88,97887.3886.87—87.87 No13,01212.6212.13—13.13n Absolute frequency, % Prevalence; $95\%$ CI $95\%$ Confidence interval $95\%$ Table 3 shows the association between clusters and social isolation variables among adolescents. In the adjusted analysis, adolescents in the Health-promoting SB and diet (OR = 0.69; $95\%$ CI = 0.62–0.76) and in the Health-promoting PA and diet (OR = 0.73; $95\%$ CI = 0.67–0.79) clusters showed reduced odds of loneliness compared to those in the Health-risk cluster. Those belonging to the Health-promoting PA and diet cluster were more likely to have close friends (OR = 1.19; $95\%$ CI = 1.01–1.41) compared to those in the health-risk cluster. Table 3Associations between the clusters and the perception of loneliness and friendships among the adolescents. PeNSE, Brazil 2015Perceived loneliness($$n = 98$$,485)Friendship($$n = 97$$,898)CrudeAdjustedaCrudeAdjustedbOR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)OR ($95\%$ CI)Clusters Health-riskRefRefRefRef Health-promoting SB and diet0.50 (0.46—0.55)0.69 (0.62—0.76)0.89 (0.76—1.03)0.86 (0.74—1.02) Health-promoting PA and diet0.50 (0.46—0.55)0.73 (0.67—0.79)1.23 (1.04—1.45)1.19 (1.002—1.41)OR Odds Ratio, $95\%$ CI $95\%$ Confidence Interval; Estimates were weighted according to the sampling design; aAdjusted for sex, skin color, age, live with mother, live with father, mother's schooling, residents of the house, cigarette smoking, alcohol consumption, drugs, physically aggression by an adult at home, involved in a fight with a firearm, involved in a fight with a melee weapon, suffered physical aggression, got involved in a fight, body satisfaction, health perception, type of school. bAdjusted for sex, skin color, age, live with mother, live with father, mother's schooling, residents of the house, cigarette smoking, drugs, physically aggression by an adult at home, involved in a fight with a firearm, involved in a fight with a melee weapon, suffered physical aggression, got involved in a fight, body satisfaction, health perception, computer at home, internet at home, full school, boarding school, have a cell phone, type of school
## Discussion
The present study investigated the association between clusters of obesogenic behaviors and social isolation in a population-based sample of Brazilian adolescents. We observed that the likelihood of loneliness and having close friends varied across the clusters, and adolescents from both healthier groups seem to have lower odds of loneliness.
The literature has shown that some psychosocial outcomes of adolescents can be influenced by obesogenic behaviors such as PA, diet, and SB [2, 11, 15]. In addition, there is evidence that adolescents with healthier obesogenic behavior profiles have better socialization, with lower chances of loneliness [5, 9, 10]. Our findings also highlight the fact that many adolescents have lifestyles defined by the coexistence of favorable and unfavorable health behaviors [16]. Thus, adolescents in clusters that include an active lifestyle seem to have more prosocial attitudes in their life, such as talking to friends and going for a walk more often [17], improving friendship quality and decreasing the chances of being socially isolated [4].
A systematic review investigating the relationship between friendship and PA analyzing cross-sectional, longitudinal, and experimental studies with adolescents in the United States showed that peers and friends play an important role in adolescents' PA levels. This association was positive in terms of peer support, presence of peers and/or friends, peer norms, friendship quality, peer acceptance and affiliation, and negative for peer victimization [18]. Despite the inverse chain to our proposed analyses, having friends helps to provide intrapersonal and interpersonal psychological support, favoring better psychological disposition for the maintenance of PA behavior during adolescence. In addition, being active favor socialization, evidencing a cyclical relationship between behaviors—socialization—behavior.
Some mediators have been hypothesized to help explain our findings. For instance, increasing self-confidence and supporting greater problem-solving ability were observed in the univariate relationships between PA and social isolation [17] and between SB and social isolation [5]. We hypothesize that PA and SB may share psychosocial mediators, and one behavior “supplies” the other in maintaining more prosocial attitudes. In other words, a cluster that lacks PA but the adolescent has a reduced time in sedentary behavior can still provide some benefits and vice-versa. Furthermore, obese adolescents report more loneliness, have fewer friends, and are more bullied [19]. In this case, it is suggested that healthier clusters can favor both better results for biological outcomes, such as decreasing overweight and obesity and minimizing interpersonal and intrapersonal vulnerabilities in adolescence [2].
Some limitations can be observed in this study. First, the cross-sectional nature of the data does not allow for a causal relationship to be established between adolescents' behaviors and our selected outcomes. The causal chain of these relationships between behavior and health outcomes is well established. However, it is plausible that the observed associations are bidirectional. Although obesity seems to influence social isolation, we ask for caution when interpreting our findings, as there was no control for this variable (not available for PeNSE—sample 1) in our adjusted analysis. Feeling lonely and having fewer friends can keep adolescents away from healthier behaviors, such as PA during leisure time. The social isolation construct is self-reported based on two indicators that may not represent the phenomenon's complexity; therefore, caution in interpreting the results is necessary. Nevertheless, the observed research problem is substantial. It has an analytical approach centered on the individual (clusters analysis); the clusters observed in this population were validated in previous research and involve a representative sample. To the authors’ knowledge this is the first study to assess the relationship between clusters of obesogenic behaviors and social isolation. In addition, this study draws attention to a less obvious association between lifestyle and health outcomes. Schools, administrators, and policymakers can look beyond obesity and realize that opportunities, attitudes, and choices (in non-particular order) about adolescent lifestyle can impact the social life and the mental health of young people. Therefore, multicomponent interventions are required, considering the synergies between lifestyle behaviors.
## Conclusions
Adolescents in clusters where health-favorable behaviors outweighed unfavorable ones were less likely to perceive themselves as lonely and without close friends. Clusters of obesogenic behaviors seem to share mediators that expose or protect adolescents not only to obesity but also to critical psychosocial outcomes for social life, such as having more friends.
## Supplementary Information
Additional file 1: Table 1. Continuation of the adolescent characteristics table, with behavioral variables, victimization and health outcomes.
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|
---
title: Fibrosis score 4 index has an independent relationship with coronary artery
diseases in patients with metabolic-associated fatty liver disease
authors:
- Maryam Namakchian
- Soghra Rabizadeh
- Sara Seifouri
- Hassan Asadigandomani
- Melika Arab Bafrani
- Kiana Seifouri
- Foroogh Alborzi Avanaki
- Armin Rajab
- Manouchehr Nakhjavani
- Alireza Esteghamati
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10039491
doi: 10.1186/s13098-023-01031-y
license: CC BY 4.0
---
# Fibrosis score 4 index has an independent relationship with coronary artery diseases in patients with metabolic-associated fatty liver disease
## Abstract
### Background
Metabolic-associated fatty liver disease (MAFLD), one of the most common liver diseases, is detected in patients with concomitant hepatic steatosis and Type 2 Diabetes (T2D). We looked into the relationship between Fibrosis-4 (FIB-4) index and coronary artery diseases (CAD) in patients with MAFLD, to further look into the efficiency of FIB-4 in screening for CAD among patients with MAFLD.
### Method
In this study, we included 1664 patients with MAFLD (T2D, who also had hepatic steatosis) during 2012–2022 and divided them into 2 groups; CAD and non-CAD. Demographic, Anthropometric indices, liver function tests, lipid profile and FIB-4 index of all patients were evaluated and compared.
### Result
Among the 1644 patients (all have MAFLD), 364($21.4\%$) had CAD. Patients with MAFLD and CAD were more probable to be hypertensive, have longer duration of diabetes and be older (with p-values < 0.001). After adjustment for confounding factors, in a multivariable logistic regression model, FIB4 showed a significant independent relationship with concomitant MAFLD and CAD. Upper Tertile FIB-4 had an odds ratio of 3.28 (P-value = 0.002) to predict CAD. Furthermore, in Receiver Operating Characteristic (ROC) *Curve analysis* with the maximum Youden Index, a FIB-4 cut-off of 0.85 (AUC = 0.656, $95\%$ CI 0.618–0.693, $P \leq 0.001$) noted to predict CAD in patients with MAFLD.
### Conclusion
This study showed that the FIB-4 score independently correlates with CAD in patients with MAFLD.
## Introduction
The number of patients with diabetes is increasing dramatically around the world; it has been estimated that at the current rate there will be 634 million patients with diabetes in the world by 2030 [1].
One of the most common liver diseases worldwide is non-alcoholic fatty liver disease (NAFLD) which has become a public health problem in recent years [2]. Metabolic-associated fatty liver disease (MAFLD) is a novel terminology that was proposed recently by international experts instead of NAFLD in patients with overweight/obesity, type 2 diabetes, or evidence of metabolic dysregulation, in addition to hepatic steatosis [3]. MAFLD is important as it can further increase the risk of cardiovascular complications which can be fatal [4]. Various studies showed that by changing the definition of NAFLD to MAFLD, high percentage of people with fatty liver disease who had metabolic dysregulation may be in higher risk of developing coronary artery diseases (CAD) [5, 6].
The overall prevalence of MAFLD is about $39\%$ among general population. Not only obese people, but lean and non-obese people are also vulnerable to MAFLD. It is worth noting that hypertension and diabetes are important comorbidities in non-obese patients with MAFLD [7, 8]. The main causes of death in MAFLD are cardiovascular, malignancy and end stage liver disease [9].
Liver biopsy is the gold standard for diagnosis of NAFLD. Because liver biopsy is an invasive procedure, alternative tools like FIB-4 which is non-invasive and inexpensive is used to estimate liver fibrosis in MAFLD [10]. FIB-4 index is calculated by using age, platelet count (PLT) and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels. Hence, this index can be used for the early detection of liver fibrosis among patients with T2D [11]. The FIB-4 index is measured according the formula that follows: Age(years)* AST(Unit/Liter)/ (PLT (109/L)*√ALT(Unit/Liter)) [12].
The aim of this study is to investigate the potential link between FIB-4 index and cardiovascular complications in patients with MAFLD.
## Study population
In this prospective study, patients with T2D referred to diabetes clinic of Vali-Asr hospital, affiliated with Tehran University of Medical Sciences during 2012 to 2022 were included. Patients with T2D based on the 2022 American Diabetes Association guideline [13] and non-alcoholic fatty liver disease based on ultrasound findings were included.
Those under the age of 18, with T1D, pregnancy, with a history of malignancy, end stage renal disease, heart failure or cirrhosis were excluded from the study. A total of 1644 patients with concomitant T2D and non-alcoholic fatty liver disease were included in this study, and then they were divided into two groups; patients with and without a coronary artery disease (CAD). In this study Patients with history of myocardial infarction, acute coronary syndrome [14], percutaneous coronary intervention (PCI), Coronary artery bypass graft (CABG) or angioplasty were considered to have CAD [15].
## Data collection
Patients’ baseline demographic and anthropometric characteristics including age, gender, duration of diabetes, history of hypertension (HTN), height, weight, and waist circumferences were recorded. Informed consent was obtained from all subjects according the declaration of Helsinki. All of the subjects were over 18 years old and all of them were qualified to give consent, so they filled the consent form individually.
Systolic and diastolic blood pressure, and laboratory data including fasting blood glucose (FBS), hemoglobin A1C (HbA1c), 2-hour post-prandial blood glucose (2hpp), creatinine, lipid profile including triglyceride (TG),cholesterol (Chol), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), liver enzymes, insulin level, platelet count were measured. Urinary albumin excretion was measured using urinary albumin-to-creatinine ratio in random urine samples. Urinary albumin concentrations were evaluated by an immunoturbidimetric assay. Albuminuria was defined as the urine albumin-to-creatinine ratio greater than 30 mg/gr. Creatinine was measured by enzymatic method on automated analyzer. Homeostatic model assessment-Insulin Resistance (HOMA-IR) was calculated. Estimated GFR was calculated by the Modification of Diet in Renal Disease (MDRD) equation.
Waist circumferences were measured in upright position as the horizontal plane midway between the costal margins and the iliac crest. Hip circumference was measured as the distance around the largest part of the hip and Waist to Hip Ratio (WHR) was calculated by dividing waist circumference by hip circumference. For body mass index (BMI) calculation, weight (in kilograms) was divided by the square of height (in meters). Blood pressure was measured after 15 min of rest after patients arrived by using an automated blood pressure device. The mean of two blood pressure recordings, that were measured 10 min apart, was recorded. All blood samples were obtained after a 10–12 h of fasting and measured with kits certified by the central reference laboratory. HbA1c was recorded via high-performance liquid chromatography (A1C, DS5 Pink kit; Drew, Marseille, France). FBS was measured by enzymatic colorimetric methods with the glucose oxidase test and serum lipid profile (TG, HDL-C, LDL-C) were measured by enzymatic methods.
For the diagnosis of NAFLD based on imaging, at least two of the following three criteria were required: echogenic liver with an existing contrast compared with renal parenchyma, blurring of the vessels, and hepatic vein narrowing [16].
## Statistical analysis
All analyses were carried out using the 24th version of the SPSS software. P-values less than 0.05 were considered statistically significant. The normal distribution of the sample was tested with Kolmogorov-Smirnov and Shapiro-Wilk tests, p-p, plot and histogram. Continuous variables with normal distribution were expressed as means ± standard deviations (SD), and continuous variables with skewed distribution were expressed as median and interquartile range. T-test was conducted to differentiate these variables among patients with and without CAD and Mann Whitney U test was used for variables without normal distribution. Categorical variables were recorded as frequencies or proportions to evaluate the association of variables with CAD; chi-square analysis was applied where appropriate. Multivariate logistic regression analysis was performed to assess the relationship between FIB4 and other indicators with CAD. Odds ratios (ORs) that were calculated in the logistic regression analysis were expressed with a $95\%$ confidence interval (CI). The area under the ROC (receiver operating characteristic) curve was estimated to determine the prognostic value of FIB4 for CAD in patients with MAFLD and the cut-off for FIB-4 was estimated using the Youden index.
## Results
A total of 1644 subjects with MAFLD were studied. These subjects were divided into two groups, including CAD and Non-CAD. The non-CAD control group consisted of 1280 ($75.3\%$) patients, whereas the CAD group was composed of 364 ($21.4\%$) patients.
Table 1 presents the baseline characteristics of the two groups. The mean age of CAD patients was 62.10 ± 10.182 and $61.5\%$ [224] of them were male. The mean age of non-CAD controls was 53.02 ± 11.023 and $48.5\%$ [621] of them were male. According to Table 1, participants with MAFLD and CAD were more likely to be older, male, hypertensive, had longer duration of diabetes, and increased frequency of albuminuria compared to patients without CAD (with all P-values < 0.001). Also, their FBS and 2hpp (2-hour post-prandial) levels were higher (with all P-values < 0.001). These patients were shown to have higher levels of SBP (P-value = 0.017) and HbA1C (P-value = 0.017), waist to hip ratio (P-value = 0.015) compared to the non-CAD patients. Whereas, the opposite was true for the levels of AST (P-value = 0.010), ALT (P-value = 0.002), ALKP (P-value = 0.005), and eGFR (P-value < 0.001). They were shown to have lower levels of cholesterol, triglyceride and LDL than their non-CAD counterparts.
Table 1Comparison of baseline characteristics of patients with MAFLD with and without CADNon-CADN = 1280CADN = 364P-Value Age, years 53.02 ± 11.0262.10 ± 10.18 < 0.001 Duration of DM, years 8.87 ± 6.6914.97 ± 8.79 < 0.001 Gender (female/male) $51.5\%$ [659] /$48.5\%$ [621]$38.5\%$ [140] / $61.5\%$ [224] < 0.001 Waist circumference, cm 103.36 ± 11.09103.72 ± 10.020.55 Waist/Hip 0.94 ± 0.050.95 ± 0.05 0.01 SBP, mmHg 128.62 ± 33.68133.00 ± 16.98 0.017 DBP, mmHg 79.75 ± 7.7879.68 ± 8.810.90 HTN $34.7\%$ [443]$58.5\%$ [213] < 0.001 BMI, kg/m² 31.36 ± 5.6331.06 ± 5.140.37 platelet 275 ± 52.7260 ± 59.80 < 0.001 FBS, mg/dl 155.55 ± 52.25169.82 ± 67.06 < 0.001 2hpp, mg/dl 211.98 ± 83.58231.28 ± 83.71 < 0.001 Hb AIC, % 7.591 ± 1.617.80 ± 1.45 0.01 Cholesterol, mg/dl 186.20 ± 43.95168.33 ± 43.46 < 0.001 HDL-C, mg/dl 44.40 ± 11.7343.92 ± 11.160.48 LDL-C, mg/dl 105.91 ± 34.2291.32 ± 32.86 < 0.001 TG, mg/dl 195.86 ± 141.84169.39 ± 83.77 < 0.001 Creatinine 0.97 ± 0.231.04 ± 0.23 < 0.001 AST, U/L 29.29 ± 17.8326.85 ± 14.92 0.010 ALT, U/L 41.07 ± 23.4336.58 ± 24.29 0.002 ALKP, U/L 169.77 ± 88.35153.34 ± 72.30 0.005 eGFR,mL/min/1.73 m² 103.96 ± 31.6295.98 ± 27.25 < 0.001 Albuminuria $17.3\%$ [134]$28.4\%$ [74] < 0.001 HOMA-IR 4.05 (2.72–5.87)4 (2.80–5.65)0.44 Smoking % (n) $4.6\%$ [59]$6\%$ [21]0.43 FIB4 0.94 ± 0.471.15 ± 0.48 < 0.001 Insulin levels 12.57 ± 7.5311.36 ± 5.97 0.003 Statin use Antidiabetic drugs $56.2\%$$74.1\%$ < 0.001 Oral agents $84.3\%$$84.1\%$0.50 Oral agents + Insulin $12.7\%$$11.6\%$ Insulin $2.9\%$$4.3\%$Data are presented as mean ± standard deviation, median (interquartile range), or counts (percentages).MAFLD: metabolic-associated fatty liver disease, DM: diabetes mellitus, SBP: systolic blood pressure, DBP: diastolic blood pressure, BMI: body mass index, FBS: fasting blood glucose, HBA1C: hemoglobin A1C, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol, TG: triglyceride, AST: aspartate transaminase, ALT: alanine transaminase, ALP: alkaline phosphatase, eGFR: estimated Glomerular Filtration Rate, HOMA-IR: homeostatic model assessment of insulin resistance BMI (P-value = 0.372), waist circumference (P-value = 0.556), smoking (P-value = 0.434), HDL-C (P-value = 0.489), HOMA-IR index (P-value = 0.397) and, DBP (P-value = 0.902), did not differ between MAFLD patients with and without CAD.
Nonetheless, FIB4 index was significantly higher among patients with both MAFLD and CAD compared those without CAD (1.15 ± 0.48 versus 0.94 ± 0.47) respectively (P-value < 0.001).
As shown in Table 2, we divided subjects according FIB-4 tertile scores to shows the number and proportion of patients with MAFLD and CAD in each group. While the lower tertile consists of 29 ($12.6\%$) patients with MAFLD and CAD, the middle and upper tertile, include 77 ($33.3\%$) and 125 ($54.1\%$), respectively.
Table 2Prevalence of CAD in three groups according to FIB-4 tertile in patients with MAFLDFIB-4 tertileNon-CADCADP-valueTertile 1st (0.22–0.74)$27234.1\%$$2912.6\%$< 0.001Tertile 2nd (0.746–1.062)$24330.5\%$$7733.3\%$< 0.001Tertile 3rd (1.063–3.78)$28335.5\%$$12554.1\%$< 0.001FIB-4 fibrosis score index, CAD coronary artery disease (the percentage of patients with CAD in the second and third tertiles of fib-4 is significantly higher compared to first tertile. While this percentage is significantly higher in Non-CAD patients in the first tertile of fib-4, these results confirm that higher fib-4 is associated with CAD).
In multivariable logistic regression analysis, FIB4 index had a significant relation with CAD in those with MAFLD. This relationship was remained significant after adjusting for multiple confounding factors including gender, age, smoking, duration of diabetes, BMI, waist-to-hip ratio, HTN, HbA1c, HDL-C, LDL-C, TG and eGFR. The odd’s ratio for the middle tertile of FIB4 index compared to lower tertile showed to be 2.59 with a P-value = 0.008, and the upper tertile had a higher odd’s ratio, at about 3.28 and a P-value = 0.002. ( Table 3) Table 3Results of Multivariate logistic regression analysisBetaStandard errorOdd’s ratio$95\%$ C.I.P-valueLowerUpperFIB-4 index:(Reference)Lower tertile-----0.004Middle tertile0.9530.3612.5941.2775.2680.008Upper tertile1.1880.3603.2801.6216.6380.001 Gender (male) 1.0110.3082.7491.5045.0260.001 Age 0.0280.0161.0280.9961.0620.087 Duration of DM 0.0940.0171.0981.0631.1350.000 History of HTN 0.1670.2321.1810.7501.8600.472 Smoking 1.0700.6612.9150.79710.6570.106 LDL-C -0.0150.0040.9850.9780.9920.000 TG, mg/dl 0.0010.0011.0010.9981.0030.468 Hb A1c -0.0190.0780.9810.8411.1440.807 eGFR -0.0160.0070.9840.9710.9970.016 Albuminuria 0.2870.2751.3320.7782.2810.297FIB-4: Fibrosis score 4, FIB-4 first tertile is considered as reference, WHR: waist to hip ratio, eGFR: estimated glomerular filtration rate measured in mL/min/1.73 m², BMI: body mass index, DM: diabetes mellitus, HTN: hypertension, HDL-C: high density lipoprotein cholesterol, TG: triglyceride, HBA1c: hemoglobin A1C In ROC analysis the predictive value of FIB4 index for CAD in patients with MAFLD is illustrated in Fig. 1; Table 4. With the maximum Youden Index, the cut-off was set at 0.85 with a sensitivity of $75\%$ and specificity of $50\%$. ( AUC = 0.656, $95\%$ CI 0.618–0.693, $P \leq 0.001$).
Fig. 1AUROC curve for FIB-4 Table 4Multivariate logistic regression analysisAUC$95\%$ CISensitivitySpecificityCut-off FIB-4 0. 6560.618–$0.69375\%$$50\%$0.85FIB-4 Fibrosis-4 index, AUC area under the curve, AUROC area under the receiver operating characteristic
## Discussion
In this study, the relationship between FIB-4 index and coronary artery disease in patients with MAFLD was evaluated. The results of the present study showed that after adjustment for multiple confounding factors, patients with a higher FIB-4 score are 2.5 to 3 times more probable to have CAD. The FIB-4 index had an independent relationship with CAD in a multivariable logistic regression.
A new definition of metabolic-associated fatty liver disease or “MAFLD” reflects metabolic dysregulation much better than NAFLD, because the term of NAFLD emphasizes just on “non-alcoholic” but MAFLD insinuates metabolic causes of liver disease. These criteria of MAFLD are based on the presence of hepatic steatosis in the presence of one or more of overweight/obesity, type 2 diabetes mellitus, or evidence of metabolic dysregulation [17]. Due to increasing frequency of obesity, and diabetes we are facing with a surge of metabolic disorders in children and adolescents. The criterion for a diagnosis of pediatric MAFLD is based on liver histopathology or imaging, serum biomarkers or score of hepatic steatosis with at least one of these criteria: excess adiposity, T2DM, or any evidence of metabolic dysregulation [18].
*Certain* genetic causes are involved in the etiology of MAFLD. Studies have shown that the breakdown of toll-like receptor (TLR) tolerance can lead to tissue damage and the activation of TLR causes inappropriate inflammatory reactions that have been implicated in the severity of MAFLD [19]. Studies has now focused on genome-wide association studies (GWAS) to discover via multi-trait GWAS, genome-wide association studies (PheWAS), Mendelian randomization and functional annotation studies [20].
Liver fibrosis is associated with CAD risk factors such as obesity, hypertension, diabetes, dyslipidemia, etc. NAFLD can impact the severity of atherosclerosis [21]. In multiple studies, the relation between NAFLD and CAD has been detected, indicating that patients with NAFLD have a higher chance of developing CAD and its life-threatening complications [22–24]. In contrast, a study by Ken Liu, et al. in 2017, reported that the amount of fat in the liver, as measured by controlled attenuation parameter (CAP), did not correlate with the incidence of cardiovascular events [25].
Though the definite cause behind the increased rate of CAD among patients with hepatic steatosis has not been determined as of yet, some speculations have been made. One of which is that NAFLD is commonly associated with T2D, a comorbidity also known as MAFLD, leading to insulin resistance and increased blood glucose. These all can in turn further increase the chances of patients developing CAD by triggering monocyte/macrophage adhesion to the vascular walls, and stimulating chemokine secretion by the smooth muscle cells of the vessels, and activating inflammation via macrophages [26]. In line with the findings in patients with NAFLD, MAFLD can potentially influence the risk of CAD. This is because of the overlap between NAFLD and MAFLD as well as the more metabolic derangements in patients with MAFLD, which in turn further increases the risk of CAD [27].
Many specialists refer patients that are at intermediate or high-risk of developing CAD to cardiologists for a cardiovascular review. That being said, currently no screening tool has been proven to be efficient enough to be adopted for asymptomatic patients with MAFLD [28].
In 2019, Song et al. carried out a study on patients with NAFLD without CAD and concluded that FIB-4 score as a noninvasive fibrosis marker is significantly associated with the coronary artery calcium score (CACS) > 100 [29]. Also Lee, J. et al. showed the association between intermediate/high FIB-4 scores and the progression of coronary artery calcification (CAC) in patients with NAFLD [30].
Tsai, T.Y, et al. study in 2022 revealed that patients with atherogenic plaque in the coronary computed tomography angiography had higher FIB4 and other liver fibrosis scores including Forns score, and NFS [31].
Jin, J.L, et al. study showed that in patients with established CAD in the general population, the FIB4 index had a positive relationship with the number of diseased vessels [32].
In 2022 Chen, X., et al. stated that the FIB-4 and other noninvasive liver fibrosis scoring systems (NFS, APRI, and BARD) are useful in assessing advanced fibrosis for patients with MAFLD [33].
In the CORONASH study, carried out in 2021, association of FIB4 index with advanced liver fibrosis in patients with established CAD was evaluated. One hundred eighty nine patients with proven CAD were assessed for a concomitant advanced liver fibrosis disease with the use of 5 different non-invasive fibrosis tests. This study showed that about $5\%$ of patients with established CAD had advanced liver fibrosis. They propose the use of non-invasive fibrosis tests in CAD patients to avoid non necessary further assessment (e.g. by Fibro scan, then liver biopsy) [34]. In our study, we mainly focused on the relationship between FIB-4 levels in patients with concomitant MAFLD and CAD. To do this, the predictive value of FIB-4 was measured after adjusting for multiple confounding factors including Age, Gender, Duration of DM, HTN, Smoking, BMI, GFR, Waist/Hip ratio, HbA1c, lipid profile, and albuminuria. Our findings were in line with the CORONASH study with regards to AUROC of FIB-4, here it was estimated at approximately 0.656, which was slightly higher compared to that of the CORONASH study (0.647).
In a prospective cohort study Chen, Q., et al. followed 3263 patients with established CAD in general population with regards to their mortality rate. 319 deaths were identified due to cardiovascular diseases. They showed that patients with the highest FIB-4 score levels had more cardiovascular mortality compared to those with the lowest FIB-4 score [35].
Han, E., et al. calculated the ASCVD risk scores among general population, where the prevalence of MAFLD was $38.0\%$. They concluded that in patients with MAFLD, higher FIB-4 score correlates with higher ASCVD risk score [27]. According to high global prevalence of obesity and other associated disease including diabetes, metabolic dysfunction-associated fatty liver disease (MAFLD), hypertension, CAD, malignancy and HTN a primary care-driven, patient-centered, multidisciplinary model is needed to provide a holistic care with focus on clinical care and new clinical trials study for management of metabolic diseases [36].
In the present study, a significantly lower LDL-C, TG and cholesterol levels were observed in the CAD group compared to the non-CAD group. We think that it may be due to suggested healthy diet, health conscious lifestyle, and statin use in patients with both MAFLD and CAD, that were recorded in the patients files.
The present study showed that FIB-4 score with a cut-off of 0.85 and AUROC of 0.656 may potentially play a role in prediction of CAD in patients with MAFLD.
However, we suggest further investigations should be conducted on the correlation between FIB-4 and CAD in patients with MAFLD.
## Conclusion
This study showed the relationship between FIB-4 index and coronary artery disease in patients with MAFLD. Hence, due to increased global prevalence of MAFLD, we suggest that this simple and non-invasive index to be investigated in further studies.
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|
---
title: The CB1 cannabinoid receptor regulates autophagy in the tibialis anterior skeletal
muscle in mice
authors:
- Carlos Sepúlveda
- Juan Manuel Rodríguez
- Matías Monsalves-Álvarez
- Camila Donoso-Barraza
- Francisco Pino-de la Fuente
- Isabelle Matías
- Thierry Leste-Lasserre
- Philippe Zizzari
- Eugenia Morselli
- Daniela Cota
- Miguel Llanos
- Rodrigo Troncoso
journal: Biological Research
year: 2023
pmcid: PMC10039507
doi: 10.1186/s40659-023-00426-5
license: CC BY 4.0
---
# The CB1 cannabinoid receptor regulates autophagy in the tibialis anterior skeletal muscle in mice
## Abstract
The endocannabinoid system (ECS) regulates energy metabolism, has been implicated in the pathogenesis of metabolic diseases and exerts its actions mainly through the type 1 cannabinoid receptor (CB1). Likewise, autophagy is involved in several cellular processes. It is required for the normal development of muscle mass and metabolism, and its deregulation is associated with diseases. It is known that the CB1 regulates signaling pathways that control autophagy, however, it is currently unknown whether the ECS could regulate autophagy in the skeletal muscle of obese mice. This study aimed to investigate the role of the CB1 in regulating autophagy in skeletal muscle. We found concomitant deregulation in the ECS and autophagy markers in high-fat diet-induced obesity. In obese CB1-KO mice, the autophagy-associated protein LC3 II does not accumulate when mTOR and AMPK phosphorylation levels do not change. Acute inhibition of the CB1 with JD-5037 decreased LC3 II protein accumulation and autophagic flux. Our results suggest that the CB1 regulates autophagy in the tibialis anterior skeletal muscle in both lean and obese mice.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40659-023-00426-5.
## Background
Obesity has become a pandemic in modern societies, and its treatment is a complex public health concern, being the fifth leading cause of death worldwide [1]. Approximately ~ 28 million people around the world die as a result of overweight or obesity co-morbidities, including hypertension, dyslipidemia, insulin resistance, stroke, diabetes mellitus, fatty liver disease, coronary heart diseases, cancer, and metabolic diseases [2, 3]. Lifestyle modifications, nutritional, surgical, and pharmacological therapeutic strategies have been used to fight this condition [4]. Macroautophagy (herein referred to as autophagy) and the endocannabinoid system (ECS) are involved in the progress of obesity. Autophagy is a lysosome-dependent catabolic process whereby misfolded proteins and organelles are degraded and recycled for multiple processes [5]. The aberrant behavior of basal autophagy contributes to the pathogenesis of several diseases, including cancer, cardiovascular diseases, obesity, diabetes mellitus, and aging. In particular, disrupted autophagy in skeletal muscle is associated with lipid droplet accumulation, muscle mass imbalance, and metabolic homeostatic alterations [6–9]. For instance, intact autophagy is essential for preserving muscle structure and fitness under basal conditions [10]. Masiero et al. showed that autophagy-incompetent muscle progressively degenerates due to aberrant proteostasis [7]. Conversely, the stimulation of autophagy induces beneficial effects, such as retarding the age-dependent loss of muscle mass [11].
The ECS encompasses a large group of endogenous molecules, and the most studied endocannabinoids include arachidonoylethanolamide (a.k.a. anandamide, AEA) and 2-arachidonoylglycerol (2-AG). Several enzymes are involved in their synthesis and degradation and two well-known G-protein coupled receptors, i.e., type 1 and type 2 cannabinoid receptors (CB1 and CB2) are involved in their signaling [12]. The main enzymes involved in the anandamide and 2-AG syntheses are the N-acyl phosphatidylethanolamine phospholipase D (Napepld) and diacylglycerol lipase alpha (Daglα), respectively. In addition, the identified hydrolyzing enzymes are fatty acid amide hydrolase (FAAH), which degrades anandamide, and monoacylglycerol lipase (MAGL), able to hydrolyze 2-AG. The CB1 is widely expressed in central and peripheral tissues, including the central nervous system, liver, adipose tissue, and skeletal muscle [13].
Diet-induced obesity leads to changes in the expression of the CB1, FAAH, and MAGL in several tissues and increased circulating endocannabinoids [14–16]. These changes affect the architecture and physiology of skeletal muscle, as previously described [17, 18]. From a general perspective, the ECS affects food intake and energy metabolism; for instance, pharmacological inhibition of CB1 by SR141716 (Rimonabant) reversed the obesity complications in rodents and improved several metabolic processes [19, 20]. Moreover, the CB1 is required for the development of diet-induced steatosis, dyslipidemia, and insulin and leptin resistance [21]. Treatment with a CB1 agonist increases de novo fatty acid synthesis in the liver or isolated hepatocytes, thus contributing to diet-induced obesity [15] and strongly suggesting a role in the progress of obesity and its comorbidities.
On the other hand, CB1 is known to influence mTOR and AMPK signaling (both known signaling pathways the regulate autophagy) [22, 23]. Although it is known that the CB1 participates in autophagy regulation in neurons [24, 25], it is unclear if the CB1 can modulate autophagy by canonical pathways in vivo in the tibialis anterior muscle. Furthermore, many studies have investigated the role of the ECS in regulating energy balance and metabolism in the nervous system and peripheral organs [14, 16, 26], however, very few have examined the physiological role in skeletal muscle and, specially, the control of relevant biological processes, such as autophagy. This study aims to investigate the role of the CB1 in regulating autophagy in the tibialis anterior muscle. Here, we show evidence that the CB1 regulates basal autophagy in the tibialis anterior muscle in normal and diet-induced obese mice.
## A high-fat diet affects the ECS and autophagy in the tibialis anterior muscle
As previously suggested, exposure to a high-fat diet (HFD) leads to the deregulation of several biological systems [27–29]. We focused on the tibialis anterior (TA) skeletal muscle due to previous reports showing that autophagy modulation in this muscle is key to maintaining muscle mass [7, 30, 31]. As we expected, body weight, fasting glycemia, and area under the curve (AUC) of an intraperitoneal glucose tolerance test were increased in mice fed with a HFD for 12 weeks (Fig. 1A–E; *$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$; and ****$p \leq 0.0001$). We found an increase in CB1 mRNA and protein levels in the TA in the HFD group (Fig. 1E–I; *$p \leq 0.05$). Moreover, mRNA expression was reduced for FAAH and increased for MAGL (Mgll), respectively, in the TA of mice fed with a HFD (Fig. 1F, G; *$p \leq 0.05$). To evaluate the effect of diet-induced obesity on autophagy markers, we then measured the protein levels of LC3 and p62/Sqstm1 (markers) in TA muscle. No significant changes were observed in the p62/Sqstm1 ratio. However, LC3 I and LC3 II accumulation was observed in the HFD group (Fig. 1J–M; **$p \leq 0.01$ and ***$p \leq 0.001$). These results suggest that diet-induced obesity increases ECS activity and LC3 II protein levels. Fig. 1Effects of a high-fat diet on body weight, glucose homeostasis, endocannabinoid system, and autophagy markers. A Study design. B Body weight. C Glycemia. D Glucose tolerance test (GTT). E Expression of CB1 mRNA levels. F Expression of FAAH mRNA levels (fatty acid amide hydroxylase). G Expression of monoacylglycerol lipase (Mgll) mRNA levels. H–I CB1 protein levels. J Representative western blot. ( K) p62/Sqstm1 protein levels. L LC3 I protein levels. M LC3 II normalized by total LC3. Two-way analysis of variance (ANOVA) repeated measurements with Bonferroni’s post hoc test and unpaired t-test were conducted. * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.0005$; ****$p \leq 0.0001.$ Values are expressed as mean and S.E.M. and scatter dot plots, as appropriate
## Deletion of the CB1 prevents LC3 II accumulation induced by a high-fat diet in the tibialis anterior muscle
Since we observed concomitant deregulation in the ECS and basal autophagy markers, we wanted to determine if the deletion of the CB1 prevented LC3 II accumulation induced by a HFD in the TA. We fed CB1-KO male mice with a control or a HFD for 12 weeks (Fig. 2A). Genetic deletion of CB1 was confirmed by western blot analysis of TA (Supp. 1A). As previously reported [32], we found that CB1-KO mice were protected from diet-induced obesity, compared to WT controls, in terms of body weight, fat mass, and tibialis anterior weight,with no changes in lean mass (Fig. 2B–F). There was a lower cumulative calorie intake effect in mice with deletion of CB1 (Fig. 2G). HFD increased plasma glucose levels, but CB1 deletion prevented this hyperglycemia (Fig. 2H). Next, we evaluated endocannabinoids plasma concentrations. Mice fed with a HFD increased AEA and PEA plasma levels (Diet: $p \leq 0.0001$). Moreover, OEA levels increased by genotype and diet effect (Diet: $$p \leq 0.0088$$; Genotype: $$p \leq 0.0243$$) (Fig. 2I–L). We then explored the effect of diet-induced obesity on the mRNA expression of genes of the endocannabinoid system and autophagy in the TA. As shown in Fig. 3M, Mgll and Napepld revealed a diet effect ($p \leq 0.05$), suggesting an increase of both enzymes. An interaction effect ($p \leq 0.05$) was found in Daglβ, showing restoration of mRNA levels in HFD-fed CB1-KO mice. Finally, Becn1 had a diet and interaction effect ($p \leq 0.05$), indicating that a HFD increases its mRNA expression levels, and that the genotype further increases it. Fig. 2Knockout of CB1 prevents impairment induced by a high-fat diet. A Study design. B Body weight curve. C Body weight gain. D Fat mass. E Lean mass. F Tibialis anterior weight. G Cumulative calorie intake. H Glycemia. I Plasma anandamide (AEA). J 2-arachidonoylglycerol (2-AG). K N-palmitoyl-ethanolamine (PEA). L Oleoylethanolamine (OEA). M mRNA levels. Two-way analysis of variance (ANOVA) repeated measurements with Bonferroni’s post hoc test. Statistical significance was set at $p \leq 0.05.$ * WT HFD vs. all groups. Two-way analysis of variance (ANOVA) with Bonferroni’s post hoc test. * WT HFD vs. all groups. # CB1-KO HFD vs WT CD and CB1-KO CD. Statistical significance was set at $p \leq 0.05.$ Values are expressed as mean and S.E.M. and scatter dot plot, as appropriateFig. 3Knockout CB1 prevents LC3 II accumulation in tibialis anterior muscle. p62/Sqstm1, LC3, AKT473, total AKT, mTOR2448, total mTOR, p70S6K389, total p70S6K, AMPK172, and total AMPK protein levels were determined by western blot. A Representative western blot image. B p62/Sqstm1 protein levels. C LC3 I protein levels. D LC3 II normalized by total LC3. E Representative western blot image. F AKT473. G mTOR2448. H p70S6K389. I AMPK172. Two-way analysis of variance (ANOVA) with Bonferroni’s post hoc test. Statistical significance was set at $p \leq 0.05.$ Values are expressed as mean and S.E.M. and scatter dot plots, as appropriate To investigate whether CB1 knockout prevented LC3 II accumulation induced by a high-fat diet, we determined autophagy-related proteins levels. No significant changes were observed in p62/Sqstm1 and LC3 I protein levels (Fig. 3A–C). Interestingly, a diet and genotype effect ($p \leq 0.0001$) was found in LC3 II (Fig. 3D), suggesting a role for the CB, independently of the diet. To further elucidate if the genotype effect of LC3 II accumulation was driven by changes in the regulator proteins of autophagy, we evaluated the levels of AMPK and AKT/mTOR/p70S6K phosphorylation, which, unexpectedly, did not change (Fig. 3E–I).
## The CB1 regulates basal autophagy
Due to the surprising absence of changes in the protein phosphorylation of AMPK and the mTOR pathway, we evaluated whether the CB1 regulated autophagic flux by using chloroquine in WT and CB1-KO mice. We found no difference in p62/Sqstm1 protein levels (Fig. 4A, B). As we expected, chloroquine induced LC3 II accumulation (CQ: $$p \leq 0.001$$ and Genotype: $$p \leq 0.0087$$, Fig. 4C). To explore whether changes in the LC3 proteins in the second experiment with the HFD were the results of alterations in the lysosome, we then evaluated the Lysosomal-associated proteins 1 (Lamp1) and 2 (Lamp2), but there were no changes (Fig. 4D–F). To determine if pharmacological inhibition of the CB1 increased autophagic flux, we injected C57 mice intraperitoneally with a dose of JD-5037 (3 mg kg−1 body weight), which is a CB1 antagonist with low brain penetration [33]. An interaction effect was found in the JD + CQ group in p62/Sqstm1 protein levels (Interaction: $$p \leq 0.0085$$; Fig. 5A, B). Regarding LC3 proteins, JD-5037 produced an effect ($$p \leq 0.0002$$), suggesting that pharmacological blockade of CB1 prevents chloroquine-induced changes in autophagic flux. Lastly, we observed an accumulation of polyubiquitinated protein in mice injected with JD-5037 (JD-5037 effect: $$p \leq 0.0163$$). These results suggest that while genetic deletion of CB1 does not regulate autophagic flux, acute pharmacological inhibition of the CB1 with JD-5037 decreases LC3 II protein accumulation and autophagic flux. Fig. 4The CB1 regulates basal autophagy in tibialis anterior muscle. p62/Sqstm1, LC3, Lamp1, and Lamp2 protein levels were determined by western blot. A Representative western blot image. B p62/Sqstm1 protein levels. C LC3 II is normalized by total LC3. D Representative western blot image. E Lamp1 protein levels. F Lamp2 protein levels. Two-way analysis of variance (ANOVA) with Bonferroni’s post hoc test and unpaired t-test were conducted. Statistical significance was set at $p \leq 0.05.$ Values are expressed as mean and S.E.M. and scatter dot plots, as appropriateFig. 5JD-5037 reduces LC3 II protein in vivo. p62/Sqstm1, LC3, and polyubiquitin protein levels were determined by western blot. A Representative western blot image. B p62/Sqstm1 protein levels. C LC3 II is normalized by total LC3. D Representative western blot image. E Polyubiquitin protein levels. A two-way analysis of variance (ANOVA) with Bonferroni’s post hoc test was conducted. Statistical significance was et al. $p \leq 0.05.$ * CQ vs. JD + CQ. Values are expressed as mean and S.E.M. and scatter dot plot, as appropriate
## Discussion
In this study, we investigated the role of CB1 in regulating autophagy in the TA skeletal muscle. We found an association between ECS deregulation and impaired basal autophagy in mice fed with a HFD. The alterations of the ECS were mainly characterized by a rise in the CB1 and a reduction of FAAH. On the other hand, interestingly, these results may constitute a concerted mechanism to activate CB1 with anandamide, its natural agonist. In addition, LC3 II accumulation is the distinctive point of autophagy impairment. We observed that the deletion of the CB1 in mice fed with a HFD prevented LC3 II accumulation, supporting the role of CB1 in favoring the increase of LC3 II observed in HFD. However, there was no change in the phosphorylation levels of the AMPK and mTOR pathways, two critical regulatory pathways of autophagy. Finally, while genetic deletion of CB1 regulated autophagy, acute pharmacological inhibition of the CB1 with JD-5037 decreased LC3 II protein accumulation and autophagic flux.
Previous studies have found an increase in CB1 levels in the liver, adipose tissue, and central nervous system of mice and humans with obesity [15, 34–37]. Accordingly, we found that CB1 mRNA and protein levels were increased in the TA of mice fed with a HFD. This fact could be relevant to muscle metabolism and physiology. Esposito et al. [ 18] exposed differentiated L6 skeletal muscle cells to SR141716 (Rimonabant), a known CB1 antagonist/inverse agonist, increasing 2-deoxyglucose uptake in a time- and dose-dependent manner. A similar effect was also induced by gene silencing of the CB1. In accordance, Liu et al. [ 38] treated female mice with a daily i.p. dose of Rimonabant, increasing glucose uptake by $68\%$ in an isolated soleus muscle preparation. In addition, oxygen consumption was higher in Rimonabant- compared with vehicle-treated muscle. These reports corroborate the significant role of CB1 in muscle metabolism and physiology. Additionally, we found decrease FAAH mRNA expression, suggesting reduced activity and, as a result, a possible rise of anandamide at the plasmatic and tissue levels, which may lead to persistent receptor activation [39]. Sipe et al. [ 40] studied a missense polymorphism in FAAH in 2667 subjects of white, black, and Asian ancestry. Their results indicate that FAAH polymorphism is a risk factor in the overweight/obese population. A role for this enzyme in energy homeostasis has also been shown in FAAH-deficient (FAAH(−/−)) mice. These FAAH(−/−) animals had increased total adipose tissue and body weight, and increased levels of anandamide in several tissues [41].
Aberrant autophagy has been reported in several tissues in obese mice [6, 42, 43]. Our findings show that basal autophagy is affected by diet-induced obesity in the tibialis anterior. Furthermore, decreased muscle mass has been found in mice fed with a HFD [44], and CB1-KO mice were protected from this effect. LC3 II accumulation correlates with either enhanced autophagosome synthesis or reduced autophagosome turnover, probably due to delayed trafficking to the lysosome, reduced fusion between compartments, or impaired lysosomal proteolytic activity [45]. Fan and Xiao [46] showed that autophagy was compromised in the muscle of rats fed with a HFD. The authors fed rats with an HFD and then treated them with the autophagy inhibitor chloroquine. Using immunostaining and western blot, they found that the LC3 II/I ratio was increased in the muscle of rats fed with a HFD, denoting impaired autophagy. In agreement, Li et al. [ 47] showed that a HFD inhibits the production of autophagic lysosomal expression of autophagy-related genes (LC3 II, Becn1, and ATG5) and increases the accumulation of p62/Sqstm1 in the gastrocnemius muscle. Our findings show that basal autophagy is affected by diet-induced obesity. Therefore, our results suggest concomitant impairments in the ECS and autophagy in the TA.
Autophagy is a conserved cellular degradation process in which parts of the cytosol and organelles are sequestered into an autophagosome and delivered into a lysosome for breakdown and eventual recycling of the resulting macromolecules. Genetic deletion of the CB1 prevented the LC3 II accumulation induced by a HFD. We can speculate that decreased LC3 II levels reflect the restoration of autophagy, an explanation that requires further study. Unexpectedly, mTOR and AMPK, the two multiprotein complexes involved in the canonical induction pathway of autophagic vesicle formation, do not seem to be altered in our experimental setting. Autophagy is also regulated by non-canonical pathways that lead to autophagosomal degradation [48]. These alternative pathways are stimuli and cell-type dependent, thus future studies are required to better understand the mechanism that leads to this non-canonical autophagy regulation.
Autophagic flux assay is considered the best characterization of autophagy levels [49, 50]. Hence, we performed an autophagic flux assay in vivo in CB1-KO mice treated with an i.p. injection of chloroquine. We found higher LC3 II accumulation in wild-type and CB1-KO mice previously treated with chloroquine and, unexpectedly, lack of the CB1 did not affect LC3 II abundance. However, mice treated only with a CB1 antagonist, JD-5037, reduced LC3 II basal levels. Furthermore, JD-5037 reverted LC3 II accumulation by blocking chloroquine-induced fusion of the autophagosome with lysosomes. As mentioned above, our data suggest that CB1-KO prevents LC3 II accumulation, indicating restoration of the autophagy in mice fed a HFD. However, CB1-KO does not impact autophagic flux, suggesting that there is a compensation mechanism in KO mice; moreover, this experiment was not done in mice fed with a high-fat diet with ECS overactivation. An alternative explanation is that the CB1 regulates LC3 lipidation through competitive anandamide production. NAPE-PLD requires calcium for the anandamide synthesis route, with phosphatidylethanolamine (PE) as a source [51]. PE is also a substrate for LC3 II production. It is known that CB1 regulates intracellular calcium flux [52]. Oláh et al. showed that the activation of the CB1 inhibits sarcoplasmic Ca2+ release and the sarcoplasmic reticulum Ca2+ ATPase during excitation–contraction coupling in a Gi/o protein-mediated manner in adult skeletal muscle fibers. This could, in part, explain the reduction of LC3 II protein levels in mice injected with JD-5037; however, more research is necessary to confirm this hypothesis. Due to a possible effect on lysosomal function, we evaluated two lysosomal proteins, Lamp1 and Lamp2. Similarly, there was no difference between wild-type and CB1 KO, suggesting that the lysosome was unaffected. We also evaluated polyubiquitinated proteins at specific lysine residues (Lys48) because it labels proteins for proteasomal degradation. Interestingly, more polyubiquitinated proteins were found in mice treated with JD-5037. This result is consistent with the reduction of LC3 II protein, but the mechanism behind this is still elusive.
Contrary to our findings, Hiebel et al. [ 53] showed that siRNA knockdown of CB1 activity affects the autophagic flux independent of the canonical pathway. They performed an autophagic flux assay in human embryonic kidney cells, showing LC3 II accumulation, suggesting a possible role of the CB1 in autophagy regulation. Piyanova et al. [ 54] treated with Rapamycin (a known mTOR inhibitor that induces autophagy) and Baf1 (to inhibit autophagic flux) to hippocampal neurons from CB1-KO. LC3 II levels were higher in Cnr1−/− neurons compared with Cnr1+/+, indicating that the autophagic rate is higher in the absence of CB1. Moreover, Blázquez et al. [ 55] treated wild-type mice with a single i.p. injection of the Δ9-tetrahydrocannabinol (THC), which exerts its biological effects mainly by activating the CB1, and evaluated LC3 protein. LC3 II levels were increased, suggesting that THC impairs the autophagy and that this process occurs selectively in the striatum. Genetic background, mouse strain, diet composition, mice age, and cell line types could explain these discrepancies with our results. Additionally, TA in C57BL/6 mice is a fast-contracting muscle with a high percentage of type IIB fibers [56]. We explored mRNA levels of autophagy proteins but did not find alterations that could explain the reduction of LC3 protein levels. Additional autophagy or lysosomal genes and proteins should be assessed to elucidate these results.
Restoring autophagy levels in several diseases is key to recovering physiological processes. Autophagy is a potential target for developing pharmacological and non-pharmacological interventions to manage obesity. Therefore, it is relevant to search for new pharmacological targets to counteract obesity-related disruptions in different physiological systems. Pharmacological blockers of the CB1, such as Rimonabant, were promising agents to reduce food intake and body weight and revert the metabolic alterations induced by obesity. However, its negative side effects related to severe complications of neuropsychiatric effects (i.e. anxiety, depression, and suicidal ideation) [57] led to its withdrawal from the market. The design of new CB1 peripheral blockers could remove these unwanted effects. JD-5037 exerts a positive impact on the control of body weight, metabolic outcome, and low brain penetration without adverse side effects [33]. However, in the current model, we observed that the inhibition of CB1 with JD-5037 produces a reduction of autophagic flux that needs further investigation.
It has been widely reported that obesity induces sarcopenia. In our results, CB1-KO prevents weight reduction in TA induced by a high-fat diet. Iannotti et al. [ 2014] showed in human and mouse myoblasts that the stimulation of CB1 with 2-AG or arachidonyl‐2′‐chloroethylamide prevents myotube formation and promotes myoblast proliferation. Interestingly, these effects are reverted with rimonabant [17]. Moreover, Iannotti et al. [ 20,018] showed that treatment with rimonabant promotes human satellite cell differentiation in vitro and increases the number of myofibers [58]. However, Le Bacquer et al. [ 2022] co-incubated C2C12 myotubes with dexamethasone and rimonabant for 24 h, and the atrophy was prevented in myotubes exposed to rimonabant, without affecting the atrogin-1/MAFbx ratio. The authors also found that rimonabant stimulates protein synthesis and CB1 agonists are unable to modulate protein synthesis, suggesting a CB1-independent mechanism [59]. In vitro and in vivo models, mouse strains, and rimonabant doses can explain these controversial results.
## Conclusions
The present investigation provides evidence of altered autophagy in diet-induced obesity and increased “endocannabinoid tone.” Our results also indicate a regulatory role of the CB1 on fast muscle, such as the tibialis anterior in mice, reducing LC3 II accumulation induced by a HFD. In addition, acute pharmacological inhibition of the CB1 reduces LC3 II accumulation induced by chloroquine, suggesting a reduction of autophagic flux. In conclusion, CB1 regulates autophagy in the tibialis anterior muscle in mice. Although our study could not clarify why and how CB1 regulates autophagy, it opened a new area of research.
## Animals and diet
All animal study protocols were reviewed and approved by the local Committee on Animal Health and Care of the University of Bordeaux and the Institute of Nutrition and Food Technology. For the first experiment, male C57BL/6 littermate mice were kept with food and tap water ad libitum, and a 12–12 h light–dark cycle. Mice were fed with a control diet (CD, $$n = 6$$) containing $70\%$ carbohydrates, $10\%$ fat, and $20\%$ protein in terms of kcals (Research Diet Inc. D12450J, New Brunswick, NJ, USA) and a high-fat diet (HFD, $$n = 6$$) containing $60\%$ fat ($90\%$ lard, $10\%$ soybean oil), $20\%$ carbohydrates and $20\%$ protein in terms of kcal (D12492, Research Diets, New Brunswick, NJ, USA). Body weight and food intake were assessed weekly. At the end of the experiment, a glucose tolerance test (GTT) was performed. Finally, mice were euthanized, and the tibialis anterior (TA) muscle was removed for analysis.
For the second experiment, male C57BL/6 CB1+/+ (WT) and CB1–/– (CB1-KO) littermate mice were maintained under standard conditions (food and tap water ad libitum; 12 h–12 h light–dark cycle). Mice were randomly assigned into 4 groups: WT CD (Wild-type control diet, $$n = 8$$), WT HFD (Wild-type high-fat diet, $$n = 8$$), CB1-KO CD (CB1 knockout control diet, $$n = 7$$), and CB1-KO HFD (CB1 knockout high-fat diet, $$n = 6$$). The mice were fed for 12 weeks with a chow diet containing $13.5\%$ fat, $61.3\%$ carbohydrates, and $25.2\%$ protein in kcal (SAFE, A03 SP/17275) or a high-fat diet (D12492, Research Diets, New Brunswick, NJ, USA). Body weight and food intake were measured each week up to the end of the experiment. Body composition was also evaluated during the first and 12 week by nuclear echo magnetic resonance imaging whole-body composition analysis (EchoMRI 900; EchoMedical Systems, USA), as described previously [60]. Glycemia was measured in mice fasting for 6 h. Finally, the mice were sacrificed the next day after measuring the glycemia, and the TA was collected and frozen (− 80 °C). For the third experiment, mice were grouped in: WT PBS (Wild-type Phophate-Buffered Saline; $$n = 5$$), WT CQ (Wild-type chloroquine; $$n = 5$$), CB1-KO PBS ($$n = 6$$), and CB1-KO CQ ($$n = 7$$). They fasted for 24 h and were subjected to an intraperitoneal injection of CQ (100 mg kg body weight) or PBS 4 h before sacrifice. Animals were sacrificed, and the TA muscle was quickly dissected and frozen for immunoblot analyses. For the fourth experiment, male C57BL/6 littermate mice were grouped in: CTRL (Control; $$n = 6$$), CQ (chloroquine; $$n = 6$$), JD (JD-5037; $$n = 6$$), and JD-CQ ($$n = 6$$). Mice were kept in fasting condition for 24 h. 4 h before sacrifice, mice were injected with 100 mg kg body weight of chloroquine or PBS. After 2 h, an intraperitoneal injection of 3 mg kg−1 body weight of JD-5037 or vehicle ($1\%$ DMSO, $1\%$ Tween80, and PBS was applied. Finally, the animals were sacrificed, and the TA muscle was quickly dissected and frozen for immunoblot analyses.
## Glucose tolerance test
Three days before sacrifice, a glucose tolerance test was performed in the morning. Mice fasted for 6 h in separated individual cages. Basal blood glucose was measured in samples collected from the tail with an Accu-Check Performa glucometer (Roche Diagnostic, Mannheim, Germany). Then, 1.5 mg kg−1 body weight of glucose in PBS was injected intraperitoneally, and blood glucose was evaluated at 15, 30, 90, and 120 min time points. At the end of the assessment, mice were placed in their cages.
## Endocannabinoid quantification
To determine endocannabinoids, we used the liquid chromatography-tandem mass spectrometric analysis method [61]. The blood samples of mice were collected and homogenized with chloroform/methanol (2:1, v/v) containing internal deuterated standards (Cayman Chemicals, Ann Arbor, MI, USA). Purified endocannabinoids were then evaluated by isotopic dilution, according to a calibration curve.
## RNA extraction and PCR procedure
Total RNA was extracted from samples using a commercial kit (Fermentas, Villebon sur Yvette, France). Random hexamer primers and Oligo(dt)18 primers (Fermentas) were used for cDNA synthesis from 2 μg of total RNA with RevertAid Premium Reverse Transcriptase (Fermentas). Real-time PCR was performed using transcript-specific primers, cDNA (1 ng), and LightCycler 480 SYBR Green I Master (Roche) in a final volume of 10 μl. The 2 − ΔΔCT method was used for relative quantification analysis, and PCR amplification of the housekeeping genes Ywhaz, Gapdh, and *Tubulin alpha* was used for controls. The primer’s sequences are reported in Additional file 1: Table S1.
## Western blot analyses
Total protein lysates were prepared using T-PER lysis buffer (78,510, ThermoScientific) with phosphatases/protease inhibitors. Lysates were centrifuged at 12,000 rpm for 10 min at 4 °C. Total protein concentrations were measured using the BCA assay kit (71,285–3, Novagen®, Merck, MA USA). Proteins were separated using 6–$15\%$ SDS-PAGE and transferred to PVDF membranes. The membranes were blocked with $5\%$ low-fat milk (Svelty, Nestlé) in T-TBS/$0.1\%$ (1 mM Tris) Tween-20 and incubated overnight at 4 °C with primary antibodies. The membranes were then extensively washed with TBS/$0.1\%$ Tween-20, and incubated with the secondary polyclonal anti-mouse (#402335, Calbiochem) or anti-rabbit (#401315, Calbiochem) antibodies. The protein bands in the blots were visualized using HRP secondary antibodies. Enhanced chemiluminescence ECL-plus reagent (DW1029, Biological Industries) and C-DiGit® Blot Scanner (LI-COR, USA) were used and bands were analyzed with the Image J version 1.51 software (National Institutes of Health, Bethesda, MD, USA). The primary antibodies used were CB1 (Cayman #10006590), LC3A/B (CST #4108), p62/Sqstm1 (NBP1-48320, Novus), GAPDH (CST #5174), phospho-p70S6K Thr389 (CST #9234), p70S6K Total (CST #2708), phospho-AMPKα Thr172 (CST #2535), AMPKα Total (CST #5831), phospho-Akt Ser473 (CST #9271), Akt Total (CST #9272), phospho-mTOR Ser2448 (CST #2971), mTOR total (CST #2972), Lamp1 (CST #3243), Lamp2 (CST #49,067), and K48-linkage Specific Polyubiquitin (CST # 4289). GAPDH or total protein content were used as loading controls.
## Statistical analysis
All values are presented as mean ± standard error of the mean (SEM). Statistical analyses were performed using Student's t-test, repeated measure analysis of variance (ANOVA), and two-way ANOVA for non-repeated measures. Diet—genotype, genotype—chloroquine, and JD-5037—chloroquine were used as the main effects for the variables. Bonferroni’s post hoc test was applied where appropriate, and statistical significances were set at $p \leq 0.05.$ All statistical analyses were performed using the GraphPad Prism software (version 8.0, San Diego, CA, USA).
## Supplementary Information
Additional file 1: Representative western blot image.
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|
---
title: Study of FOXO1-interacting proteins using TurboID-based proximity labeling
technology
authors:
- Yanting Su
- Yuanyuan Guo
- Jieyu Guo
- Ting Zeng
- Ting Wang
- Wu Liu
journal: BMC Genomics
year: 2023
pmcid: PMC10039511
doi: 10.1186/s12864-023-09238-z
license: CC BY 4.0
---
# Study of FOXO1-interacting proteins using TurboID-based proximity labeling technology
## Abstract
### Background
Protein‒protein interactions (PPIs) are the foundation of the life activities of cells. TurboID is a biotin ligase with higher catalytic efficiency than BioID or APEX that reduces the required labeling time from 18 h to 10 min. Since many proteins participate in binding and catalytic events that are very short-lived, it is theoretically possible to find relatively novel binding proteins using the TurboID technique. Cell proliferation, apoptosis, autophagy, oxidative stress and metabolic disorders underlie many diseases, and forkhead box transcription factor 1 (FOXO1) plays a key role in these physiological and pathological processes.
### Results
The FOXO1-TurboID fusion gene was transfected into U251 astrocytes, and a cell line stably expressing FOXO1 was constructed. While constructing the FOXO1 overexpression plasmid, we also added the gene sequence of TurboID to perform biotin labeling experiments in the successfully fabricated cell line to look for FOXO1 reciprocal proteins. Label-free mass spectrometry analysis was performed, and 325 interacting proteins were found. A total of 176 proteins were identified in the FOXO1 overexpression group, and 227 proteins were identified in the Lipopolysaccharide -treated group (Lipopolysaccharide, LPS). Wild-type U251 cells were used to exclude interference from nonspecific binding. The FOXO1-interacting proteins hnRNPK and RBM14 were selected for immunoprecipitation and immunofluorescence verification.
### Conclusion
The TurboID technique was used to select the FOXO1-interacting proteins, and after removing the proteins identified in the blank group, a large number of interacting proteins were found in both positive groups. This study lays a foundation for further study of the function of FOXO1 and the regulatory network in which it is involved.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12864-023-09238-z.
## Background
The forkhead transcription factor (FOXO) family of proteins can inhibit tumor proliferation, regulate energy metabolism, induce cell responses, and contribute to the regulation of human antiaging due to their widespread presence in various [1–3]. Many stimuli can induce changes in FOXO activity, such as insulin, insulin-like growth factor-1 (IGF-1), cytokines and oxidative [4]. After in-depth study of the FOXO family, forkhead box transcription factor 1 (FOXO1) has been considered a representative member because it plays a key regulatory role in many transcription [5]. A large number of studies have shown that FOXO1, regarded as an important transcription factor due to its complex activities, regulates many targets, such as genes involved in apoptosis and autophagy, antioxidant enzymes, cell cycle arrest genes, and metabolic and immune regulatory [6, 7]. However, the mechanism of FOXO1 in the occurrence and development of many diseases is still unclear and even contradictory. Therefore, in-depth study of the FOXO1 signaling pathway is of great significance for the development of targeted drugs for a variety of [8].
As people’s quality of life has improved in recent years, diabetes has become a familiar disease, with insulin resistance and β-cell function impairment being the hallmarks of type 2 diabetes mellitus (T2DM). The concept of insulin resistance emerged in the 1980 and 1990 s and has been recognized as the basic pathological state of T2DM, while β-cell exhaustion has received less attention. Studies have shown that far fewer individuals have T2DM than those that have insulin resistance alone, suggesting that insulin resistance does not necessarily lead to T2DM unless it is also accompanied by pancreatic β-cell [8–11]. Healthy β-cells can increase their numbers and functional output to compensate for the effects of insulin [12]. In T2DM, β-cell dysfunction can be induced in many ways, including oxidative [13], endoplasmic reticulum [14], hypoxic [15] and the expression of [16], which lead to apoptosis, restricted proliferation, uncontrolled autophagy, the dedifferentiation of β-cells and other adverse consequences. Studies have shown that FOXO1 is involved in the above mechanisms and exerts corresponding functions. Although FOXO1 inhibits β-cell replication and neogenesis, it is required to maintain the functions and characteristics of β-cells during times of increased metabolic [17]. Thus, FOXO1 is an indispensable factor in diabetes research. In view of the important role of FOXO1 in T2DM, we aimed to construct a cell line stably overexpressing the FOXO1 gene to identify the proteins with which it interacts to facilitate the study of its mechanism of action in the future.
Protein‒protein interactions (PPIs) are the foundation of the life activities of [18]. Protein proximity labeling technologies, such as BioID and APEX, have gradually been applied to study of [19, 20]. Since the interactions between proteins rely mostly on hydrogen bonds, salt bridges and hydrophobic interactions and their spatial distance from each other is very short, it is generally considered that interacting proteins must be adjacent to each [21]. TurboID is a protein with a higher catalytic efficiency than BioID or APEX, and it reduces the labeling time from 18 h to 10 min. Since many proteins exert binding and catalytic effects that are very short-lived, it is theoretically possible to find relatively novel binding proteins using the TurboID [22]. Here, we utilize the efficiency of TurboID to find proteins that interact with FOXO1.
To conduct in-depth research on FOXO1, this paper used lentivirus infection to transfect the FOXO1 gene into U251 astrocytes to construct a stable cell line that expresses FOXO1. While constructing the FOXO1 overexpression plasmid, we also included the gene sequence of TurboID. Therefore, we can conduct biotin labeling experiments on the successfully constructed cell line and design an experimental group for LPS treatment. A large number of proteins that interact with FOXO1 were found by silver staining, which lays a foundation for further study of the function of FOXO1 and the regulatory network in which it is involved.
## Construction of overexpression plasmid and TurboID labeling flowchart
The composition of the FOXO1-TurboID overexpression plasmid is shown in Fig. 1A, including the target gene FOXO1, biotin marker enzyme TurboID, nuclear localization signal (NLS) and tag protein Flag, as well as two different resistance screening markers, AmpR and puro, where AmpR was used to screen for positive clones in E. coli, and puro was used to screen for stably transfected cells. Figure 1B is a simple illustration of our biotin experiment. First, the successfully and stably transfected FOXO1-U251 cells were placed in an environment with a suitable concentration of biotin so that free biotin could fully enter the cells, and adjacent proteins were covalently labeled with biotin by TurboID. After the protein affinity purification experiment, proteins that were not labeled with biotin were excluded, the labeled proteins were selected for analysis by label-free quantitative mass spectrometry analysis.
Fig. 1Construction of the overexpression plasmid and TurboID labeling flowchart. ( A) Diagram of functional elements of the FOXO1-TurboID overexpression plasmid. ( B) Biotin marker experimental workflow
## Construction and validation of the stably transfected cell line
In Figs. 2A and 293T cells were cotransfected with PSPAXZ, PMD2.G and pLenti-CMV-EGFP overexpression plasmids. After 48 h of infection, intense fluorescence was observed under a fluorescence microscope, which indicated that the cells had been successfully transfected with the pLenti-CMV-EGFP gene. There were no problems in the transfection system, and so the same batch of the FOXO1-TurboID cotransfection system should proceed smoothly, and the supernatant virus solution after transfection can be collected. Figure 2B shows the U251 cells observed under a fluorescence microscope 48 h after lentivirus infection. The appearance of fluorescence also confirms that the pLenti-CMV-EGFP gene was successfully transfected into the U251 cells, making the probability of FOXO1-TurboID transfection high.
Fig. 2Validation of cell line stable transfection. ( A) Fluorescence image of 293T cells highly expressing pLenti-CMV-EGFP. ( B) Fluorescence image of U251 cells highly expressing pLenti-CMV-EGFP. ( C) Validation of FOXO1 protein expression. NC, U251 cells; OE, cells with stable overexpression of FOXO1-TurboID The cells overexpressing the FOXO1 gene were treated with Flag antibody for Western blotting and a clear band appeared at the protein molecular weight of approximately 105 kDa (FOXO1 protein molecular weight 69.6 kDa, TurboID protein molecular weight 35 kDa; the size of *Flag is* almost negligible) (Fig. 2C). This region was blank in the U251 cell control group, indicating that a stable cell line was successfully constructed. The results obtained with the FOXO1 antibody were similar.
## Biotin labeling and identification by silver staining
Before performing the biotin labeling experiments, we determined the time needed for biotin labeling. Figure 3 A shows that 12 h was the appropriate time. Therefore, 500 µmol/L biotin was used to treat the stably transfected FOXO1-TurboID cells for 12 h.
Fig. 3Biotin labeling and silver staining. ( A) Biotin labeling was assessed at different time points. 1, 0 min; 2, 10 min; 3, 1 h; 4, 3 h; 5, 6 h; 6, 12 h; 7, 24 h. (B) Protein identification by silver staining. M, protein ladder; 1, Blank Strip; 2, EGFP-U251 cells; 3, FOXO1-U251 cells; 3, LPS-treated FOXO1-U251 cells After the three groups of cells were treated with biotin for 12 h for labeling, the biotin-labeled protein was pulled down with magnetic beads and then identified by silver staining. The significant difference between the cell line stably expressing FOXO1 and control EGFP-U251 cells is shown in Fig. 3B, which proves that a large number of proteins that interact with the target protein FOXO1 had been pulled down. The difference in expression between FOXO1-U251 cells and LPS-treated FOXO1-U251 cells needs further mass spectrometry analysis.
## Mass spectrometry data analysis
By analyzing the mass spectrometry data, a total of 325 interacting proteins were collected by the proximity labeling technique. One hundred proteins were identified in the U251 blank group, and 240 proteins were identified in the FOXO1 overexpression group. After excluding 64 proteins that were also identified in the blank group, 176 interacting proteins remained (see the Additional file 1 for specific information on these proteins). Additionally, 289 proteins were identified in the LPS-treated group, and after excluding the 62 proteins that were also identified in the blank group, 227 interacting proteins remained (see the Additional file 1 for specific information on these proteins).
We used the online analysis tool Sangerbox 3.0 for GO and KEGG analyses (Sangerbox 3.0, http://vip.sangerbox.com/home.html) [23–25]. First, we selected 176 interacting proteins from the FOXO1 overexpression group. In the analysis of cellular components, proteins were found to be enriched in vesicle, extracellular region, extracellular space, extracellular region part, extracellular exosome, etc. ( Fig. 4A). In the analysis of molecular function, proteins were found to be enriched in nucleic acid binding, RNA binding, signaling receptor binding, cell adhesion molecule binding, cadherin binding, etc. ( Fig. 4B). In addition, several significantly enriched proteins were identified from the analysis of biological processes, including establishment of localization, transport, vesicle-mediated transport, interspecies interaction between organisms, mRNA metabolic process, etc. ( Fig. 4C). After KEGG analysis, proteins were found to be enriched in the following pathways: ribosome, spliceosome, complement and coagulation cascades, salivary secretion, Alzheimer’s disease, African trypanosomiasis, cholesterol metabolism, pertussis, etc. ( Fig. 4D).
Fig. 4Analysis of FOXO1 proximates. ( A) Enriched proximate proteins of the FOXO1 group based on cellular components. ( B) Enriched proximate proteins of the FOXO1 group based on molecular function. ( C) Enriched proximate proteins of the FOXO1 group based on biological process. ( D) Enriched proximate proteins of the FOXO1 group based on KEGG analysis. ( E) Enriched proximate proteins of the LPS-treated group based on cellular components. ( F) Enriched proximate proteins of the LPS-treated group based on molecular function. ( G) Enriched proximate proteins of the LPS-treated group based on biological process. ( H) Enriched proximate proteins of the LPS-treated group based on KEGG analysis Next, we selected 227 interacting proteins from the LPS-treated group for analysis. In the analysis of cellular components, the following protein enrichments were found, including cytosol, vesicle, extracellular space, extracellular region part, extracellular exosome, etc. ( Fig. 4E). In the analysis of molecular function, the search for nucleic acid binding, RNA binding, cell adhesion molecule binding, cadherin binding, protein-containing complex binding, etc. ( Fig. 4F). In addition, several significantly enriched regions were identified in the analysis of biological processes, including establishment of localization, transport, multiorganism process, vesicle-mediated transport, interspecies interaction between organisms, etc. ( Fig. 4G). After KEGG analysis, proteins were found to be enriched in the following pathways: ribosome, spliceosome, complement and coagulation cascades, cholesterol metabolism, hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), Alzheimer’s disease, glycolysis/gluconeogenesis, Parkinson’s disease, ferroptosis, etc. ( Fig. 4H).
## Validation of interacting proteins
The mass spectrometry data indicated that many hnRNP family members are FOXO1-interacting proteins, including hnRNPF, hnRNPD, hnRNPA1, hnRNPH1, hnRNPA2B1, hnRNPH3, hnRNPA3, and hnRNPK. Therefore, hnRNPK was selected for protein interaction verification. In addition, RBM14, a nuclear receptor coactivator, was selected. Cells were collected and subjected to immunoprecipitation experiments. The Flag affinity gel adsorbs the target protein with the Flag tag, so it can adsorb FOXO1-TurboID-Flag fusion proteins. Figure 5 A shows that FOXO1 can specifically bind to hnRNPK and RBM14. Therefore, these samples after immunoprecipitation could show bands for the hnRNPK and RBM14 antibodies.
Fig. 5Validation of the interacting proteins. ( A) Western blotting bands after immunoprecipitation. ( B) Immunofluorescence image of the interaction between FOXO1 and hnRNPK in FOXO1-U251 cells. ( C) Immunofluorescence image of the interaction between FOXO1 and RBM14 in FOXO1-U251 cells We used immunofluorescence techniques to verify the interaction between FOXO1 and hnRNPK. The immunofluorescence staining results were observed by confocal microscopy. In Figs. 4 and 5B’,6-diaminyl-2-phenylindoles (DAPI) emits blue fluorescence and shows the morphology of the nucleus, the fluorescent secondary antibody fluorescein isothiocyanate (FITC, green fluorescence) marks the FOXO1 gene, and the fluorescent secondary antibody Sulfo-Cyanine3 (Cy3, red fluorescence) indicates hnRNPK. The merged image has many yellow spots, which may indicate a large number of interacting FOXO1 and hnRNPK proteins. Similarly, in Fig. 5C, DAPI emits blue fluorescence, showing the morphology of the nucleus, the fluorescent secondary antibody FITC (green) marks the FOXO1 gene, and the fluorescent secondary antibody Cy3 (red) indicates RBM14. The combined image has many yellow spots, which may indicate a large number of interacting FOXO1 and RBM14 proteins. However, because the expression of FOXO1 is too high and diffuses throughout the nucleus, the combination result of co-localization may be questionable. Relevant experiments need further verification. Although the confocal image data is doubtful, we still believe that FOXO1 interacts with two proteins through immunoprecipitation experiments.
## Discussion
In recent years, people have paid increasing attention to their health, and research on health preservation and antiaging has received more interest. The FOXO family genes are recognized as a class of longevity [26]. FOXO-regulated genes mainly include those that regulate the cell cycle and cellular death, autophagy, metabolism, and antioxidative processes. FOXO, as a switch that regulates a large class of functional genes, is an important research object in medical biology and life science [27, 28]. Cell lines that stably express FOXO can provide a reliable platform to study this family. The overexpression of genes in cells can be either transient or stable. Transient overexpression is relatively limited in scope and [29]. For example, transiently overexpressed RNA can only produce interference for a certain period of time in experiments. Transient transfer will introduce unpredictable copy number expression (while transient expression tends to be high and very unstable), resulting in inaccurate experimental results due to artificial factors. However, stable gene interference effects can be achieved by constructing stable cell [30]. For accurate and stable cell experiments, it is necessary to construct stably transfected cell lines.
Since most of the proteins encoded by human genes perform their functions when in [31], it is necessary to identify protein interactions to deeply analyze the structure and function of cells and study their molecular basis. However, most protein interactions are weak and transient, and overcoming these disadvantages has been a major difficulty. In recent years, Branon et al. introduced TurboID, a novel enzyme that biotinylates adjacent proteins, which combines the simplicity and nontoxicity of BioID with the high catalytic efficiency of [32]. This technique uses biotinylated enzymes to identify adjacent proteins and then enriches the biotin-labeled proteins with avidin magnetic beads for identification by mass spectrometry. Overall, using TurboID significantly improves the efficiency and greatly reduces the labeling time required compared with the original biotin labeling [22].
Lentiviral transfection, featuring high efficiency, stability and precision, has become a common tool for research in cellular and molecular [33]. Lentiviruses not only permit the continuous expression of the target protein in cells but are also puromycin-resistant to allow for the rapid selection of cell lines. In this study, we used a lentivirus to construct FOXO1-TurboID and EGFP-TurboID overexpression vectors, and EGFP-TurboID cells showed green fluorescence, indicating that the active lentiviral particles had been successfully delivered. FOXO1-TurboID expression was detected at the protein level, and a cell line that continuously expressed the FOXO1-TurboID gene was obtained. Next, we performed biotin labeling experiments on wild-type U251 cells and U251 cells with stable overexpression of FOXO1-TurboID and treated the latter cells with LPS. Subsequently, after biotin affinity purification and silver staining, a large number of proteins that interact with FOXO1 were directly observed after excluding the endogenous biotin-interacting proteins found in the false-positive control sample.
In this experiment, a total of 325 interacting proteins were collected by the proximity labeling technique. One hundred proteins were identified in the U251 blank group, and 240 proteins were identified in the FOXO1 overexpression group. After excluding the 64 proteins that were also identified in the blank group, 176 interacting proteins remained. Moreover, 289 proteins were identified in the LPS-treated group, and after excluding the 62 proteins that were also identified in the blank group, 227 interacting proteins remained. We then performed GO analysis and KEGG analysis. All of the above analyses are in line with the characteristics of FOXO1 as a transcription factor. In the LPS-treated group, several interesting pathways were enriched according to KEGG analysis, including hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), Alzheimer’s disease, glycolysis/gluconeogenesis, Parkinson’s disease, and ferroptosis. These pathways deserve further study.
The mass spectrometry data indicated that many hnRNP family members are FOXO1-interacting proteins, including hnRNPF, hnRNPD, hnRNPA1, hnRNPH1, hnRNPA2B1, hnRNPH3, hnRNPA3, and hnRNPK. Therefore, hnRNPK was selected for protein interaction verification. In addition, RBM14, a nuclear receptor coactivator, was selected. FOXO1 interacts with hnRNPK and RBM14, as verified by immunoprecipitation and confocal experiments. hnRNPK is an important regulatory protein in the nuclear heterogeneous ribonucleoprotein (hnRNP) [34] that is widely expressed in mammalian cells and distributed in the nucleus, cytoplasm, mitochondria and cell [35]. As the center of many biological pathways, hnRNPK is crucial for gene expression regulation, cell signal transduction, DNA repair and telomere [36]. A study found that hnRNPK is a conserved DNA/RNA binding protein that has a high expression level in tumor tissues and is closely related to the prognosis of malignant [37]. In addition, Gallardo et al. found that the reduced survival rate of mouse embryos with hnRNPK knockout suggests that this protein may play a key role in the development of newborn [38]. FOXO1, an important transcription factor, is the main target of insulin signal transduction and regulates metabolic homeostasis during oxidative stress. We believe that the interaction between FOXO1 and hnRNPK may be involved in the regulation of the expression of a large group of genes and play certain roles in cancer and diabetes. RNA binding motif protein 14 (RBM14), also known as PSP2 or quasi dot protein, is an RNA binding protein belonging to the RBM (RNA binding motif) protein [39]. RBM 14 has two RNA recognition motifs (RRMs) and a prion-like domain (PLD) at the N-terminus. Because it can interact with RNA and proteins, it can play a variety of roles in eukaryotic cells, such as participating in transcriptional activation, inducing chromosome separation, helping with DNA repair and cell [40]. Studies have shown that the RBM protein family is crucial in mesodermal development, and RBM14 plays a major role in the development of the heart. When RBM14 is absent, a variety of embryonic defects may occur, such as cardiac abnormalities and heart [41]. RBM14 is also a potential factor affecting oocyte quality and meiotic maturation of [42]. We believe that the interaction between FOXO1 and RBM 14 may regulate embryonic development and participate in DNA repair and cell differentiation processes.
Although our study identified a large number of FOXO1-interacting proteins, the biological functions of such interactions need to be further confirmed. I hope our study will provide a reference for FOXO1-related disease research.
## Conclusion
In this study, label-free mass spectrometry analysis was performed to identify 325 interacting proteins. A total of 176 proteins were identified in the FOXO1 overexpression group, and 227 proteins were identified in the LPS-treated group. These numbers exclude the interfering proteins identified in the U251 blank group. Finally, the interacting proteins hnRNPK and RBM14 were selected for immunoprecipitation and immunofluorescence verification studies.
## Materials
Monoclonal antibodies [FLAG (ab205606), hnRNP K (ab134060), and RBM14 (ab70636)] were purchased from Abcam. FOXO1 (a2934) and β-actin (ac006) were purchased from ABclonal. Lipopolysaccharide (LPS, L8880) was purchased from Beijing Solarbio Technology Co., Ltd. PVDF membranes were purchased from Millipore. Dynabead™ MyOne™ streptavidin C1 [65,001], biotin (B20656) and liposomal transfection reagent (Lipofectamine 2000, 11,668,500) were obtained from Thermo Fisher. RIPA buffer (G2002-100ML), DAIP buffer (G1012-100ML), and anti-fluorescence quenching sealing agent (G1401-25ML) were purchased from Wuhan Servicebio Technology Co., Ltd. Anti-DYKDDDDK (Flag) Affinity Gel (20585ES03), Lentivirus Concentration Solution (41101ES50) and a protein silver staining kit (36244ES30) were purchased from Yisheng Biotechnology (Shanghai) Co., Ltd. A DNA product purification kit (DP204) and a high purity plasmid small amount extraction kit (DP104) were purchased from Tiangen Biotech (Beijing) Co., Ltd. All of the inorganic salts came from Sinopharm.
## Cells lines
HEK 293T and U251 cells were obtained from ATCC (Manassas, USA). All cells were grown in Dulbecco’s Modified Eagle’s Medium (Gibco, USA) supplemented with $10\%$ fetal bovine serum (Gibco, USA). All cells were cultured in a $5\%$ CO2-humidified atmosphere at 37 °C.
## Construction of the plasmids
The TurboID gene sequence was obtained from Dr. Bo Xu (Wuhan University) and it replaced EGFP for integration into the pLenti-CMV-EGFP plasmid to generate pLenti-CMV-TurboID. The FOXO1 gene was amplified with human cDNA as a template (primer, FOXO1-f: atagaagacaccaccacacttagaatggccgaggcgcctcag; FOXO1-r: ggggggtatacgtataccgcctg acaccagctatgtc). pLenti-CMV-TurboID used restriction sites Xba I and BamH I, and the FOXO1 fragment was recombined into the linearized pLenti-CMV-TurboID plasmid. The positive clones were screened by transformation. The extracted plasmid was identified by enzyme digestion and sent to a company for sequencing. The successfully sequenced plasmid was named FOXO1-TurboID (Addgene ID: 194,712).
## Lentivirus packaging of the plasmid
293T cells were plated in two 10 cm cell culture dishes and transfected when the cells reached a density of 80-$90\%$. For transfection, serum-free medium was used to dilute the plasmids. One 1.5 mL EP tube contained dilutions of the PSPAXZ, PMD2.G and FOXO1-TurboID plasmids, and another EP tube contained dilutions of the PSPAXZ, PMD2.G and pLenti-CMV-EGFP plasmids. Later, an appropriate amount of liposomal transfection reagent (approximately 60 µl in a 10 cm dish) was diluted with serum-free medium and incubated at room temperature for 5 min. Then, the diluted DNA plasmid and liposome transfection reagent were mixed in slowly and evenly, and the samples were placed at room temperature for 20 min. Next, the prepared DNA–liposome complex was added to a 10 cm Petri dish with media exchange and cultured overnight at 37 °C. After exchanging the media again and further culture for 48–72 h, the medium was collected. The culture medium was removed with a 10 mL syringe and filtered through a 0.45 mm filter membrane. The filtrate was mixed with the concentrated lentivirus reagent in an appropriate proportion (reagent:culture medium = 1:4). Finally, the supernatant was centrifuged, fresh culture medium was mixed in for precipitation, and the virus was aliquoted into several 1.5 mL EP tubes.
## Construction and validation of the FOXO1-overexpressing cell line
U251 cells in the logarithmic growth stage were inoculated into 24-well plates. When the cell density reached 70-$80\%$, one well was selected to be treated with the virus concentrate carrying the FOXO1-TurboID plasmid, and another well was selected to be treated with the virus concentrate carrying the pLenti-CMV-EGFP plasmid. Infection was carried out for 4–8 h, and the medium was replaced with fresh medium after infection. After 48 h, the fluorescence intensity of the cells infected with the pLenti-CMV-EGFP plasmid was observed. Intense fluorescence indicated that infection was successful. Next, the medium was replaced with medium containing puromycin (1.5 µg/mL) for resistance screening, and the cell state was observed within 24–48 h. After that, the surviving cells were further cultured, subcultured in 12-well plates when they were in a normal state, and then subcultured successively until the number of cells met the requirements of subsequent experiments. During this period, the cell state was observed every day, and the solution was changed every two days. Some cells were used to make a lysate for SDS‒PAGE. The cell line stably transfected with the pLenti-CMV-EGFP plasmid was named EGFP-U251, and the cell line stably transfected with the FOXO1-TurboID plasmid was named FOXO1-U251.
## Screening the time for biotin labeling
FOXO1-U251 cells were grown in 6-well plates, and when their density reached more than $80\%$, the original medium was removed, and the cells were washed twice with PBS. Biotin labeling was carried out with biotin (500 µmol·L-1), MgCl2 (1 µmol·L-1) and Adenosine 5’-triphosphate (ATP, 200 µmol·L-1), and six time points (0 min, 10 min, 1 h, 3 h, 6 and 12 h) were evaluated. At each time point, the cells were collected, and the cell lysates were subjected to SDS‒PAGE. The antibody used was streptavidin-peroxidase.
## Proximity labeling technology based on TurboID
According to the method reported by Branon et al.[22], EGFP-U251 and FOXO1-U251 cells were cultured in T75 cell culture flasks. The experiment was divided into three groups. ① In the blank group, EGFP-U251 cells were cultured in two T75 culture flasks; ② in experimental Group 1, FOXO1-U251 cells were cultured in two T75 culture flasks; and ③ in experimental Group 2, FOXO1-U251 cells were cultured in two T75 culture flasks and LPS (100 ng·L-1) was also used. All three groups of cells were treated with biotin (500 µmol·L-1), MgCl2 (1 µmol·L-1) and ATP (200 µmol·L-1) in DMEM, and the cells were collected for sample preparation after 12 h.
## Affinity purification of the biotin-labeled protein
The cells in each of the T75 culture flasks were collected in a 1.5 mL EP tube and resuspended by adding 1 mL of RIPA buffer. Then, the cells were lysed on ice for 15 min and centrifuged at 12,000 r·min− 1 for 10 min. After that, the supernatant was transferred to a clean 1.5 mL EP tube. Magnetic Dynabeads were washed with 1 mL of RIPA buffer for 2 min and placed on a magnetic stand for approximately 10 s of adsorption until the liquid became clear. This procedure was repeated five times after discarding the RIPA buffer. Following the last wash, the beads were divided into 3 aliquots, and the buffer was discarded when the beads precipitated. The above protein lysates were added to the prepared beads, sealed with parafilm, and shaken slowly at 4 °C in a 360-degree shaker overnight. After overnight incubation, the supernatant was discarded, and the beads were washed with 1 mL of RIPA buffer for approximately 1.5 min. This procedure was repeated once. The beads were then washed once with 1 mL of KCl (1 mol·L− 1), three times with Buffer 1 (100 mL of Buffer 1: 67 mL of NaCl (3 mol·L− 1), 1 mL of Tris-HCl (pH 7.4, 1 mol·L− 1), and 32 mL of H2O), and once with Na2CO3 (0.1 mol·L− 1). Then, the beads were washed once with $10\%$ SDS for 2 min (or up to 3 min). After adding RIPA buffer, the beads were placed in a metal bath at 50 °C for 1 min. Then, the beads were washed twice with 1 mL of RIPA buffer, and 1 mL of RIPA buffer was added back. One-third of the samples were used for identification by silver staining, and the remaining samples were washed twice with PBS after discarding the RIPA supernatant and finally stored at -20 °C after removal of the liquid.
## Identification of the protein with biotin affinity by silver staining
One-third of the protein sample mentioned in the previous step was used for preparing a $10\%$ SDS‒PAGE gel (1 mm thick, 10-well comb). Five microliters of 10× loading buffer was added to 30 µL of sample, and each sample (10 µL) was loaded after denaturation. The gel after electrophoresis was put into 100 mL of fixative solution (50 mL of ethanol + 10 mL of glacial acetic acid + 40 mL of deionized water) and placed on a shaker at 60–70 r·min− 1 for 20 min. After discarding the fixative solution, the gel was put into 100 mL of $30\%$ ethanol (30 mL of ethanol + 70 mL of deionized water) and shaken gently on a shaker for 10 min. After discarding the ethanol, 200 mL of deionized water was added, and the sample was shaken gently on a shaker for 10 min. After discarding the water, 100 mL of sensitizing solution (1×) (1 mL of sensitizing solution (100×) + 99 mL of deionized water; used within 2 h of preparation) was added, and the sample was shaken gently on a shaker for 4–5 min. After discarding the sensitizing solution, 200 mL of deionized water was added, and the sample was shaken gently on a shaker for 1 min; this procedure was repeated once. After discarding the water, 100 mL of silver staining solution (1×) (1 mL of silver staining solution (100×) + 99 mL of deionized water; used within 2 h of preparation) was added, and the sample was shaken gently on a shaker for 30–40 min. After discarding the silver staining solution, 100 mL of deionized water was added, and the sample was shaken on a shaker for 0.5 min; this procedure was repeated once. After discarding the water, 100 mL of chromogenic solution (0.05 mL of chromogenic solution A + 20 mL of chromogenic solution B (5×) + 80 mL of deionized water; used within 20 min of preparation) was added for gentle shaking on a shaker for 3–10 min until a band appeared. After discarding the chromogenic solution, 100 mL of stop solution (5 mL of glacial acetic acid + 95 mL of deionized water) was added, and the sample was shaken on a shaker for 10 min. After discarding the stop solution, 100 mL of deionized water was added, and the sample was shaken on a shaker for 0.5 min; this procedure was repeated once. Pictures of the bands were taken and saved.
## Identification by label-free quantitative protein mass spectrometry
After identification by silver staining, the protein samples were sent to a company for label-free quantitative protein mass spectrometry analysis. The specific steps in this procedure were as follows.
## Sample preparation
The bead samples obtained from the immunoprecipitation experiment were washed three times with precooled PBS to remove the remaining detergent. Then, bead samples were incubated in reaction buffer ($1\%$ SDC; 100 mM Tris-HCl, pH 8.5; 10 mM TCEP; 40 mM CAA) at 95 °C for 10 min for protein denaturation, cysteine reduction and alkylation. The eluates were diluted with an equal volume of H2O and subjected to trypsin digestion overnight at 37 °C by adding 1 µg of trypsin. The peptide was purified using homemade SDB desalting columns. The eluate was vacuum dried and stored at -20 °C for later use.
## LC‒MS/MS detection
LC‒MS/MS data acquisition was carried out on Orbitrap Exploris 480 mass spectrometer coupled with an EASY-nLC 1200 system (both from Thermo Scientific) [43]. Peptides were picked up by an autosampler and transferred to a C18 analytical column (75 μm × 25 cm, 1.9 μm particle size, 100 Å pore size, Thermo) for separation. Mobile phase A ($0.1\%$ formic acid) and mobile phase B ($80\%$ ACN, $0.1\%$ formic acid) were used for the 60 min gradient separation procedure. A constant flow rate of 300 nL/min was used. For DDA mode analysis, each cycle consisted of the acquisition of one full scan mass spectrum ($R = 60$ K, AGC = $300\%$, max IT = 20 ms, scan range = 350–1500 m/z) followed by 20 MS/MS events ($R = 15$ K, AGC = $100\%$, max IT = auto, cycle time = 2 s). The HCD collision energy was set to 30. The isolation window for precursor selection was set to 1.6 Da. The former target ion exclusion was set to 35 s.
## Data analysis
Raw MS data were analyzed with MaxQuant (V1.6.6) using the Andromeda database search [44]. Spectral files were searched against the [45] Human proteome database using the following parameters: LFQ mode was checked for quantification; variable modifications, oxidation (M) & acetyl (protein N-term); fixed modification, carbamidomethyl (C); and digestion, trypsin/P. Matching between runs was used for identification transfer. The search results were filtered with a $1\%$ FDR.
## FOXO1 immunoprecipitation (IP) experiment
The anti-Flag affinity gel was fully resuspended to form a homogeneous solution. Fifty microliters of the mixture (25 µL of gel) was placed into a new centrifuge tube for centrifugation at 8,000 r·min− 1 for 30 s. The gel was allowed to precipitate to the bottom of the centrifuge tube and then stand for 1–2 min before adding the sample. After removing the supernatant, 500 µL of TBS was added, and the gel was gently resuspended and centrifuged at 10,000 r·min− 1 for 30 s. Then, the supernatant was discarded. This procedure was repeated once. A certain amount of cell lysate was added, and the final volume was adjusted to 1 mL. The cell lysate was incubated slowly overnight at 4 °C. On the next day, the cell lysate was centrifuged (8,000 r·min− 1 for 30 s), the supernatant was removed, and the pellet was shaken with 0.5 mL of TBS before being centrifuged again (8,000 r·min− 1 for 30 s). All of the supernatant was removed, and the procedure was repeated three times.
## Immunofluorescence of FOXO1-overexpressing cells
FOXO1-overexpressing cells were subcultured in a 3.5 cm confocal dish to observe their growth state. When the cell density was above $80\%$, the medium was removed, and the cells were fixed with $4\%$ paraformaldehyde for 15 min. Then, the cells were treated with $0.5\%$ Triton X-100 at room temperature for 20 min, blocked with $5\%$ normal goat serum for 30 min, and incubated with FOXO1 primary antibody at 4 °C overnight. Subsequent steps were carried out in the dark. The secondary antibody was added to the cells for incubation at room temperature for 1 h, and PBST was used to wash the cells 3 times. Next, DAPI was added dropwise, the cells were incubated in the dark for 5 min, and the nuclei were counterstained. PBST was used to wash the cells 4 times. After the addition of fluorescence quencher, the cells were photographed by confocal microscopy and stored.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Additional file 1 Additional file 2
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---
title: 'A novel nomogram to predict 90-day mortality in patients with hepatitis B
virus-related acute-on-chronic liver failure: a single-center retrospective study'
authors:
- Ye Xiong
- Zuoxun Xia
- Lu Yang
- Jianrong Huang
journal: BMC Gastroenterology
year: 2023
pmcid: PMC10039517
doi: 10.1186/s12876-023-02727-1
license: CC BY 4.0
---
# A novel nomogram to predict 90-day mortality in patients with hepatitis B virus-related acute-on-chronic liver failure: a single-center retrospective study
## Abstract
### Background
Acute-on-chronic liver failure (ACLF) is a critical illness with high mortality. Herein, we developed and validated a new and simple prognostic nomogram to predict 90-day mortality in hepatitis B virus-related ACLF (HBV-ACLF) patients.
### Methods
This single-center retrospective study collected data from 181 HBV-ACLF patients treated between June 2018 and March 2020. The correlation between clinical data and 90-day mortality in patients with HBV-ACLF was assessed using univariate and multivariate logistic regression analyses.
### Results
Multivariate logistic regression analysis showed that age ($$p \leq 0.011$$), hepatic encephalopathy ($$p \leq 0.001$$), total bilirubin ($$p \leq 0.007$$), international normalized ratio ($$p \leq 0.006$$), and high-density lipoprotein cholesterol ($$p \leq 0.011$$) were independent predictors of 90-day mortality in HBV-ACLF patients. A nomogram was created to predict 90-day mortality using these risk factors. The C-index for the prognostic nomogram was calculated as 0.866, and confirmed to be 0.854 via bootstrapping verification. The area under the curve was 0.870 in the external validation cohort. The predictive value of the nomogram was similar to that of the Chinese Group on the Study of Severe Hepatitis B score, and exceeded the performance of other prognostic scores.
### Conclusion
The prognostic nomogram constructed using the factors identified in multivariate regression analysis might serve as a beneficial tool to predict 90-day mortality in HBV-ACLF patients.
## Introduction
Acute-on-chronic liver failure (ACLF) is a multifaceted condition characterized by progressive deterioration of liver function, multiple organ failure, poor treatment effects, and high short-term mortality [1]. The short-(1–3 months) and medium-term (6 months) mortality of patients with ACLF can reach as high as 50–$90\%$, and the 28-day mortality is 15 fold higher than that of patients with chronic liver disease (CLD) alone [2]. Despite the present popularity of vaccines, hepatitis B virus (HBV) infection still inflicts a devastating impact on human health, affecting approximately 240 million people worldwide [3, 4]. HBV-related ACLF (HBV-ACLF) is a serious complication of chronic hepatitis B (CHB) infection, and can develop at any stage of disease progression [5]. In China, HBV is the major cause of CLD. Moreover, it is estimated that HBV-ACLF accounts for more than $70\%$ of ACLF cases in the region, causing over 100,000 deaths every year [6, 7].
Currently, the treatment of ACLF mainly involves the use of an artificial liver support system (ALSS), cell therapies, and liver transplant (LT) [8]. Nevertheless, an accumulating body of research indicates that ALSS use exerts no significant benefit on the survival of patients with end-stage liver failure [9, 10]. Moreover, because of ethical and safety concerns, the clinical application of cell therapies has so far been limited [8, 11]. In many countries, LT has been applied as a medical intervention to save the lives of patients with ACLF. However, because of the shortage of donor liver grafts, identifying suitable recipients and prioritizing LT is crucial [12].
To date, several predictive scoring systems have been developed to evaluate the prognosis of patients with ACLF, including the Model for End-stage Liver Disease (MELD) score, Chronic Liver Failure Consortium Organ Failure score (CLIF-C OFs), CLIF-C ACLFs, and Chinese Group on the Study of Severe Hepatitis B score (COSSH-ACLFs). However, the MELD score, CLIF-C Ofs, and CLIF-C ACLFs were all established in Western countries, in which the populations are over $70\%$ Caucasians. Furthermore, the main causes of disease were hepatitis C virus and alcohol, rather than HBV. Hence, it is still challenging to use these scoring systems to predict HBV-ACLF prognosis [13, 14]. On the other hand, although the COSSH-ACLFs is a predictive scoring model based on HBV infection, its ability to accurately predict the prognosis of patients with HBV-ACLF is uncertain and requires further verification [15]. In addition, the COSSH-ACLFs also involves multiple parameters and requires complex organ failure assessments. Thus, a more simple prognostic model, which would be more feasible in clinical practice, is needed.
The purpose of this study was to establish a valid but simple prediction tool using laboratory and clinical parameters to predict 90-day mortality in patients with HBV-ACLF and provide guidance for clinical treatment and decision-making.
## Patients
For this single-center, retrospective study, we extracted data from all patients diagnosed with HBV-ACLF during admission in the First Affiliated Hospital of Zhejiang University School of Medicine from June 2018 to March 2020. HBV-ACLF was diagnosed based on the COSSH-ACLF criteria [15]. The inclusion criteria were as follows: [1] age between 18 and 80 years; [2] hospitalization for at least 2 days; [3] ACLF caused by HBV infection; [4] serum total bilirubin (TBIL) ≥ 12 mg/dL; and [5] international normalized ratio (INR) ≥ 1.5. The exclusion criteria were as follows: [1] ACLF resulting from other causes of liver damage, such as hepatitis A, C, D, and E viruses, alcohol, or autoimmune hepatitis; [2] liver cancer or other tumors; [3] coinfection with human immunodeficiency virus; [4] pregnant women; [5] severe cardiopulmonary diseases or previous renal failure; [6] incomplete data or loss to follow-up; and [7] patients who underwent LT. Based on these eligibility criteria, 181 patients were enrolled in this study (Fig. 1).Fig. 1Flowchart of the screening and enrollment of patients with HBV-ACLF. Abbreviations: HBV-ACLF, hepatitis B virus-related acute-on-chronic liver failure; HEV, hepatitis E virus; HIV, human immunodeficiency virus All patients included in this study received comprehensive treatments, including antiviral therapy for HBV-DNA-positive patients, as well as treatment for hypoproteinemia, infection, hepatic encephalopathy (HE), ascites, electrolyte disorder, and other complications.
## Data collection
We extracted patient data before treatment from the medical records, including clinical (age, sex, body mass index [BMI], oxygen saturation, and blood pressure) and laboratory (white blood cell [WBC], hemoglobin, platelet count, albumin, alanine aminotransferase, aspartate aminotransferase, TBIL level, creatinine [Cr], serum sodium, INR, D-dimer, total cholesterol [TC], triglyceride, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol, alpha fetoprotein [AFP], and HBV DNA) data, as well as data on the presence of ongoing complications such as HE, upper gastrointestinal bleeding (UGB), infection, and ascites. Furthermore, all enrolled patients were followed up from the date of their enrollment until the end of the 90-day follow-up, or death.
Several ACLF predictive scores were calculated using the following formula: MELD = 9.57 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× Ln (Cr, mg/dL) + 3.78 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× Ln (TBIL, mg/dL) + 11.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× Ln (INR) + 6.43 [16]; CLIF-C-OFs including TBIL, Cr, the grade of HE, INR, mean arterial pressure, and respiratory status were evaluated according to the standard criteria: CLIF-C-ACLF = 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× [0.33 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× CLIF-OFs + 0.04 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× Age + 0.63 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× Ln(WBC)-2] [14]; COSSH-ACLFs = 0.741 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× INR + 0.523 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× HBV-SOFA + 0.026 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× Age + 0.003 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× TBIL [15].
## Statistical analysis
SPSS software (version 26.0) was used for all statistical analyses. Continuous variables are presented as the means ± standard deviation or medians with interquartile range, and were analyzed using the independent two-sample t-test and Mann–Whitney U test, respectively. Categorical variables are presented as frequencies, and were analyzed using Pearson’s chi-squared test. Univariate and multivariate logistic regression models were also used to assess the correlation between clinical variables and 90-day mortality in patients with HBV-ACLF to screen for independent predictors, which were subsequently introduced into R v.3.6.1 (http://www.r-project.org/) to establish a nomogram prediction model. A receiver-operating characteristic curve (ROC) was drawn, and the area under the curve (AUC) was calculated to evaluate the discriminatory ability of the nomogram prediction model. Decision curve analysis was then conducted to determine the clinical applicability of the nomogram [17]. The bootstrap repeated sampling method (1000 bootstrap resampling) was used for internal verification. The corrected C-index was calculated and the calibration curve was drawn after internal verification (1000 bootstrap resampling) to evaluate the consistency of the nomogram prediction model [18]. The nomogram was then externally verified using the data of 198 HBV-ACLF patients collected from April 2020 to December 2021. In all comparisons, the results were considered statistically significant at $p \leq 0.05.$
## Demographics and characteristics of the enrolled patients
Study patients were divided into the survival (111 patients) and death/non-survival group (70 patients), based on survival condition at 90 days. The majority of patients in both groups were men ($85.6\%$ vs. $80.0\%$, respectively). The patients in the non-survival group were older than those in the survival group (52.84 ± 11.63 vs. 45.38 ± 11.25 years, $p \leq 0.001$), and non-survivors had a higher incidence of complications such as infection, UGB, ascites, and HE compared to the survivors ($$p \leq 0.004$$, $$p \leq 0.003$$, $$p \leq 0.044$$, and $p \leq 0.001$, respectively). The demographic characteristics of the participants are shown in Table 1.Table 1Patient demographics and characteristicsVariablesSurvival ($$n = 111$$)Non-survival ($$n = 70$$)pSex (male)95560.325Age (years)45.38 ± 11.2552.84 ± 11.63 < 0.001*#BMI (kg/m2)24.20 ± 3.1123.26 ± 3.420.059#HE532 < 0.001*Ascites83610.044*UGB3100.003*Infection32350.004*WBC (109/L)6.70 (4.90–8.90)6.80 (5.10–9.20)0.933Hb (g/L)126.55 ± 18.84125.40 ± 20.620.700#PLT (109/L)111.00 (77.00–149.00)103.00 (66.75–137.50)0.186ALT (U/L)322.00 (131.00–673.00)323.00 (108.25–549.25)0.795AST (U/L)180.00 (93.00–362.00)211.50 (106.00–407.75)0.290Albumin (g/L)31.24 ± 4.4031.10 ± 3.380.815#aTBIL (µmol/L)322.30 (264.40–393.90)367.30 (295.38–451.60)0.007*Cr (µmol/L)62.00 (55.00–72.00)59.50 (52.00–73.50)0.372TC (mmol/L)2.13 (1.82–2.62)2.10 (1.62–2.76)0.441TG (mmol/L)1.36 (1.11–1.75)1.18 (0.98–1.63)0.029*HDL-C (mmol/L)0.17 (0.10–0.23)0.20 (0.14–0.27)0.017LDL-C (mmol/L)0.64 (0.28–1.15)0.91 (0.48–1.31)0.070AFP (ng/mL)154.00 (49.10–365.80)56.00 (18.08–176.30)< 0.001*INR1.85 (1.68–2.09)2.26 (1.90–2.91)< 0.001*D-dimer (ug/L)1385.00 (766.00–2854.00)2890.50 (1932.50–4045.25)< 0.001*Sodium (mmol/L)137.00 (135.00–139.00)137.00 (134.00–140.00)0.432Log10HBV-DNA (IU/ml)5.17 ± 1.715.41 ± 2.200.432#aAbbreviations: BMI body mass index, HE hepatic encephalopathy, UGB upper gastrointestinal bleeding, WBC white blood cell, Hb hemoglobin, PLT platelet, ALT alanine aminotransferase, AST aspartate aminotransferase, TBIL total bilirubin, Cr creatinine, TC total cholesterol, TG triglyceride, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, AFP alpha fetoprotein, INR international normalized ratio*p value < 0.05; #conforms to normal distribution on the SK test of normality; aindicates that Levene’s test for equality of variances are not uniform, two independent T' tests of correction were applied
## Risk factors associated with 90-day mortality
Univariate analysis revealed that age, the occurrence of complications (i.e. infection, UGB, ascites, and HE), and the levels of TBIL, AFP, INR, TC, HDL-C, and D-dimer were significantly associated with 90-day mortality ($p \leq 0.05$). Results showed that there were no significant between-group differences in terms of sex, BMI, WBC, Hb, PLT, ALT, AST, albumin, Cr, TC, LDL-C, sodium, and Log10HBV-DNA (Table 1). Additionally, eleven risk factors identified on univariate analysis were screened using multivariate logistic regression analysis, which revealed that age (odds ratio [OR] 1.052, $95\%$ confidence interval [CI] 1.012–1.095), HE (OR 9.059, $95\%$ CI 2.604–31.514), TBIL (OR 1.006, $95\%$ CI 1.002–1.011), INR (OR 1.014, $95\%$ CI 1.004–1.023), and HDL-C (OR 1.080, $95\%$ CI 1.017–1.146) were independent risk factors for 90-day mortality in patients with HBV-ACLF (Table 2).Table 2Multivariate logistic regression of risk factors of 90-day mortalityVariablesB valueSEWaldOR ($95\%$CI)pAge0.0510.0206.4401.052 (1.012,1.095)0.011*HE2.2040.63612.0039.059 (2.604,31.514)0.001*Ascites0.7090.6401.232.033 (0.580,7.120)0.267UGB1.3270.8932.2073.770 (0.655,21.705)0.137Infection0.4310.4480.9281.539 (0.640,3.700)0.335TBIL0.0060.0027.2731.006 (1.002,1.011)0.007*TG0.6590.3793.0211.933 (0.919,4.064)0.082HDL-C0.0770.0306.3971.08 (1.017,1.146)0.011*AFP-0.0020.0010.1680.998 (0.996,1.001)0.168INR0.0140.0050.0061.014 (1.004,1.023)0.006*D-dimer000.3310.566Abbreviations: HE hepatic encephalopathy, UGB upper gastrointestinal bleeding, TBIL total bilirubin, TG triglyceride, HDL-C high-density lipoprotein cholesterol, AFP alpha fetoprotein, INR international normalized ratio*p value < 0.05; Since the physiological changes of INR and HDL are small, in order to facilitate the observation of the fluctuations of small amplitude values, we magnified them by 100 times and put them into binary logistic analysis
## HBV-ACLF prognostic nomogram establishment and evaluation
The five abovementioned independent risk factors identified on multivariate analysis were introduced into the R software to establish a prognostic nomogram model for the 90-day mortality of patients with HBV-ACLF (Fig. 2a). The higher the score calculated from the sum of the specified scores of each predictor in the nomogram, the higher the probability of death. Fig. 2a The nomogram to predict 90-day mortality in patients with HBV-ACLF. Abbreviations: HBV-ACLF, hepatitis B virus-related acute-on-chronic liver failure; b Calibration curve of the prognostic nomogram in patients with HBV-ACLF. Notes: The x-axis represents the nomogram-based predicted mortality, and the y-axis represents the actual mortality. The diagonal dotted line represents an ideal model with no disparities between predicted mortality and the actual clinical result. The solid line represents the performance of the nomogram; c Decision curve analysis of the prognostic nomogram. Notes: The x-axis represents the threshold probability, and the y-axis represents the net benefit. The blue, gray, and black lines represent the prognostic nomogram, the assumption that all the patients will die, and the assumption that no patients will die, respectively. The decision curve analysis shows that the nomogram has a good overall net benefit in a wide range of threshold probabilities The calibration curve of the nomogram for the prediction of 90-day mortality in patients with HBV-ACLF demonstrated a high level of agreement (Fig. 2b). The C-index for the prognostic nomogram was 0.866, and was confirmed to be 0.854 via bootstrapping verification, indicating a strong congruence between the findings on the nomogram and the actual results in the internal verification.
## Clinical usage
As shown in Fig. 2c, we performed decision curve analysis on the nomogram to estimate the net benefits of our nomogram to patients. The findings showed a significant net benefit for almost all threshold probabilities, especially the threshold probability between 2–$91\%$.
## External validation of the prognostic nomogram
In the external set, the prognostic nomogram also showed good discrimination (AUC = 0.870) (Fig. 3a). As shown in Fig. 3b, the calibration curve was close to the ideal curve, indicating strong congruence between the predicted probability and the actual probability in the external set. Additionally, the decision curve analysis (Fig. 3c) suggested that the prognostic nomogram also provided net benefit in almost all of the threshold probability range. Fig. 3The ROC curve (a), calibration curve (b), and decision curve (c) of the prognostic nomogram in the external validation cohort We plotted the ROCs to analyze and compare the predictive values of the nomogram, and COSSH-ACLFs in estimating the 90-day mortality of patients with HBV-ACLF (Fig. 4). The predictive ability of the nomogram was approximately comparable to that of the COSSH-ACLFs (AUC 0.866 vs. 0.872, $$p \leq 0.099$$).Fig. 4Comparison of abilities between the nomogram and other scoring systems in predicting the 90-day mortality of patients with HBV-ACLF
## Discussion
ACLF is characterized by a rapid progression of multiple organ failure and a low survival rate, making it an urgent global health problem [4, 19]. In Asia, HBV infection and activation are the main causes of ACLF [20]. Further, the mortality of patients diagnosed with HBV-ACLF at admission is high, necessitating further research. It is crucial to develop an accurate and feasible mortality risk prediction model to enable the application of relevant interventions. This will aid in guiding the clinical decision making of medical staff, as well as in ameliorating patient outcomes.
Nowadays, nomograms are widely used as prognostic tools in medical studies. Due to their high accuracy, they play important roles in clinical decision making. In this single-center study, we screened five independent risk factors (age, TBIL, INR, HE, and HDL-C) and established a nomogram to predict 90-day mortality in patients with HBV-ACLF.
First, both the CLIF-C ACLFs and COSSH-ACLFs already include age in their prognostic scoring systems, and both consider old age as a risk factor for poor prognosis in patients with HBV-ACLF. This is logical, as an increase in age leads to significant decreases in liver volume, blood flow, and enzyme metabolism, which reduce the liver’s tolerance to diseases [21]. Second, high serum levels of INR and TBIL generally indicate coagulation failure and liver failure, respectively. Many prognostic studies have reported that the abovementioned parameters are indicators of survival in patients with liver diseases [22, 23]. Furthermore, the parameters are widely used in prognostic scores for patients with ACLF [14, 24, 25]. Additionally, patients with HE, especially those with ACLF, reportedly have higher mortality rates [1, 26]. Verma et al. previously revealed that the presence of HE was independently associated with mortality in patients with ACLF, and that patient mortality tends to increase with higher HE grades [27].
Importantly, HDL-C level, which is not included in any of the currently used prediction models, was considered a risk factor in our prognostic nomogram. It is well-known that a low HDL-C level is an independent predictor of cardiovascular risk [28, 29], and some recent studies have shown that blood lipid levels are decreased in patients with inflammatory diseases [20]. The possible underlying mechanism involves the binding of HDL-C particles to lipopolysaccharides, resulting in the inhibition of the activation of inflammatory factors [30]. According to a cross-sectional study, low HDL-C level is a poor prognostic factor for chronic liver failure [31]. In our study, HDL-C levels in patients with HBV-ACLF were lower than the established normal values (0.78–1.81 mmol/L) in both the survival (0.17, $95\%$ CI 0.10–0.23) and non-survival groups (0.20, $95\%$ CI 0.14–0.27). However, in our prognostic nomogram, we concluded that a lower HDL-C level was beneficial for the survival of patients with HBV-ACLF. This result is contrary to the conventional view that HDL-C is a “good cholesterol” [32, 33]. Indeed, recent studies have reported that HDL-C may not always be a “good cholesterol.” Silbernagel et al. found that the protective effect of HDL-C disappeared in patients with unstable coronary heart disease [34]. Moreover, some studies have shown that, unlike in healthy individuals, HDL-C does not exert any vascular protective effects in patients with kidney disease or diabetes; in fact, increased levels of HDL-C may even have harmful effects [35–37]. We speculated that in a diseased state, HDL-C may have a negative impact on the human body, owing to disease-induced dysfunction. Therefore, in patients with HBV-ACLF, higher HDL-C levels may be more detrimental to patient survival. Nevertheless, more intensive basic experiments and mechanistic research are needed to clarify the abovementioned speculation. It is still unknown whether HDL-C plays a role in the course of HBV-ACLF.
As observed, the areas under the ROCs of the nomogram and COSSH-ACLFs were very similar. Hence, it could be considered that the predictive abilities of the nomogram and COSSH-ACLFs are comparable. The COSSH-ACLFs is a complex score based on organ failure, comprising 11 parameters which are complicated to evaluate [15]. As Chen et al. stated in their article, pulse oxygen saturation or arterial blood gas analysis were recently used in their center to routinely evaluate the respiratory function of patients with ACLF [38]. The complexity of the COSSH-ACLFs significantly limits its application. In the present study, five independent risk factors were selected to build a prognostic nomogram that is simple, intuitive, precise, and universal.
However, the present study has several limitations. First, this was a single-center, retrospective study, which lead to inevitable selection bias, and the majority of the participants were men. Moreover, we excluded patients who were lost follow-up or with incomplete data, which may have affected the efficacy of the nomogram. Second, the course of ACLF changes dynamically. The data in this study were collected at the time of diagnosis rather than at consecutive time points. Moreover, subsequent changes in treatment and nursing care may lead to variations in relevant parameters. Third, further studies are required to confirm whether HDL-C can be used as a prognostic predictor in patients with HBV-ACLF, and to clarify the mechanism of HDL-C in the pathogenesis of HBV-ACLF. Lastly, although the robustness of our prognostic nomogram was checked via internal validation (bootstrap testing) and external validation, external evaluation with a larger, multi-center population is still required.
## Conclusion
In this study, we established and validated a simple, prognostic nomogram to predict 90-day mortality in patients with HBV-ACLF. Additionally, this nomogram can be used for early detection of patients with HBV-ACLF, making clinical decisions, and guiding the allocation of medical resources. Although our nomogram was internally validated by internal verification (1000 bootstrap resampling) and assessed by using another validation cohort, this nomogram still requires external validation before its use can be popularized.
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|
---
title: 'The renal artery-aorta angle associated with renal artery plaque: a retrospective
analysis based on CT'
authors:
- Hongzhi Yang
- Ruwu Yang
journal: BMC Medical Imaging
year: 2023
pmcid: PMC10039526
doi: 10.1186/s12880-023-00997-5
license: CC BY 4.0
---
# The renal artery-aorta angle associated with renal artery plaque: a retrospective analysis based on CT
## Abstract
### Purpose
To investigate the relationship between renal artery anatomical configuration and renal artery plaque (RAP) based on 320-row CT.
### Methods
The abdominal contrast-enhanced CT data from 210 patients was retrospectively analyzed. Among 210 patients, there were 118 patients with RAP and 92 patients with no RAP. The anatomical parameters between lesion group and control group were compared and analyzed by using t-test, χ2-test and logistic regression analysis.
### Results
[1] There were statistical differences on age, hypertension, diabetes, hypertriglyceridemia and hypercholesterolemia between lesion group and control group. [ 2] The differences on the distribution and type and of RAP between lesion group and control group were statistically significant. The most common position was the proximal, and the most common type was calcified plaque. [ 3]There were significant statistical differences on the proximal diameter of renal artery and renal artery-aorta angle A between lesion group and control group. The differences on the other anatomical factors between two groups were not statistically significant. [ 4] The result of logistic regression analysis showed that right RAP was related to age, hypertension and right renal artery angle A (the AUC of ROC = 0.82), and left RAP was related to high serum cholesterol, age and left renal artery angle A(the AUC of ROC = 0.83). [ 5] The RAP was associated with renal artery-aorta angle A, but the differences on distribution, type stability of RAP between R1 (L1) group and R2 (L2) group were not statistically significant.
### Conclusions
The RAP was associated with age, hypertension, hypercholesterolemia and renal artery-aorta angle A. Adults which had the greater renal artery-aorta angle A and the other above risk factors may be at increased risk for RAP.
## Introduction
Renal artery plaque (RAP) is associated with renal artery stenosis(RAS) and is known as the predominant cause of RAS, which can result in renal resistant hypertension, ischemic nephropathy, renal artery angina, chronic kidney disease, renal dysfunction, recurrent pulmonary edema, angina colic, acute left heart failure and acute coronary syndrome [1, 2]. As a indicator of atherosclerosis, RAP not only reflects the stage of progression and severity of systemic atherosclerosis, but also plays important role on prevention of coronary heart disease and acute stroke [2, 3]. RAP rupture can lead severe stenosis or occlusion of renal artery that could cause renal infarction or kidney dysfunction [4]. One study have demonstrated that patients with RAP are more likely to appear acute coronary syndrome and acute stoke than people with no RAP [5]. Takumi et al. [ 6] found that RAP may influence renal function after renal artery intervention. Furthermore, atherosclerotic state from donor and recipient, other anatomical and technical factors can result in transplant renal artery stenosis (TRAS), which can induce graft failure and ischemic nephropathy [7–9]. In sum, RAP makes a great unfavorable impact on human health. Therefore, it is necessary to study the risk factors influencing the formation of plaque.
Up to date, the researches related with RAP mainly focus on RAP associations with subclinical disease, RAP relevance with risk factors, RAP relationship with the progression of systemic atherosclerosis, few research on RAP associations with anatomical factors [10, 11]. There are many factors that can influence the formation of RAP, including traditional atherosclerosis risk factors: age, male gender, body mass index (BMI), smoking, hypertension, diabetes, carotid artery intima-media thickness, which were considered as a useful noninvasive marker of subclinical atherosclerosis [3, 5]. In a study, Tolkien et al. [ 12] found a significant association between RAP and age, gender, hypercholesterolemia and hypertension, while Siegel et al. [ 13] concluded that calcifications of the RAP were not related to hypertension through retrospectively analysis on abdominal CT image data. In addition, children with known risk factors, including genetic diabetes, hyperlipidemias, and renal diseases, are at higher risk for atherosclerotic plaques in adolescence [14]. However, it remains unknown as to whether RAP is associated with experimental indexes related to renal function or not. Zhun et al. [ 15] indicated the atherosclerotic RAS was related with regional vascular geometry, but the quantitative analysis of correlation between RAP and the specific anatomical indexes of renal vessel was not studied. Vlach Giannis Nikolaos et al. [ 16] showed that RNA-mediated inflammation affected the process of atherosclerotic cardiovascular disease and stability of artery plaque. Wen et al. [ 17] indicated that serum cystatin C may contribute stability of plaques in normal renal function and serum cystatin C was a risk predictor of plaques in mildly impaired renal function. Although the conclusion is quite controversial, there have been many studies arguing the relationship between RAP and demography or metabolic abnormalities. Only few research is available regarding the correlation between RAP and anatomic configuration of renal arteries or veins.
In addition to, it was found that some older patients with multiple underlying metabolic diseases had no RAP, but some younger patients with no underlying metabolic diseases had RAP in the daily-routine practice, which may be speculated that RAP was connected with renal vascular anatomical configuration. In view of the above facts, this study mainly investigates whether RAP is associated with anatomical factors of renal artery and vein or not, through retrospectively analyzing abdominal contrast-enhanced CT image characteristics. The common examinations related with RAP research contain multi-detector row computed tomography (MDCT), intravascular ultrasound (IVUS), and MRI [18–21]. MDCT is the most widely used owing to clearly display lumen and have a higher sensitivity of RAP. Consequently, this study does relevant research on RAP by analysis of CT image data.
## Study design and participants
We retrospectively assessed consecutive patients who underwent abdominal contrast-enhanced CT for screening and evaluating- at a single tertiary center between May 2019 and April 2020. Inclusion criteria: (a) patients with abdominal CT enhancement or CTA from May, 2019, to April, 2020; (b) patients with abdominal CTA from May, 2019, to April, 2020. Exclusion criteria: (a)The bilateral renal arteries were showed apparent structural blur with artifacts, so that the lumen was not evaluated; (b). Laboratory indicators (including glucose, cholesterol, triglycerides, creatinine, BUN, uric acid) were incomplete; (c). The RAS was caused by non-atherosclerosis factors (arthritis, arterial dissection, fibromuscular dysplasia, tumors and so on). A total of 210 patients were finally enrolled in this study, and demographic and clinical data were retrospectively collected from the electronic medical records (Fig. 1). There were 108 male ($43\%$) and 102 female ($57\%$)with an average age of 64.1 ± 11 years old. The lesion group was defined as patients with left or right artery plaque, and the control group was defined as patients without left and right artery plaque. The lesion group was divided into two subgroups (left/right) according to the left or right renal artery of RAP. Right lesion group was divided into R1 and R2 subgroups according to the right renal artery angle A (R1 group if angle A < 61.1° and R2 group if angle A ≥ 61.1°). Left lesion group was divided into L1 and L2 subgroups according to the left renal artery angle A (L1 group if angle A < 57.9° and R2 group if angle A ≥ 57.9°). Hypertension was defined as systolic blood pressure≧140 mmHg or diastolic blood pressure above≧90 mmHg. Diabetes can be diagnosed (Fasting blood glucose is ≧ 7.0 mmol/L, or, random blood glucose and blood glucose of two hours of oral glucose tolerance test are ≧ 11.1 mmol/L). Hypercholesterolemia, hypertriglyceridemia, high serum creatinine, high-BUN and high-uric acid were defined as higher than the upper limit of the laboratory's normal reference range (cholesterol 2.48–5.17 mmol/L, triglycerides 0.57–1.70 mmol/L, creatinine 62–106 μmoI/L, BUN 2.1–7.1 mmol/L, uric acid 2.48–5.17 mmol/L). People who have smoked continuously or cumulatively for 6 months or more were smokers. Abdominal contrast-enhanced CT images demonstrated whether the RAP exits or not and showed the position and property of RAP (2.3). This study was approved and consented by the Ethics Committee of Xidian Group Hospital, and written informed consent was obtained from all participants. Fig. 1A flow diagram of the patient selection process
## CT scanning protocol
All abdominal contrast-enhanced CT examinations were performed with a 320-slice scanner (Cannon, Japan). These examinations were performed with prior placement of a 20-gauge venous access in a median cubital vein of the right arm and after administration of 80–90 mL of nonionic iodinated contrast medium (Iodine 370 mg I/mL), followed by injection of 30 mL of saline solution at a rate of 3.5 mL/s with the use of a dual-head injector (Irich, Medical). Optimal delay was performed with the automatic intelligent bolus-tracking technique (The tracer was placed in the proximal segment of abdominal aorta with threshold 320HU). Images were acquired with the following protocol: 100–120 kV, automated mA, pitch of 1.5, slice thickness of 0.5 mm, reconstruction interval of 0.5 mm, matrix of 512 × 512, and the total scan time of 182–185 s, the scan range was from the top of the diaphragm to the lower level of the kidney and beyond in a cranio-caudal direction. The dose of radiation was 3 ~ 5 mSv.
## CT image analysis
All the abdominal contrast-enhanced CT original data were transmitted to an independent imaging workstation (Cannon Vistra6.5, Japan) for post-processing. The primary methods of imaging processing were maximum intensity projection, multi-planar reconstruction, curved-planar reconstruction, and volume rendering [22]. Coronal multi-planar reconstruction (MPR, Fig. 2a) with a 3-mm thickness, Coronal curved planar reconstruction (CPR, Fig. 2b) and volume rendering (VR, Fig. 2c) were used for the evaluation of renal artery. The CT original imaging data were evaluated and analyzed by radiologists with ten years of experience. The position of plaques was documented as the following criteria: (a) the proximal section was defined as proximal $\frac{1}{3}$ segment of renal artery trunk; (b) the middle section was defined as middle $\frac{1}{3}$ segment of renal artery trunk; (c) the distal section was defined as distal $\frac{1}{3}$ segment of main renal artery; (d) the diffuse was defined as two or more segments. The type of the plaque was assessed as the following criteria: calcified plaques–CT value ≥ 130HU, non-calcified plaques–CT value < 130HU, mixed plaques–both non-calcified and calcified plaques [23, 24]. Stable plaque includes calcified plaque and mixed plaque. Unstable plaque includes non-calcified plaque. The diameter of renal artery (proximal, mid, distal) was measured in the axial image. In addition, the angle between the renal artery and the abdominal aorta (angle A) was measured in the coronal MPR image, the angle between the proximal $\frac{1}{3}$ segment and the distal $\frac{1}{3}$ segment or the rise of the most curved angle (angle B) was measured in the transverse or coronal MPR image, the distance of the bilateral renal artery origin position was measured in the coronal MPR image, the length of the main trunk was measured in the CPR image (Fig. 2b), the vascular variation include accessory renal artery or premature branch (length of the main trunk < 15 mm in CPR), the above parameters were documented by the two radiologists with ten years of experiences. They resolved inconsistent measurement results through repeated measurement (Figs. 3 and 4).Fig. 2Renal artery reconstruction image. ( a) cor-MPR, left renal artery angle A (between left renal artery and the abdominal aorta); (b) The length of left renal artery trunk (from the origin to the bifurcation); (c) Renal artery VR, which shows the origin position, route, lumen and anatomical variation of renal arteryFig. 3A participant (age range between 55 and 65 years old) with hypercholesterolaemia and normal blood pressure, renal angle A (right 41.64°, left 46.56°), cor-CPR shows that there is the calcified plaque in the proximal left renal artery and there is no RAP in the right renal arteryFig. 4A participant (age range between 80 and 85 years old) with hypertension and hypercholesterolaemia, renal angle A (right 65.86°, left 62.16°), cor-CPR shows there is the calcified plaque in the proximal and the distal left renal artery and there is the non-calcified plaque in the proximal right renal artery
## Statistical analyses
Quantitative data are expressed as mean value ± standard deviation, and qualitative data are expressed as percentage. Statistical methods included the chi-square test, two-sample t-test and logistic regression analysis. The chi-square was used to evaluate the distribution trend of RAP, the proportion of various plaques, the correlation between RAP and renal artery variation, the association between RAP and clinical variables, the difference on the incidence of RAP between the different the renal artery angle A. The two-sample t-test was applied to examine statistical differences on anatomical parameters between groups. Logistic regression analysis was carried out to investigate whether the above statistically different factors are related to the RAP or not. All statistical analyses were performed by using SPSS software (version 25.0). The p-value less than 0.05 were considered statistically significant in this study (FDR corrected).
## Comparison of clinical information
A total of 210 subjects were included in this study ($48.6\%$ female).The average age was 64.1 ± 13.8 years old. Among all patients, 118 patients had RAP, and 92 patients had no RAP. There were significant statistical differences on age, hypertension, hypercholesterolemia, hypertriglyceridemia, and diabetes between the lesion group and control group. The other clinical factors of renal artery between two groups were not significant statistical ($p \leq 0.05$ Table 1). Detailed clinical information about the study population was summarized in Table 1.Table 1Cohort characteristics stratified by the presence of renal artery plaqueTotal($$n = 210$$)Lesion group ($$n = 118$$)Control group($$n = 92$$)p-valueBasic information Gender, man (%)108(51.4)59(50.0)49(55.7)0.74 Age, yeas old64.05 ± 13.871.6 ± 9.554.4 ± 12.4< 0.01abClinical symptoms Kidney disease, n (%)80(38.1)45(38.1)35(38.0)1High risk factors Hypertension, n (%)81(38.6)59(50.0)22(23.9)< 0.01a Hypercholesterolemia, n (%)67(31.9)47(39.8)20(21.7)< 0.01b Hypertriglyceridemia, n (%)51(24.3)44(37.3)7(7.6)< 0.01 Diabetes, n (%)30(14.3)26(22.0)4(4.3)< 0.01Smoke, n (%)108(51.4)10(8.5)4(4.3)0.31Kidney function index High-serum creatinine, n (%)85(40.5)49(41.5)36(39.1)0.83 High-urea, n (%)55(26.2)33(28.0)22(23.9)0.61 High-uric acid, n (%)47(22.4)24(20.3)23(25.0)0.52Logistic regression analysis of the above statistical different indicators shows that right renal artery plaque is related to age, hypertension (a). Left renal artery plaque is related to age, hypercholesterolaemia (b)
## Comparison of distribution and type of RAP
There was no statistical difference on the incidence of RAP between unilateral side and bilateral side($p \leq 0.05$). The incidence of RAP between left renal artery and right renal artery were approximate with no statistical difference($p \leq 0.05$).There was no statistical difference on the distribution and type of RAP between left renal artery and right renal artery ($p \leq 0.05$).The differences on the distribution and type of RAP from left/right renal artery were significant statistical different($p \leq 0.05$).The RAP was found more frequently at the proximal section of renal artery. The calcified plaque was the most common. Detailed information about the RAP was summarized in Table 2.Table 2Comparison of characteristic of left and right renal artery plaqueRight renal arteryLeft renal arteryP-value(P1, P2, P1-2)Renal artery plaque88($41.9\%$)94($44.8\%$)0.55(P1-2)Distribution Proximal63($71.6\%$)67($71.3\%$)< 0.01, < 0.01, 0.18 Middle21($23.9\%$)18($19.1\%$) Diatal3($3.4\%$)2($2.1\%$) Diffuse1($1.1\%$)7($7.4\%$)Type Calcified plaque49($55.7\%$)47($50.0\%$)< 0.01, < 0.01, 0.31 Non-calcified plaque12($13.6\%$)21($22.3\%$) Mixed plaque27($30.1\%$)26($27.7\%$)Accessory renal artery Lesion group15($17.0\%$)9($10.2\%$)0.67(P1), 0.14(P2) Control group17($13.9\%$)18($14.8\%$)Premature branch Lesion group34($38.6\%$)40($45.5\%$)0.13(P1), 0.51(P2) Control group51($41.8\%$)43($35.2\%$)P1–-comparison within the right renal artery groupP2–-comparison within the left renal artery groupP1-2–-comparison between left and right renal artery groups
## Comparison of anatomical factors of renal artery
The difference on the proximal diameter of the renal artery and renal artery angle A between lesion group and control group was significant statistical ($p \leq 0.05$ Tables 3 and 4). The other anatomical factors of renal artery between two groups were not significant statistical($p \leq 0.05$ Tables 2, 3 and 4).Logistic regression analysis found that right RAP was significantly related to age, hypertension and right renal artery angle A, and left RAP was related to age, hypercholesterolemia and left renal artery angle A (Tables 1, 2, 3 and 4). The AUC of ROC (right RAP) is 0.82 (Fig. 5), and the AUC of ROC (left RAP) is 0.83 (Fig. 6).The difference on the distribution, type and stability of RAP between R1 and R2 group (between L1 and L2 group) were not statistically significant ($p \leq 0.05$ Tables 5 and 6). The renal artery angle A had no significant effect on the distribution, type and stability of RAP.Table 3Comparison of anatomical factors between lesion group and control group (right renal artery)Control group ($$n = 122$$)Lesion group ($$n = 88$$)t-valuep-valueRenal artery diameter (mm) Proximal5.30 ± 1.144.91 ± 1.012.570.01* Middle3.85 ± 0.853.76 ± 0.890.680.5 Distal3.81 ± 0.923.71 ± 1.010.710.48 Average4.29 ± 0.874.16 ± 0.891.040.3Renal artery angle(°) Angle A54.53 ± 17.0760.14 ± 14.702.550.01*a Angle B130.26 ± 31.41131.87 ± 27.620.390.7Renal artery opening distance(mm)7.68 ± 1.146.66 ± 1.141.440.15The main trunk length(mm)41.51 ± 13.7341.73 ± 13.210.120.9Renal vein diameter(mm) Proximal6.30 ± 2.055.99 ± 2.570.940.35 Middle6.84 ± 1.996.32 ± 2.141.790.08 Distal6.32 ± 2.285.92 ± 2.221.290.2 Average6.49 ± 1.746.17 ± 2.021.540.13*$p \leq 0.05$Logistic regression analysis of the above statistical different indicators show that right renal artery plaque is related to renal artery angle A(*a), and is not related to the proximal diameter(*)Table 4Comparison of anatomical factors between lesion group and control group (left renal artery)Control group ($$n = 116$$)Lesion group ($$n = 94$$)t-valuep-valueRenal artery diameter (mm) Proximal5.44 ± 1.135.04 ± 1.032.710.01* Middle4.37 ± 0.904.33 ± 0.840.340.74 Distal4.16 ± 0.904.19 ± 0.840.230.82 Average4.66 ± 0.904.52 ± 0.771.040.23Renal artery angle (°) Angle A53.98 ± 15.5962.79 ± 15.194.13< 0.01*b Angle B126.53 ± 37.28123.51 ± 34.860.60.55 Renal artery opening distance (mm)7.28 ± 13.237.20 ± 13.230.130.9 The main trunk length (mm)34.20 ± 13.2336.09 ± 13.021.040.3Renal vein diameter (mm) Proximal7.81 ± 2.167.35 ± 1.791.70.09 Middle6.77 ± 2.006.40 ± 1.941.340.18 Distal4.64 ± 2.164.38 ± 2.221.290.39 Average6.40 ± 1.696.04 ± 1.601.60.11*$p \leq 0.05$Logistic regression analysis of the above statistical different indicators show that left renal artery plaque is related to renal artery angle A(*b), and is not related to the proximal diameter(*)Fig. 5Right renal artery plaque associations with age, hypertension and right renal artery angle A (ROC curve, AUC = 0.82)Fig. 6Left renal artery plaque associations with age, hypercholesterolaemia and left renal artery angle A (ROC curve, AUC = 0.83)Table 5Comparison of distribution/type/stability of RAP within right renal artery lesion groupR1 group ($$n = 55$$)R2 group ($$n = 33$$)t-valuep-valueDistribution Proximal41($74.6\%$)22($66.7\%$)5.30.15 Middle11($20.0\%$)10($30.3\%$) Diatal2($3.6\%$)1($3.0\%$) Diffuse1($1.8\%$)0Type Calcified plaque8($14.5\%$)4($12.1\%$)1.950.38 Non-calcified plaque30($54.5\%$)19($57.6\%$) Mixed plaque17($31.0\%$)10($30.3\%$)Stability Stable plaque25($45.5\%$)14($42.4\%$)0.080.78 Unstable plaque30($54.5\%$)19($57.6\%$)Table 6Comparison of distribution/type/stability of RAP within left renal artery lesion groupL1 group ($$n = 54$$)L2 group ($$n = 40$$)t-valuep-valueDistribution Proximal38 ($70.4\%$)30($75.0\%$)4.120.25 Middle9 ($16.7\%$)8($20.0\%$) Distal2 ($3.7\%$)0 Diffuse5 ($9.2\%$)2($5.0\%$)Type Calcified plaque10 ($18.5\%$)11($27.5\%$)1.470.48 Non-calcified plaque28 ($51.9\%$)19($47.5\%$) Mixed plaque16 ($29.6\%$)10($25.0\%$)Stability Stable plaque26 ($48.1\%$)21($52.5\%$)0.170.68 Unstable plaque28 ($51.9\%$)19($47.5\%$)
## Discussion
RAP was the most common cause of RAS, which can bring about unfavorable impact on human health, including renal insufficiency, renal hypertension, ischemic nephropathy, acute cardiovascular events and so on [25–29]. This study showed that people with high-risk anatomical factor (the greater renal artery angle A) and high-risk traditional factors (older age, hypertension, hypercholesterolemia) had the higher risks of RAP. It is essential to attach importance to high risk factors related to RAP. Intervening risk factors may improve the long-term prognosis of the patients to the greatest extent [30].
## Clinical information
RAP is the local indicator of systemic atherosclerosis process [2, 12]. This research has confirmed that right RAP is significantly related to age, hypertension and right renal artery angle A, and left RAP is related to age, hypercholesterolemia and left renal artery angle A (Figs. 3 and 4). The possible reasons for this difference are as follows: First. There may exist anatomical and histological slight differences on the structures of wall between left and right renal artery, for this reason, the left renal artery is susceptible to hypercholesterolemia, and the right renal artery is susceptible to hypertension. Second. the starting position of right renal artery is usually slightly higher than that of left renal artery, and the pressure of blood vessel wall gradually decreases from the proximal side to the distal side, therefore, the starting pressure of right renal artery is slightly higher than that of left,blood vessel wall of the right starting position must bear more pressure, so the intima of this position is vulnerable to be damaged, in order to promote plaque formation. Third. The abdominal aorta is on the left, and the position of right kidney is lower than that of the left kidney, hence, the length of the right renal artery trunk is longer than that of the left and the renal artery angle A is smaller than the left one, consequently, blood hypercholesterolemia is vulnerable to deposit in the left renal artery. Finally, the degree of tortuosity of abdominal aorta affects local hemodynamics, which influence the formation of RAP. To the people with the above risk factors, the vessel wall is lack of flexibility and prone to be damaged, the bloodstream is low and the cholesterol is easy to deposit in the wall, which contribute to the formation of plaque [31].
There was no significant difference on the other factors between the two groups. The results are identical with previous researches [32].Our finding that there is no statistical difference on the incidence of RAP between men and women is consistent with previous studies.
## Distribution and type of RAP
The current study demonstrates that the probability of RAP from the left and right renal artery is similar. However, Zhun et al. [ 15] reported that the prevalence of left RAP was higher than that of the right RAP. The deviation is mainly concerned about the population of limited sample, regional differences from sample, differences in baseline diseases and the different phases of RAP.
The atherosclerotic plaques are the most prone to appear at the proximal sections, then, the middle and distal sections of renal artery, diffuse distribution is the rarest. The above outcome is correspond with the conclusions of Tafuri et al. [ 33]. The proximal segment is the most vulnerable to suffer the impact of blood flow velocity and fluid shear stress (FSS), and the vascular intima is damaged more easily, vascular smooth muscle cells are prone to differentiate into an osteoblast/chondrocyte phenotype, where inflammatory mediators, recruitment of activated macrophages, lipids and inflammatory factors are more likely to release and deposit [34]. The above factors are conducive to provoke and regulate the pathological process of plaque.
Among all plaques, calcified plaques are the most common ones, followed by mixed plaques, non-calcified plaques are the least. The possible causes are as follows: the metabolic regulation mechanism of RAP that oxidized lipids promote calcification in these vascular cells, the process of systemic atherosclerosis, the appearance time on calcification of plaque and the decrease of glomerular filtration rate [35]. In addition, calcified plaques and mixed plaques are prone to be found because of being more apparent contrast with surrounding tissues than non-calcified plaques. In the early stage of RAP, RAS is slight with positive remodeling of the lumen, and it rarely caused clinical symptoms (such as renal hypertension and renal artery insufficiency syndrome and so on). Therefore, there are fewer imaging examinations to be done, and the corresponding detection rate is relatively low [36]. RAP is strongly related to calcium deposition in the coronary, carotid, infra-renal aorta and common iliac arteries [7]. Hence, it can indirectly predict the probability of RAP through the demonstration of artery plaque from other main artery to make suitable treatment strategy [37].
## Anatomical factors of RAP
This study indicates that the RAP is significantly correlated with the renal angle A, which is in accordance with a previous study [13].When the renal artery angle A is greater, the flow from regions of the proximal sections with bifurcations or curvatures and the downstream are more prone to appear complex changes in direction and the blood FSS is greater, which is more prone to damage the vascular intima and activate FSS pathways (shear-responsive kinases, GTP-ases, ion channels, and other signaling molecules and signaling events(including inflammatory mediator), as well as many downstream genes and micro-RNAs and so on), the process of lipid deposition, inflammatory activation and thrombosis is more prone to be triggered. Finally, these factors facilitate the formation of plaque [38–40]. However, the renal artery angle A makes no significant impact on concrete distribution, type and stability of RAP, which is related to the above pathophysiology.
The angle B or the rise of the most curved arch make no noticeable impact on RAP. The possible reasons are as follows: First. The renal artery was straight in some cases. Second. The measurement deviation is displayed in view of the shorter renal artery trunk. Third. The method of evaluation has limitations. Finally. The number of selected samples is relatively few. The RAP is not linked with the diameter of the proximal/middle/distal /average of the renal artery, which is unanimous with the results of previous study. The latter suggested that there was no apparent correlation between RAP and the lumen diameter. The reasons are related to the positive remodeling of the plaque, vessel lumen self-regulation mechanisms. The length of the main renal artery may not be a primary contributor to form plaque despite affecting the passing time of the main blood flow, which is consistent with the former literature. The distance between the left and right renal artery initial positions does not affect the blood flow of the renal artery trunk. Consequently, it is not apparent correlated with RAP. The accessory renal artery and premature branch of the renal artery are the common mutations of the renal artery, which are not associated with the formation of RAP.
The present study makes a systematic and comprehensive assessment on factors related with RAP, but it still exists some limitations. First of all, the sample size of this study is relatively small, and it is still necessary to increase the sample size for further verification of the results in the future. Secondly, the relationship between the specific location on the cross section of the plaque and RAP was not further explored in depth. Thirdly, whether MRI functional imaging can be used to evaluate the correlation between plaque and relevant influencing factors or not, which need to be further studied in the future [41–43]. Finally, the plaque was analyzed only from CT images, not combined with IVUS, which may be considered as the direction of future research.
## Conclusion
In conclusion, right RAP is significantly related to age, hypertension and right renal artery angle A and left RAP is related to age, hypercholesterolemia and left renal artery angle A. Among all influence factors, only the renal artery angle A, age, hypertension and hypercholesterolemia are correlated with RAP. It can be a good precaution that those with the above factors are prone to appear RAP in clinical practice. It is beneficial for risk stratification of RAP, secondary prevention of RAP. Furthermore, it is conducive to select donor of kidney transplant. In addition, it is useful for the treatment guidance and post-treatment assessment of renal artery denervation [44]. The current study makes a systematic and comprehensive assessment about RAP, which lays a foundation for the relevant study of RAP. But this study also has forging limitations, which need to be further researched in the future.
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|
---
title: Longitudinal DNA methylation profiling of the rectal mucosa identifies cell-specific
signatures of disease status, severity and clinical outcomes in ulcerative colitis
cell-specific DNA methylation signatures of UC
authors:
- Suresh Venkateswaran
- Hari K. Somineni
- Jason D. Matthews
- Varun Kilaru
- Jeffrey S. Hyams
- Lee A. Denson
- Richard Kellamayer
- Greg Gibson
- David J. Cutler
- Karen N. Conneely
- Alicia K. Smith
- Subra Kugathasan
journal: Clinical Epigenetics
year: 2023
pmcid: PMC10039532
doi: 10.1186/s13148-023-01462-4
license: CC BY 4.0
---
# Longitudinal DNA methylation profiling of the rectal mucosa identifies cell-specific signatures of disease status, severity and clinical outcomes in ulcerative colitis cell-specific DNA methylation signatures of UC
## Abstract
### Background
In peripheral blood, DNA methylation (DNAm) patterns in inflammatory bowel disease patients reflect inflammatory status rather than disease status. Here, we examined DNAm in diseased rectal mucosa from ulcerative colitis (UC) patients, focusing on constituent cell types with the goal of identifying therapeutic targets for UC other than the immune system. We profiled DNAm of rectal mucosal biopsies of pediatric UC at diagnosis ($$n = 211$$) and non-IBD control ($$n = 85$$) patients and performed epigenome-wide association studies (EWAS) of specific cell types to understand DNAm changes in epithelial, immune and fibroblast cells across disease states, course, and clinical outcomes. We also examined longitudinal analysis on follow-up samples ($$n = 73$$), and comparisons were made among patients with clinical outcomes including those undergoing colectomy versus those who did not. Additionally, we included RNA-seq from the same subjects to assess the impact of CpG sites on the transcription of nearby genes during the disease course.
### Results
At diagnosis, UC rectal mucosa exhibited a lower proportion of epithelial cells and fibroblasts, and higher proportion of immune cells, in conjunction with variation in the DNAm pattern. While treatment had significant effects on the methylation signature of immune cells, its effects on fibroblasts and epithelial cells were attenuated. Individuals who required colectomy exhibited cell composition and DNAm patterns at follow-up more similar to disease onset than patients who did not require colectomy. Combining these results with gene expression profiles, we identify CpG sites whose methylation patterns are most consistent with a contribution to poor disease outcomes and could thus be potential therapeutic targets.
### Conclusions
Cell-specific epigenetic changes in the rectal mucosa in UC are associated with disease severity and outcome. Current therapeutics may more effectively target the immune than the epithelial and fibroblast compartments.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13148-023-01462-4.
## Background
UC is a form of inflammatory bowel disease that affects an estimated $1\%$ of the population in North America and Europe [1]. The chronic inflammation is limited to colon in UC, and is usually remitting and relapsing in nature, but repeated inflammation of the colon invariably results in progressive tissue damage. By nature, UC subjects exhibit evidence of systemic inflammation during the active disease, and ~ $10\%$ of all UC subjects also have involvement of extra-intestinal manifestations [2]. Although UC is heterogeneous in disease course and outcome, the need for colectomy shortly after diagnosis is the least favorable outcome. Genome-wide association studies have proven the role of immune-associated genetic variants beyond doubt in UC [3–5], but in total these variants have accounted for less than $10\%$ of the disease susceptibility, and an even smaller proportion of disease outcome. So far, these genetic studies have pointed to few influences of disease beyond the genetic regulation of the immune system itself. Although several drugs are already available to target the immune system in UC, and more immune targeting therapies are being developed, nearly half of UC patients show insufficient response to these therapies [6, 7]. Thus, we asked if it is possible to identify therapeutic targets for UC other than the immune system itself.
*Since* genetics itself explained only a small portion of IBD, we turned to other molecular systems implicated in UC pathogenesis [8]. Recent studies in IBD demonstrated that DNAm signatures [9–11] have been associated with CD and UC phenotypes [12–17]. However, most studies have focused on study of peripheral blood rather than the diseased tissue. In our recent study we found that DNAm changes in peripheral blood cells associate primarily with inflammatory status rather than disease status [18], and that methylation patterns in blood largely return to “normal” after anti-inflammatory treatment, regardless of the underlying disease state. Blood-based DNAm studies have underlined the potential role of epigenetics mechanisms in IBD, but to find cells whose molecular signatures might better reflect the disease state, we move to the location of disease itself. Here we examine genome-wide DNAm of the rectal tissue in an inception cohort of UC at 2 time points, once at diagnosis (treatment naïve) and subsequently at follow-up, to explore how longitudinal DNAm associates with disease onset, disease progression and outcome. Similar studies have been done only in small sets of patients, and in select cellular compartments such as purified epithelial cells [19, 20].
Site-specific DNAm differences have been reported in IBD from peripheral blood [18], and intestinal biopsies [19–21], but the analysis on the source of the cell types that are driving those signals, the temporal relationship between DNAm, disease, and the most unfavorable clinical outcomes (e.g., colectomy status) were not studied for therapeutic benefits. Thus, in this study we conducted a cell type-specific EWAS of DNA methylation (~ 850,000 sites) changes in the rectal mucosa at diagnosis, follow-up, and across disease phenotypes and clinical outcome trajectories (colectomy and mucosal healing) using rectal mucosal biopsies collected for the RISK and PROTECT pediatric CD and UC inception cohorts [22–24]. Our analysis examined interactions between DNAm-based estimates of proportions of three major cell components of intestinal tissues—epithelial, immune cells and fibroblasts (as a measure of mesenchyme) – and DNAm to identify cell-specific differential DNAm patterns and associated gene expression from the same patients to evaluate disease status, disease course, disease severity, and colectomy status as clinical outcomes [25] with the goal of finding patterns consistent with the cause of disease severity (rather than consequence) to serve as potential targets for molecular therapies.
## Altered DNAm in the epithelial, immune and fibroblast compartments are associated with UC at diagnosis
We used rectal mucosal biopsies to profile DNAm changes associated with UC. Principal component (PC) analysis of DNAm levels at ~ 820 K CpG sites in the mucosa showed separate clusters for UC at diagnosis ($$n = 211$$) and controls ($$n = 85$$) (Additional file 1: Fig. S1). We observed that PC1 and PC2 explained $20.1\%$ and $8\%$ of the variance in DNAm, respectively. Thus, we performed a traditional EWAS and identified 99,989 DNAm sites associated with UC at diagnosis (FDR < 0.05; Additional file 1: Fig. S2).
Identification of this large number of sites may reflect inflammation or unmeasured cellular heterogeneity, but is consistent with the high proportion of variation explained in our PC analysis, and suggests the possibility of a strong but complex DNAm signature of UC. To better understand what drives these large-scale differences in DNAm, we next decomposed our bulk signatures into constituent cell-type proportions (epithelial, immune, and fibroblast cells) [26].
Figure 1a shows estimated cellular proportions based on DNAm signatures in UC at diagnosis vs. control samples for three primary cell types (epithelial, immune, and fibroblast cells). The estimated cell proportions from mucosal DNAm profiles shows a decrease in the proportion of epithelial cells ($p \leq 2.2$e−16) as well as fibroblasts ($$p \leq 5$$e−07) and increasing proportions of immune cells ($p \leq 2.2$e−16), changes that are consistent with damaged and inflamed mucosa [27–29]. Cell-specific EWAS between UC at diagnosis ($$n = 211$$) and controls ($$n = 85$$) revealed 3504 (Fig. 1b top panel; Additional file 2: Table S2a), 2279 (Fig. 1b middle panel and Additional file 3: Table S3a) and 910 (Fig. 1c bottom panel and Additional file 4: Table S4a) differentially methylated CpG sites in epithelial, immune and fibroblast cells, respectively at FDR < 0.05. Overall, these data suggest the possibility that cell-type-specific changes in DNAm in the rectal mucosa can be leveraged to differentiate diseased tissue from healthy individuals as well as and behavior during disease. Fig. 1DNAm and corresponding gene signatures associated with UC at diagnosis: a Comparison of estimated cell proportions for epithelial, immune cells and fibroblasts between rectal biopsies from UC patients (at diagnosis) and controls. p Values are shown from the Wilcoxon test. b Cell-specific epigenome-wide analysis (EWAS) comparing UC patients to controls. The blue line represents significant differential methylation at false discovery rate (FDR) < 0.05, and the red line represents Bonferroni-adjusted genome-wide significance ($p \leq 1$e−08). c The volcano plot shows differentially expressed genes that are associated with cell specific CpG sites in UC for all three cell types. x-axis shows log2FC and y- axis shows the negative log p-value detected for each gene in DE analysis. d The lollipop diagram shows the top 10 Gene Ontology (GO) biological processes identified as enriched in sets of differentially methylated CpG sites (blue) and differentially expressed genes in UC. Y- axis shows the number of CpGs and genes detected for each GO term
## Regulatory potential of the differentially methylated CpG sites in UC
To gain insight into the regulatory potential of our differentially methylated CpG sites, we identified the nearest genes annotated to these CpGs in the Illumina manifest file. Differentially methylated CpG sites in epithelial ($$n = 3504$$ CpGs), immune ($$n = 2279$$ CpGs) and fibroblast ($$n = 910$$ CpGs) cells were annotated to 1932, 1338 and 561 protein-coding genes, respectively. We first examined the association between the differentially methylated CpG sites and the gene expression levels of their corresponding genes in the rectal mucosa (CpG-gene pairs) in a subset of individuals ($$n = 119$$) with both DNAm and gene expression data, and identified 1046 unique Gene-CpG associations for epithelial cells (Additional file 2: Table S2b), 537 unique Gene-CpG associations for immune cells (Additional file 3: Table S3b) and 272 unique Gene-CpG associations for fibroblasts (Additional file 4: Table S4b) at FDR < 0.05. For these sets of genes, we next performed differential expression analysis comparing expression in rectal mucosa biopsies taken from patients with UC at diagnosis ($$n = 206$$) vs. biopsies taken from non-IBD individuals ($$n = 20$$). 885 ($63\%$) of epithelial-specific differentially methylated CpG-associated genes were associated with UC at diagnosis (FDR < 0.05; (Additional file 2: Table S2c). Similarly, 442 ($81\%$) of immune-specific CpG-associated genes (Additional file 3: Table S3c) and 217 ($80\%$) of fibroblast-specific CpG-associated genes (Additional file 3: Table S3c) were differentially expressed between patients with UC at diagnosis and non-IBD controls (FDR < 0.05; Fig. 1c). The observation that the majority of the genes found to be associated with UC-specific CpG sites also demonstrated changes in steady state levels of mRNA detectable in the mucosa highlights the regulatory potential of our differentially methylated CpG sites.
## Biological relevance of the differentially methylated CpG sites in UC
GO analysis revealed that the set of genes ($$n = 488$$) associated with the differentially methylated CpG sites in epithelial cells was enriched for numerous biological processes related to epithelial function, including changes in wound response, GTPase signaling and cell migration (Fig. 1d top panel; Additional file 2: Table S2D–E). Similarly, genes associated with immune cell-specific CpGs ($$n = 150$$) showed an enrichment for immune function related processes, including cell activation and signaling (Fig. 1d bottom panel; Additional file 3: Table S3D–E), all of which are expected to occur in the damaged mucosa [30–33]. Further, a text-mining analysis [34] revealed that 99 of 885 epithelial and 53 of 442 immune genes were associated with UC in the literature (see Additional file 2: Table S2F–G and Additional file 3: Tables S2F–G). However, we did not detect any pathway level enrichment for genes associated with fibroblast-specific CpG sites ($$n = 56$$) at FDR < 0.05.
## DNAm changes in the rectal mucosa of patients with UC post-treatment
To assess the cellular and molecular changes in the rectal mucosa of patients with UC after treatment, we next examined cellular proportions and DNAm profiles at follow-up ($$n = 73$$; Fig. 2). Clinical activity and/or disease severity in pediatric patients with UC is monitored by a validated activity index known as pediatric UC activity index (PUCAI) [35]. The PUCAI scores of patients at follow-up were significantly lower than those obtained at the time of diagnosis (Additional file 1: Fig. S4a) consistent with patient’s disease activity improving, on average, after treatment. Consistent with this, we observed a corresponding change in the estimated proportions of epithelial, immune and fibroblast compartments (Fig. 2a). Interestingly we also observed that the epithelial and immune cell proportions were significantly improved ($$p \leq 0.0087$$; $$p \leq 0.0077$$) in the follow-up UC patient’s rectal biopsies who had undergone clinical remission at week 52 when compared to those who had not (Fig. 2b). We observed a similar trend where the epithelial proportions were significantly increased and immune cells were significantly depleted (similar to levels in controls) for both corticosteroid-free and calprotectin remissions at week 52 (Additional file 1: Fig. S5), but did not observe such changes in fibroblasts. Compared to UC at diagnosis, we observe an expansion in the proportion of epithelial and fibroblast compartments at follow-up, and depletion of immune cells. Fig. 2Longitudinal profiling of DNAm in UC patients at during the disease. a, b Boxplots show the rectal biopsy DNA methylation (DNAm) estimated cell proportions for epithelial, immune and fibroblast compared among a controls ($$n = 85$$), UC at diagnosis ($$n = 211$$) and UC at Follow-up ($$n = 73$$), and b UC patients who underwent clinical remission at week 52 ($$n = 22$$) vs those who did not ($$n = 45$$). p Values are from the Wilcoxon test. c Cell-specific epigenome-wide analysis (EWAS) analysis shows differentially methylated sites between UC at diagnosis and UC at follow-up (paired samples, $$n = 73$$) for all three major cell types. The blue line represents the sites significant at FDR < 0.05, and the red line represents the sites significant at epigenome-wide $p \leq 1$e−08. d The effect sizes for all the UC associated sites from all three types epithelial ($$n = 3504$$ CpG sites) and fibroblast ($$n = 910$$) and immune cells ($$n = 2279$$) of UC at diagnosis i.e., UC at diagnosis vs Controls (x-axis) were compared to UC at disease course i.e., UC at diagnosis vs follow-up (y-axis). In the immune sub-panel, maroon dots represent the 96 CpG sites that reached significance after multiple test corrections on p-values of 2279 CpGs in UC at disease course (FDR < 0.05). e The effect sizes for all the CpG sites associated with disease course i.e., UC at diagnosis vs follow-up (y-axis) from only immune cells ($$n = 668$$) were compared to UC at diagnosis i.e., UC at diagnosis versus Controls (x-axis). In the sub-panel, maroon dots represent the 154 CpG sites that reached significance after multiple test corrections on p values of 668 CpGs in UC at diagnosis (FDR < 0.05) Our cell type-specific EWAS comparing DNAm levels at the time of follow-up to those obtained at the time of diagnosis within the same 73 individuals found 668 CpG sites differentially methylated within the immune compartment (FDR < 0.05; Fig. 2c middle panel; Additional file 1: Fig. S4c, Additional file 5: Table S5a). For the majority of significant sites, the direction of the changes moved DNAm levels in cases after treatment closer to levels seen in controls. These patterns are consistent with DNAm levels in the immune compartment reflecting systemic immune status, and thus responding to systemic anti-inflammatory therapies. On the other hand, EWAS comparing DNAm levels at the time of follow-up to those obtained at the time of diagnosis did not reveal any differentially methylated CpG sites in the epithelial (Fig. 2c, top panel, Additional file 1: Fig. S4b) and fibroblast compartments (Fig. 2c, last panel, Additional file 1: Fig. 4d).
To identify the impact of treatment effects on DNAm sites, we compared the DNAm differences observed for UC at diagnosis vs. controls, and UC at disease course vs. UC at diagnosis. The EWAS in Fig. 1b shows the CpG sites that are differentially methylated between newly diagnosed UC patients and controls, while the EWAS in Fig. 2e shows the CpG sites that are differentially methylated in UC patients before and after treatment. Figure 2d shows that for CpGs significant in each of the three compartments in the UC vs. control EWAS (Fig. 1b), there is a negative correlation in effect sizes from the two EWAS; this is consistent with a pattern where CpGs showing DNAm differences in patients at the time of diagnosis return to control levels upon treatment. Stronger negative correlations were observed for sites associated with UC in epithelial cells (R = − 0.68) or fibroblasts (R = − 0.47) vs. those associated in immune cells (R =− -0.33); however, significant pre- vs. post-treatment DNAm differences were only observed in immune cells (Fig. 2c, middle panel), with 96 CpG sites showing significant DNAm differences in immune cells in both the case–control and pre-post analyses. For these 96 sites we observed a strong negative correlation (R = − 0.86), with DNAm levels of 95 of these sites moving in the opposite direction from onset and trending towards controls and only one site (cg17642041, located in the transcription start site of the CCR9 gene) trending away from control levels (maroon dot in Fig. 2C immune cell panels). We observed the same general pattern (R = − 0.34) at all the 668 CpG sites that differed between diagnosis and follow-up UC: 154 of 668 CpG sites were significantly different at follow-up (FDR < 0.05) with a very strong negative correlation (R = − 0.80) between onset and follow-up (Fig. 2e; Additional file 5: Table S5e).
Extending this analysis to all ~ 820 K CpG sites analyzed across epigenome-wide, we still observed significant negative correlations between effect sizes (R = − 0.21 for epithelial cells; R = − 0.16 for immune cells; R = − 0.21 for fibroblasts; Additional file 1: Fig. S6), suggesting that at a broad scale, the epigenomic level the DNAm profiles observed at follow-up were trending towards those observed in controls. Of the 2279 sites differing between UC at diagnosis and controls in immune cells (from Fig. 1b, middle panel), 33 showed a significant negative association (epigenome-wide) in the comparison of cases vs. controls and a significant positive association when comparing UC at diagnosis vs. follow-up in cases, with a negative correlation between the effect sizes from the two analyses (R = − 0.92; Additional file 1: Fig. S6b).
## Biological relevance of the differentially methylated CpG sites in UC at disease course
In support of these patterns showing DNAm profiles at follow-up trending towards control levels, possibly due to inflammation-reducing treatment, gene ontology analysis on the 668 CpG sites differing from UC at diagnosis to follow-up identified numerous biological processes related to immune function, including changes of B-cell/leukocyte activation and differentiation (Fig. 3a, Additional file 5: Table S5g). To gain further biological insight into the pathways related to DNAm changes in the immune compartment during treatment, we analyzed these 668 CpG sites for association between DNAm and nearby gene expression (270 genes). In this analysis we compared changes in gene expression for each gene with DNAm changes at its associated CpG site across a matched cohort of UC patients ($$n = 29$$) and observed that expression of 39 genes associated with DNAm at 42 CpG sites ($p \leq 0.05$; Additional file 5: Table S5g). Differential expression analysis at diagnosis vs. follow-up ($$n = 29$$ matched samples) [25] identified 9 genes as differentially expressed (FDR < 0.05; Fig. 3b, Additional file 5: Table S5h). As an example, we highlight two CpG-gene pairs in Fig. 3c, which shows the relative levels of DNAm at CpG sites 1500 base pairs upstream from the transcription start site (TSS) of FGD2 gene, and their corresponding gene expression level for the same UC subjects at diagnosis and follow-up. Fig. 3Regulatory importance of the differentially methylated CpG sites in UC at disease course. a The lollipop diagram shows Gene Ontology (GO) biological processes identified for 668 differentially methylated CpG sites (blue) from immune cells. Y- axis shows the number of CpGs detected in each GO term. b The volcano plot shows differentially expressed genes that are associated with immune cell specific CpG sites in UC during the course of the disease. x-axis shows log2FC and y- axis shows the negative log p-value detected for each gene in DE analysis. c Boxplots depicting the methylation proportions of UC at diagnosis and during follow-up (matched sample, $$n = 29$$, that contains both DNAm and gene expression profiles) at the two CpG sites located TSS1500 to the nearby gene, and the corresponding gene expression values are plotted. Y-axis shows DNAm values and log of read counts representing methylation and gene expression, respectively To gain a finer-grained understanding of the immune compartment, we used an immune-subtype reference panel (see methods) to make cell-type composition estimates within this compartment breaking cell-counts into 7 parts (B-, CD8T-, CD4T-cells, monocytes, neutrophils, eosinophils, and nature killer cells). We identified significant changes in the cellular proportions of B-cells and CD4 T-cells between diagnosis and follow-up ($p \leq 0.05$, Wilcoxon test, Additional file 1: Fig. S7), with decreases in B-cell and increases in T-cell proportions at follow-up. Although neutrophil abundance did not change significantly from diagnosis to follow-up, most of the significant changes in DNAm between diagnosis and follow-up (145 CpG sites) were inferred to be attributable to the neutrophil compartment (Additional file 1: Fig. S8; Additional file 5: Table S5i). We also observed at least 9 CpG sites showing different DNAm patterns in B-cells (Additional file 1: Fig. S8; Additional file 5: Table S5j). Thus, neutrophils, and to some extent B-cells, appear to be undergoing the largest amount of epigenetic change in the mucosa during UC treatment, at least as inferred from this model.
## UC-specific rectal DNAm signatures at diagnosis correlate with disease severity defined by PUCAI
Next, as a proof-of-concept, we asked if DNAm could distinguish patients with UC based on their disease severity. A comparison of 44 mild UC (10 < PUCAI < 35), 80 moderate UC (35 < PUCAI < 65) and 87 severe UC patients (PUCAI > 65) across the entire 820 K CpG panel showed significant differences between mild vs moderate, and mild vs severe UC patients in PC1 (Additional file 1: Fig. S9). However, no differences were observed between moderate vs severe UC. The estimated cell proportions from mucosal DNAm profiles show no significant differences for either epithelial cell, fibroblasts, and immune cell across disease severity in UC (Additional file 1: Fig. S9a). Consistent with this, our cell type specific EWAS among mild, moderate and severe UC patients did not identify any significant CpGs (data not shown). Therefore, to identify individual CpG sites that capture disease severity within each cell type, we grouped patients with moderate and severe UC ($$n = 167$$) and compared them to those with mild UC ($$n = 44$$), revealing 46, 127 and 13 CpGs in epithelial, immune and fibroblast compartments, respectively (Additional file 1: Fig. S9b, Additional files: Tables S6–8). GO analysis of these sites did not establish strong evidence for enrichment of any biological processes at FDR < 0.05.
We tested whether any of the cell-specific CpG sites (as a predictor) are associated with nearby gene expression (as an outcome). We observed 7 Gene-CpG associations for epithelial (Additional file 6: Table S6b) and 12 Gene-CpG associations for immune cells (FDR < 0.05; Additional file 7: Table S7b). However, none of these genes were differentially expressed in mild UC when compared to moderate and severe (data not shown).
## Rectal DNAm signatures show potential for indicating colectomy risk
The total DNAm signatures were analyzed for signals indicating predisposition towards severe disease resulting in colectomy by year 2 post diagnosis. PC1 of the total 820 K CpG sites showed a significant difference in the DNAm signatures between patients who would eventually undergo colectomy at 2 years post diagnosis with patients who did not (Additional file 1: Fig. S10). Similarly, significant differences in DNAm signatures amongst the three compartments, epithelial, immune and fibroblast were also observed between colectomy and no-colectomy. In total, 24 patients underwent colectomy in the 2 years after diagnosis, and while the “no colectomy” group showed improvements in epithelial ($$P \leq 0.01$$) and fibroblast ($$P \leq 0.002$$) proportions along with a decrease in immune cell proportions at follow-up ($$P \leq 0.004$$) (a sign of mucosal healing and reduction of inflammation), the colectomy group showed no improvement in epithelial ($$P \leq 0.89$$) and fibroblast proportions ($$P \leq 0.49$$) and no decrease in the immune proportions (remaining elevated compared to “no colectomy” at follow-up; $$P \leq 0.80$$) ($$n = 175$$; Fig. 4a).Fig. 4DNAm signatures at diagnosis associated with colectomy at 2 years. a Comparison of estimated cell proportions for epithelial, immune cells and fibroblasts obtained from rectal biopsies at diagnosis compared between colectomy and non-colectomy groups. P Values are from the Wilcoxon test. b Cell-specific epigenome-wide DNAm analysis (EWAS) comparing UC patients who underwent colectomy at 2 years ($$n = 24$$) to non-colectomy UC patients ($$n = 175$$). The blue line represents significant differential methylation at FDR < 0.05, and the red line represents Bonferroni-adjusted genome-wide significance ($p \leq 1$e−08) To identify genes whose DNAm patterns at diagnosis indicate the need for future colectomy, cell-type specific EWAS analysis was performed on non-colectomy UC patients ($$n = 175$$) vs UC patients who eventually underwent colectomy ($$n = 24$$; Fig. 4b; Additional file 1: Fig. S11). We identified 89 CpG sites associated with future colectomy in epithelial cells (Fig. 4b top panel; Additional file 9: Table S9), 257 CpG sites in immune cells (Fig. 4b middle panel and Additional file 10: Table S10), and 63 CpG sites in fibroblasts (Fig. 4b bottom panel and Additional file 11: Table S11). GO analysis of UC colectomy CpG sites did not establish strong evidence for enrichment of any biological processes at FDR < 0.05.
Among CpG sites associated with future colectomy, we observed 15 and 10 Gene-CpG associations for sites associated with colectomy in epithelial cells and immune cells respectively (Additional files 9–10: Tables S9b, S10b). However, none of these genes showed differential expression in UC.
We further analyzed differences at diagnosis in the severe UC group by comparing those patients that eventually underwent colectomy at 2 years post diagnosis ($$n = 14$$) to those that did not ($$n = 60$$). The epithelial cell proportions were nominally decreased ($$P \leq 0.05$$) at diagnosis in colectomy severe UC patients when compared to non-colectomy severe UC patients (Additional file 1: Fig. S12a, top panel). In contrast, an increase in the immune and fibroblast proportions was observed for the colectomy group, but the differences did not reach statistical significance (Additional file 1: Fig. S12a, middle and bottom panels). Only 2 CpG sites for epithelial and fibroblast cells were found by cell-specific EWAS analysis (Additional file 1: Fig. S12b, top and bottom panels; Additional files: Tables S9,11), and 64 CpG sites for immune cells (Additional file 1: Fig. S12b, middle panel; Additional file 10: Table S10).
## Clinical relevance of differentially methylated CpG sites in UC
The distinction between methylation signatures in UC at diagnosis and controls suggests that a diagnostic potential exists in these values that can be leveraged for patient stratification. Supporting this, a random forest analysis showed that CpG sites showing cell-type-specific associations with UC in epithelial, immune cells, and fibroblasts in a training dataset could indeed distinguish UC patients from controls in an independent validation dataset, with $96\%$ accuracy (AUC = 0.96); Additional file 1: Fig. S13). However, these rectal biopsy DNAm profiles did not show similar predictive power for disease progression or outcome in UC given with the limitation of the sample size in of the testing group (data not shown).
## Discussion
In this study, we used a treatment naïve inception UC cohort in which biospecimens underwent multi-omic analysis at baseline and follow-up. Taking advantage of this inception cohort with extensive prospective clinical meta-data, we estimated DNAm-based cell type proportions in the human rectal mucosa – epithelial, immune and fibroblast compartments—and examined DNAm differences in each of these compartments at the time of diagnosis and during the course of UC.
We first showed that within each compartment, UC-specific epigenetic changes are disturbed at diagnosis. Our first analysis on UC at diagnosis vs. controls DNAm patterns for different cell populations may be reflecting different aspects of the disease. Immune cell-specific responses are consistent with effects of ongoing inflammation. Immune cell DNAm in rectal tissue, as well as in blood, may be more the consequence of disease caused inflammation than it is the underlying cause of UC or disease progression. The epithelial compartment, on the other hand, with observable differences in wound response and cell migration pathways (prior to treatment) might provide insight into why some individuals fail to respond to effective anti-inflammatory treatments.
The next analysis between diagnosis and follow-up showed that the immune compartment undergoes the greatest amount of epigenetic response to treatment, with the epithelial and fibroblast compartments showing fewer changes. These findings are consistent with previous work [19] reporting that DNAm changes in purified epithelial cells from mucosa persisted across two time-points. Especially, overall cell proportions, and many of the cell-specific methylation patterns moved closer to levels seen in controls. This pattern was most pronounced in immune cells and is hardly surprising since current treatment primarily targets the immune system and clearly its effects are most direct and observable in this compartment.
A further the cell-specific disease severity analysis on the rectal biopsy DNAm from UC at diagnosis indicated that epigenetically moderate and severe UC patients group together, whereas mild UC has a distinct epigenetic profile relative to moderate and severe UC as defined by PUCAI. On the other hand, we couldn’t establish any evidence that these CpG sites are involved in nearby gene regulation or biological processes related to disease severity in UC. On the other hand, there were widespread methylation differences at follow-up between those with ongoing, severe disease who would soon need colectomy, and those who would not. Remarkably, DNAm signatures observed in the rectal mucosa at diagnosis could also distinguish patients destined to need a colectomy within 2 years and those who did not.
The epithelial barrier is the front-line defense against invading microbes, toxins, and other luminal contents, but also provides selective transport of nutrients and other beneficial substances that maintain homeostasis and carries significant inflammatory consequences to underlying mucosa upon its malfunction and degradation. The lack of epigenetic changes in the epithelial and fibroblast cells following treatment is of concern to mucosal healing. Notably, we observed that at least $25\%$ of the epithelial derived CpG sites (889 out of 3504) changing during UC are consistent with a previously published study that compared purified epithelial cell methylation patterns between UC patients and controls [19]. Damage and erosion of the intestinal epithelium is a hallmark characteristic of UC, and the large number of epithelial-specific CpG sites affected at diagnosis indicates either a response to external stimuli potentiating the disease or a response to the damage that the body is trying to repair. In fact, in the study of epithelial-derived organoids described in the Introduction, at least $25\%$ of the genes (124 out of 488) associated with epithelial derived CpG sites were also observed to be differentially expressed in UC [19], supporting a relationship between changing DNAm and gene transcription in the epithelium during UC. Our observation of decreased epithelial proportions based on changes in DNAm signals, with a corresponding increase in immune cell abundance, is in line with the well-documented biological consequences of UC. Pathway analysis of the genes influenced by the significantly changing CpG sites showed changes in pathways regulating cell migration, lipid metabolism, small GTPase signaling and wound repair/healing, suggesting that the changes in DNAm in turn influence nearby genes and processes that are critically involved in restitution and repair [36–39].
Fibroblasts originating from the mesenchymal compartment are responsible for maintaining the extracellular milieu that provides structural stability and regulatory growth and differentiation signals for the epithelium [40–42]. The decrease in proportion of this cellular compartment further correlates with symptoms of UC, but this was the one compartment that looked most similar in comparison to control abundance at follow-up. The epigenetic findings here provide new targets that should be considered for correcting UC related epigenetic changes that could promote mucosal healing. For example, UBE2G1 detected in fibroblasts was found to be hypomethylated and did not revert after treatment, and is a known IBD marker [43]. UBE2G1 is ubiquitin conjugating enzyme involved in degradation of short-lived proteins and known to have disease implications when its functionality is perturbed [44–46]. Further, whether therapeutic changes in the fibroblasts enhanced their immunosuppressive effects and facilitated a decrease in immune cell proportion was not entirely clear, but a reversion of many of the fibroblasts’ CpG signatures to non-IBD status suggested this possibility.
Clearance of the immune response by current IBD treatments (anti-TNF as an example) has long been appreciated as the standard of care for IBD and was exemplified in our DNAm analysis where healing was associated with a decrease in mucosal immune proportions and reversion of many of the immune-specific CpG sites back to non-IBD signatures. Interestingly, FOXP1 [47], IL23R [48–51], CCR9 [52, 53] have been documented to play a role in IBD, and here their corresponding CpG loci were found to maintain disease-specific DNAm signatures after treatment. The CCR9/CCL25 signaling axis is responsible for leukocyte trafficking to the gut and our data may reflect epigenetic changes during UC that are associated with the activation and recruitment of immune cells from the peripheral blood into the intestinal mucosa, consequentially sustaining inflammatory conditions and prolonging or worsening the disease. New targeted therapies that can offset epigenetic effects regulating CCR9 may show improved resolution of the inflammatory response [53].
Additionally, our study has revealed some important cell- and disease-severity specific findings that were not reported in previous literature. In our previous study, we reported that peripheral blood DNAm signatures in IBD associate with the degree of inflammation, but do not predict/characterize disease in the gut. In the current study, utilizing samples provided by the longitudinal UC patients from PROTECT cohort, we were able to address this question, demonstrating that the DNAm patterns from actual diseased tissues reflect the nature of disease rather than the inflammation status. This is further supported by our comparative analysis of cell-specific EWAS indicating that only 9 sites from both epithelium and immune cells, and 4 sites from fibroblasts, overlap with our previous total peripheral blood DNAm sites [18]. Unlike blood DNAm, the rectal tissue DNAm, particularly epithelial / mesenchymal tissue DNAm, do not revert back to normal levels. While contrasts between mild and moderate UC were largely difficult to interpret, differences between individuals requiring early colectomy and those with less severe disease were more pronounced. Methylation patterns at diagnosis seem to predict which patients are most likely to progress to colectomy after two years post treatment, and methylation and cell-composition patterns which fail to revert towards controls at follow-up are especially good predictors of who will require an early colectomy.
Finally, we note that the model we employed to decompose mucosal data (comprised of numerous cell types) is believed to be well powered to distinguish three main cellular lineages (epithelial, immune and fibroblasts), but the model relies on estimates, which may be less precise than direct measurements obtained through immunophenotyping or single-cell methodologies [19, 54, 55]. The discoveries reported here suggest that future studies performed in purified cells or using single-cell technologies profiling DNA methylation at the genome-scale will be fruitful in providing further insight into the role of specific cell types in UC. Similarly, because DNAm is influenced by age and enviromental factors, it would be interesting to see whether similar cell-type-specific patterns are observed in studies of UC in adults.
## Conclusion:
In summary, we show that cell type-specific epigenetic changes that occur in the course of UC are taking place in the diseased tissue (rectal mucosa), and that these changes are associated with disease severity and outcome and can accurately distinguish patients from controls. Based on our findings, we speculate that for individuals who do not sufficiently respond to current therapies, targeting epithelial genes with barrier function may improve the clinical outcomes.
## Overall study participants
The cases used for analysis here are a subset of the PROTECT UC cohort. PROTECT is a multicenter inception cohort with a total of 431 treatment naïve UC patients from 29 centers in the USA and Canada. This cohort was prospectively followed for at least 2 years with rectal biopsy collection at diagnosis (before treatment) and subsequent follow-up (treatment period from 8 weeks to 2 years; follow-up; $$n = 73$$). Details regarding inclusion / exclusion criteria, study protocol, approvals, and other clinical parameters assessed have been reported previously [23, 24]. Based on the availability of the rectal tissue DNA, 211 pediatric UC patients from the PROTECT cohort, aged 4–17 years old, were used. Out of the 211 had baseline rectal DNA availability, 73 of them also had follow-up rectal tissue DNA and were used. All 73 patient follow-up samples were collected as clinically indicated during the follow-up visits between 8 weeks and 2 years. All 73 patients received one or more treatment(s) prior to follow-up, described in detail in Hyams et al. [ 23, 24]. The UC diagnosis was based on conventional clinical, endoscopic, and histological parameters. For non-IBD controls, we used age- and gender-matched rectal biopsy genomic DNA samples from 85 RISK participants (RISK is described elsewhere [22]) that had no histologic or endoscopic inflammation and remained asymptomatic during the disease course. Both the PROTECT and RISK studies were approved by the Institutional Review Boards at each of the participating RISK and PROTECT sites. The same sites in North America participated in both the PROTECT and RISK studies. All relevant ethical regulations for work on human participants have been met and conducted in accordance with the criteria set by the Declaration of Helsinki. Informed consent was obtained from the parents of all study participants.
## Quantification of genome-wide DNAm
Rectal biopsy genomic DNA was extracted using the AllPrep DNA/RNA Mini Kit (Qiagen, Valencia, CA, USA), and 500 ng of DNA was subjected to bisulfite treatment using EZ DNAm-GoldTM Kits (Zymo Research, Irvine, CA, USA). MethylationEPIC BeadChip (Illumina, San Diego, CA) was used to quantify genome-wide DNAm differences across ~ 850,000 genome-wide CpG sites [56]. The R package CpGassoc [57] was used to perform the initial quality control (QC). CpG sites called with low signal or low confidence (detection $P \leq 0.05$) or with data missing for greater than $10\%$ of samples were removed, and samples with data missing or called with low confidence for greater than $10\%$ of CpG sites were removed. Probes mapping to multiple locations were also removed [58]. After QC, a total of ~ 820,000 probes and 369 samples (85 non-IBD controls, 211 UC samples at diagnosis, and 73 samples from follow-up) remained. Beta values (β) were calculated for each CpG site as the ratio of methylated (M) to the sum of methylated and unmethylated (U) signals: β = M/(M + U). Signal intensities were then normalized using the module beta-mixture quantile dilation (BMIQ) [59] to account for the probe type bias. These normalized signal intensities were used to perform principal component analysis to further identify sample outliers.
## Statistical methods
(i)Estimating the cell proportions of rectal biopsy tissues. The R/Bioconductor-package EpiDISH [26] was used to estimate cell type proportions through deconvolution of our bulk DNAm data derived from rectal tissue. Estimation was based on two reference panels: (i) an epithelial, immune and fibroblast -specific reference panel and (ii) another immune subtype cells reference panel that can also map the data to 7 immune cell types; B-cells, CD4+ T-cells, CD8+ T-cells, NK-cells, monocytes, neutrophils, and eosinophils. These estimated cell type proportions were used as covariates in all DNAm analyses to adjust for differences in DNAm due to between-sample differences in cellular composition. Briefly, for a given DNAm data matrix, the CellDMC program in EpiDISH [26] uses a reference panel of DNAm profiles in major cell-types (CTs) to estimate cell-type fractions in each sample. It then fits a statistical model to test for association between DNAm and phenotype adjusted for cell-type fractions. In addition to main effects for cell-type fractions, the model includes interaction terms between the phenotype and estimated cell-type fractions, enabling identification of DMCs in specific cell-types (DMCTs).(ii)Genotyping and data processing. Peripheral blood DNA samples of 296 cases and controls with DNAm data were genotyped using the UK Biobank array, and ~ 850,000 genotypes were called using the Axiom Suite software. All subjects had call rates > $95\%$ and consistent gender records with the clinical data. All quality control procedures were performed in PLINK [60]. Principal components were computed based on a pruned version “–hwe 0.001 –maf 0.2 –geno 0.01 –indep-pairwise 50 5 0.2” of the data set consisting of 58,237 LD-independent SNPs (r2 < 0.1). We used the first 5 genotype-based principal components to control for population stratification in all analyses.(iii)DNAm association with UC at diagnosis. To identify CpG sites associated with UC at diagnosis, we performed a case–control EWAS on 211 UC patients at diagnosis compared to 85 non-IBD controls. UC-associated methylation changes in rectal tissue regardless of cell type were first profiled using the R package CpGassoc [57]. Briefly, DNAm was regressed on disease status (0 for control, 1 for UC) with age, gender, epithelial and fibroblast cell proportions, and the first five genotype-based principal components as covariates in the model.(iv)Cell-specific methylation association with UC at diagnosis. We next used the CellDMC function from the EpiDISH [26] package to identify sets of CpGs that show a cell-type specific association with UC. We performed cell-specific EWAS within the epithelial, immune and fibroblasts to test for association between UC and methylation at the ~ 820,000 sites that passed QC. DNAm at each CpG was regressed on disease status (0 for control, 1 for UC) and age, gender, and the first five genotype-based principal components were included as covariates in the model, along with covariates for DNAm-based estimates of cellular proportions, and an interaction term between UC and cellular proportion to identify cell-specific signal. We identified significant CpGs within each EWAS using the Benjamini–Hochberg false discovery rate criterion (FDR < 0.05).(v)Cell-specific methylation association with UC at diagnosis versus follow-up. To assess longitudinal changes in DNAm in patients with UC, we compared the methylation levels in rectal biopsy samples obtained at diagnosis and follow-up ($$n = 73$$; from 8 weeks to 2 years). Age was recalculated for the follow-up samples based upon their time of visit after diagnosis. In the cell-specific EWAS, DNAm was regressed on disease course (0 for at diagnosis, 1 for at follow-up), and age, gender, and the first five genotype-based principal components were included as covariates in the model, along with the covariates and interactions for cell type proportion. To account for the two time points from each patient, representing DNAm levels at diagnosis and follow-up, we included fixed effect covariates for subject ID. To identify CpG sites associated with UC during treatment, we performed a similar cell-specific EWAS comparing diagnosis vs follow-up within the same samples ($$n = 73$$).(vi)Gene Ontology (GO) biological process enrichment analysis. Gene ontology (GO) for biological process enrichment analysis was performed for genes annotated to our UC associated CpG sites by the R/Bioconductor package missMethyl [61]. Genes with more CpG probes on the MethylationEPIC array are more likely to have differentially methylated CpGs, which could introduce potential bias when performing pathway enrichment analysis. The gometh function implemented in missMethyl considers the varying number of differentially methylated CpGs by computing a prior probability for each gene based on the gene length and the number of CpGs probed per gene on the array. Similarly, the GO biological process enrichment analysis for UC associated genes that are associated to UC associated CpGs was performed by using Toppgene [62]. In addition, we used DisGeNET [34] to perform a text-mining analysis of whether the cell-specific gene signatures of this study are enriched for genes previously associated with UC. We specifically tested for disease ID C0009324, which consists of 1458 genes associated with UC.(vii)Analysis of DNAm and gene expression. *Differential* gene expression analysis was performed on a subset of patients from whom both gene expression and DNAm data was available from the rectal mucosa ($$n = 119$$). Gene expression was previously measured using TruSeq Illumina mRNAseq (20 controls and 211 UC samples)[25] or Lexogen 3’UTR mRNAseq (39 UC at diagnosis and a matched subset of the same 39 UC patients’ sampled at 52 week follow-up) [63]. More details on RNA sequencing, data processing and QC are described elsewhere [25, 63].(viii)CpG sites associated with disease status, disease course, disease severity, or colectomy status in the EWAS were further tested for association with expression of genes as annotated by Illumina. In total we used data from 119 patients with UC who had both DNAm and TruSeq Illumina mRNAseq gene expression profiles [25]. Gene expression counts were regressed on DNAm proportions using the DESeq2 R/Bioconductor package, with age, gender and the first five genotype-based principal components included as covariates in the model. A false discovery rate criterion (FDR < 0.05) was used to define a set of significant CpG-gene pairs.(ix)*Differential* gene expression analysis on disease status, disease severity, and colectomy status. We also performed a targeted differential expression analysis of genes annotated to the CpG sites identified in the EWAS of disease status, disease course, disease severity and/or colectomy status. The TruSeq Illumina mRNAseq dataset [25] ($$n = 119$$) was used to identify genes differentially expressed between disease status, disease severity and/or colectomy status. Similarly, the Lexogen 3’UTR mRNAseq data was analyzed to identify genes differentially expressed for CpGs associated to UC at disease course. The DEseq2 package was used to perform the differential expression analysis. Briefly, gene expression values were regressed on disease status, disease severity, or colectomy status with age and gender adjusted as covariates in the model using the default normalization method. DE genes were identified using either nominal p value < 0.05 and fold change (FC) > 1.2.(x)Random forest classification The entire dataset at diagnosis ($$n = 296$$) was divided into a $75\%$ training (64 controls and 158 UC cases) and a $25\%$ validation set (21 controls and 53 UC cases). For this analysis, CellDMC was performed only on training dataset samples and the UC-associated CpG sites across all three cell types were identified. A RandomForest (RF) model was constructed using the UC-associated CpG sites identified in the training dataset as predictors and UC as the outcome, using the RandomForest [64] model in R with default parameters. The trained RF model was tested using the test dataset samples to test the prediction performance of the defined model. Accuracies (ACC) and area under the curve (AUC) were calculated by comparing of actual labels to predicted labels of test set class, separately for cases and controls.
## Supplementary Information
Additional file 1. Supplementary Figures. Additional file 2. List of UC-specific epithelial DNA methylation signatures and pathways. Additional file 3. List of UC-specific immune cells DNA methylation signatures and pathways. Additional file 4. List of UC-specific fibroblast DNA methylation signatures and pathways. Additional file 5. List of DNA methylation signatures and pathways associated in the rectal mucosa of patients with UC post-treatment. Additional file 6. List of UC-specific epithelial DNA methylation signatures at diagnosis associated with disease severity defined by PUCAI.Additional file 7. List of UC-specific immune cells DNA methylation signatures at diagnosis associated with disease severity defined by PUCAI.Additional file 8. List of UC-specific fibroblast DNA methylation signatures at diagnosis associated with disease severity defined by PUCAI.Additional file 9. List of UC-specific epithelial DNA methylation signatures at diagnosis associated with colectomy at two years. Additional file 10. List of UC-specific immune cells DNA methylation signatures at diagnosis associated with colectomy through two years. Additional file 11. List of UC-specific fibroblast DNA methylation signatures at diagnosis associated with colectomy through two years. Additional file 12. The metadata of the participants used in this study.
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|
---
title: 'Provision and experience of care among women with hypertension in pregnancy:
a multi-center qualitative study in Ghana'
authors:
- Kwame Adu-Bonsaffoh
- Evelyn Tamma
- Adanna Nwameme
- Phyllis Dako-Gyeke
- Emmanuel Srofenyoh
- Evelyn K. Ansah
- Diederick E. Grobbee
- Arie Franx
- Joyce L. Browne
journal: Reproductive Health
year: 2023
pmcid: PMC10039538
doi: 10.1186/s12978-023-01593-0
license: CC BY 4.0
---
# Provision and experience of care among women with hypertension in pregnancy: a multi-center qualitative study in Ghana
## Abstract
### Background
Hypertensive disorders of pregnancy (HDP) remain a leading global health problem with complex clinical presentations and potentially grim birth outcomes for both mother and fetus. Improvement in the quality of maternal care provision and positive women’s experiences are indispensable measures to reduce maternal and perinatal adverse outcomes.
### Objective
To explore the perspectives and lived experiences of healthcare provision among women with HDP and the associated challenges.
### Methods
A multi-center qualitative study using in-depth interviews (IDIs) and focus group discussions (FGDs) was conducted in five major referral hospitals in the Greater Accra Region of Ghana between June 2018 and March 2019. Women between 26 and 34 weeks’ gestation with confirmed HDP who received maternity care services were eligible to participate. Thematic content analysis was performed using the inductive analytic framework approach.
### Results
Fifty IDIs and three FGDs (with 22 participants) were conducted. Most women were between 20 and 30 years, Akans (ethnicity), married/cohabiting, self-employed and secondary school graduates. Women reported mixed (positive and negative) experiences of maternal care. Positive experiences reported include receiving optimal quality of care, satisfaction with care and good counselling and reassurance from the health professionals. Negative experiences of care comprised ineffective provider–client communication, inappropriate attitudes by the health professionals and disrespectful treatment including verbal and physical abuse. Major health system factors influencing women’s experiences of care included lack of logistics, substandard professionalism, inefficient national health insurance system and unexplained delays at health facilities. Patient-related factors that influenced provision of care enumerated were financial limitations, chronic psychosocial stress and inadequate awareness about HDP.
### Conclusion
Women with HDP reported both positive and negative experiences of care stemming from the healthcare system, health providers and individual factors. Given the importance of positive women’s experiences and respectful maternal care, dedicated multidisciplinary women-centered care is recommended to optimize the care for pregnant women with HDP.
## Plain language summary
High blood pressure (hypertension) in pregnancy can have severe complications for both mother and fetus including loss of life. The outcome of pregnancy for women who develop hypertension during pregnancy can be improved by ensuring optimal quality of care. In this study, we explored the opinions and experiences of women whose pregnancies were affected by hypertension concerning the care they received during their recent admission at different hospitals in Ghana and the challenges they faced. In four major referral hospitals in the Greater Accra Region of Ghana, we interviewed the women and had focus group discussions. Women who were pregnant for 26 weeks up to 34 weeks and had hypertension in pregnancy were invited for inclusion in the study.
We conducted in-depth interviews with fifty women and three focus group discussions with 22 women. Most women who participated in the study were between 20 and 30 years old, Akans (ethnicity), married/cohabiting, self-employed and secondary school graduates. The women reported both positive and negative experiences of care during their admission at the hospitals. Examples of positive experiences were receiving good quality of care, satisfaction with care, and adequate counselling from the health workers. Examples of negative experiences were poor communication between the providers and affected women, inappropriate attitudes by the healthcare providers, and disrespectful treatment such as verbal and physical abuse. The major factors in the health system that influenced women’s experiences of care were lack of logistics, substandard professionalism, inefficient national health insurance system and long delays at health facilities prior to receiving treatment. The individual women’s factors that affected the quality of care included financial constraints, psychosocial stress and inadequate knowledge about hypertension during pregnancy.
In conclusion, we determined that women with hypertension in pregnancy experience both positive and negative aspects of care and these may be due to challenges associated with the healthcare system, health providers and women themselves. There is the need to ensure optimal quality and respectful maternity care considering the nature of hypertension in pregnancy. These women require dedicated hospital staff with significant experience to improve the quality of care provided to women with hypertension in pregnancy.
## Introduction
The global maternal mortality ratio is estimated at 199 per 100,000 live births, with a lifetime risk of maternal death of 1 in 190. The lifetime risk is substantially higher in sub-Sahara Africa (1 in 38) compared to high-income countries (1 in 5400) indicating significant healthcare inequities [1]. Hypertensive disorders of pregnancy (HDP) or maternal hypertension is among the major causes of maternal mortality, with complex clinical presentations and potentially devastating birth outcomes for both the woman and fetus [2]. HDP-related maternal morbidity and mortality disproportionately affect low- and middle-income countries (LMICs) [3, 4] with approximately 1900 maternal deaths in high-income countries, compared to 20,900 maternal deaths in Sub-Saharan Africa [3].
Prevention of avoidable maternal deaths through improvement in obstetric and newborn care has been a long-standing priority for the World Health Organization (WHO) and the global agenda of the Millennium Development Goals (2001–2015) and Sustainable Development Goals (SDGs, 2015–2030). Overall, provision of care (coverage) has improved during antenatal, intrapartum and postpartum periods, globally. However, lagging improvements to optimize the quality of maternal care resulted in reductions in maternal mortality that fall short of the global ambitions [5]. For instance, the maternal mortality ratio in Ghana remains high at 308 per 100,000 live births despite demonstrable improvements (740 in 1990). [ 1] About $98\%$ of pregnant women in Ghana receive antenatal care from skilled birth attendants and the institutional deliveries rate has increased from $54\%$ in 2007 to $79\%$ in 2017 [6, 7]. The proportion of maternal deaths attributed to hypertension in pregnancy has doubled over the past decade in the country and it is the second largest cause after hemorrhage [6].
The WHO defines quality of care as the extent to which healthcare services provided to individuals and patient populations improve desired health outcomes [5]. High quality of care is multidimensional and incorporates safety, effectiveness, timeliness, efficiency, equitability and usefulness to people [5, 8]. Thus, improvement in the quality of care is critical in achieving the SDG 3’s target of reducing the global maternal mortality to less than 70 per 100,000 live births [9]. Importantly, during provision of care, the rights and dignity of the women should be respected to promote positive pregnancy and childbirth experience [10, 11]. Disrespectful care is increasingly being identified as endemic in most maternity care settings with a direct negative impact on the quality of care and can constitute a significant disincentive to future health-seeking behavior of women [11, 12].
Recently, a WHO multi-country study with Ghana inclusive reported that over $40\%$ of women experienced significant mistreatment including physical, verbal, stigmatization or discrimination [13]. A key recommendation from this study hinges on further research into a comprehensive understanding of the drivers and structural dimensions of disrespectful maternity care including socio-economic inequalities. As such, women’s perspectives and their actual experiences of care at health facilities are vital to improving the existing healthcare system and the quality care for HDPs in the country. Therefore, the main objective of this study was to explore women's perspectives on provision and lived experiences of care and identify specific challenges among women treated for hypertension during pregnancy in five health facilities in Ghana.
## Study design and setting
This multi-center qualitative study using both in-depth interviews (IDIs) and focus group discussions (FGDs) was conducted in five major health facilities in the Greater Accra Region (GAR) of Ghana. The study sites were Kore-Bu Teaching Hospital (KBTH), Greater Accra Regional Hospital, La General Hospital, Lekma Hospital and Tema General Hospital. The Greater Accra Metropolitan Area of Ghana has a population of about 4 million inhabitants with different ethnic backgrounds. The antenatal care coverage by skilled health provider is about $97.5\%$ comprising mainly midwives and doctors. The region records the highest facility-based childbirth ($91.9\%$) in the country with the majority ($71.4\%$) from public institutions and about $20.5\%$ from private health facilities [6].
This qualitative synthesis was part of a large study titled “Severe Preeclampsia adverse Outcome Triage study (SPOT study)". The overarching aim of the SPOT study was to validate the fullPIERS (Pre-eclampsia Integrated Estimate of RiSk) and miniPIERS risk prediction Models for adverse pregnancy outcomes in women with severe preeclampsia in Ghana [14, 15]. The detailed methodology of the SPOT study including the maternal outcomes has been published recently [16]. The main objective of the qualitative analyses was to comprehensively explore the quality of care for women with maternal hypertension in the clinical setting based on the lived experiences of pregnant women and perspectives of health workers. The health professionals’ perspectives on clinical challenges associated with managing maternal hypertension and context-specific recommendations have been published recently [17]. In addition, hypertensive mother’s knowledge, attitudes and misconceptions on HDP have been reported [18]. In this paper, we report hypertensive mothers’ perspectives and their lived experiences of care at health facilities in Ghana.
## Participants
Eligibility criteria were women with HDP diagnosed at gestational ages between 26 and 34 weeks, who received maternity services in any of the study centers and provided written informed consent. HDPs diagnosed before 34 weeks (early onset type) are considered severe disease with increased risk for poor outcomes and hospitalization for an extended period. We excluded women with hypertensive disorders diagnosed after gestational ages more than 34 weeks. There is evidence that planned early delivery for women with HDP after 34 weeks’ gestation is associated with less composite maternal morbidity and mortality compared with prolongation of the pregnancy [19]. Hypertensive pregnancies that occurred prior to 26 weeks were also excluded as conservative clinical management (i.e. prolongation of pregnancy) is generally not recommended due to the high risk of poor pregnancy outcomes [20].
## Participant recruitment and interviews
Data collection commenced on 1st June 2018 and was completed on 31st March 2019. Study participants were recruited via purposive sampling based on the specified inclusion criteria. Initially, a potential participants' list was compiled comprising women with hypertension in pregnancy. Patients that met the inclusion criteria were then identified by one of the authors (ET) with the help of the medical doctor in the study team. The selected potential participants were approached by ET who explained the study protocol to them individually. Women who agreed to participate in the study and provided informed consent were then assigned study identification numbers. The in-depth interviews (IDIs) were carried out immediately after discharge from the hospital. However, if IDI was missed after discharge from the hospital, the interview was re-scheduled within the postnatal period (six weeks postpartum). The IDIs were started first and continued until the point of saturation where no new information emerged from subsequent interviews. All the in-depth interviews (IDIs) were conducted by ET with regular supervision and support from KAB. The FGDs were also conducted and moderated by ET and notes were taken by another trained research assistant. We used interview guides for the IDIs and FGDs to gain a comprehensive understanding of the challenges during provision of care and experiences of hypertensive mothers. Both the IDIs and the FGDs were either conducted in Ga or Twi (local Ghanaian languages) and all were audio-recorded. The notes taken during the interviews were kept in a diary and provided additional clarification and greater transparency during the data analysis.
The IDIs and the FGDs were conducted in designated quiet rooms specifically allocated for the qualitative interviews in each facility to avoid frequent interruptions. There were no other people present in the interview rooms at the time of data collection apart from ET and the research assistant (note taker). The IDIs and FGDs usually lasted for between 30 to 60 min and 60 to 120 min respectively. The FGDs were conducted after the women had been discharged from the health facilities and within six weeks of childbirth so as to reduce recall bias and provide a clear picture of the overall quality of care they received during their admission at the health facilities.
## Ethical consideration
The study protocol was reviewed and approved by the Ghana Health Service Ethics Review Committee (Protocol ID GHSERC- GHSERC015 /$\frac{09}{17}$) and Ethical and Protocol Review Committee (EPRC) of the College of Health Sciences, University of Ghana (Protocol ID GHSERC- CHS-EtM.4-P$\frac{1.2}{2017}$-2018). We obtained written informed consent from all the study participants prior to the interviews and they were assured of strict confidentiality of the information provided. Anonymity was ensured by the non-inclusion of any identifiable information about the respondents.
## Data management and analysis
In this study, mixed methodological orientations of phenomenology and grounded theory were employed via systematic data collection and careful thematic content analysis [21]. We used an inductive analytic framework approach in the data analysis. In the inductive thematic analysis, the themes were derived mainly via coding of the data (data-driven) without being influenced by our theoretical interest in the topic. Deductive analytic approach complemented the analysis as data coding was not performed without any prior theoretical and epistemological background [21].
Transcription of the interviews and translation from Twi or Ga into English started soon after the commencement of the data collection and continued alongside the interviews. Prior to the data analysis, a two-day qualitative data analysis training session was organized for ET and KAB at the School of Public Health, University of Ghana, by the Social Scientists in the team headed by (PG and NA). After the training, the codebook was developed by ET with input from KAB based on the semi-structured interview guides. The transcripts were read multiple times by two authors (KAB and ET) in a more recursive manner to familiarize themselves with the data and to understand the train of thoughts of the respondents. During the recursive process of reading the transcripts, important notes were taken to indicate potential thematic areas and this resulted in the generation of the initial codes which were critical for the final coding of the transcripts. Coding was done by ET and KAB using NVivo software (version 12) based on the thematic content. During the data analysis, the notes that were scribed during the interviews provided clearly objective contribution and understanding via comparison with the transcripts. The study team discussed the codes and the emerging thematic areas until a consensus was reached.
In this study, triangulation of the results was ensured via the inclusion of hypertensive mothers of different backgrounds, from different health facilities (data source triangulation) and with the use of both IDIs and FGDs (method triangulation) [22]. Coding was undertaken by two authors (ET and KAB) and disagreements regarding coding were resolved via discussions by the team. The interviews were undertaken with a clear understanding of the principle of reflexibility and active note-taking during the IDI and FGDS. Reflexivity was ensured via comparison of the interview transcripts with the notes taken during the data collection to provide objective representation and greater transparency of the findings. The consolidated criteria for reporting qualitative research (COREQ) were used as a guide in reporting this paper [23].
## Characteristics of the study participants
In this multicenter study comprising five hospitals in Ghana, a total of 125 women were invited to take part out of which 72 women finally participated comprising 50 and 22 for the IDIs and FGDs respectively. For the FGDs, most of the women could not be traced following discharge from the hospital (Fig. 1). The FGDs were conducted in three out of the five hospitals: Korle-Bu Teaching Hospital ($$n = 4$$ participants, 19 could not be traced out of 23 women invited), Greater Accra Regional Hospital ($$n = 10$$ participants, 5 could not be traced out of 15 women invited) and Tema General Hospital ($$n = 8$$ participants, 7 could not be traced out of 15 women invited). Overall, 12 women ($18.5\%$) declined to participate in the FGDs. A total of 31 ($47.7\%$) women (out of 65) could not be traced during the postpartum period following invitation to participate in the FGDs. There was some challenges in recruiting participants from the two smaller hospitals (La General hospital and Lekma hospital) for the FGD as we could not assemble the minimum number for the FGD on different occasions. The socio-demographic characteristics of the participants and the facility distributions are presented in Table 1.Fig. 1Flow chart for inclusion of women with hypertension in pregnancyTable 1Socio-demographic characteristics of the study participantsVariableIDIs n (%)FGDs n (%)Total n (%)Age < 203 (6.0)03 (4.2) 20–3029 (58.0)8 (36.4)37 (51.4) 30–3914 (28.0)10 (45.5)24 (33.3) 40 + 4 (8.0)4 (18.2)8 (11.1)*Marital status* Single17 (34.0)5 (22.7)22 (30.6) Married/cohabiting33 (66.0)17 (77.3)50 (69.4)*Educational status* None/primary14 (28.0)3 (13.6)17 (23.6) Secondary27 (54.0)15 (68.2)42 (58.3) Tertiary9 (18.0)4 (18.2)13 (4.2)Number of previous births 06 (12.0)0 [0]6 (8.3) 1–441 (82.0)21 (95.5)62 (86.1) 5 + 3 (6.0)1 (4.5)4 (5.6)Residence Urban48 (96.0)22 (100.0)70 (97.2) Peri-urban2 (4.0)0 [0]2 (2.8)Ethnicity Akan28 (56.0)7 (31.8)35 (48.6) Ewe10 (20.0)6 (27.3)16 (22.2) Ga6 (12.0)4 (18.2)10 (13.9) Other6 (12.0)5 (22.7)11 (15.3)Occupation Unemployed15 (30.0)2 (9.1)17 (23.6) Formally employed6 (12.0)0 [0]6 (8.3) Self-employed26 (52.0)19 (86.4)45 (62.5) Casual worker1 (2.0)0 [0]1 (1.4) Others2 (4.0)1 (4.5)3 (4.2)Health facilities (study sites) Korle-Bu Teaching Hospital20 (40.0)4 (18.2)24 (33.3) La-General Hospital4 (8.0)–4 (5.6) Lekma Hospital5 (10.0)–5 (6.9) Greater Accra Regional Hospital16 (32.0)10 (45.5)36 (50.0) Tema General Hospital5 (10.0)8 (36.4)13 (18.1)IDIs in-depth interviews, FGDs focus group discussions Most of the women included in this study had Akan ethnicity ($48.6\%$, $$n = 35$$), and were married/co-habiting ($69.4\%$, $$n = 50$$), self-employed ($62.5\%$, $$n = 45$$) and between the age group of 20 to 30 years ($51.4\%$, $$n = 37$$). Majority had attained secondary education ($58.3\%$, $$n = 42$$) and experienced between 1 to 4 previous childbirths ($86.1\%$, $$n = 62$$) and lived in urban areas in the Accra Metropolis ($97.2\%$, $$n = 70$$). Majority of the IDIs were contributed by the Korle-Bu Teaching Hospital ($40.0\%$, $$n = 20$$) and Greater Accra Regional Hospital ($32.0\%$, $$n = 16$$). Of the 60 potential participants for the IDIs, ten ($16.7\%$) were excluded (4 declined and 6 could not be traced).
In this study, we explored the women’s perspective on provision and experiences of care, and specific challenges faced by women treated for maternal hypertension. The major themes that emerged included [1] women’s knowledge on hypertension in pregnancy, [2] women’s experiences of care and [3] challenges experienced by women while receiving care.
## 1. Women’s knowledge on hypertension in pregnancy
Most of the study participants had limited knowledge about HDP including the danger symptoms of severe hypertension, especially those with limited educational level. Most women indicated that they were ignorant about preeclampsia and other HDPs and wondered why health workers do not routinely educate them on the subject. “Please, I will like to ask that the name that they are mentioning [pre-eclampsia], is it an illness? ( Laughter by the women) Because that is what the doctors always write. Because we are illiterates we don’t understand. I don’t know what it is” (FGD, 40 years, married)“Truth be told, I had never heard about it before. And I still really don’t even know what it is in detail. I quite remember I even use to point to the wrong place when I was asked to point to my heart. In my first pregnancy, nothing about hypertension was mentioned to me (IDI, 29 years, single) However, few mothers had adequate knowledge of hypertension and its major complications including stroke. Adequate knowledge was commonly demonstrated among women who had experienced preeclampsia or other types of maternal hypertension in their previous pregnancies. Most women diagnosed with hypertension in pregnancy had limited knowledge about the condition before their diagnoses were made. Other women did not know that hypertension can affect pregnant women although they had heard about hypertension in the general population. “Your health is the most important because your BP [blood pressure], when it goes up very high it can kill you or leave you with a stroke” (IDI, 29 years, married)“I’ve heard it because I experienced it in my previous pregnancy and I know what it can bring about. So when even someone says [s]he has headache I tell the person to go and check, it might be hypertension because it can kill you easily” (IDI, 39 years, single).“I have heard about BP before but I didn’t know that you could have BP when pregnant” (IDI, 31 years, married) Majority of the respondents attributed their hypertension to stressful situations they experienced during pregnancy. Other women related the occurrence of the hypertension to grudges at workplaces and the home environment. Some participants hinted that in some situations, disturbances in the home environment by family members resulting in ‘excessive thinking’ in pregnancy were associated with hypertension in pregnancy. Most women frequently attributed hypertension in pregnancy to stressful situations which lead to heightened psychological stress and excessive thinking by the women. “Where I was staying, there were other tenants living there who always want to argue with me. Anytime they see me they start to insult and mock at me. So because of that I decided to leave that house because I was very angry so I don’t know if that is what triggered the BP” (IDI, 28 years, single).“What I can say about it is that when we think too much that is what causes it so if you are thinking reduce it and give everything to God. He does all things but when you think too much it will not be able to solve that problem, and then also you should find time to rest. You shouldn’t do too much work” (IDI, 39 years, married).
## 2. Women’s experiences of care
Experience of care was a key recurring theme reported by majority of the women with maternal hypertension. The respondents had different interpretations of what was considered “good quality of care” based on their lived experiences of care at their respective health facilities and the outcomes of their pregnancies. The reported experiences by the women relating to their care at the respective health facilities were mixed. Few women reported positive experiences and perceptions of good quality of care. The hypertensive mothers narrated mixed feelings regarding their care experiences, indicating significant dissatisfaction among participants. However, some hypertensive mothers had positive experiences and described the quality of care they received as optimal. The high quality of care experienced by some of the women commenced with excellent reception at the health facilities followed by provision of appropriate treatment (standards of professionalism) and reassurance by the health workers. “As for me I was well cared for. They've really cared for me. The way the thing [hypertension] happened to me and the way they were able to take care of me. They gave me injections when they had to. They really took very good care of me and I’m very happy” (FGD, 28 years, married)“On the day that I came, honestly, they gave me a good reception because my baby’s heartbeat was up and my Bp was also up so they calmed me down so that my Bp will come down. So they gave me excellent care and I was very happy” (FGD, 42 years, married) However, it was apparent that some women were extremely unhappy with the care they received while they were on admission. They enumerated the negative experiences they encountered and recommended measures to mitigate against such inappropriate treatment by health workers. “Getting up from the bed was very difficult for me. I could not raise my leg. A nurse will ask you to come to her for her to check your temperature and something else while she is seated at the other end. I couldn’t walk and almost fell so I had to hold unto the beds of others when I walked a little bit. On the other hand, there are those (nurses) who will come over to help you when you tell them you can’t get up” (FGD, 41 years, married).“When labour started, at the initial stages when I called any nurse who was passing by, they ignored me instead of them may be encouraging me to bear the pain. When it happens like that you think that maybe you are going to die not knowing anybody there because the person who you know as a nurse who is supposed to help you isn’t. When the baby’s head was coming out she then asked me to get up. It’s fine if you don’t know the condition in which a woman has to go through when in labour. When I had squatted when the baby’s head was coming, she was looking on but she kept urging me to get up and I told her that I couldn’t get up in that condition. There was a container under my bed, and it was in this container that I delivered into (Respondents: ooh!). I was very hurt and told my husband. I was very hurt because I’m sure she was one of the student nurses. If she had drawn closer and helped me with the delivery maybe I wouldn’t have lost so much blood. I bled a lot and suffered a lot before the baby came out” (FGD, 26 years Married).
There were mixed findings regarding provision of relevant information and counselling with respect to procedures undertaken by health professionals. Some women were given comprehensive counselling prior to the procedures they went through; they were satisfied with the care received and they commended the health professionals. “Yes, they will tell you this is going to be painful. They tell you before they inject you. When I came back from the theatre, I told the doctor that my buttocks really hurt. He told me that he will mix the drug with another drug to make it less painful. So he mixed it with another drug before injecting me and the pain was less” (IDI, 31 years, married).
## a. Experience of complications of maternal hypertension
Although the pregnancy outcomes were generally good for most of the women, some experienced adverse outcomes of maternal hypertension, such as the demise of their babies. The narratives provided by some mothers who experienced adverse outcomes clearly indicated that they had some form of postpartum psychological strain and depression. “For me, since they took the child out I didn't want anyone to come to me because in my room when the babies around me cry, I panic, so I told them to let me go home” (IDI, 29 years, single)“They did a scan and realized that the baby had died in my womb. My sister signed as a witness to the death of the baby. After my vitals and blood were checked, everything was alright except my BP” (IDI, 28 years, married) Some of the patients experienced severe complications of maternal hypertension such as convulsions or loss of consciousness (eclampsia). A typical example was a woman who collapsed (had eclampsia) and was rushed to the hospital in an unconscious state and was referred to the tertiary center for further treatment. “After collapsing at home, I was sent to a nearby clinic and after regaining consciousness the clinic transferred me here. When I got here, I was given medicine” (FGD, 26 years, married) A similar occurrence of eclampsia and prolonged loss of consciousness was reported by a young woman who regained her consciousness long after she had been operated upon (cesarean section).“I remember I started eating kenkey and started vomiting and that was it. I didn’t remember anything again…. I saw that there was plaster on my stomach and I was lying down before my mother came and I ask them what I was doing here and they said I had been operated because I was pregnant” (IDI, 18 years, single).
## b. Experience of mistreatment and disrespectful care
Some of the participants recounted unpleasant experiences of disrespectful treatment while receiving maternal care services at the health institutions. These abusive treatments were meted by different categories of health professionals (doctors, midwives) and took different forms including verbal, neglect and physical mistreatment. Verbal abuse was rampant, and most women reported their experience of being shouted at, insulted or scolded during the provision of care. In addition, non-verbal abusive attitudes were displayed toward some hypertensive women. “Some people [health workers] talk to you in a “funny way” so you won't feel it but others too will be shouting at you and you think she is doing her job so you can't say anything about it, but some do it in a very nice way” (IDI, 28 years, single)“In all the doctors take good care of us, but the nurses who work with the doctors are snobs. So you will say that I don’t like this place because when you go there the nurses are snobs. They do this too much” (IDI, 31 years, married) Various instances of neglect by healthcare providers were mentioned by the respondents. The affected women felt neglected and worried especially during the times when they needed the support of the health professionals most. Incidents of extreme forms of inadequate attention or abandonment by health workers during the critical times of childbirth in the health facilities were recounted with a lot of emotions by some of the women. “When I was in labour, I had to tell the nurse that she should come and check me so I can go to the labour ward and she only told me that I should allow them to sleep because that time it was around dawn, 2am. She said I should allow them to sleep and that I’m not in any labour. So I had to go and so it was when I started pushing that the people [other patients] on the ward called out to the nurses “she is giving birth ooo” and when they came I had given birth” (IDI, 24 years, married) In the process of provision of maternal care some women experienced physical abuse which included being hit by health providers. Reasons cited for such mistreatment include patients’ refusal to obey instructions and lack of patience on the health workers’ part. “When they [health workers] have to wake you up for you to take your medication, they hit you very hard as if you were their little sister before they will tell you to take your medication instead of tapping you gently. That was my problem” (FGD, 37 years, married) Sometimes the attitudes of some of the health workers put the patients off and made them wish they had an alternative health facility to seek treatment. Majority of the mothers felt uncomfortable and worried when health professionals who are taking care of them are unfriendly and disinterested in their welfare. “Some nurses are not all that friendly; if you are talking to them as if they are listening, sometimes the way they talk to you makes you feel uncomfortable, so I think at least they should be a little friendly to us” (IDI, 24 years, married)
Lack of interactive communication between the health professionals and hypertensive women was considered a major shortcoming in the process of providing care. Adequate communication from the doctors and nurses on the status and progress of their medical conditions with heartfelt expression of empathy was a major expectation of the women. Some women observed that the lack of communication was even worse for women with no or minimal educational attainment. “Hmmm I have a problem with the doctors and nurses, some don’t explain things to the patients. You come and they say everything is okay and fine, unless those who have gone to school a little bit and can read. But I think in everything, they should tell the patients” (IDI, 26 years, married).
## 3. Challenges experienced while receiving care
Women with hypertension in pregnancy experienced myriads of challenges related to the health system and attitudes of healthcare professionals while receiving care at health facilities. Institutional challenges include inadequate facilities such as beds or space for managing women referred for urgent care due to severe maternal hypertension. Unavailability of hospital beds for admitting mothers with hypertensive emergencies in most health facilities was frequently mentioned. However, urgent institutional arrangements were made in some cases to provide space for admission of the affected mothers following some avoidable institutional delays. “When we came in the evening, we were told there was no bed. So they came to look for a place to put a bed. So they cleared the place where they had put certain things and then put me there. They said because of my case I had to lie down. I shouldn’t be standing so they made a bed for me to lie down and then they checked my BP frequently” (IDI, 28 years, married) Situations where pregnant women on admission had no beds and slept on the floor were also mentioned. Such situations occurred frequently when the hospitals were overwhelmed with high patient loads. A typical example of these experiences encountered personally by some of the hypertensive mothers is indicated below. “Please one good experience I encountered when I was transferred after delivery to the ward was that, there were no beds for the first half and I was given a mattress which I laid on till evening. In the evening one midwife came and asked why a BP patient was lying on the floor. There was a bed then, so she carried me unto the bed like a baby. The woman [midwife] did very well. So, this is one good experience which I had” (FGD, 32 years, co-habiting) In some instances of “no bed syndrome”, some hypertensive pregnant women are managed in chairs until beds become available. The following quote describes a typical experience by one hypertensive pregnant woman who was nursed in a plastic chair when she presented with severe hypertension and required hospital admission and immediate treatment“When I arrived here, I was told there was no bed so I should look for a plastic seat and sit on. So I sat in the seat while they took care of me. I was injected and all that sitting in the chair” (FGD, 31 years, married)
## a. High cost of laboratory tests and antihypertensive medications
The cost of healthcare was a prominent theme that emerged from almost all the respondents. It became clear that the most important underlying challenge associated with the care for women with maternal hypertension was financial constraint. An important concern mentioned by majority of the women was the high cost of hospital stay. In addition, the cost of medications (antihypertensive drugs) were high for which they implored the government and other organizations to support. “The drugs are very expensive. There are some drugs which are not covered by health insurance. You will have to buy it yourself. You can buy drugs to the tune of 400, 500, 600 and sometimes 1.2 cedis [with the sum mentioned here ranging from 70 to 125 USD]. Some are even more than that. So you will buy it yourself. Health insurance doesn’t cover. It covers very little, the ones that are not expensive like 35 or 5 cedis” (IDI, 33 years, married) Financial constraint was cited as the single most important challenge encountered by women with hypertension, especially in paying for their laboratory tests and medications. The participants made recommendations to the government to either reduce the cost of the medications or supply the relevant drugs to them at no cost. “So the government should make sure that the labs [laboratory tests] done for pregnant women with hypertensive disorders of pregnancy should be made free because without the labs the doctors cannot do their work well” (IDI, 33 years, married)“The government should reduce the cost and help those of us who don’t have money so that we can receive the care the doctors are ready to give us. Because if you have high blood pressure and you don’t have money, you are still thinking how will the hypertension will go?” ( IDI, 43 years, married).
## b. Insecurity about the proficiency of the medical team
Some women with maternal hypertension had the impression that some of the medical practitioners were not adequately competent to offer optimal treatment to them on certain occasions. There were instances of arguments among the doctors about the most appropriate clinical decision in the presence of the patients and this created a feeling of insecurity, uncertainty and fear due to perceived impression of inexperienced medical personnel. These feelings of insecurity were compounded by lack of communication and interaction with the affected patients who only looked up to God for miracle. Some mothers narrated how they were scared by the actions or clinical decision of some doctors. “My problem over here is that it was like trial and error. When this person comes [referring to the doctor] he will come and write his report, “severe pre-eclampsia”. When this person [referring to another doctor] also comes, he writes mild pre-eclampsia and then leaves. What I have being yearning for my whole life [referring to a baby], you have students coming in and out. When this one comes, he comes to write then when the doctors come, they don’t read the report. This one comes to write “mild” and then the other one comes to write “severe”. So when the time came for me to go to theatre they should have found out whether it [the baby] will come on or not, but they were arguing among themselves that I was para zero or para “o” or something so they had to go and take the baby out for me. So this is the problem I had” (FGD, 42 years, married).“When I came they [doctors] did not explain things to me and when they checked they asked me to go home and come the following week but if it had not been for the head of the hospital I could have gone home and something could have happened to me” (FGD, 29 years, single)
## c. Delays in receiving care at health facilities
Majority of the mothers with maternal hypertension recounted their experiences of significant delays at the health facilities before receiving the needed care. This was reported by participants from all the health facilities included in the study. Further enquiry indicates that the actual provision of care they received at the facilities are commendable despite prior delays in accessing the care. “I leave home very early because I’m coming from afar. I get here by 6 am and start heading back home at 5 pm. We suffer a lot. We are cared for alright but some people who arrive later go ahead of us because they know someone who works here. We sit in the queue for long because we don’t know anyone who works here. So we really suffer a lot” (FGD, 31 years, married).
Some women who were referred on account of severe hypertension had to obtain folders before they were provided the needed care. The challenge of going through the long registration process without the initial triage or treatment results in significant delay in receiving the needed urgent care, especially for emergency cases. Such long processes of acquiring folders (in-hospital registration) could result in increased adverse pregnancy outcomes for undiagnosed hypertensive emergency due to delay in initiating anti-hypertensive treatment on time. “When I got here, I was asked to go and make a card. After that I was given a nurse who checked my BP so upon checking my BP, they realized it was an emergency so they saw that the BP had gone up so they gave me to a doctor” (IDI, 33 years, married)“We got here at 3 something and they asked us to go and get a folder. My husband kept long with the getting of the folder. So I was attended to after the folder was brought and they checked my Bp and then they sent me to a room. I was given injections instantly” (FGD, 34 years, Married)
## d. Shortage of health professionals
Majority of the women thought the number of health professionals (nurses and doctors) were inadequate to take care of the high patient load. Most women appreciated implicitly that the actual number of health professionals were not adequate. However, they (health workers) were mostly supported by students who were learning on the job. Some patients attributed poor professional output by the health professionals to the high patient load and the inadequate numbers to manage the high volumes of obstetric cases. “I think sometimes there is pressure on them (doctors) and some of the patients they do not abide by the instructions, and they don’t take their drugs and come with worse conditions. So, I think a lot of doctors should be employed” (IDI, 26 years, married).“The midwives are not many so if more of those who have completed school can be added. At the government hospitals the patients are more than the nurses that is why they don’t have time for us so the government should see to it for us” (FGD, 41 years, married)
## e. National health insurance associated challenges
There was a general perception that the National Health Insurance (NHIS) has major limitations and does not cover the cost of maternal care completely. Most women had extreme difficulty in procuring all the prescribed medications and laboratory tests. These sentiments were expressed by almost all the hypertensive mothers with mixed reports on whether the NHIS covers the drugs and laboratory tests fully or partly. “The NHIS doesn’t cover the BP drugs so they should improve the insurance so that it covers the drugs because not everyone can afford it. When I came, I have spent almost 600 *Ghana cedis* here and not everyone can afford it” (IDI, 29 years, single)“With the labs it was not expensive because the health insurance covers some and the rest you will have to pay for it. And the drugs too, the insurance covers some of them so the ones it does not cover you have to buy them either at the hospital when they have some or outside”. ( IDI, 31 years, single) Based on the gross limitations associated with the NHIS as enumerated by the women including its inability to pay for the cost of most medications and laboratory tests, several requests were made to the government. These appeals mainly related to increasing the coverage of the NHIS to defray the hospital bill for women with maternal hypertension. “They should increase the coverage of the insurance. We are told with insurance, delivery is free but this is not so we still pay. Even for the labs and drugs we still pay. So far, I have spent almost 500 cedis” (IDI, 33 years, married)
## f. Home care management
Some participants recommended the idea of managing some selected patients at home for less severe conditions as compared to the strict hospital confinement. Inferably, some women felt their condition were not severe enough to require admission to the hospital and that the possibility of home care and monitoring for less severe cases of maternal hypertension should be explored by health professionals. The reasons for advocating home management as compared to hospital care majorly related to reduced healthcare cost and loss of women’s productive time. “I think when someone comes and the condition is not too serious the doctors should prescribe drugs for the person and not necessarily admit the person as it takes some productive hours of work away from the person and being admitted here, the cost of admission too is expensive and not all can pay” (IDI, 29 years, single)“For me, I was not ok because I told them to allow me to go home so that I can come for review while searching for some money and they refused, and it was because of my siblings I left at home” (IDI, 25 years, single)
## Discussion
This multi-center qualitative study provides a unique opportunity to understand the quality of maternal care from the perspectives of women treated for hypertension during pregnancy and their lived experiences of care at health facilities in Ghana. Women with HDP reported mixed (positive and negative) experiences of care. Major bottlenecks in the provision of high-quality care identified relate to health system challenges such as lack of logistics, inefficient national health insurance and unexplained delays at health facilities; health professionals-related factors including ineffective provider–client communication, inappropriate attitude by the health professionals, disrespectful treatment including verbal and physical abuse; and inadequate women’s knowledge about hypertension in pregnancy.
The finding of inadequate knowledge on the preeclampsia or maternal hypertension by the hypertensive mothers is consistent with other reports across the globe especially in LMICs [24–28]. In a related study in the United Kingdom, Wotherspoon et al. determined limited knowledge of preeclampsia by women most of whom were unaware of the potential risk of developing preeclampsia [29]. In that study, majority of the women were uninformed about the rationale for regular measurement of their blood pressures and urine samples. Relatedly, majority of the mothers attributed the development of hypertension to stressful situations they experience during pregnancy especially from their workplaces and home environment. The notion of ‘stress-induced preeclampsia’ was of paramount concern to the hypertensive mothers and this emerging discovery requires further research as similar findings have been reported [28]. Although the etiology of maternal hypertensive remains elusive recent studies have demonstrated causal associations with chronic psychosocial stress [30–33].
Majority of the mothers with maternal hypertension recounted their experiences of significant delays at the health facilities before receiving healthcare services as reported by other studies [2, 34, 35]. These unnecessary and avoidable delays could result in preventable maternal deaths or severe morbidity [35, 36]. Also, high costs of laboratory tests and antihypertensive medications were considered a major challenge for women and continuous support from the government was frequently solicited. In this case, all-inclusive and effectively working NHIS is indispensable in improving the quality of care. Majority of the mothers lamented desperately on the limitations of the existing NHIS. Originally, the NHIS in Ghana was deemed to cover $95\%$ of all healthcare cost (including maternal care services) with some specific exceptions [37]. However, the constant public outcry including reports of frustrations experienced by hypertensive mothers in this multi-center study calls for a critical review of patients’ benefit and coverage of the NHIS in the country.
Another important health system-related concern was the feeling of insecurity by some women about the proficiency of the medical teams. Some hypertensive mothers had the impression that some of the health workers were not adequately competent. Unavailability of skilled personnel to make correct diagnosis and implement appropriate healthcare plan constitutes substandard care with increased risk of severe maternal near-miss and mortality [38]. The challenge of substandard care for maternal hypertension is not limited to LMICs. For instance, in the Netherlands where maternal mortality ratio is among the lowest worldwide, maternal hypertension is the leading cause of maternal deaths with about $96\%$ associated with substandard care [39]. Similar concerns related to the quality of maternal care have been reported in other high-income countries including the confidential inquiries into maternal deaths in the United Kingdom. The issue of substandard care remains a major clinical challenge that warrants urgent attention globally [27].
In addition, a significant number of mothers reported personal experiences of disrespectful treatments from some health professionals including neglect, verbal and physical abuse. Such mistreatment of women during provision of maternal care remains a global phenomenon with worse implications in the LMICS [11, 40]. In Ghana, disrespectful maternity care occurring in various forms has been reported with differing opinions about its rationale or acceptability in contemporary maternal care [12, 41–43]. Admittedly, mistreatment of women during provision of maternal care is a complex phenomenon that requires the input of all stakeholders including the government, health institutions, health professionals and society. More recently, a WHO multi-country study on mistreatment of women comprising both labour observation and postpartum community survey reported that over $40\%$ of women experienced some form of mistreatment [13]. The main public health concern about mistreatment of women with hypertensive disorder relates to its potential to disincentivize prospective mothers and their families in seeking care at health facilities. Evidence-based interventions of locally appropriate dimensions are urgently required to minimize abusive treatment of women and improve respectful maternity care in the country.
Effective communication between health care providers and women is strongly recommended by WHO to enhance positive experience of care and minimize unnecessary anxiety [10]. In our study, inadequate interactive communication was a major theme that emerged from majority the hypertensive mothers, consistent with similar reports from other studies [25, 44, 45]. In a related study in the United States, various recommendations were provided to improve effective communication between patients and healthcare providers including building trust, rapport and reflective listening [46]. Lack of effective communication negatively impact on satisfaction with care. In a study conducted in Germany, approximately $70\%$ of the hypertensive mothers reported dissatisfaction with the medical information provided by their healthcare providers on maternal hypertension [47]. This quantitative report by Leeners et al. [ 47] complements the qualitative reports demonstrated in our study and calls for a paradigm shift in provider–client communication. Re-training and empowerment of healthcare professionals, including improvement of their salaries, health facilities and personal circumstances are viable measures to improve efficient provision of care and women’s experiences.
Intriguingly, the concept of home care management and monitoring for less severe maternal hypertension was raised by some of the hypertensive mothers to reduce the burden on health facilities, health care cost and improve women’s productivity. In another study, Barlow et al. reported expression of similar recommendation by hypertensive mothers as they preferred to continue their bed rest and medications at home because they thought their condition were not severe [48]. In some high-income countries home management of women with non-severe maternal hypertension is permissible and recommended [49–51]. More recently, Perry et al. reported significant reduction in the number of hospital visits among hypertensive pregnant women when managed on home-based blood pressure monitoring without increasing adverse pregnancy outcomes [52].
## Strengths and limitations
The main strengths of this qualitative study include the multi-center nature comprising five major hospitals in Ghana and the rigorous methodology adopted. In this study, women of various age groups were sampled purposively which resulted in a comprehensive assessment of experiences of care and major determinants of quality of care. Another important aspect of this study relates to the timing of the interviews which occurred after hospital discharge which enabled participants to express the opinions freely without any fear of retribution from the healthcare providers. Also, all the IDIs and the FGDs were undertaken by a trained researcher who is non-healthcare professional, and this enhanced the women’s willingness to discuss their views freely. Finally, we employed both IDIs and FGDs to ensure comprehensive understanding (method triangulation) [22] of the process involved in the provision of care and lived experiences of hypertensive mothers.
This study has some limitations. Although this was a multi-center study, it was mainly conducted in the southern zone of the country where coverage of maternal healthcare services is highest. Therefore, women’s experiences of care including challenges associated with provision of maternal care services reported in our study may be underestimated. Also, data collection for the IDIs was mainly undertaken by only one author and this may have influenced the triangulation of the findings (investigator triangulation) with increased potential for monotony. Investigator triangulation relates to the use of more researchers in data collection or analysis resulting in improved assurance of data variety and confirmation of the findings [22]. However, the findings of this study depict the gross overview of the quality of care associated with maternal hypertension in the country.
## Conclusion
This multi-center qualitative study has highlighted hypertensive women’s perspectives on the quality of care and their lived experiences with the care for women with hypertensive disorders of pregnancy. A complex array of elements affects the provision and experience of care for women with maternal hypertension. This includes health system related factors such as lack of logistics, substandard professional attitude and unexplained delays at health facilities. Patient related factors that negatively influence the provision of care comprise inadequate awareness of maternal hypertension and its complications and financial challenges. The quality of care experienced by women with maternal hypertension was negatively influenced by ineffective provider–client communication, inappropriate attitude by the health professionals, disrespectful treatment.
The quality of provision and experience of care for maternal hypertension in the country could be improved by integration of appropriate evidence-based interventions at different levels such as health system, healthcare cost coverage, regular refresher courses for health workers and patient-centered care interventions. The emphasis should be placed on multidimensional collaboration of all stakeholders in both governmental and non-governmental organizations as well as the entire society. Well-integrated maternal health education promotion should be integrated into the educational programs to create and maintain optimal awareness about the relevance of high-quality maternal health. Further studies of high methodological quality with wider national coverage are recommended to better understand how quality and experience of care can be improved for women with maternal hypertension.
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|
---
title: Inhibition of miR-4640-5p alleviates pulmonary hypertension in chronic obstructive
pulmonary disease patients by regulating nitric oxide synthase 1
authors:
- Zhao Yang
- Ping Li
- Qun Yuan
- Xi Wang
- Hong-Hong Ma
- Bing Zhuan
journal: Respiratory Research
year: 2023
pmcid: PMC10039540
doi: 10.1186/s12931-023-02387-5
license: CC BY 4.0
---
# Inhibition of miR-4640-5p alleviates pulmonary hypertension in chronic obstructive pulmonary disease patients by regulating nitric oxide synthase 1
## Abstract
### Background
Pulmonary hypertension (PH) is a devastating disease characterized by vasoconstriction and vascular remodeling, leading to right ventricular failure and death. PH is a common complication of chronic obstructive pulmonary disease (COPD). Accumulating evidence demonstrate that microRNAs participate in the pathobiology of PH in COPD patients. In this study, we aimed to evaluate the expression and function of microRNA-4640-5p (miR-4640-5p) in PH.
### Methods
The mRNA and protein levels were determined by quantitative polymerase chain reaction (qPCR) and western blot, separately. Functional assays and western blot were performed to determine the effects of miR-4640-5p and NOS1 on cell growth, migration. Besides, the dual-luciferase reporter assays were used to validate miR-4640-5p and NOS1 interactions.
### Results
We found that miR-4640-5p expression was significantly higher in the lung tissues of COPD-PH patients than in the healthy controls while higher expression of miR-4640-5p was correlated with more severe COPD-PH. By using pulmonary artery smooth muscle cell (PASMC) in in vitro assays, we demonstrated that inhibition of miR-4640-5p suppressed cell proliferation and migration of PASMC via regulating mTOR/S6 signaling. Bioinformatics analysis and validation experiments revealed that nitric oxide synthase 1 (NOS1) was a direct downstream target of miR-4640-5p. Overexpression of NOS1 partially antagonized the effect of miR-4640-5p in regulating PASMC cell proliferation and migration. In addition, our findings suggested that miR-4640-5p/NOS1 axis regulated mitochondrial dynamics in PASMCs. Furthermore, in the hypoxia-induced PH rat model, inhibition of miR-4640-5p ameliorated PH with reduced right ventricular systolic pressure and Fulton index.
### Conclusions
miR-4640-5p regulates PH via targeting NOS1, which provides a potential diagnostic biomarker and therapeutic target for COPD-PH patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12931-023-02387-5.
## Background
Pulmonary hypertension (PH) is a devastating disease featured with elevated pulmonary vascular resistance, vasoconstriction and vascular remodeling, leading to right ventricular failure and death [1, 2]. PH is a common complication of chronic obstructive pulmonary disease (COPD) [3]. The therapeutic treatment for PH associated with COPD has been advanced significantly during the past decade [4, 5]. However, there is no cure treatment and current therapies only focus on vasodilation but not vascular remodeling [6]. Studies have demonstrated that pulmonary artery smooth muscle cells (PASMCs) play critical roles in thickening media and distal vessel muscularization [7]. Thus, it is of great importance to develop new therapeutic treatments targeting PASMC and prevent vascular remodeling in PH associated with COPD.
Accumulating evidence demonstrate that microRNAs play key functions in the pathobiology of PH in COPD patients, which could be utilized as biomarkers and therapeutic targets for the diagnosis and treatment of patients with COPD-PH [8, 9]. Liu et.al reported that high expression of microRNA-214 promoted the development of vascular remodeling in hypoxia-induced PH by targeting CCNL2 [10]. MiR-190a-5p was demonstrated to participate in the regulation of hypoxia-induced PH and could be utilized as a biomarker for diagnosis and prognosis in COPD-PH patients [11]. Similarly, increased expression of plasma miR-210 could serve as a diagnostic biomarker for COPD-PH [12]. MiR-4640-5p was found to interact with lncRNA OGFRP1 and regulate eIF5A expression in non-small cell lung cancer (NSCLC) [13]. However, the expression and function of miR-4640-5p in COPD-PH and its underlying mechanisms are not fully studied.
Nitric oxide is an endogenous pulmonary vasodilator that is synthesized by nitric oxide synthase (NOS), playing an important role in pulmonary vascular tone and vascular remodeling [14]. There are three different NOS isoforms which comprises neuronal NOS (nNOS, NOS1), inducible NOS (iNOS, NOS2) and endothelial NOS (eNOS, NOS3) [15]. NOS1 has been found to regulate pulmonary hypertension in animal models [16]. The mRNA and protein expression of NOS1 was significantly higher in lung tissues of smokers with COPD compared with nonsmoker controls [17]. How NOS1 expression was regulated in COPD-PH remains unclear.
In this study, we aimed to evaluate the expression of microRNA-4640-5p (miR-4640-5p) in the lung tissues of PH patients. The function of miR-4640-5p evaluated both in in vitro PASMC and in vivo hypoxia-induced rat PH model. Our findings suggest that miR-4640-5p regulates PH via targeting NOS1, which provides a potential diagnostic biomarker and therapeutic target for COPD-PH patients.
## COPD-PH patient specimen
Lung tissues from COPD-PH patients or normal donors were from Suzhou Science & Technology Town Hospital. Patient and specimen information from experimental group and control group were listed in Additional file 1: Tables S1, Additional file 2: Table S2 and Additional file 3: Table S3. Informed consent was obtained from all participants. The study protocols were approved by the research ethics committee of Suzhou Science & Technology Town Hospital and were conducted in accordance with the principles expressed in the Declaration of Helsinki.
## Cell culture and treatment
Human primary pulmonary artery smooth muscle cells (PASMCs) were purchased from the ScienCell (California, USA) and cultured in the DMEM (Gibco) with the recommended vascular smooth muscle cell growth kit. Cells were cultured at 37 °C with $5\%$ CO2, under normoxia ($21\%$ O2) or hypoxia ($1\%$ O2) condition.
## Transfection
MiR-4640-5p mimics (5′-UGGGCCAGGGAGCAGCUGGUGGG-3′), miR-4640-5p inhibitor (5′-CCCACCAGCUGCUCCCUGGCCCA-3′), and relative negative control (NC) mimics (#miR1N0000001-1-10; RIBOBIO) or NC inhibitor (#miR2N0000001-1-10; RIBOBIO) were purchased from RiboBio (Guangzhou, China). NOS1 overexpression vector were purchased from RiboBio (Guangzhou, China). Transfection was performed using AC0021M (AccuRef Scientific; Xi’an, Shaanxi, China) following the manufacturer’s instructions.
## CCK-8 assay
Cell counting Kit-8 (CCK-8) assay (#AC0011M, AccuRef Scientific) was performed to evaluate the PASMC proliferation following the manufacturer’s protocol. After culture for indicated time, CCK-8 reagent was added to the cell culture and the absorbance at 450 nm was measured to determine the cell viability.
## 5-Ethynyl-20-deoxyuridine (EdU) incorporation assay
PASMCs were cultured in 24-well plates for 24 h under hypoxia condition. DNA synthesis was analyzed by using an EdU assay kit reagent (RC0051M, AccuRef Scientific, China) for 2 h at 37 °C. Cells were also treated with DAPI solution for 5 min at room temperature.
## Cell migration assay
PASMCs were seeded into the upper chambers of the Transwell system (Corning 3422, USA) in serum-free medium. Medium with $10\%$ FBS was added to the lower chamber. After 36 h incubation, cells that migrated through the membrane filter were fixed and stained with $0.5\%$ crystal violet and then counted with a microscope.
## Wound-healing assay
PASMCs were seeded into six-well plates at a density of 1 × 106 cells/well and cultured until reaching $80\%$ confluence. An artificial scratch wound was generated using a sterile 200-μL tip. The streaked cells were washed with serum-free medium and cultured in complete medium for 36 h. Cell migration was recorded by an inverted microscope (20 × magnification).
## Hypoxia-induced PAH rat model
Animal experiments were performed according to the guidelines for the care and use of laboratory animals in Suzhou Science & Technology Town Hospital. The study protocol was reviewed and approved by the Institute Animal Care and Use Committees (IACUC) of Suzhou Science & Technology Town Hospital. Briefly, twenty male SPF Sprague–Dawley rats (250–275 g) were randomly divided into four groups ($$n = 5$$): Normoxia group, Hypoxia group, Hypoxia + anti-NC group, Hypoxia + anti-miR-4640-5p group. Rats in Hypoxia + anti-NC group and Hypoxia + anti-miR-4640-5p group were administrated with anti-NC and anti-miR-4640-5p intratracheally, respectively, while Normoxia group and Hypoxia group were given equal volume of PBS. Pulmonary hypertension model was established by exposing the rats in Hypoxia group, Hypoxia + anti-NC group and Hypoxia + anti-miR-4640-5p group to $10\%$ O2 for 8 h per day for 4 weeks. After the rats were anesthetized with 30 mg/kg sodium barbital, the abdominal incision was made through the xiphoid process, and the scalp needle was inserted into the right ventricle from the abdominal cavity through the diaphragm to measure the right ventricular systolic pressure (RVSP) and Cardiac output (CO) using a pressure transducer catheter (Millar Instruments).
## Reverse-transcribe quantitative polymer chain reaction (RT-qPCR)
Total RNA was extracted from lung tissue samples and PASMCs using TRIzol reagent (AccuRef Scientific, China) and then reverse transcribed to cDNA using Accuref 1st Strand cDNA synthesis kits (#RM0011, AccuRef Scientific, China) following the manufacturer's recommendations. The total RNA concentration was measured using a NanoDrop spectrophotometer. Quantitative real-time PCR was carried out on Applied Biosystems 7300 real-time PCR system (Applied Biosystems, USA) using the Accuref qPCR SYBR Green Mixture (#RM0031M, AccuRef Scientific). U6 RNA was used as internal control, and the relative expression values were normalized using Ct method (2−△△Ct). The primer sequences were listed in Table 1.Table 1Primers used for qPCRGenePrimers (5′ → 3′)miR-4640-5pForwardTGGGCCAGGGAGCAGCTGGUGGGReverseUniversal oligo dT primerU6ForwardCTCGCTTCGGCAGCACAReverseUniversal oligo dT primerDUSP13ForwardTTTCATAGGAGATGCGGCCAReverseCACTGCTGCCGTAGAAGTCATMEM184BForwardATCACATGCCACCAGCCCAReverseGCTGCTCTACCACAGTCCTCCD209ForwardTGCTGAGGAGCAGAACTTCCReverseTACTGCTTGAAGCTGGGCAAORAI2ForwardCGTATAAATGACCTGCCTGGCTReverseAGGAGCAGAGGGGTCGATAGAGO1ForwardCACGCTGGACTTCACAGTCTReverseCCCGCAGCTGCTCCCVAMP3ForwardAGTTAGACGACCGTGCAGACReverseCCGATTGCCCACATCTTGCMYO1EForwardACTGGGAGGAAAGCAGGGTAReverseAGATGCGCCGATAGGCATAGNOS1ForwardATTTATGCCGCGTTTCCAGCReverseAGGCATCATGAGCCCGTCTFCP2L1ForwardTTCCAGCCATGCTCTTCTGGReverseCGAGCACATCACGCAGGTAGAPDHForwardATGGGGAAGGTGAAGGTCGReverseGGGGTCATTGATGGCAACAATA
## Western blot
Cultured PASMCs were lysed in RIPA buffer (HAT, WB053) with proteinase inhibitor (Roche, Switzerland). Equal amounts of protein were separated by SDS-PAGE and transferred onto PVDF membrane (Immobilon-P, IPRH00010, China). The membranes were blocked with $5\%$ non-fat milk in PBST, followed by incubation with primary antibodies (β-actin, Proteintech 20536-1-AP; mTOR, MERCK, T2949-200UL; p-mTOR (pSer 2448), MERCK, SAB4504476-100UG; p-S6, Abcam, Y179; GAPHD, Proteintech, 60004-1-Ig; anti-NOS1, Santa Cruz, SC-5302) overnight at 4 °C. Subsequently, the membranes were further incubated with a secondary antibody conjugated with HRP (anti-Rabbit IgG, Biosharp, BL003A). The protein levels were visualized by using a Western blot ECL kit (#AP0082, AccuRef Scientific, China).
## Luciferase reporter assay
PASMCs were seeded into 24-well plates. Luciferase reporter vector psiCHECK2 (Promega, USA) containing WT or mutant 3′-UTR of NOS1 was transiently co-transfected with miR-4640-5p mimics or NC into PASMCs cells. The relative luciferase activity was analyzed by Dual-luciferase reporter assay system (Promega, GloMax$\frac{20}{20}$, USA).
## Statistical analysis
All results were shown as mean ± SD. The statistical analysis was performed using two-tailed student t test or ANOVA with GraphPad Prism V8 software (Prism, USA). A p value < 0.05 was considered as statistical significant.
## MiR-4640-5p is highly expressed in the lung tissues of COPD-PH patients
The expression of miR-4640-5p was examined in the lung tissues of COPD-PH patients and normal volunteers by RT-qPCR. As shown in Fig. 1A, miR-4640-5p expression was significantly higher in the lung tissue of COPD-PH patients. Pearson correlation analysis found that the expression of miR-4640-5p was positively associated with the smoking index (SI, r = − 0.5480, $$p \leq 0.042$$, the smoking index is a unit for measuring cigarettes consumption over a long period and was calculated using the following formula: smoking index = the number of cigarettes smoked per day (CPD) × years of tobacco use.) ( Fig. 1B). Moreover, the expression levels of miR-4640-5p were negatively correlated with FEV$1\%$ Pred, but not FEV1/FVC ratio (Fig. 1C, D). However, miR-4640-5p expression was significantly correlated with the pulmonary artery systolic pressure (PASP) (Fig. 1E).Fig. 1MiR-4640-5p is highly expressed in the lung tissues of COPD-PH patients and correlated with severity of COPD-PH. A The expression of miR-4640-5p was examined in the lung tissues of COPD-PH patients ($$n = 14$$) and healthy controls ($$n = 14$$) by RT-qPCR. B–E *Pearson analysis* was performed to analysis the correlation between miR-4640-5p expressions and the smoking index (SI), FEV1/FVC%, FEV$1\%$ Pred, and pulmonary artery systolic pressure (PASP). *** $p \leq 0.001$
## Inhibition of miR-4640-5p suppresses cell proliferation and migration of PASMC via regulating mTOR/S6 signaling
To further investigate the expression and function of miR-4640-5p in COPD-PH, PASMCs were cultured under hypoxia condition for different time and the expression of miR-4640-5p was analyzed. The results showed that the expression of miR-4640-5p was enhanced in PASMCs cultured under hypoxia, with the highest levels at 72 h post hypoxia treatment (Fig. 2A). Compared with normoxia condition, hypoxia treatment significantly enhanced miR-4640-5p expression while miR-4640-5p inhibitor markedly suppressed miR-4640-5p levels in PASMCs (Fig. 2B). Functionally, we demonstrated that hypoxia treatment remarkably enhanced cell proliferation and DNA incorporation and inhibition of miR-4640-5p significantly suppressed cell growth in PASMCs (Fig. 2C, D). Mammalian target of rapamycin (mTOR) signaling is a central hub regulating cell proliferation, autophagy and apoptosis [18]. Hypoxia treatment of PASMCs enhanced mTOR/S6 signaling (Fig. 2E, F). In the contrast, miR-4640-5p inhibitor could suppress the phosphorylation of mTOR and S6 in PASMCs (Fig. 2E, F). Furthermore, transwell assay and wound-healing assay revealed that hypoxia treatment enhanced PASMC cell migration while inhibition of miR-4640-5p decreased the capability of cell migration in PASMCs (Fig. 2G, H).Fig. 2Inhibition of miR-4640-5p suppresses cell proliferation and migration of PASMC via regulating mTOR/S6 signaling. A PASMCs were cultured under hypoxia condition for indicated time and the miR-4640-5p expression was analyzed by RT-qPCR. B–G PASMCs were cultured under normoxia or hypoxia condition, transfected with miR-4640-5p inhibitor or negative control (NC). B The relative expression of miR-4640-5p in PASMCs was analyzed by RT-qPCR. C Cell proliferation was analyzed by CCK-8 assay. D DNA synthesis was analyzed by EdU incorporation assay. E, F Protein expression levels of p-mTOR, mTOR, p-S6, and S6 in PASMCs were analyzed by western blot. β-actin was used as an internal control. PASMC migration was evaluated by transwell assay (G) and wound-healing assay (H). All experiments were independently repeated for at least three times. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$
## NOS1 is a downstream direct target of miR-4640-5p
By using three different bioinformatics analysis tools (TargetScan, mRDB, and miRWalk), we identified 9 genes as potential targets of miR-4640-5p (Fig. 3A). Among these 9 genes, overexpression of miR-4640-5p in PASMCs significantly suppressed the expression of DUSP13 and NOS1 (Fig. 3B). Further analysis suggested that miR-4640-5p had the complementary binding sequences against 3′-UTR of NOS1 (Fig. 3C). Luciferase reporter assay demonstrated that miR-4640-5p bound to the 3′-UTR of NOS1 and significantly inhibited the luciferase activity in PASMCs transfected with luciferase reporter vector containing WT 3’-UTR of NOS1, but not mutated 3’-UTR of NOS1 (Fig. 3C). Under normoxia condition, we found that overexpression of miR-4640-5p could markedly decreased the mRNA and protein levels of NOS1 (Fig. 3D, E). Intriguingly, the miR-4640-5p expression levels were negatively associated with NOS1 mRNA expression levels in COPD-PH patients (Fig. 3F). Moreover, hypoxia treated PASMCs had significantly lower levels of NOS1 mRNA and protein compared with that in normoxia condition (Fig. 3G, H).Fig. 3NOS1 is a downstream direct target of miR-4640-5p. A A Venn diagram showed the numbers of potential target genes of miR-4640-5p predicted by TargetScan, miRDB, and miRWalk. B PASMCs were transfected with miR-4640-5p mimic or NC. The relative mRNA expression levels of commonly predicted genes were analyzed by RT-qPCR 48 h later. C Diagrams showed the putative binding sites between miR-4640-5p and corresponding wild type (WT) or mutant (MUT) sites of NOS1. PASMCs were co-transfected with reporter vector containing WT or Mut 3’-UTR of NOS1, together with NC or miR-4640-5p mimic. The dual luciferase activity was analyzed in PASMCs at 48 h post transfection. D The mRNA and (E) protein expression levels of NOS1 in PASMCs cultured under normoxia or hypoxia, transfected with miR-4640-5p mimic or NC. F *Pearson analysis* of the correlation between NOS1 mRNA expression and miR-4640-5p expression in lung tissue from COPD-PH patients ($$n = 14$$). G, H The mRNA (G) and protein (H) expression levels of NOS1 in PASMCs cultured under normoxia or hypoxia condition were analyzed by RT-qPCR and western blot. All experiments were independently repeated for at least three times. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$
## Overexpression of NOS1 antagonizes the effect of miR-4640-5p in regulating PASMC cell proliferation and migration
To further study the functional relationship between miR-4640-5p and NOS1, we overexpressed NOS1, with or without miR-4640-5p mimics in PASMCs (Fig. 4A). Hypoxia treatment decreased the expression of NOS1, while overexpression of NOS1 antagonized the effect of miR-4640-5p and restored NOS1 expression levels in PASMCs (Fig. 4A). Consistently, hypoxia treatment enhanced PASMC cell proliferation and DNA incorporation. Overexpression of NOS1 inhibited cell proliferation, which could be partially reversed by miR-4640-5p mimics (Fig. 4B, C). In addition, NOS1 overexpression inhibited the p-mTOR and p-S6, which could be partially restored by miR-4640-5p overexpression (Fig. 4D). Similarly, hypoxia treatment enhanced cell migration and overexpression of NOS1 dampened the migration capability of PASMCs, which could be partially rescued by miR-4640-5p overexpression (Fig. 4E, F).Fig. 4Overexpression of NOS1 antagonizes the effect of miR-4640-5p in regulating PASMC cell proliferation and migration. PASMCs were cultured under normoxia or hypoxia condition, transfected with pcDNA empty vector, pcDNA-NOS1, miR-NC + pcDNA-NOS1, or miR-4640-5p mimic + pcDNA-NOS1. A The relative expression of NOS1 in PASMCs was analyzed by western blot. B Cell proliferation was analyzed by CCK-8 assay. C DNA synthesis was analyzed by EdU incorporation assay. D Protein expression levels of p-mTOR, mTOR, p-S6, and S6 in PASMCs were analyzed by western blot. β-actin was used as an internal control. PASMC migration was evaluated by transwell assay (E) and wound-healing assay (F). All experiments were independently repeated for at least three times. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$
## MiR-4640-5p/NOS1 regulates mitochondrial dynamics in PASMCs
Mitochondrial dysfunction has been reported to participate in COPD-PH pathobiology [19]. We examined the mitochondrial dynamics in PASMCs treated with hypoxia and transfected with miR-4640-5p mimics or NOS1 overexpression vector. As shown in Fig. 5A, hypoxia treatment enhanced the expression of the dynamin 1-like protein (Drp1) and mitochondrial fission 1 (FIS1), but decreased mitofusin 1 (MFN1) and NOS1 expression. In addition, overexpression miR-4640-5p further enhanced Drp1/FIS1 expression and suppressed MFN1/NOS1 expression, while the regulation mediated by miR-4640-5p could be partially antagonized by NOS1 overexpression (Fig. 5A). Consistently, hypoxia treatment or miR-4640-5p overexpression led to reduced area of mitochondria but increased mitochondria numbers, indicating enhanced cell cycle progression and cell proliferation (Fig. 5B, C). However, overexpression of NOS1 restored the area of mitochondria and decreased the number of mitochondria (Fig. 5B, C).Fig. 5MiR-4640-5p/NOS1 regulates mitochondrial dynamics in PASMCs. PASMCs were cultured under normoxia or hypoxia condition, transfected with pcDNA empty vector, pcDNA-NOS1, miR-NC + pcDNA-NOS1, or miR-4640-5p mimic + pcDNA-NOS1. A The protein expression of Drp1, FIS1, MFN1, NOS1 and GAPDH in PASMCs was analyzed by western blot. B, C Immunofluorescence staining of mitochondria in PASMCs with different treatment was performed. The area of mitochondria and number of mitochondria was analyzed. All experiments were independently repeated for at least three times. * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$
## Inhibition of miR-4640-5p ameliorates hypoxia-induced PAH in rat
To further investigate the function of miR-4640-5p in vivo, we established the hypoxia-induced PAH rat model by exposing Sprague–Dawley (SD) rat in hypoxia chamber ($10\%$ O2, 8 h per day) for 4 weeks. SD rats were treated with PBS under normoxia or hypoxia, or administrated with anti-miRNA negative control or anti-miR-4640-5p. The results showed that hypoxia exposure enhanced miR-4640-5p expression while down-regulated the mRNA and protein levels of NOS1 in rat PASMCs (Fig. 6A–C). Inhibition of miR-4640-5p enhanced NOS1 mRNA and protein expression in rat PASMCs (Fig. 6A–C). Functionally, hypoxia treatment resulted in enhanced right ventricular systolic pressure (RVSP) and Fulton index (RV/(LV + S)), with decreased cardiac output (CO) (Fig. 6D–F). Treatment with anti-miR-4640-5p ameliorated the severity of PH, showing decreased RVSP and Fulton index and increased CO (Fig. 6D–F). In addition, the RVSP/CO ratio was significantly enhanced under hypoxia treatment but markedly decreased with anti-miR-4640-5p treatment (Fig. 6G). Consistent with the in vitro results, hypoxia treatment decreased FIS1 and NOS1 expression in rat PASMCs while enhanced Drp1 and FIS1 expression (Fig. 6H). Inhibition of miR-4640-5p partially reversed the effect of hypoxia treatment (Fig. 6H).Fig. 6Inhibition of miR-4640-5p ameliorates hypoxia-induced PAH in rat. Sprague–Dawley rats were randomly divided into four groups ($$n = 5$$): Normoxia group, Hypoxia group, Hypoxia + anti-NC group, Hypoxia + anti-miR-4640-5p group. Pulmonary hypertension model was established by exposing the rats in Hypoxia group, Hypoxia + anti-NC group and Hypoxia + anti-miR-4640-5p group to $10\%$ O2 for 8 h per day for 4 weeks. After terminal harvest, the relative expression of miR-4640-5p (A), NOS1 mRNA (B), and NOS1 protein (C) in rat lung tissues was analyzed. D–G After the rats were anesthetized with 30 mg/kg sodium barbital, the abdominal incision was made through the xiphoid process, and the scalp needle was inserted into the right ventricle from the abdominal cavity through the diaphragm to measure and calculate the (D) RVSP, (E) RV/(LV + S), (F) CO, and (G) RVSP/CO. H The protein expression of Drp1, FIS1, MFN1, NOS1 and GAPDH in PASMCs was analyzed by western blot. Western blot experiments were independently repeated for at least three times *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$
## Discussion
The pathophysiology of COPD-PH is multifactorial and heterogeneous [20]. Mounting evidence show that miRNAs play critical functions in COPD-PH [21–23]. Here, we identified miR-4640-5p was highly expressed in the lung tissues of COPD-PH patients, which was correlated with more severe COPD-PH. Inhibition of miR-4640-5p suppressed cell proliferation and migration of PASMC in vitro and ameliorated PH with reduced right ventricular systolic pressure and Fulton index in hypoxia-induced rat PH in vivo. The miR-4640-5p/NOS1 axis could be utilized as a potential diagnostic biomarker and therapeutic target for COPD-PH patients.
The function of miR-4640-5p has been investigated in multiple cancer types such as laryngeal squamous cell carcinoma, endometrial cancer and palillary thyroid cancer [24–26]. In NSCLC, lncRNA OGFRP1 functions as an oncogene via sponging miR-4640-5p [13]. However, the expression profile and function of miR-4640-5p in lung tissues of COPD-PH patients have not been addressed. Our results showed higher miR-4640-5p expression in COPD-PH lung tissues, which was correlated with more severe PH (Fig. 1). PASMC cell hyper-proliferation is an essential cause of vessel intimal thickening and pulmonary vascular remodeling while PASMC proliferation could be regulated by chronic hypoxia exposure [27]. Consistently, we found that hypoxia treatment enhanced miR-4640-5p expression and promoted PASMC cell proliferation and migration, which was associated with enhanced mTOR/S6 signaling.
MicroRNA-mRNA network contributes to the pathogenesis of idiopathic pulmonary arterial hypertension [28]. In this study, bioinformatics analysis indicated that NOS1 might be a direct downstream target of miR-4640-5p. It has been reported that the mRNA and protein expression of NOS1 was significantly higher in lung tissues of smokers with COPD compared with nonsmoker controls [17]. We validated the interaction between miR-4640-5p and NOS1 by using luciferase reporter assay, while overexpression of NOS1 could functionally reverse the effect of miR-4640-5p overexpression. However, due to the multiple-to-multiple regulation between miRNAs and target genes, there might be other downstream target genes of miR-4640-5p involved in the regulation of pathogenesis of COPD-PH, which requires further investigation.
Intriguingly, mitochondrial dysfunction has been reported to participate in COPD-PH pathobiology [19]. Similarly, we found that hypoxia treatment or miR-4640-5p overexpression led to reduced area of mitochondria but increased mitochondria numbers, indicating enhanced cell cycle progression and cell proliferation, while overexpression of NOS1 restored the area of mitochondria and decreased the number of mitochondria. The function of miR-4640-5p was also evaluated in a hypoxia-induced PH rat model in vivo, suggesting that inhibition of miR-4640-5p could ameliorate the hypoxia-induced PH development in rat.
## Conclusions
In summary, our findings suggest that inhibition of miR-4640-5p could suppress PASMC cell proliferation and migration, ameliorate hypoxia-induced PH via targeting NOS1 and regulating mTOR/S6 signaling. Our results might provide a potential novel approach for COPD-PH treatment.
## Supplementary Information
Additional file 1. Table S1. Information about the patient specimens of experimental group. Additional file 2. Table S2. Information about the patient specimens of control group. Additional file 3. Table S3. Statistical information about the patient specimens of control and experimental group.
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|
---
title: 'Systolic blood pressure reduction with tirzepatide in patients with type 2
diabetes: insights from SURPASS clinical program'
authors:
- Ildiko Lingvay
- Ofri Mosenzon
- Katelyn Brown
- Xuewei Cui
- Ciara O’Neill
- Laura Fernández Landó
- Hiren Patel
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10039543
doi: 10.1186/s12933-023-01797-5
license: CC BY 4.0
---
# Systolic blood pressure reduction with tirzepatide in patients with type 2 diabetes: insights from SURPASS clinical program
## Abstract
### Background
Tirzepatide, a once-weekly glucose-dependent insulinotropic polypeptide/ glucagon-like peptide-1 receptor agonist, is approved in the United States, Europe and Japan for the treatment of type 2 diabetes. Across the SURPASS-1 to -5 clinical studies, tirzepatide 5, 10 and 15 mg demonstrated significant improvements in glycated haemoglobin A1c (HbA1c) (− 1.9 to − $2.6\%$), body weight (− 6.6 to − $13.9\%$) and systolic blood pressure (SBP) (− 2.8 to − 12.6 mmHg) at the end of study treatment.
### Methods
Post-hoc mediation analyses were conducted to evaluate weight-loss dependent and weight-loss independent effects of tirzepatide on SBP reductions across the 5 SURPASS studies. The safety population (all randomized patients who took at least 1 dose of study drug) of each study was analyzed. Additional analyses were conducted at individual study level or pooled across 5 SURPASS trials.
### Results
The difference in mean SBP change from baseline at 40 weeks (total effect) between the tirzepatide and comparator groups was − 1.3 to − 5.1 mmHg (tirzepatide 5 mg), − 1.7 to − 6.5 mmHg (tirzepatide 10 mg) and − 3.1 to − 11.5 mmHg (tirzepatide 15 mg). These SBP reductions were primarily mediated through weight loss, with different degrees of contributions from weight-loss independent effects across the different trials. In the SURPASS-4 study, which enrolled patients with established cardiovascular disease, weight-loss independent effects explained $33\%$ to $57\%$ of difference in SBP change between tirzepatide and insulin glargine groups. In a pooled analysis of the SURPASS-1 to -5 studies, there was a significant ($p \leq 0.001$) but weak correlation ($r = 0.18$ to 0.22) between change in body weight and SBP. Reductions in SBP with tirzepatide were not dependent on concomitant antihypertensive medications at baseline as similar reductions were observed whether participants were receiving them or not (interaction $$p \leq 0.77$$). The largest SBP reductions were observed in the highest baseline category (> 140 mmHg), while those in the first quartile of baseline SBP category (< 122 mmHg) observed no further decrease in SBP.
### Conclusions
Tirzepatide-induced SBP reduction was primarily mediated through weight loss, with different degrees of contributions from weight-loss independent effects across the different trials. SBP reduction was not dependent on antihypertensive medication use but dependent on baseline SBP value, alleviating theoretical concerns of hypotension.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01797-5.
## Introduction
Hypertension is a common comorbidity of type 2 diabetes (T2D) and is twice as prevalent in people with T2D compared with those without T2D [1]. Approximately half of adults with hypertension are unaware they have it and, of those with hypertension, only $42\%$ are treated while only $21\%$ have their hypertension under control [2]. Hypertension is a strong risk factor for microvascular and macrovascular diabetic complications, including retinopathy, nephropathy and atherosclerotic cardiovascular disease [3, 4]. The American Diabetes Association recommends that patients with T2D should achieve a blood pressure (BP) goal of less than $\frac{140}{90}$ mmHg. For patients with a high risk of cardiovascular (CV) disease, however, they recommend a BP below $\frac{130}{80}$ mmHg [5]. Meanwhile, the European Society of Cardiology and the European Association for the Study of Diabetes recommend a BP target of 120–$\frac{130}{70}$–80 mmHg [6].
A glucose lowering agent with clinically relevant improvements in BP and cardiovascular risk reduction may be advantageous to the majority of patients with T2D. Some glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have demonstrated CV benefits, [7–10] and generally a neutral to modest reduction in BP, making them a preferred treatment option in patients with T2D with indicators of high-risk of established atherosclerotic CV disease [11]. Even small reductions of 2.4 mmHg in systolic blood pressure (SBP) can have a significant effect in reducing CV events [12], with larger SBP reductions demonstrating greater effects [13–16].
Tirzepatide, a once-weekly glucose-dependent insulinotropic polypeptide (GIP) and GLP-1 RA, is approved in the United States, Europe and Japan for the treatment of people with T2D. In five global phase 3 clinical trials (SURPASS-1, -2, -3, -4, -5), tirzepatide produced substantial reductions in glycated haemoglobin A1c (HbA1c) (− 1.9 to − $2.6\%$), and body weight (− 6.6 to − $13.9\%$) over 40 to 52 weeks, enabling many people (23–$52\%$) with T2D to achieve normalization of glucose control (defined as HbA1c < $5.7\%$) [17–21]. Across the SURPASS studies, tirzepatide 5, 10 and 15 mg also demonstrated clinically relevant improvements in SBP (− 2.8 to − 12.6 mmHg) over 40–52 weeks [17–22]. As weight loss is known to lower SBP, it is important to assess the contribution of tirzepatide-induced weight loss on SBP reduction [23].
Tirzepatide has a safety profile consistent with that of GLP-1 RAs, with mild to moderate gastrointestinal adverse events (AEs) mostly reported during the dose escalation period which decreased over time. Additionally, in a meta-analyses conducted across seven phase 2 and 3 clinical studies, tirzepatide demonstrated CV safety when compared with pooled comparators with the hazard ratio of 0.80 ($95\%$ confidence interval [CI]: 0.57, 1.11) for major adverse cardiovascular events (MACE-4) which included death due to CV cause, myocardial infarction, stroke and hospitalization for unstable angina [24].
The objective of this report is to provide an overview of the effect of tirzepatide on SBP across the five SURPASS studies and to assess the impact of weight loss and other select variables (use of antihypertensive medication and baseline SBP value) on this effect.
## Study design and participants
A database was created using $\frac{40}{42}$-week clinical data from five randomized controlled trials, SURPASS-1, -2, -3, -4 and -5. A common $\frac{40}{42}$-week primary time point was selected for consistent assessment across the five SURPASS studies. The study design for each trial is described in detail in Additional file 1. Key eligibility criteria, and primary efficacy and safety results have been published previously for all five trials [17–21] (ClinicalTrial.gov Identifiers: NCT03954834, NCT03987919, NCT03882970, NCT03730662, NCT04039503). Two of the trials were placebo-controlled (SURPASS-1 and -5) while the remaining three trials compared tirzepatide (5 mg, 10 mg and 15 mg) to semaglutide 1 mg, titrated insulin degludec and titrated insulin glargine (SURPASS-2, -3 and -4, respectively). Participants randomized to tirzepatide started at a 2.5 mg dose once weekly and escalated the dose by 2.5 mg every 4 weeks until they reached their assigned dose. Participants continued their baseline antihypertensive medications and were permitted to adjust during the study.
Body weight measurements were carried out in a consistent manner using a calibrated electronic scale in kilograms. All weights for a given patient were measured using the same scale and patients wore light clothes with no shoes while their weight was measured. All laboratory parameters were assessed in a central laboratory. Blood pressure and pulse rate were measured after the participant sat quietly for 5 min. For each parameter, two measurements were taken using the same arm with the recordings taken at least 1 min apart. BP was taken with an automated blood pressure machine.
## Statistical analysis
Post-hoc mediation analyses were conducted to evaluate weight-loss dependent (WL-D) and weight-loss independent (WL-IND) effects of tirzepatide on SBP reductions across the five SURPASS studies individually and also pooled doses per study. The model for the estimation of WL-D and WL-IND effects on SBP at Week $\frac{40}{42}$ included the interaction between treatment and weight change, with the baseline variable for SBP, use of antihypertensive drug, country and HbA1c category ([≤ $8.0\%$, > $8.0\%$] for SURPASS-5, [≤ $8.5\%$, > $8.5\%$] for other studies) as covariates in the model. The safety population (all randomly assigned patients who took at least one dose of study drug) of each study was used in this analysis which included data regardless of adherence to study drug or initiation, modification or discontinuation of antihypertensive medications. With the integrated database from the five studies, subgroup analyses of change from baseline in SBP by baseline antihypertensive drug use (Yes, No) was performed. Correlation between SBP change from baseline and weight change from baseline were performed and the Pearson correlation coefficient was presented. SBP change from baseline was summarized by the baseline SBP quartile.
## Baseline characteristics and key demographics
Patient demographics and baseline characteristics from participants randomized in SURPASS-1 to -5 ($$n = 4199$$; $$n = 1394$$ receiving tirzepatide 5 mg, $$n = 1397$$ receiving tirzepatide 10 mg, $$n = 1408$$ receiving tirzepatide 15 mg and $$n = 2064$$ receiving placebo or active comparators) are shown in Table 1. Blood pressure at baseline is also shown in Additional file 2. Baseline characteristics and demographics were well balanced between tirzepatide and comparators for each study and pooled dataset. Table 1Baseline characteristics and key demographics (SURPASS 1–5 individual [pooled arms] and pooled data)Individual SURPASS trialsPooled SURPASS trialsSURPASS-1 ($$n = 478$$)SURPASS-2 ($$n = 1878$$)SURPASS-3 ($$n = 1437$$)SURPASS-4 ($$n = 1995$$)SURPASS-5 ($$n = 475$$)Pooled TZP ($$n = 4199$$)Pooled comparator ($$n = 2064$$)Age (years)54.1 ± 11.956.6 ± 10.457.4 ± 10.063.6 ± 8.660.6 ± 9.958.5 ± 10.460.3 ± 10.3Sex—male (n, %)247 (51.7)882 (47.0)802 (55.8)`1246 (62.5)264 (55.6)2245 (53.5)1196 (57.9)Duration of diabetes (years)4.7 ± 5.48.6 ± 6.58.4 ± 6.211.8 ± 7.513.3 ± 7.39.38 ± 7.010.12 ± 7.3Cardiovascular disease (%)a581387183535 HbA1c (%)7.9 ± 0.98.3 ± 1.08.2 ± 0.98.5 ± 0.98.3 ± 0.98.3 ± 1.08.3 ± 0.9BMI (kg/m2)31.9 ± 6.634.2 ± 6.933.5 ± 6.132.6 ± 5.533.4 ± 6.133.4 ± 6.333.0 ± 6.1Weight (kg)85.9 ± 19.893.7 ± 21.994.3 ± 20.190.3 ± 18.795.2 ± 21.692.6 ± 20.691.6 ± 20.1eGFR (mL/min/1.73m2)94.1 ± 19.796.0 ± 17.194.1 ± 17.081.3 ± 21.185.5 ± 17.891.0 ± 19.587.8 ± 20.3SBP (mmHg)127.6 ± 14.1130.6 ± 13.8131.5 ± 13.3134.4 ± 15.4137.9 ± 15.7132.0 ± 14.5133.1 ± 14.9DBP (mmHg)79.4 ± 8.879.2 ± 9.079.2 ± 8.978.4 ± 9.480.7 ± 10.879.0 ± 9.279.1 ± 9.4Antihypertensive medication use (%)b47647093757278ACE inhibitors13303340363235Angiotensin II receptor blockers22262137272730Dihydropyridine derivatives11121727301821Beta-blocking agents7112341302228Data are mean ± SD, unless otherwise indicatedACE angiotensin-converting enzyme BMI body mass index; DBP diastolic blood pressure; eGFR estimated glomerular filtration rate; HbA1c glycated hemoglobin A1c; N population size; n sample size; SBP systolic blood pressure; SD standard deviation; TZP tirzepatideaData presented for all randomised patients and for cardiovascular disease includes history of myocardial infarction, coronary revascularization, hospitalization for unstable angina or heart failure, stroke or transient ischemic attack, peripheral arterial disease, lower extremity arterial revascularization, carotid revascularization, or documented coronary artery diseasebMost frequently used classes of antihypertensive medications Across the SURPASS studies, mean age was 54–64 years and 47–$94\%$ of participants were using antihypertensive medications at baseline. At baseline, SURPASS-1 participants had the lowest SBP and duration of diabetes (127.6 mmHg and 5 years) compared to SURPASS-5 participants (137.9 mmHg and 13 years). As expected, SURPASS-4 participants had the highest prevalence of CV disease ($87\%$), use of antihypertensive medication at baseline ($94\%$) and lowest estimated glomerular filtration rate (81.3 mL/min/1.73m2), as this study enrolled patients with a high CV risk (coronary heart disease, peripheral arterial disease, cerebrovascular disease, chronic kidney disease or congestive heart failure). SURPASS-4 participants were also older and $63\%$ were male.
## Systolic blood pressure reduction with tirzepatide across SURPASS program
Across the SURPASS program, SBP reductions ranged from − 4.2 to − 12.6 mmHg in participants receiving tirzepatide. In each study, SBP reductions were greater with tirzepatide than with placebo or active comparator groups at Week $\frac{40}{42.}$
In the monotherapy placebo-controlled study (SURPASS-1), treatment with tirzepatide 10 mg resulted in significantly greater SBP reductions compared with placebo (estimated treatment difference [ETD] [$95\%$ CI] − 3.1 [− 6.2, 0.1] mmHg; $$P \leq 0.04$$). In the add-on to basal insulin placebo-controlled study (SURPASS-5), all doses (5, 10 and 15 mg) resulted in significantly greater SBP reductions compared with placebo (ETD [$95\%$ CI] − 4.4 [− 7.8, − 1.0], − 6.6 [− 9.9, − 3.2] and − 10.9 [− 14.3, − 7.5] mmHg; $$P \leq 0.01$$, $P \leq 0.001$ and $P \leq 0.001$, respectively) (Fig. 1).Fig. 1Change from baseline in systolic blood pressure at Week $\frac{40}{42.}$ Data are least-squares mean ± SE. Pooled comparator data are not presented as these comparators have varying effects on SBP. Data are taken from the safety population of each study. * $p \leq 0.05$ vs. placebo/active comparator, ***$p \leq 0.001$ vs. placebo/active comparator. CI confidence interval, SBP systolic blood pressure, SE standard error, TZP tirzepatide In the SURPASS-2 study, 10 mg and 15 mg dose groups of tirzepatide demonstrated significantly greater SBP reductions than semaglutide 1 mg (ETD [$95\%$ CI] − 1.8 [− 3.4, − 0.1] and − 3.0 [− 4.6, − 1.3] mmHg; $$P \leq 0.03$$ and $P \leq 0.001$, respectively), while in SURPASS-3 and SURPASS-4 studies, SBP reductions were greater with all tirzepatide doses compared with insulin degludec (ETD [$95\%$ CI] − 4.9 [− 6.8, − 3.0], − 6.3 [− 8.2, − 4.4], − 6.9 [− 8.8, − 5.0] mmHg) and insulin glargine (ETD [$95\%$ CI] − 4.7 [− 6.5, − 3.0], − 5.6 [− 7.4, − 3.9], − 7.2 [− 8.9, − 5.5] mmHg) for tirzepatide 5, 10 and 15 mg, respectively: $P \leq 0.001$) (Fig. 1).
Overall, SBP reductions were greater with tirzepatide than with placebo or active comparator groups and were dose dependent with the greatest SBP reductions observed in the tirzepatide 15 mg treatment groups. Similarly, pooled analysis across the five SURPASS trials at Week $\frac{40}{42}$ indicated dose dependent SBP reductions of − 4.8, − 5.8 and − 7.0-mmHg for tirzepatide 5, 10 and 15 mg treatment groups, respectively. ( Fig. 1).
## Association between systolic blood pressure and weight change from baseline
There were similar reductions in body weight for all tirzepatide doses across the five SURPASS studies. Similar to SBP, mean body weight reductions at Week $\frac{40}{42}$ were dose dependent, with the greatest reductions observed in the tirzepatide 15 mg treatment groups across the SURPASS program. ( − 7.0, − 9.1 and − 10.8 kg for tirzepatide 5, 10 and 15 mg treatment groups, respectively). Body weight reductions did not reach a plateau.
The mediation analysis showed contribution of WL-D and WL-IND effects on total effect of SBP reductions presented as difference between tirzepatide and comparator group for each study (Fig. 2). For WL-D effects between tirzepatide and comparator groups, the ETD ($95\%$ CI) in mean SBP change from baseline ranged from − 1.0 (− 1.6, − 0.5) to − 4.5 (− 6.7, − 2.4) mmHg (tirzepatide 5 mg), − 2.0 (− 2.8, − 1.4) to − 6.2 (− 8.9, − 3.6) mmHg (tirzepatide 10 mg) and − 2.4 (− 3.3, − 1.6) to − 7.5 (− 10.6, − 4.4) mmHg (tirzepatide 15 mg) (Fig. 2). WL-IND effects contributed to a lesser extent as the ETD ($95\%$ CI) in mean SBP change from baseline between tirzepatide and comparator groups ranged from − 0.3 (− 1.8, 1.3) to − 3.8 (− 8.4, 0.9) mmHg (tirzepatide 5 mg), + 2.5 (− 1.4, 6.7) to − 2.9 (− 5.9, 0.1) mmHg (tirzepatide 10 mg) and + 0.7 (− 3.3, 4.8) to − 6.5 (− 10.8, − 1.9) mmHg (tirzepatide 15 mg) (Fig. 2). Mediation analysis conducted by pooling patients across all doses of tirzepatide within each study also showed consistent results (Additional file 3).Fig. 2Mediation analyses for systolic blood pressure using weight loss as a factor at Week $\frac{40}{42}$ (SURPASS 1–5 individual data). Data are least-squares mean ETD ($95\%$ CI). Data are taken from the safety population of each study. Percentage values represent the percent of blood pressure reduction mediated by weight loss. CI confidence interval, ETD estimated treatment difference, SBP systolic blood pressure, TZP tirzepatide, WL-D weight-loss dependent, WL-IND weight-loss independent In the SURPASS-4 study where patients with high CV risk were enrolled, and in the SURPASS-5 study where patients had the longest duration of T2D, WL-IND effects explained 33–$57\%$ and 26–$73\%$ of the difference in SBP change between tirzepatide versus the insulin glargine and placebo group, respectively (Fig. 2).
In the pooled analyses, there was a significant ($p \leq 0.001$) but weak correlation ($r = 0.18$ to 0.22) between change in body weight and SBP from baseline at Week $\frac{40}{42}$ in tirzepatide-treated patients (Fig. 3).Fig. 3Correlation between change in systolic blood pressure and body weight at Week $\frac{40}{42.}$ Data taken from the safety population of SURPASS 1–5 pooled. SBP systolic blood pressure, r correlation coefficient, TZP tirzepatide
## Subgroup analysis of systolic blood pressure change from baseline by use of antihypertensive medications
At baseline, in the pooled tirzepatide treatment groups, $72.4\%$ of participants were receiving antihypertensive medications. Similarly, in the pooled comparator group, $78.1\%$ were receiving antihypertensive medications at baseline. In the SURPASS-4 study which enrolled participants with established CV disease, $94\%$ were receiving antihypertensive medications at baseline. ( Table 1).
Reductions in SBP with tirzepatide were not dependent on concomitant antihypertensive medications as similar reductions were observed whether participants were receiving them or not (tirzepatide 5 mg, − 5.0 vs − 4.3 mmHg; tirzepatide 10 mg, − 5.7 vs − 5.8 mmHg; tirzepatide 15 mg, − 7.0 vs − 7.0 mmHg) with a non-significant treatment by antihypertensive medication (Yes, No) interaction ($$P \leq 0.77$$) (Fig. 4).Fig. 4Change from baseline in systolic blood pressure at Week 40 by use of antihypertensive medication at baseline. Data taken from the safety population of SURPASS 1–5 pooled. SBP systolic blood pressure, r correlation coefficient, TZP tirzepatide
## Subgroup analysis of systolic blood pressure changes from baseline by quartile of baseline value
The fourth quartile of SBP baseline value was > 140 mmHg, the median was 132.0 mmHg and the first quartile was ≤ 122.5 mmHg. Quartile of baseline SBP value significantly influenced the SBP change at Week $\frac{40}{42}$ ($p \leq 0.0001$) (Fig. 5). The greatest SBP change with tirzepatide doses, which ranged from − 14.0 to − 17.5 mmHg, was observed in participants with the highest SBP values at baseline (Q4, > 140 mmHg). SBP was significantly reduced in all categories, except for in the lowest SBP baseline value category (Q1, ≤ 122.5 mmHg), where no clinically meaningful changes were observed. Fig. 5Change from baseline in systolic blood pressure at Week 40 by quartile of baseline value. Data are mean ± SE. Data taken from the safety population of SURPASS 1–5 pooled. N population size, SBP systolic blood pressure, SD standard deviation, TZP tirzepatide Body weight reduction was similar across all the quartiles of SBP baseline value (tirzepatide 5 mg − 6.8 kg to − 7.3 kg; tirzepatide 10 mg − 8.9 kg to − 9.4 kg; tirzepatide 15 mg − 10.3 kg to − 11.6 kg) (Fig. 5).
## Safety assessment
Tirzepatide had a safety profile consistent with that of GLP-1 RAs with the majority of gastrointestinal AEs noted during the dose escalation period and decreasing overtime [17–21]. The most commonly reported gastrointestinal AEs were nausea and diarrhea. The percentage of patients reporting ≥ 1 treatment-emergent gastrointestinal AE by preferred term ranged from 3 to $16\%$ for tirzepatide 5 mg, 3–$24\%$ for tirzepatide 10 mg and 6–$24\%$ for tirzepatide 15 mg across the SURPASS studies.
Treatment with tirzepatide resulted in a mean increase in heart rate of 1–4, 2–4 and 3–6 beats per minute (bpm) for 5-, 10- and 15-mg groups, respectively, at the end of study treatment (Week $\frac{40}{42}$ for SURPASS-1, 2 and 5 and Week 52 for SURPASS-3 and 4). For the two placebo-controlled studies, heart rate increased by 0–2 bpm on average. Treatment with active comparators semaglutide 1 mg, insulin degludec and insulin glargine resulted in mean heart rate increases of 4, 1 and 1 bpm, respectively.
## Our findings in context
Tirzepatide demonstrated clinically significant improvements in SBP ranging from 4 to 13 mmHg reduction across the three doses (5 mg, 10 mg and 15 mg) at 40 weeks in the SURPASS clinical program. Tirzepatide 10 mg and 15 mg demonstrated statistically significant reductions in SBP compared to GLP-1 RA, semaglutide 1 mg (5.3 mmHg and 6.5 mmHg vs 3.6 mmHg, respectively). The magnitude of effect was also consistent in SURPASS 4 which enrolled patients with established cardiovascular disease.
We observed that the effect of tirzepatide on SBP was mediated through weight loss with different degrees of contributions from weight-loss independent effects across the different SURPASS trials. Furthermore, SBP reduction was not dependent on antihypertensive medication use but dependent on baseline SBP value.
These findings are in agreement with a pooled analysis of six randomized phase 3 clinical trials in GLP-1 RA liraglutide [25]. Similarly, the authors reported that SBP was weakly correlated with weight loss at 26 weeks and SBP reductions were observed in the presence and absence of antihypertensive medication. However, mediation analyses were not carried out across the trials to determine the extent of the weight-loss contribution, nor was the SBP change according to baseline values evaluated. In a meta-analysis conducted across 33 randomized studies, the authors evaluated GLP-1 RAs, liraglutide and exenatide, using random-effect analysis and concluded that these GLP-1 RAs induced a small but significant change in SBP that appeared to be independent of the degree of weight loss and SBP values at baseline [26].
## The effect of weight loss and other variables on blood pressure
Weight loss of about 5–$10\%$ is expected to improve SBP by > 5 mmHg [27]. Across the SURPASS program, weight loss associated with tirzepatide explained the majority of treatment effect on SBP. Excess adiposity, insulin resistance, inflammation and higher oxidative stress are hallmarks of type 2 diabetes and obesity, which are proven to alter endothelial dysfunction and affect haemodynamics resulting in elevated blood pressure [28, 29]. It is not surprising that robust weight loss, reduction in liver and abdominal fat [30] and improvement in insulin sensitivity [31] associated with tirzepatide treatment may likely affect this key pathophysiological state associated with elevated BP in this population.
Nevertheless, there was some heterogeneity in this observation in the studies with higher baseline age, SBP, use of antihypertensive medications and duration of diabetes (SURPASS-4 and SURPASS-5) where weight loss explained roughly half of the total effect on SBP. Reduction in insulin usage in the SURPASS-5 study, particularly in the 15-mg dose group, may have also played a role in SBP reduction. There was a weak correlation between weight loss and SBP in the pooled analyses across SURPASS trials which further raises curiosity to explore potential weight-loss independent mechanisms that could be driving the reduction in SBP. These mechanisms are potentially not related to the study treatment. While the mediation analysis indicated the treatment effect on SBP reduction were mainly through the weight loss, the results of the two analyses are not contradictory. The lack of robust association between weight loss and improvement in SBP has been reported with GLP-1 RAs [24, 25]. Natriuresis [32], direct vasodilation [33], reductions in sympathetic nervous system activity [34], extracellular volume and midregional-pro-atrial natriuretic peptide (proANP) [33, 35] are potential direct mechanisms for GLP-1 RAs leading to SBP lowering.
To ascertain whether use of antihypertensive mediation impacts the degree of SBP reduction with tirzepatide, analyses were conducted for subgroups of patients using or not using antihypertensive medication at treatment initiation. These findings may be relevant for health care professionals initiating tirzepatide in patients with T2D using antihypertensive medications, as this could be a potential opportunity to adjust antihypertensive medications if target blood pressure is reached. SBP reduction with tirzepatide was highest (mean of 14–18 mmHg) in patients with a baseline value greater than 140 mmHg while there was minimal impact on SBP for patients with a baseline value of less than 123 mmHg. This is a clinically relevant finding from a patient safety perspective as this effect minimizes any potential risk of hypotension or syncope. This finding is also consistent with that reported with liraglutide [36].
Although much is known about the effects of GLP-1 RAs on blood pressure, little clinical data are available on the effects of GIP agonism. After a 6-day subcutaneous GIP infusion in patients with type 1 diabetes, a 4.6 mmHg reduction in SBP was noted [37]. In a separate study on patients with T2D already using GLP-1 RA and metformin, continuous acute infusion of GIP (6 pmol/kg/min) resulted in significant reduction in SBP compared to placebo. This effect was hypothesized to be due to elevated proANP and suggests an additive haemodynamic effect of GIP and GLP-1 receptor co-agonism [38]. Superior SBP lowering with tirzepatide compared to semaglutide may be due to greater weight loss or partly due to GIP specific mechanisms that warrant further exploration.
## Safety
Across SURPASS studies, tirzepatide was associated with an increase in heart rate of 1 to 6 beats per minute at the end of study treatment. There was no significant difference between tirzepatide and semaglutide in the SURPASS-2 study at Week 40 in change in heart rate compared to baseline despite higher reduction in HbA1c, weight and SBP [18]. Several mechanisms have been postulated for elevation in heart rate with GLP-1 RAs, such as reflex tachycardia, increase in sympathetic nervous system activity and direct sino-atrial node action but none have been clinically proven yet [39]. Several long-acting GLP-1 RAs have demonstrated CV protection in a dedicated CV outcome trial (CVOT) [7–10, 38] while the SURPASS-CVOT study (NCT04255433) for tirzepatide is ongoing and will provide further insights into whether these effects on blood pressure lowering, improvements in metabolic parameters and elevation in heart rate would combine to produce any meaningful impact on hard CV outcomes. To date, tirzepatide has demonstrated CV safety when compared with pooled comparators in a meta-analysis of phase 2 and phase 3 studies with the hazard ratio of 0.80 ($95\%$ CI: 0.57–1.11) for MACE-4 [24]. In a post-hoc analysis of SURPASS-4 data, tirzepatide slowed the rate of decline in eGFR, showed clinically meaningful improvement in albuminuria and significantly lowered occurrence of the composite kidney outcomes compared to insulin glargine which may be related to SBP lowering [40].
## Limitations and conclusions
The limitations of this study include its post-hoc nature and the fact we did not systematically collect indications for antihypertensive medications as these could have been used for other co-morbidities. Studies were not designed to systematically assess blood pressure and randomization was not stratified based on baseline status of hypertension, SBP and other relevant parameters that could have affected the outcomes. Weight loss with tirzepatide did not plateau at $\frac{40}{42}$ weeks and therefore longer term data could provide more robust assessments in future.
In conclusion, tirzepatide has demonstrated clinically relevant reductions in SBP across the SURPASS program. This effect was primarily mediated through weight loss, with different degrees of contributions from weight-loss independent effects across the different SURPASS trials. Furthermore, SBP reduction was not dependent on antihypertensive medication use and dependent on baseline SBP value, alleviating theoretical concerns of hypotension in patients with lower baseline SBP.
## Supplementary Information
Additional file 1: Summary of the SURPASS 1–5 study designs. Additional file 2: Baseline blood pressure across SURPASS studies. Additional file 3: Mediation analyses for systolic blood pressure using weight loss as a factor at Week $\frac{40}{42}$ (SURPASS 1–5, pooled data for TZP dose).
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|
---
title: 'Improving quality of life in patients with rare autoimmune liver diseases
by structured peer-delivered support (Q.RARE.LI): study protocol for a transnational
effectiveness-implementation hybrid trial'
authors:
- Natalie Uhlenbusch
- Arpinder Bal
- Boglárka Balogh
- Annika Braun
- Anja Geerts
- Gideon Hirschfield
- Maciej K. Janik
- Ansgar W. Lohse
- Piotr Milkiewicz
- Mária Papp
- Carine Poppe
- Christoph Schramm
- Bernd Löwe
journal: BMC Psychiatry
year: 2023
pmcid: PMC10039546
doi: 10.1186/s12888-023-04669-0
license: CC BY 4.0
---
# Improving quality of life in patients with rare autoimmune liver diseases by structured peer-delivered support (Q.RARE.LI): study protocol for a transnational effectiveness-implementation hybrid trial
## Abstract
### Background
Psychosocial support is a crucial component of adequate rare disease care, but to date psychosocial support needs of this patient population are insufficiently met. Within Q.RARE.LI, we strive to evaluate the effectiveness of a structured, transdiagnostic, and location-independent psychosocial support intervention in routine care of patients with rare autoimmune liver diseases in five countries and prepare its implementation.
### Methods
Within an effectiveness-implementation hybrid trial, we aim to a) investigate the effectiveness of the intervention in routine care in five diverse healthcare systems and b) assess implementation outcomes, examine and prepare the implementation context, and develop country-specific implementation strategies. To assess effectiveness, we will include $$n = 240$$ patients with rare autoimmune liver diseases. Within a two-armed randomized controlled trial (allocation ratio 1:1), we will compare structured and peer-delivered psychosocial support in addition to care-as-usual (CAU) with CAU alone. Outcomes will be assessed via electronic database entry prior to intervention, directly after, and at a three-month follow-up. Our primary effectiveness outcome will be mental health-related quality of life at post-assessment. Secondary outcomes include depression and anxiety severity, perceived social support, helplessness, and disease acceptance. Implementation outcomes will be assessed within a mixed-methods process evaluation. In a quantitative cross-sectional survey, we will examine perceived acceptability and feasibility in patients, peer-counselors, and healthcare providers involved in delivery of the intervention. In qualitative focus groups, we will analyze the implementation context and determine barriers and facilitators for implementation with different stakeholders (patients and/or representatives, peer-counselors, healthcare providers, health insurers). Based on these results, we will derive country-specific implementation strategies and develop a concrete implementation plan for each country.
### Discussion
The intervention is expected to help patients adjust to their disease and improve their mental quality of life. The transdiagnostic and location-independent program has the potential to reach patients for psychosocial support who are usually hard to reach. By preparing the implementation in five countries, the project can help to make low-threshold psychosocial support available to many patients with rare diseases and improve comprehensive healthcare for an often neglected group.
### Trial registration
ISRCTN15030282
## Background
Rare diseases per definition appear in less than one in 2000 individuals. With each rare disease having a low prevalence and a worldwide very high number of different rare diseases (~ 6000), research, healthcare, and affected individuals are confronted with a variety of challenges. In many cases, knowledge about the diseases is scarce, diagnostic processes are long and difficult, and adequate treatment is limited. Patients often are geographically dispersed, lack contact to peers, and have to travel long distances to reach specialized care [1–3]. These rarity-specific aspects add to the burden that most patients with chronic conditions face, such as persistent somatic suffering or constraints in everyday life. The majority of rare diseases are genetically caused, progressive, and mostly there is no cure available, confronting individuals with the lifelong task of adjusting to their condition. How challenging this can be, is indicated by increased rates of depressive and anxiety symptoms [4] and disorders [5] across different rare conditions. Psychological health is a key determinant for quality of life. In absence of a cure for most rare diseases, improving quality of life is a major healthcare aim. Therefore, supporting patients in adjusting to their diseases and staying mentally healthy is crucial. In a systematic review on quality of life in rare diseases, Cohen and Biesecker [2010] found that quality of life is often reduced in this patient group [6]. However, this is not necessarily the case, depending on whether disease adjustment succeeds. The authors conclude that psychological support can be essential for patients’ overall wellbeing.
To date, psychosocial support needs of patients with rare diseases are not sufficiently met [7, 8]. Patients wish for more support in different areas of their life, such as physical support and daily living, support concerning the health system and information, or psychological support [7]. Moreover, many patients do not feel sufficiently socially supported and lack contact to peers with the same condition [1, 9]. Support programs that are available for patients with common chronic conditions, such as diabetes [10] or COPD [11], may not adequately address the unique challenges patients with rare diseases face [12]. In addition, disease-specific approaches are not feasible due to the high number of different rare diseases [13]. Geographical dispersion of patients further complicates reaching individuals with rare diseases for healthcare services.
To tackle these challenges and address the unmet psychosocial support needs of the rare disease community, we developed a support program at the University Medical Center Hamburg-Eppendorf in Germany. The program was developed based on pre-assessed needs of patients with rare diseases [14]. Patients receive a structured self-help manual, which they complete from home over the course of six weeks. Once a week, they receive telephone-based support by a trained and supervised peer-counselor to reflect on the content of the self-management book. The program was evaluated within a monocentric two-armed randomized efficacy trial including $$n = 89$$ patients with four heterogeneous rare diseases [15]. The program was highly accepted by patients and demonstrated efficacy regarding mental health-related quality of life, disease acceptance, coping abilities, perceived social support, and helplessness. The program has the potential to address the unmet support needs of patients with rare diseases. It has been conceptualized transdiagnostically, focusing on the common experiences of patients with different conditions [9]. It is location-independent and therefore tackles the challenge that patients are often geographically dispersed. It is peer-delivered in order to address the need of patients to connect with each other. However, it is not yet available to patients, which is the starting point for Q.RARE.LI. As it is not feasible to provide specific psychosocial interventions for every single rare disease, we pursue a transdiagnostic approach focusing on the common needs of patients with rare diseases. One way to do so is by building subgroups, for instance based on the affected organ system [12, 16]. We aim to make the support program available to the rare disease community, starting with rare autoimmune liver diseases.
Rare autoimmune liver diseases include autoimmune hepatitis (AIH, prevalence 0.5–$\frac{1}{100}$,000), primary sclerosing cholangitis (PSC, prevalence 1–$\frac{9}{100}$,000) and primary biliary cholangitis (PBC, prevalence 1–$\frac{5}{10}$,000). All three diseases have an autoimmune and cholestatic etiology, are incurable and progressive, and go along with clinical symptom burden such as damage to the liver or chronic fatigue. The challenges patients with rare liver diseases face reflect those of the wider rare disease community (difficulties in diagnosis, lack of information, isolation, uncertainty, stigmatization [17]) and the need for psychosocial support is particularly high, as indicated by reduced quality of life [18–23] and high psychopathological burden [24, 25].
## Objectives and hypotheses
The overall aim of Q.RARE.LI is to make a structured, peer-delivered psychosocial support program available to patients with rare autoimmune liver diseases. More specifically, we aim to evaluate the effectiveness of the program in routine care of five different healthcare settings. In addition, we aim to prepare the implementation into routine care by assessing implementation outcomes and developing implementation strategies in each of the five participating countries. Regarding the effectiveness, we hypothesize that structured, peer-delivered support in addition to care-as-usual (CAU) leads to 1) an improvement in mental health-related quality of life and 2) improved outcomes regarding a) physical health-related quality of life, b) depression severity, c) anxiety severity, d) illness acceptance, e) perceived helplessness, f) social support, and g) self-management abilities in patients with rare liver diseases compared to CAU alone. Concerning implementation outcomes, we hypothesize that the program shows high feasibility and acceptability as indicated by 1) > $75\%$ of the patients completing the intervention, 2) > $75\%$ of these rating the intervention as a) helpful and b) appropriate, 3) $75\%$ of the stakeholders delivering the intervention rating it as a) feasible, b) appropriate, and c) helpful for patients.
## Methods
This study protocol follows the Standard Protocol Item: Recommendations for Interventional Trial (SPIRIT) guidelines (spirit-statement.org). The trial has been registered at https://doi.org/10.1186/ISRCTN15030282. If any important modifications from the protocol are made, the protocol will be updated there. Q.RARE.LI is funded by the Joint Transnational Call 2021 within the European Joint Programme on Rare Diseases.
## Trial design
To ensure the dual focus of investigating both effectiveness and implementability, we chose an effectiveness-implementation hybrid trial design [26]. These are study designs blending the processes of effectiveness and implementation research. This can accelerate the translation of research into clinical practice [26], and helps to better understand the contextual factors related to the success of interventions [27]. We will conduct an effectiveness-implementation hybrid trial type 1 [26] with the primary aim of assessing the effectiveness of the intervention while also analyzing its implementability.
To investigate effectiveness, we will conduct a two-armed randomized superiority trial. Within a parallel group design, patients with rare autoimmune liver diseases will be randomly assigned to either the intervention or a control group with a 1:1 allocation ratio. To evaluate implementability, we will conduct a mixed-method process evaluation assessing implementation outcomes both quantitatively (cross-sectional survey) and qualitatively (focus groups). The quantitative data enables us to measure how well the intervention was accepted and how feasible its implementation is perceived, and ensures a clear conclusion to our formulated hypothesis. The qualitative results will complement these data by helping to gain a deeper understanding of how well applying the intervention worked in routine care, and what facilitates and hinders a successful implementation from the perspective of different stakeholders (e.g. patient representatives, healthcare providers). The qualitative data further will help to develop concrete implementation strategies. Figure1 shows an overview of the study design. Fig. 1Study design. Notes. IG = intervention group, CG = control group, CAU = care-as-usual
## Study setting
The study will be conducted in routine care of patients with rare autoimmune liver diseases at five different centers: The University Medical Center Hamburg-Eppendorf (UKE) in Germany, the University Health Network in Toronto, Canada, the Ghent University Hospital in Belgium, the Medical University of Warsaw in Poland, and the University of Debrecen in Hungary. The UKE serves as coordinating center and is in charge of the study conduct. Patients will be recruited in routine care but participate from home and data will be collected centrally online by the coordinating center in Germany.
## Participants
To evaluate effectiveness of the intervention, we will include patients with rare autoimmune liver diseases (AIH, PSC, PBC). Besides being diagnosed with one of these conditions, inclusion criteria are a subjective psychosocial support need, an age of at least 18 years, and written informed consent. Exclusion criteria are a life-threatening health status, acute suicidality, ongoing psychotherapy, severe cognitive, auditory or visual impairment, and inability to complete assessments. Peer-counselors are individuals with rare autoimmune liver diseases who feel well adjusted to their disease and strive to help others in the adjustment process, and who provide written informed consent. For the quantitative implementation part, we will include patients who received the intervention, peer-counselors, and healthcare providers involved in intervention delivery. For the qualitative implementation part, we will include four groups of stakeholders: patients/ patient representatives, peer-counselors, healthcare providers, and healthcare leaders such as health insurers, who provide written informed consent. Exclusion criteria are involvement in outcome assessment or data analysis.
## Sample size
Our primary effectiveness outcome is the group difference in the baseline-adjusted mean score in mental health-related quality of life at post-assessment, assessed with the Short-Form Health Survey (SF-12 [28]). Taking the results of our prior randomized controlled trial (RCT) into account [15], we assume a between groups effect size of $d = 0.4.$ Based on two-sided testing with α = 0.05 and 1-β = 0.8, $$n = 100$$ patients are needed per group (intention-to-treat analysis), yielding in $$n = 200$$ to be analyzed. Considering our prior RCT [15], we conservatively expect an inclusion rate of $60\%$ and approximately $15\%$ loss to follow-up. Thus, $$n = 240$$ patients will be included, for which we expect to invite a total of $$n = 400$$ patients for participation at the five study sites over 15 months.
For the quantitative implementation part of the trial, all patients in the intervention group ($$n = 100$$), all peer-counselors (~ $$n = 40$$) and all involved healthcare providers (~ $$n = 25$$) will be invited to participate in the survey. With an estimated response rate of $80\%$ [15], we expect to include $$n = 132$$ participants across all sites. At least $$n = 5$$ participants per stakeholder group and site will be invited to take part in the focus groups, resulting in an estimated sample size of $$n = 100$$ in total.
## The intervention
The intervention was developed based on pre-assessed psychosocial support needs of patients with rare diseases. A detailed description of the development process has been published [29]. The program is based on structured self-help and peer-counseling. Participants receive a manual, which contains six chapters and which is based on Acceptance and Commitment Therapy (ACT [30]). The first chapter includes general information about rare diseases and a reflection exercise on how the disease affects one’s life. The second chapter focuses on dealing with difficult emotions, the third chapter is about disease acceptance, the forth one about values, the fifth one about setting meaningful goals, and the last chapter contains a review and outlook exercise. Patients complete one chapter per week and, in addition, receive a 30-minute telephone-based peer-counseling session to reflect on the content of the manual. Peer-counselors receive a two-day training, consultation guidelines with additional information and exemplary questions for each chapter as well as supervision by a psychologist or psychiatrist trained in psychotherapy. The regular duration for the program participation is six weeks, but can be expanded to up to ten weeks if any incidents in the patient’s life require a temporary interruption.
## Assessments and outcomes
We will assess quantitative outcomes in both groups at three time points: at baseline (t0), directly after patients in the intervention group completed the program (t1), and at a three-month follow up (t2). Sociodemographic and clinical variables will be assessed at baseline using single items. Qualitative data will be assessed after the follow-up assessment is completed (t3).
## Effectiveness outcomes
The primary effectiveness outcome is the group difference in mental health-related quality of life at post-assessment, assessed with the Short Form Health Survey (SF-12 [28]). Using 12 items, the SF-12 Health Survey measures psychological and physical aspects of generic, health-related quality of life [31] and is based on the 36-item version SF-36 [32]. Multiple studies demonstrated its sound psychometric properties [33, 34] and it has been used in a variety of chronic conditions, including patients with autoimmune liver diseases [18, 19, 21].
Secondary outcomes are physical health-related quality of life (SF-12), somatic symptom severity (Patient Health Questionnaire-15, PHQ-15 [35, 36]), depression severity (Patient Health Questionnaire-9, PHQ-9 [37, 38]), anxiety severity (Generalized Anxiety Disorder Scale-7, GAD-7 [39, 40]), illness cognitions (disease acceptance, helplessness, perceived benefits; Illness Cognition Questionnaire, ICQ [41]), social support (Social Support Questionnaire, F-SOZU [42]), and self-management abilities (Appraisal of Self-Care Agency Scale Revised, ASAS-R [43]). We will further assess treatment expectations (Treatment Expectation Questionnaire, TEX-Q [44, 45]), psychological burden related to somatic symptoms or associated health concerns (Somatic Symptom Disorder – B Criteria Scale, SSD-12 [46–48]), general self-efficacy (Self-efficacy scale, SWE [49]), and illness perceptions (Brief Illness Perception Questionnaire, B-IPQ [50]).
## Implementation outcomes
Quantitative implementation outcomes will be assessed at post-assessment (t1). We will ask patients and peer-counselors to evaluate the program and measure acceptability and feasibility on numeric rating scales from 0–10. We will further ask healthcare providers to evaluate the program. Qualitative data will be assessed in focus groups after the follow-up assessment is completed. We will assess perceived implementability of the program into routine care with a standardized semi-structured interview guide. The development of this guide will be supported by the Consolidated Framework for Implementation Research (CFIR) Interview Guide Tool (CFIR Booklet (cfirguide.org)).
## Procedures
Table 1 shows the timeline for patients participating in the study according to the SPIRIT guidelines. Table 1Timeline according to the SPIRIT guidelinesEnrolmentBaselinePost- and follow-upENROLLMENT-t1t0Interventiont1t2t3Eligibility screenXInformed consentXAllocationXINTERVENTIONS Support programX Care as usualXXXXXXASSESSMENTS Sociodemographic variablesX Clinical variablesX Treatment expectationsXEffectiveness outcomes Mental health-related quality of lifeXXX Physical health-related quality of lifeXXX Somatic symptom severityXXX Depression severityXXX Anxiety severityXXX Self-management abilitiesXXX Illness cognitionsXXX Social supportXXX General self-efficacyXXX Psychological burden related to somatic symptomsXXX Illness perceptionsXXXQuantitative implementation outcomesAcceptability, program evaluationXQualitative implementation outcomesProgram evaluationX
## Effectiveness focus
Each partner will recruit eligible patients on a national level by informing them about the study in their routine care. In case this does not suffice to reach the targeted sample size, each partner receives support by a collaborating national patient advocacy organization (PAO), who will recruit nationwide via their homepage, patient journal, newsletter etc. Inclusion and exclusion criteria will be assessed in a structured interview by trained research personnel. Eligible participants will sign an informed consent form. Participants will then be randomly assigned to either the intervention group or a control group. The intervention group will complete the six-week program from home in addition to CAU, whereas the control group will receive CAU alone. After the last data assessment, patients in the control group can participate in the program outside of the trial.
## Implementation focus
For patients in the intervention group, the quantitative program evaluation will be part of the post-assessment. In addition, stakeholders being involved in delivery of the intervention at each site or in long-term implementation will be invited to take part in the mixed-methods study. They will sign an informed consent form and complete a quantitative cross-sectional survey after the last patient completed the intervention. Qualitative focus groups will be conducted after the follow-up assessment. We pursue a purposeful sampling approach [39] by forming homogenous subgroups with patients/ patient representatives, peer-counselors, healthcare providers, and health insurers in order to describe the particular opinions of these groups in depth.
## Peer-counselors
Each partner will recruit approximately seven to ten peer-counselors, i.e. individuals with rare liver diseases (PSC, PBC, AIH), who feel well adjusted to their disease and strive to help others in the adjustment process. Their eligibility will be assessed in structured interviews by social science experts (e.g. psychologists) in each team. Peer-counselors then are invited to a two-day training, which will be held in groups of four to five participants at the national study site. After the training, they receive consultation guidelines and supervision. Over the course of the trial, peer-counselors will complete several counseling cycles. Outcomes will also be assessed in peer-counselors before, once during and after their counseling work and they will be invited to participate in the mixed-method process evaluation.
## Methods against bias
A fixed randomization schedule (allocation ratio 1:1) will be conducted electronically to avoid any preferential patient allocation to the two conditions. Randomization will be conducted by a researcher who is not involved in data assessment and intervention delivery. Neither the patients nor the researchers have any influence on the randomization procedure. Electronic data collection. Data will be directly entered into electronic databases by the patients. Thus, error-prone transfer from paper to the electronic databases will be avoided. Standardization of intervention. Peer-counselors will be trained and supervised and the intervention is described in peer-counseling guidelines. After each session, peer-counselors will complete a short protocol to assess adherence to the program content. Blinding. a) Raters: Outcome assessments will be either self-reported or performed by trained raters who are fully blinded regarding group allocation and not involved in intervention delivery. Thus, Q.RARE.LI is fully observer-blinded. b) Peer-counselors and patients: As in most psychotherapeutic intervention studies, full patient and therapist blinding is not feasible as their active involvement in the intervention is necessary. However, peer-counselors will not be informed about the patient’s group allocation. Minimization of patient drop-out. At follow-up, patients will be contacted by telephone according to a schedule of repeated contact attempts. Standardized report of trial and results. The trial and the results will be reported according to the CONSORT 2010 recommendations (www.consort-statement.org/).
All data on human subjects will be recorded, handled, and stored by the rules of the General Data Protection Regulation (GDPR). A Data Safety and Monitoring Board (DSMB), independent of the project team and consisting of internationally recognized experts in rare diseases and psychosocial treatments, will oversee the conduct of this study. Before the trial starts, we will prepare a data management plan and all partners will sign a data protection agreement in which they commit to comply to the GDPR.
## Data collection
The data collection will be organized and supervised by the coordinating center. All quantitative data will be collected electronically into a common database, located at the REDCap system of the University Medical Centre Hamburg-Eppendorf (REDCap, www.project-redcap.org). This will minimize data exchange between countries. Qualitative data will be generated on a national level and securely stored at each site. The focus group discussions will be audio-recorded and transcribed verbatim. The audio records will be deleted after the transcripts have been generated.
## Data handling
Individual participant data will be collected and processed in a de-identified form by a unique study identification number (ID). All data directly identifying persons will be replaced by the study ID. No data allowing participant identification will be handed to third parties. Data collected locally from the individual partners (original consent forms, audio files, and interview transcripts) will be securely archived at the specific sites for maximum 10 years after publication of the last scientific report, following the national data management strategy. If locally collected data is exchanged between partners, then only in fully anonymized form. For locally collected data, compliance with the GDPR will be determined in the data protection agreement. In accordance with the data minimization principle, we will only collect data that is relevant to the purposes of the project.
## Data analysis
We will calculate descriptive measures of all variables and examine group differences at baseline. Our primary hypothesis within the RCT will be tested with a hierarchical linear model including time (two-staged) and group as factors, time as repeated effect, the time x group interaction, the baseline outcome score as a covariate, and a random intercept. We will use the restricted maximum likelihood method to produce estimates. To assess group differences for each time point, we will determine estimated marginal mean values (EMMeans). Effect sizes will be calculated by dividing the adjusted group mean difference by the observed standard deviation of the total sample at baseline. Model assumptions will be checked by plotting residuals against estimated values and residual distribution against normal distribution. We will perform all tests two-sided and consider $p \leq 0.05$ as statistically significant. Data will be imputed if more than $5\%$ are missing. In accordance with White et al. [ 51], the number of imputations will be chosen depending on the proportion of missing data.
Quantitative data will be analyzed descriptively (mean, standard deviation, percentages, figures). Qualitative data will be analyzed with qualitative content analysis according to Mayring [52]. We will deductively derive implementation barriers and facilitators based on the CFIR (www.cfirguide.org), i.e. considering the outer and inner setting, intervention characteristics, individuals involved, and the process. Based on these contextual conditions, we will derive implementation strategies with the CFIR.
## Data sharing
In accordance with the ethics committee approval and the 2015 German Research Foundation (DFG) guidelines for the handling of research data, the quantitative individual patient data will be made publicly available in a de-identified form. The full data package (i.e. analyzable data set, protocol, statistical analysis plan, statistical programming code) will be made freely available through a clinical data repository (e.g. Dryad Digital Repository) and saved for at least 10 years. Data sharing will follow the FAIR Data Principles (Findable, Accessible, Interoperable, and Reusable) and international naming conventions (e.g. Systematized Nomenclature of Medicine) to maximize transparency and scientific reproducibility.
## Ethical aspects
All research activities in Q.RARE.LI will respect fundamental ethics principles, including those reflected in the Charter of Fundamental Rights of the European Union and the WMA Declaration of Helsinki, and comply with international, EU, and national laws. Each partner will obtain an ethics approval by an independent local ethics committee (see Table2 in the declarations section for an overview). The coordinator will ensure compliance with ethical principles and is advised by the data protection officer of the UKE.Table 2Overview of ethics approvalsPartnerName of ethics committeeIDStatusGermany, coordinating centerIndependent Ethics Committee of the Hamburg Medical Chamber, Weidestr. 122 b, 22,083 Hamburg, GermanyPhone: + 49 40 202,299–240, E-mail: ethik@aekhh.de2021–100,757-BO-ffApproved January 31st, 2022CanadaUniversity Health Network Research Ethics Board, 700 University Ave, 4th Floor, Toronto, Ontario, M5G 1Z5Phone: [416] 581–784922–5056Approved February 14th, 2023BelgiumCommittee on Medical Ethics, UZ Gent, Corneel Heymanslaan 10, 9000 Ghent, BelgiumPhone: + 32 9 3,322,111, E-mail: ethisch.comite@uzgent.beBC-10401Approved February 22nd, 2023PolandThe Local Ethics Committee of Medical University of Warsaw, ul. Pawińskiego 3C, 02–106 Warszawa, Poland,Phone: + 48 22 57 20 303, E-mail: komisja.bioetyczna@wum.edu.plKB/26/ 2022Approved February 21st, 2022HungaryMedical Research Council, Scientific and Research Ethics Committee (ETT TUKEB), 25 Alkotmány u., Budapest, H-1054, Hungary, Phone: (+ 36 1) 795 1192, E-mail: attilane.gombos@bm.gov.hu40,513–$\frac{5}{2021}$/EÜIG,Approved August 16th, 2021
## Informed consent
Before inclusion, eligible participants are informed about the study procedure and data protection verbally and in written form by the local research team. They are also informed that consent to participate is voluntary and can be withdrawn at any time without giving reasons, and without any disadvantages. Written information will be provided in participants’ local language and checked by a patient representative regarding comprehensibility beforehand. Written informed consent forms will be signed by all participants and sent to the local research team. Participants will receive a financial compensation for their participation in the study.
## Risks and benefits
*In* general, psychosocial support interventions bear no major risk for severe adverse events. In our efficacy trial [15], no major adverse events occurred and no individual stated that the intervention harmed them. Rather, participating in the intervention was beneficial for patients. Being involved as a peer-counselor can potentially be beneficial, too [53]. However, adverse events unrelated to the intervention may occur. If suicidal ideation is detected (e.g. during selection interviews or in peer-counseling sessions), we will apply a proven algorithm (e.g. contact the physician or consider psychiatric treatment) that is already available from other studies of the project team. Before the trial, the staff (including peer-counselors) will be carefully advised to follow these guidelines and provided with an emergency number. In cases of acute suicidality, the patient will be transferred to receive psychiatric treatment and excluded from the study. Any questions regarding patient exclusions, serious adverse events, and potential study termination will be reported to and reviewed by the DSMB. The DSMB will annually monitor the study and evaluate the collected study data with regard to participant safety, study conduct, compliance with the study protocol and progress. Where appropriate, recommendations will be made to continue, modify, or terminate the study or to unmask participants in case of adverse events. For the individual patient, the trial procedure will stop if any adverse events or withdrawal of informed consent occur. The whole trial will be discontinued if the consortium or the DSMB detect significant associations between study participation and adverse events.
## Dissemination
The results will be published and disseminated nationally and internationally to the scientific community as well as to the patient community and public. The main findings will be submitted for publication in a high-impact peer-reviewed journal with open-access mechanism within 12 months of study completion. Authorship eligibility will be decided based on the International Committee of Medical Journal Editors (ICMJE). We will also regularly communicate the results in lay language via press releases, social media, our own homepages and forums which are popular amongst patients. The close collaboration with PAOs in each participating country ensures adequate communication to the patient community via newsletters and yearly events, which we plan to attend in order to present the results.
## Patient involvement
The intervention has been developed based on pre-assessed needs of patients with different rare diseases [29] and was supported by the German patient organization ACHSE e.V. Within Q.RARE.LI, patients are actively involved as stakeholders when evaluating the implementability of the intervention into routine care. Moreover, the engagement of peer-counselors meets the need of a greater patient empowerment and has been formulated by the Word Health Organization as a strategy to improve care [54].
## Discussion
Q.RARE.LI aims to pave the way for widely available psychosocial support for the rare disease community. We expect to demonstrate the effectiveness of the peer-delivered psychosocial support intervention in routine care of patients with rare autoimmune liver diseases in five different national health systems. With respect to implementation outcomes, we expect high acceptability and perceived feasibility of the intervention from the perspective of patients, peer-counselors, and healthcare providers. In addition, we expect to gain a deep understanding of the implementation context in five different countries and be able to derive barriers and facilitators for implementation based on the CFIR framework. This will lead to a profound understanding of the intervention characteristics, the outer setting, the inner setting, the individuals involved as well as the process from different perspectives (including patients and healthcare providers). Identifying these contextual determinants will help us to derive appropriate implementation strategies and develop a concrete implementation plan in each country. If our hypotheses are confirmed, we will be one step closer to making targeted psychosocial support available to a large group of patients with rare diseases. Independent of the effectiveness results, the results on implementation will lead to a better understanding of the implementation conditions in five different healthcare settings and facilitate future implementation of any psychosocial support program.
With Q.RARE.LI, we pursue a transdiagnostic approach in psychosocial care. Considering the high clinical heterogeneity between different rare diseases, even among conditions of the same group such as autoimmune liver diseases, this may seem counterintuitive. However, the transdiagnostic approach offers a major advantage in psychosocial care of this patient population. At a structural level, cross-disease services are what makes comprehensive psychosocial care possible in the first place as offering disease-specific services for the multitude of different rare diseases is considered impossible [13]. At a content level, this approach is adequate as well. Despite being affected by heterogeneous clinical symptoms, patients with different rare diseases experience a variety of similar problems, including psychological burden, constraints in everyday and social life, and problems related to the healthcare system [3]. Addressing these in transdiagnostic psychological interventions can help to reach many individuals within this hard-to-reach patient population for psychosocial support.
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|
---
title: 'Association of prognostic nutritional index level and diabetes status with
the prognosis of coronary artery disease: a cohort study'
authors:
- Tianyu Li
- Deshan Yuan
- Peizhi Wang
- Guyu Zeng
- Sida Jia
- Ce Zhang
- Pei Zhu
- Ying Song
- Xiaofang Tang
- Runlin Gao
- Bo Xu
- Jinqing Yuan
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10039549
doi: 10.1186/s13098-023-01019-8
license: CC BY 4.0
---
# Association of prognostic nutritional index level and diabetes status with the prognosis of coronary artery disease: a cohort study
## Abstract
### Background
Malnutrition and inflammation are associated with adverse clinical outcomes in patients with diabetes or coronary artery disease (CAD). Prognostic nutritional index (PNI) is a comprehensive and simple indicator reflecting nutritional condition and immunological status. Whether there is a crosstalk between nutritional-immunological status and diabetes status for the impact on the prognosis of coronary artery disease (CAD) is unclear.
### Methods
A total of 9429 consecutive CAD patients undergoing percutaneous coronary intervention were grouped by diabetes status [diabetes (DM) and non-diabetes (non-DM)] and preprocedural PNI level [high PNI (H-PNI) and low PNI (L-PNI)] categorized by the statistically optimal cut-off value of 48.49. The primary endpoint was all-cause death.
### Results
During a median follow-up of 5.1 years (interquartile range: 5.0–5.1 years), 366 patients died. Compared with the non-DM/H-PNI group, the DM/L-PNI group yielded the highest risk of all-cause death (adjusted hazard ratio: 2.65, $95\%$ confidence interval: 1.97–3.56, $p \leq 0.001$), followed by the non-DM/L-PNI group (adjusted hazard ratio: 1.44, $95\%$ confidence interval: 1.05–1.98, $$p \leq 0.026$$), while DM/H-PNI was not associated with the risk of all-cause death. The negative effect of L-PNI on all-cause death was significantly stronger in diabetic patients than in nondiabetic patients (p for interaction = 0.037). Preprocedural PNI category significantly improved the Global Registry of Acute Coronary Events (GRACE) risk score for predicting all-cause death in patients with acute coronary syndrome, especially in those with diabetes.
### Conclusions
CAD patients with diabetes and L-PNI experienced the worst prognosis. The presence of diabetes amplifies the negative effect of L-PNI on all-cause death. Poor nutritional-immunological status outweighs diabetes in increasing the risk of all-cause death in CAD patients. Preprocedural PNI can serve as an assessment tool for nutritional and inflammatory risk and an independent prognostic factor in CAD patients, especially in those with diabetes.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-023-01019-8.
## Backgroud
Malnutrition, manifested as altered body composition and diminished biological function, is not rare in patients with coronary artery disease (CAD) and has been reported to be associated with adverse clinical outcomes [1]. Inflammation has been recognized as a key mediator in the negative impact of malnutrition on the prognosis of cardiovascular disease [2]. Prognostic Nutritional Index (PNI) was first introduced by Buzby et al. in the context of gastrointestinal surgery [3] and modified by Onodera et al. [ 4]. Calculated from serum albumin levels and absolute lymphocyte counts, this simple and comprehensive index reflects not only protein stores but also the immunological status. Its prognostic value has been examined in malignancy [5], autoimmune disease [6], and heart failure [7–12] and has been reported in several small-scale studies for patients with acute coronary syndrome (ACS) or stable CAD [13–16].
Diabetes is a common cardiovascular risk factor and has been reported to be associated with increased risk of malnutrition [17]. Both malnutrition and diabetes affect systemic metabolism and exacerbate inflammation, driving the development of CAD. However, noModification of Diet in Renal Disease studies have examined how diabetes and coexisting malnutrition affect the prognosis of CAD. Only one study so far has reported the prevalence and prognostic value of malnutrition in CAD patients accompanied by diabetes [18]. Accordingly, this study aimed to investigate the joint effect and interaction between nutritional-immunological status assessed by PNI and diabetes status on the prognosis of the overall CAD population.
## Study design, setting, and participants
From January 2013 to December 2013, the cohort study prospectively recruited 10,724 consecutive patients undergoing percutaneous coronary intervention (PCI) at Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China. PCI was performed by experienced interventional cardiologists blinded to the study protocol. Details on catheterization procedures and periprocedural medication were in line with contemporaneous practice guidelines in China. At discharge, all patients without documented contraindications were prescribed statins and dual anti-platelet therapy with aspirin plus clopidogrel. Other cardiovascular medications, such as β-blockers, angiotensin-converting enzyme inhibitors, or angiotensin-receptor blockers, were prescribed according to patients' conditions and contemporaneous guidelines. Baseline and angiography data were extracted from the electronic medical record. Patients were followed up since the date of PCI. Follow-up and outcome information was obtained through clinic visits or telephone interviews by an independent group of clinical research coordinators at one, six, 12, 24 months, and 5 years after discharge. Investigator training and telephone recording were conducted to achieve high-quality results. Endpoint events were adjudicated by two independent cardiologists, and disagreement was resolved by consensus. This study complied with the Declaration of Helsinki. The Ethics Committee of Fuwai Hospital, National Center for Cardiovascular Diseases, approved the study protocol before enrolment (No. 2013–449). All participants provided written informed consent before intervention.
This post hoc analysis investigated the joint effect and interaction between PNI level and diabetes status on 5 year outcomes for CAD patients after PCI. Exclusion criteria were age less than 18 years, unsuccessful PCI, bare-metal stent implantation, end-stage liver or renal disease, systemic inflammatory disease, and missing preprocedural serum albumin and absolute lymphocyte counts data. Participants entering the final analysis were stratified by diabetes status (diabetes [DM] and non-diabetes [non-DM]) and further categorized by the optimal cut-off value of preprocedural PNI (high PNI [H-PNI] and low PNI [L-PNI]) into four groups.
## Blood sampling and laboratory testing
Preprocedural blood samples were collected after emergency admission for unstable patients and after fasting for at least 12 h for stable patients. Postprocedural blood samples were collected within 24 h after PCI. Fasting blood glucose was assayed by an enzymatic hexokinase method. Glycated hemoglobin was assayed using a Tosoh Automated Glycohemoglobin Analyzer (HLC-723G8, Tokyo, Japan). Blood cell counts were measured by an automated blood cell counter. Serum albumin was measured using an automated chemistry analyzer (AU5400, Olympus, Japan) by the bromocresol green dye method. PNI was calculated as 10 × serum albumin (g/L) + 5 × absolute lymphocyte counts (109/L). Estimated glomerular filtration rate was calculated with the modified Modification of Diet in Renal Disease equation [19].
## Outcomes and covariables
The primary endpoint was all-cause death. Secondary endpoints included cardiac death, non-fatal myocardial infarction (MI), non-fatal stroke, unplanned revascularization, and major adverse cardiovascular and cerebrovascular events. All deaths were considered cardiac unless an unequivocal non-cardiac cause could be established. MI was diagnosed based on the Third Universal Definition of Myocardial Infarction. Strokes included ischemic stroke, hemorrhagic stroke, and transient ischemic attack. Unplanned revascularization was defined as repeated coronary artery bypass grafting or PCI of any vessel driven by ischemic symptoms and events.
Body mass index ≥ 25 kg/m2 was considered obese based on the World Health Organization standard for Asian populations [20]. Diabetes was defined as fasting blood glucose ≥ 7.0 mmol/L, glycated hemoglobin ≥ $6.5\%$, oral antidiabetic medication or insulin use, or self-reported diabetes. Hypertension was defined as systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, antihypertensive medication use, or self-reported hypertension. Dyslipidemia was diagnosed when at least one of the following criteria was met: total cholesterol ≥ 6.22 mmol/L, total triglyceride ≥ 2.26 mmol/L, low-density lipoprotein cholesterol ≥ 4.14 mmol/L, high-density lipoprotein cholesterol < 1.04 mmol/L, lipid-lowering medication use, or self-reported dyslipidemia [21].
## Statistical analysis
Preprocedural PNI was categorized by the statistically optimal cut-off value for predicting all-cause death determined by recursive partitioning and log-rank tests. Baseline characteristics were compared using Mann–Whitney U tests, Kruskal–Wallis tests, or χ2 tests as appropriate. Categorical and continuous variables were expressed as numbers (percentages) and median [interquartile range], respectively. Correlation between preprocedural PNI and glycemic measures was assessed using Spearman rank correlation analysis.
Survival curves were plotted using Kaplan–Meier method and compared using log-rank tests. Association of preprocedural PNI category and diabetes status with clinical outcomes was examined using Cox proportional-hazards regression by estimating hazard ratios (HRs) and $95\%$ confidence intervals (CIs). Covariables for adjustment included sex, age, hypertension, chronic obstructive pulmonary disease, previous revascularization, previous MI, previous stroke, high-sensitivity C-reactive protein, estimated glomerular filtration rate, and left ventricular ejection fraction, according to clinical plausibility and significance in univariate analysis. In addition, an inverse probability of treatment weighting analysis based on propensity score was undertaken. The propensity score was calculated by logistic regression with variables related to DM, PNI, and/or the outcomes.
Subgroup analysis for all-cause death was performed according to four variables of interest: age (≥ 65 years versus < 65 years), sex (women versus men), body mass index (≥ 25 kg/m2 versus < 25 kg/m2), and admission presentation (ACS versus chronic coronary syndrome). In sensitivity analysis for all-cause death, we applied five indexes: [1] preprocedural dichotomous PNI grouped by median; [2] preprocedural continuous PNI; [3] postprocedural PNI categorized by the optimal cut-off value; [4] the change in PNI before and after PCI (ΔPNI); [5] malnutrition defined based on the Global Leadership Initiative on Malnutrition (GLIM) criteria [10, 22]—an etiological criterion of inflammation (high-sensitivity C-reactive protein > 3.0 mg/L) plus any of the following phenotypic criteria: low body mass index (< 18.5 kg/m2 if < 70 years, or < 20.0 kg/m2 if ≥ 70 years) or reduced muscle mass (free fat mass index < 17.0 kg/m2 in men or < 15.0 kg/m2 in women). Association of preprocedural continuous PNI and ΔPNI with all-cause death was examined with restricted cubic splines with 4 knots.
The added value of the six indexes beyond the Global Register Acute Coronary Events (GRACE) risk score for the ACS population was evaluated by receiver operating characteristic curves and the decision curve analysis and was compared by the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).
Statistical analyses were conducted with R version 4.2.0 (R Core Team 2022, Vienna, Austria. www.R-project.org). Figures were created by GraphPad Prism version 9.0.0 (GraphPad Software, San Diego, California, USA, www.graphpad.com). Two-tailed p-values of < 0.05 were considered statistically significant.
## Study population and baseline characteristics
The study population comprised 10,263 patients, of which 9429 ($91.87\%$) patients with complete 5 year follow-up data were available for the final analysis. The number of participants at each stage is described in Additional file 1: Fig. S1. All baseline characteristics of patients followed up and lost to follow-up were comparable (Additional file 1: Table S1). During a median follow-up of 5.1 years (interquartile range: 5.0–5.1 years), 366 all-cause deaths, 219 cardiac deaths, 551 non-fatal MIs, 345 non-fatal strokes, 1371 unplanned revascularizations, and 2143 major adverse cardiovascular and cerebrovascular events were documented. No correlation was observed between preprocedural PNI and fasting blood glucose or glycated hemoglobin (r < 0.200) (Additional file 1: Table S2).
As shown in Table 1, the median age of the study population was 59 years (interquartile range: 51–66 years), 2163 ($22.93\%$) were women, and 3956 ($41.96\%$) had diabetes. The median value of preprocedural PNI was 52.60 for all participants. When patients were stratified by vital status, absolute lymphocyte counts, serum albumin levels, and PNI were significantly lower in patients who had died than in those still alive. Unsurprisingly, patients who survived to the end of5 year follow-up were younger, had fewer comorbidities (diabetes, hypertension, peripheral artery disease, and chronic obstructive pulmonary disease), were less likely to have a previous history of revascularization, MI and stroke, and had higher estimated glomerular filtration rate and left ventricular ejection fraction. The clinical presentation of CAD, cardiovascular medication use, and angiographic characteristics were well-balanced between the two groups. Table 1Baseline characteristics stratified by vital status at the end of follow-upVariableAll participants ($$n = 9429$$)Deceased ($$n = 366$$)Survival ($$n = 9063$$)pDemographic characteristics Sex (Women)2162 (22.93)95 (25.96)1067 (22.81)0.160 Age, years59 [51, 66]66 [58, 73]58 [51, 65] < 0.001 ≥ 652623 (27.82)203 (55.46)2420 (26.70) < 0.001 BMI, kg/m225.91 [23.88, 27.76]25.71 [23.40, 27.73]25.91 [23.94, 27.76]0.052 ≥ 255742 (60.90)213 [58.20]5529 (61.01)0.280 Current smoking5363 (56.88)210 (57.38)5153 (56.86)0.844Clinical characteristics Clinical presentation0.642 ACS5583 (59.21)221 (60.38)5362 (59.16) CCS3846 (40.79)145 (39.62)3701 (40.84) Hypertension6576 (69.74)291 (79.51)6285 (69.35) < 0.001 Dyslipidemia7121 (75.52)269 (73.50)6852 (75.60)0.358 Diabetes3956 (41.96)189 (51.64)3767 (41.56) < 0.001 Peripheral artery disease252 (2.67)16 (4.37)236 (2.60)0.040COPD220 (2.33)24 (6.56)196 (2.16) < 0.001 Previous revascularization2468 (26.17)134 (36.61)2334 (25.75) < 0.001 Previous MI1826 (19.37)92 (25.14)1734 (19.13)0.004 Previous stroke990 (10.50)51 (13.93)939 (10.36)0.029Medication at admission Aspirin9315 (98.79)359 (90.09)8956 (98.82)0.209 Clopidogrel9412 (99.82)365 (99.73)9047 (99.82)0.576 Statins9051 (95.99)351 (95.90)8700 (95.99)0.929 β-blockers8493 (90.07)323 (88.25)8170 (90.15)0.234 ACEIs/ARBs4929 (52.27)204 (55.74)4725 (52.14)0.176Preprocedural laboratory tests ALC, 109/L1.87 [1.51, 2.30]1.76 [1.45, 2.20]1.87 [1.51, 2.31] < 0.001 Serum albumin, g/L42.70 [39.90, 45.90]41.30 [38.70, 44.60]42.80 [40.00, 45.90] < 0.001 PNI52.60 [49.00, 56.15]50.98 [46.70, 54.65]52.65 [49.05, 56.25] < 0.001 hs-CRP, mg/L1.60 [0.80, 3.59]2.08 [1.05, 5.25]1.58 [0.79, 3.54] < 0.001 Fasting blood glucose, mmol/L5.48 [4.93, 6.63]5.70 [5.04, 7.08]5.47 [4.93, 6.62] < 0.001 Glycated hemoglobin, %6.2 [5.8, 6.9]6.4 [6.0, 7.3]6.2 [5.8, 6.9]0.002 eGFR, ml/min/1.73m2118.11 [102.63, 133.24]111.64 [89.10, 127.53]118.27 [10.300, 133.50] < 0.001 ≤ 6092 (0.98)16 (4.37)76 (0.84) < 0.001 LVEF, %64 [60, 67]62 [58, 66]64 [60, 67] < 0.001 < 40102 (1.08)14 (3.83)88 (0.97) < 0.001Angiographic characteristics LM/TVD412 (4.37)18 (4.92)394 (4.35)0.601 SYNTAX score10 [6, 17]10 [5, 17]10 [6, 17]0.911 SYNTAX category0.110 ≤ 228367 (88.74)313 (85.52)8054 (88.87) 22–32893 (9.47)43 (11.75)850 (9.38) ≥ 33169 (1.79)10 (2.73)159 (1.75) DES implantation8950 (94.92)340 (92.90)8610 (95.00)0.072Values are presented as number (%) or median [interquartile range]ACEI angiotensin-converting enzyme inhibitor, ACS acute coronary syndrome, ALC absolute lymphocyte counts, ARB angiotensin-receptor blocker, BMI body mass index, CCS chronic coronary syndrome, COPD chronic obstructive pulmonary disease, DES drug-eluting stent, eGFR estimated glomerular filtration rate, hs-CRP high-sensitivity C-reactive protein, LM/TVD left main or three-vessel disease, LVEF left ventricular ejection fraction, MI myocardial infarction, PNI prognostic nutritional index, SYNTAX synergy between percutaneous coronary intervention with Taxus and cardiac surgery The optimal cut-off value of preprocedural PNI for predicting all-cause death was 48.49. Table 2 shows baseline characteristics among four groups stratified by preprocedural PNI category and diabetes status. Patients with L-PNI accounted for $22.08\%$ of all participants, $20.88\%$ of the diabetes population, and $22.95\%$ of the nondiabetic population. The DM/L-PNI group had more women and elderly patients than other groups. Patients in the DM/L-PNI group tended to have more comorbidities and previous adverse events and were more likely to have declined renal and cardiac function. The severity of coronary lesions sequentially increased from the non-DM/H-PNI group to the DM/L-PNI group, reflected by more left main or three-vessel disease and higher Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) score. Table 2Baseline characteristics stratified by DM status and preprocedural PNI levelVariableNon-DM/H-PNI ($$n = 4217$$)Non-DM/L-PNI ($$n = 1256$$)DM/H-PNI ($$n = 3130$$)DM/L-PNI ($$n = 826$$)pDemographic characteristics Sex (Women)835 (19.80)318 (25.32)784 (25.05)225 (27.24) < 0.001 Age, years56 [49, 63]62 [55, 70]58 [51, 65]64 [58, 71] < 0.001 ≥ 65872 (20.68)528 (42.04)835 (26.68)388 (46.97) < 0.001 BMI, kg/m225.9 [23.9, 27.8]25.0 [22.9, 26.8]26.2 [24.2, 28.3]25.7 [23.7, 27.7] < 0.001 ≥ 252555 (60.59)619 (49.28)2088 (66.71)480 (58.11) < 0.001 Current smoking2497 (59.21)672 (53.50)1750 (55.91)444 (53.75) < 0.001Clinical characteristics Clinical presentation < 0.001 ACS1744 (41.36)424 (33.76)1409 (45.02)269 (32.57) CCS2473 (58.64)832 (66.24)1721 (54.98)557 (67.43) Hypertension2793 (66.23)852 (67.83)2294 (73.29)637 (77.12) < 0.001 Dyslipidemia3085 (73.16)861 (68.55)2526 (80.70)649 (78.57) < 0.001 Peripheral artery disease88 (2.09)26 (2.07)105 (3.35)33 (4.00) < 0.001 COPD84 (1.99)45 (3.58)63 (2.01)28 (3.39)0.001 Previous revascularization947 (22.46)311 (24.76)927 (29.62)283 (34.26) < 0.001 Previous MI772 (18.31)229 (18.23)634 (20.26)191 (23.12)0.004 Previous stroke359 (8.51)131 (10.43)359 (11.47)141 (17.07) < 0.001Medication at admission Aspirin4174 (98.98)1230 (97.93)3097 (98.95)814 (98.55)0.018 Clopidogrel4209 (99.81)1253 (99.76)3126 (99.87)824 (99.76)0.822 Statins4060 (96.28)1222 (97.29)2985 (95.37)784 (94.92)0.007 β-blockers3761 (89.19)1114 (88.69)2872 (91.76)746 (90.31) < 0.001 ACEIs/ARBs2048 (48.57)622 (49.52)1772 (56.61)487 (58.96) < 0.001Preprocedural laboratory tests ALC, 109/L2.00 [1.64, 2.38]1.46 [1.20, 1.71]2.04 [1.67, 2.49]1.47 [1.19, 1.74] < 0.001 Serum albumin, g/L44.20 [41.70, 46.70]38.60 [37.1, 40.00]44.10 [41.50, 46.70]38.40 [36.70, 40.08] < 0.001 PNI54.00 [51.35, 57.00]46.25 [44.55, 47.45]54.25 [51.50, 57.33]46.15 [44.31, 47.45] < 0.001 hs-CRP, mg/L1.40 [0.72, 2.99]1.74 [0.82, 4.69]1.72 [0.87, 3.54]2.27 [0.98, 9.15] < 0.001 Fasting blood glucose, mmol/L5.13 [4.80, 5.54]4.95 [4.63, 5.34]7.04 [5.90, 8.45]6.74 [5.50, 8.40] < 0.001 Glycated hemoglobin, %5.9 [5.7, 6.2]6.0 [5.7, 6.2]7.2 [6.7, 8.2]7.2 [6.6, 8.2] < 0.001 eGFR, ml/min/1.73m2119.16 [104.99, 133.64]115.59 [101.22, 132.21]118.23 [102.19, 133.70]114.85 [94.54, 131.67] < 0.001 ≤ 6021 (0.50)11 (0.88)29 (0.93)31 (3.75) < 0.001 LVEF, %64 [60, 68]63 [60, 68]64 [60, 67]62 [58, 66] < 0.001 < 4035 (0.83)17 (1.35)30 (0.96)20 (2.42) < 0.001Angiographic characteristics LM/TVD166 (3.94)55 (4.38)140 (4.47)51 (6.17)0.038 SYNTAX score9 [6, 16]10 [6, 17]10 [6, 17]11 [6, 19] < 0.001SYNTAX category < 0.001 ≤ 223794 (89.97)1122 (89.33)2757 (88.08)694 (84.02) 22–32359 (8.51)111 (8.84)315 (10.06)108 (13.08) ≥ 3364 (1.52)23 (1.83)58 (1.85)24 (2.91) DES implantation4031 (95.59)1185 (94.35)2968 (94.82)766 (92.74)0.005Preprocedural PNI was categorized by the optimal cut-off value for all-cause death of 48.49Values are presented as number (%) or median [interquartile range]ACEI angiotensin-converting enzyme inhibitor, ACS acute coronary syndrome, ALC absolute lymphocyte counts, ARB angiotensin-receptor blocker, BMI body mass index, CCS chronic coronary syndrome, COPD chronic obstructive pulmonary disease, DES drug-eluting stent, DM diabetes, eGFR estimated glomerular filtration rate, H high, hs-CRP high-sensitivity C-reactive protein; L low LM/TVD left main or three-vessel disease, LVEF left ventricular ejection fraction, MI myocardial infarction, PNI prognostic nutritional index, SYNTAX synergy between percutaneous coronary intervention with Taxus and cardiac surgery
## Effect of preprocedural PNI category and diabetes status on clinical outcomes
Kaplan–Meier curves illustrate that patients in the DM/L-PNI group experienced more all-cause deaths than in other groups (log-rank $p \leq 0.001$; Fig. 1).Fig. 1Kaplan–*Meier analysis* for all-cause death. Survival curves stratified by diabetes status (A), PNI level (B), and both (C). Preprocedural PNI was categorized by the optimal cut-off value for all-cause death of 48.49. DM diabetes, H high, L low, PNI prognostic nutritional index.
Univariate analysis for all-cause death is shown in Additional file 1: Table S3. Analyses before and after adjustment generated consistent results that the DM/L-PNI group yielded the highest risk of all-cause death (adjusted HR: 2.65, $95\%$ CI 1.97–3.56, $p \leq 0.001$) compared with the non-DM/H-PNI group, followed by the non-DM/L-PNI group (adjusted HR: 1.44, $95\%$ CI 1.05–1.98, $$p \leq 0.026$$), while DM/H-PNI was not associated with the risk of all-cause death (Table 3). The negative effect of L-PNI on all-cause death was significantly stronger in diabetic patients than in nondiabetic patients (p for interaction = 0.037; Fig. 2). The inverse probability of treatment weighting analysis produced similar results (Additional file 1: Table S4). Baseline characteristics after weighting were shown in Additional file 1: Table S5.Table 3Associations of DM status and PNI level with clinical outcomesOutcomeEvents/TotalEvent rate per 1000 pysCrude HR ($95\%$ CI)pAdjusted HR ($95\%$ CI)pAll-cause death$\frac{366}{94297.87}$–––– Non-DM/H-PNI$\frac{122}{42175.83}$Reference–Reference– Non-DM/L-PNI$\frac{55}{12568.891.53}$ (1.11, 2.10)0.0091.44 (1.05, 1.98)0.026 DM/H-PNI$\frac{113}{31307.321.26}$ (0.97, 1.62)0.0801.16 (0.90, 1.51)0.248 DM/L-PNI$\frac{76}{82619.163.30}$ (2.47, 4.39) < 0.0012.65 (1.97, 3.56) < 0.001p for trend–– < 0.001– < 0.001– Cardiac death$\frac{219}{94294.71}$–––– Non-DM/H-PNI$\frac{69}{42173.30}$Reference–Reference– Non-DM/L-PNI$\frac{35}{12565.661.72}$ (1.14, 2.58)0.0091.61 (1.07, 2.43)0.022 DM/H-PNI$\frac{67}{31304.341.32}$ (0.94, 1.84)0.1071.21 (0.86, 1.69)0.274 DM/L-PNI$\frac{48}{82612.103.68}$ (2.54, 5.31) < 0.0012.83 (1.94, 4.14) < 0.001p for trend–– < 0.001– < 0.001– Non-fatal MI$\frac{551}{942912.18}$–––– Non-DM/H-PNI$\frac{236}{421711.58}$Reference–Reference– Non-DM/L-PNI$\frac{65}{125610.750.93}$ (0.71, 1.22)0.6000.91 (0.69, 1.20)0.495 DM/H-PNI$\frac{199}{313013.281.15}$ (0.95, 1.39)0.1521.08 (0.89, 1.31)0.423 DM/L-PNI$\frac{51}{82613.281.14}$ (0.84, 1.55)0.3861.03 (0.76, 1.40)0.837p for trend––0.13580.4946 Non-fatal stroke$\frac{345}{94297.54}$–––– Non-DM/H-PNI$\frac{115}{42175.57}$Reference–Reference– Non-DM/L-PNI$\frac{59}{12569.741.75}$ (1.28, 2.39)0.0011.68 (1.22, 2.30)0.001 DM/H-PNI$\frac{131}{31308.661.56}$ (1.21, 2.00)0.0011.46 (1.14, 1.88)0.003 DM/L-PNI$\frac{40}{82610.321.85}$ (1.29, 2.66)0.0011.63 (1.13, 2.35)0.009p for trend–– < 0.001–0.001– Unplanned revascularization$\frac{1371}{942932.46}$–––– Non-DM/H-PNI$\frac{577}{421730.21}$Reference–Reference– Non-DM/L-PNI$\frac{154}{125626.980.89}$ (0.75, 1.07)0.2170.89 (0.75, 1.07)0.220 DM/H-PNI$\frac{519}{313037.501.23}$ (1.10, 1.39)0.0011.21 (1.07, 1.36)0.002 DM/L-PNI$\frac{121}{82633.811.11}$ (0.91, 1.35)0.3081.07 (0.88, 1.31)0.475p for trend––0.003–0.011– MACCE$\frac{2143}{942952.36}$–––– Non-DM/H-PNI$\frac{851}{421745.71}$Reference–Reference– Non-DM/L-PNI$\frac{269}{125648.661.06}$ (0.93, 1.22)0.3751.04 (0.91, 1.20)0.543 DM/H-PNI$\frac{777}{313058.171.26}$ (1.55, 1.39) < 0.0011.21 (1.10, 1.34) < 0.001 DM/L-PNI$\frac{246}{82671.861.55}$ (1.35, 1.79) < 0.0011.42 (1.23, 1.65) < 0.001p for trend–– < 0.001– < 0.001–Preprocedural PNI was categorized by the optimal cut-off value for all-cause death of 48.49. Adjusted for sex, age, hypertension, chronic obstructive pulmonary disease, previous revascularization, previous myocardial infarction, previous stroke, high-sensitivity C-reactive protein, estimated glomerular filtration rate, and left ventricular ejection fractionCI confidence interval, DM diabetes, H high, HR hazard ratio, L low, PNI prognostic nutritional index, pys person years, MACCE major adverse cardiovascular and cerebrovascular events, MI myocardial infarctionFig. 2Association of PNI level and diabetes status with all-cause death. Forrest plots for all participants (A) and subgroups (B). Preprocedural PNI was categorized by the optimal cut-off value for all-cause death of 48.49. Subgroups were defined by age category, sex, BMI category, and admission presentation. ACS acute coronary syndrome, BMI body mass index, CCS chronic coronary syndrome, CI confidence interval, HR hazard ratio, other abbreviations as in Fig. 1.
No significant interaction between subgroups and preprocedural PNI category and diabetes status (all p for interaction > 0.05) was detected. DM/L-PNI remained associated with the highest risk of all-cause death, except in the subgroup aged < 65 years which limited statistical power with only 15 all-cause deaths and 438 individuals (Fig. 2, Additional file 1: Table S6).
Kaplan–Meier curves for secondary endpoints are shown in Additional file 1: SFigs. S2, S3, S4, S5, and S6. The same pattern of the association of preprocedural PNI category and diabetes status with all-cause death was observed for cardiac death. The non-DM/H-PNI group yielded a significantly lower risk of non-fatal stroke than the other three groups. DM/H-PNI was associated with an increased risk of unplanned revascularization. DM/H-PNI and DM/L-PNI were associated with an increased risk of major adverse cardiovascular and cerebrovascular events. No association of the four groups with non-fatal MI was observed.
## Sensitivity analysis
Postprocedural PNI decreased in approximately $85\%$ of patients. Analyses applying preprocedural dichotomous PNI and postprocedural PNI category generated robust results with the main analysis, whereas ΔPNI had no association with all-cause death. Only 471 patients were diagnosed with malnutrition based on the GLIM criteria, and the association with all-cause death remained similar to the main analysis (Additional file 1: Table S7).
On a continuous scale, elevated preprocedural PNI was associated with a decreased risk of all-cause death. For a 1-standard deviation increase in PNI, adjusted HRs and $95\%$ Cis were 0.94 (0.92–0.96) in all participants, 0.92 (0.89–0.95) in diabetic patients, and 0.96 (0.93–0.99) in nondiabetic patients. When PNI was below 48.49, the risk of all-cause death decreased sharply with elevating PNI in both diabetic and nondiabetic patients, while a PNI above 48.49 yielded a trend toward a slight but steady reduction in the risk of all-cause death, which was only significant in diabetic patients (Fig. 3).Fig. 3Association of preprocedural continuous PNI with all-cause death. Restricted cubic spline curves for all participants (A), diabetic (B), and nondiabetic (C) patients. A preprocedural PNI level of 48.49 was set as a reference. Adjusted for sex, age, hypertension, chronic obstructive pulmonary disease, previous revascularization, previous myocardial infarction, previous stroke, high-sensitivity C-reactive protein, estimated glomerular filtration rate, and left ventricular ejection fraction. SD, standard deviation; other abbreviations as in Figs. 1, 2.
## Added value of nutritional indexes beyond the GRACE risk score
For the prediction of all-cause death in the entire ACS patients, the addition of preprocedural PNI category significantly improved discrimination (AUC and $95\%$ CI 0.733 [0.698–0.768] vs. 0.688 [0.651–0.725], ΔAUC: 0.045, $p \leq 0.001$) and reclassification (NRI: 0.323, $95\%$ CI 0.186–0.466, $p \leq 0.001$; IDI: 0.080, $95\%$ CI 0.023–0.137, $$p \leq 0.006$$) of the GRACE risk score (Table 4). The decision curve illustrates that the GRACE + PNI category model outperformed the GRACE risk score, with a higher clinical net benefit within a threshold probability range from 0.05 to 0.25 (Fig. 4A). In diabetic ACS patients, the added value of preprocedural PNI category was more significant, with a higher clinical net benefit within a threshold probability range from 0.05 to 0.30 (Table 4; Fig. 4B). In nondiabetic ACS patients, the addition of preprocedural PNI category also achieved model improvement, whereas the decision curve reveals no clear increase in clinical net benefit (Fig. 4C).Table 4Model performance after adding nutrition indexes to the GRACE risk score for predicting all-cause deathAUC ($95\%$ CI)pNRI ($95\%$ CI)pIDI ($95\%$ CI)pAll participants GRACE0.688 (0.651, 0.725)–Reference–Reference– GRACE+PNI categorya0.733 (0.698, 0.768)<0.0010.323 (0.186, 0.466)<0.0010.080 (0.023, 0.137)0.006 GRACE+dichotomous PNIb0.730 (0.695, 0.765)<0.0010.221 (− 0.176, 0.350)0.8190.088 (0.032, 0.144)0.002 GRACE+continuous PNIc0.733 (0.698, 0.768)<0.0010.102 (− 0.041, 0.262)0.3260.094 (0.037, 0.151)0.001 GRACE+postprocedural PNId0.731 (0.694, 0.768)<0.0010.202 (− 0.203, 0.340)0.1420.075 (0.014, 0.136)0.015 GRACE+ΔPNIe0.694 (0.656, 0.733)0.1190.079 (− 0.048, 0.230)0.4491x10-4 (-0.044, 0.044)0.998 GRACE+GLIMf0.706 (0.670, 0.743)0.026− 0.128 (− 0.326, 0.201)0.3180.024 (− 0.027, 0.074)0.363Diabetic patients GRACE0.707 (0.657, 0.756)–Reference–Reference– GRACE+PNI categorya0.763 (0.713, 0.813)<0.0010.414 (0.179, 0.628)<0.0010.089 (0.023, 0.154)0.008 GRACE+dichotomous PNIb0.762 (0.713, 0.811)<0.0010.346 (0.129, 0.528)<0.0010.091 (0.018, 0.164)0.015 GRACE+continuous PNIc0.766 (0.718, 0.815)<0.0010.228 (0.018, 0.441)0.0190.104 (0.031, 0.178)0.005 GRACE+postprocedural PNId0.746 (0.692, 0.800)0.0030.240 (− 0.236, 0.404)0.3490.055 (-0.010, 0.120)0.097 GRACE+ΔPNIe0.704 (0.652, 0.755)0.3100.147 (− 0.067, 0.375)0.493− 0.019 (− 0.073, 0.035)0.495 GRACE+GLIMf0.741 (0.692,0.790)0.009− 0.267 (− 0.394, 0.421)0.2880.08 (0.005, 0.149)0.036Nondiabetic patients GRACE0.662 (0.608, 0.716)–Reference–Reference– GRACE+PNI categorya0.716 (0.667, 0.764)0.0010.261 (0.118, 0.449)0.0160.104 (0.019, 0.188)0.017 GRACE+dichotomous PNIb0.714 (0.665, 0.762)0.002− 0.104 (-0.161, 0.294)0.5430.090 (0.006, 0.173)0.036 GRACE+continuous PNIc0.713 (0.665, 0.762)0.002− 0.007 (-0.117, 0.266)0.6070.103 (0.020, 0.187)0.015 GRACE+postprocedural PNId0.725 (0.674, 0.775)0.002− 0.179 (-0.198, 0.369)0.4160.045 (− 0.017, 0.160)0.112 GRACE+ΔPNIe0.675 (0.617, 0.732)0.1330.037 (− 0.095, 0.242)0.562− 3x10-4 (-0.002, 0.002)0.706 GRACE+GLIMf0.678 (0.626, 0.731)0.243− 0.131 (− 0.241, 0.270)0.3520.025 (− 0.052, 0.101)0.527aPreprocedural PNI categorized by the optimal cut-off value for all-cause death of 48.49bPreprocedural PNI grouped by the mediancPreprocedural PNI analyzed as a continuous variabledPostprocedural PNI categorized by 48.49eA continuous variable calculated as postprocedural PNI minus preprocedural PNIfMalnutrition defined by the GLIM criteriaAUC: area under the curve, CI confidence interval, GLIM Global Leadership Initiative on Malnutrition, GRACE global register acute coronary events, IDI integrated discrimination improvement, NRI net reclassification improvement, PNI prognostic nutritional indexFig. 4Decision curve analysis for models predicting all-cause death. Decision curves for all participants (A), diabetic (B), and nondiabetic (C) patients. Preprocedural PNI was categorized by the optimal cut-off value for all-cause death of 48.49. GRACE, Global Register Acute Coronary Events; other abbreviations as in Fig. 1.
The addition of preprocedural dichotomous PNI, preprocedural continuous PNI, postprocedural PNI category, and malnutrition defined by the GLIM criteria to the GRACE risk score improved the AUC to varying extents. However, NRI and IDI indicate that these indexes were inferior to preprocedural PNI category. ΔPNI provided no improvement in the GRACE risk score (Table 4).
## Discussion
This study presents the first evaluation of the joint effect and interaction between PNI level and diabetes status on 5 year outcomes after PCI in CAD patients. We found that patients with diabetes and L-PNI experienced the highest risk of all-cause death; the negative effect of L-PNI on all-cause death was significantly stronger in diabetic patients than in nondiabetic patients; the addition of preprocedural PNI category significantly improved model performance and clinical net benefit of the GRACE risk score for predicting all-cause death, especially in diabetic patients. These findings emphasize the prognostic significance of nutritional-immunological status and its interaction with diabetes status for CAD patients.
Previous small-scale studies have reported reduced coronary blood flow [15] and survival rate related to L-PNI in the ACS or stable CAD population [13–16]. This study demonstrated the adverse prognostic significance of low PNI for the overall CAD population. Hypoalbuminemia raises cardiovascular risk mainly related to weakened antioxidant, oncotic pressure-maintaining, and antithrombotic capacities of albumin [24]. In addition, decreased serum albumin indicates underlying inflammation, which provokes the progress of atherosclerosis [25]. Reduced absolute lymphocyte counts indicate impaired immune defenses due to malnutrition [26], reflecting increased susceptibility to infection and inflammation, which translate into atherosclerotic burden [2]. Additionally, different lymphocyte subsets are known to have opposite roles: T helper-1 and B2 cells can induce atherosclerosis, while regulatory T cells and B1 cells have atheroprotective properties [27]. Malnutrition may alter the proportions of lymphocyte subsets, causing an imbalance between proatherogenic and antiatherogenic immune microenvironments [26].
After considering diabetes status, we found that CAD patients accompanied by diabetes and L-PNI experienced the highest risk of all-cause death, the L-PNI-related risk outweighed the diabetes-related risk, while diabetes aggravated the negative impact of L-PNI (Additional file 1: Fig. S7). First, diabetic patients are often in a negative nitrogen balance due to increased protein catabolism and excretion and decreased protein anabolism. This raises the risk of malnutrition, [17] which in turn exacerbates insulin resistance, leading to a vicious cycle that impairs patients’ general conditions. Both diabetes and malnutrition can exacerbate the imbalance between cardioprotective immune response and inflammation, synergistically promoting the progression of CAD, resulting in worse prognosis in patients with combined traits [26, 28, 29]. Second, L-PNI/nondiabetic patients had a higher HR for all-cause death than H-PNI/diabetic patients, which is in line with previous research suggesting that the mortality risk related to malnutrition is higher than that associated with other chronic comorbidities [17], highlighting the value of PNI as a potent and general prognostic indicator. The differential impact of PNI and diabetes on all-cause death may be attributed to the fact that diabetes is typically subjected to active management, whereas subclinical malnutrition often goes undetected and therefore lacks intervention. Last, the adverse prognostic effect of L-PNI was aggravated in the presence of diabetes, which should be explained by the distinct pathophysiological state of diabetic patients. One possible example is that serum albumin might play a role in preventing autophagy; [30] however, the level of autophagy in diabetic heart tissue is significantly increased, [31] thereby amplifying the deleterious impact of hypoalbuminemia.
This study provides a comprehensive analysis of PNI. Except for ΔPNI, preprocedural PNI category, preprocedural dichotomous PNI, preprocedural continuous PNI, and postprocedural PNI category were all significantly associated with all-cause death in CAD patients and improved the AUC of the GRACE risk score. The finding is supported by previous studies [13, 16]. In this study, the GRACE + PNI category model showed the best performance, and only this model achieved significant improvement in both diabetes and nondiabetic patients. Restricted cubic spline for the association of preprocedural continuous PNI with the risk of all-cause death presents an inflection, illustrating that categorizing PNI by a certain cut-off value to identify malnourished patients is clinically realistic. The observed decrease in PNI after PCI may be attributable to the acute stress of catheterization. Therefore, preprocedural PNI is a more appropriate index of nutrition status than postprocedural PNI.
The GLIM has built a global consensus for malnutrition diagnosis with consideration of inflammation. However, the addition of malnutrition defined by the GLIM criteria had limited improvement in the GRACE risk score. This finding can be attributed to two reasons: first, we applied only one etiological criterion and two phenotypic criteria and thus failed to identify all malnourished patients; second, the GLIM still primarily considers body weight, thereby underestimating malnutrition in this study population. Moreover, GLIM criteria involve a multi-step diagnostic approach. In contrast, due to the wide availability of serum albumin and absolute lymphocyte counts, preprocedural PNI is a convenient and potent prognostic factor for CAD patients.
To our knowledge, this large-scale cohort study presents the first evaluation of the prognostic significance of PNI in the overall CAD population, the first investigation of the joint effect and interaction between PNI level and diabetes status on the prognosis of CAD patients, and the most comprehensive analysis for PNI.
This study also has some limitations. First, the observational nature raises concerns about residual confounding. Second, this single-center study was conducted only in Chinese population, which restricts the generalizability of our work. Large-scale studies in different countries and races are needed to determine a universal or race-specific cut-off value of PNI. Third, we did not follow up on nutritional status, which might have changed during the five-year follow-up period. Randomized trials are necessary to evaluate the value of PNI as an indicator of the efficacy of oral nutritional support in improving prognosis of CAD in a context of reduction of inflammatory drivers of both diabetes and CAD.
## Conclusions
CAD patients with diabetes and L-PNI experienced the worst prognosis. The presence of diabetes amplifies the negative effect of status-PNI on all-cause death. Poor nutritional-immunological status outweighs diabetes in increasing the risk of all-cause death in CAD patients. Preprocedural PNI can serve as an assessment tool of nutritional and inflammatory risk and an independent prognostic factor in CAD patients, especially in those with diabetes.
## Supplementary Information
Additional file1: Table S1. Baseline characteristics stratified by follow-up status. Table S2. Correlation between preprocedural PNI and glycemic measures in all participants and stratified by DM status. Table S3. Univariate and multivariate Cox proportional-hazard regression analysis for all-cause death. Table S4. Subgroup analysis for all-cause death. Figure S1. The study flowchart. Figure S2. Kaplan-Meier curves for cardiac death by diabetes status (A), PNI level (B) and both (C). Figure S3. Kaplan-Meier curves for non-fatal MI by diabetes status (A), PNI level (B) and both (C). Figure S4. Kaplan-Meier curves for non-fatal stroke by diabetes status (A), PNI level (B) and both (C). Figure S5. Kaplan-Meier curves for unplanned revascularization by diabetes status (A), PNI level (B) and both (C). Figure S6. Kaplan-Meier curves for MACCE by diabetes status (A), PNI level (B) and both (C). Figure S7. Central illustration.
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|
---
title: Prevalence of chronic periodontitis in patients undergoing peritoneal dialysis
and its correlation with peritoneal dialysis-related complications
authors:
- Zhihao Chen
- Hai Deng
- Kristine Sun
- Zehui Huang
- Shan Wei
- Yunyao Lin
- Zhongchen Song
- Yingli Liu
journal: BMC Nephrology
year: 2023
pmcid: PMC10039550
doi: 10.1186/s12882-023-03102-8
license: CC BY 4.0
---
# Prevalence of chronic periodontitis in patients undergoing peritoneal dialysis and its correlation with peritoneal dialysis-related complications
## Abstract
### Objective
The microinflammatory state can influence the occurrence of dialysis-related complications in dialysis patients. Chronic periodontitis (CP), in which plaque biofilm is considered to be the initiating factor, is a chronic infectious disease in the oral cavity. It is still uncertain whether CP affects the microinflammatory state in peritoneal dialysis (PD) and the occurrence of dialysis-related complications. The purpose of this study was to investigate the correlation between the periodontal index and clinical parameters in peritoneal dialysis patients with CP and dialysis-related complications, including peritoneal dialysis-associated peritonitis (PDAP) and cardiovascular and cerebrovascular events (CCEs).
### Methods
This was a retrospective cohort study, and 76 patients undergoing PD were enrolled. Clinical parameters, the occurrence of PD-related complications and periodontitis-related indicators, including the gingival index (GI), plaque index (PLI), probing depth (PPD) and clinical attachment loss (CAL), were collected. Correlation analysis was used to explore the correlation between periodontal or clinical parameters and the occurrence of PD-related complications.
### Results
All the patients had different degrees of periodontitis (mild $9.2\%$, moderate $72.4\%$, severe $18.4\%$); PPD was inversely related to serum albumin (r = − 0.235, $$p \leq 0.041$$); CAL has a positive correlation with serum C-reactive protein (rs = 0.242, $$p \leq 0.035$$); PLI was positively correlated with serum calcium ($r = 0.314$, $$p \leq 0.006$$). ANOVA, multivariate logistic regression analysis and Kaplan-Meier Survival curve suggested that CAL was a risk factor for the occurrence of PDAP. There was no correlation between periodontal parameters and CCEs or poor prognosis.
### Conclusion
CP is universally present in PD patients, and the presentation of periodontitis influences the systemic inflammatory state in PD patients. CP is a risk factor for PDAP.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12882-023-03102-8.
## Introduction
Chronic kidney disease (CKD) affects approximately $10\%$ of adults worldwide, leading to a large number of end-stage renal disease (ESRD) patients [1]. Peritoneal dialysis (PD), a type of renal replacement therapy, has the advantages of preserving residual renal function and mitigating cardiovascular risk; thus, it is a high-quality and cost-effective treatment for ESRD patients [2]. Current studies suggest that ESRD patients are in a state of continuous systemic microinflammation, and the levels of various inflammatory cytokines in blood circulation are continuously increasing [3]. Microinflammatory status is an important risk factor for cardiovascular events in dialysis patients [4, 5].
Chronic periodontitis (CP) is an inflammatory and destructive disease with multiple microbial sources and is characterized by gingival inflammation, periodontal attachment loss, alveolar bone resorption and periodontal pocket formation [6, 7]. The natural history of CP slowly progresses from the early gingivitis stage to irreversible severe periodontitis [7]. There is growing evidence that CP is associated with systemic diseases, including diabetes mellitus, cardiovascular and cerebrovascular events, pulmonary infectious disease and chronic kidney disease [6–8]. Some studies have shown that there is a bidirectional relationship between CKD and CP. As CKD progresses, patients show considerable susceptibility to CP, and patients with poor renal function usually have poor oral hygiene and a higher proportion of moderate or severe CP [9]. Similarly, CP, a nontraditional risk factor for CKD, can accelerate the progression of CKD, and periodontal therapy can significantly improve renal function [10–12]. However, high-quality clinical data on whether the presence of CP affects PD patients’ microinflammatory state and the occurrence of dialysis complications is still lacking.
Local inflammation of the periodontal tissue affects body systems by altering the levels of blood inflammatory mediators, increasing the probability of systemic inflammation in PD patients [7, 13]. Peritoneal dialysis-associated peritonitis (PDAP) is the most common clinical complication of PD and seriously affects peritoneal ultrafiltration and dialysis efficiency, and it is an important cause of the termination of PD and even death [14–17]. Cardio- and cerebrovascular events (CCEs) are the first and most important cause of death among PD patients [18]. PD-related complications seriously affect treatment efficacy and quality of life. A large number of studies have shown that age, sex, diabetes, hypoalbuminemia and long dialysis vintage are risk factors for PDAP [5, 19]. However, few studies have assessed the correlation between oral condition or periodontitis incidence and PD-related complications in PD patients.
This was a retrospective cohort study that enrolled 76 PD patients and aimed to analyze the correlation between periodontitis parameters and the clinical indexes of PD patients and whether CP is associated with the occurrence of PDAP or CCEs. It is hoped that the risk factors that affect the quality of life and survival rate of PD patients can be identified, leading to early intervention, thus reducing the incidence of PD-related complications, improving the quality of life of PD patients and improving their long-term survival rate.
## Study population
This study included a convenience sample of 76 PD patients enrolled between January 2017 and January 2022 from Shanghai Ninth People’s Hospital attached to Shanghai Jiao Tong University School of Medicine. The inclusion criteria for PD patients were as follows:1) age 18–80 years and 2) regular stable PD for more than 3 months. The exclusion criteria were as follows:1) completely edentulous patients; 2) those with a history of any periodontal treatment in the past 3 months; 3) those who underwent conventional hemodialysis or renal transplantation; 4) those who received immunosuppressive therapy within the past 3 months; 5) those with uncontrolled systemic diseases like malignant tumors or acquired immune deficiency syndrome; and 6) nicotine or alcohol addiction. The end point of follow-up was as follows: 1) patients who converted to hemodialysis or underwent renal transplantation, and 2) death.
## Study methods
A full-time peritoneal dialysis nurse assumed responsibility for the patients’ medical records and blood sample database. A specific person took charge of the data entry and verification of the database. PD patients underwent full-mouth clinical periodontal examination by a trained and calibrated periodontist.
We collected all enrolled PD patients’ baseline characteristics (including age, sex, dialysis vintage, primary etiology and diabetes mellitus history DM), and patients were divided into a long PD vintage group (dialysis time ≥ 36 months, 47 patients) and a short PD vintage group (dialysis time < 36 months, 29 patients). The relevant laboratory data from the last PD adequacy assessment after January 2017 (including C-reactive protein (CRP), albumin (Alb), hemoglobin (Hb), calcium (Ca), phosphorus (P), and parathyroid hormone (PTH)) were collected. After professional periodontal examination, we recorded the occurrence of PDAP and CCEs. The PDAP diagnostic criteria were 1. clinical manifestations of peritonitis, including abdominal pain and cloudy peritoneal fluid; 2. dialysis effluent leukocyte count > 100/μl and neutrophils > $50\%$; and 3. positive bacterial culture results from dialysis effluent. A diagnosis could be made when the patient met at least two of the above criteria. CCEs included myocardial infarction, heart failure, acute coronary syndrome, cerebral infarction and cerebral hemorrhage.
The clinical evaluation was performed by UNC-15 periodontal probe (Hu-Friedy, Chicago, United States). Six sites (mesio-buccal, mid-buccal, distal-buccal, mesio-lingual, midlingual, and distal-lingual) per tooth were measured. Periodontal professional examination included the gingival index (GI), plaque index (PLI), probing depth (PPD), clinical attachment loss (CAL) and bleeding on probing (BOP).
According to the gingival color and tendency of bleeding on probing, the GI was used to evaluate gingival inflammation (most commonly, Löe and Silness, 0 to 3 scale). GI values were scored as: 0-normal gingiva; 1-mild inflammation (slight change in color, slight edema, no bleeding on probing); 2-moderate inflammation (redness, edema and glazing, bleeding on probing); 3-severe inflammation (marked redness and edema, ulceration, tendency to spontaneous bleeding). Based on the presence of bleeding after probing, BOP(+)% was calculated as the ratio of the number of bleeding sites to the number of examined sites, which is an objective indicator of inflammation. Patients with a mean percentage of sites with BOP (BOP%) of ≤$20\%$ are periodontally stable, while those with a mean BOP% of ≥$30\%$ are considered to have an increased risk of periodontal disease progression. PLI was used to evaluate oral hygiene based on dental plaque thickness. PLI values were evaluated as: 0-no bacterial plaque deposits; 1-the plaque deposit did not cover more than $\frac{1}{3}$ of the coronary area; 2-the plaque deposit covered more than $\frac{1}{3}$ but did not exceed $\frac{2}{3}$ of the coronary area; 3-the plaque covered more than $\frac{2}{3}$ of the dental surface. GI, PLI and BOP% could represent the oral hygiene and gingival condition, not the definition of periodontitis, but they are still the essential periodontal examinations.
The diagnosis of *Periodontitis is* mainly determined by CAL. According to the classification criteria for periodontal diseases, gingival inflammation and bleeding on probing, PPD ≤ 4 mm, and 1 mm ≤ CAL ≤ 2 mm was defined as mild CP; gingival inflammation and bleeding on probing, 4 mm < PPD ≤ 6 mm, 3 mm ≤ CAL ≤ 4 mm, and a probable loose tooth was defined as moderate CP; PPD>6 mm, CAL ≥ 5 mm, periodontal lesions including furcation involvement, and significant inflammation or periodontal abscess was defined as severe CP. When CAL was between 2 and 3 mm or 4–5 mm, the degree of periodontitis was determined by PPD and periodontal symptoms [20].
Grouping was based on the severity of CP and the presence of adverse events after periodontal examination. According to the severity of CP, patients were divided into mild group (7 patients), moderate group (55 patients) and severe group (14 patients). According to the presence or absence of PDAP, patients were divided into a PDAP group (33 patients) and a non-PDAP group (43 patients); similarly, patients were divided into a CCE group (26 patients) and a non-CCE group (50 patients).
All patients did not receive the periodontal therapy.
## Statistical analyses
All data were analyzed using SPSS software (version 23.0;IBM Corporation)for Microsoft Windows. The normally distributed variables are described using the mean and standard deviation (SD), while the median and interquartile range (IQR) are used to describe nonnormally distributed data, and frequency (percentage) is used to describe qualitative data. Statistical significance was determined using independent samples t tests when the data were normally distributed. Multigroup comparisons were performed using ANOVA. The Wilcoxon rank-sum test was used if the results consisted of nonnormally distributed data or ordinal data. The chi-squared test was used for categorical variables. Correlation analysis was performed by Spearman’s or Pearson’s correlation method for nonnormally or normally distributed data. Binary logistic regression models were constructed to investigate risk factors. Survival curves were constructed by the Kaplan-Meier method. A p value < 0.05 was considered to indicate statistical significance.
## Baseline characteristics and clinical parameters
A total of 76 patients (45 males and 31 females, mean age: 60.37 years) were included in this study. As shown in Table 1, all patients had different degrees of periodontitis, and the proportion of patients with moderate or severe periodontitis reached $90.8\%$. Table 1 also shows other clinical parameters and periodontal indexes in PD patients. Table 1Basic characteristics and clinical parametersIndexTotal($$n = 76$$)Age(years)60.37 ± 14.35Gender(M/F)$\frac{45}{31}$Primary etiology Diabetic nephropathy23($30.7\%$) Hypertensive nephropathy12($15.8\%$) Chronic glomerulonephritis17($22.4\%$) Polycystic kidney2($2.6\%$) *Systemic lupus* erythematosus1($1.3\%$) Multiple myeloma1($1.3\%$) Pyelonephritis1($1.3\%$) Unknown19($25.0\%$)Comorbidities/n(%) DM33($43.4\%$) CHD62($81.6\%$)PD vintage/n(%) 3–36 months29($38.2\%$) >36 months47($61.8\%$)Alb(g/L)33.26 ± 5.00Hb(g/L)102.54 ± 20.61Ca(mmol/L)2.24 ± 0.27P(mmol/L)1.80 ± 0.55PTH(pg/mL)243.18(149.20,480.43)ALP(U/L)88.00(72.00,146.75)CRP(mg/L)3.34(0.79, 9.20)Periodontal parameters PPD(mm)3.66 ± 0.94 CAL(mm)3.91 ± 1.21 GI1.84(1.73, 2.00) PLI1.82 ± 0.43 BOP(+)%81.98(72.22, 90.96)Degrees of CP/n(%) mild7($9.2\%$) moderate55($72.4\%$) severe14($18.4\%$)M/F Male/female, DM Diabetes, CHD Coronary heart disease, PD Peritoneal dialysis, CP Chronic periodontitis, PPD Probing depth, CAL Clinical attachment loss, GI Gingival index, PLI Plaque index, BOP Bleeding on probing Tables 2 and 3 show the incident rate of PDAP and CCEs. During 208.7 patient-years, 26 patients developed 32 episodes of CCEs and the incident rate of CCEs was 0.15 episodes/patient-year. During 229.3 patient-years, 33 patients developed 44 episodes of peritonitis and the incident rate of peritonitis was 0.19 episodes/patient-year. Table 2Basic characteristics and the data on CCECCE($$n = 26$$)Episodes/patients-years0.15Classification of Disease Heart failure13($40.6\%$) Acute Coronary Syndrome5($15.6\%$) Infarct of Brain12($37.5\%$) Hematencephalon2($6.3\%$)CCEs Cardiovascular and cerebrovascular eventsTable 3Basic characteristics and the data on PDAPPDAP($$n = 33$$)Episodes/patients-years0.19Bacteria Gram-positive cultures26($59.1\%$) Staphylococcus species12($27.3\%$) Streptococcus species10($22.7\%$) Others4($8.7\%$) Gram-negative cultures10($22.7\%$) Culture-negative8($18.2\%$)PDAP Peritoneal dialysis-associated peritonitis
## The correlation between clinical parameters and periodontal indexes
As shown in Table 4, PPD was weakly correlated with Alb (r = − 0.235, $$p \leq 0.041$$). There were positive correlations of CAL with CRP and of PLI with Ca (rs = 0.242, $$p \leq 0.035$$; $r = 0.314$, $$p \leq 0.006$$). The correlations between the remaining clinical and periodontal parameters were not statistically significant. Table 4Pearson correlation coefficient(r) or Spearman’s rank correlation coefficient(rs) between PD clinical parameters and indices of CPPPD(mm)CAL(mm)PLIGI(rs)BOP(+)%(rs)Alb(g/L)−0.235①− 0.165− 0.016−0.016− 0.047Hb(g/L)0.166−0.0670.140−0.0730.007Ca(mmol/L)−0.015−0.0530.314③0.1580.130P(mmol/L)−0.158−0.070− 0.1420.076− 0.067PTH(pg/mL, rs)−0.184− 0.086−0.105− 0.053−0.057ALP(U/L, rs)0.0200.0620.0830.0290.052CRP(mg/L, rs)0.1370.242②0.0860.1380.054PPD Probing depth, CAL Clinical attachment loss, GI Gingival index, PLI Plaque index, BOP Bleeding on probing①:$$p \leq 0.041$$;②:$$p \leq 0.035$$;③:$$p \leq 0.006$$
## Differential analysis with ANOVA
As shown in Table 5, patients’ characteristic were compared between patients with mild, moderate and severe CP. Long PD vintage ($$p \leq 0.029$$), CRP ($p \leq 0.01$) and the occurrence of PDAP ($p \leq 0.01$) were significantly different between patients with the different severity of CP.Table 5ANOVA test of the different severity of CPMild($$n = 7$$)Moderate($$n = 55$$)Severe($$n = 14$$)χ2/HP valueAge(years)61.71 ± 15.8859.04 ± 15.0064.93 ± 10.300.9740.382Gender(M/F)$\frac{5}{231}$/$\frac{229}{50.3720.697}$Long PD Vintage6($85.71\%$)29($52.73\%$)12($85.71\%$)3.7070.029DM4($57.14\%$)21($38.18\%$)8($57.14\%$)1.1000.338Alb(g/L)36.71 ± 3.9033.11 ± 5.1532.14 ± 4.402.1020.130Hb(g/L)105.14 ± 27.16102.75 ± 20.56100.43 ± 18.600.1290.879Ca(mmol/L)2.40 ± 0.492.20 ± 0.222.31 ± 0.292.4960.089P(mmol/L)1.59 ± 0.451.86 ± 0.591.67 ± 0.431.2320.298PTH(pg/mL)238.05(46.98,456.80)252.60(167.50,525.99)150.57(95.19,374.48)0.7660.468ALP(U/L)71.00(63.00,138.00)92.00(77.00,146.00)86.00(67.25,151.75)0.3730.690CRP(mg/L)1.68(0.00,4.26)2.43(0.00,47.56)18.25(3.00,40.14)16.107< 0.001The occurrence of complications CCEs4($57.14\%$)18($32.73\%$)4($28.57\%$)0.9290.399 PDAP0($0.00\%$)21($38.18\%$)12($85.71\%$)9.872< 0.001CP Chronic periodontitis, M/F Male/female, DM Diabetes, CCEs Cardio- and cerebrovascular events, PDAP Peritoneal dialysis-associated peritonitis
## Single-factor analysis in patients with peritoneal dialysis-related complications
There were no significant differences in any periodontal indexes (PPD, CAL, PLI, GI, BOP+%) between patients with or without CCEs ($$p \leq 0.948$$, $$p \leq 0.616$$, $$p \leq 0.431$$, $$p \leq 0.473$$, $$p \leq 0.974$$) (Table S.1).
As shown in Table S.2, the values of CP (z = − 3.992, $p \leq 0.001$), CRP (z = − 2.582, $$p \leq 0.01$$) and long dialysis vintage (χ2 = 5.663, $$p \leq 0.017$$) were significantly different between patients with and those without PDAP. The differences between other variables and the occurrence of PDAP were not statistically significant.
## Multivariate logistic regression analysis of risk factors affecting the occurrence of PDAP
As shown in Table 6, we use three multivariate-adjusted models to evaluate the association of CP and the occurrence of PDAP. In multivariate-adjusted model 1, dialysis vintage, CRP and periodontal parameters (PPD and PLI) were adjusted in the binary logistic regression analysis (all variables were significantly different between patients with and those without PDAP in single-factor analysis). In model 2, we adjusted DM and Alb, which are known to associate with a higher risk of peritonitis, but show no significant differences in this study. In model 3, we adjusted for potential confounders (age and sex). After adjusting for all the variables, Table 6 shows that the occurrence of PDAP were significantly associated with increasing CAL.Table 6Multivariate logistic regression analysis of risk factors affecting the occurrence of PDAPVariableMultivariate-Adjusted Model 1Multivariate-Adjusted Model 2Multivariate-Adjusted Model 3OR($95\%$ CI)P valueOR($95\%$ CI)P valueOR($95\%$ CI)P valueCAL(mm)2.135(1.094–4.168)0.0262.107(1.056–4.205)0.0342.159(1.047–4.455)0.037Adjusted 1 model adjust for: PPD; PLI; CRP; Long PD vintage. Adjusted 2 model adjust for:PPD; PLI; CRP; Long PD vintage; Alb; DM; Adjusted 3 model adjust for: Age; Sex; PPD; PLI; CRP; Long PD vintage; Alb; DM
## Kaplan-Meier survival curve for the occurrence of PDAP
In order to reduce the lead-time bias, the risk factor of PDAP was estimated using the Kaplan-Meier method. The result was presented in Fig. 1. It revealed that the severity of CP is the risk factor of PDAP ($$p \leq 0.007$$).Fig. 1Kaplan-Meier Survival Curve for the occurrence of PDAP
## Discussion
There are many common risk factors for the occurrence and progression of CP and CKD (including age and environmental factors). The risk of the occurrence of severe periodontal disease increases as CKD progresses to ESRD [12]. There are many reasons for susceptibility to periodontal disease in ESRD patients. To prevent overload volume,dialysis patients need to restrict fluid intake and use diuretics as adjuvant therapy, especially patients with residual renal function; thus, xerostomia becomes the most common oral symptom [7, 21]. On the other hand, uremia alters the inflammatory response to bacterial plaques in gingival tissue, and reduced salivation can further lead to plaque formation [7]. Currently, the proportion of regular stomatology follow-up in ESRD patients is very low [9]. Negligence of oral hygiene is one of the main causes of a higher prevalence and severity of CP [7, 22, 23]. Numerous studies have shown that the prevalence of moderate or severe periodontitis in PD patients was significantly higher than that in those without CKD, and CP was widespread in patients with renal replacement therapy. Patients with CKD stage 3 or higher also had a significantly increased prevalence of CP [7, 12, 22, 24].
Similar results were obtained in this study. Based on the collected periodontal clinical data, patients’ GI, PLI and BOP(+)% were high, indicating poor oral hygiene and gingival status. PPD and CAL are the most important parameters for periodontitis. PPD is closely related to subgingival plaque biofilms and inflammatory status, representing the current CP condition. CAL is an indicator of previous cumulative tissue destruction [7]. Our study found that all PD patients had different degrees of CP(mild: $9.2\%$, moderate: $72.4\%$, severe $18.4\%$), and more than $90\%$ of patients had moderate/severe CP. The mean value of CAL reached 3.91 mm. Ibrahim et al. [ 7] reported that the mean CAL reached 3.97 mm in predialysis patients. Kristine Sun et al. [ 24] found that there were significant differences in CAL between PD patients and healthy patients, indicating more severe periodontitis in PD patients. In a previous study of serological examination in dialysis patients, the serum CRP levels in hemodialysis patients with CP were significantly higher than those without CP. The serum CRP levels significantly decreased after periodontal treatment [25, 26]. Kristine Sun et al. [ 24] showed that various inflammatory factors (including IL-6, IL-8, hs-CRP, etc.) in gingival crevicular fluid were correlated with the degree of periodontitis and were much higher than those in healthy people. This phenomenon was related to periodontal tissue destruction caused by severe periodontal disease in PD patients. This study found a positive correlation between CRP and CAL, the serum CRP levels significantly positive correlated with the CP severity increasing in dialysis patients, indicating that periodontitis was probably an important source of systemic inflammation in PD patients. This is in keeping with the result of previous study [27].
The physiological function and metabolism were mediated with the prolonging of the dialysis time. It is interesting that duration of PD is significant correlated with the severity of CP in our study. Bayraktar G et al. [ 27] showed that GI and PPD scores were significantly higher in subgroups receiving HD for 3 or more years, and were positively correlated with HD duration. Several reports have indicated periodontal disease-related parameters, such as PPD, CAL, GI, and PLI, worsed with longer duration of HD [28, 29], but the relationship between the degree of periodontitis and dialysis vintage remains controversial.
Hypoalbuminemia not only is an indicator of malnutrition but also is related to clinical complications, influencing the prognosis [30, 31]. This study showed a weak negative correlation between PPD and Alb (r = − 0.235, $$p \leq 0.041$$), probably because persistent inflammatory reactions and reduced oral intake are associated with malnutrition [9, 24, 26]. Periodontal status can affect nutritional parameters, and periodontal treatment can improve periodontal status as well as inflammatory status and the nutritional state [19]. However, some studies found no statistically significant association between Alb and the severity of periodontitis. In addition to nutritional status, these studies suggested that *Alb is* regulated by other factors, such as protein loss caused by peritoneal dialysis and gastric anorexia caused by inadequate dialysis [26, 31]. In our study, we just collect patients’ clinical parameter-Alb. More reliable nutritional markers, such as anthropometric assessment, body composition, other biochemical parameters (transthyretin, cholesterol and total lymphocytes, etc.) are needed to evaluate patients’ nutritional status [32]. Increased calcium in the saliva can promote dental calculus formation [7], which may be supported by the positive correlation between PLI and serum Ca in this study. On the other hand, secondary hyperparathyroidism can alter the levels of serum calcium and phosphorus metabolism and PTH, which are common complications observed in dialysis patients [22]. Kristine Sun et al. [ 24] and Naghsh N et al. [ 31] reported a positive correlation between PTH and alveolar bone loss and suggested that CKD-mineral and bone disorder (MBD) was an important risk factor for alveolar bone loss. Furthermore, alveolar bone loss can be improved by increasing calcium intake and worsened by a high-phosphorus diet [12, 33]. This study found only a weak correlation between Ca and PPD, while several studies showed that there were no statistically significant differences in Ca, P and PTH between PD patients with or without CP [26, 31, 33]. Interestingly, completely edentulous are common in PD patients. Although these patients were ineligible in this study, it may indicate cross-talk between CKD and CP. Therefore, we need further studies to prove that periodontitis is correlated with secondary hyperparathyroidism and calcium and phosphorus metabolism. Hematopoietic raw material, nutritional status and inflammatory states result in anemia in dialysis patients. This study showed no significant correlation between serum Hb and periodontitis, which is consistent with the results of most recent studies [31].
The presence of microinflammatory status in dialysis can lead to an increased risk for cardiovascular and infectious disease, seriously affecting the quality of life and prognosis [2, 7]. Some studies have suggested that the degree of CP is positively related to inflammatory parameters (including hs-CRP, etc.) and atherosclerosis risk factors [20, 25]. The relationship between periodontitis and systemic inflammation includes the cytokine response in the gingival epithelium caused by bacterial plaque, bacteremia caused by oral bacteria, circulating oral microbial toxins and immune responses to oral microorganisms [23]. Bacteremia and the systemic inflammatory response associated with CP are not only initiating factors for vascular endothelial lesions but also important factors of the vascular wall inflammatory process [20, 21]. Patients with CKD and CP as comorbidities can experience the progression of renal and cardiovascular disease due to systemic chronic inflammation [12]. This study also found a correlation between CAL and CRP. However, there were no significant differences in any periodontal parameters between patients with and those without CCEs. And the occurrence of CCEs did not differ significantly in the different severity PD patients. Tasdemir Z et al. [ 34] reported that periodontal therapy can reduce the systemic inflammatory response, thus contributing to a reduction in the risk of cardiovascular and cerebrovascular disease. However, evidence that CP determines the long-term effects on CCEs is still limited, and there is little evidence that periodontal treatment can prevent atherosclerosis or change its prognosis [20, 35]. Further intervention studies are needed to confirm the relationship between CP and CCEs in dialysis patients.
Currently, there are few studies on the correlation between oral infection and dialysis-related infection. Research on dialysis-related infections mostly focuses on the analysis of infectious pathogens and lacks research data on oral health conditions and oral microorganisms. No studies have reported the association between periodontitis and PDAP. ISPD guidelines [36] reported that oral streptococci can cause PDAP. Hideaki Oka et al. [ 23] suggested that better oral hygiene habits were associated with a lower incidence of PDAP and streptococcal infection. At present, the risk factors for PDAP are considered to include age, hypoalbuminemia, DM, etc. [ 19]. Moreover, a long dialysis vintage can aggravate periodontal damage [24]. In this study, all variables were included in the univariate analysis, and we found statistically significant differences in CAL, PPD, PLI, CRP, long PD vintage and the severity of CP between the occurrences of PDAP and non-PDAP. Then, these statistically significant variables were included in the multivariate regression analysis, we found that the severity of CP is significantly associated with the occurrence of PDAP, and this result was still significant after adjusting for confounding variables such as age, sex, Alb and the history of diabetes mellitus.
Bacteremia caused by invasive dental manipulation is considered one of the secondary causes of PDAP [23]. Oka H et al. [ 23] reported that oral streptococci can be found in PD effluent from some PDAP patients, and streptococci in the oral cavity was the most important bacterial cause of PDAP in the oral cavity. The study also summarized other previous studies to prove the theory that oral splash contamination leads to PDAP. To prevent exogenous peritonitis, the study recommended hand washing and wearing a face mask before fluid exchange. At the same time, the positive culture rate of gram-negative Enterobacteriaceae was 1.2–$3.2\%$ on the tongue and in saliva and gingival crevicular fluid, indicating that gram-negative Enterobacteriaceae derives from the gastrointestinal tract but also comes from the oral cavity as the main pathogenic bacteria. However, the main causal organisms of PDAP are still gram-positive bacteria, and staphylococci, as the most common bacteria found in PDAP patients, are not oral colonization bacteria [17]. In our study, gram-positive bacteria were the predominant pathogens ($57.8\%$), with staphylococci and streptococci each accounting for $24.4\%$ of infections. Unfortunately, we were not able to detect the presence of oral flora, so the relationship between oral pathogens and PDAP pathogens requires further study.
In addition, periodontitis can lead to poor glycemic control, consequently increasing the risk of other complications of DM [20]. After treatment, the inflammatory markers in PD patients, especially those with DM, are slightly reduced, and blood glucose control also improves [7, 20]. These results suggest that periodontal disease is the main source of inflammation in PD patients with DM. Due to the small sample of DM patients, the daily glucose levels of patients during follow-up were not accurately recorded, so data on the correlation between periodontitis and glycemic control were not available.
The main limitation of this study is that this is an observational cohort study with no periodontal therapy intervention, a lack of long-term control, a small sample size, and a short follow-up duration. It is just a convenience sample, this conclusion does not apply to all PD patients. Although our study found that CP can influence systemic inflammatory status, which is a risk factor for PDAP, it is not certain whether the improvement in periodontitis reduces systemic inflammation, improves nutritional status, slows the process of alveolar bone loss and reduces the incidence of PDAP and CCEs. The answers to these questions need to be confirmed by further prospective randomized controlled studies.
## Conclusion
In summary, chronic periodontitis is prevalent among peritoneal dialysis patients. The presence of periodontitis, which affects the inflammatory status, is a risk factor for PDAP. Additional study is needed to verify the correlation between periodontitis and nutritional condition or calcium-phosphorus metabolism. Therefore, clinicians need to pay attention to PD patients’ oral hygiene in their work, intervene in a timely manner, and treat periodontitis. After the improvement of oral hygiene, the patients’ immune defense ability and nutritional status may be improved. Further investigation needs to be performed in this area. To a certain extent, it is beneficial to prevent the occurrence of PDAP, improve the quality of life and reduce the occurrence of dialysis complications.
## Supplementary Information
Additional file 1: Table S.1. Comparison of the periodontal clinical parameters and the occurrence of cardiovascular complications. Additional file 2: Table S.2. Univariate analysis of risk factors for PDAP.
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|
---
title: Reference values for wrist-worn accelerometer physical activity metrics in
England children and adolescents
authors:
- Stuart J. Fairclough
- Alex V. Rowlands
- Borja del Pozo Cruz
- Matteo Crotti
- Lawrence Foweather
- Lee E. F. Graves
- Liezel Hurter
- Owen Jones
- Mhairi MacDonald
- Deborah A. McCann
- Caitlin Miller
- Robert J. Noonan
- Michael B. Owen
- James R. Rudd
- Sarah L. Taylor
- Richard Tyler
- Lynne M. Boddy
journal: The International Journal of Behavioral Nutrition and Physical Activity
year: 2023
pmcid: PMC10039565
doi: 10.1186/s12966-023-01435-z
license: CC BY 4.0
---
# Reference values for wrist-worn accelerometer physical activity metrics in England children and adolescents
## Abstract
### Background
Over the last decade use of raw acceleration metrics to assess physical activity has increased. Metrics such as Euclidean Norm Minus One (ENMO), and Mean Amplitude Deviation (MAD) can be used to generate metrics which describe physical activity volume (average acceleration), intensity distribution (intensity gradient), and intensity of the most active periods (MX metrics) of the day. Presently, relatively little comparative data for these metrics exists in youth. To address this need, this study presents age- and sex-specific reference percentile values in England youth and compares physical activity volume and intensity profiles by age and sex.
### Methods
Wrist-worn accelerometer data from 10 studies involving youth aged 5 to 15 y were pooled. Weekday and weekend waking hours were first calculated for youth in school Years (Y) 1&2, Y4&5, Y6&7, and Y8&9 to determine waking hours durations by age-groups and day types. A valid waking hours day was defined as accelerometer wear for ≥ 600 min·d−1 and participants with ≥ 3 valid weekdays and ≥ 1 valid weekend day were included. Mean ENMO- and MAD-generated average acceleration, intensity gradient, and MX metrics were calculated and summarised as weighted week averages. Sex-specific smoothed percentile curves were generated for each metric using Generalized Additive Models for Location Scale and Shape. Linear mixed models examined age and sex differences.
### Results
The analytical sample included 1250 participants. Physical activity peaked between ages 6.5–10.5 y, depending on metric. For all metrics the highest activity levels occurred in less active participants (3rd-50th percentile) and girls, 0.5 to 1.5 y earlier than more active peers, and boys, respectively. Irrespective of metric, boys were more active than girls ($p \leq .001$) and physical activity was lowest in the Y8&9 group, particularly when compared to the Y1&2 group ($p \leq .001$).
### Conclusions
Percentile reference values for average acceleration, intensity gradient, and MX metrics have utility in describing age- and sex-specific values for physical activity volume and intensity in youth. There is a need to generate nationally-representative wrist-acceleration population-referenced norms for these metrics to further facilitate health-related physical activity research and promotion.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12966-023-01435-z.
## Background
Accelerometers are commonly used in physical activity and health research concerning surveillance [1, 2], epidemiology [3, 4], and intervention evaluation [5, 6]. The utility of accelerometers is well established and evidenced by observed relationships between physical activity and health outcomes across the life-course including all-cause mortality [3], cardiovascular disease [7], cancer [8], obesity [9], musculoskeletal health [10], and psychosocial wellbeing [11].
Historically, most accelerometer data have been derived from hip-worn devices, but over the last decade there has been a shift towards alternative wear sites, such as the wrist, thigh, and back [2, 12, 13] to improve participant compliance to wear protocols and therefore enhance the reliability of resultant data [14, 15]. In particular, large scale cohort studies such as the UK Biobank [2] and the Brazilian Birth Cohort Study [16] have employed wrist-worn devices to produce population-level data associated with health. Similarly, the US NHANES and the NHANES National Youth Fitness Survey moved from hip- to wrist-worn physical activity assessments to reduce participant burden, resulting in an increase in device wear compliance [17–19]. This change also provided a more secure and comfortable accelerometer attachment site which aligns better with the 24-h movement behaviours paradigm by capturing data over the 24-h cycle [20].
In step with these changes has been the increased transition from proprietary accelerometer metrics (i.e., counts), to use of potentially device-agnostic raw acceleration data-driven metrics. Metrics such as Euclidean Norm Minus One (ENMO) [21], Mean Amplitude Deviation (MAD) [22], and Monitor Independent Movement Summary (MIMS) units [23] provide composite summary acceleration values and have been increasingly used over the last decade. These metrics can be generated in a relatively straightforward and cost-effective manner due to the increased accessibility of raw acceleration data and open-source processing and analysis applications, such as the GGIR and MIMS-unit R packages [24, 25].
The potential utility of such metrics was recently expanded by Belcher et al. who published MIMS-units US population-referenced percentiles for wrist-worn accelerometry [1]. This study, which was based on similar analyses of hip-worn ActiGraph counts from 2014 [26] and 2015 [27], included children and adolescents aged 3- to 19-years and adults up to age 80 + years. The authors reported MIMS-units in over 6000 youth and found that activity peaked in both sexes at age 6 years and were lowest at age 17 years and 18 years in males and females, respectively. It was also observed that females accumulated higher MIMS-units than males at lower percentiles (< 50th), while the opposite was observed at the higher percentiles up to age 11 years [1]. The analysis of Belcher and colleagues provides unique insights into age- and sex-related activity differences derived from wrist raw acceleration metrics, which in some instances were counter to those previously reported for hip-worn accelerometer proprietary counts data (e.g., males being more active than females at all ages) [1].
In addition to MIMS-units, ENMO and, to a lesser extent, MAD metrics have been used as summary acceleration metrics with increasing frequency. Presently, the differences and patterns in youth physical activity for both summary metrics based on average acceleration (i.e., proxy for activity volume) [28] are unknown. Furthermore, age- and sex-related differences have not been described for additional device-agnostic metrics that describe the specific physical activity dimensions of intensity (i.e., intensity gradient) [28], and time-related intensity (i.e., MX: minimum acceleration for the most active accumulated period of time, where X = the period of time) [29]. Average acceleration and intensity gradient are independently-related to various health and wellbeing outcomes in different populations [28, 30, 31], while MX metrics can be used to estimate prevalence of meeting physical activity guidelines [29, 32]. As the use of these metrics continues to increase, there is a need for reference values to help the physical activity and health research community interpret activity levels from continuous raw acceleration data [33]. However, relatively little comparative data for average acceleration, intensity gradient, and MX metrics are available for children and adolescents, and where it does exist it is limited by narrow age groups and/or modest sample sizes [28, 34, 35]. To address this need our aims for this novel study were:To present age- and sex-group reference percentile values for ENMO- and MAD-generated average acceleration, intensity gradient, and MX metrics in a wide age-range of England children and adolescents;To compare volume and intensity physical activity profiles for ENMO- and MAD-generated metrics by age- and sex-groups.
## Data acquisition and study eligibility
Ten ethically approved wrist accelerometry studies led or supervised by the first or last authors were identified for inclusion in this pooled individual participant data analysis. Eligible studies involved school-aged youth who provided assent and who had parental/carer written informed consent to participate in physical activity research studies during school term time. Seven studies were cross-sectional and three were interventions, six focused on primary school students only, two focused on secondary school students, and two studies included primary and secondary school students. Participant inclusion criteria varied by study but as a minimum, participants were required to be physically able to regularly take part in physical education classes. Individual study sample sizes ranged from $$n = 29$$ to 311, with a mean of $$n = 150$$ ± 84 participants. Seven studies included recruitment information to determine the participation rate (mean = $72\%$). The participants’ socio-economic position (SEP) spanned English Indices of Multiple Deprivation deciles 1 (low SEP) through 10 (high SEP) [36]. The median decile was 4 (IQR = 2, 8) which reflects the established health inequalities in northwest England. Participant characteristics are presented by study in Additional file 14. For inclusion in the analysis, studies required non-intervention assessments of wrist accelerometer-derived physical activity. For the included intervention studies only baseline data were used. In addition to raw acceleration data, as a minimum, studies needed to provide stature, body mass, and demographic data including age and sex. Where published, details of these studies can be found elsewhere [37–42]. Investigators with a major involvement in the eligible studies (e.g., past PhD students, co-supervisors) were approached by email and invited to contribute individual participant data to allow data harmonisation and subsequent pooled analysis. On receipt of signed data transfer agreements all contributing investigators transferred their de-identified data via a secure file sharing system. Ethical approval for this pooled individual participant data study was granted by Edge Hill University’s Science Research Ethics Committee (#ETH2021-0034). Data were available from 10 studies conducted in 71 schools between 2015 and 2022 in the Merseyside, Lancashire, and Greater Manchester counties of northwest England.
## Anthropometric and demographic variables
In all contributing studies stature and body mass were measured to the nearest 0.1 cm/kg using a portable stadiometer and digital scales, respectively. Standard anthropometrical procedures were followed with participants wearing light clothing and shoes removed [43]. International Obesity Task Force age- and sex-specific body mass index (BMI) cut-points were applied to classify participants by weight status [44], and BMI z-scores were computed using UK 1990 reference data [45].
## Physical activity acceleration metrics
In the contributing studies ActiGraph GT9X (ActiGraph, Pensacola, FL; 8 studies), or GENEActiv Original (Activinsights, Cambs, UK; 2 studies) triaxial accelerometers were used. The devices have a dynamic range of ± 8 g and were requested to be worn for up to 7 consecutive days on the non-dominant wrist using either 24-h (8 studies) or waking hours wear protocols (2 studies), with a sampling frequency set at 100 Hz (8 studies) or 30 Hz (2 studies). The devices were initialised and data downloaded using the latest releases of the respective ActiLife (versions 6.13.1 to 6.13.4) and GENEActiv (versions 2.2 to 3.1) available at the time of data collection. Physical activity metrics were generated from the raw accelerometer data files (ActiGraph: gt3x then conversion to.csv format; GENEActiv:.bin format) and were processed in R using package GGIR version 2.6–0 [24].
## Accelerometer data harmonisation
To harmonise data collected from 24-h and waking hours protocols it was first necessary to define the age and day-specific waking windows of interest, as follows: Firstly, accelerometer files were sorted into four age-groups based on school Year (i.e., Grade) group (Year (Y) 1&2 (age 5–7 y), Y4&5 (age 8–10 y), Y6&7 (age 10–12 y), and Y8&9 (age 12–14 y), which were used in all subsequent steps. Next, accelerometer files from 24-h data collection protocol studies were processed in GGIR parts 1 to 4 using the default sleep detection algorithm [46] to estimate average waking and sleep times (and therefore duration of a ‘waking hours’ day) for weekdays and weekend days. Accelerometer files for 1297 participants were processed with the resultant sleep data representing 5627 participant-days. The averaged waking and sleep times, and total awake duration for each group and day type (i.e., weekdays, weekend days) are presented in Table 1.Table 1Weekday and weekend averaged wake and sleep times (Mean (SD)) and waking hours durationWeekdayWeekendAge groupsWaking timeSleep timeTotal awake duration (min)Waking timeSleep timeTotal awake duration (min)Y1&207:20 (00:16)21:16 (00:21)83707:59 (00:33)21:49 (00:29)830Y4&507:04 (00:11)21:35 (00:22)87107:43 (00:35)21:55 (00:49)852Y6&707:14 (00:28)22:16 (00:21)90208:05 (00:35)22:42 (00:35)867Y8&907:09 (00:50)22:49 (00:27)94008:02 (01:21)23:22 (00:41)860Note: Times are in 24-h clock format These averaged waking and sleep times were then used in separate GGIR shell R scripts to populate the qwindows argument to define waking hours during subsequent data processing. This ensured that determination of the waking day for data processing was specific for each age group and day type. Age group accelerometer files, including those from the two studies that did not use a 24-h wear protocol, were then re-processed separately in GGIR part 2 to calculate the ENMO and MAD-derived waking hours physical activity acceleration outcome metrics. This processing was undertaken for weekdays and for weekend days for each of the four age groups (i.e., eight separate data processing runs). Signal processing included autocalibration using local gravity as a reference [47], detection of implausible values, and detection of non-wear. Non-wear was imputed by default in GGIR whereby invalid data were imputed by the average at similar time points on other days of the week [21]. An example GGIR configuration file contains details of the parameters selected (Additional file 1). Wear time criteria were: at least three valid weekdays and one valid weekend day, with ≥ 600 min·d–1 of accelerometer wear during waking hours defined as a valid wear day. Participants’ accelerometer data were excluded from analyses if post-calibration error was > 10 mg (milli-gravitational units) and/or the wear time criteria were not achieved.
## Acceleration metrics
Average acceleration (i.e., average magnitude of dynamic acceleration) was calculated during GGIR part 1 processing using ENMO (i.e., the Euclidean norm of the three accelerometer axes with 1 g subtracted and negative values truncated to zero [21], and MAD (i.e., the mean of the dynamic acceleration signal with the static component removed) [48]. To reflect the intermittent nature of youth physical activity behaviour and to ensure higher intensity activities were captured both summary metrics were averaged over 1-s epochs [49, 50]. The metrics were expressed in mg to represent activity volume and were used to generate all subsequent metrics. Average acceleration from the ActiGraph and GENEActiv devices worn on the non-dominant wrist has demonstrated equivalence in adults [51].
The intensity gradient reflects the negative curvilinear relationship between intensity and time accumulated at any given intensity, and describes an individual’s intensity profile during the measurement period [28]. A higher intensity gradient (i.e., less negative value) reflects proportionately more time being spread across the intensity profile, whereas a lower or more negative gradient reflects proportionately less time spent in mid-range and higher intensities. Intensity gradient was selected in GGIR part 2 using the iglevels = TRUE argument.
The MX metric (where X refers to an accumulated duration of time) is the acceleration in mg above which the most active X minutes are accumulated. The MX metric is a population-independent continuous variable, derived from directly measured acceleration, and captures intensity irrespective of level of activity, or fitness status (unlike absolute intensity cut-points) [29]. Fourteen different MX metrics were computed to cover different durations of interest and thus give a comprehensive picture of profile of physical activity. These were M2, M5, M10, M15, M20, M30, M45, M60, M120, M240, M360, M480, M600, and M720. These metrics were generated in GGIR part 2 using the qlevels argument aligned to the waking hours duration indicated by the qwindows time range.
To allow comparisons with previous studies employing wrist-accelerometer cut-points, we also calculated time spent in moderate-to-vigorous physical activity (MVPA), using an ENMO threshold of 200 mg, which approximates the ActiGraph and GENEActiv cut-points of 201.4 mg and 191.6 mg, respectively [52]. Using 200 mg allowed comparison with previous studies that had used either device [28, 31]. We did not calculate MVPA for MAD because to our knowledge published cut-points for non-dominant wrist data in youth do not exist for this summary metric. Additional file 2 provides a summary of the GGIR output variables selected.
## Data analysis
Processed accelerometer data for each age group were firstly combined and then average weighted week (5:2 ratio) values were computed for average acceleration, intensity gradient, MX metrics, and MVPA (ENMO only). These data were then harmonised with the corresponding anthropometric and demographic data using each participant’s unique ID code. Sex- and age-group descriptive statistics were calculated for average acceleration, intensity gradient, M60 (which relates most closely to the youth physical activity guideline of at least 60 min MVPA·d−1 averaged across the week), and MVPA. We used the gamlss R package (v.5.4–10) [53] to create sex- and age-specific percentile curves (3rd, 5th, 10th, 25th, 50th, 75th, 90th, 95th, and 97th) for each metric using the Generalized Additive Models for Location Scale and Shape (GAMLSS) method [54]. All metrics were modelled at each age (in increments of 0.1 years) with a parametric distribution (Box-Cox t, Box-Cox Power Exponential, or Box-Cox normal; depending on which distribution had the best model fit). The characteristics of the chosen parametric distribution (i.e., the location, scale, skewness, and kurtosis) were then modelled to vary smoothly across age using penalised B-splines [55]. Goodness of fit was checked for all models using worm plots.
The GAMLj R package (v. 2.4.0) [56] was used to generate linear-mixed models to examine age and sex differences for each metric, while accounting for individual participant data being clustered in schools. Season of data collection, accelerometer wear time, accelerometer type, and recording frequency were included as covariates. Radar plots were constructed to present the sex- and age-group MX metrics. These provide a visual translation of between-group activity intensity profiles. Each MX metric is plotted on one radius and the points joined to give a distinct shape for each age- and sex-group. Higher intensity profiles are indicated by a greater surface area of the plotted shape on the left of the plot, where the shorter duration MX metrics were positioned (i.e., M30 through to M2) [33]. To enable translation of the MX metrics with traditional time-use intensity thresholds, dashed lines are included in the plots at 200 mg and 700 mg, to represent moderate (MPA) and vigorous physical activity (VPA), respectively [52]. To reflect that ENMO has been used more extensively than MAD in a range of age groups and populations [28, 30–32], we report our results on the former. Equivalent data for MAD are presented as supplementary material (Additional files 3, 4, 5, 6, 7 and 8)
## Results
From the 10 contributing studies $$n = 2011$$ participants had informed parental consent to participate. When participants with missing descriptive data and without accelerometer output data were removed the sample was $$n = 1503$$ ($75\%$ of consented sample). Two-hundred-and-fifty-three participants did not achieve the accelerometer wear time criteria, resulting in a final analytical sample of $$n = 1250$$ ($62.2\%$ of consented sample and $83.2\%$ of sample with accelerometer data; Fig. 1).Fig. 1Data flowchart for the analytical sample There were no significant differences in age ($$p \leq 0.26$$), BMI z-score ($$p \leq 0.98$$) or sex ($$p \leq 0.09$$) between the participants that achieved wear time compliance and those that did not. Participant descriptive characteristics for the analytical sample are presented in Table 2. The sample consisted of $59\%$ girls, which was mainly due to there being substantially more girls than boys in the 12–14 y age group. This reflected the inclusion of a girls-only study in the pooled dataset. The proportion of participants classified as normal-weight ranged from 69 to $77\%$ (boys) and from 70 to $78\%$ (girls), which is broadly in line with national data in England corresponding with the years when the included studies were conducted [57]. Compliance to the accelerometer wear protocol was very good among participants in the analytical sample (Table 3). On average the accelerometers were worn for 6.0 days out of 7 for 14.0 h·d−1 with the highest compliance seen in the older groups, and lowest compliance in the youngest group. Table 2Participants’ descriptive characteristics, grouped by sex and age (Mean (SD), unless stated otherwise)BoysGirlsY1&2Y4&5Y6&7Y8&9Y1&2Y4&5Y6&7Y8&9n822011636495253155237Age (y)6.0 (0.3)9.7 (0.5)10.7 (0.6)13.0 (0.4)6.0 (0.3)9.7 (0.5)10.7 (0.7)13.7 (0.5)Height (cm)116.5 (6.2)138.3 (6.7)143.8 (8.0)160.0 (8.8)115.8 (5.1)138.2 (7.2)144.3 (7.7)160.2 (6.4)Weight (kg)22.3 (3.7)34.5 (8.5)38.8 (10.1)53.5 (11.7)22.0 (3.7)35.7 (8.4)40.3 (11.0)55.2 (11.3)BMI (kg‧m2)16.3 (1.7)17.9 (3.1)18.6 (3.5)20.8 (4.1)16.3 (2.1)18.5 (3.2)19.2 (4.0)21.4 (3.9)BMI-z0.39 (0.97)0.48 (1.20)0.46 (1.38)0.79 (1.15)0.38 (1.30)0.55 (1.12)0.47 (1.31)0.58 (1.17)Normal-weight (%)73.077.072.069.078.070.072.076.0Overweight/obese (%)27.023.028.031.022.030.028.024.0Table 3Participants’ unadjusted waking hours average weighted week accelerometer data, grouped by sex and age (Mean (SD), unless stated otherwise)BoysGirlsY1&2Y4&5Y6&7Y8&9Y1&2Y4&5Y6&7Y8&9n822011636495253155237Valid days (n)5.1 (0.5)6.4 (0.7)6.2 (0.7)6.1 (0.6)5.1 (0.4)6.4 (0.7)6.3 (0.6)6.6 (0.7)Wear (h‧d−1)13.6 (0.8)13.9 (0.8)14.3 (0.8)14.5 (0.8)13.6 (0.6)13.9 (0.8)14.4 (0.7)14.6 (0.8)Average acceleration (mg)75.9 (19.8)73.4 (21.5)74.0 (25.5)59.2 (27.1)65.1 (13.6)62.1 (17.3)64.3 (17.1)43.6 (12.2)Intensity gradient-2.07 (0.12)-2.04 (0.12)-2.07 (0.14)-2.20 (0.18)-2.15 (0.11)-2.14 (0.12)-2.16 (0.14)-2.38 (0.17)M2 (mg)1949.1 (423.2)2088.8 (530.2)2018.1 (607.1)1445.1 (662.5)1552.5 (345.3)1664.3 (439.7)1661.0 (519.4)899.8 (392.0)M5 (mg)1386.2 (319.0)1425.1 (428.6)1377.5 (481.8)936.7 (520.9)1081.3 (262.7)1096.1 (328.6)1095.6 (369.8)593.5 (277.1)M10 (mg)963.7 (240.8)960.4 (323.5)937.9 (362.6)634.4 (381.6)744.4 (188.9)723.6 (230.9)733.3 (249.0)417.1 (210.3)M15 (mg)737.01 (198.3)726.5 (258.9)716.1 (289.0)501.0 (325.1)570.8 (142.5)547.3 (175.0)560.9 (183.4)341.1 (191.0)M20 (mg)593.0 (165.7)583.4 (213.4)580.6 (237.9)421.2 (283.1)463.9 (111.5)444.8 (139.2)458.5 (143.0)291.6 (165.7)M30 (mg)423.4 (121.1)417.1 (146.4)423.6 (170.2)324.1 (205.1)339.6 (73.8)329.7 (97.1)343.0 (97.9)236.3 (149.7)M45 (mg)287.4 (82.9)295.0 (95.9)303.4 (112.4)246.3 (128.9)246.9 (47.9)244.2 (66.7)256.3 (66.3)183.84 (45.8)M60 (mg)230.8 (61.4)230.2 (69.0)238.3 (82.0)204.6 (109.4)196.7 (35.8)196.9 (51.5)207.8 (50.5)154.9 (33.4)M120 (mg)122.1 (30.5)121.5 (31.7)129.07 (40.9)112.9 (33.0108.8 (19.6)110.6 (28.7)119.5 (27.7)93.8 (21.9)M240 (mg)52.9 (13.7)51.7 (14.3)57.4 (19.9)49.7 (17.6)48.6 (10.7)49.3 (15.1)54.2 (14.7)41.6 (12.0)M360 (mg)41.8 (10.9)38.6 (10.8)41.1 (14.5)33.5 (12.6)38.8 (8.9)37.2 (11.95)38.7 (10.9)28.3 (8.6)M480 (mg)26.8 (6.8)26.3 (7.4)29.6 (10.4)24.8 (9.1)25.7 (6.1)25.7 (8.5)27.6 (7.9)21.4 (6.7)M600 (mg)14.8 (4.0)14.4 (4.5)16.5 (6.1)13.6 (5.2)14.5 (4.1)14.2 (5.4)15.1 (4.7)12.0 (4.3)M720 (mg)7.0 (2.7)7.0 (3.0)8.7 (3.9)7.3 (3.4)7.1 (2.7)7.0 (3.5)7.8 (3.0)6.2 (2.8)MVPA (min‧d−1)67.1 (21.1)65.8 (22.1)68.2 (27.7)53.9 (27.2)56.5 (15.0)55.2 (20.3)59.8 (20.8)36.5 (15.0)Notes. Accelerometer outcomes calculated using the ENMO metric; MX metrics = minimum acceleration for the most active accumulated X minutes; MVPA = Moderate-to-vigorous physical activity
## Results for study aim 1
Figure 2 displays the age-specific average acceleration percentiles for boys and girls. At all ages boys’ average acceleration was greater than girls across the full percentile range. Average acceleration peaked between ages 6.5 and 7.5 y in the less active boys (up to 50th percentile) and between 8.5 and 9.5 y in the more active boys, compared to ages 7 and 7.5 y in the less active girls (up to 50th percentile) and 7.5 and 8 y in the more active girls. The observed age-related decline was gradual in boys and relatively steep in girls from age 11 y. Girls’ average acceleration declined most from age 11y with the steepest reductions in the most active girls (i.e., 50th-97th percentile).Fig. 2Percentiles of waking hours wrist-worn average acceleration for boys (panel a) and girls (panel b) Figure 3 presents age- and sex-specific percentiles for intensity gradient. At all ages and activity levels boys’ intensity gradient values were higher (less negative) than girls. Intensity gradient was highest between ages 8–9 y and between 7.5–8.5 y in the less active boys and girls, respectively (up to 50th percentile). Among the most active, intensity gradient peaked between 9.5 and 10 y (girls) and between 9 and 10 y (boys). In both sexes the age-related decline in intensity gradient was somewhat steeper than for average acceleration and was most pronounced from age 10 to 11.5 y, with the less active participants reducing intensity gradient earlier than more active peers. Fig. 3Percentiles of waking hours wrist-worn intensity gradient for boys (panel a) and girls (panel b) Age- and sex-specific M60 percentiles are shown in Fig. 4, with girls’ values consistently lower than boys at all ages. M60 was highest between ages 7.5–8.5 y in the less active boys (up to 50th percentile) and between 9.5 and 11 y in the more active boys. In girls M60 peaked between ages 6.5–8 y (up to 50th percentile) and between 8.5 and 9.5 y (50th -97th percentile). M60 declined gradually in boys across almost all percentiles, although at age 13–14 y the slope plateaued in the most active group (97th percentile). However, at the extremes of the curves only a small number of participants were represented (e.g., 15 boys in the $97\%$ percentile), therefore these data should be interpreted with caution. In contrast, girls’ M60 values fell most from age 11 y with the steepest reductions at the 50th-97th percentiles. Fig. 4Percentiles of waking hours wrist-worn M60 metric for boys (panel a) and girls (panel b) Figure 5 displays time spent in MVPA. At all ages boys’ MVPA was greater than girls’ at each percentile. Among the less active participants (up to 50th percentile) MVPA peaked between ages 6.5–8.5 y in the less active boys and between 8.5 and 9.5 y in the more active boys. In more active peers MVPA was highest between 8.5 and 9.5 y (boys) and between 8 and 10.5 y (girls). The age-related decline in MVPA was gradual in boys but was more pronounced in girls from age 11 y, particularly among the more active girls (50th-97th percentiles). Tables detailing percentiles values for each metric are presented in Additional file 10 (Additional file 9 for the equivalent tables relating to the MAD-derived metrics).Fig. 5Percentiles of waking hours wrist-worn MVPA for boys (panel a) and girls (panel b)
## Results for study aim 2
For each metric boys were more active than girls ($p \leq 0.001$) at all Year-groups (Table 3 and Additional file 11). For all metrics physical activity was lowest in the Y8&9 group, who were significantly less active than the Y1&2 group for average acceleration ($p \leq 0.001$), intensity gradient ($p \leq 0.001$), M2 to M60, and M360 metrics ($p \leq 0.0001$ to $$p \leq 0.048$$), and MVPA ($$p \leq 0.01$$). Figure 6 presents the range of MX metrics across age-groups for boys and girls, respectively, and shows that the main age-related differences in physical activity occurred among Y8&9 boys and girls. The plots illustrate the physical activity profiles underlying the statistical analyses, demonstrating how these differences were most apparent at higher intensities represented by the shorter duration MX metrics. This was more noticeable in girls and reflected the timing of the steepest age-related decline in intensity gradient. Using the indicative thresholds for MPA and VPA (dotted and dashed lines on the plots) [58], boys at all ages accumulated 60 min·d−1 in MVPA. Among girls 60 min·d−1 of MVPA was accrued by the Y6&7 group, with 45 min·d−1 achieved by the younger age groups, and around 30 min·d−1 by the oldest girls. Approximately 10 min·d−1 and 2 min·d−1 of VPA was achieved by the Y8&9 boys and girls, respectively. In contrast, the younger age groups accumulated VPA for 15 min·d−1 (boys) and 10 min·d−1 (girls).Fig. 6Waking hours physical activity profiles described by MX metrics for boys (panel a) and girls (panel b) The percentile curves and statistical analyses results by age-group and sex were similar for MAD generated metrics (Additional files 3, 4, 5, 6, 7 and 8).
## Discussion
This study is the first to report reference percentile values for ENMO- and MAD-generated average acceleration, intensity gradient, M60, and MVPA across a wide age range of boys and girls, derived from wrist-worn accelerometers. Our study is timely given the growing use of raw acceleration data and in particular, the ENMO metric to report comparable device-agnostic metrics describing youth physical activity. Providing reference values for these metrics may help researchers distinguish how physical activity volume or intensity (or both) differ by age and sex and where participants are positioned in terms of their activity levels.
Irrespective of age and metric used (ENMO or MAD), boys were more active than girls for all physical activity outcomes. This concurs with our previous findings in children [32] but contrasts with recent population-referenced data from the US, where girls’ mean MIMS-units‧day−1 were higher than boys’ from age 6 to 19 y [1]. MIMS-units reflect total physical activity volume and of the metrics reported in our study, are most comparable to average acceleration. A recent comparison of ENMO, MAD and MIMS-units to ActiGraph counts showed that ENMO and MAD increase proportionately more at higher intensities than MIMS-units and counts [59]. As boys’ undertook more high intensity activity, this difference between the metrics at higher intensities would likely affect boys’ overall activity volume more so than girls’, perhaps contributing to this discrepancy between studies [59]. Variations in girls’ and boys’ accelerometer wear time in the NHANES data [1], the inherent differences in the signal processing algorithms used to determine MIMS-units and ENMO, and the different population groups also likely contributed to these dissimilar findings.
The largest magnitude of differences between boys and girls was for the metrics related most to higher intensity physical activity (i.e., intensity gradient, lower duration MX metrics). This suggests that time in higher intensity physical activity was the main driver for the overall sex-related differences. This is consistent with findings from cut-point studies reporting significant sex differences in MVPA and VPA [60, 61] and our recent intensity spectrum work [62] which showed that the largest sex differences in 5-to-15 y olds occurred at accelerations ≥ 700 mg, which are indicative of activities with an equivalent intensity at least to jogging [63]. Such differences may reflect multidimensional influences on boys’ and girls’ structured physical activity, such as sports. For example, more opportunities typically exist for boys than for girls to do a wider range of organised out-of-school activities [64, 65], boys have superior perceived [66, 67] and actual [68, 69] motor competence than girls, and stronger parental social support for physical activity and praise for boys to engage in active pursuits such as sports have been reported [70, 71].
Age-related differences were similar for boys and girls across the four metrics. Of these metrics average acceleration is the most comparable to the recently published MIMS-unit reference values for youth aged 5 and 15 y [1]. However, Belcher et al. showed that MIMS-units·d−1 peaked at age 6 y in both sexes [1], which was earlier than we observed in our sample for any metric. The age at which physical activity was highest varied between ages 6.5 y through to 10 y across the different metrics and both sexes. Generally, average acceleration peaked earlier than metrics which had an intensity component; (e.g., intensity gradient typically peaked 1-year later than average acceleration). This could relate to age-linked increases in motor skill proficiency which predispose relatively older children to take part in more structured and higher intensity physical activities [72]. The only other comparable study of youth accelerometer reference values reported total activity counts and MVPA from NHANES hip accelerometer counts data collected between 2003 to 2006 [26]. This also showed boys’ and girls’ physical activity from both metrics peaking at age 6 y, but differed from our findings where average acceleration and MVPA were highest between ages 6.5–10 y. These differences were most likely associated with disparities in accelerometer-related factors (i.e., signal processing algorithms used to determine outcome metrics, device placement, selected cut-points), and the characteristics of the respective samples.
We observed that the age of peak physical activity also differed by activity level. For less active participants all physical activity metrics were highest around 1 y earlier than for more active peers. This could have reflected the influence of developmental differences and environmental factors that differentially active children are exposed to. A combination of variations in motor competence proficiency [68], physical activity and sport opportunities [65], structure and intensity of these opportunities [73], and psychosocial correlates [71] may have underpinned the observed age-related differences. Moreover, we found that across all metrics the highest reference values for girls occurred at younger ages than for boys. This likely relates to differences in the timing and tempo of maturation between boys and girls of the same chronological age [74], with girls typically undergoing physical changes earlier than boys. Studies have shown that when boys and girls are compared by biological age, sex- and chronological age-related differences in total physical activity and MVPA are attenuated [74, 75]. Further, recent review evidence supports the notion that maturational timing is inversely associated with overall physical activity and sports participation [76]. This evidence though is inconsistent, due in part to differences in study methodologies (e.g., maturity indicator, device vs. self-report physical activity assessments) and adjustment for important confounding variables such as chronological age [76].
The MX metrics demonstrated that age-related activity differences occurred mainly in relation to the Y8&9 group, which concurs with the percentile reference data. Further, these differences were most apparent at higher intensities (i.e., lower duration MX metrics), which reflects the higher tempo and intermittent nature of physical activity in younger children [77]. Moreover, there is greater engagement in structured physical activity and sport among children compared to adolescents [65] as a consequence of various factors (e.g., greater activity/sport sampling among younger vs. increased specialisation among older groups [78], age-related drop-out from structured sport [79], and increased academic and social time demands among adolescents). When indicative thresholds for MPA and VPA were overlayed on the MX radar plots, sex-differences in the time accumulated in MVPA were observed, but more striking was the modest duration of accumulated VPA particularly among the oldest group (i.e., 10 and 2 min·day−1 for boys and girls, respectively). This stark age-related difference in higher intensity physical activity is concerning, particularly in light of the known associated physical health benefits of VPA [62, 80].
Strengths of the study include using 24-h data to establish waking and sleep times that were specific to age-groups and day-types, which allowed the summary physical activity metrics to reflect actual waking hours durations, rather than them being defined by an arbitrary value (e.g., 16 h reflecting 07.00 to 23.00) [62]. Although we used stringent wear time criteria there was a high level of compliance to wearing the wrist accelerometers, with data available from $83\%$ of participants who had some recorded accelerometer outcome data, which exceeds the compliance level reported in Belcher’s NHANES sample [1]. Further, participants in our study averaged 5.1 to 6.6 d of wear and average wear time of 14.0 h·d−1 from an average 14.6 h·d−1 waking hours. Additionally, use of standardised accelerometer data processing decisions with non-proprietary raw acceleration data gathered from different accelerometers allowed data from a large number of studies to be pooled. This approach enables comparability between wrist-accelerometer studies and thus can advance assessment of youth physical activity in future.
Study limitations include use of cross-sectional data that were not nationally representative of English youth; for these reasons the reference values are not intended to be generalised beyond the sample population. There were also disproportionately more girls than boys in the sample and relatively fewer children in the youngest age group. This should be taken into account when comparing percentile values between age- and sex-groups, by considering the potential for values to be less representative if sample characteristics vary substantially to the reference group. Furthermore, participation rate data was not available for all included studies, thus there was a risk of sampling bias which could have influenced the results. Acceleration recording frequency was 100 Hz in eight of the pooled studies and 30 Hz in two. ENMO and MAD describe accelerations averaged over a given epoch, and thus the influence of recording frequency should be minimal, particularly when the same epoch duration is used, as was the case in our pooled sample. However, we acknowledge that the lower sampling frequency in two of the ten studies may have impacted on the resultant output [81]. This is the largest pooled youth dataset reporting average acceleration, intensity gradient, and MX metrics and our analyses provide important age- and sex-specific reference data. We hope that this will provide the impetus for international efforts to pool raw acceleration data on larger scales in order to produce nationally-representative reference values for each acceleration metric.
## Conclusions
This is the first study to present age- and sex-specific reference values for average acceleration, intensity gradient, MVPA, and MX metrics. Physical activity volume and intensity peaked between ages 6.5–10.5 y, and were significantly lower in the Y8&9 group and girls. Our findings demonstrate the feasibility and utility of generating percentile curves for data-driven acceleration metrics to aid health-related physical activity research and promotion. We provide these values as a first step towards more comprehensive internationally representative reference value and view them as a marker for researchers to use when interpreting values from their own samples. In future there is a need to generate country-specific wrist-acceleration population-referenced norms for average acceleration, intensity gradient, and MX metrics. This would enable better quality physical activity surveillance, monitoring, and health promotion through standardised comparisons of device-independent accelerometer metrics across populations and subgroups.
## Supplementary Information
Additional file 1. Example GGIR config file listing of arguments and parameters used in the GGIR accelerometer data processing. Additional file 2. Example GGIR physical activity metric output variables. Additional file 3. Unadjusted mean values for all metrics by age-group and sex for MAD-derived metrics. Additional file 4. Average acceleration percentile plots for MAD metric. Additional file 5. Intensity gradient percentile plots for MAD metric. Additional file 6. M60 percentile plots for MAD metric. Additional file 7. Linear mixed models results for MAD metric. Additional file 8. Radar plots showing intensity profile by age and sex for MAD MX metrics. Additional file 9. Tables of percentile values for MAD metric. Additional file 10. Tables of percentile values for ENMO metric. Additional file 11. Linear mixed models results for ENMO metric. Additional file 12. STROBE checklist. Additional file 13. Recruitment and sampling information. Additional file 14. Participant characteristics by study.
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title: The psychometric properties of Binge Eating Scale among overweight college
students in Taiwan
authors:
- Huey-Yeu Yan
- Fu-Gong Lin
- Mei-Chih Meg Tseng
- Yue-Lin Fang
- Hung-Ru Lin
journal: Journal of Eating Disorders
year: 2023
pmcid: PMC10039566
doi: 10.1186/s40337-023-00774-3
license: CC BY 4.0
---
# The psychometric properties of Binge Eating Scale among overweight college students in Taiwan
## Abstract
The Binge Eating Scale (BES) is a screening tool that has been widely used to assess binge eating problems in Western countries. The current study aimed to test the validity and reliability properties of the BES among overweight college students in Taiwan. This research involved 300 overweight and obese college students while using a traditional Chinese-translated questionnaire in the survey and analyzed with subjective and scientific statistics methods afterward. The results indicated that BES has good cross-cultural applicability and can be used as a first-line measuring tool by mental health professionals to identify the severity of binge eating behavior among overweight or obese college students in Taiwan.
### Background
The Binge Eating Scale (BES) is a widely used measuring tool to assess binge eating problems in Western countries. However, the psychometric properties of such scales among cross-cultural youth groups are insufficient, and the factor structure continues to be debated; therefore, further research is needed. The aim of this study was to examine the properties of BES among overweight college students in Taiwan.
### Methods
A cross-sectional design and convenience sampling were adopted to recruit 300 overweight students from five universities. A translated Traditional Chinese version of BES was used for the survey, and the validity of the scale was tested using the Confirmatory Factor Analysis (CFA) and Bulimic Investigatory Test, Edinburgh (BITE). The reliability was evaluated using internal consistency and test–retest reliability.
### Results
The CFA results showed a reasonable model fit. The first-order two-factor model was consistent with that of the original BES and significantly correlated with the criterion of BITE score. Cronbach’s α value, representing internal consistency reliability, and the intraclass correlation coefficient of repeated measures made one month apart were both 0.83, indicating good reliability and stability. Significant correlations were observed between the BES score and sex and BMI; however, no correlation was observed between BES scores and age.
### Conclusion
The BES presents sound psychometric properties, has good cross-cultural applicability, and can be used as a first-line screening tool by mental health professionals to identify the severity of binge eating behavior among overweight college students in Taiwan. It is recommended that participant diversity and obesity indicators be incorporated into the scale in the future to establish a universal psychometric tool.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40337-023-00774-3.
The online version contains supplementary material available at 10.1186/s40337-023-00774-3.
## Background
Binge eating disorder (BED) is the most common type of eating disorder and is one mental illness with rather high morbidity [1]. A national research report in the U.S. indicated that the lifetime prevalence of BED was $0.85\%$ among adults over the age of 18 years in the United States ($$n = 36$$,306). This prevalence was higher than that of two other eating disorders: anorexia nervosa (AN), at $0.8\%$, and bulimia nervosa (BN), at $0.28\%$ [2]. The primary symptom of BED according to DSM-5 criteria is recurrent episodes of binge eating, characterized by eating a large amount of food in a short period (usually two hours) and a sense of losing control over eating [1]. Due to the highest incidence rate found at the age of 14 and 19–24 years old and mostly related to body weight and emotional distress, it seriously impacts physical and mental health [3–6].
Influenced by Western eating habits and culture in the past two decades, binge eating problems have emerged in Taiwan. A clinical study found that as high as $15.9\%$ ($$n = 189$$) of those who participated in weight-loss courses in hospitals revealed binge eating symptoms, most of whom were young and had early-onset obesity and psychological distress [7]. Another study also found that $42\%$ of people with obesity whoreceived clinical weight loss treatment ($$n = 841$$) were diagnosed with at least one mental disorder, of which eating disorders, mood disorders, and anxiety disorders accounted for the highest proportions [8]. In addition, another study analyzed National Health *Insurance data* from 2002 to 2013 and found that the incidence rate of BN, which has the same binge-eating symptoms as BED, also increased year by year, with 6.1 cases per 100,000 samples and an average increase of $4.96\%$ each year until 2009, dropping slightly to $4.95\%$ afterward. The highest incidence rate ($51.3\%$) was found in the age group of 20–29 years old (5406 cases) [9]. Compared with the peak onset cited above (14 and 19–24 years old), the age group with the highest incidence rate in Taiwan appeared to be more than ten years older than the United States. It was estimated that a group of young people had suffered from binge eating but were not used to accessing medical help [9]. In addition, this hidden group was mostly at college age and underwent great academic pressure, which may trigger eating disorder relapse [10]. Therefore, it is imperative to develop an appropriate traditional Chinese version of the BES screening tool in Taiwan for early detection of BED and other eating disorders and assistance with psychological treatment or in conjunction with other medical treatments.
The Binge Eating Scale (BES) is a tool used to assess binge eating problems among obese persons. It consists of 16 items of self-report questions. This scale was developed by Gormally et al. [ 11] based on years of clinical experience and psychometric data in treating patients with binge eating. The content of the scale included 8 items describing behavior manifestations and 8 items about feelings and cognitions surrounding a binge episode. Each item contains three to four levels of symptom descriptions with 0–2 or 0–3 points. The total score ranged from 0 to 46. The higher the score, the more severe the binge eating problems. To distinguish levels of binge-eating severity, Marcus et al. [ 12] set three cutoff scores by subtracting or adding half of the standard deviation to the mean BES score and created three score ranges of ≤ 17 = none to mild, 18–26 = moderate, or ≥ 27 points = severe binge eating problems. This scale, with an internal consistency reliability α value ranging from 0.80 to 0.93, was widely used, not only by people with overweight and obesity but also by community residents and college students from different countries, for example, being translated and tested in France (by French), Portugal (by Portuguese), Spain (by Spanish), Malaysia (by Malay), Lebanon (by Arabic), and China (by Simplified Chinese) [5, 13–17].
Although the BES has been widely used in Western countries and is convenient to score and easy to administer, the composition dimension is controversial when applied cross-culturally. For example, studies in China, Puerto Rico, France, and Spain showed that the best goodness of fit for the translated questionnaire was a one-factor model [5, 13, 14, 16], which is different from the original version. In addition, when assessing reliability, some studies used the Pearson correlation coefficients to analyze repeated measures of the same sample [14, 16], which may not clearly reflect the correlation and consistency between the two levels of measurement [18]. In addition, the interval between tests was not clearly stated, which may affect the test–retest reliability results. Although one study by China translated the original BES into a Simplified Chinese version of BES (SC-BES), Traditional *Chinese is* still the official language in Taiwan [19], so people may have problems reading and understanding the SC-BES correctly. In addition, the glyph images of certain simplified words are prone to mistakes, and certain simplified words are unable to present unique connotations and significance [20]. They may not fit the cultural and habitual needs in Taiwan [21]. Furthermore, the age of participants in the SC-BES ranged from 12 to 18, so we evaluated a different group from this study. In light of the prevalence of overweight and obesity in the 18–24-year-old college stage in Taiwan being considerably high ($29.3\%$) [22] but lacking applicable assessment tools, this study aimed to test the psychometric properties of a Traditional Chinese version of BES (TC-BES; see Additional file 1) by establishing its factor structure, internal consistency, and construct validity in overweight college students. It is hoped to develop and construct a reliable and valid BE screening tool for mental health professionals’ reference and usage.
## Participants
This study adopted a cross-sectional design and convenience sampling method to recruit 300 students with overweight or obesity from five universities in Taiwan based on Asian anthropometric standards and cultural background. The inclusion criteria were as follows: [1] young people aged 18–24; [2] who had a body mass index (BMI) ≥ 24; and [3] who were willing to participate and sign a consent form (if younger than 20 years old, a parent's signature on a parental consent form was required). The exclusion conditions included [1] pregnancy; [2] breastfeeding; [3] a history of severe mental illness diagnosed by a physician; and [4] refusal to participate in this study.
The sample size was calculated based on the recommendation by Anderson and Grebing [23] that each variable in the factor analysis required at least 10–20 samples. Thus, this study invited 335 potential participants, among whom 310 were willing to participate. After excluding 10 people who did not meet the inclusion criteria (BMI < 24), a total of 300 subjects, 215 women ($71.7\%$) and 85 men ($28.3\%$), participated in the study. The participants' ages ranged from 18 to 24 years, with a mean age of 20.37 (SD = 1.31). Their BMI ranged from 24.01 to 49.67 kg/m2 with a mean of 29.82 (SD = 4.63). Among the participants, $27.0\%$ were categorized as overweight (≥ 24 to < 27), $29.0\%$ as mildly obese (≥ 27 to < 30), $28.7\%$ as moderately obese (≥ 30 to < 35), and $15.3\%$ as severely obese (≥ 35).
## Binge Eating Scale (BES)
This study used BES as the research instrument. With the consent of the original author Jim Gormally, and based on the recommendations by Streiner et al. [ 24], the BES was translated into Mandarin Chinese using the following steps: [1] Forward translation: Two bilingual Taiwanese (one had a psychology background) translated BES into Chinese separately; [2] Comparison of the translations and synthesis version: The research team discussed the first drafts with translators and integrated them into a preliminary version (PV); [3] Backward translation/Blind back-translation: Two professional translators translated the PV back to the English version without reviewing the original questionnaire and then discussed with the research team and merged the drafts into one. The original author, Jim Gormally, was invited to assist in the review and revision. [ 4] Expert verification: Seven experts reviewed the semantic and conceptual equivalence between the Chinese PV and the original English scale using a four-point scale (1 point = very inappropriate; 4 points = very appropriate). The scale content validity index (scale-level CVI; S-CVI) was found to be 0.99, implying that the overall expert consistency was excellent. [ 5] Pilot test: Thirty overweight students were recruited to take a precursor test, and the internal consistency was found to be 0.88 (Cronbach’s α).
## Bulimic Investigatory Test, Edinburgh (BITE)
The Bulimic Investigatory Test, Edinburgh (BITE) is a self-administered questionnaire designed to identify subjects with symptoms of bulimia or binge eating [25]. It consists of two subscales: [1] Symptom Scale (30 questions): To assess the current level of bulimia symptoms based on yes and no questions. One point was assigned to "yes", while 0 points were assigned to "no"; [2] Severity Scale (3 questions): Scores were assigned to questions 6, 7, and 27 according to the frequency of binge eating and compensatory behaviors. The total score was the sum of the numbers corresponding to the circled responses with a maximum of 39 points. Thus, the total possible score of BITE was 69 points. The validity criterion was set at 25 points. A score higher than 25 points was indicative of a severe current eating disorder and existing binge eating problems [13, 25].
The Chinese version of the BITE was translated by Tseng et al. [ 26] after obtaining the original author's consent. The internal consistency reliability as represented by Cronbach α values for the subscales were 0.95 and 0.77, while the ICC values were 0.87 and 0.88. Based on the recommendation from previous studies [9, 26, 27], BITE scores 26–28 were used as the validity criterion. With the above settings, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic efficiency were all 1. On the other hand, the diagnostic efficiency dropped to 0.98 or 0.99 when set to 21–25 or 29, respectively [26]. To avoid false negative errors and achieve the study goal of early detection, the cutoff point was set to 26. The reliability and diagnosability of this scale were good. The scale is currently considered an appropriate self-administered questionnaire that could assist in the diagnosis of bulimia in practice in Taiwan [7, 26, 27] and is suitable for use as a relative criterion with the assessment tool in this research.
## BMI
The participants' BMIs were determined using a body fat meter (model, OMRON HBF-216, medical instrument certificate No. 000704) with the logged individual's height and the actual measured weight, calculated automatically by the instrument in real-time.
## Procedure
After receiving IRB approval (TCHIRB-10910001-E), data collection was conducted at five different universities. Most participants, 260 out of 300 ($86.7\%$), were from four universities in the metropolitan area of northern Taiwan. The remaining 40 participants ($13.3\%$) came from a university in the suburbs of central Taiwan. During the data collection process, the investigator first explained study objectives and procedures to potential participants both in writing and verbally. Data collection was conducted in a face-to-face manner and only initiated after obtaining the participants’ and their guardians’ written consent. Participation was voluntary. The questionnaires were self-administered, and the data were kept anonymous. Anyone who refused to take part was excluded before data collection. The participants completed questionnaires in a quiet campus environment, such as meeting rooms, libraries, and classrooms. The investigator checked the completeness of the questionnaire on-site when it was returned. The participants were asked to complete unanswered questions instantly. We set the test–retest reliability to 0.8 based on a previous study [14]. The study adopted 1.5 times the required sample size [19] to offset any potential attrition rate. As a result, this study used a subsample size of 30 to ensure test–retest reliability, and a sample suit for the study criteria was adopted to invite 30 participants to repeat the survey after one month. The data collection period was from February 11, 2011, to May 31, 2021.
## Statistical analysis
SPSS 22 and LISREL 8.51 statistical software were used in data analysis. A CFA was performed in construct validity testing based on the data of Bagozzi and Yi [28] and of Bollen [29] with the following model fit indices used: [1] Chi-square (χ2); [2] χ2 with degree of freedom (χ2/df; ideal value = 1 ~ 3); [3] goodness-of-fit index (GFI; ideal value > 0.90); [4] root mean square error of approximation (RMSEA; good fit < 0.05; fair fit = 0.05–0.08); [5] standardized root mean square residual (SRMR; good fit < 0.05; fair fit = 0.05–0.08); [6] adjusted goodness-of-fit index (AGFI; ideal value > 0.90; acceptable value > 0.8); [7] comparative fit index (CFI; ideal value > 0.95); and [8] nonnormed fit index (NNFI; ideal value > 0.95). Spearman's rank correlation coefficient (rho) was performed to test concurrent validity with BITE (> 0.5–0.75 indicating a moderate to a good relationship) [30], and the receiver operating characteristic curve (ROC) was used to describe the instrument’s performance. Reliability was determined by the internal consistency of Cronbach's alpha, and the test–retest reliability was assessed using the intraclass correlation coefficient (ICC). In addition, demographic data were analyzed using descriptive and correlational statistics.
## Participants descriptive
The mean BES score of the 300 participants was 10.67 (SD = 6.66; range: 0–34). According to the cutoffs recommended by Marcus et al. [ 12], 248 ($82.7\%$) participants who scored less than or equal to 17 points were considered to have no to mild binge eating issues. Forty-six participants ($15.3\%$) had moderate binge eating with scores ranging from 18 to 26, while 6 participants ($2\%$) had severe binge eating with scores ≥ 27. In addition, the mean BITE score was 10.20 (SD = 6.72; range: 1–40). The cutoff for the Chinese version of the BITE was 26 points. Among the participants, 291 ($97\%$) scored less than 26 points, indicating an unlikely presence of an eating disorder or existing binge eating. Alternatively, nine participants ($3\%$) had scores ≥ 26 points, indicating the likely presence of bulimia nervosa.
## Factor structure
CFA was used to evaluate whether the latent variables of the TC-BES components were consistent with the empirical data. The maximum likelihood (ML) estimation method was adopted for model estimation. Provided there were no offending estimates, the overall model fit test result was χ2 = 222.23 ($p \leq 0.001$), df = 103, χ2/df = 2.16 (< 3 is considered good fit), GFI = 0.91 (> 0.90 is considered good fit), AGFI = 0.89 (> 0.8 and not exceeding the GFI value is considered acceptable), CFI = 0.95 and NNFI = 0.94 (> 0.9 is a good fit), SRMR = 0.06 and RMSEA = 0.06 (values between 0.05 and 0.08 are fair fit). Therefore, it was confirmed that the first-order two-factor model had fair construct validity, and the factor structure and correlational relationship between variables were reasonable and acceptable.
## Concurrent validity
Residual analysis suggested that $p \leq 0.001$, and the data were not normally distributed. Therefore, Spearman's rank correlation coefficient was adopted to determine the correlation between BES and BITE, which revealed a significant positive correlation (rs = 0.69, $p \leq 0.01$). Next, the C-BITE cutoff score of 26 was used for diagnostic evaluation. The results of the receiver operating characteristic curve (ROC) analysis (Fig. 1) showed that the area under the curve (AUC) = 0.9 ($95\%$ CI = 0.83–0.97), which was higher than the standard of 0.7. In addition, the significance test revealed that $p \leq 0.001$, which suggested that the BES scale exhibited a significant effect in predicting symptoms. Subsequently, the Youden index was utilized to determine the best TC-BES cutoff score, which showed a sensitivity of 0.889, a specificity of 0.818, and an optimal cutoff score of 17 points. Fig. 1ROC curve showing the accuracy of BES as a screening tool for binge eating behavior (AUC = 0.9; $95\%$ CI = 0.83–0.97)
## Internal consistency reliability
The result of the TC-BES internal consistency reliability test, as indicated by Cronbach's α, was 0.83. After removing the individual item, Cronbach's α ranged from 0.80 to 0.83, indicating good correlations between items in the scale. In addition, the homogeneity of the questions was tested using the corrected item-total correlation (r tot). The correlation coefficients of the 16 items were all significantly higher than the standard value of 0.3 ($p \leq 0.01$), except for questions 6 and 13 ($r = 0.26$). Table 1 presents means, standard deviations, and percentages of BE symptoms (i.e., scores of 1, 2, or 3), corrected item-total correlations (r tot), and Cronbach's α values if any BES items were deleted. Table 1Mean scores (M), standard deviations (SD), percentages of symptoms (%), corrected item-total correlation (rtot), and Cronbach's alpha (α) if any BES items were deletedItemsMSD%r totα if item deletedItem 10.500.8037.700.330.82Item 21.280.9475.700.360.82Item 30.470.8232.700.570.81Item 40.140.517.000.310.82Item 50.680.6161.000.470.82Item 60.810.7567.700.260.83Item 70.971.2738.300.510.81Item 81.040.8568.300.510.81Item 90.910.7667.700.490.81Item 100.520.7934.700.640.80Item 110.610.5657.700.520.81Item 120.480.7336.000.390.82Item 130.220.699.300.260.83Item 140.940.8863.700.410.82Item 150.670.7853.000.470.81Item 160.440.6336.700.580.81Sample Size ($$n = 300$$), % = Percentage of the samples selecting 1, 2, or 3Table 2Results of ICC calculation in SPSS using 2-way mixed-effects model, single measurement, absolute-agreementIntraclass correlation$95\%$ Confidence intervalF test with true value 0Lower boundUpper boundValuedf1df2SigSingle measures0.8300.6520.91812.29429290.000
## Test–retest reliability
Thirty participants were randomly selected from the total sample ($$n = 300$$) to answer a second administration of the BES to test the scale’s temporal stability. The coefficient of stability was estimated using a two-way mixed-effects model based on a single measurement type and the absolute agreement relationship. The ICC for the results of the scale repeated one month after the first test was 0.83 ($p \leq 0.01$), which was greater than the standard of 0.75 [18], indicating good stability and good reliability (Table 2).
## Correlation with sex, age and BMI
Spearman’s rank correlation coefficient was adopted to determine the correlation between the BES score and various demographic variables. The correlation coefficients (rs) between the BES score and sex and BMI were − 0.14 and 0.12, respectively, and both reached the significance level ($p \leq 0.05$). In contrast, age (rs = − 0.02) had no significant relationship with the BES score.
## Discussion
In this study, LISREL was used to test the construct validity of the C-BES. The results from CFA revealed that the overall model fit was good, and there was a good and acceptable relationship between the factor structures. Although the χ2 value showed a significant difference ($p \leq 0.01$), it is important to note that the chi-square test is sensitive to sample size, and most differences will appear statistically significant when the sample size is large [31–33]. A further examination at χ2/df showed that the adjusted value was 2.66, which was smaller than the standard value of 3 [34]. In this study, most of the indices met the criteria of good model fit. The testing results were in line with the original version of the scale and consistent with findings by researchers from other countries [15, 17]. When compared with other studies, our study provided two-factor results instead of one-factor results [5, 16], and different results could also come from recruiting participants of different age groups. We recommend testing participants of various ages and BMI in future studies to develop a BE screening tool that applies to the general population.
In terms of content validity testing, the original author of the scale, Dr. Gormally, was invited to assist in reviewing the content of the backward translation. Seven Taiwanese experts were also invited to help with the review of the questionnaire translation, including two psychiatrists with expertise in binge eating disorders, three mental health experts specializing in eating disorder patient care, and two linguistics professors. After two consensus meetings and revisions, the content validity indices (item-level content validity index; I-CVI) for all questions (16 items, a total of 62 options) were 1. Only three options had indices of 0.86, which were well above the standard value of 0.78 for I-CVI [35]. This result showed that the translated scale was very representative, and the expert content validity was excellent. This research advocates the need to have five to seven expert reviewers in any future translation of study instruments. Furthermore, the expertise of the reviewers should be aligned with the area of the review to achieve effectiveness in cross-cultural language communication [20, 36].
In testing criterion-related validity, clinical evidence showed that BED was closely related to bulimia nervosa (BN), and both have obvious binge eating symptoms. Patients with BN also regularly exhibit compensatory behaviors, such as rigorous exercise, induced vomiting, laxative use, or fasting [1, 37, 38]. This study used BITE, the scale for diagnosing bulimia, as the key indicator for assessing criterion-related validity. The testing results were consistent with the empirical data, indicating a positive and significant relationship. The results support the C-BES as a validity tool for assessing BE behaviors among overweight or obese college students in Taiwan.
In terms of reliability, the internal consistency of the C-BES was good (α = 0.83), well above the acceptable value of 0.7, indicating good reliability [39]. Although the corrected item-total correlations (rtot) for items 6 and 13 were 0.26, the two items were retained in the model, as they were essential in identifying important characteristics of BED, including the extent of eaters' guilt after overeating and dietary abstinence between meals. The other reason was that the removal of individual questions did not increase the internal consistency of the overall scale. It is recommended that item clarity be further enhanced in terms of semantics to increase the level of discernment in the future. The results of the test–retest reliability were quite good. The scores of the two repeated measurements, with a one-month interval, had a significant correlation, and the ICC exceeded the reference value of 0.8 [18]. The C-BES had good reliability and stability and can be employed in large-scale surveys cost-effectively.
Demographic data suggested that subjects’ BES scores were significantly associated with their gender and BMI, which is consistent with the findings of most Western studies [40–42]. In addition, a recent national survey on young adults in the United States ($$n = 14$$,322; aged between 18 and 24 years) found that the prevalence of binge eating among overweight or obese individuals was substantially higher than that among normal or underweight individuals. More specifically, the prevalence was $29.3\%$ versus $15.8\%$ among women and $15.4\%$ versus $7.5\%$ among men. Subsequent logistic regression analysis indicated that the risk (odds ratio) of women developing binge eating was 2.32 times that of men ($95\%$ CI = 2.05–2.61) [43]. This result revealed the need for school health units to focus on the binge eating problem among college students and prioritize overweight or obese women for screening.
Since this study focused on college students, the inclusion criteria limited the age of participants to 18–24 years old. Such a narrow range in age may cause an insignificant difference in statistical testing. The results of this study support the above viewpoints and recommend that future studies be conducted to thoroughly explore the psychological factors associated with binge eating among people of diverse cultures and different genders to help develop positive coping strategies for regulating emotional stress.
## Limitations
This study adopted convenience sampling to survey young students from five colleges and universities characterized by their focus on developing healthcare programs. As most of these schools are in the metropolitan area of northern Taiwan, this study may not reach all young people with binge eating disorders. It is suggested that future studies should include diversified participants, schools of higher heterogeneity, or even those of different age groups to enhance the applicability and popularity of the scale. In addition, this study defined overweight and obese individuals based on World Health Organization [WHO] recommendations. The diagnostic criteria for overweight (BMI ≥ 24 kg/m2) and obese (BMI ≥ BMI 27 kg/m2) were based on data published by Taiwan’s Ministry of Health and Welfare [22]. The cutoff BMI values were different from the standards of Western countries. Due to racial differences and other discrepancies in disease-related conditions, how to clearly define the criteria for overweight and obesity is a highly discussed topic. It is recommended that future studies adopt multiple indicators, such as the body fat ratio, waist circumference, or waist-hip ratio (WHR), to diagnose overweight and obesity holistically.
## Conclusion
The aim of this study was to examine the psychometric properties of the BES scale among overweight college students in Taiwan. The original BES was translated into Mandarin Chinese by our research team and was tested with good reliability and stability. Diagnostic evaluation using BITE with 26 as the cutoff score created an optimal C-BES cutoff score of 17 and a high level of both sensitivity ($88.9\%$) and specificity ($81.8\%$), indicating that the scale could effectively predict BE symptoms. The results indicate that the BES presents sound psychometric properties, has good cross-cultural applicability, and can be used as a first-line screening tool by mental health professionals to identify the severity of binge eating behaviors among overweight college students in Taiwan. It is recommended that participant diversity and obesity indicators be incorporated into the scale in the future to establish a universal psychometric tool.
## Supplementary Information
Additional file 1. Appetite Scale.
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|
---
title: 'Characterization of renal artery variation in patients with clear cell renal
cell carcinoma and the predictive value of accessory renal artery in pathological
grading of renal cell carcinoma: a retrospective and observational study'
authors:
- Dingyang Lv
- Huiyu Zhou
- Fan Cui
- Jie Wen
- Weibing Shuang
journal: BMC Cancer
year: 2023
pmcid: PMC10039570
doi: 10.1186/s12885-023-10756-y
license: CC BY 4.0
---
# Characterization of renal artery variation in patients with clear cell renal cell carcinoma and the predictive value of accessory renal artery in pathological grading of renal cell carcinoma: a retrospective and observational study
## Abstract
### Objective
To explore the characteristics of renal artery variation in patients with renal cell carcinoma and to evaluate the predicting value of accessory renal artery in the pathological grading of renal cell carcinoma.
### Methods
The clinicopathological data of patients with clear cell renal cell carcinoma diagnosed in the Department of Urology of the First Hospital of Shanxi Medical University from September 2019 to March 2023 were retrospectively analyzed. All patients underwent visual three-dimensional model reconstruction from computed tomography images. All kidneys were divided into two groups: the affected kidney and the healthy kidney, and the incidence of renal artery variation in the two groups was analyzed. Then, according to the existence of accessory renal artery in the affected kidney, the patients were divided into two groups, and the relationship between accessory renal artery and clinicopathological features of patients with clear cell renal cell carcinoma was analyzed. Finally, univariate and multivariate logistic regression analyses were performed to determine the predictors of Fuhrman grading of clear cell renal cell carcinoma, and the predictive ability of the model was evaluated by the receiver operating characteristic curve.
### Results
The incidence of renal artery variation and accessory renal artery in the affected kidney was significantly higher than them in the healthy kidney. The patients with accessory renal artery in the affected kidney had larger tumor maximum diameter, higher Fuhrman grade and more exophytic growth. The presence of accessory renal artery on the affected kidney and the maximum diameter of tumor are independent predictors of high-grade renal cell carcinoma. The receiver operating characteristic curve suggests that the model has a good predictive ability.
### Conclusion
The existence of accessory renal artery on the affected kidney may be related to the occurrence and development of clear cell renal cell carcinoma, and can better predict Fuhrman grade of clear cell renal cell carcinoma. The finding provides a reference for the future diagnostic evaluation of RCC, and provides a new direction for the study of the pathogenesis of RCC.
## Introduction
Renal cell carcinoma (RCC) is one of the common malignant tumors of the urinary system, and it ranks third for incidence, after prostate and bladder cancer, accounting for $2\%$-$3\%$ of adult malignant tumors [1]. Among the histological subtypes of RCC, clear cell RCC (ccRCC) is the most common one, accounting for 70–$80\%$ of cases [2]. About $16\%$ of the patients present with metastatic renal cell carcinoma at their first visits. In recent years, the use of advanced imaging technologies has promoted early diagnosis of RCC. The incidence of RCC is on the rise, however, its etiology is still poorly understood. At present, the main surgical approaches of RCC are radical nephrectomy and partial nephrectomy, and there are still greater risks of recurrence and metastasis after surgery [3–5]. Therefore, it is necessary to further explore the mechanisms underlying the occurrence and development of RCC.
The evaluation of tumors has been one of the core tasks in clinical diagnosis and treatment of diseases, especially for ccRCC, a malignant tumor with poor prognosis. Tumor grade describes the degree of differentiation of tumor cells relative to normal cells, and it is a reliable indicator of tumor growth and metastasis. Studies have pointed out that the pathological grade of RCC is one of the major predictors of prognosis. Usually, the higher the grade, the worse the prognosis [6]. Fuhrman grading system has been widely used in pathological grading of RCC, and it is established based on the evaluation of several nuclear characteristics: nuclear size, nuclear shape and nucleolar prominence. According to these characteristics, tumors are classified into four different grades (I-IV). Grades I and II are considered to be low-grade tumors with good prognosis, while grades III and IV are considered to be high-grade tumors with poor prognosis [7]. Yu et al. [ 8] found that the prognosis of patients with low-grade RCC is relatively better than that of high-grade patients, the safety of partial nephrectomy is higher, and the incidence of postoperative complications is lower. Tabourin et al. [ 9] reported that Fuhrman grade > 2 is one of the important risk factors for postoperative recurrence of RCC. However, Fuhrman grade can only be determined by postoperative pathology examination, and it is still challenging to obtain them timely and effectively before surgical treatment of RCC.
Studies have found that the incidence of renal artery variation (RAV) in patients with RCC ranges between $17.5\%$ and $25.1\%$, which is higher than $14.01\%$ in the general population [10, 11]. And in the clinical work, we often found that the affect kidney of patients with renal cell carcinoma was often accompanied by accessory renal artery. Therefore, we speculate that there is a certain relationship between the occurrence and development of RCC and RAV. To confirm this hypothesis, we observed the characteristics of RAV in patients with RCC based visual three-dimensional (3D) models reconstructed from computed tomography (CT) images, and explored the correlation between accessory renal artery (ARA) and clinicopathological features of patients, as well as the predictive factors of tumor pathological grade. The findings are expected to enhance our understanding of the mechanism of occurrence and development and treatment of RCC.
## Data collection
We retrospectively collected the clinical data of patients with ccRCC in the Department of Urology of First Hospital of Shanxi Medical University from September 2019 to March 2023. The clinicopathological features of patients included sex, age, body mass index (BMI), tumor location, maximum tumor diameter, tumor growth pattern, Fuhrman grade, and hypertension. Inclusion criteria were as follows: [1] patients had unilateral single tumor; [2] patients underwent plain and enhanced urinary CT scans or abdominal and pelvic CT scans, as well as visual 3D model reconstruction from CT images; and [3] ccRCC was confirmed by renal puncture or postoperative pathology report. Patients with incomplete data were excluded. This study complied with the Declaration of Helsinki. The study was approved by the ethics committee of the First Hospital of Shanxi Medical University (Ethical code: [2021] K048). The written informed consent was waived by the First Hospital of Shanxi Medical University of the Institutional Review Board.
All patients underwent CT scanning, and CT images were used for 3D model reconstruction. The typical reconstructed 3D models are shown in Fig. 1.Fig. 1Representative CT images and reconstructed 3D models for one male patient and one female patient. Legends: A Male, 70 years old, right renal tumor, the maximum tumor diameter was 7.5 cm, there were 2 ARA and 1 branch of renal artery before the renal hilum. B Female, 50 years old, right renal tumor, the maximum tumor diameter was 3.0 cm, there is 1 ARA
## Classification of RAV
There are different classification methods of RAV. ARA and branches of renal artery before renal hilum are the most clinically significant, which have attracted much attention from scholars. According to the study of Satyapal et al. [ 12], we defined ARAs as one or more arteries originating from thoracic aorta, abdominal aorta or its branches (superior and inferior mesenteric artery, common iliac artery, etc.), except renal artery. Branches of renal artery before renal hilum are defined as one or more branches from the trunk of the renal artery before entering the renal hilum. The number of renal arteries, ARA and branches of renal artery before renal hilum were recorded by observing CT images and reconstructed visual 3D model, and the incidence of all sorts of variations was calculated.
## Pathological grading of ccRCC
The pathological grading was conducted by the pathologists of our hospital using the Fuhrman grading system. Patients with ccRCC were grouped into a low-grade group (Fuhrman grades I and II) and a high-grade group (Fuhrman grades III and IV).
## Statistical analysis
Continuous variables were expressed as means ± standard deviation and analyzed by the Student’s t-test, while categorical variables were presented as counts (percentages) and analyzed by the Chi-squared test. All kidneys were divided into two groups: the affected kidney and the healthy kidney. The incidence of RAV in the two groups was analyzed. Then, according to the presence or absence of ARA on the affected kidney, the patients were divided into two groups, and the relationship between ARA and clinicopathological features of patients with ccRCC was analyzed. Finally, the presence or absence of ARA on the affected kidney, sex, age, body mass index (BMI), tumor location, maximum tumor diameter, tumor growth pattern, and hypertension were included in univariate and multivariate logistic regression analysis to determine the independent predictors of tumor pathological grade. The receiver operating characteristic (ROC) curve and AUC were used to evaluate its predictive ability. All statistical analyses were performed using SPSS version 26 (IBM Corporation). Differences were considered statistically significant at $P \leq 0.05.$
## Clinical characteristics of patients
A total of 99 patients with ccRCC were enrolled in the study. The baseline characteristics of the patients are summarised in Table 1. The mean age of the patients was 57.99 ± 9.99 years, and there were 54 males ($54.5\%$) and 46 patients with hypertension ($46.5\%$). The mean BMI was 25.46 ± 3.21 kg/m2, and the mean maximum tumor diameter was 4.34 ± 2.73 cm. The inferior pole of kidney ($43.4\%$) was the major site where ccRCC was located. The high-grade ccRCC accounted for $26.3\%$, and $80.8\%$ of ccRCC showed an exophytic growth pattern. Table 1The baseline characteristics of the patientsVariablesValueN99Age, years57.99 ± 9.99Sex, n (%) Male54 (54.5) Female45 (45.5)BMI, kg/m225.46 ± 3.21Maximum tumor diameter, cm4.34 ± 2.73Tumor location, n (%) Superior pole of kidney26 (26.2) Middle pole of kidney30 (30.3) Inferior pole of kidney43 (43.4)Tumor growth pattern, n (%) Exogenous80 (80.8) Endogeny19 (19.2)Hypertension, n (%) Yes46 (46.5) No53 (53.5)Fuhrman grade, n (%) High26 (26.3) Low73 (73.7)Continuous variables are expressed as mean ± standard deviation and categorical variables as counts (percentages)
## Correlation between RAV and ccRCC
All the kidneys were divided into two groups: the affected kidney and the healthy kidney. The incidence of RAV in the affected kidney was significantly higher than that in the healthy kidney ($$P \leq 0.003$$). Furthermore, we divided RAV into ARA and the branches of renal artery before renal hilum. The incidence of ARA in the affected kidney was significantly higher than that in the healthy kidney ($$P \leq 0.000$$, Table 2), but there was no significant difference in the incidence of the branches of renal artery before renal hilum between the two groups ($$P \leq 0.144$$, Table 2).Table 2Correlation between RAV and ccRCCGroupRAVARABranches of renal artery before the renal hilumYesNoYesNoYesNoThe affected kidney, n (%)57 (57.6)42 (42.4)42 (42.4)57 (57.6)30 (30.3)69 (69.7)The healthy kidney, n (%)36 (36.4)63 (63.6)19 (19.2)80 (80.8)21 (21.2)78 (78.8)χ2 value8.94212.5332.139P value0.0030.0000.144
## Correlation between ARA and clinicopathological features of patients with ccRCC
The patients were divided into two groups according to the presence or absence of ARA in the affected kidney. There was no significant difference in sex, age, BMI, tumor location and hypertension between the two groups. However, compared to the group without ARA in the affected kidney, the group with ARA in the affected kidney had a larger tumor diameter ($$P \leq 0.000$$, Table 3) and a higher Fuhrman grade ($$P \leq 0.000$$, Table 3) and exhibited more exophytic growth ($$P \leq 0.000$$, Table 3).Table 3Correlation between ARA and clinicopathological features of patients with ccRCCVariablesGroup with ARA in the affected kidney ($$n = 41$$)Group without ARA in the affected kidney ($$n = 58$$)P valueSex, n (%)0.503 Male24 (58.54)30 (51.72) Female17 (41.46)28 (48.28)Age, years59.14 ± 10.4657.57 ± 9.380.711BMI, kg/m224.85 ± 2.5225.52 ± 3.630.855Hypertension, n (%)0.228 Yes22 (53.66)24 (41.38) No19 (46.34)34 (58.62)Maximum tumor diameter, cm5.61 ± 2.863.11 ± 1.460.000Tumor location, n (%)0.449 Superior pole of kidney13 (31.70)13 (22.41) Middle pole of kidney10 (24.39)20 (34.48) Inferior pole of kidney18 (43.90)25 (43.10)Tumor growth pattern, n (%)0.000 Exophytic39 (95.12)41 (70.69) Endogenetic2 (4.88)17 (29.31)Fuhrman grade, n (%)0.000 High20 (48.78)6 (10.34) Low21 (51.22)52 (89.66)
## ARA predicts Fuhrman high-grade ccRCC in patients
To explore the association between ARA and Fuhrman grade of ccRCC, Logistic regression analysis was performed on each variable for the univariate model (Table 4). In the univariate model, the presence of ARA on the affected kidney (OR, 8.254; $95\%$CI, 2.907–23.437; $$P \leq 0.000$$) and maximum tumor diameter (OR, 1.700; $95\%$CI, 1.300–2.224; $$P \leq 0.000$$) were found to be significantly associated with high-grade ccRCC. Logistic regression was then performed for these two variables for the multivariate model (Table 4). In the multivariate model, the presence of ARA on the affected kidney (OR, 4.242; $95\%$CI, 1.135–13.331; $$P \leq 0.013$$) and maximum tumor diameter (OR, 1.536; $95\%$CI, 1.170–2.017; $$P \leq 0.002$$) remained significant predictors of high-grade ccRCC. As shown in Fig. 2, the AUC of the multivariate model was 0.841 ($95\%$CI, 0.739–0.942; $$P \leq 0.000$$), indicating that this model had high predictive ability. Table 4Univariate and multivariate models for predicting high-grade ccRCCVariablesUnivariate analysisMultivariate analysisOR$95\%$CIP valueOR$95\%$CIP valueSex, male/female1.8380.726–4.6540.199Age, years1.0170.971–1.0660.477BMI, kg/m20.9540.823–1.1060.533Hypertension, yes/no0.7960.323–1.9650.621ARA on the affected kidney, presence/absence8.2542.907–23.4370.0004.2421.350–13.3310.013Tumor location0.113 Superior pole of kidneyreferencereferencereference Middle pole of kidney0.2460.066–0.9180.037 Inferior pole of kidney0.6190.220–1.7410.364Maximum tumor diameter, cm1.7001.300–2.2240.0001.5361.170–2.0170.002Tumor growth pattern, exophytic/endogenetic3.6430.780–17.0130.100Fig. 2ROC curve. Legends: The AUC of the multivariate model was 0.841 ($95\%$CI, 0.739–0.942; $$P \leq 0.000$$), indicating that this model had high predictive ability
## Discussion
*In* general, each kidney of a normal adult is supplied by a single artery. However, in the embryonic stage, the developing mesonephros, metanephros, suprarenal glands, and gonads are supplied by nine pairs of lateral mesonephric arteries arising from the dorsal aorta. These nine pairs of arteries are divided into three groups: cranial (1st and 2nd pair), middle (3rd–5th pair), and caudal (6th–9th) [13]. The renal artery develops from one of the pairs in the middle group. With the gradual upward movement of the metanephros, renal artery variantions can be formed if the remaining arteries are not completely degenerated. The incidence of RAV varies widely worldwide. The incidence of RAV in the Chinese population is $14.5\%$ [14]. In the Greek population, the incidence of RAV is $11.2\%$ [15]. In Colombia, nearly a third of its population has one additional renal artery, and about $3\%$ of the same population has two additional renal arteries, most of which reach the kidney through its hilar region [16]. In addition, the incidence of RAV in patients with RCC is higher than that in normal people [10, 11], suggesting that the occurrence and development of RCC may be related to RAV. To elucidate the relationship between RAV and RCC, we divided RAV into two categories: ARA and the branches of renal artery before renal hilum. It is found that the incidence of ARA in the affected kidney was significantly higher than that in the healthy kidney, indicating that ARA may play an important role in the occurrence and development of RCC. At present, there is no evidence showing the relationship between ARA and RCC.
In order to explore the correlation between RAV and ccRCC, we divided all kidneys into two groups: the affected kidney and the healthy kidney, and found that the incidence of RAV in the affected kidney was significantly higher than that in the healthy kidney. Furthermore, we found that the incidence of ARA in the affected kidney was significantly higher than that in the healthy side of kidney, but we did not find significant difference in the incidence of branches of renal artery before renal hilum between the two groups. This result indicated that the significant difference in the incidence of RAV between the two groups is caused by the difference of in the incidence of ARA.
Furthermore, we found that the group with ARA in the affected kidney had a larger tumor maximum diameter and higher Fuhrman grades, and ccRCC showed more exophytic growth. The reason may be that the blood supply of the tumor with ARA in the affected kidney is so abundant that the tumor grows faster, which leads to high tumor invasiveness and a high degree of malignancy. Previous studies have shown that tumor growth patterns can predict ccRCC pathological grade [17–20] and high-grade ccRCC mostly show exophytic growth. Our study found that most of the tumors with ARA in the affected kidney showed exophytic growth and higher Fuhrman grades, which indirectly confirmed the above conclusion.
Previous studies have shown that tumor diameter, BMI and tumor growth pattern can well predict the pathological grade of RCC. Zhu et al. [ 21] analyzed 186 patients with RCC and found that tumor size (OR, 1.91; $95\%$CI, 1.01–3.60; $$P \leq 0.047$$) was one of the independent risk factors for high-grade RCC. Hu et al. [ 22] found that maximum tumor diameter was a significant predictor of high-grade ccRCC in both univariate (OR, 1.136; $95\%$CI, 1.003–1.287; $$P \leq 0.045$$) and multivariate (OR, 1.159; $95\%$CI, 1.018–1.320; $$P \leq 0.025$$) models. Our study obtained the same results through univariate and multivariate logistic regression analysis. In addition, previous studies have confirmed that obesity is closely related to the occurrence and development of RCC [23, 24]. A meta-analysis research found that BMI is associated with the risk of RCC [25]. However, Parker et al. [ 26] suggest that patients with high BMI in RCC had a lower degree of tumor malignancy. Our results showed that there was no significant correlation between BMI and pathological grades of ccRCC. Therefore, these contradictory results suggest that the relationship between obesity and RCC is complex and needs further study. Some studies showed that tumor growth patterns can predict pathological grades of ccRCC [15–18], but our study did not find a significant correlation between them, possibly due to the small sample size.
An important new finding of this study is that the presence of ARA in the affected kidney is an independent risk factor for high-grade ccRCC, which has not been reported in previous studies. Moreover, AUC showed that the multivariate prediction model of ARA combined with tumor maximum diameter could better predict high-grade ccRCC. At present, the mechanism of RAV and its effect on the pathological grade of RCC are still unclear. A study [27] performed in 2014 showed that in the embryonic kidney, several possible progenitor cells express the transcription factor Foxd1 precursor. Foxd1 itself regulates the origin, number, orientation, and cellular composition of renal vessels. Multiple accessory and aberrant arteries that originate from the abdominal aorta and iliac arteries likely cause the kidneys’ failure to ascend. Whether mutations of the *Foxd1* gene in humans are associated with accessory or aberrant renal arteries remains to be investigated. In addition, Foxd1 is found to inhibit the ectopic differentiation and composition of the renal vascular system, but the molecular mechanisms involved need to be further studied.
The limitations of this study are as follows: [1] this study was a retrospective study, which might lead to selection bias although strict screening had been carried out; [2] this study was a single-center study with a small sample size, and the number of patients with high-grade ccRCC was small; and [3] only patients with ccRCC were included in this study, without comparison with normal population.
## Conclusion
In patients with ccRCC, the incidence of RAV in the affected kidney was significantly higher than that in the healthy kidney due to the influence of ARA. Importantly, this study found for the first time that ARA could be used as an independent predictor for Fuhrman grades of ccRCC. The finding provides a reference for the future diagnostic evaluation of RCC, and provides a new direction for the study of the pathogenesis of RCC. More prospective clinical studies with a large sample size, as well as relevant basic research, are needed in the future to explore the underlying biological mechanisms by which ARA affects the Fuhrman grade of ccRCC.
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|
---
title: Mitochondrial ROS driven by NOX4 upregulation promotes hepatocellular carcinoma
cell survival after incomplete radiofrequency ablation by inducing of mitophagy
via Nrf2/PINK1
authors:
- Chao Peng
- Xi Li
- Feng Ao
- Ting Li
- Jingpei Guo
- Junfeng Liu
- Xiaoting Zhang
- Jinyan Gu
- Junjie Mao
- Bin Zhou
journal: Journal of Translational Medicine
year: 2023
pmcid: PMC10039571
doi: 10.1186/s12967-023-04067-w
license: CC BY 4.0
---
# Mitochondrial ROS driven by NOX4 upregulation promotes hepatocellular carcinoma cell survival after incomplete radiofrequency ablation by inducing of mitophagy via Nrf2/PINK1
## Abstract
### Background
The recurrence of hepatocellular carcinoma (HCC) after radiofrequency ablation (RFA) remains a major clinical problem. Cells that survive the sublethal heat stress that is induced by incomplete RFA are the main source of HCC relapse. Heat stress has long been reported to increase intracellular reactive oxygen species (ROS) generation. Although ROS can induce apoptosis, a pro-survival effect of ROS has also been demonstrated. However, the role of ROS in HCC cells exposed to sublethal heat stress remains unclear.
### Methods
HepG2 and HuH7 cells were used for this experiment. Insufficient RFA was performed in cells and in a xenograft model. ROS and antioxidant levels were measured. Apoptosis was analyed by Annexin-V/PI staining and flow cytometry. Protein expression was measured using western blotting. Colocalization of lysosomes and mitochondria was analyzed to assess mitophagy. Corresponding activators or inhibitors were applied to verify the function of specific objectives.
### Results
Here,we showed that sublethal heat stress induced a ROS burst, which caused acute oxidative stress. This ROS burst was generated by mitochondria, and it was initiated by upregulated NOX4 expression in the mitochondria. n-acetylcysteine (NAC) decreased HCC cell survival under sublethal heat stress conditions in vivo and in vitro. NOX4 triggers the production of mitochondrial ROS (mtROS), and NOX4 inhibitors or siNOX4 also decreased HCC cell survival under sublethal heat stress conditions in vitro. Increased mtROS trigger PINK1-dependent mitophagy to eliminate the mitochondria that are damaged by sublethal heat stress and to protect cells from apoptosis. Nrf2 expression was elevated in response to this ROS burst and mediated the ROS burst-induced increase in PINK1 expression after sublethal heat stress.
### Conclusion
These data confirmed that the ROS burst that occurs after iRFA exerted a pro-survival effect. NOX4 increased the generation of ROS by mitochondria. This short-term ROS burst induced PINK1-dependent mitophagy to eliminate damaged mitochondria by increasing Nrf2 expression.
## Introduction
Radiofrequency thermal ablation (RFA) is widely considered to be an effective local therapy for hepatocellular carcinoma (HCC) because of its minimal invasiveness and limited complications [1]. However, although the outcomes after RFA appear to be comparable to those reported after surgical resection when the tumor diameter is < 1 cm, HCC recurrence after RFA remains a major problem and is associated with a poor prognosis [2]. For patients with HCC tumors less than 3 cm in diameter, five-year recurrence rates are reported to reach 50–$70\%$ [3, 4]. Destroying all the malignant cells within the target region is the primary aim of RFA treatment. However, tumor heterogeneity, the quality of imaging guidance, variation in the achieved temperature within a tumor, and the heat sink effect are factors that lead to incomplete RFA (iRFA) [1]. Cancer cells in the iRFA zone are exposed to sublethal hyperthermia, and they consequently either undergo apoptosis or recover from reversible injury [5]. The survival of cells in the sublethal hyperthermia zone is the main cause of HCC recurrence after RFA. Thus, targeting the molecular mechanisms that facilitate HCC cell survival after iRFA may increase the ablation zone and improve prognosis.
Hyperthermal treatment has been reported to increase intracellular reactive oxygen species (ROS) levels [6–9]. Modulation of intracellular ROS levels is crucial for cellular homeostasis, as cells respond differently to varying levels of ROS [10]. Toxic levels of ROS can kill tumor cells, while the nonlethal increase in ROS levels facilitates tumor progression and metastasis [10]. Sustained exposure to heat stress (> 1 h) can induce sufficient ROS accumulation and eventually lead to cell death [6, 7, 11, 12]. However, HCC cells in the iRFA zone suffer a state of hyperthermia for approximately 15 min at temperatures between 38 and 50 °C [1]. Most cells that are exposed to such heat stress do not die and instead exhibit greater migration and invasion abilities [13–15], which indicates that the increased ROS levels under such stress conditions may be nonlethal. At nonlethal levels, ROS can act as signaling molecules to activate stress-responsive pathways [10, 16]. Mildly elevated ROS levels can activate the PI3K-Akt pathway, and ERK$\frac{1}{2}$ pathway, upregulate heat shock protein expression (HSP) [17–19], and induce autophagy [16], which have all been reported to be crucial for survival during heat stress. Moreover, ROS has been reported to promote the survival of liver endothelial cells suffering from ischemia/reperfusion injury [20], the adaptation and survival of isolated hepatocytes [17] and the survival of human placenta-derived multipotent cells [21]. Thus, we postulate that the changes in ROS levels may also contribute to the survival of HCC cells after iRFA. However, the exact dynamic change in ROS levels and the related mechanism have not yet been fully explored.
Mitophagy is a type of selective autophagy that mediates the clearance of damaged mitochondria and is involved in survival [22]. ROS have been reported to induce PINK1-dependent mitophagy [23–25]. Short-term sublethal heat exposure can induce mitochondrial depolarization [25] and partially damage the outer mitochondrial membrane of isolated mitochondria [26]. However, the mitochondrial electron transport chain (ETC), which is the source of mitochondrial ROS (mtROS) located at the inner mitochondrial membrane, is not markedly affected by short sublethal heat exposure [26]. *The* generation of more mitochondrial ROS under sublethal heat stress conditions may require a trigger. Recently, “ROS-induced ROS release” was reported to be a mechanism underlying ROS augmentation [27]. In addition to mitochondria, NADPH oxidases are another main source of ROS in mammalian cells [16]. There are seven isoforms of NADPH oxidase (NOX1–5 and DUOX1–2) [16]. It has been reported that NADPH oxidases (NOX2 and NOX4) can localize to the mitochondria and affect mtROS generation [28–30]. NOX4 has also been reported to be upregulated by heat stress [31]. Moreover, ROS generated by NADPH oxidases have been shown to inhibit necrosis or apoptosis in many types of cells [31–36]. However, little is known about the role of NADPH oxidases in ROS production in the context of iRFA, and mitophagy has not been investigated as a mechanism that can be initiated by ROS and is involved in survival.
In this study, we show that the upregulation of NOX4 in mitochondria increases mtROS generation, which triggers PINK1-dependent mitophagy to clear mitochondria that are damaged by sublethal heat stress by increasing the expression of Nrf2, thereby promoting cell survival after iRFA. This pro-survival effect of ROS may be an important mechanism underlying the heat stress resistance of HCC cells induced by iRFA.
## Reagents
N-acetylcysteine (NAC), trypan blue and MitoTEMPO were obtained from Sigma‒Aldrich (USA). Diphenyleneiodonium chloride (DPI), VAS2870, Mdivi-1, carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazon(FCCP), lucigenin, bardoxolone and 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA) were obtained from MCE(USA), and NADPH was purchased from Beyotime Biotechnology (China). MitoTracker™ Deep Red, MitotrackerFM Green and MitoSOX™ were obtained from Invitrogen (UK). Specific antibodies were purchased from different companies as follows: GAPDH, LC3B,Nrf2 (CST, USA), NOX4, P62, TOMM20, VDAC1, Cleaved caspase-3 (Abcam, UK), BCL-2, and BAX (Affinity, China). The mitochondrial stress test complete assay kit (ab232857) and extracellular oxygen consumption assay kit (ab197243) were obtained from Abcam(UK).
## Sublethal heat treatment
An in vitro model of insufficient thermal ablation was established based on the method described in a previous report, with minor modifications [37]. Briefly, HCC cells were seeded in 6-well plates. The cells reached 70–$80\%$ confluence after 24 h of culture. Then, medium that was prewarmed at 46 °C was added to the wells. The cells were subsequently submerged into a water bath that was preheated at 46 °C for 15 min. After with the medium was replaced with fresh medium that had been kept at 37 °C, the cells were returned to the incubator. NAC (5 mM) or MitoTEMPO (500 nM) was administered 1 h after heat treatment and incubated for 4 h. DPI (10 μM) and VAS2870 (10 μM) were added 30 min after heat treatment and incubated for 4 h. FCCP (1 μM), bardoxolone(0. 5 μM) and Mdivi-1 (10 μM) were used for pretreatment for 1 h.
## Cell viability assays
Cells were seeded and reached 70–$80\%$ confluence after 24 h of culture. The cell viability was first determined before heat treatment. The cells were then exposed to 46 °C for 15 min, and cell viability was measured at 0, 2, 6, 12, and 24 h. At each indicated time point after heat treatment, the cells were trypsinized and harvested by centrifugation (1200 × rpm, 4 min). The cells were resuspended and mixed with $0.4\%$ trypan blue solution. Cell viability was evaluated by counting the number of cells that excluded the dye.
## ROS and mitochondrial superoxide production measurement
ROS production was measured using DCFH-DA. MitoSOX™ Red was used to measure mitochondrial superoxide production. Both indicators were dissolved in dimethyl sulfoxide (DMSO) and diluted to working concentrations in Hank’s balanced salt solution. Cells exposed to heat stress were trypsinized and harvested at the indicated times. Then, the collected cells were resuspended in PBS with either 10 μM DCFH-DA or 5 mM MitoSOX™ Red and incubated at 37 °C for 20 min. The fluorescence signal was measured by using flow cytometry(CytoFLEX) at 485 nm/538 nm for DCFH-DA and 510 nm/590 nm for MitoSOX™ Red.
## Determination of the glutathione (GSH) levels
A commercial glutathione assay kit(Beyotime, China) was used for GSH level measurement. The experiment was carried out based on the manufacturer’s instructions. Cells were collected by centrifugation(1000 × rpm,5 min). The precipitates were mixed with a triple volume of protein removal reagent. After being frozen twice, the cells were centrifuged at 10000 × g for 10 min and the supernatants were collected. The GSH detection agent was mixed and 150 μL was added per well. After adding 10 μL of sample and incubating at room temperature for 5 min, 50 μL of NADPH was added, and the absorbance of the well was measured at 412 nm. The GSH levels were calculated based on a standard curve.
## Measurement of total antioxidant capacity (T-COA) and superoxide dismutase (SOD) activity
The total antioxidant capacity assay was conducted based on the manufacturer’s instructions (Beyotime, China). Cells were washed and transferred to a homogenizer. The homogenates were centrifuged at 12000 × g for 5 min. The total antioxidant capacity detection agent was prepared based on the instructions, and 200 μL was added per well. After adding 20 μL of sample and incubating, the absorbance of the wells was measured at 734 nm. A commercial total SOD activity assay kit (Beyotime, China)based on WST-8 was used. The cells were lysed and centrifuged at 12000 × g for 5 min. The supernatants were collected. The working solution was prepared based on the instructions. Twenty microliters of sample was mixed with SOD detection buffer and WST-8 and incubated at 37 °C for 30 min. The absorbance of the well was measured at 450 nm. The T-COA and SOD activities were calculated based on a standard curve.
## Measurement of NADPH oxidase activity
The NADPH oxidase activity of total cell homogenate was assessed by superoxide production. The method was based on lucigenin chemiluminescence described previously [36, 38]. Briefly, cells were lysed in lysis buffer (Beyotime, China) and the proteins were harvested by centrifugation(10000 × rpm,15 min).Working buffer(phosphate buffer(50 mM),EDTA(1 mM),sucrose(150 mM) was prepared. After the protein concentrations were measured, 50 μg of protein was added to 500 μL of working buffer. Then, dark-adapted lucigenin(100 μM) was added. Chemiluminescence was measured immediately after NADPH (100 μM) was added at 15 s intervals for 1 min by an EnVision® luminometer.
## Mitochondrial membrane potential (MMP) measurement
TMRE was used to assess the MMP. Cells exposed to heat stress or not were treated with NAC or DMSO for the indicated times. Then, the cells were incubated with medium containing 150 nM TMRE. After staining for 20 min at 37 °C, cells were washed, and the fluorescence signal was acquired by flow cytometry (CytoFLEX).
## Determination of apoptosis
An Annexin V/PI apoptosis detection kit (BD Biosciences) was used to assess apoptosis. Briefly, cells were trypsinized without EDTA and harvested by centrifugation(200 × g, 5 min). Then, 100 μL of 1 × binding buffer was used to resuspend the cells. Five microliters of Annexin V and PI were added. After incubation for 15 min at room temperature in the dark, the samples were immediately assessed by using a flow cytometer (CytoFLEX). Apoptotic cells were divided into cells undergoing early(Annexin V + /PI −) or late apoptosis (Annexin V + /PI +). A total of 10,000 cells per condition were analyzed for these measurements..
## Small interfering RNA (siRNA) -mediated knockdown
Human NOX4 siRNA was obtained from GenePharma (China). The siRNAs were transfected into cells by using Lipofectamine® 3000 Reagent (Life Technologies) based on the manufacturer’s protocol. Twenty-five picomoles of siRNA per well was used for transfection. The sequences of siRNAs was NOX4 siRNA(5′- AACCUCUUCUUUGUCUUCUACAUGCUGCU − 3′) and control siRNA(5′-UUCUCCGAACGUGUCACGUTT -3′).
## RNA isolation and real-time PCR
An RNeasy kit (QIAGEN, USA) was adopted to extract total RNA from cultured cells. PrimeScript™ RT Master Mix (Takara, Japan) was used for genomic DNA digestion and reverse transcription. Real-time PCR was performed using SYBR® Green Master Mix (Applied Biosystems) on a thermal cycler (Bio-Rad, USA). GAPDH was chosen as an internal control. The primer sequences of the genes were described as follows:NOX1-F 5'-GGTCAACACGAGGAGAGC-3ʹ; NOX1-R: 5'-CAAGGATCCACTTCCAAGACTC-3'; NOX2-F: 5'-CCCAATCCCTCAGTTTGCT-3'; NOX2-R: 5'-CCTTCTGTTGAGATCGCCAA-3'; NOX3-F: 5'-ACCTTCTGTAGAGACCGCTAT-3'; NOX3-R:5'-TCACATGCATACAAGACCACA.
-3'; NOX4-F:5'-CTGTGGTGTTACTATCTGTATTTTCTC-3''; NOX4-R:5'-CTTGCTGCATTCAGTTCAACA-3'; NOX5-F: 5'-GCCAGTGCCTCAACTTCG-3'; NOX5-R:5'-CCACTACCACGTAGCCCATA-3'; DUOX1-F: 5'-GCGTCTACATG AGAAATGCCA-3'; DUOX1-R: 5'-GCAGCAGTGCATCCACAT-3'’; DUOX2-F:5'-CGCCACCTACCAGAACATC-3''; DUOX2-R: 5'-GGTAGAGAAGAACTGCTC AGAG-3'. RIPK1-F: 5′-TCCTCGTTGACCGTGAC-3′; RIPK1-R: 5 -GCCTC
CCTCTGCTTGTT-3′; RIPK3-F: 5′-CCAGCTCGTGCTCCTTGACT-3′:; RIPK3-R: 5′-TTGCGGTCCTTGTAGGTTTG-3′; GAPDH-F:5'-TGACAACAGCCTCAAGAT-3'; GAPDH-R:5'-GAGTCCTTCCACGATACC-3'.
## Western blotting analysis
Cells were collected and lysed using RIPA lysis buffer (Beyotime, China) containing $1\%$ protease inhibitor on ice. The supernatants containing proteins were harvested by centrifugation(10,000 × rpm,15 min) at 4 °C. Equal protein extracts were separated using SDS‒PAGE ($15\%$ or $10\%$) and then transferred onto polyvinylidene difluoride membranes. After blocked with $5\%$ BSA solution, the membranes were incubated with the relevant antibodies at 4 °C overnight.
## Separation of nucleari and mitochondrial fractions
A commercial kit(Beyotime, China) was used to separate separating mitochondria. Based on the manufacturer’s protocol, cells were trypsinized and harvested by centrifugation(200 × g,10 min). After washing with PBS, 1 ml mitochondrial isolation solution was added for 10 min on ice, and then the cells were transferred into a homogenizer. Then, the homogenates were centrifuged at 600 × g for 10 min at 4 °C. The supernatants were collected and centrifuged at 11000 × g for 10 min at 4 °C. The precipitates contained mitochondria and were harvested. The nuclear protein extraction reagent was added to the precipitates..After vortexing, nuclear proteins were harvested by centrifugation(16000 × g, 10 min). The supernatants were collected.
## Mitochondrial DNA (mtDNA) extraction and copy number quantification
Mitochondria were lysed with mitochondrial lysis buffer on ice for 10 min. Then, 5 μL of enzyme mix was added. After incubation in a 50 °C water bath for 60 min, absolute ethanol was added and incubated for an additional 10 min at − 20 °C. mtDNA was collected by centrifugation at top speed for 5 min. The mtDNA was analyzed by PCR using MT-CO2 (mitochondrially encoded cytochrome c oxidase II): F: 5'-CAAACCTACGCCAAAATCCA-3', R:5'-GAAATGAATGAGCCTACAGA-3'. 18S rDNA (F:5'-TAGAGGGACAAGTGGCGTTC-3' and R:5'-CGCTGAGCCAGTCAGTGT-3') was choosed for measuring nuclear DNA.
## Immunofluorescence confocal microscopy (colocalization)
The cells were incubated with MitoTracker™ Deep Red (500 nM) for 10 min. After the medium was removed, the cells were washed, and $4\%$ paraformaldehyde was added to fix for 15 min. Then, the cells were permeabilized using $0.2\%$ Triton X-100 for 10 min. After a 30 min block with $5\%$ BSA, primary antibody was added to the culture dish and incubated at room temperature for 30 min. FITC-conjugated secondary antibodies were added, and further incubated for 30 min. The cells were then stained with DAPI for 10 min. After washing, the cells were observed using a confocal microscope (Zeiss, Germany). MitoTracker™ Deep Red fluorescence was determined at wavelengths of $\frac{647}{668}$ nm. All fields were imaged using a 63 × objective lens.
## Assessment of lysosome and mitochondria colocalization
Mitophagy was assessed by the colocalization of the mitochondria and lysosomes using a confocal system. The cells were incubated with MitoTracker™ Red (500 nM) for 10 min before heat treatment in an incubator. The cells were then washed with fresh medium and subjected to heat treatment. After 12 h, the cells were incubated with LysoTracker™ Green (50 nM) and Hoechst 33,342 for 15 min. The cells were then washed and observed with a Leica confocal system (Zeiss, Germany). MitoTracker™ Red fluorescence was measured at wavelengths of $\frac{647}{668}$ nm and LysoTracker™ Green fluorescence was measured at wavelengths of $\frac{488}{560}$ nm. Images were taken with a 63 × objective.
## Measurement of ATP levels
A commercial ATP determination kit(Beyotime, China) was used to measure intracellular ATP levels. After adding lysis buffer to the plates, the cells were scraped. The supernatants were harvested by centrifugation (12,000 × g, 5 min). Diluted ATP detection reagent was prepared on ice, and 100 μL was added per well. Then,20 μL of supernatant was then added per well. Luminescence was measured using an EnVision® illuminometer at 5 s after mixing. A BCA protein assay kit was used to measure theprotein concentrations of the supernatant. The ATP levels were normalized to the protein concentration of each sample.
## Oxygen consumption rate (OCR) measurement
Extracellular Oxygen Consumption Assay (Abcam, ab197243) and Mitochondrial Stress Test Complete Assay(Abcam, ab232857) kits were combined to measure the extracellular OCR according to the protocol. Briefly, approximately 4 × 104 cells were seeded in a 96-well clear bottom black plate. After being cultured for the indicated times, the cells were subjected to sublethal heat stress or not. Medium containing NAC or not was added to the cells at 1 h after heat exposure. Ten microliters of fresh medium, oligomycin (15 μM), FCCP (25 μM), or antimycin A (15 μM) was added to the indicated wells 12 h after heat exposure. Then, high sensitivity mineral oil warmed to 37 °C was applied. Signal intensity was immediately measured using the TR fluorescence intensity mode at $\frac{380}{650}$ nm by an EnVision® reader.
## TUNEL staining
Apoptosis was analyzed in 5 μm thick frozen sections of xenografts that were collected one day after iRFA using TUNEL staining. The sections were dried at room temperature for 20 min and then submerged in $4\%$ paraformaldehyde to fix for 30 min. After washing twice with PBS, 0. $2\%$ Triton X-100 was added and incubated at room temperature for 15 min. The sections were washed twice with PBS again. Equilibration buffer(1X,100 μL) was added and incubated at room temperature for 30 min. Then, TdT enzyme solution was added and incubated at 37 °C for 60 min. The sections were then stained with DAPI for 15 min. Fluorescence was measured using a fluorescence microscope (Leica,Germany). Apoptotic cells displayed red fluorescence under excitation. Blue fluorescence of DAPI was used to quantify the nuclei. Fiji software (ImageJ, Bethesda, MD) was applied to analyze the acquired images. TUNEL-positive cells were normalized to the total number of nuclei in each image.
## Establishment of a subcutaneous HCC xenograft model and thermal ablation model in vivo
The HCC xenograft model was established in BALB/c nude mice (male, 4–6 weeks) obtained from the Guangdong Medical Laboratory Animal Center (Guangzhou, China). Isoflurane anesthesia was used for the anesthesia of the animals. Surgical procedures were conducted under aseptic conditions. After local disinfection,HepG2 cells (2 × 106) were injected subcutaneously into the right axilla of mice using 1 ml syringe. The experiment was not carried out until the diameter of subcutaneous tumors reached approximately 10 mm. The mice were randomly divided into four groups, namely, the control, iRFA, heat, and heat + iRFA groups, with each group including five mice. The RFA electrode was inserted into the tumor and positioned at one-third of the length of the tumor under ultrasound guidance. For iRFA, the ablation power was 5 W for 1 min. Sham ablation was conducted without ablation power. To evaluate postoperative recurrence, the tumor dimensions were measured every 4 days. The volume of tumors was calculated based on the formula: V = (length × width2)/2. On Day 20 after ablation,the mice were sacrificed and the tumor volume and weight were measured and recorded. The mice in the NAC or NAC + iRFA group were injected with 100 mg/kg NAC for 5 days via the tail vein starting 2 h after iRFA.
## Statistical analysis
All the data are expressed as the mean ± standard deviation. Statistical analysis was performed on SPSS Statistics 25 (USA) using Student’s t test and one-way analysis of variance (ANOVA). $p \leq 0.05$ was considered statistically significant. All the figures associated with statistics were generated with GraphPad Prism (Version 8.0).
## Cells undergo oxidative stress after sublethal heat stress in vivo
To examine the dynamic changes in ROS levels after sublethal heat stress, we used a ROS indicator to determine the intracellular ROS levels at the indicated time points after sublethal heat stress. As shown in Fig. 1A, B, the ROS levels in both HepG2 and HuH7 cells gradually increased from 2 to 6 h after sublethal heat stress, then gradually decreased, and finally maintained at a level higher than that observed at 37 °C. As the antioxidant system is another factor that determines the fate of cells under oxidative stress, we also investigated the intracellular T-COA of cells at the indicated time points after sublethal heat stress. The results showed that it decreased in the first 2 h after sublethal heat stress, then gradually increased, and was finally maintained at a level higher than that observed at 37 °C (Fig. 1C). Therefore, we further separately quantified the levels of reduced GSH, a major antioxidant in cells, and total activity of SOD, an enzymatic antioxidant, after sublethal heat stress at the indicated time points. The results showed that the reduced level of GSH also gradually decreased from 2 to 6 h after sublethal heat stress, then gradually increased, and was finally maintained at a level higher than that observed at 37 °C (Fig. 1D). The total SOD activity gradually increased within 12 h after sublethal heat stress (Fig. 1E).These observations showed a transient impairment of antioxidant capacity and were in accordance with the dynamic changes in ROS levels after sublethal heat stress. Taken together, these data suggested that the cells undergo short-term oxidative stress after sublethal lethal heat stress. Given that oxidative stress is a trigger of cell death, we further assessed cell viability at 6, 12, and 24 h after sublethal heat stress. As shown in Fig. 1F, no significant cell death was observed in either HepG2 or HuH7 cells at 12 h or 24 h after sublethal heat stress, compared to 6 h after sublethal heat stress. This indicates that the ROS burst after sublethal heat stress was not strong enough to induce cell death. Fig. 1Dynamic changes in ROS production and antioxidant capacity after sublethal heat stress. HepG2 and HuH7 cells were incubated at 46 °C for 15 min or not. A The changes in ROS fluorescence B total antioxidant capacity, C reduced GSH level, and total D SOD activity were measured at the indicated time points (37 °C, 0, 2, 6, 12, and 24 h) after sublethal heat stress. E Cell viability measured at 37 °C,6, 12, and 24 h after sublethal heat stress. Values are the mean ± SE. * $p \leq 0.05$ vs. 37 °C; **$p \leq 0.01$ vs. 37 °C; ***$p \leq 0.001$ vs. 37 °C; ##$p \leq 0.01$ vs. 6 h; ###$p \leq 0.001$ vs. 6 h;ns no significance
## Inhibition of ROS production sensitizes cells to sublethal heat stress due to increased apoptosis
To further clarify the role of ROS after sublethal heat stress, we eliminated intercellular ROS by adding the ROS scavenger NAC 1 h after sublethal heat stress. First, we measured the MMP, a classical marker of the induction of cell death. Both HepG2 and HuH7 cells exhibited a significant decrease in the MMP at 12 h in the presence of NAC (Fig. 2A, B).This suggest that inhibition of ROS may cause cell death after sublethal heat stress. Then, cell death was assessed by flow cytometry using Annexin V-FITC/PI labeling. Cell death was significantly increased in the NAC treatment group (Fig. 2C, D). Notably, Annexin V + cells were the most significantly increased portion (Fig. 2C, E and F). This suggested that apoptosis is the main type of cell death induced by ROS inhibiton after sublethal heat stress. To further confirm the effect of ROS inhibiton, we measured the expression of apoptosis-related proteins. As shown in Fig. 2G, the protein levels of the active forms of Caspase-3 and BAX were increased, and the protein level of BCL-2 was decreased. In contrast, the mRNA levels of RIPK1 and RIPK3, markers of necroptosis, were not significantly altered (Fig. 2H). These results suggest that the ROS burst after sublethal heat stress exerts a pro-survival effect. The ROS burst after sublethal heat stress was confined to within 12 h (Fig. 1A). To further confirm the pro-survival effect of the ROS burst within 12 h after sublethal heat stress, we determined the effect on apoptosis by adding NAC 12 h after sublethal heat stress. As shown in Fig. 2I, no apoptosis was observed in the group treated with NAC at 12 h after sublethal heat stress. This suggests that the ROS burst within 12 h after sublethal heat stress contributed to the cells survival after sublethal heat stress. Fig. 2NAC induces apoptosis after sublethal heat stress. HepG2 and HuH7 cells that were untreated or subjected to sublethal heat stress were cultured for 12 h in medium with or without NAC (5 mM). A and B Mitochondrial membrane potential was assessed by TMRE staining and analyzed by flow cytometry at 12 h after sublethal heat stress. C and D Cell death was quantified using Annexin V-FITC/PI staining and flow cytometry at 24 h after sublethal heat stress. Summary graph representing the percentage of single PI + cells (Q1 areas) (E) and Annexin V + cells (Q2 and Q3 areas) (F) in each group. G Western blot of GAPDH, BAX, BCL-2, and Cleaved-caspase-3 expression. H mRNA expression of RIPK1 and RIPK3.HepG2 and HuH7 cells incubated with NAC(5 mM) at 12 h after sublethal heat stress. ( I)Cell death was quantified using Annexin V-FITC/PI staining and flow cytometry at 24 h after sublethal heat stress. Values are the mean ± SE.**$p \leq 0.01$; ***$p \leq 0.001$; ns no significance
## Enhanced production of mtROS driven by NADPH oxidase
ROS can be generated in various cellular compartments, and they have different reactivities and stabilities. Mitochondria and cellular NADPH oxidase are the two main sources of cellular ROS [16]. To investigate the source of increased ROS production, mitochondrial superoxide production was assayed using MitoSOX™ staining, and NADPH oxidase activity was measured by the lucigenin-enhanced chemiluminescence method. As shown in Fig. 3A, mtROS levels gradually increased from 2 to 6 h after sublethal heat stress, then gradually decreased, and were finally maintained at a level higher than that observed at 37℃. NADPH oxidase activity also increased from 2 h after sublethal heat stress (Fig. 3B). Furthermore, both pharmacological inhibition of NADPH oxidase with DPI or VAS2870 and cleavage of mtROS with mitoTEMPO induced apoptosis and reduced the ROS levels after sublethal heat stress (Fig. 3C, D). This suggests that both sources contributed to the increased generation of ROS and cell survival after sublethal heat stress. Notably, the level of ROS reduction by NAC, a nonspecific ROS scavenger, was not more pronounced than that by mitoTEMPO, DPI, or VAS2870 (Fig. 3D). There may be an upstream and downstream relationship between mitochondria and NADPH oxidase. Reports suggest that NADPH oxidase is also located within mitochondria and induces mtROS release by generating ROS [29, 39, 40]. Therefore, we further investigated the effect of NADPH oxidase inhibition on mtROS generation. As shown in Fig. 3E, inhibition of NADPH oxidase by DPI or VAS2870 inhibited mtROS levels at 6 h after sublethal heat stress. Thus, NADPH oxidase is upstream of mtROS generation after sublethal heat stress. Fig. 3Elevated production of mitochondrial ROS driven by NADPH oxidases. A Changes in MitoSOX™ fluorescence as measured using flow cytometry at the indicated time points (37 °C, 0, 2, 6, 12, and 24 h) after sublethal heat stress. B Superoxide production by NADPH oxidase was measured in total cell homogenates. C Heated or unheated HCC cells incubated with or without inhibitors of ROS: NAC(5 mM), MitoTEMPO (500 nM), DPI (10 μM), or VAS2870 (10 μM). Apoptosis was quantified using Annexin V-FITC/PI staining and flow cytometry at 24 h after sublethal heat stress. D ROS levels as measured by flow cytometry at 6 h after sublethal heat stress. E Heated or unheated HCC cells preincubated with or without inhibitors of NADPH oxidase: DPI (10 μM) and VAS2870 (10 μM), MitoSOX™ fluorescence as measured using flow cytometry at 6 h after sublethal heat stress. Values are the mean ± SE.**$p \leq 0.01$ vs. 37℃; ***$p \leq 0.001$ vs. 37℃; ###$p \leq 0.001$ vs. 6 h; ns no significance
## The upregulation of NOX4 in mitochondria contributes to the increase in ROS production after sublethal heat stress
The NOX family contains seven members, and the mRNA expression of these genes was determined. The results showed that only NOX4 mRNA expression was significantly upregulated at 6 h after sublethal heat stress (Fig. 4A). Western blotting analysis also revealed that NOX4 protein expression was markedly increased in both cell types after sublethal heat stress (Fig. 4B). Studies have reported that NOX4 is located in mitochondria and is associated with the generation of mtROS in both breast cancer cells and renal cells [29, 41]. Therefore, we further investigated whether NOX4 is located in the mitochondria of HepG2 and HuH7 cells by using confocal microscopy. The overlap of NOX4 staining and MitoTracker™ red staining suggested that NOX4 localized at mitochondria in both HepG2 and HuH7 cells (Fig. 4C). To further verify this hypothesis, we measured NOX4 expression in the mitochondrial fractions of HepG2 and HuH7 cells by Western blotting. The results confirmed that NOX4 was located in the mitochondria, and its level was also increased in this compartment after sublethal heat stress (Fig. 4D). We then used siRNA-mediated knockdown of NOX4 to investigate its function (Fig. 4E). This approach was also successful in reducing mitochondrial NOX4 expression (Fig. 4F). Consistent with the pharmacological inhibition of NADPH oxidase, the knockdown of NOX4 significantly suppressed both total ROS and mtROS production at 6 h after sublethal heat stress (Fig. 4G, H). As expected, inhibition of NOX4 also increased apoptosis at 24 h after sublethal heat stress (Fig. 4I, J). These results suggest that upregulation of NOX4 in mitochondria is the trigger of increased mtROS generation after sublethal heat stress. Fig. 4NOX4 localizes at mitochondria and contributes to the increase in ROS production after sublethal heat stress. A RT-qPCR analysis of NOX1–5 and DUOX1–2 mRNAs expression after sublethal heat stress. B Western blot of NOX4 expression in total cell lysates after sublethal heat stress. C Colocalization of mitochondria and NOX4. MitoTracker™ red and FITC- conjugated secondary antibody was used for NOX4. Nuclei were counterstained with Hoechst 33,342. Yellow displays colocalization. D Western blot of NOX4 expression in mitochondria after sublethal heat stress. E Protein levels of NOX4 in whole cell extracts. F Protein levels of NOX4 in mitochondria. G DCFH-DA and H MitoSOX™ fluorescence as measured using flow cytometry at 6 h after sublethal heat stress. I and J Apoptosis was quantified using Annexin V-FITC/PI staining and flow cytometry at 24 h after sublethal heat stress. Values are the mean ± SE. * $p \leq 0.05$;***$p \leq 0.001$; ns no significance
## Inhibition of ROS levels after sublethal heat stress induces the accumulation of damaged mitochondria and results in severe mitochondrial dysfunction
Short-term heat exposure can cause damage to the mitochondria [26]. Removal of damaged mitochondria by autophagy is an essential survival response. ROS are known to trigger both autophagy and, mitophay and may be essential for removing damaged mitochondria after sublethal heat stress. We first assessed the content of mtDNA,a biochemical marker of mitochondrial number that is affected by inhibition of ROS after sublethal heat stress. The results showed that NAC treatment after sublethal heat stress increased the mtDNA copy number (Fig. 5A). Then, we further investigated the effect of inhibiting ROS production on removing damaged mitochondria using a fluorescence-based assay. As shown in Fig. 5B, the accumulation of damaged mitochondria was observed in the NAC treatment group after sublethal heat stress(Fig. 5B, C). Then, the morphology, quantity, and function of mitochondria were assessed. As shown in Fig. 5D, the NAC treatment group showed more round mitochondrial structures. Increased mitochondrial mass and reduced ATP production were also observed in the NAC treatment group (Fig. 5E, F). Basel respiration, max respiration, ATP-coupled oxygen consumption and proton leak were significantly decreased in cells by NAC treatment after sublethal heat stress(Fig. 5G). These results suggest that the ROS burst after sublethal heat stress is necessary to remove damaged mitochondria and sustain mitochondrial function. Fig. 5Inhibition of ROS production results in accumulation of damaged mitochondria and severe mitochondrial dysfunction after sublethal heat stress. A The mitochondrial DNA copies of each group. B Flow cytometry of mitochondrial status. Gates represent cells with damaged mitochondria. C The statistical results of B in three independent experiments. D Representative images of mitochondrial morphology. E Mitochondrial as mass assessed by staining with MitoTracker™ Green using a flow cytometric. F ATP contents of each group. G Results of the Mito stress test. Values are the mean ± SE.*$p \leq 0.05$;**$p \leq 0.01$; ***$p \leq 0.001$; ns no significance
## Mitophagy is induced by sublethal heat stress and is involved in survival
Mitophagy is a type of selective autophagy that mediates the clearance of damaged or dysfunctional mitochondria from the cell. ROS can induce the induction of PINK1-dependent mitophagy. It is unclear whether mitophagy plays a role in cell survival during heat stress. To determine whether mitophagy can be induced by sublethal heat stress, we assessed the LC3B, PINK1, P62, and TOMM20 protein levels via Western blotting. As shown in Fig. 6A, cells exposed to sublethal heat stress showed significantly increased protein expression levels of LC3B-II and PINK1, accompanied by significantly reduced P62 and TOMM20 protein levels. To further verify the induction of mitophagy by sublethal heat stress, we assessed mitophagy after sublethal heat stress based on the colocalization of labeled mitochondria and lysosomes. The results showed that sublethal heat stress exposure caused significant increase in the colocalization of mitochondria and lysosomes (Fig. 6B). This suggests that mitophagy was activated by sublethal heat stress in cells. Therefore, we further investigated whether mitophagy is involved in cell survival after sublethal heat stress. We pretreated cells with FCCP, an inducer of mitophagy, and Mdivi-1,an inhibitor of mitophagy, to observe the changes in apoptosis after sublethal heat stress. As shown in Fig. 6C, inhibition of mitophagy resulted in a 1.5-to 2.5-fold increase in Annexin V + staining, and inducing mitophagy before sublethal heat stress significantly inhibited apoptosis. This confirmed that mitophagy exerts a pro-survival effect after sublethal heat stress. Fig. 6Mitophagy is induced by sublethal heat stress and is involved in survival. A WB of GAPDH, P62, LC3B, and TOMM20. B Representative confocal images of cells coloaded with MitoTracker™ Red and LysoTracker™ Green at 24 h after sublethal heat stress. Colocalization is shown in yellow and indicated by arrows. C Flow cytometry analysis of apoptosis of heated or unheated HCC cells after inducing or inhibiting mitophagy at 24 h after sublethal heat stress. Values are the mean ± SE.**$p \leq 0.01$; ***$p \leq 0.001$; ns no significance
## Inhibition of ROS production induces apoptosis after sublethal heat stress by inhibiting mitophagy
To determine whether the apoptosis induced by ROS inhibition was mediated by mitophagy, we examined alterations in LC3B-II, PINK1, P62, and TOMM20 protein levels in cells following inhibition of ROS production after sublethal heat stress. As shown in Fig. 7A, decreased PINK1 and LC3B-II protein expression, and P62 and TOMM20 accumulation were observed upon inhibition of ROS production after sublethal heat stress. Moreover, the colocalization between mitochondria and lysosomes was also markedly decreased in the group treated with NAC (Fig. 7B). To further confirm, we examined the impact of the preinduced of mitophagy on NAC induced apoptosis after sublethal heat stress. As shown in Fig. 7C, the effect of NAC on inducing apoptosis after sublethal heat stress was completely eliminated by the predinduction of mitophagy. These data confirmed that mitophagy was suppressed by inhibition of ROS production after sublethal heat stress. Fig. 7Inhibition of ROS production induces apoptosis after iRFA via mitophagy inhibition. A WB of GAPDH, P62, LC3B, and TOMM20 expression in heated or unheated HCC cells treated with or without 5 mM NAC at 24 h after sublethal heat stress (B) Representative confocal images of cells coloaded with MitoTracker™ Red and LysoTracker™ green after ROS inhibition. Colocalization is shown in yellow and indicated by arrows. C Apoptosis was quantified using Annexin V-FITC/PI staining and flow cytometry of heated cells treated with NAC (5 mM) or FCCP (1 μM) at 24 h after sublethal heat stress. Values are the mean ± SE. *** $p \leq 0.001$; ns no significance
## Nrf2 was elevated in response to oxidative stress after sublethal heat stress and mediated ROS induced mitophagy
Nrf2 is usually activated to promote tumor cell survival under oxidative stress conditions[42].To determine whether the oxidative stress induced by acute sublethal heat stress is sufficient to activate Nrf2, we measured the change in Nrf2 by Western blotting. As shown in Fig. 8A, elevated Nrf2 protein expression was observed after sublethal heat stress and was accompanied by decreased Keap1 expression. Nrf2 is a transcription factor that performs its function in the nucleus. Thus, we futher measured the nuclear protein expression of Nrf2. Increased protein expression of Nrf2 was also observed in the nucleus after sublethal heat stress(Fig. 8B). This was also confirmed by immunofluorescence (Fig. 8C). To determine whether this elevated Nrf2 expression was a responsed to the ROS burst that occurs after sublethal heat stress, we examined the effect of NAC on the protein expression of Nrf2. As shown in Fig. 8C, D, the expression of Nrf2 was significantly inhibited by NAC treatment after sublethal heat stress. We further measured the effect of adding NAC 12 h after sublethal heat stress on the protein expression of Nrf2, since the oxidative stress was confined to within 12 h after sublethal heat stress. The results showed that there was no effect on the protein expression of Nrf2 when NAC was added at 12 h after sublethal heat stress(Fig. 8E). This confirmed that the Nrf2 elevation occurred in response to the ROS burst that occurs after sublethal heat stress. Reports also suggest that Nrf2 can induce PINK1 expression during stress. Thus, we further explored the effect of Nrf2 on the protein expression of PINK1. As shown in Fig. 8F, the NAC-mediated inhibition of PINK1 expression after sublethal heat stress could be reversed by bardoxolone, an activator of Nrf2 (Fig. 8F). Similarly, the apoptosis induced by NAC after sublethal heat stress was also reversed by bardoxolone(Fig. 8G). Taken together, under sublethal heat stress, Nrf2 expression was increased in response to the ROS burst, and then it translocated to the nucleus to induce the expression of PINK1, which is involved in to involve the mitophagy after sublethal heat stress. Fig. 8Nrf2 was elevated in response to oxidative stress after sublethal heat stress and mediated ROS induced mitophagy. A Western blot of Keap1 and Nrf2 expression in whole cell lysates. B Changes in the protein expression of Nrf2 in nucleus. C Representative image of Nrf2 localization in cells in different groups. DEffect of NAC(5 mM) on Nrf2 E Effect of adding NAC(5 mM) 12 h after sublethal heat stress on Nrf2. F Inhibition effect of NAC on PINK1 after sublethal heat stress was reversed by bardoxolone. G Apoptosis induced by NAC after sublethal heat stress was reversed by bardoxolone at 24 h after sublethal heat stress
## Inhibition of ROS production sensitizes tumors to iRFA in vivo
We then evaluated the effect of inhibition of ROS production on tumor growth in vivo in response to iRFA. Tumors grown from subcutaneously injected HepG2 cells were subjected to iRFA or sham operation. As shown in Fig. 9A, B, administration of NAC after iRFA significantly increased the percentage of TUNEL positive cells. This demonstrated the pro-survival effect of the ROS burst after sublethal heat stress, which was also showed after iRFA in vivo. We then harvested the tumors on Day 21. As shown in Fig. 9C–E, iRFA significantly increased the tumor weight and volume compared to the control group. In the iRFA group, tumor growth was markedly suppressed by the administration of NAC 2 h after treatment. These results show that inhibition of ROS production also sensitizes cells to hyperthermia treatment in vivo. Fig. 9Inhibition of ROS production sensitizes HCC cells to hyperthermia in vivo. NAC (100 mg/kg/day) was administered via tail vein injection starting 2 h after iRFA for 5 days. A and B A significantly higher portion of TUNEL positive cells was observed in iRFA + NAC Group 1 day after iRFA. The tumors were collected on Day 21. Tumor growth was monitored at the indicated times. The C tumor weights and D tumor volume growth curves indicated that NAC sensitized HepG2 cells to hyperthermia. E Image showing the tumor burdens in mice. F Schematic diagram of the ROS burst that is involved in cell survival after sublethal heat stress. Values are the mean ± SE.**$p \leq 0.01$;***$p \leq 0.001$; ns no significance
## Discussion
This study revealed that short-term (within 12 h) elevation in ROS levels exerts a pro-survival effect in HCC after iRFA. The NOX4 isoform of NADPH oxidase located in the mitochondria is responsible for the stimulation of mtROS production by heat stress in HCC cells. Our study also showed that the ROS burst played a pivotal role in the initial induction of PINK1-dependent mitophagy by increasing the expression of Nrf2, and inhibiting the increase in ROS levels after iRFA resulted in the accumulation of damaged mitochondria, which eventually initiated apoptosis.
It is well established that heat stress results in increased ROS levels, which are considered to be important for the induction of cell death [6]. However, the key factor determining whether an adverse effect is induced by stress is the dose–response relationship. The cell-killing effect of hyperthermal treatment is time and temperature dependent [43]. With sublethal heat stress, irreversible cell damage occurs only after prolonged exposure (30 to 60 min) [5]. Clearly, this is not representative of RFA treatment. The thermal dose during iRFA is not sufficient to induce apoptosis. However, increased ROS generation has been observed in cells exposed to sublethal temperatures ranging from 40 to 47 °C regardless of the duration [12, 21, 44–46]. Our data showed that cells suffered short-term oxidative stress after iRFA without cell death. This confirmed that the increased ROS production after iRFA was moderate and noncytotoxic. The apoptosis induced by ROS production inhibition after iRFA demonstrated a pro-survival effect of ROS and confirmed the function of ROS as signaling molecules. This finding is also consistent with previous reports that increased ROS levels can activate prosurvival signaling pathways [10, 16]. Moreover, it has previously been reported that ROS generation at mild temperatures is involved in the induction of cellular defense molecules [12, 47] such as HSPs [18, 21]. Thus, considering that the ROS level after 12 h was still higher than that at pre-iRFA, the ROS burst after iRFA may also protect cells from a higher ROS level post-iRFA by enhancing the antioxidant system.
In our study, we also investigated the source of ROS overproduction in HCC cells after iRFA. These results indicated that ROS were generated by both mitochondria and NADPH oxidases. However, the effects of both DPI and MitoTEMPO on ROS production did not differ from those of NAC, a nonspecific ROS scavenger. This suggests that an upstream and downstream relationship may exist between these two sources of ROS. Further inhibition of NADPH oxidase activity by DPI or VAS2870 attenuated mtROS production, demonstrating an upstream role of NADPH oxidase in mtROS activation. Recent studies have also reported crosstalk between these two producers [39, 48–50]. Indeed, a previous study showed that the mitochondrial inner membrane was still intact, and there was no significant change in the composition of electron transport chain complexes in isolated mitochondria after acute heat treatment [26]. This suggests that the integrity of the ETC in isolated mitochondria may not be affected by short-term heat stress, and there is an factor that initiates increased mtROS production. Recently, “ROS-induced ROS release” was described as a mechanism underlying ROS augmentation [27]. In the present study, we found that NOX4 was upregulated by iRFA, and depletion of NOX4 inhibited iRFA-induced increases in mtROS and total ROS. NOX4 was recently reported to be present in the mitochondria of many types of cells [28, 29, 51]. Our study showed that NOX4 was also located in the mitochondria of HCC cells. This suggests that NOX4 may impact mtROS through ROS generation confined to the mitochondria. Moreover, NOX4 within the mitochondria is regulated by ATP, as described in a previous study [41]. Mitochondrial damage is accompanied by low ATP levels. This indicates that individual generation of ROS in the mitochondria may be highly dependent on ATP and NOX4 levels. Thus, the levels of ROS not only removed the damaged mitochondria, but also avoided the induction of apoptosis.
One of the consequences of increased ROS production is the induction of autophagy [52, 53]. Heat stress has been reported to initiate autophagy in many cell types [54, 55]. Moreover, autophagy has been reported to be a pro-survival mechanism during stress, including iRFA-induced stress [54]. Mitophagy is a type of selective autophagy that mediates the clearance of damaged mitochondria. An increase in ROS levels triggers mitophagy to limit ROS production [56], and we also observed a decline in ROS levels 6 h after iRFA. This may be due to enhanced mitophagy. The mitochondrial outer membrane has been reported to be partially damaged under heat stress conditions (incubation of isolated mitochondria for 20 min at 42 °C) [26]. Our study also showed that mitochondrial damage occurred after heat treatment, and removal of damaged mitochondria by mitophagy is essential for HCC cell survival after iRFA. Consistent with this finding, in Caenorhabditis elegans larvae, rebuilding of the mitochondrial network after heat stress has been reported to depend on mitophagy, which is essential for recovery [57]. Moderate mitochondrial ROS levels have been reported to trigger PINK1-dependent mitophagy [23, 24, 58]. In our study, we also observed that inhibition of ROS production blocked PINK1-dependent mitophagy, resulting in accumulation of damaged mitochondria and ultimately inducing apoptosis after iRFA.
Nrf2 plays a vital role in cells encountering oxidative stress, and the protumor role of Nrf2 is also well established [42]. Cells usually adapt acute oxidative stress usually through metabolic reprogramming. Chronic oxidative stress usually activates genetic programs [10].Nrf2 was reported to be activated under persistent moderate heat stress [12]. In our study, Nrf2 was also activated even by acute oxidative stress. Moreover, we also showed a synchronized changes between Nrf2 and PINK1. Over 200 genes can be modulated by Nrf2, including PINK1, whose transcription is upregulated under stress [59, 60].NRF2 has recently been considered to be a key transcription factor of metabolic reprogramming in cancer cells. Activation of NRF2 increases glucose uptake and enhances glycolysis, which is important for the thermotolerance of cells [61]. In addition, Nrf2 affects glutathione synthesis and lipoxidation [62]. Tumors driven by oncogenic KRAS sustain their redox balance through Nrf2 associated glutaminolysis [63, 64]. Moreover, activated Nrf2 increases GPX4, a well established downstream target, and can decrease lipoxidation [65]. Although the role of ferroptosis in iRFA is still unclear, resistance to the ferroptosis inducer sorafenib after iRFA has been reported [66, 67]. This means that increased Nrf2 could also affect ferroptosis. However, this may need to be further explored.
## Conclusions
In summary, we obtained the evidence that increased NOX4 after iRFA significantly increased mtROS production and activated Nrf2 to increase the expression of PINK1, resulting in the induction of mitophagy (Fig. 9F). Enhanced mitophagy removed damaged mitochondria, resulting in increased cell survival after iRFA.
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|
---
title: Season, storage and extraction method impact on the phytochemical profile of
Terminalia ivorensis
authors:
- Aliu Moomin
- Wendy R. Russell
- Rachel M. Knott
- Lorraine Scobbie
- Kwesi Boadu Mensah
- Paa Kofi Tawiah Adu-Gyamfi
- Susan J. Duthie
journal: BMC Plant Biology
year: 2023
pmcid: PMC10039578
doi: 10.1186/s12870-023-04144-8
license: CC BY 4.0
---
# Season, storage and extraction method impact on the phytochemical profile of Terminalia ivorensis
## Abstract
### Background
Terminalia ivorensis (TI) is used in West African ethnomedicine for the treatment of conditions including ulcers, malaria and wounds. Despite its widespread use, the phytochemical profile of TI remains largely undetermined. This research investigated the effects of extraction method, season, and storage conditions on the phytochemical composition of TI to contribute towards understanding the potential benefits.
### Methods
TI bark was collected in September 2014, September 2018 and February 2018 during the rainy or dry seasons in Eastern Region, Ghana. Samples were extracted sequentially with organic solvents (petroleum ether, chloroform, ethyl acetate and ethanol) or using water (traditional). Metabolites were identified by liquid chromatography–mass spectrometry/mass spectrometry and compared statistically by ANOVA.
### Results
A total of 82 different phytochemicals were identified across all samples. A greater yield of the major phytochemicals ($44\%$, $p \leq 0.05$) was obtained by water as compared with organic extraction. There was also a higher concentration of metabolites present in cold ($63\%$, $p \leq 0.05$) compared with hot water extraction. A significantly ($p \leq 0.05$) higher number of phytochemicals were identified from TI collected in the dry ($85\%$) compared to the rainy season ($69\%$). TI bark stored for four years retained $84\%$ of the major phytochemicals.
### Conclusion
This work provides important information on composition and how this is modified by growing conditions, storage and method of extraction informing progress on the development of TI as a prophylactic formulation or medicine.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12870-023-04144-8.
## Introduction
Plant secondary metabolites are of interest to the food and pharmaceutical industries due to their potential use in the prevention and treatment of various health disorders, as well as their use as dietary supplements [1]. Analysis of the molecular structures of secondary plant metabolites in plants can provide a potential mechanism for observed health benefits, as well as a route to isolation and/or synthesis at lower production cost than by isolating them from natural sources [2]. It also aids in the study of their efficiency, absorption, solubility, and stability in the human body [2, 3]. One notable example was the structural elucidation of salicylic acid which led to the synthesis of acetylsalicylic acid, the widely use non-steroidal anti-inflammatory [3].
Terminalia ivorensis (TI, Ivory Coast almond) of the family Combretaceae, is found in tropical and sub-tropical zones of the world [4, 5]. It is a large forest tree that grows up to 15–50 m in height and is branchless for up to 30 m [4]. TI trees are used commercially as a supply of solid timber for the building and construction industries and the wood for firewood and the production of charcoal [4–6]. Ghana’s economy benefited from exports of timber and timber products worth 73.35 million US dollars in 2021 and 134 million US dollars in 2020 [7]. The yellow extract from the bark is used as dye in the textile industry while the leaves have been reported to be a good material for producing conductive composites and an adsorbent for the sequestration of pollutants from the environment [4, 8].
Besides its economic usage, TI also serves as a good source of phytochemicals for ethno-medicinal purposes [9]. In in vitro studies, TI has been reported to show antibacterial, antifungal, antioxidant and anti-plasmodial effects [10–13]. Whereas several in vivo studies have found TI to possess anti-inflammatory, anti-nociceptive, anti-psychotic, hepatoprotective and nephroprotective properties in mice and rats [14–17]. In traditional medicine, TI is used in the West African region for the treatment of diuresis, general body pains, haemorrhoids, malaria, wounds and yellow fever [18–20]. However, there are limited data on the metabolite profile of TI and no information comparing the phytochemical profiles derived from traditional or more controlled chemical methods of extraction of TI. There are also no data showing the effect of herbalists’ preferential use of cold or hot extraction procedures as well as time of sampling on metabolites extracted from TI and hence the need to understand practice.
Practitioners of traditional medicine in Ghana usually employ the use of various solvents for extraction of plant products for therapeutic purposes including the proportional mixing of available solvents (such as alcohol, vinegar and water) soaking plant samples, such as flowers, leaves, bark or roots in these solutions at ambient temperatures overnight or boiling in water for several hours.
The climate of *Ghana is* tropical and characterised by rainy and dry seasons [21]. The northern part of the country has only one rainy season which occurs from April to September while the southern part of Ghana, where the study samples were obtained, has two rainy seasons which occur from April to July and from September to November. The national mean annual rainfall in *Ghana is* 1100–1900 mm. The dry season spans November to April [21–23]. There is approximately 12 h of daylight daily, and the temperature in the country fluctuates with season, with mean temperatures generally between 21 ºC and 35 ºC [24].
It is well established that seasonality impacts on the life cycle, distribution and composition of phytochemical in plants [25]. Changes in season, which are characterised by changes in light intensity, temperature, rain and wind patterns, affect plant morphology, flowering, fruiting, phytochemical profile and ability to compete with other species for survival [25, 26]. Being relatively immobile organisms, plants have developed alternative defence mechanisms to overcome stress conditions resulting from changes in weather, herbivory and microbial attack [26]. The production of a wide variety of secondary metabolites, including anthocyanins, cinnamic acids and flavonoids, is a major adaptation used by plants to overcome stressful conditions [25, 26]. The synthesis of secondary metabolites is closely regulated and restricted to specific plant tissues and developmental stages and is produced in response to stimuli such as reduced water, high temperature or decreased light intensity [27, 28]. Several studies have reported changes in secondary plant metabolites at the genetic or protein level due to stressful conditions [29–31]. For instance, decreased irrigation increased red beet total phenolics by $82\%$ and by $98\%$ in lettuce when copared with dequate water provision [32, 33]. Soil pollution with heavy metals such as cadmium, chromium and lead, have been shown to increase total phenolics (by 18 and $6\%$) and flavonoid content (by 12 and $7\%$) in *Ficus carica* and Shinus molle resectively when compared with samples from less polluted soils [34]. Aloe vera collected across different locations of India at varying altitudes, temperatures and rainfall patterns contain different amounts of alkaloids, flavonoids, glycosides, phenolic compounds, reducing sugars, saponins, steroids, tannins and terpenoids [25].
Herbalists usually store medicinal plant samples from days to several months in the chain of production of their medicines, particularly herbal bitters [35]. However, many plant metabolites are unstable and easily degraded or metabolised during storage [36]. For instance, flavonones are modified into anthocyanins in raspberry [37]. Moreover, phytochemical metabolites, such as α-tocopherol, benzoic acid, catechin, cyclohexen-1-carboxylic acid, lycopene, myoinositol and stigmasterol can be depleted during storage in *Cosmos caudatus* stored at room temperature for more than 12 h [36]. Investigating the impact of storage on metabolites within plant samples will provide important information for herbalists for designing sustainable plant harvesting, processing and storage techniques for medicine production [35] and will provide evidence-based knowledge on the “shelf-life” of plants used in traditional medicine [38].
This research investigated the comprehensive metabolite profile of TI and compared the aqueous method of extraction commonly used by traditional medical practitioners in Ghana with typical organic solvent extraction (i.e., sequential Soxhlet extraction). The research also compared the two commonly used traditional extraction methods: hot and cold-water extraction. This study further investigated the effects of season and storage on the phytochemical profile of TI.
## Collection and preparation of plant material
Fresh TI bark samples were collected from a forest in Asakraka Kwahu in the Eastern region of Ghana in both the dry (February) and wet (September) seasons of 2018. An additional sample from September 2014 which was stored for 4-years was also included to investigate the effect of long-term storage. Appropriate permissions were obtained for the collection of the plant and its use was executed in accordance with relevant guidelines. The samples were collected from trunks of TI trees assessed by a certified herbalist to be of approximately the same size and age and collected in an ethical and sustainable manner. The samples were authenticated by Dr George Henry Sam at the Department of Pharmacognosy, Faculty of Pharmacy and Pharmaceutical Sciences, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana and assigned a voucher herbarium specimen number KNUST/HEB/TI/SB/$\frac{10}{12}$ which was prepared and deposited in the department’s herbarium. The initial preparation of the samples was carried out at the Department of Pharmacology, KNUST and then transported at room temperature to the United Kingdom (Robert Gordon University and Rowett Institute) for further analysis. Immediately after harvesting the fresh TI bark samples, they were initially processed by washing them thoroughly with tap water and air drying them at room temperature for 2 weeks. They were then sent to the Rowett Institute for further processing and analysis. The dried samples were broken down into smaller pieces with a domestic food processor and then fine-powdered using a freezer mill (SPEX sample prep 6870, Fisher Scientific, Loughborough, UK). The fine powdered samples were vacuum sealed to minimise evaporation, oxidation and microbial growth and stored at -80 °C until required for extraction of phytochemicals.
## Hot water extraction of phytochemicals from TI
To mimic the traditional hot water extraction, TI sample (25 g, $$n = 3$$) collected in February 2018 was weighed into a conical flask and 250 mL of distilled water at ambient temperature was added with gentle swirling of the flask for 2 min to ensure that the sample was soaked properly without forming bubbles. The sample was heated on a hot plate (FB 15001, Fisher Scientific, UK) at 100 °C for 1 h. The extracted sample in the water solution was allowed to cool to room temperature and then filtered under vacuum through a 70 mm filter paper (FB 59017, Fisher Scientific, UK). The filtrate was frozen at -20 °C and freeze-dried (freeze-drier: Virtis Advantage EL, Biopharma Process Systems, Hampshire, UK) over 2 days to obtain dried powdered TI hot water extract for further analysis. After the completion of the hot water extraction, the residual TI was discarded and a new sample from the same batch of TI was used to repeat the extraction process. The samples were extracted in triplicates. These extracts were stored at -20 °C until required for analysis.
## Cold water extraction of phytochemicals from TI
TI sample collected in February 2018 was prepared and soaked in distilled water as described earlier (Sect. 2.1) and extracted similarly to the traditional cold-water extraction. The conical flask containing the sample was covered with aluminium foil and allowed to stand for 48 h and extraction was carried out at ambient temperature. The content of the flask was filtered, freeze-dried, and stored as described earlier (Sect. 2.2.1).
## Extraction of phytochemicals from TI samples using organic solvents
Sequential Soxhlet extraction is a common scientific method to extract and characterise secondary plant metabolites. Using automated Soxtherm equipment (Gerhardt Soxtherm, SX PC 1.40, Gerhardt, Germany) the procedure was performed for all three TI samples as described previously with some modifications [39]. Solvents were used in order of increasing polarity: petroleum ether (40–60 °C, extra dry, Fischer Scientific), chloroform (99.8+%, stabilized with ethanol, Fischer Scientific), ethyl acetate ($99.98\%$, HPLC grade, Fischer Scientific), and ethanol ($99.8\%$, absolute, Fischer Scientific). The freeze-dried TI sample (6 g) was weighed into each of the six cellulose thimbles and extracted with 140 mL of petroleum ether for 118 min at 150 °C. The remaining solvent was allowed to cool to room temperature and each of the extracts transferred into pre-weighed 25 mL round bottom flasks. Each replicate was evaporated under vacuum at 40 °C using a Buchi rotavapor (R-200, Sigma-Aldrich, UK) to obtain the petroleum ether extract of TI. The dried extracts were weighed separately to record yield before being pooled into a pre-weighed bottle, wrapped with tin foil to prevent light degradation and stored at -80 °C for further analysis. The TI residue left in the thimbles after the petroleum ether extraction was vacuum dried for 18 h at room temperature using a Heraeus vacutherm (VT 6025, Kendro, Germany) and reweighed. The drying was carried out in a vacuum to minimise oxidation. Using the specific extraction program on the Soxtherm machine for each solvent, the residue was sequentially extracted with chloroform (125 min extraction) with rotary evaporation at 62 °C, ethyl acetate (110 min extraction) with rotary evaporation at 77 °C and ethanol (115 min extraction) with rotary evaporation at 78 °C. After the completion of the sequential extraction, the residual TI was discarded and a new sample from the same batch of TI was used to repeat the extraction process. All samples were extracted in triplicates.
## Identification of phytochemicals isolated from TI samples
The method used for the identification of phytochemicals was as described previously [40]. Extracts from samples (10 mg/mL) were prepared in methanol (with $0.1\%$ cetic acid) and 20 µL of each suspension was added to 40 µL of an internal standard and 40 µL of methanol. The internal positive standard was 2-amino-3,4,7,8-tetramethylimidazol [4,5-f] quinoxaline (100 ng/µL) and the negative standard was 13 C benzoic acid (400 ng/µL). The samples were centrifuged for 3 min at 10,000 x g at 4 °C, and the supernatant was subjected to liquid chromatography mass spectroscopy / mass spectroscopy (LC MS/MS) analysis. Phytochemicals were separated by liquid chromatography using an Agilent 1100 HPLC system with Zorbax Eclipse 5-µm, 150 × 4 mm column (Agilent Technologies, Wokingham, UK).
Three-gradient elution method was used with the mobile phase solvents as water and acetonitrile containing $0.1\%$ acetic acid. An injection volume of 5 µL was used with a flow rate of 300 µL/min. The liquid chromatography eluent was directed into an ABI 3200 triple quadruple mass spectrometer (Applied Biosystems, Warrington, UK) fitted with a turbo ion-spray source. The mass spectrometer was run in a negative-ion mode for the analysis of indoles and phenolics with the settings: ion-spray voltage of -4500, source temperature of 400 °C, gases 1, 2 and curtain gas were set at 15, 40 and 10 respectively. For the analysis of heterocyclic amines, the mass spectrometer was run in positive ion mode with the settings: ion-spray voltage of 5500 V, source temperature of 400 °C, gases 1, 2 and curtain gas were set at 14, 40 and 10 respectively. All metabolites were quantified by multiple-reaction monitoring and ion transition for each of the analytes determined based on their molecular ion and a strong fragment ion. Differing elution times were used to overcome similarities in molecular ions and fragment ions for the different categories of compounds. Declustering potential, voltage variables, collision energy, collision cell entrance potential and collision cell exit potential were individually optimized for each analyte and the molecular weight were quantified in relation to the internal standards [40].
## Data analysis
The identity and quantity of phytochemicals from TI (freshly obtained in February 2018) by organic solvent extraction was compared with phytochemicals recovered from aqueous extraction with the two commonly used traditional methods: hot and cold-water. Venn diagrams are used to visualise the relationship between the metabolites from the different extraction methods and to show the common phytochemicals that were compared by further statistical analysis (Figs. 1 and 2). Comparisons were made between the samples collected in September 2018 (rainy season) versus February 2018 (dry season) to assess the impact of season on phytochemical profile, and in September 2014 versus September 2018 to assess the impact of storage on phytochemical profile. These comparisons were made for the organic solvent extraction methods using petroleum ether, chloroform, ethyl acetate or ethanol. The concentration of phytochemicals present in the different samples were compared using analysis of variance (ANOVA) followed by Bonferroni’s post hoc test, with $p \leq 0.05$ considered as significant. TI samples obtained in September 2014, February 2018 and September 2018 were represented as 1, 2 and 3 respectively and a principal component analysis was carried out to determine the differences between the samples when extracted with the different solvents.
## Effect of organic versus water extraction on the phytochemical profile of TI
There was a total of 82 phytochemicals identified using both organic (chloroform, ethanol, ethyl acetate and petroleum ether) and water (cold and hot) extraction of TI obtained during the dry season. There was a greater number of metabolites isolated by organic solvent extraction as compared to water extraction. Phytochemicals including some amines (serotonin, spermine and tyramine), cinnamic acids (3,4-dimethoxycinnamic acid), flavonoids (biochanin A, eriocitrin, formononetin and hesperidin), indoles (indole and indole-3-carboxylic acid), mandelic acids (particularly 3-hydroxymandelic acid) and phenylpyruvic acid (phenylpyruvic acid) were found exclusively using water extraction. Conversely, certain as amines (spermidine), benzoic acids (2,3-dihydroxybenzoic acid, 2,4-dihydroxybenzoic acid, 2,5-dihydroxybenzoic acid, 2,6-dihydroxybenzoic acid, 3,4-dimethoxybenzoic acid and p-anisic acid), benzenes (particularly 1,2,3-trihydroxybenzene and 1,2-hydroxybenzene), coumarins (coumarin), flavonoids (bergapten, ethylferulate, imperatorin and niacin) and phenylpropionic acids (4-hydroxy-3-methoxyphenylpropionic acid and 4-hydroxyphenylpropionic acid) were exclusive to the organic extracted fractions (Fig. 1).
Fig. 1Distribution of phytochemicals between aqueous (combined hot and cold) extraction shown in green and all six organic solvents extraction shown in grey from the TI sample collected in the dry season (February 2018). Metabolites common to both extraction procedures is shown in gold Common to both extraction procedures, catechin was found to be the most abundant phytochemical present at a concentration of 12248.7 ± 1594.3 ng/mg in the organic fraction, which was significantly ($p \leq 0.05$) lower in all six of the organic fractions than the aqueous fractions (cold and hot) at 40,220 ± 17790.8 ng/mg (Table 1). Also, significantly ($p \leq 0.05$) higher in the aqueous fractions were 3,4-dimethoxybenzaldehyde, 4-hydroxyphenylpyruvic acid, apigenin, benzoic acid, epigallocatechin gallate, gallocatechin, genstein, indole-3-carboxaldehyde, indole-3-pyruvic acid, kaempferol, luteolin, myricetin, naringenin, phloretin, phloridzin, p-hydroxybenzoic acid, protocatachaldehyde, protocatechuic acid, quercetin, quercetin-3-glucoside, resveratrol, scopoletin, syringaresinol and taxifolin. On the other hand, significantly ($p \leq 0.05$) lower amounts of anthranilic acid, phenyllactic acid and salicylic acid were found in the aqueous extracts (Table 1).
Table 1Comparison of phytochemical profile from TI sample obtained in February 2018 and extracted using organic solvents or traditional (both cold and hot water) extraction methodsPhytochemical classPhytochemical metabolitesOrganic solvents extraction (ng/mg)Organic solvents extraction relative abundance (%)Traditional (aqueous) extraction (ng/mg)Traditional (aqueous) extraction relative abundance (%)Acetophenones3,4,5-Trimethoxyacetophenone2.2 ± 0.50.014.2 ± 1.90.014-Hydroxy-3-methoxyacetophenone12.4 ± 2.30.0320.0 ± 4.50.034-Hydroxyacetophenone8.1 ± 3.70.0244.2 ± 8.9**0.06Benzaldehydes3,4-Dimethoxybenzaldehyde3.3 ± 0.50.0113.4 ± 4.7**0.02P-Hydroxybenzaldehyde20.6 ± 5.80.0516.9 ± 5.40.02Protocatachaldehyde68.8 ± 8.90.17240.8 ± 52.2***0.33Syringin27.6 ± 4.30.0725.8 ± 5.10.04Vanillin135.8 ± 48.10.34103.4 ± 23.80.14Benzoic acidsAnthranilic acid7.2 ± 0.90.023.6 ± 0.4*0.00Benzoic acid360.6 ± 173.80.90793.8 ± 70.4**1.09Chlorogenic acid10.1 ± 2.50.0311.2 ± 1.10.02Gallic acid6199.3 ± 808.915.526520 ± 507.38.95P-hydroxybenzoic acid34.2 ± 5.60.0991 ± 25.3***0.12Protocatechuic acid266.9 ± 61.80.67692 ± 237.5***0.95Salicylic acid6357.9 ± 1261.415.9244.4 ± 17.0***0.06Syringic acid65.5 ± 13.40.1655.3 ± 15.90.08Vanillic acid145.6 ± 37.20.36182 ± 43.90.25Cinnamic acidsCaffeic acid347.6 ± 62.20.87211.8 ± 64.80.29Cinnamic acid7.0 ± 2.10.026.6 ± 1.90.01Ferulic acid255.9 ± 40.30.64210.6 ± 51.70.29P-coumaric acid151.6 ± 45.20.38167.6 ± 34.60.23Sinapic acid11.4 ± 4.80.037.6 ± 2.10.01FlavonoidsApigenin12.8 ± 0.60.0337.2 ± 16.9**0.05Catechin12248.7 ± 1594.330.6740,220 ± 17790.8***55.18Epicatechin2806 ± 562.87.032264 ± 814.53.11Epigallocatechin2152 ± 226.25.391968 ± 452.52.70Epigallocatechin gallate581.2 ± 73.31.465240 ± 113.1***7.19Gallocatechin5340 ± 537.413.378360 ± 961.6**11.47Genstein15.3 ± 0.90.0441.1 ± 17.6**0.06Hesperitin7.7 ± 2.20.028.4 ± 3.50.01Luteolin14.8 ± 4.80.0469.6 ± 14.4***0.10Morin25.1 ± 5.30.0636.8 ± 5.80.05Myricetin69.5 ± 16.60.17198.2 ± 40.8***0.27Naringenin42.6 ± 14.80.11232.4 ± 74.6***0.32Neohesperidin24.7 ± 5.30.0629.6 ± 4.80.04Phloretin42.4 ± 2.80.11322.6 ± 137.7***0.44Phloridzin14.6 ± 5.10.04116.4 ± 35.6***0.16Psoralen2.3 ± 0.50.011.6 ± 0.40.00Quercetin63.7 ± 9.90.16216.4 ± 65.6***0.30Quercetin-3-glucoside30 ± 5.80.08133.2 ± 40.7***0.18Reservatrol17.9 ± 4.90.0446.4 ± 6.7**0.06Scopoletin20.3 ± 4.40.0578 ± 12.4***0.11Tangeretin10.5 ± 5.10.037.6 ± 4.40.01Taxifolin73.5 ± 19.90.18329.2 ± 145.3***0.45IndolesIndole-3-acetic acid21.0 ± 5.20.0531.3 ± 5.40.04Indole-3-pyruvic acid201.2 ± 51.70.501028 ± 400.1***1.41LignansIndole-3-carboxaldehyde7.2 ± 2.60.0251.6 ± 5.1***0.07Pinoresinol45.6 ± 10.70.1135.1 ± 8.40.05Secoisolariciresinol257.5 ± 65.10.64199.4 ± 67.50.27Syringaresinol540 ± 118.21.351114 ± 280***1.53PhenolsHydroxytyrosol90.6 ± 23.50.2386.4 ± 31.10.12Phenylacetic acidsPhenylacetic acids16.2 ± 4.30.0430.3 ± 5.80.04Phenyllactic acidsPhenyllactic acid20.3 ± 5.10.053.0 ± 1.8***0.00Phenylpyruvic acids4-hydroxyphenylpyruvic acid624 ± 69.61.56882 ± 308.6**1.21 SUM 39938.8 100 72884.0 100 Data are presented as mean amount ± SD (ng/mg) and mean relative abundances (%), $$n = 3$$, *$p \leq 0.05$, **$p \leq 0.01$ and ***$p \leq 0.001$ as compared to organic solvents extraction of TI sample (obtained in dry season) by ANOVA followed by Bonferroni’s post hoc test
## Effect of hot water versus cold water extraction on the phytochemical profile of TI
There was a total of 67 phytochemicals identified using both cold and hot water. There was a greater number of phytochemicals extracted using cold water compared to hot water extraction. Metabolites including cinnamic acids (3,4-dimethoxycinnamic acid and cinnamic acid), mandelic acids (3-hydroxymandelic acid), flavonoids (hesperidin, morin and neohesperidin), indoles (indole-3-carboxylic acid) and phenylpyruvic acids (phenylpyruvic acid) were present only in the cold-water extract, while amines (spermine), benzoic acids (anthranilic acid), flavonoids (eriocitrin and psoralen) or indoles (indole-3-pyruvic acid) were absent in the cold-water extract (Fig. 2).
Fig. 2Distribution of phytochemicals between cold (blue), metabolites found in both extracts (green) or hot (pink) water extraction from TI sample (February 2018) Again, catechin was the most abundant phytochemical detected using both cold (52,800 ± 13,200 ng/mg) and hot water (27,640 ± 6910 ng/mg) extracts (Table 2).
Table 2Comparison of phytochemical profile from TI sample obtained in February 2018 and extracted using cold versus hot water traditional extraction methodsPhytochemical classPhytochemical metabolitesHot water (ng/mg)Hot water relative abundance (%)Cold water (ng/mg)Cold water relative abundance (%)Acetophenones3,4,5-Trimethoxyacetophenone1.8 ± 0.40.006.5 ± 1.8***0.014-Hydroxy-3-methoxyacetophenone6.8 ± 1.70.0133.3 ± 8.2***0.044-Hydroxyacetophenone8.0 ± 2.00.0180.4 ± 20.1***0.09AminesSerotonin9.1 ± 2.20.025.0 ± 1.20.01Tyramine0.7 ± 0.20.001.1 ± 0.40.00Benzaldehydes3,4-Dimethoxybenzaldehyde10.0 ± 2.20.0216.7 ± 4.10.02P-Hydroxybenzaldehyde5.8 ± 1.40.0128 ± 6.9***0.03Protocatachaldehyde133.2 ± 33.30.24348.4 ± 87.1***0.39Syringin10.8 ± 2.40.0240.8 ± 9.8***0.05Vanillin51.2 ± 12.80.09155.6 ± 38.8***0.18Benzoic acidsBenzoic acid107.6 ± 26.90.191480 ± 370***1.67Chlorogenic acid10.4 ± 2.60.0211.9 ± 2.90.01Gallic acid4040 ± 10107.309000 ± 2250***10.15P-Hydroxybenzoic acid44.8 ± 11.20.08137.2 ± 34.3***0.15Protocatechuic acid524 ± 1310.95860 ± 215*0.97Salicylic acid32.3 ± 8.00.0656.4 ± 14.10.06Syringic acid36.9 ± 9.20.0773.6 ± 18.4**0.08Vanillic acid109.2 ± 27.20.20254.8 ± 63.7**0.29Cinnamic acids4-Methoxycinnamic acid7.8 ± 1.90.017.7 ± 1.90.01Caffeic acid95.2 ± 23.80.17328.4 ± 82.1***0.37Ferulic acid131.6 ± 32.90.24289.6 ± 72.4***0.33P-Coumaric acid108.8 ± 27.20.20226.4 ± 56.5**0.26Sinapic acid1.0 ± 0.20.0014.2 ± 3.5***0.02FlavonoidsApigenin25.2 ± 6.30.0549.2 ± 12.3*0.06Biochanin A16.3 ± 4.00.0318.9 ± 4.70.02Catechin27,640 ± 691049.9552,800 ± 13,200**59.52Epicatechin2840 ± 7105.131688 ± 4221.90Epigallocatechin2128 ± 5323.851808 ± 4522.04Epigallocatechin gallate5320 ± 13309.615160 ± 12905.82Formononetin3.1 ± 0.70.013.2 ± 0.80.00Gallocatechin7680 ± 192013.889040 ± 226010.19Genstein28.6 ± 7.10.0553.6 ± 13.4*0.06Hesperitin5.9 ± 1.90.0110.9 ± 2.7*0.01Kaempferol89.2 ± 22.30.16184.8 ± 46.2**0.21Luteolin59.6 ± 14.90.1179.6 ± 19.90.09Myricetin210 ± 52.50.38186.4 ± 46.60.21Naringenin179.6 ± 44.90.32285.2 ± 71.3*0.32Phloretin225.2 ± 56.30.41420 ± 105*0.47Phloridzin91.2 ± 22.80.16141.6 ± 35.40.16Quercetin170 ± 42.50.31262.8 ± 65.7*0.30Quercetin-3-glucoside83.6 ± 20.90.15182.8 ± 45.7**0.21Resveratrol41.6 ± 10.40.0851.2 ± 12.80.06Scopoletin42 ± 10.50.08114 ± 28.5***0.13Tangeretin10.7 ± 2.60.024.4 ± 1.1*0.00Taxifolin226.4 ± 56.60.41432 ± 108**0.49IndolesIndole24.2 ± 16.10.04110.4 ± 27.6***0.12Indole-3-acetic acid6.2 ± 1.50.0156.4 ± 14.1***0.06LignansIndole-3-carboxaldehyde19.6 ± 4.90.0483.6 ± 20.9***0.09Pinoresinol35.0 ± 8.70.0635.1 ± 8.70.04Secoisolariciresinol151.6 ± 37.90.27247.2 ± 61.80.28Syringaresinol1312 ± 3292.37916 ± 2291.03PhenolsHydroxytyrosol64.4 ± 16.10.12108.4 ± 27.1**0.12Phenylacetic acidsPhenylacetic acids14.9 ± 3.70.0345.6 ± 11.4***0.05Phenyllactic acidsPhenyllactic acid1.7 ± 0.40.004.2 ± 1.0*0.00Phenylpyruvic acids4-hydroxyphenylpyruvic acid1100 ± 2751.99664 ± 166**0.75 SUM 55331.8 88703.5 100 100 Data are presented as mean amount ± SD (ng/mg) and mean relative abundances (%), $$n = 3$$, *$p \leq 0.05$, **$p \leq 0.01$ and ***$p \leq 0.001$ as compared to hot water extraction of TI sample (obtained in dry season) by ANOVA followed by Bonferroni’s post hoc test Significantly ($p \leq 0.05$) higher concentrations of 3,4,5-trimethoxyacetophenone, 4-hydroxy-3-methoxyacetophenone, 4-hydroxyacetophenone, apigenin, benzoic acid, caffeic acid, catechin, ferulic acid, gallic acid, genstein, hydroxytyrosol, indole, indole-3-acetic acid, indole-3-carboxaldehyde, kaempferol, naringenin, p-coumaric acid, phenylacetic acid, phenyllacetic acid, phloretin, p-hydroxybenzaldehyde, p-hydroxybenzoic acid, protocatachaldehyde, protocatechuic acid, quercetin, quercetin-3-glucoside, scopoletin, sinapic acid, syringic acid, syringin, taxifolin, vanillic acid and vanillin were found in the cold water extract. In contrast, significantly ($p \leq 0.05$) higher concentrations of 4-hydroxyphenylpyruvic acid and tangeretin were found in the hot water extract (Table 2).
## Effect of season on the phytochemical profile of TI
There was a total number of 77 phytochemicals were identified across TI samples obtained in both the dry and rainy seasons. Certain metabolites were absent from the TI sample obtained in the rainy season. These included certain amines (spermidine), acetophenones (3,4,5-trimethoxyacetophenone), benzaldehydes (protocatachaldehyde), benzenes (1,2-dihydroxybenzene and 1,2,3-trihydroxybenzene), benzoic acids (2,3-dihydroxybenzoic acid, 2,4-dihydroxybenzoic acid, 2,5-dihydroxybenzoic acid, 2,6-dihydroxybenzoic acid, 3,4-dimethoxybenzoic acid and anthranilic acid), cinnamic acids (sinapic acid), coumarins (coumarin), flavonoids (ethylferulate, epigallocatechin, kaempferol, niacin and), indoles (indole-3-acetic acid), phenols (hydroxytyrosol), phenylacetic acids (phenylacetic acid), phenyllactic acids (phenyllactic acid) and phenylpropionic acids (4-hydroxyphenylpropionic acid). Only 2-hydroxybenzyl alcohol, biochanin A, didymin, dopamine, formononetin, hesperidin and indole were detected exclusively in the TI sample obtained in the rainy season (Table 3).
Table 3Comparison of phytochemical profile from TI samples obtained in February 2018 (dry season) or September 2018 (rainy season) extracted using different organic solventsPhytochemical classPhytochemical metabolitesPetroleum ether extract (ng/mg)Chloroform extract (ng/mg)Ethyl acetate extract (ng/mg)Ethanol extract (ng/mg)Dry seasonRainy seasonDry seasonRainy seasonDry seasonRainy seasonDry seasonRainy seasonAcetophenones3,4,5-trimethoxyacetophenone2.7 ± 0.6NDNDND2.0 ± 0.2ND2.1 ± 0.5ND4-hydroxy-3-methoxyacetophenone36.4 ± 6.20.2 ± 0.1***4.6 ± 1.1ND4.8 ± 1.1ND3.9 ± 0.9ND4-hydroxyacetophenone10.1 ± 2.1ND12.2 ± 2.84.1 ± 1.1**5.3 ± 1.28.1 ± 1.84.6 ± 1.1NDAminesDopamineND3.1 ± 0.7ND3.0 ± 0.7ND7.4 ± 1.8ND6.6 ± 1.6Spermidine20.6 ± 5.1NDNDNDNDNDNDNDBenzaldehydes3,4-dimethoxybenzaldehyde4.9 ± 1.19.3 ± 1.31.7 ± 0.4NDNDNDNDNDP-hydroxybenzaldehydeND9.9 ± 2.434.4 ± 4.724.7 ± 5.1ND9.0 ± 2.26.6 ± 1.33.7 ± 0.9ProtocatachaldehydeNDND54.8 ± 13.7ND36.7 ± 9.1ND114.8 ± 28.7NDSyringin21.2 ± 4.255.2 ± 6.5*77.6 ± 14.257.2 ± 9.61.0 ± 0.117.2 ± 3.4***10.4 ± 2.14.8 ± 1.2*Vanillin132.8 ± 23.7210.4 ± 23.8359.6 ± 48.6179.2 ± 23.3*7.7 ± 1.448.2 ± 8.3*43.2 ± 6.142.3 ± 5.6Benzenes1,2-dihydroxybenzeneNDNDNDND984 ± 246NDNDND1,2,3-trihydroxybenzeneNDNDNDNDNDND64.0 ± 16.0NDBenzoic acids2,3-dihydroxybenzoic acid14.2 ± 3.5NDNDND58 ± 14.5NDNDND2,4-dihydroxybenzoic acid18 ± 4.5NDNDNDNDNDNDND2,5-dihydroxybenzoic acidNDNDNDND22.1 ± 5.5NDNDND2,6-dihydroxybenzoic acidNDNDNDND5.2 ± 1.3NDNDND3,4-dimethoxybenzoic acidNDNDNDND99.6 ± 24.9NDNDNDAnthranilic acidNDNDNDND6.8 ± 1.6ND7.6 ± 1.8NDBenzoic acid291.6 ± 29.3165.6 ± 31.1*620.1 ± 79.7468.3 ± 72.2281.6 ± 37.5218.0 ± 34.6249.2 ± 31.6476 ± 49.8*Chlorogenic acidNDNDNDNDNDND10.1 ± 2.227.2 ± 3.4*Gallic acidNDND37.9 ± 9.4ND3200 ± 512.33880 ± 486.815,360 ± 41022684 ± 245.3***P-anisic acid31.1 ± 4.826.3 ± 5.130.1 ± 4.618.6 ± 2.3ND27.2 ± 6.8ND18.4 ± 4.6P-hydroxybenzoic acidNDND55.6 ± 5.81.2 ± 0.4***6.2 ± 1.822.1 ± 3.240.8 ± 3.67.8 ± 2.2*Protocatechuic acidNDND19.4 ± 4.8ND249.2 ± 25.6134 ± 18.7*532 ± 54.8119.2 ± 32.4**Salicylic acid25,280 ± 302671.2 ± 14.7***39.1 ± 3.157.6 ± 8.276.4 ± 16.846.4 ± 13.3*36.1 ± 4.850.8 ± 4.9Syringic acidND0.1 ± 0.0262.4 ± 12.137.6 ± 2.5100.4 ± 21.517.1 ± 3.3***33.7 ± 8.4NDVanillic acid48.4 ± 6.939.8 ± 11.4348.8 ± 47.896.4 ± 15.3**84.8 ± 16.244.0 ± 8.1*100.4 ± 17.329.2 ± 7.2***Cinnamic acidsCaffeic acidNDNDNDND391.6 ± 42.823.5 ± 4.1***303.6 ± 43.419.3 ± 4.2***Cinnamic acidND3.3 ± 0.6ND5.0 ± 1.27.0 ± 1.7NDNDNDFerulic acid15 ± 3.63.1 ± 0.7**92.4 ± 8.354.4 ± 7.9516.0 ± 52.752.4 ± 8.4***400.9 ± 76.522.2 ± 3.2***P-coumaric acidNDND11.2 ± 2.211.4 ± 2.558.8 ± 6.854.8 ± 7.1384.8 ± 34.838.8 ± 5.5***Sinapic acidNDNDNDND11.4 ± 2.8NDNDNDCoumarinsCoumarin3.0 ± 0.6NDNDNDNDNDNDNDFlavonoidsApigeninNDND12.7 ± 2.69.6 ± 2.912.2 ± 2.314.9 ± 3.413.4 ± 3.212.4 ± 3.4BergaptenNDND1.1 ± 0.20.8 ± 0.1ND1.7 ± 0.4NDNDBiochanin AND14.3 ± 3.3NDNDNDNDNDNDCatechinNDND25.9 ± 3.432.5 ± 5.718,800 ± 4286.116,280 ± 3621.317,920 ± 353212,480 ± 2642.4DidyminND9.4 ± 2.5ND9.2 ± 2.3ND10.1 ± 2.5ND10.2 ± 2.5EpicatechinNDNDNDND3204 ± 801ND2408 ± 147.756.2 ± 14.2***EpigallocatechinNDNDNDND2472 ± 618ND1832 ± 458NDEpigallocatechin gallateNDNDNDND34.3 ± 3.9165.2 ± 31.4***1128 ± 122.4114.4 ± 21.6***Ethylferulate198.1 ± 49.6NDNDNDNDNDNDNDFormononetinND3.7 ± 0.9ND4.4 ± 1.1ND3.8 ± 0.9ND3.9 ± 0.9GallocatechinNDNDNDND5720 ± 652.5684 ± 55.8***4960 ± 672.5456 ± 59.8***GensteinNDND16.1 ± 2.113.2 ± 3.214.4 ± 4.116.9 ± 3.815.4 ± 2.815.0 ± 3.2HesperidinNDNDNDNDND24.2 ± 6.0ND22.8 ± 5.7HesperitinND17.4 ± 4.517.7 ± 3.582.6 ± 13.4***3.3 ± 0.455.6 ± 5.1***2.8 ± 0.357.6 ± 4.4***ImperatorinND10.2 ± 2.53.5 ± 0.93.7 ± 0.7ND5.1 ± 1.25.6 ± 1.54.8 ± 1.4KaempferolNDND27.1 ± 6.7ND35.0 ± 8.7ND56 ± 14NDLuteolinNDNDNDND31.2 ± 2.533.2 ± 4.828.1 ± 5.231.8 ± 4.5MorinND47.2 ± 11.2100.4 ± 15.7208.5 ± 34.2**ND82.0 ± 20.5ND82.8 ± 20.7MyricetinNDNDNDND138.8 ± 32.394.8 ± 19.8139.2 ± 24.494.4 ± 21.2NaringeninNDND66.4 ± 11.277.2 ± 13.642.4 ± 6.7114.8 ± 16.8***61.6 ± 17.99.2 ± 15.3**NeohesperidinNDNDND10.6 ± 2.616.9 ± 2.525.9 ± 4.632.4 ± 5.7124 ± 23.5***NiacinNDNDNDND322.8 ± 80.7ND54.8 ± 13.7NDPhloretinNDNDND3.8 ± 0.940.4 ± 5.264 ± 7.344.4 ± 5.334.1 ± 5.1Phloridzin1.7 ± 0.4NDNDND8.1 ± 1.275.2 ± 12.5***34.0 ± 4.558.4 ± 6.2PsoralenNDNDNDND1.3 ± 0.3ND3.2 ± 0.72.6 ± 0.4QuercetinNDND31.9 ± 7.9ND68.8 ± 9.263.2 ± 8.490.4 ± 8.752.8 ± 6.3*Quercetin-3-glucosideNDNDNDNDND65.6 ± 16.430.1 ± 3.851.2 ± 4.4ResveratrolNDNDNDND23.8 ± 5.45.1 ± 1.3*12.7 ± 3.1NDTangeretin6.5 ± 2.17.6 ± 2.212.8 ± 3.19.0 ± 2.75.9 ± 1.315.1 ± 2.4**16.5 ± 3.413.1 ± 2.8TaxifolinNDND8.4 ± 2.1ND112 ± 25.6135.6 ± 24.3100 ± 19.7111.2 ± 24.4Scopoletin13.1 ± 3.31.5 ± 0.1***6.3 ± 1.241.2 ± 4.4*30.2 ± 7.5ND31.3 ± 7.8NDIndolesIndoleNDNDND11.4 ± 2.8NDNDNDNDIndole-3-acetic acidNDNDNDND21.0 ± 5.2NDNDNDIndole-3-pyruvic acidNDNDNDND201.2 ± 31.7624 ± 121.7***ND844 ± 211LignansIndole-3-carboxaldehyde2.2 ± 0.22.6 ± 0.42.3 ± 0.49.6 ± 1.8*7.0 ± 1.311.2 ± 2.417.2 ± 4.25.8 ± 1.3**PinoresinolND18.3 ± 4.545.6 ± 5.629.9 ± 3.7NDNDNDNDSecoisolariciresinolND2.0 ± 0.5468.2 ± 38.4145.6 ± 23.3***221.6 ± 39.7112.8 ± 23.8*82.8 ± 5.782.4 ± 8.2SyringaresinolNDND444.0 ± 50.2440.0 ± 46.7504 ± 126NDNDNDPhenols2-hydroxybenzyl alcoholND18.3 ± 4.5ND25.4 ± 6.2NDNDNDNDHydroxytyrosolNDNDNDND159.2 ± 39.8NDNDNDPhenylacetic acidsPhenylacetic acids12.2 ± 4.349.6 ± 6.5*2.4 ± 0.734.4 ± 4.9**37.3 ± 9.3ND12.9 ± 3.2NDPhenyllactic acidsPhenyllactic acidNDNDNDND40.4 ± 10.1ND0.9 ± 0.1NDPhenylpropionic acids4-hydroxyphenylpropionic acidNDND368.0 ± 96.0NDNDNDNDND4-hydroxy-3-methoxyphenylpropionic acidND1.8 ± 0.4ND17.5 ± 4.32.8 ± 0.3***1.6 ± 0.3NDNDPhenylpyruvic acids4-hydroxyphenylpyruvic acid540 ± 60.22796 ± 306.8***708.2 ± 65.5408.3 ± 40.75160 ± 1290NDND608 ± 152Data are presented as amount ± SD, $$n = 3$$, *$p \leq 0.05$, **$p \leq 0.01$ and ***$p \leq 0.001$ as compared to TI sample obtained in the dry season by ANOVA followed by Bonferroni’s post hoc test. ND represent not detected (minimum detectable limit of 0.1 ng/mg) Again, catechin was the most abundant phytochemical measured in TI samples obtained from both seasons (dry season 18,800 ± 4286.1 ng/mg, rainy season 16,280 ± 3621.3 ng/mg) although the differences were not significant. Significantly ($p \leq 0.05$) lower concentrations, of some phytochemicals, however, were found in the TI sample obtained in the rainy season as compared to TI obtained in the dry season. These included certain acetophenones (4-hydroxy-3-methoxyacetophenone), benzoic acids (gallic acid, p-hydroxybenzoic acid, protocatechuic acid, salicylic acid and vanillic acid), cinnamic acids (caffeic acid, ferulic acid and p-coumaric acid), flavonoids (epicatechin, gallocatechin, hesperitin, quercetin, resveratrol and scopoletin), indoles (indole-3-pyruvic acid), lignans (secoisolariciresinol) and phenylacetic acids (phenylacetic acid). Conversely, the TI sample obtained in the rainy season showed significantly ($p \leq 0.05$) higher concentrations of 4-hydroxy-3-methoxyphenylpropionic acid, benzoic acid, chlorogenic acid, morin, naringenin, phloridzin and syringin as compared to the sample obtained in the dry season (Table 3).
## Effect of storage on the phytochemical profile of TI
There was a total of 69 phytochemicals identified in both the fresh (September 2018) and stored (September 2014) TI samples obtained in the rainy season. Relative to the fresh TI sample, phytochemicals including 3,4-dihydroxyphenylpropionic acid, 3,4-dimethoxybenzoic acid, 4-hydroxyphenylpropionic acid, caffeine, eriocitrin, ethylferulate, indole-3-carboxylic acid, kaempferol, kynurenic acid, protocatachaldehyde, spermidine and spermine were found only in the stored TI sample. Moreover, storage of TI sample resulted in the loss of certain phytochemicals found in the fresh TI sample. These included certain benzoic acids (chlorogenic acid and p-anisic acid), cinnamic acid, flavonoids (biochanin A, didymin, epicatechin, formononetin, hesperidin, imperatorin and neohesperidin), 2-hydroxybenzyl alcohol and phenylacetic acid which were lost due to storage of the TI sample (Table 4).
Table 4Comparison of phytochemical profile from TI samples obtained in September 2014 (stored) or September 2018 (fresh) extracted using different organic solventsPhytochemical classPhytochemical metabolitesPetroleum ether extract (ng/mg)Chloroform extract (ng/mg)Ethyl acetate extract (ng/mg)Ethanol extract (ng/mg)Fresh sampleStored sampleFresh sampleStored sampleFresh sampleStored sampleFresh sampleStored sampleAcetophenones4-hydroxy-3-methoxyacetophenone0.2 ± 0.1NDND43.2 ± 10.8NDNDNDND4-hydroxyacetophenoneNDND4.1 ± 1.164.0 ± 16.0***8.1 ± 1.836.9 ± 5.1*NDNDAminesDopamine3.1 ± 0.73.7 ± 0.93.0 ± 0.73.7 ± 0.97.4 ± 1.8ND6.6 ± 1.6NDSpermidineND190.4 ± 47.6NDNDNDNDNDNDSpermineND16.8 ± 4.1ND8.2 ± 2.0NDNDNDNDBenzaldehydes3,4-dimethoxybenzaldehyde9.3 ± 1.3NDND4.3 ± 1.1ND8.2 ± 2.0ND10.5 ± 2.6P-hydroxybenzaldehyde9.9 ± 2.4ND24.7 ± 5.1121.6 ± 30.4***9.0 ± 2.237.4 ± 8.1*3.7 ± 0.98.0 ± 2.0*ProtocatachaldehydeNDNDND284.8 ± 71.2ND278.8 ± 69.7ND96.8 ± 24.2Syringin55.2 ± 6.59.1 ± 2.2***57.2 ± 9.6271.2 ± 52.8***17.2 ± 3.4110.1 ± 16.8***4.8 ± 1.232.1 ± 8.0***Vanillin210.4 ± 23.8147.6 ± 36.9179.2 ± 23.31276 ± 319***48.2 ± 8.3416 ± 42.7***42.3 ± 5.6138 ± 34.5***Benzoic acids3,4-dimethoxybenzoic acidNDNDND30 ± 7.5NDNDNDNDBenzoic acid165.6 ± 31.1130.4 ± 32.6468.3 ± 72.21204 ± 301***218.0 ± 34.674.8 ± 9.9*476 ± 49.8209.2 ± 52.3***Chlorogenic acidNDNDNDNDNDND27.2 ± 3.4NDGallic acidNDNDND78 ± 19.53880 ± 486.814,720 ± 1525.6*2684 ± 245.36080 ± 1520***P-anisic acid26.3 ± 5.1ND18.6 ± 2.3ND27.2 ± 6.8ND18.4 ± 4.6NDP-hydroxybenzoic acidNDND1.2 ± 0.4149.6 ± 37.4***22.1 ± 3.2157.6 ± 16.9***7.8 ± 2.240.8 ± 9.6***Protocatechuic acidNDNDND162 ± 40.5134 ± 18.71900 ± 354.6***119.2 ± 32.4656 ± 164***Salicylic acid71.2 ± 14.7ND57.6 ± 8.2142.8 ± 35.7***46.4 ± 13.354.4 ± 8.350.8 ± 4.921.6 ± 5.4**Syringic acid0.1 ± 0.02ND37.6 ± 2.5234 ± 58.5***17.1 ± 3.3110.4 ± 23.5***ND31.6 ± 7.8Vanillic acid39.8 ± 11.433.6 ± 8.496.4 ± 15.31200 ± 300***44.0 ± 8.1428 ± 40.1***29.2 ± 7.2134.4 ± 33.6***Cinnamic acidsCaffeic acidNDNDNDND23.5 ± 4.142.8 ± 8.719.3 ± 4.28.4 ± 2.1*Cinnamic acid3.3 ± 0.6ND5.0 ± 1.2NDNDNDNDNDFerulic acid3.1 ± 0.7ND54.4 ± 7.9400 ± 96.4***52.4 ± 8.4135.2 ± 15.9***22.2 ± 3.232.8 ± 8.2P-coumaric acidNDND11.4 ± 2.597.6 ± 21.7***54.8 ± 7.1143.2 ± 21.3***38.8 ± 5.548.8 ± 12.2FlavonoidsApigeninNDND9.6 ± 2.914.8 ± 3.714.9 ± 3.415.4 ± 3.112.4 ± 3.415.2 ± 3.8BergaptenND0.8 ± 0.20.8 ± 0.10.8 ± 0.21.7 ± 0.4NDNDNDBiochanin A14.3 ± 3.3NDNDNDNDNDNDNDCatechinND115.6 ± 28.932.5 ± 5.737.7 ± 9.416,280 ± 3621.314,000 ± 1261.212,480 ± 2642.45640 ± 1410***Didymin9.4 ± 2.5ND9.2 ± 2.3ND10.1 ± 2.5ND10.2 ± 2.5NDEpicatechinNDNDNDNDNDND56.2 ± 14.2NDEpigallocatechin gallateNDNDNDND165.2 ± 31.486.8 ± 16.4*114.4 ± 21.642 ± 10.5***EriocitrinNDNDNDNDND5.8 ± 1.4NDNDEthylferulateND23.4 ± 5.8ND8.3 ± 2.0NDNDNDNDFormononetin3.7 ± 0.9ND4.4 ± 1.1ND3.8 ± 0.9ND3.9 ± 0.9NDGallocatechinND29.2 ± 7.2NDND684 ± 55.8800 ± 67.9456 ± 59.8273.2 ± 68.3**GensteinNDND13.2 ± 3.218 ± 4.516.9 ± 3.820.9 ± 3.615.0 ± 3.217.0 ± 4.2HesperidinNDNDNDND24.2 ± 6.0ND22.8 ± 5.7NDHesperitin17.4 ± 4.5ND82.6 ± 13.421.9 ± 5.4***55.6 ± 5.115.4 ± 3.3***57.6 ± 4.48.2 ± 2.0***Imperatorin10.2 ± 2.5ND3.7 ± 0.7ND5.1 ± 1.2ND4.8 ± 1.4NDKaempferolNDNDND35.4 ± 8.8ND36.9 ± 9.2ND44.4 ± 11.1Kynurenic acidNDNDNDNDNDNDND36.1 ± 9.0LuteolinNDNDNDND33.2 ± 4.859.2 ± 8.331.8 ± 4.550 ± 12.5Morin47.2 ± 11.2ND208.5 ± 34.298 ± 19.5*82.0 ± 20.551.2 ± 7.1*82.8 ± 20.7NDMyricetinND99.2 ± 24.8ND96.8 ± 24.294.8 ± 19.8104.4 ± 8.694.4 ± 21.2101.2 ± 25.3NaringeninNDND77.2 ± 13.6106 ± 22.8114.8 ± 16.8122.4 ± 10.399.2 ± 15.3105.6 ± 26.4NeohesperidinNDND10.6 ± 2.6ND25.9 ± 4.6ND124 ± 23.5NDPhloretinNDND3.8 ± 0.915.0 ± 3.5**64 ± 7.365.6 ± 5.534.1 ± 5.126.2 ± 6.5PhloridzinNDNDNDND75.2 ± 12.5158.8 ± 24.2*58.4 ± 6.219.7 ± 4.9***PsoralenNDNDNDNDNDND2.6 ± 0.43.6 ± 0.9QuercetinND34.6 ± 8.6ND48.8 ± 12.263.2 ± 8.4125.2 ± 16.7**52.8 ± 6.390.4 ± 22.6**Quercetin-3-glucosideNDNDNDND65.6 ± 16.494.2 ± 12.6*51.2 ± 4.425.8 ± 6.4*ResveratrolNDNDNDND5.1 ± 1.38.4 ± 1.4ND13.3 ± 3.3Tangeretin7.6 ± 2.28.0 ± 2.09.0 ± 2.79.4 ± 2.315.1 ± 2.46.9 ± 1.213.1 ± 2.810.1 ± 2.5TaxifolinNDNDND31.7 ± 7.9135.6 ± 24.3211.2 ± 28.4*111.2 ± 24.4207.2 ± 51.8**Scopoletin1.5 ± 0.1ND41.2 ± 4.42.7 ± 0.6***NDNDNDNDIndolesIndoleNDND11.4 ± 2.86.2 ± 1.5*NDNDNDNDIndole-3-carboxylic acidNDNDND3.1 ± 0.7NDNDNDNDIndole-3-pyruvic acidNDNDNDND624 ± 121.7724 ± 43.8844 ± 2111400 ± 350*LignansIndole-3-carboxaldehyde2.6 ± 0.4ND9.6 ± 1.85.2 ± 1.311.2 ± 2.42.6 ± 0.3**5.8 ± 1.36.0 ± 1.5Pinoresinol18.3 ± 4.5ND29.9 ± 3.760.4 ± 15.1**ND23.7 ± 5.9ND62.4 ± 15.6Secoisolariciresinol2.0 ± 0.5ND145.6 ± 23.3572 ± 143***112.8 ± 23.8261.2 ± 34.5**82.4 ± 8.2249.6 ± 62.4***SyringaresinolNDND440.0 ± 46.7496 ± 124NDNDND524 ± 131Phenols2-hydroxybenzyl alcohol18.3 ± 4.5ND25.4 ± 6.2NDNDNDNDNDCaffeineNDNDND3.9 ± 0.9NDNDNDNDPhenylacetic acidsPhenylacetic acid49.6 ± 6.5ND34.4 ± 4.9NDNDNDNDNDPhenylpropionic acids3,4-dihydroxyphenylpropionic acidNDNDND66.4 ± 16.6NDNDNDND4-hydroxyphenylpropionic acidNDNDND1388 ± 347ND664 ± 166NDND4-hydroxy-3-methoxyphenylpropionic acid1.8 ± 0.4ND17.5 ± 4.314.6 ± 3.61.6 ± 0.3NDNDNDPhenylpyruvic acids4-hydroxyphenylpyruvic acid2796 ± 306.8664 ± 166***408.3 ± 40.7504 ± 1265160 ± 1290ND608 ± 1521560 ± 390***Data are presented as amount ± SD, $$n = 3$$, *$p \leq 0.05$, **$p \leq 0.01$ and ***$p \leq 0.001$ as compared to fresh TI sample by ANOVA followed by Bonferroni’s post hoc test. ND represent not detected (minimum detectable limit of 0.1 ng/mg) Again, catechin was the most abundant phytochemical measured in fresh TI sample (16,280 ± 3621.3 ng/mg) and gallic acid was the most abundant phytochemical present in stored TI sample (14,720 ± 1525.6 ng/mg) although the differences were not significant.
The stored TI sample showed significantly ($p \leq 0.05$) higher concentrations of acetophenones (4-hydroxyacetophenone, benzaldehydes p-hydroxybenzaldehyde), benzoic acids (gallic acid, p-hydroxybenzoic acid, protocatechuic acid and syringic acid), cinnamic acids (ferulic acid and p-coumaric acid), flavonoids (phloretin and tangeretin) and indoles (indole-3-pyruvic acid). Conversely, the stored TI sample showed significantly ($p \leq 0.05$) lower concentrations of certain flavonoids (catechin, epigallocatechin gallate, gallocatechin, hesperitin, morin, scopoletin, and taxifolin), indole and indole-3-carboxaldehyde when compared with the fresh sample (Table 4).
## Principal components analysis of TI samples
Principal component analysis (PCA) on a univariate scale was used to assess differences in the main phytochemical metabolites extracted from the TI samples (obtained in September 2014, February 2018 and September 2018 which were represented as 1, 2 and 3 respectively). Similarity in phytochemicals between samples was shown by how closely clustered they were to each other in the same quadrant of a plot. From the PCA plot (Supplementary Fig. 1A), sample obtained in February 2018 [2] was different from those obtained in September 2014 and September 2018 when extracted using water (hot and cold), ethanol or ethyl acetate. Looking across the 3 samples, similar metabolites were obtained from petroleum ether extraction. Metabolites from samples obtained in September 2014 and September 2018 were observed to be more similar by using all the solvents: water (hot or cold), ethanol, ethyl acetate except chloroform.
## DISCUSSION
Plants are essentially immobile in nature and cannot escape unfavourable environmental conditions. However, they produce secondary metabolites through various physiological and biochemical processes that improve their chances of survival and growth in response to changes in the environment [41, 42]. The type and concentration of secondary metabolites produced by a plant are species-dependent, are influenced by developmental stage, and by environmental conditions during growth [43]. The physiological processes involved in the synthesis of phytochemicals result in alterations in gene expression, regulation of protein activity, ion homeostasis and endogenous levels of phytochemicals [42]. For centuries, humans have exploited such physiological changes in plants for drug discovery or by directly using the plants as medicines or using metabolites from the plants to produce synthetic drugs [44]. TI is a tree that grows in the tropical and sub-tropical regions of the world and is mostly used for medicinal purposes [43]. This study focused on the impact of solvent (aqueous versus organic solvent) extraction on the phytochemical profile of TI and also investigated the influence of change in season (dry and rainy) and storage on secondary metabolites for its implication on traditional medicine.
Successful isolation of biologically active compounds from a plant material is principally dependent on the type of solvent used in the extraction procedure [45]. In scientific research, samples are frequently extracted with organic solvents like acetone, chloroform, dichloromethane, ethyl acetate, ethanol, petroleum ether [46–48]. Different solvent extracts of plant samples contain different phytochemicals and hence, have different biological activity [46, 47]. For example, water and ethanol extracts of several plants including Hypoxia hemerocallidea, *Ocimum basilicum* and *Senna petersiana* have higher antibacterial and antioxidant activity than other extracts such as chloroform or dichloromethane [47, 49] Traditional medicines are not generally extracted with chemical solvents but are rather routinely extracted using available solvents such as water or alcohol, and this results in differences in biological activity between extracts from traditional and chemical methods [50]. Therefore, this study compared phytochemicals isolated using traditional method of aqueous extraction (hot and cold water) with organic sequential solvent extraction.
The organic solvent extraction produced a wider profile of phytochemicals as compared to aqueous extraction, with $20\%$ and $14\%$ of the total phytochemicals being identified exclusively in the organic or aqueous extracts respectively. However, for the phytochemicals common to both extraction methods, water extraction showed an approximately $44\%$ higher concentration of phytochemicals than the organic solvent extraction method (Table 1). Assuming the bioactive constituents are present using the traditional extraction methods, then no further benefits are likely to be realised by utilising solvent extraction.
Selection of a good solvent for extraction should be based on a high yield of target compounds, but should also allow for ease of subsequent handling of the extracts and minimal deterioration or metabolism of the compounds present [45]. Traditional extraction with cold and hot water showed higher amounts of several phytochemicals, while the sequential organic extraction with chloroform, ethanol, ethyl acetate and petroleum ether showed a wider profile of phytochemicals extracted. This could suggest metabolism of parent compounds during the extraction procedure, again advocating benefits of the traditional extraction methods. In addition to polarity of the extraction solvent, composition and yield of phytochemicals are highly dependent on temperature and duration of extraction [51, 52]. For this reason, the two most commonly used traditional extraction methods: hot and cold-water extraction. There was a greater number of secondary metabolites detected following cold-water extraction with significantly ($p \leq 0.05$) higher amounts of certain acetophenones, benzaldehydes, benzoic acids, cinnamic acids, flavonoids, indoles, lignans and and only a few compounds (4-hydroxyphenylpyruvic acid and tangeretin) being significantly ($p \leq 0.05$) lower in the cold-water extract. The decrease in number of secondary metabolites detected with hot water extraction, as for sequential Soxhlet extraction again could be due to the loss or metabolism of thermo-unstable compounds.
Analysis of the influence of season on TI secondary metabolites showed that 82 phytochemical metabolites were commonly identified from TI samples obtained in both the dry and the rainy seasons. Four organic solvents were used to obtain different phytochemical profiles from the TI samples. A total number of 85 and $69\%$ of the phytochemicals were isolated from the TI sample obtained in the dry and rainy seasons respectively. As many as $24\%$ of the 82 phytochemicals were found exclusively in the sample obtained in the dry season, while only $8\%$ were identified exclusively in TI sampled in the rainy season.
During the dry season, there is a decrease in water and nutrient supply to plants [53]. Nutritional stress can result in the accumulation of osmo-protectants to stabilise proteins structure and maintain membrane integrity and scavenge reactive oxygen species (ROS), with biomass and secondary metabolites production [53, 54]. Phenolic compounds are involved in plant reproduction, growth and tolerance of stress [55, 56]. Moreover, plants that produce phenolics with allelopathic activity can compete with and suppress the growth of surrounding plants [57]. Phenolics also play other essential functions in plants such as indicators of stress, nutrient uptake, photosynthesis, and protein synthesis [58, 59]. There are a wide variety of compounds classified as phenolics which include coumarins, flavonoids, cinnamic acids and lignans [60].
Plant hormones such as auxins, salicylic acid, cytokinin, ethylene, gibberellic acid and jasmonic acid act to modulate developmental processes in plants and determine plant responses to environmental stresses [42, 59, 61]. In agreement with previous findings, this study observed higher levels of salicylic acid in TI collected in the more stressful dry season when compared to that obtained in the rainy season. The characteristic water deficit of the dry season has also been associated with induction of the synthesis of flavonoids, anthocyanins and phenolic acids in fruits and vegetables such as grape, lettuce, pomegranate and red beet [37]. This work also found that certain amines, acetophenones, benzaldehydes, benzenes, benzoic acids, cinnamic acids, coumarins, flavonoids, indoles, phenols, phenylacetic acids, phenyllactic acids and phenylpropionic acids exclusively in the TI sample obtained in dry season. Absence of adequate amounts of water or higher transpiration rates also result in drought stress in plants and changes secondary metabolite production [42, 61]. In contrast, a deficit in irrigation has been observed to reduce total anthocyanins and total phenolics in pomegranate [62].
Conversely, the rainy season is characterised by cloudy weather and relatively lower temperatures as compared to the sunny and high temperatures (up to 35–40 °C) observed in the dry season [23]. In this study, significantly ($p \leq 0.05$) higher amounts of cinnamic acids (chlorogenic acid), among other compounds were measured in TI sampled in the rainy season showed as compared to dry season.
In agreement with this study, a previous study reported that cooler weather was linked with the production of high levels of chlorogenic acid [63]. Plants growing in lower temperatures develop significant adjustments in several physiological and biochemical processes that enable them to survive under low temperature stress, and this causes inhibition in the synthesis and storage of secondary metabolites [64]. Moreover, water uptake, dehydration and metabolism in plants are reduced at low temperatures [65]. The light intensity and exposure period also have significant influence on the production and storage of secondary metabolites [66]. Coumarin levels have been shown to significantly decrease in different plant parts due to shorter light period [67].
Medicinal plant gatherers or traders usually wait to collect sufficient plant stock before supplying the market [68]. In the chain of production of medicinal products, herbalists usually store medicinal plant samples for days up to several months before use [35, 38]. Many of the bioactive compounds from these medicinal products may degrade during storage and where possible, it is recommended that plant samples should be extracted and analysed shortly after collection, as secondary metabolites can decompose even when stored under liquid nitrogen [69]. Analysing fresh and stored (4 years) TI samples, a total of 69 metabolites were identified common to both obtained in the rainy season. Further 12 different phytochemicals were detected exclusively in the fresh TI sample (Table 4) suggesting that these may be degraded during storage.
To the best of our knowledge, there are no data investigating metabolite profiles in fresh and stored TI, and hence, published findings describing the effect of storage on phytochemicals come from other plant sources. Storage of Lilium bulbs for 30 days results in a decrease in free amino acids, total polysaccharides and reducing sugars by approximately 39, 63 and $18\%$ respectively [70]. It has also been reported that storage of *Cosmos caudatus* at room temperature for 12 h causes depletion of phenolic compounds (such as α-tocopherol, benzoic acid, catechin, cyclohexen-1-carboxylic acid, lycopene, myoinositol and stigmasterol) with phenolic compounds degraded into free sugars such as α-D-galactopyranose, sucrose and turanose, [36]. The depletion of phenolic compounds on storage is attributed to plant dehydration which occurs postharvest [71]. In the presence of oxygen, polyphenol oxidase converts phenolic compounds to quinones [72]. The activity of polyphenol oxidase in Lilium bulbs has been shown to increase by approximately $100\%$ after 30 ays of storage [70]. Traditional healers commonly prefer fresh plant material because of doubts around the degree of biological activity of stored plants [49].
Contrary to the negative considerations about storage of plant material, modification of secondary metabolites during storage is not always detrimental and the increased activity or levels of specific compounds may be associated with a higher value product [47]. This study observed that higher levels of gallic acid (a natural antioxidant), and caffeine (a central nervous system stimulant) were present in the stored TI sample. Monribot-Villanueva et al. [ 2019] also found that gallic acid levels were elevated in some varieties of mango fruits after 6 days of storage [73].
Many traditional medical practitioners consider that the efficacy of stored plant material does not vary [50]. The results obtained from this current study, and other published findings confirms this since this study showed that $84\%$ of the metabolites are still present after 4 years of storing TI bark sample.
Plant parts such as bark, roots or underground storage organs (such as bulbs, corms, rhizomes and tubers) may have a longer storage life compared to fruits or leaves while the compounds present within are more stable [49]. Moreover, these organs are better suited to protect the stored phytochemicals from degradation due to their lower surface area to volume ratio [47]. This confirms the findings of this study where TI bark that was stored for 4 years retained most of its secondary metabolites. However, while stored TI bark retained approximately $84\%$ of econdary metabolites, fresh TI sample contained higher amounts. It was also found that the two TI samples obtained in the rainy season were more similar to each other but different from the TI sample obtained in the dry season.
## Conclusion
Method of extraction including temperature and solvent selection impacted on both the profile and quantity of metabolites measured in the extracts. *In* general, the cold water extracted sample had the highest amount of phytochemicals, albeit it with a narrower profile (compare to organic extracted) indicating different solubilities and thermo-stability of the metabolites. Higher phytochemical levels were also measured in the TI sample collected in the dry rather than wet season, likely due to harsher environmental conditions which is known to induce the synthesis and storage of secondary protective metabolites. TI that was stored for 4 years retained the majority of secondary metabolites measured in freshly collected TI, which could suggest a longer shelf-life for TI. This may be due to the ability of stem bark to provide a greater protection of the secondary metabolites thereby offering the metabolites more stability against the environment and hence, offering the medicinal product more stability. However, until the bioactive molecule (or molecules) responsible for the desired therapeutic effect are identified, optimal conditions for preparation cannot be fully demonstrated. This work, however, provides important information on composition and how this is modified by growing conditions, storage and method of extraction informing progress on the development of TI as a prophylactic formulation or medicine.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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|
---
title: Adenosine 2 receptor regulates autophagy and apoptosis to alleviate ischemia
reperfusion injury in type 2 diabetes via IRE-1 signaling
authors:
- Mohamed Bassirou Yacouba Moukeila
- Erick Thokerunga
- Feng He
- Christian Cedric Bongolo
- Yun Xia
- Fuyu Wang
- Adamou Foumakoye Gado
- Hama Mamoudou
- Shahzad Khan
- Bonkano Ousseina
- Hadjara Abdoulkarim Ousmane
- Drissa Diarra
- Jianjuan Ke
- Zongze Zhang
- Yanlin Wang
journal: BMC Cardiovascular Disorders
year: 2023
pmcid: PMC10039586
doi: 10.1186/s12872-023-03116-y
license: CC BY 4.0
---
# Adenosine 2 receptor regulates autophagy and apoptosis to alleviate ischemia reperfusion injury in type 2 diabetes via IRE-1 signaling
## Abstract
### Purpose
This study aimed to determine the effect and mechanism of action of adenosine 2 receptor (A2R) activation on myocardial ischemia reperfusion injury (MIRI) under diabetic conditions.
### Methods
MIRI type 2 diabetic rats and H9C2 cardiomyocytes were treated with A2R agonist and then subjected to hypoxia for 6 h and reoxygenation for 18 h. Myocardial damage, and infarct size were determined by cardiac ultrasound. Indicators of cardiomyocyte injury, creatine kinase-MB and cardiac troponin I were detected by Enzyme Linked Immunosorbent Assay. Endoplasmic reticulum stress (ERS) was determined through measuring the expression levels of ERS related genes GRP78, p-IRE1/IRE1, and p-JNKJNK. The mechanism of A2R cardio protection in MIRI through regulating ERS induced autophagy was determined by investigating the ER resident protein IRE-1. The ER-stress inducer Tunicamycin, and the IRE-1 inhibitor STF in combination with the A2R agonist NECA were used, and the cellular responses were assessed through autophagy proteins expression Beclin-1, p62, LC3 and apoptosis.
### Results
NECA improved left ventricular function post MIRI, limited myocardial infarct size, reduced myocardial damage, decreased cardiomyocytes apoptosis, and attenuated ERS induced autophagy through regulating the IRE-XBP1s-CHOP pathway. These actions resulted into overall protection of the myocardium against MIRI.
### Conclusion
In summary, A2R activation by NECA prior to ischemia attenuates apoptosis, reduces ERS induced autophagy and restores left ventricular function. This protective effect occurs through regulating the IRE1-XBPs-CHOP related mechanisms. NECA is thus a potential target for the treatment of MIRI in patient with type 2 diabetes.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-023-03116-y.
## Introduction
Myocardial infarction (MI) is an ischemic heart condition whose risk remains very high among type 2 diabetic (T2DM) patients [1, 2]. Indeed, T2DM patients are twice as likely to suffer ischemic heart disease, and have a significantly raised post MI mortality rate compared to non-diabetics [3, 4]. First line treatment for acute myocardial ischemia to save the ischemic myocardium is reperfusion. However, this treatment is often associated with tissue injury in a condition called “myocardial ischemia reperfusion injury” (MIRI). Various factors are responsible for MIRI including cellular autophagy, which decreases the number of cardiomyocytes needed to repair cardiac systolic function [5]. Moreover, T2DM has been shown to exacerbate endoplasmic reticulum stress mediated autophagy in the pancreas [6]. Therefore, inhibition of autophagy could be a way of alleviating MIRI.
During MI, the extracellular concentration of the purine nucleoside molecule, Adenosine, has been found to dramatically increase, indicating a possible function in MI [7]. Studies have shown that binding of adenosine to its receptors (A1, A2a, A2b and A3); that are extensively expressed on the mammalian myocardium, has beneficial effects on MIRI [8]. Adenosine 2a receptor in particular is cardioprotective during reperfusion and this cardio protection is associated with downregulated autophagy [9]. A recent study demonstrated that activation of A2R in diabetic rats protects against MIRI by preventing apoptosis [10]. Whether the endoplasmic reticulum stress induced autophagy is involved is not yet clear.
Endoplasmic reticulum stress (ERS) activates a process called autophagy, where nutrients are recycled through lysosomal degradation of unfolded or misfolded proteins and worn out cellular organelles [11]. Under extreme stress such as those induced by MIRI, autophagy is activated to encourage cellular survival and energy conservation [12]. However, autophagy is a double edged sword; enhanced autophagy following MIRI can lead to significant reduction of viable cardiomyocytes which impairs recovery of ventricular functions and exacerbate the condition. In normal cells, activation of A2R promotes cardio-protection through regulating autophagy [9]. Whether the ER is involved has not been elucidated. Previous studies have demonstrated that the endoplasmic reticulum membrane protein, IRE-1 regulates autophagy in various cells in response to ER- stress [13–15]. In this study, we examined whether A2R activation regulates ERS induced autophagy in diabetic rats to offer cardio protection during MIRI, and the mechanism involved.
## Animals
Fifty Sprague Dawley (SD) adult rats (250–280 g) were offered by Hubei Experimental Animal Research Center for this experiment (License No.: SCXK (E) 2015-0018). All animal experiments were approved by the Animal Experiment Committee of Wuhan University (Ethical approval number: WP2020-01108), and strictly conducted in compliance with the study protocol, the ARRIVE guidelines, and the Guide for the Care and Use of Laboratory animals (No. 85-23, revised 1996) published by National Institutes of Health (NIH).
## H9C2 myocardial cells
The rat embryonic cells H9C2 cells were provided by Procell (Wuhan, China) and maintained in Dulbecco’s Modified Eagle Medium (DMEM, Thermofischer scientific, USA) supplemented with $10\%$ fetal bovine serum (FBS, Life technologies, Carlsbad, Ca, USA) and $1\%$ Streptomycin-Penicillin antibiotics (FBS, Life technologies, Carlsbad, Ca, USA). Cells were cultured at 37 °C in $5\%$ CO2.
## Establishment of ischemia reperfusion injury model in vitro
After 72 h of culture, the neonatal rat cardiomyocytes (NRCM) were subjected to hypoxia reperfusion (H/R) to induce ischemia reperfusion injury. The cells were cultured for 6 h in a glucose-free, serum-free medium in the presence of $95\%$ N2 and $5\%$ CO2. Cells were then re-oxygenated for 18 h in fresh DMEM/F12 supplemented with $10\%$ FBS, $1\%$ penicillin–streptomycin, $1\%$ BrdU an incubated at 37 °C and $5\%$ CO2 [29].
## Measurement of autophagic flux
One day prior to ischemia reperfusion (I/R), the H9C2 cells were transfected with RFP-GFP-LC3 and LAMP-2 for 24 h to observe co-localization. Immediately after reoxygenation, cells were fixed with $4\%$ paraformaldehyde, and then stained with DAPI. Confocal microscope (LEICA TCS SP8, Germany) was used to observe the fluorescence signal under multiple visions; yellow spots representing autophagosomes, and red spots representing autolysosomes. Images were analyzed using image J software.
## Measurement of endoplasmic reticulum stress
Cultured in a six plate well were removed, culture medium discarded and the cells washed 3 times with PBS for 5 min each. Cells were then fixed for 20 min with $4\%$ paraformaldehyde, and then washed again 3 times with PBS for 5 min each. A histochemical pen was used to draw a circle to prevent the incubation solution from flowing away in the following process. Cells were then incubated overnight at 4 °C in primary antibodies (GRP8, dilution: 1: 1000; p-IRE1, dilution: 1: 500; XBP1, dilution: 1: 500) diluted with $5\%$ BSA. The following day, they were washed 3 times with PBS for 5 min each and re-incubated in the dark in corresponding secondary antibodies (HRP-Goat anti Rabbit, and HRP-Goat anti mouse, dilutions: 1: 10,000 each) at 37 °C for 40 min, later washed with PBS and then stained with DAPI, incubated in the dark for 20 min at room temperature. They were washed with PBS and the film sealed with an anti-fluorescence quenching solution, then observed under the microscope.
## Detection of cell survival rate by CCK-8
Cells were cultured for 16 h at 9.5 × 105/cm2 density in a 48-well plate, $10\%$ CCK-8 solution was then added to the culture medium and further incubated for 2 h. A cell free control group was created and CCK-8 solution added as well. Absorbance was then measured at 450 nm (PerkinElmer EnSpire Microplate Reader, USA) and the optical density (OD) determined to calculate cell viability.
## RNA extraction, reverse transcription and qRT-PCR
Total RNA was extracted from the cardiomyocytes using TRIpure Total RNA Extraction kit (Cat: EP013) form ELK Biotechnology Co. Ltd., China according to the manufacturer’s instructions. Reverse transcription to make cDNA was conducted using M-MLV Reverse Transcriptase kit (Cat: EQ002) also from ELK Biotechnology Co. Ltd., China. Quantitative real-time PCR was performed using StepOne™ Real-Time PCR system (Life Technologies, CA, USA) using QuFast SYBR Green PCR Master Mix (Cat: EQ001, ELK Biotechnology Co.Ltd., China. A 10-μl total reaction volume was used, and the reaction were as follows: 95 °C for 1 min; 40 cycles of 95 for 15 s, 58 °C for 20 s and 72 °C for 45 s, melting curve 60 °C → 95 °C, with 1 °C temperature increase every 20 s. Beta-actin was used as the endogenous control for data normalization and the double delta method used to calculate gene expression. Ct values used for calculations were averages of three independent repeats. All primer information is provided in Additional file 1: Table S1.
## Establishment of diabetic rats’ model
To establish the diabetic model, SD rats were fed on a high fat diet containing $60\%$ fats, $20\%$ carbohydrates and $20\%$ proteins for 30 days. After the 30 days, they were allowed to fast for 12 h, weighed and then injected with l% Streptozotocin citrate buffer at a 45 mg/kg dose. They continued to be fed on the high fat diet, and after 72 h, blood glucose from the tail veins were measured. The glucose levels were all greater than 16.7 mmol/L, indicating successful development of type 2 diabetes mellitus. The rats continued to be fed on high fat diet to maintain chronic type II diabetes (Additional file 2, Additional file 3, Additional file 4, Additional file 5 and Additional file 6).
## Myocardial ischemia-reperfusion injury model
To develop myocardial ischemia reperfusion models, each rat was weighed, and anesthetized by injection with $1\%$ sodium pentobarbital intraperitoneally at 50 mg/Kg. A cut was then made along the midline of the neck, muscles and tissues separated, the trachea exposed and an inverted T-shaped incision made along the cartilage ring of the trachea to intubate it. Parameters of the ventilator were set as: VT = 30–50ML/; frequency = 50–70 times/min; and breathing ratio = 1:1. The left thorax was opened to expose the heart. A 5–0 suture was used to ligate the left anterior descending coronary artery (LAD) at about 2 mm under the junction between the pulmonary artery cone and the left atrial appendage. To ensure reversible LAD occlusion, an openable knot was created by passing a ploythene tube through it just before it was tightened. In the sham group, no ligation was conducted. Ischemia was confirmed when transient drop in blood pressure was noticed, and the surface of the myocardium turned blue, indicating cyanosis. Reperfusion recovery was demonstrated by the rapid disappearance of cyanosis and hyperemic responses in the epicardium. Occlusion was conducted for 30 min and reperfusion 120 min then the rats euthanized by anesthesia overdose.
## Hemodynamic measurements
Following anesthesia, while in the supine position, a puncture was made in the right internal carotid artery and an electrocardiogram connected. ( BL-420, Taimeng Informatization Biological Signal Acquisition and Analysis System, China). 100U/Kg heparin anticoagulant was then injected and a catheter inserted into the right internal carotid artery to monitor the arterial blood pressure of the rat.
## Echocardiography
The GE Vivid 7 (GE Health Medical, USA), was used to conduct transthoracic echocardiography. It was fitted with an 11 MHz image transducer to generate images. The ultrasound personnel was blinded to the study. Data collected were on: structure of the left ventricle, systolic ventricular septal thickness (IVSs), diastolic ventricular septal thickness (IVSd), left ventricular end-systolic diameter (LVIDs), left ventricular end-diastolic diameter (LVIDd), left ventricular diastolic posterior wall thickness (LVPWd), and left posterior wall thickness (LVPWs). These were all collected by M-mode ultrasound, while left ventricular end-systolic volume (LVESV), Left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), stroke volume (SV) and left ventricular fractional shortening (FS) were calculated automatically using alogarithms in the computer.
## Measurement of myocardial infarction area (Evans blue staining)
Once reperfusion was complete, the reversible LAD occlusion was fully ligated. Large amounts of pentobarbital sodium was administered via the tail vain to euthanize the rats, while Evans blue dye was injected using the femoral vein. The hearts were removed, washed with phosphate buffered saline 3 times and then quickly frozen for 10 min at − 80 °C. The ventricular part was then sliced from the top into small equal parts. They were then incubated in $1\%$ triphenyl tetrazolium chloride (TTC) for 15 min at 37 °C and allowed to slowly stain. The sections were then converted into digital photographs, and measurements of the myocardial infarction area (pale), ischemic risk area (red) and the non-infarct area (blue) carried out using the Image *Pro plus* 6.0 software.
## Experimental procedures
The diabetic rats were randomly divided into sham operation group (Sham group), myocardial ischemia–reperfusion group (I/R group), myocardial ischemia–reperfusion + Adenosine receptor A2 agonist group (I/R + NECA group), myocardial ischemia–reperfusion + NECA + ERS inducer group (I/R + NECA + TM group), myocardial ischemia–reperfusion + adenosine receptor A2 agonist + ERS inducer group + IRE1 inhibitor group (I/R + NECA + TM + STF group). NECA was administered 10 min before ischemia.
The cardiomyocytes HC9c2 after high glucose treatments were randomly divided into sham operation group (HC9c2 + high glucose), Hypoxia-reperfusion group (HC9c2 + high glucose + H/R), myocardial ischemia–reperfusion + Adenosine receptor A2 agonist group (HC9c2 + high glucose + NECA + H/R), myocardial ischemia–reperfusion + NECA + ERS inducer group (HC9c2 + high glucose + NECA + Tunicamycin + H/R), myocardial ischemia–reperfusion + adenosine receptor A2 agonist + ERS inducer group + IRE1 inhibitor group (HC9c2 + high glucose + NECA + Tunicamycin + STF + H/R). NECA was administered 10 min before hypoxia.
## Protein extraction and western blot
Radio immunoprecipitation assay (RIPA) lysis buffer (Beyotime Biotechnology, Shanghai, China), mixed with protease inhibitor, phenylmethylsulfonyl fluoride (PMSF) (Thermofischer scientific, USA) was used to extract total protein from cells and tissues. Final protein concentration was measured using Bicinchoninic acid (BCA) assay kit (Beyotime Biotechnology, Shanghai, China). Appropriate concentrations were determined and the proteins loaded onto $10\%$ SDS PAGE gels and separated by electrophoresis. The proteins were then transferred on to polyvinylidene difluoride (PVDF) membranes, (Sigma-Aldrich, Beijing, China) incubated in primary antibodies overnight at 4 °C, then incubated in appropriate secondary antibodies for 1 h at room temperature and finally target bands visualized using the ECL imaging system (model 5200, Tianneng, China).
## Detection of cardiac troponin and creatine kinase-MB levels
Enzyme linked immunosorbent assay (ELISA) was used to detect indicators of cardiomyocyte injury, CK-MB (creatine kinase-MB) and cTnI (cardiac troponin I), using kits obtained from CUSABIO BIOTECH Co., Ltd., Wuhan, China following the corresponding manufacturer’s instruction. 4 ml of blood samples were tapped into EDTA anticoagulant tubes from the femoral vein 2 h post reperfusion, centrifuged at 2000 rpm for 20 min at 4 °C and ELISA conducted.
## Hematoxylin and Eosin staining
Heart tissues quickly harvested after reperfusion were washed 3 times in sterile PBS, and fixed using formalin for 24 h. The fixed tissues were then successively dehydrated in ethanol of increasing concentration; $70\%$, $80\%$, $90\%$, $95\%$, and $100\%$, then washed in xylene before paraffin embedding. 5 μm paraffin tissue blocks were prepared, and the tissue sections progressively dewaxed in xylene. They were then dehydrated in decreasing concentration of ethanol; $100\%$, $95\%$, $80\%$, and $75\%$), and then stained using hematoxylin and eosin. The tissues were then dehydrated, and sealed with neutral resins, and observed under the microscope.
## Tunel assay
To further demonstrate apoptosis, Tunnel assay was conducted. Briefly, cells were grown on small glass slides inside a six well plate. The cells were fixed with $4\%$ formaldehyde, washed 3 times with PBS, incubated in $70\%$ ethanol for 30 min at 4 °C, again washed again 3 times with PBS, then incubated in a mixture of TdT enzyme and dUTP for 60 min at 37 °C. The cells were then rinsed with PBS, and incubated with DAPI counter stain for 10 min at room temperature and kept in the dark. They were then washed 3 times in PBS and re incubated with 7-AAD / RNase A solution and incubated for 30 min at room temperature then observed under a fluorescence microscope and images collected.
## Electron microscopy
Following reoxygenation, cells were fixed in $2.5\%$ glutaraldehyde for 4 h at 4 °C, rinsed 3 times using 0.1 M phosphoric acid buffer then fixed for 2 h in $1\%$ osmic acid. Gradual dehydration in increasing concentration of ethanol was conducted; ($50\%$, $70\%$, $90\%$, $100\%$), then permeated, and embedded in epoxy resin for 48 h at 60 °C. An ultrathin slicer (Leica, EM UC7, German) was used to make 80 nm slices of the embedded tissues and then stained with $2\%$ uranyl acetate and lead citrate. Cellular morphology and autophagic flux were observed under an electron transmission microscope (Hitachi TEM system, HT7800, Japan).
## Statistical analysis
All data were expressed as mean ± standard error of the mean unless explicitly specified, and analyzed statistically using GraphPad Prism 8.0 (GraphPad Software, Inc., La Jolla, CA). Normality tests were conducted and data normalized to the sham/control group. The unpaired Student's t test was used to compare the differences between two groups, and one-way ANOVA used to verify differences among more than two groups with Bonferroni or Dunnett post-hoc conducted, unless otherwise stated. P value of < 0.05 was considered significant.
## Activation of A2R facilitated myocardial function recovery after MI/RI in diabetic rats
To evaluate the effect of adenosine A2a receptor activation on myocardial reperfusion injury in diabetic rats, an in vivo diabetic rat model of MIRI was established, and NECA, a potent, non-selective A2R agonist was administered to activate A2R prior to reperfusion. Successful establishment of the model was confirmed by using an electrocardiogram (ECG), as indicated by the presence of ST segment elevation on the ECG following left anterior descending coronary artery (LAD) ligation of the rats. After 120 min of reperfusion, there was an obvious inverted Q wave and a prolonged QT interval in the ECG of the I/R group of rats. In contrast, QT interval prolongation was relatively improved in the I/R + NECA group (Fig. 1A). The changes in heart rate and mean arterial pressure during I/R were not statistically significant among the groups, (Fig. 1B, C). These results suggested that activation of adenosine A2a receptor prior to reperfusion, facilitated quick ventricular repolarization after ischemia of the myocardium. Fig. 1Dynamic electrocardiogram and hemodynamic monitoring of the heart. AECG performance at baseline, ischemia, and reperfusion for each group. B Changes in heart rate at each time point expressed as (X ± SD); C Changes in mean arterial pressure in each group (X ± SD). D Echocardiographic manifestations of each group; E, F Left ventricular ejection fraction (LVEF) and left ventricular shortening fraction (FS) of rats in each group compared with control group (sham) and IR group (IR), ** $P \leq 0.01$ About five minutes to the end of reperfusion, cardiac ultrasound was conducted to observe changes in cardiac functions of the rats, (Fig. 1D). Echocardiography revealed that the left ventricular ejection function (LVEF) and shortening fraction (lvfs) in I/R group were significantly lower than those in the sham group (Fig. 1E, F; $P \leq 0.05$), indicating that I/R significantly damaged the systolic function of the heart. Compared with IR group, LVEF and lvfs in I/R + NECA group were significantly improved (Fig. 1E, F; $P \leq 0.05$). This indicates that the activation of adenosine A2a receptor significantly facilitated recovery of cardiac systolic function.
## Activation of adenosine A2R attenuated MIRI in diabetic rats
To determine the effects of A2R activation on myocardial damage following ischemia, Serum levels of the myocardial enzymes creatinine kinase MB (CK-MB) and cardiac troponin (cTnI) were determined. CK-MB was significantly increased following ischemia, from 731.45 ± 49.61 in the sham group to 1329.30 ± 44.23 in the IR group. This was dramatically reduced to 656.91 ± 7.02 in I/R + NECA group. Similarly, cTnI increased following ischemia from 117.26 ± 27.53 in the sham group to 215.98 ± 42.97 in I/R group. This was also significantly reduced to 165.55 ± 30.36 in I/R + NECA group, demonstrating that A2R activation by NECA attenuated ischemic myocardial damage (Fig. 2A, B). Hematoxylin and eosin (H&E) staining and electron microcopy were then conducted on the myocardial tissues obtained before and after reperfusion. The results revealed that the myocardial damage seen in the I/R group was almost completely reversed after A2R activation prior to reperfusion in the I/R + NECA group (Fig. 2C–E). To determine the size of myocardial infarction area, Evans Blue staining was used. Compared to the IR group, the myocardial infarction area in the I/R + NECA group decreased by about $41\%$ (Fig. 2F, G). The ischemic area of the two groups did not differ significantly signifying consistency in the LAD ligation sites of the models. Taken together, these result demonstrated that activation of adenosine A2a receptor using NECA prior to reperfusion attenuated myocardial ischemia reperfusion injury in the diabetic rats. Fig. 2Adenosine A2 receptors activation attenuated MI/RI in the rats. A, B Plasma levels of cTnI and CK-MB in the different groups of rats. Data were presented as mean ± SD and analyzed by one-way ANOVA with uncorrected Fisher’s LSD post hoc test, ***$P \leq 0.001$, and ****$P \leq 0.0001$ respectively. C–E H&E staining and electron microscopy of the cardiac tissues. F, G Comparative percentage of myocardial ischemic area in each group. Infarct size was significantly reduced in the IR + NECA group versus IR group, * $P \leq 0.05$
## Activation of A2R protected the myocardium through regulating endoplasmic reticulum stress-induced autophagy and apoptosis
To explore the roles of the endoplasmic reticulum stress (ERS) induced autophagy in MIRI in the diabetic rats in response to A2R activation, its agonist NECA was administered 1 h prior to reoxygenation. NECA administration in the I/R + NECA group significantly decreased the expression of the autophagy promoting protein, Beclin-1 compared to the I/R and the sham groups. On the other hand, its administration attenuated the decrease in the autophagosome substrate p62. ( Fig. 3A–C). These results indicated that A2R activation had an anti-autophagy effect following ERS induction. ERS was determined by measuring the expression levels of ERS related genes GRP78, p-IRE1/IRE1, and p-JNK/JNK and their corresponding protein levels. Increase in GRP78, p-IRE1/IRE1, and p-JNK/JNK in the IR group of rats indicated increase in ERS induced apoptosis via the IRE-1/JNK pathway, which was significantly attenuated by A2R activation in the IR + NECA group (Fig. 3D–G). The above results demonstrated that A2R activation could have protected the myocardium during MIRI through regulating ERS induced autophagy. Fig. 3Effects of A2R activation on autophagy and ERS. A–C Expression of autophagy proteins Beclin and p62. Beclin is decreased while p62 decline is attenuated by A2R activation in the IR + NECA group compared to sham group and the IR group, * $P \leq 0.05$, ** $P \leq 0.01.$ D–G ERS proteins (GRP78, p-IRE-1/IRE1, p-JNK / JNK) are increased in IR group but administration of NECA significantly reduced their expression in the IR + NECA group, **$P \leq 0.01$ To explore the mechanism by which A2R activation influences the endoplasmic reticulum stress induced autophagy to confer cardio protection in MIRI, the ER resident protein IRE-1 was modulated. Previous studies have shown that IRE-1 regulates ER-stress induced autophagy in various cells [13–15]. The ER-stress inducer Tunicamycin (TM), and the IRE-1 inhibitor STF in combination with the A2R agonist NECA, were used, and the cellular responses measured using expression of the autophagy proteins Beclin-1, p62 and LC3. H&E staining of the I/R + NECA + TM group revealed broken muscle fibers, interrupted and discontinuous Z-line, and shifted & vacuolated mitochondria indicating induction of ER stress, (Fig. 4A–C). These were however all reversed following administration of the IRE-1 inhibitor in the I/R + NECA + TM + STF group; the myocardial fibers were continuous, Z-line was continuous, and mitochondrial edema and vacuolation were significantly reduced. Similar results were observed with the electron microscopy, indicating that inhibition of IRE-1 alleviated ER-stress, (Fig. 4D–F). In the western blot analysis, the administration of IRE-1 inhibitor prior to induction of ERS by TM, significantly reduced the expression levels of Beclin-1, prevented the conversion of LC3I to II and attenuated the reduction of p62, indicating attenuation of ERS-induced autophagy (Fig. 4G–J). Moreover, the protein expression levels of the IRE1 signaling pathway-related proteins GRP78, IRE1, and p-JNK were substantially raised during I/R injury yet significantly decreased following treatment with STF These results indicated that inhibition of IRE-1attenuated ERS-induced autophagy in the diabetic rats (Fig. 3D–G).Fig. 4H&E staining, A In the Sham group, the myocardial fibers are complete; the Z-line is clear, continuous and straight, and the structural position is normal. B After ERS induction with TM, the heart muscle fibers were broken, Z-line was interrupted and discontinuous, and the mitochondria shifted and vacuolation increased. C After administration of IRE-1 inhibitor before reperfusion, the myocardial fibers were again continuous, Z-line was continuous, and mitochondrial edema and vacuolation were reduced. Electron microscopy: D In the Sham group, the myocardial fibers are complete; the mitochondria normal, and the cristae are clear. E After ERS induction using TM, muscle fibers were broken, Z-line was interrupted and discontinuous, mitochondria shifted and vacuolation increased; F After administration of IRE-1 inhibitor prior to reperfusion, the, myocardial fibers were continuous again, Z-line was continuous, and mitochondrial edema and vacuolation were reduced. G–J Administration of IRE-1 inhibitor significantly reduced the expression levels of Beclin-1, prevented the conversion of LC3I to II and attenuated the reduction of p62
## A2R activation attenuated ERS induced apoptosis under hypoxia/reperfusion
To further assess the regulatory effects of adenosine A2 receptor agonists on endoplasmic reticulum stress induced autophagy and apoptosis in MIRI and the interaction between the two mechanisms, an in vitro ischemia reperfusion injury model in diabetic cardiomyocytes was developed. H9C2 cardiomyocytes cell line was used. Exposing the H9C2 cells to hypoxia induced significant cell death, which was alleviated by administration of the adenosine A2R agonist NECA (Fig. 5A). Apoptosis related proteins CHOP, and Caspase 12 were significantly increased in the H9C2 + high glucose + H/R group compared to Sham group, and sharply decreased following administration of the adenosine A2R agonist, NECA in the H9C2 + high glucose + H/R + NECA group (Fig. 5B–D). However, the total amount of JNK did not change (Fig. 5B, E), while its activated form that is known to induce apoptosis [16], significantly increased in the I/R group following reperfusion and was attenuated by A2R activation in the IR + NECA group (Fig. 3D, F). Similarly, tunnel staining indicated increased apoptosis in the H9C2 + high glucose + H/R group, which was alleviated in the H9C2 + Glu + high glucose + NECA group (Fig. 5F).Fig. 5Effect of A2R activation on autophagy. A Cell viability was detected by CCK-8. B–E Western blot analysis of apoptosis related proteins Caspase-12, CHOP, and JNK analyzed by Image J. All data presented as mean ± SEM and analyzed by one-way ANOVA and Dunnett’s post hoc test; $$n = 3$.$ ** $P \leq 0.01$, ***$P \leq 0.001$ and *$P \leq 0.05$ respectively. F TUNEL staining demonstrating apoptosis
## Cardio-protection effect of A2R activation in MIRI is by regulating endoplasmic reticulum stress induced autophagy through IRE-1 protein
To investigate the protective role of A2R in H/R, its effect on endoplasmic reticulum stress, and autophagy was evaluated. NECA was used to activate A2R while Tunicamycin was used to induce endoplasmic reticulum stress (ERS) and the levels of ERS related proteins GRP78, p-ire1and XBP1s determined. In addition, p-IRE-1 inhibitor STF was used to evaluate the possible involvement of p-IRE-1 protein in regulating autophagy in response to ERS. As expected, the levels of ERS proteins GRP78, p-IRE1and XBP1 were all increased in response to Tunicamycin treatment (Fig. 6A–D), while the levels of autophagy related proteins Beclin-1 and LC3 were increased and the autophagosome substrate protein, p62 decreased indicating increased autophagy (Fig. 6E–H). Similarly, increased co-localization of LC3 and LAMP-2 was observed in the ERS induced group using immunofluorescence staining (Fig. 6I) indicating that ERS induction promoted autophagosomes and lysosomes fusion. These were however all reversed following treatment with p-IRE-1 inhibitor STF, indicating that p-IRE-1 could be involved in the regulation of ERS induced autophagy. Fig. 6Effect of A2R activation on autophagy and endoplasmic reticulum stress. A–D Expression levels ERS proteins GRP78, p-IRE-1 and XBP1s evaluated by western blot and analyzed by image J. *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ E–H Expression levels of autophagy proteins, p62, Beclin-1, LC3I and LC3 II evaluated by western blot and analyzed by image J. ***$P \leq 0.001$, ****$P \leq 0.0001.$ *All data* presented as mean ± SEM, analyzed by one way ANOVA and Tukey’s post hoc test. I LC3-LAMP-2 co-localization viewed by fluorescence microscopy
## Discussion
MIRI remains a major cause of death of cardiomyocytes in patients with coronary heart disease [17]. Mechanisms responsible for cardiomyocytes death during MIRI include; autophagy, apoptosis, ferroptosis and pyroptosis [18]. Autophagy is a highly conserved cellular process where nutrients are catabolized and recycled through lysosomal degradation of unfolded or misfolded proteins and worn out cellular organelles [11]. It releases amino acids, fatty acids, ATP, and other high energy molecules that are essential for cell survival and normal tissue function during ischemia or nutrient deficiency [11]. This is particularly important for non-proliferative cells such as cardiomyocytes as it ensures cell survival and maintains structural and functional stability of the heart. The endoplasmic reticulum (ER) is one of the major cellular organelles that regulates autophagy [19]. Under extreme external stress, protein folding in the ER is disrupted and so misfolded and unfolded proteins accumulate in the ER lumen and the cytoplasm. These proteins interact with p62, which then recruits LC3II [20, 21], and together form autophagosomes. In the presence of LAMP2, these autophagosomes fuse with lysosomes to form autolysosomes leading to lysis and recycling of the contents [22, 23], and eventual survival of the cell. This process is called endoplasmic reticulum stress (ERS) induced autophagy. However, autophagy is a double edged sword; enhanced autophagy following MIRI can lead to significant reduction of viable cardiomyocytes which impairs recovery of ventricular functions and exacerbate the condition [24–27].
Given the upregulation of CHOP and caspase 12 in our experiment, we investigated ERS involvement in the resultant autophagy and apoptosis using the ERS inducer Tunicamycin. Induction of ERS resulted into increased expression of BiP (GRP78), p-IRE1 and XBP1s. The response of CHOP to ERS is often regulated by three factors; protein kinase RNA-like endoplasmic reticulum kinase (PERK), activating transcription factor 6 (ATF6), and inositol requiring protein 1 (IRE1) [28]. The dramatic increase in the expression of IRE1 here suggested that it was the likely pathway in this experiment. IRE1 is kept in its inactive state by GRP78 interaction. Upon accumulation of unfolded proteins, GRP78, having more affinity for unfolded proteins, disintegrates from IRE1 thus activating it [29]. In its active form, IRE1 cleaves its substrate precursor XBP1 to its active form XBP1s [30, 31]. XBP1s then upregulates the expression of CHOP and many other genes involved in UPR to restore protein homeostasis [32–34]. However, when ERS is prolonged or severe, IRE1 stimulates the activation of the apoptotic-signaling kinase-1 (ASK1), which in turn activates JNK and p38 MAK hence inducing apoptosis [35]. To confirm the involvement of IRE1, the cardiomyocytes were treated with the IRE1 inhibitor STF. This significantly inhibited the autophagy promoter Beclin-1 and resulted into accumulation of p62 indicating reduced or inhibited autophagy.
In this experiment, subjecting the cardiomyocytes to hypoxia and reoxygenation (H/R) induced significant apoptosis as demonstrated by the CCK-8 assay and the dramatic increase in apoptosis related proteins CHOP and caspase-12. CHOP and caspase-12 are particularly associated with endoplasmic reticulum stress (ERS) induced apoptosis [36, 37]. Since JNK levels remained constant, this apoptosis was most likely independent of the JNK pathway. ERS-induced apoptosis follows three major pathway; the IRE1/ASK1/JNK pathway, the caspase-12 kinase pathway, and the C/EBP homologous protein (CHOP)/GADD153 pathway [28, 30], so it was likely that the later pathway was involved. Activation of A2R using its agonist NECA swiftly reversed apoptosis. This suggested that NECA could be a very useful therapeutic for early stage MIRI management in diabetics. The finding are consistent with our previous results [9, 38] which demonstrated that A2aR activation protects the myocardium against MIRI through downregulating autophagy and regulating autophagy flux and apoptosis in non-diabetic cardiomyocytes. In our most recent work, [10], we demonstrated that NECA attenuated MIRI in type-2 diabetic rats through A2aR/PCK/miR-15a mechanism. However, we speculated that the autophagic flux seen by Xia et al. [ 38] in nondiabetic rats could also occur in type-2 diabetic rats, and decided to use NECA, a non-selective activator of adenosine receptors to demonstrate ERS involvement in this cardio protection. This results has thus demonstrated that indeed A2R activation in type-2 diabetic rats by NECA could also be cardioprotective via ERS inhibition mechanisms.
In the animal experiment, myocardial ischemia (MI) resulted into leakage of cTnI and CK-MB into the blood stream and infarction of the myocardium. These myocardial damages were promptly reversed by activation of A2R by NECA. Similarly, the ventricular systolic function that was impaired by MI also resolved following A2R activation. MI also impaired the LVEF and LVFS indicating impaired ventricular systolic function. However, A2R activation ameliorated both conditions, in effect restoring left ventricular systolic function. Studies have shown that recovery of ventricular systolic function following MIRI is dependent on A2R inducing the elevation of cAMP and PKA, which then triggers multiple cAMP-PKA dependent calcium ion channels, heightening the maximum peak transient outward current thus enabling post ischemia repolarization of the ventricles [39–41]. Therefore, cAMP mediated influx of Ca2 + promotes reestablishment of myocardial contractility.
On the electrocardiogram, MI elevated the ST segment and caused prolongation of the QT interval. This was possibly caused by the imbalance in the in and out flows of intracellular and extracellular ion (Ca2 +, K +, H +, and Na +), due to the interrupted blood and oxygen supply following ischemia, hence changing the repolarization current [42]. This current change in the ischemic area thus prolonged the QT wave and elevated the ST-segment. Activation of A2R during ischemic post conditioning has an antiarrhythmic effect associated with action potential shortening [44, 45].
This study had the following limitations: [1] we have previously demonstrated that A2aR activation protects the myocardium against MIRI through regulating autophagy flux and apoptosis in non-diabetic cardiomyocytes, and that NECA attenuates MIRI in type-2 diabetic rats through A2aR/PCK/miR-15a mechanism [10, 38]. Here, to investigate ERS involvement in MIRI in diabetic rats we used NECA, a nonselective A2R activator. Our result here is therefore, not specific to which adenosine receptor subtype is responsible for this ERS involved cardio protection in type-2 diabetic rats. In a future study, this will be resolved. [ 2] According to the IMproving Preclinical Assessment of Cardioprotective Therapies (IMPACT) criteria by the European Union-CARDIOPROTECTION COST Action [46], in small animal models of acute ischemia reperfusion injury (IRI), it is desirable to demonstrate benefit of the intervention after at least 28 days post intervention. However, this was not the case in this study, therefore these results should be interpreted accordingly.
## Conclusions
In summary, activation of A2R prior to reperfusion following myocardial ischemia effectively attenuated apoptosis, reduced ERS induced autophagy and re-established impaired left ventricular function, hence reducing the damaging effects of MIRI on diabetic cardiomyocytes. This protective effect occurs through regulating the IRE1-XBPs-CHOP related mechanisms.
## Supplementary Information
Additional file 1. Table S1. Information on all the primers used for qPCR in the study. Additional file 2. Figure F1. Original blots for the effects of A2R activation on autophagy and ERS. The autophagy proteins Beclin 1 and P62 were assessed. The 60Kda protein is Beclin 1, 62Kda protein is P62 and the 36Kda protein is GAPDH; the internal control protein. Additional file 3. Figure F2. Original blots for the effects of A2R activation on autophagy and ERS. The ERS proteins Grp78, P-JKN, and P-IRE1 were assessed. The 78Kda protein is Grp78, 54Kda and 46Kda proteins are isoforms of P-JKN, and the 110Kda protein is P-IRE1. The 36Kda protein is GAPDH; the internal control protein. Additional file 4. Figure F3. Original blots of autophagy proteins measured in response to P-IRE1 inhibition. The 16Kda protein is LC3, the 62Kda protein is P62 and the 60Kda protein is Beclin 1. The 36Kda protein is GAPDH as the internal control protein. Additional file 5. Figure F4. Original blots of apoptosis related proteins in H9C2 cells following hypoxia. The proteins assessed were CHOP, JKN, and Caspase 12. The 27Kda protein is CHOP, the 54Kda and 46Kda proteins are isoforms of JKN and the 46Kda protein is Caspase 12. The 43Kda protein is Beta-actin; the internal control protein. Additional file 6. Figure F5. Original blots of autophagy and ERS proteins in H9C2 cells following activation of A2R. The autophagy proteins assessed were Beclin 1, p62, and LC3, while the ERS proteins were Grp78, P-IRE1 and XBP1s. Beta actin was the internal control protein.
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|
---
title: Impact of anemia treatment for left ventricular hypertrophy using long-acting
erythropoietin-stimulating agents from the pre-dialysis to maintenance dialysis
period in patients with chronic kidney disease, retrospective longitudinal cohort
study
authors:
- Hiroaki Io
- Masahiro Muto
- Yu Sasaki
- Masanori Ishizaka
- Toshiki Kano
- Haruna Fukuzaki
- Takuya Maeda
- Yuki Shimizu
- Junichiro Nakata
- Yusuke Suzuki
journal: BMC Nephrology
year: 2023
pmcid: PMC10039592
doi: 10.1186/s12882-023-03133-1
license: CC BY 4.0
---
# Impact of anemia treatment for left ventricular hypertrophy using long-acting erythropoietin-stimulating agents from the pre-dialysis to maintenance dialysis period in patients with chronic kidney disease, retrospective longitudinal cohort study
## Abstract
### Background
Anemia in patients with chronic kidney disease (p-CKDs) may initiate or exacerbate left ventricular hypertrophy (LVH). This study aimed to determine whether treatment using long-acting erythropoietin-stimulating agents (L-ESAs) is independently associated with LVH during the pre-dialysis to maintenance dialysis period in p-CKDs.
### Methods
Physical and laboratory examinations were performed 120 days before initiating dialysis in p-CKDs (baseline). To evaluate the left ventricular mass index (LVMI) after starting dialysis, the mean hemoglobin (Hb) was defined as the average at the start of dialysis and 6 months after starting dialysis. Changes in the LVMI were observed in three groups according to mean Hb levels (Hb < 10.1, 10.1 < Hb < 11.0, and Hb > 11.0 g/dL for Groups 1, 2, and 3, respectively). LVMI was evaluated using echocardiography at the pre-dialysis, initiation, and maintenance dialysis periods.
### Results
A lower LVMI at dialysis initiation and an improvement in LVMI were detected in the highest tertile group of mean Hb (11.0 g/dl). Consequently, in the high Hb group (Hb level > 11.0 g/dl), LVMI remained low from dialysis initiation until after 6 months. The relationship between Hb and LVMI was not significant; however, a constant correlation with β ≥ 0.4 in the absolute value was maintained.
### Conclusion
L-ESAs may correlate with Hb and LVMI after administration, independent of the baseline LVMI and Hb values. These findings have therapeutic implications in the treatment strategies for p-CKDs during the pre-dialysis to maintenance dialysis period.
## Background
Cardiovascular disease is the primary reason for morbidity and mortality in patients with pre-dialysis chronic kidney disease (CKD) [1, 2]. Previously, we reported that the incidence of left ventricular hypertrophy (LVH) increased in direct correlation with the progression of CKD in patients with kidney failure [3]. The prevalence of LVH increases with the progression of kidney dysfunction. Before initiating dialysis, $85\%$ of patients with stages 4 and 5 CKD undergo left ventricular (LV) remodeling [3]. LVH is recognized as a primary risk factor for cardiovascular death in patients undergoing dialysis [4]. It is a strong predictor of cardiac failure, sudden death, myocardial infarction, and stroke [5]. A previous study reported systolic blood pressure, residual glomerular filtration rate, and serum albumin (Alb) levels as predictive factors for LV mass index (LVMI), and these were measured using echocardiography at hemodialysis initiation [6]. Renal anemia is a common complication in patients with CKD (p-CKDs) [7]; it usually develops because of erythropoietin deficiency. In a previous study, the mean hemoglobin (Hb) value was found to be higher in an LVH-uncomplicated group than in an LVH-combined group from the pre-dialysis to dialysis initiation period when using long-acting erythropoietin-stimulating agents (L-ESAs) [8]. However, few longitudinal analyses have evaluated the factors associated with LVMI. Therefore, this retrospective cohort study investigated whether anemia treatment is independently associated with LVH during the pre-dialysis to maintenance dialysis period in p-CKDs using L-ESAs.
## Study design and cohort
This retrospective longitudinal cohort study investigated 32 p-CKDs before dialysis initiation. The study protocol was in conformance with the ethical guidelines of our institution (Ethics Committee Approval No. 22–78 at Juntendo university Nerima hospital and No. 2015026 at Juntendo university) and was registered in the University Hospital Medical Information Network (UMIN; ID: UMIN000018312). Informed consent was obtained from each patient before they participated in the study. The criteria for enrollment in this study included no history of congestive heart failure (defined as dyspnea or interstitial edema on chest X-rays), valvular disease, LV systolic dysfunction with an ejection fraction less than $50\%$, arrhythmia, or abnormal electrocardiography. The study period was from July 2015 to July 2018. All patients underwent hemodialysis.
The physical and laboratory examinations and LVMI measured using echocardiography were performed three times (at baseline, dialysis initiation, and during the maintenance dialysis period). Regarding the data points, the first point (baseline) was when the patients were injected subcutaneously with L-ESAs, namely, epoetin beta pegol ($$n = 17$$) or epoetin beta ($$n = 15$$). The second point was when the patients were hospitalized to undergo preparation for dialysis (before starting dialysis, at the time of the creation of the arteriovenous fistula). The third point was at 6 months after dialysis initiation. Blood and urine samples were collected during the echocardiographic study. The clinical, laboratory, and urinary parameters of all patients were recorded at baseline, 6 months before starting dialysis, 3 months before starting dialysis, and at the start of dialysis. The patients were followed for 120 days or more from baseline to the start of dialysis to determine if L-ESAs were effective and stable in treating anemia and LV remodeling. The target Hb levels for the patients in the study were based on the Japanese Society for Dialysis Therapy treatment guidelines for renal anemia [9]. None of the patients were administered iron during the pre-dialysis period. To evaluate LVMI after the start of dialysis, the mean hemoglobin (Hb) was defined as the average at the start of dialysis and 6 months after starting dialysis. The patients were divided into tertile groups based on the mean Hb level during the observation period.
## Physical and laboratory examinations
Blood pressure (BP) was measured using a manual sphygmomanometer with the patient in a sitting position either after 5 min of rest before echocardiography or at regular visits to the outpatient clinic. For the study parameters, we selected traditional risk factors that have been reported to be closely associated with CKD and LVH. The laboratory parameters were as follows: serum creatinine, serum Alb, Hb, correct serum calcium (cCa), serum phosphorus (P), intact parathyroid hormone, brain natriuretic peptide (BNP), high-sensitivity C-reactive protein, and ferritin levels as well as transferrin saturation (TSAT). The cCa was calculated as follows: cCa = Ca + (4-Alb)]. The TSAT was calculated as follows: serum iron/total iron-binding capacity) × 100 (%). The erythropoietin resistance index (ERI; a dosage of epoetin/body weight/Hb level) was also calculated. ESA doses were converted to epoetin doses using dose conversion ratios (epoetin beta:epoetin beta pegol = 200:0.93) [10]. The serial course of Hb levels and ERI was compared with the amount of epoetin beta pegol used during each observation period.
## Echocardiography
For all patients, echocardiography (two-dimensional and M-mode measurement) was performed using LOGIQe with a 2.5 MHz phased array transducer from GE Yokogawa Medical Systems (Tokyo, Japan). Echocardiographic examinations were conducted on a non-hemodialysis day. All echocardiographic data were evaluated according to the American Society of Echocardiography guidelines [11]. The left atrial and LV size, intraventricular septal thickness, posterior LV wall thickness (PWT), and LV mass were recorded [12]. LVMI was used to express the LV mass, which was corrected using the body surface area [13]. The relative wall thickness (RWT) was calculated as follows: 2 × PWT/LV diastolic diameter (LVDd). The severity of LVH was thus assessed based on LVMI and RWT.
## Statistical analyses
We consulted Statista (Hamburg, Germany) for statistical analyses. This was a pilot study, and the number of cases was limited; thus, we did not correct the multiplicity of the tests. All data are expressed as means ± standard deviations. The Student’s t-test was used for the univariate analysis. Variables with p-values < 0.05 were analyzed using a stepwise linear regression analysis based on a forward–backward procedure. The F-value for entry or removal of candidate variables from the discriminant function was set at 4.0.
## Patient characteristics
A flow chart depicting the patient selection criteria is shown in Fig. 1. The patients were divided into three groups based on their mean Hb levels during the observation period as follows: Group 1 (G1), Hb < 10.1 g/dL; Group 2 (G2), 10.1 < Hb < 11.0; and Group 3 (G3), Hb > 11.0 g/dL. No significant differences were observed between the groups in terms of baseline patient characteristics, except for the use of epoetin beta pegol (MIRCERA), primary renal disease (percentage of diabetic patients with primary renal disease), and urinary protein excretion (Table 1).Fig. 1Patient disposition and flow chart showing patient selection criteria. LVMI Left ventricular mass index, ESA Erythropoietin-stimulating agents, Hb Hemoglobin, G1, Group 1; G2, Group 2; Group 3 Table 1Patients’ characteristicsAverage Hb < 10.110.1 ≤ Average Hb < 11.011.0 ≤ Average HbBaselineAt dialysisEndpointBaselineAt dialysisEndpointBaselineAt dialysisEndpointN566Term before dialysis (days)340.8 ± 32.8283.7 ± 132.0251.7 ± 221.4Age (years old)71.8 ± 10.762.7 ± 11.256.3 ± 13.4Sex (n, (% men))5, (100.0)4, (66.7)4, (66.7)Diabetes (n, %)4, (80.0)4, (66.7)5, (83.3)Weight (kg)59.2 ± 13.460.4 ± 14.458.0 ± 11.568.8 ± 10.469.0 ± 10.867.0 ± 10.864.5 ± 18.265.2 ± 17.361.0 ± 12.5SBP (mmHg)138.0 ± 14.0141.2 ± 19.7137.6 ± 17.8143.0 ± 13.5143.0 ± 11.5142.0 ± 10.1134.0 ± 9.9128.7 ± 17.4139.2 ± 11.1DBP (mmHg)76.5 ± 5.775.6 ± 12.773.2 ± 20.073.5 ± 14.372.7 ± 11.072.0 ± 15.982.5 ± 14.570.3 ± 9.985.2 ± 4.1Hemoglobin (g/dL)9.6 ± 0.89.7 ± 0.710.1 ± 0.89.5 ± 1.49.9 ± 0.710.9 ± 0.5#9.9 ± 0.810.9 ± 0.8*12.2 ± 0.8*, **, #ERI42.0 ± 32.637.1 ± 16.833.1 ± 5.232.7 ± 21.826.8 ± 14.729.8 ± 19.436.1 ± 20.137.4 ± 20.723.6 ± 20.8##TSAT (%)34.5 [30.1, 45.0]23.7 [19.4, 43.7]20.4 [16.9, 21.2]32.8 [29.7, 36.2]17.2 [11.6, 38.5]31.6 [24.8, 45.0]*32.8 [23.3, 44.4]17.1 [12.6, 23.1]#29.6 [22.2, 62.7]Ferritin (mg/dL)290 [121, 507]176 [69, 256]231 [69, 265]139 [107, 341]193 [62, 537]247 [125, 361]140 [103, 271]122 [68, 310]172 [97, 442]BNP (pg/mL)74 [32, 163]482 [106, 1130]648 [67, 1189]138 [62, 177]47 [18, 409]269 [153, 343]26 [19, 34]41 [21, 88]50 [19, 191]Intact PTH (mg/dL)219 [161, 700]215 [115, 548]320 [156, 648]406 [139, 817]237 [15, 673]141 [93, 282]287 [283, 722]220 [65, 485]161 [93, 320]Correction Ca (mg/dL)8.7 ± 0.29.1 ± 0.99.3 ± 0.7#8.8 ± 0.48.7 ± 0.38.6 ± 0.28.6 ± 0.59.4 ± 0.99.5 ± 0.8Phosphorus (mg/dL)4.2 ± 1.35.1 ± 1.35.7 ± 1.1#4.4 ± 0.55.0 ± 1.25.0 ± 1.55.7 ± 0.5**6.0 ± 1.25.7 ± 1.7Albumin (mg/dL)3.8 ± 0.43.3 ± 0.52.9 ± 0.7#4.0 ± 0.53.6 ± 0.43.6 ± 0.54.0 ± 0.43.7 ± 0.43.5 ± 0.4hsCRP (mg/dL)0.07 [0.03, 0.10]0.03 [0.01, 0.36]0.03 [0.01, 0.14]0.28 [0.15, 0.40]0.14 [0.09, 0.80]0.05 [0.03, 0.06]0.09 [0.08, 0.10]0.11 [0.06, 5.95]0.16 [0.06, 0.30]Creatinine (mg/dL)4.7 ± 1.79.3 ± 3.4#-5.7 ± 2.18.7 ± 1.9#-7.0 ± 0.9*10.3 ± 1.9#-Urinary protein (g/g Cr)2.3 ± 1.03.9 ± 4.5-2.6 ± 1.91.9 ± 0.6-1.4 ± 1.11.5 ± 0.7-Echocardiography LVDd (mm)52.0 ± 0.151.7 ± 3.455.7 ± 7.549.4 ± 7.248.9 ± 4.847.7 ± 3.849.8 ± 1.144.6 ± 4.9*46.1 ± 4.3* Ejection fraction (%)0.61 ± 0.190.60 ± 0.120.58 ± 0.100.70 ± 0.150.67 ± 0.130.67 ± 0.110.67 ± 0.010.71 ± 0.060.64 ± 0.09##LVMI (g/m2)163.5 ± 39.6162.7 ± 45.7205.9 ± 41.5143.3 ± 45.3143.1 ± 32.8149.7 ± 25.8*117.6 ± 17.7*110.5 ± 31.1123.2 ± 29.7* RWT0.41 ± 0.020.48 ± 0.060.44 ± 0.060.45 ± 0.070.48 ± 0.040.50 ± 0.060.43 ± 0.040.49 ± 0.05#0.48 ± 0.07Treatment (n, (%)) ARB2, (40.0)1, (20.0)1, (20.0)1, (16.7)1, (16.7)2, (33.3)2, (33.3)3, (50.0)3, (50.0) CCB1, (20.0)1, (20.0)1, (20.0)0, (0.0)0, (0.0)1, (16.7)3, (50.0)3, (50.0)4, (66.7) β-blocker4, (80.0)4, (80.0)3, (60.0)4, (66.7)3, (50.0)2, (33.3)5, (83.3)5, (83.3)5, (83.3)Abbreviations: SBP Systolic blood pressure, DBP Diastolic blood pressure, ERI Erythropoietin resistance index, TSAT Transferrin saturation, BNP brain natriuretic peptide, PTH Parathyroid hormone, Ca Calcium, hsCRP High-sensitivity C-reactive protein, LVDd Left ventricular diastolic dysfunction, LVMI Left ventricular mass index, RWT Relative wall thickness. Average Hb: average at baseline and dialysis * $p \leq 0.05$ vs. Average Hb < 10.1, **$p \leq 0.05$ vs. 10.1 ≤ Average Hb < 11.0 (unpaired test: Fisher’s exact test [nominal scale], t-test [continuous scale and parametric], Mann–Whitney U test [continuous scale and non-parametric]) # $p \leq 0.05$ vs. Baseline, ##$p \leq 0.05$ vs. At dialysis (paired test: paired t-test [continuous scale and parametric], Wilcoxson signed-rank test [continuous scale and non-parametric])
## Evaluation of LV remodeling at follow-up using LVMI
A comparison of LVMI at the baseline, initiation, and after 6 months is shown in Fig. 2. We detected a lower LVMI at baseline (pre-dialysis period) along with improvements in the LVMI during the observation period in G3 (> 11.0 g/dl). The LVMI of the patients in G3 (> 11.0 g/dl) was significantly lower than that of the other groups. The LVMI showed no difference at the start of ESA administration and dialysis initiation between the groups using epoetin beta pegol and epoetin beta (data not shown, see Reference 8). Changes in LVMI were observed in the three groups according to the mean Hb level from the start of ESA administration to dialysis initiation. As a result, in the high Hb group, where the Hb level exceeded 11.0 g/dl, the LVMI remained low from the time of dialysis initiation to after 6 months (Fig. 2).Fig. 2Comparison of the left ventricular mass index at the pre-dialysis to maintenance dialysis periods. Hb Hemoglobin
## Factors associated with LVMI during the observation period
As shown in Table 1, there was a clear difference in LVMI after 6 months. Table 2 shows the results from the univariate linear regression analysis of factors associated with LVMI after 6 months of dialysis. The LVMI after 6 months was significantly associated with age, male sex, Hb, TSAT, and mean Hb. Table 3 shows the results from the multivariate linear regression analysis of factors associated with LVMI after 6 months of dialysis. In Model 1, age and sex were significant factors; however, in Model 2, the relationship between Hb and LVMI was not significant, and a constant correlation with β ≥ 0.4 in absolute value was maintained. This suggested that Hb and LVMI after continuous administration of erythropoiesis receptor activator might be correlated independent of the initial LVMI and Hb values. Table 2Univariate linear regression analysis of factors associated with LVMILVMI after 6 months of dialysis (univariate analysis)Regression coefficientsStandard errorβ p-valueAge1.9410.7760.543 0.024 Sex: male56.23422.9010.535 0.027 Baseline Hb-15.13111.080-0.3330.192After 6 months of dialysis Weight-0.5991.080-0.1470.588 Systolic blood pressure-0.4141.055-0.1080.701 Diastolic blood pressure-1.1120.825-0.3500.201 Hemoglobin-26.9108.466-0.634 0.006 ERI-0.2490.738-0.0900.741 TSAT-191.51381.409-0.597 0.040 Ferritin-0.0890.117-0.2350.462 BNP0.1300.0280.840 0.001 Intact PTH0.0260.0870.0940.771 Correct calcium-28.35429.733-0.3190.368 Phosphorus-2.77915.001-0.0650.858 Mean Hb-30.96913.765-0.502 0.040 β: Standardized regression coefficientsAbbreviations: BNP Brain natriuretic peptide, PTH Parathyroid hormone, hsCRP High-sensitivity C reactive protein, TSAT Transferrin saturation, Hb Hemoglobin, ERI Erythropoietin resistance index, LVMI Left ventricular mass indexTable 3Multivariate linear regression analysis of factors associated with LVMILVMI after 6 months of dialysis (multivariate analysis)Regression coefficientsStandard errorβ p-valueModel 1: Age, Sex adjusted Hb of After 6 months-15.5577.721-0.3670.065 Age1.6830.5740.471 0.012 Sex38.33518.7200.3650.061Model 2: Baseline Hb and LVMI adjusted Hb of After 6 months-18.0779.537-0.4260.080 Baseline LVMI0.5460.3680.4600.161 Baseline Hb3.76512.5700.0830.769β: Standardized regression coefficients LVMI Left ventricular mass index, Hb Hemoglobin
## Serial processing of Hb levels and ERI
When epoetin beta pegol was used, Hb levels after 6 months of dialysis were significantly higher than those at baseline and the start of dialysis (Fig. 3). Hb levels at dialysis initiation were lower than baseline Hb levels in the group using short-acting ESA (S-ESA) (epoetin beta) (data not shown, see Reference 8). The ERI after 6 months of dialysis was lower than that at baseline and dialysis initiation in patients using L-ESAs (Fig. 4).Fig. 3Comparison of hemoglobin levels during the pre-dialysis to maintenance dialysis period. Serial process of levels of hemoglobinFig. 4Comparison of the levels of erythropoietin resistance index during the pre-dialysis to maintenance dialysis periods. Serial process of levels of erythropoietin resistance index The ERI at dialysis initiation and 3 months prior was significantly higher than that at baseline in the group using S-ESA (data not shown, see Reference 8). Patients using L-ESAs showed a significant improvement in ERI compared with patients using S-ESA in the multivariate analysis of variance (ANOVA) (data not shown, see Reference 8).
## Discussion
We aimed to determine whether treatment using long-acting erythropoietin-stimulating agents (L-ESAs) is independently associated with LVH during the pre-dialysis to maintenance dialysis period in p-CKDs. We found that L-ESAs were effective and stable when treating anemia until the maintenance dialysis period. Anemia is a causative factor for LVH in p-CKDs, and studies have shown correlations between age, anemia, LV mass, and CKD [3]. Conversely, anemia and LVH have indicated associations with declining renal function in p-CKDs; the interactions among aging, anemia, LVH, and CKD are complex [7, 14]. LVH is an independent risk factor for cardiac death in p-CKDs and end-stage renal disease. Severe CKD is associated with a higher prevalence of and more severe LVH. Interestingly, an elevated LV mass and LV geometry pattern appear to be important in predicting the response to anemia correction in p-CKDs. Previous reports have shown that eccentric hypertrophy is a greater risk factor than concentric hypertrophy for poor cardiovascular outcomes and worsening renal function in anemic p-CKDs [15, 16]. In this study, the measurement of the LVDd decreased upon initiating dialysis (Table 1). There was no significant difference in RWT throughout the observation period, which indicated that the patients in our study had concentric LVH. Several studies have examined the value of aggressively treating p-CKDs with recombinant erythropoietin to prevent the development of LVH [16]. Possible explanations for this finding include increased BP and blood viscosity in the treated patients. The Alb, urinary proteins, BNP, and BP levels from this study were neither significantly correlated nor independently associated with LVMI, as seen in the multivariate regression analysis (Tables 2 and 3). We believe that anemia causes high cardiac output to normalize wall stress, causing LV dilation due to increased preload followed by compensatory hypertrophy. There was no significant difference among the groups in terms of BP (Table 1).
Treating anemia improves survival, thereby decreasing morbidity and mortality and improving the quality of life in p-CKDs. A previous study reported that LVMI decreased due to increased Hb levels [17]. Our results are consistent with the findings from previous studies evaluating p-CKDs [3, 6, 7]. However, the Correction of Hemoglobin and Outcome in Renal Insufficiency study revealed that a targeted Hb level of 13.5 g/dl was more harmful than 11.3 g/dl in pre-dialysis p-CKDs and resulted in no incremental improvement in the quality of life [18]. The Cardiovascular Reduction Early Anemia Treatment Epoetin β study showed that with mild-to-moderate anemia in p-CKDs, the normalization of Hb levels in the range of 13.0–15.0 g/dl did not reduce cardiovascular events compared with the effects of achieving a lower target range (10.5–11.5 g/dl) [19]. Evidence regarding the target value for anemia treatment in p-CKDs is insufficient, especially for the upper limit target value, and further research is warranted. In this study, the Hb level reached the target value. For such cases, aggressive treatment (nephrologist follow-up; patients with a history of nephropathy treatment) is considered necessary [8]. In an observational study, the conversion from epoetin beta to epoetin beta pegol maintained a Hb level between 11.2 g/dl and 11.4 g/dl after 12 months [20]. Our previous study showed that the treatment of anemia prevented LVH, and the use of L-ESAs led to a more significant improvement in the ERI than using S-ESA in a few instances, as was recorded in the multivariate ANOVA analysis [8].
A previous study indicated that older age, high body mass index, pretreatment for Hb, use of angiotensin conversion enzyme/angiotensin receptor blocker, and diabetic nephropathy were associated with increased erythropoietin requirements in anemic p-CKDs [21]. In this study, most patients had diabetic nephropathy in the lowest tertile of mean Hb (< 10.1 g/dl). In p-CKDs with a history of nephropathy treatment who were not referred to a nephrologist, the risks of end-stage renal disease were higher in those with stages 3b-5 CKD [22]. Previous reports have shown that anemia and resistance to ESAs are prognostic factors in hemodialysis patients [23]. In addition, an association was found between Hb levels and the cardiothoracic ratio in patients on incident dialysis [24]. These reports partly supported our results.
This study has a few limitations. First, the duration of the observation periods was different among the patients. Additionally, this was a pilot study and the number of cases was limited; thus, we did not correct the multiplicity for the tests. Second, echocardiography data were examined using only three data points. We could have established a more accurate difference if five or six points had been used. An increase in LVMI is a prerequisite for developing LVH. Accurate echocardiographic screening of p-CKDs is an available tool to rule out the presence of LVH. Third, all the patients had different personal, medical, and treatment histories. In the future, studies with larger sample sizes and comprising patients with similar characteristics need to be performed to determine the outcomes of this treatment more accurately. Moreover, the results of comparative studies between ESA and hypoxia-inducible factor-prolyl hydroxylase inhibitors are expected.
In conclusion, using L-ESAs appears to be an effective and stable method of treating anemia until after 6 months of dialysis. Therefore, it is essential to treat anemia to prevent LV remodeling in p-CKDs. These findings may have therapeutic implications for treatment strategies in p-CKDs with L-ESAs administered during the pre-dialysis phase until maintenance dialysis.
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---
title: Luteolin intake is negatively associated with all-cause and cardiac mortality
among patients with type 2 diabetes mellitus
authors:
- Wenbin Zhang
- Duanbin Li
- Yu Shan
- Yecheng Tao
- Qingqing Chen
- Tianli Hu
- Menghan Gao
- Zhezhe Chen
- Hangpan Jiang
- Changqin Du
- Min Wang
- Kai Guo
journal: Diabetology & Metabolic Syndrome
year: 2023
pmcid: PMC10039598
doi: 10.1186/s13098-023-01026-9
license: CC BY 4.0
---
# Luteolin intake is negatively associated with all-cause and cardiac mortality among patients with type 2 diabetes mellitus
## Abstract
### Background
Luteolin, a common flavonoid in our daily diet, has potent anti-diabetic effects. However, its prognostic impact on type 2 diabetes mellitus (T2DM) is still uncertain. This study aimed to clarify this association.
### Methods
In this prospective cohort study, 2,461 patients with T2DM were included from the National Health and Nutrition Examination Survey. Dietary luteolin intake was estimated by the type and amount of food consumed in a 24-hour dietary recall. All-cause and cardiac mortality were ascertained by National Death Index *Mortality data* (as of December 31, 2019). The association of luteolin intake with mortality risk was estimated by Cox proportional hazards model.
### Results
The median (interquartile range) luteolin intake was 0.355 (0.130, 0.835) mg/day. During the follow-up (median, 8.4 years), 561 all-cause deaths (including 136 cardiac deaths) were documented. Per-unit increment of luteolin intake (natural logarithm transformed) was found to reduce all-cause mortality by $7.0\%$ ($$P \leq 0.024$$) and cardiac mortality by $22.6\%$ ($$P \leq 0.001$$) in patients with T2DM. An inverse dose-response association was identified between luteolin intake (range: 0.005–9.870 mg/day) and mortality risk. The consistent result was also shown when stratified by age, gender, race, body mass index, HbA1c level, and T2DM duration. Moreover, luteolin intake increment was also shown to be associated with a lower C-reactive protein level at baseline (β =-0.332; $95\%$ CI =-0.541, -0.122).
### Conclusion
The current study confirmed that the dietary luteolin intake increment reduced all-cause mortality (especially cardiac mortality) in patients with T2DM, which may be attributed to the anti-inflammatory property of luteolin.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13098-023-01026-9.
## Background
Type 2 diabetes mellitus (T2DM) is a major global public health concern, with a total of 415 million people living with diabetes worldwide [1]. As the sixth leading cause of disability, T2DM carries tremendous healthcare and financial burden [2, 3]. Patients with T2DM have substantially higher cardiovascular disease morbidity and mortality [4]. The World Health Organization reported that cardiovascular disease accounted for more than two-thirds of diabetes-related deaths in the elderly [5]. For mechanisms, T2DM accelerates most cardiac pathologies, including microvascular dysfunction, vascular endothelial inflammation and injury, thrombogenesis, and autonomic nerve disorder [6]. As a metabolic disease, T2DM is inevitably related to our daily diet. In the United States, almost half of cardiovascular and diabetes deaths are linked to poor dietary practices, corresponding to nearly 1,000 deaths per day [7]. Therefore, it is important to identify a healthy dietary pattern to avoid premature cardiovascular complications and mortality in patients with T2DM.
Luteolin (molecular formula C15H10O6), a naturally occurring metabolite, is commonly found in our dietary components, such as parsley, thyme, peppermint, basil, celery, and artichoke [8]. Among the thousands of flavonoids, luteolin belongs to the flavones class and is often present in the form of its glycoside (luteolin-7-O-glucoside or luteolin-7G) [9, 10]. The health-promoting benefits of luteolin have been well documented due to its pleiotropic properties, including anti-diabetic [11], cardiovascular protective [12], anti-inflammatory [13], and anti-cancer [14] effects. In diabetic animal models, luteolin has been identified to reverse glucose intolerance [15], delay renal function decline [16], improve learning and memory [17], and promote wound healing [18]. Moreover, in various animal models of cardiac disease, the therapeutic effects of luteolin were also shown, including promoting cytoprotection in myocardial ischemia/reperfusion injury [19], improving heart function in heart failure [20], and reversing atherosclerosis in coronary artery disease [21]. Overall, the potent antidiabetic and cardiovascular protective effects of luteolin have been extensively documented in these animal studies. However, there are few population-based studies to investigate its relationship with relevant clinical outcomes.
Therefore, we designed this population-based cohort study to investigate the impact of dietary luteolin intake on the prognosis of T2DM patients, including all-cause and cardiac mortality.
## Study population
This study employed the data from the National Health and Nutrition Examination Survey (NHANES) (3 cycles of 2007–2008, 2009–2010, and 2017–2018). NHANES was sponsored and administered by the National Center for Health Statistics, which investigated the US civilians’ health and nutritional status through a nationally representative sample [22]. Participants were required to receive a series of questionnaires, physical examinations, and laboratory tests at home or the mobile examination center (MEC). More details were presented elsewhere [22].
Overall, 3,489 diabetic patients were identified according to diabetic questionnaires (variable diq010), blood glucose testing in MEC (variables lbxgh, lbdglusi, lbdsglsi, lbdgltsi), and records of anti-diabetic agents use (variable rxddcn). Furthermore, we excluded those with luteolin intake missing ($$n = 623$$), sample weights missing ($$n = 196$$), follow-up data missing ($$n = 64$$), underlying type 1 diabetes (told to have diabetes when < 30 years, $$n = 145$$) [23], and pregnant ($$n = 0$$). Finally, 2,461 patients with T2DM were enrolled from NHANES (Figure S1).
## Dietary intakes
NHANES collected the amount of food and beverages consumed by participants in the past 24 h. Two 24-hour dietary recall interviews were performed, including an in-person interview in MEC and a telephone interview 3–10 days afterward. The specific nutrient was further calculated according to the Food and Nutrient Database for Dietary Studies (FNDDS) [24]. These nutrient intakes were averaged if participants underwent both the first and second dietary recall interviews. We extracted the intake data on 29 types of flavonoids in 6 categories (including luteolin). The dictionary of luteolin content in foods and beverages (≥ 1 mg/100 mg) was shown in Table S1.
## Mortality outcomes
NHANES Public-Use Linked Mortality Files were used to determine the survival status of participants (as of December 31, 2019) [25]. The International Classification of Diseases, Tenth Revision (ICD-10) was used to define the cause-specific death [26]. We examined the all-cause death and the top four cause-specific death, in order of cardiac diseases (ICD-10: I00-I09, I11, I13, I20-I51), malignant neoplasms (ICD-10: C00-C97), diabetes mellitus (ICD-10: E10-E14), and cerebrovascular diseases (ICD-10: I60-I69). The definitions and proportions of cause-specific death were shown in Table S2.
## Covariate definitions
Demographic parameters were extracted from the questionnaire data. Races were classified into non-Hispanic whites, non-Hispanic blacks, and others. Alcohol consumption in the past 12 months was defined as heavy drinking (≥ 2 drinks/day), mild drinking (1 drink/day), and non-drinking (no drink). Cigarette consumption was defined as never smoked (< 100 cigarettes in a lifetime), formerly smoked (≥ 100 cigarettes in a lifetime and quit now), and currently smoked (≥ 100 cigarettes in a lifetime and smoke some days or every day). Physical activity in leisure time was categorized into no or unable to activity, moderate activity, and vigorous activity. The family poverty income ratio is equal to the family income divided by the poverty guideline, which is corresponding to the year and the state of the participants. Body mass index (BMI, kg/m2) is equal to weight (kg) divided by height (m) squared.
Hypertension was defined when diastolic blood pressure ≥ 90 mmHg, systolic blood pressure ≥ 140 mmHg, use of anti-hypertensive drugs (bpq040a), or “yes” code for “told you had high blood pressure” (variable bpq$\frac{020}{030}$). Hyperlipidemia was defined as current use of lipid-lowering agents or the presence of abnormal lipid profiles, including total cholesterol ≥ 200 mg/dL, triglycerides ≥ 150 mg/dL, low-density lipoprotein cholesterol (LDL-C) ≥ 130 mg/dL, or high-density lipoprotein cholesterol < 40 mg/dL for male whereas < 50 mg/dL for female [27].
## Statistical analysis
Due to the complex survey design, sample weights (day one dietary weights) were taken into account in analyses. The continuous variable was presented as weighted mean ± standard error with comparisons by the Kruskal-Wallis test, and the categorical variable was presented as unweighted count (weighted percentage) with comparisons by the chi-square test.
The events per variable (EPV) criterion was used to evaluate the sample size. If the EPV of the model exceeded 10, the sample size was considered sufficient to meet the statistical requirements [28, 29]. The relative risks of all-cause and cardiac mortality were calculated for luteolin intake tertiles. Kaplan-Meier survival analyses and Cox proportional hazards models were employed to process the survival data. Three statistical models were fitted. The first model adjusted for age (continuous), race (non-Hispanic white, non-Hispanic black, or others), and gender (male or female). The second model further adjusted for BMI (< 20, 20–24, 25–29, or ≥ 30 kg/m2), educational attainment (below high school, high school, or college or above), alcohol consumption (none, mild, or heavy), cigarette consumption (never, former, or current), poverty income ratio (≤ 1, 1–3, or > 3), energy intake (< 1500 or ≥ 1500 kcal/day), and physical activity in leisure time (no or unable, moderate, or vigorous activity). Moreover, the third model additionally adjusted for hyperlipidemia diagnosis (no or yes), hypertension diagnosis (no or yes), HbA1c level (< 6.5 or ≥ $6.5\%$), and use of oral anti-diabetic agents or insulin (no or yes). To minimize the removal of samples, missing covariates were treated by multiple imputations.
A dose-response association between continuous luteolin intake (Ln-transformed) and mortality risk was visualized by the restricted cubic spline model. Four knots of the spline model were determined at specific distribution percentiles ($5\%$, $35\%$, $65\%$, and $95\%$). The likelihood ratio test was used to determine the non-linearity of the dose-response association by comparing the model with and without spline terms.
Subgroup analysis was conducted when patients were stratified by age (< 65, ≥ 65 years), gender (male, female), race (non-Hispanic white, others), BMI (< 30, ≥ 30 kg/m2), energy intake (< 1500, ≥ 1500 kcal/day), HbA1c level (< 6.5, ≥ $6.5\%$), and T2DM duration (< 10, ≥ 10 years). Interactions were examined by integrating the product term between continuous luteolin intake and stratified factors.
To test the robustness, we also conducted several sensitivity analyses. First, the flavonoid database provided the amounts of 29 flavonoids (belonging to 6 flavonoid categories) (Table S3). To avoid the potential effect of remaining flavonoids, we further adjusted other flavonoid intakes in the model. Second, given the underlying luteolin intake outliers, we fitted a new dose-response association after excluding luteolin intake outside the 5th and 95th percentiles. Third, we also examined the association of luteolin intake with cause-specific death, including malignant neoplasms (C00-C97), diabetes mellitus (E10-E14), and cerebrovascular diseases (I60-I69). Finally, to investigate the potential mechanism by which luteolin reduces mortality risk, we examined the association between dietary luteolin intake and baseline cardiometabolic risk factors, including HbA1c, fasting blood glucose, homeostasis model assessment-insulin resistance (HOMA-IR), HOMA-insulin sensitivity (HOMA-IS), HOMA-β, arterial blood pressure, LDL-C, C-reactive protein (CRP).
Two-sided P values < 0.05 were considered statistical significance. Data were analyzed by R software (R version 4.1.1).
## Results
The characteristics of 2,461 patients with T2DM were summarized according to luteolin intake tertiles (mean age, 61.3 years; male, $49.4\%$) (Table 1). The median (interquartile range) of luteolin intake was 0.355 (0.130, 0.835) mg/day. Patients with higher luteolin intake were more likely to be male, tended to have higher educational attainment, higher energy intake, and more intense leisure-time physical activity.
Table 1Baseline characteristics based on luteolin intake among patients with type 2 diabetes mellitusCharacteristicTotalLuteolin intake (mg/day)P valueTertile 1Tertile 2Tertile 3 [0.005,0.195) [0.195,0.640) [0.640,9.870]Number of patients2461841803817Age, years61.28 ± 0.4260.69 ± 0.7461.80 ± 0.7261.36 ± 0.620.560Gender (%)0.040* Female1222 (50.63)432 (52.54)410 (54.01)380 (45.95) Male1239 (49.37)409 (47.46)393 (45.99)437 (54.05)Race (%)0.001* Non-Hispanic White971 (63.58)340 (65.30)312 (61.43)319 (63.86) Non-Hispanic Black603 (14.37)247 (17.98)207 (15.23)149 (10.30) Others887 (22.05)254 (16.72)284 (23.35)349 (25.84)Educational attainment (%)< 0.001* Below high school407 (9.34)147 (9.07)137 (10.61)123 (8.49) High school or equivalent1002 (40.64)379 (46.53)347 (44.87)276 (31.55) College or above1052 (50.02)315 (44.41)319 (44.52)418 (59.96)Body mass index, kg/m233.34 ± 0.2633.64 ± 0.3732.74 ± 0.3533.58 ± 0.430.150Family income-poverty ratio (%)0.002* ≤1.0475 (13.35)201 (16.73)167 (13.67)107 (9.96) 1.0–3.01178 (41.81)422 (45.01)384 (42.88)372 (37.94) >3.0808 (44.84)218 (38.26)252 (43.45)338 (52.10)Leisure-time physical activity (%)< 0.001* No or unable1662 (62.90)619 (70.43)562 (66.29)481 (53.04) Moderate621 (28.67)179 (25.00)189 (25.38)253 (34.89) Vigorous178 (8.43)43 (4.57)52 (8.33)83 (12.07)*Smoking status* (%)0.010* Never smoker1210 (49.51)390 (48.45)398 (48.77)422 (51.12) Former smoker872 (35.87)286 (32.47)282 (36.20)304 (38.72) Current smoker379 (14.62)165 (19.09)123 (15.03)91 (10.16)Alcohol consumption (%)< 0.001* Non-drinker1235 (44.32)462 (48.53)427 (47.67)346 (37.53) Mild drinker554 (25.14)138 (18.87)176 (22.68)240 (33.04) Heavy drinker672 (30.54)241 (32.59)200 (29.65)231 (29.43)Energy intake (%)0.002* <1500 kcal/day971 (34.37)389 (40.73)318 (35.82)264 (27.27) ≥1500 kcal/day1490 (65.63)452 (59.27)485 (64.18)553 (72.73)Hypertension (%)0.001* No632 (27.66)193 (20.67)209 (33.36)230 (29.15) Yes1829 (72.34)648 (79.33)594 (66.64)587 (70.85)Hyperlipidemia (%)0.130No314 (11.05)109 (12.33)112 (12.43)93 (8.66)Yes2147 (88.95)732 (87.67)691 (87.57)724 (91.34)HbA1c, %7.02 ± 0.047.02 ± 0.066.99 ± 0.077.04 ± 0.070.880Fasting blood glucose, mmol/L8.34 ± 0.098.29 ± 0.208.21 ± 0.148.50 ± 0.140.340Duration of diabetes, years10.19 ± 0.299.78 ± 0.4010.49 ± 0.5010.33 ± 0.430.480Use of oral anti-diabetic agents (%)0.500 No1084 (46.84)377 (47.37)347 (44.27)360 (48.59) Yes1377 (53.16)464 (52.63)456 (55.73)457 (51.41)Use of insulin (%)0.070 No2054 (82.79)684 (80.65)662 (81.08)708 (86.26) Yes407 (17.21)157 (19.35)141 (18.92)109 (13.74)*The continuous* variable is presented as weighted mean ± standard error. The categorical variable is presented as unweighted count (weighted percentage)*$P \leq 0.05$ During follow-up periods (median, 8.7 years), 561 all-cause deaths (including 136 cardiac deaths) were documented. Given the sufficient positive outcomes, the sample size was considered adequate based on the criterion of EPV greater than 10. Kaplan-Meier survival analyses showed that a higher luteolin intake contributed to a lower all-cause mortality (Log-rank $$P \leq 0.010$$) and cardiac mortality (Log-rank $$P \leq 0.003$$) (Fig. 1). The relative risks ($95\%$ CIs) across luteolin intake tertiles were 1 (reference), 0.910 (0.770, 1.076), and 0.689 (0.573, 0.829) for all-cause mortality and 1 (reference), 0.760 (0.524, 1.102), and 0.481 (0.313,0.740) for cardiac mortality (Table S4). In Cox regression analyses, adjusted-HRs ($95\%$ CIs) across luteolin intake tertiles were 1.000 (reference), 0.885 (0.683, 1.146), and 0.855 (0.691, 1.057) for all-cause mortality and 1.000 (reference), 0.662 (0.413, 1.063), and 0.487 (0.284, 0.836) for cardiac mortality (Table 2). Per-unit increment of luteolin intake (Ln-transformed) contributed to a $7.0\%$ reduction in all-cause mortality (adjusted-HR = 0.930; $95\%$ CI = 0.874, 0.991; $$P \leq 0.024$$) and a $22.6\%$ reduction in cardiac mortality (adjusted-HR = 0.774; $95\%$ CI = 0.668, 0.898; $$P \leq 0.001$$) (Table 2). Moreover, the spline plot showed an inverse dose-response association of luteolin intake (range: 0.005–9.870 mg/day) with all-cause mortality (P-nonlinearity = 0.828) and cardiac mortality (P-nonlinearity = 0.638) (Fig. 2).
Fig. 1Kaplan-Meier survival curve of all-cause mortality (A) and cardiac mortality (B) based on luteolin intake tertiles among patients with type 2 diabetes mellitus Table 2HR ($95\%$ CI) for all-cause and cardiac mortality based on luteolin intake among patients with type 2 diabetes mellitusCharacteristicLuteolin intake (mg/day)Per-unit incrementof luteolin intake(Ln-transformed)P valueTertile 1 [0.005, 0.195)Tertile 2 [0.195, 0.640)Tertile 3 [0.640, 9.870]All-cause mortality No. deaths/total (%)$\frac{221}{841}$ (26.3)$\frac{192}{803}$ (23.9)$\frac{148}{817}$ (18.1)$\frac{561}{2461}$ (22.8) Model 111 (reference)0.866 (0.664, 1.130)0.668 (0.539, 0.830)0.864 (0.807, 0.926)< 0.001 Model 221 (reference)0.907 (0.701, 1.173)0.856 (0.690, 1.062)0.927 (0.869, 0.990)0.023 Model 331 (reference)0.885 (0.683, 1.146)0.855 (0.691, 1.057)0.930 (0.874, 0.991)0.024Cardiac mortality No. deaths/total (%)$\frac{62}{841}$ (7.4)$\frac{45}{803}$ (5.6)$\frac{29}{817}$ (3.5)$\frac{136}{2461}$ (5.5) Model 111 (reference)0.649 (0.397, 1.06)0.385 (0.221, 0.672)0.725 (0.621, 0.847)< 0.001 Model 221 (reference)0.677 (0.408, 1.122)0.487 (0.279, 0.850)0.770 (0.659, 0.901)0.001 Model 331 (reference)0.662 (0.413, 1.063)0.487 (0.284, 0.836)0.774 (0.668, 0.898)0.001HR ($95\%$ CI) was estimated by Cox proportional hazards model and accounted for the sample weights. Cardiac mortality was defined as I00-I09, I11, I13, I20-I51 according to the ICD-10 criteria1Model 1 was adjusted for age (continuous), race (non-Hispanic white, non-Hispanic black, or others), and gender (male or female)2Model 2 was additionally adjusted for BMI (< 20, 20–24, 25–29, or ≥ 30 kg/m2), educational attainment (below high school, high school, or college or above), alcohol consumption (none, mild, or heavy), cigarette consumption (never, former, or current), poverty income ratio (≤ 1, 1–3, or > 3), energy intake (< 1500 or ≥ 1500 kcal/day), and physical activity in leisure time (no or unable, moderate, or vigorous)3Model 3 was additionally adjusted for hyperlipidemia (no or yes), hypertension (no or yes), HbA1c level (< 6.5 or ≥ $6.5\%$), and use of oral anti-diabetic agents or insulin (no or yes) Fig. 2The dose-response association of luteolin intake with all-cause mortality (A) and cardiac mortality (B) among patients with type 2 diabetes mellitusThe dose-response association of continuous luteolin intake (Ln-transformed) with mortality risk was visualized by the restricted cubic spline model (corresponding to the range of luteolin intake: 0.005–9.870 mg/day). Four knots of the spline model were determined at specific distribution percentiles ($5\%$, $35\%$, $65\%$, and $95\%$). The spline model was adjusted for consistent confounding factors, including age, gender, race, income-poverty ratio, body mass index, educational attainment, smoking, drinking, energy intake, physical activity, hypertension, hyperlipidemia, HbA1c level, and use of oral anti-diabetic agents or insulin. For more details on confounding factors processing, refer to Table 2. The non-linearity of the dose-response association was examined by the likelihood ratio test. The Y-axis represents the adjusted HR given the value of luteolin intake compared to the corresponding median. The shadow area depicts the $95\%$ confidence intervals. The density diagram at the top depicts the distribution of luteolin intake (Ln-transformed) In Fig. 3 and Figure S3, subgroup analyses revealed a consistent result when patients were stratified by age (< 65, ≥ 65 years), gender (male, female), race (non-Hispanic white, others), BMI (< 30, ≥ 30 kg/m2), energy intake (< 1500, ≥ 1500 kcal/day), HbA1c level (< 6.5, ≥ $6.5\%$), and T2DM duration (< 10, ≥ 10 years). For cardiac mortality (Fig. 3), patients with T2DM duration ≥ 10 years were more likely to get benefits from dietary luteolin intake increment compared to those with T2DM duration < 10 years (P for interaction = 0.048). Moreover, patients with HbA1c ≥ $6.5\%$ (adjusted-HR = 0.731, $95\%$ CI = 0.614, 0.871) had a more remarkable cardiac mortality reduction compared to those with HbA1c < $6.5\%$ (adjusted-HR = 0.824, $95\%$ CI = 0.658, 1.032).
Fig. 3Subgroup analyses of luteolin intake with cardiac mortality among patients with type 2 diabetes mellitusHR ($95\%$ CI) was assessed by Cox proportional hazards model. The model was adjusted for covariates including age, race, gender, body mass index, income-poverty ratio, smoking, drinking, energy intake, physical activity, hypertension, hyperlipidemia, HbA1c level, and use of oral anti-diabetic agents or insulin (except the stratified variable itself). Due to not being informed of their diabetes, 833 patients were unable to determine the diabetes duration and were excluded from the stratified analysis of type 2 DM duration1 The interaction between luteolin intake (continuous) and the stratified variable was assessed by the Wald test*$P \leq 0.05$ Several sensitivity analyses confirmed the robustness of our findings. First, when additionally adjusting for remaining flavonoids in the model, luteolin intake increment remained independently reduced all-cause and cardiac mortality (Table S5). Second, after excluding potential outliers of luteolin intake (excluding 273 samples outside the 5th and 95th percentiles), the inverse dose-response association between luteolin intake and mortality risk remained (Figure S2). Third, except for cardiac death, there was no significant association between luteolin intake increment and cause-specific death, including malignant neoplasms death (adjusted-HR = 1.039; $95\%$CI = 0.879, 1.222), diabetes mellitus death (adjusted-HR = 1.112; $95\%$CI = 0.918, 1.348), and cerebrovascular diseases death (adjusted-HR = 0.863; $95\%$CI = 0.646, 1.152) (Table S6). Finally, a cross-sectional investigation indicated that luteolin intake increment was related to a lower CRP level at baseline (adjusted-β =-0.332; $95\%$ CI =-0.541, -0.122), but not associated with other cardiometabolic risk factors including HbA1c, fasting blood glucose, HOMA-IR, HOMA-IS, HOMA-β, systolic blood pressure, diastolic blood pressure, and LDL-C (Table S7).
## Discussion
The current study prospectively examined the association of luteolin intake with all-cause and cardiac mortality among 2,461 patients with T2DM. During the follow-up period (median, 8.7 years), per-unit increment of luteolin intake (Ln-transformed) contributed to a $7.0\%$ reduction in all-cause mortality ($$P \leq 0.024$$) and a $22.6\%$ reduction in cardiac mortality ($$P \leq 0.001$$). This association was independent of other flavonoid intakes and consistent across the stratified population. The anti-inflammatory effect of luteolin was also identified at baseline, which may account for the remarkable prognostic improvement.
Flavonoids are a group of polyphenolic compounds produced in plants, and more than 10,000 flavonoids have been isolated and identified [30]. Among US adults, the average flavonoids intake from the daily diet is about 200–250 mg/day, including flavan-3-ols ($80\%$), flavonols ($8\%$), flavanones ($6\%$), anthocyanidins ($5\%$), isoflavones (< $1\%$), and flavones (< $1\%$) [31]. Luteolin (3,4,5,7-tetrahydroxy flavone) is a kind of flavones that has been commercially developed as a dietary supplement and cosmetic additive due to its safety properties and multiple biological effects, including anti-diabetic [11], cardiovascular protective [12], anti-inflammatory [13], and anti-cancer [14] effects.
The potent anti-diabetic effect of luteolin has been demonstrated by various animal studies. In the insulin-resistant mouse, Shao et al. found that oral administration of luteolin can reverse glucose intolerance and improve insulin sensitivity [15]. In streptozotocin-induced diabetic nephropathy rats, Xiong et al. found that luteolin administration (80 mg/kg daily for 8 weeks) protected the renal filtration and inhibited glomerulosclerosis and thus delaying the progression of diabetic nephropathy [16]. In diabetic encephalopathy rats, Ren et al. found that luteolin administration improves learning and memory function by inhibiting hyperglycemia-mediated apoptosis in hippocampi [17]. In diabetic rats with chronic wounds, a histopathological study proved that the application of luteolin ointment ($0.5\%$ w/w) was effective in accelerating wound healing [18]. Consistently, for the first time, this population-based study confirmed that dietary luteolin intake increment contributed to a risk reduction in both all-cause and cardiac mortality among patients with T2DM. Therefore, it is recommended that T2DM patients consume more luteolin-rich food in their daily diet. To consume more luteolin-rich foods may be a promising dietary intervention strategy for T2DM. Compared to using anti-diabetic drugs, dietary intervention could be safer and can be more easily accepted by patients. Besides, given its anti-diabetic effects, luteolin-rich dietary strategy is also optional for pre-diabetes patients who do not require anti-diabetic drugs.
It is well known that T2DM patients shared a substantially higher cardiovascular risk [32, 33]. In the sensitivity analysis, except for cardiac mortality, there was no significant association of luteolin intake increment with the leading cause-specific mortality (malignant neoplasms, diabetes mellitus, and cerebrovascular diseases). The prognostic improvement of T2DM could be mainly due to cardiac mortality reduction. Consistently, previous animal studies have also identified several therapeutic effects of luteolin on cardiac-related diseases. First, in myocardial ischemia/reperfusion injury, luteolin alleviates cardiomyocyte apoptosis and blocks oxidative stress, thus promoting cytoprotection and reducing ischemia/reperfusion injury [19]. Second, in heart failure, luteolin improves heart function by enhancing contractility, upregulating autophagy, and preventing cardiac fibrosis [20]. Third, in coronary artery disease, luteolin reverses atherosclerosis by ameliorating oxidative damage, decreasing vascular inflammation, and inhibiting the proliferation and migration of vascular smooth muscle cells [21]. In addition, some epidemiological evidence also supports the primary protection of the flavonoid intake increment in reducing mortality risk from coronary heart disease among the disease-free elderly [34].
There are still several findings in our study that are worth noting. First, the anti-inflammatory effect of luteolin may account for cardiac-specific prognosis improvement. Low-grade inflammation is a common feature in subjects with both T2DM and cardiac disease [35]. The potent anti-inflammatory property of luteolin has been well established by previous studies [36, 37]. Consistently, in the sensitivity analysis, we identified the anti-inflammatory property and found that luteolin intake increment was associated with a lower baseline CRP level (β =-0.332, $95\%$ CI =-0.541, -0.122). However, we did not find a significant association between luteolin intake and other cardiometabolic risk factors, including HbA1c, fasting blood glucose, HOMA-IR, HOMA-IS, HOMA-β, arterial blood pressure, and LDL-C. Although some of these have been previously reported. This discrepancy may be due to several reasons. First, the pleiotropic effects of luteolin were primarily confirmed in preclinical animal studies, which may not be generalized to population-based studies. In addition, the dose of luteolin intake in our study was low, which only came from the general diet rather than additional supplements. These low-dose intakes may not reach the threshold of the pleiotropic effect.
For another noteworthy finding, the impact of luteolin intake on cardiac mortality may vary depending on T2DM duration and blood glucose levels. For cardiac mortality, we found that patients with T2DM duration ≥ 10 years were more likely to get benefits from dietary luteolin intake increment compared to those with T2DM duration < 10 years (P for interaction = 0.048, Fig. 3). Moreover, patients with HbA1c ≥ $6.5\%$ had a more remarkable cardiac mortality reduction compared to those with HbA1c < $6.5\%$. The anti-inflammatory mechanism of luteolin may account for this discrepancy. First, low-grade inflammation status is a chronic process that gradually promotes the development of cardiac disease [38]. Therefore, with the T2DM duration increasing, the benefit of luteolin intake then may become more pronounced. Second, patients with uncontrolled blood glucose tend to have a higher level of inflammation [39, 40]. Thus, patients with HbA1c ≥ $6.5\%$ were more likely to benefit from the anti-inflammatory effect of luteolin.
This study still has several limitations. First, the dose of luteolin intake was only estimated at baseline, which might not accurately represent the luteolin intake during follow-up. Second, NHANES took the representative US civilians. In the United States, the western dietary pattern is predominant, which determines the dietary composition of luteolin and other nutrients. Therefore, our findings may not generalize to populations with non-western dietary patterns. Third, some T2DM patients were determined based on questionnaire data and medication data, which may be subject to self-report bias. Fourth, to remove T1DM from all diabetics, we excluded patients who were informed of their diabetes at age < 30 years ($$n = 145$$). This may introduce sample selection bias due to considering only the epidemiological characteristics of T1DM. Fifth, distinguishing luteolin from nutrients in foods (especially other flavonoids) remains difficult, although potential dietary confounding factors (energy and various flavonoids) have been adjusted in analyses. Finally, because of the nature of observational studies, we cannot draw a causal inference, and residual or unknown confounding factors may still exist.
## Conclusion
This study confirmed that the dietary luteolin intake increment reduced all-cause mortality (especially cardiac mortality) in patients with T2DM. To avoid premature cardiac complications, it is recommended that T2DM patients consume more luteolin-rich foods in their daily diet. However, further studies are needed to determine whether additional luteolin intake from supplementations is an optional strategy for the primary prevention of cardiac diseases among T2DM patients.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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|
---
title: Rate of progressive healing with a carbon-fiber orthosis in cases of partial
union and nonunion after ankle arthrodesis using the Ilizarov external fixator
authors:
- Charlotte Cibura
- Sebastian Lotzien
- Thomas Rosteius
- Christopher Ull
- Periklis Godolias
- Thomas Armin Schildhauer
- Matthias Königshausen
journal: Journal of Foot and Ankle Research
year: 2023
pmcid: PMC10039608
doi: 10.1186/s13047-023-00613-3
license: CC BY 4.0
---
# Rate of progressive healing with a carbon-fiber orthosis in cases of partial union and nonunion after ankle arthrodesis using the Ilizarov external fixator
## Abstract
### Background
The Ilizarov fixator is a popular device for treating arthrodesis of the ankle joint in complex situations. However, the therapy can fail, with nonunion or partial union that might not be load stable. There is the possibility of follow-up surgery or extended wearing of the fixator. Full weight bearing with a carbon orthosis remains another treatment option, which has not yet been investigated. The aim of the study was to determine the rate of progress that can be obtained with a carbon orthosis in cases of partial union or nonunion after fixator removal.
### Methods
In this retrospective observational study thirty-three patients received a carbon orthosis after fixator removal due to nonunion or partial union. All patients were allowed to walk with the orthosis under full load. The consolidation rate was determined radiologically and compared with the imaging data obtained during the last follow-up. In addition to demographic data, the Foot and Ankle Ability Measure and pain using a numeric rating scale were determined. Nine patients had to be excluded due to insufficient follow-up, and finally $$n = 24$$ patients were included in the study.
### Results
The average duration of fixator use was 21 weeks (range 15–40 weeks), and the total average follow-up after removal of the fixator was 16 months (range 4- 56). For 14 ($58.33\%$) patients, there was a further increase in consolidation with the orthosis after the fixator was removed.
### Conclusion
The results show that if there is only partial union or nonunion, further consolidations can be achieved after the application of a carbon orthosis. In a difficult patient population, using an orthosis should therefore be attempted to avoid unnecessary revision operations.
## Background
For many years, good treatment results have been achieved with the Ilizarov fixator for arthrodesis in the ankle joint area. Ilizarov fixator application is an external procedure that has been described in particular for patients complicated with acute or chronic infections, soft tissue defects, axial malpositions (varus/valgus) and relevant comorbidities (diabetes mellitus [DM], polyneuropathy [PNP], peripheral vascular disease [PVD]). The healing results described range between 73 and $100\%$ [1–13]. Most authors only describe the result of arthrodesis either as a union or as a failure in the sense of nonunion and/or infection. Only a few studies have described individual cases of partial consolidation or stable pseudarthrosis that were treated with an orthosis, and only in recent individual studies were the consolidation rates determined adequately. However, these were internal arthrodesis procedures, and there is no uniform consensus on when arthrodesis is likely to be stable [1, 4, 9, 10, 14–17]. In cases of nonunion or partial consolidation, there is the possibility of extending fixator application, follow-up surgery to perform cancellous bone apposition with or without a change of procedure to an internal procedure (plates/screws), rearthrodesis using an Ilizarov fixator or lower leg amputation [1, 4, 7–10]. Both rearthrodesis via the Ilizarov fixator and a process change to an internal procedure, however, can be made more difficult by the presence of vulnerable soft tissues, chronic osteomyelitis, multiple previous surgeries with already significant leg length differences, or incompliance or rejection by the patient because of repeated long and complex therapy. Additionally, a prolonged fixator wearing time is often not tolerated by the patient because of the high rates of complications such as recurrent pin infections or broken pins [1, 18]. The alternative to lower leg amputation in these cases can be the adaptation of a custom-made orthosis, provided there is no fulminant infection. The attempt to mobilize in an adapted orthosis in cases of partial union or nonunion after *Ilizarov arthrodesis* without any further internal osteosynthesis has only been described in very few cases, and so far, we are concerned that there are no studies on the further courses of these patients [1, 2, 4, 9, 10, 13, 19]. The aim of this study was therefore to determine clinical and radiological results in a difficult patient population with orthosis after fixator removal and existing nonunion or partial union. Can bony consolidation increase even after several months of wearing the fixator due to a full load in the orthosis? What clinical results can this procedure achieve? Could the orthosis be a possible sensible alternative to another revision or amputation in such a difficult patient population?
## Study design
The present study was performed in accordance with the Declaration of Helsinki. Ethical permission for this study was obtained from the ethics committee and informed consent was obtained from all patients before participation in the study (registration number: 18–6582-BR). This is a retrospective register study with a prospective follow-up.
## Patient selection
All patients who underwent ankle fusion using the Ilizarov external fixator at our institution (level 1 trauma center) from $\frac{01}{2013}$ to $\frac{01}{2020}$ were retrospectively reviewed. All patients were included if they had tibiotalar joint (TT) arthrodesis, subtalar joint arthrodesis, simultaneous TT and subtalar joint arthrodesis or tibiocalcanear (TC) arthrodesis and had received a carbon orthosis after fixator removal due to lack of consolidation or partial consolidation that was (in our opinion) not guaranteed to be load stable without additional stabilization. For all these patients, further surgical treatment or extended fixator wearing was ruled out if they presented with vulnerable soft tissues/chronic osteomyelitis (COM)/previous illnesses/recurrent complications or if they refused such treatments. To capture all patients with these criteria, a keyword analysis of all digitized files was performed by the authors. The medical records of these patients were retrospectively reviewed for the following factors: sex, age, associated relevant concomitant diseases, body mass index (BMI), smoking, reason for arthrodesis, time spent in the fixator, follow-up, consolidation rate, consolidation rate at the last follow-up and wearing time of the orthosis. The data were collected anonymously using Microsoft Excel © Version 14.7.7. The exclusion criteria were as follows:Follow-up less than six months from the start of Ilizarov fixator applicationPatients treated with the Ilizarov fixator for bone transport or fracture treatmentWearing time of the orthosis less than three monthsAge < 18 years
## Incidence of fusion
The Incidence of fusion was determined according to the imaging results following fixator removal. It was divided into five groups ($0\%$, 5–$20\%$, 21–$40\%$, 41–$60\%$, 61–$80\%$) and compared and evaluated with the imaging findings during the last follow-up examination. Fusion of a joint segment was defined as trabeculation or calcific density crossing the former space. In a total of 22 ($91.67\%$) patients, computed tomography (CT) was performed at the time of fixator removal to determine consolidation. In 10 ($41.67\%$) patients, this CT scan was compared with a scan obtained during the last follow-up. The consolidation rate was calculated based on the method by Jones et al. All sagittal images were retrospectively reviewed, the lengths of the fused segments and the lengths of each joint on each slice were recorded, and the incidence of fusion was then calculated: fusion incidence = 100x (sum of lengths of fused segments on all slices/sum of lengths of joint surface) [14].
For 12 ($50\%$) patients, X-ray images + CT were compared with the X-ray images obtained at the last follow-up. For these patients, consolidation was also first determined using the abovementioned CT-based method, and then the course was determined using the X-ray images. For two ($8.33\%$) patients, only X-rays were compared with regard to trabeculation or calcific density crossing the former space. Follow-up CT was often not performed due to a lack of symptoms, the detection of an already clear change on X-ray or the patient’s refusal of further treatment and thus ethically intolerable increased radiation exposure. All patients were in our regular treatment while the fixator was in place (presentation took place every 14 days), and the indication for fixator removal and the application of an orthosis was made by the authors in each case.
For the evaluation of this study, all images were finally evaluated independently by two investigators (orthopedic surgeons), and if there were any discrepancies, the images were rated by a third independent person.
## Treatment with the carbon orthosis
All patients included in this study received a custom-made lower leg carbon orthosis, which they were instructed to wear daily for mobilization for at least 3–6 months or longer if necessary. Full weight bearing was allowed with the orthosis.
Lower leg orthoses with permanently fixed ankle joints consist essentially of a lower leg shaft that is open towards the front, crosses the calves and extends proximally to about the knee bend, which merges distally into a foot section that is also open toward the front, reinforced in the ankle area and elastic in the forefoot area. The entire orthosis is mainly made of carbon fiber, with the exception of the elastic forefoot, which is made of aramid fiber. The orthosis is fixed to the lower leg with Velcro fasteners. The foot area is designed so that, typically, commercially available shoes can be worn, which in turn hold the foot/ankle area (Figs. 1, 2 and 3). The orthosis was made by the orthopedic and medical supply store Care Center Rhein Ruhr GmbH, Germany. Fig. 1Picture of a custom-made carbon orthosisFig. 2Picture of a custom-made carbon orthosisFig. 3Picture of a custom-made carbon orthosis All patients wore the orthosis regularly during the daytime and for any kind of mobilization for at least 3 months. Sixteen ($66.67\%$) patients were able to train off the orthosis after an average of 10 months (range 3–36) and switch to custom-made orthopedic shoes. Of these, nine patients had a consolidation of 80–$100\%$ in the last FU, two of 60–$80\%$, one of 40–$60\%$, two of 5–$20\%$, and one of $0\%$, while one had no change (80–$100\%$ tibiotalar, 5–$20\%$ subtalar) in the last FU. Seven ($29.17\%$) patients continue to wear the orthosis during mobilization today. Of these, two and one patient had $0\%$ and 5–$20\%$ consolidation, respectively (all with clinical instability), one patient had 60–$80\%$ consolidation and three patients had 80–$100\%$ consolidation in the last FU. One further patient was amputated.
## Outcome score
To be able to determine pain and further outcomes, a prospective questionnaire with a numeric rating scale (NRS) and the validated German version of the Foot and Ankle Ability Measure (FAAM) was sent by mail and completed. The numeric rating scale was used to determine the actual pain, ranging from 0–10, with 0 representing no pain at all and 10 representing maximum pain. The FAAM questionnaire was used to determine the possibility of everyday and sporting activities for the patients [20]. The resulting score has previously been shown to be a reliable, valid and responsive outcome score of physical function in individuals with foot and ankle dysfunction [21]. The score comprises two subareas, one with 21 questions relating to everyday problems (activities of daily life [ADLs]) and a sports subarea with eight questions. The results of the individual questions are added and shown as a percentage. A maximum of $100\%$ can be achieved in each case. Additionally, patients had to rate their functioning in ADLs and sports with a percentage ranging from 0 to 100 (0 indicating an inability to perform ADLs or sport activities) and had to provide information about the functional status of their ankles/feet using a four-point rating scale.
The NRS and FAAM scores were fully recorded for 16 of 23 ($69.57\%$) patients after an average of 3,2 years (after fixator removal). Seven ($43.75\%$) patients stated that they had no pain at all. Of these, three patients had a consolidation of 81–$100\%$ at the last radiological FU, one patient had a consolidation of 61–$80\%$, one had 81–$100\%$ TT and 61–$80\%$ subtalar consolidation, one patient had 41–$60\%$ consolidation, and one had a consolidation of 5–$20\%$. The remaining nine ($56.25\%$) patients reported pain in the ankle, which averaged 4,8 on the numeric rating scale (range 2–8). Of these, at the last FU, two patients had $0\%$, one had 5–$20\%$, one had 61–$80\%$, four had 81–$100\%$ and one had 81–$100\%$ tibiotalar and 0–$20\%$ subtalar consolidation. However, three of these last six patients already had advanced arthrosis in the subtalar or calcaneocuboid joint (CC) and talonavicular joints (TN), and one patient already had consolidation of the CC and TN during the last radiologic FU.
The FAAM score showed an average value of $49\%$ (SD ± 18) on the subscale for ADL and $24\%$ (SD ± 17) on the sports subscale. The results of the FAAM score along with a listing of the individual subscales are shown in Table 6.Table 6FAAM score for 16 patients (ADLs and Sports)FAAM, mean ± SD, %ValueADL subscale49 ± 18ADL global 0–100 rating scale38 ± 20Sport subscale24 ± 17Sport global 0–100 rating scale21 ± 26Higher scores represent higher levels of function for each subscale, with $100\%$ representing no dysfunctionFAAM overall level of function in %Normal0Nearly normal18Abnormal47Severely abnormal35
## Patients
In the specified period, 319 patients experienced arthrodesis in the area of the ankle using an Ilizarov fixator. For 33 ($10.34\%$) of these patients, after removal of the fixator, a carbon orthosis was applied due to an existing nonunion or partial consolidation and the exclusion of further therapy options. Of these 33 patients, nine had to be excluded due to insufficient follow-up, and finally $$n = 24$$ ($7.52\%$) patients (follow-up rate: $72.73\%$) were included in the study. Table 1 provides an overview of the patient group. The total average follow-up after removal of the fixator was 16 months (range 4- 56). The Ilizarov fixator was used externally for all patients because of the presence of an acute or chronic infection (17 [$70.83\%$] cases) and/or poor soft tissue, secondary diseases or noncompliance. Table 1Study group $$n = 24$$Study groupAverage age (years)59 (range 27–77)Sex Male15 ($62.5\%$) Female9 ($37.5\%$)Duration in frame (weeks)21 (range 15–40)Average FU after fixator removal16 months (range 4- 56)TT arthrodesis14 ($85.33\%$)Simultaneous TT/subtalar arthrodesis5 ($20.83\%$)TC arthrodesis5 ($20.83\%$)Rearthrodesis12 ($50\%$)Average number of presurgeries6 (range 0–24)Comorbidities Nicotine abuse11 ($45.83\%$) DM4 ($16.67\%$) Mean BMI31.0 (range 21–41) Wound healing disorders after fixator removal3 (12,$5\%$)*Autogenous cancellous* bone10 (41,$67\%$)Abbreviations: TT tibiotalar, TC tibiocalcanear, FU follow up, DM diabetes mellitus, BMI body measure index
## Incidence of Fusion
When evaluating the imaging findings in all cases, there was an agreement between the observers regarding an increase, decrease or no change in the consolidation. Different percentages were given for seven ($29.17\%$) patients. In these cases, the images were also assessed by a third independent person, and a consensus could be found in all cases.
Figure 4 gives an overview of the course of the consolidation. For 14 ($58.33\%$) patients, there was an increase in consolidation with the orthosis after the fixator was removed (Table 2); for seven ($29.17\%$) patients, no change could be recorded (Table 3); for two ($8.33\%$) patients, the consolidation decreased (Table 4); and for one ($4.17\%$) patient, there was an increase in consolidation in the upper ankle and a decrease in consolidation in the lower ankle (simultaneous arthrodesis) (Table 5, Figs. 5, 6, 7, 8 and 9—Case 1, Figs. 10, 11, 12 and 13—Case 2). However, this last patient was amputated after 15 months due to an acute subtalar infection. Fig. 4Overview of the courses in the orthosis after fixator removalTable 2Patient demographics and consolidation—14 ($58.33\%$) patients with increase in consolidation ($$n = 24$$)IDAgeSexBMISmokingDMComorbiditiesDiagnosisType of ArthrodesisConsolidation at fixator removal in %Consolidation at last FU in %668m30,7NoYesHTN, asthma, alcohol abuse, PVD, sleep apnea, chronic kidney failureRearthrodesis, acute infectionTT + subtalarTT 0, subtalar 0TT 61–80, subtalar 61–801957m39,9NoNononeRearthrodesis, COMTT5–2081–1001751f41,0YesNoHTN,PNP, alcohol abusePost-traumatic arthrosisTT21–4081–1002261f21,0NoNoHTN, rheumatoid arthritis, total hip replacement + total knee replacement, wound healing disorderRearthrodesis, COMTC21–4081–100157f36,7YesNoRheumatoid arthritis, HTN, depression, scheuermann´s diseaseRearthrodesis, COMTT41–6081–100256m31,6YesYesPNP, depression, neuropathic pain syndrome, wound healing disorderRearthrodesis, COMTT41–6081–100450m31,9YesNoHTN, wound healing disorderPost-traumatic, acute infectionTT41–6081–100953m21,2NoNoPNPRe-rearthrodesis, post-traumatic arthrosisTT41–6081–1001376f39,9NoNoHTN,CAD,MIPost-traumatic arthrosisTT41–6081–1002077m21,9NoNoHTN, MI, artrial fibrillationPost-traumatic, acute infectionTT41–6081–1002160m36,0NoNoHTN, thyroid cancerPost-traumatic arthrosisTT41–6081–1002367f28,4NoNoRheumatoid arthritisPost-traumatic arthrosisTT + subtalarTT 41–$60\%$, subtalar 81–100TT 81–100, subtlar 81–100558f20,8YesNononeRearthrodesis, COMTT61–8081–1001127m29,1YesNoHypothyroidismRearthrodesis, COMTT41–6061–80Table 3Patient demographics and consolidation—seven ($29.17\%$) patients with no change in consolidation ($$n = 24$$)IDAgeSexBMISmokingDMComorbiditiesDiagnosisType of ArthrodesisConsolidation at fixator removal in %Consolidation at last FU in %351f32,6NoYesAsthmaAcute infectionTT61–8061–80764m22,2YesNoHTN, PVDRearthrodesis, COMTT + subtalarTT 81–100, Subtalar 61–80TT 81–100, subtalar 61–80854m32,1YesNoHTN, wound healing disorderPosttraumatic arthrosisTT + subtalarTT 81–100, subtalar 5–20TT 81–100, subtalat 5–201248m31,0YesNoHTN, lower leg amputation oppositeRearthrodesis, COMTC001460m26,3NoNononeCOMTC5–205–201552m38,0YesNoHTN, asthma, chronic pain syndrome, tumor prosthesis hipRearthrodesis, idiopathic necrosis of the talusTC5–205–202475f24,5NoNoHTN, PNPRearthrodesis, acute infectionTC00Table 4Patient demographics and consolidation—consolidation decreased in two ($8.33\%$) patients ($$n = 24$$)IDAgeSexBMISmokingDMComorbiditiesDiagnosisType of ArthrodesisConsolidation at fixator removal in %Consolidation at last FU in %1066m38,8NoNoHTN, artrial fibrillation, alcohol abuse, varicosisPost-traumatic, acute infectionTT61–805–201860m38,6NoYesVaricosis, chronic pain syndromePost-traumatic, COMTT21–400Table 5Patient demographics and consolidation—one ($4.17\%$) patient with an increase in consolidation in the upper ankle and a decrease in the lower ankle ($$n = 24$$)IDAgeSexBMISmokingDMComorbiditiesDiagnosisType of ArthrodesisConsolidation at fixator removal in %Consolidation at last FU in %1660f32YesNoRheumatoid arthritis, PNP, COPD, osteoporosis, depressionPost-traumatic, Acute infectionTT + subtalarTT 61–80, subtalar 61–80TT 81–100, subtalar 21–40Abbreviations: F female, M male, COM chronic osteomyelitis, DM diabetes mellitus, PNP polyneuropathy, PVD peripheral vascular disease, HTN hypertension, CAD coronary artery disease, MI myocardial infarction, COPD chronic obstructive lung disease, TT tibiotalar, TC tibiocalcanearFig. 5Case 1: X-ray (a.p. and lateral) and sagittal CT of a 57-year-old patient with a partial consolidation of 5–$20\%$ after fixator removal. The wearing time was 23 weeks. The initial ankle fracture was followed by multiple surgeries, leading to the development of chronic osteomyelitis and an already frustrating attempt at arthrodesis. In the last X-ray (a.p. and lateral), and after a follow-up of 19.5 months, there was a clear increase in consolidation. The patient stated that he had no pain. The FAAM score was 33 for the ADL part and 0 for the sports part. However, his BMI was 39.9, and the patient continued wearing the orthosis because it made him feel saferFig. 6Case 1: X-ray (a.p. and lateral) and sagittal CT of a 57-year-old patient with a partial consolidation of 5–$20\%$ after fixator removal. The wearing time was 23 weeks. The initial ankle fracture was followed by multiple surgeries, leading to the development of chronic osteomyelitis and an already frustrating attempt at arthrodesis. In the last X-ray (a.p. and lateral), and after a follow-up of 19.5 months, there was a clear increase in consolidation. The patient stated that he had no pain. The FAAM score was 33 for the ADL part and 0 for the sports part. However, his BMI was 39.9, and the patient continued wearing the orthosis because it made him feel saferFig. 7Case 1: X-ray (a.p. and lateral) and sagittal CT of a 57-year-old patient with a partial consolidation of 5–$20\%$ after fixator removal. The wearing time was 23 weeks. The initial ankle fracture was followed by multiple surgeries, leading to the development of chronic osteomyelitis and an already frustrating attempt at arthrodesis. In the last X-ray (a.p. and lateral), and after a follow-up of 19.5 months, there was a clear increase in consolidation. The patient stated that he had no pain. The FAAM score was 33 for the ADL part and 0 for the sports part. However, his BMI was 39.9, and the patient continued wearing the orthosis because it made him feel saferFig. 8Case 1: X-ray (a.p. and lateral) and sagittal CT of a 57-year-old patient with a partial consolidation of 5–$20\%$ after fixator removal. The wearing time was 23 weeks. The initial ankle fracture was followed by multiple surgeries, leading to the development of chronic osteomyelitis and an already frustrating attempt at arthrodesis. In the last X-ray (a.p. and lateral), and after a follow-up of 19.5 months, there was a clear increase in consolidation. The patient stated that he had no pain. The FAAM score was 33 for the ADL part and 0 for the sports part. However, his BMI was 39.9, and the patient continued wearing the orthosis because it made him feel saferFig. 9Case 1: X-ray (a.p. and lateral) and sagittal CT of a 57-year-old patient with a partial consolidation of 5–$20\%$ after fixator removal. The wearing time was 23 weeks. The initial ankle fracture was followed by multiple surgeries, leading to the development of chronic osteomyelitis and an already frustrating attempt at arthrodesis. In the last X-ray (a.p. and lateral), and after a follow-up of 19.5 months, there was a clear increase in consolidation. The patient stated that he had no pain. The FAAM score was 33 for the ADL part and 0 for the sports part. However, his BMI was 39.9, and the patient continued wearing the orthosis because it made him feel saferFig. 10Case 2: CT (coronal and sagittal) of a 53-year-old patient with multiple surgeries and two frustrated attempts at arthrodesis using internal procedures. Four-month follow-up CT shows an increase in TT consolidation of up to 80–$100\%$. The patient was able to train off the orthosis and switch to custom-made orthopedic shoes. The patient currently complains of pain with a visual analog scale score of 5 in the foot/ankle joint during exercise, but the CT now also shows clear arthrosis in the TN and CC jointsFig. 11Case 2: CT (coronal and sagittal) of a 53-year-old patient with multiple surgeries and two frustrated attempts at arthrodesis using internal procedures. Four-month follow-up CT shows an increase in TT consolidation of up to 80–$100\%$. The patient was able to train off the orthosis and switch to custom-made orthopedic shoes. The patient currently complains of pain with a visual analog scale score of 5 in the foot/ankle joint during exercise, but the CT now also shows clear arthrosis in the TN and CC jointsFig. 12Case 2: CT (coronal and sagittal) of a 53-year-old patient with multiple surgeries and two frustrated attempts at arthrodesis using internal procedures. Four-month follow-up CT shows an increase in TT consolidation of up to 80–$100\%$. The patient was able to train off the orthosis and switch to custom-made orthopedic shoes. The patient currently complains of pain with a visual analog scale score of 5 in the foot/ankle joint during exercise, but the CT now also shows clear arthrosis in the TN and CC jointsFig. 13Case 2: CT (coronal and sagittal) of a 53-year-old patient with multiple surgeries and two frustrated attempts at arthrodesis using internal procedures. Four-month follow-up CT shows an increase in TT consolidation of up to 80–$100\%$. The patient was able to train off the orthosis and switch to custom-made orthopedic shoes. The patient currently complains of pain with a visual analog scale score of 5 in the foot/ankle joint during exercise, but the CT now also shows clear arthrosis in the TN and CC joints Of the 14 patients with an increase in consolidation, one ($7.14\%$) patient initially had a consolidation rate of 61–$80\%$, nine ($64.29\%$) had a rate of 41–$60\%$, two ($14.29\%$) had a rate of 21–$40\%$, one ($7.14\%$) had a rate of 5–$20\%$ and one ($7.14\%$) had a rate of $0\%$. In 12 ($85.71\%$) of the 14 patients, there was 81–$100\%$ consolidation during an average follow-up of 18 months (range 4—56) (Table 2).
Of the seven patients without change, there was a consolidation of $0\%$ among two ($28.57\%$), 5–$20\%$ among three ($42.86\%$) and 61–$80\%$ among two ($28.57\%$) (Table 3). Here, the average follow-up was 11 months (range 4–33).
In two patients, the rate was between 21–$40\%$ and 61–$80\%$ after removal of the fixator and continued to decrease over the course to $0\%$ and 5–$20\%$. Here, the follow-up was seven and 46 months, respectively (Table 4).
## Discussion
In the difficult patient population presented in this study, after fixator removal and under full load in a carbon orthosis, there was an increase in the bony consolidation of the arthrodesis zone in $58.33\%$ ($$n = 14$$) of all 24 cases. This took place after the fixator had already been worn for an average of five months, and further risky surgeries were thus avoided. In most cases, there was consolidation between 41–$60\%$ after fixator removal, but an increase could also be recorded in patients with 21–$40\%$, 5–$20\%$ and $0\%$ consolidation. Mobilization under full load was possible for all patients.
In complex patient populations such as that described in this study, with multiple previous surgeries, already failed arthrodesis attempts, an infection history, poor soft tissues, a long medical history and/or relevant previous illnesses, the decision to repeat surgical treatment in cases of nonunion or partial union after wearing the Ilizarov fixator for several months is often difficult. In our opinion, the risk of a repeated infection following introduction of internal osteosynthesis material would have been too high for many of these patients, and/or the soft tissue situation and previous illnesses would have meant an increased risk of a defect/wound healing disorder. An extension of the wearing time of the fixator or even another attempt at arthrodesis using the Ilizarov fixator is often not tolerated by the patient due to its bulky structure and the possibility of recurrent pin infections, further complications, injuries to the opposite leg and destruction of clothing and bed linen. The alternative is amputation. To prevent this, a carbon orthosis was first applied in this patient population with the provision of full weight bearing. To the best of our knowledge, the possibility of further treatment using a carbon orthosis in the case of partial consolidation or nonunion has not yet been described in cases of arthrodesis via an Ilizarov fixator.
In recent studies, primarily good results have been achieved with the Ilizarov fixator [1–13, 18]. However, most of these studies only described a union or a failure in cases of nonunion or infection. Arthrodesis is considered permanent if it is clinically stable and if X-ray or CT shows bridges, although this is described differently depending on the author. Khanfour et al. described arthrodesis as the detection of bridging trabeculation in at least 2 planes at the arthrodesis site on the radiogram [8]. Katensis et al. required evidence of bridging trabeculae without a change in the position of the ankle under weight bearing, and Salem et al. confirmed successful arthrodesis by painless weight bearing as well as radiologically using plain radiographs or CT scans [7, 18]. Since X-ray was not always sufficiently meaningful in the complex courses of the patients in the current study, CT was also performed for almost all patients after the fixator was removed. A much more precise statement about the consolidation of the arthrodesis could thus be made [14–17]. The abovementioned patients all showed only partial consolidation or nonunion. To avoid breaking the existing bone bridges in this complicated patient group and to achieve a further increase in consolidation even without a fixator or additional internal osteosynthesis material, an orthosis was applied. This allowed all patients to walk, and repeated surgical treatment with all risks and complications was avoided.
The grouping of the consolidation rate as sets of percentages was based on Jones et al. [ 14]. He described a system for calculating the extent of consolidation and divided it into the following groups: 0–$33\%$, nonunion, 34–$66\%$, partial consolidation, and 67–$100\%$, consolidation. However, this was via the application of an internal arthrodesis procedure. Further studies by Dorsey et al. and Glazebrook et al. followed this system for internal arthrodesis as well and stated that arthrodesis is stable from over $33\%$ or over 25- $49\%$ [15, 17]. For internal arthrodesis, there is therefore no consensus regarding the consolidation rate on CT when partial consolidation is sufficiently stable. After external arthrodesis using an Ilizarov fixator, very few authors describe partial consolidations or tight pseudarthroses that were subsequently treated with an orthosis. However, the further course of the patients remains unclear. Kugan et al. described a patient with nonunion who could mobilize with an orthosis with pain. El Alfy et al. also described a patient with fibrous nonunion who was able to mobilize with an orthosis, and Zarutzy et al. described five patients who needed a supporting orthosis (four stable pseudarthroses and one malunion) [4, 9, 10]. In a study on tibiocalcanear arthrodesis by Reinke et al., a patient with a partial consolidation of 40–$50\%$ and four patients with stable pseudarthrosis were described, all of whom were also treated with an orthosis [1].
In this study, 14 ($58.33\%$) patients under load in the orthosis showed an increase in consolidation six months after the start of arthrodesis. In nine ($64.29\%$) patients, there was an initial consolidation of 41–$60\%$, and in some cases, an orthosis might not have been necessary. However, given the long course of the disease, we did not want to take any risks, and the orthosis initially provides the patient with security so that a full load is possible. In four other patients, the consolidation was $0\%$ [1], 5–$20\%$ [1] and 21–$40\%$ [2]. The risk of a break in areas with few bridges would have been too high, and a safe increase could be achieved with the orthosis. Among the seven ($29.17\%$) patients with no change, the majority only had $0\%$ [2] or 5–$20\%$ consolidation [3], which could indicate that an increase occurs sooner when at least 21–$40\%$ ossification has already occurred. However, the follow-up for these patients was shorter at 11 months (average) than for the patients with an increase (17.4 months), and with such a small number, no definitive statement can be made.
The $58.33\%$ of the patients who experienced an increase in consolidation does not seem very high at first, but this must be considered against the background of a difficult patient population. Eleven ($45.83\%$) patients were smokers, the average BMI of 31.0 was in the range of obesity grade 1, four ($16.67\%$) patients had diabetes mellitus, three ($12.5\%$) patients presented with wound healing disorders during as well as after fixator removal, and other difficult diseases such as chronic kidney failure with renal osteopenia, osteoporosis, rheumatoid arthritis, PVD, depression, and alcohol abuse were recorded (Tables 2, 3, 4 and 5). Twelve ($50\%$) patients experienced a second attempt after a previous frustrating arthrodesis, and one patient experienced a third attempt. In a study on 88 patients with internal subtalar arthrodesis, Chahal et al. showed that smokers and patients with DM had a 3.8- and 18.7-fold higher probability of malunion and that the worst functional outcome was observed in patients with DM [22].
The results of the FAAM score were significantly worse for the ADL subscale with $49\%$ (SD ± 18) and for the sport subscale with $24\%$ (SD ± 17) than for other studies. Kerkhoff et al. described an average value of $70\%$ (SD ± 22.3) for the ADL subscale and $29\%$ (SD ± 27.8) for the sport subscale in 122 patients. However, these were primary internal arthrodeses in the case of degenerative changes, and the BMI was lower at 27.5 (SD ± 4,9); no statement was made regarding DM [23]. Strasser et al. described a score of 81.5 (SD ± 18.3) in patients over 70 after internal arthrodesis [24]. The BMI or other previous illnesses were not reported. A division into subscales was not made here either. Morasiewicz et al. achieved values of $79\%$ (56–88) after *Ilizarov arthrodesis* and $70\%$ (49–91) after internal arthrodesis [25]. Although this included patients with infection, vulnerable soft tissues and deformities, there are no reports of complicated courses with frustrating arthrodesis in advance. The average age of 44 years was also significantly younger than that of 59 years in this study. However, it shows that the overall outcome with partial consolidation and orthosis for complicated patients (multiple interventions, frustrating previous arthrodesis attempts, long disease courses and/or multiple previous illnesses) is worse overall, so orthosis should be seen as a salvage procedure and only be provided an alternative for individual cases.
## Limitations
This study has several limitations. The study sample of 24 patients was small, and the study mainly had a retrospective design. However, these are complex individual patients after following Ilizarov arthrodesis, which is otherwise a well-investigated therapeutic procedure; therefore, it is not expected that a large number of patients should be encountered. Even if CT had been performed for almost all patients after fixator removal, only 10 ($41.67\%$) patients underwent follow-up CT. In the remaining patients, CT and X-rays were compared with only X-rays. However, further CT diagnostics would in some cases not have been ethically justifiable if the change was already clearly visible on comparable X-ray images or because there would have been no further consequences for the patient. Thus, the radiological percentages could not be determined exactly.
For patients without a change, the follow-up was significantly shorter than for patients who did demonstrate change, so no statement can be made here as to whether a change might still occur in the course of the process, which would potentially increase the rate of ossification even further. The outcome scores could not be recorded for all patients because some had moved to an unknown location or had already died from other causes. A longer follow-up would be necessary here to make a reliable statement regarding the long-term outcome. In addition, there was no comparable control group without an orthotic device, so no statement can be made regarding the course in this case.
## Conclusion
In conclusion we found that in cases of difficult patients or tissue conditions and partial consolidation after arthrodesis in the ankle joint using the Ilizarov fixator, further ossification can be detected in some patients after fixator removal following the use of a carbon orthosis. Thus, further surgical treatment or extended wearing of the fixator could be avoided in such complex cases.
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|
---
title: 'Alcohol consumption and microvascular dysfunction: a J-shaped association:
The Maastricht Study'
authors:
- Frank C. T. van der Heide
- Simone J. P. M. Eussen
- Alfons J. H. M. Houben
- Ronald M. A. Henry
- Abraham A. Kroon
- Carla J. H. van der Kallen
- Pieter C. Dagnelie
- Martien C. J. M. van Dongen
- Tos T. J. M. Berendschot
- Jan S. A. G. Schouten
- Carroll A. B. Webers
- Marleen M. J. van Greevenbroek
- Anke Wesselius
- Casper G. Schalkwijk
- Annemarie Koster
- Jacobus F. A. Jansen
- Walter H. Backes
- Joline W. J. Beulens
- Coen D. A. Stehouwer
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10039613
doi: 10.1186/s12933-023-01783-x
license: CC BY 4.0
---
# Alcohol consumption and microvascular dysfunction: a J-shaped association: The Maastricht Study
## Abstract
### Background
Microvascular dysfunction (MVD) is an important contributor to major clinical disease such as stroke, dementia, depression, retinopathy, and chronic kidney disease. Alcohol consumption may be a determinant of MVD.
### Objective
Main objectives were [1] to study whether alcohol consumption was associated with MVD as assessed in the brain, retina, skin, kidney and in the blood; and [2] to investigate whether associations differed by history of cardiovascular disease or sex.
### Design
We used cross-sectional data from The Maastricht Study ($$n = 3$$,120 participants, $50.9\%$ men, mean age 60 years, and $27.5\%$ with type 2 diabetes [the latter oversampled by design]). We used regression analyses to study the association between total alcohol (per unit and in the categories, i.e. none, light, moderate, high) and MVD, where all measures of MVD were combined into a total MVD composite score (expressed in SD). We adjusted all associations for potential confounders; and tested for interaction by sex, and history of cardiovascular disease. Additionally we tested for interaction with glucose metabolism status.
### Results
The association between total alcohol consumption and MVD was non-linear, i.e. J-shaped. Moderate versus light total alcohol consumption was significantly associated with less MVD, after full adjustment (beta [$95\%$ confidence interval], -0.10 [-0.19; -0.01]). The shape of the curve differed with sex (Pinteraction = 0.03), history of cardiovascular disease (Pinteraction < 0.001), and glucose metabolism status (Pinteraction = 0.02).
### Conclusions
The present cross-sectional, population-based study found evidence that alcohol consumption may have an effect on MVD. Hence, although increasing alcohol consumption cannot be recommended as a policy, this study suggests that prevention of MVD may be possible through dietary interventions.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01783-x.
## Introduction
Major clinical diseases such as stroke [1], dementia [1], depression [1], retinopathy [2], and chronic kidney disease [3] are thought to be (in part) caused by microvascular dysfunction (MVD). Mechanistically, MVD is thought to hamper hemodynamic autoregulation, which can predispose capillaries to a detrimentally high pressure, leading to capillary dilation, leakage, rupture, nonperfusion (i.e. ischemia), and, ultimately, clinical symptoms of stroke [1], dementia [1], depression [1], retinopathy [2], and chronic kidney disease [3]. Biologically, MVD is thought to be to an important extent caused by an impaired endothelial cell nitric oxide (NO) bioavailability, a hall mark feature of endothelial cell dysfunction [2].
Subtle functional and structural changes of the microvasculature, which reflect (more) MVD, can be non-invasively assessed in various organs [2]. First, presence of features of cerebral small vessel disease (CSVD; i.e. greater white matter hyperintensity volume, more cerebral microbleeds, and more lacunar infarcts) can be assessed in the brain [2]. These features are thought to reflect structural deterioration of the brain and are thought to be (in part) caused by MVD [2].Second, MVD in the retina can be inferred from wider or narrower retinal arteriolar diameters, wider retinal venular diameters, or as lower flicker light-induced increase in retinal microvascular diameters [2]. The interpretation of the retinal arteriolar diameter is thought to depend on the stage of MVD, with widening as an early-stage and narrowing as a later-stage feature of MVD [2, 4]. Third, MVD in skin, kidney, and blood can respectively be assessed as lower heat-induced skin hyperemia, higher urinary albumin excretion (UAE), and higher levels of plasma biomarkers of MVD (i.e. higher levels of soluble intercellular adhesion molecule-1 [sICAM-1], soluble vascular adhesion molecule-1 [sVCAM-1], soluble E-selectin [sE-selectin] and Von Willebrand Factor [vWF]) [2].
Alcohol consumption may be a potentially modifiable determinant of MVD and many studies suggest that the association between alcohol consumption and MVD may be J-shaped [2, 5, 6]. Mechanistically, at certain lower levels of alcohol consumption, ethanol and polyphenols, the main bioactive constituents in alcoholic beverages, may be able to reduce MVD via increasing endothelial cell NO bioavailability. First, ethanol can increase NO bioavailability via stimulating NO synthesis by the endothelial cell NO synthase enzyme (eNOS) [7, 8]. Second, polyphenols are thought to increase NO bioavailability via reducing oxidative stress (oxidative stress is a potent reductor of NO bioavailability) [2, 9, 10]. Additionally, as wine and beer contain more polyphenols than spirits, wine and beer may be stronger stimulators of NO bioavailability than spirits [5]. In contrast, at certain higher levels of alcohol consumption, ethanol can induce oxidative stress [5]. Therefore, there may be a threshold where NO bioavailability is more impaired by ethanol than increased by polyphenols and ethanol, resulting in more instead of less MVD [5]. In addition, at which levels of alcohol consumption this threshold occurs and how strong the effects of alcohol consumption on MVD are may differ by background levels of oxidative stress (which are presumably higher in e.g. individuals with, versus without, a history of cardiovascular disease) [10–12] and by sex [13, 14].
Indeed, there is some evidence that alcohol consumption may be a determinant of MVD, and that the association of alcohol consumption with MVD may be J-shaped, however, this evidence has important limitations [15–53]. First, many population-based studies did not quantify the amount of alcohol consumption [15, 18, 21, 27, 28, 32, 33, 37, 40, 44, 47, 50, 52]; did not take potential cardiovascular [16–20, 22, 25, 26, 29, 34, 35, 38, 39, 41, 45, 46, 48, 49, 51, 53] and/or lifestyle [23, 24, 31, 32, 42, 43] confounders in to account; and/or did not account for sick quitters [19, 22, 30, 34, 36, 42, 43] (i.e. individuals who quit drinking and are thought to have an increased cardiovascular risk) [54, 55]. Second, only few studies investigated the associations of wine, beer, and spirits consumption with MVD [23, 26, 27, 42, 43, 45]. Third, no population-based studies have yet reported the association between alcohol consumption and MVD in individuals with and without a history of cardiovascular disease.
In view of the above, we investigated, using a large, well-characterized population-based cohort study, whether total alcohol, wine, beer, and spirits consumption were associated with MVD, estimated from features of CSVD, retinal microvascular diameters, flicker light-induced increase in retinal microvascular diameters, heat-induced skin hyperemia, UAE, and plasma biomarkers of MVD. In addition, we tested whether associations were modified by history of cardiovascular disease or sex.
## Study population and design
The present study used data from The Maastricht Study, an observational population-based cohort study. The rationale and methodology have been described previously [56]. In brief, the study focuses on the etiology, pathophysiology, complications and comorbidities of diabetes mellitus type 2 and is characterized by an extensive phenotyping approach. Eligible for participation were all individuals aged between 40 and 75 years and living in the southern part of the Netherlands. Participants were recruited through mass media campaigns, the municipal registries and the regional Diabetes Patient *Registry via* mailings. Recruitment was stratified according to known type 2 diabetes status, with an oversampling of individuals with type 2 diabetes for reasons of efficiency. The present report includes cross-sectional data from 3,451 participants who completed the baseline survey between November 2010 and September 2013.
Magnetic resonance imaging (MRI) measurements were implemented from December 2013 onwards until February 2017 and were available in 2,318 out of 3,451 participants [57]. The examinations of each participant were performed within a time window of three months. The study has been approved by the institutional medical ethical committee (NL31329.068.10) and the Minister of Health, Welfare, and Sports of the Netherlands (Permit 131088–105234-PG). All participants gave written informed consent.
## Alcohol consumption
Habitual alcohol consumption over the past 12 months was assessed via a self-administered validated food frequency questionnaire (FFQ) [58]. Total alcohol consumption was calculated from the questionnaire-assessed average consumption of individual types of wine (i.e. red wine, white wine, strong wine [such as sherry]), individual types of beer (i.e. pilsner, light alcoholic beer, high alcoholic beer) and spirits [58]. The intraclass correlation coefficient for alcohol consumption assessed by FFQ versus (up to 5) 24-h recalls was 0.78 ($95\%$ confidence interval, 0.70–0.83; $$n = 135$$) [58]. We categorized alcohol consumption into none (< 1 unit per week [for both men and women]), light (≥ 1 unit/week to 1 unit/day for men, ≥ 1 unit/week to 0.5 unit/day for women), moderate (> 1 to 2 units/day for men, > 0.5 to 1 unit/day for women), and high (> 2 units/day for men, > 1 units/day for women) where 1 unit was defined as 10 g/day (g/d) of total alcohol (i.e. ethanol) consumption, 100 g/d of red or white wine consumption, 50 g/day of strong wine consumption, 225 g/d of pilsner, 320 g/d light alcoholic beer consumption, 160 g/d of high alcoholic beer consumption, or 35 g/d of spirits consumption [59].
## Features of CSVD, microvascular retinal diameters, and measures of MVD
Here, we briefly describe the methods used; a detailed description is provided in the Extended Methods (Additional file 1).
## Features of CSVD
We evaluated three CSVD features, i.e. white matter hyperintensity volume, cerebral microbleeds, and lacunar infarcts with a 3 T brain MRI scanner (Siemens Magnetom Prisma-fit Syngo MR D13D, Erlangen, Germany).
## Retinal microvascular diameters
We measured retinal microvascular diameters with static retinal vessel analysis from an optic disk-centered fundus photograph with the retinal health information and notification system (RHINO) software, as described previously [60]. In brief, we measured the diameter (expressed in measurement units [MU]) of the six largest retinal vessels at 0.5–1.0-disc diameter away from the optic disc margin. Diameters of arteriolar or venular vessels were combined into an average arteriolar retinal diameter (i.e. central retinal arteriolar equivalent [CRAE]) or venular retinal diameter (i.e. central retinal venular equivalent [CRVE]).
## Flicker light-induced increase in retinal arteriolar and venular diameter
We assessed the flicker light-induced increase in retinal arteriolar and venular diameters (in MU) with the Dynamic Vessel Analyzer (Imedos, Jena, Germany), as previously described [61–63]. Briefly, a 50 s-baseline recording was consecutively followed by a 40-s flicker light exposure and a 60-s recovery period. Baseline diameter was calculated as the average diameter between 20 and 50 s of the baseline recording. The diameter during flicker light exposure was calculated as the mean of the diameters assessed at time points 10 and 40 s of flicker light stimulation exposure. Flicker light-induced increase in retinal diameter was calculated as the diameter during flicker light exposure minus the baseline diameter.
## Heat-induced skin hyperemia
We measured heat-induced skin hyperemia by laser Doppler flowmetry (Perimed, Järfälla, Sweden), as previously described [61, 63]. Briefly, at the wrist skin blood flow, expressed in arbitrary perfusion units (PU), was recorded unheated for 2 min to serve as a baseline. After 2 min, the temperature of the laser Doppler probe was rapidly and locally increased to 44 °C and was kept constant until the end of the registration. Heat-induced increase in skin blood flow was expressed as the increase in skin blood flow during the 23 min heating phase. We calculated heat-induced increase in skin blood flow as the average skin blood flow during the 23 min heating phase minus the baseline skin blood flow (i.e. average skin blood flow during the first 2 min).
## Urinary albumin excretion
Urinary albumin excretion (UAE) was calculated as the average UAE of two 24-h urine collections (two collections were available for $91.3\%$ of participants). We used an automatic analyzer to measure urinary albumin concentration with a standard immunoturbidimetric assay. We multiplied urinary albumin concentration by collection volume to obtain 24-h UAE. The detection limit for assessment of urinary albumin concentration was set at 1.5 mg/L.
## Plasma biomarkers of microvascular dysfunction
We evaluated four plasma biomarkers of microvascular dysfunction (MVD) i.e. soluble intercellular adhesion molecule-1 [sICAM-1], soluble vascular adhesion molecule-1 [sVCAM-1], soluble E-selectin [sE-selectin], and Von Willebrand Factor [vWF] [64]. sICAM-1, sVCAM-1, and sE-selectin were measured in EDTA plasma samples with commercially available 4-plex sandwich immunoassay kits with different standards and antibodies (Meso Scale Discovery, Rockville, Maryland, United States of America). vWF was quantified in citrate plasma using ELISA (Dako, Glostrup, Denmark).
## Covariates
As described previously [56], we determined glucose metabolism status according to the World Health Organization 2006 criteria as normal glucose metabolism, prediabetes, type 2 diabetes, or other types of diabetes than type 2 [65]; assessed educational level (low, intermediate, high), income level and occupational status (low, intermediate, high) as measures of socioeconomic status [66], smoking status (never, former, current), and history of cardiovascular disease by questionnaire; assessed dietary habits with the Dutch Healthy Diet index sum score, a measure of adherence to the Dutch dietary guidelines 2015 [67], based on a validated food frequency questionnaire [58]; assessed lipid-modifying, antihypertensive, and glucose-lowering medication use as part of a medication interview; assessed weight, height, waist circumference, office and 24-h ambulatory blood pressure during a physical examination; calculated body-mass index (BMI) based on body weight and height; measured total daily physical activity (hours/day) with an accelerometer [68]; measured fasting plasma glucose, 2-h post load glucose, hemoglobin A1c (HbA1c), lipid profile, serum creatinine, serum cystatin C, and plasma biomarkers of low-grade inflammation [69] (i.e. high-sensitive C-reactive protein, serum amyloid A, interleukin-6, interleukin-8 and tumor necrosis factor alpha) in fasting venous blood samples; calculated the estimated glomerular filtration rate (eGFR) with the CKD-EPI (Chronic Kidney Disease Epidemiology collaboration) formula using serum creatinine and cystatin C [70]; and assessed presence of retinopathy in both eyes via fundus photography.
## Statistical analyses
We used a total MVD composite score as endpoint. We composed a total MVD composite score because we assume that all measures of MVD under study represent a similar underlying measure of MVD [2]. In order to perform analyses we recalculated several variables. First, we inversed (i.e. multiplied by -1) flicker light-induced increase in retinal arteriolar and venular diameters and heat-induced skin hyperemia so that higher values indicate more MVD. Second, we logarithmically transformed white matter hyperintensity volume, cerebral microbleeds, lacunar infarcts, and UAE as these outcome variables were not normally distributed. Third, to reduce noise (i.e. measurement error) we calculated composite scores for CSVD features, retinal microvascular diameters, flicker light-induced increase in retinal microvascular diameters, plasma biomarkers of MVD, and total MVD [71]. To maximize the number of participants that we could use in the main analyses, we included participants in the main analyses if data were available for at least two out of six measures of MVD. Then, we performed complete cases analyses, i.e. we included individuals in the main analyses if they had complete data on the total MVD composite score, alcohol consumption, and covariates required for the main statistical models (shown below). Last, we recalculated the Dutch Healthy Diet score so that the “diet score” reflects dietary intake without alcohol consumption.
As the association between alcohol consumption and MVD may be non-linear and quadratic (i.e. J-shaped), as previously described, we tested for a quadratic association [72]. To test for a quadratic association, we entered a quadratic term of total alcohol consumption in to the model (we used the formula y = x + x2). If the P-value of the quadratic term was < 0.05, we considered the association statistically better described by a quadratic association than by a linear association. We performed this test for total alcohol consumption instead of individual types of alcohol for reasons of statistical power, i.e. as the range of total alcohol consumption is greater than the range of individual types of alcohol consumption, the statistical power to detect a non-linear association is likely greater [73]. In these analyses we did not exclude zero alcohol consumers (more details in the next paragraph).
We used multivariable regression analyses to analyze non-linear and linear associations of total alcohol consumption, wine, beer, and spirits consumption with the total MVD composite score.
We analyzed both linear and non-linear associations. For non-linear analyses, we entered total alcohol consumption, wine, beer, and spirits consumption into the statistical model as dummies of none, moderate, or high, versus light, alcohol consumption. Wine, beer, and spirits consumption were entered in the same model to allow mutual adjustment for consumption of other alcoholic beverages. In these analyses we used light drinkers as a reference group as we cannot distinguish so-called sick quitters from never drinkers (i.e. life-long abstainers) within the none consumers [55]. For linear analyses, we entered alcohol in the model as a continuous variable (per unit). For P for trend analyses, we entered alcohol consumption in the model as a categorical variable (coded 0 = none, 1 = light, 2 = moderate, and 3 = high alcohol consumption). For all linear analyses, we used zero drinkers as reference group. We did not use light drinkers as a reference group for linear analyses because in order to perform such analyses zero drinkers should be excluded, a methodological choice which would result in a substantial reduction in the size of the study population and a considerable loss of statistical power [73].
Model 1 shows crude results. In model 2 we adjusted for age, sex, glucose metabolism status (entered as dummies of prediabetes, type 2 diabetes, or other types of diabetes versus normal glucose metabolism status [reference]) and educational level (low [reference], middle, high). We chose these variables as they are key potential confounders (all) or because individuals were oversampled by design according to a certain condition (type 2 diabetes). In model 3A we additionally adjusted for potential confounders (waist circumference, smoking status [never {reference, current, former,}], and diet score). In model 3B we additionally adjusted for variables which are potential confounders and may additionally also be potential mediators (office systolic blood pressure, use of antihypertensive medication [yes/no], total cholesterol/HDL cholesterol ratio, lipid-modifying medication [yes/no], and history of cardiovascular disease [yes/no]). Data were expressed as regression coefficients and corresponding $95\%$ confidence intervals.
We tested for interaction by history of cardiovascular disease and sex. We a priori hypothesized that the shape of the association may differ between individuals with and without a history of cardiovascular disease [10–12] and between men and women [13, 14]. We used a likelihood ratio test to test for interaction. The likelihood ratio test compares the goodness in fit between the fully adjusted model (model 3B) with and without an interaction term (e.g. history of cardiovascular disease*total alcohol consumption). A statistically significant P-value from the likelihood ratio test indicates that the shape of the association under study is (statistically) different between subgroups (e.g. between individuals with and without a history of cardiovascular disease).
To test robustness of our observations we performed several additional analyses. Here we highlight a selection, all additional analyses are presented in the Supplemental Methods section. First, we analyzed associations of alcohol consumption with individual measures of MVD under study. Second, we tested whether the association of total alcohol consumption with the total MVD composite score was modified by individual cardiovascular risk factors (i.e. glucose metabolism status, hypertension, current smoking, and dyslipidemia). To test whether this association was modified by individual cardiovascular risk factors, we tested for interaction by these factors. Third, we investigated the association between total alcohol consumption and the total MVD composite score in individuals with zero, one, two, three, or four of the above cardiovascular risk factors to investigate whether associations were stronger in individuals with presumed increasingly higher levels of oxidative stress. Last, we investigated how the shape of the association was impacted when we left either wine, beer, or spirits out of the total alcohol consumption index.
We performed all regression analyses with Statistical Package for Social Sciences version 23.0 (IBM SPSS, IBM Corp, Armonk, NY, USA) and likelihood ratio tests with Software for Statistics and Data Sciences version 14.0 (StataCorp, Texas, USA). For all analyses, including interaction analyses, a P-value < 0.05 was considered statistically significant.
## Selection and characteristics of the study population
Figure 1 shows an overview of the study population selection and Tables 1A and 1B show general characteristics according to total alcohol consumption (shown for individuals with complete data on UAE [$$n = 3$$,107]). General characteristics of individuals in the study population are: mean age 60 years old, $51\%$ men, $27.5\%$ type 2 diabetes. Next, $16\%$, $31\%$, $20\%$, and $33\%$ of participants were, respectively, none, light, moderate, and high total alcohol consumers, and $59\%$, $43\%$, and $10\%$ of participants were, wine, beer, and/or spirits consumers, respectively. Overall, participants who consumed more alcohol were older and less likely to have type 2 diabetes. General characteristics of participants included in the study were highly comparable to those of participants with missing data (Additional file 1: Tables S1 and S2 show general characteristics of individuals who had available and missing data).Fig. 1Delineates the selection of participants for inclusion. CSVD, cerebral small vessel disease; UAE, urinary albumin excretion; MVD, microvascular dysfunctionTable 1General characteristics of the MVD study population in the UAE study populationCharacteristicTotal study population($$n = 3107$$)Total alcohol consumptionNone($$n = 498$$)Light($$n = 964$$)Moderate($$n = 620$$)High($$n = 1$$,025)Demographics Age, years59.9 ± 8.259.0 ± 8.659.1 ± 8.860.1 ± 8.261.0 ± 7.2 Men1,582 (50.9)163 (32.7)555 (57.6)357 (57.6)507 (49.5)Lifestyle factors *Smoking status* Never1088 (35.0)214 (43.0)379 (39.3)236 (38.1)259 (25.3) Former1,624 (52.3)196 (39.4)480 (49.8)313 (50.5)635 (62.0) Current395 (12.7)88 (17.7)105 (10.9)71 (11.5)131 (12.8)Body mass index*, kg/m227.0 ± 4.528.2 ± 5.527.5 ± 4.626.4 ± 3.926.3 ± 4.0Waist circumference, cm95.8 ± 13.797.4 ± 15.797.3 ± 14.094.5 ± 12.794.2 ± 12.7Physical activity*, hours/day2.0 ± 0.71.9 ± 0.81.9 ± 0.72.0 ± 0.62.1 ± 0.7Dutch Healthy Diet score, points83.2 ± 14.784.8 ± 14.784.9 ± 14.185.7 ± 14.479.4 ± 14.8Cardiovascular risk factors Glucose metabolism status Normal glucose metabolism1,760 (56.6)209 (42.0)538 (55.8)385 (62.1)628 (61.3) Prediabetes460 (14.8)59 (11.8)137 (14.2)90 (14.5)174 (17.0) Type 2 diabetes854 (27.5)225 (45.2)282 (29.3)137 (22.1)210 (20.5) Other types of diabetes33 (1.1)5 (1.0)7 (0.7)8 (1.3)13 (1.3) *Fasting plasma* glucose*, mmol/l5.5 [5.1–6.5]5.8 [5.1–7.3]5.5 [5.0–6.7]5.5 [5.0–6.4]5.5 [5.1–6.2] 2-h post load plasma glucose*, mmol/l9.3 [6.3–9.3]7.4 [5.5–13.0]6.3 [5.0–9.4]6.1 [4.9–8.3]6.1 [5.1–8.4] HbA1c*, %5.7 [5.4–6.2]5.9 [5.5–6.7]5.7 [5.3–6.3]5.6 [5.3–6.1]5.6 [5.3–6.0] Use of glucose-lowering medication697 (22.4)196 (39.4)244 (25.3)102 (16.5)155 (15.1) Total/HDL cholesterol ratio3.7 ± 1.23.8 ± 1.23.8 ± 1.23.6 ± 1.13.5 ± 1.2 Use of lipid-modifying medication1,137 (36.6)231 (46.4)347 (36.0)222 (35.8)337 (32.9) Office systolic blood pressure, mm Hg135.0 ± 18.3134.4 ± 18.3134.3 ± 18.1135.9 ± 18.4135.5 ± 18.3 Office diastolic blood pressure, mm Hg76.1 ± 9.975.0 ± 9.276.4 ± 10.076.3 ± 10.676.4 ± 9.8 Ambulatory systolic blood pressure, mm Hg118.9 ± 11.7116.7 ± 11.1118.6 ± 11.7119.8 ± 11.8119.7 ± 11.7 Ambulatory diastolic blood pressure, mm Hg73.4 ± 7.171.9 ± 9.073.7 ± 7.273.6 ± 7.373.7 ± 7.1 Use of antihypertensive medication1,252 (40.3)261 (52.4)387 (40.1)236 (38.1)368 (35.9) History of cardiovascular disease522 (16.8)115 (23.1)175 (18.2)101 (16.3)131 (12.8) Diabetic retinopathy*41 (1.6)9 (2.1)20 (2.5)6 (1.1)6 (0.6) eGFR, ml/min/1.73288.0 ± 14.986.4 ± 17.387.3 ± 15.588.6 ± 14.689.0 ± 13.2Biomarkers of low-grade inflammation* C-reactive protein, µg/ml1.2 [6.1–2.8]1.7 [0.7–3.8]1.4 [0.7–3.0]1.2 [0.6–2.5]1.0 [0.6–2.3] Serum amyloid A, µg/ml3.3 [2.1–5.4]3.7 [2.3–6.4]3.3 [1.9–5.7]3.2 [2.0–5.3]3.2 [2.1–5.1] *Tumour necrosis* factor alpha, pg/ml2.2 [1.9–2.6]2.3 [1.9–2.7]2.2 [1.9–2.6]2.2 [1.9–2.5]2.1 [1.8–2.5] Interleukin-6, pg/ml4.1 [3.3–5.3]0.7 [0.5–1.0]0.6 [0.4–0.9]0.6 [0.4–0.8]0.6 [0.4–0.9] Interleukin-8, pg/ml4.1 [3.3–5.3]4.2 [3.4–5.4]4.2 [3.3–5.4]3.9 [3.2–5.3]4.2 [3.3–5.3]Other *Educational status* Low1041 (33.5)225 (45.2)331 (34.3)190 (30.6)295 (28.8) Medium877 (28.2)158 (31.7)285 (29.6)172 (27.7)262 (25.6) High1189 (38.3)115 (23.1)348 (36.1)258 (41.6)468 (45.7) Occupational status* Low801 (31.0)177 (46.6)265 (32.5)151 (28.8)208 (20.3) Middle922 (35.7)130 (34.2)289 (35.5)198 (37.7)305 (35.3) High860 (33.3)73 (19.2)261 (32.0)176 (33.5)350 (40.6) Income per month*, euros2028 ± 8181653 ± 7041919 ± 7252123 ± 8232229 ± 869Alcohol consumption Total alcohol consumption, units/day0.85 [0.2–1.9]0.0 ± 0.00.3 [0.1–0.5]1.1 [0.8–1.5]2.3 [1.8–3.1] Total wine consumption, units/day0.3 [0.0–1.1]0.0 ± 0.00.1 [0.0–0.3]0.6 [0.3–0.9]1.7 [0.9–2.1] Total beer consumption, gram/day0.1 [0.0–0.5]0.0 ± 0.00.1 [0.0–0.3]0.3 [0.0–0.8]0.3 [0.0–1.6] Total spirits consumption, units/day0.0 [0.0–0.0]0.0 ± 0.00.0 [0.0–0.0]0.0 [0.0–0.0]0.0 [0.0–0.1]Endpoints CSVD features White matter hyperintensity volume, ml†0.0 [0.0–0.1]0.0 [0.0–0.1]0.0 [0.0–0.0]0.0 [0.0–0.1]0.0 [0.0–0.1] Presence of cerebral microbleeds†245 (11.8)26 (8.3)74 (11.8)60 (14.2)85 (11.9) Number of cerebral microbleeds0.0 [0.0–0.0]0.0 [0.0–0.0]0.0 [0.0–0.0]0.0 [0.0–0.0]0.0 [0.0–0.0] Presence of lacunar infarcts†114 (5.5)20 (6.4)37 (5.9)21 (5.0)36 (5.0) Number of lacunar infarcts0.0 [0.0–0.0]0.0 [0.0–0.0]0.0 [0.0–0.0]0.0 [0.0–0.0]0.0 [0.0–0.0] Composite score†0.0 ± 1.0− 0.0 ± 1.0− 0.0 ± 1.00.0 ± 1.00.0 ± 1.0Retinal microvascular diameters Arteriolar diameter, MU†142.3 ± 20.2145.3 ± 19.9143.0 ± 20.1140.9 ± 20.7141.1 ± 20.1 Venular diameter, MU†214.6 ± 31.4218.2 ± 31.3215.6 ± 32.2211.0 ± 31.4214.0 ± 30.4 Composite score†0.0 ± 1.0− 0.0 ± 1.0− 0.0 ± 1.00.1 ± 1.00.0 ± 1.0Flicker light-induced increase in retinal microvascular diameters Arteriolar flicker light-induced dilation, MU†4.4 ± 3.64.1 ± 3.74.3 ± 3.44.8 ± 3.84.3 ± 3.5 Venular flicker light-induced dilation, MU†7.6 ± 4.17.7 ± 4.27.5 ± 4.07.5 ± 4.17.7 ± 4.1 Composite score†0.0 ± 1.00.0 ± 1.00.0 ± 1.0-0.1 ± 1.0-0.0 ± 1.0 Heat-induced skin hyperemia, PU†112.1 ± 57.3113.1 ± 61.4107.7 ± 53.0109.9 ± 55.5116.9 ± 59.8 UAE, mg/24 h†6.7 [4.0–11.9]7.3 [4.4–14.2]6.7 [4.2–12.3]6.4 [3.8–11.0]6.5 [3.9–11.4] ≥ 30 mg/24 h†270 (18.7)59 (11.8)86 (8.9)47 (7.6)78 (7.6)Plasma biomarkers of MVD composite score sICAM-1, ng/ml†353.9 ± 99.8390.0 ± 134.2349.2 ± 89.6347.2 ± 95.5344.6 ± 87.2 sVCAM-1, ng/ml†428 ± 101.0450.1 ± 125.9432.8 ± 99.2427.8 ± 96.3412.9 ± 88.6 sE-selectin, ng/ml†117.8 ± 65.7131.8 ± 90.7117.9 ± 64.2119 ± 58.2114.3 ± 55.0 vWF, %†132.6 ± 48.4140.4 ± 52.0133.7 ± 47.1131.5 ± 48.4128.4 ± 47.3 Composite score†0.0 ± 1.00.4 ± 1.30.0 ± 0.9− 0.1 ± 1.0− 0.1 ± 0.9Data are presented as mean ± standard deviation, median [interquartile range] or n (%)Definitions of alcohol consumption categories: none (< 1 unit per week [for both men and women]); light (≥ 1 unit/week to 1 unit/day for men, ≥ 1 unit/week to 0.5 unit/day for women); moderate (> 1 to 2 units/day for men, > 0.5 to 1 unit/day for women); and high (> 2 units/day for men, > 1 units/day for women)HbA1c glycated hemoglobin, HDL high-density lipoprotein, SD standard deviation, CSVD cerebral small vessel disease, SD standard deviation, MVD microvascular dysfunction, PU perfusion units, ICAM soluble intercellular adhesion molecule-1, sVCAM soluble vascular adhesion molecule-1, sE-selectin soluble E-selectin, vWF von Willebrand factor, UAE urinary albumin excretion, MU measurement units†value shown for study population with complete data on cerebral small vessel disease, or retinal arteriolar and venular diameters, or flicker light-induced increase in retinal arteriolar and venular diameter, or heat-induced skin hyperemia, or UAE, or plasma biomarkers of microvascular dysfunction i.e. for features of cerebral small vessel disease $$n = 2075$$; for retinal arteriolar and venular diameters $$n = 2721$$; for flicker light-induced increase in retinal arteriolar and venular diameter $$n = 2090$$; for heat-induced skin hyperemia $$n = 1517$$; for urinary albumin excretion $$n = 3107$$; and for plasma biomarkers of microvascular dysfunction $$n = 3078$$*Data were available for: ambulatory blood pressure, $$n = 1345$$; BMI, $$n = 3106$$; physical activity, $$n = 2408$$; fasting plasma glucose, $$n = 3106$$; 2-h post load glucose, $$n = 2868$$; HbA1c, $$n = 3100$$; diabetic retinopathy, $$n = 2610$$; eGFR, $$n = 3082$$; biomarkers of low grade-inflammation, $$n = 2079$$; occupational status, $$n = 2$$,583; income, $$n = 2$$,368
## Associations between alcohol consumption and measures of MVD
The association between total alcohol consumption and MVD was non-linear, i.e. J-shaped (model 3B; Pquadratic-value = 0.01; Fig. 2). The mathematical minimum of the J-curve (“minimum”) was at approximately 4 units/day in the crude model (i.e. the amount of total alcohol consumption where the association becomes directionally different; Fig. 2). After full adjustment (model 3B), moderate versus light total alcohol, wine, beer, and spirits consumption was statistically significantly associated with less MVD (model 3B; moderate versus light total alcohol, wine, beer, and spirits consumption, respectively; standardized betas [$95\%$ confidence interval], − 0.10 [− 0.19; − 0.01]; − 0.15 [− 0.25; − 0.05]; − 0.13 [− 0.25; − 0.02]; and − 0.16 [− 0.29; − 0.04]; Table 2 and Fig. 3).Fig. 2General population ($$n = 3$$,120; minimum of the J-curve at 4 units/day). Figure 2 The Scatter plot shows data points for total alcohol consumption (x-axis; per unit) and the total MVD composite score (y-axis; in SD) where a quadratic association was modeled (blue line). In the general population the minimum of the J-curve was located at approximately 4 units/dayTable 2Associations of total alcohol, wine, beer, and spirits consumption with the total MVD composite score in the general populationAlcohol consumptionModelContinuousNone vs. lightModerate vs. lightHigh vs. lightP for trendβ ($95\%$ CI)β ($95\%$ CI)β ($95\%$ CI)β ($95\%$ CI)P−valueGeneral population, $$n = 3120$$ Total alcohol consumption123A3B− 0.02 (− 0.05; 0.00)− 0.05 (− 0.07; − 0.02)− 0.05 (− 0.08; − 0.03)− 0.04 (− 0.07; − 0.02)0.23 (− 0.12; 0.33)0.16 (0.06; 0.26)0.15 (0.05; 0.25)0.12 (0.02; 0.22)− 0.15 (− 0.25; − 0.05)− 0.12 (− 0.22; − 0.03)− 0.10 (− 0.19; − 0.01)− 0.10 (− 0.19; − 0.01)− 0.15 (− 0.24; − 0.06)− 0.13 (− 0.21; − 0.05)− 0.14 (− 0.22; − 0.06)− 0.11 (− 0.19; − 0.03)0.000.000.000.00 Wine consumption123A3B− 0.11 (− 0.15; − 0.08)− 0.10 (− 0.13; − 0.06)− 0.09 (− 0.12; − 0.06)− 0.08 (− 0.11; − 0.04)0.24 (0.15; 0.33)0.15 (0.07; 0.24)0.11 (0.03; 0.20)0.10 (0.02; 0.18)− 0.20 (− 0.31; − 0.09)− 0.17 (− 0.27; − 0.06)− 0.16 (− 0.26; − 0.06)− 0.15 (− 0.25; − 0.05)− 0.12 (− 0.23; − 0.01)− 0.06 (− 0.17; 0.04)− 0.07 (− 0.17; 0.03)− 0.05 (− 0.15; 0.05)0.000.000.000.00 Beer consumption123A3B0.04 (0.01; 0.08)− 0.01 (− 0.04; 0.03)− 0.02 (− 0.05; 0.02)− 0.01 (− 0.04; 0.03)− 0.08 (− 0.17; − 0.00)− 0.01 (− 0.10; 0.07)− 0.02 (− 0.10; 0.07)− 0.02 (− 0.10; 0.06)− 0.14 (− 0.27; − 0.01)− 0.11 (− 0.23; 0.01)− 0.14 (− 0.25; − 0.02)− 0.13 (− 0.25; − 0.02)0.00 (− 0.15; 0.15)− 0.05 (− 0.19; 0.08)− 0.10 (− 0.23; 0.04)− 0.07 (− 0.20; 0.06)0.570.300.090.21 Spirits consumption123A3B0.17 (0.03; 0.31)0.05 (− 0.08; 0.17)− 0.00 (− 0.13; 0.12)− 0.01 (− 0.13; 0.11)− 0.05 (− 0.13; 0.04)0.00 (− 0.09; 0.09)− 0.01 (− 0.09; 0.08)− 0.01 (− 0.09; 0.07)− 0.24 (− 0.39; − 0.10)− 0.15 (− 0.28; − 0.02)− 0.17 (− 0.30; − 0.04)− 0.16 (− 0.29; − 0.04)− 0.33 (− 0.58; − 0.08)− 0.18 (− 0.41; 0.05)− 0.19 (− 0.42; 0.03)− 0.17 (− 0.40; 0.05)0.010.400.830.69Betas and $95\%$ confidence intervals indicate the strength of the association between total alcohol, wine, beer, and spirits consumption with the total MVD composite score where a negative beta indicates less MVD. Total alcohol, wine, beer, and spirits consumption were entered in the models as a continuous variable (per unit, i.e. 10 g/day), as dummies (none, moderate or high versus light alcohol consumption) or (for the P-for trend analyses) as a categorical variable (none, light, moderate, and high alcohol consumption)One SD corresponds with 1.6 ml white matter hyperintensity volume (logarithmic scale), 2.4 cerebral microbleeds (logarithmic scale), 1.6 lacunar infarcts (logarithmic scale; all three combined in the CSVD features composite score); 20.2 MU of CRAE, 31.4 MU of CRVE (combined in the retinal microvascular diameter composite score); 3.6 MU of flicker light-induced increase in retinal arteriolar diameter, 4.1 MU of flicker light-induced increase in retinal venular diameter (combined in the flicker light-induced increase in retinal microvascular diameter composite score); 0.98 mg/24 h of logarithmically transformed UAE; 57.3 PU of heat-induced skin hyperemia; or 99.8 ng/ml sICAM-1, 101.0 ng/ml of sVCAM-1, 65.7 ng/ml of sE-selectin, or $48.4\%$ vWF (combined in the plasma biomarkers of MVD composite score)The numbers of participants with complete data on CSVD features, retinal microvascular diameters, flicker light-induced increase in retinal microvascular diameters, heat-induced skin hyperemia, UAE, and plasma biomarkers of MVD respectively are $$n = 2075$$; $$n = 2721$$; $$n = 2090$$; $$n = 1$$,517; $$n = 3107$$; and $$n = 3078$$Model 1: crude; Model 2: age, sex, glucose metabolism status (entered as dummies of type 2 diabetes, prediabetes, or other types of diabetes versus normal glucose metabolism status), education level [low, middle, high]; model 3A: model 2 + waist circumference, smoking status [current, ever, never], diet score; model 3B: model 3A + office systolic blood pressure, use of antihypertensive medication [yes/no] total cholesterol/HDL cholesterol ratio, lipid-modifying medication, prior cardiovascular disease. Additionally, for associations with heat-induced skin hyperemia baseline skin blood flow was entered in model 1Bold denotes P-value < 0.05CI confidence interval, CSVD cerebral small vessel disease, CRAE central retina arteriolar equivalent, CRVE central retinal venular equivalent, SD standard deviation, PU perfusion units, UAE urinary albumin excretion, sICAM-1 soluble intercellular adhesion molecule-1, sVCAM-1 soluble vascular adhesion molecule-1, sE-selectin soluble E-selectin, vWF von Willebrand factor, MVD microvascular dysfunctionFig. 3Associations of moderate versus light total alcohol consumption with the total MVD composite score in the general population. Betas and $95\%$ confidence intervals indicate the strength of the associations of total alcohol consumption (moderate versus light) with total MVD composite score (per SD) where a negative beta indicates less MVD. The number of participants in analyses and the numerical values per SD for all endpoints are reported in the legends of Table 2 (general population), Additional file 1: Table S4 (history of cardiovascular disease strata) and Additional file 1: Table S5 (sex strata). Variables included in model 3B are age, sex (where applicable), glucose metabolism status, education level, waist circumference, smoking status, diet score, office systolic blood pressure, use of antihypertensive medication, total cholesterol/HDL cholesterol ratio, lipid-modifying medication, and history of cardiovascular disease (where applicable). * indicates P-value < 0.05. B, beta; CI: confidence interval; SD: standard deviation; MVD, microvascular dysfunction
## Interaction analyses
History of cardiovascular disease and sex modified the association between total alcohol consumption and the total MVD composite score (P-for-interaction values: < 0.001 and = 0.03, respectively). Additional file 1: Table S3 shows all P-for-interaction values.
## History of cardiovascular disease
In individuals with and without a history of cardiovascular disease the shapes of the non-linear association of total alcohol consumption with the total MVD composite score were different, both with regard to the location of the minimum of the J-curve as well as the depth of the minimum of the J-curve. The minimum of the J-curve, was at approximately 6 units/day in the crude model in individuals with a history of cardiovascular disease; and at approximately 2 units/day in the crude model in individuals without a history of cardiovascular disease (Additional file 1: Figure S1). Then, the minimum of the J-curve was lower in individuals with, versus without, a history of cardiovascular disease (indicating that higher than light total alcohol consumption was more strongly associated with less MVD in individuals with, versus without, a history of cardiovascular disease; e.g. model 3B; for moderate versus light total alcohol consumption, -0.22 [-0.44; -0.01] in individuals with a history of cardiovascular disease versus -0.09 [-0.19; 0.01] in individuals without a history of cardiovascular disease; Fig. 3 and Additional file 1: Table S4). Next, in individuals with a history of cardiovascular disease, wine and beer consumption were more strongly associated with less MVD than spirits consumption (model 3B; for moderate versus light wine, beer, and spirits consumption, respectively, -0.29 [-0.56; -0.03]; -0.28 [-0.55; 0.00]; and -0.21 [-0.51; 0.10]; Additional file 1: Table S4 and S Additional file 1: Figure S2).
## Sex
In men and women, the shapes of the associations of total alcohol consumption with the total MVD composite score clearly differed with regard to the location of the minimum of the J-curve and somewhat, but not materially, differed with regard to the depth of the minimum of the J-curve. The minimum of the J-curve for total alcohol consumption in the association with the total MVD composite score was at approximately 5 units/day in men in the crude model; and at approximately 3 units per/day in women in the crude model (Additional file 1: Figure S1). Then, the strength of this association was somewhat stronger in women than in men (e.g. model 3B; moderate versus light total alcohol consumption, -0.11 [-0.25; 0.04] in women versus − 0.09 [− 0.20; 0.03] in men; Fig. 3 and Additional file 1: Table S5). Additionally, in both men and women, wine consumption was somewhat more strongly associated with less MVD, estimated from the total MVD composite score, than beer or spirits consumption, where wine, but not beer or spirits, consumption was somewhat more strongly associated with less MVD in women than in men (model 3B; moderate versus light total alcohol consumption, − 0.09 [− 0.14; − 0.01] in women versus -0.06 [-0.11; -0.02] in men; Additional file 1: Table S5).
## Additional analyses
We observed quantitatively similar results in a range of additional analyses (all results are reported in the Extended Results section in the Additional file 1: Tables S6-S18 and Additional file 1: Figures S1-S14). We highlight three findings. First, we found that alcohol consumption was in the same direction associated with retinal arteriolar and venular diameters (model 3B; Additional file 1: Table S9). Second, we found that the minimum in the J-curve was at increasingly higher levels of total alcohol consumption in individuals with increasingly more cardiovascular risk factors (Additional file 1: Table S7 and Additional file 1: Figure S5). Third, when we left wine or beer consumption out of the total alcohol consumption index the location of the minimum of the J-curve was different (at higher levels of alcohol consumption when wine was left out of the index and at lower levels of alcohol consumption when beer was left out of the index; Additional file 1: Figure S8). We did not see material changes when we left spirit out of the index (Additional file 1: Figure S8).
## Discussion
The present population-based study has three main findings. First, in the complete population we found a J-shaped association between total alcohol consumption with MVD, indicating that moderate versus light total alcohol consumption was associated with less MVD and higher than moderate versus light total alcohol consumption was associated with more MVD. In addition, associations with MVD were similar for wine, beer, and spirits. Second, in individuals with, versus without, a history of the cardiovascular disease, the minimum of the J-curve was at higher levels of total alcohol consumption; and the depth of the minimum of the J-curve was considerably lower. In addition, in individuals with a history of cardiovascular disease, the depth of the minimum of the J-curve was considerably lower for wine and beer consumption than for spirits consumption. Third, in men, the minimum of the J-curve was at higher levels of alcohol consumption than in women; and in women the depth of the minimum of the J-curve was somewhat lower than in men (indicating a somewhat stronger association in women than in men). In addition, wine consumption was somewhat more strongly associated with less MVD in women than in men. We did, however, not see a consistent pattern for other types of alcoholic beverages (i.e. beer and spirits).
Our findings are in line with observations from most previous studies [15–53]. Importantly, the present study is the first large population-based study to comprehensively report associations of total alcohol, wine, beer, and spirits consumption with MVD assessed in various organs, both in the general population as well as in substrata of individuals with a history of cardiovascular disease or a cardiovascular risk factor. Further, the present study is the first study to report the associations of total alcohol, wine, beer, and spirits consumption with flicker light-induced increase in retinal diameters and heat-induced skin hyperemia.
Our observations support the concept that alcohol consumption is a determinant of MVD. All measures of MVD under study likely (in part) reflect endothelial cell function, which relies on NO bioavailability, and NO bioavailability can be modified by alcohol consumption [2, 6, 7]. Mechanistically, the J-shaped association between alcohol and MVD may reflect a triphasic balance, where in the descending part of the curve alcohol consumption induces a net increase in NO bioavailability (reducing MVD); at the minimum of the curve there is an equilibrium between increasing and reducing effects of alcohol on NO bioavailability (net no effect on MVD); and in the ascending part of the curve, alcohol consumption induces a net reduction in NO bioavailability (increasing MVD) [5, 9, 74–76]. Biologically, at lower levels of alcohol consumption ethanol likely increases NO bioavailability via stimulation of NO synthesis by the enzyme eNOS; [5, 9, 74–76] and polyphenols likely increase NO bioavailability via reducing eNOS uncoupling and scavenging of NO by oxidative stress [5, 9, 74–76]. Then, at higher levels of alcohol consumption ethanol likely reduces NO bioavailability by inducing oxidative stress [5, 9, 74–76].
In individuals with, versus without, a history of the cardiovascular disease, the minimum of the J-curve in the association of total alcohol consumption with the total MVD composite score was located at higher levels of total alcohol consumption, possibly because levels of background oxidative stress are higher in individuals with, versus without, a history of cardiovascular disease [5, 74]. Biologically, higher levels of ethanol-induced oxidative stress may be required to induce more oxidative stress than already present in the background [5, 27]. Indeed, consistent with this concept, we found that the minimum in the J-curve was located at higher levels of alcohol consumption in individuals with, versus without, a cardiovascular risk factor (for any individual risk factor under study).
In individuals with, versus without, a history of the cardiovascular disease the depth of the minimum of the J-shaped association of total alcohol consumption with MVD was considerably lower (indicating a stronger association of total alcohol consumption with MVD), possibly because at higher, versus lower, levels of background oxidative stress polyphenols can more potently increase NO bioavailability [5, 5, 27, 74]. Biologically, polyphenols can both increase NO bioavailability via preventing the scavenging of co-factors that are required for NO synthesis and via inhibiting a vicious circle in which oxidative stress scavenges NO and oxidizes NO into more oxidative stress (i.e. peroxynitrate, a reactive nitrogen species) [5, 27]. Indeed, consistent with this concept, in individuals with a history of cardiovascular disease, wine and beer consumption, which reflect greater intake of polyphenols than spirits consumption, were more strongly associated with less MVD than spirits consumption.
In men, versus women, the minimum of the J-curve was located at higher levels of total alcohol consumption, likely due to sex differences in the pharmacokinetics of ethanol [77, 78]. Biologically, as in women, versus men, the gastric activity of the antidiuretic hormone (ADH) is lower, which regulates the clearance of ethanol (first-pass metabolism), the consumption of a comparable quantity of ethanol likely leads to a higher level of ethanol in the blood of women than men [77, 78]. Additionally, as women on average have a lower volume of body water than men and ethanol is distributed in water in the body, the consumption of a comparable quantity of ethanol likely leads to higher blood concentrations of ethanol in women than in men [77, 78].
In women the depth of the minimum of the J-curve was somewhat, but not materially, lower than in men, possibly because certain small polyphenol-based pharmacodynamic sex differences exist [14, 79]. Biologically, as certain polyphenols in alcoholic beverages (e.g. resveratrol) can, via binding to the estrogen receptor, in a sex-specific manner alter intra-endothelial cell signaling pathways that regulate NO bioavailability, alcohol consumption may more strongly lead to an increase in endothelial cell NO bioavailability in women than in men [14, 79, 80]. Indeed, consistent with this concept, we found that wine consumption, which reflects greater resveratrol intake (mainly from red wine), was somewhat more strongly associated with less MVD in women than in men [14, 79].
In analyses with individual measures of MVD we observed that higher alcohol consumption was associated with narrower retinal microvascular diameters. Retinal arteriolar widening is thought to occur in early stages of MVD; thus, a narrower arteriolar diameter may represent less widening (i.e. indicating less MVD) [2, 4]. Biologically, widening of retinal arteriolar diameter is thought to reflect impairment of autoregulation, which is (in part) thought to be caused by endothelial cell dysfunction, as well as focal downstream ischemia [2, 4]. Indeed, human and animal data from observational and experimental studies in the retina, as well as in other organs such as the kidney, support this concept [2, 4].
Our findings should not be interpreted as to imply that changing alcohol consumption can be used to prevent MVD. Every unit increase in consumption of alcohol is associated with increased risk of loss of disability-adjusted life-years, as found in The Global Burden of Alcohol study which used data from 195 countries [81]. In addition, another important point is that it remains under debate which threshold for alcohol consumption should be recommended. Previous studies found differing thresholds at which alcohol consumption was associated with more favorable health outcomes. For example, a recent individual participant data analysis of n ~ 600,000 participants found that 100 g/week of alcohol consumption (for both men and women) was associated with a lower risk of all-cause mortality; [82] and a recent randomized clinical trial found that < 1 unit of alcohol consumption was associated with a decrease in arterial stiffness [83]. These results differ from our results, in which we found that up to two units per day of alcohol consumption for women and up to five units per day or alcohol consumption for men were associated with less MVD. Nevertheless, our findings add to the increasing body of evidence that it may be possible to reduce MVD via dietary interventions; and that it may be possible to personalize recommendations on alcohol consumption according to the presence of risk factors for cardiovascular disease [84].
Main strengths of this study are the large size of this population-based cohort study with oversampling of individuals with type 2 diabetes, which enables accurate comparison of individuals with and without diabetes [73]; the large number of potential confounders that was considered [85]; and the use of state-of-the-art techniques to assess CSVD features and MVD in various organ beds [60]. In addition, a strength of this study is that sick quitters were accounted for in analyses in which light alcohol consumption was used as a reference group [55].
Limitations include the following. First, due to the cross-sectional nature of the study causal inferences should be made with considerable caution. Second, some misclassification of high drinkers may have occurred as high drinkers may be more likely to self-underreport their alcohol consumption [36]. This may have led to an underestimation of strength of the associations in this study [71]. Third, even though we took an extensive set of confounders into account, we cannot fully exclude unmeasured confounding. For example, we did not take binge drinking into account and binge drinking may be more detrimental than chronic high alcohol consumption [6]. Fourth, there were relatively low numbers of high beer consumers (≤ $7\%$ of participants) and moderate or high spirits consumers (≤ $2\%$ of participants) in this study and this may resulted in a lack of statistical power to be able to detect statistically significant associations of beer and spirits consumption with endpoints under study (i.e. type 2 error) [73]. Fifth, we could not account for how drinking behavior changes related to the presence of certain medical conditions may have affected the analyses. For example, certain individuals with type 2 diabetes may have quit alcohol consumption due to dietary restrictions (imposed by their medical doctors due to the presence of cardiovascular risk factors). Sixth, we studied Caucasian individuals aged 40–75 years and therefore our results may be generalizable to such a population; whether these results also apply to other populations requires further study [86].
In conclusion, in this cross-sectional study we found a J-shaped association between total alcohol, wine, beer, and spirits consumption and MVD, indicating that moderate versus light alcohol consumption was associated with less MVD and higher than moderate versus light alcohol consumption was associated with more MVD. Additionally, the location and the depth of the minimum of the J-curve differed by history of cardiovascular disease and sex. Therefore, alcohol consumption may have an effect on MVD and via MVD mitigate microvascular clinical disease such as stroke, dementia, depression, retinopathy, and chronic kidney disease [1–3]. Although increasing alcohol consumption cannot be recommended as a policy, this study suggests that prevention of MVD may be possible through dietary interventions.
## Supplementary Information
Additional file 1.
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|
---
title: 'Time-dependent effect of GLP-1 receptor agonists on cardiovascular benefits:
a real-world study'
authors:
- Sara Piccini
- Giuseppe Favacchio
- Cristina Panico
- Emanuela Morenghi
- Franco Folli
- Gherardo Mazziotti
- Andrea Gerardo Lania
- Marco Mirani
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10039680
doi: 10.1186/s12933-023-01800-z
license: CC BY 4.0
---
# Time-dependent effect of GLP-1 receptor agonists on cardiovascular benefits: a real-world study
## Abstract
### Background
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have shown cardiovascular benefits in cardiovascular outcome trials in type 2 diabetes mellitus. However, the most convincing evidence was obtained in subjects with established cardiovascular (CV) disease. We analyzed the determinants of GLP-1 RA-mediated CV protection in a real-world population of persons with type 2 diabetes with and without a history of CV events with long-term follow-up.
### Methods
Retrospective cohort study of 550 individuals with type 2 diabetes (395 in primary CV prevention, 155 in secondary CV prevention), followed at a single center after the first prescription of a GLP-1 RA between 2009 and 2019. CV and metabolic outcomes were assessed.
### Results
Median duration of follow-up was 5.0 years (0.25–10.8) in primary prevention and 3.6 years (0–10.3) in secondary prevention, with a median duration of treatment of 3.2 years (0–10.8) and 2.5 years (0–10.3) respectively. In the multivariable Cox regression model considering GLP-1 RA treatment as a time-dependent covariate, in the primary prevention group, changes in BMI and glycated hemoglobin did not have an impact on MACE risk, while age at the time of GLP-1 initiation (HR 1.08, $95\%$ CI 1.03–1.14, $$p \leq 0.001$$) and GLP-1 RA cessation by time (HR 3.40, $95\%$ CI 1.82–6.32, $p \leq 0.001$) increased the risk of MACE. Regarding the secondary prevention group, only GLP-1 RA cessation by time (HR 2.71, $95\%$ CI 1.46–5.01, $$p \leq 0.002$$) increased the risk of MACE. With respect to those who withdrew treatment, subjects who continued the GLP-1 RA had significantly greater weight loss and lower glycated hemoglobin levels during follow-up.
### Conclusions
In this real-world type 2 diabetes population, discontinuation of GLP-1 RA treatment was associated to a higher risk of major cardiovascular events, in both subjects with and without a history of CV events.
## Background
GLP-1 receptor agonists (GLP-1 RAs) have been employed for over a decade for the treatment of type 2 diabetes mellitus. At present, several GLP-1 RA formulations are available: liraglutide and lixisenatide are subcutaneously administered once per day; dulaglutide, semaglutide, exenatide LAR are injected weekly. GLP-1 RAs share multiple mechanisms of action, including improvement of insulin secretion in response to hyperglycemia, suppression of glucagon hypersecretion, deceleration of gastric emptying reducing post-meal glycemic excursions, and changes in appetite and satiety leading to a reduction in body weight [1–4].
As for all new anti-diabetic medications, before approval for clinical use, GLP-1 RAs demonstrated cardiovascular safety in placebo controlled cardiovascular outcome trials (CVOTs). The primary endpoint of most of these studies were major adverse cardiovascular events (MACE), a combined endpoint of either cardiovascular (CV) death or non-fatal myocardial infarction or stroke. All GLP-1 RA, except for lixisenatide, showed decreased incidence of MACE, which was statistically significant in four of the studies (LEADER for liraglutide, SUSTAIN-6 for weekly semaglutide, Harmony Outcomes for albiglutide and REWIND for dulaglutide) [5–11].
GLP-1 RA CVOTs were quite heterogeneous in study design, sample size, duration, and proportion of patients with established CV disease (CVD). The individual studies were not powered to evaluate individual CV events, and, in fact, single CV events were seldom significantly decreased (for instance, liraglutide significantly decreased CV and all-cause mortality in the LEADER trial). Nevertheless, several meta-analyses of CVOTs showed that GLP-1 RA decreased MACE risk by 14–$16\%$, and, as a class, they did significantly decrease hazard ratios for individual events, including death from CV causes, fatal or non-fatal stroke, fatal or non-fatal myocardial infarction, and all-cause mortality [12–15]. Interestingly, two of the most recent meta-analyses that included subgroup analyses of subjects with or without established CVD, suggest similar favorable effects in patients with and without CVD [12, 14]. However, the number of events in primary prevention was small, due to the relatively short duration of the trials and the lower risk in these subjects.
GLP-1 RAs exert their beneficial CV effects through incompletely characterized mechanisms. Between the potential determinants of CV benefits, GLP-1 RAs modify CV risk factors by reducing body weight, systolic blood pressure, plasma LDL cholesterol and tryglycerides and by improving glycemic control (reduced HbA1c, avoidance of severe hypoglycemia). In addition, GLP-1 RAs have been shown to exert direct effects on the CV system, potentially leading to improved endothelial function, improved cardiac function under conditions of coronary ischemia, anti-inflammatory, and anti-atherosclerotic effects [16].
The first GLP-1 RA to be approved in Italy was exenatide b.i.d. in 2008, and since then we have collected more than 10 years of experience on diabetic patients treated with these agents. Considering the difference between randomized controlled trials (RCT) and real-life clinical practice, we analyzed the determinants of GLP-1 RA-mediated CV protection in a real-world population of patients with type 2 diabetes attending the outpatient clinic of a large university hospital located in the metropolitan area of Milan, Italy.
## Methods
This was a retrospective cohort study of patients with type 2 diabetes mellitus who were followed at a single high-volume center (Humanitas Research Hospital, Milan). The institutional Ethical Committee approved this study and patients gave standard written informed consent to use their anonymized clinical data for research purposes.
Using electronic medical records, patients receiving for the first time an injectable GLP-1 receptor agonist (i.e.: liraglutide, exenatide, lixisenatide, dulaglutide, semaglutide) between December 1st, 2009, and December 31st, 2019, were identified. The date of the visit in which the GLP-1 RA was first prescribed, was considered as baseline. Follow-up data until December 31st, 2021, were included. Inclusion criteria were the following: age > 18 years, signed informed consent, a diagnosis of type 2 diabetes mellitus, being naïve to GLP-1 RA, and having a minimum follow-up of 2 years at our center with complete availability of the clinical and biochemical data relating to weight, glycemic control and CV events. We excluded patients who started a GLP-1 RA after 2020, since they started treatment during the COVID-19 pandemic and were less likely to have reliable follow-up data. Exclusion criteria were the following: malignant tumor first diagnosed, relapsed, or undergoing active treatment (i.e.: chemotherapy, targeted therapies, radiotherapy) at the time of the first prescription of a GLP-1 RA; gestational diabetes and any form of secondary diabetes (e.g.: diagnosis of active Cushing's disease, pancreatectomy, corticosteroid treatment); bariatric surgery at any time during follow-up; diagnosis of type 1 diabetes or latent autoimmune diabetes of the adult (LADA).
Treatment persistence, side effects, cardiovascular and metabolic outcomes were evaluated during the follow-up period.
The dataset supporting the conclusions of this article is available in the Zenodo repository, https://doi.org/10.5281/zenodo.7533472.
## Clinical data
Demographic (age and gender) and clinical characteristics were evaluated at baseline (first prescription of a GLP-1 RA). Weight and height were measured and used to calculate body mass index (BMI). The presence of diabetes complications (retinopathy, nephropathy, neuropathy) and of additional CV risk factors was assessed (arterial hypertension, dyslipidemia, obesity, smoking, coronary, carotid, or lower extremity artery stenosis > $50\%$, left ventricular hypertrophy). Arterial hypertension was defined as a systolic blood pressure (BP) ≥ 140 mmHg or a diastolic BP ≥ 90 mmHg or current antihypertensive treatment. Dyslipidemia was defined by elevated serum lipid levels according to the current guidelines at the time of the baseline visit or current lipid-lowering treatment. Established CVD was defined as previous stroke, MI, unstable angina, myocardial ischemia on imaging or stress test, or coronary, carotid, or peripheral artery revascularization.
Medication history included glucose-lowering treatments used until the baseline visit, use of cardioactive drugs, including statins, ezetimibe, proprotein convertase subtilisin/kexin type 9 (PCSK-9) inhibitors, antiplatelet drugs, antihypertensive drugs, angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARB), mineralocorticoid receptor antagonists (MRA), and sacubitril/valsartan. All glucose-lowering therapies prescribed during the follow-up period were recorded. Duration of treatment with GLP-1 RA was computed, and, in case of discontinuation, the reason was evaluated. During each visit, self-reported treatment adherence to glucose-lowering medication was assessed. Duration of treatment with GLP-1 RA was computed, and, in case of discontinuation, the reason was evaluated. Treatment discontinuation was defined as the interruption of GLP-1 RA therapy for at least 3 months. If a subject resumed a GLP-1 RA after 3 months, his follow up ended at that time.
Laboratory data and body weight were recorded at baseline, and at 1 year, 2, 4, 6, 8 and 10 years (when available), and at the end of follow-up (the most recent visit available for each patient).
The primary outcome was a composite of major adverse CV events (MACE): non-fatal myocardial infarction (MI) or unstable angina, non-fatal stroke, and all-cause death. All-cause death was identified from the administrative data repository of the Lombardy Region (Italy). Death from any cause was used in place of cardiovascular death, due to uncertainty in establishing the latter in most of the cases.
Glycemic control and weight changes over time were also assessed.
## Statistical analysis
Due to the exploratory nature of the study, we enrolled all the patients receiving any GLP-1 receptor agonist in a 10-year period, estimating the number of subjects to be around 500. Due to the deep expected difference in MACE incidence, we conducted our analysis in parallel in patients with or without a previous CV event.
Data were described as number and percentage, if categorical, or mean and standard deviation, if continuous. If required by the description, a $95\%$ confidence interval has been added. Adherence to Gaussian distribution was verified with the Shapiro–Wilk test.
Survival from MACE was explored with survival analysis, considering as time to failure the first occurrence of non-fatal myocardial infarction (MI) or unstable angina, non-fatal stroke, and all-cause death, or the last contact date on censored patients. All potential prognostic factors were submitted to univariable proportional hazard Cox regression analysis. To include the duration of treatment in the analysis, we used a Cox regression model considering GLP-1 RA treatment as a time dependent covariate. Each patient with MACE was matched with a censored patient by year of therapy initiation, corrected for age at the beginning of therapy, BMI and glycated hemoglobin considered at beginning and end of therapy.
Changes in biological parameters (weight, blood glucose, and glycated hemoglobin) were described, comparing persistent and non-persistent GLP-1 users. Difference between the two groups were explored with Mann Whitney test, due to the non-Gaussian nature of the variables.
A p under 0.05 was considered as significant. All analyses were performed with Stata version 17.
## Study population
A total of 837 patients had a first prescription for a GLP-1 RA during the index period. 550 patients met the criteria for inclusion ($65.7\%$). Of those not included, 245 patients ($29.3\%$) did not have the minimum follow-up period of 2 years, 11 ($1.3\%$) had a concomitant active malignancy, 5 ($0.6\%$) were classified as LADA, 15 ($1.8\%$) had bariatric surgery during follow-up, and 11 ($1.3\%$) had secondary diabetes (Fig. 1).Fig. 1Study profile Subjects were split into two groups, namely a primary prevention group of persons without a history of CV events ($$n = 395$$ subjects), and a secondary prevention group of patients with a history of CV events ($$n = 155$$ subjects), defined as previous stroke, MI, unstable angina, myocardial ischemia on imaging or stress test, or coronary, carotid, or peripheral revascularization.
Baseline clinical and laboratory characteristics of study participants are shown in Table 1. All patients were residents in the Milan metropolitan area. Table 1Baseline characteristicsPrimary preventionSecondary preventionN395155Gender (males)193 ($48.86\%$)119 ($76.77\%$)Age, years61.6 ± 10.166.3 ± 7.8BMI, kg/m234.2 ± 5.832.2 ± 5.9 BMI < 3092 ($23.35\%$)62 ($40.00\%$) BMI 30–35148 ($37.56\%$)56 ($36.13\%$) BMI > 35154 ($39.09\%$)37 ($23.87\%$)Obese (BMI > 30 kg/m2)302 ($76.5\%$)93 ($60.0\%$)Diabetes duration, years8 (0–48)11 (0–50)Diabetes duration > 10 years136 ($34.43\%$)78 ($50.32\%$)sBP137.9 ± 15.6132.9 ± 14.7dBP79.9 ± 8.876.6 ± 8.3Fasting blood glucose, mg/dL174.1 ± 53.8161.9 ± 50.2HbA1c, %8.22 ± 1.367.87 ± 1.33HbA1c < $7\%$50 ($12.66\%$)35 ($22.58\%$)Total cholesterol, mg/dL181.6 ± 38.6149.3 ± 32.8HDL, mg/dL45.8 ± 11.341.8 ± 10.3Tryglicerides, mg/dL178.4 ± 122.4159.4 ± 79.6LDLca, mg/dL102.7 ± 34.176.3 ± 26.6Creatinine, mg/dL0.86 ± 0.261.01 ± 0.32eGFR, ml/min/1.73 m285.3 ± 19.376.7 ± 20.6Proteinuria11 ($2.89\%$)9 ($6.04\%$)Diabetic kidney disease91 ($23.27\%$)52 ($33.77\%$)Diabetic retinopathy51 ($12.9\%$)37 ($23.9\%$)*Smoking status* Never236 ($59.75\%$)75 ($48.39\%$) Current smokers71 ($17.97\%$)22 ($14.19\%$) Former smokers88 ($22.28\%$)58 ($37.42\%$)Ischemic heart disease0137 ($88.39\%$)Stroke08 ($5.16\%$)Peripheral artery revascularization028 ($18.06\%$)Arterial hypertension296 ($74.94\%$)150 ($96.77\%$)Dyslipidemia247 ($62.53\%$)149 ($96.13\%$)> $50\%$ carotid, coronary, or peripheral artery stenosis22 ($5.6\%$)31 ($20.6\%$)Heart failure7 ($1.8\%$)13 ($8.4\%$)LDLc was calculated using the Friedewald formula. eGFR, estimated glomerular filtration rate calculated with the CKD-EPI formula. sBP, dBP, systolic and diastolic blood pressure, respectivelyaMissing values in 68 subjects in primary prevention and in 27 subjects in secondary prevention In the primary prevention group, mean age was 61.6 years, $48.9\%$ were males, mean diabetes duration was 8 years, mean glycated hemoglobin at baseline was $8.2\%$, and $12.7\%$ had a glycated hemoglobin below $7\%$. The mean duration of follow-up was 5 years. In the secondary prevention group, mean age was 66.3 years, $76.8\%$ were males, mean diabetes duration was 11 years, mean glycated hemoglobin at baseline was $7.8\%$, and $22.6\%$ had a glycated hemoglobin below $7\%$.
At baseline, mean BMI was 34.2 and 32.2 kg/m2 in the primary prevention and in the secondary prevention groups respectively. In those without a history of CV events, $62.5\%$ had dyslipidemia, $74.9\%$ had arterial hypertension, $76.5\%$ were obese (BMI > 30 kg/m2), $18.0\%$ were active smokers, $22.3\%$ were former smokers, and $1.8\%$ had a diagnosis of heart failure. In the secondary prevention group, $88.4\%$ had a history of ischemic heart disease, $5.2\%$ had had a stroke, and $18.1\%$ had received an arterial revascularization procedure, $60\%$ were obese (BMI > 30 kg/m2), $14.2\%$ were active smokers, $37.4\%$ were former smokers, and $8.4\%$ had a diagnosis of heart failure.
## Treatment patterns
Before the baseline visit, most patients were taking metformin ($86.8\%$ in primary prevention, $83.2\%$ in secondary prevention), whereas $36.7\%$ in primary prevention and $32.3\%$ in secondary prevention were treated with either sulfonylureas or meglitinides, $21\%$ were on insulin in the primary prevention group and $27.7\%$ in the secondary prevention group, $23.3\%$ and $21.3\%$ respectively were on DPP-4 inhibitors, while very few were taking SGLT2 inhibitors (Table 2).Table 2Treatment patterns at baselinePrimary preventionSecondary preventionOngoing anti-diabetic treatments before baseline visitMetformin343 ($86.8\%$)129 ($83.2\%$)Pioglitazone68 ($17.2\%$)23 ($14.8\%$)Acarbose5 ($1.3\%$)2 ($1.3\%$)Sulfonylureas or meglitinides145 ($36.7\%$)50 ($32.3\%$)Insulin83 ($21\%$)43 ($27.7\%$)SGLT2 inhibitors12 ($3.0\%$)5 ($3.2\%$)DPP4 inhibitors92 ($23.3\%$)33 ($21.3\%$)Cardioactive therapies at baselineStatins177 ($44.8\%$)140 ($90.3\%$)Ezetimibe17 ($4.3\%$)19 ($12.3\%$)PCSK-9 inhibitors01 ($0.6\%$)Antiplatelet agents113 ($28.6\%$)147 ($94.8\%$)Anti-hypertensives282 ($71.4\%$)151 ($97.4\%$)ACEi o ARBs o MRAs244 ($61.8\%$)132 ($85.2\%$)GLP-1 RA prescribed at baselineLixisenatide1 ($0.3\%$)0Exenatide BID6 ($1.5\%$)1 ($0.6\%$)Exenatide LAR4 ($1.0\%$)0Liraglutide247 ($62.5\%$)92 ($59.4\%$)Dulaglutide134 ($33.9\%$)62 ($40\%$)Semaglutide (weekly)3 ($0.8\%$)0SGLT2, sodium-glucose cotransporter-2; DPP4, Dipeptidyl peptidase-4; PCSK-9, Proprotein convertase subtilisin/kexin type 9; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; MRA, mineralocorticoid receptor antagonist As shown in Table 2, in the primary prevention group, $71.4\%$ of the subjects were treated with anti-hypertensive drugs, $28.6\%$ were on anti-platelet drugs, while statins and ezetimibe were used by $44.8\%$ and $4.3\%$ of the subjects respectively. At baseline, 14 of the subjects with hypertension and 61 of those with dyslipidemia were newly diagnosed, and therefore they were not yet receiving pharmacological treatments. In those with previous CV events, nearly all patients were receiving cardioactive therapies in secondary prevention.
The mean duration of follow-up was 3.6 years. After initiating the GLP-1 RA, median duration of treatment was 3.2 years in primary prevention and 2.5 years in secondary prevention (Table 3).Table 3Treatment patterns during follow-upPrimary preventionSecondary preventionN395155Duration of follow-up5.0 (0.25–10.8)3.6 (0–10.3)GLP-1 RA during follow-upaLixisenatide2 ($0.5\%$)0Exenatide BID6 ($1.5\%$)1 ($0.6\%$)Exenatide LAR12 ($3.0\%$)3 ($1.9\%$)Liraglutide257 ($65.1\%$)94 ($60.6\%$)Dulaglutide182 ($46.1\%$)80 ($51.6\%$)Semaglutide (weekly)63 ($15.9\%$)7 ($4.5\%$)Insulin degludec + liraglutide21 ($5.3\%$)5 ($3.2\%$)Duration of GLP-1 treatment, years3.2 (0–10.8)2.5 (0–10.3)GLP-1 RA discontinuation159 ($40.25\%$)63 ($40.65\%$)Reasons for GLP-1 RA discontinuationGI symptoms50 ($31.4\%$b)24 ($38.1\%$c)Inefficacy74 ($46.5\%$b)25 ($40.0\%$c)Noncompliance11 ($6.9\%$b)3 ($4.8\%$c)Other (e.g.: drug discontinued during hospitalizations, prescription expired, switch to SGLT2i…)11 ($6.9\%$b)3 ($4.8\%$c)Worsening of kidney function5 ($3.1\%$b)1 ($1.6\%$c)Malaise, fatigue, dizziness or myalgias4 ($2.5\%$b)2 ($3.2\%$c)Allergic or cutaneous reactions3 ($1.9\%$b)2 ($3.2\%$c)*Incident pancreatitis* or biliary disorders2 ($1.3\%$b)1 ($1.6\%$c)Tachycardia1 ($0.6\%$b)1 ($1.6\%$c)Treatment persistenceGLP-1 RA ongoing at year 1333 ($84.3\%$)121 ($78.1\%$)GLP-1 RA ongoing at year 2297 ($75.2\%$)104 ($67.1\%$)GLP-1 RA ongoing at year 4163 ($64.9\%$d)42 ($56\%$d)GLP-1 RA ongoing at year 6100 ($57.1\%$d)24 ($55.8\%$d)GLP-1 RA ongoing at year 865 ($60.2\%$d)14 ($50.0\%$d)GLP-1 RA ongoing at year 1015 ($53.6\%$d)3 ($75\%$d)Switched to a different GLP-1 RA133 ($33.7\%$)32 ($20.6\%$)Treated with a single molecule262 ($66.3\%$)123 ($79.4\%$)Switched to lixisenatide00Switched to exenatide BID00Switched to exenatide LAR6 ($1.5\%$)3 ($1.9\%$)Switched to liraglutide9 ($2.3\%$)2 ($1.3\%$)Switched to dulaglutide46 ($11.6\%$)17 ($11.0\%$)Switched to semaglutide56 ($14.2\%$)6 ($3.9\%$)Switched to insulin degludec + liraglutide15 ($3.8\%$)4 ($2.6\%$)Anti-diabetic medications taken with GLP-1 RAsMetformin359 ($90.89\%$)134 ($86.45\%$)Pioglitazone86 ($21.77\%$)15 ($9.68\%$)Acarbose9 ($2.28\%$)1 ($0.65\%$)Sulfonylureas/meglitinides158 ($40.00\%$)53 ($34.19\%$)Insulin109 ($27.59\%$)42 ($27.10\%$)SGLT-2 inhibitors13 ($3.29\%$)4 ($2.58\%$)Anti-diabetic medications after GLP-1 RA withdrawalN = 159N = 63Metformin139 ($86.3\%$b)51 ($82.3\%$c)Pioglitazone32 ($19.9\%$b)5 ($8.1\%$c)Acarbose8 ($5.0\%$b)2 ($3.2\%$c)Sulfonylureas/meglitinides84 ($52.2\%$b)26 ($41.9\%$c)Insulin100 ($62.1\%$b)43 ($69.4\%$c)SGLT-2 inhibitors70 ($44.03\%$b)25 ($39.68\%$c)DPP-4 inhibitors40 ($24.8\%$b)22 ($35.5\%$c)Severe hypoglycemic events5 ($1.0\%$; 3 patients with 1 episode, 1 patient with 2 episodes)0aDuring follow-up, patients could switch to different GLP-1 RAs from the ones prescribed at baselinebPercentage of those who discontinued the drug, $$n = 159$$ in primary preventioncPercentage of those who discontinued the drug, $$n = 63$$ in secondary preventiondThe percentages refer to the numbers of patients still on follow-up: year 4, primary prevention 251, secondary prevention 75; year 6 primary 175, secondary 43; year 8 primary 108, secondary 28; year 10 primary 28, secondary 4 Liraglutide and dulaglutide were the most frequently prescribed GLP-1 RAs (liraglutide: $65.1\%$ and $60.6\%$, dulaglutide: $46.1\%$ and $51.6\%$ in primary and in secondary prevention respectively). $33.7\%$ of the subjects in primary prevention and $20.6\%$ of those in secondary prevention were prescribed two or more different medications during follow-up, mostly because of switching from daily to weekly formulations.
The majority of the subjects in both groups were on metformin in association to the GLP-1 RA, more than one third was on sulfonylureas or meglitinides, about $27\%$ were on insulin, while pioglitazone was used by $21.8\%$ of those in primary prevention and by $9.7\%$ of those with a history of CV events (Table 3).
The percentage of patients still taking the GLP-1 RA gradually declined during follow-up, from $84\%$ in primary prevention and $78\%$ in secondary prevention at one year, to $65\%$ and $56\%$ at four years, and $60\%$ and $50\%$ at eight years, respectively (Table 3). The most common causes of drug discontinuation were either gastrointestinal side effects ($31\%$ of those who discontinued the GLP-1 in primary prevention and $38\%$ in secondary prevention) or inefficacy to achieve glycemic targets or weight loss ($46\%$ in primary prevention and $40\%$ in secondary prevention). With respect to glycemic control, target HbA1c was personalized according to the applicable guidelines at the time of the visit. Clinically relevant weight change was defined as a weight loss of at least $5\%$.
After GLP-1 discontinuation, half of the patients started a sodium-glucose cotransporter-2 (SGLT-2) inhibitor ($48\%$ in primary prevention and $58\%$ in secondary prevention), whereas the proportion of insulin and sulfonylurea users increased (insulin: $62\%$ and $69\%$, sulfonylureas/meglitinides: $52\%$ and $42\%$ in primary and in secondary prevention respectively). $25\%$ of those in primary prevention and $36\%$ of those in secondary prevention were switched to a DPP-4 inhibitor (Table 3).
## CV outcomes
During a median follow-up of 5.0 years in primary prevention and 3.6 years in secondary prevention, the primary composite outcome (MACE) occurred in 34 patients ($8.6\%$) in the first group and 32 patients ($20.7\%$) in latter. The total number of events in primary prevention was: 9 ($2.28\%$) myocardial infarctions or unstable anginas, 6 ($1.52\%$) strokes, and 19 ($4.81\%$) deaths. In those with a history of previous CV events, there were 11 ($7.10\%$) myocardial infarctions or unstable anginas, 6 ($3.87\%$) strokes, and 15 ($9.68\%$) deaths. Data are summarized in Table 4.Table 4CV events during follow-upPrimary preventionSecondary preventionn395155MACE34 ($8.61\%$)32 ($20.65\%$)Stroke6 ($1.52\%$)6 ($3.87\%$)Myocardial infarction or unstable angina9 ($2.28\%$)11 ($7.10\%$)All-cause death19 ($4.81\%$)15 ($9.68\%$)The main composite CV outcome (MACE) included non-fatal myocardial infarction or unstable angina, non-fatal stroke, all-cause death In the primary prevention group, after GLP-1 RA withdrawal, patients who experienced a MACE, compared to patients without events were taking less frequently metformin ($59\%$ vs. $92\%$, $$p \leq 0.001$$), pioglitazone ($0\%$ vs. $22\%$, $$p \leq 0.042$$) and SGLT-2i ($17.65\%$ vs. $50\%$, $$p \leq 0.018$$), while insulin users were more numerous ($88\%$ vs. $60\%$, $$p \leq 0.031$$). In the secondary prevention group, fewer patients who had a MACE were using SGLT-2i ($14.29\%$ vs. $53\%$, $$p \leq 0.013$$), while more patients were on insulin ($93\%$ vs. $63\%$, $$p \leq 0.044$$). Data are summarized in Table 5.Table 5Diabetes medications after GLP-1 RA withdrawalNPrimary preventionSecondary preventionMACENo MACEpMACENo MACEP171351443Metformin10 ($58.82\%$)124 ($91.85\%$)0.0019 ($64.29\%$)37 ($86.05\%$)0.115Pioglitazone030 ($22.22\%$)0.04204 ($9.30\%$)0.563Acarbose1 ($5.88\%$)6 ($4.44\%$)0.5511 ($7.14\%$)1 ($2.33\%$)0.434Sulfonylureas/meglitinides8 ($47.06\%$)78 ($57.78\%$)0.5526 ($42.86\%$)17 ($40.48\%$)0.875Insulin$\frac{15}{17}$ ($88.24\%$)81 ($60.00\%$)0.03113 ($92.86\%$)27 ($62.79\%$)0.044SGLT-2 inhibitors3 ($17.65\%$)67 ($49.63\%$)0.0182 ($14.29\%$)23 ($53.49\%$)0.013DPP-4 inhibitors2 ($11.76\%$)37 ($27.82\%$)0.2405 ($35.71\%$)17 ($39.53\%$)0.705 In the univariable Cox regression analysis for those in primary prevention (Table 6) older age (HR 1.10, $95\%$ CI 1.04–1.15, $p \leq 0.001$), longer diabetes duration (HR 1.07, $95\%$ CI 1.03–1.10, $p \leq 0.001$), the presence of kidney disease (HR 2.17, $95\%$ CI 1.10–4.31, $$p \leq 0.026$$), were associated to an increased HR for MACE, diastolic blood pressure at baseline (HR 0.96, $95\%$ CI 0.93–1.00, $$p \leq 0.046$$), and better kidney function at baseline (HR 0.97, $95\%$ CI 0.96–0.99, $p \leq 0.001$) were associated to a lower HR for MACE.Table 6Results of the univariable Cox regression analysisUnivariable analysisPrimary preventionSecondary preventionHR ($95\%$ CI)pHR ($95\%$ CI)pGender (male)0.91 (0.46–1.81)0.7801.17 (0.52–2.65)0.698Age, years1.10 (1.04–1.15)< 0.0011.05 (1.00–1.11)0.038SmokingNever11Current0.63 (0.21–1.82)0.3911.09 (0.40–3.01)0.864Former0.90 (0.39–2.10)0.8060.99 (0.44–2.20)0.976BMI, kg/m21.01 (0.95–1.07)0.7581.03 (0.97–1.09)0.378< 301130–350.46 (0.18–1.19)0.1081.69 (0.70–4.08)0.243> 350.66 (0.29–1.51)0.3251.34 (0.52–3.43)0.548sBP at baseline1.00 (0.98–1.02)0.9981.00 (0.98–1.02)0.817dBP at baseline0.96 (0.93–1.00)0.0460.98 (0.94–1.02)0.264FBG, mg/dl1.00 (1.00–1.01)0.3441.00 (0.99–1.01)0.915HbA1c, %1.08 (0.87–1.35)0.4711.04 (0.81–1.35)0.739HbA1c < $7\%$0.32 (0.08–1.35)0.1210.66 (0.20–2.22)0.507Total cholesterol, mg/dl1.01 (1.00–1.01)0.1681.01 (1.00–1.02)0.035HDL, mg/dl1.00 (0.97–1.04)0.8131.00 (0.97–10.04)0.831Triglycerides, mg/dl1.001 (0.998–1.004)0.5391.003 (0.999–1.008)0.174LDLc, mg/dl1.01 (1.00–1.02)0.1721.01 (1.00–1.02)a0.056eGFR, ml/min/1.73 m20.97 (0.96–0.99)< 0.0010.98 (0.96–1.00)0.022Diabetes duration, years1.07 (1.03–1.10)< 0.0011.02 (0.99–1.06)0.197Diabetes duration > 10 years2.13 (1.08–4.19)0.0291.51 (0.72–3.18)0.278 > $50\%$ coronary, carotid, or lower extremity artery stenosis1.96 (0.68–5.63)0.2134.00 (1.97–8.14)< 0.001Diabetic kidney disease2.17 (1.10–4.31)0.0261.91 (0.94–3.92)0.076Anti-diabetic medications taken with GLP-1 RAsMetformin + GLP-10.49 (0.17–1.41)0.1880.53 (0.20–1.39)0.196Sulphonylureas + GLP-12.01 (0.97–4.17)0.0621.49 (0.72–3.08)0.282Pioglitazone + GLP-10.35 (0.12–1.01)0.0520.94 (0.32–2.74)0.909Acarbose + GLP-13.72 (0.87–15.83)0.076NCInsulin + GLP-11.05 (0.50–2.19)0.9061.40 (0.68–2.90)0.363SGLT2i + GLP-1NCNCCardioactive therapies at baselineStatins0.70 (0.35–1.39)0.306Ezetemibe2.48 (0.75–8.17)0.134Antiplatelet agents0.90 (0.42–1.93)0.783Anti-hypertensives1.89 (0.73–4.90)0.190ACEi o ARBs o MRAs1.09 (0.53–2.24)0.811Other CV risk factorsArterial hypertension2.10 (0.74–5.97)0.164Dyslipidemia0.91 (0.45–1.82)0.780NC, not calculated; ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin II receptor blocker; MRA, mineralocorticoid receptor antagonist; CV, cardiovascular; LDL was calculated using the Friedewald formula; eGFR, estimated glomerular filtration rate calculated with the CKD-EPI formulaaMissing values in 27 subjects.
In secondary prevention, age (HR 1.05, $95\%$ CI 1.00–1.11, $$p \leq 0.038$$), the presence of an arterial stenosis > $50\%$ (HR 4.00, $95\%$ CI 1.97–8.14, $p \leq 0.001$), total cholesterol (HR 1.01, $95\%$ CI 1.00–1.02, $$p \leq 0.035$$), and kidney function (HR 0.98, $95\%$ CI 0.96–1.00, $$p \leq 0.022$$) were associated to MACE risk (Table 6).
In the multivariable analysis, we considered the use of GPL-1 RA as a time dependent variable.
In the primary prevention group, change in BMI and glycated hemoglobin did not have an impact on MACE risk, while age at the time of GLP-1 initiation (HR 1.08, $95\%$ CI 1.03–1.14, $$p \leq 0.001$$) and GLP-1 cessation by time (HR 3.40, $95\%$ CI 1.82–6.32, $p \leq 0.001$) increased the risk of MACE, corrected by year of start of GLP-1 RA treatment. The impact of diabetes duration was not strictly significant, but again suggested an increase of MACE risk (HR 1.03 $95\%$ CI 1.00–1.07, $$p \leq 0.078$$).
Regarding the secondary prevention group, only GLP-1 RA cessation by time (HR 2.71, $95\%$ CI 1.46–5.01, $$p \leq 0.002$$) increased the risk of MACE, corrected by year of start of GLP-1 RA treatment, while age at the time of GLP-1 RA initiation wasn’t strictly significant anymore, but still suggested an increased risk (HR 1.05, $95\%$ CI 1.00–1.10, $$p \leq 0.067$$). Data are shown in Table 7.Table 7Results of multivariable time dependent Cox regression analysis for MACEHR ($95\%$ CI)pPrimary preventionAge at beginning of GLP1, years1.08 (1.03–1.14)0.001GLP-1 RA withdrawal by time3.40 (1.82–6.32)< 0.001Secondary preventionGLP-1 RA withdrawal by time2.71 (1.46–5.01)0.002Results are adjusted by year.
## Glycemic control and weight changes
Baseline characteristics (sex, age, diabetes duration) and weight changes, fasting blood glucose, and HbA1c during follow-up in primary and secondary prevention are shown in Table 8, comparing subjects who discontinued or not GLP-1 RA. No significant differences were observed in sex and age both in primary and secondary prevention group, diabetes duration was significantly longer in the primary prevention group who discontinued the GLP-RA treatment. There were significant differences in metabolic parameters between GLP-1 RA persistent and non-persistent subjects mainly in the primary prevention group. Table 8Weight changes, fasting blood glucose, and HbA1c during follow-up in primary and secondary prevention, comparing subjects who continued the GLP-1 RA to those who discontinued treatmentPrimary preventionSecondary preventionnGLP-1 RA ongoingnGLP-1 RA discontinuedpnGLP-1 RA ongoingnGLP-1 RA discontinuedpSex (M)236121 ($51.3\%$)15972 ($45.3\%$)0.2439275 ($81.5\%$)6344 ($69.8\%$)0.091Age at baseline23662.2 ± 8.915960.7 ± 11.60.6199266.9 ± 7.16365.5 ± 8.80.352Diabetes duration at baseline2368.5 ± 6.615910.4 ± 8.30.0209211.1 ± 6.56313.4 ± 9.60.318Weight changes vs. baseline, kgYear 1226− 2.93 ± 5.11155− 1.66 ± 6.010.02192− 2.33 ± 4.9260− 2.16 ± 4.900.983Year 2230− 3.17 ± 6.12151− 1.42 ± 8.19< 0.00188− 2.18 ± 6.0558− 2.07 ± 5.820.713Year 4132− 4.14 ± 5.69115− 1.19 ± 8.26< 0.00140− 4.46 ± 6.0035− 0.75 ± 6.840.006Year 685− 5.37 ± 7.1488− 2.15 ± 8.030.02225− 5.00 ± 8.5818− 2.53 ± 9.530.475Year 847− 6.64 ± 9.0860− 1.82 ± 11.790.01015− 8.71 ± 12.5112− 3.77 ± 14.860.463Year 1010− 7.26 ± 11.1016− 2.46 ± 11.310.0362− 1.10 ± 1.132− 19.10 ± 21.780.333Final235− 2.75 ± 9.71157− 1.09 ± 9.770.00291− 2.69 ± 8.0162− 3.05 ± $8.990.67795\%$ CI final235− 4.00; − 1.50157− 2.63; 0.4591− 4.38; − 1.0162− 5.34;− 0.77Fasting blood glucose, mg/dlBaseline231169.7 ± 49.9156180.6 ± 58.60.05289158.6 ± 45.461166.9 ± 56.60.331Year 1219136.5 ± 31.6150156.5 ± 49.7< 0.00189133.5 ± 34.057150.9 ± 39.30.001Year 2225137.4 ± 31.0149167.2 ± 60.2< 0.00189131.6 ± 27.759156.7 ± 54.80.002Year 4132136.9 ± 32.0115164.1 ± 60.1< 0.00139146.4 ± 37.733152.1 ± 58.40.734Year 682141.6 ± 37.186156.0 ± 54.70.11424140.4 ± 41.818148.9 ± 47.60.806Year 843135.3 ± 40.759145.6 ± 40.90.04115136.4 ± 45.513156.0 ± 66.50.578Year 109121.8 ± 27.916148.3 ± 83.60.6072123.0 ± 17.02173.0 ± 33.90.333Final229141.1 ± 37.0152155.8 ± 57.50.00489135.8 ± 33.661154.2 ± 61.70.104Baseline vs. final< 0.001< 0.0010.0010.046HbA1c, %Baseline2348.09 ± 1.221548.41 ± 1.520.037877.62 ± 1.11638.23 ± 1.540.002Year 12236.90 ± 0.821527.58 ± 1.30 < 0.001896.86 ± 0.87587.73 ± 1.02< 0.001Year 22276.96 ± 0.941487.77 ± 1.62 < 0.001906.84 ± 0.74597,83 ± 1.39< 0.001Year 41336.88 ± 0.791148.01 ± 1.58< 0.001387.28 ± 0.95317.96 ± 1.330.014Year 6847.06 ± 1.07877.80 ± 1.39< 0.001247.21 ± 1.10187.81 ± 1.350.195Year 8466.75 ± 0.75607.88 ± 1.81< 0.001156.80 ± 1.07127.79 ± 1.040.004Year 10106.54 ± 0.53177.83 ± 1.540.01026.15 ± 0.3528.50 ± 0.280.333Final2337.08 ± 1.081517.76 ± 1.46< 0.001916.85 ± 0.82607.55 ± 1.400.002Baseline vs. final< 0.001< 0.001< 0.0010.001"Final" refers to the last visit available for each patient There were five severe hypoglycemic events, defined as hypoglycemia requiring external assistance for reversal, in four patients ($0.7\%$).
## Discussion
In this real-life study of type 2 diabetes patients initiating a GLP-1 RA, longer duration of GLP-1 RA treatment was associated to a lower rate of a composite of non-fatal myocardial infarction or unstable angina, non-fatal stroke, and all-cause death in both primary and secondary prevention. To date, several metanalyses of RCTs have consistently shown the cardiovascular benefit of GLP-1 RAs, and growing evidence from real-world studies is supporting the efficacy of these drugs also in broader populations and in less controlled environments than those of RCTs [12–15, 17–20]. In fact, recent real-world studies comparing the cardiovascular effects of GLP-1 RAs versus other glucose-lowering drugs have shown a significant reduction in the composite cardiovascular outcome, to an extent varying from 30 to $33\%$ [20, 21].
To date, the most convincing evidence of GLP-1 RA CV protection was obtained in secondary prevention populations, as CVOTs were initially designed as safety trials, and therefore enrolling subjects at very high CV risk was a strategy to increase the number of events and thus decrease trial duration and sample size. Specifically, the proportion of subjects in secondary prevention went from $70\%$ in EXCSEL to $80\%$ in SUSTAIN-6, and $100\%$ in ELIXA and Harmony. The REWIND trial (dulaglutide vs. placebo), which included $69\%$ of individuals in primary prevention, was an exception, and was the first study to suggest GLP-1 CV benefits even in subjects without a history of CV events. In fact, subgroup analyses from REWIND showed that MACE reduction in those treated with dulaglutide was similar in those with and without a history of previous CV events, but slightly missed statistical significance in both groups.
Similar to REWIND, the majority of the subjects included in our population was in primary CV prevention ($72\%$), and the protection offered by continued GLP-1 RA treatment was similar in those with and without previous CV events. Also, GLP-1 RA treatment withdrawal (i.e.: before MACE or end of follow-up), considered as a time-dependent variable, significantly increased the risk of MACE in both groups. Interestingly, two of the most recent meta-analyses that included subgroup analyses of subjects with or without established CVD, suggest similar favorable effects in patients with and without CVD, but with a smaller reduction in absolute risk in those in primary prevention [12, 14].
Over half of the patients in the cohort were able to continue a GLP-1 RA in the long term (four to ten years). These subjects exhibited long-term benefits in terms of glycemic control, with a significant decrease in HbA1c of approximately -$1\%$ that was maintained over time, and a significant weight loss which increased in magnitude over the years, reaching a maximum of − 6.6 kg at eight years of follow-up.
When analyzing the possible variables associated with cardiovascular outcomes, we found that discontinuation of GLP-1 RA treatment was independently associated to a strong significant increase in the risk of developing a MACE. This finding is consistent with and reinforces the recent hypothesis that GLP-1 RAs appear to exert CV protection as long as patients are exposed to the individual drug for a sufficiently long amount of time. Indeed, the percentage of time of exposure to the investigational GLP-1 RA in individual CVOTs showed a positive correlation with MACE absolute risk reduction [22].
We may also note that our patients showed a greater treatment persistence compared to other real-world studies from other countries. In a retrospective study from a large cohort (8698 patients) in the United States, over half of the patients initiating GLP-1 RA were non-adherent and the majority ($70.1\%$) discontinued therapy by 24 months [23]. Similarly, in another real-world study conducted in the UK among 589 patients initiating a GLP-1 RA, $45.2\%$ and $64.7\%$ of the subjects discontinued the treatment, at 12 and 24 months respectively [24].
Further, most of the patients during the observation period were treated with liraglutide ($65.1\%$ in primary prevention and $60.6\%$ in secondary prevention), dulaglutide ($46.1\%$ in primary prevention and $51.6\%$ in secondary prevention) and semaglutide ($15.9\%$ in primary prevention and $4.5\%$ in secondary prevention), with liraglutide being prescribed more during the first years of observation and then partially switched to the newer weekly formulations. Indeed, all these molecules in their CVOTs have shown a superiority versus placebo in reducing MACE [6, 7, 9]. We may infer that the nature of the GLP-1 RAs used (long-acting and with easy-to-use devices) and the persistence to treatment observed in our cohort may have favored an exposure time sufficient to induce the clinical CV benefit, thus corroborating the link between treatment persistence and MACE HR.
Concerning the withdrawal of treatment, $17\%$ of the patients ($$n = 96$$) experienced adverse events that led to drug discontinuation, in the vast majority being gastrointestinal symptoms ($13\%$), a proportion slightly higher than in most CVOTs, in which drug discontinuation due to adverse events varied from $4.5\%$ for once-weekly exenatide in EXSCEL (only gastrointestinal side effects were assessed) to $13.2\%$ for subcutaneous semaglutide in SUSTAIN-6 [7, 8]. In $18\%$ of the subjects, the molecule was withdrawn because glycemic or weight outcomes were not met, and therefore they were switched to a different class of diabetes medication. In this respect, in Italy, by regulatory decision, GLP-1 RAs were not allowed in combination with insulin until 2018, or with SGLT2-inhibitors until 2020. Therefore, patients needing a more intense treatment regimen had to be taken off GLP-1 RA. Indeed, $80\%$ of those who withdrew the GLP-1 RA for inefficacy used insulin; $59\%$ started a SGLT2i. Some concern may arise about the possible favorable interference deriving from SGLT-2i utilization, which have a proven CV protection [25], after GLP-1 RAs discontinuation. Indeed, if GLP-1 RA withdrawal by time is associated to an increased HR for MACE, the subsequent use of a SGLT2i could have partly mitigated (by adding CV protection) the effect of the sole GLP-1 RA discontinuation. In this view, we may speculate that if the patients who have discontinued the GLP-1 RA would have not subsequently started a SGLT-2i, they would have had an even greater risk of MACE.
Multiple mechanisms have been studied to explain the cardiovascular benefits of GLP-1 RAs. On one hand, GLP-1 RAs have been shown to effectively improve CV risk factors, including body weight, systolic blood pressure, LDL cholesterol and triglyceride concentrations, and glycemic control [16]. On the other hand, GLP-1 RAs also seem to directly interfere with atherogenic processes. Animal studies and studies on human cells indicate that GLP-1 RAs interfere with the atherogenesis process through GLP-1 receptors expressed by various cell populations involved in plaque development and rupture, including endothelial cells, monocytes, macrophages, and vascular smooth muscle cells [26–34]. The overall result is slower plaque progression and plaque stabilization. Epigenetic mechanisms may play a relevant role, since GLP-1 RAs may revert the DNA hypomethylation induced by hyperglycemia, once again leading to decreased activation of pro-inflammatory and pro-atherogenic pathways [35]. These mechanisms, involving anti-inflammatory and anti-atherogenic effects which lead to a plaque stabilization over time, may potentially explain the time-dependent CV risk reduction observed in our cohort. Remarkably, in our study population, longer duration of GLP-1 RA treatment positively affected metabolic outcomes in the primary CV prevention group, in accord with similar evidence reporting that GLP-1 treatment was statistically associated with a long-lasting decrease in HbA1c over time [36]. Indeed, tighter glycemic control alone could counteract the development of atherosclerosis in its early stages, but not in the presence of overt vascular damage [37] and intensive glucose control has been shown to reduce the risk of major CV events, mainly in type 2 diabetes patients without evidence of macrovascular disease [38]. Interestingly, an association between the magnitude of HbA1c reduction (versus placebo) and MACE hazard ratio in an analysis of GLP-1 RA CVOTs was previously suggested [39]. Also, a mediation analysis of the REWIND trial concluded that the improvement in HbA1c was a potential mediator of CV protection, responsible for up to $82\%$ of the total effect on MACE [40].
In fact, in our study, we observed a sustained reduction of HbA1c in patients treated with GLP-1 RA, with most values during follow up being under $7\%$. This could be considered a remarkable result in a real-life setting, in which the treatment targets have changed over the years according to guidelines’ updates.
We are aware that the lack of a control group prevents further speculations, but we may note that in our population the incidence of MACE was 26 per 1000 person-years, which is relatively close to the rate observed in the dulaglutide group of the REWIND trial (namely 35.8 per 1000 person-years), further corroborating the expected beneficial CV effect of GLP-1 RAs use [41].
This study has some limitations. The first is its retrospective nature which may carry the risk of measurements errors, non-standardized data elements and selection bias. Another limitation is the lack of a control group; to overcome this issue we applied a propensity score matching with the rest of diabetic population attending our center (i.e.: subjects treated with SGLT2 inhibitors or with DPP4 inhibitors or with insulin and never prescribed GLP-1 RAs), but the number yielded was too low to obtain reliable statistical results. Treatment adherence was self-reported by patients, which may raise concerns due to its vulnerability to social desirability and memory biases that tend to overestimate the degree to which patients execute medication regimens. Also, patients were followed at a single tertiary university center, thus involving a population that may be different to other territorial realities. These aspects may limit the generalizability of the conclusion drawn here.
Strengths of the present study include the relatively long follow-up period, compared to most CVOTs and real-world studies, and the real-world population of subjects, most of whom without established CVD. Additionally, at the time most subjects entered the study, the results from CVOTs were not yet available, and thus did not heavily affect the selection of patients based on CV risk parameters, but rather more based on metabolic parameters, such as HbA1c not at target, or need for weight loss.
## Conclusions
Our study showed that in a real-life population of type 2 diabetes patients, GLP-1 RAs may reduce the risk of MACE in a time-dependent manner, thus establishing an association between duration of treatment with GLP-1 RAs and CV outcomes. Since discontinuation of GLP-1 RAs increased the risk of MACE this treatment should be maintained over time, whenever possible.
Further investigations involving long term treatment with GLP-1 RA on larger real-world populations are warranted to confirm the time-dependent benefit on CV outcomes.
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|
---
title: 'Association Between Metabolic Syndrome and Decline in Cognitive Function:
A Cross-Sectional Study'
authors:
- Hissa N Alsuwaidi
- Ashraf I Ahmed
- Hamad A Alkorbi
- Sara M Ali
- Lina N Altarawneh
- Shooq I Uddin
- Sara R Roueentan
- Asmaa A Alhitmi
- Laiche Djouhri
- Tawanda Chivese
journal: Diabetes, Metabolic Syndrome and Obesity
year: 2023
pmcid: PMC10039709
doi: 10.2147/DMSO.S393282
license: CC BY 4.0
---
# Association Between Metabolic Syndrome and Decline in Cognitive Function: A Cross-Sectional Study
## Abstract
### Aim
We investigated whether metabolic syndrome (MetS) is associated with a decline in cognitive function in a cohort of middle-aged and elderly individuals without known cognitive dysfunction diseases in Qatar.
### Methods
We conducted a cross-sectional study on randomly selected participants aged 40–80 years from the Qatar Biobank, with data on cognitive tests and MetS components. Participants with a history of dementia, stroke, or mental disorders were excluded. MetS was diagnosed using the NCEP-ATP III criteria and cognitive performance was assessed using the Cambridge Neuropsychological Test Automated Battery (CANTAB). Two cognitive function domains were assessed. These are speed of reaction, measured using the Reaction Time (RT), and short-term visual memory, measured using the Paired Associate Learning (PAL) test. Multivariable logistic regression models were used to determine associations between MetS and poor speed of reaction and poor memory performance.
### Results
The mean age of the participants included was 49.8 years (SD 6.7). Of these, $51.9\%$ were females and $88.0\%$ were of Qatari nationality. Most of the 1000 participants had MetS ($$n = 302$$) or 1–2 MetS components ($$n = 523$$), whereas only 170 had no MetS components. There was a strong association between MetS and poor memory performance (OR 1.76, $95\%$ CI 1.04–2.96, $$P \leq 0.034$$), but a weaker association with poor speed of reaction (OR 1.5, $95\%$ CI 0.89–2.50, $$P \leq 0.125$$).
### Conclusion
In middle-aged and elderly individuals, MetS was strongly associated with diminished short-term visual memory, psychomotor coordination and motor speed.
## Introduction
Metabolic syndrome (MetS) is a cluster of metabolic risk factors known to increase the risk of cardiovascular disease and death, and is a global problem that affects nearly 20–$25\%$ of adults1 and about $28\%$ of Qatari adults.2 The risk factors, which are the components of MetS, include raised blood pressure, visceral obesity, dysglycemia and dyslipidaemia. Although MetS is mostly known as a risk factor for cardiovascular disease,3 there is some evidence that it may lead to worsening cognitive function, especially in older adults. However, the association between MetS and cognitive dysfunction remains debatable.6 It may not be possible to restore cognitive function if it declines, especially in older adults.4 Therefore, identifying individuals who are susceptible to cognitive dysfunction would be of great value to reduce the associated decrease in quality of life. One prospective cohort study of Danish postmenopausal women showed that impaired fasting plasma glucose was the one metabolic risk factor that showed a strong association with cognitive dysfunction.5 However, results from other longitudinal studies were inconclusive, perhaps due to differences in age, gender, genetic predisposition, length of follow-up, and type of cognitive test. Although one meta-analysis showed that MetS increases the risk of mild cognitive impairment,6 the findings were not conclusive, and this area remains under-researched, therefore, warrants further investigations.
Several important knowledge gaps remain concerning MetS and cognitive function. First, data on the association between cognitive dysfunction and MetS are scarce in middle-aged individuals.7 In addition, the association between MetS and cognitive dysfunction remains debatable. Indeed, some studies have reported that MetS is an independent risk factor for cognitive impairment, whereas other studies found no significant association between MetS and cognitive function. Further, findings on associations between MetS and domain-specific cognitive function remain limited.6 Lastly, data on the association between MetS and cognitive function is lacking in the Middle East and North Africa regions, which are regions with a high prevalence of MetS. Therefore, the aim of this current study was to contribute to this relatively under-researched field in the Middle East by investigating the association between MetS and decline in cognitive function in middle-aged and elderly participants without any cognitive disorders in Qatar. Additionally, we investigated the association between the individual components of MetS and decline in cognitive function.
## Study Design and Setting
We conducted a cross-sectional study that included participants who were randomly chosen from the Qatar Biobank (QBB), which is a nationwide longitudinal cohort and repository of samples and data on different aspects of health and lifestyle of participants, who are citizens and long-term residents of Qatar.8 The design and methods of the QBB have been described previously.8 In the current study, we will refer to the methods used by the parent QBB. The data that were obtained from QBB included data on the Cambridge Neuropsychological Test Automated Battery (CANTAB), demographics, laboratory results, and comorbidities. Men and women aged 40–80 years who had cognitive test results were included in this study. Participants with a history of dementia, stroke, or mental disorders were excluded.
## Collected Data
The following data were obtained for each participant from the QBB: demographic data, such as age, gender, education level, marital status, nationality, occupation, and family medical history. Data on chronic disease history, blood pressure and anthropometric measurements (BMI, waist-to-hip ratio, systolic blood pressure, diastolic blood pressure, mean arterial pressure, and use of antihypertensive medication), history of cardiovascular diseases (confirmed diagnosis of stroke and acute coronary syndrome), data on diabetes (confirmed diagnosis of diabetes, fasting glucose levels, glycated haemoglobin, and use of diabetic medications), and data on dyslipidaemia (levels of HDL, LDL, cholesterol, triglycerides, and use of lipid-lowering agents). Further, data on smoking history (cigarette, shisha, passive smoking), diet, physical activity, and cognitive test data were obtained.
## Metabolic Syndrome (MetS)
MetS was diagnosed using NCEP-ATP III criteria.9 For participants to be considered to have MetS; they must have three or more of the following risk factors: dysglycaemia (HbA1C ≥ $5.7\%$) or previously diagnosed with type 2 diabetes mellitus, raised blood pressure (systolic ≥ 130 mmHg and/or diastolic ≥ 85 mm Hg), dyslipidaemia which is defined as triglycerides ≥1.7 mmol/L or low HDL-C (men – less than or equal 1.03 mmol/l, women – less than or equal 1.3 mmol/l), and abdominal obesity (waist circumference ≥ 102 cm in men, waist circumference ≥ 88 cm in women). Participants in this study were divided into three groups; those with MetS (at least 3 MetS components), those with 1–2 MetS components but not meeting the strict definition of MetS and those without any MetS components.
## Assessment of Cognitive Function
The CANTAB (Cambridge Neuropsychological Test Automated Battery) system was used to measure cognitive function. Two tests were used to assess cognitive function at the Qatar Biobank, which are the Reaction Time (RT) test that measures psychomotor coordination and motor speed, and the Paired Associate Learning (PAL) test that measures short-term visual memory.10 The RT consists of 60 trials per participant, where the participant must keep their finger on a button that is presented at the bottom of the screen. The participants must then react as fast as they can to select the target that will be presented as a small box within one of the two larger black boxes. This test evaluates the time needed to respond and the total mistakes a participant made in those 60 trials. *We* generated a novel reaction score, which was the product of the total mistakes a participant made in the 60 trials multiplied by the time they took. *The* generated score reflects both variables collectively, and therefore the participant with the lowest score had the best performance.
On the other hand, the PAL test evaluates the visual short-term memory function of participants via displaying a series of images located within certain boxes on the screen in which participants must memorize them within a short time period. The images displayed in the boxes were then hidden. After that, the participant would see a certain image in the middle of the screen where they are expected to match this image to the box that displayed it. The test involves a total of 7 levels, and the participant moves to a higher level of difficulty with more boxes if all the shapes were matched correctly. However, a total of 10 unsuccessful attempts at any level would terminate the test. This test reports the number of attempts needed to correctly identify the location of each target at any level, the maximum level of difficulty reached by the participant, and the total time needed to end the test. Based on the test results, we generated a novel memory scoring scheme that incorporates all the aforementioned variables to rank the participants from 1 to N by sorting on levels of difficulty, total mistakes, and time taken (in that order), with difficulty level given the highest priority and time taken given the least priority. *The* generated score reflected all variables, as shown in the example in Supplementary Table 1. The numerical ranking was then converted into a percentile and those below the 25th percentile were considered to have significantly lower cognitive function, compared to the rest of the sample. The participants were then classified into poor memory performance (below 25th percentile) and normal, and this served as the binary variable for our analysis.
## Statistical Analysis
First, participants were divided into two groups, one with MetS and the other without MetS, according to NCEP-ATP III criteria. Then, we subdivided the latter group into those with 1–2 MetS components and those who were totally free of the MetScomponents of interest. The participants’ demographics and characteristics as well as their performance scores in both tests (RT and PAL) were compared between the three groups. For categorical variables, frequencies and proportions were used to summarise the data. The Chi-squared test and Fisher's Exact (when there were small frequencies) were used to compare categorical variables. The continuous data were first tested for normality using histograms. Continuous variables were described as mean and standard deviations (SD) if normally distributed or as median and interquartile ranges if the variables were not normally distributed. ANOVA was used to compare continuous variables across the MetS groups if data were normally distributed. For the variables that were not normally distributed, comparisons were tested using the Kruskal–Wallis test. Post hoc tests were done with the Bonferroni correction for the comparison groups only when ANOVA P≤0.05.
We compared cognitive function by MetS status using the generated scores for both tests (RT & PAL). For the primary analysis, the presence of MetS was defined as the independent variable to predict the changes in the performance scores in either of the tests (dependent variables). Both continuous outcome scores did not meet the assumptions of linear regression, as their residuals were skewed. Thus, we identified cut-off values for RT and PAL scores using the quartiles to categorize the poorest performers in the two cognitive tests. For the PAL test, a cut-off value below the 25th percentile was indicative of poor memory performance in comparison to all participants. A score value above the 75th percentile reflected poor reaction performance for the RT test. The cut-offs were then used to categorize the continuous performance scores, into binary outcomes. We used logistic regression to investigate the association of metabolic syndrome with a poor performance score in each test independently. The components of metabolic syndrome such as (dysglycemia, dyslipidaemia, elevated blood pressure, and abdominal obesity) were also evaluated for associations with poor performance in either the RT or PAL test. Odds ratios (ORs), their $95\%$ CIs, and P-values were reported. We adjusted for confounders identified through a directed acyclic graph (DAG), namely; age, sex, income, physical activity, diet, cigarette smoking, education, and shisha smoking.
## Ethics
Ethics approval for this study was provided by the Qatar Biobank (QBB) (Ref - EX-2019-RES-ACC-0182-0107). During their participation in the QBB, participants gave written informed consent. The research also received waiver of ethics approval from the Qatar University Institutional Review Board (Ref - QU-IRB 1223-E/20).
## Characteristics of Participants by Metabolic Syndrome
Of the 1000 participants included in the present study, 175 were with no MetS components, 523 with 1–2 MetS components, and 302 with MetS. The characteristics of the participants are shown in Table 1. Overall, the mean age was 49.8 years (SD 6.7 years, range 41–69 years), $51.9\%$ were females and $88.0\%$ were of Qatari nationality. Post hoc analysis showed that the MetS group had a higher median age, increased BMI, raised waist circumference, elevated HbA1c, raised triglycerides, low HDL, and raised compared to the other two groups, with strong evidence against the null hypothesis in all these comparisons (Supplementary Table 2). Table 1Characteristics of Participants by Metabolic SyndromeFactorNo MetS (No Components)No MetS (1–2 Components)MetSP-valueN175523302Age, median (IQR)46.0 (43.0, 49.0)48.0 (44.0, 54.0)50.0 (46.0, 56.0)<0.001BMI, mean (SD)27.0 (3.7)29.6 (5.2)32.3 (5.3)<0.001Gender Female100 ($57.1\%$)263 ($50.3\%$)156 ($51.7\%$) Male75 ($42.9\%$)260 ($49.7\%$)146 ($48.3\%$)0.29Nationality Non-Qatari17 ($9.7\%$)58 ($11.1\%$)45 ($14.9\%$) Qatari158 ($90.3\%$)465 ($88.9\%$)257 ($85.1\%$)0.16Waist Circumference Normal175 ($100.0\%$)346 ($66.2\%$)82 ($27.2\%$) Raised0 ($0.0\%$)177 ($33.8\%$)220 ($72.8\%$)<0.001HbA1c Normal175 ($100.0\%$)250 ($47.8\%$)47 ($15.6\%$) Elevated0 ($0.0\%$)273 ($52.2\%$)255 ($84.4\%$)<0.001Triglycerides Normal175 ($100.0\%$)411 ($78.6\%$)99 ($32.8\%$) Raised0 ($0.0\%$)112 ($21.4\%$)203 ($67.2\%$)<0.001HDL Normal175 ($100.0\%$)409 ($78.2\%$)98 ($32.5\%$) Decreased0 ($0.0\%$)114 ($21.8\%$)204 ($67.5\%$)<0.001Blood pressure Normal175 ($100.0\%$)423 ($80.9\%$)150 ($49.7\%$) Raised0 ($0.0\%$)100 ($19.1\%$)152 ($50.3\%$)<0.001Diet Less than 3 times/ week167 ($97.1\%$)497 ($96.7\%$)290 ($97.0\%$) More than 3 times/ week5 ($2.9\%$)17 ($3.3\%$)9 ($3.0\%$)0.95Physical activity Yes48 ($27.4\%$)143 ($27.3\%$)63 ($20.9\%$) No127 ($72.6\%$)380 ($72.7\%$)239 ($79.1\%$)0.095EducationBelow tertiary education48 ($27.4\%$)197 ($38.0\%$)129 ($42.9\%$)Tertiary education and above127 ($72.6\%$)322 ($62.0\%$)172 ($57.1\%$)0.004Cigarette smoking Smokers24 ($21.6\%$)66 ($20.6\%$)42 ($23.1\%$)0.63 Ex-smokers12 ($10.8\%$)38 ($11.8\%$)28 ($15.4\%$) Non-smokers75 ($67.6\%$)217 ($67.6\%$)112 ($61.5\%$)Shisha smoking Yes41 ($36.3\%$)117 ($36.0\%$)57 ($31.3\%$)0.53 No72 ($63.7\%$)208 ($64.0\%$)125 ($68.7\%$)
## Comparison of Memory Performance by MetS Status
Table 2 and Figure 1 show comparisons of memory performance by MetS status. The group with MetS had worse memory test perfomance, with a lower median memory performance score compared to the group with no MetS (median 42.9, IQR 21.4–67.3, median 62.3, IQR 38.4–83.8; respectively), with very strong evidence against the null hypothesis ($P \leq 0.001$). Further, the group with MetS had a lower memory performance median compared to the group with MetS 1–2 components (median 42.9, IQR 21.4–67.3, median 48.6, IQR 24.2–74.9; respectively), with little evidence against the null hypothesis at this sample size ($$P \leq 0.117$$). In addition, the group with 1–2 MetS components also had a lower memory performance median compared to the group with no MetS (median 48.6, IQR 24.2–74.9, median 62.3, IQR 38.4–83.8; respectively), with strong evidence against the null hypothesis ($$P \leq 0.003$$). Table 2Comparison of Memory Performance and Speed of Reaction Score by Metabolic SyndromeFactorNo MetS (No Components)No MetS (1–2 Components)With MetSP-valueN175523302Memory performance, median (IQR)62.3 (38.4, 83.8)48.6 (24.2, 74.9)42.9 (21.4, 67.3)$P \leq 0.001$*Speed of reaction score, median (IQR)82.5 (49.2, 168.3)87.7 (51.8, 168.8)105.7 (55.9, 219.2)$P \leq 0.01$**Notes: Post hoc analysis (using Bonferroni adjustment): *MetS vs No MetS (1–2 components), $$P \leq 0.117.$$ MetS vs No MetS (no components), $P \leq 0.001.$ No MetS (1–2 components) vs No MetS (no components), $$P \leq 0.003.$$ ** MetS vs No MetS (1–2 components), $$P \leq 0.012.$$ MetS vs No MetS (no components), $$P \leq 0.007.$$ No MetS (1–2 components) vs No MetS (no components), $$P \leq 0.586.$$ Figure 1Box plot showing comparison of memory performance score by MetS status. NB: The memory score is a ranking of participants based on highest difficulty level reached, total mistakes, and the total time taken to complete the test. Participants who reached to the maximum level of difficulty with lowest mistakes and shortest duration of time were assigned a higher ranking in comparison to the other participants and the participant with the lowest rank had the worst memory performance score.
For MetS components, participants with dysglycaemia, raised blood pressure, and raised waist circumference performed worse, with lower median scores in memory performance, compared to those without those components (Supplementary Table 3, and Supplementary Figure 1). However, there were no differences in memory performance scores between participants with dyslipidaemia (low HDL, raised triglycerides), and those without dyslipidaemia (Supplementary Table 3, and Supplementary Figure 1).
## Comparison of Speed of Reaction by MetS Status
Table 2 and Figure 2 show comparisons of speed of reaction by MetS status. The group with MetS had worse speed of reaction, with a higher speed of reaction score median compared to the group with no MetS (median 105.7, IQR 55.9–219.2, median 82.5, IQR 49.2 −168.3; respectively), with strong evidence against the null hypothesis ($$P \leq 0.007$$). Further, the group with MetS had worse speed of reaction, with a higher speed of reaction score median compared to the group with 1–2 MetS components (median 105.7, IQR 55.9–219.2, median 87.7, IQR 51.8 −168.8; respectively), with moderate evidence against the null hypothesis ($$P \leq 0.012$$). However, there were no clinically and statistically significant differences in the speed of reaction score between the individuals with no MetS and those with 1–2 MetS components (Table 2 and Figure 2). Figure 2Box plot showing comparison of speed of reaction performance by MetS status. NB: The reaction score is a product of the total mistakes a participant made in 60 trials multiplied by the time they took. Therefore, the participant with the lowest score had the best performance.
For MetS components, participants with dysglycaemia and raised waist circumference had a worse speed of reaction performance with higher median scores compared to those without those components (Supplementary Table 3, and Supplementary Figure 2). However, there were no differences in speed of reaction scores between participants with dyslipidaemia (low HDL, raised triglycerides), and raised blood pressure compared to those without the components of interest (Supplementary Table 3, and Supplementary Figure 2).
## Memory Performance
After multivariable logistic regression, MetS was associated with clinically significant higher odds of poor memory performance compared to having no MetS (OR 1.76, $95\%$ CI 1.04 −2.96), with moderate evidence against the null hypothesis ($$P \leq 0.034$$). Having 1–2 MetS components showed a $62\%$ increase in the odds of poor memory perfomance (OR 1.62, $95\%$ CI 1.00–2.66), but with weak evidence against the null hypothesis ($$P \leq 0.051$$) (Table 3). Furthermore, all components of MetS, except triglycerides, showed an increase in odds of poor memory performance, albeit, with little evidence against the null hypothesis (Supplementary Table 4). Upon stratification by education level, compared to those without MetS, participants with MetS who did not attain tertiary education showed a 2.5-fold increase in the odds of having poor memory performance score (OR 2.51, $95\%$ CI 1.08 −5.83), with moderate evidence against the null hypothesis ($$P \leq 0.032$$), while weaker associations were observed in those with tertiary education (Supplementary Table 5). Table 3Association Between Metabolic Syndrome and Cognitive Dysfunction – Multivariable Logistic RegressionUnadjusted AnalysisAdjusted AnalysisOR$95\%$ CIP-valueOR$95\%$ CIP-valueMemory performance scoreNo MetS componentsRef1–2 MetS components2.081.31 −3.320.0021.621.00 −2.660.051MetS2.561.57 −4.180.0011.761.04 −2.960.034Constant (baseline odds)0.170.89Speed of reaction scoreNo MetS componentsRef1–2 MetS components1.200.76 −1.890.4421.110.64–1.800.677MetS1.721.06–2.770.0271.500.89–2.500.125Constant (baseline odds)0.200.46Note: Adjusted for age, sex, income, physical activity, diet, cigarette, and shisha smoking, education.
## Speed of Reaction
After multivariable logistic regression, MetS was associated with a $50\%$ increase in the odds of poor speed of reaction (OR 1.5, $95\%$ CI 0.89–2.50), but with weak evidence against the null hypothesis ($$P \leq 0.125$$). Having 1–2 MetS components was also associated with higher odds of poor speed of reaction, but this was neither clinically nor statistically significant (OR 1.11, $95\%$ CI 0.64 −1.80, $$P \leq 0.677$$) (Table 3). In stratified analysis, in participants who did not attain tertiary level education, compared to having no MetS, MetS showed an almost 3-fold significantly higher odds of having poor speed of reaction score (OR 2.9, $95\%$ CI 1.18–7.15, $$p \leq 0.021$$), while no association was observed in those with tertiary education (Supplementary Table 5). For MetS components, dysglycaemia and raised waist circumference were associated with higher odds of poor speed of reaction, while the other components did not show significant associations with poor speed of reaction (Supplementary Table 4).
## Discussion
In this cross-sectional analysis of middle-aged and elderly adults without cognitive dysfunction diseases in Qatar, we found that MetS was associated with poor cognitive function in the domains of psychomotor coordination and motor speed and short-term visual memory. In addition, we found that certain MetS components, such as dysglycaemia and raised waist circumference were associated with poor psychomotor coordination and motor speed. In stratified analysis, MetS was associated with poor cognitive function in those withouttertiary education, but not in those with tertiary education.
We found a strong association between MetS and poor short-term visual memory, with MetS increasing the odds of poor performance in the PAL memory test by $76\%$. Our findings were in line with other studies.11,12 The precise mechanisms by which MetS could influence cognitive functions remain unknown, but it has been hypothesised that there are neuroanatomical changes in individuals with MetS and that these are likely due to MetS-induced alterations in the hippocampal region of the brain, which plays an essential role in memory.13,14 It has also been suggested that increased inflammation and endothelial dysfunction in patients with MetS increase the risk for neurological degeneration.15 Among all MetS components, blood pressure was the most associated with increased odds of poor memory function in MetS patients with an OR of 1.4, followed by dysglycemia, HDL, and raised waist circumference. However, we observed no association with raised triglycerides. The association of hypertension with poor short term memory in individuals with MetS has been poorly studied, and research is scarce. However, previous explanatory models hypothesised that increased carotid artery stiffness is associated with cognitive impairment, specifically vascular dementia.16 For dysglycemia, a previous study performed on middle-aged patients identified that dysglycemia was clinically associated with less grey matter density and reduced glucose metabolism in the frontotemporal regions of the brain.17 Further, in line with our findings, a cohort study following 1147 patients for 7 years found no association between levels of triglycerides and cognitive dysfunction.18 However, in contrast to the findings of these studies, a recent study that involved participants from Tianjin, China, aged 45 years and older with poor income showed an association between high triglycerides and cognitive decline.19 The researchers of the Chinese study19 suggested that the differences between these various studies might be related to the nutritional and metabolic status of the different populations. Other studies that have used triglyceride-glucose index (TyG), an insulin resistance biomarker (calculated from triglyceride values and fasting serum glucose), reported that an increased TyG index is strongly associated with the risk of developing cognitive decline.20,21 Another study found that in participants with type 2 diabetes, the TyG index could be used to predict the risk of developing mild cognitive decline.22 In the present study, we found that participants with MetS had a $50\%$ increase in odds of poor speed of reaction scores, although this change was not statistically significant. This finding is consistent with that of a previous study that reported an association between MetS and poor speed of reaction in the elderly.23 It has been suggested that speed of reaction is altered due to changes in fractional anisotropy, which is a measure of connectivity in the brain.23 We observed that the association between individual components of MetS and speed of reaction differed across the different risk factors. Participants with raised waist circumference had the highest odds of poor speed of reaction scores with an OR of 1.53, followed by dysglycemia with an OR of 1.38. To the best of our knowledge, there have been no studies that have investigated the association between waist circumference in MetS and cognition. However, a few previous studies that were conducted specifically on individuals with diabetes mellitus have found an association between waist circumference and cognitive impairment and dementia.24,25 One study showed that participants with obesity had damaged brain structure on neuroimaging, which included loss of brain volume and brain atrophy particularly of the grey matter, suggesting that inflammation related to obesity might cause cognitive decline.26 It should be noted that excess macronutrients of the adipose tissues stimulate the release of inflammatory mediators, such as tumor necrosis factor α and interleukin 6, and reduce the production of adiponectin, predisposing to a pro-inflammatory state and oxidative stress.27 The increased level of interleukin 6 stimulates the liver to synthesize and secrete C-reactive protein.27 *As a* risk factor, inflammation is an imbedded mechanism of developing cardiovascular diseases including coagulation, atherosclerosis, MetS, insulin resistance, and diabetes mellitus.27 Our current findings extend those from other studies of waist circumference by showing an association with MetS and distinguishing among cognitive domains, namely speed of reaction, which reflects connectivity in the brain. These findings indicate the importance of clinical screening for cognitive decline and early intervention in middle-aged patients with MetS to slow the progression of memory decline. Interventions and cognitive function screening and interventions in those at high risk of cognitive impairment should be added to the management of MetS.28 A narrative review concluded that dietary intervention and support of the gut-brain axis are potential early interventions to decrease the risk of developing cognitive dysfunction.29 *It is* noteworthy that interest in investigating the interaction between gut microbiome and neurological function is increasing. Specific microbial-derived metabolites present in individuals with MetS, such as trimethylamine N-oxide (TMAO), have been associated with cognitive decline.30–32 The precise underlying mechanism through which this occurs remains to be unidentified. However, animal studies have suggested that the metabolite leads to increased oxidative stress and neuroinflammation, and that the hippocampus area is affected.33,34 Given the well-established role of the hippocampus in memory formation,35 changes in its structure and/or function might contribute to the high increase in the odds of poor memory performance in the participants with MetS found in the present study. Additionally, education has been implicated to provide better resilience of cognition, thus, better cognitive reserve at higher levels of brain maintenance.36 Upon stratifying for education, we found that in participants with MetS who did not attain tertiary education, there was an almost 3-fold increase in odds of poor speed of reaction, and an odds of 2.51 for poor memory performance. In a longitudinal study conducted on an older population, it was found that in participants with higher education there was lesser rapid decline in cognition early in follow-up, however, with moderately faster decline in later follow-up.37 One limitation of our study is that the CANTAB tests done by the QBB only provided information about memory performance and speed of reaction, without testing other cognitive domains. The cross-sectional nature of this study implies that temporality cannot be assumed in this study. There was also lack of concurrent functional MRI of brain activity, which could identify any early cerebral abnormalities, especially in the middle-aged population.38 To our knowledge, this is also the first study to investigate the risk of developing cognitive decline in middle-aged individuals with MetS in the Middle East, and therefore adds a valuable contribution to this under-researched field. Future studies are needed to study the required length of exposure to MetS to develop cognitive dysfunction. In addition, further research can investigate if early administration of therapeutic agents would be effective in preventing cognitive decline.
## Conclusion
In middle-aged and elderly participants in the Middle East, MetS and some of its components were strongly associated with diminished short-term visual memory, psychomotor coordination and motor speed. Our findings suggest a need for screening and prevention of cognitive function in individuals with MetS, not only in the older populations, but also in the middle-aged.
## Disclosure
The authors report no conflicts of interest in this work.
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|
---
title: Increase in serum brain-derived neurotrophic factor levels during early withdrawal
in severe alcohol users
authors:
- Andrei Garziera Valerio
- Felipe Ornell
- Vinicius Serafini Roglio
- Juliana Nichterwitz Scherer
- Jaqueline Bohrer Schuch
- Giovana Bristot
- Flavio Pechansky
- Flavio Kapczinski
- Felix Henrique Paim Kessler
- Lisia von Diemen
journal: Trends in Psychiatry and Psychotherapy
year: 2022
pmcid: PMC10039723
doi: 10.47626/2237-6089-2021-0254
license: CC BY 4.0
---
# Increase in serum brain-derived neurotrophic factor levels during early withdrawal in severe alcohol users
## Abstract
### Introduction
Changes in brain-derived neurotrophic factor (BDNF) have been linked to the neuroadaptative consequences of chronic alcohol use and associated with disease severity and prognosis. Few studies have evaluated the influence of drug withdrawal and clinical and sociodemographic data on BDNF levels in severe alcohol users.
### Objectives
Our goals were [1] to evaluate variation in BDNF levels during alcohol withdrawal and, [2] to assess the influence of putative confounding factors on BDNF levels.
### Methods
Our sample consists of 62 men with alcohol use disorder undergoing a detoxification process. Serum BDNF levels were measured using a commercial sandwich-ELISA kit, at two points: before and after the detoxification period.
### Results
We found an increase in BDNF levels during alcohol withdrawal (25.4±9.6 at admission vs. 29.8±10.2 ng/ml at discharge; $p \leq 0.001$), even after controlling for potential confounders (positive family history, number of days between blood sample collections, and age) (Generalized Estimating Equation: coefficient = -4.37, $95\%$ confidence interval [$95\%$CI] -6.3; -2.4; $p \leq 0.001$). Moreover, individuals who had first-degree relative with alcohol dependence had smaller increases in BDNF levels than individuals with no family history (14.8 [$95\%$CI -5.3; 35.6] vs. 35.3 [$95\%$CI 15.4; 74.8]; $$p \leq 0.005$$).
### Conclusions
In summary, variation in BDNF levels seems to be influenced by withdrawal in severe alcohol users. A positive family history of alcohol dependence could also be a factor that influences variation in this biomarker.
## Introduction
The pathogenesis of substance use disorder (SUD) involves many biological mechanisms and neuroadaptive changes, with notable involvement of neurotrophins. Brain-derived neurotrophic factor (BDNF) is the most abundant neurotrophin in the human brain and is associated with neurogenesis, cognitive functions, cerebral neuroplasticity, learning, and memory. 1 - 3 Consistent evidence shows changes in BDNF regulation underlying several behaviors and psychiatric disorders. 4 In fact, changes to serum and plasma BDNF levels were observed in individuals with SUD. 5 Moreover, it has been shown that severity of drug abuse was inversely correlated to BDNF levels, 6 - 9 suggesting that BDNF could be a prognostic marker in SUD. 10 - 12 Alcohol use disorder is the most prevalent SUD, with a prevalence of $5.1\%$ among adults, affecting approximately 283 million people worldwide. 13 Lower levels of BDNF have been observed among current alcohol users, but studies are still inconsistent and controversial, depending on the characteristics of the samples. 11, 14 - 16 During the withdrawal phase, some studies detected a small increase in serum BDNF levels, 9, 17 while others studies show decreases in this neurotrophin during the first days of alcohol abstinence. 18, 19 In addition, lower levels of BDNF were found in individuals with delirium tremens (DT), even after detoxification. 7 A follow-up study showed that individuals who were abstinent for 180 days had higher levels of serum BDNF compared to baseline measures and compared to those who relapsed during this same period. 11 Beyond use of drugs, other factors may also be related to the BDNF variation during alcohol abstinence and could be influencing the results detected so far, including age, sex, and age at first drug use, 5 presence of psychiatric disorders, 20, 21 neurodegenerative diseases, chronic inflammatory state, 22 tobacco consumption, 23 family history of alcohol, 24, 25 and genetic predisposition. 26, 27 Currently, there are no biomarkers that can predict the overall severity or disease stage in SUD, although assessment of peripheral biomarkers in specific populations might shed light on the relationship between such markers, including BDNF, and clinical characteristics and disease progression.
The overall scenario suggests that BDNF could be a candidate biomarker of severity and prognosis in alcohol addiction. Nonetheless, only a few studies have evaluated BDNF levels in severe alcohol users during early withdrawal. In this sense, our main goal was to evaluate the variation of BDNF levels before and after alcohol withdrawal in individuals with alcohol use disorder during an inpatient treatment program. The influence of putative confounding factors on BDNF levels during alcohol withdrawal was also assessed.
## Sample selection
Alcohol users were recruited at the Álvaro Alvim Unit, a specialized service for the treatment of addiction in male patients at the Hospital de Clínicas de Porto Alegre (HCPA), a public hospital located in Southern Brazil. The study was approved by the HCPA Institutional Review Boards and Ethics Committees (Number 14-0249), and all subjects enrolled provided written informed consent.
Inclusion criteria were: [1] a diagnosis of alcohol use disorder according to the criteria from the fourth version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV); [2] age 18 years or older; and [3] consent to provide two blood samples during inpatient treatment. Individuals were excluded if they were unable to participate or understand the research protocol, judged on clinical status. Individuals were recruited consecutively between October 2013 and May 2016, during which time all patients admitted were invited to participate in the study.
The research protocol was applied by trained junior researchers, after initial detoxification and stabilization of withdrawal symptoms. Sociodemographic data and psychiatric disorders respectively were assessed using the Addiction Severity Index – 6th Version (ASI-6), previously validated for Brazilian Portuguese, 28 and the Structured Clinical Interview for DSM-IV. These interviews were conducted between the fifth and 12th days in hospital. Initially, 94 inpatients agreed to participate in the study. However, only 62 individuals completed the research protocol and provided two blood samples (one at hospital admission and another one while in hospital) and were therefore included in the study.
## Blood collection and processing
Two blood samples were collected. The first blood sample was collected within the initial 24 hours after admission and the second was taken after 15 days in hospital. For both samples, ten milliliters of blood were collected from each patient after 8h fasting by venipuncture into an anticoagulant-free vacuum tube. Immediately after collection, blood samples were centrifuged at 4000rpm for 10 min and the serum was aliquoted, labeled, and stored at -80ºC until assay testing.
## BDNF measurement
Serum BDNF levels were measured by sandwich-ELISA using a commercial kit, according to the manufacturer’s instructions (Millipore, USA). Briefly, microtiter plates (96-well, flat-bottom) were incubated overnight at 4ºC with the samples diluted 1:75 in sample diluent and standard curve ranging from 15.63 to 1000 pg/mL of BDNF. Plates were washed four times with wash buffer followed by addition of biotinylated mouse anti-human BDNF monoclonal antibody (diluted 1:1000 in sample diluent), which was incubated for 3 hours at room temperature. After washing, samples were incubated with streptavidin-horseradish peroxidase conjugate solution (diluted 1:1000 in sample diluent) for 1 hour at room temperature. After addition of the substrate and stop solution, the amount of BDNF was determined (absorbance set at 450 nm). The standard curve demonstrates a direct relation between optical density and BDNF concentration.
## Statistical analysis
Distributions of continuous data were assessed using the Shapiro-Wilk test. Variables with normal distribution were expressed as mean and standard deviation, while other variables were expressed as median and 1st-3rd quartile (IQR). Categorical variables were expressed as absolute and relative frequency.
Two different measures of BDNF levels were obtained: at hospital admission and after 15 days in hospital (named “BDNF discharge”). Initially, the mean difference between these two measures was assessed using the paired t test. For subsequent analyses, BDNF levels were transformed into a single measure that considers the percentage variation in levels, using the following formula: Bivariate analyses were conducted to assess the relationships between the percentage variation in BDNF levels and continuous data (i.e.: age, years of regular use of alcohol) or categorical data (i.e.: presence of psychiatric disorders, family history) using Spearman’s coefficient or the Mann-Whitney test, respectively. Moreover, a generalized estimating equation (GEE) model was run to analyze serum BDNF levels at admission and discharge, controlling for potentially confounding variables: [1] presence of first-degree relatives with alcohol dependence (yes/no); [2] number of days between the two blood collections; and [3] age.
## Demographic characteristics and psychiatric diagnoses
The sample consisted mostly of white men ($$n = 46$$, $74.2\%$), with mean age of 48.9 (SD = 9.2) years, lower education level ($59.7\%$ with less than 8 years’ schooling), and currently living without a partner ($64.5\%$). Patients had high prevalence of current anxiety symptoms ($29.4\%$) and major depressive episodes ($19.6\%$). At least two previous treatments for problems with alcohol were reported by $50\%$ of the sample, and almost $76\%$ had a positive family history of alcohol use disorder. Also, half of the sample had consumed alcohol three or more times per week for more than 22 years (Table 1).
Table 1Sociodemographic and clinical data and percentage variation of BDNF between admission and discharge Total ($$n = 62$$)BDNF %varp-valueAge at first use of alcohol*15.4±4.00.1710.184Years of regular use of alcohol (3+ times/week)* ($$n = 58$$)22 [10; 30]-0.1170.700Number of hospitalizations for alcohol use*2 [0; 5]0.0380.778Age (years)*48.9±9.20.0180.887BMI*25.8±4.10.0330.798Skin color † 0.664White46 (74.2)16.7 [-2.2; 37.5] Non-white16 (25.8)19.8 [6.2; 42.7]Educational level † 0.971≤ 8 years of schooling37 (59.7)15.2 [5; 38.7] > 8 years of schooling25 (40.3)24.3 [-2.2; 38.5]*Marital status* † 0.871Not married40 (64.5)16.2 [3.6; 36.6] Married22 (35.5)19.8 [-1; 41.4]Homeless † ($$n = 49$$ ‡) 0.834Yes7 (14.3)15 [3.6; 43.1] No42 (85.7)16.2 [-1; 38.7] First-degree relative with alcohol use disorder † 0.005Yes47 (75.8)14.8 [-5.3; 35.6] No15 (24.2)35.3 [15.4; 74.8]Major depressive episode † ($$n = 51$$) 0.367Current presence10 (19.6)13.3 [-6.5; 34.1] Absence41 (80.4)18.8 [5; 41.4] Anxiety disorders † ($$n = 51$$) 0.352Current presence15 (29.4)24.3 [3.7; 71.7] Absence36 (70.6)15.3 [1.3; 38.6] Smoking (current) † 0.249Yes41 (66.1)17.1 [-1.0; 34.1] No21 (33.9)15.4 [5.0; 74.9]Chronic diseases§ 0.249Yes16 (27.6)40.2 [0.3; 74.3] No42 (72.4)14.9 [3.6; 32.6]*Categorical data* were expressed as n (%), and continuous data as mean ± standard deviation or median [1st; 3rd quartiles].* Spearman correlations; † Mann-Whitney test.‡ Has been homeless at some time in life. § At least one of the following diseases: diabetes, cancer, HIV, stroke, or liver disease.
## BDNF levels
The comparison between admission and discharge measurements showed an increase in BDNF levels after alcohol withdrawal (25.4±9.6 vs. 29.8±10.2 ng/mL; $p \leq 0.001$, Figure 1A). Furthermore, analyses considering sociodemographic and clinical data demonstrated that the percentage of variation in BDNF levels was significantly lower for those who had a first-degree relative with alcohol dependence (14.8 [-5.3;35.6] vs. 35.3 [15.4;74.8]; $$p \leq 0.005$$, Table 1 and Figure 1B). No other associations were found with sociodemographic or clinical characteristics or psychiatric diagnoses (Table 1). Liver function tests (alanine transaminase, aspartate transaminase, and gamma-glutamyltransferase) were also unrelated to BDNF variation (Table S1, available as online-only supplementary material).
Figure 1Variation in BDNF levels between hospital admission and discharge ($$n = 62$$). A) BDNF levels after alcohol withdrawal (paired t test). B) Percentage variation in BDNF and first-degree relatives with alcohol dependence (no relative ($$n = 15$$), has relative ($$n = 47$$); Mann-Whitney test).
Taking into account these results, and to confirm our initial finding, the comparison between BDNF levels at admission and discharge was assessed controlling for presence of first-degree relatives with alcohol dependence and also considering the number of days between blood collections and age as possible confounding variables. The increase in BDNF levels during withdrawal remained significant (coef. = -4.37, $95\%$ confidence interval [$95\%$CI] -6.3; -2.4; $p \leq 0.001$, Table 2).
Table 2Generalized estimating equation (GEE) model for change in BDNF levels between hospital admission and discharge Coef$.95\%$CIp-valueAge-0.126(-0.4; 0.2)0.405Admission (discharge ref.)-4.367(-6.3; -2.4)< 0.001First-degree relatives with alcohol dependence0.112(-5.9; 6.1)0.971Days between blood sample collections0.003(-0.1; 0.1)$0.96495\%$CI = $95\%$ confidence interval.
## Discussion
Our main finding involves an increase of BDNF levels during early withdrawal in severe alcohol users. These results are in line with previous studies conducted with alcohol users. 7, 11 Of note, our results also suggest that the presence of family history of alcohol use disorder contributes to the variation in BDNF levels during abstinence, perhaps due to genetic influence.
Increased BDNF levels during abstinence have been observed in alcohol addiction, although some aspects may differ between studies. For instance, different abstinence periods can be assessed and considered. Our study observed that the variation in BDNF levels occurs shortly after alcohol withdrawal (on average 15 days later). Sönmez et al. 29 also assessed BDNF levels 2 weeks after alcohol withdrawal and suggested that BDNF could be involved in neuroadaptation during abstinence. Similar evidence has been observed in relation to other drugs, like crack cocaine 6, 30 - 32 and heroin. 33 Furthermore, preclinical studies indicate that BDNF levels appear to vary according to the pattern of alcohol consumption, whether recreational use, abuse, or dependence. 34, 35 Acute and moderate use of alcohol temporarily increases BDNF levels, while chronic and excessive use seems to lead to a reduction in levels. 34, 36 The withdrawal period appears to bring BDNF back to baseline levels, 19, 34, 36 - 38 which corroborates our results, since our sample includes chronic and severe alcohol users. In fact, the severity of abstinence may also influence BDNF levels. 7 Patients with DT have lower levels of BDNF compared to healthy controls and patients without DT. After detoxification, BDNF levels increase in alcoholic patients, but to a lesser extent in those with DT. 7, 39 This pattern of BDNF levels during withdrawal may be related to the brain’s capacity to regenerate after discontinuation of substance use. 40 Chronic use of psychoactive substances involves repeated hyperactivation of the dopaminergic pathway, leading to neuroadaptive mechanisms that can cause disruption in the brain’s reward system and in regulation of BDNF. 41 - 44 Although, this reorganization may indicate a functional response mechanism in the short term, over the long term, when related to chronic use of alcohol for many years, it can generate dysfunctional changes (allostatic load), resulting in a harmful response. 45 - 51 *It is* suggested that in advanced stages of the disorder, inadequate responses may persist even after abstinence. 52, 53 This phenomenon is encompassed by the term neuroprogression, which is related to pathological reorganization of the central nervous system (CNS) along the course of severe psychiatric mental disorders. Understanding of the biological underpinnings of neuroprogression is still recent. 54 In alcohol addiction, as well as in other psychiatric disorders, it is believed that homeostatic functioning is disturbed over the course of the pathology by remodeling of the CNS. 55 - 57 Investigations that have evaluated BDNF in other psychiatric disorders, such as schizophrenia, major depressive disorder, bipolar disorder, and suicide behavior, also showed a similar pattern to that detected in our analyses. 58 - 65 Although presence of psychiatric disorders could lead to changes in BDNF levels, no influence associated with such comorbidities was observed in our study. The concentration and function of BDNF might also be influenced by chronic conditions such as diabetes, 66 cancer, 67 HIV, 68 stroke. 69 and liver disease. 70 Nonetheless, no association was found between presence of these diseases and variation in BDNF levels in our sample, emphasizing that the increase in BDNF levels is mainly due to alcohol withdrawal.
This study has some limitations. Previous research has shown that sex-related hormonal, genetic, and epigenetic factors can modulate BDNF activity. 71 - 74 However, this study was carried out at an exclusively male psychiatric hospital and therefore does not allow us to evaluate the effect of variables related to sex. Some other factors regarding the hospitalization process, such as use of medication, 75 may have also contributed to the variation in BDNF levels. However, during the initial period of abstinence, patients are only given medications to manage withdrawal symptoms (such as benzodiazepines). Other medications (such as antidepressants and mood stabilizers) are usually prescribed after this initial period. Other non-pharmacological measures, such as group therapy sessions 76 and physical exercises 76 - 78 were also very similar among all patients. These factors may not therefore have an influence on our findings. On the other hand, individual variations related to clinical improvement and presence of withdrawal symptoms may also impact BDNF levels. Although all patients included in the study exhibited clinical improvement during follow-up, we did not apply any scale that specifically assesses the progression of withdrawal symptoms. Also, the sample size is small, which may have prevented us from detecting other significant findings. Nonetheless, it should be noted that this is a more homogeneous sample since all patients are men, who were admitted to hospital for treatment of severe cases of addiction, were refractory to outpatient treatment, and were possibly at a more advanced stage of the condition.
## Conclusions
Our findings reinforce the role of BDNF as a neurotrophin involved in alcohol use disorder. The variation in BDNF levels during alcohol withdrawal reinforces the hypothesis that BNDF is a possible biomarker of this pathology. Also, our study identified that presence of family history of alcohol use disorder could be a factor that influences the variation in this biomarker. Further studies are needed to understand the relationship between BDNF, severity (or staging), and prognosis in alcohol use disorders, since this topic is of pivotal importance for clinical practice as well as for scientific research.
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|
---
title: 'Bipolar disorder: an association between body mass index and cingulate gyrus
fractional anisotropy not mediated by systemic inflammation'
authors:
- Ramiro Reckziegel
- Francisco Diego Rabelo-da-Ponte
- Jacson Gabriel Feiten
- Isadora Bosini Remus
- Pedro Domingues Goi
- Miréia Fortes Vianna-Sulzbach
- Raffael Massuda
- Danielle Macedo
- David de Lucena
- Letícia Sanguinetti Czepielewski
- Clarissa Severino Gama
journal: Trends in Psychiatry and Psychotherapy
year: 2022
pmcid: PMC10039724
doi: 10.47626/2237-6089-2020-0132
license: CC BY 4.0
---
# Bipolar disorder: an association between body mass index and cingulate gyrus fractional anisotropy not mediated by systemic inflammation
## Abstract
### Objective
To investigate associations between body mass index (BMI), white matter fractional anisotropy (FA), and C-reactive protein (CRP) in a group of individuals with bipolar disorder (BD) during euthymia and compare them with a control group of healthy subjects (CTR).
### Methods
The sample consisted of 101 individuals (BD $$n = 35$$ and CTR $$n = 66$$). Regions of interest (ROI) were defined using a machine learning approach. For each ROI, a regression model tested the association between FA and BMI, controlling for covariates. Peripheral CRP levels were assayed, correlated with BMI, and included in a mediation analysis.
### Results
BMI predicted the FA of the right cingulate gyrus in BD (AdjR2 = 0.312 F[3] = 5.537 $$p \leq 0.004$$; β = -0.340 $$p \leq 0.034$$), while there was no association in CTR. There was an interaction effect between BMI and BD diagnosis (F[5] = 3.5857 $$p \leq 0.012$$; Fchange = 0.227 AdjR2 = 0.093; β = -1.093, $$p \leq 0.048$$). Furthermore, there was a positive correlation between BMI and CRP in both groups (AdjR2 = 0.170 F[3] = 7.337 $p \leq 0.001$; β = 0.364 $$p \leq 0.001$$), but it did not act as a mediator of the effect on FA.
### Conclusion
Higher BMI is associated with right cingulate microstructure in BD, but not in CTR, and this effect could not be explained by inflammatory mediation alone.
## Introduction
Obesity is disquietingly common among individuals living with bipolar disorder (BD).1, 2 Higher body mass index (BMI) is not only associated with increased cardiovascular risk,3 but is also associated with illness severity, with worse global functioning status, and with cognitive impairment,4, 5 possibly through damage to neural substrates.6 Compromised white matter (WM) integrity estimated by fiber fractional anisotropy (FA) could be a candidate pathway for such deficits.7, 8 The association between BMI and FA has been previously explored in the context of mood episodes in BD.9, 10 During depression, BMI is associated with structural connectivity in cortico-limbic networks.9 Following the first episode of mania, the main findings are disruptions in right parietal, temporal, and occipital regions of overweight and obese patients.10 To our knowledge, there are no studies in euthymic patients. Although abnormalities during euthymia are less pronounced, they seem to reflect long-term and possibly irreversible structural damage and act as more stable markers in BD.11 Although the association between BMI and WM microstructure has been described previously,9, 10 the neurobiological pathway linking this association with conditions remains unclear. The authors discussed a possible inflammatory process, but no inflammatory markers were actually assayed.9, 10 C-reactive protein (CRP) is a sensitive marker of peripheral inflammation that has been extensively reported in obesity.12 Also, it has already been associated with WM microstructural damage in severe mental illness,13 so it constitutes a promising candidate to test the hypothesis of inflammatory damage in obese bipolar patients.
We hypothesize that obese individuals with BD in a euthymic phase present WM microstructural damage related to BMI, as a possible consequence of an unbalanced allostatic and pro-inflammatory profile.4 Therefore, this is a proof-of-concept study that aims to: 1) investigate associations between BMI and FA in individuals with BD during euthymia in comparison with a control group of healthy individuals (CTR) and 2) test whether any possible association is mediated by inflammation measured by CRP.
## Material and methods
This is a cross-sectional observational study that included 101 subjects, 35 BD and 66 CTR. To be included in the study, all participants needed to be adults (age > 18 years) at the time of enrollment and sign an informed consent form. The project was approved by the Research Ethics Committee at the Hospital de Clínicas de Porto Alegre (HCPA, Project ID 10-0348) and is in accordance with the Declaration of Helsinki.
BD patients diagnosed with type I BD receiving outpatient psychiatric care at the HCPA were eligible for enrollment on the study if euthymic. A trained psychiatrist confirmed the diagnosis using the structured clinical interview for DSM-IV (SCID), and euthymia was defined as Hamilton Rating Scale for Depression14 and Young Mania Rating Scale15 scores less than 7. Recruitment of CTR members followed the same logistics, selecting from among companions of individuals attending outpatient care of another medical specialty at the HCPA or community volunteers from the same socioeconomic background as the cases. For each CTR, the psychiatrist performed a clinical interview to rule out history of mental illness in the candidate or a first-degree relative.
The following exclusion criteria were applied for both groups: 1) age > 70 years; 2) pregnancy; 3) substance abuse or addiction (other than smoking); 3) endocrine or cardiovascular disease requiring medical attention or treatment adjustment; 4) rheumatological, neurological, autoimmune, infectious, or chronic inflammatory diseases; 5) immunosuppressive therapy; and 6) any contraindication precluding Magnetic Resonance Imaging (MRI) scanning.
Weight and height were obtained for each individual to calculate BMI (weight/height*height; kg/m2). The same instruments (scale and height ruler) were used for all participants, wearing light clothing but no shoes. A trained technician collected five milliliters of blood from each subject by venipuncture. High sensitivity CRP was measured using a latex particle-enhanced immunoturbidimetric assay following the manufacturer`s instructions (Roche Diagnostics, Indianapolis, IN, United States).
MRI images were acquired by a Philips Achieva 1.5T (Bethesda/Netherlands, 2009) with a dedicated 8 channel headcoil. Diffusion weighted MRI images were acquired using single-shot spin-echo echo-planar imaging (SE-EPI) sequence: TR/TE/Flip angle (10000 ms/124 ms/90º); b-value of 0 and 1000 s/mm2 with 15 directions; and voxel sizes: 2×2×3mm3 (high resolution). A trained researcher was responsible for processing and verifying all the volumetric segmentation using Freesurfer image analysis suite v.5.1.0, (http://surfer.nmr.mgh.harvard.edu/). TRACULA, a toolbox package within Freesurfer, was used for automated segmentation of major WM tracts.16 The t test for independent samples was used to assess differences between groups in age, BMI, and years of education, and the chi-square test was employed for sex and smoking status. Continuous variables are described as mean and standard deviation and categorical variables as percentages unless otherwise specified. Lithium use, age, and sex were included as covariates in all subsequent analyses because they are intimately related with brain microstructure.17 The Kolmogorov-Smirnov test was used to check the parametric distribution of the variables, including regression residuals. Because serum CRP levels were not normally distributed, we used the parametric distribution of logarithmic-transformed data for all analyses of this variable.
To avoid multiple comparisons problems, we applied a feature selection algorithm titled least absolute shrinkage and selection operator (LASSO) to select the most important regions of interest (ROI), using leaving-one-out cross validation (LOOCV). Afterwards, we ran Linear Regression Models for BMI main effect on FA ROI selected by LASSO and then conducted an analysis of mediation by serum CRP levels. The statistical program IBM SPSS Statistics 18.0 was used to compile data and conduct statistical analysis using regression models, while the statistical software R version 3.6.1 and the caret version 6.0 package were used for the feature selection algorithm.
## Results
The sample groups did not differ in age, smoking status, or years of education, as summarized in Table 1. As expected, the average BMI was greater among patients with BD. Nineteen ($54\%$) of the individuals with BD were taking lithium. Other medications in use by patients included valproate and atypical antipsychotics.
Table 1Sociodemographic data of the sample across groups Individuals with bipolar disorder ($$n = 35$$)Unaffected controls ($$n = 66$$)Group comparisonsAge,* mean (SD)42.35 (15.05)37.58 (14.05)t[100] = -1.57, $$p \leq 0.119$$Sex† male/female, n (%)11 [31]/24 [69]38 [58]/28 [42]χ2[1] = 6.26, $$p \leq 0.012$$*Smoking status,† n (%)8 [23]8 [12]χ2[1] = 1.70, $$p \leq 0.192$$Education years,* mean (SD)9.94 (3.02)10.88 (3.52)t[100] = 1.31, $$p \leq 0.193$$BMI (kg/m2),* mean (SD)29.70 (6.55)25.54 (4.24)t(48.57) = -3.34, $$p \leq 0.002$$*Obese individuals,† n (%)14 [40]11 [16]χ2[1] = 6.26, $$p \leq 0.012$$*YMRS, median (IR)0.50 (3.00)--HAM-D, median (IR)2.0 (5.00)--Lithium users, n (%)19 [54]--Valproate users, n (%)15 [42]--Atypical antipsychotic users, n (%)22 [63]--BMI = body mass index; HAM-D = Hamilton Depression Rating Scale; IR = interquartile range; SD = standard deviation; YMRS = Young Mania Rating Scale.* t test for independent variables.† Chi-square test.
The LASSO feature selection model consisted of 4 variables: sex (male as reference), left corticospinal tract FA, right cingulate gyrus endings FA, and forceps major of the corpus callosum FA with coefficients of -0.5, 0.16, -0.08, -0.06, respectively. The area under the curve was 0.62, sensitivity was 0.63, specificity was 0.62, and balanced accuracy was 0.63.
Each of the tracts that were identified in the LASSO model were considered ROI for further analyses and were entered into linear regression models. BMI and left corticospinal tract FA were positively associated in the overall sample (BD + CTR), controlled for covariates (AdjR2 = 0.319 F[4] = 10.841 $p \leq 0.001$; β = 0.247 $t = 2.460$ $$p \leq 0.016$$).
In the right cingulate gyrus, BMI showed a trend but did not reach statistical significance to predict the FA of the overall sample (AdjR2 = 0.139 F[4] = 4.396 $$p \leq 0.003$$; β = -0.209 t = -1.855 $$p \leq 0.067$$). There was an interaction effect between BMI and diagnostic group (F[5] = 3.5857 $$p \leq 0.012$$; F change = 0.227; AdjR2 = 0.093; BMI*Group effect: t = -2.011, β = -1.093, $$p \leq 0.048$$). This effect was not mediated by inflammation measured by CRP, since the direct effect of BMI*Group on right cingulate gyrus (c’= -0.0009 $$p \leq 0.0086$$) was greater than the total effect counting the indirect effect of CRP (c = -0.0008 $$p \leq 0.0203$$). In the BD subgroup analysis, BMI showed a negative correlation (AdjR2 = 0.312 F[3] = 5.537 $$p \leq 0.004$$; β = -0.340 t = -2.235 $$p \leq 0.034$$), while in CTR there was no association (Figure 1).
Figure 1Regression analysis of BMI and FA of the right cingulate fibers across groups. Fractional anisotropy values shown are corrected for lithium use (for BD patients only), age, and sex. BD = bipolar disorder patients; BMI = body mass index (kg/m2); CTR = control group.
Fractional anisotropy values for the forceps major of the corpus callosum showed no significant correlation with BMI of the sample (AdjR2 = 0.088 F[4] = 3.015 $$p \leq 0.023$$; β = 0.118 $t = 1.014$ $$p \leq 0.313$$).
In the overall sample, BMI was strongly correlated with higher levels of CRP (AdjR2 = 0.170 F[3] = 7.337 $p \leq 0.001$; β = 0.364 $t = 3.549$ $$p \leq 0.001$$). In the analysis by group, this effect was prominent and probably driven by the CTR group (AdjR2 = 0.268 F[3] = 8.327 $p \leq 0.001$; β = 0.418 $t = 3.092$ $$p \leq 0.003$$). However, there was no significant correlation among BD individuals, (AdjR2 = -0.003 F[3] = 0.095 $$p \leq 0.422$$; β = 0.289 $t = 1.622$ $$p \leq 0.116$$).
## Discussion
The results of this study point to an association between BMI and decreased FA in the right cingulate gyrus of individuals with BD, but not in CTR. This association could indicate WM microstructural damage in this region.18 Decreased cingulate FA is one of the most consistent findings in BD.18, 19 Loss of integrity in this structure is highly deleterious considering its central role of connection in the limbic system.18 Its posterior portion subserves an integrative network of cognitive tasks, top-down attentional control, visual processing, and memory systems for recognition,20 while the anterior portion is fundamental for the processing of executive functions related to emotional and visceromotor stimuli.21 Indeed, the cingulum links together regions critical for these processes, including the cingulate cortex, the ventral visual stream, and the hippocampal complex.20 In sum, decreased FA in association fibers such as the cingulate gyrus provide evidence of WM dysconnectivity in BD,17 and our findings suggest that it may be associated with BMI.
Accelerated aging of the WM, especially in limbic communication structures, could be the pathway underlying such microstructural damage associated with BMI in BD, but with uncertain etiology.7 Chronic inflammatory status is a good candidate for the neuroimmunological abnormalities that occur in severe psychiatric disorders8 and could mediate the deleterious effect of BMI in WM.9, 10 In our study, higher BMI was indeed correlated with increased pro-inflammatory cytokines in our population measured by CRP, but this correlation was not seen in BD, only among CTR.
In contrast to what we had expected, the decreased right cingulate fiber FA in BD was not mediated by CRP. This suggests that a metabolic process other than inflammation may be playing a role in the structures we studied, conferring microstructural damage or protection related to changes in BMI. Cholesterol, triglycerides, and glucose levels have also been correlated with FA structures in BD.9 Insulin resistance plays a prominent role in obese BD, modulating white matter abnormalities and synaptic plasticity alongside inflammatory dysfunction and oxidative stress.4 Furthermore, adipose tissue hormonal secretion and behavioral challenges such as food intake and exercise balance have been consistently reported among obese BD patients2, 3 and could be mediating WM microstructural damage.6 One possible explanation for the lack of mediation effect of CRP on the FA of cingulate fibers is that our BD sample was out of episode during the assessment, when inflammation may decrease.8, 11 *Our hypothesis* was that even in recovery obese patients would present higher inflammatory status, which was not confirmed.
We also found a positive association between BMI and the left corticospinal tract among both BD and CTR. In contrast to right cingulate fibers, corticospinal tracts are reported to have higher FA in BD than in the general population.22 Higher FA in this essentially motor tract could perpetuate deficits in motor inhibition,17 which in turn can contribute to behavioral features observed in obese BD as they struggle with inhibition of exaggerated physical and emotional responses.23 There are important limitations to address in the present study. The lack of association with BMI in CTR should be interpreted with caution, because of limited variability of BMI among this CTR sample. Compromised WM has been described in larger obese non-psychiatric populations.7 The cross-sectional design of this study does not allow inferences about causality and this limitation cannot be adequately mitigated without a longitudinal follow-up. The sample size was modest, which limited the possibility of including more covariates and performing sensitivity analyses. Also, patients were on continuous use of medications that might influence the integrity of the neural bundles,24 such as anticonvulsants and antipsychotics, which we could not control for because of the small sample size. Nonetheless, we were able to control for lithium use.
In summary, we found that BMI was associated with WM microstructure in euthymic bipolar patients. Such results reinforce the hypothesis that there are convergent pathways between BD and systemic alterations associated with obesity, contributing to the understanding of both conditions and their bilateral relations. Furthermore, our findings do not corroborate an inflammatory pathophysiology underlying this association and future efforts in this field could include the endocrine profile beyond inflammatory markers.
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17. Heng S, Song AW, Sim K. **White matter abnormalities in bipolar disorder: insights from diffusion tensor imaging studies**. *J Neural Transm (Vienna)* (2010) **117** 639-654. PMID: 20107844
18. Duarte JA, de Araújo e Silva JQ, Goldani AA, Massuda R, Gama CS. **Neurobiological underpinnings of bipolar disorder focusing on findings of diffusion tensor imaging: a systematic review**. *Rev Bras Psiquiatr* (2016) **38** 167-175. PMID: 27007148
19. Favre P, Pauling M, Stout J, Hozer F, Sarrazin S, Abé C. **Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals**. *Neuropsychopharmacology* (2019) **44** 2285-2293. PMID: 31434102
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|
---
title: 'An invisible villain: high perceived stress, its associated factors, and possible
consequences in a population-based survey in southern Brazil'
authors:
- Lauro Miranda Demenech
- Sara S. Fernandes
- Renata Gomes Paulitsch
- Samuel C. Dumith
journal: Trends in Psychiatry and Psychotherapy
year: 2022
pmcid: PMC10039725
doi: 10.47626/2237-6089-2021-0228
license: CC BY 4.0
---
# An invisible villain: high perceived stress, its associated factors, and possible consequences in a population-based survey in southern Brazil
## Abstract
### Introduction
Much of the evidence on the relationship between stress, lifestyle, and other physical and mental health outcomes comes from studies conducted in high-income countries. There is therefore a need for research among populations in low and middle-income settings.
### Objectives
To measure stress levels and identify factors associated with a high stress level and its consequences for health.
### Methods
This was a population-based cross-sectional study carried out in 2016 with adults aged 18 years or older in a municipality in southern Brazil. A two-stage sampling strategy based on census tracts was used. Stress levels were measured with the Perceived Stress Scale (PSS-14) and classified into quartiles. The impact of the highest stress levelon each outcome was assessed with etiologic fractions (EF).
### Results
The most stressed groups were: females (PR = 1.51, $95\%$CI 1.25-1.81), younger people (PR = 1.76, $95\%$CI 1.26-2.46), middle-aged individuals (PR = 1.60, $95\%$CI 1.17-2.19), those with lower schooling (PR = 1.56, $95\%$CI 1.20-2.02), the physically inactive (PR = 1.51, $95\%$CI 1.20-1.91), people who spent three or more hours watching television per day (PR = 1.29, $95\%$CI 1.12-1.50), and those with food insecurity (PR = 1.44, $95\%$CI 1.19-175). Possible consequences of high stress level were regular or poor self-perception of health (EF = $29.6\%$), poor or very poor sleep quality (EF = $17.3\%$), lower quality of life (EF = $45.6\%$), sadness (EF = $24.2\%$), and depressive symptoms (EF = $35.8\%$).
### Conclusions
Stress plays an important role in several domains of health. Both public policies that target reduction of inequalities and specific stress-management interventions can reduce stress levels in populations, thereby decreasing the burden of other negative physical and mental health outcomes related to stress.
## Introduction
Stress can be defined as the body’s response pattern to external demands, regardless of the nature of the causative agent, and the implications of stress are of considerable interest in health research.1 While responses to acute stress are adaptive, chronic stress can predispose individuals to a lower quality of life and increased health problems.2 Evidence indicates that stress can hinder development of a healthy lifestyle. People under stress are more likely to adopt harmful health behaviors, such as physical inactivity, smoking, and drinking alcoholic beverages.3 Stress seems to have a complex and bidirectional relationship with mental disorders, especially depression.4 Much of the available evidence on the relationship between stress, lifestyle, and other physical and mental health outcomes comes from studies conducted in high-income countries.5 - 7 *There is* therefore a need for research among populations in low and middle-income settings. There are also complex interrelations between individual (gender, education, occupation, income, behaviors) and contextual factors (structure, culture, and values of the region or country in which one lives) that predispose a person to be more stressed. Failure to account for these mechanisms may lead to incomplete interpretations of possible adverse outcomes resulting from stress.
High levels of perceived stress have been associated with poorer overall physical and mental health, in addition to increased risk of premature death.6 Investigating the association between psychological stress and health-related outcomes is of foremost relevance. Mapping how stress can be shaped according to individuals’ characteristics and to modifiable lifestyle behaviors, as well as its effects, can provide health professionals and key stakeholders with helpful insights and information for development of better health plans, policies, and practices.
Therefore, the objective of the present study was to identify the social, economic, demographic, and behavioral factors that are associated with perceived stress and to investigate the possible consequences of stress for the physical and mental health of the population of a municipality in southern Brazil.
## Study design
This cross-sectional study is part of a population-based study, titled “Health of the Population of Rio Grande” (Saúde da População Rio-Grandina]. The aim of this research was to evaluate the health of adults and the elderly in the city of Rio Grande in southern Brazil.
The project was approved by the Research Ethics Committee at the Universidade Federal do Rio Grande (protocol $\frac{20}{2016}$; CAAE: 52939016.0.0000.5324).
## Sampling plan
The sample size estimate was calculated by considering the different outcomes evaluated in the research project. The parameters considered were as follows: significance level of $5\%$, power of $80\%$, prevalence of outcome of $10\%$, frequency of exposure between 20 and $60\%$, and prevalence ratio of 2.0. Then, $50\%$ was added to the sample size estimate to account for the sampling design effect (because sampling units were households, resulting in a cluster effect by which members of the same household tend to have more similar characteristics and responses), $15\%$ was added to account for possible confounders, and $10\%$ was added to account for attrition and refusals. The final sample size consisted of 1,423 individuals.
The sampling process was conducted in two stages based on the 2010 Demographic Census Data.8 First, 70 of the 293 census tracts in the municipality (approximately $25\%$) were systematically selected. Considering that it was expected that there would be an average of two individuals aged 18 years or over per household,8 711 households (1,423 ÷ 2) were selected, with a probability proportional to the size of the census tract. All individuals aged 18 years or older in each of the households selected were invited to take part in the study.
## Procedures
First, the study was publicized via interviews on local radio stations, publications in local newspapers, and television newscasts, and on a social media page on the internet. Second, preliminary visits were made to selected households by study supervisors, in order to verify household characteristics (whether it was a residential address and to determine number of residents eligible for the study), to inform residents about the study, and to schedule interviews. Data collection was conducted between April and July 2016 using a precoded and standardized questionnaire that had been tested previously and was administered by trained interviewers. People who agreed to participate signed a consent form. Data on sex and age were collected from those who did not agree to participate. Data were input twice by different supervisors using EpiData 3.1 software. More details regarding the sample size calculation, sampling plan, and fieldwork logistics can be found elsewhere.9
## Variables and instruments
The outcome was perceived stress, measured using the Perceived Stress Scale,10 which is a 14-item instrument that assesses the frequency with which an individual has experienced certain feelings and situations. This scale has been translated and validated for use in the Brazilian setting.11 A Likert scale was used, with response options ranging from 0 (never) to 4 (always) points. A total score ranging from 0 to 56 points is generated by summing the scores for all questions. The variable perceived stress was operationalized in quartiles based on the total score. The top quartile was considered the group with the highest stress levels (the most stressed individuals).
Independent variables included in the analyses were sex, age, skin color, marital status, living alone, schooling level, wealth index (generated through a principal component analysis with 11 items of home assets or house characteristics and then categorized into tertiles), smoking, excessive alcohol consumption (five or more drinks for men and four or more drinks for women on a single occasion in the previous 30 days12), physical activity during leisure time (some or none), hours per day watching television, food insecurity (defined as any score above zero on the Brazilian Scale of Food Insecurity13), and whether the respondent had visited a physician in the previous month.
The possible physical and psychological consequences of stress evaluated herein were back pain in the previous 12 months (self-reported, no/yes), obesity (self-reported body mass index ≥ 30 kg/m2), self-perception of health (excellent, very good or good/regular or poor), quality of sleep (very good, good or regular/poor or very poor), quality of life as measured by the World Health Organization instrument validated in Brazil (total score operationalized into quintiles),14 sadness as measured by the Andrews and Whitney face scale (defined as those who reported feeling unhappy or very unhappy),15 depressive symptoms (absent/present) as measured by the Patient Health Questionnaire (PHQ-9) validated in the Brazilian population,16 and self-reported medical diagnosis of hypertension, diabetes or cardiopathy (no/yes). It is worth noting that the term “possible consequences” refers to outcomes that are theoretically believed to have increased probability of occurrence when levels of stress are high, but causality cannot be determined within the constraints of the design of this study.
## Data analysis
The statistical analyses were conducted in Stata 15.1. First, a descriptive analysis of sample characteristics was performed. After this step, bivariate analyses were performed to calculate the proportions of highly stressed individuals (top quartile) according to each of the independent variables. Then, a multivariate analysis was carried out with Poisson regression with robust adjustment of variance to identify the factors associated with the highest stress level or the consequences of the highest stress level.17 The conceptual model for these analyses is illustrated in Figure 1.
Figure 1Conceptual model of analysis of associated factors and possible consequences of high stress levels. Rio Grande, Brazil, 2016 ($$n = 1$$,295).
For the multivariate analysis, a hierarchical model was constructed on two levels by the backward method (Figure 1).18 Variables with a p-value < 0.20 were retained in the final model. The remaining variables were used for adjustment purposes to verify the effects of the highest stress level on the different outcomes (Model 1). The analysis of the consequences of stress was also adjusted for all other outcomes (Model 2) to verify the effect of stress on each outcome independently of the other outcomes. The results of crude and multivariate analyses are presented as prevalence ratios (PR) and $95\%$ confidence intervals ($95\%$CI).
Finally, we calculated the etiologic fraction (EF) of the stress on the physical and psychological outcomes using the formula. The EF is interpreted as the proportion of the outcome that might be reduced if the group with the highest stress level were omitted from the population. All analyses were performed considering the sample design effect and a significance level of $5\%$ for two-tailed tests.
## Data availability statement
We declare that the data used in this manuscript is available upon request from the corresponding author.
## Results
The sample comprised 1,295 individuals, which corresponds to a response rate of $91.0\%$. Among non-respondents ($9\%$), $6.9\%$ were refusals and $2.1\%$ were losses. The proportion of males was significantly higher among non-respondents than among participants ($p \leq 0.001$), with no significant difference in age ($$p \leq 0.64$$). Majorities of the sample were female ($56.6\%$), had white skin color ($82.9\%$), and were not living alone ($90.4\%$). Almost half of the sample ($41.8\%$) had eight years or less of schooling, $39.3\%$ were aged between 18 and 39 years, $11.7\%$ reported excessive alcohol consumption, $17.9\%$ were current smokers, $31.8\%$ watched television for three or more hours per day, and $33.3\%$ engaged in some physical activity. Approximately one-third of the sample reported food insecurity and had visited a doctor in the previous month (Table 1). The mean perceived stress score was 23.6 points (standard deviation [SD] = 7.4), and scores ranged from 3 to 50 points. The cutoff for the quartile with the highest stress level was ≥ 29 points.
Table 1Description of the characteristics of the sample of adults aged 18 years or older from Rio Grande, Brazil, 2016 ($$n = 1$$,295).Variablen%Sex ($$n = 1$$,295) Male56243.4Female73356.6Age (years) ($$n = 1$$,295) 18 to 3950839.340 to 5947736.8≥ 6031023.9Skin color ($$n = 1$$,293) White1,07282.9Black and others22117.1Marital status ($$n = 1$$,295) Single60046.3Not single69553.7Living alone ($$n = 1$$,294) No1,17090.4Yes1249.6Schooling (years) ($$n = 1$$,293) 0 to 854141.89 to 1139730.7≥ 1235527.5Wealth index (tertiles) ($$n = 1$$,294) 1st (poorest)44434.32nd41732.23rd (richest)43333.5Smoking ($$n = 1$$,295) No1,06382.1Yes23217.9Excessive alcohol consumption ($$n = 1$$,292) No1,14088.2Yes15211.8Physical activity in leisure ($$n = 1$$,294) No86266.6Yes43233.4Television time (hours per day) ($$n = 1$$,279) < 387268.2≥ 340731.8Food insecurity ($$n = 1$$,295) No84164.9Yes45435.1Visit to physician (last month) ($$n = 1$$,295) No87967.9Yes$41632.1\%$ = prevalence; N = total number of observations per category; n = absolute frequency.
Table 2 shows the distribution of individuals with the highest stress level, grouped by the independent variables. Individuals with food insecurity had the greatest proportion of highly stressed individuals ($35\%$), whereas individuals who were physically active had the lowest proportion of highly stressed individuals ($16\%$). In the crude analyses, the variables age, marital status, schooling, wealth index, physical activity, time watching television, food insecurity, and having visited a doctor were significantly associated with the highest level of stress. In the adjusted analysis, the following variables remained significant: female sex (PR = 1.51, $95\%$CI 1.25-1.81), age between 18 and 39 years old (PR = 1.76, $95\%$CI 1.26-2.46) or between 40 to 59 years old (PR = 1.60, $95\%$CI 1.17-2.19), schooling less than or equal to eight years (PR = 1.56, $95\%$CI 1.20-2.02), physical inactivity (PR = 1.51, $95\%$CI 1.20-1.91), watching television for three hours or more per day (PR = 1.29, $95\%$CI 1.12-1.50), and food insecurity (PR = 1.44, $95\%$CI 1.19-175).
Table 2Crude and adjusted prevalence ratios for associations between the highest stress level and independent variables. Multivariate analysis conducted with two hierarchical levels, through Poisson regression with robust adjust for variance, accounting for design effect. Sample of adults aged 18 years or older in Rio Grande, Brazil, 2016 ($$n = 1$$,295).Level/variablePrevalence* (%)*Crude analysis* PR ($95\%$CI)*Adjusted analysis* PR ($95\%$CI)First level Sex Male19.41.001.00Female28.81.48 (1.23; 1.80)1.51 (1.25; 1.81)Age (years) 18 to 3928.51.70 (1.30; 2.24)1.76 (1.26; 2.46)40 to 5925.81.54 (1.13; 2.10)1.60 (1.17; 2.19)≥ 6016.81.001.00Skin color White23.71.001.00Black and others29.41.24 (1.00; 1.55)1.13 (0.90; 1.42)*Marital status* Single29.31.42 (1.13; 1.78)1.23 (0.94; 1.61)Not single20.71.001.00Living alone No25.51.001.00Yes17.70.70 (0.47; 1.04)0.72 (0.47; 1.10)Schooling (years) 0 to 827.91.42 (1.11; 1.80)1.56 (1.20; 2.02)9 to 1124.71.25 (0.96; 1.63)1.27 (0.98; 1.63)≥ 1219.71.001.00Wealth index (tertiles) 1st (poorest)30.21.52 (1.18; 1.96)1.30 (0.99; 1.71)2nd24.01.21 (0.92; 1.59)1.06 (0.81; 1.39)3rd (richest)19.91.001.00Second level Smoking No23.81.001.00Yes28.91.21 (0.96; 1.53)0.99 (0.77; 1.26)Excessive alcohol consumption No25.41.001.00Yes20.40.81 (0.59; 1.09)0.91 (0.66; 1.25)Physical activity in leisure No29.01.82 (1.42; 2.33)1.51 (1.20; 1.91)Yes16.01.001.00Television time (hours per day) < 322.41.001.00≥ 330.21.35 (1.15; 1.59)1.29 (1.12; 1.50)Food insecurity No19.11.001.00Yes35.01.83 (1.51; 2.22)1.44 (1.19; 1.75)Visit to physician (last month) No22.81.001.00Yes28.91.27 (1.04; 1.55)1.14 (0.91; 1.44)$95\%$CI = $95\%$ confidence interval; PR = prevalence ratio.* Prevalence of the highest perceived stress quartile by category.
Table 3 shows the prevalence of physical and psychological outcomes and their associations with the highest stress level. The most frequent outcomes were regular or poor self-rated health ($33.7\%$), hypertension ($28.1\%$), and obesity ($23.7\%$). In the crude analysis, the highest stress level was significantly associated with back pain, cardiopathy, regular or poor self-rated health, poor or very poor quality of sleep, lower quality of life, sadness, and depressive symptoms. In the adjusted analysis, controlling for possible confounders (Model 1), the highest stress level was still significantly associated with these variables in addition to being associated with hypertension. In Model 2 (also adjusting for other outcomes), the highest stress level remained associated with regular or poor self-perception of health (PR = 1.53, $95\%$CI 1.29-1.81), poor or very poor quality of sleep (PR = 1.62, $95\%$CI 1.09-2.40), lower quality of life (PR = 2.70, $95\%$CI 2.05-3.05), sadness (PR = 2.27, $95\%$CI 1.43-3.57), and depressive symptoms (PR = 3.02, $95\%$CI 1.95-4.68). The highest stress level stress levels showed a protective effect against obesity (PR = 0.75, $95\%$CI 0.58-0.97).
Table 3Crude and adjusted analysis of possible consequences of high levels of stress. Sample of adults aged 18 years or older in Rio Grande, Brazil, 2016 ($$n = 1$$,295).OutcomesPrevalence* (%)*Crude analysis* PR ($95\%$CI)Adjusted model 1† PR ($95\%$CI)Adjusted model 2‡ PR ($95\%$CI)EF (%)Back pain20.72.06 (1.71; 2.47)1.91 (1.55; 2.34)1.20 (0.93; 1.56)18.0Obesity23.71.11 (0.91; 1.35)1.01 (0.81; 1.27)0.75 (0.58; 0.97)2.5Hypertension28.11.15 (0.96; 1.39)1.26 (1.05; 1.51)1.08 (0.88; 1.32)4.0Diabetes7.01.30 (0.84; 2.00)1.54 (0.98; 2.43)1.06 (0.64; 1.75)2.1Cardiopathy10.21.47 (1.01; 2.14)1.69 (1.18; 2.42)1.17 (0.85; 1.62)4.6Regular or poor self-perception of health33.72.25 (2.00; 2.53)2.08 (1.84; 2.34)1.53 (1.29; 1.81)29.6Poor or very poor sleep quality10.72.96 (2.21; 3.96)2.76 (2.08; 3.66)1.62 (1.09; 2.40)17.3Lowest quintile of quality of life19.95.22 (4.16; 6.56)4.53 (3.59; 5.71)2.70 (2.05; 3.05)45.6Sadness9.04.54 (3.18; 6.48)3.67 (2.51; 5.37)2.27 (1.43; 3.57)24.2Depressive symptoms11.25.97 (4.18; 8.53)5.04 (3.51; 7.22)3.02 (1.95; 4.68)$35.895\%$CI = $95\%$ confidence interval; PR = prevalence ratio.* Prevalence of outcome.† Adjusted for sex, age, marital status, living alone, schooling, wealth index, physical activity in leisure time, television time per day, and food insecurity.‡ Adjusted for the same variables in model 1 and all outcomes adjusted for each other; EF = etiologic fraction.
Regarding the EF, the highest stress level made a substantial contribution to most outcomes (Table 3). The EF was $45.6\%$ for lower quality of life, $35.8\%$ for depressive symptoms, $29.6\%$ for regular or poor self-rated health, $24.2\%$ for sadness, and $17.3\%$ for poor or very poor quality of sleep. The results showed that stress made a low, but still significant, contribution to occurrence of obesity (EF = $2.5\%$).
## Main finding of this study
This study evaluated perceived stress levels of the population of a municipality in southern Brazil and attempted to identify the possible risk factors for and the consequences of high levels of stress. The mean perceived stress score in this sample was 23.6 (SD = 7.4). It was shown that female, younger, and less educated individuals had a higher probability of being more stressed. Participants who were physically inactive, watched more television, and reported food insecurity had a higher probability of being more stressed.
One of the possible consequences of high levels of stress was self-rated regular or poor health. In addition to a modest association (PR = 1.53), stress explained $29.6\%$ of the variation in this outcome. An unexpected result was that participants with the highest stress level had a lower probability of being obese, although stress levels explained a low proportion of the variance in this outcome (only $2.5\%$). One of the main consequences of high levels of stress in this study was a reduction in quality of life. The most stressed participants had a $170\%$ greater probability of having a lower quality of life, and stress alone accounted for $45.6\%$ of the variance in this outcome. In this study, the highest stress level was significantly associated with and explained $17.3\%$ of the variance in poor or very poor sleep quality. The most stressed individuals were two and three times more likely to present symptoms of sadness and depression, respectively, than their less stressed counterparts. In addition, the highest stress level explained a high proportion of the variance in these outcomes ($24.2\%$ for sadness and $35.8\%$ for depression).
## What is already known on this topic?
The stress scores reported in studies conducted in low and middle-income countries, such as Jordan (17.7)19 and India (19.3)20 were lower than that those found in this study. However, the scores reported in high-income countries, such as Italy (15.2), Germany (14.9), France (15.0), and Poland (17.6) were higher than that found in our study.21 In addition, the mean score for perceived stress in our investigation was similar to the score reported in a study conducted in Greece (25.4),21 but it should be noted that Greece was about to enter into a profound social and economic crisis when that study was conducted. Thus, it is plausible that population stress levels are closely related to the degree of social and economic development of the community, possibly due to the direct and indirect benefits of these resources on the general quality of life. In low and middle-income settings, income inequality, unequal distribution of job opportunities, and low-quality working conditions can erode the social cohesion that allows people to live and work together. This process may decrease social resources, trust, and civic participation, and increase crime and deterioration of public structures and institutions, increasing overall levels of stress in populations.
There is evidence that women report being more stressed than men,22 possibly due to hormonal influences and social issues,23 such as the devaluation of their work, the need for more working hours, and the objectification of their bodies.24 Studies indicate that older people have lower levels of anxiety, depression and stress, as well as higher levels of happiness, satisfaction, and well-being,25, 26 which can be explained by an increase in wisdom and an increased ability to deal with daily life stressors.27 Finally, individuals with less education may have greater difficulty finding optimal occupations and attaining higher socioeconomic status, which may expose them to greater and more persistent psychosocial stressors.28 Physical activity has a bidirectional relationship with stress, since physically active individuals tend to be less stressed and, consequently, are more likely to remain active.29 Individuals who spend more time in front of television tend to have higher levels of sedentary behavior (i.e., sitting and/or lying down),30 which has also been strongly associated with high levels of stress.31 Respondents with food insecurity may experience higher levels of prolonged and toxic stress, as they lack basic resources for survival and citizenship.32 Furthermore, food insecurity may result in insufficient intake of nutrients, generating physiological sequelae that may predispose individuals to psychological suffering.32, 33 With respect to the finding about obesity, the initial hypothesis was that individuals with high levels of stress would be more prone to obesity, since stress plays a role in its development and maintenance.34 Notwithstanding, the results found in this study may have occurred due to two phenomena. First, individuals who eat for comfort seem to achieve lower levels of perceived stress, which could result in people with higher BMI having lower perceived stress scores.35 Second, the results may be due to a negative confounding effect, because obese people tend to have a worse perception of health,36 are sadder and more depressed,37 and have worse quality of sleep38 and quality of life.39 *It is* therefore plausible that when we control for these variables in multivariate analysis, obese people can, in fact, be less stressed.
The association between stress level and poorer self-perceived health corroborates the literature that emphasizes that self-perceived health can be referred to as a health indicator.40, 41 Regarding quality of life, both acute and chronic stress have effects that compromise health, which can affect people’s quality of life.42 Although low quality of life is not considered a morbidity, it is associated with a wide range of physical and mental health outcomes with corresponding implications for public health.43 *It is* important to state that sleep and stress can have a bidirectional relationship. Poor sleep quality can cause impairments such as chronic stress and multimorbidities,44 which, in turn, can increase sleep-related problems, increasing stress. Concerning mental health, stress increases the risk of developing physical and mental disorders45 and is strongly associated with depression4, 46, 47 and suicidal thoughts.48 *There is* a biological mechanism for these effects, since stress causes neurochemical, immunological, and autonomic changes related to emotional and cognitive regulation, which may lead to manifestations of depressive symptoms.49
## What does this study add?
It should be highlighted that this is the first study of the risk factors for and consequences of perceived stress to be conducted in a representative sample of a Brazilian municipality. Etiologic factor estimates of the possible consequences associated with high levels of stress can also be especially interesting because they enable us to forecast the proportion of each outcome that would be reduced if we were able to eliminate high levels of stress.
This sample had a high mean perceived stress score, especially when compared to samples from high-income countries. Individuals who were female, younger, less educated, physically inactive, and subject to food insecurity, and people who watched more television per day had a higher probability of being more stressed. Consequences related to the highest stress level were regular or poor self-perceived health, poor or very poor sleep quality, lower quality of life, sadness, and depression. Stress alone explained a large proportion of the variability in these outcomes. An unexpected result was that the highest stress level was associated with a lower probability of being obese, even though this association was weak and poorly explained by stress.
This article sought to present the importance that stress plays throughout several domains of health and relate it to a wide range of individual characteristics and consequences. Results of this investigation can have at least three implications: First, strengthening public policies that promote gender equality, education and occupation opportunities for younger individuals, and access to healthy food, physical activities, and diverse leisure options may reduce stress levels in the population. These may be interpreted as broad recommendations, but without these basic actions, targeted interventions are likely to be less effective (or ineffective). Second, stress seems to play an important role in the development of several negative health outcomes. It can therefore be used as a proxy to screen for psychological and physical comorbidities by health professionals, considering that more stressed people were more likely to report poor health, poor quality of sleep, lower quality of life, and sadness and depression. Third, specific interventions targeting reductions in stress (at the individual and collective levels) can reduce the burden of physical and psychological suffering, considering that stress alone contributed to a significant proportion of the abovementioned consequences. Including psychologists in the family health strategy could facilitate community access to mental health assessment, prevention, and treatment, improving people’s overall quality of life.
## Limitations of this study
The findings of this investigation should be interpreted in light of its limitations. First, all variables were measured through self-report, which might produce less precise results. However, most large-scale epidemiological studies collect data using self-report measures, enabling us to compare our results with existing findings. Second, work-related characteristics were not assessed in this investigation, and considering its possible impacts on stress, this should be considered as a limitation. Future research conducted within the same (or a similar) context should address this topic in the investigation. Work conditions may influence key factors significantly associated with stress identified in this study. Job opportunities can be unequally distributed according to gender, age, and educational level, especially in low and middle-income countries28 such as Brazil. It can therefore result in better (or worse) material conditions (access to household assets and availability of healthy food), and in higher (or lower) opportunities to engage in physical activities and in leisure activities (other than watching television). Finally, the data were collected in 2016. Despite possible concerns regarding timeliness, this study is still relevant because, apart from shedding light on an important issue, it registers stress levels in a population in the pre covid-19 pandemic setting, allowing future comparisons of scenarios.
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|
---
title: Collagen-based injectable and self-healing hydrogel with multifunction for
regenerative repairment of infected wounds
authors:
- Haojie Gu
- Han Li
- Liren Wei
- Jian Lu
- Qingrong Wei
journal: Regenerative Biomaterials
year: 2023
pmcid: PMC10039733
doi: 10.1093/rb/rbad018
license: CC BY 4.0
---
# Collagen-based injectable and self-healing hydrogel with multifunction for regenerative repairment of infected wounds
## Abstract
At present, the development trend of dressing materials is being multifunctional for convenient and long-term nursing care process of some complicated wounds. Here, basing on the theory of wound moist healing, an injectable and self-healing hydrogel comprising of collagen (COL), chitosan (CS) and oxidation modified Konjac glucomannan (OKGM), which acts as a macromolecular cross-linker to construct dynamic Schiff-base bonds was smartly designed. The strategy of introducing the silver nanoparticles (Ag NPs) into the COL–CS–OKGM hydrogel matrix achieved a markedly enhanced antibacterial activity derived from the synergistical effect between the Ag+ and the mild photothermal efficacy of Ag NPs, which also improved the local capillary blood circulation of the wound area to further facilitate wound healing process. The excellent syringeability and self-healing behaviors endowed the COL–CS–OKGM–Ag hydrogel with self-adapting ability for the wounds with irregular and large area needing frequent applying and changing without secondary injuries. In vitro and in vivo evaluations verified that so-designed COL–CS–OKGM–Ag hydrogel also with hemostatic performance is a promising multifunctional dressing for the treatments of infected wound with not only good biocompatibility and convenient use, but also with desired regenerative healing prognoses benefited from hydrogel moist environment and physiotherapy.
## Graphical Abstract
## Introduction
Healthy skin is not only a physical barrier but also an important immune organ, protecting internal organs from pathogen invasion and other external threats [1]. Wound infections are usually caused by severe microbial invasion derived from skin tissue damage, which can produce some inflammation that significantly increases the infected wound related diseases and reduces the quality of wound healing [2]. Additionally, various complications from wound infection with impaired healing could be life-threatening in some cases [3]. Traditional therapeutic materials such as gauze easily dehydrates and scabs the wound, increasing pain and infection rate, and also cannot conducive to the migration of epithelial cells, consequently resulting in poor-quality healing [4]. To make matters worse, the strong attachment of gauze to wound usually causes unpleasant pain and further damage when replacing the dressing [5].
In recent decades, studies have found that a moderately moist microenvironment is more suitable and ideal for the regenerative repair of the wound tissues, minimizing or even avoiding scarring repair [6].
Owning to possessing the excellent properties such as biomimetic porosity and good water retention for creating a local moist ambience etc., hydrogels are desirable materials for facilitating the regenerative healing of wound tissues [7]. Additionally, hydrogels are also widely utilized as efficient carriers for functional nanoparticles and drug delivering, particularly as new platform materials for surgical dressing in biomedical applications [8, 9].
Although hydrogels are used as a carrier of antibacterial drugs for wound infection treatments, the abuse of antibiotics and the drug resistance of bacteria have greatly weakened the treatment effect of infected wounds in these year [10]. Near-infrared (NIR) laser-triggered photothermal therapy (PTT) based on nanoparticle has become one of the most effective antibacterial strategies for its few side effects, low systemic toxicity, high spatial resolution and tissue penetration depth [11]. In addition, NIR laser can focus on wound area to improve local blood circulation and alleviate tissue inflammation [12]. More importantly, the antimicrobial efficacy of PTT is not limited by bacterial resistance when compared with conventional antibiotic treatment. Used alone, however, PTT easily causes a certain degree of damage to healthy skin tissue around the wound because the temperature required for killing bacteria is relatively high [13]. An option to overcome this defect is to develop a synergy therapy based on PTT, which integrates the advantages of single therapy, to strengthen the antibacterial effect and minimize possible damage in normal cells. This synergy not only can generate more efficient antibacterial behavior but also can control area temperature at 40–45°C to make a favorable environment for wound healing. Such mild photothermal physiotherapy also facilitates promoting local blood circulation and angiogenesis of the wound tissue [14].
Hydrogels are appropriate to be used for synergistic therapy including wound healing [15]. Especially injectable self-healing hydrogels have become concerns because of its convenient operation, flexible application and self-repairing after damage by external forces. Such kind of hydrogel as dressing is based on the significant theory of moist wound healing [16], and therefore quite suitable for clinical uses aiming at various wounds including infected wounds [17] due to maintaining a necessary condition of sustained moist environment for scheme regenerative wound healing [18]. Both collagen (COL) and chitosan (CS) are natural biomacromolecules developed as the ideal biomedical materials with excellent biocompatibility. As a main component of mammalian tissue extracellular matrix (ECM), collagen has good adhesion to various cells and promotion of rapid angiogenesis in skin tissue and corneal [19, 20]. Chitosan is characteristic of the properties of antibacterial activity and non-toxicity [21]. Collagen and chitosan have a good synergistic compatibility, and a complex of these two macromolecules is expected to mimic the composition of ECM [22], which has been demonstrated to have new or improved properties [23].
However, most of the traditional collagen–chitosan composite hydrogels relying on collagen macromolecular self-assembly to achieve gelation, which are normally permanent network produced by irreversible interactions between molecular chains [24]. For these COL–CS hydrogels, there are some shortcomings such as too short operation window period, an inconvenience of formulating dressing material upon applying and the difficulty to maintain complete cover on wound area due to their absence of self-healing properties after being cracked [25]. Konjac glucomannan (KGM) is another natural polysaccharide as the main component of the tubers and roots of Amorphophallus konjac plants, which is recognized as safe material by Food and Drug Administration (FDA) regulations for its healthcare benefits [26]. Oxidation modified konjac glucomannan (OKGM) is usually applied as a macromolecular cross-linker agent for positive polyelectrolyte such as chitosan to construct injectable and self-healing hydrogels for minimally invasive treatment of cancer [27] and irregular or chronic wounds [28].
Hence, basing on our previous work [27], and inspired by the photothermal effect along with the inherent antibacterial properties of silver nanoparticles, we synthesized gallic acid-modified silver nanoparticles (GA–Ag NPs) by one-step method firstly, then we dispersed these silver nanoparticles into OKGM crosslinked collagen–chitosan composite hydrogel (COL–CS–OKGM–Ag), in which the principle of Schiff base reactions was utilized to design a typical injectable and self-healing hydrogel as wound dressing with hemostasis and antibacterial activities. When such hydrogel was irradiated under NIR light, the local temperature of wound area can be elevated to cause the destruction of bacterial cell membranes and protein denaturation [29]. Simultaneously, the mild temperature also can facilitate the regeneration of capillaries in wound area. Additionally, the stimulation of NIR laser irradiation could further stimulate the release of Ag+, thus increasing the interaction with the protein sulfhydryl groups on the damaged membrane and inducing the death of bacteria [30]. Consequently, so-designed hydrogel on one hand can perform a dual antibacterial strategy for the treatment of infected wounds. On the other hand, it can provide an indispensable moist environment for wound tissue healing by regeneration other than cicatrix formation. Significantly, the injectable and self-healing behaviors of COL–CS–OKGM–Ag composite hydrogel significantly expand the function and scene scope in clinical applications.
## Materials
Medical-grade collagen (Type I) with high purity was extracted from calf skin by modified method in our laboratory basing on Miller description [31]. Chitosan (CS, molecular weight (Mw): 310–375 kDa, degree of deacetylation >$75\%$) was obtained from Sigma-Aldrich. KGM powder (purity ≥ $90\%$, 200-mesh) was purchased from Sheli Ltd (Hongkong, China) and further purified upon receiving. Sodium periodate (NaIO4, ≥$99.5\%$) were provided by Aladdin (Shanghai, China). Chloral hydrate and fluorescein diacetate (FDA) were purchased from Sigma-Aldrich (St Louis, MO, USA). Phosphate buffered saline (PBS) and all sterile consumables used in cell experiments were obtained from Corning (USA). CCK-8 kits were provided by Beyotime (Shanghai, China). Bovine serum albumin $4\%$ and paraformaldehyde solution were supplied by Solarbio (Beijing, China). The ultrapure water from a Milli-Q system (Millipore, Billerica, MA, USA) was used in all procedures of our experiments.
## Preparation of GA–Ag NPs and OKGM
Ten milliliters of saturated gallic acid solution were added to 80 ml of silver nitrate aqueous solution (0.01 M) with stirring vigorously. Then the temperature of the mixture was raised to 80°C, while aqueous NaOH solution (1 M) was added dropwise to adjust the pH to about 11 for the solution, followed by a stirring of 30 min in dark. The obtained mixture was dialyzed (molecule cutoff 8000–12 000) for 48 h after cooling to room temperature and stored. To embed these silver nanoparticles into the hydrogel prepared later, the silver nanoparticle suspension was concentrated by swirling evaporation to obtain the final suspension with a concentration of about 1290 µg/ml Ag NPs, which was used for following experiments.
A certain amount of KGM aqueous solution ($0.8\%$, w/v) was mixed with sodium periodate aqueous solution, whose final concentration in the mixture was 18 mM. Then the mixture was stirred at room temperature in dark for 12 h, whereafter 200 µl of ethylene glycol was added to terminate the reaction. The obtained product was dialyzed (molecule cut off 8000–12 000) for 72 h with following concentration by reduced-pressure distillation to obtain the needed OKGM solution.
## Preparation of COL–CS–OKGM–Ag hydrogel
Five milliliters of collagen acid solution ($1\%$, w/v) and 5 ml of CS acid solution ($2\%$, w/v) were mixed at low temperature maintained by ice bath with stirring at 500 rpm for 10 min. A certain amount of OKGM solution ($2.4\%$, w/v) was added dropwise under stirring. Next, a certain amount of concentrated silver nanoparticle suspension was then added to make the final concentration of silver nanoparticles in the hydrogel to be 0, 50, 100, 150 and 200 µg/ml, which are labeled as COL–CS–OKGM hydrogel or COL–CS–OKGM–Ag hydrogels for all the following references. Subsequently, the pH value of the solution was adjusted to about 6–7 with 1 M NaOH to obtain a composite hydrogel of COL–CS–OKGM–Ag, which was poured into a clean centrifuge tube and stored at 4°C.
Characterizations of the COL–CS–OKGM–Ag hydrogel were detailed in Supplementary data.
## In vivo antibacterial and wound healing
All in vivo experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of Sichuan University and carried out in accordance with the guidelines for animal rights formulated by Sichuan University, which comply with EU Directive $\frac{2010}{63}$/EU for animal experiments. All animals were housed under standard specific pathogen-free animal facilities. The relevant details were available in the Supplementary data.
## Statistical analysis
All data were presented as mean ± SD of at least three representative trials, with the use of one-way statistical analysis of variance. The significance levels were set at $P \leq 0.05$ (*), $P \leq 0.01$ (**), $P \leq 0.001$ (***); and n.s. represents no significant difference.
## Synthesis and characterization of the GA–Ag nanoparticles
In view of the advantages of good antibacterial potency, antioxidant property and biocompatibility [32], gallic acid was chosen as a reducing agent for silver nanoparticle synthesis from silver ions. GA–Ag NPs are spherical in shape, with good stability and polydispersity (Fig. 1a) because of the adsorption of gallic acid molecules on the surface of the silver nanoparticles (Fig. 1b). Dynamic light scattering analyses revealed an average diameter of 18 nm with narrow size distribution of GA–Ag nanoparticles in water (Fig. 1c).
**Figure 1.:** *TEM images of GA–Ag NPs under low power (a) and at high magnification (b). Size distribution of GA–Ag NPs (c). TGA (d), ultraviolet spectrophotometer (e) and XRD profile (f) of GA–Ag NPs. SEM images of CS-OKGM hydrogel (g), COL–CS–OKGM hydrogel (h) and COL–CS–OKGM–Ag hydrogel (i). Bar = 500 μm.*
The proportion GA in GA–Ag nanoparticles was estimated to be ∼$37\%$ from the thermogravimetric analysis (TGA) curve (Fig. 1d). The GA–Ag NPs have two peaks under UV spectrophotometer (Fig. 1e). The one peak at 396 nm is the absorption peak of silver nanoparticles [33], the other peak at 262 nm indicates the existence of GA molecules on the surface of the silver nanoparticles, which agrees with the absorption peak of pure GA [34]. X-ray diffraction (XRD) analysis showed that the crystal structure of GA–Ag NPs was face-centered cubic (Fig. 1f). There were four typical diffraction peaks at 2θ of 37.98° [111], 44.02° [200], 64.22° [220] and 77.22° [311], which completely conform to the Joint Committee on Powder Diffraction Standards (JCPDS, file no. 01-1164).
The FTIR spectra of gallic acid modified silver nanoparticles and pure gallic acid are shown in Fig. 2a. The peak at 793 cm−1 is ascribed to the benzene ring vibration in gallic acid molecules on GA–Ag NPs. The peak at 1359 and 1562 cm−1 correspond to the C=C stretching vibrations in the benzene rings. The functional groups of oxhydryl deriving the peak at 3184 cm−1 belong to the carboxyl or phenolic hydroxyl groups. These evidences indicate a successful modification for Ag NPs with GA [34, 35].
**Figure 2.:** *FTIR spectra of (a) GA–Ag NPs and (b) hydrogels. (c) Strain amplitude sweep and (d) step–strain measurements of COL–CS–OKGM–Ag hydrogel. Viscosity and shear-thinning behavior of COL–CS–OKGM–Ag hydrogel (e). Photographs of hydrogel injection via 26G syringe needle (f) and (g) fusion of two separated hydrogels showing the easy self-healing behavior of COL–CS–OKGM–Ag hydrogel.*
## Synthesis of injectable and self-healing hydrogels basing on collagen and OKGM
In this study, a hydrogel dressing possessing injectable and self-healing properties for surgical applications was designed, integrating various advantages of excellent antibacterial properties, biocompatibility and blood coagulation capacity. Traditionally, the composite of collagen and chitosan has many merits for biomedical applications but forming irreversible hydrogel network and failure to self-repair after destruction limit their further development. In this work, OKGM was selected as a macromolecular cross-linking agent to endow the collagen–chitosan-based hydrogels with injectable and self-healing properties. Aldehyde groups from OKGM and amino groups from collagen and chitosan formed dynamic chemical bonds of Schiff base, which results in dynamic and reversible rather than permanent hydrogel network. Moreover, with temperature increasing to physiological level, collagen macromolecules were activated to occur local self-assembly process. Therefore, a novel type of hydrogel with physicochemical cross-linked double network comprising of dynamic chemical bonds and self-assembly fibrils was constructed.
As shown in Fig. 1g and h, after the incorporation of collagen, the pore morphology of CS-OKGM hydrogel almost remained unchanged, but the pore size was significantly reduced, which contained filamentous fibrils mainly generated by self-assembly behavior of part collagen. For the COL–CS–OKGM–Ag composite hydrogel containing 200 μg/ml GA–Ag NPs, its micropores seems to become further small and uniform (Fig. 1i), whose swelling rate of lyophilized sponge shape reached about $1675\%$ at 2 min in PBS (Supplementary Fig. S1), which could facilitate absorbing a large amount of wound effusion. The anti-dehydration performance of the hydrogels was evaluated in Supplementary Fig. S2, demonstrating these hydrogels could provide a relatively humid environment for up to 9 h, which was conductive to wound healing. Further, the anti-dehydration time could be further extended by absorbing blood or exudates seeping out of wounds early in healing. The surface elements of COL–CS–OKGM–Ag composite hydrogel were analyzed under energy dispersive spectrometer (Supplementary Fig. S3), whose result indicated that GA–Ag NPs were evenly distributed in the hydrogel matrix.
The chemical structures and characteristic groups of OKGM (Supplementary Fig. S4) and as-prepared hydrogels (Fig. 2b) were verified via FT-IR. Compared with the FTIR spectrum of KGM, the spectrum of OKGM showed two characteristic bands at 1730 and 893 cm−1. The one at 1730 cm−1 represents the symmetrical vibration band of the aldehyde group [36], while the other one at 830 cm−1 is attributed to the hemiacetal structure formed between the aldehyde group and the adjacent hydroxyl group [37]. Meanwhile, the peak at 806 cm−1 corresponds to the respiratory vibration peak of the pyran ring in the KGM macromolecular chain, which almost disappeared in the OKGM spectrum, further suggesting the ring-opening oxidation of KGM macromolecules [38]. The Mw of OKGM was determined to be ∼72.8 kDa by gel permeation chromatography (Supplementary Table S1), indicating that OKGM is a typical biological macromolecular crosslinking agent. As shown in Fig. 2b, the peak at 1637 cm−1 corresponds to the stretching vibration of C=N [39], which confirms successful construction of the dynamic hydrogel network basing on Schiff-base bonds. The broad band at 3405 cm−1 should be attributed to the stretching vibrations of O–H and N–H in chitosan [40]. In the spectra of the COL–CS–OKGM hydrogel, the characteristic peaks at 1687, 1545 and 1247 cm−1 correspond to the amide I (C=O stretching), amide II (N–H bending) and amide III (C–N stretching) bands in collagen macromolecules, respectively [41, 42].
## Injectable and self-healing properties of the hydrogel
Hydrogels possessing permanent network formed by irreversible crosslinking could not restore to their previous whole upon a split by external force which exceed their own endurance. As functional wound dressing, the repairable self-healing hydrogels based on Schiff bases have received intensive attentions benefit from the excellent performances of use convenience and versatile clinical services [43, 44]. Rheological recovery test was applied for COL–CS–OKGM–Ag hydrogel to explore its self-healing behavior.
First, strain amplitude sweep tests were performed to confirm the transition point between the liquid phase and solid phase of the hydrogel. As shown in Fig. 2c, at a frequency of 1 Hz, a plateau area was appeared first in the range strain of 1–$70\%$, in where the storage modulus (G′) and loss modulus (G″) had no obvious changes. As the strain continued to increase, the G′ values decreased rapidly and the G″ values rose sharply. After the strain went beyond the yield point ($197\%$), the G′ values fell to be less than G″, indicating that the hydrogel network was broken.
Then the self-healing capability of the hydrogel was evaluated by step strain tests (1 Hz) (Fig. 2d). The G′ value was higher than G″ in low strain ($1\%$), suggesting the sample was in a hydrogel state at this time. When a high strain ($500\%$) was applied, G′ quickly decreased to ∼7 Pa. In this case, the sample underwent a sol–gel transition and presented a viscous liquid state with a G′ value lower than G″. After experiencing multiple damage-healing cycles later, the storage modulus and loss modulus of the hydrogel were basically unaffected, which is the result of the dynamic formation of Schiff base between the amino groups and the aldehyde groups. These reversible and dynamic bonds derived the mechanical properties of self-healing and syringeability. In addition, the viscosity changes of the hydrogel were monitored by a step-shear measurement at high magnitude shear rate of 100 s−1 (Fig. 2e). The viscosity of the hydrogels decreased with the increase of the shear rate, showing a recovery cycle with the shear rate decreasing, which demonstrated a typical shear thinning behavior to simulate the hydrogel state under the injection conditions in needle. By some further intuitive methods, the hydrogel was injected into water through a syringe (Fig. 2f) to observe its injecting behavior in liquid environment and its self-healing capability after incision (Fig. 2g).
## Photothermal effect of COL–CS–OKGM–Ag hydrogels
The photothermal performance of as-constructed composite hydrogel of COL–CS–OKGM–Ag was studied by irradiation under 808 nm NIR laser with a power of 2 W/cm2. After 10 min of irradiation (Fig. 3a), the temperature of the hydrogels containing 200 μg/ml GA–Ag NPs rose rapidly, while the temperature of the hydrogels without silver nanoparticles almost occurred no any changes. For the concentration of 200 μg/ml GA–Ag NPs in the hydrogels, the temperature of the hydrogel increased 21.5°C within 10 min, while the temperature increase of only 7°C for the hydrogels containing 50 μg/ml GA–Ag NPs (Fig. 3b). This difference indicated that GA–Ag NPs have a significant photothermal efficacy, and the hydrogel temperature can be precisely adjusted by controlling the concentration of GA–Ag NPs in these hydrogels. After multiple cycles of irradiation, the maximum temperature of the hydrogel containing 200 μg/ml GA–Ag NPs had no decrease, indicating its good photothermal stability (Fig. 3c). The photothermal conversion efficiency (η) of COL–CS–OKGM–Ag composite hydrogel calculated to be $27.5\%$ (Supplementary Fig. S5), which was higher than some other photothermal agents, such as PVP-Pt NPs ($22.99\%$) [45], Cu9S5 nanocrystals ($25.7\%$) [46] and Prussian blue nanocages ($26\%$) [47]. The increased temperature of the hydrogel facilitated the release of more Ag+ from Ag NPs, which significantly improved the antibacterial capability of the composite hydrogel of COL–CS–OKGM–Ag [48].
**Figure 3.:** *(a) Thermal infrared images of hydrogels with different Ag NPs content under 808 nm NIR irradiation. (b) Temperature profiles of COL–CS–OKGM–Ag hydrogels with different silver concentrations during the extension of irradiation time. (c) Temperature variation of the COL–CS–OKGM–Ag hydrogel containing 200 μg/ml Ag NPs during three on/off laser irradiation cycles (808 nm 2 W/cm2). Photograph of differently treated hydrogels incubated at 37°C for 12 h against inhibitory bands of (d) E. coli and (e) S. aureus. (f) Photographs of E. coli and S. aureus treated by (I) PBS, (II) COL–CS–OKGM hydrogel, (III) COL–CS–OKGM–Ag hydrogel containing 200 μg/ml Ag, (IV) COL–CS–OKGM–Ag hydrogel containing 200 μg/ml Ag + NIR. (g) Statistical analyses for the bacterial numbers of E. coli and S. aureus (mean ± SD, n = 3, ***P < 0.001).*
## Antibacterial properties of COL–CS–OKGM–Ag hydrogels
Escherichia coli and *Staphylococcus aureus* were chosen to evaluate the antibacterial activity of the hydrogels containing Ag NPs, of which the superior photothermal performance enhanced the inherent bactericidal properties of Ag ions alone. This synergistic action not only can effectively prevent bacterial infections but also promote angiogenesis to accelerate wound healing. The group of the COL–CS–OKGM hydrogel did not generate inhibition zones, while the group of the COL–CS–OKGM–Ag hydrogel and the group of the COL–CS–OKGM–Ag hydrogel + NIR produced obvious inhibition areas (Fig. 3d and e). For E. coli and S. aureus, the diameters of the inhibition zone of the COL–CS–OKGM–Ag hydrogel without NIR irradiation were 2.5 ± 0.52 and 2.4 ± 0.1 cm, respectively. And the diameters of the inhibition zone of the COL–CS–OKGM–Ag hydrogel + NIR were 2.7 ± 0.32 and 2.4 ± 0.1 cm, independently, indicating that the Ag NPs-containing hydrogels had a relatively long-term of at least 12 h antibacterial effect. The Ag+ released from the Ag NPs, which can rapidly bind to the negatively charged cell membranes of bacteria, disrupting the normal metabolism of bacteria to result in bacterial death. Compared with the experiment group without NIR, the influence of the photothermal treatments from the group applying NIR on the diameters of inhibition zones was limited over time. From a relatively long-term, the diameter of the inhibition zone mainly depended on the silver ion concentration endowed by the corresponding composite hydrogel containing Ag NPs.
However, from the short-term effects, the auxiliary antibacterial effect of NIR irradiation on the bacterial suspension was prominent in a short time. When E. coli and S. aureus were in contact with hydrogels for 1 h, the CFU numbers of the bacteria treated with PBS and silver-free COL–CS–OKGM hydrogel, respectively, were almost similar (Fig. 3f). Some specific antibacterial properties were not observed. The hydrogel containing GA–Ag NPs without photothermal treatment exhibited limited antibacterial effect. Although it showed very obvious antibacterial activity during long-term contact, the slow release of Ag+ under short-term contact was quite not enough for rapid and effective sterilization. In addition, the CFU number decreased significantly for the group of the hydrogel containing GA–Ag NPs plus NIR (Fig. 3g). This outstanding short-term antibacterial effect was mainly ascribed to the synergistic action of the photothermal efficacy of the GA–Ag NPs and the inherent antibacterial properties of the released Ag+, which made up for the insufficient antibacterial effect of Ag+ itself within a short period of time and meanwhile maintained the antibacterial activity for a long time.
The morphology of the bacteria under different treatments was revealed by SEM to further demonstrate the influence of the GA–Ag NPs within the hydrogel on bacteria (Fig. 4a). The untreated E. coli had smooth surface and complete morphology. On the other hand, E. coli treated with GA–Ag NPs-contained hydrogel without NIR was slightly damaged. Most of the cell membrane of E. coli in the group of COL–CS–OKGM–Ag hydrogel + NIR showed shrinkage and damage. The same changes occurred to S. aureus, after irradiated under NIR light, whose cell membrane treated with COL–CS–OKGM–Ag hydrogel showed pits and shrinkage. It was obvious that the COL–CS–OKGM–Ag hydrogel along with NIR irradiation assistance had much better antibacterial activity in a short period of time at initial treatment. The optimum temperature for enzyme activity in bacteria is 30–40°C, and the enzyme activity could be strongly inhibited at higher temperatures. The irradiation of NIR laser promoted the elevation of local temperature and led to bacterial cell membrane destruction and protein denaturation [49]. Simultaneously, the release of Ag+ was also accelerated by high temperature and combined with the sulfhydryl groups on the surface of bacteria [50], which can destroy the bacterial cell membrane and thus interact with the nucleic acid to result in bacteria death [51].
**Figure 4.:** *(a) SEM images of E. coli and S. aureus and (b) CLSM images of S. aureus treated by (I) PBS, (II) COL–CS–OKGM hydrogel, (III) COL–CS–OKGM–Ag hydrogel containing 200 μg/ml Ag NPs, (IV) COL–CS–OKGM–Ag hydrogel containing 200 μg/ml Ag NPs + NIR. PI labeling dead bacteria, and SYTO-9 labeling live bacteria. (c) A schematic illustration for the mouse liver hemorrhage model treated by COL–CS–OKGM–Ag hydrogel. (d) Accumulated blood loss in 120 s for hepatic hemorrhage under different treatments (mean ± SD, n = 3, ***P < 0.001). (e) The hemostasis effect of COL–CS–OKGM–Ag hydrogel on damaged mouse liver within 120 s as positive control and untreated group as negative control.*
As shown in Fig. 4b, there were almost no PI fluorescence in the groups of the bacteria treated with PBS or with the non-silver-containing COL–CS–OKGM hydrogel. A few PI fluorescence spots appeared in the group of the hydrogel containing GA–Ag NPs, indicating that GA–Ag NPs exhibited natural antibacterial ability. When the NIR laser irradiation was performed for 10 min, the bacteria emitted massive PI fluorescence, indicating that most of bacteria had died. The confocal laser scanning microscope (CLSM) images further confirmed that the synergy between the photothermal properties of GA–Ag NPs and the inherent bactericidal activity of Ag+ during the short-term contact can destroy the integrity of cell membranes and induce the death of bacteria.
## Evaluation of in vivo hemostatic effects of COL–CS–OKGM–Ag hydrogel
Infection from wound bleeding is one of the main causes of tissue complications, leading to inflammation and delayed wound healing [52]. Timely hemostasis is the first step in wound repair. Therefore, mouse liver hemorrhage models were applied to evaluate the hemostatic ability of the COL–CS–OKGM–Ag hydrogel (Fig. 4c). First of all, from Fig. 4e, it was obvious that due to a puncture on the liver, the blood seeped out from the pinhole, which will flow out continuously if no effective treatment was adopted. In contrast, when the hydrogel was injected onto the wound area immediately after the occurrence of bleeding, the bleeding was controlled and stopped within 30 s. As shown in Fig. 4d, 2 min after the hydrogel injection onto the liver wound, the bleeding volume was 39.33 ± 11.4 mg, while the bleeding volume from the untreated liver wound reached up to 234.33 ± 24.7 mg. The rapid hemostatic ability of the COL–CS–OKGM–Ag hydrogel should be attributed to the excellent performance of collagen and chitosan. Positively charged collagen could attract platelet aggregation and activate intrinsic pathways of the secondary hemostatic process. Chitosan could agglutinate with negatively charged erythrocytes and effectively activate platelets to achieve good hemostasis efficacy [53].
## In vitro biocompatibility evaluation for hydrogel of COL–CS–OKGM–Ag
Firstly, human dermal fibroblasts (HDF) cells were cultured on the surface of COL–CS–OKGM–Ag hydrogels (Fig. 5a). After 24 h, whether for the silver-free hydrogels or the COL–CS–OKGM–Ag hydrogels containing 200 μg/ml Ag NPs, these spindle-shaped cells had adhered to the surface of the hydrogels and diffused well. An important sign of early cell proliferation is the extension of membranous processes such as filopodia or lamellar feet [54]. HDF cells produced visible filopodia and formed slender spindle shape to effectively promote cell proliferation. After 3 or 5 days of cultivation, the cells showed evident proliferation. In addition, HUVEC cells and NIH3T3 cells were also inoculated on the surface of hydrogel (Fig. 5c and e), respectively. Both HUVEC and NIH3T3 cells could attach to the hydrogel within 1 day and proliferated over the next 4 days. Compared with the blank group, the cells cultured on the hydrogels with different silver content all showed contiguous cell viability (Fig. 5d and f). The cell viability of the COL–CS–OKGM–Ag hydrogel groups remained above $85\%$ after incubation for 1, 3 and 5 days. To further verify the cell compatibility of so-designed hydrogel material, HDF cells (Fig. 5b) and NIH3T3 cells (Supplementary Fig. S6) were co-mixed with the hydrogel precursor solution before gelation, respectively, thus to culture these cells inside the hydrogel. After 5 days of culture, HDF and NIH3T3 cells proliferated and grew significantly inside the hydrogel with few dead cells. Then histological analyses were performed on the major organs of mice treated with the hydrogel material for 14 days (Supplementary Fig. S8). All tissues showed a normal structural state without obvious organ damage and inflammation. These results confirmed the excellent biocompatibility of COL–CS–OKGM–Ag hydrogels, which can provide a suitable microenvironment for cell survival and growth. According to ISO/TR 7406 [55], hemolysis ratio (<$5\%$) for the COL–CS–OKGM hydrogels with different silver concentration is in the critical safe range of hemolysis for biomaterials, suggesting the good blood compatibility (Supplementary Fig. S7).
**Figure 5.:** *CLSM images of (a) HDF cells, (c) HUVEC and (e) NIH3T3 cells cultured on the surface of COL–CS–OKGM–Ag hydrogel containing from 0 to 200 μg/ml Ag NPs (bar = 200 μm, 50 μm). (b) Three-dimensional reconstruction of CLSM image of HDF cells embedded and cultured inside the hydrogel for 5 days. Cells were cultured on hydrogels with different silver content for 1, 3 and 5 days, the proliferation of (d) HUVECs and (f) NIH3T3 cells was detected by CCK-8. (g) Photographs of S. aureus infected wounds at predetermined time points for different treatment groups. (h) Temperature changes showed by thermal infrared images of mice irradiated by infrared laser and (i) percentage of wound area in vivo at 7 and 14 days.*
## In vivo evaluation for wound healing
According to wet-wound healing theory, COL–CS–OKGM–Ag hydrogel, as a water-rich dressing containing bioactive macromolecules and Ag nanoparticles, assisted complex wounds including infected wounds with regenerative healing. A full-thickness wound of a round incision with a diameter of 1.0 cm on the back of the mouse, which was infected by S. aureus, was constructed as an infection wound model. The degree of wound healing in each group of mice (Fig. 5g) was recorded by camera at different times. Under the irradiation of 808 nm NIR light for 1 min (Fig. 5h), the local temperature of the wound area, which was covered by the COL–CS–OKGM–Ag hydrogel, rose rapidly from 27°C to 44°C. The wound area was maintained at a mild temperature of <45°C without burning the skin by controlling the power of the NIR light emitter. The results showed that this COL–CS–OKGM–Ag hydrogel could be used as a good PTT for human wound healing. After 7 days of treatment, compared with the untreated group, the wound area of the treated group decreased to a higher degree. After 14 days of treatment, the new epidermis gradually extended to the center and covered the former wound area. During the recovering period, the group of COL–CS–OKGM–Ag + NIR hydrogel exhibited the fastest healing process compared with the other groups, of which new growth skin was very similar to the normal skin. The wound area decreased only to $3.2\%$ on the 14th day (Fig. 5i), while the wounds of the blank group still had obvious scabs. Such designed hydrogel dressings based on collagen and chitosan had been proved to have expected potential of promoting wound healing [56]. This COL–CS–OKGM–Ag hydrogel combining NIR laser irradiation had effectively increased antibacterial activity on infectious wounds with accelerating the wound healing process due to the thermal therapy efficacy from Ag nanoparticles.
Histological analyses further evaluated the regenerative skin tissue of the hydrogel-treated infected wound. Inflammatory cell infiltration still existed in the wound area on the seventh day (Fig. 6a), while the wound edge was in healing and the new epidermis became thicker compared with the surrounding normal skin. Fine neovascularization appeared in the lower layer of the wound. On the 14th day, it can be seen that the wound of the hydrogel treatment group has healed basically and formed a complete epidermis layer. Both the epithelium and connective tissue were shown to have greater regularity, and new hair follicles were obvious at the wound site, which implied the functional regenerative repair of the skin tissue. The epidermal thickness of the wound treated with COL–CS–OKGM–Ag + NIR was thinner than that of the other three groups (Fig. 6b). Since epidermal thickening is related to hypertrophic scar formation [57], it was further suggested that COL–CS–OKGM–Ag hydrogel plus photothermal treatment reduced the formation of scars. In addition, in the process of wound repair, collagen is a necessary condition for wound healing and dermal reconstruction [58]. In Masson’s three-color staining slices, a small amount of collagen deposition occurred in the treatment group on Day 7 and the area of new collagen fibers increased greatly on the 14th day. Compared with the other groups, the wounds treated by the group of COL–CS–OKGM–Ag + NIR hydrogel showed more collagen deposition (Fig. 6c), which were dense and orderly. These results had demonstrated that the group of COL–CS–OKGM–Ag + NIR exhibited better healing effect than other groups, because collagen deposition and granulation formation were positively correlated with the state of wound healing [59].
**Figure 6.:** *(a) H&E staining and Masson staining of the wound sites on 7th and 14th day. Scale bars = 200 μm. Arrow 1: scab skin; Arrow 2: blood vessels; Arrow 3: thickened epidermis; Arrow 4: collagen fiber; Arrow 5: epidermal tissue; Arrow 6: hair follicle; Arrow 7: fibroblasts (n = 3). (b) The epidermal thickness and (c) collagen deposition was measured on 14th day (mean ± SD, n = 3, **P < 0.01).*
Wound healing needs new blood vessels to transport nutrients and metabolism to achieve reconstruction for tissue repair [60]. Therefore, angiogenesis is also an important indicator for wound healing evaluation [61]. As showed by CD31 immunohistochemical analyses (Fig. 7a), on the seventh day, new vessels already appeared in the wound area for different groups, with fewest new vessels in the blank group while most vessels in the COL–CS–OKGM–Ag + NIR treated group. On Day 14, there were more new vessels in the VI group than in the control group (Fig. 7b). These phenomena indicated that the COL–CS–OKGM–Ag hydrogel plus mild thermal stimulation can facilitate the neovascularization. Remarkably, the healing process of infected wounds and the regeneration of new blood vessels are slow and persistent, so the irradiation of NIR light in animal experiments is also a long-term regular process to ensure effective killing of bacteria and stimulation of angiogenesis. The syringeability and the self-healing properties of the COL–CS–OKGM hydrogels enable this dressing type to fully fit to irregular wound surface, providing an appropriately moist environment for healing process. The antibacterial effect of the COL–CS–OKGM–Ag hydrogel can be synergistically enhanced by the photothermal function of the Ag NPs, which meanwhile can give mild photothermal stimulation for accelerating wound healing in turn.
**Figure 7.:** *(a) CD31 immunohistochemically staining on the 7th and 14th day of the wound treatment. Scale bars =100μm, black arrows: blood vessels. (b) Number of newly formed blood vessels from the immunohistochemical images on Day 14 (mean ± SD, n = 3, **P < 0.01). Note: I: blank; II: CS–OKGM–Ag hydrogel + NIR; III: COL–CS–OKGM–Ag hydrogel; IV: COL–CS–OKGM–Ag hydrogel + NIR.*
## Conclusions
A multifunctional hydrogel dressing integrating antibacterial and hemostasis activities together with mild PTT was smartly designed for treating complicated wounds by injecting this hydrogel on wound area to maintain a moist environment for regenerative healing rather than cicatrix repair. This collagen-based hydrogel was successfully constructed through incorporating Ag-NPs into COL–CS–OKGM hydrogel matrix, which was confirmed by rheological characterization for its excellent injectable and self-healing behaviors originating from reversible Schiff-base linkages. In vitro and in vivo evaluations demonstrated the good biocompatibility of the COL–CS–OKGM–Ag hydrogel and the markedly enhanced antibacterial action provided by the synergistical effect of Ag+ and mild photothermal efficacy of Ag NPs, which improved the local capillary circulation of the wound area to further accelerate wound healing process. Meanwhile, this hydrogel matrix of COL–CS–OKGM–Ag has also been demonstrated to be an excellent hemostasis material for inhibiting wound bleeding. The injectable self-healing properties make the COL–CS–OKGM–Ag hydrogel a convenient dressing material for the wounds with irregular and large area needing frequent applying and changing without secondary injury. So-designed composite hydrogel is a promising multifunction platform for wound therapies with great safety and desired regenerative prognoses.
This study emphasizes the proof-of-concept applications of such multifunctional hydrogel for the treatment of various wounds derived from some diseases or tumor resection. But as many reported relevant studies, our works were conducted only on infectious wound models. Further investigations based on more complicated wound models will be necessary to confirm the adjuvant therapy and management strategy for the regenerative healing achieved by this multifunctional hydrogel dressing.
## Supplementary data
Supplementary data are available at Regenerative Biomaterials online.
## Author contributions
H.G.: Methodology, Investigation, Formal Analyses, Writing—Original Draft. H.L.: Investigation. L.W.: Review & Checking. J.L.: Resources. Q.W.: Funding Acquisition, Conceptualization, Investigation, Revising, Supervision.
## Funding
This work was supported by Sichuan Province Key Research and Development Project (2018SZ0046) and the National Natural Science Foundation of China [51373105].
Conflicts of interest statement. The authors have declared no conflicts of interest.
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|
---
title: Weighted gene co-expression network analysis revealed T cell differentiation
associated with the age-related phenotypes in COVID-19 patients
authors:
- Yao Lin
- Yueqi Li
- Hubin Chen
- Jun Meng
- Jingyi Li
- Jiemei Chu
- Ruili Zheng
- Hailong Wang
- Peijiang Pan
- Jinming Su
- Junjun Jiang
- Li Ye
- Hao Liang
- Sanqi An
journal: BMC Medical Genomics
year: 2023
pmcid: PMC10039774
doi: 10.1186/s12920-023-01490-2
license: CC BY 4.0
---
# Weighted gene co-expression network analysis revealed T cell differentiation associated with the age-related phenotypes in COVID-19 patients
## Abstract
The risk of severe condition caused by Corona Virus Disease 2019 (COVID-19) increases with age. However, the underlying mechanisms have not been clearly understood. The dataset GSE157103 was used to perform weighted gene co-expression network analysis on 100 COVID-19 patients in our analysis. Through weighted gene co-expression network analysis, we identified a key module which was significantly related with age. This age-related module could predict Intensive Care *Unit status* and mechanical-ventilation usage, and enriched with positive regulation of T cell receptor signaling pathway biological progress. Moreover, 10 hub genes were identified as crucial gene of the age-related module. Protein–protein interaction network and transcription factors-gene interactions were established. Lastly, independent data sets and RT-qPCR were used to validate the key module and hub genes. Our conclusion revealed that key genes were associated with the age-related phenotypes in COVID-19 patients, and it would be beneficial for clinical doctors to develop reasonable therapeutic strategies in elderly COVID-19 patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12920-023-01490-2.
## Background
The Corona Virus Disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a significant impact on society and the economy worldwide, resulting in a large number of deaths. It has been identified that age-related vulnerability is a key factor in the burden of disease during this pandemic [1]. Research has shown that COVID-19 mainly affects the elderly population and many risk factors, such as advanced age [2], male gender, and clinical complications, contribute to the progression of the disease to severe and critical stages [3]. Despite the efforts made in researching and developing vaccines and drugs, COVID-19 has not been well controlled or cured, and there are still many irreversible complications [4–6]. Therefore, it is crucial to investigate the spectrum and pathogenesis of the disease in susceptible populations during this pandemic.
Macrophage infiltration into the lung cause a rapid and intense cytokine storm in COVID-19 patients, ultimately leading to complications such as metabolic syndrome, obesity, type 2 diabetes, lung disease, and cardiovascular disease. These age-related diseases increase the severity and lethality of COVID-19, and in some cases, can even lead to multi-organ failure and death [7]. It is widely accepted that elderly patients with COVID-19 are more likely to progress to a severe condition and unfavourable outcome due to age-related biological alterations [2, 8, 9]. The underlying mechanisms that cause different outcomes in different age groups may include age-related pattern recognition receptor activation, type 1 interferon activation, functionally impaired neutrophils, macrophages, T cells response, and other complex progressive changes in the immune system [10, 11]. However, the exact mechanism is still unclear, and it is urgent for us to investigate the most important factor causing different outcomes in different age groups of COVID-19 patients.
Several computational and research methods have been developed in order to understand the pathogenesis of specific disease states. Part of these methods are used to explore underlying gene networks, which are useful in guiding diseases understanding and their mechanistic pathways. Notably, co-expression analysis is one of many approaches. In this analysis, clusters genes into co-expression groups are called modules. Genes which belong to the same module are considered to have the same functional properties [12]. By using the concepts of graph-theory, it enables researchers to systematically understand the relationship between module genes and phenotypes based on module characteristic genes [12]. Indeed, weighted correlation network analysis (WGCNA) of co-expression analysis has been applied to many biological processes [13–18]. In short, gene networks provide tools to perform group level gene comparisons and identify biologically relationships between genes and phenotypes comprehensively.
Therefore, in this study, we analysed age-related gene networks through co-expression analysis in 100 COVID-19 patients. The SRP279280 dataset was used to identify the gene module which tightly connected with age trait. The function and hub genes of the key module were identified, the protein–protein interaction network and transcription factor-gene interaction were constructed. The results of our study may provide new insights into the mechanisms underlying adverse progression of COVID-19 in advanced age and may help identify potential diagnostic biomarkers and therapeutic targets.
## Data collection and pre-processing
*The* gene expression profile and clinical information of GSE157103 were downloaded from publicly Sequence Read Archive (SRA) database SRP279280 (https://www.ncbi.nlm.nih.gov/sra/). Dataset GSE157103 contained a total of 100 COVID-19 patients’ normalized peripheral blood mononuclear cell (PBMC) data in vivo, including 62 males and 38 females, 50 ICU-patients and 50 non-ICU-patients, 42 mechanical-ventilation-patients and 58 non-mechanical-ventilation-patients [19]. Of note, there was a sample for which age information was missing. The age of rest samples ranged from 21 to 90. The 10,985 genes whose transcript per million (TPM) was more than 1 were selected for further analysis from all 19,472 gene set in expression profile. Average method was used to calculate the distance between samples, and 3 samples were eliminated as outliers.
In addition, independent dataset, which was collected from the research of Huang et al. [ 20], are used to verify the consistency and reliability of our results. It contains 4 mild COVID-19 patients’ and 4 severe COVID-19 patients’ normalized PBMC RNA-seq data.
## Weighted gene co-expression network analysis and screening for key modules
The WGCNA R package (version 1.7.1) based on R (version 4.2.1) were performed to identify key genes associated with different age groups in dataset GSE157103 COVID-19 patients [19]. WGCNA is the most widely used methods to identify genes and modules associated with specific phenotypes [21]. *All* genes and samples were checked for excessive deletion values and for abnormal samples. And three outliers were cut out. R2 was set as 0.8 to weight highly correlated genes, and the soft thresholding power (β) was 14. For the purpose of defining clusters of genes and getting a more suitable number of modules, the minimal module size was set as 30, mergeCutHeight was set as 0.05 and other parameters retain the default values. The adjacency matrix was used to calculate the topological overlap matrix (TOM) and a heatmap was used to show the degree of overlap in share neighbours between pairs of genes in the network.
In order to identify the key module with significant correlation, we calculated the correlation between each module eigengenes connectivity and the age of samples. The correlation coefficient values were displayed within a heatmap. The module that was most significantly associated with age was considered as the key module. The correlation analysis in this study were performed using the Pearson correlation as described in the “WGCNA” package [12].
## Functional enrichment analysis and immune evaluation of key module
The enrichment analysis was performed to explore the functions of the key modules. DAVID (https://david.ncifcrf.gov/) [22] is a powerful tool for gene functional annotation and analysis. The comprehensive resource such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [23–26], Gene Ontology (GO) etc. datasets were included in the enriched pathways. The Benjamini–Hochberg (BH) adjusted p-values were sorted for visualization.
We used single-sample GSEA (ssGSEA) which is the measure of genes that are co-ordinately up- or down-regulated within a sample to calculate immune enrichment scores for each sample. ssGSEA define an enrichment score that represents the absolute degree of enrichment of the gene set in each sample within a dataset. Sequencing normalization of gene expression values was performed, and enrichment scores were generated using the empirical cumulative distribution function (ECDF) of the genes in the signature and the remaining genes. We used the immune cell set as a reference to perform immune scores between 100 samples.
## Principal component analysis and receiver operating characteristic curve analysis
Principal component analysis (PCA) is one of the most widely applied data dimensionality reduction algorithms [27]. It was used to remove noise and unimportant features and retain the most important features of the key module. The key module genes with possible correlation were transformed into linearly uncorrelated variables through orthogonal transformation, and the first principal component (PC1) was chosen for further analysis according to the contribution rate. The first two principal components were selected for two-dimensional visualization. To further evaluate the potential diagnostic value of the first principal component in key module for COVID-19, the receiver operating characteristic (ROC) curve analysis was performed by using the pROC (version 1.18.0) [28] routine in R. We evaluated the diagnostic efficacy of PC1 and age for sex, ICU admission, and mechanical-ventilation.
## Protein–protein interaction analysis and network construction
In biological systems, protein–protein interaction (PPI) networks are evaluated and analysed to understand the interaction between intracellular protein, which can improve the comprehension of protein function. The key module genes were used to construct PPI network through STRING web tools [29], and all parameters were set to default values. STRING provides the PPI network that shows how the identified hub genes (proteins) interrelate functionally and physically with each other through encoding the gene list as input [30]. The PPI results were analysed and visualized by Cytoscape (V3.8.2) [31–33]. The hub genes were chosen based on degree connectivity via cytoHubba [34] in Cytoscape. Among the 11 methods in Cytoscape’s plug-in ctyoHubba, Maximal Clique Centrality (MCC) performs the best, with the advantage of precision predictive function in essential proteins from the Yeast PPI network [34]. After MCC algorithm, all module genes were ranked according to their intramodular connectivity, and only the top ten genes were selected as hub genes.
## Transcription factors-genes interactions
Transcription Factors (TFs) are proteins that bind DNA and regulate transcription in a sequence specified manner. In order to further understand the regulation of hub genes, we found out the TFs of the hub genes and constructed the TFs-genes network map through NetworkAnalyst 3.0 (https://www.networkanalyst.ca/) [35]. The TFs and gene targets data were obtained based on the ENCODE ChIP-seq database (https://www.encodeproject.org/) [36]. By BETA Minus algorithm, only peak intensity signal < 500 and the predicted regulatory potential score < 1 was included.
## Collecting experimental samples, isolating PBMC and extracting total RNA
5 mL of fasting elbow venous blood was collected from 6 samples (3 samples in 20–30 years old considered as younger group and 3 samples in 40–50 years old considered as older group) and centrifuged at 800 g/min at 25 °C for 8 min. The supernatant was thoroughly mixed with the same amount of PBS, 6 mL of Ficoll separation solution was added and centrifuged at 1200 g/min at 25 °C for 10 min. The PBMC was absorbed and placed in a new 50 mL centrifuge tube, thoroughly mixed with PBS by 5 times, centrifuged at 300 g/min at 25 °C for 10 min. The supernatant was discarded and 2 mL RBC cracking solution was added into the tube, stood at room temperature for 5 min. An appropriate amount of PBS was added and centrifuged at 2500 rpm/min at 25 °C for 5 min. The supernatant is discarded and the precipitation is retained. Trizol (American ambion) was added into the precipitation, and the total RNA was extracted by Trizol method. Then the dissolved RNA was transferred to the ice box, and the concentration and quality of total RNA were evaluated by NanoDrop ND-2000 spectrophotometer. The samples were stored in the refrigerator at − 80 °C for further using.
## RT-qPCR
Total RNA was reverse-transcribed into cDNA using PrimerScript RT Master Mix kit (Japan Takara), and then real-time fluorescent quantitative PCR was performed according to the instructions of TB Green Premix Ex Taq II kit (Japan Takara). The primers were synthesized by Shanghai Shengong Biological Engineering Company, and the sequence was:RORC: upstream primer 5ʹ-AGCGCTCCAACATCTTCTCC-3ʹ, downstream primer 5ʹ-ACCACTTCCATTGCTCCTGC-3ʹ,CCR7: upstream primer 5ʹ-TTCCAGCTGCCCTACAATGG-3ʹ, downstream primer 5ʹ-CAAGAAAGGGTTGACGCAGC-3ʹ;MYC: upstream primer 5ʹ-GGCTCCTGGCAAAAGGTCA-3ʹ, downstream primer 5ʹ-CTGCGTAGTTGTGCTGATGT-3ʹ.
The reaction total volume was 20 uL of PCR reaction system, including SYBR Premix Ex Taq 10 uL, upper and downstream primers 0.4uL of each, ROX Reference DyeII 0.4 uL, cDNA template 2 uL and sterilized distilled water supplement system to 20 uL. Using GAPDH as the internal reference, the experiment was repeated for 3 times to take the mean value, and the relative mRNA expression was calculated by 2-ΔΔCt method.
SPSS 23.0 and Graphpad prism 9.0 were used for statistical analysis, and T-test analysis was performed for comparison between groups. $p \leq 0.05$ was considered statistically significant.
## Identification of key module with highest correlation with age phenotype
We developed a flowchart to systematically analyze the age-related genes in COVID-19 (Fig. 1). First, 10,985 genes (TPM > 1) in the 100 samples of COVID-19 patients were used to set up the co-expression network. Three outliers which found by samples clustering (Additional file 1: Fig. S1A) were eliminated in order to make the subsequent analysis more reliable. To construct the network, a soft-threshold of 14 was used to obtain the approximate scale-free topology (Additional file 1: Fig. S1B, C). Genes across the 97 samples were hierarchically clustered based on topological overlap (Fig. 2A). 39 modules were identified in which genes are co-expressed and random colours were allotted to distinguish different modules. To examine the relation of these modules, we built an eigengene adjacency matrix by calculating the correlation of the eigengenes matrix. Correlation between modules were represented by heatmap (Fig. 2B), and it indicated relative independence among these modules. Fig. 1Schematic flow chart demonstrating the process of the analysisFig. 2Results of Weight Gene Co-expression Network Analysis (WGCNA) in 100 COVID-19 patients of different ages. A Clustering dendrogram of 10,985 co-expression genes (TPM > 1) based on topological overlap. B Eigengene adjacency heatmap of different gene co-expression modules. C Module-age correlative analysis. Each row corresponds to a module eigengene and each column corresponds to different ages. Heatmap block with p-values and correlation coefficients. The red box in the figure shows the module with the highest correlation coefficient in the age range. D The barplot shows the absolute value of the correlation coefficient of each module for different ages For the purpose of determining if any of the identified co-expression modules were associated with age, the correlation coefficient were calculated between each module and aging status by correlating the eigengene values of each module with the age trait (Fig. 2C). We found more modules were negatively correlated with age phenotype, and only a few were positively correlated with this trait. Barplot was used to further represent gene significance across modules (Fig. 2D). The results demonstrated that age was most significantly correlated with the lightgreen module, which composed of 108 genes, suggested those 108 genes were mostly associated with the age-related phenotypes in COVID-19 patients. Therefore, this module can be adopted to represent the ageing stage of COVID-19 patients. For those, we selected lightgreen module for further investigation and will use the key module to refer to them.
## Key module was shown to correlate with T cell function
To further investigate the relationship between key module’s gene expression and distribution in COVID-19 patients, patients were divided into 7 age groups every 10 years [37]. By performing the eigengene variation analysis (Fig. 3A), we found module eigengenes (ME) values were generally higher in younger patients than in older patients ($$p \leq 0.00035$$). *In* general, the ME value gradually decreased with the increase of age. *The* gene expression was further analysed and results revealed that higher expression of key module genes in young adults, while lower expression in aged patients (Fig. 3B).Fig. 3Lightgreen module gene features. A *Variation analysis* of the lightgreen module eigengene values in different age groups (p-value = 0.00035). B Gene expression heatmap of 93 genes in the lightgreen module in COVID-19 patients with different ages. C KEGG pathway enrichment analysis of genes in the lightgreen module. D GO enrichment analysis of genes in the lightgreen module. E Differential enrichment scores of 9 immune cell signatures among ICU group and non-ICU group In order to further explore the function of the key module. Enrichment analysis were performed by DAVID platform [22]. KEGG pathway enrichment analysis showed that T cell-related categories, including Th17 cell differentiation and Th1 and Th2 cell differentiation, were enriched in these key module genes (Fig. 3C). We also performed GO enrichment analysis (Fig. 3D). The key module genes were also significantly enriched in T cell related pathways, such as positive regulation of T cell receptor signaling pathway, thymic T cell selection, T cell receptor signaling pathway and T cell differentiation. It suggested that T cell-related genes expressed variant in different age groups of COVID-19 patients. We further characterized the immune cell components in 100 COVID-19 patients by scoring key module genes using ssGSEA (Fig. 3E). Interestingly, Key module genes were scored mainly in T cells subtypes, including natural killer T cell, type 17 T helper cell, activated CD4 + T cell, central memory CD4 + T cell, effector memory CD8 + T cell, activated CD8 + T cell, type 1 T helper cell and type 2 T helper cell. Nevertheless, the immune cell composition was significantly different among 50 ICU-patients and 50 non-ICU-patients. Except effector memory CD8 + T cell which was not significant, the composition of other subtypes of T cells was higher in non-ICU patients than in ICU patients while memory B cell was the opposite. Thus, T cell depletion characterizes severe COVID-19 disease.
## Key module genes can distinguish COVID-19 patients between different status
To further determine the power of key module in COVID-19 patients, principal component analysis (PCA) was proceeded to construct the key module signatures. We selected the top two significant components with the higher contribution degree, which explained $81.7\%$ and $2.68\%$ of the key module variation, to identify COVID-19 patients’ different status. Surprisingly, the first principal component (PC1) mainly separated mechanical-ventilation patients from non-mechanical-ventilation patients (Fig. 4A), and it also effectively separated ICU patients from non-ICU patients (Fig. 4B). We also analysis the gender distribution in two dimensions by the top two significant components (Additional file 2: Fig. S2A), just like we thought, there was little discrimination between gender. Taken together, measures mentioned above confirmed the importance of the key module in COVID-19 status. Fig. 4Performance of lightgreen module analysis. A, B Principal component analysis (PCA) of lightgreen module. Each dot represents one sample. Green: non-mechanical-ventilation. Purple: mechanical-ventilation. Orange: ICU. Yellow: non-ICU. C Receiver operating curve (ROC) plot of the performance based on accuracy using PC1 of lightgreen module genes for severity, mechanical ventilation and gender Then, principal component 1 was selected to act as a signature score predicting COVID-19 status on the basis of its highest contribution degree. To further explore the diagnostic potential of the key module, we perform receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) for discriminating whether required to ICU was 0.836, and for discriminating whether used mechanical ventilation was 0.817 (Fig. 4C). For contrast, we demonstrated the diagnostic power of PC1 for gender, and the AUC was only 0.588. This suggests that the PC1 has a strong diagnostic capacity for patients’ severity and mechanical-ventilation and there is little discrimination for gender. To further demonstrate the potential capabilities of age-related key module, rather than the role of age itself. We also performed ROC analysis by replacing PC1 with age, and analyzed the diagnostic power of age for severity, mechanical-ventilation and gender (Additional file 2: Fig. S2B). The AUC were 0.548, 0.475 and 0.494, suggesting age-related key module has high diagnostic potential for severe COVID-19, while age cannot predict the severity of patients. Because of the correlation between the key module and T cell-related functions, we assumed that alteration of the key T cell-related gene expression may be a reason for elderly COVID-19 patients progressed to severe disease with an unfavourable outcome.
## Ten genes were identified as hub genes and three genes were considered as key TF-genes
The PPI network was constructed with 108 key module genes using the STRING database [29]. The network diagram contains 47 nodes and 60 edges (Fig. 5A). By MCC algorithm [38], the network interaction was presented among these genes and identified 10 hub genes (the core components of the module that were representative of the module’s function), including GATA3, CCR7, IL2RB, CD5, SLAMF1, TCF7, MYC, RORC, IL23A and CARD11, based on a higher degree of connectivity in the key module. Fig. 5Network analysis. A PPI network diagram of lightgreen module genes. The network diagram contains 47 nodes and 60 edges. The first 10 genes are selected as GATA3, CCR7, IL2RB, CD5, SLAMF1, TCF7, MYC, RORC, IL23A and CARD11. B Transcriptional factors-genes network diagram. The red circle represents the hub genes of PPI, and the blue circle represents the transcription factor TFs–genes interactions showed the interaction between hub genes selected above by using NetworkAnalyst. The TFs–genes interaction network consisted of 157 nodes and 448 edges (Fig. 5B). Among them, MYC was regulated by 62 TF genes, IL23A was regulated by 48 genes, CCR7 was regulated by 41 genes, GATA3 was regulated by 35 genes and RORC was regulated by 19 genes. Among these TFs-genes, HDGF, SUPT5H and MLLT1 with 4 edges was considered as key TF-genes which was highly related with regulation of T cell differentiation in elderly COVID-19 patients.
## Validation of the key genes revealed significant differences in the expression of genes related to T cell differentiation in different age groups
To validate our results, we analysed RNA-seq data from other independent COVID-19 datasets in vivo and observed the same results (Fig. 6A). The expression of key module genes was higher in the younger group. To further validate the aforementioned bioinformatics analysis, we had consulted additional literature and found more evidence of the association of RORC, CCR7, and MYC with COVID-19 and T cells before we performed laboratory experiments [39–47]. The mRNA expression levels of these genes were obtained by the RT-qPCR experiment in key genes which was highly ranked in relation to transcription factors and associated with important phenotype (Fig. 6B, C, D). In different age groups, the mRNA relative expression of RORC and CCR7 gene were significantly higher in younger group compared with older group (RORC: $$p \leq 0.0393$$, CCR7: $$p \leq 0.0104$$). MYC had the same trend although there was no statistical difference (MYC: $$p \leq 0.1765$$).Fig. 6Independent data sets and RT-qPCR experiments. A Heatmap represent the key module genes in independent data sets. B, C, D Validation of the expression level of three key genes using RT-qPCR: B RORC, C CCR7 and D MYC. unpaired Student’s t-test
## Discussion
Due to the pandemic of COVID-19, millions of people are severely affected. More and more people passed away, especially among the elderly. Even though several drug candidates and vaccines have been researched and applied to treat the patients, there is still no reliable treatments for elderly. *Inspecting* gene co-expression patterns is proven to be an effective method to analyse and uncover complicated genetic network. In our study, gene co-expression analysis was performed on RNA-sequencing (RNA-seq) data set, which contained gene expression data from 100 COVID-19 patients. We identified a key module, consisting of 108 genes, which is most associated with age phenotypes. The results of the enrichment analysis suggested that the key module and the underlying pathological processes of the disease were associated with T cell-related pathways.
Carolyn et al. focused on the different SARS-CoV-2 specific T cell responses between in children and adults and found low T cell responses in children [11]. We focused on young and old patients and hypothesised that T cell differentiation played a key role in different outcomes of different age groups of COVID-19 patients. Our results may complement those of Carolyn et al. T cell differentiation is not only different in children and adult, but also in different age groups of adult patients with COVID-19. An age-dependent stratification of severe COVID-19 patients was also reported in previous study [48]. Other researches have shown that severe COVID-19 patients have a significant age-associated increase of autoantibody levels against 16 targets (e.g., amyloid β peptide, β catenin, cardiolipin, claudin, enteric nerve, fibulin, insulin receptor α, and platelet glycoprotein). These findings have provided key new insights into the reason why the prognosis for older patients is worse compared with younger ones [48].
T cells play a vital role in viral clearance, but the underlying pathophysiology of T cells in COVID-19 is extremely complex [49]. It has been reported that CD8 + cytotoxic T cells secrete a range of molecules such as perforin, granzymes, and IFN-γ to remove viruses from the host [50]. At the same time, CD4 + helper T cells (Th cell) assist cytotoxic T cells and B cells, enhancing their ability to clear pathogens [51]. However, continuous viral stimulation may induce T cell failure, resulting in loss of cytokine production and reduced function [52, 53]. Earlier studies have been unclear regarding the numbers and function of T cells in COVID-19 patients, albeit with suggestions of depressed lymphocyte counts [54, 55]. Diao et al. recently reported that the novel coronavirus could trigger the release of cytokines which in turn drive T cells depletion and exhaustion, instead of attacking T cells directly. Beside, they also found the number of T cells was negatively correlated with case severity [56]. In our study, we identified a key module, consisting of 108 genes through 100 COVID-19 patients. And we found immunocyte enrichment scores of key module genes were higher in the mild group than in the severe group. It is consistent with the results of Diao et al. [ 56], and also positive for the reliability of the key module.
Our findings also highlight the strong discrimination ability of the key module for mild and severe cases of COVID-19. As we all know, ICU and mechanical ventilation are important indicators in severe respiratory disease. And there was no direct correlation between gender and age-related module. At the same time, we demonstrate that age is not an independent determinant of disease progression. At present, the global rate of severe cases of COVID-19 is gradually decreasing, which may be due to the fact that doctors have more experience in the diagnosis and treatment of COVID-19 as time goes on. The mutation of the virus may also lead to the gradual weakening of virulence. Of course, all of this has yet to be further confirmed. Nevertheless, the progress of elderly patients to severe disease still poses a great burden to society. The hub genes and TFs we identified may help further reduce the rate of severe progression in older patients.
Our approach is markedly different from previous bioinformatics reports in COVID-19 studies [57–60], which relied on identifying genes by differential analysis. However, in order to provide insights into systems biology, we implemented gene co-expression module analysis to provide key gene modules instead of looking for differential gene expression (DEGs). We clustered age-related genes and detected hub genes according to the highly connected nodes in each module network. Lightgreen module was identified as the key module with the highest level of significant association. And *Ten* genes in the key module were further identified as hub genes, including GATA3, CCR7, IL2RB, CD5, SLAMF1, TCF7, MYC, RORC, IL23A and CARD11. Most of these 10 hub genes have been reported to be significantly associated with T cell differentiation. For example, CCR7 activated mutations in T cell receptor-NF-κB signaling, T cell trafficking and other T cell-related pathways [61]. Expression of the Th17 transcription factor RORC was high in rheumatoid arthritis [62]. CCR7, RORC and MYC were not only associated with T cell differentiation, but also considered to be transcription factors that regulate many downstream immune-related or T cell-related genes [62–65]. These 10 hub genes may have the most important effect on outcomes in different age COVID-19 patients through regulating downstream key module genes. However, how those hub genes control the key modules needs further investigation and requires more examinations.
The TFs-genes network showed that HDGF, SUPT5H and MLLT1, with the most edges, played important roles in age-related processes of COVID-19. To our surprise, these key TFs were associated with T cells. For instance, HDGF can induce the immune suppressor functions on CD8(+) T cell activities [66]. SUPT5H was positively correlated with activated memory CD4 + T cells [67]. MLLT1 is down-regulated in NK cells expressing T cell immunoglobulin [68]. These TFs had been studied in kinds of diseases, particularly in cancer, but the role of these genes in advanced age COVID-19 patients need further study.
Zhao et al. published a study by single-cell omics which revealed T cell immune response in severe COVID-19 patients [69]. They suggested tissue-resident memory-like Th17 cells were associated with disease severity and lung damage. As mentioned above, RORC gene is related to Th17 cells. Meanwhile, Hassaniazad et al. showed that RORC is related to the recovery of acute inflammatory response of COVID-19 [39]. Therefore, RORC was identified as an experiment-candidate gene. We also found CCR7 shares edges with all the key TFs (HDGF, SUPT5H and MLLT1) identified in this study. It was reported that the specific marker CCR7 of immune cell subsets in critically ill patients was significantly lower than that in healthy case [44] and specific T cells trend to differentiate to CCR7-CD45RA + effectors after exposure to SARS-CoV-2 antigen [45]. In addition, MYC has the largest number of TFs connected in our study. T cell proliferation is closely related to MYC gene expression [70, 71], and coronavirus infection can affect MYC gene expression [47]. Therefore, we performed RT-qPCR of RORC, CCR7 and MYC. It showed that the expression of RORC and CCR7 in PBMC in younger group (20–30 years old) was significantly higher than older group (40–50 years old). The expression of MYC in younger group was higher than that older group, the difference was not statistically significant. We considered the individual differences in MYC may be due to more transcription factors and more complex regulatory networks.
Our analysis focused on PBMC gene expression analysis to obtain further insights regarding the potential utilization of the identified hub genes in diagnostic development for COVID-19. Our analysis shows that the key module has excellent diagnostic capabilities. This may have potential in the future diagnosis and treatment of mild and severe cases of COVID-19. However, several limitations of the study should be noted as expanding the sample size will be more helpful to our conclusion. In addition, it has been reported that the most striking phenotypic differences between young and old humans in immune parameters is the distribution of T cell differentiation phenotypes [72]. This also provides evidence for our conclusion. Changes in T cell differentiation-related genes play a key role in the outcomes of COVID-19 patients in different age groups. Our conclusion will be useful for the development of therapeutic strategies in elderly COVID-19 patients and give us a hint that we should pay more attention to research about T cell differentiation status in aged COVID-19 patients.
## Conclusions
In conclusion, we identified 10 age-related genes associated with the COVID-19 patients’ status, and constructed a TFs-genes interactions network of the hub genes. And we discovered age-related genes were associated with T-cell function. These biomarkers can properly predict the status of COVID-19 patients. Further studies should be performed to explore the precise role of these genes in COVID-19.
## Supplementary Information
Additional file 1: Fig. S1. Co-expression construction. A Sample clustering dendrogram. The outliers are GSMA4753070, GSMA4753095 and GSMA4753087. B The relationship between soft-threshold (power) and scale-free topology. C The relationship between soft threshold (power) and mean connectivityAdditional file 2: Fig. S2. Performance of the key module. A Principal component analysis (PCA) of lightgreen module genes and gender. Each dot represents one sample. Red: female. Blue: male. B Receiver operating curve (ROC) plot of the performance based on accuracy using age for severity, mechanical ventilation and gender
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|
---
title: Awareness Level of Hypoglycemia Among Diabetes Mellitus Type 2 Patients in
Al Qassim Region
journal: Cureus
year: 2023
pmcid: PMC10039802
doi: 10.7759/cureus.35285
license: CC BY 3.0
---
# Awareness Level of Hypoglycemia Among Diabetes Mellitus Type 2 Patients in Al Qassim Region
## Abstract
Background Hypoglycemia has a major impact on patient health and glycemic management during insulin therapy for both type 1 (T1DM) and type 2 diabetes mellitus (T2DM). It is the rate-limiting complication in diabetes management that prevents stringent glucose control.
Objectives To assess the knowledge and awareness about hypoglycemia as a complication of T2D in adults in Al Qassim, Saudi Arabia.
Methods *This is* a cross-sectional study done among type 2 diabetes patients in Al-Qassim, Kingdom of Saudi Arabia, from January to June 2022. A previously validated online questionnaire was disseminated through social media to gather information from respondents. Participants were chosen via a simple random sampling technique. Data analysis was completed using SPSS (version 23; IBM Corp., Armonk, NY).
Results Overall, 213 respondents were included in our study. The majority of them were females ($70.9\%$). The participants' average age was 35.9 + 13.0 years. Our results revealed that the average awareness score of the study population was found to be 3.6 ± 1.1 (by using the Clarke method) and 3.7 ± 2.1 (by using the Gold method). Moreover, we found that impaired awareness of hypoglycemia’s prevalence by Clarke's questionnaire was $52.1\%$ and $53.5\%$ by using the Gold questionnaire. In addition, almost half of the respondents reported weakness as a symptom of hypoglycemia over the last six months and unconsciousness over the last 12 months. Hypertension was the most commonly reported chronic disease by our participants. Lastly, factors such as age, gender, educational level, geographic distribution, and history of chronic illness did not show any significant association with impaired awareness of the prevalence of hypoglycemia.
Conclusion According to our research, we concluded that patients with type 2 diabetes mellitus in the region of Al-Qassim, Saudi Arabia, had insufficient knowledge about hypoglycemia as a complication of T2D. Moreover, the impaired awareness of hypoglycemia in diabetic patients was found to be high. Hence, there is a need for interventional programs to raise public awareness.
## Introduction
Diabetes mellitus is a chronic disease that affects the body's metabolism and is defined by hyperglycemia caused by the impairment of insulin secretion, increased peripheral resistance, or both. Diabetes is caused by an imbalance in carbohydrate, protein, and lipid metabolism as a result of complete or partial insulin secretion and/or activity. Type I diabetes, which is insulin-dependent, and type II diabetes, which is insulin-independent, are the two main types of diabetes [1,2]. This disease has become an epidemic all around the world. As of 2019, DM had affected 463 million people around the world and more, and it was expected to rise to 700 million by 2045 [3]. According to an epidemiological study of Saudis aged 15 and up from various areas of KSA, the age-adjusted prevalence (using WHO criteria) was higher in urban areas (males $12\%$, females $14\%$) than in rural areas ($7\%$ male and $7.7\%$ female). The frequency was $29\%$ among rural females of similar ages. Throughout the course of the study, $56\%$ of those diagnosed with diabetes had no prior knowledge of their condition. In another study, it was discovered that $17\%$ of people aged 30 and up had DM [4].
In most developed countries, diabetes is the fourth-leading cause of mortality. Diabetes complications like coronary artery disease and vascular disease, acute ischemic stroke, peripheral neuropathy leading to chronic wounds and amputations, acute kidney injury, and loss of vision are causing increased disability, lower life quality, and massive medical expenses in almost every culture. It is undoubtedly one of the most difficult health problems of the twenty-first century. On the other hand, the acute consequences of diabetes include diabetic ketoacidosis (DKA), hyperosmolar hyperglycemic nonketotic coma (HHNC), and hypoglycemia. DKA and HHNC are associated with insulin deficiency [5]. In hypoglycemia, where blood glucose ≤70 mg/dL, this is a serious diabetic complication with high individual and community costs [6]. Furthermore, Insulin-induced hypoglycemia is one of the typical side effects of insulin treatment. According to the data, insulin-related hypoglycemia results in about 98 thousand emergency room visits annually, as well as 30.000 hospitalizations in the US [7]. Preventing or limiting its occurrence and impact necessitates a thorough understanding of the implications [6]. Despite the fact that hypoglycemia occurs more commonly in people with type 1 diabetes, hypoglycemia requiring emergency medical intervention is just as common in people with type 2 diabetes as in people with type 1 diabetes, according to previous research [8]. Hypoglycemia causes neurogenic symptoms including tremors, palpitations, anxiety/arousal, sweating, hunger, and paresthesias. In addition to neuroglycopenic symptoms, including dizziness, weakness, drowsiness, delirium, confusion, and, at lower plasma glucose concentrations, seizure, and coma [9,10]. There are many long- and short-term complications of hypoglycemia in diabetic patients, and it can affect many systems in the body, including cerebrovascular, cardiovascular, retinal cell death, vision loss, and neurocognitive impairment, as well as health-related quality-of-life difficulties [11]. Furthermore, older diabetes patients who had hypoglycemia had a two-fold greater risk of falling [12]. As a result, there is a six-fold higher chance of dying from diabetes in patients with severe hypoglycemia than in those who do not have it [11].
Hypoglycemia has an impact on medical resources, leading to consumption and productivity losses that are significantly costly, and if this cost burden is not taken into consideration, diabetes care might be underestimated [13]. According to previous observational studies, the hypoglycemia risk is greater in insulin-treated patients who have been diabetic for a long time and have been on insulin for a long period of time [10,14,15]. Furthermore, severe hypoglycemia has been shown to happen up to five times more often in individuals with poor awareness [16]. Insufficient food consumption ($47\%$), physical exercise ($23\%$), insulin dose miscalculation ($16\%$), and impaired hypoglycemia awareness ($5\%$) were the most common causes of severe hypoglycemia, according to the findings of the Kedia N study [17]. Moreover, according to prospective research evaluating the hypoglycemic frequency and associated symptoms, participants with reduced awareness suffered more moderate and severe hypoglycemic episodes [18]. Research was conducted in South India, and 366 type 2 diabetes patients were included. Overall, $34\%$ of participants had a poor understanding of hypoglycemia. Poor knowledge was linked to older age, illiteracy, and poor socioeconomic status [19]. According to a survey that included 2530 type 2 diabetes patients and was done by the American Association of Clinical Endocrinology, despite the fact that half or more of the research participants had previously experienced hypoglycemia, many patients had no idea of the causes or conditions [20]. Other research done in Najran showed that $44\%$ of diabetics had limited knowledge of hypoglycemia symptoms [21].
Type 2 DM is now becoming a worldwide problem in healthcare. And the current findings show that improving diabetic patients' awareness, knowledge, and attitude toward the disease could result in improved glycemic control. It will help people to understand the risk of diabetes, motivate them to seek appropriate medical care, and teach them how to control the disease [22]. In the context of this, understanding the symptoms of hypoglycemia and being aware of potential preventative measures would be beneficial to type 2 diabetic care. We proposed to investigate type 2 diabetes patients' understanding of hypoglycemia in the Al Qassim region.
## Materials and methods
Study design This cross-sectional study was conducted in Al-Qassim, Kingdom of Saudi Arabia, from January to June 2022. We collected the data using a validated, self-administrator questionnaire and modified Clarke's and Gold questionnaires (Figure 1 and Figure 2). We conducted this through social media to assess knowledge and attitude toward hypoglycemic attacks.
**Figure 1:** *Modified Clarke's questionnaire* **Figure 2:** *Gold questionnaire*
Study setting *The data* were collected through a previously published validated online questionnaire about knowledge and attitude toward hypoglycemic attacks.
Sample size The sample size was calculated by using the EPI-Info app (Centers for Disease Control and Prevention, Atlanta, Georgia). The estimated sample size was 312. We used a simple random sampling technique by selecting patients from different areas in Al-Qassim. People who have diabetes mellitus type 2 and were on treatment with either oral hypoglycemic agents or insulin despite their gender and duration of disease were included, whereas people who were diagnosed with diabetes mellitus type 1 and gestational diabetes were excluded.
Data collection methods We collected the data using a validated, self-administrator questionnaire. The questionnaire was translated into Arabic. We conducted it through social media. We targeted different cities in the Al-Qassim region to increase the chance of generalizing the findings. We obtained informed consent and assured that confidentiality was maintained on the first page of our questionnaire. The questionnaire is divided into three sections: the first section includes socio-demographic data (age, gender, nationality, the city where they live, education level, occupation, and marital status). The second section is Clarke's questionnaire, which comprises eight questions assessing awareness of hypoglycemia. The total score ranges from "0" to "7," and a higher score indicates impaired awareness. The four or more points indicate impaired awareness of hypoglycemia (IAH). The third section of the gold questionnaire is a single question, "Do you know when your hypos are commencing?" to which the participant responds on a seven-point Likert scale (1 = Always aware of hypoglycemia, 7 = Never aware of hypoglycemia). A score ≥4 suggests IAH.
Pilot study A pilot study was conducted on 20 participants to estimate the clarity of data collection tools and the timing for data collection.
Data analysis plan *Data analysis* was performed using SPSS (version 23; IBM Corp., Armonk, NY). The chi-square test was used for analyzing qualitative data. A p-value of 0.05 or less was considered statistically significant.
Study limitations This questionnaire is self-administered by respondents, so it may be influenced by a recall bias.
Ethical considerations Ethical approval was obtained from the Qassim Research Ethics Committee. We obtained informed consent and ensured that confidentiality was maintained.
## Results
Characteristics of the participants A total of 213 individuals participated in the study. They were predominantly female ($70.9\%$). According to our findings, participants were 35.9 +/-13.0 years old on average. In addition, we saw that the majority of participants ($73.7\%$) had university-level education. We discovered that roughly one-third of the study population was from Al Rass city when it came to geographic distribution. Then, $21.1\%$ of them were from Unaizah city while $28.6\%$ were from Buraidah city. Table 1 displays the distribution for additional cities. Additionally, our results showed that the majority of participants had no history of chronic illness (Table 1). Figure 3 shows the frequency of chronic diseases among the study participants.
Awareness of hypoglycemia According to our findings, the study population's average hypoglycemic awareness score, calculated using Clarke's questionnaire, was 3.6 +/-1.1 (Range 1-7). We used the Gold questionnaire, and the typical awareness score was 3.7 +/- 2.1. ( Range 1-7). According to our research, the prevalence of impaired awareness of hypoglycemia (IAH) of study subjects who had a score of >/= 4 in the Clarke's and Gold questionnaires was $52.1\%$ and $53.5\%$, respectively.
Our research revealed that most participants occasionally had hypoglycemia symptoms when their blood sugar level was low. The majority of individuals ($62.4\%$) did not completely lose the hypoglycemia symptoms they used to experience when their blood glucose level was low. In addition, over half of the respondents noted weakness as a sign of hypoglycemia in the previous six months while $30.5\%$ mentioned confusion, $10.3\%$ mentioned unconsciousness, and $8\%$ required assistance. In terms of the frequency of these symptoms, we discovered that the majority of respondents experienced them once every six months.
When we evaluated individuals' symptoms over the course of the previous year, we found that unconsciousness was reported by $46.9\%$ of participants, improvement after an intravenous glucose injection by $31.9\%$ of people, and seizures by $21.1\%$ of participants. More than half of respondents acknowledged having these symptoms more than once. Our results showed that the majority of subjects had no episodes of hypoglycemia in the previous month, either with or without symptoms. Twenty-three percent ($23\%$) of them had periods of hypoglycemia without symptoms while $26.8\%$ of them had episodes of hypoglycemia lasting one to three months.
When we asked people what their lowest blood sugar level was before experiencing hypoglycemic symptoms, we discovered that $34.7\%$ of them said it was above 70 mg/dl, which means they didn't have hypoglycemia and their symptoms were due to something else and $26.8\%$ said it was between 50 and 59 mg/dl. Our results showed that just $6.6\%$ of individuals experienced hypoglycemic symptoms when their blood glucose level was less than 70, and $36.6\%$ of participants indicated that this happened occasionally and that they have hypoglycemia symptoms without any evidence of actual hypoglycemia (Table 2).
**Table 2**
| Variable | Category | Frequency | Percent |
| --- | --- | --- | --- |
| 1. Do you feel hypoglycemia symptoms when your blood glucose is low? | Always | 56 | 26.3% |
| 1. Do you feel hypoglycemia symptoms when your blood glucose is low? | Sometimes | 128 | 60.1% |
| 1. Do you feel hypoglycemia symptoms when your blood glucose is low? | Never | 29 | 13.6% |
| 2. Have you lost symptoms of hypoglycemia that used to occur when your blood glucose is low? | No | 133 | 62.4% |
| 2. Have you lost symptoms of hypoglycemia that used to occur when your blood glucose is low? | Yes | 80 | 37.6% |
| 3. Check all your symptoms of hypoglycemia over the last 6 months | Disorientation | 65 | 30.5% |
| 3. Check all your symptoms of hypoglycemia over the last 6 months | Weakness | 109 | 51.2% |
| 3. Check all your symptoms of hypoglycemia over the last 6 months | Unconsciousness | 22 | 10.3% |
| 3. Check all your symptoms of hypoglycemia over the last 6 months | Need help from someone else | 17 | 8% |
| 3-1. If you answered “yes” to any one of the above questions, how often do you have it? | | 46 | 21.6% |
| 3-1. If you answered “yes” to any one of the above questions, how often do you have it? | Once / 6 months | 50 | 23.5% |
| 3-1. If you answered “yes” to any one of the above questions, how often do you have it? | Once / 2 months | 42 | 19.7% |
| 3-1. If you answered “yes” to any one of the above questions, how often do you have it? | Once / month | 41 | 19.2% |
| 3-1. If you answered “yes” to any one of the above questions, how often do you have it? | > Once / month | 34 | 16% |
| 4. Check all your symptoms of hypoglycemia over the last 12 months | Unconsciousness | 100 | 46.9% |
| 4. Check all your symptoms of hypoglycemia over the last 12 months | Seizure | 45 | 21.1% |
| 4. Check all your symptoms of hypoglycemia over the last 12 months | Improved after I/V glucose injection | 68 | 31.9% |
| 4-1. If you answered “yes” to any one of the above questions, how often do you have it? | | 105 | 49.3% |
| 4-1. If you answered “yes” to any one of the above questions, how often do you have it? | More than 1 time | 108 | 50.7% |
| 5. How often did you have an episode of hypoglycemia with symptoms during the last month? | Never | 76 | 35.7% |
| 5. How often did you have an episode of hypoglycemia with symptoms during the last month? | 1-3 / month | 57 | 26.8% |
| 5. How often did you have an episode of hypoglycemia with symptoms during the last month? | 1 / week | 53 | 24.9% |
| 5. How often did you have an episode of hypoglycemia with symptoms during the last month? | 2-3 / week | 17 | 8% |
| 5. How often did you have an episode of hypoglycemia with symptoms during the last month? | 4-5 / week | 3 | 1.4% |
| 5. How often did you have an episode of hypoglycemia with symptoms during the last month? | Almost daily | 7 | 3.3% |
| 6. How often did you have an episode of hypoglycemia without symptoms during the last month? | Never | 91 | 42.7% |
| 6. How often did you have an episode of hypoglycemia without symptoms during the last month? | 1-3 / month | 49 | 23% |
| 6. How often did you have an episode of hypoglycemia without symptoms during the last month? | 1 / week | 42 | 19.7% |
| 6. How often did you have an episode of hypoglycemia without symptoms during the last month? | 2-3 / week | 22 | 10.3% |
| 6. How often did you have an episode of hypoglycemia without symptoms during the last month? | 4-5 / week | 5 | 2.3% |
| 6. How often did you have an episode of hypoglycemia without symptoms during the last month? | Almost daily | 4 | 1.9% |
| 7. What was the lowest blood glucose level before feeling the symptoms of hypoglycemia? | > 70 mg/dl | 74 | 34.7% |
| 7. What was the lowest blood glucose level before feeling the symptoms of hypoglycemia? | 60-69 mg/dl | 43 | 20.2% |
| 7. What was the lowest blood glucose level before feeling the symptoms of hypoglycemia? | 50-59 mg/dl | 57 | 26.8% |
| 7. What was the lowest blood glucose level before feeling the symptoms of hypoglycemia? | 40-49 mg/dl | 17 | 8% |
| 7. What was the lowest blood glucose level before feeling the symptoms of hypoglycemia? | < 40 mg/dl | 22 | 10.3% |
| 8. How often did you have symptoms of hypoglycemia when your blood glucose level was low? | Always | 14 | 6.6% |
| 8. How often did you have symptoms of hypoglycemia when your blood glucose level was low? | Often | 20 | 9.4% |
| 8. How often did you have symptoms of hypoglycemia when your blood glucose level was low? | Sometimes | 78 | 36.6% |
| 8. How often did you have symptoms of hypoglycemia when your blood glucose level was low? | Rarely | 61 | 28.6% |
| 8. How often did you have symptoms of hypoglycemia when your blood glucose level was low? | Never | 40 | 18.8% |
Factors associated with the prevalence of impaired awareness of hypoglycemia Our findings showed that there was no correlation between age, gender, education levels, geographic distribution, or a history of chronic illness and the prevalence of decreased awareness of hypoglycemia. The prevalence of impaired awareness of hypoglycemia showed a relatively higher degree of association with geographic distribution and educational levels (P values = 0.088* and 0.187*, respectively) (Table 3).
**Table 3**
| Variable | Category | Prevalence of IAH | Prevalence of IAH.1 | Prevalence of IAH.2 | Prevalence of IAH.3 |
| --- | --- | --- | --- | --- | --- |
| Variable | Category | Gold Q n (%) | P value | Clarke Q n (%) | P value |
| Age (in years) | 20 - 25 | 41 (60.3) | 0.350 | 36 (52.9) | 0.930 |
| Age (in years) | 26 - 35 | 17 (41.5) | 0.350 | 19 (46.3) | 0.930 |
| Age (in years) | 36 - 45 | 31 (55.4) | 0.350 | 30 (53.6) | 0.930 |
| Age (in years) | 46 - 55 | 18 (56.3) | 0.350 | 18 (56.3) | 0.930 |
| Age (in years) | > 55 | 7 (43.8) | 0.350 | 8 (50) | 0.930 |
| Gender | Male | 33 (53.2) | 0.956 | 37 (59.7) | 0.157 |
| Gender | Female | 81 (53.6) | 0.956 | 74 (49) | 0.157 |
| Educational level | Primary & illiterate | 6 (54.5) | 0.349* | 5 (45.5) | 0.187* |
| Educational level | Intermediate | 4 (66.7) | 0.349* | 3 (50) | 0.187* |
| Educational level | Secondary | 24 (68.6) | 0.349* | 22 (62.9) | 0.187* |
| Educational level | Diploma | 2 (50) | 0.349* | 0 (0) | 0.187* |
| Educational level | University | 78 (49.7) | 0.349* | 81 (51.6) | 0.187* |
| City | Buraidah | 33 (54.1) | 0.519* | 25 (41) | 0.088* |
| City | Unaizah | 19 (42.2) | 0.519* | 29 (64.4) | 0.088* |
| City | Al Rass | 38 (55.9) | 0.519* | 39 (57.4) | 0.088* |
| City | Al Mithnab | 2 (66.7) | 0.519* | 2 (66.7) | 0.088* |
| City | Al Bukayriyah | 4 (80) | 0.519* | 4 (80) | 0.088* |
| City | Al Badayea | 9 (64.3) | 0.519* | 6 (42.9) | 0.088* |
| City | Asyah | 1 (100) | 0.519* | 1 (100) | 0.088* |
| City | Uyun Al Jiwa | 0 (0) | 0.519* | 0 (0) | 0.088* |
| City | Riyadh Al Khabra | 6 (60) | 0.519* | 2 (20) | 0.088* |
| City | Uglat Asugour | 2 (66.7) | 0.519* | 2 (66.7) | 0.088* |
| City | Dariyah | 0 (0) | 0.519* | 1 (50) | 0.088* |
| History of chronic disease | Yes | 30 (54.5) | 0.860 | 25 (45.5) | 0.251 |
| History of chronic disease | No | 84 (53.2) | 0.860 | 86 (54.4) | 0.251 |
## Discussion
The present study aimed to assess the knowledge and awareness about hypoglycemia as a complication of T2DM among the general population in the Al Qassim region, Saudi Arabia. It is difficult to achieve optimal blood glucose control since it involves the necessity for glycemic control with the risk of hypoglycemia [23,24]. Despite the increasing use of insulin to treat T2DM, hypoglycemia and IAH are not thought to pose substantial issues in the treatment of insulin-treated type 2 diabetes (T2DM). It has been proven that the duration of insulin treatment directly correlates with the risk of hypoglycemia in this population [25,26].
Our results revealed that the average awareness score of the study population was found to be 3.6 ± 1.1 (by using Clarke's method) and 3.7 ± 2.1 (by using the Gold method), which indicated that our respondents had inadequate knowledge of hypoglycemia. This was consistent with another study that was conducted in Saudi Arabia, which stated that participants ($61.4\%$) had good knowledge of hypoglycemia, but only $38.6\%$ had poor knowledge [27]. Another Indian study reported that $66.1\%$ of diabetic patients had good knowledge of hypoglycemia [19]. Furthermore, we found that the prevalence of impaired awareness of hypoglycemia (IAH) by Clarke's questionnaire was $52.1\%$ and $53.5\%$ by using the Gold questionnaire. These results were lower than another study in Turkey, which showed that $83.4\%$ had impaired awareness of their hypoglycemia [28], but higher than other studies in the UK ($8\%$) [29], Brazil ($35\%$) [30], and Malaysia ($36.9\%$) [31]. These differences are most likely due to variations in scoring systems and the variability of the sampled population. Any hypoglycemia symptom could manifest, and typical symptoms aren't necessarily the first to show up [32]. Patients must therefore be aware of any signs in order to identify them early and take the necessary action. According to our research, weakness was the hypoglycemic symptom that people most frequently reported experiencing in the previous six months. Dizziness and weakness were the most prevalent signs of hypoglycemia among the study individuals, according to a previous Indian study [19]. In addition, we found that most respondents only encountered it once every six months.
In a prior study conducted in Brazil, more hypoglycemia episodes were discovered. During the four-week follow-up period, $61.8\%$ of T2DM patients experienced at least one hypoglycemic incident [30]. Another study in the UK discovered that severe hypoglycemia had occurred in $15\%$ of people the year before, with an estimated incidence of 0.28 episodes/patient/year for the general population [29]. In addition, we found that $23\%$ experienced one to three episodes of asymptomatic hypoglycemia every month. In similar research, $10.6\%$ of T2DM patients were found to have asymptomatic hypoglycemia [30]. The prevalence of poor awareness of hypoglycemia was found to have no significant association with age, gender, education levels, geographic distribution, or history of chronic illness. Another study, however, found that older age, illiteracy, and low socioeconomic position were related to poor knowledge, but insulin combined with orally administered antihyperglycemic agents (OHAs) was associated with strong knowledge [19]. This disparity may be attributable to differences in research population characteristics, as the majority of our study participants were young.
Our study had some limitations, including the potential for question misinterpretation, patients' inaccurate self-reporting of hypoglycemic events, and recall bias, particularly for baseline questions referring to the previous six months. Information is based on how well patients can recall and understand the signs and symptoms of low blood sugar.
## Conclusions
Our study revealed that type 2 diabetic patients in Saudi Arabia's Al-Qassim region lacked knowledge about hypoglycemia as a consequence of T2D. Furthermore, there was a high prevalence of impaired awareness of hypoglycemia. The current research highlights the value of patient education and doctor knowledge in the management of hypoglycemia, specifically the burden of hypoglycemic unawareness. To raise public awareness, interventional programs are required.
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|
---
title: Composite nanofiber formation using a mixture of cellulose acetate and activated
carbon for oil spill treatment
authors:
- Nehad A. Elmaghraby
- Ahmed M. Omer
- El-Refaie Kenawy
- Mohamed Gaber
- Safaa Ragab
- Ahmed El Nemr
journal: Environmental Science and Pollution Research International
year: 2022
pmcid: PMC10039825
doi: 10.1007/s11356-022-24982-7
license: CC BY 4.0
---
# Composite nanofiber formation using a mixture of cellulose acetate and activated carbon for oil spill treatment
## Abstract
Oil and organic pollutants are significant disasters affecting the aquatic ecosystem and human health. A novel nanofiber composite from cellulose acetate/activated carbon (CA/AC) was successfully fabricated by the electrospinning technique. CA/AC nanofiber composites were prepared from $10\%$ (w/v) polymer solutions dissolving in DMA/acetone ratio 1:3 (v/v) with adding three different percentages of AC (3.7, 5.5, and $6.7\%$) to the total weight of CA. The prepared CA/AC nanofiber composite morphology reveals randomly oriented bead-free fibers with submicron fiber diameter. CA/AC nanofiber composites were further characterized by TGA, DSC, and surface area analysis. Water uptake was investigated for fabricated fibers at different pH. Oil adsorption was conducted in both static (oil only) and dynamic (oil/water) systems to estimate the adsorption capacity of prepared composites to treat heavy and light machine oils. The results showed increased oil adsorption capacity incorporating activated carbon into CA nanofiber mats. The maximum sorption capacity reached 8.3 and 5.5 g/g for heavy and light machine oils obtained by CA/AC5.5 (AC, $5.5\%$). A higher oil uptake was reported for the CA/AC composite nanofibers and showed a constant sorption capacity after the second recycles in the reusability test. Of isotherm models, the most applicable model was the Freundlich isotherm model. The result of kinetic models proved the fit of the pseudo-second-order kinetic model to the adsorption system.
## Introduction
Petroleum hydrocarbons have drawn wide attention due to their carcinogenicity, toxicity, and mutagenicity (El Nemr 2005; Salem et al. 2014; El Nemr et al. 2016). The oil spill is considered one of the significant challenges for water pollution that can cause severe damage to both the ecosystem and human health (Khan et al. 2020; Dhaka and Chattopadhyay 2021). The most used oil/water separation methods, such as centrifugation, gravity, coalescence, and flotation methods have some limits like poor efficiency of oil separation, second pollution, high costs, and time-consuming (Wang et al. 2020). Another alternative oil/water separation technology is membrane technology with low energy consumption, hydrophobic, lipophilic, facile operation, and excellent separation efficiency (Darmanin and Guittard 2015). On the other hand, it has some drawbacks such as weak flexibility, recyclability, and durability, as well as the pores of the membrane are easily polluted or blocked by oil, and also a water membrane was formed at the surface of the membrane due to water denser than oil that hinders the contact between the oil phase and membrane (Ma et al. 2019; Satapathy et al. 2017; El Nemr and Ragab 2018). Therefore, adsorption is considered a preferable method for oil removal due to its high performance, low cost, and easy reusability (Ao et al. 2020).
A specific porous polymer-based composite nanofiber (CNFs), like low cost, high efficiency, excellent recyclability, and environmental friendliness, make them a promising adsorption material (Yue et al. 2018). Cellulose acetate nanofibers are one of the most suitable polymers for oil adsorption materials attributed to the attractive properties such as biocompatibility, small diameter of the nanofibers, non-toxicity, porosity, eco-friendly, biodegradability, and moisture retaining properties (Jatoi et al. 2019, 2020). Because of CA’s poor mechanical strength and high-temperature sensitivity, it is not favorable to use CA alone (Sabir et al. 2016, 2015; Wasim et al. 2017). These addressed issues can be overcome by addition of inorganic materials such as carbon materials, zeolite, alumina (Al2O3), silica (SiO2), and zirconia (ZrO2) which provide the possibility of additional properties that are very difficult to achieve using organic materials only (Wasim et al. 2019). Cellulose acetate nanofiber can be produced by a simple, cost-effective electrospinning technique (Wang and Nakane 2021). Electrospinning is an electrostatic technique that uses an applied voltage to charge the polymer particles then the nanofibers were deposited at the collector. The prepared nanofibers or sub-micron fibers are characterized by small diameter and highly porous surfacing. Because of these characteristics, electrospun fiber mats are often used in smart clothing, electrode, pharmaceutical, sensors, filtration, environmental engineering, and tissue scaffolds (Angel et al. 2020; Serag et al. 2022). Electrospinning is the method that will be used in this study to fabricate new effective eco-friendly cellulose acetate composite nanofibers with varying loads of activated carbon. These nanofibers will then be studied for their potential use in oil spill treatment. CA/C were produced using the electrospinning technology under optimal conditions into one mat. This allowed for the exponential benefits of both materials to be merged. The characterization of the generated composite nanofibers was looked into using various techniques, and the oil adsorption capacity of the nanofibers was measured using multiple adsorption settings. Additionally, the kinetics of the adsorption process of heavy machine oil (HMO) and light machine oil (LMO) as well as the reusability potential of the newly produced composite nanofibers were tested.
## Materials
ALPHA Chemie in India was the supplier of CA, which had an acetyl concentration of 29–$46\%$ and a molecular weight of 50,000. n-Hexane with a $95\%$ purity level from M-TEDIA, India. Activated carbon is produced by Fisher Company. It has a microporous structure with an average pore diameter of 1.96 nm, a BET surface area of 1460 m2/g, and a particle size of 58.5 nm. Polyethylene glycol (PEG) is manufactured by ACROS Organic with a mass weight of 200 g/mole. Assay of $99.8\%$ for acetone of the HPLC grade was obtained from Central Drug House, India. Merck supplies N,N-dimethylacetamide (DMAA). Ethyl alcohol is supplied by the International Company for sup and MED, Industries, located in Egypt. Exxon Mobil in Egypt (fully synthetic motor oil 5 W-50, Rally formula) supplied heavy (viscosity 32 cSt at 40 °C) and light (viscosity 5.5 cSt at 40 °C) machine oils. All of the chemicals were utilized without any further purification.
## Preparation of cellulose acetate/activated carbon solution
CA/AC composite was obtained by dissolving CA in DMA/acetone (4:1) (v/v) mixture by continuous stirring for 2 h at room temperature (24 ± 2 °C) followed by ultra-sonication overnight at room temperature to obtain a homogenous solution. The ratio of solid materials to the solvent mixture was $10\%$ (w/v). PEG was used to facilitate the electrospinning process and overcome the solution mixture surface tension. The PEG to CA ratio was 1:1 wt/wt. Three different AC percentages (3.7, 5.5, and $6.7\%$) of the total cellulose acetate weight were added to CA solution (Nasir et al. 2017).
## Electrospinning of CA/AC solutions
A + 26 kV voltage was used at the injector, and –10 kV voltage was applied for the collector. A polymer solution intended for electrospinning was inserted into a syringe with a volume of 20 mL and a needle made of metal with a blunt end. At a temperature of 24 − 2 °C, the syringe pump was programmed to deliver the polymer solution at a rate of 10 mL/min with a tip-to-collector distance of 10 cm. The webs were collected on a square collector made of aluminum. After removing the nanofiber mats from the collector and washing them with ethanol and water to completely remove the PEG, the mats were dried in a vacuum oven at 50 °C for 24 h (Salihu et al. 2012; Elmaghraby et al. 2022).
## Characterization of the CA/AC nanofiber composite
The morphological characterization of the CA/AC nanofiber composites was investigated by JEOL, Model JSM 6360LA, Japan Scanning Electron Microscope (SEM) after applying a gold coating. The IMAGE-G program was used to conduct an analysis of the SEM images in order to determine the average fiber diameters. A V-100 VERTEX70 spectrophotometer was used to conduct the Fourier transform infrared spectroscopy (FTIR) examination on the samples. The thermogravimetric analyses (TGA) of the composite nanofibers were also carried out with differential scanning calorimetry (DSC) utilizing an SDT 650 simultaneous thermal analyzer from the USA. Each powdered sample was heated at a rate of 10 °C/min. In a silica crucible heated to temperatures of up to 900 °C, approximately 10 mg of each sample was paralyzed, while nitrogen gas flowed through the apparatus (Elmaghraby et al. 2022; Antunes dos Santos et al. 2021). To determine the surface area and pore size distribution of the CA/AC composite nanofibers that were formed, nitrogen adsorption–desorption isotherms were measured at 77 K using a device called BELSORP mini II. This device was developed and supplied in Japan by BEL Japan, Inc. The Brunauer–Emmett–Teller (BET) equation was used to measure the real surface area from the N2 adsorption isotherm, and the single point total pore volume was determined from the nitrogen sum adsorbed at 0.95 relative pressure. Both of these measurements were performed at atmospheric pressure. These two data were obtained from an isotherm depicting the adsorption of nitrogen (Elmaghraby et al. 2022; de Almeida et al. 2020).
## Water uptake capacity
The electrospun CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers were further characterized by calculating their swelling and their ability to absorb water, which is essential for oil removal application. Approximately 0.1 g of CA/AC nanofiber composites was incubated at room temperature (24 ± 2 °C) for 24 h in three mediums of pH 3, 6, and 11 following the previous works of Shi et al. [ 2014] and Bhandari et al. [ 2017]. The samples were softly dried using filter paper to get rid of the excess water and then weighed to obtain swelling weight (Ws). After that, the dry weight (Wd) was acquired by reweighing the swollen samples after drying them in an oven at 50 °C to a constant weight. This was done to get an accurate reading. Using the following Eq. [ 1], we could determine the product’s swelling ratio as a percentage. In this equation, WS represents the weight of the swollen composite nanofibers, and Wd represents the weight of the dried composite nanofibers (Elmaghraby et al. 2022; Serag et al. 2018).1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Selling\;ratio\;\left(\%\right)= \frac{{W}_{S}-{W}_{d}}{{W}_{d}}\times 100$$\end{document}Sellingratio%=WS-WdWd×100
## Oil sorption and retention capacity (static test)
The sorption test was done as 30 mL of heavy and machine oil was poured into a 250-mL beaker, then added 0.5 g of each sample of CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites for 1 h in room temperature (24 ± 2 °C). The composite samples were lifted, placed on a wire mesh, drained for 5 min to eliminate any loosely attached oil, and weighed. Then, the samples were elevated with free oil dripping out for 24 h to determine the oil retention capacity. All investigations were repeated 3 times, and only the mean value was reported as the final results. The oil sorption capacity (Qosc) (g/g) and retention capacity (Qorc) (g/g) were calculated according to Eqs. [ 2] and [3] (Elmaghraby et al. 2022; Dong et al. 2015).2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Oil\;sorption\;capacity\;\left({Q}_{osc}\right)=\frac{{W}_{5}-{W}_{i} }{{W}_{i}}$$\end{document}OilsorptioncapacityQosc=W5-WiWi3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Oil\;retention\;capacity\;({Q}_{orc})=\frac{{W}_{24}-{W}_{i}}{{W}_{5}- {W}_{i}}$$\end{document}Oilretentioncapacity(Qorc)=W24-WiW5-Wiwhere Wi, W5, and W24 (g) are the initial mass of the sample before sorption, the mass of oil wetted sample at 5 min and 24 h dripping, respectively.
## Batch sorption experiments (oil water system)
An experiment series was carried out to study the sorption removal performance of the CA/AC nanofiber composites for absorbing light and heavy machine oils from the water surface. Different sorption parameters have been investigated as sorbent dosage, sorption time, and oil thickness. CA/AC nanofiber composites were placed into a 250-mL beaker with a certain amount of machine oil and 50 mL of seawater, and then they were vibrated at 200 rpm, for various interval times of 5, 10, 15, 30, and 60 min. Then the samples were removed into a wire mesh, lifted to free oil draining for 5 min and weighted. Subsequently, the composite samples were squeezed five times to eliminate the residual oil, and a small amount of hexane was added to help extract oil from the fibers. The residual liquid was centrifuged for water content determination by using a graduated centrifuge tube. The capacity of oil sorption was measured following Eq. [ 4], where Wi, Ww, and Ws are the initial dry weight of sorbent, the weight of adsorbed water, and the weight of saturated sorbent (water + oil + sorbent), respectively (Elmaghraby et al. 2022; Alaa El-Din et al 2017; Martins et al. 2021).4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Q}_{osc}=\frac{{W}_{S}-{W}_{w}-{W}_{i}}{{W}_{i}}$$\end{document}Qosc=WS-Ww-WiWi
## Reusability test
The reusability of the fabricated CA/AC composite nanofibers was investigated for static and dynamic tests. The oil-observed fibers were placed in a centrifuge to separate the absorbed oil and then washed with 3 mL n-hexane and centrifuge again. After washing, the fibers were dried for 24 h at 60 °C in an oven and then the regenerated composite nanofibers were utilized again in the next oil-sorption process. The static test was done as 0.5 g of regenerated fibers inserted into 30 mL of oil for 1 h at RT (24 ± 2 °C) for both LMO and HMO. Where the oil/water test was performed as 0.4 g of regenerated fibers with seawater (50 mL) and 5 mm oil thickness layer of HMO and 7-mm-oil thickness of LMO for 15 min and 1-h contact times, respectively. The recycling steps were repeated for 4 cycles, and the oil sorption capacity of the fibers after each cycle was calculated (Elmaghraby et al. 2022; Long et al. 2021).
## Adsorption isotherm studies
The absorption isotherm of the experimental data was investigated using Freundlich, Langmuir, and Tempkin isotherm models (IM) (Elmaghraby et al. 2022; El Nemr et al. 2010). The theory of Langmuir suggests that the adsorption takes place on a certain homogenous site of the adsorbent. The maximum sorption capacity was calculated according to the saturation of the monolayer on the surface of the sorbent. The linear Langmuir model form is expressed in Eq. [ 5] (Langmuir 1916; Longhinotti et al. 1998).5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{C}_{e}}{{q}_{e}}=\frac{1}{{K}_{a}{Q}_{m}}+\frac{1}{{Q}_{m}}\times {C}_{e}$$\end{document}Ceqe=1KaQm+1Qm×Cewhere Ce (mg/L) is the equilibrium concentration; qe (mg/g) denotes the amount of oil sorbed; Qm (mg/g) represents the maximum sorption capacity; Ka (L/mg) is the sorption equilibrium constant.
The key assumption of a Freundlich equation is that the surface has a heterogeneous composition and a non-uniform distribution of heat due to adsorption processes taking place on the surface. The Freundlich exponential equation, which postulated that the adsorbate concentration would increase as the oil concentration on the adsorbent surface increased, assumed this would be the case. From a value of 1/n from Freundlich equation it was clearly found that a normal Langmuir isotherm occurred at a value for 1/n below one, while cooperative adsorption occurred at a value for 1/n above one. Equation [6] expressed the linear form of the Freundlich isotherm (Freundlich 1906):6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\log\;q_e=\log\;K_f+\frac1n\log\;C_e$$\end{document}logqe=logKf+1nlogCewhere Kf and n showed the adsorption capacity of the Freundlich constant and the adsorption rate constant, respectively (Nasseh et al. 2019).
The Tempkin isotherm model supposed that the adsorption heat decreases linearly with coverage due to the interaction between adsorbent and adsorbate (Tempkin and Pyzhev 1940). The linear Tempkin model is expressed in the simplified Eq. [ 7] (Aharoni and Ungarish 1977; Aharoni and Sparks 1991; Wang and Qin 2005):7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${q}_{e}=\beta lnA+\beta ln{C}_{e}$$\end{document}qe=βlnA+βlnCewhere β = (RT)/b, R (8.314 J/mol K) is the universal gas constant; T (Kelvin) is the absolute temperature; and b is a constant related to the heat of adsorption (Pearce et al. 2003; Akkaya and Ozer 2005).
## Best-fit IM
To estimate the most suited IM for experimental data, the error functions were investigated for the studied IM, where the symbol N and P refer to the number of experimental data points and the number of IM parameters, respectively. The average percentage errors (APE) showed the fit between the predicted and experimental adsorption capacity data, respectively, and can be determined by the Eq. [ 8] (Ng et al. 2002).8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$APE\left(\%\right)=\frac{100}N\sum\nolimits_{$i = 1$}^N\left|\frac{qe,\;isotherm-qe,\;calc}{qe,\;isothem}\right|\mathrm i$$\end{document}APE%=100N∑$i = 1$Nqe,isotherm-qe,calcqe,isothemi The hybrid fractional error (Hybrid) function is the most dependable error function because it accounts for low concentrations by balancing absolute deviation against fractional error and being represented as the Eq. [ 9] (Porter et al. 1999; Allen et al. 2003).9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Hybrid=\frac{100}{N-P}\sum\nolimits_{$i = 1$}^N\left|\frac{qe,\;isotherm-qe,\;calc}{qe,\;isothem}\right|\mathrm i$$\end{document}Hybrid=100N-P∑$i = 1$Nqe,isotherm-qe,calcqe,isothemi The chi-square error, X2 is given as the equation (El Nemr et al. 2010).10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X^2=\sum\nolimits_{$i = 1$}^N\frac{\left(qe,\;isotherm-qe,\;calc\right)^2}{qe,\;isothem}$$\end{document}X2=∑$i = 1$Nqe,isotherm-qe,calc2qe,isothem *The sum* of the squares of the errors (ERRSQ) is given by the following Eq. [ 11] (Ng et al. 2002).11\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ERRSQ=\sum\nolimits_{$i = 1$}^N{(qe,\;calc-qe,\;isotherm)}^2\mathrm i$$\end{document}ERRSQ=∑$i = 1$N(qe,calc-qe,isotherm)2i
The Marquart’s percentage standard deviation (MPSD) is given by the following Eq. [ 12] (Ng et al. 2002).12\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MPSD={\sqrt{\frac1{N-P}\sum_{$i = 1$}^N(\frac{qe,\;calc-qe,\;isotherm}{qe,\;isotherm}})}^2\mathrm i$$\end{document}MPSD=1N-P∑$i = 1$N(qe,calc-qe,isothermqe,isotherm)2i *The sum* of the absolute errors (EABS) is given by the following Eq. [ 13] (Ng et al. 2002).13\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$EABS={\sum }_{$i = 1$}^{N}\left|q e, calc- q e, isotherm\right|\mathrm{i}$$\end{document}EABS=∑$i = 1$Nqe,calc-qe,isothermi The root mean square errors (RMS) are given by Eq. [ 14] (Ng et al. 2002).14\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RMS=100\times{\sqrt{\frac1N\sum \nolimits_{$i = 1$}^N(1-\frac{qe,\;calc}{qe,\;isotherm}})}^2$$\end{document}RMS=100×1N∑$i = 1$N(1-qe,calcqe,isotherm)2
## Kinetic studies
The sorption kinetics investigates the rate of oil sorption, and it is essential to find the best conditions for the batch experiments. The most common kinetics models for studying the equilibrium data are pseudo-first-order (PFO) (Lagergren 1898) and pseudo-second-order models (PSO) (Ho et al. 2005). The correlation coefficient (R2, with values close to or equal to 1) was deemed to be a measure of concordance between experimental data and the model-predicted values; the model with the relatively higher value is the one that is more appropriate to the kinetics of oil sorption. The PFO model was used to characterize the kinetic data. This model characterizes the rate of oil sorption in accordance with the number of vacant sites. The ratio of occupied adsorption sites to the total number of adsorption sites is directly related to the occupation rate (Choi et al. 2020). The PFO equation is given by Eq. [ 15]:15\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\log\;\left(q_e-q_t\right)=\log\;q_e-\frac{K_1}{2.303}t$$\end{document}logqe-qt=logqe-K12.303twhere, qt (mg/g) and qe (mg/g) represent the amount of oil on the composite at time t (min) and at equilibrium time, respectively, and K1 (L/min) is the adsorption rate constant of PFO.
The PSO kinetic model supposed that the adsorption takes place in the square-shaped sites, where at equilibrium, there is a relationship between the number of available adsorption sites on the absorbent and that of the occupied sites. There is a correlation between the square of the product of the number of vacant sites and the number of occupied sites and the adsorption rate (Choi et al. 2020). The PSO can be represented by Eq. [ 16]:16\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{t}{{q}_{t}}=\frac{1}{{K}_{2}{q}_{e}^{2}}+\frac{1}{{q}_{e}}t$$\end{document}tqt=1K2qe2+1qetwhere, K2 represented the PSO rate constant of sorption (g/mg min). Using the following Eq. [ 17], the PSO rate constants were utilized to obtain the initial sorption rate.17\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$h={K}_{2}{q}_{e}^{2}$$\end{document}h=K2qe2
## Analyses of CA/AC nanofiber composites
Figure 1 (a, b, c, and d) show the SEM images of CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite mats prepared by electrospinning. The surface morphology of the prepared mats showed a randomly oriented bead-free matrix nanofiber network. The average fiber diameters of CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites were 495, 740, 745, and 759 nm, respectively, which were calculated as a mean value of 20 measurements using ImageJ software (Elmaghraby et al. 2022). It is clearly found that the average fiber diameter increases with increased AC content to CA nanofibers, as it enlarges the fiber diameter without destroying the fiber structures (Liu et al. 2020).Fig. 1The SEM images were taken at 15 kV and magnification of 5000 × for a CA nanofiber mats, b CA/AC3.7, c CA/AC5.5, and d CA/AC6.7 composite nanofibers The FT-IR spectra of CA, AC, CA/AC3.7, CA/AC5.5, and CA/AC6.7 samples are given in Fig. 2. CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 samples exhibited same beaks at 3395 cm−1 due to the O–H, and beak at around 1737 cm−1 originates from C = O ester stretching vibration. The beaks at 1228 and 1368 cm−1 are due to the bending vibration of C–O and C–H groups, respectively. A strong beak at 1050 cm−1 was due to the stretching vibration of C–O–C in CA (Mohy Eldin et al. 2016). The FT-IR spectra of AC showed three peaks at 3477 cm–1 for O–H, 1741 cm–1 for C = O, and 1340 cm–1 for C–C vibrations. Clearly, by adding activated carbon to CA nanofiber mats, no change in the beaks was observed even if increasing activated carbon content. Fig. 2FT-IR analyses of a CA, b AC, c CA/AC3.7, d CA/AC5.5, and e CA/AC6.7 composite nanofibers; f represents image (a) to image (e) in one image for comparison According to the IUPAC classification, the adsorption–desorption curve of CA, AC, CA/AC3.7, CA/AC5.5, and CA/AC6.7 belonged to type V isotherm, as shown in Fig. 3 (Gregg and Sing 1982; Sing et al. 1985). The adsorption–desorption curve type V isotherm indicates that the adsorbent-adsorbate interactions are weak with mesoporous or microporous adsorbents. Because the isothermal loop deceleration does not stabilize at relative pressures that are close to the saturation vapor pressure, it is possible to deduce that mats have pores that are similar to slits. This can be inferred from the fact that the saturation vapor pressure is close to the relative pressure. The BET measurements and surface parameters of CA and CA/AC composite are detailed in the report that can be found in Table 1. These parameters include the surface area, average pore diameter, and pore volume. The surface area of the CA was found to be somewhat decreased with increased AC content in nanofibers and then increased with $6.7\%$ AC load to CA, which may be related to the surface imperfections of the CA/AC composite. This was observed both before and after the addition of $6.7\%$ AC load to CA. The largest surface area was 7.6913 m2/g for CA/AC6.7 composite, which was closed to the surface area of cellulose acetate nanofiber, which was 7.6522 m2/g. The surface area of CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites were 6.744, 2.1374, and 7.6522 m2/g, respectively. The mean pore diameters (MPD) of CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite mats were 1.5113, 5.9447, and 7.1716 nm, respectively. Fig. 3a Adsorption/desorption of N2 gas isotherms, b BET analysis, and c BJH analysis of CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofiber. Table 1The CA/AC composite nanofiber was analyzed for its total surface area (TSA), total pore volume (TPV), and MPDSampleTSA (m2/g)TPV (cm3/g)MPD (nm)CA7.65220.023510.6380CA/AC3.76.74420.00261.5113CA/AC5.52.13740.00325.9447CA/AC6.77.69130.01387.1716 The TGA curves of CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites are shown in Fig. 4 a. The fabricated composite nanofiber mat displayed two steps of thermal transition, the first at a low temperature between 50 and 150 °C with weight loss 10.32, 12.42, 10.68, and $10.71\%$ for CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites, respectively. These were equivalent to moisture loss, whereas the second was at a higher temperature between 350 and 400 °C with weight loss of 73.01, 79.57, 80.47, and $80.13\%$ for CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites, respectively, which represents the primary thermal degradation of cellulose acetate chains. In addition, the residues of CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites after thermal decomposition at 900 °C were 16.67, 8.01, 8.85, and 9.16 wt%, respectively. The differential scanning calorimetry (DSC) technique is used for determining thermal transitions without weight change (El Nemr et al. 2021). It provides insight into a material’s phase shifts (melting point, boiling point, and fusion), heat capacity (Cp), and glass transition temperatures by monitoring heat flow over temperature (Tg). DSC analysis of CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers is shown in Fig. 4 b. CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers displayed two peaks in DSC plot. All composite samples fabricated were exhibited a crystallization endo peak at a crystallization temperature of 61.07, 86.44, 83.87, and 93.15 °C for CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers, respectively, and are due to sample water content loss. The exo melting peak represented a degradation temperature of CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers were 575.17, 575.55, 565.75, and 434.68 °C, respectively that was attributed to the degradation of cellulose acetate. Figure 4 b shows clearly that by adding AC to hybrid mats, the observed maximum degradation temperatures of CA increased to 575.55 for CA/AC hybrid mats, respectively, as AC supports CA fiber. As revealed in SEM pictures, there is a point bonded structure in the hybrid mat due to the homogeneous mixing of CA and AC. For CA/AC composite nanofibers, a higher concentration of AC in the CA nanofibers resulted in a lower decomposition onset temperature. Fig. 4a TGA curves, b DSC curves of the CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 underflow of N2 (100 mL/min) using heating temperature from 50 to 900 °C.
## Water uptake
The swelling behavior of a CA/AC nanofiber composite is vital in the sorption application, as it allows the absorption of body fluid within the nanofiber giving a proof for good sorption properties (Tan et al 2020). The swelling performance of CA/AC nanofiber composites was studied at different pH media (3, 6, and 11 following reported data by Shi et al. [ 2014] and Bhandari et al. [ 2017]), as shown in Fig. 5. The swelling test was done for 24 h, and the maximum swelling ratio of CA membrane reached $130\%$ at pH 6, and for the CA/AC composite nanofiber mats displayed higher values of swelling at the same pH (150.8, 144.11, and 97.93) for CA/AC3.7, CA/AC5.5, and CA/AC6.7, respectively. Subsequently, the highest swelling ratio was obtained at neutral and alkaline medium, whereas at pH 3 the swelling ratio was significantly low. Similar to what has been reported (Shi et al. 2014; Bhandari et al. 2017), the alkaline condition increased swelling, while the acidic condition decreased it. The swelling ratio of the pure CA nanofibers increases with adding AC content up to $5.5\%$ AC which may be due to activated carbon allowing penetration of water molecules into the nanofiber network, which leads to increase the interspace between the polymer molecules, so the swelling ratio increases (Serag et al. 2018). However, by increasing AC content to $6.7\%$, the swelling ratio decreased, that is attributed to AC clogging the pores in fiber network and causing aggregations which hinders the water uptake process. Fig. 5Results of the CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites’ equilibrium swelling at pH media (3, 6, and 11)
## ORC and sorption OSC
The oil retention capacity and adsorption of the CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 nanofiber mats are reported in Table 2. The maximum oil sorption capacity (OSC) for CA/AC nanofiber composite reached 9.06 g/g for heavy machine oil with CA/AC 3.7, which means that it can absorb more than 9 times of oil over its own weight. The best light machine oil sorption capacity was 5.8 g/g obtained by CA/AC5.5. CA/AC nanofiber composite showed a high oil retention capacity that reached $91.3\%$ for heavy oil and $93\%$ for light oil after 24-h dripping. It is clearly found that adding AC to CA mats increased the OSC and ORC. It was shown from the result, the sorption of HMO is slightly more than LMO, which is attributed to HMO being denser than LMO within the same volume unit. Generally, viscosity plays an essential role in enhancing the oil sorption process by boosting the adhered oil molecules on the adsorbent surface (Mohy Eldin et al. 2017). In contrast, LMO was more easily dropped out from steel mesh than HMO (Abdullah et al. 2010).Table 2ORC and OSC of the fabricated fiber samples CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 compositesSample codeHMOLMOQoscQorcQoscQorcCA8.2391.34.4580CA/AC 3.79.0691.35.5992.2CA/AC5.58.9490.45.8293.7CA/AC6.78.2391.35.3492.8
## Batch adsorption experiments
CA/AC composite nanofibers were examined for their ability to absorb HMO and LMO from water. Different sorption parameters were studied, including sorbent dose, contact time, and oil thickness, to determine the high sorption capacity.
## Effect of time
About 0.4 g of each fabricated composite fiber was tested for heavy and light machine oil sorption with 5-mm-oil thickness of HMO and 7-mm-oil thickness of LMO at different sorption times of (5, 10, 15, 30, and 60) min. Figure 6 displays the effect of sorption time on the OSC of HMO and LMO by CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite mats. It is obviously shown from the results that the CA/AC nanofiber composite have a fast sorption rate during the first 5 min for HMO. The maximum sorption capacity was obtained within 15 min for HMO, and 30 min for LMO reached 8.3 and 5.5 g/, respectively, for CA/AC5.5 composite. In addition, the OSC somewhat increases with the contact time during the first 15 min; beyond that, the process for all of the adsorbents is quite slow. This can be explained by the fact that the oil on the exterior surface of the fibers can gently work its way into the hollow lumen. The findings of Thompson et al. [ 2010] and Wang et al. [ 2013] are in agreement with this observation. Fig. 6Impact of contact time on the OSC of a HMO and b LMO onto CA, CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites
## Effect of sorbent dosage
The quantity or mass used from CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites were studied for the removal of HMO and LMO, as it considered an important parameter in industrial applications. Different sorbent doses of (0.1, 0.2, 0.3, 0.4, and 0.5 g) of each fabricated fiber were examined for oil sorption with 5-mm-oil thickness at 15 min of HMO and 7-mm-oil thickness at 30 min of LMO. Figure 7 investigates the HMO and LMO sorption capacity increase by increasing the sorbent dose from 0.1 to 0.5 g. The maximum sorption capacity for HMO reached 8.2 g/g by CA/AC5.5, while it reached 5.2 g/g by CA/AC5.5 for LMO. Increases in the number of exposed sorption sites that are available for the sorption process per unit gram of CA and CA/AC composites may be responsible for the outcomes that were observed (Liu et al. 2014). Also, it is essential to note that the highest values of the sorption capacity were recorded using which confirmed static oil sorption. Furthermore, the highest sorption capacity values, which were given by CA/AC5.5, indicate that adding activated carbon to CA mats increased the oil sorption capacity, but further increase of activated carbon cause aggregations which hinders the water uptake process. Fig. 7Impact of composite weight on the sorption capacity of a HMO and b LMO onto CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers
## Impact of oil spill thickness
The different oil layer thickness was studied on the sorption capacity of CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers. The sorption experiments were done as 0.4 g of the fabricated fiber was added to the oil/seawater system with different thicknesses of HMO (3, 4, 5, and 6) mm for 15 min and LMO (3, 5, 7, and 10) mm for 30 min. Figure 8 shows the effect of HMO and LMO thicknesses on the sorption capacity, as there is an increase in the sorption capacity with increasing the thickness of HMO and LMO. Then, further increases of oil amount up to 5 mm of HMO thickness layer and 7 mm of LMO thickness layer; the sorption capacity tends to be constant at constant seawater (50 mL); this may be due to the sorbent sites being saturated with oil spills. The maximum sorption capacity for HMO reached 8.35 g/g, while for LMO reached 5.13 g/g by CA/5.5AC composite nanofibers. It accomplishes this by increasing the oil thickness, which supplies a key driving force to overcome all resistances of the oil between the aqueous and solid phases, ultimately resulting in an increase in oil uptake. This is done by means of the oil’s interaction with the aqueous phase. In addition, there is a correlation between a rise in the initial oil thickness and an increase in the number of collisions between the oil and the adsorbent, which in turn leads to an increase in the OSC (Omer et al.2019).Fig. 8Impact of oil thickness layer on the sorption capacity of a HMO and b LMO onto CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers
## Reusability
The reusability is an essential factor for the selection of sorbent materials. The oil-saturated samples were recycled by using the centrifuge method and n-hexane extraction for further use in several sorption/desorption cycles. The OSC after regeneration of CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers for oil/seawater system are shown in Fig. 9, while Table 3 shows the reusability of CA/AC composites in static oil solution. The regeneration method showed easy reuse of sorbent materials where the fibers show higher oil uptake in the first cycle. Then the absorption capacity decreased, which can be attributed to the oil remaining in the fiber structure or due to the washing process causing deterioration of fiber structure, and the sorption capacity at the last two reusability cycles remained constant. Furthermore, the highest sorption capacity of oil/water solution for CA/AC composite was 8.3 and 5.2 g/g, which decreased to about 4.6 and 3.9 g/g for HMO and LMO, respectively. Thus, CA/AC composites are considered highly potential and promising oil-absorbing materials for oil spill cleanup (Teli and Valia 2013).Fig. 9The reusability of CA/AC3.7, CA/AC5.5, and CA/AC6.7 for HMO/water and LMO/water systems (4 cycles)Table 3The reusability of CA/AC3.7, CA/AC5.5, and CA/AC6.7 for static oil (4 cycles) for HMO and LMONo. of cycles0123401234Type of oilHMOLMOCA/AC3.7OSC (g/g)8.236.076.074.174.805.594.674.374.344.22St. dv0. 0810.0620.0710.0430.0520.0930.0850.0630.0690.078PL (%)-26.2426.2449.3341.67-16.4521.8222.3624.50CA/AC5.5OSC (g/g)9.066.006.004.544.495.824.404.443.623.54St. dv0. 0880.0520.0650.0670.0590.0820.0740.0690.0530.063PL (%)-33.7733.7749.8850.44-24.3923.7137.839.18CA/AC6.7OSC (g/g)8.945.575.574.473.275.344.493.253.033.06St. dv0.670.0560.0700.0720.0680.0790.0630.0540.0530.060PL (%)-37.6937.695063.42-15.9139.1343.2542.69St. dv. standard deviation, PL (%) percentage loss adsorption capacity.
## Adsorption isotherm
The adsorption isotherm behaviors are investigated for the connection that exists between the equilibrium concentrations of CA and CA/AC composites in the bulk and the oil amount at the solid surface. Langmuir, Freundlich, and Temkin isotherm equations were tested to investigate the correlation between the oil amount adsorbed and the oil concentration, which are the most widely accepted surface sorption models. The correlation coefficient (R2) was evaluated to determine the applicability of the isotherm equations. The result in Table 4 shows that the adsorption data of oil fitted with the Freundlich model better than another isotherm model, as represented by higher (R2) = 1 (Fig. 10). The Freundlich isotherm model studies the adsorption on heterogeneous surfaces and also suggests that the energy of sorption decreases exponentially when the sorption centers of an adsorbent are completed (El Nemr et al. 2010). The amount of oil adsorbed onto adsorbent in unit-balance-concern is represented by KF, and the deviation from the linearity of adsorption is represented by the heterogeneity factor (1/n). The value of n indicates the non-linearity degree between the concentration of solution and adsorption: where $$n = 1$$ indicates that the adsorption is linear; n < 1 represents that the adsorption process is chemical; n > 1 shows that the adsorption is a physical process (Kargi and Cikla 2006). The data in Table 4 describe that the adsorption of oil by CA/AC3.6, CA/AC5.5, and CA/AC6.7 was on heterogeneous surfaces with the interaction between adsorbed molecules and from the value of n > 1 means that the adsorption process is a favorable physical process. Table 4The isotherm model parameters for HMO and LMO adsorption by CA/AC3.7, CA/AC5.5, and CA/AC6.7 compositesIsotherm modelIsotherm parametersHMOLMOCA/AC3.7CA/AC5.5CA/AC6.7CA/AC3.7CA/AC5.5CA/AC6.7LangmuirQm10.3036.3626.886.569.1913.46Ka × 103131.0749.9865.25191.79146.57113.89R20.980.971.000.960.910.99Freundlich1/n1.321.171.191.401.341.27KF1.531.791.761.531.541.65R21.001.001.001.001.001.00TempkinA0.2679.761.510.252.722.19B0.220.140.160.270.400.21R21.001.001.000.991.001.00Fig. 10Freundlich model for adsorption of a HMO and b LMO over CA/AC3.7, CA/AC5.5, and CA/AC6.7 composite nanofibers
## Error function studies for best-fit IM
Error function equations were investigated to study the best-fit IM. Table 5 summarizes the data obtained from different error functions, as the lowest accuracy isotherm model is the Langmuir isotherm. The most fitted isotherm models are Freundlich and Tempkin isotherm models. Nevertheless, the error functions that were investigated produced diverse outcomes for each isotherm model. Therefore, the comparison of the isotherm models ought to be centered on each error function in its own right. Table 5Several alternative error functions are used to represent the isotherm model that best matches the experimental equilibrium data for CA/AC3.7, CA/AC5.5, and CA/AC6.7 compositesType of machine oilIMAPE%X2HybridERRSQMPSDEABSRMSHeavy oilLangmuir2679.723,7643002.397,3028351.8116.467624.1Freundlich0.430.000.520.000.780.020.71Tempkin96.185.10156230.98105.775.3496.56Light oilLangmuir1289.511,1131378.597,5553903.9106.673563.8Freundlich0.890.001.080.001.390.031.27Tempkin97.178.98409.6096.85106.559.1997.27
## Adsorption kinetic studies
The rate of adsorption was investigated using a number of different kinetic models. These models are essential for the design and modeling of adsorption processes because they control chemical reaction, the diffusion process, and mass transfer. PFO (Ho et al. 2000) and PSO (Zeldowitsch 1934) investigated the kinetics of the adsorption of oil onto CA and CA/AC composites, and the correlation coefficients (R2) expressed the conformity between experimental data and the model-predicted values. The PFO equation is used to represent the adsorption rate based on the adsorption capacity, which is often expressed by a plot of the values of log (qe – qt) vs t, and the Lagergren parameters, k1 and qe, may be determined from the slope and intercept, respectively. According to the findings in Table 6, the experimental qe acquired using the PFO model does not equal the estimated qe derived from all of the data. Therefore, it is highly unlikely that the adsorption reaction will be a first-order reaction with a low correlation coefficient. While the PSO equation was used to describe the adsorption kinetic of CA/AC nanofiber composites, a plot of t/qt vs t was used to illustrate the process. The slope and intercept of the line can be used to experimentally calculate the initial adsorption rate (h), the equilibrium adsorption capacity (qe), and the second-order constants (k2, g/mg min) (Fig. 11). According to the findings presented in Table 6, the computed correlations are less than 0.991 for the second-order kinetic model. As a result, the adsorption kinetics may very likely be more favorably represented by the PSO kinetic model for oil absorption. In addition, the values of qe that were computed were, for the most part, equivalent to the qe values that were measured experimentally, which demonstrated that the adsorption system conforms to the PSO kinetic model. The values of the initial adsorption rate (h), which represent the rate of initial adsorption, practically increased with the increase in initial oil concentrations. In contrast, the PSO rate constant (k2) decreased with an increase in oil amount (Table 6) (El Nemr et al. 2010).Table 6Data of PFO and PSO rate constants as well as the qe values calculated and experimental for HMO and LMO adsorption onto CA/AC compositesParameterType of machine oilqe (exp.)PFO kinetic modelPSO kinetic modelSamplesk1 × 103qe (calc.)R2k2 × 103qe (calc.)hR2CA/AC3.7Heavy oil6.4813.70196.450.94035.187.231.840.997CA/AC5.58.080.7240.530.11127.678.902.190.991CA/AC6.75.952.080.110.612224.455.967.970.998CA/AC5.5Light oil3.86137.037.9420.93117.124.970.420.996CA/AC3.75.13115.151.5490.66218.385.990.660.994CA/AC6.74.4540.073.330.991140.644.002.250.999Fig. 11Plot t/qt vs t of the PSO model for sorption of a HMO and b LMO by CA/AC3.7, CA/AC5.5, and CA/AC6.7 composites
## Comparison study for removal of MO by CA/AC nanofiber composite with other adsorbents
The manufactured nanofiber composite had a maximum oil sorption capacity of 8.2 g/g. As shown in Table 7, the observed result was compared to earlier information reported in other literature. This comparison demonstrated that the manufactured CA/AC nanofiber composite demonstrates excellent efficiency for MO sorption, as it absorbs almost eight times as much oil as *Salvinia cucullata* Roxb and Slovenian woody material. Moreover, it absorbs forty times than rice husks. The manufactured CA/AC nanofiber composite might therefore be employed as an adsorbent to remove oil. Table 7Oil sorption capacities of various adsorbents given in the literatureSorbentSorption capacity (g/g)ReferenceCarbonized cotton husks5.1Yang et al. [ 2020]Slovenian woody material1.3Piperopoulos et al. [ 2020]Rice husks after Pyrolysis (T: 480 °C, t: 3 h, under vacuum)7.5Angelova et al. [ 2011]Sugarcane bagasse3.2–5.3Said et al. [ 2009]Sugarcane bagasse esterification with stearic acid1.3–3.2Said et al. [ 2009]Wool fibers0.225, 5.56Rajakovic et al. [ 2007]Kapok fiber0.827Khan et al. [ 2004]Rice husks0.298[Khan et al. ( 2004]Coconut husk0.058Khan et al. [ 2004]*Salvinia cucullata* Roxb0.944Khan et al. [ 2004]Wood chips0.343Khan et al. [ 2004]Reed canary grass (Phalaris arundinacea) screening with flax (*Linum usitatissimum* L.) and Hemp fiber (*Cannabis sativa* L.)1.107Khan et al. [ 2004]Our study8.2-
## Conclusion
In conclusion, the CA/AC composites have a great potential to be used as efficient sorbent materials of HMO and LMO. Under optimum conditions, the CA/AC composite nanofiber mats absorbed more than their weight of HMO and LMO in 30 min. The maximum sorption capacity reached 8.3 and 5.5 g/g for HMO and LMO, respectively, obtained by CA/AC5.5 composite nanofibers. The CA/AC composite nanofibers showed higher oil uptake in the reusability test, and then its sorption capacity remained constant after the second cycle. Thus, CA/AC composite nanofibers are considered as a promising material for HMO and LMO adsorption from aquatic environment.
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|
---
title: 'Hesperidin protects against aluminum-induced renal injury in rats via modulating
MMP-9 and apoptosis: biochemical, histological, and ultrastructural study'
authors:
- Nancy Husseiny Hassan
- Doaa Mohammed Yousef
- Amira Ebrahim Alsemeh
journal: Environmental Science and Pollution Research International
year: 2022
pmcid: PMC10039835
doi: 10.1007/s11356-022-24800-0
license: CC BY 4.0
---
# Hesperidin protects against aluminum-induced renal injury in rats via modulating MMP-9 and apoptosis: biochemical, histological, and ultrastructural study
## Abstract
Aluminum, one of the most abundant metallic elements, is known to be toxic to multiple organs including the kidneys. This study aimed to investigate the pleiotropic nephroprotective effects of Hesperidin in aluminum chloride (ALCL3)-induced renal injury, highlighting the potential molecular mechanisms underlying. Twenty-four male albino rats were divided into four groups: control, Hesperidin (80 mg/kg BW, orally), ALCL3 (10 mg/kg BW, IP), and ALCL3 + Hesperidin groups. By the end of the study, blood samples were collected, and tissue samples were harvested at sacrifice. ALCL3 rats showed dramatically declined renal function, enhanced intrarenal oxidative stress, inflammation, apoptosis, and extravagant renal histopathological damage with interstitial fibrosis as shown by a higher Endothelial, Glomerular, Tubular, and Interstitial (EGTI) score. Hesperidin significantly reversed all the aforementioned detrimental effects in ALCL3-treated rats. The study verified the nephroprotective effects of Hesperidin on ALCL3-induced renal damage and confirmed the critical role of extracellular matrix (ECM) remodeling and apoptosis inhibition.
## Introduction
Aluminum is one of the most plentiful metallic components on the planet; it establishes around $8\%$ of the complete mineral substance of the world outside. Recently, increased attention to the biotoxicity of aluminum is considered due to its availability (Al Eisa and Al Nahari 2016). Aluminum is used as food additive. Moreover, aluminum pots represent around $20\%$ of the relative multitude of pots on the planet. Aluminum is additionally present in water suitable for human use with a convergence of 0.2 mg/L, water-filtering specialists, jars and containers, foil paper made from aluminum, and beauty care products (Fatima et al. 2016). Thusly, human beings are highly susceptible to aluminum toxicity as it may accumulate in the kidney causing nephrotoxicity due to its high availability (Fatima et al. 2016). Additionally, aluminum can liberate receptive oxygen species (ROS) and apoptosis by animating the favorable to oxidant effects of iron and copper, resulting in mitochondrial dysfunction and subsequent oxidative deterioration of macromolecules and releasing of cytochrome C from the mitochondria (Al-Olayan et al. 2015; Zahedi-Amiri et al. 2019).
Renal function contributes to the elimination of aluminum, such as aluminum chloride (ALCL3) via glomerular filtration, reabsorption of filtrated ALCL3 in tubules, secretion, and excretion in distal tubules (al Kahtani 2010). The extreme exposure of aluminum due to dissimilar human daily lifestyles raised the threat of renal aluminum withholding due to the accumulation of aluminum to renal tubules resulting in renal dysfunction (Al Dera 2016; Hasona and Ahmed 2017).
According to this background, the kidney is a vigorous tissue that is more liable to toxic insults of ALCL3 which impairs the pro-oxidant/antioxidant stability, which motivates the pro-oxidant effects of iron or copper (Zahedi-Amiri et al. 2019). This augments the lipid peroxidation (LPO) process and decreases the actions of the antioxidants, which in turn overgenerates oxidative stress, leading to renal toxicity. Therefore, antioxidant compounds that can mitigate the oxidative insult may be a nominee to relieve ALCL3 toxicity (Hasona and Ahmed 2017).
Hesperidin—a flavanone glycoside—is a cheap rich byproduct of citrus cultivation (Rushdy et al. 2012). Hesperidin displayed an obvious superoxide radical scavenging action (Cho 2006; Kumar et al. 2011). Hesperidin is a potential antioxidant mediator against free radicals and justifies clinical trials (Wilmsen et al. 2005; Balakrishnan and Menon 2007). Mishra [2013] reported Hesperidin’s effects on antioxidative properties, examined with a free-radical scavenging system, including reducing power, chelating activity on Fe2+ free radical scavenging, hydrogen peroxide scavenging, and hydroxyl radical scavenging activities, in an attempt to understand its mechanism of action which may pave the way for possible therapeutic applications (Pradeep et al. 2008; Kamaraj et al. 2009).
Also, a high expression of MMP-9 can make an epithelial-mesenchymal transformation (EMT) in tubular cells, which could be an additional mechanism for the induction of renal fibrosis (Yang et al. 2002aa; Tan et al. 2013).
According to the previously stated properties of Hesperidin, we designed this work to clarify Hesperidin’s potential effect in modifying the probable biochemical, histological, and immunohistochemical renal deterioration caused by ALCL3.
## Test chemicals
Aluminum chloride (ALCL3), Hesperidin, and additional routine chemicals (analytical grade) were purchased from Sigma-Aldrich® Chemical Company (St. Louis, MO, USA).
## Experimental animals
Twenty-four male Wistar rats (6 weeks old, with weight of 180–200 g) were accustomed for one week before ALCL3 administration and afterward randomly distributed into four groups ($$n = 6$$). The rats were housed in the Animal House, Faculty of Medicine Zagazig University, with access to food and water ad libitum. The housing conditions were maintained at a constant temperature (24 ± 1 °C), relative humidity (55 ± $5\%$), ventilation frequency (18 times/h), and a 12 h light/dark cycle. The rats were housed in plastic cages (four rats per cage) with soft chip bedding. The size of the cage was 47 × 30 × 15 cm, which was large enough for the growth of four rats. Throughout the experiment, the wood chips were renewed every 3 days. The health status of the rats was monitored daily. All measures to minimize pain or discomfort were taken by the investigators. The Institutional Animal Care and Use Committee of Zagazig University accepted the rat experiments (approval no. ZU-IACUC/3/F/$\frac{79}{2020}$). All maneuvers followed the guidelines recognized in the Guidelines for the Care and Use of Laboratory Animals.
## Experimental strategy
Twenty-four rats were allocated to the following groups: Control group: contained 6 animals injected with sterile physiological saline intraperitoneally for 5 weeks.
Hesperidin group: the 6 rats were orally treated with Hesperidin only (80 mg/kg body weight) dissolved in dimethyl sulfoxide (DMSO), by gavage for 5 weeks (Pari et al. 2015).
Aluminum chloride (ALCL3) group: which included 6 rats that were given intraperitoneally 10 mg/kg body weight of ALCL3 diluted in normal saline every day for 5 weeks, 1 g dissolved in 500 mL distilled water. So, each mL of distilled water contains 2 mg of ALCL3 (Mostafa et al. 2015).
Aluminum chloride (ALCL3) + Hesperidin group: included 6 rats; each rat was injected intraperitoneally with 10 mg/kg BW of ALCL3 in concomitant with the oral intake of Hesperidin 80 mg/kg once daily by gavage for 5 weeks.
## Tissue sampling
By the end of the experiment, animals were exposed to fasting over the night. In the morning, they were anesthetized by a single intraperitoneal (I.P.) injection of thiopental (75 mg/kg/BW) (Tardif et al. 2013). Blood samples were directly taken from the retro-orbital venous sinuses of the rats in each group (Parasuraman et al. 2015). The samples were kept to coagulate at room temperature and then centrifuged at 3000 R.P.M. for 10 min. The serum was collected and frozen at − 20 °C for the urea, creatinine, and uric acid biochemical analysis in serum. After that, laparotomy was done, and the right and left kidneys were sensibly dissected and rapidly isolated from each group. The right kidneys of the animals in each group were used for histopathological examination, so they were prepared in $10\%$ neutral buffered formalin solution for 2 h to be hardened. The left renal specimens of each group were divided into two portions; one portion was preserved at − 80 °C for further homogenization. They were homogenized in 50 mM Tris–HCl pH 7.4 and 300 mM sucrose, making up $10\%$ (w/v) homogenate with a tissue homogenizer (Heidolph Instruments, Donau, Germany). The obtained homogenate was centrifuged at 4000 R.P.M. for 15 min. at 4 °C, and the supernatant was utilized for the malondialdehyde (MDA) estimation, the total antioxidant capacity (TAC), and the inflammation marker (Interleukin-6 and 10). The other portion of the left kidney specimens was processed for electron microscopic examination; renal cortex samples (1 mm thickness) were preserved in a mix of $2.5\%$ glutaraldehyde and $2.5\%$ paraformaldehyde.
## Measurement of serum urea, creatinine, and uric acid
Sera were collected and kept in aliquots at − 20 °C until they were utilized to estimate serum urea, uric acid, and creatinine levels. Their readings were estimated with commercially accessible kits as stated by the manufacturer’s directions (SPECTRUM Diagnostic kits were acquired by the Egyptian Company for Biotechnology (S.A.E.), Obour city industrial area, block 20,008-piece 19 A. Cairo, Egypt).
## Assessment of the proinflammatory cytokines (IL-6) and the anti-inflammatory cytokines (IL-10) in renal tissue homogenates
Proinflammatory cytokine IL-6 and anti-inflammatory cytokine IL-10 concentration was assessed in renal samples frozen at − 80 C. IL-6 (SEA079Ra) was assessed with RAT Platinum Enzyme-Linked Immunosorbent Assay Kits (Wuhan USCN Business Co. Ltd. Houston, TX) according to the manufacturer’s directions with an absorbance reader (BioTek ELx800, BioTek, Winooski, VT) at 450 nm. These results were considered by the four-parameter curve method and stated as picograms (pg)/gm tissue.
## Assessment of tissue malondialdehyde (MDA) and total antioxidant capacity (TAC) in renal tissue homogenates
The degree of lipid peroxidation was checked by estimating the degree of MDA utilizing commercial kits as defined by Ohkawa et al. [ 1979]. The method depends on the interaction of thiobarbituric acid with MDA in acidic medium at a temperature of 95 °C to form thiobarbituric acid reactive product, and the absorbance of the resultant pink product was measured at 534 nm.
The TAC was estimated using commercial kits bought from Bio-diagnostics dependent on the technique depicted by Koracevic et al. [ 2001]. The antioxidative capacity was performed by the antioxidants’ reaction in the specimen with a characterized measure of an exogenously given hydrogen peroxide (H2O2). The antioxidants in the sample eliminated a certain amount of the provided hydrogen peroxide. The residual H2O2 was determined colorimetrically by an enzymatic reaction which involves the conversion of 3,5-dichloro2-hydroxy benzene sulfonate to a colored product. The color was measured spectrophotometrically at 505 nm. Protein content in tissue homogenate was measured according to the method of (Lowry et al. 1951).
## Real-time polymerase chain reaction (RT–PCR) analysis for MMP-9 gene expression in renal tissues
Real-time PCR RNA was used to detect the mRNA expression of MMP-9. Total RNAs were isolated using an RNA commercial kit (Intron, Sungnam, Korea) according to the manufacturer’s instructions. The extracted RNA was reverse transcribed by QuantiTect RT–PCR kit (Qiagen; catalog no. 204243) to form cDNA as recommended by the manufacturer. The quantity and quality of the RNA were confirmed by measuring the A260/A280 percentage. The meditation and purity of RNA were assessed using a spectrophotometer (UNICO, UV2000, China). Glyceraldehyde-3- phosphate dehydrogenase (GAPDH) was used as a reference gene for standardizing the expression data as presented in Table 1 (Zawada et al. 2015).Table 1Primer sequencesGenePrimer 3′ → 5′Reverse primer 3 → 5’PbAccession noMMP-9CAAACCCTGCGTATTTCCAGAGTACTGCTTGCCCAGGA223NM_031055.2GapdhCTCATGACCACAGTCCATGCTTCATCGGGATGACCTT152NM_001394060.2
## General histopathological characteristics by hematoxylin and eosin staining
All procedures were performed in the Department of Pathology, Faculty of Medicine, Zagazig University. As per usual methodology (Suvarna et al. 2018), the right renal samples were secured in $10\%$ neutral buffered formalin and inserted in paraffin. Sections of 5 μm thickness were mounted on glass slides, deparaffinized in xylene, and stained utilizing hematoxylin and eosin stain (H&E).
## Histopathological scoring
Renal sections were examined and scored to assess the degree of injury. A skilled histopathologist, who was blinded to the group allocation, assessed histological damage and measured it with the Endothelial, Glomerular, Tubular, and Interstitial (EGTI) scoring system developed precisely for animal studies on kidney tissue in the context of injury (Table 2). The scoring of the renal cortex was made in all studied groups of the renal cortex (Khalid et al. 2016).Table 2The EGTI histology scoring system (Khalid et al. 2016)
## Histopathological evaluation of renal fibrosis
We used the paraffin blocks of formalin-fixed renal specimens in Masson’s trichrome to stain the collagen fibers and assess the area percent of fibrosis. The areas of fibrosis appeared in green while the parenchyma appeared in red color.
## Immunohistochemistry (IHC) for assessment of matrix metalloproteinase-9 (MMP-9), (FS7-associated cell surface antigen) FAS protein, caspase-3, BAX, and BCL2 expression
We utilized the paraffin specimens of formalin-fixed renal samples for immunohistochemical examination. Paraffin sections of 4 μm thickness were prepared and mounted on positively charged slides, deparaffinized in xylene, hydrated in descendant ratings of ethanol, and treated with $3\%$ hydrogen peroxide in methanol to block the endogenous peroxidase action. Antigens were retrieved using 0.01 mol/L citrate-buffered saline (pH 6.0), and endogenous peroxidase activity was quenched using $0.3\%$ (v/v) H2O2 in phosphate-buffered saline. Then, the non-specific binding of immunological reagents was blocked by incubating the samples with normal goat serum $10\%$ (v/v) for one hour. A goat anti-mouse MMP-9 antibody (Santa Cruz Biotechnology, Santa Cruz, CA, USA) diluted 1:100 was applied to ultrathin sections overnight at 4 °C, followed by a second layer of biotin-conjugated, affinity purified rabbit anti-goat immunoglobulin G (Santa Cruz) diluted 1:100 with phosphate-buffered saline and $0.1\%$ bovine serum albumin for 15 min at room temperature, and finally by streptavidin-conjugated horseradish peroxidase, rabbit monoclonal FAS antibody (ab-15285 Abcam, Cambridge, UK) with a dilution of 1:100 in PBS for 1 h at room temperature and anti-BAX, rat monoclonal antibody (1:50, no. 13401A, clone G206-1276, immunoglobulin (Ig) M, 0.5 mg/mL, PharMingen, San Diego, CA). Additionally, anti-active caspase-3 antibody incubation (Abcam, Cambridge, MA, USA) was used. Anti-active caspase-3 antibody was diluted to 1:300. Moreover, monoclonal mouse using a Bio-Rad GS-690 densitometer and Molecular anti-rat Bcl-2 (to confirm Bcl-2 staining; Santa Cruz BioAnalyst version 4 analysis software) (Bio-Rad Laboratories chemicals) were also used. Polyclonal mega using the same analysis package and confirmed rabbit anti-rat Bcl-2 (1:400; PharMingen, San Diego, CA, by visual comparison to the ribosomal RNA subunits, USA) was diluted at 1:400. The primary antibodies were detected with a biotin-streptavidin detection system with 3,3′-Diaminobenzidine (DAB) (Sigma Aldrich, USA) as a chromogen, and then, counter-staining with Mayer’s hematoxylin was performed. Negative control segments were prepared with similar rules with replacement of the definite primary antibodies with normal rabbit immunoglobulin G (IgG) (Suvarna et al. 2018).
## Histological assessment by transmission electron microscopic (TEM) examination
The renal cortex samples (1 mm thickness) were preserved in a mix of $2.5\%$ glutaraldehyde and $2.5\%$ paraformaldehyde, put in phosphate buffer for 24 h, post-fixed in $1\%$ osmium tetra-oxide, dehydrated, and fixed in resin. The samples were cut and sectioned into the semi-thin and ultrathin sections. Semi-thin Sects. ( 1 μm) were stained with toluidine blue and observed using a light microscope. Then, the thin slices were cut on an ultra-microtome via a diamond knife to yield the ultrathin sections of 60–90 nm thick, and they were moved to copper grids for staining with lead citrate and uranyl acetate. The sections were studied by a transmission electron microscope in Electron Microscopic Examination Unit in Mansoura University using a Zeiss EM 100 S transmission electron microscope at 60 kV to perceive the ultrastructural changes.
## Histomorphometric analysis
Morphometric examination was done on every rat in each group. After immunostaining with the anti-MMP-9, anti-FAS, anti-caspase-3, anti-BAX, and anti-BCL2 antibodies, perceptive fields across the pictures caught by the light microscope at 400 × amplification were chosen to quantify the percentage area of FAS, caspase-3, BAX, and BCL2-positive response and the optical density (pixel) of MMP-9 protein in the renal cortex from 6 rats/group. Furthermore, the percentage area of Masson’s trichrome-positive region was likewise determined in the caught discerning fields from 6 rats/group, and the mean/median values were accounted for ImageJ analysis software (Fiji ImageJ; 1.51 n, NIH, USA) used at Human Anatomy and Embryology Department, Zagazig University.
Furthermore, the BAX to Bcl-2 ratio was determined for each sample individually by dividing the mean area percent for BAX by that for Bcl-2.
## Statistical analysis
Continuous variables were stated as the mean ± standard error of the mean (SEM) if the data were normally distributed. Normality was patterned by the Kolmogorov–Smirnov test. The one-way ANOVA was utilized to recognize significant changes between groups. Post hoc Tukey’s test was done for numerous comparisons among groups. Skewed continuous data were stated using the median and interquartile range (IQR). The Kruskal–Wallis test and Dunn’s multiple comparison test were used when equal variances were not present. The threshold for statistical significance was set at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05.$ All statistical computations were completed utilizing GraphPad Prism software, version 5.0 (GraphPad Software, San Diego, CA, USA).
## Serum biochemical parameter levels of kidney function
All groups’ serum urea, uric acid, and creatinine levels are shown in Table 3. ALCL3 injection for 5 weeks significantly increased both serum urea and creatinine levels compared with the control and Hesperidin values (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$ vs control), which was more prominent in the creatinine level; however, uric acid level did not significantly change. Hesperidin co-treatment with ALCL3 injection further attenuated the changes in the level of serum markers of renal damage which revealed a significant decrement compared with the ALCL3 group, but a statistical equality with the control group was provided in serum urea (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$) only. Table 3Effect of ALCL3 and Hesperidin after ALCL3 injection for 5 weeks on kidney function of the different experimental groupsParametersControlHesperidinALCL3 groupALCL3 + HesperidinSerum urea (mg/dL)69.15 ± 2.38a70.88 ± 2.68a83.40 ± 0.91b75.30 ± 0.58cSerum uric acid (mg/dL)2.11 ± 0.66a2.13 ± 0.12a2.677 ± 0.17a2.302 ± 0.12aSerum creatinine (mg/dL)0.57 ± 0.03a0.56 ± 0.03a0.9285 ± 0.04b0.6507 ± 0.06b,cValues are represented as mean ± SEMaControl and Hesperidin-treated groupsbSignificantly different from the control and Hesperidin groups at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$cSignificantly different from the ALCL3 group at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$
## Effects of ALCL3 and Hesperidin on proinflammatory cytokine IL-6 and anti-inflammatory cytokine IL-10
As shown in Table 4, rats treated with ALCL3 for 5 weeks exhibited significantly raised levels of cytokine IL-6. They showed decreased levels of cytokine IL-10 in renal tissue compared with rats of the control and Hesperidin groups. Renal tissue gained from rats co-treated with Hesperidin for 5 weeks with ALCL3 injection displayed significantly attenuated cytokine IL-6 levels and an increment in cytokine IL-10 levels when compared with the ALCL3 group rats. However, IL-6 levels still reveal a significant change when compared with the control group (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$).Table 4Effect of ALCL3 and Hesperidin after ALCL3 injection for 5 weeks on proinflammatory cytokine IL-6 and anti-inflammatory cytokine IL-10 in renal tissue homogenates of the different experimental groupsParametersControlHesperidinALCL3 groupALCL3 + HesperidinInterleukin-6 (pg/gm tissue)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$106.4\pm 2.22$$\end{document}106.4±2.22 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$106.0 \pm 3.36$$\end{document}106.0±3.36 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$146.1 \pm 5.57$$\end{document}146.1±5.57 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$126.2 \pm 3.19$$\end{document}126.2±3.19 cInterleukin-10 (pg/gm tissue)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$104.3 \pm 1.63$$\end{document}104.3±1.63 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$102.6 \pm 1.96$$\end{document}102.6±1.96 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$80.62 \pm 1.53$$\end{document}80.62±1.53 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$100.5 \pm 2.31$$\end{document}100.5±2.31 cValues are represented as mean ± SEMaControl and Hesperidin-treated groupsbSignificantly different from the control and Hesperidin groups at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$cSignificantly different from the ALCL3 group at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$
## Effect of ALCL3 and Hesperidin on oxidative stress markers in renal tissue
As presented in Table 5, lipid peroxidation, measured in terms of malondialdehyde (MDA), increased significantly in rat renal tissues treated with ALCL3 for 5 weeks compared with the control and Hesperidin groups (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$). The co-treatment with Hesperidin for 5 weeks with ALCL3 injection significantly decreased MDA concentration compared with the ALCL3 group (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$) but still revealed a significant change from the control and Hesperidin groups. Conversely, the TAC significantly decreased in the ALCL3 group compared with the control group (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$). However, the co-treatment with Hesperidin significantly increased TAC, with a non-significant difference from the control and Hesperidin groups. Table 5Effect of ALCL3 and Hesperidin after ALCL3 injection for 5 weeks on MDA level and total antioxidant capacity (TAC) in renal tissue homogenates of the different experimental groupsParametersControlHesperidinALCL3 groupALCL3 + HesperidinMDA (nmol/mL-tissue)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$22.11 \pm 1.65$$\end{document}22.11±1.65 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$21.98\pm 1.26$$\end{document}21.98±1.26 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$50.99 \pm 1.24$$\end{document}50.99±1.24 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$32.25 \pm 1.11$$\end{document}32.25±1.11 bcTotal antioxidant capacity (Mm-tissue)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$9.48 \pm 0.59$$\end{document}9.48±0.59 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10.04\pm 0.59$$\end{document}10.04±0.59 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.46 \pm 0.64$$\end{document}5.46±0.64 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.29 \pm 0.64$$\end{document}7.29±0.64 cValues are represented as mean ± SEMaControl and Hesperidin-treated groupsbSignificantly different from the control and Hesperidin groups at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$cSignificantly different from the ALCL3 group at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$
## Effect of ALCL3 and Hesperidin on the gene expression of MMP-9
Real-time PCR was used to study the expression of MMP-9 mRNA. The mRNA expression of MMP-9 was significantly elevated (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$) in the ALCL3 group compared with the control and Hesperidin groups. The co-treatment with Hesperidin with the ALCL3 injection for 5 weeks revealed that the gene expression of MMP-9 was significantly reduced at the end of the experiment. There was a significant downregulation compared with ALCL3 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$), as presented in Table 6.Table 6Effect of AlCl3 and Hesperidin on the gene expression of MMP-9ParametersControlHesperidinALCL3 groupALCL3 + HesperidinRelative mRNA of MMP-9\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$9.58 \pm 1.17$$\end{document}9.58±1.17 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$9.24 \pm 0.8$$\end{document}9.24±0.8 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$76.67 \pm 4.34$$\end{document}76.67±4.34 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$24.17 \pm 2.12$$\end{document}24.17±2.12 bcValues are represented as mean ± SEMaControl and Hesperidin-treated groupsbSignificantly different from the control and Hesperidin groups at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$cSignificantly different from the ALCL3 group at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$
## Hematoxylin and eosin staining results
The rat renal cortex of the control and Hesperidin groups presented a typical histological construction of glomeruli with narrow renal glomerular capsular space. Proximal convoluted tubular cells with vesicular rounded basally situated nuclei and acidophilic granular cytoplasm and distal convoluted tubular cells were seen with less acidophilic cytoplasm (Fig. 1a, b) respectively. The rat renal cortex of groups treated with ALCL3 showed obvious pathological lesions, characterized by glomerular atrophy, and another segmented one with a wide renal glomerular capsular space, multiple renal tubular epithelial cell degeneration, and exfoliation in some tubules. Other tubules had pyknotic nuclei, renal interstitial hemorrhage, and interstitial cellular infiltration. Large, thick-walled congested blood vessels with irregular endothelial linings could be observed (Fig. 1c, d). The rats’ renal cortices of groups co-treated with Hesperidin with ALCL3 injection for 5 weeks displayed glomeruli. Proximal and distal convoluted tubular cells preserved their control character to a certain degree (Fig. 1e).Fig. 1Illustrative images of rat renal cortex stained with H&E technique of different experimental groups. a The control group shows normal histological structure, glomeruli (G), proximal convoluted tubule (PT), distal convoluted tubule (DT), and Bowman’s space (asterisk). b The Hesperidin group shows normal histological structure, glomeruli (G), proximal convoluted tubule (PT), distal convoluted tubule (DT), and Bowman’s space (asterisk). c, d Rat’s renal cortex of the ALCL3-treated group shows different pathological lesions, shrunken glomerulus (G +), segmented glomerulus (G#), destructed tubules (T), interstitial hemorrhage (Hg), pyknotic nuclei (zigzag arrow), exfoliated epithelial lining (short arrow), large thick wall congested blood vessel (BV), interstitial cellular infiltrations (IF), and dilated Bowman’s space (asterisk). e Rat renal cortex of the ALCL3 + Hesperidin-treated group preserves their control character to a certain degree glomerulus (G), proximal convoluted tubule (PT), and distal convoluted tubule (DT). Few destructed tubules (T) show exfoliated epithelial lining (short arrow). H&E × 400, scale bar 50 μm
## The EGTI histopathological scoring system
The scoring system consisted of histological injury in 4 individual components: Endothelial, Glomerular, Tubular, and Interstitial. The scoring was made in all studied groups of the renal cortex after histopathological examination by light and electron microscopy. The control rats exhibited a normal appearance of the renal cortex; the brush border of the tubular cells is intact, with no thickening of the basal membrane. No inflammation or necrosis was seen (Tubular score 0). There is no noticeable interstitium suggesting no damage/abnormality within the interstitial compartment (Interstitial score 0). The ALCL3 group showed varying degrees of damage to the renal cortex, inflammation, and hemorrhage within the interstitium. It presented in less than $25\%$ of the tissue with thickened basal membranes of the tubular cells with loss of the brush border in more than $25\%$ of the tubular cells, with necrosis (Tubular score 3, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$) compared with the control and Hesperidin groups.
Additionally, there were inflammation and hemorrhage within the interstitial space, with necrosis in up to $60\%$ of the cells (Interstitial score 3). In the ALCL3 + Hesperidin group, the brush border of the tubular cells is intact, with a mild thickening of the basal membrane. Few inflammations with absent necrosis appeared (Tubular score 1). There is no noticeable interstitium, suggesting no injury within the interstitial compartment (Interstitial score 0, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$), compared with the aluminum chloride-treated group (Table 7).Table 7The EGTI histology scoring resultsEGTI damage scoreControlHesperidinAlCl3AlCl3 + HesperidinEndothelial (median (IQR))0.0 (0.25)a0.0 (0.25)a3.0 (0.0)b0.5 (1.25)bcGlomerular (median (IQR))0.0 (0.25)a0.0 (0.25)a3.0 (1.0)b1.0 (1.25)bcTubular (median (IQR))0.0 (1.0)a0.0 (1.25)a4.0 (1.0)b1.0 (2.0)bcInterstitial (median (IQR))0.0 (1.0)a0.0 (1.25)a3.0 (1.25)b1.0 (2.0)bcaControl and Hesperidin-treated groupsbSignificantly different from the control and Hesperidin groups at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$cSignificantly different from the ALCL3 group at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$
## Masson’s trichrome staining results
The results obtained by Masson’s trichrome staining of all experimental groups are presented in Fig. 2. The control and Hesperidin groups showed minimal basophilic collagen fibers adjacent to the glomeruli (Fig. 2a, b). In contrast, the ALCL3-treated group exhibited abundant collagen fibers surrounding the renal tubules and around the blood vessels (Fig. 2c). However, the concomitant administration of Hesperidin with ALCL3 exhibited few collagen fibers around the glomeruli and the blood vessels (Fig. 2d). These findings were confirmed through the statistical analyses of the area percentage of collagen fibers in all experiment groups (Table 8).Fig. 2Illustrative images of Masson’s trichrome-stained sections of different experimental groups showing the collagen fiber distribution. a Control group, b Hesperidin group, c ALCL3, and d ALCL3 + Hesperidin groups. Arrows indicating green staining of the collagen fibers in the interstitium and around normal glomeruli (G), destructive ones (G#), and the blood vessels (bv). Masson’s trichrome, scale bar × 50 μm, × 400Table 8Statistical analysis of the histomorphometric measurementsMorphometric measurementsControlHesperidinALCL3 groupALCL3 + HesperidinArea percent of Masson’s trichrome\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.2 \pm 0.39$$\end{document}5.2±0.39 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.04 \pm 0.29$$\end{document}5.04±0.29 a12.88 ± 0.29b7.87 ± 0.39bcOptical density of MMP-90.2 ± 0.01a0.22 ± 0.01a0.39 ± 0.002b0.31 ± 0.002bcArea percent of FAS\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.87 \pm 0.5$$\end{document}2.87±0.5 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.77 \pm 0.47$$\end{document}2.77±0.47 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$17.33\pm 0.82$$\end{document}17.33±0.82 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$8.83\pm 0.84$$\end{document}8.83±0.84 bcArea percent of caspase-3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.32 \pm 0.039$$\end{document}0.32±0.039 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.73 \pm 0.13$$\end{document}1.73±0.13 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20.23 \pm 0.6$$\end{document}20.23±0.6 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.1 \pm 0.37$$\end{document}7.1±0.37 bcArea percent of BAX\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.92 \pm 0.17$$\end{document}1.92±0.17 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.91 \pm 0.24$$\end{document}1.91±0.24 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$28.02\pm 1.36$$\end{document}28.02±1.36 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$8.01\pm 0.9$$\end{document}8.01±0.9 bcArea percent of BCL2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$25.49 \pm 1.53$$\end{document}25.49±1.53 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$28.99\pm 2.26$$\end{document}28.99±2.26 a\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.61\pm 0.24$$\end{document}3.61±0.24 b\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$17.91\pm 1.96$$\end{document}17.91±1.96 bcValues are represented as mean ± SEMaControl and Hesperidin-treated groupsbSignificantly different from the control and Hesperidin groups at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$cSignificantly different from the ALCL3 group at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$
## Immune histochemical results
To explore the effects of ALCL3 and Hesperidin on the oxidation process, we measured the matrix metalloproteinase-9 (MMP-9) as an oxidative and fibrotic marker. Immunoexpression in the renal specimens of all groups was analyzed. Immunohistochemically stained renal sections of the control and Hesperidin groups exhibited faint positive reactions for MMP-9 in the cytoplasm of tubular cells (Fig. 3a, b), respectively. Strong positive reactions for MMP-9 in the cytoplasm of degenerated tubular cells appeared in the ALCL3 group (Fig. 3c). The ALCL3 + Hesperidin group revealed weak positive reactions for MMP-9 in the cytoplasm of most tubular cells compared with the treated group (Fig. 3d). The outcomes confirmed statistically in the ALCL3-treated group displayed a significant increase in the optical density (OD) of the positive immune appearance of the MMP-9 compared with the control and Hesperidin groups. These results were significantly lower in rats receiving ALCL3 and Hesperidin together (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$) (Table 8).Fig. 3Illustrative images of sections of rat renal cortex showing matrix metalloproteinase-9 (MMP-9) immunoreactivity in the different experimental groups: a control, b Hesperidin, c ALCL3-treated, and d ALCL3 + Hesperidin groups. The dark brown expression indicates the MMP-9 immunopositivity. Immunoperoxidase technique for MMP-9, × 50 μm, × 400 To detect the effect of ALCL3 and Hesperidin on cell survival, we measured FAS, caspase-3, and BAX proteins as apoptotic markers and Bcl2 as antiapoptotic marker. Immunoexpression in the renal samples was analyzed (Figs. 4, 5, 6, and 7), respectively. In the renal tubules of the control and Hesperidin groups, FAS, caspase-3, and BAX-positive apoptotic cells were hardly demonstrated (Figs. 4a, b, 5a, b, and 6a, b), respectively, and Bcl2-positive cells were markedly expressed in the renal tissue (Fig. 7a, b). In the ALCL3-treated group, abundant FAS, caspase-3, and BAX-positive apoptotic cells were demonstrated in the renal tubules (Figs. 4c, 5c, and 6c), respectively, and Bcl2-positive cells were hardly demonstrated in the renal tubules (Fig. 7c). On the other hand, the use of Hesperidin in concomitant with ALCL3 greatly decreased the FAS, caspase-3, and BAX expression that exhibited relatively fewer apoptotic cells in the renal tissues (Figs. 4d, 5d, and 6d), respectively, and greatly increased the Bcl2 expression in the renal tissues (Fig. 7d). These results confirmed statistically that the ALCL3-treated group exhibited a significant increase in the area percentage of the immune-positive appearance of the FAS, caspase-3, and BAX and a significant decrease in the area percentage of the immune-positive appearance of the Bcl2 compared with the control group. Fig. 4Illustrative images of sections of rat renal cortex showing FAS immunoreactivity in the different experimental groups: a control group, b Hesperidin, c ALCL3, and d ALCL3 + Hesperidin groups, respectively. Arrow heads are signifying the dark brown expression of FAS-positive cells. Immunoperoxidase technique for FAS, × 50 μm, × 400Fig. 5Illustrative images of sections of rat renal cortex showing caspase-3 immunoreactivity in the different experimental groups: a control, b Hesperidin, c ALCL3, and d ALCL3 + Hesperidin groups, respectively. Arrow heads are signifying the dark brown expression of caspase-3-positive cells. Immunoperoxidase technique for caspase-3, × 50 μm, × 400Fig. 6Illustrative images of sections of rat renal cortex showing BAX immunoreactivity in the different experimental groups: a control, b Hesperidin, c ALCL3, and d ALCL3 + Hesperidin groups, respectively. Arrow heads are signifying the dark brown expression of BAX-positive cells. Immunoperoxidase technique for BAX, × 50 μm, × 400Fig. 7Illustrative images of sections of rat renal cortex showing BCL2 immunoreactivity in the different experimental groups: a control, b Hesperidin, c ALCL3, and d ALCL3 + Hesperidin groups, respectively. Arrows are signifying the markedly expressed reaction of BCL2-positive cells. Arrow heads are corresponding to the hardly demonstrated BCL2-positive cells. Immunoperoxidase technique for BCL2, × 50 μm, × 400 Interestingly, Hesperidin co-administration with ALCL3 showed antiapoptotic effect as it significantly decreased BAX and increased BCL-2 immune-expression area percent at (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$) as compared to the ALCL3-treated group (Table 8).
Furthermore, by BAX/BCL2 protein ratio application, there was a statistically significant increase in the ALCL3-treated group in comparison with the control and Hesperidin-treated groups with statistically significant decrease in the ALCL3 + Hesperidin group (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$) (Table 9).Table 9The BAX to Bcl-2 protein ratioMorphometric measurementsControlHesperidinALCL3 groupALCL3 + HesperidinThe BAX to Bcl-2 ratio0.075a0.065a7.76b0.44bcaControl and Hesperidin-treated groupsbSignificantly different from the control and Hesperidin groups at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$cSignificantly different from the ALCL3 group at\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$p \leq 0.05$$$\end{document}$p \leq 0.05$
## Semi-thin toluidine blue-stained sections
Examination of the semi-sections stained by toluidine blue of renal cortex from the different experimental groups is shown in Fig. 8. In the control and Hesperidin groups, the renal corpuscle was lined by the parietal layer of Bowman’s capsule. The podocytes, which lined the visceral layer, embraced the glomerular capillaries and mesangial cells (Fig. 8a, b), respectively. The proximal convoluted tubule had narrow lumen, vesicular nuclei with nucleoli, and basal striations. The distal tubules had cuboidal epithelia with rounded vesicular nuclei and basal striations (Fig. 8e, f), respectively. In the ALCL3 group, the renal corpuscle exhibited a narrowed renal space; however, there were dilated inter-tubular capillaries in the interstitium with thick-walled blood vessels. Some nuclei of tubular cells appeared dense and distorted, whereas others exhibited rarified chromatin (Fig. 8c). Renal tubules had vacuolated cytoplasm with loss of the cellular architecture (Fig. 8g). In the ALCL3 + Hesperidin group, the renal corpuscles had preserved architecture and capsular thickness and space (Fig. 8d). There was a notable restoration of the brush border and the vesicular nuclei in the proximal and distal convoluted tubules (Fig. 8h).Fig. 8Semi-thin sections in the rat renal cortex from the different experimental groups. a, e, b, f Control and Hesperidin groups, respectively. a, b Tubules (T) and a part of the renal corpuscle (RC) is lined by parietal layer of Bowman’s capsule (arrowhead). The podocytes (O), which line the visceral layer, embrace the glomerular capillaries (C) and mesangial cells (M) in both control and Hesperidin groups, respectively. e, f Proximal convoluted tubule has narrow lumen vesicular nuclei (N) with nucleoli (n) and basal striations (PT), distal tubules have cuboidal epithelium with rounded vesicular nuclei and basal striation (DT) in both control and Hesperidin groups, respectively. c, g ALCL3 group. c The renal corpuscle exhibits narrowed renal space (arrowhead), but there are dilated inter-tubular capillaries (**) in the interstitium with thick wall blood vessels (v). g Some nuclei of proximal (PT) and distal (DT) tubular cells appear dense and distorted (N). Renal tubules have vacuolated cytoplasm with loss of the cellular architecture. d, h ALCL3 + Hesperidin group. d The renal corpuscles (RC) with preserved architecture and capsular thickness and space (arrowhead). The podocytes (O) embrace mesangial cells (M). h Notable restoration of the brush border and the vesicular nuclei (N) in both proximal (PT) and distal convoluted tubules (DT). Toluidine blue, × 20 μm, × 1000
## Podocytes, glomerular capillaries, and glomerular basement membrane
Transmission electron microscopic assessment of the ultrathin section of the control and Hesperidin groups documented that the glomerular capillaries had fenestrated endothelial lining surrounded by the glomerular basement membrane. Podocytes showed folded nuclei and major processes (primary processes) extending parallel to the glomerular basement membrane (GBM). Numerous minor processes extended from the major ones (secondary processes) and passed perpendicularly to the GBM to end by feet-like plates separated by filtration slits in both control and Hesperidin groups (Fig. 9a, c), respectively. The glomerular filtration barrier was formed by three layers: fenestrated capillary endothelium, trilaminar GBM, and filtration slits between the minor processes of podocytes in both control and Hesperidin groups (Fig. 9b, d), respectively. In the ALCL3 group, TEM results of the renal cortex showed an irregular thickening of the GBM with the loss of its trilaminar appearance (Fig. 9e). In addition, the minor processes of podocytes appeared enlarged and fused, obliterating the filtration slits (Fig. 9f). In the ALCL3 + Hesperidin group, the GBM appeared to be mostly of regular thickness, except for some thickened areas (Fig. 9g). Filtration slits separated the minor processes of the podocytes. The glomerular capillaries were lined by fenestrated endothelium with partially intact primary and secondary foot processes (Fig. 9h).Fig. 9Transmission electron microscopy (TEM) of the renal corpuscle of rat renal cortex showing a panoramic view of a glomerulus from the different experimental groups. a, b The control group shows a higher magnification of the right upper and lower corners of TEM, respectively, showing podocytes (PC) with well-defined intact foot processes primary (P1) and secondary (P2), glomerular capillaries (C), intact basement membrane (GBM), fenestrated endothelium (FE), filtration slits (FS). c, d The Hesperidin group shows podocytes (PC) with well-defined intact foot processes primary (P1) and secondary (P2), glomerular capillaries (C), intact basement membrane (GBM), fenestrated endothelium (FE), filtration slits (FS). e, f The ALCL3 group shows podocytes (PC) with dark-stained nuclei and fused and ill-defined or even disappeared primary (P1) and secondary (P2) foot processes, congested glomerular capillaries (C), thickened basement membrane (GBM), fenestrated endothelium (FE), filtration slits (FS). g, h The ALCL3 + Hesperidin group shows podocytes (PC), with partially intact primary (P1) and secondary (P2) foot processes, glomerular capillaries (C), partially thickened basement membrane (GBM), fenestrated endothelium (FE), filtration slits (FS). TEM: a, b, f, h: scale bar = 2 μm; c, d, e, g: scale bar = 5 μm
## Proximal convoluted tubules
The cells lining the PCTs appeared cuboidal in the control and Hesperidin groups, with euchromatic central nuclei resting on a thin intact basement membrane. Their apical borders demonstrated numerous closely packed, well-developed microvilli projecting into the lumen. Also, numerous tubules, longitudinally arranged mitochondria, and rough and smooth endoplasmic reticula were seen in both control and Hesperidin groups (Fig. 10a–d), respectively. In the ALCL3 group, the lining cells of PCTs showed cytoplasmic vacuoles, increased density of the nuclei, thickened basement membranes, loss of mitochondrial longitudinal arrangement, hypertrophied RER and SER, and distorted appearances of the apical microvilli (Fig. 10e, f). Regarding the ALCL3 + Hesperidin group, the cells lining the PCTs had euchromatic central nuclei resting on a moderately thin, intact basement membrane, intact apical microvilli, and a preserved mitochondrial longitudinal arrangement (Fig. 10g, h).Fig. 10Transmission electron microscopy (TEM) of the proximal convoluted tubules (PCT) of rat renal cortex from the different experimental groups. a, b, c, d The control and Hesperidin groups, respectively, show PCT cells with euchromatic nuclei (N), resting on a thin intact basement membrane (BM). Their apical borders demonstrated numerous closely packed microvilli (MV). In addition, numerous tubular longitudinally arranged mitochondria (M), well-developed rough (RER) and smooth (SER) endoplasmic reticula are seen. e, f The ALCL3 group shows lining cells of PCTs with cytoplasmic vacuoles (V), increased density of the nucleus (N), thickened basement membrane (BM), loss of mitochondrial (M) longitudinal arrangement, hypertrophied RER and SER, and disturbed appearance of the apical microvilli (MV). g, h The ALCL3 + Hesperidin group shows the cells lining PCTs have euchromatic central nuclei (N) resting on a moderately thin intact basement membrane (BM), nearly normal apical microvilli (MV) nearly preserved RER and SER, and preserved mitochondrial (M) longitudinal arrangement. TEM, scale bar = 5 μm
## Distal convoluted tubules
The cubical cells lining the DCTs had few short-scattered microvilli on their luminal surfaces in the control and Hesperidin groups. Each cell of the DCT contained an ovoid euchromatic nucleus with less extended chromatin resting on a thin, intact basement membrane. Furthermore, basally arranged mitochondria and well-developed rough and smooth endoplasmic reticula occurred in both control and Hesperidin groups (Fig. 11a–d), respectively. In the ALCL3 group, the lining cells of the DCTs showed widening of the intercellular spaces and irregular nuclear outlines, together with the loss of the basal arrangement of its mitochondria and thickened basement membrane with hypertrophied (RER) and (SER) (Fig. 11e, f). Regarding the ALCL3 + Hesperidin group, the cells lining the DCTs had moderately thin, intact basement membranes, nearly preserved RER and SER, and a mild disarrangement of the mitochondria (Fig. 11g, h).Fig. 11Transmission electron microscopy (TEM) of the distal convoluted tubules (DCT) of rat renal cortex from the different experimental groups. a, b, c, d The control and Hesperidin groups, respectively, show DCT cells with euchromatic nuclei (N), resting on a thin intact basement membrane (BM). In addition, basally arranged mitochondria (M), well-developed rough (RER) and smooth (SER) endoplasmic reticula are seen. e, f The ALCL3 group shows lining cells of DCTs with widening of the intercellular spaces and irregular nuclear outlines (N), thickened basement membrane (BM), loss of mitochondrial (M) basal arrangement, hypertrophied RER and SER. g, h The ALCL3 + Hesperidin group shows the cells lining DCTs had euchromatic central nuclei (N) resting on a moderately thin intact basement membrane (BM), nearly preserved RER and SER, and mild disarrangement of mitochondria (M). TEM: a–h: scale bar = 5 μm; g: scale bar = 10 μm
## Discussion
Nowadays, the extreme exposure to aluminum due to dissimilar human daily lifestyles raised the threat of renal aluminum withholding due to the accumulation of aluminum to the renal tubules which resulted in renal dysfunction as reported by Al Dera [2016] and Hasona and Ahmed [2017].
The present study has provided a more direct investigation of the precise role of ALCL3 in the incidence of renal dysfunction. The study aimed to investigate the potential molecular mechanisms of Hesperidin-induced nephroprotection in ALCL3-induced renal injury. We utilized 6-week-old age rats as it is the age of rat maturity according to the international experimental guidelines (ARRIVE guidelines) (Percie du Sert et al. 2020). We administered ALCL3 in a dose of 10 mg /kg as documented by (Mostafa et al. 2015), but we decided to increase the duration from 4 to 5 weeks to ensure the occurrence of the renal injury at all levels.
Our findings accounted for the reported disturbance in plasma level of creatinine, urea, and uric acid as evidenced by significant elevated level of serum creatinine and urea as well as uric acid levels as compared to the control and Hesperidin groups which were assessed as significant indicators of renal function and increased their levels after ALCL3 administration, indicating impaired renal function and provoking nephrotoxicity. Such outcomes were consistent with the previous literature reported results by Al-Kahtani and Morsy [2019], Balgoon [2019], and Okail et al. [ 2020]. Additionally, Imam et al. [ 2016] informed that the raised plasma urea and creatinine readings in ALCL3-treated rats are reflected as a considerable marker of renal dysfunction.
Moreover, in the current study, a noticeably enhanced intrarenal oxidative stress with significant elevation in renal MDA level (the byproduct of lipid peroxidation) as compared to the control and Hesperidin groups and overwhelming defensive antioxidant molecules proved by the diminished TAC of the renal tissue against the harm of ROS following ALCL3 administration. Such outcomes are consistent with those of Tribble et al. [ 1987] and Pari et al. [ 2015].
Furthermore, ALCL3 significantly elevated proinflammatory cytokine levels (including IL-6) and decreased anti-inflammatory markers such as IL-10, which is consistent with many previous reports that revealed significant elevations in proinflammatory cytokine levels (including TNF-α and IL-6) in ALCL3-intoxicated animals compared with control rats (Al Dera 2016).
It is of great interest that previous studies reported that there is a crosstalk between the damage elucidated by oxidative factors and inflammatory cytokines with the upregulation of matrix metalloproteinases (MMP) expression (Catania et al. 2007; Dejonckheere et al. 2011). Matrix metalloproteinases (MMPs) are from a group of Zn2 + -dependent and Ca2 + -dependent endopeptidases and unusually stated in numerous renal disorders. Prior researches have proved that MMP-9 can control extracellular matrix (ECM) deprivation throughout renal fibrosis (Kolset et al. 2012; Tsioufis et al. 2012). Therefore, MMPs have a master impact on renal tissue damage which control essential cellular actions as cell proliferation, migration, and differentiation by degrading extracellular matrix (Newby 2006).
The current study revealed that ALCL3 administration upregulated the MMP-9 m RNA expression and elevate the MMP-9-positive immunoreactivity within the cytoplasm of degenerated tubular cells. The results were confirmed statistically by a significant rise in the OD of the immune-positive appearance of MMP-9 in comparison to the control and Hesperidin groups.
Such outcomes were in agreement with the recent study, which demonstrated that the treatment of cells with ALCL3 augmented the action of MMP-9 and mRNA expression of matrix metalloproteinase-9 (MMP-9) and myosin light-chain kinase (MLCK) in a concentration-dependent way in intestinal cells. ALCL3 stimulated extracellular signal-regulated kinase $\frac{1}{2}$ and nuclear factor-kappa B (NF-κB), resulting in mRNA expression of (MMP-9), MLCK, and inflammatory cytokines (tumor necrosis factor-alpha (TNF-α), interleukin-1β (IL-1β), and IL-6) in HT-29 cells (Jeong et al. 2020). Moreover, another work added that, in most tissues, the expression level of MMP-9 was very low but rose in response to local secretion of inflammatory cytokines and growth factors, most notably interleukin-1 (IL-1) and tumor necrosis factor-alpha (TNF-α) (Labrie and St-Pierre 2013).
Interestingly, previous studies have revealed that MMPs are implicated in initiation and progression of renal fibrosis (Zhao et al. 2013) which is produced by extreme accumulation of collagen in renal tissue that evaluated the main process responsible for the progression of chronic kidney disorders (Pradère et al. 2008).
Our findings confirmed accumulation of collagen fibers in the interstitium of renal cortex and around the blood vessels of ALCL3-treated rats which was proved by statistical analysis of area percentage of Masson’s trichrome staining of collagen fibers that revealed a significant increase compared with the control and Hesperidin groups. This finding was in agreement with other previous studies (Hanafy and Soltan 2004). Tsai et al. [ 2012] observed that raised MMP-9 expression in human atrophic tubular nuclear was accompanied by higher interstitial fibrosis score (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r\hspace{0.17em}=\hspace{0.17em}0.40$$\end{document}$r = 0.40$, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p\hspace{0.17em}=\hspace{0.17em}0.002$$\end{document}$$p \leq 0.002$$), signifying that it might be a protagonist in the process of renal damage. Moreover, Tan et al. [ 2013] demonstrated a profibrotic role of MMP-9 in tubular cell EMT. They established the pathogenesis of MMP-9’s influence to renal fibrosis via osteopontin cleavage. Correspondingly, Ling et al. [ 2018] reported that the atypical appearance of MMP-9 was associated with EMT in the podocytes of diabetic rats.
In contrast to our result, a prior work reported that in the model of MMP-9 deficiency mice had increased glomerular fibrin deposition and the fibrin-degrading activity of MMP-9 was clearly beneficial in this model of acute glomerular disease (Lelongt et al. 2001). Moreover, Wozniak et al. [ 2021] explained this phenomenon by postulating that MMP-9 activity appears to have protective roles in the acute glomerular and tubular damage phase but might be detrimental during later stages and fibrosis development.
In an attempt to further clarify the core mechanisms that might be related to the deleterious effect of ALCL3 on renal tissue was activation of apoptosis as confirmed in our study where the ALCL3-treated group exhibited a significant rise in the area percentage of FAS, caspase-3, and BAX-positive immunoexpressed cells and decrease in Bcl-2-positive immunoexpressed cells. This was in agreement with Xu et al. [ 2018] who stated that ALCL3 exposure stimulated Fas/Fas ligand signaling pathway, accessible as Fas, Fas ligand, and Fas-associated death domain expression augmentation and caspase-8 initiation. Furthermore, ALCL3 exposure suppressed Bcl-2 protein expression and upregulated the expressions of BAX. These results indicated that ALCL3 exposure encouraged apoptosis through activating FAS- and mitochondria-mediated signaling pathway.
In the nephrotoxic nephritis (NTN) kidneys, the ratio of Bax to Bcl-2 at protein levels was consistently elevated in the ALCL3-treated group and exhibited strong correlations with caspase-3 activity, apoptosis, inflammation, and renal fibrosis. This suggests that the Bax/Bcl-2 ratio may be significant and that it likely affects caspase-3 in mediating the apoptosis linked to inflammation and renal cell loss throughout the development of renal fibrosis. These findings were in concordance with Yang et al. ( 2002b)b.
Besides the previously mentioned mechanism of ALCL3 injury, renal tissue injury was evident upon H&E examination. Notable microscopic variations in the construction of the renal cortex were characterized by areas of tubular damage ranging from mild to severe in all treated animals, as hypertrophy and degeneration of the epithelia of the renal tubules with a distinction of the mononuclear cells’ infiltration. In this experiment, the reported histopathological changes were in agreement with the study by Sargazi et al. [ 2006]. Similar effects have been described by Mahieu et al. [ 2005], who reported that aluminum had been concerned in the pathogenesis of numerous clinical problems, including renal dysfunction.
Additionally, ultrastructure examination of the renal cortex of the kidneys of animals in the ALCL3 group revealed a shorter and irregular brush border membrane, split mitochondria, and vacuolization of the cytoplasm. In the ALCL3-injected group, numerous proximal renal tubule cells exhibited thickened basement membranes, tubular epithelial damage, and changes in epithelial cell shape. This was associated with occasional tubule dilatation, interstitial tubule fibrosis, and lymphocyte infiltration into some interstitial areas.
These results were congruent with those of previously reported studies by Al Kahtani et al. [ 2014], who reported that ALCL3 produced ultrastructural alterations on proximal cortical tubules, the target of aluminum that exhibited nuclear-cytoplasmic changes. Necrotic nuclei and the detachment of brush borders indicated a functional impairment of urinary reabsorption.
Ultimately, the current study has proved a crucial role of Hesperidin against development of ALCL3-induced renal dysfunction. It has provided lots of evidence of the potential protective effect of Hesperidin. In fact, ALCL3 rats treated with Hesperidin displayed a remarkably improved renal function with significantly reduced serum creatinine and urea levels and uric acid in agreement with (Rushdy et al. 2012).
In an attempt to further explain the underlying molecular mechanisms of Hesperidin-induced renoprotection in ALCL3-treated rats, our study has established a vital role of Hesperidin in suppressing all the aforementioned ALCL3-induced oxidative stress, inflammatory, apoptotic, and profibrotic signaling pathways in kidney tissue which were significant and approaching the near normal healthy state of the control rats.
These findings are in accordance with those of Sahu et al. [ 2013], who reported that Hesperidin treatment significantly weakened the cisplatin-induced oxidative stress/lipid peroxidation and inflammation (infiltration of leukocytes and proinflammatory cytokines).
Moreover, Hanedan et al. [ 2018] stated that Hesperidin and chrysin could attenuate colistin-induced nephrotoxicity via antioxidant and anti-inflammatory activities. Elhelaly et al. [ 2019] added that Hesperidin had potent protective effects against oxidative stress, lipid peroxidation, and DNA damage induced by acrylamide-induced renal toxicity in rats.
In harmony with our results, Anandan and Subramanian [2012] reported that Hesperidin acted as a potent scavenger of free radicals in the kidney to prevent the toxic effects of gentamycin both at the biochemical and histopathological levels.
Consistent with our findings, Li et al. [ 2020] documented that flavonoid could impact learning and memory role by preventing extreme apoptosis and oxidative stress in ALCL3-exposed rats. Furthermore, Muhammad et al. [ 2019] reported that Hesperidin rescued lipopolysaccharide (LPS)-induced neuronal apoptosis by reducing the expression of associated X protein (BAX) and caspase-3 protein and promoting the Bcl-2 protein level.
In support to our results, Park et al. [ 2019] reported that Hesperidin improved the renal dysfunction and reduced inflammation and apoptosis after ischemia/reperfusion injury as it has potent antiapoptotic and anti-inflammatory properties due to its antioxidant property.
These results were in contrary to Aboismaiel et al. [ 2020] who stated the antitumor effect of Hesperidin through induction of Fas/FasL apoptotic pathway and inhibiting of Bcl-2 gene expression. Hesperidin was reported to induce apoptosis in cancer cell line by stimulating ROS-mediated apoptosis along with cell cycle arrest at G2/M phase in human gall bladder carcinoma (Pandey et al. 2019), by inhibiting Sp1 and its regulatory protein in MSTO-211H cells (Lee et al. 2012), through the mitochondrial apoptotic pathway by decreasing the expression of cyclin D1 and increasing the expression of p21 and p53 (Xia et al. 2018), and through CASP3 activation in human colon cancer cells (Park et al. 2008).
Interestingly, Hesperidin downregulated MMP-9 mRNA expression with a little positive reaction in the cytoplasm of most tubular cells and the glomerulus. The results were confirmed statistically by a significant decrease in the OD of the immune-positive appearance of MMP-9 compared with the ALCL3-treated rats.
In harmony with our results, a recent report showed that Hesperidin inhibits the expression of members of the MMP family, as reported by Lee et al. [ 2018], who stated that Hesperidin inhibited the MMP-9-related signaling pathway activated by UVB irradiation. Moreover, Kongtawelert et al. [ 2020] reported that Hesperidin significantly reduced the levels of MMP-9 and MMP-2 secreted from PD-L1 high-expressing MDA-MB231 cells. The current results were supported by these recent studies.
Remarkably, Hesperidin prohibited extreme collagen accumulation in the ALCL3 renal cortex as our results revealed that concomitant administration of Hesperidin with ALCL3 exhibited few accumulations of collagen fibers surrounding the glomeruli and around the blood vessel that exhibited a significant decrease in the area percentage of Masson’s trichrome staining of collagen fibers compared with the ALCL3 group signifying the antifibrogenic action of Hesperidin. The antifibrotic action of Hesperidin against liver fibrosis in rats has been reported in a previous study (Pérez-Vargas et al. 2014).
In further agreement, other studies confirmed that direct or indirect inhibition of MMP-9 activity by Hesperidin resulted in a decrease in renal fibrosis in obstructive nephropathy (Wang et al. 2010). Another study added that broad-spectrum metalloproteinase inhibitors have been used to treat fibrotic kidney diseases experimentally (Wozniak et al. 2021) (Abdel-Hamid 2013).
The aforementioned microscopic renal tissue degenerative changes were attenuated by the administration of Hesperidin. These results are constant with those of the study by Abd Alsalam et al. [ 2016], who found that the oral administration of Hesperidin (100 mg/kg and 200 mg/kg) improved the microscopic renal construction in a dose-dependent method. Moreover, Aly et al. [ 2017] demonstrated that treating the DEN/CCl4-induced rats with Hesperidin significantly prevented kidney injury. Additionally, Meng et al. [ 2020] reported that treatment with Hesperidin attenuated renal injury in I/R kidney-injured rats.
Finally, administration of Hesperidin significantly mitigated the ALCL3-induced intrarenal oxidative stress, inflammatory, and profibrotic changes and reversed all the related deleterious sequelae (Jeon et al. 2014). Our study has paid an attention to the role of MMP-9 expression as in the nephroprotective effects of Hesperidin which inhibited the MMP-9-related signaling pathway (Kongtawelert et al. 2020) activated by ALCL3 (Jeong et al. 2020).
## Conclusion
Collectively, the interpretation of our results proved tangible antioxidant, anti-inflammatory, antifibrotic, and antiapoptotic ameliorative influences of Hesperidin on the renal biochemical, cytomorphological, and immunohistochemical effects of ALCL3-induced renal damage. Furthermore, MMP-9 played a key role in the underlying molecular machinery of these results.
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|
---
title: Lnc_000048 Promotes Histone H3K4 Methylation of MAP2K2 to Reduce Plaque Stability
by Recruiting KDM1A in Carotid Atherosclerosis
authors:
- Shuai Zhang
- Yu Sun
- Qi Xiao
- Mengying Niu
- Xudong Pan
- Xiaoyan Zhu
journal: Molecular Neurobiology
year: 2023
pmcid: PMC10039837
doi: 10.1007/s12035-023-03214-0
license: CC BY 4.0
---
# Lnc_000048 Promotes Histone H3K4 Methylation of MAP2K2 to Reduce Plaque Stability by Recruiting KDM1A in Carotid Atherosclerosis
## Abstract
Stabilizing and inhibiting plaque formation is a key challenge for preventing and treating ischemic stroke. KDM1A-mediated histone modifications, which involved in the development of training immunity, ultimately exacerbate the outcomes of inflammation. Although lncRNAs can recruit KDM1A to participate in histone methylation modification and regulate inflammation, cell proliferation, and other biological processes, little is known about the role of KDM1A-lncRNA interaction during atherosclerosis. The present study sought to delineate the effect of the interaction between lnc_000048 and KDM1A on plaque rupture in carotid atherosclerosis, as well as the potential mechanism. Our results revealed that lnc_000048 reduced the activity of histone demethylase and activated MAP2K2 expression by interacting with KDM1A. Furthermore, upregulated lnc_000048 indirectly regulated ERK phosphorylation by MAP2K2 and eventually activated the inflammatory response through the MAPK pathway, which was involved in atherosclerosis. Importantly, our study using ApoE-/- mice confirmed the regulatory role of lnc_000048 in promoting inflammation and collagen degradation in atherosclerotic plaques. These results suggest that targeting the lnc_000048 /KDM1A/MAP2K2/ERK axis may be a promising strategy for preventing atherosclerosis.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s12035-023-03214-0.
## Background
Atherosclerosis-induced unstable plaque plays a crucial role in ischemic stroke [1, 2]. Understanding the pathophysiological process and regulatory mechanism of atherosclerosis is of strategic significance for preventing and treating ischemic stroke. Aggravated inflammation and increased expression levels of matrix metalloproteinases (MMPs) can weaken plaque caps and promote plaque rupture [3, 4]. Further, lncRNA-mediated histone methylation plays an important role in chronic inflammatory vascular diseases, such as atherosclerosis [5–7].
LncRNAs contribute to atherosclerotic processes such as lipid metabolism disorders, inflammatory responses, and plaque formation [8]. Leisegang et al. found that the lncRNA MANTIS limited ICAM-1 mediated monocyte adhesion to endothelial cells, which may accelerate the development of atherosclerosis [9]. LncRNAs can also participate in histone modification by binding proteins and interfering with the transcription of inflammatory factors, chemokines, and metabolic pathways [10, 11]. KDM1A (also known as LSD1) plays an important pathological role by regulating histone methylation modification (H3K4me$\frac{1}{2}$ and H3K9me$\frac{1}{2}$), which leads to epigenetic reprogramming and affects gene expression [12, 13]. Choi et al. reported that the lncRNA MEF2 recruited histone demethylase KDM1A to decrease the levels of inhibitory markers such as H3K9me2 and H3K9me3 from the promoter region of muscle-specific genes and subsequently promoted the differentiation of muscle cells [14]. The binding of lncRNAs to KDM1A is becoming an important target for disease treatment as it leads to reversible changes in gene transcription caused by histone modification [15–17]. However, the specific mechanisms by which lncRNAs interact with KDM1A during atherosclerosis initiation and development need to be further investigated.
Our earlier research showed that the lnc_000048 was highly expressed in plasma exosomes of patients with large-artery atherosclerotic stroke, and the elevation of its level was related to the rupture of atherosclerotic plaques [18]. But it is still unknown how lnc_000048 contributes to atherosclerosis. We hypothesized that lnc_000048 could interact with KDM1A in target genes’ promoter regions and thus involved in atherosclerosis. We designed this study to explore the potential impact of histone modification induced by the interaction between lnc_000048 and KDM1A on plaque formation and stability in atherosclerosis, with the aim of identifying a new potential molecular mechanism underlying atherosclerosis progression.
## Bioinformatic Analysis
Lnc_000048 was identified from the RNA sequencing [18]. In the Supplementary Materials and Methods, a comprehensive bioinformatic analysis was detailed.
## Cell Culture and Drug Treatment
Procell Life Science&Technology Co., Ltd. provided the human mononuclear cell line (THP-1, Procell Life Science&Technology, CL-0233) for purchase (Wuhan, China). The Supplementary Materials and Methods described the specifics of cell culture. THP-1 cells were cultured in 6-well plates or 10 cm2 dishes as required for the exponential phase. The cells were then given a 100 nM PMA (Sigma-Aldrich Chemical Company, USA) treatment for 48 h to induce monocytes into macrophages. Next, 100 μg/mL ox-LDL (Yiyuan Biotechnology, Guangzhou, China) was added to serum-free RPMI 1640 medium for 48 h to construct the atherosclerosis model in vitro for subsequent experiments. According to the experimental requirements, the model cells were pretreated with signaling pathway inhibitors or enzyme inhibitors, including MEK/ERK inhibitors (FR 180,204, terminal concentration 1 µM) and GSK-LSD1 (final concentration 5 µM) [19, 20]. All inhibitors were purchased from MCE. Lentivirus was transfected into THP-1 cells in accordance with the manufacturer’s recommendations. The Supplementary Materials and Methods provided a description of the specifics.
## Carotid Atherosclerosis Model and Transfection in ApoE-/- mice
All experimental animal protocols were approved by the Animal Management Committee and Animal Ethics and Welfare Committee of the Affiliated Hospital of Qingdao University. The US National Institutes of Health’s Guide for the Care and Use of Laboratory Animals was strictly followed when carrying out the protocols (NIH Publication No. 85–23, revised 1996). The model of carotid atherosclerosis was constructed as explained previously [21, 22]. Then, as previously mentioned, the virus-infected cells were injected into the mice’s tail vein for a total of 4 weeks [23–25].
## RNA Isolation and Quantification
RNA isolation and quantification were carried out as previously described [26]. The Supplementary Materials went into detail about these specifics and Methods and primers used for the genes of interest are listed in Supplementary Table S1.
## Protein Extraction and Western Blotting Analysis
Western blotting was carried out in the manner previously described [27]. The Supplementary Materials and Methods provided a description of the specifics.
## Oil Red O Staining
To assess the accumulation of lipid in cells, the Oil Red O Stain Kit was performed as per manufacturer’s instructions (G1262; Solarbio; Beijing, China). The Supplementary Materials and Methods contained further information.
## RNA Pull-Down Assay and Mass Spectrometry Analysis
Using the PierceTM Magnetic RNA–Protein Pull-Down Kit’s instructions, RNA pull-down assays were used to look at the lnc_000048-binding proteins (Thermo Scientific). Details are provided in the Supplementary Materials and Methods and primer sequences of lnc_000048 for RNA pull-down are listed in Supplementary Table S2.
## Fluorescence In Situ Hybridization (FISH)
Fluorescence in situ hybridization assays were performed as per manufacturer’s instructions (C10910; RiboBio; Guangzhou) [28]. FISH was used to assess lnc_000048 distribution in THP-1 cells. Details were provided in the Supplementary Materials and Methods.
## Immunofluorescence
Immunofluorescence was used to assess the distribution of KDM1A in THP-1 cells. The details were described in the Supplementary Materials and Methods.
## Chromatin Immunoprecipitation (ChIP)
To assess H3K4me2, Chromatin immunoprecipitation (ChIP) assays were carried out in accordance with the manufacturer’s instructions (26,156; Thermo Scientific) [29]. The details were described in the Supplementary Materials and Methods and primers used for the genes of interest are listed in Supplementary Table S3.
## HE Staining
HE staining was performed as described previously [30]. The Supplementary Materials and Methods provide details.
## Masson Staining
Masson staining was conducted using a ready-to-use kit (Masson’s Trichrome Stain Kit, Solarbio) [31]. The Supplementary Materials and Methods provide details.
## Statistical Analysis
Data represent the mean ± standard error. Comparisons between the controls and treatment groups were performed using one-way ANOVA. The Mann–Whitney U test or Student’s t-test was used to compare continuous variables between patients and controls. Statistical significance was defined as $p \leq 0.05$ for all tests. Statistical analyses were performed using Statistical Package for the Social Sciences software version 17.0 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism 6.
## Characteristics of lnc_000048
Our previous studies found that up-regulated lnc_000048 may be associated with the rupture of atherosclerotic plaques [18]. qRT-PCR was used to confirm lnc_000048 expression in vitro atherosclerosis model, and the results showed that lnc_000048 was up-regulated in THP-1 macrophage-derived foam cells (Fig. 1A).Fig. 1Characteristics of lnc_000048. A The expression of lnc_000048 in macrophages and THP-1 macrophage-derived foam cells; B CPAT evaluated the ability of coding for lnc_000048, GADPH, ACTB, HULC, SNHG3, and BACER; C, D GO and KEGG analysis of target genes for lnc_000048 We then evaluated the coding potential of lnc_000048 using CPC2, CPAT, and Pfam, and found that lnc_000048 had no coding potential, similar to HOTAIR or NEAT1 (Fig. 1B).
Furthermore, we predicted the target genes of lnc_000048 by using TargetScan, miRanda, and RNAhybrid. The top 10 target genes of lnc_000048 were listed in Supplementary Table S4. Importantly, the biological function of lnc_000048 was assessed using GO and KEGG, and our results indicated that lnc_000048 might be related to immune and platelet aggregation (Fig. 1C, D). Together, these findings imply that the amount of lnc_000048 level is elevated in foam cells produced fromTHP-1 macrophage and probably involved in the process of atherosclerosis.
## Lnc_000048 Increased the Expression Levels of Inflammatory Factors and Matrix Metalloproteinases in THP-1 Macrophage-Derived Foam Cells
We used qRT-PCR and western blotting to analyze the expression of inflammatory cytokines (IL-1, IL-6, and TNF-), as well as matrix metalloproteinases (MMP-2 and MMP-9), in THP-1 macrophage-derived foam cells to assess the phenotypic change. The results showed an increase in inflammatory factors and matrix metalloproteinases in THP-1 macrophage-derived foam cells (Additional file: Fig. S1 A-B). Oil Red O staining indicated the formation of foam cells induced by ox-LDL from THP-1-derived macrophage and the successful establishment of an atherosclerosis model in vitro (Additional file: Fig. S1C).
To further evaluate the effect of lnc_000048 on atherosclerosis, we constructed and transfected lentiviruses with lnc_000048 knockdown (sh-lnc_000048) or overexpression (oe-lnc_000048). In THP-1 macrophage-derived foam cells, the expression levels of lnc_000048 were different in the sh-lnc_000048 and oe-lnc_000048 groups compared with that in the control group ($p \leq 0.05$), and there was no difference in the expression level of lnc_000048 between the sh-NC and oe-NC groups (Fig. 2A) ($p \leq 0.05$).Fig. 2Lnc_000048 accelerated inflammatory responses and collagen degradation matrix in THP-1 macrophage-derived foam cells. A qRT-PCR assay was performed in THP-1 macrophage-derived foam cells transfected with lnc_000048 knocked down and overexpressed RNA to evaluate the relative level of lnc_000048. B The effects of lnc_000048 on inflammatory cytokine and matrix metalloproteinase gene transcriptional expression were assessed using qRT-PCR among control, sh-lnc_000048, oe-lnc_000048, sh-NC, and oe-NC groups. C The effects of lnc_000048 on inflammatory cytokine and matrix metalloproteinase expression were assessed using western blotting among control, sh-lnc_000048, oe-lnc_000048, sh-NC, and oe-NC groups. D Effect of lnc_000048 on intracellular lipid accumulation examined using oil red O staining among control, sh-lnc_000048, oe-lnc_000048, sh-NC, and oe-NC groups. * $P \leq 0.05$ versus the control group; **$P \leq 0.01.$ Three times each of the cellular tests were done. The one-way ANOVA method was used to assess comparisons between various groups The effects of lnc_000048 overexpression or knockdown on THP-1 macrophage-derived foam cells were next investigated using western blotting and qRT-PCR. After lnc_000048 knockdown, the expression levels of inflammatory cytokines (IL-1β, IL-6, TNF-α) and matrix metalloproteinases (MMP-2, MMP-9) decreased compared with those in the sh-NC group, following the overexpression of lnc_000048, the release of these factors was higher than that in the oe-NC group (Fig. 2B and C) ($p \leq 0.05$), highlighting the ability of lnc_000048 to accelerate the inflammation and degradation of collagen in THP-1 macrophage-derived foam cells. The effects of lnc_000048 overexpression and knockdown on intracellular lipid accumulation in THP-1 macrophage-derived foam cells were examined using Oil Red O staining. As shown in Fig. 2D, when compared with the sh-NC group, knockdown of lnc_000048 inhibited intracellular lipid accumulation and overexpression of lnc_000048 increased intracellular lipid accumulation ($p \leq 0.05$). These findings suggest that lnc_000048 promotes lipid accumulation in foam cells generated from THP-1 macrophages.
Together, all the results suggest that lnc_000048 boosted inflammatory responses s and collagen degradation matrix in THP-1 macrophage-induced foam cells.
## KDM1A Was Essential for ox-LDL-Induced Atherosclerosis in THP-1-Derived Macrophages
To elucidate the potential mechanism by which lnc_000048 promotes atherosclerosis, we focused on KDM1A, the key player in histone modification. We first identified the potential binding protein of lnc_000048 by RNA pull-down and mass spectrometry. After functional analysis, 319 proteins were identified, and 6 proteins related to epigenetics were obtained (Fig. 3A and B, Additional file: Fig. S2A). Finally, we selected KDM1A, which participates in histone modification and might be involved in the development of atherosclerosis, as an lnc_000048-binding protein. The potential interaction between lnc_000048 and KDM1A was evaluated using the catRAPID bioassay, and the results demonstrated that the interaction between lnc_000048 and KDM1A was likely with a high score of 0.7 (a score of 0.5 is considered as a high possibility of interaction) (Additional file: Fig. S2B).Fig. 3KDM1A was essential for atherosclerosis. A Analysis combining mass spectrometry and an RNA pull-down assay to create a heatmap of the proteins bound to lnc_000048. B The lnc_000048-binding proteins that are related to epigenetics. C Subcellular localization of lnc_000048 and KDM1A in THP-1 cells detected using FISH and immunofluorescence assays. D By using an RNA pull-down experiment and western blotting, the interaction between lnc_000048 and KDM1A was confirmed. E The effects of KDM1A on inflammatory cytokines and matrix metalloproteinases expression were assessed using western blotting. F The effects of an inhibitor of KDM1A (GSK-LSD1) on inflammatory cytokines and matrix metalloproteinases expression were assessed using western blotting. * $P \leq 0.05$ versus the control group; **$P \leq 0.01.$ All cellular experiments were repeated 3 times. Comparison among multiple groups was analyzed using one-way ANOVA Furthermore, RNA-FISH assay revealed that lnc_000048 was located in the nuclei of THP-1 cells. Similarly, immunofluorescence results showed that KDM1A was present in the nucleus (Fig. 3C). Spatial localization suggested the possibility of an interaction between lnc_000048 and KDM1A. To further confirm the interaction between lnc_000048 and KDM1A, we conducted WB experiments after RNA pull-down and found that the binding of KDM1A to lnc_000048 was specific (Fig. 3D).
The influences of knockdown or overexpression of KDM1A on THP-1 macrophage-derived foam cells were examined by western blotting. After KDM1A knockdown, the expression levels of inflammatory cytokines (IL-1β, IL-6, and TNF-α) and matrix metalloproteinase (MMP-2 and MP-9) were elevated compared with those in the sh-NC group. Following the overexpression of KDM1A, the release of these factors was lower than that in the oe-NC group (Fig. 3E) ($p \leq 0.05$), highlighting the ability of KDM1A to reduce the occurrence of inflammation and degradation of collagen in THP-1 macrophage-derived foam cells. More importantly, treating cells with KDM1A inhibitors can significantly accelerate the occurrence of inflammation, suggesting that targeting KDM1A can disturb the process of atherosclerosis (Fig. 3F). Interestingly, we observed no significant change in KDM1A expression level in THP-1 macrophage-derived foam cells, while histone methylation levels, which were regulated by KDM1A, increased. This finding indicated that the activity of KDM1A was altered (Additional file: Fig. S3A–B).
Together, the results shown in Fig. 3A–F and Additional file Fig. S2A-B, S3A–B suggested that in a KDM1A-dependent manner, lnc_000048 involved in the process of atherosclerosis.
## Lnc_000048 Enhanced MAP2K2 Methylation via the Attenuated Activity of KDM1A to Promote Atherosclerosis
To investigate how lnc_000048 functions via KDM1A to induce atherosclerosis, we first evaluated the effect of the interaction between lnc_000048 and KDM1A on their expression levels. The results showed that the knockdown or overexpression of lnc_000048 did not affect the expression level of KDM1A (Fig. 4A), and the expression level of lnc_000048 was unaffected by KDM1A overexpression or knockdown (Fig. 4B), suggesting that there was no mutual regulation in expression between lnc_000048 and KDM1A.Fig. 4Lnc_000048 elevated the expression of MAP2K2 by attenuating the activity of KDM1A. A The effects of KDM1A on lnc_000048 transcriptional expression were assessed using qRT-PCR. B The effects of lnc_000048 on KDM1A expression were assessed using western blotting. C The effects of lnc_000048 on the activity of KDM1A were assessed using western blotting by combination use of KDM1A inhibitors (GSK-LSD1). D The reverse effects of KDM1A on lnc_000048 were assessed using western blotting. E Enriched histone 3 lysine 4 dimethylation (H3K4me2) on the promotors of proatherogenic genes. ChIP of H3K4me2 was performed on chromatin from THP-1 macrophage-derived foam cells. H3K4me2 quantification was performed by qRT-PCR analysis of the promotors of IL-1β, IL-6, TNF-α, MMP-2, and MMP-9. F The effects of lnc_000048 on enrichment H3K4me2 on the promotors of MAP2K2 were assessed using ChIP and qRT-PCR. G The effects of KDM1A on enrichment H3K4me2 on the promotors of MAP2K2 were assessed using ChIP and qRT-PCR. H The effects of lnc_000048 on MAP2K2 expression were assessed using WB. ** $P \leq 0.01$, ***$P \leq 0.001.$ Three times each of the cellular tests were done. The one-way ANOVA method was used to assess comparisons between various groups To further investigate the effect of lnc_000048 on KDM1A, we combined GSK-LSD1 to detect the expression of H3K4me2. The results showed that lnc_000048 attenuated the activity of KDM1A and enhanced the levels of H3K4me2 (Fig. 4C).
Furthermore, to further determine whether the interaction between lnc_000048 and KDM1A contributes to the progression of atherosclerosis, we performed rescue experiments. The results showed that the aggravation of inflammation and collagen degradation caused by lnc_000048 overexpression could be alleviated by KDM1A overexpressed (Fig. 4D). These results further confirmed that KDM1A rescued the pro-atherosclerotic effects induced by lnc_000048.
As a histone demethylase, KDM1A can reduce H3K4me2 levels in the promoter regions of target genes. To further clarify the target molecules of KDM1A, we first performed ChIP to analyze the H3K4me2 levels in the promoter region of inflammatory factors, and the results showed that overexpression or knockdown of lnc_000048 did not affect the levels of histone methylation (Fig. 4E), indicating that lnc_000048 targeted other factors via KDM1A.
In Noonan syndrome, KDM1A could affect the histone methylation level of the MAP2K2 promoter region. By weakening KDM1A, we predicted that lnc_000048 would have an impact on the levels of H3K4me2 in the promoter region of MAP2K2. The results of ChIP showed that H3K4me2 was increased in the promoter region of MAP2K2 in overexpression of lnc_000048 (Fig. 4F). To further clarify whether lnc_000048 affects MAP2K2 through KDM1A, we analyzed the levels of histone methylation of MAP2K2 by knocking down or overexpressing KDM1A. The results demonstrated that knockdown KDM1A and overexpressed lnc_000048 had similar effects (Fig. 4G). Western blotting showed the similar results (Fig. 4H, Additional file: Fig. S4B).
Together, the results from multiple approaches shown in Fig. 4A–H and Additional file Fig. S4B suggested that lnc_000048 could enhance MAP2K2 function by attenuating the histone demethylase activity of KDM1A to promote atherosclerosis.
## Lnc_000048 Indirectly Enhanced the Phosphorylation of ERK
Atherosclerosis develops in large part as a result of the MAPK pathway, and our research found that the expression level of MAP2K2 was elevated during atherosclerosis development (Additional file: Fig. S4A). As an important member of the MAPK pathway, MAP2K2 activates ERK, leading to its phosphorylation. We further measured the levels of total and phosphorylated ERK (p-ERK) and found that the expression level of total ERK was unchanged and p-ERK was enhanced in THP-1 macrophage-derived foam cells (Additional file: Fig. S4A).
Our previous study found that lnc_00048 increased the levels of histone methylation in the promoter region of MAP2K2. We performed a ChIP experiment to explore whether lnc_000048 plays a similar role in ERK. The results showed that there were no significant changes of H3K4me2 in ERK among knockdown or overexpressed lnc_000048 and KDM1A groups (Fig. 5A and B), which suggested that phosphorylation of ERK was induced by lnc_000048 in an indirect manner. Western blotting confirmed that overexpression of lnc_000048 promoted the expression of MAP2K2 and p-ERK, whereas knockdown of lnc_000048 had the opposite effect. However, lnc_000048 overexpression or knockdown had little effect on the overall amount of ERK expression in THP-1 macrophage-derived foam cells (Fig. 4H and Fig. 5C), suggesting that lnc_000048 interferes with the phosphorylation of ERK by affecting MAP2K2 rather than total ERK. In addition, we found that MAP2K2 expression and ERK phosphorylation levels increased in the KDM1A knockdown group (Additional file: Fig. S4B). More importantly, the increased MAP2K2 and p-ERK levels caused by lnc_000048 overexpression could be alleviated by KDM1A overexpression (Fig. 5D). These results further confirmed that KDM1A rescued the pro-atherosclerotic effects induced by lnc_000048.Fig. 5Lnc_000048 enhanced the phosphorylation of ERK. A The effects of lnc_000048 on the enrichment of H3K4me2 on ERK promotors were assessed using ChIP and qRT-PCR. B The effects of KDM1A on the enrichment of H3K4me2 on ERK promotors were assessed using ChIP and qRT-PCR. C The effects of lnc_000048 on ERK and p-EKR expression were assessed using western blotting. D The reverse effects of KDM1A on changes in MAPKs pathway induced by lnc_000048 were assessed using western blotting. E, F The reverse effects of MAPKs pathway inhibitor (FR180204) on lnc_000048 were assessed using western blotting: ERK and p-ERK(E) and inflammatory cytokines and matrix metalloproteinases (F). *** $P \leq 0.001$; ****$P \leq 0.0001.$ Three times each of the cellular tests were done. The one-way ANOVA method was used to assess comparisons between various groupsFig. 6Lnc_000048 promoted atherosclerosis progression in ApoE–/– mice. A Analysis of carotid plaque development in ApoE-/- mice with atherosclerosis following treatment with sh-lnc_000048 or oe-lnc_000048 using HE staining. B Masson staining analysis of collagen degradation of carotid plaque in ApoE-/- mice after sh-lnc_000048 or oe-lnc_000048 treatment. C The effects of lnc_000048 on inflammatory cytokine and matrix metalloprotein expression were assessed using western blotting. D The effects of lnc_000048 on the expression of factors of MAPKs pathway assessed using western blotting. * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001.$ All experiments were repeated three times. The one-way ANOVA method was used to assess comparisons between various groups Furthermore, we used the MAPK inhibitor, FR180021, to observe the role of the MAPK pathway in atherosclerosis. The results demonstrate that FR180712 significantly inhibited lnc_000048-induced activation of the MAPK signaling pathway and the release of inflammatory factors and matrix metalloproteins in THP-1 macrophage-derived foam cells (Fig. 5E and F).
Together, the results shown in Fig. 5A–F and Additional file Figure S4A-B indicated that lnc_000048 participated in atherosclerosis through the MAPK pathway.
## Lnc_000048 Accelerated the Inflammation and Degradation of Collagen in ApoE-/- Mice with Carotid Atherosclerosis
To confirm the role of lnc_000048 in an in vivo mouse model, we successfully simulated carotid atherosclerosis through partial carotid ligation with or without lentivirus vector infection for the knockdown or overexpression of lnc_000048 in ApoE-/- mice. HE staining showed that the structure of the carotid intima was disordered, inflammatory cells were aggregated, and plaques were formed in the carotid artery, at the same time, we have tested the level of KDM1A in the plaque and the results showed that the level of KDM1A has no changes, which is consistent with our results in vitro (Additional file: Fig. S5A and Fig. S5B). The qRT-PCR results demonstrated that knockdown and overexpression of lnc_000048 were effective in the release of inflammatory factors and matrix metalloproteinases in atherosclerotic plaques (Additional file: Fig. S5C).
The results of HE staining of the carotid artery showed that ApoE-/- mice with lnc_000048 knockdown exhibited less plaque; however, in the overexpressed lnc_000048 group, the structure of the carotid intima was disturbed, the intima thickened, and the plaque volume increased significantly (Fig. 6A). Masson staining indicated that the volume of carotid plaque increased significantly and decreased, and collagen degradation was more serious in ApoE-/- mice overexpressing lnc_000048. However, lnc_000048 knockdown exhibited the opposite effect, suggesting that lnc_000048 can accelerate the process of atherosclerosis (Fig. 6B).
Finally, the overexpression of lnc_000048 enhanced the expression of inflammatory factors and matrix metalloproteinases in carotid plaque samples, activating the MAP2K2-ERK axis, according to the results of our western blotting (Fig. 6C and D).
Together, the results from our studies in ApoE-/- mice validated that lnc_000048 promoted atherosclerosis by activating the lnc_000048/MAP2K2/ERK axis.
## Discussion
LncRNAs participate in various stages of atherosclerosis by mediating histone modification [32–34]. In this study, we presented a new perspective that lnc_000048 participated in atherosclerosis via epigenetic modifications caused by its interaction with KDM1A. Our results suggested that lnc_000048 weakened the enzymatic activity of KDM1A to increase histone methylation levels in the promoter region of MAP2K2 and stimulate the phosphorylation of ERK, resulting in increased expression levels of inflammatory cytokines and matrix metalloproteinases (Fig. 7). These findings suggested that lnc_000048 /KDM1A/MAP2K2/ERK was a key regulatory axis in atherosclerosis progression. Fig. 7Schematic depiction of lnc_000048 /KDM1A/MAP2K2/ERK pathway in atherosclerosis and plaque Our study found that lnc_000048 levels were increased in ox-LDL-treated macrophages, which was consistent with our previous RNA-seq findings. Lnc_000048 is a long non-coding RNA with a transcript of 1.128 kb, functional enrichment suggested that lnc_000048 may be involved in atherosclerotic biological processes, such as inflammation, platelet-activation, and epigenetic modification. However, the specific role of lnc_000048 in atherosclerosis remains unknown.
LncRNAs can participate in histone modification and regulate gene expression by recruiting proteins that regulate disease progression [35]. However, current studies on histone modification by lncRNAs are mostly focused on cancer, and their role in atherosclerosis is still in its infancy. In our study, through RNA pull-down and mass spectrometry analyses, we found that lnc_000048 could bind KDM1A to participate in the development of atherosclerosis through histone modification.
Subsequent subcellular localization showed that both lnc_000048 and KDM1A localize to the nucleus, suggesting the possibility of a spatial interaction. Subcellular localization of lncRNAs can indicate their possible mode of action in cells such as competing endogenous RNA (ceRNA) [36]. LncRNAs can induce epigenetic modifications and regulate the transcription of target genes by co-locating with proteins in the nucleus [37]. Ren et al. proposed that lncRNA PLACT1 and hnRNPA1 co-localized in the nucleus and then induced an increase of H3K27, which reduces the transcription level of IκBα [38]. We further confirmed the physical binding relationship between lnc_000048 and KDM1A using the RNA pull-down combined WB technique.
Furthermore, we found that lnc_000048 recruited KDM1A and inhibited its demethylase activity of KDM1A, which further mediated the enrichment of H3K4me2 in the promoter region and activated the expression of MAP2K2. LncRNAs can recruit histone modification enzymes as well as transcription factors to specific genomic sites and exert transcriptional inhibition or activation of downstream target genes [39, 40]. For example, lncRNA-Zeb1-AS1 activates ZEB1(a transcription factor) through epigenetic activation, indirectly regulates downstream target molecules of ZEB1, and is eventually involved in tumors [41]. Pandey et al. found that lncRNA Kcnq1ot could specifically interact with H3K9- and H3K27-specific histone methyltransferase G9a and PRC2 complexes, ultimately promoting increased levels of H3K27me3 and H3K9me3 [42]. In our study, we found that lnc_000048 could recruit and affect the demethylase activity of KDM1A, eventually regulating the expression of downstream target genes. These results were similar to those reported by Choi et al. [ 14]. Future studies will be of interest to explore the bonding point between lnc_000048 and KDM1A to further investigate the mechanism of lnc_000048 in the process of atherosclerotic plaques.
As an important histone demethylase, KDM1A regulates gene transcription in an epigenetic manner by altering histone or non-histone methylation levels in the promoter region [43–45]. Our study found that lnc_000048 participated in MAP2K2 transcription by affecting histone methylation in the promoter region of MAP2K2, which was caused by KDM1A. LncRNAs can recruit KDM1A to participate in the biological processes of tumors and angiogenesis. Pu et al. found that MAGI2-AS3 might attract KDM1A to encourage the demethylation of H3K4me2 in the RACGAP1 promoter, which would ultimately lower the level of RACGAP1 expression and control the proliferative, migratory, and invading capacities of HCC cells [46]. Di Zhao et al. reported that lncRNA-HOTAIR interacted with KDM1A to induce partial transcriptomic reprogramming in the endothelial cell [47]. Our results are consistent with Kent’s report that silencing KDM1A leads to an increased expression level of MAP2K2, which activates downstream MAPK pathway factors, such as the phosphorylation of ERK [48]. However, these therapeutic targets still need more validation in vivo trials in future.
Finally, the results showed that lnc_000048 accelerated the formation and instability of atherosclerotic plaques in ApoE-/- mice in vivo. Overexpression of lnc_000048 enhances the accumulation of inflammatory cells and collagen degradation in plaques.
## Conclusions
In conclusion, our study found that lnc_000048 significantly induced the release of matrix metalloproteinases and inflammatory factors in models of atherosclerosis. Mechanistically, lnc_000048 affected histone demethylase activity by recruiting KDM1A, promoting the transcription of MAP2K2 by accumulating H3K4me2 in the promoter region, further accelerating the phosphorylation of ERK, and eventually promoting downstream inflammatory factors. More importantly, our results suggested that lnc_000048 affected atherosclerosis progression by inhibiting KDM1A activity rather than its expression. Our results indicated how lnc_0000048 promoted atherosclerosis and provide new potential strategies to target the lnc_000048 /KDM1A/MAP2K2/ERK axis to inhibit the progression of atherosclerosis.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 1.97 MB)
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|
---
title: 'Immortalized Alzheimer’s Disease Astrocytes: Characterization of Their Proteolytic
Systems'
authors:
- Chunmei Gong
- Laura Bonfili
- Yadong Zheng
- Valentina Cecarini
- Massimiliano Cuccioloni
- Mauro Angeletti
- Giulia Dematteis
- Laura Tapella
- Armando A. Genazzani
- Dmitry Lim
- Anna Maria Eleuteri
journal: Molecular Neurobiology
year: 2023
pmcid: PMC10039838
doi: 10.1007/s12035-023-03231-z
license: CC BY 4.0
---
# Immortalized Alzheimer’s Disease Astrocytes: Characterization of Their Proteolytic Systems
## Abstract
Alzheimer’s disease (AD) is a progressive neurodegeneration with dysfunctions in both the ubiquitin–proteasome system (UPS) and autophagy. Astroglia participation in AD is an attractive topic of research, but molecular patterns are partially defined and available in vitro models have technical limitations. Immortalized astrocytes from the hippocampus of 3xTg-AD and wild-type mice (3Tg-iAstro and WT-iAstro, respectively) have been obtained as an attempt to overcome primary cell line limitations and this study aims at characterizing their proteolytic systems, focusing on UPS and autophagy. Both 26S and 20S proteasomal activities were downregulated in 3Tg-iAstro, in which a shift in catalytic subunits from constitutive 20S proteasome to immunoproteasome occurred, with consequences on immune functions. In fact, immunoproteasome is the specific complex in charge of clearing damaged proteins under inflammatory conditions. Parallelly, augmented expression and activity of the lysosomal cathepsin B, enhanced levels of lysosomal-associated membrane protein 1, beclin1, and LC3-II, together with an increased uptake of monodansylcadaverine in autophagic vacuoles, suggested autophagy activation in 3Tg-iAstro. The two proteolytic pathways were linked by p62 that accumulated in 3Tg-iAstro due to both increased synthesis and decreased degradation in the UPS defective astrocytes. Treatment with 4-phenylbutyric acid, a neuroprotective small chemical chaperone, partially restored proteasome and autophagy-mediated proteolysis in 3Tg-iAstro. Our data shed light on the impaired proteostasis in 3Tg-iAstro with proteasome inhibition and autophagic compensatory activation, providing additional validation of this AD in vitro model, and propose a new mechanism of action of 4-phenylbutyric acid in neurodegenerative disorders.
## Introduction
Alzheimer disease (AD) is the most common type of dementia that has become a rapidly increasing public health concern. The biological construct that helps defining AD comprises the deposition of amyloid-β (Aβ) plaques, pathological tau phosphorylation, and neurodegeneration [1, 2]. A growing body of evidence identified that oxidative stress, chronic inflammation, mitochondrial dysfunction, and endoplasmic reticulum (ER) stress have a role in AD development [3, 4].
Astrocytes, the most abundant glial cells in the central nervous system (CNS), are involved in numerous aspects of CNS physiology. Specifically, astrocytes act as scavengers for reactive oxygen species and supply cysteine precursor for neuronal glutathione [5]. Astrocytes are involved in the removal of toxins, production and release of trophic factors, regulation of neurotransmitters, and ion concentrations, thereby maintaining the overall cell homeostasis, including optimal synaptic glutamate levels and neuroimmune status [6, 7]. During AD progression, astrocytes undergo complex alterations becoming first asthenic and hypotrophic, while later they turn to be reactive and hypertrophic, mostly around developing senile plaques [7, 8]. During pathological remodeling, astrocytes lose their homeostatic and neuroprotective functions, thus representing a potential preventative or therapeutic target in the preclinical phase of AD.
Cells possess two major intracellular proteolytic pathways, namely the ubiquitin–proteasome system (UPS) and autophagy. The UPS is the major degradation system used by cells and involved in the disposal of misfolded and unfolded proteins accumulated in the endoplasmic reticulum (ER) [9]. The eukaryotic 26S proteasome is a large, multi-catalytic protease complex in charge of the removal of intracellular misfolded, oxidized, or aggregated ubiquitin-tagged-proteins, in an ATP-dependent manner [10]. The catalytic core of this structure is the 20S proteasome, consisting of two rings made of seven α subunits, flanking two superimposed rings made of seven β subunits. Among β-subunits, only three (β1, β2, β5) possess an intact active site and cleave proteins into the proteolytic chamber with a total of six active sites inside a functional proteasome [11–13]. Specifically, subunit β1 is associated with the caspase-like activity or peptidylglutamyl-peptide hydrolysing (PGPH) activity; subunit β2 exerts a trypsin-like (T-L) activity; subunit β5 is related to the chymotrypsin-like (ChT-L) activity, but given its tendency to cleave after small neutral and branched side chains also the small neutral amino acid preferring (SNAAP) and branched-chain amino acid preferring (BrAAP) activities can be assigned to this subunit [14]. Numerous data have demonstrated that AD and other neurodegenerations are characterized by an impaired UPS functionality and that amyloid aggregates are able to further inhibit such complex [15]. However, in cells exposed to IFN-γ or tumor necrosis factor-α (TNF-α), the 20S proteasome is converted into the immunoproteasome with its constitutive catalytic subunits, β1, β2, and β5, being replaced by the inducible subunits β1i, β2i, and β5i [16]. In immunoproteasome, β2i and β5i express trypsin-like and chymotrypsin-like enzymatic activity similarly to the constitutive counterparts. β1i can exert chymotrypsin-like activity, differently from the constitutive β1 which is associated to PGPH activity [17]. Due to distinct cleavage sites, inducible β subunits can hydrolyze proteins in a distinct manner with respect to constitutive subunits and can generate peptides binding major histocompatibility complex (MHC) class I molecules, playing a role in antigen presentation process [18]. Depending on the tissue and cell type, different proteasome arrangements can coexist [19]. Previous works have shown that immunoproteasome expression is related not only to neuroinflammation in AD but also to aging [20].
Autophagy is a highly conserved lysosome-dependent proteolytic system that attempts to restore cellular homeostasis through the degradation of unfolded/misfolded or aggregated proteins and damaged subcellular organelles [21]. The autophagy process involves several steps, including nucleation of the isolation membrane named phagophore, expansion and closure of the phagophore, fusion between the resulting autophagosomes and multivesicular endosomes or lysosomes, and degradation of autophagosome contents [22]. Autophagy is involved in AD pathogenesis [23]. Specifically, it is involved in Aβ aggregates clearance and it preserves neuronal function [24]. Cathepsins B and L are cysteine proteases contained in lysosomes with a role in AD pathogenesis, being involved in cholesterol metabolism, Aβ peptide degradation, and amyloid precursor protein (APP) processing, thus representing a therapeutic target in neurodegenerations [25]. Changes in cathepsins activity are normally found in aging neurons and are considered as a cause of age-related neuropathologic variations. Increased cathepsin B levels have been previously observed in AD animal models. Cathepsin B is associated with amyloid plaques in AD brains and has been suggested to be responsible for the increased Aβ production. Conversely, cathepsin L activity increases α-secretase-mediated non-amyloidogenic pathway [25, 26]. Lysosomal-associated membrane protein 1 (LAMP1) is considered a lysosome marker, whereas microtubule-associated protein light chain 3 (LC3) is an indicator of autophagosome formation, which preferentially interacts with the autophagy-adaptor protein SQSTM1/p62, thus mediating selective degradation during autophagy [27]. p62 has been demonstrated to work as a receptor for ubiquitinated proteins and organelles to be degraded by lysosomal enzymes. It is a common component of protein inclusions in several neurodegenerative disorders, among them neurofibrillary tangles in AD [28]. Moreover, beclin-1 plays an important role in membrane isolation and nucleation, contributing to the formation of early autophagosomes [29]. Interestingly, deletion of BECN1 in AD animal models enhanced the intracellular and extracellular Aβ loads [30]. However, molecular mechanisms underlying defective proteolysis in astrocytes in AD are not well clarified.
Immortalized astrocytic cell lines were generated from hippocampi of 3xTg-AD mice, a well-established AD mouse model, and from the wild-type counterpart [31]. These lines named 3Tg-iAstro and WT-iAstro faithfully reproduce the features of primary astrocytic cultures from 3xTg-AD mice and WT mice in terms of gene profiling, proteostasis, Ca2+ signaling, and ER-mitochondria interaction [31–33], but further analysis must be carried out to reach a complete characterization of such cell lines.
Interestingly, chaperones have been shown to reduce levels of misfolded proteins, thus minimizing the accumulation of aggregates and their downstream pathological consequences [34]. The chemical chaperone, sodium 4‐phenylbutyric acid (4‐PBA), is a small‐molecular‐weight and blood‐brain barrier permeable fatty acid, able to regulate ER stress, attenuate cell damage, and help unfolded protein remodeling [34–36]. It has been already used for the treatment of urea cycle disorders and has been recently considered with high potential as a new drug for preventing cognitive decline [37].
In the current study, the proteolytic pathways of both 3Tg-iAstro and WT-iAstro have been characterized, focusing on proteasome subunit composition and functionality and on key autophagic markers [38]. In addition, the ability of 4‐PBA treatment to affect the interconnected proteolytic systems in both WT-iAstro and 3Tg-iAstro has been investigated, as a possible mechanism of action of the neuroprotective compound.
## Reagents and Chemicals
The substrates Suc-Leu-Leu-Val-Tyr-7-Amino-4-methylcoumarin (AMC), Z-Leu-Ser-Thr-Arg-AMC, Z-Leu-Leu-Glu-AMC for assaying the chymotrypsin-like (ChT-L), trypsin-like (T-L), and peptidyl glutamyl-peptide hydrolyzing (PGPH) proteasomal activities were purchased from Sigma-Aldrich S.r. L. (Milano, Italy). Z-Gly-Pro-Ala-Leu-Ala-MCA to measure BrAAP activity was from Biomatik (Cambridge, Ontario). Aminopeptidase N (EC 3.4.11.2) was purified from pig kidney [39]. Cathepsin B and cathepsin L substrates (Z-Arg-Arg-AMC and Z-Phe-Arg-7-amino-4-trifluoromethyl-coumarin (AFC) respectively), cathepsins inhibitors (CA074Me and N-(1-Naphthalenylsulfonyl)-Ile-Trp-aldehyde), and monodansylcadaverine (MDC) were obtained from Sigma-Aldrich S.r. L. (Milano, Italy). Media and chemicals used for cell cultures were purchased from Enzo Life Sciences, Inc. The membranes for western blot analyses were purchased from Millipore (Milan, Italy). Proteins immobilized on polyvinylidene fluoride membranes were detected with the enhanced chemiluminescence (ECL) technique (Amersham Pharmacia Biotech, Milan, Italy) on a ChemiDoc MP, Chemiluminescence system (Biorad, Italy).
## Cell Lines
Generation of immortalized astrocytes from hippocampi of WT and 3xTg-AD mice (WT-iAstro and 3Tg-iAstro cells) was described elsewhere [31]. Briefly, the immortalized cell lines were generated from separate primary astrocyte cell cultures deriving from both WT and AD mice. For immortalization, primary astroglia cultures were depleted of microglial cells by magnetic-assisted cell sorting using anti-CD11b-conjugated microbeads obtaining a population of highly purified astrocytes. Cells were transduced using retrovirus expressing Simian Virus 40 large T antigen (SVLT). Transformed cells were selected in G418, amplified, and stabilized for 12 passages prior to characterization. No clonal selection was performed to maintain the natural heterogeneity of the cultures.
In this study, three independent WT-iAstro and three independent 3Tg-iAstro lines were grown in complete culture media containing Dulbecco’s modified Eagle’s medium (DMEM; Sigma-Aldrich, Cat. No. D5671) supplemented with $10\%$ fetal bovine serum (Gibco, Cat. No. 10270), 2 mM L-glutamine (Sigma-Aldrich), and $1\%$ penicillin/streptomycin solution (Sigma-Aldrich). Cells were incubated with growth medium at 37 °C equilibrated with $95\%$ air and $5\%$ CO2 in flasks or dishes. Cells were passaged once a week and used for experiments between passages 12 and 20 from establishment [31].
## Cell Treatment with 4-Phenylbutyric Acid (4-PBA)
WT-iAstro and 3Tg-iAstro cells were treated with 4-PBA (Sant Cruz Biotechnology, Cat. sc-232961) [35, 40] for 48 h at a concentration of 3 μM, which has been chosen as a minimal concentration able to revert protein synthesis, ER-mitochondrial interaction, and secretome alterations in 3Tg-iAstro cells [33].
## Cell Lysis
After removing the medium and washing with cold phosphate buffered saline (PBS), cells were harvested in PBS and centrifuged at 1600 × g for 5 min. The pellet was resuspended in a lysis buffer (20 mM Tris, pH 7.4, 250 mM sucrose, 1 mM EDTA, and 5 mM β-mercaptoethanol) and passed through a 29-gauge needle at least ten times. Lysates were centrifuged at 12,000 × g for 15 min and the supernatants were stored at –80 °C. Protein concentration was estimated by the Bradford method [41] using bovine serum albumin as standard.
## Proteasome Activities
Proteasome activities were measured in cell lysates through fluorimetric assays, as previously reported [42], using the following synthetic substrates: Leu-Leu-Val-Tyr-AMC for ChT-L, Leu-Ser-Thr-Arg-AMC for T-L, Leu-Leu-Glu-AMC for PGPH. BrAAP activity was measured using Gly-Pro-Ala-Leu-Ala-AMC substrate in the presence of aminopeptidase-N (AP-N). The incubation mixture contained 1 μg of cell lysate, the appropriate substrate, and 50 mM Tris/HCl pH 8.0, up to a final volume of 100 μL. Incubation was performed at 37 °C, and after 60 min, the fluorescence of the hydrolyzed 7-amino-4-methyl-coumarin (AMC) was recorded (AMC, λexc = 365 nm, λem = 449 nm) on a SpectraMax Gemini XPS microplate reader (Molecular Devices, Sunnyvale, CA, USA). In order to test the 26S proteasome ChT-L, we used Suc-Leu-Leu-Val-Tyr-AMC as substrate and 50 mM Tris/HCl pH 8.0 buffer containing 10 mM MgCl2, 1 mM dithiothreitol, and 2 mM ATP. The effective 20S proteasome contribution to short peptide cleavage was evaluated with control experiments performed using specific proteasome inhibitors, Z-Gly-Pro-Phe-Leu-CHO and lactacystin (5 μM in the reaction mixture), and then subtracting the obtained fluorescence values from the values obtained in cell lysates.
## Cathepsins Activities
Cathepsin B and L proteolytic activities were measured in cell lysates following the protocol described by Tchoupè et al. [ 43] using the fluorogenic peptides Z-Arg-Arg-AMC and Z-Phe-Arg-AFC respectively, at a final concentration of 5 μM. The mixture for cathepsin B, containing 7 µg of protein lysate, was pre-incubated in 100 mM phosphate buffer pH 6.0, 1 mM EDTA, and 2 mM dithiothreitol for 5 min at 30 °C. Upon the addition of the substrate, the mixture was incubated for 15 min at 30 °C. Similarly, the mixture for cathepsin L, containing 7 µg of protein lysate, was incubated in 100 mM sodium acetate buffer pH 5.5, 1 mM EDTA, and 2 mM 37 dithiothreitol for 5 min at 30 °C and, upon the addition of the substrate, the mixture was incubated for 15 min at 30 °C. The fluorescence of the hydrolyzed 7-amino-4-methyl-coumarin (AMC, λexc = 365 nm, λem = 449 nm) and 7-amino-4-trifluoromethylcoumarin (AFC, λexc = 397 nm, λem = 500 nm) was detected on a SpectraMax Gemini XPS microplate reader. The effective cathepsin contribution to the proteolysis was evaluated through control experiments performed using the specific inhibitor CA074Me and subtracting these values from the fluorescence values obtained by analyzing cell lysates.
## Western Blotting Analysis
Cell lysates (20 μg of total proteins) were resolved by $12\%$ or $15\%$ SDS/PAGE and electroblotted onto PVDF membranes. Membranes with transferred proteins were incubated with the specific primary monoclonal antibody and successively with the proper peroxidase conjugated secondary antibody. Monoclonal antibodies against β5, β2, and β1 proteasomal subunits, ubiquitin, beclin-1, LAMP1, nuclear factor erythroid 2 (NF-E2)-related factor 2 transcription factor (Nrf2), histone deacetylase 6 (HDAC6), IFN-γ, and TNF-α were obtained from Santa Cruz Biotechnology, Inc. (Heidelberg, Germany). Anti-β5i, anti-β2i, and anti-β1i rabbit monoclonal antibodies were from AFFINITI Research Products Ltd, (Mamhead, UK). SQSTM1/p62 (sequestosome 1/p62) mouse monoclonal antibody was from Sigma-Aldrich S.r. L. (Milano, Italy), and the anti-LC3B antibody and cathepsin B were purchased from Cell Signaling Technology, Inc. Anti-amyloid precursor protein antibody was from Abcam (Milano, Italy). ECL western blotting detection reagents were used on a Biorad ChemiDoc MP system. Each gel was loaded with molecular weight markers in the range of 12 to 225 kDa (GE Healthcare). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) or β-actin was utilized as controls for equal protein loading: membranes were stripped and re-probed with anti-GAPDH or anti β-actin monoclonal antibodies (Santa Cruz Biotechnology, Heidelberg, Germany). Stripping buffer contained 200 mM glycine, $0.1\%$ SDS, and $1\%$ Tween 20. ChemiDoc-acquired immunoblot images were processed through Image J software (NIH, USA) to calculate the background mean value and its standard deviation. The background-free image was obtained subtracting the background intensity mean value from the original digital data. The integrated densitometric value associated with each band was calculated as the sum of the density values over all the pixels belonging to the selected band with a density value higher than the background standard deviation. For each band, densitometric value was normalized to the relative GAPDH or β-actin signal intensity. The ratios of band intensities were calculated within the same Western blot. All the calculations were carried out using the Matlab environment (The MathWorks Inc., Natick, MA, USA).
## Monodansylcadaverine Assay
Autophagy induction was assayed in both WT-iAstro and 3Tg-iAstro with MDC staining, using the fluorescent lysosomotropic compound that is incorporated into multilamellar bodies as a probe for labeling of autophagic vacuoles in cultured cells by fluorescence microscopy [44]. In detail, 6 × 105 cells/well were seeded into 6-well culture plates. Following overnight growth, 50 μM MDC was added to cell medium. After 10-min incubation at 37 °C, cells were washed three times with phosphate buffered solution (PBS) and immediately analyzed with a Leica DMI6000B epifluorescence microscope equipped with a S Fluor 40 × /1.3 objective and a 380–420 nm filter. At least 16 areas were scanned for each sample and images are representative of three distinct subgroups for both cell lines. Fluorescence intensity was quantified using FIJI Image J software, by obtaining the corrected total cell fluorescence upon subtracting the mean fluorescence of background readings.
## ELISA Assay for Aβ Levels Determination
Aβ1–42 levels were measured in both cell lysates and cell supernatant using the mouse amyloid beta 40 and mouse amyloid beta 42 solid-phase sandwich ELISA kits (Thermo Fisher Scientific, Italia), following the kits manufacturer instruction.
## Statistical Analysis
Biochemical data is expressed as mean values ± S.D. Statistical analysis was performed using the Student t-test or one‐way ANOVA, followed by the Bonferroni test using Sigma-Stat 3.1 software (SPSS, Chicago, IL, USA). p-values $p \leq 0.05$ (*) and $p \leq 0.01$ (**) were considered to be statistically significant.
## Impaired 26S Proteasome Functionality in 3Tg-iAstro
A markedly reduced proteasomal 26S ChT-L activity was observed in 3Tg-iAstro compared to WT-iAstro ($40\%$ reduction, Fig. 1A). Accordingly, Western blotting results showed higher levels of ubiquitinated substrates in 3Tg-iAstro compared to WT astrocytes (Fig. 1B), in agreement with previous studies in which increased levels of ubiquitin conjugates were demonstrated to correlate with inhibited proteasomal activity [45, 46]. As UPS is able to degrade soluble misfolded proteins or small oligomers, large protein aggregates prevent degradation and inhibit UPS activity [47, 48].Fig. 1Deregulation of UPS in 3Tg-iAstro. 26S Proteasome activity in WT-iAstro (WT) and 3Tg-iAstro (AD) (panel A) and Western blotting of ubiquitin conjugates (panel B). Panel (C) 20S proteasomal activities measured in WT-iAstro (WT) and 3Tg-iAstro (AD) through enzymatic fluorimetry assays. From left to right chymotrypsin-like activity (ChT-L), trypsin-like activity (T-L), peptidyl-glutamyl peptide-hydrolyzing activity (PGPH), and branched-chain amino acid activity (BrAAP) of the 20S proteasome are reported. Activities are expressed as percentage of WT (data from three independent subgroups of both cell types at the same passage number). Panel (D) 20S proteasome subunits composition. Representative Western blots of 20S proteasome constitutive (β5, β2, β1) and inducible (β5i, β2i, β1i) subunits in WT-iAstro (WT) and 3Tg-iAstro (AD) immortalized murine astrocytes. Equal protein loading was verified by using an anti-β-actin antibody and normalized expression of the target protein is reported as arbitrary units (a.u.). The densitometry from five separate blots and an immunoblot which is representative of three distinct cellular subgroups are reported. Equal protein loading was verified by using an anti-β-actin antibody and normalized expression of the target proteins is reported as arbitrary units (a.u.). Data points marked with asterisks are statistically significant compared to the WT counterpart (at the same passage number) (*$p \leq 0.05$, **$p \leq 0.01$), upon Student’s t-test
## Downregulated 20S Proteasome Activities in 3Tg-iAstro
26S proteasome analysis provides an insight in the ATP-Ubiquitin-dependent protein degradation pathway, whereas 20S proteasome, lacking 19S regulatory subunits, is regulated by ubiquitin-independent mechanisms with a different target-specificity. Despite most of the research focusing on 26S proteasome, several works have demonstrated that the 20S proteasome is one of the cytoplasmic components that adapts to oxidative stress and promotes oxidized protein degradation [49, 50] preventing the formation of protein aggregates that can block both 26S proteasome and 20S proteasome independently from the ubiquitin post-translational modification.
20S proteasomal chymotrypsin-like and trypsin-like activities, attributed to β5 and β2 respectively, were the most significantly decreased in 3Tg-iAstro compared to WT-iAstro cells, in line with previous studies on a human cellular model of AD [51]. The ability to cleave after branched-chain amino acids (BrAAP activity) showed a moderate decrease in 3Tg-iAstro, whereas no significant difference was observed in PGPH activity, constitutively attributed to β1 subunit (Fig. 1C).
## Constitutive Proteasome and Immunoproteasome Were Differentially Expressed in 3Tg-iAstro Compared to WT-iAstro
Western blotting detection of constitutive and inducible proteasomal catalytic β subunits (Fig. 1D) revealed a reduced expression of constitutive 20S β subunits, specifically β2 and β5, and increased protein levels of β2i and β5i subunits in 3Tg-iAstro compared to WT-iAstro cells (*$p \leq 0.05$, Fig. 3), in agreement with data from enzymatic assays (Fig. 1C). β1 and β1i subunits were equally expressed in hippocampal astrocytes from 3xTg-AD and WT mice, in line with the stable PGPH activity in the two cellular types. Results are coherent with previous research data which demonstrated that, upon protein aggregate production during AD pathogenesis, in the attempt to cope with the increased cytoplasmic burden, cells prioritize the expression of immunoproteasome subunits to clear protein aggregates or oxidized proteins [20, 52].
UPS deregulation in 3Tg-iAstro reflects patterns already occurring in AD neural cells from both mouse and human subjects [15, 53].
## Altered Autophagic Pathway in 3Tg-iAstro
Upon UPS failure, the cell’s attempt to remove protein aggregates exploits autophagy, which is therefore upregulated to cope with the extended demand [54]. Accordingly, lysosomal cathepsin B expression levels (Fig. 2A) and activity (Fig. 2B) were significantly higher in 3Tg-iAstro compared to WT-iAstro, whereas no significant differences were observed in cathepsin L expression and activity (Fig. 2A-B).Fig. 2Autophagy deregulation in 3Tg-iAstro. Panel (A) Cathepsin B and cathepsin L representative western blotting; panel (B) Cathepsin B and cathepsin L enzymatic activities measured in WT-iAstro (WT) and 3Tg-iAstro (AD) and expressed as fluorescence unit (FU). Panel (C) Monodansilcadaverine (MDC) staining of autophagic vacuoles. 40 × microscope representative images and FIJI Image J quantitative fluorescence analysis from 16 areas for each sample and from three distinct subgroups for both cell line are reported. Panel (D) Autophagic markers. Representative Western blots of Beclin-1, LC3, p62, Nrf2, HDAC6, and LAMP1 in WT-iAstro (WT) and 3Tg-iAstro (AD). The densitometric analyses derive from five separate blots and three independent cell lines. For LC3, LC3-II analysis is reported [59]. Equal protein loading was verified by using an anti-β-actin or an anti-GAPDH antibody and normalized expression of the target protein is reported as arbitrary units (a.u.). Detection was executed by ECL. Data points marked with asterisks (*$p \leq 0.05$, **$p \leq 0.01$) are statistically significant compared to WT cells (at the same passage number) upon Student’s t-test MDC staining was used to deeply monitor the autophagic cascade [44], by measuring the amounts of autophagic vacuoles, the most abundant organelles contained within the dystrophic neurites that are associated to β-amyloid in senile plaques [55]. The analyses revealed an increased uptake of MDC into vacuoles in 3Tg-iAstro with the classical punctate pattern of MDC-labeled fluorescence. In detail, MDC-positive vesicles in WT-iAstro are smaller with respect to 3Tg-iAstro that contain numerous, enlarged MDC-positive vesicles (Fig. 2C).
We next evaluated the levels of the autophagy-related proteins beclin-1, LC3-II, lysosomal-associated membrane protein 1 (LAMP1), and p62. Beclin-1 plays an important role in autophagy, being involved in the enrolment of membranes to form autophagosomes [29]. LC3-II is strongly bound to the autophagosome membrane and it is considered a well-established autophagic marker [56]. LAMP1 plays an important role in lysosome biogenesis [57]. Interestingly, hippocampal 3Tg-iAstro displayed higher levels of LAMP1 and beclin-1 with respect to WT-iAstro, suggesting that autophagy was initiated (Fig. 2D). The higher levels of LC3-II in 3Tg-iAstro are in agreement with increased amount of autophagic vacuoles (Fig. 2C).
p62 is considered a scaffold protein and a signaling hub for multiple pathways including UPS and autophagy-mediated protein turnover. p62 can interact with both autophagosomes-associated LC3-II and ubiquitinated conjugates to engulf the aggregates in autophagosomes. It also interacts with HDAC6 by inhibiting its deacetylase activity to maintain acetylated α-tubulin levels and stabilize microtubules in order to enhance autophagosome trafficking. Moreover, p62 synthesis is regulated by Nrf2 [27, 56, 58].
Western blotting analyses revealed that p62 intracellular levels were markedly higher in 3Tg-iAstro, compared with WT-iAstro, suggesting an altered autophagic flux in AD, possibly due to both an increased p62 synthesis and a decreased p62 proteolysis as indicated by the enhanced Nrf2 and the decreased HDAC6 intracellular levels in 3Tg-iAstro (Fig. 2D), in agreement with studies showing that p62 synthesis can be induced by an increase in Nrf2 expression upon UPS deficiency.
These results suggest that the autophagic cascade is altered in 3Tg-iAstro, accurately reproducing AD pathology.
## 4-Phenylbutyric Acid Partially Restored Both Proteasomal and Autophagic Deficits of 3Tg-iAstro Cells
The ability of 4-PBA to restore proteolysis in 3Tg-iAstro has been investigated, because the neuroprotective effects of this FDA-approved compound are not fully elucidated [36, 60]. Upon 48-h incubation with 3 μM 4-PBA, significantly increased 20S proteasome chymotrypsin-like, trypsin-like, BrAAP activities (Fig. 3A) and 26S chymotrypsin-like activity (Fig. 3C) were observed in 3Tg-iAstro cells, with consequent reduced levels of ubiquitin conjugated proteins (Fig. 3D). 4-PBA did not significantly affect the UPS-mediated proteolysis in WT cells (Fig. 3C, D). Interestingly, upon treatment with 4-PBA, a partial restoration of the expression levels of constitutive and inducible proteasome subunits has been observed in 3Tg-iAstro (Fig. 3B).Fig. 3Partially restored UPS functionality in 3Tg-iAstro treated with 4-PBA. Panel (A) 20S Chymotrypsin-like, Trypsin-like, branched-chain amino acid cleavage (BrAAP) activities in WT-iAstro, 3Tg-iAstro, WT-iAstro treated with 4-PBA (WT + 4-PBA), and 3Tg-iAstro treated with 4-PBA (AD + 4-PBA). Values are expressed as % of control. Panel (B) Representative Western blots of β5, β5i, β2, and β2i in WT-iAstro, 3Tg-iAstro, WT-iAstro treated with 4-PBA (WT + 4-PBA), and 3Tg-iAstro treated with 4-PBA (AD + 4-PBA). The densitometric analyses of β5, β5i, β2, and β2i expression obtained from five separate blots and three independent experiments are shown. Equal protein loading was verified by using an anti-GAPDH antibody and normalized expression of the target protein is reported as arbitrary units (a.u.). Detection was executed by ECL. Panel (C) 26S proteasome activity in WT-iAstro, 3Tg-iAstro, WT-iAstro treated with 4-PBA (WT + 4-PBA), and 3Tg-iAstro treated with 4-PBA (AD + 4-PBA). Panel (D) Western blots of ubiquitin conjugates in treated and untreated cells. Densitometry from five separate blots and three independent experiments is reported. Anti-GAPDH was used as equal loading control. Data points marked with asterisks (*$p \leq 0.05$, **$p \leq 0.01$) are statistically significant compared to untreated cells using one‐way ANOVA, followed by the Bonferroni test As expected, the immunoproteasome overexpression in 3Tg-iAstro was associated to increased levels of TNFα and IFNγ pro-inflammatory cytokines (Fig. 4). Interestingly, treatment with 4-PBA significantly reduced the levels of these cytokines, required for activating the innate immune response (Fig. 4), in agreement with the reduced levels of inducible proteasome subunits and with the partial recovery of proteasome functionality (Fig. 3).Fig. 4Inflammatory cytokines. Western blotting detection of TNFα and IFNγ levels in WT-Astro, 3Tg-iAstro, WT-iAstro treated with 4-PBA (WT + 4-PBA), and 3Tg-iAstro treated with 4-PBA (AD + 4-PBA). Densitometry from five separate blots and three independent experiments normalized by GAPDH is reported and expressed as arbitrary units (a.u.) p62, LC3-II, beclin-1, LAMP1, and cathepsin B are crucial components of the autophagy system. A significantly downregulated p62, LC3-II, beclin-1, LAMP1, and cathepsin B levels in 3Tg-iAstro cells treated with 4-PBA was observed (Fig. 5A), in agreement with previous data demonstrating that 4-PBA inhibited ER stress-induced autophagy [61]. 4-PBA significantly decreased Nrf-2 levels and increased HDAC6 levels in 3Tg-iAstro, indicating an effect on both synthesis and degradation of p62.Fig. 5Partially restored autophagy in 4-PBA-treated 3Tg-iAstro. WT-iAstro (WT) and 3Tg-iAstro (AD) were treated or not with 4-PBA 3 μM, for 48 h respectively. Panel (A) Representative Western blots of Beclin-1, LAMP-1, LC3-II, p62, Nrf2, HDAC6, and Cathepsin B in WT, AD, WT treated with 4-PBA (WT + 4-PBA), and AD treated with 4-PBA (AD + 4-PBA) immortalized astrocytes. The densitometric analyses were obtained from five separate blots and three independent experiments are shown. Equal protein loading was verified by using an anti-GAPDH antibody and normalized expression of the target protein is reported as arbitrary units (a.u.). Detection was executed by ECL. Panel (B) MDC staining of autophagic vacuoles. 40 × microscope representative images and FIJI Image J quantitative fluorescence analysis from16 areas for each sample and from three distinct subgroups for both cell line are reported. Data points marked with asterisks (*$p \leq 0.05$, **$p \leq 0.01$) are statistically significant compared to untreated cells using one‐way ANOVA, followed by the Bonferroni test Interestingly, the increased MDC uptake in autophagic vacuoles in 3Tg-AD compared to WT-iAstro is mitigated by 4-PBA treatment (Fig. 5B).
Taken together, our data indicate that proteolytic pathway alterations compromise protein quality control in AD astrocytes and the chaperone may correct these alterations.
Additionally, APP protein expression was higher in 3Tg-iAstro (Fig. 6A), as well as Aβ peptide concentrations (Fig. 6B) confirming that astrocytes are capable of producing Aβ, in agreement with previous reports [62] and indicating that astrocytes can contribute to total brain amyloid load. Treatment with 4-PBA significantly reduced APP, Aβ40, and Aβ42 levels in 3Tg-iAstro (Fig. 6), confirming the positive effects of the drug in the regulation of proteolysis. Fig. 6Effects on amyloid beta. Panel (A) Western blotting analysis of amyloid precursor protein (APP) expression in WT-iAstro and 3Tg-iAstro treated or not with 4-PBA. Densitometry from five separate blots and three independent experiments normalized by GAPDH is reported and expressed as arbitrary units (a.u.). Panel (B) Aβ40 and Aβ42 concentrations, expressed as pg/mL, determined by ELISA in WT-iAstro and 3Tg-iAstro treated or not with 4-PBA. Data points marked with asterisks (*$p \leq 0.05$, **$p \leq 0.01$) are statistically significant compared to untreated cells using one‐way ANOVA, followed by the Bonferroni test
## Discussion
AD can be described a proteinopathy characterized by the progressive accumulation of toxic Aβ peptides and hyperphosphorylated tau proteins in specific brain regions. Although the major focus of AD research has been neural cells, considered the main contributor of the synthesis and release of toxic Aβ species, recent evidences indicated that microglial cells and astrocytes intervene in early stages of AD pathogenic process, but more studies are necessary to provide a better understanding of their specific roles [6, 63].
In the present work, we provide the characterization of the proteolytic systems of murine immortalized hippocampal astrocytes, obtained from 3xTg-AD mice and a wild-type counterpart, to further understand the key pathways involved in protein degradation.
In spite of a number of limitations, which include their mouse origin, being generated from newborn pups, and being immortalized cells, 3Tg-iAstro cells present a series of advantages. They are a low cost and an easy-to-handle model which can be adopted to a large number of interdisciplinary and inter-laboratory investigations from single cell analysis to high throughput assays and analyses requiring large amount of starting material, such as fractionation. In particular, we used 3Tg-iAstro cells to investigate AD-related alterations of astrocytic proteostasis and found an impairment of ribosomal protein synthesis [32, 33]. In this frame, the UPS and the autophagy-lysosomal pathway are crucial for controlling cellular proteostasis [9] and we focused on both pathways to understand how their impairment contributes to the pathology. Significant differences between WT and AD immortalized hippocampal astrocytes were observed, coherently with previous research on human and murine samples [3, 15, 64] and confirming the validity of these immortalized astrocytes.
Reactive astrocytes in AD assume a heterogenous array of phenotypes which vary according to brain region and pathology. Thus, hippocampal astrocytes from a model that mimics amyloidosis in AD are likely to represent those reactive astrocytes in AD hippocampus, being the most affected brain area in AD.
Ubiquitin conjugates accumulation reflected 26S proteasomal activity impairment in 3Tg-iAstro, in accordance with previous studies demonstrating an impaired ATP synthesis in 3Tg-iAstro cells [32], since the degradation of ubiquitin conjugates by the 26S proteasome is coupled to ATP hydrolysis [65]. Moreover, since the discovery of high levels of ubiquitin in senile plaques, correlation between Aβ and 26S proteasome has been confirmed in cultured neurons and astrocytes [66, 67] and our data on APP, Aβ40, and Aβ42 levels in 3Tg-iAstro are in line with these studies (Fig. 6).
The 20S proteasome is able to degrade proteins which have not been ubiquitinated. We firstly investigated the intrinsic enzymatic activity of 20S proteasome. Despite the trend to decrease in activity, coherently with the 26S proteasome results, each activity showed a different pattern compared to WT-iAstro. ChT-L and T-L activities come from β5 and β2 subunits respectively and were both decreased in 3Tg-iAstro, while PGPH activity was not downregulated by the exposure to intracellular protein aggregates. BrAAP functionality was also slightly reduced in 3Tg-iAstro compared to the WT counterpart. This body of evidence highlights how ChT-L and T-L are preferentially impaired in AD astrocytes. This hints to a stronger involvement of β5 and β2 subunits in the attempt to remove intracellular protein aggregates, which are already known to halt the proteasome [48, 68, 69]. Interestingly, our results confirmed the shift from 20S constitutive proteasome to immunoproteasome as β5i and β2i are increased and inversely correlated with constitutive β5 and β2 expression [70]. In addition, the increase in inducible subunits can be seen as a reaction to the inflammatory state promoting the production of immunopeptides that will be exposed on MHC surface molecules. The increase in β5i and β2i correlates with mRNA levels of β and βi subunits previously measured in human hippocampal astrocytes [71] and with increased levels of IFNγ and TNFα proinflammatory cytokines in untreated 3Tg-iAstro (Fig. 4).
Autophagic flux alterations by amyloid peptides have been demonstrated in the brain of postmortem Alzheimer’s disease patients, animal models, and cell models [72]. Considering the complex and dynamic nature of the autophagy-lysosomal pathway, we monitored several markers associated with different steps of this process. Specifically, cathepsin B and cathepsin L lysosomal enzymes are differentially involved in AD development. Cathepsin B is known to possess β-secretase-like activity and thus contribute to Aβ production. Cathepsin L instead has shown the capability of increasing α-secretase activity. From literature [73, 74], when UPS is impaired, autophagy upregulation occurs to enable clearance of larger aggregates.
In this study, enhanced uptake of MDC in autophagic vacuoles was observed in AD cells (Fig. 2C). Besides, the levels of LAMP1, Beclin-1, and LC3-II as well as the level of p62 were noticeable increased in AD astrocytes compared to WT, confirming a deficient autophagic function in 3Tg-iAstro. p62 higher expression levels can be induced by an increased Nrf2 following UPS deficiency together with an inhibited autophagy reducing p62 degradation, in agreement with the accumulation of ubiquitin conjugates in 3Tg-iAstro and with previous studies [27]. Altogether, these results provide a clearer understanding of proteolytic machinery dysregulation of astroglia cells in AD pathology. Impaired proteolysis implies a reduced capacity to clear toxic, damaged, and misfolded proteins, consequently contributing to neurodegeneration. In terms of the homeostatic support to neurons and other CNS cells, impaired proteolysis may severely affect astrocytic secretome, including neurogenic and neuroprotective molecules [33, 75].
Treatment with the molecular chaperone 4-PBA partially restored proteolytic activities in 3Tg-iAstro cells. 4‐PBA interacts with hydrophobic domains of misfolded proteins preventing their aggregation [34]. Thus, this chaperone shows the beneficial role on correcting protein folding and trafficking leading to the partial recovery of UPS and autophagy functionality in AD astrocytes, with effect on the immune function, as indicated by the decreased concentrations of pro inflammatory cytokines (Fig. 4) and consequent modulation of immunoproteasome (Fig. 3B) and an effect on APP processing (Fig. 6). The higher APP protein expression in 3Tg-iAstro was significantly reduced upon 4-PBA treatment. Interestingly, significantly lower amount of both Aβ40 and Aβ42 were detected in 3Tg-iAstro upon 48-h treatment with the small chaperone confirming the positive effects of the drug in the regulation of proteolysis.
Collectively, our data shed light on proteolytic systems alterations in these reliable immortalized hippocampal astrocytes and their role in AD. Furthermore, we propose a new mechanism involved in the beneficial effect of 4-PBA in neurodegenerative disorders.
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|
---
title: IKBA phosphorylation governs human sperm motility through ACC-mediated fatty
acid beta-oxidation
authors:
- Yanquan Li
- Youwei Hu
- Zhengquan Wang
- Tingting Lu
- Yiting Yang
- Hua Diao
- Xiaoguo Zheng
- Chong Xie
- Ping Zhang
- Xuelian Zhang
- Yuchuan Zhou
journal: Communications Biology
year: 2023
pmcid: PMC10039860
doi: 10.1038/s42003-023-04693-6
license: CC BY 4.0
---
# IKBA phosphorylation governs human sperm motility through ACC-mediated fatty acid beta-oxidation
## Abstract
The nuclear factor-κB (NF-κB) signaling pathway regulates specific immunological responses and controls a wide range of physiological processes. NF-κB inhibitor alpha (IKBA) is an NF-κB inhibitory mediator in the cytoplasm that modulates the nuclear translocation and DNA binding activities of NF-κB proteins. However, whether the upstream cascade of the canonical NF-κB signaling pathway has physiological roles independent of IKBA-mediated transcriptional activation remains unclear. Herein we investigated the function of IKBA in mature sperm in which transcriptional and translational events do not occur. IKBA was highly expressed in human sperm. The repression of IKBA phosphorylation by its inhibitor Bay117082 markedly enhanced sperm motility. On the contrary, lipopolysaccharide-stimulated IKBA phosphorylation significantly decreased sperm motility. Nevertheless, Bay117082 treatment did not affect the motility of IKBA-knockout sperm. Further, untargeted metabolomic analysis and pharmacological blocking assays revealed that the Bay117082-induced increase in sperm motility was attributable to fatty acid β-oxidation (FAO) enhancement. In addition, we found that IKBA phosphorylation inhibition resulted in a significant reduction of acetyl-CoA carboxylase levels in the FAO metabolic pathway. Our findings indicate that IKBA-mediated signaling orchestrates sperm motility program and improves our understanding of transcription-independent NF-κB signaling pathway in cells.
NF-κB inhibitor alpha (IKBA) signaling in mature sperm drives sperm motility and controls fatty acid b-oxidation metabolism.
## Introduction
The nuclear factor-κB (NF-κB) signaling pathway plays a pivotal role in immune, inflammatory, and stress responses, as well as in cell differentiation, proliferation, survival, and apoptosis1,2. The NF-κB inhibitor alpha (IKBA) plays a key role in this canonical pathway maintaining the inactive state of the NF-κB complex in the cytoplasm of most nucleated cells. When cells are stimulated by lipopolysaccharide (LPS) or tumor necrosis factor, IKBA is phosphorylated, ubiquitinated, and degraded by a proteosome-dependent pathway; consequently, the NF-κB complex dissociates and translocates to the nucleus where it then regulates downstream target genes3–5. This regulatory process mediated by IKBA and its phosphorylation is well characterized and widely accepted to be primarily involved in nuclear transcriptional response6,7. Yet, it has recently been shown that there appears to be a non-genomic pathway of NF-κB signaling molecules in anucleated cells8–11. Most members of NF-κB pathway proteins are expressed in platelets which are sensitive to NF-κB inhibitors9,12–14. Erythrocytes contain NF-κB and IKBA proteins and can be induced to programmed cell death by Bay117082, an irreversible inhibitor of IKBA phosphorylation that interfering with NF-κB-dependent signaling8,15. In addition, some studies on the interaction between NF-κB members and mitochondrial proteins have confirmed the non-classical function of the NF-κB signaling pathway in cell activities16–18. Despite these new developments, the non-genomic mechanism of NF-κB signaling regulation remains unclear.
Sperm are highly differentiated terminal cells with tightly condensed chromosomes and in which no transcription and translation occur19. Their functional transformation entirely relies on changes in protein composition and a complex array of post-translational modifications. The transcription-dependent effects of NF-κB signaling are nonexistent in spermatozoa; thus, they are ideal for elucidating the non-classical mechanism of the NF-κB pathway. Motility is a characteristic function of sperm. Sperm motility generates enough force to free the shackle sperm cell from the oviductal reservoir. Hyperactivation causes them to penetrate cumulus matrix and is a must for penetrating the oocyte pellucida and consequently achieving fertilization20–22. Sperm motility is highly dependent on ATP production. Although glycolysis has been suggested to be the primary energy source for mammalian spermatozoa motility23–25, sperm of many species, including humans, can reportedly remain motile for long periods in sugar-free media26,27. This suggests that energy sources other than glycolysis help sperm maintain their motility. Mitochondrial fatty acid β-oxidation (FAO), for which exogenous fatty acids are the main source of energy, is an active regulator of bovine and boar sperm motility28–30. Some enzymes in FAO reaction are evidently expressed in sperm. Some studies found that the inhibition of FAO by etomoxir resulted in a significant reduction in sperm motility30–33. According to proteomics- and metabolomics-based studies, human sperm cells contain enzymes involved in lipid metabolism, including those associated with mitochondrial FAO for energy production31,34. Thus, it is debatable whether glycolysis and/or oxidative phosphorylation is the only or the major supplier of ATP needed for human sperm motility. Further, it not fully understood how sperm maneuver between these processes for energy production26,27,35,36. In addition, the characteristics of FAO-related modulators in human spermatozoa is unknown.
Herein, we investigated the role of IKBA phosphorylation in sperm motility and elucidated the underlying molecular mechanism via metabolic tracing, pharmacological assessments, and IKBA-knockout mouse model.
## Expression of canonical NF-κB components in human sperm
To investigate the expression of NF-κB components in human sperm, we performed western blotting and indirect immunofluorescence assays. We found the presence of most NF-κB proteins, including IKKA, IKKB, IKKγ, p$\frac{105}{50}$, p$\frac{100}{52}$, RELA/B, cREL, and IKBA in human sperm (Fig. 1a). The phosphorylation state of IKKA/B, RELA, and IKBA constitutively existed in these cells, and their phosphorylation was further enhanced when sperm were incubated in Biggers–Whitten–Whittingham (BWW) media (Fig. 1b). We selected several proteins in NF-κB components and observed their localization. IKKA, IKKB, IKBA, and RELA subunits were found to be distributed in the midpiece of sperm (Fig. 1c–f), where mitochondria are localized. The expression and distribution of NF-κB proteins suggested their involvement in the regulation of human sperm functions. Fig. 1Expression and localization of canonical NF-κB proteins in human sperm.a Expression of IKKA, IKKB, IKKγ, p105/p50 (NF-κB1), p100/p52 (NF-κB2), RELA (p65), RELB, cREL, and IKBA in human sperm. α-Tubulin served as the loading control. Numbers represent samples of different individuals. b Phosphorylation states of IKKA/B, RELA and IKBA proteins in human sperm in PBS and BWW media. α-Tubulin served as the loading control. c–f Localization of IKKA (c), IKKB (d), IKBA (e) and RELA (f) proteins in human sperm. DAPI (blue) labeled the nuclei; IKKA, IKKB, IKBA, and RELA were stained with AlexaFluor488 (green) separately. Fluorescent images were merged with bright-field images (Bright) shown in the MERGE panels. Scale bar: 5 μm.
## IKBA phosphorylation regulates sperm motility
IKBA phosphorylation is the core event in NF-κB signaling activation. To investigate the functional roles of IKBA proteins, we assessed the effect of Bay117082, an IKBA phosphorylation inhibitor, on human sperm motility. Bay117082 was found to inhibit IKBA phosphorylation (Fig. 2a). Correspondingly, Bay117082 strongly enhanced the curvilinear velocity (VCL) and amplitude of lateral head displacement (ALH) of human sperm in a dose-dependent but not in a time-dependent manner (Fig. 2b, c). At 25 μM, the enhancement of curvilinear movement was still effective for 60 min. No difference in VCL were detected between 25 μM and 50 μM at 10 and 60 mins (Fig. 2b). Therefore, we selected 25 μM Bay117082 as the ideal concentration and 10 min as the ideal treatment duration for subsequent experiments, unless otherwise specified. To verify the role of IKBA phosphorylation activation in motility, sperm were treated with LPS, a stimulator of IKBA phosphorylation. LPS activated IKBA phosphorylation (Fig. 2d) and significantly inhibited sperm motility (Fig. 2e, f). Collectively, these findings suggest that IKBA phosphorylation regulates parameters related to human sperm hyperactivation. Fig. 2IKBA phosphorylation regulates sperm motility.a Western blotting of IKBA and pIKBA in human sperm treated with different concentrations of Bay117082 (0, 12.5, 25 and 50 μM) for 10 min. α-Tubulin served as the loading control. b, c Dose-dependent effect of Bay117082 on sperm motility. Sperm were incubated with 0, 12.5, 25, and 50 μM Bay117082 for 10 and 60 mins in BWW media. The sperm motility parameters VCL (b) and ALH (c) were measured by CASA. Values represent mean ± SEM ($$n = 8$$). * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$, as compared with the corresponding control (0 µM). ns, no significance. d Western blotting of IKBA and pIKBA in human sperm treated with different concentrations of LPS (0, 0.2, 2 and 20 μg/mL) for 10 min. α-Tubulin served as the loading control. e, f Dose-dependent effects of LPS on sperm motility. Sperm were incubated with final concentrations of LPS at 0, 0.2, 2, and 20 μg/mL for 10 and 30 mins. VCL (e) and ALH (f) were examined by CASA. Values represent mean ± SEM ($$n = 9$$). * $p \leq 0.05$; **$p \leq 0.01$, as compared with the corresponding control (0 μg/mL). ns, no significance.
## IKBA phosphorylation alters sperm swimming patterns from forward to curvilinear movement
Hyperactivated sperm mainly display low straight-line velocity (VSL) or high VCL and ALH. We investigated Bay117082-induced changes in sperm motility pattern. Trajectories of control (Fig. 3a) and Bay117082-treated (Fig. 3b) sperm recorded by computer-assisted sperm analysis (CASA) showed an obvious change from long linear trajectories to short wide wavy trajectories. Next, we analyzed the linearity and curvilinearity of sperm movement and found that regardless of normozoospermia or asthenozoospermia, when the basal VCL value was ≤ 100 μm/s, Bay117082 significantly increased the VCL of normozoospermic sperm from 81.7570 ± 7.7440 μm/s to 101.4965 ± 18.4700 μm/s ($p \leq 0.001$) and VCL of asthenozoospermic sperm from 76.5007 ± 10.3311 μm/s to 99.2404 ± 13.5237 μm/s ($p \leq 0.001$) (Fig. 3c, e). However, when basal VCL was >100 μm/s, the VCL of both normozoospermic (111.9934 ± 13.3063 μm/s vs 113.9119 ± 15.2855, $$p \leq 0.245$$) and asthenozoospermic (107.1364 ± 14.2916 μm/s vs 106.4524 ± 14.3532, $$p \leq 0.741$$) sperm did not show further increase on Bay117082 treatment (Fig. 3g, i). Under all conditions, Bay117082 significantly increased ALH (Fig. 3d, f, h, j). In contrast, Bay117082 reduced VSL, linearity (LIN) and straightness (STR) (Fig. 3c, e, g, i), which are forward movement-related parameters. Altogether, we observed that Bay117082 altered sperm swimming patterns from forward to curvilinear movement (see Supplementary Videos 1–4 for corresponding videos).Fig. 3IκBα phosphorylation alters sperm swimming patterns from forward to curvilinear movement.a, b Sperm trajectories before (a) and after (b) treatment with 25 μM Bay117082, as recorded by CASA. c–j Changes in sperm motility parameters after treatment with 25 μM Bay117082 for 10 min. VCL, VAP, VSL, LIN, STR and WOB are presented in radar charts. ALH is shown in bar graphs. c–f Bay117082-induced changes in parameters related to the motility of normozoospermic (c, d) and asthenozoospermic (e, f) sperm, with basal VCL ≤ 100 μm/s. g–j Bay117082-induced changes in parameters related to the motility of normozoospermic (g, h) and asthenozoospermic (i, j) sperm with basal VCL > 100 μm/s. *$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$, as compared with the control (0 μM).
## Bay117082-induced enhancement of sperm motility involves fatty acids consumption for energy production
Sperm motility depends on energy supply. Herein on measuring changes in ATP content in sperm without or with Bay117082 treatment, we found that Bay117082 treatment significantly increased ATP levels in sperm (Fig. 4a). Next, we equally divided sperm into control group (C) and treated group (T), followed incubation for 10 min without or with 25 μM Bay117082, respectively. Sperm were subsequently analyzed via untargeted metabolomics by liquid chromatography-mass spectrometry (LC-MS/MS) analysis. Principal component analysis indicated that the treated and control groups could be significantly grouped (Fig. 4b). As evident from the heatmap shown in Fig. 4c, relative to the control group, the level of four metabolites showed significant up-regulation [Thioetheramide-PC, 1-Palmitoyl-sn-glycero-3-phosphocholine, PC (16:$\frac{0}{16}$:0), and Triethanolamine] and those of nine showed down-regulation [palmitic acid, phosphorylcholine, oleic acid, stearic acid, adrenic acid, adenosine monophosphate (AMP), adenosine diphosphate (ADP), adenine, and taurine] in the treated group. Further, in the treated group, the levels of several fatty acids (Fig. 4d–g) including oleic acid (C18H34O2), adrenic acid (C22H36O2), stearic acid (C18H36O2) and palmitic acid (C16H32O2), were markedly downregulated, suggesting the consumption of long-chain fatty acids on treatment with Bay117082. Furthermore, metabolite set enrichment analysis that based on the small molecule pathway database highlighted several metabolic pathways of sperm (Fig. 4h). β-oxidation of long-chain fatty acids was significantly enriched, indicating its important role in sperm motility alteration. These results suggest that Bay117082-activated sperm motility is closely related to FAO.Fig. 4Bay117082-induced sperm motility enhancement consumes fatty acids as the energy source.a Effects of 25 μM Bay117082 on sperm ATP level. Values represent mean ± SEM ($$n = 10$$), *$p \leq 0.05$, as compared with the control (0 μM). b Principal component (PCA) analysis of untargeted metabolomics data obtained from Bay117082-treated sperm and control sperm. Each dot on plot represents an individual sample ($$n = 20$$). Green dots represented the control group (C); purple dots represent the Bay117082-treated group (T); blue dots represent the quality control samples. c Heatmap of significantly different metabolites in the control (C) and Bay117082-treated (T) group. The variable importance in the projection (VIP) value of each variable in the orthogonal partial least squares discriminant analysis (OPLS-DA) model was calculated. Significance was determined using paired Student’s t test. VIP > 1 and $p \leq 0.05$ were applied to identify statistically significant differential metabolites. The x-axis represents comparative changes in each sperm sample before (C1–C20) and after (T1–T20) Bay117082 treatment. Significantly different metabolites were labeled on the y-axis. d–g Bay117082-induced changes in oleic acid (d), adrenic acid (e), stearic acid (f), and palmitic acid (g) in sperm. Values represent mean ± SEM ($$n = 20$$), *$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$, as compared with the control (0 μM). h Metabolite Set Enrichment Analysis based on the Small Molecule Pathway Database. The top 25 of enriched pathways are shown.
## Sperm motility enhancement by Bay117082 is not dependent on glucose metabolism
In view of previous studies, glycolysis is the primary supplier of ATP to human sperm24. We investigated the effects of Bay117082 on sperm motility in the absence of glucose, pyruvate, and lactic acid (BWW-GPL). Bay117082 was found to still significantly promote sperm motility in BWW-GPL media (Fig. 5a, b). Furthermore, human sperm were treated with α-chlorohydrin (ACH), an inhibitor of glycolysis, to verify if the effects of Bay117082 on sperm motility were independent on glycolysis. As indicated in Fig. 5c and by a previous study37, treatment with 500 μM ACH for 1 h effectively downregulated the percentage of sperm motility. However, sperm motility stimulated by Bay117082 could not be significantly blocked by ACH (Fig. 5d). The results suggest that the promotion of sperm motility by Bay117082 is not mainly dependent on glucose metabolism. Fig. 5Bay117082 promotes sperm motility independently of glucose metabolism.a, b Changes in VCL and ALH of sperm treated with 25 μM Bay117082 in BWW-GPL or BWW media for 10 and 30 mins. BWW-GPL: BWW media lacking glucose (G), pyruvate (P) and lactic acid (L). Values represent mean ± SEM ($$n = 7$$), *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, as compared with the corresponding control (0 μM) in each medium. c Changes in motile percent of sperm incubated with 500 μM α-chlorohydrin (ACH) for 1 h. Values represent mean ± SEM ($$n = 7$$), **$p \leq 0.01.$ d Changes in VCL of sperm treated with 25 μM Bay117082 in the absence or presence of 500 μM ACH for 1 h. Values represent mean ± SEM ($$n = 7$$), ***$p \leq 0.001$, as compared with the corresponding control.
## IKBA phosphorylation inhibition by Bay117082 regulates FAO in human sperm
To validate Bay117082-induced utilization of FAO, we examined the expression of some key FAO-associated proteins or enzymes. Carnitine palmitoyl transferase 1A (CPT1A), acyl-CoA synthetase long-chain family member 1 (ACSL1), long-chain specific acyl-CoA dehydrogenase (ACADL), very long-chain specific acyl-CoA dehydrogenase (ACADVL) and hydratase subunit A (HADHA) were expressed in sperm and found in the midpiece of sperm tail, where mitochondria are located (Fig. 6a–f). CPT1A is a rate-limiting enzyme of FAO and transports activated long-chain fatty acids from the cytoplasm into mitochondria. Etomoxir, an inhibitor of CPT1A, can reportedly significantly decrease human sperm motility31. Herein we treated sperm with etomoxir and the same result was obtained (Fig. 6g). Furthermore, etomoxir significantly abolished sperm motility enhancement induced by Bay117082 (Fig. 6h). The AMP-activated protein kinase (AMPK)-acetyl-CoA carboxylase (ACC)-CPT1A axis tightly regulates long-chain fatty acid oxidation in mitochondria38. We investigated the state of AMPK and ACC proteins as well as their phosphorylation before and after Bay117082 treatment. On Bay117082 treatment, ACC protein content showed a significant decrease in sperm, whereas the level of pACC/ACC showed an upward trend (Fig. 6i–k). There was no significant change in AMPKA, the catalytic subunit of AMPK, and its phosphorylation level in sperm before and after Bay117082 treatment (Fig. 6i, l). We also found that ACC was localized to the mitochondria in sperm midpiece (Fig. 6m), similar to the localization of IKBA and key FAO enzymes. In addition, Bay117082 treatment elevated the content of NADH/NAD+, the products of FAO, in BWW-GPL media (Fig. 6n). These results suggest that Bay117082 activates FAO to release energy and enhance movement of sperm. Fig. 6IKBA phosphorylation inhibition by Bay117082 regulates FAO in human sperm.a Expression of ACSL1, ACADL, ACADVL, HADHA, and CPT1A in sperm. α-Tubulin was applied to assess protein loading. Numbers represent samples of different individuals. b–f ACSL1, ACADL, ACADVL, HADHA and CPT1A localization in sperm. DAPI (blue) labeled the nuclei. CPT1A (b), ACSL1 (c), ACADL (d), ACADVL (e), and HADHA (f) were stained with AlexaFluor488 (green). Images were merged (MERGE) with bright-field images (Bright). Scale bar: 5 μm. g Motile percent of sperm treated with etomoxir (400 μM) for 1 h in BWW media. Values represent mean ± SEM ($$n = 9$$), **$p \leq 0.01$, compared with the corresponding control (0 μM). h Changes in VCL of sperm treated with 25 μM Bay117082 in the absence or presence of etomoxir (400 μM) for 1 h. Values represent mean ± SEM ($$n = 9$$), **$p \leq 0.01$, compared with the corresponding control. ns, no significance. i–l Western blotting showing AMPKA, ACC, pAMPKA, and pACC levels (i) in sperm treated with or without Bay117082 at 25 μM for 10 min. Gray-scale value analysis of ACC and pACC (j), pACC/ACC (k), as well as AMPKA and pAMPKA (l). $$n = 7$$, *$p \leq 0.05$, compared with the corresponding control (0 μM). ns, no significance. α-Tubulin was applied to assess protein loading. m Indirect immunofluorescence showing ACC localization in human sperm. DAPI (blue) labeled the nuclei; ACC was stained with Alexa Fluor 488 (green). Images were merged (MERGE) with bright-field images (Bright). Scale bar: 5 μm. n Changes in NADH/NAD+ levels in sperm treated with or without Bay117082 in BWW-GPL media. Values represent mean ± SEM ($$n = 8$$), *$p \leq 0.05.$
## IKBA-/- mouse sperm lost their response to Bay117082
Bay117082 is a broad-spectrum inhibitor with multiple targets39. To exclude the nonspecific role of Bay117082 in sperm motility regulation, we generated an IKBA conditional knockout mouse (IKBA cKO) model based on the CRISPR-Cas9 and Cre-/LoxP technologies (Nfkbiaf/f; Stra8-Cre) (Supplementary Fig. 1a, b). Genotyping PCR was performed to assess the genotype of mice (Supplementary Fig. 1c, d). Western blotting and immunofluorescence analysis indicated the deletion of IKBA in (Nfkbiaf/f; Stra8-Cre +) mouse (IKBA-/-) sperm (Fig. 7a, b). Motility parameters showed no statistically significant difference between IKBA-/- and control sperm (Supplementary Fig. 1e). However, IKBA-/- sperm lost their response to Bay117082 (Fig. 7c, d). Altogether, these findings indicated that Bay117082 regulates sperm motility by specifically targeting IKBA phosphorylation. Fig. 7IKBA-/- mouse sperm showed no response to Bay117082.a Expression level of IKBA in control and Nfkbia cKO mice sperm. α-Tubulin was applied to assess protein loading. b Distribution of IKBA in sperm of control mice and Nfkbia cKO mice. DAPI (blue) labeled the nuclei, and IKBA was stained with Alexa Fluor 488 (green). Images were merged (MERGE) with bright-field images (Bright). Scale bar: 10 μm. c, d Changes in VCL and ALH of control and Nfkbia cKO mice sperm treated with or without Bay117082 for 10 min. Values represent mean ± SEM ($$n = 9$$), *$p \leq 0.05$, ***$p \leq 0.001$, compared with the corresponding control (0 μM). ns, no significance.
## Discussion
Both activated and hyperactivated sperm motility are pivotal for fertilization20. Sperm motility requires large amounts of ATP. According to previous studies24,25,40, carbohydrates are the main source of energy for sperm motility. In this study, FAO was found to play a chief role in generating energy needed for human sperm motility. Moreover, we believe that Bay117082 can be used to enhance human sperm motility. Our results provide novel insights into the molecular mechanisms underlying the regulation of energy needed for sperm motility and lay a foundation for the development of Bay117082 as a potential therapeutic to tackle male infertility. We also found that almost all known NF-κB family members were expressed in human sperm. Besides, IKBA phosphorylation, a modification of specific NF-κB proteins, was sensitive to stimulation and served functions other than the regulation of transcription. We report a novel and ideal cell model to study the molecular mechanism of the NF-κB signaling pathway in the absence of NF-κB translocation to the nucleus. Our findings indicate that IKBA-mediated signaling orchestrates a transcription-independent sperm motility system and invite to rethink and evaluate non-classic NF-κB signaling in somatic cells.
Mammalian sperm constantly metabolize extracellular or/and intracellular energy substrates to generate ATP so as to maintain their motility for prolonged periods. ATP is mainly derived from glycolysis and mitochondrial oxidative phosphorylation with glucose, pyruvate, and lactic acid serving as the substrates. However, according to recent studies, sperm of different species employ diverse metabolic strategies to alter their motility based on physiological or pathological stimuli41. For instance, dog sperm are capable of synthesizing and degrading glycogen, which is transformed into glucose-6-phosphate and then via glycolysis, energy is supplied to sperm42,43. Ketone body catabolism also reportedly contributes to ATP production to support mouse sperm motility44. The hydrolysis of phospholipids to produce glycerol, which can enter the glycolytic pathway through the sequential conversion to glycerol-3-phosphate and dihydroxyacetone phosphate, is evidently correlated to pig and bull sperm motility41. It has been previously suggested that fatty acids are the main source of energy and improve bovine and boar sperm motility28–30. Furthermore, dolphin sperm motility appears to depend almost exclusively on the oxidation of endogenous fatty acids45. An earlier study reported that Slc22a14-mediated FAO is essential for spermatozoa energy generation and motility in mice33. In case of human sperm, ATP required for sperm motility is usually believed to be mainly derived from oxidative phosphorylation and glycolysis, and sperm response to changes in energy demand is regulated by glycolytic flux rather than by mitochondrial respiration24,26,27,41,46. Nevertheless, it has been found that human sperm can remain motile for quite a long period in the absence of glucose, pyruvate, and lactic acid26,27,47, suggesting that besides glycolysis and carbohydrates, other energy sources are available to support spermatozoa motility. Proteomic studies have suggested that FAO contributes to ATP generation and human sperm motility. Enzymes associated with mitochondrial β-oxidation have been identified in sperm tail, and etomoxir, a fatty acid oxidation inhibitor, was reported to significantly decrease sperm motility31. Our results indicated FAO can be mobilized by IKBA phosphorylation to provide energy to human sperm and regulate their motility, supporting the view that in addition to carbohydrates (glucose, pyruvate, and lactic acid), lipids are an important source of energy for human sperm motility. This implies that most mammalian spermatozoa employ a versatile metabolic strategy to maintain their motility. Future studies should focus on comprehensively investigating these strategies under specific circumstances. It is notable that the inhibition of IKBA phosphorylation by Bay117082 accelerated sperm motility within 10 min. One molecule of glucose generates only two ATP molecules if incompletely metabolized by glycolysis and lactic acid fermentation, and there are 32-26 ATP molecules are produced when one glucose molecule is completely aerobically combusted24. In the other hand, β-oxidation of one molecule 16 C fatty acid, such as palmitoyl-CoA, generates 106 ATP molecules48. Therefore, the activation of β-oxidation can quickly improve sperm motility. The lipid composition of spermatozoa shows certain specific characteristics, such as a higher proportion of neutral lipids49. In asthenozoospermic men, oleic acid and palmitic acid levels are relatively higher, signifying a metabolic disorder of the sperm lipids50,51. We believe that in human sperm, FAO is an important backup strategy for the regulation of hyperactivation motility.
Initially, Bay117082 was identified to be a specific inhibitor of NF-κB kinase (IKK) in the NF-κB signaling pathway; it was found to selectively inhibit cytokine-induced IKBA phosphorylation to exert anti-inflammatory activity in vivo52. Later, it was demonstrated that Bay117082 was a broad-spectrum inhibitor with anti-inflammatory activity against multiple targets39,53,54. In addition, Bay117082 displayed other pharmacological activities that include anticancer and neuroprotective effects39. A previous report demonstrated Bay117082 to be an irreversible inhibitor of protein tyrosine phosphatases (PTPs), which specifically dephosphorylate tyrosine residues in proteins to modify their tyrosine phosphorylation status in cells and evidently enhance protein phosphorylation55. In sperm, the enhancement of protein tyrosine phosphorylation is the indicator of capacitation which is accompanied by hyperactivation. PTPs activity has a positive role in the regulation of mammalian sperm motility and protein tyrosine phosphorylation56. Herein we ruled out the possibility that Bay117082 regulates sperm motility by inhibiting PTPs. In bull and mouse sperm, hyperactivated motility is not always positively correlated to an increase in capacitation-associated protein tyrosine phosphorylation57,58. Further, there exists no significant relationship between human sperm motility and tyrosine phosphorylation59. Previous studies have reported that tyrosine phosphorylation shows a slow and gradual time-dependent increase during sperm capacitation60–62. In this study, Bay117082 induced an obvious increase in protein tyrosine phosphorylation at 60 min (Supplementary Fig. 2a). Nevertheless, the effect of Bay117082 on sperm movement was rapid: it significantly accelerated sperm motility in 10 min or even as fast as 2 min in our experiments. A significant increase in tyrosine phosphorylation by Bay117082 at 1 h did not further enhance sperm motility (Supplementary Fig. 2b and Fig. 2b, c). It has been previously reported that, PTPs activity inhibition reduces the overall velocity of hamster, boar, stallion, dog mouse and human spermatozoa56,63,64. While Bay117082 significantly increased sperm motility, it was unable to induce any such change after IKBA gene deletion in our mouse model. This suggests that Bay117082 specifically targets the phosphorylation of IKBA protein. Without a doubt, we cannot completely exclude the influence of other unknown targets of Bay117082 on sperm motility.
As mature sperm lacks complete cytoplasm and most organelles, it is plausible that IKBA serves some function other than the regulation of transcription. The involvement of NF-κB in non-canonical signaling pathways has been previously demonstrated in anucleated cells8–11. IKBA evidently interacts with mitochondrial proteins to regulate specific functions16–18. In the present study, IKBA protein localized to the mitochondria in sperm midpiece, suggesting that IKBA plays a vital role in supplying energy to sperm. We also confirmed that IKBA phosphorylation was closely related to β-oxidation of mitochondria in human sperm. Altogether, we believe that the IKBA-mediated β-oxidation of activated fatty acids within the mitochondrial matrix represents a key energy source for sperm motility.
Lipids are primarily consumed through mitochondrial FAO. Free fatty acids are esterified with CoA and then transferred into the mitochondria matrix for β-oxidation, where they are oxidized into acetyl-CoA and NADH and FADH2 are generated. In this process, ACC converts acetyl-CoA to malonyl-CoA. Malonyl-CoA inhibits CPT1, a key enzyme that initiates free fatty acids transportation into mitochondria. Several studies have demonstrated that the AMPK/ACC/CPT1 pathway regulates FAO38,65. AMPK inactivates ACC via phosphorylation, preventing malonyl-CoA generation38,66. The inhibition of the AMPK/ACC/CPT1A signaling pathway is favorable for the recovery of CPT1 activity and FAO. Our data showed that the inhibition of IKBA phosphorylation by Bay117082 significantly decreased ACC, which explained why Bay117082 promoted β-oxidation. In somatic cells, the activity of ACC can be regulated by transcription and post-transcription as well as degradation according to the metabolic status of cells67. No transcription or translation occurs in sperm, and post-translational modification and degradation of proteins are accordingly observed. ACC is reportedly ubiquitylated on interaction with E3 ubiquitin ligase, which subsequently causes ACC degradation by proteasome in adipose tissue68,69. E3 ubiquitin ligase is expressed in sperm and performs an essential ubiquitination function70–72. Therefore, it is reasonable to speculate that ACC degradation is caused by the ubiquitination degradation pathway. Further studies are warranted to determine whether IKBA directly or indirectly regulates ACC degradation and how this degradation is modulated. Figure 8 shows the potential mechanism via which FAO is activated by Bay117082/IKBA/ACC.Fig. 8Diagram depicting the regulation mechanism of IKBA phosphorylation in sperm motility. IKBA and IKK subunits located on the midpiece of human sperm. Bay117082 inhibits IKBA phosphorylation and changes the swimming pattern from forward movement to curvilinear movement. On the contrary, LPS induces IKBA phosphorylation and weakens sperm motility. ACC level decrease and pACC/ACC level shows an upward trend when sperm are treated with Bay117082; thus, less acetyl-CoA is catalyzed by ACC to form malonyl-CoA. As malonyl-CoA inactivates CPT1A, the decrease in ACC level or the increase in pACC/ACC level reduces malonyl-CoA production and promote the entry of fatty acids into the mitochondria for catabolism. The key enzymes of FAO, including ACSL1, ACADL, ACADVL, and HADHA, are located on the midpiece of human sperm. NADH levels are elevated in sperm treated with Bay117082, suggesting that increased ATP levels from FAO support increased sperm motility. Etomoxir decreases sperm motility and blocks the action of Bay117082 on sperm motility. Thin black arrows represent known positive physiological process. Thin black dashed arrows indicate a speculative process. Blocked lines represent repressive effect. Thick arrows (up or down) indicate the alteration of substance after treatment with Bay117082 (yellow), LPS (blue), and etomoxir (purple); solid lines represent verified trends and dashed lines represent conjectural trends. The elements “sperm” and “mitochondria” were modified from our previous article62.
In summary, we discover the non-classical role of IKBA in mature human sperm. Our results indicate that IKBA phosphorylation regulates sperm motility through ACC-mediated FAO. Our data provide novel insights into the regulatory mechanisms associated with supplying energy for human sperm motility. Finally, we believe that Bay117082 can be potentially used to enhance sperm motility and to alleviate male infertility problems, such as asthenozoospermia.
## Chemicals/reagents
As recommended by the WHO Laboratory manual for the examination and processing of human semen (fifth edition) and our earlier study62, we used BWW media (94.8 mM NaCl, 4.8 mM KCl, 1.7 mM CaCl2, 1.2 mM MgSO4, 1.2 mM KH2PO4, 5.5 mM glucose, 13.21 mM sodium lactate, 0.27 mM sodium pyruvate, 25 mM NaHCO3, and 3.5 mg/mL BSA) for collecting and cultivating human sperm. The aforementioned reagents were purchased from Sigma-Aldrich (Saint Louis, MO, USA). Bay117082, LPS, α-chlorohydrin, etomoxir and DAPI were also obtained from Sigma-Aldrich. Antibody-related information is shown in Supplementary Table 1.
## Ethical approval
This study, including the process for semen sample collection, was approved by the Ethics Committee on human subjects of International Peace Maternity and Child Health Hospital (GKLW2018-03).
## Sperm sample collection and treatments
Semen samples were collected and treated as previously reported62. Briefly, semen was obtained via masturbation from participants with 3-5 days of sexual abstinence who visited outpatient clinics at Reproductive Medicine Center of International Peace Maternity and Child Health Hospital, Shanghai, China, between January 2019 to September 2022. The samples were liquefied at room temperature for at least 30 min before diagnostic semen analysis and scientific research. Subsequently, the samples were subjected to CASA (Hamilton-Thorne, Beverly, MA, USA) to assess sperm motility parameters and classified into normozoospermia or asthenozoospermia according to the WHO laboratory manual for the examination and processing of human semen (fifth edition). We then centrifuged spermatozoa at 500 g for 3 min to remove plasma, followed by a BWW medium or PBS buffer wash. Then samples were centrifuged and resuspended in BWW medium or PBS buffer to a final concentration of 10-20 ×106 cells/mL for subsequent experiments. This suspension was divided into several equal parts and treated with Bay117082, LPS, α-chlorohydrin, or etomoxir, either individually or in combination as required. Samples were finally incubated in a $5\%$ CO2 incubator at constant temperature of 37 °C for specific time.
## Assessment of sperm motility
Sperm motility and concentration were measured by CASA, as previously described62. After treatment with required reagent for specific time, 5 μl of each sample was added to the sperm-counting chamber (Netherland, Leja). Ten microscopic fields of each chamber were analyzed, and at least 200 spermatozoa were evaluated. The following parameters were recorded: percentage of motile sperm (motile percent), average path velocity (VAP, μm/s), progressive velocity (VSL, μm/s), curvilinear line velocity (VCL, μm/s), straightness (STR, %), linearity (LIN, %), amplitude of lateral head (ALH, μm), beat cross frequency (BCF, Hz), and wobble (WOB, %). The playback function of the system was used to verify its accuracy.
## Western blotting
Sperm samples were collected and purified. Pellets of spermatozoa were lysed in Laemmli buffer (0.0625 M Tris base, $2\%$ SDS, $10\%$ glycerol, $0.02\%$ bromophenol blue, and $5\%$ β-mercaptoethanol) with protease inhibitors, phosphatase inhibitors, and $1\%$ phenylmethysulphonyl fluoride. Total protein was separated by electrophoresis on a $10\%$ SDS-polyacrylamide gel, and protein bands were transferred onto a polyvinylidene difluoride membrane. The membrane was then incubated with primary antibodies at 4°C overnight. Antibody concentration ratios are listed in Supplementary Table 1. Subsequently, the membrane was incubated with horseradish peroxidase-conjugated anti-rabbit or anti-mouse secondary antibody for 1 hour at room temperature or 4°C overnight. Chemiluminescence was detected by Immobilon ECL Ultra Western HRP Substrate (Darmstadt Germany, Millipore).
## Immunofluorescence staining
Motile sperm were washed and resuspended at a concentration of 10 × 106 cells/mL in PBS. A drop of 20 μL of sperm suspension was smeared onto a slide and allowed to air dry at room temperature. These slides were then fixed in $4\%$ paraformaldehyde for 10 min and washed with PBS three times for 5 min each time. The sperm slides were subsequently perforated by $0.3\%$ Triton X-100 with $0.2\%$ Tween-20 in PBS for 30 min at room temperature. For antigen blocking, $3\%$ BSA in $10\%$ goat serum solution was used, followed by incubation at room temperature for 1 h. The slides were incubated with the primary antibody solution at a final concentration of 1:50 - 1:100 (Supplementary Table 1) at 4 °C overnight. After washing with PBST (PBS with $0.5\%$ Tween-20) three times for 5 min each time, the samples were incubated with a polyclonal anti-rabbit/mouse IgG-FITC antibody (1:1000 dilution) for 1 h in the dark at room temperature. After three washes, the samples were then incubated with DAPI for 30 min at room temperature to colorize the nucleus. Finally, Vectashield® Antifade Mounting Medium (Vector Laboratories, CA, USA) was used to mount coverslips. Images were captured using a fluorescence confocal microscope (TCS SP8 SR, Leica, Germany).
## Untargeted metabolomics analysis
Sperm were treated in the absence or presence of 25 μM Bay117082 for 10 min, and then collected and purified. To remove proteins and extract metabolites, 800 μL cold methanol/acetonitrile (1:1, v/v) was added. This mixture was collected and centrifuged at 14000 g for 5 min at 4°C and the supernatant thus obtained was collected. After drying the supernatant in a vacuum centrifuge, it was redissolved in 100 μL acetonitrile/water (1:1, v/v) and subjected to LC-MS/MS. A quadrupole time-of-flight mass spectrometer (Sciex TripleTOF 6600, USA) coupled to hydrophilic interaction chromatography via electrospray ionization was used to analyze these extracts by Shanghai Applied Protein Technology Company Limited. LC separation was achieved on an ACQUIY UPLC BEH Amide column (2.1 mm×100 mm, 1.7 μm particle size) using an aqueous gradient of solvent A (25 mM ammonium acetate and 25 mM ammonium hydroxide) and solvent B (acetonitrile). Mass spectra were obtained in negative and positive ionizations mode. Data were acquired in the mass range from 60 to 1000 Da m/z range during MS acquisition, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. Further, during auto-MS/MS acquisition, data were acquired in the mass range from 25 to 1000 Da m/z, and the accumulation time for product ion scan was set at 0.05 s/spectra. Information-dependent acquisition was used to acquire product ion scan in the high sensitivity mode.
## ATP and NADH/NAD+ level determination
Spermatozoa were treated with or without 25 μM Bay117082 for 10 min, and sperm were then collected and purified. Spermatozoa pellets were resuspended in lysis buffer from a kit and ultrasonically lysed (20 kHz, 750 W, $20\%$ power, cycles of 2 s on and 5 s off for 1 min; Intelligent Ultrasonic Processor, SHUNMATECH, Nanjing, China) on ice. Sperm ATP levels were measured by the Enhanced ATP Assay Kit (Beyotime, S0027, China), according to manufacturer instructions. ATP concentration between 0.1 nM to 10 μM is proportional to the fluorescence when fluorescein and luciferase are extreme. Spermatozoa NADH/NAD+ levels were determined using the Coenzyme I NAD (H) content test kit (Nanjing Jiancheng Bioengineering Institute, A114-1-1, China). The oxidized thiazole blue (MTT) was reduced to formazan by NADH through the hydrogen transfer of phenazine methyl sulfate; the absorbance at 570 nm was determined, according to the manufacturer instructions. NAD+ is reduced to NADH by alcohol dehydrogenase and further detected by the MTT reduction method.
## Conditional knockout mouse model
Mice carrying a LoxP-flanked Nfkbia allele (Nfkbiaflox/flox) were generated using the CRISPR-Cas9 technology and homologous recombination in fertilized eggs. LoxP sites were designed to be located at both ends of exons 1 and 2 of Nfkbia. Briefly, Cas9 mRNA and sgRNA (Supplementary Table 2) and donor vector were microinjected into the fertilized eggs of C57BL/6 J mice to obtain F0, which were then mated with C57BL/6 J mice and passaged to obtain a stable genetic (Nfkbiaflox/flox) generation. Nfkbiaflox/flox females were mated with Stra8-Cre knock-in male mice expressing CRE from A1 spermatogonia onward73 (Stra8-GFPCre mice were generously provided by Prof. MingHan Tong) to generate (Nfkbiaflox/-; Stra8-Cre) fitters and mate with (Nfkbiaflox/flox) mice. Mice genotypically identified as (Nfkbiaflox/flox; Stra8-Cre) were considered to be conditional knockout mice. Western blotting and immunofluorescence assays were performed to verify knockout efficiency.
## Statistics and reproducibility
Values represent mean ± standard error of the mean (SEM), with the number of samples (n) being ≥3 in independent experiments. GraphPad Prism 8.3 (Prism, USA) was used for data analysis. Data between the control and treatment groups were analyzed using two-tailed t tests, one-way ANOVA or two-way ANOVA to determine statistical significance. Tukey’s multiple comparison test was applied to analyze data for multiple comparisons. $p \leq 0.05$ indicated statistical significance. The statistical programming language R (www.R-project.org) and the web server for metabolomic data analysis MetaboAnalyst (https://www.metaboanalyst.ca) were used to analyze metabolomics data.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Video 1 Supplementary Video 2 Supplementary Video 3 Supplementary Video 4 Supplementary Data 1 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04693-6.
## Peer review information
Communications Biology thanks Fei Sun and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: Manuel Breuer.
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|
---
title: Rutin alleviates colon lesions and regulates gut microbiota in diabetic mice
authors:
- Cifeng Cai
- Wenwen Cheng
- Tiantian Shi
- Yueling Liao
- Meiliang Zhou
- Zhiyong Liao
journal: Scientific Reports
year: 2023
pmcid: PMC10039872
doi: 10.1038/s41598-023-31647-z
license: CC BY 4.0
---
# Rutin alleviates colon lesions and regulates gut microbiota in diabetic mice
## Abstract
Diabetes is a common metabolic disorder that has become a major health problem worldwide. In this study, we investigated the role of rutin in attenuating diabetes and preventing diabetes-related colon lesions in mice potentially through regulation of gut microbiota. The rutin from tartary buckwheat as analyzed by HPLC was administered intragastrically to diabetic mice, and then the biochemical parameters, overall community structure and composition of gut microbiota in diabetic mice were assayed. The results showed that rutin lowered serum glucose and improved serum total cholesterol, low-density lipoprotein, high-density lipoprotein, triglyceride concentrations, tumor necrosis factor-α, interleukin-6, and serum insulin in diabetic mice. Notably, rutin obviously alleviated colon lesions in diabetic mice. Moreover, rutin also significantly regulated gut microbiota dysbiosis and enriched beneficial microbiota, such as Akkermansia ($p \leq 0.05$). Rutin selectively increased short-chain fatty acid producing bacteria, such as Alistipes ($p \leq 0.05$) and Roseburia ($p \leq 0.05$), and decreased the abundance of diabetes-related gut microbiota, such as Escherichia ($p \leq 0.05$) and Mucispirillum ($p \leq 0.05$). Our data suggested that rutin exerted an antidiabetic effect and alleviated colon lesions in diabetic mice possibly by regulating gut microbiota dysbiosis, which might be a potential mechanism through which rutin alleviates diabetes-related symptoms.
## Introduction
Diabetes, classified as type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), is a metabolic disorder accompanied by insufficient insulin production or insulin resistance1. To date, although there have been some antidiabetic drugs in clinical practice, there is still a general lack of effective approaches to prevent the initiation and development of diabetes and diabetes-related diseases2. In T2DM patients, the incidence of colorectal cancer (CRC) is higher than that in nondiabetic populations3. Hyperinsulinemia, hyperglycemia and chronic inflammation are common risk factors for CRC and liver cancer4. Colon lesions appear in an initial phase of diabetes-induced CRC. Due to the lack of understanding of the CRC development process, there are currently no effective methods to prevent the development of colon lesions. Studies have shown that the incidence of diabetes is closely related to intestinal flora which plays an important role in host metabolism, immune regulation, the development of inflammation-derived disease and chronic inflammation5–7. The composition of the gut microbiota is also significantly correlated with obesity and diabetes8–10. Although an increasing number of drugs are being used to treat diabetes and related diseases, the side effects of many drugs cannot be ignored11. Therefore, it is of great value to develop new effective and nontoxic drugs for the treatment of diabetes and its related diseases. At present, increasing attention has been paid to the development of natural products due to their low cost and minimal side effects in diabetes treatment.
Rutin, a type of flavonoid glycoside, is mainly found in plants, such as apple, black tea, vegetables and buckwheat12,13. It was reported that rutin had antioxidant activity and promoted intestinal absorption14. Rutin has attracted extensive attention due to its various biological activities, such as antioxidant15, antiinflammatory16,17, antidiabetic13,18–22, and anticancer activities23–25. Recent studies have indicated that the regulation of gut microbiota might be one of the potential mechanisms by which natural products achieve antidiabetic effects26. However, whether rutin from tartary buckwheat could improve diabetes and colon lesions by modulating gut microbiota is still not clear. Here, we investigated the effect of rutin on reducing the diabetes pathological state, diabetes-related colon lesions, overall community structure and composition of gut microbiota in diabetic mice and their potential relationship. Our discovery of rutin reducing colon lesions and modulating the gut microbiota composition in diabetic mice provided evidence for the therapeutic potential of rutin to treat diabetes-related symptoms possibly by promoting intestinal health.
## Chemicals and reagents
Control diet ($10\%$ lipids, $19\%$ protein, $71\%$ carbohydrates, Cat. AIN-93) was obtained from Jiangsu-Xietong, Inc, Nanjing, China. A high-fat diet (HFD) ($60\%$ fat, Cat. D12492) was from Research Diets Incorporated Company, New Brunswick, New Jersey, USA. Streptozotocin (STZ, Cat. 18883-66-4) and saline (Cat. 231-598-3) were purchased from Sigma-Aldrich Co., Ltd (St. Louis, MO, USA). Tartary buckwheat powder (Zhongku No. 3) was obtained from Weining Dongfang Shengu Co., Ltd. (Guizhou, China). The AB-8 macroporous resins (Cat. M0042) were purchased from Solarbio Technology Ltd (Beijing, China). All other chemical reagents used in this study were of analytical grade.
## Rutin preparation from tartary buckwheat
The rutin used in this study was obtained by ethanol extraction from tartary buckwheat powder, the purity of rutin prepared by us reached more than $96\%$ and stored in our laboratory as previously described27.
## Establishment and treatment of the diabetic mouse model
Forty male C57BL/6 J mice (8 weeks old) were obtained from Shanghai Slac Laboratory Animal Co., Ltd (Shanghai, China). All mice were raised in separate cages under conditions in which they could freely ingest available solid feed and water. The mice were caged at a room temperature of 23 ± 1 °C, humidity of $60\%$ and a 12 h light–dark cycle. The mice were provided with ad libitum access to food and water for 1 week. The STZ-induced T1DM mouse model and HFD/STZ-induced T2DM mouse model were established as previously described28,29. Briefly, the mice received intraperitoneal injections of STZ (45 mg/kg/day) for five consecutive days. Four weeks after diabetes induction, fasting blood glucose level of 16.7 mM and above were considered diabetic. The HFD/STZ-induced T2DM mouse model were established, the ND group was fed with a normal diet ($10\%$ lipids, $19\%$ protein, $71\%$ carbohydrates) sustainably, the other three groups of mice were fed the HFD ($45\%$ lipids, $19\%$ proteins and $36\%$ carbohydrates, research diets incorporated company, New Brunswick, New Jersey, USA) for 8 weeks. After 8 weeks of HFD feeding, the mice were fasted for 12 h overnight. The HFD groups were received intraperitoneal injections of STZ (35 mg·kg − 1, dissolved at 0.1 mol/L cold citrate buffer, pH 4.4) daily for three consecutive days. The fasting serum glucose was sampled from the tail vein and determined by an enzymatic colorimetric assay using a modified glucose oxidase–peroxidase method (Roche Diagnostics, Mannheim, Germany) and a glucose analyzer (Roche-Hitachi 917). Diabetes was defined as abdominal serum glucose ≥ 16.7 mmol/L for two consecutive days30–32. In a follow-up experiment, the successfully established diabetes mice were used in the experiment, and the failed models were excluded. The dosage of rutin was optimized according to previous reports33. Rutin or saline was administered intragastrically in T1DM and T2DM mice groups (5 mice per group) once a day for 4 weeks as follows: ND group (normal diet mice with daily administration of saline), T1DM group (T1DM mice with daily administration of saline), T1DM-Rutin100 group (mice with daily administration of 100 mg Rutin/kg body weight), and T1DM-Rutin200 group (mice with daily administration of 200 mg Rutin/kg body weight), T2DM control group (mice with daily administration of normal saline), T2DM-Rutin100 group (mice with daily administration of 200 mg Rutin/kg body weight), T2DM-Rutin200 group (mice with daily administration of 200 mg Rutin/kg body weight). Four weeks after rutin treatment, fresh fecal samples were collected under a sterile environment and frozen at − 80 °C for subsequent analysis. Serum samples were collected and centrifuged at 3000 rpm for 15 min at 4 °C. Colon tissues and intestinal contents were collected and frozen in liquid nitrogen and stored at − 80 °C. All animal experiments were approved by the Wenzhou University Animal Care and Use Committee (Wenzhou, China) with approval number WZU-2020-010. All methods were performed in accordance with the relevant guidelines and regulations. This study is reported in accordance with ARRIVE guidelines (Animal Research: Reporting of in vivo Experiments; https://arriveguidelines.org).
## Biochemical analysis
Commercial detection kits (Jiancheng Bioengineering Institute, Nanjing, China) were used to measure total serum cholesterol (TC) (Cat. A111-1-1), triglyceride (TG) (Cat. A110-1-1), low-density lipoprotein cholesterol (LDL-C) (Cat. A113-1-1) and high-density lipoprotein cholesterol (HDL-C) (Cat. A112-1-1). Commercial detection kits (MultiSciences Biotech Co., Ltd., Hangzhou, China) were used to measure serum TNF-α (Cat. EK$\frac{282}{4}$-01) and IL-6 (Cat. 70-EK$\frac{206}{3}$-96) levels. The fasting serum insulin levels were measured using a Mouse INS(Insulin) ELISA Kit (Cat. D721159-0096, Sangon Biotech Co., Ltd., Shanghai, China).
## Haematoxylin & Eosin (H&E) staining
Colon tissues were fixed with $4\%$ paraformaldehyde for 48 h and all samples were dehydrated with ethanol and embedded in paraffin wax. Tissues were sliced into 5 μm-thick Sections (3–5 sections/specimen) and stained with H&E. Samples were sealed with neutral resin and observed under optical microscope (× 400 magnification) for evaluation. The histopathological score of colonic lesions was measured using the following parameters: the degree of inflammation, crypt damage, number of vacuoles and arrangement of villi. The final pathological scores were described as previously reported34.
## Immunohistochemistry
For the immunohistochemistry assays, the colon tissues were dissected, fixed in $4\%$ paraformaldehyde for 12 h at 4 °C, dehydrated overnight with $30\%$ sucrose at 4 °C, embedded in optimal cutting temperature compound and immediately frozen at − 80 °C. Samples sectioned at a thickness of 10 μm were washed with PBS and permeabilization solution with $0.5\%$ Triton X-100 in turn. Samples were then blocked in $3\%$ horse serum in PBS for 30 min and incubated sequentially with the following primary antibodies: anti-collagen I (Cat. ab260043, Abcam Ltd., UK). After incubation with corresponding secondary antibodies (Cat. A11006, Invitrogen, Carlsbad, CA, USA) at room temperature for 1 h, sample sections were treated with a peroxidase substrate DAB kit (Cat. ab64238, Abcam Ltd., UK) and counterstained with hematoxylin.
## 16S rRNA gene sequencing
Total bacterial genomic DNA of fecal samples was extracted using a rapid DNA spin extraction kit (Cat. 117033600, MP Biomedicals, Santa Ana, CA, USA) in accordance with the manufacturer’s instructions. The DNA content was then detected by agarose gel electrophoresis and Nanodrop method (Thermo Scientific, NC2000). The DNA was amplified with specific bacterial primers targeting the 16S rRNA gene containing the V3-V4 region using universal primers 338F (5’-ACTCCTACGGGAGGCAGCA-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’). PCR amplification was purified using Agencourt AMPure Beads (Beckman Coulter, Indianapolis, IN). The PCR amplification products were quantified using the fluorescence reagent Quant-iT PicoGreen dsDNA Assay Kit (Cat. P11496, Invitrogen, Carlsand, CA, USA) and a quantitative microplate reader (BioTek, FLx800). Sequencing was carried out using the Illumina MiSeq platform and MiSeq-pe250 in Shanghai Pesino Biotechnology Co., Ltd (Shanghai, China). All sample sequences were clustered according to the distance between the sequences using Uparse software (v7. 0. 1001), and were divided into operational classification units (OTUs) by plotting the relationship graph between the changes in OTUs and the clustering similarity value. A $97\%$ similarity value was selected for OTU analysis and taxonomic analysis. QIIME 2.0 was then applied to calculate the α-diversity index, including the Chao1 index, Shannon index and Simpson index. UniFrac distance measurement and principal coordinate analysis (PCoA) were selected for β-diversity analysis of the gut microbiota structure, and the gplots package from R software (R 3.4.0) was used for heatmap analysis. Linear discriminant analysis (LDA) of the effect size (LEfSe) was performed with LEfSe software (Version 1.0) and applied to calculate the OTU abundance to determine the differences between groups. Heatmap analysis was performed using R Statistical Software (R version 3.4.0: Foundation for Statistical Computing, Vienna, Austria).
## Statistical analysis
First, the normality of the distribution of dates was tested using the Shapiro–Wilk normality test. Second, statistical comparisons of different groups were evaluated by unpaired Student's t test or one-way ANOVA followed by Tukey’s multiple comparisons. If the data were nonnormally distributed, the Kruskal–Wallis test followed by the Mann–Whitney U test was used. Spearman’s correlation analysis was performed using R Statistical Software (R version 3.4.0: Foundation for Statistical Computing, Vienna, Austria)35, and a clustering heatmap of correlation coefficients was calculated by Ward's hierarchy. GraphPad Prism 6.00 (GraphPad Prism Software, San Diego, California, USA, www.graphpad.com) software was used for statis-tical analysis. All values were expressed as the means ± standard deviation (SD). A value of $p \leq 0.05$ was considered statistically significant.
## Rutin improved glucose and lipids metabolism in diabetic mice
To investigate the effect of rutin on glucose and lipids in diabetic mice, the STZ-induced T1DM mouse model and HFD/STZ-induced T2DM mouse model were successfully established, followed by administration of 100 or 200 mg rutin per kg body weight for 4 weeks. The metabolic performances of diabetic mice showed that T1DM mice featured decreased body weights and HDL-C levels, increased fasting serum glucose, increased concentrations of TC, TG, and LDL-C, increased secretion of proinflammatory cytokines TNF-α and IL-6, and less secretion of fasting insulin (Fig. 1, Supplementary Table 1) compared with normal mice. Similar metabolic performances occurred in T2DM mice except for fasting serum insulin, which was upregulated in T2DM mice (Fig. 2, Supplementary Table 2). However, rutin alleviated the symptoms that are typical disorders related to glucose and lipid metabolism in T1DM and T2DM mice. Collectively, these observations suggest that rutin could effectively improve glucose and lipids metabolism in diabetic mice. Figure 1Effects of rutin on serum glucose and lipids in STZ-induced diabetic mice. ( A) *Fasting serum* glucose. ( B–I) Serum TC, TG, LDL-C, HDL-C, TNF-α, IL-6, and insulin levels. Values are means ± SD ($$n = 5$$): *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$Figure 2Effects of rutin on serum glucose and lipids in HFD/STZ-induced diabetic mice. ( A) *Fasting serum* glucose. ( B–I) Serum TC, TG, LDL-C, HDL-C, TNF-α, IL-6, and insulin levels. Values are means ± SD ($$n = 5$$): *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## Rutin alleviated colon lesions in diabetic mice
To investigate the effect of rutin on colonic lesions in diabetic mice, colon tissues were stained with H&E. Figure 3 and Supplementary Table 3 show that the villi of colon tissues in the T1DM and T2DM groups were arranged in a disorderly manner and were easy to break, along with many vacuolar changes and damaged crypts, compared to those in the ND group. Treatment with rutin exhibited significant protection against diabetes-related colon lesions. In addition, the collagen I protein level was increased in diabetic mice, which was reversed by rutin treatment. These results indicated that rutin could ameliorate the progression of intestinal fibrosis diseases in diabetic mice. Furthermore, the greater the amount of rutin used, the better the colon lesions were improved. Figure 3Rutin improved colon lesions in diabetic mice. ( A) H&E staining of colon tissues in ND, T1DM, T1DM-Rutin100 and T1DM-Rutin200 mouse groups (scale bar: 20 μm). Red arrows: crypts; black arrows: villi. ( B) Histopathological scores of colon tissues in T1DM mice using H&E staining. ( C) H&E staining of colon tissues in ND, T2DM, T2DM-Rutin100 and T2DM-Rutin200 groups. Red arrows: vacuole; black arrows: villi (scale bar: 20 μm). ( D) Histopathological scores of colon tissues in T2DM mice by H&E staining. ( E) Immunohistochemistry indicated that collagen I protein levels were increased in diabetic mice, which was recovered by rutin treatment (scale bar: 20 μm). Values are means ± SD ($$n = 5$$): *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## Rutin changed the overall community structure of the gut microbiota in diabetic mice
The V3-V4 region of bacterial 16S rRNA in fecal samples from the experimental mice was classified and sequenced to explore the effects of rutin on the overall community structure of gut microbiota in diabetic mice. For the experiment with T1DM mice, a total of 2,672,620 original sequences were obtained and an average of 127,267 ± 17,666 sequences were acquired for each sample. After quality screening, 2,119,396 sequences were generated with an average of 100,923 ± 13,879 sequences for each fecal sample. The abundance grade curves and species accumulation curves indicated that most of the gut microbial diversity was captured with the current sequencing depth in each sample (Fig. S1). Based on the α-diversity analysis, the results showed that the Chao1, Shannon and Simpson indexes reflected both the abundance and diversity of microbial communities. The Chao1 index was significantly downregulated in T1DM mice. This change was reversed by rutin treatment (Fig. 4A and Supplementary Table 4). The Simpson and Shannon indexes were not significantly different among the groups (Fig. S2 and Supplementary Table 5). According to the principal coordinate analysis (PCoA) of weighted UniFrac and unweighted UniFrac based on the β-diversity analysis, it was revealed that gut microbiota in the ND group, T1DM group, T1DM-Rutin100 group and T1DM-Rutin200 group were clearly distinguished, suggesting that there were different microbiota structures among those groups (Fig. 4B, Fig. S3, Supplementary Table 6). In T2DM mice, a total of 2,017,480 original sequences were obtained, and an average of 106,183 ± 6479 sequences were acquired for each sample. A total of 1,562,265 effective sequences were generated with an average of 82,224 ± 499 sequences for each sample after quality screening. The abundance grade curves and species accumulation curves were drawn as shown in Fig. S4. The α-diversity analysis showed that the Chao1 index was significantly decreased in the T2DM group, indicating lower gut microbiota diversity in this group, which was reversed by rutin (Fig. 4C and Supplementary Table 7). No significant difference in the Simpson and Shannon indexes were observed among the different groups (Fig. S5 and Supplementary Table 8). The β-diversity calculated by unweighted UniFrac distances showed that the gut microbiota was clearly distinguished in the ND, T2DM, T2DM-Rutin100, and T2DM-Rutin200 groups, suggesting different microbiota among those four groups (Fig. 4D, Fig. S6, and Supplementary Table 9). The results showed that rutin changed the overall community structure of the gut microbiota in both T1DM and T2DM mice. Figure 4Rutin altered the gut microbiota diversity in T1DM and T2DM mice. ( A) The Chao 1 index in the α-diversity analysis in T1DM mice. ( B) β-diversity analysis in the ND, T1DM, T1DM-Rutin100, and T1DM-Rutin200 groups. ( C) The Chao 1 index in α-diversity analysis in T2DM mice. ( D) The β-diversity analysis in the ND, T2DM, T2DM-Rutin150, and T2DM-Rutin200 groups. One-way ANOVA was used for statistical comparisons of different groups. Values are means ± SD ($$n = 5$$): *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$
## Rutin regulated the composition of the gut microbiota in diabetic mice
To study whether rutin regulates the composition of the gut microbiota in diabetic mice. We performed 16S rRNA sequencing for each mouse fecal sample. Firmicutes and Bacteroidetes at phylum level accounted for approximately $82.5\%$ in the T1DM group (Fig. 5A and Supplementary table 10). Our data showed that the Bacteroidetes/Firmicutes (B/F) ratio in T1DM group was significantly lower than that in the ND group, but the B/F ratios in both the T1DM-Rutin100 group and T1DM-Rutin200 were higher than in the T1DM group. Compared to ND group, the relative abundance of Proteobacteria was significantly increased in the T1DM group, which was significantly reversed by rutin (Fig. 5B). LEfSe analysis was conducted to identify differentially abundant bacterial taxa among the 4 groups (LDA score > 2) (Fig. 5C and Supplementary Table 11). T1DM showed selective enrichment in 11 communities including f_Enterobacteriaceae, g_Helicobacter, g_Escherichia, and g_Mycoplasma, while T1DM-Rutin100 showed selective enrichment in 4 communities, including f_Bacteroidaceae, g_Bacteroides, o_Rickettsiales and g_Anaerotruncus. Moreover, two microbial communities were enriched in the TIDM-Rutin200 group, including o_Bacteroidales and f_Rikenellaceae. At the order level, the relative abundances of Lactobacillales, Coriobacteriales, and Campylobacterales were increased in the T1DM group, but decreased after rutin treatment. Furthermore, the relative abundances of Bacteroidales and Verrucomicrobiales were increased by rutin (Fig. S7 and Supplementary Table 12). The heatmap clustering analysis showed that compared to the ND group, the relative abundances of Dorea, Coprococcus, Enterobacter, Flexispira, Helicobacter, Proteus, Streptococcus, Coprobacillus, *Escherichia and* Lactococcus were increased in the T1DM group, which was reversed by rutin. Furthermore, rutin increased the relative abundances of Odoribacter, Rikenella, Staphylococcus, Akkermansia, Alisipes, Lactobacillus, Prevotella and Roseburia. These bacteria were negatively associated with diabetes (Fig. 5D). Additionally, Spearman’s correlation analysis showed that the gut microbiota was correlated with metabolic parameters related to diabetes (Fig. 5E and Supplementary Table 13). Serum glucose was positively correlated with the relative abundance of Flexispira and Helicobacter. Serum TC, TG, LDL-C, TNF-α, and IL-6 levels were positively correlated with the relative abundances of Enterobacteriaceae, Lactobacillus, and Lactococcus. The abundance of Sutterella was negatively associated with insulin. Figure 5Effects of rutin on the gut microbiota composition in T1DM mice. ( A) The relative abundances of gut microbiota constituents at the phylum level. ( B) Bacteroidetes/Firmicutes in feces of mice and relative abundance of Proteobacteria at the phylum level. ( C) LEfSe analysis of gut microbiota in the ND, T1DM, T1DM-Rutin100, and T1DM-Rutin200 groups. LDA scores > 2 and $P \leq 0.$ 05 are the taxonomic groups with significant differences between groups. ( D) Heatmap of the relative abundance of gut microbiota at the genus level. Each column in the plot represents a sample, and each row represents the community structure. The color represents the relative abundance of the species. The heatmap was created using Sangerbox 3.0 (http://vip.sangerbox.com/). ( E): Spearman’s correlation analysis of gut microbiota and metabolic parameters. Values are means ± SD ($$n = 5$$): *$p \leq 0.05$, **$p \leq 0.01.$
In the experiments with T2DM mice, Firmicutes and Bacteroidetes at the phylum level accounted for approximately $83\%$ in the T2DM group (Fig. 6A and Supplementary table 14). The B/F ratio in T2DM group was lower than that in the ND group, however, rutin significantly reversed this change. The relative abundance of Proteobacteria in the T2DM group was significantly higher than ND group, and rutin treatment significantly decreased the relative abundance of Proteobacteria (Fig. 6B). Furthermore, the LEfSe analysis showed that T2DM had selective enrichment in 14 communities, including f_Desulfovibrionaceae, o_Clostridiales, c_Erysipelotrichi, g_Bilophila and p_Firmicutes, while the T2DM-Rutin100 group had selective enrichment in 14 communities including f_Lachnospiraceae, g_Blautia, g_Anoxybacillus and g_Coprobacillus. However, 19 communities were selectively enriched in the T2DM-Rutin200 group, such as g_Bacteroides, g_Oscillospira, p_Verrucomicrobia, g_Akkermansia and g_Butyricimonas (Fig. 6C and Supplementary Table 15). Rutin decreased the upregulated relative abundances of Erysipelotrichales, Lactobacillales and Bacillales in T2DM mice at the order level but increased those of Bacteroidales, Burkholderiales and Coriobacteriales (Fig. S8). The relative abundances of Helicobacter, Allobaculum, Parabacteroides, Mucispirillum, Enterococcus, Lactococcus, Proteus, *Staphylococcus and* Enterobacter were upregulated in T2DM mice at the genus level, which were reversed by rutin treatment. Additionally, rutin increased the relative abundances of Klebsiella, Bacteroides, Lactobacillus, Akkermansia, Pseudoramibacter, Eubacterium, Roseburia, Butyricimonas and Alisipes (Fig. 6D and Supplementary Table 16). The serum glucose level was positively correlated with the relative abundance of Mogibacteriaceae. The relative abundances of Clostridiales, Mogibacteriaceae, and Lachnospiraceae were positively correlated with LDL-C, and IL-6 levels, while those of Mogibacteriaceae and Lachnospiraceae were negatively associated with HDL-C (Fig. 6E and Supplementary Table 17). These findings indicated that rutin could regulate the composition of the gut microbiota in both T1DM and T2DM mice. Figure 6Effects of rutin on the gut microbiota composition in T2DM mice. ( A) The relative abundances of gut microbiota constituents at the phylum level. ( B) Bacteroidetes/Firmicutes in feces of mice and relative abundance of Proteobacteria at the phylum level. ( C) LEfSe analysis of gut microbiota in the ND, T2DM, T2DM-Rutin100, and T2DM-Rutin200 groups, LDA scores > 2 and $P \leq 0.$ 05 are the taxonomic groups with significant differences between groups. ( D) Heatmap of the relative abundance of gut microbiota at the genus level. Each column in the plot represents a sample, and each row represents the community structure. The color represents the relative abundance of the species. The heatmap was created using Sangerbox 3.0 (http://vip.sangerbox.com/). ( E): Spearman’s correlation analysis of gut microbiota and metabolic parameters. Values are means ± SD ($$n = 5$$): *$p \leq 0.05$, **$p \leq 0.01.$
## Discussion
Accumulating evidence suggests that the gut microbiota plays an important role in regulating obesity, diabetes, colon cancer and nonalcoholic fatty liver disease8. Rutin is a type of flavonoid glycoside enriched in tea and buckwheat that has multiple biological activities, such as antioxidant, anti-inflammatory, antidiabetic and free radical scavenging activities. A previous study suggested that the combination of rutin and vitamin C can effectively improve oxidative stress and lower serum glucose in patients with T2DM22. Rutin can not only prevent STZ-induced oxidative damage, but also protect islet β cells to increase insulin secretion and therefore lower serum glucose36. However, whether rutin alleviates colon lesions and regulates the gut microbiota in diabetic mice is entirely unknown. Our study found that rutin could significantly improve the levels of serum glucose, TC, TG, and LDL-C in diabetic mice and increase HDL-C levels. Chronic inflammation caused by high serum glucose and excessive visceral fat accumulation could increase the risk of many different cancers, such as colon cancer and liver cancer37. We found that the villi of colon tissues were arranged disorderly and easy to break, along with many vacuolar changes and damaged crypts in diabetic mice, this phenomenon is might strongly associated with the loss of tight junction proteins, which are the expression levels of tight junction proteins in patients with diabetes are abnormal, which play an important role in maintaining the structure of colon38, however, whether rutin affects the expression levels of tight junction proteins in colon tissue of diabetic mice, we will further explore this hypothesis in the future. Disorders of the gut microbiota were reported to be closely related to the occurrence and development of colonic diseases39,40. We determined the effects of rutin on gut microbiota dysbiosis and colon lesions in diabetic mice and found that rutin significantly alleviated colon lesions in T1DM and T2DM mice (Fig. S9).
It is known that the gut microbiota condition affects the pathophysiological states of metabolic diseases. The abnormal gut microbiota in diabetic mice might be related to the occurrence of obesity, diabetes, inflammation and pathological microenvironments41,42. Reduced bacterial function diversity and low community stability were characterized in diabetic mice43. We hypothesized that the antidiabetic effects of rutin and its ability to repair colon lesion damage in diabetic mice might be closely related to the regulation of the gut microbiota. In our study, the abundance grade curves and species accumulation curves indicated that most of the gut microbial diversity was captured with the current sequencing depth in each sample. However, the rank abundance curve estimate for diversity might not be fully representative of reality. In addition, Chao1 and Shannon indexes were higher in the rutin treatment groups than in the T1DM, or T2DM group. Diversity analysis showed differences in gut microbiota distribution among the groups. However, there was still a difference between the intestinal flora after rutin treatment and that of nondiabetic mice. It is possible that the dosage of rutin is not enough to achieve the therapeutic effect, which might have an auxiliary therapeutic effect. The above results showed that rutin significantly changed the structure of the intestinal flora in diabetic mice, indicating a novel pathway through which rutin alleviates diabetes-related symptoms. It has been reported that obesity and metabolic syndromes are associated with changes in gut microbiota composition, including a decreased Bacteroidetes/Firmicutes (B/F) ratio and relative abundance of Proteobacteria44. It was also found that the abundance of Firmicutes and Bacteroidetes in diabetic mice was related to hyperglycemia and inflammatory state45. In accordance with previous studies, our results showed that rutin effectively increased Bacteroidetes and decreased Firmicutes, along with an increased ratio of Bacteroidetes to Firmicutes. The increase in the abundance of Proteobacteria could promote the endotoxin content, thus leading to the occurrence and development of chronic inflammatory diseases46. We also demonstrated that rutin reversed the relative abundance of Proteobacteria in diabetic mice. Furthermore, previous studies suggested that Erysipelotrichales were correlated with the development of obesity, systemic inflammation and metabolic diseases47,48. In our study, the Erysipelotrichaeae abundance in T2DM mice was much higher than that in the control ND group, which was reversed by rutin. Cani et al. reported a positive relationship between *Escherichia and* the occurrence and development of diabetes and obesity49, whereas rutin reduced the presence of Helicobacter, Enterobacter, and *Escherichia in* diabetic mice. The increase in the F/B ratio was related to obesity induced by a high-fat diet in mice50. Rutin could increase the B/F ratio and reduce the content of Proteobacteria, which was closely related to inflammation.
It was reported that Akkermansia was a mucin-degrading bacterium and improved insulin resistance51. Our data showed that rutin increased Akkermansia abundance. The relative abundances of some bacteria, such as Bilophila and Mucispirillum, showed a significant positive correlation with the development of diabetes47,52. Therefore, the protective effect of rutin on diabetic symptoms might be due to the decrease in Mucispirillum, Helicobacter, Enterobacter, Proteus, and Streptococcus abundances. In addition, rutin increases the relative abundances of bacteria, such as Odoribacter, Rikenella, Akkermansia, Alistipes and Roseburia, which impairs the development of diabetes. Spearman correlation analysis showed that key communities such as Enterobacteriaceae, Lactobacillus and Sutterella were related to the main metabolic parameters of diabetic mice. It is worth noting that blood lipids and inflammatory factors in diabetes mice are positively correlated with Lactobacillus, it is possible that the increase of Lactobacillus abundance can protect the damage of inflammation to the body, and may play an important role in immune defenses. Sutterella has anti-inflammatory properties and is negatively correlated with the content of IL-653,54. Therefore, the reduction in inflammation in diabetic mice treated with rutin might be due to the increase in Sutterella abundance, which was positively related to the decrease in serum IL-6, suggesting that the effect of rutin on the flora might be related to metabolic disorders. Several studies have demonstrated that short-chain fatty acids (SCFAs) are the main metabolites of gut microbiota that play a critical role in regulating metabolic syndrome and maintaining energy homeostasis and host insulin sensitivity55,56. Short chain fatty acids such as acetic acid, propionic acid and butyric acid play an important role in improving chronic inflammatory diseases and promoting colon cell health57. Alisipes, Roseburia, Rikenella and Odoribactor are bacteria that produce butyric acid, propionic acid and butyric acid in the intestine58,59. SCFAs produced by these microorganisms can provide energy for the host, improve the acidic environment in the colon, inhibit the production of inflammatory factors, and finally repair mucositis. Our study found that rutin significantly increased the abundances of Alisipes, Odoribacter, Roseburia, and Rikenella, which might lead to increased production of SCFAs that act beneficially on colon villi in our experimental diabetic mice. The recovery of colon damage by rutin might therefore be due to the enrichment of SCFA-producing bacteria. Targeted recovery of these SCFA products will provide a new therapeutic approach for diabetes patients.
## Conclusions
In conclusion, rutin exhibited an antidiabetic effect and improved colon lesions in diabetic mice, possibly by regulating the gut microbiota community structure and composition, which might be a potential new strategy for treating diabetic patients, protecting intestinal health and/or preventing colon cancer carcinogenesis. Further study is needed to reveal the detailed mechanisms through which rutin regulates the gut microbiota in diabetic mice.
## Supplementary Information
Supplementary Figures. Supplementary Tables. The online version contains supplementary material available at 10.1038/s41598-023-31647-z.
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|
---
title: 'Vision-related quality of life after surgery for vitreoretinal disorders in
a Mexican population: an observational study'
authors:
- Ilse Sarahí Márquez-Vergara
- Geovanni Jassiel Ríos-Nequis
- Ingrid Yazmín Pita-Ortíz
- Héctor Javier Pérez-Cano
- Selma Alin Somilleda-Ventura
journal: Scientific Reports
year: 2023
pmcid: PMC10039874
doi: 10.1038/s41598-023-32152-z
license: CC BY 4.0
---
# Vision-related quality of life after surgery for vitreoretinal disorders in a Mexican population: an observational study
## Abstract
Visual-related quality of life in retinal diseases has not been explored in the Mexican population, so the study aims to identify it in patients undergoing surgery due to advanced diabetic retinopathy, rhegmatogenous retinal detachment, and other causes of vitrectomy; the Visual Function Quality-25 questionnaire was applied to 76 patients, pre-and postoperative. It was divided into 10 domains and interpreted according to the National Eye Institute scores, where the highest value was the best visual function. Student's t-test for related samples and Wilcoxon’s t-test were used to compare each domain between measurements, and Pearson’s R test to correlate the total score of age and quality of life; a p value < 0.05 was considered significant. Diabetic retinopathy patients showed an improvement 1 and 3 months after surgery in all domains; in rhegmatogenous retinal detachment, there was an improvement observed up to 3 months, while a decrease in ocular pain was observed in other causes of vitrectomy. Differences found in all the quality-of-life scores were not statistical, but clinically significant. The study shows that visual-related quality of life domains improves after vitrectomy; the inclusion of this analysis might be considered relevant within the parameters of surgical success of the most prevalent vitreoretinal diseases.
## Introduction
The World Health Organization (WHO) defines quality of life as the perception that an individual has in the context of their culture, the values in which they live, their expectations, norms, and concerns1. Consists of both objective components and material circumstances of life, for example, illness, pain, disability, and other factors added to subjective components that are mainly the degree of satisfaction and the perception of living conditions2.
Currently, it is not enough to assess the value of visual acuity and the visual field as surgical success, but the quality of life perceived after eye surgery must be included. There are some instruments to assess the quality of life and visual function, including The Visual Disability Assessment (VF-14)3, the Activities of Daily Vision Scale (AVDS)4, the Quality of Life Questionnaire (QOLQ)5, Visual Function Quality-39 (VFQ-39)6 and Visual Function Quality-25 (VFQ-25)7; the latter has demonstrated validity and reliability for different populations in distinct languages in the 25-item adaptation derived from the original 51-item8.
The questionnaire measures the influence of visual disability and visual symptoms in physical and mental health domains and those related to visual functioning, problems involving vision, or the patient’s perception of their vision condition9. It has been evaluated in Spanish10, for Latino patients living in the United States9, Cubans8, and Colombians11, where internal reliability coefficients are lower than those necessary in the items of general health and driving (so they are eliminated from the questionnaire in its Spanish version)8,10.
It has been translated and adapted to the cultural environment and validated in Serbian, Turkish, Chinese, Japanese, Greek, French, Italian, Polish, Portuguese, and Spanish9,10. Among the retinal pathologies in which it has been evaluated, Okamoto and his group12 report a higher preoperative and postoperative score for macular hole and epiretinal membrane and retinal detachment. The VQF-25 has become a tool that has been used to assess the quality of life in subjects with cataracts13, glaucoma14, age-related macular degeneration15, and diabetic retinopathy16, among others.
However, the VFQ-25 questionnaire in its Spanish version has not been applied to the most prevalent retinal pathologies in Mexico. Our objective was to identify the quality-of-life score related to vision in patients with vitreoretinal diseases who undergo retinal surgery in a reference ophthalmological hospital and determine which are the domains of quality of life with higher impact after surgery.
## Methods
It is an observational, prospective, comparative, and longitudinal study. Seventy-six patients between 18 and 80 years, who had diabetic retinopathy, rhegmatogenous retinal detachment, and other vitreoretinal pathologies (macular hole, epiretinal membrane, and causes of vitreous hemorrhage) in one eye, were included by non-probabilistic sampling determined by time; patients with other pathologies such as cataracts or glaucoma that compromises the visual function were excluded. Patients were followed-up after a vitrectomy at 1 and 3 months in person or by telephone between May and August 2019; they agreed to participate, understood, and signed the informed consent. This study followed the principles established in the Declaration of Helsinki and was approved by the Institutional Review Board of the hospital where it took place. The data was handled and analyzed with strict confidentiality and according to good practices.
The demographic variables considered were age and sex, and the best-corrected visual acuity (BCVA) was recorded before the intervention, which was converted to the minimum angle of resolution (logMAR) for statistical calculations. Within the approximations, finger count was considered as $\frac{20}{2000}$, hand movement = $\frac{20}{4000}$, and light perception = $\frac{20}{8000.}$
The researchers applied the Visual Function Quality-25 test; the questionnaire was divided into 10 domains: [1] general vision, [2] eye pain, [3] close activities, [4] distance activities, [5] social functioning, [6] mental health, [7] role limitations, [8] dependence, [9] color vision, and [10] peripheral vision. The Spanish version excludes the general health and driving items from the algorithm, for which there are 23 items8; its application time was less than 15 min. The researchers explained the questionnaire to the patients at the beginning of the application and provided additional support when necessary.
For the calculation and interpretation of the results, the National Eye Institute (NEI) scoring algorithm was used17, which considers the subscales with a score from 0 to 100, where 100 is the highest function. The total score was the mean of all items; in case an answer to a question was not related to vision or did not apply to the context, it was excluded from the overall score.
The Kolmogorov–Smirnov statistical test evaluated the data distribution; only items 3, 7, and 8 in the diabetic retinopathy group did not have a normal distribution. The means of each domain with missing data imputed by the last observation carried forward (LOCF) were compared using Student’s t-test for related samples and Wilcoxon’s t-test, as appropriate, and the variable quality of life total score between the different types of tamponades was calculated using Kruskal–Wallis. Also, the correlation coefficient of the total score concerning age was obtained by Pearson’s R test, which considered an r < 0.4 as a weak correlation, r ≥ 0.4–0.7 as moderate, and r > 0.7 as a strong correlation; a p value < 0.05 was considered significant. Data were stored and analyzed in GraphPad Prism version 8.0 for Mac.
## Ethics approval
Approval was obtained from the ethics committee of Fundación Hospital Nuestra Señora de la Luz IAP. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.
## Consent to participate
Informed consent was obtained from all individual participants included in the study.
## Results
Of the presurgical sample, the mean age was 50.4 ± standard deviation (S.D.) 10.19 years, 44 men ($57.89\%$) and 32 women ($42.11\%$); $54\%$ of the patients undergoing surgery for advanced diabetic retinopathy ($$n = 41$$), $30\%$ for rhegmatogenous retinal detachment ($$n = 23$$), and $12\%$ for other causes of vitrectomy ($$n = 12$$).
Forty-one patients diagnosed with advanced diabetic retinopathy aged 53.27 years ± 9.06; $61\%$ ($$n = 25$$) were men. For this group, the preoperative quality-of-life total score was 43.01 ± 18.19 points, after 1 month 57.62 ± 20.95, and at 3 months 59.17 ± 19.11 (Table 1); the rest of the items also showed improvement in quality of life at 1 month, although the subjects evaluated reported higher ocular pain after surgery than before it ($p \leq 0.001$). This statistical difference persisted when the pre-surgical evaluation was compared with the 3 months follow-up after the intervention, except in the general vision and social performance items, although in both cases quality of life improved. Table 1Comparison of quality-of-life related variables of pre-and post-surgical evaluations, in advanced diabetic retinopathy. DomainPreOp versus 1-month*PreOp versus 3-months*One month versus 3 months*Mean (SD)pMean (SD)pMean (SD)pTotal score43.00 (18.18)–57.51 (20.94) < 0.00143.00 (18.18)–59.17 (19.11)0.00157.51 (20.94)–59.17 (19.11)0.60General vision41.95 (14.70)–65.52 (23.23)0.0341.95 (14.70)–63.95 (18.15)0.4165.52 (23.23)–63.95 (18.15)0.16Ocular pain85.67 (15.94)–73.27 (23.08) < 0.00185.67 (15.94)–81.57 (17.86) < 0.00173.27 (23.08)–81.57 (17.86)0.19Near vision**16.67 (8.33–45.83)–50 (25–79.17) < 0.00116.67 (8.33–45.83)–50 (33.33–75)0.00150 (25–79.17)–50 (33.33–75)0.20Distance vision39.32 (27.99)–52.68 (29.24) < 0.00139.32 (27.99)–51.09 (29.32)0.00152.68 (29.24)–51.09 (29.32)0.91Social functionality43.90 (32.37)–64.65 (31.53)0.0443.90 (32.37)–63.81 (29.43)0.1264.65 (31.53)–63.81 (29.43)0.94Mental health32.93 (26.33)–44.18 (26.51)0.00832.93 (26.33)–43.42 (29.71)0.0144.18 (26.51)–43.42 (29.71)0.29Role limitations**25 (0–50)–50 (25–75)0.0225 (0–50)–50 (25–75)0.0150 (25–75)–50 (25–75)0.30Dependence**25 (0–50)–50 (16.67–83.33)0.00225 (0–50)–50 (25–75) < 0.00150 (16.67–83.33)–50 (25–75)0.74Color vision75 (34.46)–81.89 (26.64)0.00175 (34.46)–77.63 (23.41) < 0.00181.89 (26.64)–77.63 (23.41)0.73Peripheral vision46.34 (22.05)–65.52 (29.43) < 0.00146.34 (22.05)–69.74 (24.41) < 0.00165.52 (29.43)–69.74 (24.41)0.74PreOp Preoperative.*Paired student’s t-test.**Wilcoxon’s t test.
Neither of the cases had a significant difference in the scores obtained when the 1-month and 3 months after surgery; there was also no difference between sex and the quality-of-life total score before surgery ($$p \leq 0.27$$), at 1 month ($$p \leq 0.93$$), and 3 months ($$p \leq 0.18$$). Regarding the type of tamponade, there was a higher quality of life pre-surgical score when the tamponade was air/gas versus silicone ($$p \leq 0.04$$). The correlation between age and the pre-surgical total score had a weak positive linear correlation ($r = 0.15$, $$p \leq 0.34$$), but this changed when evaluating the total score 1 month ($r = 0.43$) and 3 months ($r = 0.47$) after surgery, where existed moderate positive correlations with statistical significance ($p \leq 0.05$, Figs. 1, 2).Figure 1Moderate positive linear correlation between age and quality-of-life total score in diabetic retinopathy subjects, 1 month after undergoing vitrectomy. Figure 2Moderate positive linear correlation between age and quality-of-life total score in diabetic retinopathy subjects, 3 months after undergoing vitrectomy.
In patients undergoing surgery for rhegmatogenous retinal detachment, the mean age was 51.43 ± 12.31 years; $61\%$ ($$n = 14$$) were men. The evaluation included 15 right eyes and eight left eyes. The higher quality of life scores were mental health and role difficulty items 1 month after surgery compared to the pre-surgical evaluation ($p \leq 0.05$, Table 2); however, the difference did not appear between the two post-surgical total scores. Five items showed significant improvement 3 months after surgery, and only the ocular pain variable had a higher score before surgery. There was also no change at 1 month and 3 months after surgery in the pre-surgical total score concerning patient sex or the type of tamponade ($p \leq 0.05$). In the correlation analysis, existed negative linear relations between age and the total score at 1 month (r = -0.44) and 3 months (r = -0.27), although there was no statistical difference ($p \leq 0.05$).Table 2Comparison of quality-of-life related variables of pre-and post-surgical evaluations, in rhegmatogenous retinal detachment. DomainPreOp versus 1-month*PreOp versus 3-months*One month versus 3 months*Mean (SD)pMean (SD)pMean (SD)pTotal score58.04 (18.30)–61.65 (17.77)0.0258.04 (18.30)–67.88 (11.01)0.0661.65 (17.77)–67.88 (11.01)0.99General vision46.09 (12.70)–66.67 [10]0.0946.09 (12.70)–65.71 (9.76)0.0466.67 [10]–65.71 (9.76)0.99Ocular pain85.76 (20.54)–79.16 (20.73)0.5985.76 (20.54)–82.14 (14.17)0.0479.16 (20.73)–82.14 (14.17)0.21Near vision48.19 (28.42)–49.07 (33.18)0.1148.19 (28.42)–65.48 (12.20)0.1949.07 (33.18)–65.48 (12.20)0.74Distance vision59.78 (34.23)–62.96 (34.45)0.6459.78 (34.23)0.1562.96 (34.45)–0.39Social functionality67.93 (33.03)–63.89 (36.14)0.3567.93 (33.03)–65.48 (27.92)0.1863.89 (36.14)–65.48 (27.92)0.39Mental health44.84 (25.95)–51.39 (220.5)0.0244.84 (25.95)–76.78 (34.93)0.00251.39 (220.5)–76.78 (34.93)0.69Role limitations25 (0–50)–62.50 (12.50–87.50)0.0225 (0–50)–75 (37.50–87.50)0.0262.50 (12.50–87.50)–75 (37.50–87.50)0.71Dependence57.61 (35.35)–65.74 (29.30)0.9957.61 (35.35)–70.23 [23]0.4165.74 (29.30)–70.23 [23]0.36Color vision90.21 (19.57)–83.33 (21.65)0.2490.21 (19.57)–89.28 (13.36)0.0383.33 (21.65)–89.28 (13.36)0.69Peripheral vision58.69 (22.12)–75 (33.07)0.1458.69 (22.12)–78.57 (22.49)0.0275 (33.07)–78.57 (22.49)0.52PreOp Preoperative.*Paired student’s t-test.
Regarding other causes of vitrectomy analyzed, the mean age was 53.17 ± 10.12 years; $58.3\%$ ($$n = 7$$) were women. The evaluation included 11 right eyes and one left. The quality-of-life total score was 56.14 ± 10.11 before surgery, 66.49 ± 18.63 at 1 month, and 63.59 ± 24.59 at 3 months. Only the ocular pain variable showed a higher quality of life after surgery with statistical significance when compared with pre-surgical evaluation (Table 3). Quality-of-life items compared with the type of tamponade or patient sex had no difference but a weak positive correlation between age and the pre-surgical total score, not significant ($r = 0.17$, $$p \leq 0.59$$).Table 3Comparison of quality-of-life related variables of pre-and post-surgical evaluations, in other causes of vitrectomy. DomainPreOp versus 1-month*PreOp versus 3-months*One month versus 3 months*Mean (SD)pMean (SD)pMean (SD)pTotal score56.14 (14.44)–66.49 (18.62)0.2756.14 (14.44)–63.59 (24.32)0.4466.49 (18.62)–63.59 (24.32)0.73General vision56.67 (14.35)–54 (12.65)0.1756.67 (14.35)–64.44 (19.43)0.4054 (12.65)–64.44 (19.43)0.91Ocular pain81.25 (17.27)–71.25 (24.33)0.00281.25 (17.27)–66.67 (31.87)0.00871.25 (24.33)–66.67 (31.87)0.67Near vision33.33 (21.90)–58.33 (27.78)0.8733.33 (21.90)–62.03 (25.72)0.6158.33 (27.78)–62.03 (25.72)0.50Distance vision61.11 (19.08)–65.41 (28.87)0.1861.11 (19.08)–59.72 (28.87)0.9965.41 (28.87)–59.72 (28.87)0.17Social functionality63.54 (22.27)–77.50 (22.67)0.1463.54 (22.27)–66.67 (32.47)0.8777.50 (22.67)–66.67 (32.47)0.14Mental health48.44 (23.10)–61.87 (18.97)0.5348.44 (23.10)–51.39 (23.13)0.3361.87 (18.97)–51.39 (23.13)0.18Role limitations41.66 (35.89)–50 (28.87)0.2741.66 (35.89)–58.33 (27.95)0.6150 (28.87)–58.33 (27.95)0.90Dependence54.86 (28.30)–69.17 (30.44)0.6854.86 (28.30)–68.52 (34.80)0.2269.17 (30.44)–68.52 (34.80)0.17Color vision93.75 (15.54)–90 (21.08)0.0593.75 (15.54)–83.33 (27.95)0.5990 (21.08)–83.33 (27.95)0.41Peripheral vision54.17 (20.87)–72.22 (23.20)0.1454.17 (20.87)–63.89 (35.60)0.5872.22 (23.20)–63.89 (35.60)0.77PreOp Preoperative.*Paired student’s t-test.
## Discussion
Our study found an increase in quality of life 1 month after vitrectomy in each group and increased up to 16 points when compared to pre-surgical vision quality versus 3 months after vitrectomy evaluation, in patients with advanced diabetic retinopathy, 9 points in patients with rhegmatogenous retinal detachment, and 6 points in patients undergoing surgery for other reasons. The type of air/gas tamponade showed a better quality of life score compared to those in which silicone was used, this may be because its use is generally limited to the most complex retinal disease cases who perceive a lower quality of life-related to vision; additionally, a correlation was identified between older age and an improvement in the quality-of-life score after surgery, particularly in patients with diabetic retinopathy.
These findings contrast with the reported by Okamoto and his group18, who applied the test to 51 patients diagnosed with diabetic retinopathy before undergoing surgery and 3 months later; the score reported in vitreous hemorrhage was 51.2 to 62.3, tractional retinal detachment 61.1 to 70.3 and excessive macular traction 55.2 to 59.4. The subscales that reported significant improvement after surgery were general vision, near and distance activities, social function, mental health, role difficulty, driving, and peripheral vision. However, no correlation was shown with age, duration of diabetes, HbA1C levels, or fasting glucose. Our study observed that the preoperative score was below the reported by Okamoto (43.00); however, the results after surgery are similar (57.51 and 59.17), indicating a significant improvement in the quality of life of patients undergoing vitrectomy in our country.
The quality-of-life total score in cases of rhegmatogenous retinal detachment has been reported at 80 points in women and 74.7 in men with follow-up at 6 months; according to Smeretschnig, the items with the greatest difference concerning normal controls were general vision, mental health, social functionality, driving, and color vision20. On the other hand, a group reported this difference at 3 months of operated patients versus controls, in near activities, peripheral vision, dependency, and mental health19, whose findings coincide with those of Du and his team21, although the latter applied the questionnaire one day before surgery. We compared the same patients over time and found a significant difference in the composite score, regarding mental health and role difficulty during the first month, while at 3 months these same variables are preserved, general vision showed an improvement, and eye pain, color vision, and peripheral vision scores decreased; it may be possible that the changes reported in each item are higher the more time passes after surgery, but this requires an additional long-term study.
In epiretinal membranes, vitrectomy has significantly improved the components of quality of life, being the most important post-surgical improvement among the pathologies studied12. It mainly impacts 10 subscales except for peripheral vision and general health (mean score 77.9). The results seem to be directly related to the presence and severity of preoperative metamorphopsia, but not visual acuity, contrast sensitivity, or central macular thickness22. On the contrary, Ghazi reports that there is an improvement of the metamorphopsia perception after surgery, and considers that the initial visual acuity correlates with the initial VFQ-25 score; however, in his study, VA does not improve considerably after surgery, and only remote activities, general vision, and the overall score had an improvement23. Matsuoka24 has reported that the greatest improvement-related items at 3 months after surgery are visual capacity, general vision, close activities, role difficulties, and the composite score, while at 12 months it is the improvement of the metamorphopsia-perception, general vision, close activities, distance activities, mental health, role difficulty, and the composite score. In this study, we found no difference in the pre-and post-surgical quality of life for this pathology, which was included in other causes of vitrectomy; the only item with a statistical difference was the eye pain that improved after surgery in the first month. However, further analysis is required to evaluate the quality of life and its correlation with the visual function test.
It is important to consider the quality of life as one of the variables within surgical success that are observed to increase, after surgery for retinal pathologies, which is more noticeable in older patients ($43\%$ in the evolution at 1 month), and in $47\%$ of the cases 3 months after the intervention, which represents a $30\%$ of change concerning the quality of life prior the vitrectomy, according to our findings. The relevance lies in the high prevalence of these diseases in our population and their poor control; to our knowledge, it is the first report of quality of life-related to vision in patients undergoing vitrectomy in Mexico. However, we recognized that it is necessary to generate effective strategies for achieving a complete follow-up in all temporalities of the evaluation, to limit a possible bias along the analysis; this must be taken into account for further studies.
In conclusion, vitrectomy performed in patients with advanced diabetic retinopathy improves the quality-of-life score associated with vision and in each of the items from the first month after surgery, while for rhegmatogenous retinal detachment, the relevant improvement is observed up to 3 months after surgery. Another prospective study with a long-term follow-up that considers vision and sensory tests is suggested, in addition to increasing the sample of patients.
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|
---
title: The endocannabinoid system promotes hepatocyte progenitor cell proliferation
and maturation by modulating cellular energetics
authors:
- Bani Mukhopadhyay
- Kellie Holovac
- Kornel Schuebel
- Partha Mukhopadhyay
- Resat Cinar
- Sindhu Iyer
- Cheryl Marietta
- David Goldman
- George Kunos
journal: Cell Death Discovery
year: 2023
pmcid: PMC10039889
doi: 10.1038/s41420-023-01400-6
license: CC BY 4.0
---
# The endocannabinoid system promotes hepatocyte progenitor cell proliferation and maturation by modulating cellular energetics
## Abstract
The proliferation and differentiation of hepatic progenitor cells (HPCs) drive the homeostatic renewal of the liver under diverse conditions. Liver regeneration is associated with an increase in Axin2+Cnr1+ HPCs, along with a marked increase in the levels of the endocannabinoid anandamide (AEA). But the molecular mechanism linking AEA signaling to HPC proliferation and/or differentiation has not been explored. Here, we show that in vitro exposure of HPCs to AEA triggers both cell cycling and differentiation along with increased expression of Cnr1, Krt19, and Axin2. Mechanistically, we found that AEA promotes the nuclear localization of the transcription factor β-catenin, with subsequent induction of its downstream targets. Systemic analyses of cells after CRISPR-mediated knockout of the β-catenin-regulated transcriptome revealed that AEA modulates β-catenin-dependent cell cycling and differentiation, as well as interleukin pathways. Further, we found that AEA promotes OXPHOS in HPCs when amino acids and glucose are readily available as substrates, but AEA inhibits it when the cells rely primarily on fatty acid oxidation. Thus, the endocannabinoid system promotes hepatocyte renewal and maturation by stimulating the proliferation of Axin2+Cnr1+ HPCs via the β-catenin pathways while modulating the metabolic activity of their precursor cells.
## Introduction
Understanding the cellular and molecular mechanisms underlying liver regeneration is of immense interest as such insight could potentially be leveraged to provide effective therapies for life-threatening liver failure, including those caused by alcohol or non-alcoholic liver cirrhosis, hepatocellular carcinoma, viral hepatitis, toxin-induced liver damage or other forms of fulminant hepatic failure or chronic liver disease [1, 2]. Such pathology is a largely unmet clinical need, and, according to a report from the Centers for Disease Control and Prevention, in 2017 there were 4.5 million Americans diagnosed with chronic liver diseases resulting in 41,743 deaths. One known molecular pathway in liver metabolism and disease is the endocannabinoid-CB1 receptor (CB1R, which is encoded by Cnr1) system (ECS) [3–6]. Further, this pathway plays a critical role in liver regeneration [7, 8] and also modulates liver function in alcohol-induced steatosis, non-alcoholic fatty liver disease, and hepatocellular carcinoma [9–11].
Oval cells, which are liver cells with a high nuclear:cytoplasmic ratio and an ovoid nucleus, are generally considered hepatic progenitor cells (HPCs) [12]. They are generated from biliary epithelial cells and differentiate into hepatocytes [13]. However, significant variability in their morphology and gene expression profiles suggests that ovoid cells are a heterogeneous population, and some may be more likely than others to differentiate into distinct cell types [14]. Their exact role in liver regeneration is still debatable, but their presence in liver development is very well established [15]. Recently, liver cells expressing LGR5 or Axin2 have been proposed as unique precursors of hepatocytes [16, 17]. Self-renewing Axin2+ hepatic progenitor cells are the source of clones of hepatocytes expanding from the central vein towards the portal vein and are responsible for the natural homeostatic renewal of the liver [17]. Axin2 expression is regulated by the transcription factor β-catenin, which, in turn, is under the control of Wnt proteins expressed in the stem cell niche [18]. Recently, multiple single-cell RNA-sequencing analyses revealed the existence of various subsets of immature/mature hepatocytes in the mammalian liver, each with a unique metabolic role [19, 20]. The aim of this study was to characterize and explore the role of Axin2+Cnr1+ HPC cells in liver regeneration and the interplay of the ECS with the β-catenin pathways as a regulatory mechanism. We also investigated the role of the endogenous endocannabinoid, anandamide (AEA), in mitochondrial energy metabolism—in Axin2+Cnr1+ HPCs at the molecular level during liver regeneration.
## Partial hepatectomy induces Axin2 and Cnr1 expression in mouse liver
To explore the potential interaction between self-renewing Axin2+ cells and the ECS and its role in liver regeneration, we used the in situ hybridization RNAscope technique to analyze the mRNA levels of Axin2 and Cnr1 in the remnant liver at 0 and 40 h following $\frac{2}{3}$rd partial hepatectomy (PHx) in mice. We found a strikingly greater degree of Axin2 expression at 40 h post-PHx compared to baseline, which was accompanied by greater expression of Cnr1, and there was notable co-localization of Cnr1 and Axin2 staining in the same cells (Fig. 1A). These changes paralleled the earlier reported upregulation of AEA production in the regenerating liver [7]. The protein expression of β-catenin, the transcriptional regulator of Axin2, was also notably greater at 40 h post-PHx compared to baseline (Fig. 1B). Two other targets of β-catenin, GSK3β and the cell cycle marker Cyclin D1, were significantly induced at 40 h relative to 0 h post-PHx (Fig. 1C).Fig. 1Expression of Axin2 and Cnr1 in post-hepatectomy remnant liver from wild-type (CB1R+/+) and CB1R−/− mice. A Representative RNAScope analyses of Axin2, Cnr1 and their co-expression in wild-type (CB1R+/+) and CB1R−/− liver samples at 0 and 40 h after PHx. B Representative immunohistochemical analyses and quantification of β-catenin protein expression in 0 h control and 40 h post-PHx liver from wild-type mice. C Representative western blot analyses and quantification by densitometry of the β-catenin target proteins GSK3β and Cyclin D1 in 0 h control and 40 h post-PHx liver samples.
## Activation of CB1R induces the proliferation of mouse and rat hepatocyte progenitor cells
Next, we wanted to test the functional consequences of AEA signaling on HPC proliferation. Thus, we synchronized the mouse hepatocyte progenitor cell line BMOL and the rat progenitor cell line LE2 by serum deprivation, and we monitored cell proliferation for 24 h in the presence of vehicle or different concentrations of AEA. Cell proliferation was monitored by the reduction of tetrazolium salt by cellular dehydrogenases and by BrdU labeling, followed by spectrometry and confocal microscopy. We found enhanced proliferation in the presence of 50 nM AEA compared to vehicle, but less so at higher AEA concentrations (Fig. 2A, B and Supplementary Fig. S1A, B). However, cell proliferation slightly slowed at 24 h in the presence of 50 nM AEA compared to 0.3 µM AEA, possibly due to AEA’s instability in the media. Therefore, we used 0.3 µM AEA throughout the study for the 24-h endpoint. The effects of AEA were abrogated in the presence of the CB1R antagonist rimonabant (SR1) (Fig. 2B and Supplementary Fig. S1B), indicating that they are mediated by CB1R.Fig. 2Effect of AEA on the proliferation and transcriptome analyses reveal distinct modulation of cell cycle and cell differentiation pathways by AEA in mouse HPCs. A A concentration-dependent increase in proliferation of cells exposed to 50–300 nM anandamide. B Inhibition of the pro-proliferative effect of AEA by the CB1R antagonist SR1 (rimonabant), as monitored by BrdU incorporation and detected by fluorescence confocal microscopy in BMOL. C–F RNA-seq analyses of six pooled samples of BMOL cells treated with vehicle (VEH) or AEA (300 nM) are presented as heat maps. These heatmap data reveal AEA-induced genes involved in cell cycle regulation, including regulation of G1/S transition (C) and Ras and Rho proteins (D); and cellular differentiation, including TGFß-receptor signaling (E) and transcription factors involved in hepatocyte lineage segregation (F). Differentially expressed genes are highlighted by color coding and by a solid blue line in the heatmap. G Real-time PCR validation of five highly regulated target genes in cell cycle pathways (PCNA, cMyc, GSK3β, Rac1, and Fzr1) from three additional samples ($$n = 3$$/group, * $P \leq 0.05$ AEA-treated vs vehicle-treated cells). H Real-time PCR validation of four target genes in cell differentiation pathways (TGFβR-1, SP1, ROCK2, and CDC42) were verified by real-time PCR analyses ($$n = 3$$/groups, *$P \leq 0.05$ AEA-treated vs vehicle-treated cells). I Schematic summary model of the effect of AEA in HPC.
## AEA modulates cell cycle and cellular differentiation pathways in mouse progenitor cells
To understand the mechanistic role of AEA in cell proliferation of HPCs, we performed transcriptome analyses of mouse HPCs (BMOL) using a pool of six samples from such cells treated either with vehicle or AEA at 0.3 µM. GeneGo analyses of the transcriptome indicated that AEA markedly upregulated the expression of cell cycle proteins involved in G1/S transition, as well as Ras and Rho proteins in HPCs. Transcripts differentially expressed by >$20\%$ are illustrated in a heatmap (Fig. 2C, D).
The most striking effects of AEA on gene expression were on components of cellular differentiation pathways (Fig. 2EF). The most robustly induced pathway was the TGFβ receptor signaling pathway, of which 56 genes were altered in response to AEA treatment. Similarly, large differences in transcript abundance were observed in a gene network laden with transcription factors involved in the segregation of the hepatocytic lineage. The AEA-induced enrichment of genes within these two pathways was statistically significant. The top inducible targets were Ccnd1 (encoding cyclin D1) and its regulator Ctnnb1 (encoding β-catenin) (Fig. 2C). Induction of key target genes (TgfβR-1, Sp1, Rock2, and Cdc42) was verified by real-time PCR (Fig. 2G). AEA-induced upregulation of five genes, Pcna, Myc, Rac1, Gsk3β, and Fzr1, was confirmed by real-time PCR, with the degree of upregulation being consistent with the transcriptome data (Fig. 2H).
Both Ccnd1 and Axin2 were induced in mouse liver by PHx, and both are known to be regulated by β-catenin [21, 22]. To further explore the relationship among Axin2, Cnr1, and an additional progenitor cell marker Krt19 (encoding CK19) in HPCs, we performed RNAscope analyses in both mice and rat HPCs. HPC proliferation induced by 0.3 µM AEA was associated with increased expression of Axin2, Cnr1, and Krt19, and there was a partial co-expression of these three transcripts in the same cells (Fig. 3A).Fig. 3AEA-induced co-expression of Axin2, CB1R, and CK19 (Krt19) mRNA, as well as that for β-catenin and its target genes, in mouse HPCs. A RNAscope analyses of the effect of AEA (300 nM) on the expression of Axin2, Cnr1, and CK19 and in BMOL. Yellow arrows in panel A illustrate co-expression of the three mRNAs in the same cell. Co-expression is also illustrated by an overlay. B, C Effect of AEA (300 nM) on β-catenin protein localization in the nuclear membrane/nucleus in BMOL cells, as visualized by confocal immuno-histochemistry, including two sets of overlaid images at higher magnifications for AEA group. D Immunoblot analyses of β-catenin from nuclear fraction obtained four independent experiments and their quantifications. Histone H3 was used as a nuclear fraction validation marker. E CHIP analyses of Axin-2 promoter pulled by β-catenin antibody. Input DNA control was validated by β-actin. F Real-time PCR analysis of AEA induction of β-catenin target genes Axin2, Mmp7, Snail, Ccnd1, Atl1, and PAI1 ($$n = 3$$/groups, *$P \leq 0.05$ compared to vehicle-treated cells.
## AEA induces nuclear localization of β-catenin in mouse and rat HPCs
The Wnt-β-catenin pathway in self-renewing Axin2+ cells plays a critical role in hepatocyte homeostasis in the liver [17]. Exposure of rat HPCs to 0.3 µM AEA triggered an increase in the gene and protein expression of β-catenin and induced its translocation to the nucleus (Fig. 4A). Similar changes were observed in mouse HPCs with prominent localization of β-catenin to the nuclear envelope (Fig. 3B). At higher magnification, we also observed β-catenin distribution in both nucleus and its envelope after AEA treatment (Fig. 3C). Isolation of nuclear fraction followed by western blot and its quantification from four experiments demonstrated an increase in β-catenin/histone H3 ratio (Fig. 3D). β-catenin is known to regulate Axin2 through its T-cell factor binding site at the promoter [18, 23]. Therefore, we tested with CHIP assay whether it regulate in BMOL cells. We have observed increase in β-catenin binding on Axin2 promoter upon treatment with AEA in BMOL cells (Fig. 3E). Increase in nuclear localization of β-catenin was associated with the activation of its target genes, including Axin2, Snail, Ccnd1, Mmp7, Atl11 and Pal1 as documented by real-time PCR in mouse BMOL cells (Fig. 3F), and Axin2, Mmp7, Ccnd1, and Pal1 in rat LE2 cells (Fig. 4B).Fig. 4AEA induces the expression of β-catenin and its target genes in rat HPCs. A Fluorescence immune-histochemistry for β-catenin (middle), as well as DAPI staining (left), of LE2 cells treated with AEA (300 nM) and visualized by confocal microscopy. Co-localization is shown by overlay (right). B Real-time PCR analyses of the expression of β-catenin target genes Axin2, Mmp7, Ccnd1, and PAL1 in the same cells ($$n = 3$$/groups, *$P \leq 0.05$ compared to vehicle).
## CRISPR-mediated knockout of β-catenin alters AEA-induced cell proliferation and differentiation in mouse HPCs
We next analyzed the functional role of β-catenin in AEA-induced proliferation and differentiation of the mouse HPC BMOL cell line by knocking out Ctnnb1 via CRISPR technology. *We* generated β-catenin knockout cells (Ctnnb1CRISPR), including a clonal population, which was further confirmed by DNA sequencing (Fig. 5A). A pure, single clonal population was isolated by growing the knockout cells in the presence of puromycin and blasticidin, and a 2-base pair deletion in Ctnnb1 in the clone was verified by sequencing (Fig. 5B).Fig. 5AEA induction of cellular differentiation in mouse HPCs is β-catenin-dependent. A Sequence of gRNA for targeting β-catenin in BMOL cells and sequencing of the pooled CRISPER library. B Characterization of β-catenin KO clone in BMOL cells by sequencing. C Gene expression analyses by RNA-seq, presented as a heatmap, in pooled samples of wild-type and Ctnb1CRISPR (KO) cells treated with vehicle (VEH) or AEA. D Radar plot of cellular processes significantly affected by AEA from transcriptome analyses of wild-type and β-catenin KO HPCs. E Radar plot of AEA-induced (>1.5-fold increase) genes in wild-type HPCs, which were not similarly induced by AEA in KO cells. F Verification of the induction of selected genes from the AEA-induced pool by real-time PCR ($$n = 3$$/groups, $P \leq 0.05$ compared to vehicle (VEH) in wild-type (*) or KO cells (#); G Real-time PCR analyses of genes involved in hepatocyte maturation (Afp, Cyp1a2, Ugt2b35 and Oatp1b2) in wild-type and β-catenin KO BMOL cells ($$n = 3$$/groups, $P \leq 0.05$ compared to vehicle in wild-type (*) or β-catenin KO BMOL cells (#)). H Flow cytometry analyses of intracellular HNF4 and albumin in wild-type and Ctnnb1CRISPR BMOL cells treated with vehicle or AEA for 5 days: decrease in hepatocyte-like cell marker HNF4 and albumin in Ctnnb1CRISPR cells.
Heatmap representation of hierarchical clustering analyses of transcriptome data from vehicle-treated or AEA-treated Ctnnb1CRISPR HPCs demonstrated significant differential gene expression compared to wild-type cells (Fig. 5C). When transcripts were analyzed by the function of the encoded proteins, the list comprised mainly enzymes (Z-score 18.49), transcription factors (11.49), receptors [11], kinases (9.2), proteases (6.2), and phosphatases (data not shown). We further analyzed genes representing markedly enriched pathways that displayed 1.5-fold or greater induction by AEA in pooled samples of wild-type cells and blunted expression of those genes in cells with β-catenin deletion. We detected 4068 such genes and analyzed the molecular processes likely affected by them, using GeneGo. Among significantly affected processes were development (253 genes, including Hedgehog signaling), signal transduction (235 genes including NOTCH signaling), cell proliferation (221 genes in positive regulation), cell cycle (206 genes in G2-M; 195 genes in G1-S, growth factor regulation), development (195 genes in nuclear receptors, transcriptional regulation and 177 genes in WNT signaling), response to hypoxia and oxidative stress (161 genes), interleukin regulation in hepatocytes (128 genes in G1-S interleukin regulation and 59 genes in inflammation involving IL12, IL15, and IL18 signaling) and metabolic pathways (129 genes in phosphatidylcholine pathways) (Fig. 5D, E). These changes in network processes suggested an important role for AEA in β-catenin-mediated regulation of cell proliferation, cell cycle, and differentiation.
Significantly affected networks of pathways involved cMyc, FAK1, GSK3 beta, Paxillin, and STAT3, with a highly significant P value (1.90e-48), high g-score (33.12) and high Z-score (20.62Pathways significantly affected by AEA in a β-catenin-dependent manner, as revealed by RNA-seq analyses, were associated with the development and cell cycle (Supplementary Figs. S2–S4). First, WNT-β-catenin signaling in the nucleus of HPCs is part of the cellular differentiation process. Multiple stimuli lead to the induction of β-catenin and its translocation to the nucleus. CB1R activation phosphorylates and thus inhibits GSK3β activity [24], which could contribute to the activation of β-catenin. Similar pathways also modulate p38 MAPK/MEK1/ERK1, triggering the phosphorylation of MEF2, which associates with β-catenin and promotes its nuclear retention [25]. Based on Metcore-Clavariate transcriptome analyses, this pathway is significantly affected by AEA ($$P \leq 1.337$$e-4) with a low false discovery rate (FDR = 7.905e−4) (Supplementary Fig S2). AEA also appears to be involved in regulating TGF-β receptor signaling (Supplementary Fig. S3). Interactions of CB1R and TGF-β have been reported in cardiac, renal, brain, and liver tissue under various pathophysiological conditions [26–30]. This pathway is highly statistically significant (P value = 6.664e−9) with a low false discovery rate (FDR = 6.010e−7) (Supplementary Fig. S3). Another significantly affected pathway mediates the effects of endocannabinoids on the cell cycle process via regulation of G1 to S transition involving TGF-β (Supplementary Fig. S4). TGF-β factors induce an association of its receptor with the regulatory subunit of protein phosphatase-2A (PP2A) [31]. Endocannabinoids also influence the cell cycle through the effects of Ras and Rho proteins on G/S transition (Supplementary Fig. S5). This pathway is highly statistically significant ($$P \leq 4.423$$e−12) and has a low false discovery rate (FDR = 5.519e−9) (Supplementary Fig. S5).
We confirmed these changes by real-time PCR for genes selected from each pathway (Fig. 4F), in addition to six genes from each pathway (Supplementary Figs. S2–S5). Most pathways in development and cell cycle-related genes (Cdh1, BMP4, Cdkn1a, Ccna2, Kpnb1, and Rac1) were induced by AEA, and such inductions were absent in the β-catenin KO cell line. HIF1a and Bnip3, which encode for factors in the hypoxia pathway, were also induced by AEA in wild-type but not in β-catenin KO cells (Fig. 5F). A similar pattern was evident for the hepatocyte maturation markers Afp, Cyp1a2, Ugt2b35, and Oatp1b2, which were induced by AEA in wild-type but not in β-catenin KO BMOL cells (Fig. 5G).
We next used flow cytometry to assess the effects of sustained exposure of BMOL cells to AEA (added daily for 5 days at 0.3 μM/day) on the hepatocyte differentiation markers HNF4 and albumin (Fig. 5H). AEA significantly induced the expression of both proteins in wild-type but not in Ctnnb1CRISPR KO cells, indicating the β-catenin-dependence of these effects.
## Effects of AEA on mitochondrial bioenergetics in mouse HPCs
To understand the energy requirements and metabolism of a self-renewing Axin2+Cnr1+ HPC cell line, we quantified parameters of mitochondrial respiration in BMOL cells by Seahorse instrument analysis. In an assay medium supplemented with 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose (“Full Substrate”), the addition of 0.3 μM AEA significantly increased the oxygen consumption rate (OCR) under both basal and maximally stressed conditions and increased ATP production (Fig. 6A). All three effects were significantly attenuated by simultaneous exposure of the cells to SR1 (Fig. 6A). The drug treatments did not significantly modify the extracellular acidification rate (ECAR) (Fig. 6B), suggesting that the AEA-induced increase in oxidative phosphorylation (OXPHOS) was due primarily to an increase in the oxidative metabolism of glutamine and pyruvate, which, unlike glycolysis, does not result in acidification [32]. AEA elicited similar, but much smaller, effects when cells were preincubated in a limited substrate supplemented with 2.5 mM glucose and 0.5 mM carnitine (Fig. 6C, left three columns of each bar graph set). The addition of palmitate (175 μM) caused dramatic increases in both basal and maximal respiration, which were completely reversed by AEA, and the effect of AEA was again attenuated in the presence of SR1 (Fig. 6C, right three columns). Thus, AEA has opposite effects on HPC oxidative metabolism, increasing it when amino acids and glucose are used primarily as substrates and inhibits it when oxygen is used primarily for fatty acid oxidation. Fig. 6AEA regulates mitochondrial metabolism and energy homeostasis in mouse HPCs. A Oxygen consumption rate (OCR) under “full substrate” (see “Methods”) measured in the presence of vehicle, AEA (0.3 μM) or AEA + SR1 (1 μM). Basal and maximal respiration and ATP production were quantified. Columns and vertical bars denote means and SE, * and # indicate significant difference from the vehicle or AEA group, respectively, ($P \leq 0.05$, $$n = 6$$/group). B Energy map plot of OCR versus extracellular acidification rate (ECAR). C OCR under “limited substrate” in the absence (left 3 columns) or presence of 175 μM palmitate (right 3 columns). Parameters measured as in (A). Statistics and symbols as in (A). D Serum free fatty acid (FFA) content at 0 h and 40 h post-PHX); E Liver TG in content at 40 h post-PHX and in the remnant liver (0 h). F Effect of AEA on lipid droplet accumulation in the absence or presence of added palmitate and rimonabant (SR1) (left) and the quantitation on TG content (right). Columns and vertical bars denote means and SE, * indicates $P \leq 0.05$; $$n = 6$$/group.
PHx-induced liver regeneration is associated with transient steatosis mediated by mobilization of adipose tissue lipids [33]. Accordingly, liver triglyceride (TG) levels were significantly increased at 40 h post-PHx compared to the remnant liver (Fig. 6E), and there was a corresponding reduction in plasma-free fatty acids (Fig. 6D). AEA is known to promote TG accumulation in the liver [9, 34]. Incubation of BMOL cells with 0.3 μM AEA increased the TG content of the cells, which was greatly enhanced in the presence of exogenous palmitate and was inhibited by SR1 (Fig. 6F).
## Discussion
The observations presented here reveal a hitherto unknown function of the ECS as a modulator of the proliferation and differentiation of HPCs, with relevance to the process of liver regeneration. Previously, we documented a surge in the synthesis of AEA and the expression of CB1R in the remnant liver following PHX in mice, with the resulting activation of CB1R inducing the expression of the FOXM1 transcription factor and promoting cell cycle progression in the regenerating liver [7]. Here, we have demonstrated that a distinct population of Cnr1+Axin2+ HPCs contributes to their subsequent differentiation into mature hepatocytes via signaling pathways that include β-catenin and utilizes substrate-specific cellular bioenergetics (Fig. 7).Fig. 7Schematic representation of similar molecular events in liver regeneration after PHx and AEA-induced cell proliferation and differentiation of hepatic progenitor cells. AEA promotes the nuclear localization of the transcription factor β-catenin, with subsequent induction of its downstream targets leading to cell cycling and differentiation. AEA also promotes mitochondrial energetics in HPCs when amino acids and glucose are readily available as substrates, but AEA inhibits it when the cells rely primarily on fatty acid oxidation. The figure was made from Servier Medical Art by Servier, licensed under a Creative Commons Attribution 3.0 Unported License.
Self-renewing Axin2+ HPCs are involved in the homeostatic replenishment of mature and aging hepatocytes [17]. We observed a significant increase of Axin2+ cells 40 h after PHX, which closely paralleled an increase in Cnr1 expression. Furthermore, PHX induced the hepatic gene expression of β-catenin, the transcriptional regulator of Axin2, with a similar time course. The tissue levels of GSK3β protein and cyclin D1, upstream regulator and downstream target of β-catenin, respectively, were also induced following PHX. Similar to the present findings, upregulation of β-catenin and GSK3 at 5 days after PHX has been reported earlier in a rat model involving oval cells [35]. Together, these findings strongly suggest that the pro-proliferative effect of endocannabinoids following PHX is mediated via CB1R-dependent activation of a GSK3β-β-catenin–CyclinD–Axin2+ pathway in the liver.
Due to the difficulty in isolating and maintaining pure populations of primary HPCs, we used a rat and a mouse HPC cell line for further mechanistic studies to test the above hypothesis. In both cell lines, AEA-induced proliferation displayed a bell-shaped concentration-response relationship or hormesis, with the peak proliferative response observed at the low concentration of 50 nM, which is close to the affinity constant of AEA binding to the CB1R as observed in this study. The proliferative effect of AEA was attenuated by rimonabant, indicating its mediation via CB1R.
Transcriptome analyses had been previously used to uncover novel metabolic pathways in liver diseases [36, 37]. In this study, RNA-sequencing analyses revealed a comprehensive landscape of cellular pathways and networks targeted by AEA in HPCs. The most significant network processes affected by AEA were cell cycle and differentiation pathways through which AEA can influence HPC growth and simultaneously promote the maturation of HPCs into a hepatocyte lineage. AEA-regulated genes involved in cell cycle processes were confirmed by real-time PCR and include Pcna, cMyc, Gsk3β, Rac1, and Fzr1 (hedgehog signaling), which were similarly modulated by PHX [11, 38–40]. Furthermore, some AEA-induced genes involved in cellular differentiation, such as TgfβR1 and Cdc42, are also induced following PHX [41]. Notably, AEA-induced genes identified by transcriptome analyses include Ctnnb1 (encoding β-catenin) and Ccnd1 (encoding Cyclin D1), proteins that are known to have crucial roles in hepatocyte lineage differentiation.
Endocannabinoid-mediated Axin2+ cell proliferation also correlated with enhanced expression of CB1R and cytokeratin 19 (CK19) in both mouse and rat HPCs. CK19 is commonly used as an HPC marker, and its increased expression has also been reported in patients with hepatocellular carcinoma (HCC) [42, 43]. As for the AEA-induced increase in Cnr1 expression, this is in agreement with earlier findings of the autoinduction of Cnr1 expression by CB1R activation [44].
To understand the mechanism by which endocannabinoids induce Axin2 expression, we analyzed the expression of its transcriptional regulator β-catenin. In mouse HPCs treated with AEA, distinct mesh-like staining of β-catenin appeared to accumulate around the nuclear membrane. Staining was also observed in many cell nuclei and the plasma membrane when viewed at higher magnification. Furthermore, numerous β-catenin target genes were induced, suggesting that nuclear localization of β-catenin resulted in the activation of downstream targets. AEA-induced nuclear localization of β-catenin and induction of its target genes were also evident in rat HPCs. These data support the hypothesis that endocannabinoids activate β-catenin and its downstream targets in HPCs. β-catenin plays a pivotal role in the development of alcohol-associated liver steatosis, bile duct carcinoma and HCC [45–47]. CB1R activation was also reported to have a pathogenic role in these conditions [10, 11], which further supports a functional link between CB1R and β-catenin.
To further interrogate the link between β-catenin and endocannabinoid-induced HPC proliferation and differentiation, we generated β-catenin-deficient HPCs by CRISPR-Cas9 and analyzed the differential effects of AEA on gene expression in the KO cells and their wild-type controls using whole transcriptome analyses. Heatmap representation of those gene networks demonstrated a critical role for β-catenin in AEA-induced network processes including essential proteins, such as transcription factors, enzymes, receptors, kinases, proteases and phosphatases. Specifically, we screened for genes induced by AEA by more than $50\%$ in wild-type but not in β-catenin KO cells. When subjected to GeneGo analyses, the most strongly induced genes fulfilling this criterion were components of cell differentiation and cell cycle-proliferation pathways. The expression pattern of these genes was further validated by real-time PCR, which supported the key role of β-catenin pathways in AEA-induced proliferation and differentiation of HPCs.
Most cell cycle and cell proliferative genes were constitutively upregulated in β-catenin KO cells, indicating loss of regulation. β-catenin interacts with many cell cycle proteins and transcription factors and its absence may trigger compensatory mechanisms unrelated to its regulatory pathways. Thyroid hormone receptor β agonizts also induce hepatocyte proliferation in mice during liver regeneration and the same β-catenin pathway is involved [48].
Our transcriptome data indicated significant activation of both β-catenin signaling and TGF-β receptor signaling pathways by AEA. This suggests that during liver regeneration, endocannabinoids not only promote cell cycle progression but also stimulate the maturation of HPCs. Previously, β-catenin was implicated in lineage reprogramming of hepatic cells in the endoderm of Xenopus embryos [49]. CB1R signaling plays a critical role in liver development in Danio rerio (zebrafish) embryos [50]. Transcriptome analyses suggest that metabolic remodeling occurs during the process of liver regeneration [51]. Another unexpected and interesting observation in the present study is the AEA-induced, β-catenin-mediated regulation of inflammation and cell cycle regulation of interleukin expression in HPCs. Immune cells play a critical role in liver regeneration [52], during which premature hepatocytes release a variety of interleukins, thus behaving similar to immune cells in this process.
To further dissect the molecular machinery involved in the initiation of differentiation, we incubated wild-type and β-catenin KO mouse HPCs with AEA for five days and estimated the cellular levels of the hepatocyte markers HNF4 and albumin [53, 54], by immunohistochemical staining followed by flow cytometry. The observed AEA-induced increase of HNF4 and albumin levels in wild-type cells but not in β-catenin KO HPCs indicates that CB1R activation promotes maturation of cells in the hepatocyte lineage. AEA likely has a similar role in the regenerating liver, as suggested by its marked and sustained increase in the remnant liver following PHx [7, 8]. Because of the link between CB1R activation and β-catenin expression, we may infer that β-catenin is responsible for the endocannabinoid-mediated initiation of hepatocyte maturation. This, of course, does not negate the possible role of numerous other paracrine factors in the hepatocyte maturation process.
There is a physiological gradient in the liver where the oxygen tension of 60–65 mmHg in the periportal region declines to 30–35 mmHg in the perivenous zone 3 regions [55]. Axin2+ HPCs are preferentially localized in the perivenous region where the hypoxic milieu promotes their maintenance in the quiescent state, by analogy to hematopoietic stem cells localized in hypoxic niches of the bone marrow [56]. Under these conditions, HPCs must rely on anaerobic glycolysis for ATP production for their self-renewal, similar to hematopoietic stem cells [57]. Hypoxia induces the expression of HIF1α and we found that AEA induces HIF1α and Bnip3 expression in a β-catenin-dependent manner. Wnt-β-catenin signaling in the liver was shown to promote cell proliferation by increasing glutamine metabolism [58]. HIF1α in complex with HIF1β is also known to promote the expression of multiple glycolytic genes [59]. Together, these observations suggest that in the early stages following PHx, characterized by hypoglycemia and reduced availability of free fatty acids due to an increase in de novo lipogenesis [60], the energy requirements of AEA-induced HPC proliferation is provided by increased glutamine metabolism and, to a lesser degree, by glycolysis.
The most striking effect of AEA was modulation of mitochondrial energy metabolism. Mitochondrial energy metabolism plays a critical role in immune cell differentiation and cell cycle [61, 62]. In sharp contrast to the AEA-induced increase in oxygen consumption rate (OCR) derived from glutamine, pyruvate, and glucose substrates, AEA robustly inhibits the palmitate-induced increase in OCR. This effect may not be unexpected in view of the well-established role of CB1R activation to inhibit fatty acid oxidation in a variety of tissues, including liver [9, 63], brown fat [64], kidney [65], sperm cells [66], and ghrelin-producing cells of the stomach mucosa [67]. As discussed above, in the late phases of liver regeneration activation of CB1R by AEA promotes the terminal differentiation of hepatocytes, which would require a brake on the energetic support of cell proliferation. Inhibition of OXPHOS may also be a mechanism to preserve the self-renewing potential of HPCs and prevent their senescence and depletion that can result from uncontrolled proliferation.
A limitation of this study is that primary HPCs were not studied due to technical challenges of indistinguishable heterogeneous population of HPCs and hepatocytes in the PHx model. However, the insight that Axin2+Cnr1+ HPCs have a unique endogenous cannabinoid-modulated role in hepatic cell proliferation, maturation and metabolic remodeling during liver regeneration. These findings could potentially be leveraged to improve liver regeneration and thus treat acute liver failure or organ dysfunction in chronic liver disease.
## Cell culture
Mouse BMOL hepatocyte progenitor cells and permission for their genetic manipulation were obtained from Dr. George Yeoh (The University of Western Australia). Cells were cultured in Williams’ E medium containing $5\%$ FCS, antibiotics, glutamine, 20 ng/mL epidermal growth factor (BD Biosciences) and 30 ng/mL human insulin (Sigma-Aldrich) [68].
The rat LE2 non-tumorigenic hepatocyte progenitor cell line was generated by Dr. N. Fausto’s lab [69] and was kindly provided by Drs. Jean Campbell and Renay Bauer (University of Washington). Cells were maintained in a 1:1 mix of DMEM (Thermo Fisher Scientific) and Ham’s F10 supplement (Thermo Fisher Scientific) with $10\%$ fetal bovine serum (Thermo Fisher Scientific), insulin at 1 µg/mL (Sigma), hydrocortisone (0.5 µg/mL, Sigma), and gentamicin (10 µg/mL, Thermo Fisher Scientific). Cell proliferation method provided in the supplemental section.
All other methods are provided with details as supplemental materials.
## Supplementary information
Supplemental Method Supplemental Figure legends Figure S1 Figure S2 Figure S3 Figure S4 Figure S5 RNASEQ data Original Data File The online version contains supplementary material available at 10.1038/s41420-023-01400-6.
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|
---
title: Lacking mechanistic disease definitions and corresponding association data
hamper progress in network medicine and beyond
authors:
- Sepideh Sadegh
- James Skelton
- Elisa Anastasi
- Andreas Maier
- Klaudia Adamowicz
- Anna Möller
- Nils M. Kriege
- Jaanika Kronberg
- Toomas Haller
- Tim Kacprowski
- Anil Wipat
- Jan Baumbach
- David B. Blumenthal
journal: Nature Communications
year: 2023
pmcid: PMC10039912
doi: 10.1038/s41467-023-37349-4
license: CC BY 4.0
---
# Lacking mechanistic disease definitions and corresponding association data hamper progress in network medicine and beyond
## Abstract
A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts.
Large-scale disease-association data are widely used for pathomechanism mining, even if disease definitions used for annotation are mostly phenotype-based. Here, the authors show that this bias can lead to a blurred view on disease mechanisms, highlighting the need for close-up studies based on molecular data for well-characterized patient cohorts.
## Introduction
Since the seminal articles by Goh et al. 1 and Barabási et al. 2, network medicine has developed into an increasingly mature and diverse research field with its own dedicated journals3, associations4, and subfields. One of the network medicine field’s long-term objectives is to replace our current mainly phenotype-based disease classification systems by a mechanistically grounded disease vocabulary5–7. In such a vocabulary, phenotype-based disease definitions are replaced by so-called endotypes, i.e., distinct molecular mechanisms underlying the disease phenotypes. Once properly disentangled into disjoint, individually targetable endotypes5, disease-modifying treatment strategies might become available for diseases which, at the moment, can be treated only symptomatically.
Two clarifications are required to define the scope of this paper: Firstly, we use the term “endotype” to denote molecular endotypes as explained by Anderson8, Lötvall et al. 9, and Nogales et al. 5 – i.e., the underlying molecular mechanisms driving disease phenotypes. There are other works where the term “endo(patho)phenotype” denotes common intermediate phenotypes6 such as inflammation, fibrosis, or thrombosis which drive phenotypic disease manifestations10,11. Secondly, we would like to stress that compiling a endotype-based disease vocabulary is a genuinely biomedical rather than a semantic endeavor: It does not consist in redefining semantic relationships between existing disease terms but in uncovering currently unknown molecular disease mechanisms and dissecting umbrella diseases such as Alzheimer’s disease or coronary artery disease into endotypes which are clearly characterized at a molecular level5.
In order to reach the objective of an endotype-based disease vocabulary, network medicine approaches aim at uncovering pathomechanisms driving diseases. Here, we broadly distinguish between close-up and bird’s-eye-view (BEV) network medicine approaches, depending on the data used as primary input towards this task (this distinction is of course an idealized binarization of a continuous spectrum, but serves as a conceptual framework for this article). Close-up network medicine studies focus on a specific disease and start their analyses with molecular data for well-characterized patient cohorts. Such studies are typically carried out as close collaborations between bioinformaticians and domain experts from the biomedical sciences. They tend to be time- and labor-intensive and often involve the development or customization of data analysis methods for specific datasets. The most impressive translational results of the network medicine field have been reached via such close-up studies. For instance, close-up studies have led to novel mechanistic insights into type 2 diabetes12, liver fibrosis13, pulmonary arterial hypertension14, asthma15, hypertrophic cardiomyopathy16, pre-eclampsia17, chronic obstructive pulmonary disease, and idiopathic pulmonary fibrosis18.
In contrast to that, BEV approaches use large-scale disease association data that are typically gathered from several data sources. Various studies have generated evidence for the validity of this overall approach: For instance, Menche et al. 19 demonstrated that disease-associated genes form so-called disease modules, i.e., highly connected subnetworks within protein-protein interaction (PPI) networks, and that biological and clinical similarity of two diseases results in significant topological proximity of these modules. In a similar vein, Iida et al. 20 showed that shared therapeutic targets or shared drug indications are correlated with high topological module proximity. Guney et al. 21 and Cheng et al. 22 showed that the network-based separation between drug targets and disease modules is indicative of drug efficacy. Cheng et al. 23 and Zhou et al. 24 found that FDA-approved drug combinations are proximal to each other and to the modules of the targeted diseases in the interactome.
Despite the promising findings summarized above, several studies have pointed out important biases in the data used by BEV approaches. Menche et al. 19 have studied the effect of incompleteness of disease-gene association and protein-protein interaction (PPI) data on network medicine. Schaefer et al. 25 have shown that the previously observed26–28 high node degree of cancer-associated proteins in PPI networks can largely be explained by the fact that cancer-associated proteins are tested more often for interaction than others. Lazareva et al. 29 found that widely used methods to mine PPI networks for pathomechanisms inherit this bias in that they mainly learn from the node degrees instead of exploiting the biological knowledge encoded in the edges of the PPI networks. Haynes et al. 30 showed that study bias also distorts functional gene annotation resources such as the Gene Ontology (GO)31. Kustatcher et al. 32 made a similar point for functional protein annotations and sketched a roadmap for systematically exploring the understudied part of the proteome. Stoeger et al. 33 and Rodriguez-Esteban34 looked into reasons that might lead to the emergence of gene study bias and identified, respectively, a limited number of biological characteristics33 and speed of information propagation between scientific communities as potential drivers34.
While the aforementioned studies have analyzed the impact of various types of data biases related to genes and proteins (and, to a lesser extent, also variants), the disease part of disease-gene and other disease association data introduces another, so far unstudied type of data bias: In currently available large-scale disease association data, diseases are annotated with the very phenotype-based disease definitions the network medicine field aims to overcome. BEV approaches hence risk to systematically reproduce the biases introduced by these disease definitions. Consequently, BEV approaches make the implicit assumption that the biases introduced by phenotype-based disease definitions even out and that, despite those biases, disease association data using these definitions still contain useful information about the pathomechanism that are to be uncovered.
In this work, we quantify to which extent this implicit assumption is indeed backed by data. Towards this end, we construct disease-disease networks (called “diseasomes” in the remainder of this article) based on [1] disease-gene associations, [2] disease-variant associations, [3] comorbidity data, [4] symptom data, and [5] drug-indication data, as well as drug-disease and drug-drug networks (called “drugomes”) based on drug-indication and drug-target data. We then formulate two testable hypotheses that follow from the implicit assumption of BEV network medicine: The global-scale hypothesis states that, globally, networks constructed from two different types of association data that both contain useful information about endotypes should be pairwise more similar than expected by chance. The local-scale hypothesis states that this should hold not only globally but also for the neighborhoods of the individual diseases and drugs represented by nodes in the constructed networks.
In line with the findings of prior studies20–24, our analyses provide solid evidence for the global-scale hypothesis. However, they only partially support the local-scale hypothesis. Figuratively speaking, BEV network medicine hence only allows a distal view at the endotypes that are to be discovered. When zooming in on individual diseases, the picture becomes blurred and less reliable (see Fig. 1 for a conceptual visualization and Fig. 2 for a concrete exemplification of this phenomenon in the context of neurodegenerative diseases). This implies that, in order to yield translational results, BEV approaches need to be supplemented with additional layers of molecular data for well-characterized patient cohorts and a dedicated focus on the specific diseases which are being investigated. In particular, fine-grained molecular patient data are crucial for implementing network medicine’s long-term objective to replace current phenotype- or organ-based disease definitions by mechanistically grounded endotypes. The main finding of this study is hence that the biases current disease definitions introduce in large-scale disease association databases such as OMIM and DisGeNET do not even out and that such databases should be used with care in all fields of data-centric biomedicine: Instead of blindly using public disease association data out of convenience for pathomechanism mining, we strongly recommend biomedical researchers to always consciously ponder to which extent biases in these data introduced by phenotype-based disease terms threaten to distort their potential findings. Fig. 1BEV vs. close-up network medicine.a BEV network medicine mainly utilizes large-scale disease association data where diseases are annotated with phenotype-based disease definitions (b, bottom). BEV network medicine inherits the bias introduced by these definitions, which leads to a blurred view on individual pathomechanisms (b, top). c Close-up network medicine uses patient-level molecular data and is hence less dependent on the phenotype-based disease definitions that network medicine aims to replace by mechanism-based endotypes. Fig. 2Locally blurred results for neurodegenerative diseases. The color gradient visualizes local-, global-, and cluster-level empirical P-values (one-sided, unadjusted) obtained from the comparison of gene- and drug-based diseasomes in MONDO vocabulary. *The* gene-based diseasome was constructed based on disease-gene association data integrated from DisGeNET36 and OMIM43 and two diseases were connected by an edge if they share at least one disease associated gene. The drug-based diseasome was constructed based on drug-indication data integrated from CTD48 and DrugCentral37 and two diseases were connected by an edge if they share at least one indicated drug.
## Neurodegenerative diseases as case example
Before presenting the comprehensive results of our analyses, we visualize the phenomenon of local blurriness in BEV network medicine with a small example. We compiled a list of diseases that fall under the parent term “neurodegenerative disease” in the MONDO disease hierarchy. From those, we kept diseases for which we have nodes in the aligned gene- and drug-based diseasomes. This led to a cluster of seven neurodegenerative diseases which are highly connected in both diseasomes. Figure 2 shows this cluster, together with the contained diseases’ local empirical P-values obtained from the comparison of gene- and drug-based diseasomes in MONDO space, the global empirical P-value, as well as the cluster-level empirical P-value (see next subsection and Methods for explanations on how we obtained the P-values). While only two local empirical P-values are significant at 0.05 level, the cluster-level and global empirical P-values are significant at levels 0.01 and 0.001, respectively.
## Overview of analyses
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We can capture such similarities in diseasomes, where diseases \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d}_{1}$$\end{document}d1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d}_{2}$$\end{document}d2 are connected by an edge if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}(d_{1})$$\end{document}D(d1) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}(d_{2})$$\end{document}D(d2) are sufficiently similar. In order to assess the implicit assumption of BEV network medicine approaches with quantitative means, we hence formulate the following testable hypotheses (see Methods for an argument to support these hypotheses):Global-scale hypothesis: For all disease association data \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{2}$$\end{document}D2 that are assumed to contain useful information about endotypes (e.g., disease-gene association and drug-indication data from databases such as DisGeNET36 and DrugCentral37), diseasomes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2 constructed based on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{2}$$\end{document}D2 should be pairwise more similar than expected by chance. Local-scale hypothesis: For all disease association data \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{2}$$\end{document}D2 that are assumed to contain useful information about endotypes and any disease term \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d that appear in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{2}$$\end{document}D2, the direct neighborhood of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d$$\end{document}d in the diseasomes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2 constructed based on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{2}$$\end{document}D2 should be pairwise more similar than expected by chance. For example, under the assumption that disease-gene and drug-indication databases such as DisGeNET and DrugCentral contain useful information about Alzheimer’s disease (AD) mechanisms, there should be a significant overlap between the set of diseases whose associated genes overlap with AD-associated genes and the set of diseases which can be treated with drugs also indicated for AD.
To test these two hypotheses, we constructed various diseasomes, drugomes, and drug-disease networks based on different data types. An overview of the used data types and derived networks is shown in Fig. 3a. Using customized versions of the graph edit distance (GED)38,39, we then compared these networks in a pairwise manner both on a local scale, i.e. zoomed-in on individual disease or drug nodes, and on a global scale. More precisely, we generated 1000 permuted networks as randomized counterparts for each network. Subsequently, we compared the distributions of local and global GEDs obtained for the original networks to GED distributions obtained for randomized counterparts. Network randomization and computation of local and global GED are illustrated in Fig. 3b, c. While local GED measures the dissimilarity between the individual nodes’ neighborhoods in the compared networks, global GED is a measure for the overall dissimilarity of the networks. Fig. 3Overview of compared networks and graph edit distance computation.a We compared five different types of disease-disease networks (diseasomes), two different types of drug-drug networks (drugomes), and two different types of drug-disease networks. Pairwise comparisons between those networks were carried out using local and global graph edit distance (GED). b Local GED was used to quantify the dissimilarities of the individual nodes’ neighborhoods across different networks in comparison to pairs of randomly rewired networks. c Global network dissimilarities were computed using global GED, obtained by summing up the local GEDs of the individual nodes.
We also evaluated how annotating the data using disease vocabularies of different granularity affect the results, by carrying out the analyses using MONDO IDs40 and UMLS CUIs41 (finer granularity) and ICD-1042 three-character codes (coarser granularity) as node IDs in the constructed networks, respectively. To this end, where possible, we constructed the networks in MONDO, UMLS CUI, and in ICD-10 vocabulary (using three-character level codes). Note that analyses involving comorbidity data were carried out only in ICD-10 and the comparison between target- and indication-based drugomes only in MONDO vocabulary (see Methods for an explanation). Moreover, neither the semantic layers of the MONDO disease ontology nor the hierarchy of the UMLS CUI and ICD-10 classification system were used to add edges to our diseasomes. MONDO, UMLS CUI, and ICD-10 were only used as vocabularies, i.e., to provide the node IDs in our networks. Whether two disease nodes are connected by an edge exclusively depends on the primary databases containing the association data (upon mapping to MONDO, UMLS CUI, or ICD-10). For instance, two diseases are connected in the gene-based diseasome in MONDO vocabulary if the intersection of the sets of genes associated with their MONDO IDs is non-empty, where disease-gene associations were obtained from OMIM43 and DisGeNET36.
GED quantifies the dissimilarity between two networks as the minimum cost of an edit path transforming one network into the other. Edit paths are sequences of elementary edit operations (node and edge insertions, substitutions, and deletions), all of which come with associated edit costs. Hence, the GED is a distance measure between two networks. We computed three different versions of GED using uniform, weight-based, and rank-based edge editing costs, respectively. Uniform edit costs discard the association strengths of the edges in the compared networks; weight- and rank-based edit costs incorporate them by making it more expensive to delete or insert edges with strong associations or to substitute them by edges with weak associations. Corroborating the robustness of our analysis method, we obtained similar results for all three versions of GED. In the following, only the results of uniform edit costs are reported. Results for rank- and weight-based edit costs can be found in Supplementary Figs. 1–4 and 9–12, respectively. More details on disease vocabulary mapping, network construction, and GED computation can be found in Methods.
## Results of global-scale analyses
To test the global-scale hypothesis, we computed empirical P-values for each pair of networks based on global GEDs (Fig. 4a, left panel). For all evaluated pairs of networks (in MONDO, UMLS CUI, and ICD-10 vocabularies), we obtained smaller global GEDs for the original diseaseomes, drugomes, or drug-disease networks than for randomized counterparts, leading to empirical P-values which are significant at 0.001 level. Differences between GEDs obtained for permuted and a selection of original networks are shown in Fig. 4b. For the full results of our global-scale analyses, see Supplementary Fig. 5.Fig. 4Global-scale analyses.a Illustration of global-scale analysis methods. Left panel: Statistical analyses based on global GED via empirical P-values. Right panel: Statistical analyses based on shortest path distances via MWU test. b Differences of global GEDs (based on uniform edge edit costs) between a selection of original networks and their counterpart permuted networks, and corresponding global empirical P-values (one-sided, unadjusted) in MONDO, UMLS, and ICD-10 vocabularies. All obtained global empirical P-values are at the lower resolution limit of our permutation tests with 1000 randomized network pairs. c Selected results of shortest path analyses and the corresponding MWU P-values (one-sided, unadjusted). Left: Disease distances in gene-based disease-disease network vs. comorbidity-based diseasome as the reference network. Middle: Drug-disease distances in protein-based drug-disease network vs. drug-indication network as the reference network. Right: Drug distances in protein-based drug-drug network vs. indication-based drugome as the reference network. All networks underlying the results shown in (c) are constructed in the MONDO vocabulary.
Moreover, we performed analyses based on shortest path distances between disease-disease, drug-drug, and drug-disease pairs in disease-gene-gene-disease, drug-protein-protein-drug, and disease-protein-protein-drug networks, where protein-protein and gene-gene links were obtained from PPIs. We then compared shortest path distances for node pairs which do and node pairs which do not have a link in different reference networks, using the Mann-Whitney U (MWU) test (Fig. 4a, right panel).
For all shortest path analyses, we observed that shortest path distances are significantly shorter for node pairs that are connected by a link in the reference networks (see Fig. 4c for a selection of the results). In particular, the results show [1] that distances between diseases that are connected by edges in diseasomes constructed based on comorbidities, shared drugs, shared symptoms, or shared genetic variants are significantly shorter than distances between diseases without such edges (Supplementary Fig. 6a–d); [2] that distances of disease-drug pairs with shared indication edges are significantly shorter than distances of disease-drug pairs without such edges (Supplementary Fig. 6e); and [3] that distances between drug pairs with shared indication are significantly shorter than distances for drug pairs without shared indications (Supplementary Fig. 6f). In sum, our global analyses hence provide solid evidence for the global validity of the BEV network medicine paradigm and hence further corroborate the findings of previous studies19–24.
## Results of local-scale analyses
To test the local-scale hypothesis, we computed P-values using the one-sided MWU test based on local GEDs to evaluate whether the local distances for the original networks are significantly smaller than the local distances for the permuted counterparts (Fig. 5a, left panel). Local GEDs of nodes obtained for the permuted and a selection of original networks and the corresponding MWU P-values are shown in Fig. 5b (for the full results of the local-scale analyses, see Supplementary Fig. 7). The overview of the results of the local GED analyses in different vocabularies shows that the comparisons performed in ICD-10 vocabulary (at three-character level) led to more significant similarities than the ones performed in MONDO or UMLS CUI vocabulary (Fig. 6a and Supplementary Fig. 4a). As an example, the P-value computed from the local GEDs of drug-based vs. gene-based diseasomes in ICD-10 vocabulary is significant at 0.0001 level (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\approx 7.1\times {10}^{-7}$$\end{document}P≈7.1×10−7), while it is not significant in the MONDO and UMLS CUI vocabularies (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\approx 0.071$$\end{document}P≈0.071 for MONDO, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\approx 0.079$$\end{document}P≈0.079 for UMLS CUI).Fig. 5Local-scale analyses: methods and local GEDs.a Illustration of local-scale analysis methods. Left panel: Statistical analyses based on local GED via MWU test. Right panel: Computation of empirical P-values (one-sided, unadjusted) of each node based on local GEDs. b Local GEDs (of all nodes) between a selection of original networks vs. their permuted counterpart networks and corresponding MWU P-values. Left: Similarities between gene- and drug-based diseasome. Middle: Similarities between indication- and protein-based drug-disease network (for drugs). Right: Similarities between indication- and protein-based drug-disease network (for diseases). Results shown in (b) are based on uniform edge edit cost. Fig. 6Local-scale analyses: MWU P-values and local empirical P-values.a Overview of MWU P-values (one-sided, unadjusted) computed from local GEDs with levels of significance. b Fraction of significant local empirical P-values (one-sided, unadjusted) at 0.05 level computed from local GEDs on a pair of networks for the original vs. permuted network. All results are based on uniform edge edit cost.
The results of the MWU test for local GED analyses point out that we have more significant similarities in ICD-10 (8 out of 10 significant at 0.05 level) than in MONDO vocabulary (2 out of 6 significant at 0.05 level) or UMLS CUI vocabulary (1 out of 6 significant at 0.05 level). The results also suggest that variant-based diseasomes have higher similarities with other diseasomes (7 out of 10 comparisons significant at 0.05 level) than gene-based diseasomes (5 out of 10 comparisons significant at 0.05 level), considering all three vocabularies. By inspecting the P-values of drug nodes (3 out of 3 comparisons significant at 0.05 level) against disease nodes (0 out of 3 comparisons significant at 0.05 level) obtained from local-similarity analyses of indication- versus protein-based drug-disease network as well as P-values obtained from target- and indication-based drugome (significant at 0.001 level), we discovered that, in general, drug neighborhoods are better preserved across the compared networks than disease neighborhoods (Fig. 6a, bottom right panel).
Furthermore, we computed local empirical P-values individually for nodes based on local GEDs (Fig. 5a, right panel). The local empirical P-values for all network comparisons are shown in Supplementary Fig. 8. The fractions of significant local empirical P-values at 0.05 level are shown in Fig. 6b and Supplementary Figs. 4b and 12b. Our results show that, for a substantial fraction of disease nodes, local neighborhoods are preserved not only not significantly better but worse than expected by chance across the different diseasomes (compare sigmoidal shape of curves in Supplementary Fig. 8). The local-scale hypothesis hence seems to hold for some diseases, but does not hold at all for others.
In follow-up analyses, we tried to identify patterns explaining these results, e.g., by assessing whether there are certain chapters of the ICD-10 disease vocabulary which are enriched with diseases with very small or very large empirical P-values. However, no clear patterns could be discovered, indicating that it is very hard to predict for which concrete diseases BEV network medicine approaches can be expected to yield robust and reliable results. Our local analyses hence only provide weak evidence for the local-scale hypothesis, indicating the BEV network medicine tends to produce locally blurred results.
## Web tool for interactive exploration of results
In order to make our results explorable and actionable, we developed the GraphSimViz (graph similarity visualizer) web interface, which is freely available at https://graphsimviz.net. GraphSimViz allows biomedical researchers to query and visualize our findings for user-selected drugs, diseases, network types, and disease vocabularies. Using GraphSimViz, biomedical researchers can assess if a specific type of disease association data is likely to contain reliable information about pathomechanisms underlying their diseases of interest. Below, we illustrate how GraphSimViz can be employed for interactive exploration of our results, using neurodegenerative diseases as a case example. To enable quantification of the effect of biases introduced by mechanistically ungrounded disease definitions in data sources not covered by our study, we provide the GraphSimQT (graph similarity quantification tool) Python package, which is freely available on GitHub (https://github.com/repotrial/graphsimqt).
## Discussion
Our results strongly support the global-scale hypothesis and, in line with previous studies19–24, provide solid evidence for the overall validity of the BEV network medicine paradigm. However, they also indicate that results generated via BEV network medicine approaches become less reliable when zooming-in on individual diseases. Our results hence confirm that it is problematic to exclusively rely on data annotated with phenotype-based definitions if the objective is to uncover molecular pathomechanisms. As long as phenotype-based disease definitions have not been replaced by endotypes, large-scale disease association databases should therefore be used with care in network medicine and should be combined with additional layers of disease-specific omics data. In the following, we further speculate on issues that might play a role in the local blurriness of BEV network medicine and sketch a roadmap to overcome this problem.
While there are vast amounts of datasets online that contain useful information about diseases such as genetic associations, comorbidities, and symptoms, each of these datasets may use different disease vocabularies to describe their associations. The vocabularies have different degrees of granularity and are generated in different ways and for different purposes. However, for downstream (BEV) network medicine analyses, in order to jointly leverage the disease association from various data sources that use disease terms from different vocabularies as disease identifiers, we have to map data to a joint target vocabulary. This is a mammoth task that inevitably involves losing some data due to unmappable terms (see Fig. 7 for the levels of completeness of disease vocabulary mappings underlying this study).Fig. 7Levels of completeness of disease vocabulary mappings underlying this article. For each source-target vocabulary pair, mappability is computed as the percentage of terms in the source vocabulary used in this study that could be mapped to a term in the target vocabulary.
The choice of the disease vocabulary has the potential to dramatically affect the results of downstream analyses (see discordant results of local-scale analyses carried out using ICD-10 three-character codes, on the one hand, and UMLS CUIs or MONDO IDs, on the other hand, shown in Fig. 6a and Supplementary Fig. 4a). At the same time, for most analysis tasks, the choice of the disease vocabulary is dictated by the format of the data and, thus, often impossible to change without losing information at the time of analysis. The vocabularies used to annotate disease-associated data must hence be viewed as confounders which are very difficult if not impossible to control for.
Currently used disease vocabularies are not only used discordantly, but also mechanistically inadequate: Since causal molecular disease mechanisms are often unknown, disease names often do not denote such mechanisms but rather reflect the person who coined the disease term (e.g., “Alzheimer’s disease”), areas in the body that are affected (e.g., “kidney stones”) or symptoms of the disease (e.g., “irritable bowel syndrome”). ICD-10 codes are considered inadequate due to their overly inclusive designations, ranging from symptoms (e.g., cough) over syndromes (e.g., cachexia) to true endotypes with definable molecular determinants (e.g., Mendelian disorders). This leads to data that is blurred, as diseases with distinct pathomechanisms are being aggregated together, e.g., due to symptom or organ commonality. This blurriness not only has severe clinical consequences (patients with mechanistically distinct diseases receive the same untargeted treatment), but also makes it very challenging to mine disease-associated data for pathomechanisms via BEV network medicine approaches44. Since such analyses often require case-versus-control or subtype annotations as input, it is very difficult to obtain meaningful results if the employed disease definitions are too unspecific.
The results presented in this study, where drugome comparisons have led to more significant results on a local level than diseasome comparisons, are evidence that network-based analyses yield more targeted and reliable results when the underlying annotations are well-defined (such as in drug vocabularies). Comparing the results of the GED-based analyses for full diseasomes (global analyses) with those obtained for analyses based on local GEDs in diseasomes with ICD-10 three-character codes, UMLS CUIs, and MONDO terms as nodes, respectively, further highlights the detrimental effect of local blurriness in currently used disease definitions: The higher the resolution of the analysis, the less significant the obtained P-values (see Fig. 8). When using MONDO or UMLS CUI terms (fine granularity) as nodes in the diseasomes, only the comparisons between gene- and variant-based diseasomes consistently (with respect to uniform, weight-based, and rank-based edit costs) led to smaller local distances in the original networks than in their randomized counterparts. No other network comparisons in the MONDO or UMLS vocabularies yielded significant P-values for all three types of edit costs. When using ICD-10 three-character codes (which denote disease clusters rather than individual diseases), around $50\%$ of all computed MWU P-values are significant at 0.001 level. When comparing the entire diseasomes via global GEDs, all empirical P-values are significant. Fig. 8Effect of disease term granularity on results of GED-based analyses. For the individual P-values summarized in this figure, see Fig. 6a, as well as Supplementary Figs. 1, 4a, 5, 9, and 12a.
The fact that we could not identify any clear patterns among diseases with small or large empirical P-values computed based on local GEDs may be a consequence of some of the current phenotype-based disease entities already corresponding to true endotypes. We speculate that, for diseases where our current definitions already have a one-to-one mapping to true endotypes, the local-scale hypothesis holds.
Even though we expected to obtain similar results for variant-based and gene-based diseasomes, the local-similarity analyses show that variant-based diseasomes have higher similarities with other diseasomes compared to gene-based diseasomes. This indicates that the disease-gene associations underlying the gene-based diseasomes contain less targeted information than the disease-variant associations underlying the variant-based diseasomes. Hence, using disease-variant data might yield more reliable results in the context of BEV network medicine applications.
To seek a possible explanation for this difference, we had a closer look at the associations underlying these two types of diseasomes. In our study, as well as in many other network medicine studies1,2,22,45,46, disease-gene associations were taken from OMIM and DisGeNET curated databases. The latter collates disease-gene associations from different databases: UniProt47, CTD48, Orphanet49, ClinGen50, Genomics England51, CGI52, and PsyGeNET53. These constituent databases comprise multiple types of disease-gene associations such as causal mutations (mutations known to cause the disease), modifying mutations (mutations known to modify the clinical presentation of the disease), or merely statistical associations without evidence of causality. Disease-variant associations used in our study were extracted from DisGeNET, which itself integrates various databases: GWASdb54, ClinVar55, GWAS Catalog56, UniProt, and BeFree57. Like for disease-gene associations, there are different types of disease-variant associations, ranging from known causal variants to variants with merely statistical evidence. However, the heterogeneity of the association types is higher for disease-gene associations than for disease-variant associations. Moreover, the genetic variation data from the constituent disease-variant databases of DisGeNET is mainly taken from genome-wide association studies (GWAS), which identify associations between common genetic variants and phenotypic traits via hypothesis-free, genome-wide scans. In contrast, in the disease-gene databases used by DisGeNET, parts of the data are curated from studies where evidence for disease-gene associations stems from a very limited number of patients or where hypothesis-driven approaches were used (i.e. the analyzed genetic variants were limited to those contained in candidate genes selected a priori).
Another reason for the difference in results between gene-based and variant-based diseasomes may consist in the loss of detail resulting from mapping variants to genes. Distinct mutations in one gene may cause different phenotypes, but this information cannot be captured at the level of disease-gene associations and is better conserved at disease-variant level. A very good example is the LMNA gene, where different mutations can cause 13 different diseases such as Hutchinson-*Gilford progeria* syndrome and the Dunnigan-type familial partial lipodystrophy58. Finally, the difference in results between gene- and variant-based diseasomes may also partly be due to loss of information introduced when aggregating P-values for disease-variant associations at gene level59.
A limitation of our study is that our results do not rule out the possibility that confounders other than mechanistically inadequate disease definitions lead to the observed local blurriness of BEV network medicine. For instance, off-target effects might introduce biases in our analyses using drug association data, while the known biases in gene association data discussed above might explain the results obtained for analyses involving gene association data. However, we would like to stress that the obtained results are remarkably stable across all employed data modalities (see distributions of the obtained local empirical P-values in Supplementary Figs. 3, 8, and 11). Since phenotype-based disease definitions are the only confounders that affect all data types, this is strong (but of course not conclusive) evidence that the observed local blurriness can indeed mainly be attributed to them.
We started our investigation with the question of whether biases introduced by phenotype- and organ-based disease mechanisms even out when mining large-scale disease association data for disease mechanisms – an assumption implicitly made by BEV medical research approaches. Our results indicate that this question has to be answered negatively, which has several consequences for the network medicine field and beyond.
Firstly, our findings imply that uncritical use of databases such as DisGeNET or OMIM which rely on phenotype-based disease definitions is problematic. Instead, we emphasize that close-up approaches remain the gold standard in network medicine, where data scientists collaborate with researchers from the biomedical sciences and jointly analyze molecular as well as deep phenotype data for the same patients. In such a collaborative setup, a positive feedback loop can emerge, where initial hypotheses about disease subtypes and their underlying pathomechanisms are formulated based on the analysis of molecular data, further refined using deep phenotyping (e.g., histological images, blood-derived biomarkers, etc.) and expert knowledge of the clinicians, and finally validated in preclinical studies (e.g., gain- or loss-of-function studies). As mentioned above, such approaches have already led to various important insights into specific disease mechanisms.
Secondly, unsupervised network medicine methods are needed, which not only return candidate pathomechanisms but at the same time de novo stratify patients into mechanistically distinct subgroups and hence do not rely on potentially misleading priorly available phenotypically defined subtype annotations. While few such approaches exist60–62, most existing pathomechanism mining methods still rely on phenotypic case-versus-control annotations63,64 or lists of genes associated with a (potentially ill-defined) disease term65–67.
Finally, we would like to point out that the current lack of mechanistic disease definitions not only hampers progress in (BEV) network medicine, but also has a detrimental effect on virtually all other data-centric approaches to, e.g., treatment design or diagnosis which rely on disease association data that utilize phenotype-based disease definitions. For instance, an artificial intelligence model for diagnosis assistance trained on genetic disease signatures will systematically produce unreliable results if the disease annotations used for training do not correspond to true endotypes. While we here quantified the effect of this problem in the context of BEV medicine, overcoming it would hence be beneficial for a large fraction of the biomedical research community.
## Compliance with ethical regulations
Our research complies with all relevant ethical regulations. The only non-public data used for this study is the comorbidity data we obtained from the Estonian Biobank. The Estonian *Biobank is* a population-based biobank managed by the Institute of Genomics at the University of Tartu. All participants have signed a broad consent upon joining the biobank, allowing their sample and data to be used for further research. ICD-10 diagnoses are obtained from epicrises, prescriptions and bills to the Health Insurance Fund. The work in this article was covered by the ethics approval “234T-12 Omics for Health” (March 19, 2014) by the Estonian Committee of Bioethics and Human Research. Data was released by the Estonian Biobank (release M11, July 24, 2019).
## Data integration
As shown in Table 1, the data sources used to create the different networks use a range of competing disease vocabularies to refer to diseases. We hence had to map these vocabularies to a common vocabulary to be able to investigate network (dis-)similarities. The similarity analyses were performed in MONDO (Monarch Disease Ontology), UMLS CUI, and ICD-10 vocabularies. Disease ID mapping to MONDO and ICD-10 was carried out via the two-step approach implemented in the NeDRex platform68: First, MONDO contains mappings between its own disease vocabulary and various other vocabularies, including OMIM, MeSH69, and ICD-10. Then, mappings between several vocabularies and ICD-10 could be achieved by mapping disease terms to MONDO, followed by mapping MONDO to ICD-10. Mapping to UMLS CUI was carried out using the mappings provided in the UMLS Metathesaurus 2022AA full release. For all pairwise analyses, the two compared networks were aligned before computing GEDs, i.e., only the nodes contained in both of them were taken into account. Table 1Data sources used for network constructionData sourceUsed disease vocabulariesData typeNetworks constructed from data sourceHPO86OMIM, Orphanet (ORPHA)Disease-symptomSymptom-based diseasomeDisGeNETConcept Unique Identifiers of Unified Medical Language System (UMLS CUI)Disease-gene, disease-variantGene-based diseasome, variant-based diseasome, disease-gene-gene-disease network, drug-protein-protein-drug network, drug-protein-protein-disease networkOMIMOMIMDisease-geneGene-based diseasome, disease-gene-gene-disease network, drug-protein-protein-disease networkDrugCentral37SNOMED Clinical Terms87 (SNOMEDCT)Drug-target, drug-indicationTarget-based drugome, indication-based drugome and drug-disease network, drug-protein-protein-drug network, drug-protein-protein-disease networkDrugBank88–Drug-targetTarget-based drugome, drug-protein-protein-drug network, drug-protein-protein-disease networkCTD48MeSHDrug-indicationDrug-disease network, indication-based drugomeIID89–Protein-protein interactionDisease-gene-gene-disease network, drug-protein-protein-drug network, drug-protein-protein-disease networkUniProt–Gene-proteinDrug-protein-protein-disease networkEstonian Biobank90ICD-10 (mixed three- and four-character codes)Comorbidity dataComorbidity-based diseasome The comorbidity data was obtained from the Estonian Biobank, which uses originally ICD-10 codes. In order to carry out analyses involving comorbidity data in MONDO or UMLS CUI vocabulary, the comorbidity data needed to be mapped from a coarser-grained (ICD-10) to a finer-grained disease vocabulary (MONDO and UMLS CUI). Although this is possible from a technical point of view, it would have introduced a lot of noise in the obtained comorbidity networks. To avoid overshadowing all other effects by the introduced noise, we decided to carry out analyses involving comorbidity data only in ICD-10 vocabulary. Consequently, all analyses involving comorbidity data were carried out only in ICD-10 vocabulary. On the other hand, the comparison between the target- and the indication-based drugomes was carried out only in MONDO vocabulary. In these networks, nodes are drugs and not diseases and using different disease vocabularies leaves the nodes of the networks unchanged. In the indication-based drogomes, the choice of the disease vocabulary can change the edges of the networks, but, in practice, we observed that the differences are small. Target-based drugomes are not affected at all by the choice of the disease ontology. Therefore, we only use MONDO for the comparison of drugomes.
Additionally, further data harmonization steps were carried out: Since HPO contains both general and specific terms, we pruned the data by removing very general symptom terms, using the existing hierarchy in HPO. More specifically, we decomposed the generated hierarchical phenotype network into its levels and removed the terms from the top three levels.
The diagnoses in around 140 K patients records available in the Estonian Biobank (April 2020 version used for this study) are encoded in ICD-10 vocabulary, and the records contain both three- and four-character ICD-10 codes. In order to generate uniform data, we therefore truncated all four-character codes to three-character level. Moreover, we removed diseases with incidence below five from the data, as well as the codes from the ICD-10 chapters XV (“Pregnancy, childbirth and the puerperium”), XVI (“Certain conditions originating in the perinatal period”), XVIII (“Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified”), XIX (“Injury, poisoning and certain other consequences of external causes”), XX (“External causes of morbidity and mortality”), XXI (“Factors influencing health status and contact with health services”), and XXII (“Codes for special purposes”).
## Network construction
For network construction, some part of the data such as disease-gene, drug-indication, drug-target, gene-encoding-protein, and PPI data were obtained from the databases shown in Table 1, using the data access and mapping provided by the NeDRex platform68. Disease-variant and disease-symptom associations were directly obtained from DisGeNET and HPO, respectively.
Supplementary Table 1 shows the most important properties of all constructed networks. The comorbidity-based diseasome was constructed via \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi$$\end{document}ϕ-correlation. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${I}_{i}$$\end{document}Ii denote the incidence of disease \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${C}_{{ij}}$$\end{document}Cij be the number of patients who were simultaneously diagnosed with diseases \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j. The comorbidity between the two diseases can be measured by1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\phi }_{{ij}}=\frac{{C}_{{ij}}N-{I}_{i}{I}_{j}}{\sqrt{{I}_{i}{I}_{j}(N-{I}_{i})(N-{I}_{j})}},$$\end{document}ϕij=CijN−IiIjIiIj(N−Ii)(N−Ij),where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N$$\end{document}N is the total number of patient records (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$N={{{{\mathrm{139,065}}}}}$$\end{document}$$n = 139$$, 065 for the Estonian Biobank data). When two diseases co-occur more frequently than expected by chance, we have \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\phi }_{{ij}} > 0$$\end{document}ϕij>0. We used one-tailed Fisher’s exact test followed by Benjamini-Hochberg correction for multiple testing to determine the significance of comorbidity associations and connected two diseases by an edge if adjusted \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\le 0.05$$\end{document}P≤0.05. Edge weights were defined using the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\phi$$\end{document}ϕ-correlation, i.e., we set \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{{ij}}={\phi }_{{ij}}$$\end{document}wij=ϕij for all diseases \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j with significant comorbidity association.
The indication- and target-based drugomes as well as the gene-, variant-, symptom-, and indication-based diseasomes were constructed based on the Jaccard index of the respective annotations. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${A}_{i}$$\end{document}Ai denotes the set of annotations for a disease or drug \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i used as node in the network under construction (e.g., when constructing the gene-based diseasome, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${A}_{i}$$\end{document}*Ai is* the set of all genes associated with disease \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i). We connected diseases \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j by an edge if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{|A}}_{i}\cap {A}_{j}|\ge 1$$\end{document}∣Ai∩Aj∣≥1 and defined the edge weights as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${w}_{{ij}}=\left|{A}_{i}\cap {A}_{j}\right|/\left|{A}_{i}\cup {A}_{j}\right|$$\end{document}wij=Ai∩Aj/Ai∪Aj. Disease nodes with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{|A}}_{i} |=0$$\end{document}∣Ai∣=0 were removed from the networks, i.e., empty annotation sets were treated as missing data.
The bipartite indication-based drug-disease network was directly constructed from the data source, i.e., we connected a disease \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i with a drug \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i is an indication for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j. For the bipartite target-based drug-disease network, we connected a disease \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i with a drug \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j if \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$j$$\end{document}j targets a protein encoded by a gene associated to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$i$$\end{document}i. In both drug-disease networks, edges are unweighted. Finally, we constructed drug-protein-protein-disease networks where drugs are connected to their targets, experimentally validated PPIs from IID are used to connect proteins, and diseases are connected to proteins encoded by disease-associated genes.
## Graph edit distance
GED is a widely used and generically applicable distance measure for attributed graphs38,39,70. It is defined as the minimum cost of transforming a source graph \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}=({V}_{1},{E}_{1})$$\end{document}G1=(V1,E1) into a target graph \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}=({V}_{2},{E}_{2})$$\end{document}G2=(V2,E2) via elementary edit operations, i.e., by deleting, inserting, and substituting nodes and edges. Equivalently, GED can be defined as the minimum edit cost induced by a node map \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2, where nodes maps \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi \subseteq ({V}_{1}\cup \{{\epsilon }_{1}\})\times ({V}_{2}\cup \{{\epsilon }_{2}\})$$\end{document}π⊆(V1∪{ϵ1})×(V2∪{ϵ2}) are relations that cover all nodes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u\in {V}_{1}$$\end{document}u∈V1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v\in {V}_{2}$$\end{document}v∈V2 exactly once (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\epsilon }_{1}$$\end{document}ϵ1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\epsilon }_{2}$$\end{document}ϵ2 are dummy nodes that may be covered multiple times or left uncovered)71.
We used a customized version of GED to compare the different diseasomes, drugomes, and drug-disease networks constructed as detailed in the previous section as well as their randomized counterparts. Since the networks were aligned before all pairwise comparisons, we had \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${V}_{1}={V}_{2}=V$$\end{document}V1=V2=V (node sets are identical) whenever comparing two networks. Consequently, we fixed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π as the identity and computed GED as the sum of edge edit costs induced by the identity (the edge edit cost functions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{sub}}}}}}$$\end{document}sub, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{del}}}}}}$$\end{document}del, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{ins}}}}}}$$\end{document}ins are explained below):2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}\left({G}_{1},\,{G}_{2}\right)={\sum }_{{uv}\in {E}_{1}\cap {E}_{2}}{{{{{\rm{sub}}}}}}\left({uv}\right)+{\sum }_{{uv}\in {E}_{1}{{\backslash }}{E}_{2}}{{{{{\rm{del}}}}}}({uv})+{\sum }_{{uv}\in {E}_{2}{{\backslash }}{E}_{1}}{{{{{\rm{ins}}}}}}({uv})$$\end{document}GEDG1,G2=∑uv∈E1∩E2subuv+∑uv∈E1\E2del(uv)+∑uv∈E2\E1ins(uv) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1},\, {G}_{2})$$\end{document}GED(G1,G2) quantifies the global distance between the graphs \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2. Since the node sets of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2 are identical in our analyses, it can be decomposed as3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1},\,{G}_{2})={\sum }_{u\in V}{{{{{\rm{GED}}}}}}({G}_{1},\, {G}_{2},\, u)/2,$$\end{document}GED(G1,G2)=∑u∈VGED(G1,G2,u)/2,where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1},\, {G}_{2},\, u)$$\end{document}GED(G1,G2,u) is the local distance between the neighborhood \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${N}_{1}(u)$$\end{document}N1(u) of node \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and its neighborhood \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${N}_{2}(u)$$\end{document}N2(u) in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2. The local distances are defined as follows:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1},\, {G}_{2},\, u)={\sum }_{v\in {N}_{1}(u)\cap {N}_{2}(u)}{{{{{\rm{sub}}}}}}({uv})+{\sum }_{v\in {N}_{1}(u){{{{{\rm{\backslash }}}}}}{N}_{2}(u)}{{{{{\rm{del}}}}}}({uv})+{\sum }_{v\in {N}_{2}(u){{{{{\rm{\backslash }}}}}}{N}_{1}(u)}{{{{{\rm{ins}}}}}}({uv})$$\end{document}GED(G1,G2,u)=∑v∈N1(u)∩N2(u)sub(uv)+∑v∈N1(u)\N2(u)del(uv)+∑v∈N2(u)\N1(u)ins(uv) Based on the local distances, we also computed cluster-level distances for a cluster of nodes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C\subseteq V$$\end{document}C⊆V as follows:5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1},\, {G}_{2},\, C)={\sum }_{u\in C}{{{{{\rm{GED}}}}}}({G}_{1},\, {G}_{2},\, u)/2$$\end{document}GED(G1,G2,C)=∑u∈CGED(G1,G2,u)/2 We used three types of edge edit cost functions, namely, uniform costs and costs based on normalized edge ranks or normalized edge weights. The uniform costs are defined by simply setting \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{sub}}}}}}({uv})=0$$\end{document}sub(uv)=0 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{del}}}}}}({uv})={{{{{\rm{ins}}}}}}({uv})=1$$\end{document}del(uv)=ins(uv)=1 for all edges \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${uv}$$\end{document}uv. GED with uniform costs quantifies topological (dis-)similarity between two graphs but does not consider edge weights. Since edges are weighted in all compared diseasomes, we additionally defined edge edit costs based on normalized weights and normalized ranks. For the normalized weights, we scaled all edge weights to the interval \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[{{{{\mathrm{0,1}}}}}]$$\end{document}[0, 1] via division by the maximum. For the normalized ranks, we sorted the diseasomes’ edges in increasing order with respect to their weights and then again normalized the obtained ranks to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[{{{{\mathrm{0,\, 1}}}}}]$$\end{document}[0, 1] via division by the maximum rank. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{1}({uv})$$\end{document}x1(uv) be the normalized weight/rank of edge \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${uv}$$\end{document}uv in diseasome \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{2}({uv})$$\end{document}x2(uv) be its normalized weight/rank in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2. Then we defined the weight-/rank-based edit costs as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{sub}}}}}}({uv})=|{x}_{1}({uv})-{x}_{2}({uv})|$$\end{document}sub(uv)=∣x1(uv)−x2(uv)∣, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{del}}}}}}({uv})={x}_{1}({uv})$$\end{document}del(uv)=x1(uv), and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{ins}}}}}}({uv})={x}_{2}({uv})$$\end{document}ins(uv)=x2(uv). That is, substitutions are expensive if the involved edge’s normalized weight/rank differs a lot in the two graphs and deletions and insertions are more expensive for high-weighed/high-ranked than for low-weighed/low-ranked edges. Since uniform, weight-based and rank-based edit costs led to similar results, we only present the results for uniform costs in the main article. Results for weight- and rank-based edit costs are shown in the supplement.
## Statistical analyses based on graph edit distances
Using GED, we tested the local- and the global-scale hypotheses as follows: For each pair \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2 of compared networks, we generated 1,000 randomized counterparts \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}^{1},\ldots,{G}_{1}^{1000}$$\end{document}G11,…,G11000 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}^{1},\ldots,{G}_{2}^{1000}$$\end{document}G21,…,G21000. For this, we used a random network generator which repeatedly swaps edges and non-edges to obtain randomized counterparts which exactly preserve the node degrees of the original networks72,73. For each node \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u, we then computed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1},\, {G}_{2},\, u)$$\end{document}GED(G1,G2,u) as well as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1}^{i},\, {G}_{2}^{i},\, u)$$\end{document}GED(G1i,G2i,u) for each \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$i = 1$,\ldots,1000$$\end{document}$i = 1$,…,1000 and also computed the global distances \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1},\,{G}_{2})$$\end{document}GED(G1,G2) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{\rm{GED}}}}}}({G}_{1}^{i},\,{G}_{2}^{i})$$\end{document}GED(G1i,G2i).
To test the global-scale hypothesis, we computed one-sided empirical P-values as6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P=\left(1+{\sum }_{$i = 1$}^{1000}\left[{{{{{\rm{GED}}}}}}\left({G}_{1},\, {G}_{2}\right)\ge {{{{{\rm{GED}}}}}}\left({G}_{1}^{i},\, {G}_{2}^{i}\right)\right]\right)/(1+1000),$$\end{document}$$P \leq 1$$+∑$i = 11000$GEDG1,G2≥GEDG1i,G2i/(1+1000),where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[{{{{{\rm{true}}}}}}]=1$$\end{document}[true]=1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[{{{{{\rm{false}}}}}}]=0$$\end{document}[false]=0. To test the local-scale hypothesis, we used the one-sided MWU test to assess whether the local distances \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{{{{{{\rm{GED}}}}}}({G}_{1},\,{G}_{2},\,u){|u}\in V\}$$\end{document}{GED(G1,G2,u)∣u∈V} for the original networks are significantly smaller than the local distances \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{{{{{{\rm{GED}}}}}}({G}_{1}^{i},\, {G}_{2}^{i},\, u){|u}\in V,$i = 1$,\ldots,1000\}$$\end{document}{GED(G1i,G2i,u)∣u∈V,$i = 1$,…,1000} for the randomized counterparts. Moreover, we computed node-specific local empirical P-values as7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(u)=\left(1+{\sum }_{$i = 1$}^{1000}\left[{{{{{\rm{GED}}}}}}\left({G}_{1},\, {G}_{2},\, u\right)\ge {{{{{\rm{GED}}}}}}\left({G}_{1}^{i},\, {G}_{2}^{i},\, u\right)\right]\right)/\left(1+1000\right)$$\end{document}P(u)=1+∑$i = 11000$GEDG1,G2,u≥GEDG1i,G2i,u/1+1000and cluster-level empirical P-values as8\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P(C)=\left(1+{\sum }_{$i = 1$}^{1000}\left[{{{{{\rm{GED}}}}}}\left({G}_{1},\, {G}_{2},\, C\right)\ge {{{{{\rm{GED}}}}}}\left({G}_{1}^{i},\, {G}_{2}^{i},\, C\right)\right]\right)/\left(1+1000\right)$$\end{document}P(C)=1+∑$i = 11000$GEDG1,G2,C≥GEDG1i,G2i,C/1+1000where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C\subseteq V$$\end{document}C⊆V is a cluster of nodes.
Note that we consciously refrained from adjusting P-values for multiple testing. The reason for this choice is that the relevance of our results stems from the non-significance of a large fraction of the obtained P-values. If we had corrected for multiple testing, we would have inflated this fraction.
## Rationale for using the graph edit distance as a measure of network dissimilarity
In addition to our version of GED, there are various other network dissimilarity measures–most notably, embedding-based74,75, kernel-based76, and message-passing-based77,78 approaches. We decided to use GED because, to the best of our knowledge, it is the only distance measure satisfying the following requirements necessary for our analyses:To allow testing both the global- and the local-scale hypothesis, we need a graph distance measure \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d({G}_{1},\,{G}_{2})$$\end{document}d(G1,G2) which is decomposable into local node distances \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d({G}_{1},\,{G}_{2},\,u)$$\end{document}d(G1,G2,u).The local node distances \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d({G}_{1},\,{G}_{2},\,u)$$\end{document}d(G1,G2,u) should depend on \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u’s local neighborhoods in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2 but not on the overall network topologies (otherwise, we would not be testing the local-scale hypothesis when comparing local node distances).*Since a* node alignment between the compared networks is given (disease and drug terms are aligned between the networks), both the global network distance \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d({G}_{1},\,{G}_{2})$$\end{document}d(G1,G2) and the local distances \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$d({G}_{1},\,{G}_{2},\,u)$$\end{document}d(G1,G2,u) should be node-identity-aware rather than permutation-invariant. The distances need to be computable in linear time w.r.t. the size of the networks in order to enable our large-scale permutation tests.
While most of the kernel-based methods already fall short of requirement 1, popular node-embedding-based approaches (e.g., node2vec74 with subsequent distance computation in embedding space) typically do not satisfy requirements 2 through 4. Exceptions we are aware of are DeltaCon79 (which satisfies requirements 1, 3, and 4 but not requirement 2) and the graphlet degree signature80 (which satisfies requirements 1 and 2 but not requirements 3 and 4). Highly successful techniques in graph learning follow a message passing concept77,78. When restricted to a single hop (as needed to satisfy requirement 2), these methods define node \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u’s embedding in the graph \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{1}(u)=g(\{l(v){|v}\in {N}_{1}(u)\})$$\end{document}x1(u)=g({l(v)∣v∈N1(u)}), where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l(v)$$\end{document}l(v) is the label of node \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$v$$\end{document}v (its disease or drug term) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g$$\end{document}g is a permutation-invariant function77 mapping sets to vectors (e.g., indicator function). Here, using unique node labels renders the method node-identity-aware and allows to drop \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l(\cdot)$$\end{document}l(⋅) as a parameter of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g$$\end{document}g. Such approaches fulfill all four requirements, but are essentially equivalent to GED with uniform edge costs: By comparing \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$u$$\end{document}u’s embeddings \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{1}(u)$$\end{document}x1(u) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{2}(u)$$\end{document}x2(u), we compare the node labels of its neighboring nodes in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2, which is exactly what we do with uniform GED.
## Statistical analyses based on shortest path distances
We carried out analyses based on shortest path distances between [1] all disease-disease pairs in a disease-gene-gene-disease network, [2] all drug-drug pairs in a drug-protein-protein-drug network, and [3] all disease-drug pairs in a disease-protein-protein-drug network. For each network, we split the multi-set of obtained distances into multi-sets \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{0}$$\end{document}X0 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1}$$\end{document}X1, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1}$$\end{document}X1 contains the shortest path distances for all nodes pairs contained as edge in a reference network and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{0}$$\end{document}X0 contains all other shortest path distances. As reference networks, we used [1] drug-, symptom-, comorbidity-, and variant-based diseasomes, [2] a bipartite drug-indication network, and [3] an indication-based drug-drug network. We then used the one-sided MWU test to assess whether the shortest path distances contained in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{1}$$\end{document}X1 are significantly smaller than those contained in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{0}$$\end{document}X0.
## BEV network medicine is committed to the local- and the global-scale hypotheses
Recall that we have introduced BEV network medicine as the subfield of network medicine which aims at uncovering disease mechanisms by mining large-scale disease-association data. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 be data used towards this end by BEV network medicine approaches and let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d}_{1}$$\end{document}d1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${d}_{2}$$\end{document}d2 be two diseases sharing an (unknown) molecular mechanisms \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M$$\end{document}M such that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 contains entries \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}({d}_{1})$$\end{document}D1(d1) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}({d}_{2})$$\end{document}D1(d2). If \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 contains any useful information about disease mechanisms as assumed by BEV network medicine, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M$$\end{document}M should lead to significant similarities between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}({d}_{1})$$\end{document}D1(d1) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}({d}_{2})$$\end{document}D1(d2). The same holds for any other data \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{2}$$\end{document}D2 used as input by BEV network medicine. BEV network medicine is hence implicitly committed to the claim that the edge distributions of diseasomes \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{1}$$\end{document}G1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${G}_{2}$$\end{document}G2 constructed based on similarities in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{1}$$\end{document}D1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${D}_{2}$$\end{document}D2 exhibit a higher correlation than expected by chance. This, in turn, implies both the global- and the local-scale hypothesis.
## Implementation
We have implemented all network analysis approaches underlying this article in a Python package called GraphSimQT. GraphSimQT uses graph-tool81 for network handling and Scipy82 for carrying out statistical tests and comes with all networks and scripts to reproduce the results reported in this paper. Moreover, GraphSimQT can be used to compare user-provided networks, using the techniques presented in this paper. Significance of comorbidity associations was evaluated using the Scipy implementation of Fisher’s exact test and the statsmodels83 implementation of Benjamini-Hochberg multiple testing correction. The GraphSimViz web tool (https://graphsimviz.net) was implemented using Vue.js as a frontend framework, the Drugst. One (https://drugst.one) plugin as network explorer and a Django backend with a PostgreSQL database.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37349-4.
## Source data
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## Peer review information
Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: Gene-environment interaction explains a part of missing heritability in human
body mass index
authors:
- Hae-Un Jung
- Dong Jun Kim
- Eun Ju Baek
- Ju Yeon Chung
- Tae Woong Ha
- Han-Kyul. Kim
- Ji-One Kang
- Ji Eun Lim
- Bermseok Oh
journal: Communications Biology
year: 2023
pmcid: PMC10039928
doi: 10.1038/s42003-023-04679-4
license: CC BY 4.0
---
# Gene-environment interaction explains a part of missing heritability in human body mass index
## Abstract
Gene-environment (G×E) interaction could partially explain missing heritability in traits; however, the magnitudes of G×E interaction effects remain unclear. Here, we estimate the heritability of G×E interaction for body mass index (BMI) by subjecting genome-wide interaction study data of 331,282 participants in the UK Biobank to linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics (LDAK-SumHer) analyses. Among 14 obesity-related lifestyle factors, MET score, pack years of smoking, and alcohol intake frequency significantly interact with genetic factors in both analyses, accounting for the partial variance of BMI. The G×E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer are as follows: MET score, $0.45\%$ (0.12) and $0.65\%$ (0.24); pack years of smoking, $0.52\%$ (0.13) and $0.93\%$ (0.26); and alcohol intake frequency, $0.32\%$ (0.10) and $0.80\%$ (0.17), respectively. Moreover, these three factors are partially validated for their interactions with genetic factors in other obesity-related traits, including waist circumference, hip circumference, waist-to-hip ratio adjusted with BMI, and body fat percentage. Our results suggest that G×E interaction may partly explain the missing heritability in BMI, and two G×E interaction loci identified could help in understanding the genetic architecture of obesity.
A study on the effects of gene-environment interactions on body mass index (BMI) in the UK Biobank cohort finds BMI genetic loci that interact with lifestyle traits such as physical activity, smoking, and alcohol consumption.
## Introduction
Genome-wide association studies (GWASs) have uncovered several genetic variants that affect complex traits1–3, enhanced our understanding of the development of diseases, and provided genetic targets for the treatment of diseases2,4. However, the effect sizes of individual variants discovered by GWAS are relatively small—in the range of 1.01–1.20—such that the polygenic risk score (PRS), summing up all effects of GWAS single nucleotide polymorphisms (SNPs), explains less than $10\%$ of the phenotypic variance in most traits4–7. Therefore, heritability (the estimated genetic effects on traits) based on SNPs was far lower than that estimated by traditional analyses based on twin and family studies7, even though they may be inflated due to confounding with shared environmental effects with in families or twins8. Recent studies, including those using the Genome-wide complex trait analysis, a method calculating the SNP heritability from all possible common variants, suggested that common genetic variants can explain up to $70\%$ of the variation in height and $40\%$ of the variation in body mass index (BMI)9–12. Despite these progresses, substantial missing heritability remains in most complex traits7,13–16.
Complex traits are also affected by environmental factors, and the environmental effect can modify genetic risk factors, known as gene-environment (G × E) interaction17–21. Genetic variants within the FTO gene that exhibit the largest effect size on obesity were found to interact with environmental factors, such as physical activity, diet, alcohol consumption, and sleep duration22–27. The effect size of FTO variants on BMI was reduced by increased physical activity and enhanced by decreased physical activity22–25. These findings indicate that genetic susceptibility to disease can be modulated by altering environmental factors22,25,28,29, and also suggest that the G × E interaction may contribute to missing heritability30.
Generally, it is difficult to accurately measure environmental exposures, because many cases depend on self-reported questionnaires for past exposures29,31. In addition, the analysis of G × E interaction generally requires a higher sample size than that used in genetic association analysis20. Therefore, the identification of genetic variants having true G × E interaction effects in complex human traits has been challenging21,31–33. Nevertheless, recent progress in the analysis of G × E interaction has shed light on the importance of their effect on trait phenotype. Poveda et al. investigated the G × E interaction in cardiometabolic traits in the VIKING study, which involved a cohort of 16,430 Swedish adults from 1682 extended pedigrees34. They found statistically significant effects of gene-age and gene-alcohol intake interactions on weight, as well as effects of gene-age interactions on systolic blood pressure, using quantitative genetic analysis in extended pedigrees. Further, Justice et al. performed a genome-wide analysis of gene-smoking interaction on obesity-related traits in 241,258 samples (51,080 current smokers and 190,178 nonsmokers) to understand the effects of smoking on genetic susceptibility to obesity35. They identified 31 genetic loci for gene-smoking interaction, and gene set analysis using these genetic loci revealed various pathways, including response to oxidative stress and addictive behavior, where dysregulation may lead to increased susceptibility to obesity. In addition, two studies investigated the presence of G × E interaction in BMI and estimated genetic risk using PRS calculated from GWAS statistics28,29. They used the UK Biobank GWAS data, which include 500,000 participants with lifestyle measurements, and found significant G × E interactions of PRS with physical activity, alcohol consumption, and socioeconomic status in BMI. These studies demonstrate the statistical significance of the G × E interaction in complex traits. However, it is still unclear how much the G × E interaction can explain the effect of phenotypic variance on the extent of heritability.
Recently, two studies estimated the extent by which the G × E interaction explained the phenotypic variance of obesity. Robinson et al. estimated the difference in the heritability of G × E interaction in eight self-reported lifestyle variables, including diet, exercise, and smoking36. They found an evidence for the genetic interaction effect with smoking behavior, which was estimated to contribute $4.0\%$ to the BMI variation. Sulc et al. investigated the contribution of G × E interaction to obesity-related traits from the UK Biobank using a maximum likelihood method37. Previously, Wang et al. showed the presence of G × E interaction by estimating the difference in phenotype variance in different genotype groups of subjects31. Sulc et al. applied the assumption of Wang et al. that the estimation of G × E interaction in a genome-wide level is presumably affected by all environmental risk factors. Using this approach, Sulc et al. found that G × E interaction effects of genome-wide SNPs explained $1.9\%$ of the variance in BMI in addition to the $15\%$ contributed by genetic risk factors. However, Sulc et al. did not use the real measurements of environmental factors; therefore, their findings for the effect size of G × E interaction on BMI require validation.
In this study, we aimed to [1] provide evidence for the heritability of G × E interaction for BMI using real environment measurements, [2] estimate the effect size of the G × E interaction heritability for BMI, and [3] identify novel genetic loci that interact with environmental factors to affect BMI. We used the UK *Biobank data* for this analysis, which included 331,282 participants with 4,143,506 SNPs and 14 obesogenic lifestyles. To calculate G × E interaction heritability, we used both linkage disequilibrium score regression (LDSC) and linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics (LDAK-SumHer)38,39.
## Basic characteristics of participants related to BMI
We selected unrelated 331,282 UK Biobank participants of “White-British” European ancestry for this study, similar to those used in the Neale lab (https://github.com/Nealelab/UK_Biobank_GWAS). The lifestyle characteristics of the 331,282 participants are summarized in Table 1. The participants were divided into quartiles based on their BMI values: men and women were separately divided into quartiles and then the men and women in the same quartile group were combined into one responding quartile group. The average BMI of all participants was 27.39 (SD = 4.75), and the average BMIs were 22.32 (SD = 1.62) for the first, 25.40 (SD = 1.01) for the second, 28.10 (SD = 1.02) for the third, and 33.75 (SD = 3.85) for the fourth quartile groups. Table 1Basic characteristics of UK Biobank participants included in this study. GroupQuartile 1 groupQuartile 2 groupQuartile 3 groupQuartile 4 groupFemale thresholdBMI ≤ 23.4223.42 < BMI ≤ 26.0626.06 < BMI ≤ 29.6329.63 < BMIMale thresholdBMI ≤ 24.9924.99 < BMI ≤ 27.3027.30 < BMI ≤ 30.0430.04 < BMINumber of participants82,86982,79982,82382,791Males (%)46.2346.2646.2146.24Age at assessment center (years) (mean, SD)55.81 ± 8.1856.89 ± 7.9857.40 ± 7.8657.16 ± 7.72Body mass index (kg/m2) (mean, SD)22.32 ± 1.6225.40 ± 1.0128.10 ± 1.0233.75 ± 3.85Met score Met score mean (SD)2895.06 ± 2771.152768.21 ±2699.872657.59 ± 2719.932323.56 ±2603.84Moderate Physical activity Frequency mean (SD)3.87 ± 2.313.72 ± 2.293.58 ± 2.313.29 ± 2.36Time spent watching television (TV) Spent time mean (SD)2.35 ± 1.552.66 ± 1.522.92 ± 1.583.29 ± 1.75Time spent using computer Spent time mean (SD)0.98 ± 1.241.02 ± 1.261.06 ± 1.331.16 ± 1.47Smoking status (%) Never59.3556.1152.7250.76 Previous smoker29.0334.2737.6939.95 Current smoker11.629.619.69.28Pack years of smoking Pack years of smoking mean (SD)6.26 ± 13.497.09 ± 14.118.83 ± 15.8411.12 ± 18.87Alcohol intake status (%) Never2.932.482.883.93 Previous3.252.693.194.46 Current93.8294.8393.9391.61Alcohol intake frequency Category mean (SD)2.70 ± 1.472.69 ± 1.422.82 ± 1.463.16 ± 1.52Neuroticism score Neuroticism score mean (SD)4.17 ± 3.263.99 ± 3.204.06 ± 3.254.23 ± 3.30Fed-up feelings Yes (%)36.6937.3540.8847.42Sleep duration Sleep duration mean (SD)7.18 ± 1.017.19 ± 1.027.18 ± 1.097.13 ± 1.21Nap during day (%) Never/rarely63.159.1355.3349.07 Sometimes32.7836.439.543.96 Usually4.124.475.176.97Average total household income before tax Category mean (SD)2.76 ± 1.202.71 ± 1.182.60 ± 1.182.44 ± 1.16Townsend deprivation index (TDI) Townsend deprivation index mean (SD)−1.68 ± 2.91−1.83 ± 2.79−1.67 ± 2.85−1.14 ± 3.10Data, mean ± standard deviation (SD).BMI body mass index, MET score metabolic equivalent of task score.
Physical activity (category ID: 100054), metabolic equivalent task (MET) scores (category ID: 54), mental health (category ID: 100060), smoking (category ID: 100058), alcohol (category ID: 100051), sleep (category ID: 100057), household (category ID: 100066), and baseline characteristics (category ID: 100094) have been repeatedly reported to be related to obesity24,29,40. We selected 14 lifestyle factors among these categories for this study, based on previous reports, while ensuring a sample size of at least 250,000 individuals with lifestyle measures29. The 14 lifestyle variables are shown in Table 1, and the field IDs of these lifestyle variables are described in the Materials and Methods. The average values of lifestyle variables or the percentages in each BMI group are also provided in Table 1. Moreover, the distributions of the 14 lifestyle variables in all 331,282 participants are depicted as histograms, and both distributions of raw and processed variables for normalization are shown in Supplementary Fig. 1. The method for the processing of raw data is described in the Materials and Methods.
We performed association analysis between BMI as a continuous trait and the 14 lifestyle factors using linear regression (Supplementary Table 1). All 14 lifestyle factors showed a significant association under multiple testing ($P \leq 3.57$ × 10−3) with BMI adjusted for age and sex in both raw and processed variables. We found positive correlations between BMI and pack years of smoking, smoking status, time spent watching TV, fed-up feelings, neuroticism score, townsend deprivation index at recruitment (TDI), nap during day, alcohol intake frequency, and time spent using computer. In contrast, negative correlations were seen between BMI and MET score, sleep duration, moderate physical activity, alcohol intake status, and average total household income before tax (Supplementary Table 1). The analysis of lifestyle factors in the physical activity and MET category, such as summed MET minutes per week for all activity, moderate physical activity, time spent using computer, and time spent watching television, clearly demonstrated that participants with more active lifestyles tended to have a lower BMI, and participants with sedentary lifestyles exhibited a higher BMI. However, the relationships of other lifestyle factors with BMI were not as clear (Table 1). Moreover, we assessed the correlation coefficients between BMI and lifestyle factors as well as between lifestyle factors using a correlogram (Supplementary Fig. 2).
## Effect of G × E interaction on BMI
We tested the effect of G × E interactions between the 4,143,506 SNPs (minor allele frequency [MAF] ≥ 0.05) and individual lifestyle factors on BMI as a continuous trait, using fixed effect models of linear regression (PLINK v.1.90). To investigate genomic inflation, we estimated the genomic control lambda and the intercept value of LD score regression on each GWIS result (Supplementary Table 2). The intercept value of LD score regression indicates statistical inflation adjusted for LD structure and is considered as a tool for a more powerful and accurate correction than the genomic control lambda38. Since the intercept value of alcohol intake status (1.38), alcohol intake frequency (1.17), and sleep duration (1.17) were higher than 1.1, we applied the genomic control correction using lambda values to these lifestyle factors. As a result, the intercept values become decreased after the correction: alcohol intake status, 0.99; alcohol intake frequency; 0.96; sleep duration 0.98 (Supplementary Table 2). The results of G × E interaction using processed variables are depicted in Fig. 1 as Manhattan plots and in Supplementary Fig. 3 as quantile–quantile (Q–Q) plots. We found two genome-wide significant signals for multiple testing ($P \leq 3.57$ × 10−9 calculated by 5.00 × 10−$\frac{8}{14}$) in the lifestyle factors analyzed, as shown in Supplementary Table 3. The suggestive associations of G × E interaction with $P \leq 5.00$ × 10−6 are provided in Supplementary Table 4.Fig. 1Manhattan plots of gene-environment interaction study. Manhattan plots showing the –log10-transformed gene-environment interaction P-value of each SNP on the y-axis and base-pair positions along the chromosomes on the x-axis. SNP P-values were computed in Plink 1.90. The blue line indicates the suggestive threshold ($P \leq 5.00$ × 10−6). The red line indicates the genome-wide significance for multiple testing ($P \leq 3.57$ × 10−9). a Metabolic equivalent task (MET) score, b Moderate physical activity, c Time spent wathcing television (TV), d Time spent using computer, e Smoking status, f Pack years of smoking, g Alcohol intake status (the GWIS after genomic control), h Alcohol intake frequency (the GWIS after genomic control), i Neuroticism score, j Fed-up feelings, k Sleep duration (the GWIS after genomic control), l Nap during day, m Average total household income before tax, n Townsen deprivation index at recruitment.
Further, we investigated functional annotation of these two G × E interaction lead SNPs. First, we searched for functional importance of these SNPs using RegulomeDB (Supplementary Table 5). Second, we evaluated any association of genetic variants existing within the 1 Mb flanking region of the lead SNPs with obesity or obesity-related traits (Supplementary Table 6). The direct association of lead SNPs with any traits was also determined using the GWAS catalog (Supplementary Table 7). Third, we investigated the expression quantitative trait loci (eQTL) information using Genotype-Tissue Expression (GTEx) version 8 for lead SNPs shown genome-wide significance level (Supplementary Table 8)41. All two lead SNPs had genetic variants within the 1 Mb flanking region associated with BMI. The direct association of rs11642015 (interacting with alcohol intake frequency) with the G × E interaction in BMI and that of rs12438181 (interacting with smoking status) with pulmonary function in smokers were reported, and two lead SNPs showed eQTL genes as sentinel SNPs (r2 = 1).
## Heritability of G × E interaction
Based on the results of genome-wide G × E interaction tests, we calculated the heritability of G × E interaction for BMI using LDSC v1.0.1 (https://github.com/bulik/ldsc) (Table 2)38. Among the 14 lifestyle factors, MET score ($$P \leq 3.38$$ × 10−3), time spent watching television ($$P \leq 1.13$$ × 10−3), pack years of smoking ($$P \leq 1.74$$ × 10−4), alcohol intake frequency ($$P \leq 1.26$$ × 10−6), and fed-up feeling ($$P \leq 2.24$$ × 10−3) showed statistical significance, based on the Bonferroni multiple correction ($P \leq 3.57$ × 10−3) (Table 2). The heritability of G × E interaction for alcohol intake frequency in BMI was hG×E2 = 0.0080, which suggests that $0.80\%$ of the BMI phenotypic variance in the population might be attributed to the genetic interaction with alcohol intake frequency. Similarly, the heritability of G × E interaction for MET score was hG×E2 = 0.0065, that of time spent watching TV was hG×E2 = 0.0058, that of pack years of smoking was hG×E2 = 0.0093, and that of fed-up feelings was hG×E2 = 0.0054.Table 2G × E interaction heritability in BMI calculated using the LDSC method. CategoryLifestyle factorNG × E interaction heritability % (SE)P-valueInterceptPhysical activityMET scorea268,5360.65 (0.24)3.38 × 10−31.084Moderate physical activity315,7470.41 (0.16)5.20 × 10−31.025Physical activityTime spent watching television (TV)a328,9430.58 (0.19)1.13 × 10−31.090(Leisure life)Time spent using computer328,8241.22 × 10−3 (0.21)5.00 × 10−11.087SmokingSmoking Status330,1380.31 (0.18)4.17 × 10−21.086Pack years of smokinga279,7580.93 (0.26)1.74 × 10−41.070AlcoholAlcohol intake statusb330,9950.32 (0.17)2.99 × 10−20.985Alcohol intake frequencya,b331,0490.80 (0.17)1.26 × 10−60.960Mental healthNeuroticism score269,3920.52 (0.22)9.05 × 10−31.040Fed-up feelingsa324,7530.54 (0.19)2.24 × 10−31.023SleepSleep durationb329,5230.09 (0.16)2.87 × 10−10.981Nap during day331,1410.18 (0.19)1.70 × 10−11.060Social economic statusAverage total household income before tax285,5440.28 (0.18)5.99 × 10−20.996Townsend deprivation index at recruitment330,8930.42 (0.17)6.70 × 10−31.083MET score metabolic equivalent of task score, SE standard error.aIndicates the statistically significant G × E interaction heritability based on the Bonferroni corrected P-value threshold (P-value < 3.57 × 10−3).bIndicates the GWIS after genomic control.
To confirm these estimates of G × E heritability calculated by LDSC, we used another approach, the LDAK-SumHer v.5.1 method (http://dougspeed.com/ldak/), which is based on a different algorithm from LDSC39,42. The heritabilities of G × E interaction calculated using the LDAK-SumHer method are presented in Table 3. Similar to the LDSC results, MET score ($$P \leq 1.28$$ × 10−4), pack years of smoking ($$P \leq 3.04$$ × 10−5), and alcohol intake frequency ($$P \leq 5.15$$ × 10−4) showed statistical significances, based on the Bonferroni multiple correction ($P \leq 3.57$ × 10−3) (Table 3). However, the effects of time spent watching TV ($$P \leq 1.29$$ × 10−2) and fed-up feelings ($$P \leq 9.74$$ × 10−3) did not reach a significance level based on the multiple correction in the LDAK-SumHer analysis. The heritability of G × E interaction for alcohol intake frequency was hG×E2 = 0.0032, that for MET score was hG×E2 = 0.0045, and that for pack years of smoking was hG×E2 = 0.0052 in LDAK-SumHer analysis. The P-values for the heritability of G × E interaction calculated by LDSC and LDAK-SumHer methods are compared in Fig. 2, which show a similar pattern between them across lifestyle factors. Table 3G × E interaction heritability calculated using the LDAK-SumHer method. CategoryLifestyle factorNG × E interaction heritability % (SE)P-valuePhysical activityMET scorea268,5360.45 (0.12)1.28 × 10−4Moderate physical activity315,7470.17 (0.10)4.48 × 10−2Physical activityTime spent watching television (TV)328,9430.23 (0.10)1.29 × 10−2(Leisure life)Time spent using computer328,8245.00 × 10−4 (0.097)5.00 × 10−1SmokingSmoking Status330,1380.22 (0.10)1.79 × 10−2Pack years of smokinga279,7580.52 (0.13)3.04 × 10−5AlcoholAlcohol intake statusb330,9950.14 (0.09)5.71 × 10−2Alcohol intake frequencya,b331,0490.32 (0.10)5.15 × 10−4Mental healthNeuroticism score269,3920.14 (0.12)1.14 × 10−1Fed-up feelings324,7530.23 (0.098)9.74 × 10−3SleepSleep durationb329,5230.14 (0.09)5.12 × 10−2Nap during day331,1410.13 (0.099)9.90 × 10−2Social economic statusAverage total household income before tax285,5440.18 (0.11)4.48 × 10−2Townsend deprivation index at recruitment330,8930.23 (0.10)1.16 × 10−2MET score metabolic equivalent of task score, SE standard error.aIndicates the statistically significant G × E interaction heritability based on the Bonferroni corrected P-value threshold (P-value < 3.57 × 10−3).bIndicates the GWIS after genomic control. Fig. 2Bar plots of G × E interaction heritability in BMI.Comparison of results for G × E interaction heritability calculated by LDSC (a) and LDAK-SumHer (b). The x-axis indicates the –log10 G × E interaction heritability P-value. MET: metabolic equivalent task; TDI: Townsend depriviation index at recruitment; LDSC: linkage disequilibrium score regression; LDAK-SumHer: linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics.
## G × E interaction of MET score, pack years of smoking, and alcohol intake frequency in other obesity-related traits
To investigate whether MET score, pack years of smoking, and alcohol intake frequency also showed statistically significant heritability of G × E interaction in other obesity-related traits, we tested the G × E interaction in waist circumference (WC), hip circumference (HC), waist-to-hip ratio adjusted with BMI (WHRadjBMI), and body fat percentage (BFP). The lifestyle characteristics of participants related to these obesity-related traits are summarized in Supplementary Tables 9–12. Similar to that done when determining BMI, the participants were divided into quartiles based on their trait values, and as expected, the average BMI increased in each successive group for all traits. The mean value of MET score in each quartile group demonstrated that participants with more active lifestyles generally exhibited lower values of those obesity-related traits. Higher pack years of smoking and alcohol intake frequency were generally related to higher BMI values. The MET score, pack years of smoking, and alcohol intake frequency showed a significant association under multiple testing ($P \leq 4.16$ × 10−3) with all obesity-related traits (Supplementary Table 13). Similar to the results for BMI, MET score exhibited negative effect size (β) in all obesity-related traits, whereas alcohol intake frequency and pack years of smoking showed positive effect size (β) in all obesity-related traits. The distributions of raw and processed variables for obesity-related traits in the population are shown in Supplementary Fig. 4 as histograms.
Further, we examined correlations among these obesity-related traits in the study population (Supplementary Fig. 5). As expected, all traits showed positive correlations with BMI, with strongest correlation seen in HC (correlation coefficient value: 0.86), followed by that in WC (correlation coefficient value: 0.81), BFP (correlation coefficient value: 0.57), and WHRadjBMI (correlation coefficient value: 0.44).
Using the same method as that for BMI analysis, we tested G × E interactions between the 4,143,506 SNPs and MET score, pack years of smoking, and alcohol intake frequency in WC, HC, WHRadjBMI, and BFP adjusted age, sex, genotyping array, and PC1–PC10. To investigate genomic inflation, we estimated the intercept of LD score regression from the each GWIS result in the Supplementary Table 14. Since the intercept values of alcohol intake frequency on WC (1.16), alcohol intake frequency on HC (1.17), and alcohol intake frequency on WHRadjBMI (1.13) were higher than 1.1, we applied the genomic control correction using lambda value to these traits. As a result, the intercept values become decreased after the correction: alcohol intake frequency on WC (0.97), alcohol intake frequency on HC (0.98), and alcohol intake frequency on WHRadjBMI (0.97) (Supplementary Table 14). The GWIS results are shown in Supplementary Figs. 6–8 as Manhattan plots in Supplementary Figs. 9–11 as Q–Q plots. We could not find genome-wide significant signals after multiple correction ($P \leq 4.17$ × 10−9 calculated by 5.00 × 10−$\frac{8}{12}$) for G × E interaction in the MET score and pack years of smoking, while two statistically significant signals (rs5729295 and rs11642015 at the FTO locus) were identified in alcohol intake frequency (Supplementary Table 15). Using RegulomeDB and the similar method as that used with BMI, we investigated functional annotation of these two statistical significant ($P \leq 4.17$ × 10−9) SNPs (Supplementary Table 16). Further, we determined any association of genetic variants existing within the 10 Mb flanking region of the lead SNPs with obesity or obesity-related traits (Supplementary Table 17), a direct association of the lead SNPs with any traits using the GWAS catalog (Supplementary Table 18), and the eQTL information using GTEx version 8 (Supplementary Table 19)41. We found that two lead SNPs were previously reported to be associated with BMI. These SNPs of the FTO locus (rs57292959, and rs11642015) were associated with diverse traits, including BMI, and showed eQTL genes as sentinel SNPs (r2 = 1).
Additionally, we compared the results of suggestive G × E interactions in BMI ($P \leq 5.00$ × 10−6) with the results in other obesity-related traits (Supplementary Tables 20–23). Notably, rs11642015 that yielded genome-wide significant interaction in BMI (near FTO gene, β = 8.29E-04, SE = 1.31E-03, $$P \leq 2.79$$ × 10−10) was also statistically significant for WC (β = 1.11E-02, SE = 1.47E-03, $$P \leq 5.33$$ × 10−12) and BFP (β = 9.41E-03, SE = 1.60E-03, $$P \leq 4.48$$ × 10−9).
Based on these results, we calculated the heritability of G × E interaction for MET score, pack years of smoking, and alcohol intake frequency using LDSC and LDAK-SumHer for each trait (Fig. 3 and Tables 4–5). Using the Bonferroni multiple correction ($P \leq 4.17$ × 10−3), the heritabilities of G × E interaction for pack years of smoking in WC (hG×E2 = 0.0065, $$P \leq 2.36$$ × 10−3), alcohol intake frequency in WC (hG×E2 = 0.0083, $$P \leq 1.66$$ × 10−5), pack years of smoking in HC (hG×E2 = 0.0102, $$P \leq 2.25$$ × 10−5), and alcohol intake frequency in WHRadjBMI (hG×E2 = 0.0059, $$P \leq 3.66$$ × 10−3) were statistically significant according to the LDSC analysis. In case of LDAK-SumHer analysis, the heritabilities of G × E interaction for MET score in WC (hG×E2 = 0.0044, $$P \leq 1.84$$ × 10−4), pack years of smoking in WC (hG×E2 = 0.0034, $$P \leq 2.00$$ × 10−3), alcohol intake frequency in WC (hG×E2 = 0.0039, $$P \leq 4.34$$ × 10−4), pack years of smoking in HC (hG×E2 = 0.0043, $$P \leq 3.20$$ × 10−4), and MET score in BFP (hG×E2 = 0.0033, $$P \leq 2.88$$ × 10−3) were statistically significant. Fig. 3Bar plots of G × E interaction heritability in obesity-related traits. Comparison of results for G × E interaction heritability calculated by LDSC (a, c, e, g) and LDAK-SumHer (b, d, f, h) in waist circumference (a, b), hip circumference (c, d), WHRadjBMI (e, f), and body fat percentage (g, h). The x-axis indicates the –log10 G × E interaction heritability P-value. MET metabolic equivalent task, WHRadjBMI waist-to-hip ratio adjusted with body mass index, LDSC linkage disequilibrium score regression, LDAK-SumHer linkage disequilibrium adjusted kinships–software for estimating SNP heritability from summary statistics. Table 4G × E interaction heritability calculated using the LDSC method. G × E interaction heritabilityPhenotypeLifestyle factorNG × E interaction heritability % (SE)P-valueInterceptWaist circumferenceMET score268,4840.51 (0.24)1.68 × 10−21.06Pack years of smokinga279,7040.65 (0.23)2.36 × 10−31.03Alcohol intake frequencya,b330,9860.70 (0.16)6.07 × 10−60.97Hip circumferenceMET score268,4870.23 (0.24)1.69 × 10−11.07Pack years of smokinga279,7071.02 (0.25)2.25 × 10−51.05Alcohol intake frequencya,b330,9940.51 (0.17)1.35 × 10−30.98WHRadjBMIMET score268,4650.29 (0.21)8.36 × 10−21.07Pack years of smoking279,684X-1.02Alcohol intake frequencyb330,9650.43 (0.19)1.18 × 10−20.97Body fat percentageMET score264,6670.53 (0.25)1.70 × 10−21.03Pack years of smoking275,8850.42 (0.21)2.28 × 10−21.03Alcohol intake frequency326,0770.40 (0.19)1.76 × 10−21.09MET score metabolic equivalent of task score, WHRadjBMI waist to hip ratio adjusted for body mass index, SE standard error.aIndicates the statistically significant G × E interaction heritability based on the Bonferroni corrected P-value threshold (P-value < 4.17 × 10−3).bIndicates the GWIS after genomic control. Table 5G × E interaction heritability calculated using the LDAK-Sum Her method. G × E interaction heritabilityPhenotypeLifestyle factorNG × E interaction heritability % (SE)P-valueWaist circumferenceMET scorea268,4840.44 (0.12)1.84 × 10−4Pack years of smokinga279,7040.34 (0.12)2.00 × 10−3Alcohol intake frequencya,b330,9860.33 (0.10)3.88 × 10−4Hip circumferenceMET score268,4870.13 (0.12)1.50 × 10−1Pack years of smokinga279,7070.43 (0.13)3.20 × 10−4Alcohol intake frequencyb330,9940.11 (0.10)1.30 × 10−1WHRadjBMIMET score268,4650.12 (0.12)1.74 × 10−1Pack years of smoking279,6840.03 (0.10)3.85 × 10−1Alcohol intake frequencyb330,9650.07 (0.09)2.16 × 10−1Body fat percentageMET scorea264,6670.33 (0.12)2.88 × 10−3Pack years of smoking275,8850.24 (0.12)2.07 × 10−2Alcohol intake frequency326,0770.19 (0.10)3.34 × 10−2MET score metabolic equivalent of task score, WHRadjBMI waist to hip ratio adjusted for body mass index, SE standard error.aIndicates the statistically significant G × E interaction heritability based on the Bonferroni corrected P-value threshold (P-value < 4.17 × 10−3).bIndicates the GWIS after genomic control.
## Discussion
In this study, to estimate the proportion of missing heritability that could be explained by G × E interaction, we determined the heritability of G × E interaction for BMI and other obesity-related traits in a large sample of 331,282 participants from the UK Biobank. Three lifestyle factors—MET score, pack years of smoking, and alcohol intake frequency—showed statistically significant interaction with genetic factors for BMI in both LDSC and LDAK-SumHer analyses. The G × E interaction heritability (%) and standard error of these factors by LDSC and LDAK-SumHer were as follows: MET score, $0.45\%$ (0.12) and $0.65\%$ (0.24); pack years of smoking, $0.52\%$ (0.13) and $0.93\%$ (0.26); and alcohol intake frequency, $0.32\%$ (0.10) and $0.80\%$ (0.17), respectively. Moreover, we identified the statistical significance of the G × E interaction heritability of these three lifestyle factors in WC, HC, WHRadjBMI, and BFP. Additionally, we identified two genome-wide significant loci interacting with lifestyle factors in these obesity-related traits.
Recently, Rask-Andersen et al.29 and Tyrrell et al.28 calculated the PRS for BMI in the European sample referring to SNPs discovered by Locke et al.43. They investigated the interactions between the PRS and lifestyle factors using the linear regression model. Rask-Andersen et al. found physical activity, alcohol intake frequency, and socioeconomic status to interact with PRS in BMI. Tyrrell et al. found physical activity and socioeconomic status to interact with PRS in BMI. Similarly, we also found a statistical significance in the G × E interaction heritability of physical activity (MET score) and alcohol intake frequency in BMI. While smoking (pack years of smoking) was found to be significant only in this study, Robinson et al. previously demonstrated the significance of G × E interaction smoking interaction heritability34.
Robinson et al. proposed the heritability of G × E interaction by estimating the difference in heritabilities between subgroups classified by environmental exposure using mixed-effect models36. They found evidence for the contribution of G × E interaction for smoking behavior to BMI, which explains $4.0\%$ of the phenotypic variance. Our analysis estimated statistically significant heritability of G × E interaction in the pack years of smoking in BMI with somewhat less value (hG×E2 = $0.93\%$, $$P \leq 1.74$$ × 10−4 calculated by LDSC; hG×E2 = $0.52\%$, $$P \leq 3.04$$ × 10−5 calculated by LDAK-SumHer). Sulc et al. also provided evidence for G × E interaction effect on BMI based on the calculation of phenotypic variance across the different PRS groups and found PRS × E to contribute $1.9\%$ to BMI37. If we assume that there is no correlation between G × E interaction of three lifestyle factors, the G × E interaction effect on BMI may be calculation by the summation of the heritabilities of three lifestyle factors. The summed values account to $1.3\%$ for LDAK-SumHer and $2.38\%$ for LDSC in this study. Sulc et al. reported TDI and alcohol intake frequency as lifestyle factors for the PRS × E contribution of $1.9\%$ to BMI. We also found marginally significant heritability for G × E interaction in TDI (hG×E2 = $0.42\%$, $$P \leq 6.70$$ × 10−3 calculated by LDSC; hG×E2 = $0.23\%$, $$P \leq 1.16$$ × 10−2 calculated by LDAK-SumHer). And Shin et al. estimated heritability of G × E interaction in BMI using the GxEsum program, which was built on LDSC approach44. The GxEsum is a method for estimating the phenotypic variance explained by genome-wide G x E terms for large-scale biobank dataset. They provided the heritability of G × E interaction for age (hG×E2 = $0.4\%$, $$P \leq 0.019$$), neuroticism score (hG×E2 = $0.7\%$, $$P \leq 1.61$$ × 10−5), physical activity (hG×E2 = $0.3\%$ $$P \leq 0.026$$) and alcohol intake frequency (hG×E2 = $0.3\%$, $$P \leq 0.060$$) in BMI. Moreover, we also found marginally significant heritability for G × E interaction using the LDSC method as follows: neuroticism score (hG×E2 = $0.52\%$, $$P \leq 9.05$$ × 10−3), MET score (hG×E2 = $0.65\%$ $$P \leq 3.38$$ × 10−3) and alcohol intake frequency (hG×E2 = $0.80\%$, $$P \leq 1.26$$ × 10−6).
Our analysis for the G × E interaction found two genome-wide significant loci for multiple testing ($P \leq 3.57$ × 10−9 on BMI, $P \leq 4.17$ × 10−9 on obesity-related traits) (Supplementary Tables 3 and 15). All two loci have been reported to be associated with BMI (Supplementary Tables 6 and 17). Notably, two loci were previously reported for their interactions with lifestyle factors in BMI, supporting the validity of our G × E interaction analysis. First, the locus of the CHRNA (cholinergic receptor nicotinic alpha subunit) gene cluster on chromosome 15 has been well-reported to be associated with smoking addiction45–48. For rs12438181, which showed significant effects on BMI by interacting with smoking behaviors in this study, eQTL analysis of GTEx (ver. 8) data revealed that this SNP is associated with both CHRNA3 and CHRNA5 genes in the brain (Supplementary Table 8)41. Further, two recent studies have provided evidence for the interaction between CHRNA3-A5-B4 gene cluster variants and smoking behavior in BMI35,49. Taylor et al. suggested that CHRNAs modulate responses such as food appetite to rewarding stimuli, including smoking49. The FTO (fat mass and obesity associated-alpha-ketoglutarate dependent dioxygenase) gene is a well-known, strong genetic factor for obesity, of which the mechanism is mainly attributable to the role in energy metabolism50. Previous studies demonstrated that the FTO gene is not only associated with BMI, but also interacts with lifestyle factors to influence BMI24,26,31. In this study, rs11642015, and rs57292959 at the FTO locus interacted with alcohol intake frequency to affect BMI, WC, and BFP, respectively (Supplementary Tables 3 and 15). Moreover, GTEx (ver. 8) analysis of these SNPs showed the FTO gene as an eQTL gene in the skeletal muscle and pancreas (Supplementary Tables 8 and 19)41. Further, it was reported that FTO [rs1421085 (r2 = 0.95 from rs11642015)] interacts with the frequency of alcohol consumption in BMI, which supports our findings on the interaction of these lead SNPs with alcohol intake frequency24.
There are several limitations to our study. First, some of the GWIS results may be statistically inflated. This can be inferred from the Q–Q plot and the intercept values calculated through the LDSC (Supplementary Fig. 3 and Table 2)38. This statistical inflation can occur because of trait polygenicity and large sample size in the study51,52. Therefore, these findings await replication and more careful testing with a larger GWAS data. Second, to estimate the GxE interaction heritability, we used LDSC and LDAK-SumHer methods. Both methods were optimized to calculate genetic heritability using the summary statistics estimated fixed effects of linear regression. However, although GWIS is calculated as fixed effects of linear regression model, it is necessary to validate that G × E interaction heritability is estimated using GWIS simulated with various cases. Third, we simply summed the heritabilities of G × E interaction for individual lifestyle factors to estimate total G × E interaction heritability as 1.29–$2.38\%$ in BMI. If these heritabilities of different lifestyle factors are dependent on each other, the summed heritability for BMI may be lower than 1.35–$2.55\%$. However, Sulc et al. reported $1.9\%$ total heritability for the contribution of PRS × E interaction to the phenotypic variance of BMI35. Moreover, the heritabilities of G × E interaction for other obesity-related traits analyzed in this study were not much different from these. The hG×E2 of WC was $1.35\%$ (LDSC) and $0.67\%$ (LDAK-SumHer), and hG×E2 of HC was $1.02\%$ (LDSC) and $0.43\%$ (LDAK-SumHer), indicating that if our results are inflated, the inflation may be small.
In summary, we performed GWIS for BMI using 331,282 participants in the UK Biobank, and calculated the heritability of G × E interaction for 14 lifestyle factors. Among the lifestyle variables, MET score, pack years of smoking, and alcohol intake frequency consistently showed statistically significant G × E interaction heritability for BMI. Our results suggest that apart of the missing heritability in BMI may be explained by the G × E interaction, indicating that consideration of G × E interaction could improve the accuracy of predicting obesity genetically.
## UK Biobank resource
We used the UK Biobank database, which is a population‐based database that recruited more than 487,409 individuals aged 40–69 years during 2006–1053. For quality control of the sample, we used Neale lab filters (https://github.com/Nealelab/UK_Biobank_GWAS). The sample filters are as follows: PCA calculation filter for selection of unrelated samples; sex chromosome filter for aneuploidy removal; filter of principal components (PCs) for European sample selection to determine British ancestry; and filters for selection of self-reported “White-British”, “Irish”, and “White”. In addition, we selected samples based on the filter for self-reported “White-British”, remaining the final sample as 331,282.
## Ethics approval and consent to participate
All participants provided signed consent to participate in the UK Biobank54. UK Biobank has been given ethical approval to collect participant data by the North West Multicentre Research Ethics Committee, the National Information Governance Board for Health & Social Care, and the Community Health Index Advisory Group.
## Genotype data
*Baseline* genetic imputation data of 93,095,623 SNPs were available in 487,409 participants. UK Biobank participants used the UK Biobank Axiom Array and the UK BiLEVE Axiom Array from Affymetrix (Santa Clara, CA)55. Genotyping imputation was performed using UK10K Project and 1000 Genome Project Phase 3 reference panel. We performed quality control analysis using PLINK v.1.9056, based on the following exclusion criteria: SNPs with missing genotype call rates > 0.05, MAF < 0.05, and Hardy–*Weinberg equilibrium* P-value < 1.00 × 10−6. We excluded SNPs with MAF smaller than 0.05 to avoid potential false-positive results due to the coincidence of a low-frequency variant31. Consequently, 4,143,506 SNPs were retained for further analysis.
## Phenotype data
Participants’ weights were assessed using various methods during the initial UK Biobank assessment center visit. Additionally, standing height was measured on a SECA 240 Height Measure. For BMI, we used data-field 21001, which is constructed from height and weight measured. For this study, BMI was transformed using log transformation.
The lifestyle factors used in GWIS were selected based on previous studies on obesity24,29,40. The 14 lifestyle factors were metabolic equivalent task score (MET score; field ID: 22040), moderate physical activity (field ID: 884), time spent using computer (field ID: 1080), time spent watching TV (field ID: 1070), neuroticism score (field ID: 20127), fed-up feelings (field ID: 1960), smoking status (field ID: 20116), pack years of smoking (field ID: 20161), alcohol intake frequency (field ID: 1558), alcohol intake status (field ID: 20117), sleep duration (field ID: 1160), nap during day (field ID: 1190), average total household income before tax (field ID: 738), and TDI (field ID: 189) (Table 1). For the GWIS, lifestyle factors (MET score, time spent using computer, time spent watching TV, pack years of smoking, neuroticism score, sleep duration) were transformed to normal distribution using Gaussian function in structured linear mixed model v.0.3.1 (Struct-LMM) (Supplementary Fig. 1)26. For moderate physical activity analysis, participants were divided into four groups. For smoking analysis, participants were divided into two groups, one with or without previous history of smoking and the other with current smoking40.
## Statistics and reproducibility
The G × E interaction analysis for the 14 lifestyle factors on BMI as a continuous trait was performed fixed effect model of linear regression using the PLINK v.1.9056. Also, we analyzed each obesity-related trait as a continuous trait in the same methods. The formula of the linear regression model is such that:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{c}{{{{{\rm{Phenotype}}}}}}={\beta }_{0}+{\beta }_{1}{{{{{\rm{G}}}}}}+{\beta }_{2}{{{{{\rm{G}}}}}}\times {{{{{\rm{E}}}}}}+{\beta }_{3}{{{{{\rm{E}}}}}}+{\beta }_{4}{{{{{\rm{age}}}}}}+{\beta }_{5}{{{{{\rm{sex}}}}}}+{\beta }_{6}{{{{{\rm{array}}}}}}+{\beta }_{7}{{{{{\rm{PC}}}}}}1+{\beta }_{8}{{{{{\rm{PC}}}}}}2 \,\\ +{\beta }_{9}{{{{{\rm{PC3}}}}}}+{\beta }_{10}{{{{{\rm{PC4}}}}}}+{\beta }_{11}{{{{{\rm{PC5}}}}}}+{\beta }_{12}{{{{{\rm{PC}}}}}}6+{\beta }_{13}{{{{{\rm{PC}}}}}}7+{\beta }_{14}{{{{{\rm{PC}}}}}}8+{\beta }_{15}{{{{{\rm{PC}}}}}}9+{\beta }_{16}{{{{{\rm{PC}}}}}}10+\varepsilon \end{array}$$\end{document}Phenotype=β0+β1G+β2G×E+β3E+β4age+β5sex+β6array+β7PC1+β8PC2+β9PC3+β10PC4+β11PC5+β12PC6+β13PC7+β14PC8+β15PC9+β16PC10+εβ1 denote the effect size of genotype β2 denote the effect size of G × E interaction β3 denote the effect size of lifestyle factor β4 denote the effect size of age β5 denote the effect size of sex β6 denote the effect size of genotyping array β7 denote the effect size of PC1 β8 denote the effect size of PC2 β9 denote the effect size of PC3 β10 denote the effect size of PC4 β11 denote the effect size of PC5 β12 denote the effect size of PC6 β13 denote the effect size of PC7 β14 denote the effect size of PC8 β15 denote the effect size of PC9 β16 denote the effect size of PC10 To stabilize the genomic inflation, we applied the genomic control to GWIS results showing an intercept value of LD score regression higher than 1.1 (Supplementary Tables 2 and14). For this purpose, firstly, we estimated the median chi-square distribution of the GWIS result. Next, the lambda value was estimated by the median of chi-square divided by 0.4565657 Finally, each chi-square of SNP is divided by the lambda value58.
We used PLINK to identify independent SNPs. We used LD clumping to retain the most strongly associated SNPs in each region (PLINK v.1.90 –clump-p1 5e-06 –clump-p2 5e-06 –clump-r2 0.01 –clump-kb 1000) in GWIS analysis.
We used LDSC38 and LDAK-SumHer39 to estimate the G × E interaction heritability from the 14 GWIS summary statistics. The statistical evaluation method is different between two methods. LDSC methods estimates genetic heritability using a regression model, whereas LDAK-SumHer methods calculated genetic heritability using a likelihood model. When using LDSC to estimate G × E interaction heritability, it is necessary to LD score of SNP. Bulik-Sullivan et al. estimated the 1,217,312 SNPs of LD score based on the European 1000 Genomes database and Hap-Map3 SNPs (https://github.com/bulik/ldsc)38. We also used LD score to estimate G × E interaction heritability. When using LDAK-SumHer, it is required for well-imputed common SNPs panel. So, we remained SNP satisfied criteria as follows41: [1] 1000 Genomes imputation database, [2] Non-ambiguous SNPs, [3] SNPs not in MHC region (http://dougspeed.com/)39. Based on these SNPs, we used GCTA model of LDAK-SumHer to estimate the G × E interaction heritability39.
We created Manhattan plots, histograms, Q–Q plots, bar plots, and correlograms and performed association and correlation analysis in R version 4.0.3 (www.r-project.org). We used qqman package for Manhattan plots and corrplot package for calculating correlation coefficients and plotting the correlogram. The ggplot2 package was used for plotting bar plots and lme4 package was used for association analysis.
## Investigation of the biological function of significant loci
To investigate the biological function and possible effects of significant variants on various traits, we investigated this information on the GWAS catalog, RegulomeDB, and GTEx version 8. For GWAS catalog, we determined any association of genetic variants existing within the 10 Mb flanking region of the lead SNPs with obesity or obesity-related traits, a direct association of the lead SNPs with any traits using the GWAS catalog59 (https://www.ebi.ac.uk/gwas/). We investigated the eQTL information for lead SNPs shown statistical significance using GTEx version 8 database41 (https://gtexportal.org/home/). We searched for functional importance of lead SNPs using RegulomeDB60 (https://regulomedb.org/regulome-search/).
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04679-4.
## Peer review information
Communications Biology thanks David Meyre, Doug Speed and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editors: Hélène Choquet and Gene Chong.
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|
---
title: 'Sedentary lifestyle with increased risk of obesity in urban adult academic
professionals: an epidemiological study in West Bengal, India'
authors:
- Sunandini Ghosh
- Manabi Paul
- Kousik Kumar Mondal
- Sandip Bhattacharjee
- Pritha Bhattacharjee
journal: Scientific Reports
year: 2023
pmcid: PMC10039938
doi: 10.1038/s41598-023-31977-y
license: CC BY 4.0
---
# Sedentary lifestyle with increased risk of obesity in urban adult academic professionals: an epidemiological study in West Bengal, India
## Abstract
Ectopic fat deposition is more strongly associated with obesity-related health problems including type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), hypothyroidism, arthritis, etc. Our study aimed at identifying the cumulative role of several risk factors in developing obesity and the role of ectopic fat (visceral fat) in predicting cardiovascular disease risk in varied age groups among urban adult academic professionals in West Bengal. 650 adults (Male = 456; Female = 194) associated with the academic job (age 20–65 years) in urban West Bengal were randomly selected for anthropometric, blood biochemical, and questionnaire-based analyses. Body Mass Index and Visceral Fat% exhibited comparable association with all the other anthropometric parameters (e.g. Whole body Subcutaneous fat%: male-Linear Regression Comparison: $F = 11.68$; $P \leq 0.001$; female-$F = 6.11$; $P \leq 0.01$). Therefore, VF% acts as a risk factor alongside BMI in instances where BMI fails alone. The presence of T2DM, hypertension, and hypothyroidism in the case groups confirmed their obesity-associated longitudinal pattern of inheritance. Unhealthy diet pattern indicates improper liver function, vitamin D deficiency, and increased erythrocytic inflammation. An overall sedentary lifestyle with parental history of obesity was found to be significant in the longitudinal transmission of the disease.
## Introduction
Obesity, commonly caused due to abnormal fat deposition, is primarily measured by increased body mass index (BMI)1. This global pandemic has increased concern regardless of the economic condition of a country2. In 2015, about $30\%$ of the world’s total population, including 107.7 million children and 603.7 million adults were found to have obesity worldwide3. It can also be predicted that in 2030, the global population of overweight and obesity will increase to 2.16 billion and 1.12 billion, respectively4. In India, the National Family Health Survey-4 (NFHS) reported$18.9\%$ of men as overweight, including $26.6\%$ of urban and $14.3\%$ of rural men, whereas $20.6\%$ of all women were found to be overweight, accounting for $31.3\%$ of urban and $15.0\%$ rural women5. Furthermore, a study also indicates that the rate of increase in obesity becomes highest in early adulthood6.
The obesity-associated health problems refer to complex metabolic diseases, also known as lifestyle diseases, including diabetes, cardiovascular diseases, arthritis, polycystic ovarian diseases, etc. that are intimately associated with obesity7,8. It has become necessary to identify and estimate the factors associated with increasing co-morbidities leading to obesity to curb the exponential growth curve. Although fat accumulation mostly occurs in subcutaneous adipocytes, the deposition has also been found in ectopic sites such as the visceral area, liver, muscle, heart, and pancreas9. Increasing age influences the distribution of adipose, shifting it from subcutaneous depots to intra-abdominal and ectopic fat deposition10. BMI is considered to be the most common yardstick to measure obesity worldwide, yet as it is not capable of differentiating between body fat and muscle mass, it fails to be a reliable predictor of disease risk11. Therefore, body composition monitoring can become crucial to identify the visceral fat percentage (VF%) which signifies central obesity. Accumulated fat like epicardial and extra-pericardial are responsible for CVD risk and correlate well with increased VF%. Moreover, VF has been related to both cardiovascular and metabolic dysfunction12. In Asian Indians, intra-abdominal VF accumulation causes central obesity rather than a generalized one13.
The environment plays a complex interactive role along with genetic imprints. Modifiable factors like lack of physical activity, calorie-rich diet, sleeping disorders, etc. are one of the major game-changers of obesity, while the other one is parental history14,15. Poor dietary patterns coupled with increased leisure time including television watching and minuscule physical activity owing to technological development (using elevators, digitalization of manual labor, etc.) and selection of residential environment (metropolitan cities with higher facilities to minimize physical movement) accounts for increasing obesity16. Studies on children and adolescents indicate that not only maternal but the paternal history of obesity and associated co-morbidities also account for the offspring’s health17,18. Even though limited information is available on central obesity among adults in West Bengal, however, there seems that the frequency of overweight among the studied Bengali population is at an alarming stage19. A comparative study of 224 urban and 224 rural Bengali adults reported the prevalence of metabolic syndromes to be highly significant in both sexes in the urban population20. Diet, along with physical activity, plays an important role in lifestyle. Urbanization, advertisement, and easy access to supermarkets have westernized the dietary pattern in modern urban India21. The industrialization of the agricultural sector over time has increased the chemical burden on the neutral ecosystem which has affected the healthier food habit of the Bengali population22. A sedentary lifestyle involving a lack of physical exercise is prominent in professions like academics, judicial, information technology, etc. Physical activity is the only established modifiable variable that can be considered to regulate the total energy expenditure, thereby directly influencing the obesity status of a nation23.
The lack of any previously published data has driven an utmost urge to analyze the present obesity status of an occupation-oriented sedentary lifestyle in academic professionals. Hence our study focussed on the assessment of the cumulative role played by several risk factors on the vulnerable unique urban adult population in West Bengal of different age groups with similar food habits and sedentary lifestyles.
## Study area
Our study was conducted between September 2017 and September 2018. We selected three campuses of the University of Calcutta, West Bengal, India, as the study areas based on the participants, viz. the urban academic professionals (Supplementary Fig. 1). Epidemiological surveys were conducted during September, October, and November 2017 in three different campuses. Sample collections were done in December 2017; while data analyses were conducted in 2018.
## Study participants
Random sample collection was conducted in all three study sites. Sample size calculation following Cochran’s Formula24, indicated a required minimum sample size of 281 assuming the prevalence of obesity to be $24\%$ at sample collection time25 with a $95\%$ confidence interval to achieve the power of $80\%$. Consecutive sampling resulted in the voluntary participation of a total of 671 individuals including both male and female college students, research fellows, faculties, and non-teaching staff of different age groups ranging from 20 to 65 years. The male–female ratio was found to be approx. 2:1.
## Data collection
The data collected in the three respective study sites involved three phases. Phase I consisted of socio-demographic data collection. The participants were interviewed privately using a predesigned pretested questionnaire only after receiving the University Ethical Clearance and their written informed consent. All the methods were carried out in accordance with relevant guidelines and regulations approved by Institutional Ethical Committee of the University of Calcutta (Ref No. $\frac{003}{17}$-$\frac{18}{1676}$). Participation was voluntary. Phase I was followed by Phase II involving anthropometric measurements and Phase III including blood biochemical analysis.
## Anthropometric measurement
Anthropometric measurements were initiated by recording the height of the individual (without shoes) in centimeters. The bioelectrical Impedance Analysis (BIA) technique was applied to the other anthropometric parameters. For ideal weight management and a more accurate and precise body composition analysis, a full Body Sensing Technology Karada Scan Body Composition Monitor (Omron HBF-375, Kyoto, Japan) was used following the detailed mechanism26 which measured body composition such as weight (in Kg), total body fat percentage, visceral fat percentage (VF%), subcutaneous fat (WbSb%) and skeletal muscle percentage (WbSk%) and body mass index (BMI) (in kg/m2). Systolic and diastolic blood pressures (SBP and DBP respectively) were measured using an automatic digital blood pressure monitor (Omron HEM 7120, Kyoto, Japan) according to the protocol explained previously27. The data collection was followed by the calculation of the mean arterial pressure (MAP) (in mm/Hg) using the standard formula i.e., MAP = DBP + $\frac{1}{3}$(SBP − DBP). Out of the total 671 participants, 650 (456 male and 194 female) were shortlisted for our statistical analysis based on individuals without obesity (BMI: 18.5–24.9 kg/m2) and with overweight and obesity (BMI ≥ 25 kg/m2). We excluded the under-aged (< 20 years), underweight (BMI < 18.5 kg/m2), and pregnant female participants. The total 650 candidates were arranged into two separate groups according to their age, Group I (age < 35) years and Group II (age ≥ 35 years). Both of these groups were further classified into two subgroups based on their sex (male and female) i.e., Group I male, Group I female, Group II male, and Group II female. Furthermore, each subgroup was classified into two case–control groups based on BMI (‘case’ referring to overweight-obese and ‘control’ referring to non-obese healthy adults) viz. Group I case male ($$n = 51$$), Group I case female ($$n = 65$$), Group I control male ($$n = 56$$) and Group I control female ($$n = 60$$); Group II case male ($$n = 190$$), Group II case female ($$n = 50$$), Group II control male ($$n = 159$$) and Group II control female ($$n = 19$$) (Supplementary Table 1a). Confidentiality of the data was maintained throughout.
## Biochemical analysis
100 age-sexes matched overweight and obese individuals (50 male and 50 female) voluntarily registered for the biochemical analysis, of which only 25 male and 20 female representatives finally participated. 5 ml of blood was collected from each subject in (EDTA) coated vacutainer (BD Pharmaceuticals Pvt. Ltd., West Bengal, India) and outsourced to the pathological laboratory (Thyrocare Technologies Limited, Mumbai, India), for analysis of 48 biochemical parameters (Supplementary Table 3).
## Statistical analysis
All the demographic data were expressed as Mean ± SD. The anthropometric data were recorded and maintained using Microsoft Excel 2010. Statistical analyses were performed between all the subgroups in a case–control pattern. Linear regression, comparative linear regression, and 2-tailed unpaired t-test were done using Microsoft Excel (Washington, USA) and StatistiXL (Version 2.0, Broadway-Nedlands, Australia). The study was conducted following the STROBE checklist.
## Ethics guidelines
The authors confirm that all the experimental methods were carried out in accordance with relevant guidelines and regulations by the institutional ethical committee of the University of Calcutta (Ref No. $\frac{003}{17}$-$\frac{18}{1676}$).
## Ethics approval
The study was approved by Institutional Ethical Committee of the University of Calcutta (Ref No. $\frac{003}{17}$-$\frac{18}{1676}$).
## The anthropometric study
In our study design, $35.74\%$ of the total study participants were aged < 35 years (Group I), and $64.25\%$ were aged ≥ 35 years (Group II). The case–control (i.e. overweight-obese and non-obese normal weight) distribution pattern accounted for $54.76\%$ and $45.23\%$ respectively. Supplementary Table 1a represented the distribution details, mean age, and BMI of the subcategories of our studied population. The demographic details (Supplementary Table 1b) were not significantly different in the subgroups to be considered for causing any major disease manifestation.
In Group I, $96.42\%$ of the male control participants, showed VF% ≤ 91. In contrast, the rest had higher VF% despite having a normal BMI. However, for the case group of males, $86.27\%$ revealed a VF% > 9. Unlike the males, $100\%$ of the control females, as well as $66.15\%$ of case females, showed VF% ≤ 9.
In Group II, $36\%$ of the control male population reflected VF% > 9, in contrast to the case population (where $100\%$ showed VF% > 9). Whereas for females, $0\%$ of the control population showed VF% > 9 while, $68\%$ of the case population had VF% > 9.
The 2-tailed unpaired t-Test showed that there was a significant difference between the male and female groups concerning BMI ($T = 2.62$, DF1,2 = 193,455, $P \leq 0.05$), VF% ($T = 9.14$, DF1,2 = 193,455, $P \leq 0.001$), WbSb% ($T = 27.51$, DF1,2 = 193,455, $P \leq 0.001$), WbSk% ($T = 23.5$, DF1,2 = 193,455, $P \leq 0.001$), SBP ($T = 10.72$, DF1,2 = 193,455, $P \leq 0.001$), DBP ($T = 4.66$, DF1,2 = 193,455, $P \leq 0.001$) and MAP ($T = 8.13$, DF1,2 = 193,455, $P \leq 0.001$).
### BMI and WbSb%
In the total male population, both VF% and BMI were found to be in statistically significant positive correlation with WbSb% but no common slope existed between them (Linear Regression Comparison: $F = 11.68$; $P \leq 0.001$) (Fig. 1a,b). In Group I, WbSb% was found to be positively correlated with VF% and BMI respectively in both the control (Linear Regression Comparison: $F = 0.29$; $$P \leq 0.58$$) and case groups (Linear Regression Comparison: $F = 0.26$; $$P \leq 0.6$$). In study Group II also, WbSb% was found to be positively correlated with VF% and BMI respectively in both the control (Linear Regression Comparison: $F = 0.53$; $$P \leq 0.46$$) and case groups (Linear Regression Comparison: $F = 1.31$; $$P \leq 0.25$$).Figure 1Association between (i) VF% and WbSb% (a & c) (ii) BMI and WbSb% (b & d) in the total male and female population respectively.
In the total female participants, VF% and BMI were both significantly positively associated with WbSb% (Linear Regression Comparison: $F = 6.11$; $P \leq 0.01$) (Fig. 1c,d). In Group I, a similar result was observed in both control (Linear Regression Comparison: $F = 0.88$; $$P \leq 0.35$$) and case groups (Linear Regression Comparison: $F = 0.9$; $$P \leq 0.34$$). In Group II females, although the control group reflected an absolutely different result with no significant association of WbSb% with VF% and BMI, here also both VF% and BMI exhibited identical patterns of action (Linear Regression Comparison: $F = 0.97$; $$P \leq 0.33$$). WbSb% showed a significant positive association with VF% and BMI in the case group (Linear Regression Comparison: $F = 2.46$; $$P \leq 0.12$$).
### BMI and WbSk%
VF% and BMI were both significantly negatively correlated with WbSk% in both sexes. In the total male population, both VF% and BMI showed a similar negative correlation with WbSk% (Linear Regression Comparison: $F = 0.35$; $$P \leq 0.55$$) (Fig. 2a,b). In Group I male participants, WbSk% did not show any significant association with VF% or BMI in the control group (Linear Regression Comparison: $F = 0.03$; $$P \leq 0.85$$), contradictory to the result in the case group (Linear Regression Comparison: $F = 0.004$; $$P \leq 0.95$$). In Group II, WbSk% indicated a significant negative association with VF% and BMI in both control (Linear Regression Comparison: $F = 0.69$; $$P \leq 0.4$$) and the case groups (Linear Regression Comparison: $F = 1.27$; $$P \leq 0.25$$).Figure 2Association between (i) VF% and WbSk% (a & c) (ii) BMI and WbSk% (b & d) in the total male and female population respectively.
In the total female population also, VF% and BMI showed a comparable significant negative correlation with WbSk% (Linear Regression Comparison: $F = 0.11$; $$P \leq 0.73$$) (Fig. 2c,d). Both in control and case subgroups of Group I, WbSk% were significantly negatively correlated with VF% and BMI (Linear Regression Comparison: $F = 0.03$, $$P \leq 0.85$$; Linear Regression Comparison: $F = 0.003$, $$P \leq 0.95$$). In contrast, the control and case females of Group II did not show any significant correlation (Linear Regression Comparison: $F = 0.11$, $$P \leq 0.74$$; Linear Regression Comparison: $F = 0.06$; $$P \leq 0.78$$). Common slopes between VF% with WbSk% and BMI with WbSk% linear regression curves were present in all the groups irrespective of age and sex.
## Association of VF% and BMI with the blood pressure parameters (DBP and MAP)
SBP association with VF% and BMI indicated gender biases. It was not significantly positively correlated in males (Linear Regression Comparison: $F = 0.13$, $$P \leq 0.71$$) (Supplementary Fig. 2a,b), but it was in the female group (Linear Regression Comparison: $F = 0.51$, $$P \leq 0.47$$) (Supplementary Fig. 2c,d). In the total male population, DBP and MAP were both significantly positively correlated with VF% and BMI (DBP: Linear Regression Comparison: $F = 0.09$, $$P \leq 0.75$$; MAP: Linear Regression Comparison: $F = 0.00$, $$P \leq 0.99$$) separately (Figs. 3a,b, 4a,b). In the total female population, DBP and MAP were both significantly positively correlated with VF% and BMI (DBP: Linear Regression Comparison: $F = 0.003$, $$P \leq 0.95$$; MAP: Linear Regression Comparison: $F = 0.14$, $$P \leq 0.7$$) respectively (Figs. 3c,d, 4c,d). The overweight and obese females in both Group I and Group II showed a significant positive correlation of both DBP and MAP with BMI and VF% (Supplementary Table 2).Figure 3Association between (i) VF% and DBP (a & c) (ii) BMI and DBP (b & d) in the total male and female population respectively. Figure 4Association between (i) VF% and MAP (a & c) (ii) BMI and MAP (b & d) in the total male and female population respectively.
## Questionnaire-based analysis
The parameters studied using structured questionnaire-based analysis represented a significant impact on obesity-related health outcomes.
## Working and sleeping durations were significantly different among individuals of the subgroups in the study population
There were significant differences observed among Group I as well as Group II participants (Working hours: $T = 5.84$, DF1,2 = 229,415, $P \leq 0.001$; Sleeping hours: $T = 6.95$, DF1,2 = 229,415, $P \leq 0.001$) (Supplementary Table 1b).
## Physical activity is not a significant factor associated with decreasing VF% and BMI in our population
Physical activity showed no significant correlation with VF% and BMI in the total study participants ($$n = 559$$) (Supplementary Fig. 3a–d).
## Presence of parental history of obesity, CVD, T2DM, and hypothyroidism significantly affects the occurrence of obesity in the offspring
The data scoring of the parental history of mentioned diseases in 411 study participants indicated a significant positive correlation between the case and control groups of both the sexes ($T = 12.81$, DF1,2 = 223,186, $P \leq 0.001$). Increasing percentages of individuals with parental history of the concerned diseases were observed in all the case groups, irrespective of sexes (Fig. 5a,b). The score analysis could thereby predict that the parental history of the disease can significantly affect the offspring’s health, whereas score 2 is the transition score between the lower and higher risk of parental disease inheritance. Figure 5Comparative parental history score in different subgroups of Group I (a) and Group II (b) of the studied population.
## The presence of obesity-associated co-morbidities in the population
The results of the analyses identified the presence of T2DM in the Group I case individuals with higher incidents in Group II. Hypothyroidism expressed a sex-biased pattern of occurrence in females of both groups. The initial manifestation of arthritis was also observed in case individuals of Group I, increasing from ten to fifteen folds with age. The rate of hypertension among the Group I case males ($33\%$) and females ($17\%$) established a predominant risk of CVD with their increasing age (Supplementary Fig. 4).
## Blood biochemical analysis
Analyses of the biochemical parameters (Supplementary Fig. 5) from the selected 45 case individuals supported the presence of 25-OH Vit D deficiency in $65\%$ of the studied samples. Significant anomalies in liver function tests and complete blood count were also observed.
## Discussion
Recent studies have indicated that South Asian populations have a higher tendency of increasing obesity-linked non-communicable diseases as compared to Caucasians28. These findings along with insufficient information on obesity-associated non-communicable diseases in a varied age group have aroused our interest to study a South-East Indian adult population. In our study, we observed significant differences in both the BMI and VF% between the sexes. In the studied diversified age groups, VF% and BMI both showed a significant positive correlation with WbSb% and a negative correlation with WbSk% irrespective of individual obesity status. The control females of Group II indicated an entirely different result, but it could be considered an error due to a very low sample size. Therefore, as per our observation, VF% exhibited similar relation with fat deposition (WbSb%) and skeletal muscle mass (WbSk%) as in the case of BMI. Thus, VF% along with BMI portrayed a more authentic way to diagnose the lifestyle disease predisposition. The absence of any correlation between both BMI and VF% with WbSk% in Group I control males and Group II control females is a point of consideration. Young men without obesity have a higher lean skeletal muscle mass as compared to the overweight and obese individuals of the same age and sex-matched group. Both fat deposition and muscle loss are minimal in this group. Increased sarcopenia, i.e., lower WbSk% with higher VF% was observed in Group II male individuals. Earlier studies may have an answer to this observation, as sarcopenia increases both with age and lifestyle diseases like obesity29. Moreover, elder men with obesity are highly susceptible to sarcopenia due to hypogonadism30.
Cardiovascular diseases (CVD) including coronary heart diseases (CHD), heart failure (HF), hypertension (HT), and arrhythmias are the leading cause of global death31. About one-quarter of the adult population in the world is hypertensive and by 2025 the proportion would increase to $29\%$32,33. CVD accounted for 15–$20\%$ of all deaths in the Indian subcontinent34. It was seen in several studies that the prevalence of hypertension was more in females ($52.5\%$) as compared to males ($27.3\%$)35. A recent study on women showed a high prevalence of hypertension with the strongest association with overweight and obesity36. Our study results reflected obesity to be a potential allied factor for increasing blood pressure parameters only in females with overweight and obesity, of both age groups. But more precisely, it could be said that central obesity (VF%) affected hypertension in only case women of Group II. Moreover, hypertension was more predominant in the Group I case individuals, than in the control ones, indicating the tendency to develop CVD to be more among the young case subjects. Thereby, controlling obesity from early childhood may be a preventive measure to decrease the risk of developing CVD at an older age.
It has been recorded that T2DM in India has already affected 77 million in 202037. T2DM is significantly present in Group II irrespective of sex, however, the Group I case individuals also reported the disease incidences. The prevailing risk of T2DM was thereby found to be influenced by increased age and enhanced by obesity. In the case of hypothyroidism, both obesity and sex biases reflected their role in the occurrence of disease among case females of all age groups. Arthritis, on the other hand, showed only an age-dependent pattern, increasing prominently in Group II individuals.
Sedentary behavior along with a lack of moderate-vigorous physical activity is negatively associated with obesity-related co-morbidities38. A 150–300 min of moderate or 75–150 min of vigorous aerobic physical activity per day is considered to be ideal for a healthy lifestyle in adults up to 64 years of age39. Automation and digitization have increased physical inactivity by several folds40. A sedentary work pattern in information technology professionals, similar to our study population, should have reflected a similar scenario. Yet in a study, only $16\%$ were found to have increased adiposity41. The installation of fitness equipment and healthcare facilities in such multinational companies might be the game-changer there. Analysis of our data revealed physical activity as an insignificant factor to be considered in our study as all the participants irrespective of age and body composition were not even engaged in moderate aerobic exercises. Despite being aware of their health, they lacked the motivation for physical activity as well as easily accessible physical workout facilities and were comfortable in a sedentary lifestyle with very minimal physical movement, where the most physically active group showed Mean = 203.7 min of physical activities per week, the Mode being 0 (Group I control males). The increasing pattern of obesity thus becomes difficult to control.
The significance of the parental history of obesity and related co-morbidities alongside environmental effects has always been inevitable in studying the disease risk in the offspring42. Several studies have confirmed this association in children and adolescents18,42, yet its long-term effects have been left unnoticed in the adult population. This purpose was addressed in our study. The parental history of obesity-associated heritable diseases was found significantly higher in the case group of our studied population irrespective of age and sex. A complex gene-environment interaction thereby indicates the disease predisposition.
Energy intake, although an inevitable phenomenon, is often irregular and uncontrolled in different individuals. In Kolkata, West Bengal, a study reported employees with a sedentary lifestyle have equivalent energy intake to that of physically active laborers, thereby resulting in significantly higher body weight43. In the Indian population, energy intake is restricted to 39 kcal/kg body weight/day in men and 35 kcal/kg body weight/day in women with a sedentary lifestyle44. Indian diets have gradually become more westernized, influenced by a multitude of factors such as rising income, demographic transition, urbanization, and the spread of retail chains or supermarkets45. In the current years, expenses for staple cereal consumption in urban India have decreased to $6.6\%$ of the total expenses for food whereas expenses for processed and protein-rich food have increased to $30\%$46. In our studied case population with a similar urbanized food habit, the blood profiling reflected certain distinct parameters to be significantly associated with increased BMI. Vitamin D deficiency, as observed in $65\%$ of our case individuals, may be responsible primarily for increased bone turnover, increased fracture risk, and secondarily for other metabolic and autoimmune disorders, even cancer47. The lack of adverse effects on bone in obese individuals may indicate that serum 25(OH)D is low due to volumetric dilution as the adipose tissue acts as the reservoir of vitamin D48. In individuals with cardiovascular disease risk, vitamin D deficiency was found to be associated with a decrease in high-density lipoprotein (HDL) concentration and an increase in low-density lipoprotein (LDL) concentration. It triggered inflammation both in epicardial fat and in the vascular walls thereby increasing vascular rigidity49. Excess fat accumulation also affects the liver’s functioning as it is the major organ controlling fat metabolism. Among our studied parameters, serum glutamic-pyruvic transaminase (SGPT) was observed to be high in $55.8\%$ of individuals which may indicate the onset of non-alcoholic fatty liver disease (NAFLD)50. Although it is believed that the RBC count increases with increasing physical activity51, in our study high RBC count was observed in $66.67\%$ of the population. An increased level of RBC count may be an indicator of developing metabolic diseases, as observed in the Iranian population52. An improper diet with insufficient nutrients may contribute to the development of obesity-associated metabolic disease risk.
Our study had certain limitations as well. The population distribution was randomized, hence sex biases were unavoidable (male and female staffs are in the ratio are 2:1)53. It is noteworthy to mention here that information regarding physical activity, diet, etc. in the questionnaire was recorded as per our study participants’ statements.
Parental history of obesity-related co-morbidities, as evident in our study, results in longitudinal transmission. Moreover, a sedentary lifestyle amplifies it several folds. In our studied population, the obesity rate although similar in Group I and Group II individuals, the co-morbidity effects express more in case participants in Group II, women being the vulnerable clan. The current study observations can be utilized for pathophysiological implementation of diagnostic techniques involving screening of obesity in adolescence. Health care programs, incorporation of physical fitness activities in academic houses, awareness and motivation including a prescribed diet, balanced lifestyle with sufficient physical activity, and regular monitoring of the VF% alongside BMI will provide satisfactory results in the long run. Early diagnosis and control of obesity through school and higher academic-based health-planning programs is one of the most effective measures to curb the growing graph of global obesity.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31977-y.
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|
---
title: 'Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data
Analysis'
authors:
- Furui Cheng
- Mark S Keller
- Huamin Qu
- Nils Gehlenborg
- Qianwen Wang
journal: IEEE transactions on visualization and computer graphics
year: 2022
pmcid: PMC10039961
doi: 10.1109/TVCG.2022.3209408
license: CC BY 4.0
---
# Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis
## Abstract
Reference-based cell-type annotation can significantly reduce time and effort in single-cell analysis by transferring labels from a previously-annotated dataset to a new dataset. However, label transfer by end-to-end computational methods is challenging due to the entanglement of technical (e.g., from different sequencing batches or techniques) and biological (e.g., from different cellular microenvironments) variations, only the first of which must be removed. To address this issue, we propose Polyphony, an interactive transfer learning (ITL) framework, to complement biologists’ knowledge with advanced computational methods. Polyphony is motivated and guided by domain experts’ needs for a controllable, interactive, and algorithm-assisted annotation process, identified through interviews with seven biologists. We introduce anchors, i.e., analogous cell populations across datasets, as a paradigm to explain the computational process and collect user feedback for model improvement. We further design a set of visualizations and interactions to empower users to add, delete, or modify anchors, resulting in refined cell type annotations. The effectiveness of this approach is demonstrated through quantitative experiments, two hypothetical use cases, and interviews with two biologists. The results show that our anchor-based ITL method takes advantage of both human and machine intelligence in annotating massive single-cell datasets.
## Introduction
The last decade has witnessed great progress in the area of single-cell omics technologies, which allow researchers to profile individual cells from complex tissues in increasingly efficient ways. Substantial efforts have been made in the Human Cell Atlas (HCA) Project [48], the Human Tumor Atlas Network (HTAN) [51], and the Human BioMolecular Atlas Program (HuBMAP) [24] to study human tissues at the single-cell level to create large-scale datasets, often referred to as single-cell atlases. These data provide promising opportunities to systematically understand complex biological processes at the cellular level, from discerning transitions of bone marrow cells, to understanding immune cell response to SARS-CoV-2 infection [26].
With the availability of single-cell atlases, it is of growing interest to molecular biologists to leverage the high-quality labeled datasets as references to annotate the sequencing results from new studies. Compared with conventional methods of using unsupervised clustering algorithms and manually labeling cell populations, reference-based methods are more efficient and reproducible. However, despite its greater efficiency, reference-based annotation can be challenging due to the technical variation (e.g., different laboratory conditions) among different studies, referred to as batch effects in molecular biology literature [29] (Fig. 2). The results from different studies can be systematically biased, making it impossible to directly apply a supervised annotation model trained on the reference dataset to predict labels for the query dataset (unlabeled results from a new study). To make the problem more challenging, true biological variation (e.g., in the form of unique cell populations that are only present in some samples but not in others) often exists among different studies and must be retained when removing batch effects. To achieve accurate reference-based annotation, a fundamental task is to integrate the reference dataset with a query dataset by learning a joint embedding space, where batch effects are removed while biological variation is preserved.
Existing work in bioinformatics and artificial intelligence domains focuses on designing computational and machine learning methods to solve this problem. For example, Symphony by Kang et al. [ 26], uses a linear mixed model to update the embedding of cells from the query dataset to fit the reference. ScArches by Lotfollahi et al. [ 37] is a transfer-learning framework that adapts the model trained on the reference dataset to the query dataset. However, computational methods are usually based on certain assumptions that do not always hold. For example, existing methods typically assume that query datasets share similar cell types and cell type distributions with the reference dataset, making them unstable for identifying novel cell populations in the query dataset. To conduct accurate and reliable reference-based annotation, it is important to involve biologists and integrate their domain knowledge into the annotation process, examining, refining, updating, and validating the automatic annotations and their underlying assumptions. Therefore, an interactive learning framework and a set of suitable interactive visualizations are needed.
Single-cell data analysis heavily relies on interactive visualizations [8]. A large number of interactive visualization tools are widely used in the community of single-cell analysis, such as Vitessce [28], CellxGene [9], Pagoda2 [3], and UCSC Cell Browser [59]. However, existing visualization tools are primarily designed for manually exploring and annotating individual datasets, with limited support for examining and modifying multiple datasets or reference-based annotation models. While valuable lessons can be learned from existing single-cell visualization tools, the set of visualizations and interactions required to facilitate human-AI collaboration in single-cell annotation has not been well explored.
In this work, we propose Polyphony, an interactive visualization approach to make the reference-query integration and label transfer process understandable, interactive, and controllable. The design and development of Polyphony have been driven and guided by multiple rounds of interviews with a total of seven experts in single-cell analysis. Core to *Polyphony is* an interactive transfer learning (ITL) framework and a set of interactive visualizations that support user-involvement in the ITL process. Specifically, the ITL framework extends scArches [37] to enable human interpretation and participation through anchors, analogous cell populations across datasets, a concept that is understood by both the back-end ITL model and human domain experts. For the back-end model, anchors guide the integration of different datasets, help remove batch effects, and enable more accurate label transfer and cell annotation. For human users, anchors are easily interpreted by comparing their biological markers such as gene expression levels. As shown in Fig. 3, Polyphony presents potential anchors to users through an anchor recommender and fine-tunes the back-end model through an anchor processor using anchors that are confirmed, refined, or added by users. We propose three coordinated views to empower users to interpret and interact with anchors by considering multiple types of information, including the structure of the embedding space, cell clusters, top differentially-expressed genes, and per-cell gene expression values. We conduct a quantitative experiment to evaluate the effectiveness of the Polyphony model by simulating different strategies that experts may apply to confirm anchors during the model iteration. We further introduce two use cases of Polyphony and report feedback from two individual interviews with experts to demonstrate the effectiveness of our approach. The results show that the anchor-based ITL method helps users to conduct reference-based joint analysis of single-cell datasets to obtain more accurate and trustworthy integration results.
The major contributions of this paper are:
## Domain Problem.
Single-cell transcriptomics examines the gene expression levels of thousands to millions of individual cells to understand cellular heterogeneity, reconstruct developmental trajectories, and model transcriptional dynamics. In this field, cell type identification is considered the most elementary analysis task, for which a large number of computational methods have been developed [32].
Reference-based annotation enables efficient cell type identification by transferring labels from reference datasets to new datasets. A critical step in the reference-based annotation is to integrate different datasets into the same embedding space, which allows labels to be transferred from one dataset to another. Batch effects occur when non-biological factors (e.g., sequencing instrument differences) result in systematic biases in the data produced by an experiment. Batch effects can be difficult to resolve since they are usually coupled with biological variability among different experimental samples. Under-correction (i.e., failure to fully remove batch effects) or over-correction (i.e., removal of both technical and biological variability) of batch effects will lead to inaccurate dataset integration and label transfer.
This study aims to improve data integration and annotation results by involving human users and utilizing their feedback to better distinguish batch effects from biologically meaningful variation.
## Data Abstraction.
As shown in Fig. 2A, single-cell transcriptomics data can be represented by a matrix G ∈ Rg×n that records the amount of RNA corresponding to g genes (rows) detected in n cells (columns). Cells in the reference dataset have been assigned cell type labels, while cells in the query dataset have not.
To facilitate the analysis of this high-dimensional data, researchers typically map the original data into a low-dimensional embedding space (Zr ∈ Rd×n, d < g), which can be used to cluster similar cells. The goal of reference-based cell annotation is to create a joint embedding space where the same types of cells in the reference dataset and the query dataset are well aligned (Fig. 2B-C).
## RELATED WORK
In this section, we summarize the related literature about visualization and integration of single-cell data and visualization in interactive machine learning.
## Single-Cell Data Visualization
Interactive visualization techniques are widely used in analyzing molecular measurements in single cells.
Most existing methods for single-cell data visualization focus on gene expression data (e.g., single-cell RNA sequencing or scRNA-seq data) represented as a cell-by-gene expression matrix with cell-level metadata [1]. These methods are often generalizable to other single-cell data modalities (e.g., scATAC-seq data can be represented as a cell-by-peak matrix). Cakir et al. [ 8] summarized and compared these works, including Cytosplore [23], CellxGene [9], Pagoda2 [3], UCSC Cell Browser [59], and ASAP [17]. These tools display dimensionally-reduced single-cell data [7] and support common tasks, including understanding cell clusters, inspecting the expression magnitude of certain genes and annotating cell selections. Driven by these tasks, most visualization tools feature dimensionality reduction (DR) scatterplot visualizations in their largest, main views. Tools tailored for single-cell data often additionally support expression heatmap visualization, genomic sequence visualization, biomedical file formats, experimental metadata, or contextual ontology information [12].
Single-cell technologies are increasingly able to measure multiple modalities (e.g., quantification of both gene expression and chromatin accessibility in the same cells [11]), temporal context [40], and spatial context (e.g., microscopy images with cell segmentations [35] or spot detections [10], or spatially-resolved sequencing measurements [49]). The existing tools, Facetto [30], SpaCeCo [57], ImaCytE [58], and Vitessce [28], support visualization of microscopy images and cell or organelle segmentations, in addition to the aforementioned features for DR and expression matrix visualization.
The visualization techniques used in Polyphony take inspiration from and build upon the existing visualizations for analyzing homogeneous single-cell transcriptomic data. We target an under-explored scenario in which users explore and annotate single-cell transcriptomic data by comparing it to well-established reference datasets. We identify the activities required by this scenario and propose new visualizations to support the workflow in which two datasets are jointly analyzed.
## Single-Cell Data Integration
Based on related surveys and benchmarks [2, 39, 63], existing computational methods can be categorized into three main groups: anchor-based, linear mixed model (LMM)-based, and conditional variational autoencoder (CVAE)-based.
Anchor-based methods measure cell-cell similarities and identify correspondences between cells across different datasets [4, 21, 47, 60]. For example, Haghverd et al. proposed the mutual nearest neighbors (MNNs) [21] method for single-cell batch correction. MNNs are pairs of cell populations from different datasets close to each other in the latent embedding space. After identifying the MNNs, a transformation can be made to align the datasets according to these cells. Seurat V3 [60] expands upon this method and enables users to integrate single-cell measurements across different modalities. These anchors play an important role in integrating different datasets but are usually hidden from end users, making it difficult for users to understand the model working mechanism and refine model performance.
Another group of works uses LMMs to harmonize single-cell datasets [26, 29]. Symphony [26], a recent work by Kang et al., scales LMM methods to integrate newly produced data with atlas-level references in an acceptable time and memory footprint by compressing each reference into a set of cluster descriptors.
The third group of works uses CVAEs to learn a harmonized joint embedding space [36, 38], as implemented by SCVI [36]. Lotfollahi et al. proposed single-cell architectural surgery (scArches), a transfer learning framework that decouples the reference building step from the query data adaption step.
While existing methods demonstrate great performance in many settings, they share certain limitations due to their lack of interactivity and explainability. For example, a novel cell population in the query dataset can be unexpectedly fused to a well-characterized population [26]. Our work aims to incorporate interactivity and explainability in reference-based cell annotation through an interactive machine learning framework. This framework is built upon a CVAE and leverages anchor cell sets, which not only explain the progressive cell annotation process to the user, but also offer users control in the learning process.
## Visualization in Interactive Machine Learning
Interactive machine learning (IML) [15] integrates human intelligence with data-intensive computational methods. Various visualization methods have been proposed to reduce user efforts in this process.
Many studies involve users to refine model hyperparameters and working mechanisms. For example, EnsembleMatrix [62] enables users to integrate their knowledge by adjusting the weights of each classifier in an ensemble model. Kapoor et al. [ 27] allow users to adjust the weights of a confusion matrix by assigning a cost for each type of mistake. RetainVis [31] leverages an attention module where users can directly edit attention weights to update the model. Jia et al. [ 25] designed Semantic Navigator, which guides users to steer the model by editing a class-attribute matrix in a zero-shot learning process.
However, model refinement can be challenging for users with limited ML expertise. Therefore, other studies take a different approach, allowing users to inspect and label instances as a way to interact with the ML model [14, 16, 66]. For example, ProtoSteer [41] asks users to edit a list of representative examples from the training data, called prototypes, to steer a deep sequence model [42]. However, biologists conduct population-level analysis using statistical methods on single-cell data. Labeling each individual data instance (i.e., cell) is not feasible.
In this work, we propose an anchor-based approach. Users specify anchors, analogous cell populations across the two datasets, to integrate the datasets and transfer the labels from the reference to the query cells.
## Informing the Design
This section presents our collaboration with the domain experts during the design process and the analysis tasks that guide the system design.
## Design Process
This study is primarily guided by the Nested Model for visualization design and validation proposed by Munzner [43] and the nine-stage design study framework proposed by Sedlmair et al. [ 52], with focus on the discovery, design, and implementation steps.
We closely collaborated with seven domain experts (E1-E7, four females, three males; six postdoctoral researchers and one assistant professor) in single-cell analysis throughout the system design and development process. All experts are experienced in single-cell analysis and have basic knowledge of machine learning methods. None of the experts are authors of this paper. Multiple rounds of expert interviews were conducted at different stages of the study to understand the domain problem and data, clarify analysis tasks, iterate upon the design, and test the prototype system. Finally, Polyphony was evaluated with two experts specializing in single-cell transcriptomics data analysis.
## Analysis Tasks
Based on the feedback from experts, we summarize four tasks (T1-T4) that users commonly conduct in reference-based single-cell analysis and two tasks (T5-T6) that are desired by users but not well supported in existing tools. While this study focuses on reference-based single-cell analysis, we believe the reported analysis needs and tasks are also insightful for general ML-assisted single-cell analysis.
## Gain an overview of the joint embedding space.
Experts usually start their analysis with an overview of the cell embedding space, which is typically mapped into a 2D coordinate system using dimensionality reduction methods (E1-E7). This overview enables a preliminary assessment of the clustering quality (i.e., whether cell type clusters are easily distinguishable) and the cell integration quality (i.e., how the cells from different datasets are fused). The annotation results are trustworthy only when the cells are well clustered and integrated under the latent space.
## Understand cell correspondence across datasets.
After gaining an overview of the embedding space, experts expect to further explore the cell correspondences across the datasets (i.e., which query clusters are nearby which reference cell types, raised by E5). The correspondence helps experts understand how their data is mapped onto the reference and suggests how the reference labels might be used to annotate their data.
## Identify potential unique cell populations.
Experts are generally interested in identifying unique cell populations that appear in the query dataset but not the reference. In specific, when such cell populations are present, experts expect to select query cell clusters that do not overlap with well-characterized cell types for further inspection (suggested by E2). In some cases, experts have unique prior knowledge of certain genes, such as cell marker genes that distinguish cell types that have been poorly defined in the literature. They expect to evaluate marker gene expression magnitude to verify and refine the cell population selection (suggested by E3).
## Understand cell identities.
The analysis ends with a full list of annotated cell types. For each cell cluster, experts not only record the cell type label but also the biomarkers (i.e., genes that are differentially expressed among clusters and define cell types). These biomarkers are often related to cell morphology and physiology and play an important role in downstream analysis. Typical machine learning models for cell type annotation only produce cell type labels. Experts (E2, E3, E6) expressed their need to identify biomarkers through separate statistical analyses.
## Understand the integration process.
While ML-based annotation methods enable efficient analyses, the computational process is often opaque to users. Experts expressed the need to understand how each step of the data integration process changes their data. For example, E6 mentioned that they usually investigate how the clusters in the embedding space change before and after data integration. Importantly, the analysis goal is to gain a deep understanding about the data. Experts wish for a tool that is, “more than just giving me an answer” (E4).
## Refine the model to align human knowledge.
The annotation results are not always satisfactory, where the model may under-correct or over-correct the dataset. Instead of checking and manipulating the annotation results one by one, an alternative method is to correct the model systemically, as suggested by E5. Enabling users to examine, validate, and refine the model can increase user trust, improve their understanding about the data, and generate more accurate annotations [55].
## Interactive Transfer Learning
We propose an ITL framework that integrates and annotates single-cell query datasets into reference datasets under human supervision. In this section, we first provide an overview of this framework and then introduce the computational method.
## Overview
The ITL process (Fig. 3) includes four steps. First, the model compares the two datasets and recommends anchors to users (Fig. 3A). Afterwards, users inspect and update these anchors according to their domain knowledge (Fig. 3B). The model is then fine-tuned using the user-confirmed anchors (Fig. 3C), updating the annotations and embedding for the query dataset (Fig. 3D). The model then recommends new anchors based on the updated embedding space. These four steps repeat until the user is satisfied with the integration and decides to apply the predictions to annotate the remaining cells.
The following sections introduce the three major computational components in the framework: model setup, anchor recommendation, and model fine-tuning.
## Model Setup
The proposed framework is built upon scArches (single-cell architectural surgery) [37], a transfer learning framework using conditional variational autoencoders (CVAEs) [56] for single-cell data integration. We use SCVI [36] as the backbone model because it is a commonly-used and well-performing single-cell data integration and representation learning method [39].
A CVAE model can generate latent embeddings for the input data where the effects of condition labels are regressed out. In the framework inherited from scArches, such condition labels are used to index batch effects (e.g., sequencing technologies, experiment laboratories, patients, or a combination of these categorical variables). The CVAE model is first trained on the reference dataset and then adapted to the query dataset by adding a conditional node to index batches in the query dataset (Fig. 3C). The parameters in the original model are fixed while the weights related to the new conditional node are learned on the query dataset. As a result, the CVAE model can generate embeddings for the query dataset while removing the batch effects between reference and query. Based on the latent representation learned by the CVAE model, a cell type classifier is trained on the reference dataset to identify the query cell types. We choose a k-nearest-neighbor (kNN) classifier since previous studies [26] illustrate that kNN classifiers can achieve the highest performance while remaining interpretable in this setting.
As with other end-to-end reference-based annotation methods, the CVAE model can potentially over-correct and confuse biological variability with batch effects. Our ITL framework aims to address this issue through a progressive integration method shepherded by users. We set up the CVAE model similar to the settings in scArches to provide a warm start. This model is then refined based on user feedback about the recommended anchors.
## Anchor Recommendation
An integration anchor is a pair of cell sets (one from each dataset: query and reference) that share biological similarities and should be proximal in the joint embedding space. Many anchor-based techniques have been proposed to integrate single-cell datasets and remove batch effects [4, 21, 26, 29, 64]. However, the identification of anchors is typically based on a statistical assumption between datasets, which may be invalid in certain scenarios (e.g., the query dataset has a novel cell type). To address this issue, we generate anchors in a progressive manner and involve humans to validate and refine them.
Aiming for scalability to large reference datasets and low time-cost in the interactive system, we adopt the efficient algorithm from Symphony [26], the state-of-the-art atlas-level integration method, to build the anchor recommender. The algorithm contains two steps: reference building and query cell mapping. The reference building step facilitates scalability by compressing reference datasets into referential elements. Specifically, this step generates a description for each cluster in the reference dataset, including the cluster centers and the number of cells within. We compute this description using the Harmony algorithm [29]. The query cell mapping step aims to find anchors by calculating the assignment probability of each query cell to each cluster. Query cells with high assignment confidence are mapped to the query set of corresponding anchors.
The recommended anchors are represented to the users in a visual interface (Sect. 6), where users can inspect and interact with them through a set of interactive visualizations.
## Model Fine-Tuning
We fine-tune the CVAE model based on users’ feedback about the recommended anchors. First, we “correct” the embedding of the user-confirmed anchors by mapping them to the corresponding reference cluster. Inspired by Symphony’s correction algorithm, we use a linear mixed model to update the query cell embeddings by considering only the corrections of the most likely cluster, which users have verified. Then, we fine-tune the model under a semi-supervised setting. Specifically, we expect that the generated latent embeddings of the anchor query cells match the corrected embeddings. Therefore, we add a penalization term in the loss function to measure the distance between the generated and the corrected embeddings for each cell.
## Polyphony
In this section, we introduce the visualization and interaction designs in Polyphony. We first provide an overview and briefly introduce an expected analysis workflow. Then, we introduce the design details, rationales, and feedback received from experts during the design process.
The interface of *Polyphony is* composed of three coordinated views, namely the comparison view, the anchor set view, and the marker view. The comparison view (Fig. 1A) offers an overview of the embedding spaces (T1), enabling users to understand the distributions of cells and identify cell populations of interest (T3). The anchor set view (Fig. 1B) shows statistical information about each anchor (T2). The marker view (Fig. 1C) displays differential gene expression analysis results for a focal anchor (T4). These views support interactions that enable users to communicate feedback to the model via anchors (T5, T6).
## Workflow.
A typical workflow starts with the comparison view, where the user first understands the quality of the data integration. The user may also find some interesting cell populations as candidate anchors and select them for further inspection. In the anchor set view, the user checks the anchors that have either been recommended by the system or selected by themselves. At this stage, the user may directly delete low-quality anchors and confirm promising anchors according to the high-level statistical information. In some cases, the user will check the gene expression values of the anchor cells to make the decision. In the marker view, the user inspects and compares the significant genes in both the query cell set and the reference cell set of the anchor. *For* genes of interest, the user may select each gene and check the expression distribution in the comparison view. Finally, after confirming several anchors, the user submits their feedback to the model, prompting fine-tuning and embedding updates. After multiple rounds of model updates, the data integration will have been improved by taking into account human domain knowledge.
## Comparison View
The comparison view (Fig. 4A) presents both the query and the reference datasets along with different properties of the cells (e.g., cell type labels or predictions, dataset membership, and expression magnitude of selected genes) and their geometry structures in the latent space. This view helps users obtain an overview of the embedding space, evaluate the integration quality (T1), and identify cell populations of interest for further examination (T3). Further, the anchor visualization (Fig. 4B) in the embedding space provides an intuitive approach for users to understand the cell correspondence across datasets (T2).
## Hybrid design.
We use different visual representations to encode query (Fig. 4C) and reference cells (Fig. 4D) in the same view. For the query cells, we use a scatterplot to provide detailed information about these cells and offer flexible selections. The color of each point encodes the predicted cell type. For the reference cells, we first calculate the density of each type of cell across a grid (with an adjustable grid size) in the projected space. Then, we draw contours to outline dense grid regions according to a unified density threshold adjustable by users. The contour areas are textured to distinguish them from the query cell scatterplot points.
## Anchor annotation.
Layered above the hybrid visualization of cells from both datasets, we display anchor annotations to help users understand anchor quality and membership in the embedding space context (T2). The annotation design for an anchor (Fig. 4B) is composed of two parts. The inner part contains two linked dots which indicate the centers of the query (solid dot) and reference (hollow dot) cell sets. The outer part encodes the quality of the anchor using the line width (e.g., a wider line denotes lower quality). As suggested by the experts, we use gene expression (GE) similarity as the anchor quality metric. We define GE similarity as the intersection over the union of the gene sets that express significantly differentially between the anchor cells and the rest of the cells, where the significant genes are selected as the top-100 genes when ranked by the z-score under a Wilcoxon test. This design enables users to assess anchor quality and identify anchors to inspect and modify depending on the analysis task at hand.
## Design rationale.
This visualization is an evolution of an initial design, where we used two additional coordinated scatterplots to support the comparison task: one using color to encode the dataset membership of each cell, another using color to encode cell types (or cell type predictions). Despite being straightforward and intuitive to experts, we found that the users were not able to easily join this information together when many cell types were present (e.g., making it difficult to distinguish well-integrated cell types from poorly-integrated cell types). Distinguishing cell type-specific integration quality is essential in selecting proper anchors (T2). This observation motivated us to design a single-view visualization to encode the dataset and cell type information simultaneously. In doing so, we tested both heatmaps and contour lines to visualize the reference cell density layered below the query cell scatterplot. We found that experts prefer contours because heatmaps not only take longer to render but also are visually overwhelming.
## Anchor Set View
The anchor set view arranges the anchors in a table (Fig. 1B), where each entry represents an anchor that has either been recommended by the system or created by the user. The recommended anchors denote those cells that the model considers most similar between the query and reference datasets (T2). By interacting with these anchors, users understand the model results (T5) and provide feedback for further model refinement (T6).
For each anchor, the table presents the composition of the cell type predictions, the distance between the pair of sets in the latent space, and the GE similarity (introduced in the previous section). These properties were verified by experts to ensure that they represent the most important descriptive information for an anchor. We use horizontal stacked bar plots to show the proportions of predicted cell types, where the color encodes the cell type, consistent with the comparison view. In the distance column, we use bar length to indicate the median distance between cells in the query set and the center of the reference set. In the GE similarity column, we use the sweep angle of an arc within a circle to encode the similarity score (in the range 0 to 1).
The GE similarity scores help users make sense of the differences between the query and reference cells in the gene expression space. A lower similarity score indicates that the two cell groups share very different marker genes, suggesting the anchor should be removed in most cases. In contrast, when the similarity score is high, users may still need to investigate the top differentially expressed genes. Sometimes, two similar cell types may share expressions of all but a few critical marker genes whose expression patterns distinguish the cell types. In such a case, expert knowledge is required to make the judgement.
## Marker View
The marker view (Fig. 5A) enables users to conduct gene-level analysis on the significantly differentially-expressed genes (T4).
Suggested by the domain experts, we horizontally divide the interface that lists the significant genes into three columns: significant in both query and reference (“shared”), significant only in query, and significant only in reference. This design helps them distinguish the shared significant genes and those specific to each dataset.
For each gene, we display a glyph (Fig. 5B) to enable the comparison of gene significance (z-scores in our case) and gene significance rankings simultaneously in both the query and reference sets. We use two bars with contrasting colors to visualize the z-scores of a gene in the query and the reference datasets. A longer bar indicates a higher level of significance. Stacked triangles represent the ranking of a gene in the query dataset (triangles on the left side) and the reference dataset (the ones on the right side). More triangles indicate a higher ranking and imply a more important gene. Three triangles indicate a top-10 ranking. Two or one triangle(s) indicates a top-20 or top-100 ranking, respectively. These thresholds were suggested by the domain experts in our interviews.
To help users better understand the distribution of gene expression in the whole cell population, we coordinate the marker view with the comparison view. When users click a gene, the color encoding in the comparison view will be changed to represent the gene expression magnitude for each cell (Fig. 1A1).
## Interacting with Anchors
In this section, we introduce the interactions supported by Polyphony to help users explore and update the anchors (T5, T6).
## Confirm or reject.
The most straightforward interaction with anchors is to directly confirm or reject an anchor. Once an anchor is confirmed, it will be used to fine-tune the back-end model and improve the data integration. In most cases, users need to further examine anchors before they can make a decision confidently, including highlighting anchors, editing anchors, or marking anchors.
## Highlight.
When users hover over an anchor in the anchor set view, the comparison view will automatically respond by zooming in and highlighting the corresponding cells and anchor annotations.
## Edit.
The system provides lasso tools for users to select a group of cells to create a new anchor or update the cells in an existing anchor.
## Mark.
For anchors that the users are interested in but cannot make a decision confidently, Polyphony allows users to affix them to the top of table in the anchor set view. Users can keep track of the marked anchors across model iterations and reevaluate their confidence after each iteration.
## System Implementation
Polyphony contains a front-end user interface for data visualization and a back-end server for data storage, model training, and anchor operations. The front-end is built upon Vitessce [28], a web-based framework for single-cell data visualization. We extend this framework by creating additional reusable components to support reference-based analysis. The back-end is a Python application that relies on Scanpy [65], SCVI-tools [18], scArches [37], PyTorch [45], and scikit-learn [46]. We use Scanpy to preprocess the single-cell data and calculate differential gene expression. The other three packages are used to build the machine learning models. We implement the anchor-powered CVAE model by extending the SCVI model (under the scArches framework) and build the kNN classifiers with scikit-learn. All cell-related data produced in this system (e.g., latent vectors, model predictions, and anchor sets) are compressed and stored in h5ad [65] files allowing users to easily share the results. Finally, we use Flask to develop a server for communicating between the front-end and the back-end.
## Evaluation
We evaluate Polyphony from both the algorithmic perspective and the user perspective. In the algorithm evaluation, we conduct quantitative experiments to evaluate the performance of the Polyphony model under different anchor selection policies. In user-centered evaluations, we evaluate the utility and usability of the whole system from hypothetical use cases and qualitative feedback from experts.
## Datasets.
We apply two commonly-used benchmark datasets in single-cell transcriptomics data integration: the Human Pancreas Dataset, which is constructed by combining results from [5, 20, 33, 44, 53], and Human Peripheral Blood Mononuclear Cell (PBMC) Dataset [13]. The Pancreas dataset contains gene expression measurements of 15,681 cells across 9 cell types from human pancreas. We separate the datasets into a reference set (cells generated using a plate-based protocol, $$n = 7290$$ cells) and a query set (cells generated using a droplet-based protocol, $$n = 8391$$ cells). The PBMC dataset contains gene expression profiles produced by various sequencing techniques for over 32,300 total cells. We divide them into a reference set (21,573 cells from eight batches profiled by multiple technologies) and a query set (10,727 cells profiled by the 10X Genomics Chromium technology).
## Algorithm Evaluation
To study the effectiveness of the Polyphony model in improving the quality of single-cell data integration and annotation, we designed and conducted the following experiments.
## Methodology.
We evaluate the model performance by simulating different strategies that the experts may apply to confirm anchors during the model iteration (e.g., accept most anchors recommended by the model vs. select a few high-quality anchors). To simulate how experts assess anchor quality using their domain knowledge of particular cell types, we estimated the quality of an anchor based on the cell-type consistency between query and reference cells. Specifically, we calculated a normalized entropy value for each anchor based on the ground-truth types of the query and reference cells. We use a threshold θ to denote an anchor selection policy (anchors whose normalized entropy values are smaller than θ are selected). A lower threshold θ indicates a stricter policy where fewer high-quality anchors are selected. And θ = 1 indicates that all recommended anchors are selected for model updating.
## Settings.
In total, we run four experiments under different anchor selection policies (θ ∈ {0,0.25,0.5,1}) for each of the two datasets (i.e., Pancreas and PBMC). With θ = 0, the model uses no anchors (i.e., under the same settings with scArches [37]). We denote this condition as the baseline. Each experiment starts with a unified warm-up session (100 epochs) where the models initially trained on the reference dataset incompletely adapt to the query dataset. Then we run each experiment with multiple rounds of anchor-involved model updating. We report the results at the fourth round since most valid anchors are already selected after four rounds according to our experiments. We apply two commonly used metrics based on local inverse Simpson’s Index (LISI), iLISI and cLISI [29], to evaluate the integration quality and use the macro-averaged F1 score to evaluate the annotation accuracy. All three scores are normalized from 0 to 1 [39], where higher iLISI, cLISI, and F1 scores indicate better batch-effect removal, cell type separation, and annotation accuracy, respectively. We run each experiment ten times and report the average results and the variance in Table 1.
## Results.
As shown in Table 1, with both datasets, the Polyphony models (i.e., policies where θ > 0) outperform the baseline models in single-cell data integration (based on iLISI and cLISI scores) and annotation (based on F1 scores). Besides, models with more confirmed anchors (i.e., a higher θ) achieve higher iLISI scores, indicating that the cells between the query set and the reference set are better mixed and batch effects are more sufficiently removed. However, models with more anchors do not necessarily have more accurate annotations. For example, in the Pancreas dataset, the models with the anchor selection threshold θ = 0.5 achieve the highest F1 score on average (0.806). And in the PBMC dataset, the highest F1 score (0.621) is achieved under a stricter threshold θ = 0.25. We hypothesize that this is due to the confirmation of low-quality anchors, which mix different cell types and reduce the annotation accuracy. The hypothesis is supported by the cLISI scores, where models confirming all anchors (θ = 1) achieve lower cLISI scores than the models with stricter policies.
The experiment results confirm the effectiveness of the Polyphony model in single-cell data integration and annotation. The results also reveal the importance of making selections of faithful anchors, where expert judgement is required.
## User-Centered Evaluation
In this section, we introduce the user-centered evaluation of Polyphony with two use cases and qualitative feedback from experts.
## Use Case I - Pancreas Dataset Integration
In the first use case, we use the Pancreas dataset. A hypothetical biologist wants to annotate the cells in the query set using the reference cells. However, the technical variation between the two datasets introduces batch effects and prevents direct annotations. We describe how the biologist would use Polyphony to overcome this challenge and conduct an integrative analysis.
## Understand the quality of integration.
First, the user looks at the comparison view (Fig. 1A) to understand how the two datasets have been integrated together (T1). The user notices that few cells from the query dataset (represented by scatterplot points) fall within the textured contour areas, where the cells from the reference dataset are most dense. This indicates that batch effects are still present despite an initial round of integration. The user feels that biologically relevant conclusions cannot be obtained until such batch effects are removed.
## Understand and refine anchor recommendations.
The user then inspects the anchors recommended by the system (T2). The user feels that most of these recommended anchors look reasonable. One exception is “anchor-9”, which seems to link a group of Pancreas Ductal cells (■) from the query dataset to Pancreas Beta cells (■) (Fig. 1A2) in the reference (T5). Referring to the anchor set view, the user notices that this anchor contains multiple types of cells (Fig. 1B1), indicating its low quality and thus should be removed (T6). Instead, the user wants to create a more reasonable anchor to integrate Pancreas Ductal cells (■). The user uses the lasso to select a group of such cells near the reference contour (Fig. 1A3) and confirm this newly created anchor.
## Inspect gene expressions and modify anchor.
The user wants to further understand the gene profiles of the remaining anchors (T4). The user selects “anchor-14” (Fig. 1B2), which is mainly composed of Pancreas Gamma cells (■). From the marker view, the user notices that PPY is the top-most significant gene for both the query and the reference cells. The user knows that this gene is a widely accepted marker gene for this cell type and anticipates that this anchor will be helpful in integrating the Pancreas Gamma cells (■) from the two datasets together. The user then checks the expression magnitude of PPY by clicking on it. The user notices that the anchor cells are colored bright yellow (Fig. 1A1), distinguishing them from the rest of the cells, which confirms the hypothesis. After applying the same strategy to check and confirm other anchors, the user updates the model.
## Summary.
The model returns illustrate a better integration between the query and the reference datasets (Fig. 6B). Involved in the integration process, the user feels confident about the integration results.
Through this analysis, the user finds a rare cell population from the query dataset. The finding is supported by the cluster structure in the refined joint embedding space and the statistical evidence of the differentially expressed genes.
## Use Case II - PBMC Dataset Understanding
We consider a more challenging scenario in which the cell types of the query dataset are not fully reflected in the reference dataset (i.e., the query dataset includes novel cell types). We use the PBMC dataset. Following common practices in molecular biology, we simulate the mismatch in cell-type composition between the query and reference by intentionally removing all plasmacytoid dendritic cells (pDCs. 184 cells, $0.8\%$) from the reference dataset [26]. In this use case, the user is aware of the potential cell composition mismatch between the query and reference. We show how Polyphony can be used to distinguish these unseen cells in a reference-based analysis.
## Bad integration or novel cell population.
The user first checks the comparison view to gain an overall understanding of the two datasets (Fig. 7A) (T1). At a glance, the user notices that multiple cell clusters in the query dataset do not overlap with any reference cluster. Besides, some anchors have thick borders, indicating that batch effects remain (T2). Then, the user turns to the anchor set view and finds some “suspicious” clusters with either a very low GE similarity or multiple predicted cell types at high proportions (Fig. 7B1). However, the noise introduced by batch effects makes it hard to distinguish whether the “suspicious” clusters represent novel cell populations or simply misalignments (T3). The user decides to improve the integration quality and return later to investigate the “suspicious” clusters.
## Improve the integration with anchors.
The user selects anchors with homogeneous cell type prediction composition and high similarity scores (Fig. 7B2). These anchors contain common immune cells, including CD20+ B cells (■), CD4+ T cells (■), CD14+ Monocytes (■), and NK cells (■). The user is familiar with these cell types and thus can make confident decisions about whether to confirm them. After ensuring that the corresponding marker genes are highly expressed in each anchor (T4), the user confirms them (T6). After the model has been updated, the user finds that most query cells are well aligned with the reference in the new latent space (Fig. 7C). It indicates that batch effects have been removed, and most query cells have been mapped into the reference space such that similar cells are nearby.
## Identify unknown cell populations.
Despite the good alignment of most query cells, the previously-marked anchor “marked-0” stands out with a relatively long distance between the query and the reference cell sets (Fig. 7C1). Most importantly, the query and the reference sets are marked by different genes, as indicated by the low GE similarity (Fig. 7D1). The user feels that the query cells likely belong to a novel cell type. After inspecting the marker genes and referring to related literature and external tools, the user finds the marker genes related to stimulus response, defense response, and immune effector processes. The user finally confirms that these cells belong to the pDC cell type.
## Expert Interview
We conducted individual interviews with two experts (E3, E4) in a semi-structured way. Since the experts were already familiar with this project from previous interviews, we briefly reminded them about the background and demonstrated Polyphony with a showcase using the pancreas dataset. Then, we let the experts explore the system for 30 minutes freely. The participants were encouraged to ask questions during the exploration. We took notes on the system usage and asked the experts the rationale behind their operations upon anchors. Afterward, we collected their feedback on their usage experience, expected usage in their work, and desired improvements.
## System design.
Both experts agreed that visualization designs in Polyphony were intuitive and that the system was easy to use. At first, E3 expressed confusion that an anchor did not contain all cells in what appeared to be a cluster in the embedding. After explaining that anchors are analogous cell sets across datasets that the model is confident about, they understood. They highly appreciated the flexibility in selecting cell populations of interest and obtaining recommended reference cells. They commented, “this tool does not just give me an answer but allows me to interact with it.” E4 suggested that in their studies, they noticed that existing computational methods for integration sometimes produce undesired results. They highly appreciated that Polyphony enables improving the integration through their feedback.
## Anchor validation.
E4 confirmed that whether an expert confirms or rejects anchor recommendations highly depends on the expert’s knowledge about the corresponding cell type and marker genes. They commented, “…if I knew this type of cell and saw related marker genes expressed, I would be pretty sure about the anchor.” Otherwise, they need to check the literature to make the decision. E3 expressed similar opinions and suggested that certain types of cells are more likely to be chosen as the anchors by users than others, stating, “…some cell types are well-understood by biologists, while some are hard to define.”
## Application scenarios.
Both experts said that they would use the system to understand how the integration was performed and make further improvements. E4 suggested that this process can help them to understand unfamiliar cell populations in their data. They described the process as first using the familiar cell populations as anchors to integrate the datasets. Then, they would check how the unfamiliar populations are mapped to the reference for further understanding. From a different angle, E3 commented that they would like to use the system to conduct comparative studies of cells from different sources. For instance, they might compare cells from different organs or from different patients by dividing them into a treatment group and a control group.
## Desired improvements.
Both experts are eager to use the system with their own data (with over 100,000 cells). They also wanted the system to contain multiple pre-loaded reference datasets (e.g., cells collected from different organs) to select from. Further, E3 hoped that the system could enable users to simultaneously select multiple cell populations and perform differential expression analysis within the selected sub-populations of cells. E4 suggested that the system could also integrate gene set enrichment analysis [61] to provide annotations for genes, which would help experts quickly gain a high-level understanding of the potential functions of the cell populations.
## Discussion
We discuss the impact on the target domain, design implications, limitations of this work, and directions for further improvements.
## Impact on Single-Cell Data Analysis
Our system improves the biologist’s workflow in reference-based single-cell data analysis by combining two traditionally separate analysis stages—understanding and integration—together in an interactive and iterative process. We extend the usage of anchors by supporting user inspection of and interaction with them through tailored visual summaries and interaction designs. Such improvements allow users to gain insights into cell correspondences and interact with the model.
A typical challenge in the joint analysis is to distinguish whether an unexpected integration result is an insight (e.g., novel cell populations) or an artifact (e.g., results from an imperfect integration algorithm). In current practice, biologists run an integration algorithm to gain a rough result and manually check the results on cell clusters of interest. When they find unexpected results (e.g., remaining batch effects), there is limited support for model refinement. Some biologists may choose to run the integration algorithm with different parameters multiple times. Others may abandon the results and refer to different integration algorithms. Both approaches are inefficient. Polyphony aims to improve this workflow by providing controllability and interactivity for an integration model. Specifically, our system enables users to steer the integration process via anchors based on their observation of the integration results and their domain knowledge about cell types. Observing these cells and interactively steering the model allows users to conduct the joint analysis with much-improved efficiency.
## Support human-model communication with anchors.
In the proposed framework, anchors serve as a medium for human-model communication and allow experts to monitor and steer the model. We use anchors based on the following considerations. First, case-based reasoning (or analogical reasoning) [50] is one of the most common human reasoning procedures. Thus, showing concrete examples (i.e., anchors in this work) rather than model parameters or metrics helps experts understand and improve integration results in a more user-friendly way. Second, using analogous cell populations to “anchor” the two datasets is similar to biologists’ problem-solving process, where they use familiar cell populations to ensure the integration quality and make further inferences about the unknown populations. Third, from a technical perspective, anchors allow the model to be refined through semi-supervised learning, which helps to improve the integration quality with human knowledge. According to our evaluation, this approach is intuitive to experts and can greatly improve the model performance.
## Compare embedding spaces.
Most existing embedding-space comparison techniques assume that the correspondences between the two embedding spaces are clear [6, 54]. For example, they compare the embeddings of the same group of words generated by different embedding algorithms. In this work, we focus on a different problem setting where the two datasets have different embeddings (caused by batch effects) and contain different elements (i.e., the correspondences between the two datasets are ambiguous). Specially, we use links to indicate anchors and thus visually enhance users’ perception of the correspondences (Fig. 4D). Furthermore, considering the ambiguity of correspondences and the risks of leading users to integrate two different cell populations incorrectly, we encode the GE similarity using the width of the lines. This helps users to quickly find potential low-quality anchors, which may provide insights into novel cell populations.
## Scale to large datasets.
Polyphony currently supports the analysis of moderately-sized real-world datasets containing tens of thousands of cells. We tested Polyphony using three datasets. Two are moderately-sized datasets that are introduced in Sect. 7 and one is a large-scale dataset containing 274,346 immune cells from patients with COVID-19 (154,723 reference cells and 62,469 query cells) [34]. The model updates (with 50 epochs) take less than a minute for the two moderately-sized datasets and take about four minutes for the COVID dataset. These experiments were conducted on an Amazon server with an NVIDIA Tesla K80 GPU.
While the visualization front-end in Polyphony can display hundreds of thousands of cells with smooth interactions, preprocessing steps (e.g., computing anchor centroids) are a bottleneck (three minutes for 200,000 cells). This can be reduced by moving steps to the back end, facilitating use with the largest available references (e.g., Azimuth references, which range from 76,533 to 584,884 cells [22]).
## Select meaningful reference dataset.
Polyphony assumes a high-quality reference dataset and focuses on transferring labels from the reference to annotate query datasets. However, users may inadvertently use a low-quality reference (e.g., wrong labels, mismatched cells), which can severely undermine the annotation results. In the future, we intend to learn how reference quality influences system usage and make improvements in two areas. We plan to provide pre-loaded high-quality reference datasets in Polyphony. To achieve this, we will deploy the system to a cloud server, making it accessible to biologists worldwide, and prepare a gallery of pre-loaded reference datasets.
## Support multi-modal and spatial omics data.
In this work, we focus on the use of Polyphony with single-cell transcriptomics datasets. In recent years, multi-modal omics measurement techniques have enabled biologists to simultaneously measure DNA, RNA, and protein abundance and accessibility in the same cells, often with spatial context, enabling a more comprehensive understanding of biological processes. In future work, we plan to extend the anchor-based framework to support multi-modal and spatial omics data. A promising direction is to leverage multi-modal models, such as totalVI [19], to learn joint embeddings for which visualizations can be designed to support group-level comparisons across different modalities.
## Conclusion
In this work, we propose Polyphony, an interactive transfer learning (ITL) framework that helps biologists integrate and jointly analyze single-cell data with annotated references. The framework leverages anchors, analogous cell populations across datasets, to support interactions between humans and machines. We develop an interface through an iterative design process to support user understanding of the integration quality and enable integration improvements through a set of operations on anchors. We demonstrate the usefulness and effectiveness of this approach through quantitative experiments, two use cases, and interviews with two biologists. The results reveal that the anchor-based approach offers users an efficient way to interact with machine learning models for understanding and improving single-cell data integration results. Finally, we summarize the lessons learned from this study to inspire future studies on reference-based single-cell analysis and human-model interactions.
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|
---
title: 'Evaluation of the Relationship Between Smoking and Insulin Resistance: A Case-Control
Study'
journal: Cureus
year: 2023
pmcid: PMC10039987
doi: 10.7759/cureus.36684
license: CC BY 3.0
---
# Evaluation of the Relationship Between Smoking and Insulin Resistance: A Case-Control Study
## Abstract
Introduction: In recent years, there has been a surge in research focusing on the link between smoking and insulin resistance in the context of obesity and diabetes. In this study, our objective was to investigate the relationship between smoking and insulin resistance.
Materials and Methods: *This is* a case-control study. The case and control groups were formed using the hospital patient information database and clinically randomized using data obtained, including age, gender, height, and weight. The case group for this study consisted of smokers, whereas the control group consisted of non-smokers. Chi-square tests were used to compare numbers and rates, and independent sample t-tests were used for the averages. Binary logistic regression analysis was performed between the case and control groups.
Results: According to logistic regression analysis, the odds ratio for non-smokers was 0.59 (0.31-1.14). The risk of insulin resistance is decreased by $41\%$ non-significantly in non-smokers. The odds ratio for age was 1.03 (1.01-1.05). When the age variable increases by one unit, the risk of insulin resistance increase by 1.03 times.
Conclusion: Our study found no significant relationship between smoking and insulin resistance in healthy individuals. The relationship between smoking and insulin resistance, as reported in the scientific literature, may be suggestive of an association in which smoking exacerbates insulin resistance as a result of other contributing factors rather than serving as a direct causal factor. Further studies are warranted to elucidate the potential mechanisms underlying this association fully.
## Introduction
Tobacco smoke includes many chemical compounds [1]. Among these compounds, nicotine is the most prominent and well-established substance, which is responsible for the addictive effect of smoking [2]. Recently, there has been an increase in the number of studies investigating the association between smoking, obesity, and diabetes [3,4].
It is stated that nicotine is associated with type-2 diabetes by causing an increase in the glycemic index through autonomous nicotinic receptors [5]. In addition, although it has been suggested that nicotine plays a role in obesity and insulin resistance via leptin and adiponectin, the evidence has not been fully clarified [6]. Therefore, we aim to investigate the effects of smoking on insulin resistance.
## Materials and methods
Study design This was designed to be a case-control study. The sample size was determined for logistic regression analysis, considering the number of five independent variables, such as age, sex, body mass index (BMI), smoking, and alcohol consumption. For each group, using the power analysis method, the minimum sample size of at least 42 was needed to detect a significant difference when taken into account at 0.05 type-I error (Alpha), 0.35 effect size, 0.80 power (1-beta).
Population selection The case and control groups for this study were selected from the patient information database of Maltepe University Hospital, Istanbul, Türkiye, which included patients admitted between March 2020 and April 2021. The groups were matched based on age, gender, height, and weight criteria obtained from the database. The case group included 66 individuals with a smoking habit. The control group consisted of 150 individuals with non-smokers, matching the case group's gender, age, and BMI. The laboratory data of patients who were admitted to the family medicine outpatient during the last six months that met the inclusion criteria were analyzed. The study did not include patients with known diseases or those using medications.
Laboratory testing At admission, blood samples were obtained from the study participants. Nine parameters, including glucose, insulin, thyroid stimulating hormone (TSH), cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), vitamin D, and vitamin B12 were measured simultaneously for the analysis. Insulin resistance was defined using the homeostatic model assessment of insulin resistance (HOMA-IR) method. This involved calculating the product of fasting glucose (measured in mg/dL) and fasting insulin (measured in microU/mL), and dividing this value by a constant of 405. Higher HOMA-IR values indicate greater insulin resistance, with values above 2.7 considered indicative of insulin resistance.
Statistical analysis All statistical analyzes were performed by using IBM SPSS Statistics for Windows, Version 25.0 (Released 2017; IBM Corp., Armonk, New York, United States). A value of $P \leq 0.05$ was considered statistically significant. In the data expression, categorical data, numbers and percentages, and numerical data were expressed with averages and standard deviation. The distribution of demographic data was analyzed by frequency tests, categorical data comparison by chi-square test, and comparison of numerical data by independent sample t-test. The stepwise enter model was used in the binary logistic regression test to evaluate the effect of smoking on insulin resistance. Skewness and kurtosis analyses were used to conform the data to the normal distribution.
Ethical statement The study did not require ethics committee approval because of retrospectively. The study was conducted retrospectively by collecting data using hospital information systems and patient records. All patients who applied to the hospital had signed an informed consent form that included information about the process and biochemical analyses to be conducted during their application and using their data. To ensure confidentiality, the data of each patient was transferred to the SPSS environment with a code, and all identification data were removed. The researchers followed all international conventions related to patient confidentiality and research ethics. All procedures performed in this study were by the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
## Results
There was no difference between the case and control groups regarding alcohol consumption and BMI. However, the rate of women was higher in the control group than in the case group. In addition, the mean age was higher in the control group compared to the case group (Table 1).
**Table 1**
| Characteristics of participants. | Characteristics of participants..1 | Case (n=66) | Control (n=150) | p-value |
| --- | --- | --- | --- | --- |
| Age: Mean (SD)* | Age: Mean (SD)* | 33.83 (12.57) | 38.11 (15.16) | 0.032 |
| BMI: Mean (SD) | BMI: Mean (SD) | 24.76 (4.32) | 25.66 (4.77) | 0.193 |
| Gender: n (%)* | Female | 34 (51.5) | 106 (70.7) | 0.006 |
| Gender: n (%)* | Male | 32 (48.5) | 44 (29.3) | 0.006 |
| Alcohol consumption: n (%) | Yes | 10 (15.2) | 15.0 (10.0) | 0.355 |
| Alcohol consumption: n (%) | No | 56(84.8) | 135 (90.0) | 0.355 |
Based on the independent sample t-test analysis, there was no difference in HOMA-IR values between smokers and non-smokers; additionally, significant differences were observed in terms of serum vitamin B12 levels, with the control group demonstrating higher vitamin B12 levels (Table 2).
**Table 2**
| Unnamed: 0 | Case (n=66) | Control (n=150) | t | p-value |
| --- | --- | --- | --- | --- |
| | Mean ± SD | Mean ± SD | t | p-value |
| Cholesterol (mg/dL) | 188.85 ± 49.82 | 198.03 ± 44.97 | -1.33 | 0.183 |
| Triglyceride (mg/dL) | 113.61 ± 54.75 | 112.28 ± 70.08 | 0.13 | 0.892 |
| HDL-C (mg/dL) | 52.39 ± 11.56 | 54.63 ± 11.32 | -1.33 | 0.185 |
| LDL-C (mg/dL) | 116.03 ± 40.95 | 120.01 ± 39.20 | -0.67 | 0.499 |
| Glucose (mg/dL) | 92.61 ± 12.76 | 94.75 ± 13.24 | -1.10 | 0.270 |
| Insulin (µU/mL) | 8.30 ± 5.11 | 7.55 ± 5.22 | 0.96 | 0.333 |
| TSH (mIU/L) | 1.64 ± 1.15 | 1.89 ± 1.12 | -1.51 | 0.132 |
| Vitamin D (ng/mL) | 17.48 ± 6.88 | 18.85 ± 10.23 | -1.15 | 0.322 |
| Vitamin B12 (ng/L) | 160.35 ± 94.62 | 218.48 ± 117.35 | -3.54 | < 0.001* |
| HOMA-IR | 1.91 ± 1.19 | 1.81 ± 1.43 | 0.45 | 0.647 |
Binary logistic regression analysis was performed considering the parameters that differ between smokers and non-smokers. The effects of smoking, age, sex, and vitamin B12 parameters on insulin resistance were analyzed. When solely evaluating smoking as an independent variable, the Nagelkerke R2 values were less than 0.2, indicating non-significant findings within the model. In addition, when other independent variables such as age, gender, and vitamin B12 are added, the model did not reach significance. As the Hosmer-Lemeshow test yielded a p-value greater than 0.05, the model's suitability of the model was considered satisfactory. However, changes in -2 Log Likelihood values in Step-1 and Step-2 (Chi-square, $p \leq 0.05$) did not reach significance. These results show that the logistic regression analysis is valid in general. As a result, our model predicts the outcome correctly with a probability of $69.9\%$ (Table 3).
**Table 3**
| Method=Enter: Stepwise | Method=Enter: Stepwise.1 | -2 Log Likelihood | Omnibus Tests of Model Coefficients | Omnibus Tests of Model Coefficients.1 | Cox and Snell R Square | Nagelkerke R Square | Hosmer and Leweshow Test | Predicted percentage |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method=Enter: Stepwise | Method=Enter: Stepwise | -2 Log Likelihood | Chi-square | p | Cox and Snell R Square | Nagelkerke R Square | Hosmer and Leweshow Test | Predicted percentage |
| Beginning | Beginning | 267.590 | | | | | | 69.0 |
| Step1 | Smoking | 264.620 | 3.049 | 0.081 | 0.014 | 0.020 | 0.207 | 69.0 |
| Step2 | +Age, Gender, Vitamin B12 | 256.478 | 1.525 | 0.217 | 0.050 | 0.070 | 0.256 | 69.9 |
According to logistic regression analysis, the odds ratio for non-smokers was 0.59 (0.31-1.14). The risk of insulin resistance is decreased by $41\%$ in non-smokers. However, since $p \leq 0.05$, the difference did not reach statistical significance, and there was no difference between smokers and non-smokers. ( Table 4). The odds ratio for age was 1.03 (1.01-1.05). When the age variable increase by one unit, the risk of insulin resistance increases by 1.03 times (Table 4).
**Table 4**
| Insulin resistance a | Unnamed: 1 | B | SE | Wald test | p | Risk (Odds) coefficient (Exp B) | 95% CI for (Exp B) | 95% CI for (Exp B).1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Insulin resistance a | | B | SE | Wald test | p | Risk (Odds) coefficient (Exp B) | Lower | Upper |
| Step1 | Smoking (1) | -0.547 | 0.31 | 3.08 | 0.079 | 0.58 | 0.31 | 1.06 |
| Step2 | Smoking (1) | -0.527 | 0.35 | 2.48 | 0.115 | 0.59 | 0.31 | 1.14 |
| | Age | 0.027 | 0.01 | 6.21 | 0.013* | 1.03 | 1.01 | 1.05 |
| | Gender (1) | 0.294 | 0.32 | 0.86 | 0.352 | 1.34 | 0.72 | 2.49 |
| | Vitamin B12 (ng/L) | -0.002 | 0.01 | 1.41 | 0.998 | 0.99 | 0.99 | 1.01 |
| | Constant | -1.32 | 0.47 | 7.93 | 0.005 | 0.27 | | |
## Discussion
In the present study, we found no significant difference between the case and control groups regarding alcohol consumption status and BMI. However, we observed that the control group had a higher proportion of women and a higher mean age compared to the case group. These findings suggest that the randomization process may not have been sufficient to balance these factors between the groups. Nevertheless, we were able to overcome this limitation by performing regression analysis, which allowed us to adjust for potential confounding variables. Overall, our study highlights the importance of careful consideration and appropriate adjustments for confounding factors in order to obtain accurate and reliable results. As this is a retrospective study conducted in a single center, our findings may not be generalizable to other populations or healthcare settings, which can be considered a limitation of this study.
We found that serum vitamin B12 values were lower in the case group compared to the control group. These findings support the existing knowledge in the literature. The observed lower serum vitamin B12 levels in the case group compared to the control group align with previous research. It has been well-established that smoking, a source of free radicals, is associated with decreased serum vitamin B12 levels [7,8]. The cyanide in tobacco smoke leads to an increase in serum cyanide levels, which in turn increases the excretion of thiocyanate from the kidneys and is linked to lower serum vitamin B12 levels [9]. The relationship between vitamin B12 levels and insulin resistance has been previously reported in non-diabetic obese individuals, with low levels of vitamin B12 being associated with insulin resistance [10]. Furthermore, a study has also demonstrated a relationship between vitamin B12 and insulin resistance in morbidly obese patients [11]. Although there are still some unclear findings regarding the relationship between vitamin B12 and type-2 diabetes [12], our study did not find any significant association between vitamin B12 and insulin resistance. This may suggest that the role of vitamin B12 in the development of insulin resistance and type-2 diabetes requires further investigation.
In our study, no significant difference was found between smokers and non-smokers in terms of HOMA-IR values, serum glycemic index, and lipid profile. According to regression analysis, the risk of insulin resistance is decreased by $41\%$ in non-smokers, with smoking having a very low predictive power of only $2\%$ on insulin resistance. However, since $p \leq 0.05$, this relationship is not statistically significant. Our findings are inconsistent with the literature. Only a few studies mention no relationship between smoking and insulin resistance in healthy people [13]. However, while smoking did not significantly affect insulin resistance in normal and overweight groups, it showed a significant increase in HOMA-IR in obese patients [13]. In a meta-analysis study, a significant association between passive smoking and type-2 diabetes was observed [14]. Additionally, a recent study found that smoking acts in synergy with genetic susceptibility to promote latent autoimmune diabetes in adults (LADA) [15].
All of this information suggests that the relationship between smoking and insulin resistance is not yet fully understood. It is known that nicotine stimulates catecholamine-mediated glucagon release from the adrenal medulla, which can increase gluconeogenesis and lead to hyperglycemia [4]. Therefore, the relationship between smoking and insulin resistance, frequently highlighted in the literature, may be a result rather than a cause. As a result, given the assumption that hyperinsulinemia-induced hypoglycemia episodes seen at the onset of type-2 diabetes can be prevented by increasing gluconeogenesis, nicotine may be preferred due to the avoidance behavior.
According to our study, insulin resistance increases with age. Age is associated with insulin resistance and mitochondrial muscle dysfunction, as also changes in body composition, which likely contribute to the development of age-related insulin resistance [16]. During aging, oxidative stress, intramyocellular lipid accumulation, the modified activity of insulin sensitivity regulatory enzymes, decreased autophagy, sarcopenia, mitochondrial dysfunction, and an over-activated renin-angiotensin system may occur. These modifications have the potential to negatively impact the insulin sensitivity of skeletal muscles and increase the risk of insulin resistance and type-2 diabetes during skeletal muscle aging [17].
## Conclusions
Our study provides evidence that there is no significant relationship between smoking and insulin resistance in healthy individuals. This may challenge the prevailing notion in the literature that smoking is a key risk factor for insulin resistance. Instead, it is possible that smoking and insulin resistance are both consequences of underlying pathophysiological mechanisms that have yet to be fully elucidated. Therefore, further studies involving detailed evaluations of healthy volunteers are needed to shed light on this complex relationship and inform clinical practice.
## References
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|
---
title: Are there inequalities in the attendance and effectiveness of behavioural weight
management interventions for adults in the UK? Protocol for an individual participant
data (IPD) meta-analysis
authors:
- Jack M Birch
- Julia Mueller
- Stephen Sharp
- Jennifer Logue
- Michael P Kelly
- Simon J Griffin
- Amy Ahern
journal: BMJ Open
year: 2023
pmcid: PMC10039995
doi: 10.1136/bmjopen-2022-067607
license: CC BY 4.0
---
# Are there inequalities in the attendance and effectiveness of behavioural weight management interventions for adults in the UK? Protocol for an individual participant data (IPD) meta-analysis
## Abstract
### Introduction
It is important to identify whether behavioural weight management interventions work well across different groups in the population so health inequalities in obesity are not widened. Previous systematic reviews of inequalities in the attendance and effectiveness of behavioural weight management interventions have been limited because few trials report relevant analyses and heterogeneity in the categorisation of inequality characteristics prevents meta-analysis. An individual participant data meta-analysis (IPD-MA) allows us to reanalyse all trials with available data in a uniform way. We aim to conduct an IPD meta-analysis of UK randomised controlled trials to examine whether there are inequalities in the attendance and effectiveness of behavioural weight interventions.
### Methods and analysis
In a recently published systematic review, we identified 17 UK-based randomised controlled trials of primary care-relevant behavioural interventions, conducted in adults living with overweight or obesity and reporting weight outcomes at baseline and 1-year follow-up. The corresponding author of each trial will be invited to contribute data to the IPD-MA. The outcomes of interest are weight at 12-months and intervention attendance (number of sessions offered vs number of sessions attended). We will primarily consider whether there is an interaction between intervention group and characteristics where inequalities occur, such as by gender/sex, socioeconomic status or age. The IPD-MA will be conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-analyses of IPD guidelines.
### Ethics and dissemination
No further ethical approval was required as ethical approval for each individual study was obtained by the original trial investigators from appropriate ethics committees. The completed IPD-MA will be disseminated at conferences, in a peer-reviewed journal and contribute to the lead author’s PhD thesis. Investigators of each individual study included in the final IPD-MA will be invited to collaborate on any publications that arise from the project.
## Introduction
Inequalities in overweight and obesity are widely recognised—those who experience higher levels of socioeconomic deprivation, especially women, are more likely to live with obesity than those who more affluent.1 2 Similarly, comorbidities of obesity are more common in those experiencing socioeconomic deprivation.3 It has also been suggested that interventions focusing on individual behaviour change, such as behavioural weight management interventions, may exacerbate health inequalities.4 5 These intervention-generated inequalities may occur at different stages, including intervention uptake, attendance and effectiveness, and across many individual characteristics that stratify health opportunities (such as access to healthcare) and outcomes. These characteristics are summarised by the PROGRESS-Plus framework: Place of Residence, Race/ethnicity, Occupation, Gender/sex, Education, Socioeconomic status, Social Capital, plus other factors for which discrimination could occur such as age and sexual orientation.6 We recently conducted a systematic review to synthesise evidence on how different measures of inequality moderate the uptake, attendance and effectiveness of behavioural weight management interventions in adults.7 We found that most trials did not consider whether inequalities were generated in the studied intervention; where these analyses were conducted, most found no evidence of inequalities. Where an inequalities gradient was observed, intervention uptake, adherence and attrition generally favoured those considered as ‘more advantaged’ (such as those who are white, with higher income or older). Due to substantial differences in the reporting of measures of inequality, together with the low level of reporting of analyses of inequalities, we were unable to perform a quantitative synthesis of the reported results. Hence, it is not possible to fully explore inequalities using aggregated data from published literature alone. This lack of reporting may have occurred because individual trials may not be large enough to detect an interaction between moderators such as socioeconomic status (SES) and the outcome; the trials are likely to have been designed to just detect an overall effect.
These limitations can be addressed in part by conducting a meta-analysis of individual participant data (IPD), which requires the central collation, aggregation and reanalysis of IPD from relevant trials.8 9 This allows for data in each study to be analysed and defined in a uniform way, overcoming heterogeneity issues associated with using aggregate data. Meta-analysis of IPD may provide sufficient statistical power to consider whether there are inequalities in uptake, attendance and effectiveness of interventions.8 10
## Methods and analysis
This IPD meta-analysis responds to limitations identified in our previous systematic review on inequalities in the uptake of, adherence to and effectiveness of behavioural weight management interventions. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses IPD (PRISMA-IPD) extension will be followed when reporting this study.11
## Search strategy
This study includes UK-based trials of behavioural weight management interventions that we identified through a previous systematic review.7 We focused on UK-based trials to reduce heterogeneity in measures of the PROGRESS-Plus characteristics and in the context in which the interventions were delivered. Characteristics such as ethnicity and socioeconomic status are conceptualised differently in different countries, which make synthesising data across these characteristics inappropriate or not possible. For example, socioeconomic status in the UK is often captured using Indices of Multiple Deprivation (IMD), an area-based measure, which is not replicated in other countries. There are also pragmatic reasons for focusing on UK-based trials; the complexity of arranging cross-country data sharing would have made the timelines for this project unviable.
Studies published since the search strategy in the systematic review was conducted, were identified through an updated Medline search and through discussions with the corresponding authors of the included trials. The inclusion and exclusion criteria we used to identify relevant trials for this IPD meta-analysis are: The trials we identified as being eligible for inclusion are outlined in online supplemental table 1.13–29
## Outcomes
Outcomes are weight (kg) at 12-month follow-up and intervention attendance. Where data allow, attendance will be measured as the percentage of offered sessions which were attended.
## Exposures
Exposure variables are selected measures of the PROGRESS-*Plus criteria* where data are likely to be available in UK-based trials of behavioural weight management interventions. Coding of each exposure variable will depend on the variables and coding used in each study providing IPD. Should the provided variables be suitably homogeneous, then we anticipate that the coding for the IPD meta-analysis will consist of the following:
## Risk of bias assessment
We will use Cochrane’s risk of bias tool for RCTs (RoB 2) to assess the risk of bias in all studies meeting our inclusion criteria.30 The tool facilitates researchers to consider bias across six domains: the randomisation process; allocation concealment; participant and trial personnel blinding; blinding of outcome assessment; incomplete outcome data; and selective reporting. A rating of ‘low risk’, ‘high risk’ or ‘unclear’ will be assigned to each domain by two contributors independently. Where disagreements occur, these will be resolved by discussion to reach consensus or through consultation with a third contributor. We will present the results of the risk of bias assessment in a summary figure outlining a study’s overall risk of bias in addition to the risk of bias in each domain.
## Data collection and management
Our approach to collecting and aggregating the IPD was informed by the PRISMA-IPD extension and previously published IPD meta-analysis protocols.8 11 31–33
## Invitation of authors
Trial investigators of all eligible trials were invited by email, using contact details acquired through trial publications, to contribute data and collaborate on this study. The email outlined our research aims and the specific data we were requesting.
## Data collection
Standardised data specification forms will be sent to trial authors. Data will be requested in Microsoft Excel format; however, data will be accepted in any format. Once received, a master copy of each trial dataset will be saved in its original format and preserved. Any non-Microsoft Excel format datasets will be converted and then imported into Stata V.17 (StataCorp. 2021. Stata Statistical Software: Release 17.). We will also ask for detailed definitions of the measures used in the trial so we can ensure appropriate harmonisation.
## Data checking
Once data are received from trial authors, they will be checked for quality and to ensure they pertain to the correct trial. Descriptive statistics (sample size, demographic variables, weight loss or BMI change) will be performed for each individual trial; should discrepancies occur between our analysis and the original trial publication, then the study authors will be contacted for clarification. Should clarification not be received, then the size of the discrepancy will be considered. If it is small and unlikely to bias the results, then the data will be included in the IPD meta-analysis. Large data inaccuracy and excessive missing data (vs what is reported in the trial publication) may lead to a trial being excluded.
## Database creation and aggregation
A single database will be created containing data from all the trials. There is likely to be differences between studies in the coding of measures of inequalities. For example, there is variation in measures of SES that are used. Hence, once all data are received and the differences in coding across the measures become apparent, we will discuss among the core research team (JMB, MPK, SJG and AA) the best approach to achieve consistent reporting across all measures.
Once the data checking has been completed, variables will be recoded to match the coding of the IPD database. The data from each individual trial will then be copied into the IPD database and checked to ensure the integrity of the data has been maintained through the merge. Each individual trial dataset will be given a unique identifier prior to the merge.
## Trials where IPD are not available
Where we are unable to obtain IPD for an eligible trial, we will ask the trial investigator if they are able to conduct the analyses using the same coding of variables as defined in the Analysis of study outcomes section and provide us with the outcome statistics. We will offer this as an option to ensure that we are able to include as much relevant data as possible and we will provide the relevant code to facilitate this. If the outcome statistics are provided from any trials, we will meta-analyse this together with the trials for which we obtained IPD. If we synthesise results from data that we did not receive IPD for, we will conduct sensitivity analyses excluding these data. Further sensitivity analyses excluding studies with ‘high’ risk of bias will also be conducted.
## Statistical analysis
Due to our research questions exploring treatment effect and covariate interactions, we decided that a two-stage IPD meta-analysis would be most appropriate. In the first stage, regression analyses are performed individually in each trial. Then in the second stage combines the outcome estimates from each model using a standard meta-analysis approach (eg, random-effects meta-analysis).34 We are using a two-stage approach because it inherently avoids aggregation bias and controls for trial-level confounding, to which one-stage IPD meta-analyses are more susceptible.34 An additional benefit of performing a two-stage IPD meta-analysis is that trials for which we are unable to acquire individual-level data may still be included in the synthesis provided the relevant outcome statistics can be obtained. Data analysis will be conducted using Stata V.17 (StataCorp. 2021. Stata Statistical Software: Release 17, StataCorp).
## Baseline characteristics
We will describe the baseline characteristics for randomised group and each PROGRESS-Plus characteristic. This will be completed for each trial and as an overall aggregate of all participants included in the meta-analysis. We will compare these characteristics descriptively with data on the prevalence of obesity in the population, such as the Health Survey for England and other studies that have considered who routinely accesses behavioural weight management interventions.35–37
## Analysis of study outcomes
We will conduct six sets of analyses (if there are sufficient data for each outcome), two for each of our research questions, as we will synthesise data on weight loss interventions separately to data for weight loss maintenance interventions. As all outcomes of interest are continuous, we will use multivariable linear regression models, and include the relevant parameter estimates and standard errors from the models in random-effects meta-analyses. Heterogeneity will be assessed using tau2, which summarises between-studies variance, and a $95\%$ prediction interval which indicates the range in which $95\%$ of the true effects lie. Inconsistency will be assessed using I2, which indicates the proportion of total variability in the observed effects that is due to heterogeneity.
The subgroups used for each exposure variable are listed below (reference subgroup in bold). If free-text responses are available for any ‘other’ subgroup for each exposure, we will recode to the most appropriate subgroup in that exposure. If this is not possible, we will recode ‘other’ to missing. ‘ Prefer not to say’ responses will also be recoded to missing. We anticipate that certain subgroups of some variables will likely have few, if any, data—in particular some subcategories of religion or relationship status. These will be recoded to missing and excluded from the analyses.
We will present summary statistics for weight and attendance outcomes separately for each trial and as combined values across all trials.
## Research question 1: to what extent does the effectiveness of behavioural weight management interventions (defined as the difference in weight change between intervention and control) differ by individual characteristics that stratify health opportunities and outcomes (as defined using the PROGRESS-Plus Framework)?
These analyses will focus on intervention effects by subgroup. Multivariable linear regression models will be used to test the null hypothesis that there is no interaction between each PROGRESS-Plus characteristic and intervention group on weight at 12 months. Each model will be adjusted for age and gender/sex (with the exception of the models where age and gender/sex are considered as the exposure variables) and baseline weight. The interaction terms will then be meta-analysed across trials.
## Research question 2: to what extent do the weight outcomes of those who have participated in a behavioural weight management trial (defined as weight change in the overall cohort) differ by individual characteristics that stratify health opportunities and outcomes?
In these analyses, each trial will be analysed as a cohort study. Using multivariable regression, we will estimate the association between the ‘exposure’ variable (ie, each PROGRESS-Plus characteristic we have sufficient data for), and weight at 12-month follow-up. Each model will be adjusted for age and gender/sex (with the exception of the models where age and gender/sex are considered as the exposure variables), baseline weight and assigned intervention. Associations will be estimated for each exposure subgroup within a trial, and these associations will then be meta-analysed across trials.
## Research question 3: to what extent does attendance of behavioural weight management interventions differ by individual characteristics that stratify health opportunities and outcomes?
For the third research question, each trial will be analysed as a cohort study. Attendance will be considered as a percentage—the number of sessions attended divided by the maximum possible number of sessions a participant could attend—and treated as a continuous variable. Multivariable regression models will be used to estimate the association between each PROGRESS-Plus characteristic and attendance. Each model will be adjusted for age and gender/sex (with the exception of the models where age and gender/sex are considered as the exposure variables). Associations will be estimated for each exposure subgroup within a trial, and these associations will then be meta-analysed across trials.
## Missing data
A complete-case analysis will be performed, that is, participants who have missing data for either the outcome, exposure or covariates will be excluded.
## Sensitivity analysis
As highlighted in the ‘Trials where IPD are not available’ section, if we synthesise results from data that we did not receive IPD for, we will conduct sensitivity analyses excluding these data. Sensitivity analyses excluding studies with ‘high’ risk of bias will also be conducted to consider whether these studies have an impact on the results. Should sufficient data be obtained, we will also conduct further analyses to consider whether intervention characteristics affect inequalities in attendance and effectiveness. These analyses may include comparisons by intervention length, digital versus non-digital and group based versus individually based.
## Patient and public involvement
As part of the protocol development for the preceding systematic review, we received comments on a lay summary from a patient and public involvement (PPI) representative on the project aims and our definition of the PROGRESS-Plus characteristics.38 These aims and definitions have been brought forward into this IPD meta-analysis project. We will seek further PPI input on our harmonisation of the subgroups of the exposure variables to ensure our categorisations are appropriate, and a PPI representative will contribute to the interpretation of data and will coauthor the final manuscript.
## Discussion
There is some evidence from our previously conducted systematic review that those we may consider to be ‘more advantaged’ (such as having more years of education, higher income, being white and being older) may be the most likely to maintain attendance to and have better outcomes from behavioural weight management interventions.7 However, evidence was mixed and in that review we were unable to quantitatively synthesise data on attendance and weight outcomes due to heterogeneity in study populations (our review focused on all Organisation for Economic Co-operation and Development countries) and measures of the PROGRESS-Plus characteristics (eg, race and ethnicity are captured very differently in the USA vs the UK). This heterogeneity will be partly addressed in this IPD meta-analysis by focusing on trials from a single country (the UK) and through the data harmonisation that can be achieved when access to IPD is obtained.
This IPD meta-analysis will have several implications for public health policy, practice and research. The analyses may identify certain sociodemographic groups that have lesser attendance, or attain lesser weight loss. From a research perspective, future work could seek to establish why this may be the case; for public health policy, it is important to identify groups where interventions may be generating or exacerbating inequalities so additional support or provision can be offered to prevent this from occurring.
## Strengths and limitations
There are several strengths of conducting an IPD meta-analysis in comparison to a conventional meta-analysis. IPD meta-analyses are particularly useful for considering moderators of intervention outcomes,8 due to the increased statistical power gained by pooling data (although this is not guaranteed for all moderating variables, as it depends on the available data). Harmonisation of variables across studies means more data can be pooled together, leading to more robust analyses and conclusions. A further strength of IPD meta-analyses is that they go beyond published data, which may be limited in the measures reported. Receiving the original trial data also allows for increased data checking and increased validation of previously published results.9 However, there are also limitations of conducting an IPD meta-analysis. Even though the raw trial data will be acquired, analysis is dependent on the measures assessed in each original trial, and may be limited. Data harmonisation that is required to conduct an IPD meta-analysis may lead to some data being excluded from the analyses as it is unlikely to be possible to harmonise all data from different measures of each PROGRESS-Plus characteristic. A further limitation is that the estimates of inequality are influenced by the distribution of characteristics within each study. For example, studies with a narrow age range might not identify interactions between intervention effects and age. Finally, we are only looking at UK-based trials of behavioural weight management interventions, which may limit the generalisability of our findings to other countries or healthcare systems.
## Ethics and dissemination
Ethical approval was not required for this study as no primary data are to be collected, and the IPD are to be analysed in accordance to the purpose for which they were originally collected for. Ethical approval for each eligible trial for this IPD meta-analysis was obtained by the original investigators of each trial.
We anticipate that the completed IPD meta-analysis will be published in a scientific journal; one collaborator from each trial contributing IPD will be invited to be a coauthor on the publication. The findings from this IPD meta-analysis study may also be presented at relevant public health and obesity research conferences, and will contribute to the lead investigator’s PhD thesis.
## Patient consent for publication
Not applicable.
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|
---
title: Physiotherapists’ Perceptions of the Influence of Their Health Behaviours on
Their Advice to Patients
journal: Cureus
year: 2023
pmcid: PMC10040091
doi: 10.7759/cureus.35396
license: CC BY 3.0
---
# Physiotherapists’ Perceptions of the Influence of Their Health Behaviours on Their Advice to Patients
## Abstract
Background Healthcare professionals (HCPs) lifestyle behaviours can impact their health promotion counselling to patients. However, there is a lack of qualitative studies to understand the physiotherapists' perceptions of the influence of their lifestyle behaviours on their patient's advice.
Aim This research aims to explore the physiotherapists' perceptions of the influence of their health behaviours on their counselling of patients.
Method This research was a qualitative study based on the interpretivism paradigm. 15 virtual semi-structured interviews were performed with physiotherapists working for a private company in the United Kingdom (UK). Thematic analysis was used to create four themes and ten sub-themes.
Results 13 out of 15 participants stated that their lifestyle impacts their counselling, while for the other two, their counselling was based on their knowledge. Some drivers for health promotion included role modelling, having some knowledge concerning certain lifestyle behaviours and understanding their importance in health. Barriers to health promotion included lack of time and knowledge, some confusion if discussing some of these behaviours included in their scope of practice and perception of patients’ reactions to certain questions and their relevance to the musculoskeletal (MSK) condition their patients were experiencing. Some strategies proposed to improve their health promotion skills included improvements in communication skills, discussions and sharing of evidence between peers and informative leaflets to distribute to patients.
Conclusion *In this* study, 13 out of 15 participants believed their lifestyle impacts their counselling to patients. Despite this, multiple barriers to health promotion were identified. This study highlights the need for training physiotherapists about certain lifestyle behaviours, including smoking cessation, alcohol misuse and basic nutrition counselling which may improve their health promotion messages, potentially leading to patient behaviour change which ultimately may have a positive public health impact.
## Introduction
In 2016, around $71\%$ of deaths (40.5 million individuals) globally were attributed to non-communicable diseases (NCDs) [1]. Of these, $80\%$ (32.2 million individuals) were due to cancers, cardiovascular diseases, chronic respiratory diseases and diabetes, which are the most common NCDs [1,2]. In the same year, in England, $89\%$ of its mortality was due to NCDs (533,100 individuals) and $11\%$ of these deaths were likely premature [2]. These health conditions, which at times can be prevented, are associated with behavioural risk factors such as poor nutrition, lower physical activity (PA) levels and alcohol and tobacco intake [3].
To help individuals and their communities to reduce their risk of disease, the “Making Every Contact Count” (MECC) was implemented in the United Kingdom (UK) [4]. MECC helps the National Health Service (NHS), relevant agencies, local authorities and healthcare professionals (HCPs), to promote behaviour change within the population to improve people’s mental and physical health as well as their well-being and to help HCPs signposting patients to local services [4]. Another aim of this program is to assist individuals to make better decisions about their health, by developing their behaviour change skills [4]. Physiotherapists, who, to improve an injury or disability, provide non-invasive treatment such as prescribing therapeutic exercise and education and have relatively frequent and prolonged contact with patients, are also HCPs with qualifications to educate people to improve their lifestyle [5,6]. An initial evaluation of the MECC showed that it has the potential to be a simple and low-cost resource for HCPs to use across varied health issues [7]. However, evidence showed that HCPs, including nurses, physicians and physiotherapists, reported some barriers when discussing lifestyle recommendations such as nutrition, PA levels, and tobacco and alcohol intake with their patients [8,9]. Some of these barriers included inadequate monetary compensation, lack of knowledge and confidence in advising patients, lack of time and patient non-compliance with the advice given [8,9].
A representative study of the English population, published by Kyle et al. in 2017 [10], indicated a high obesity prevalence in HCPs, such as nurses ($25.12\%$) and other HCPs ($14.39\%$), which included doctors and physiotherapists. This has important implications as it can impact the delivery of health promotion messages from the HCPs to the patients [10]. Black et al. [ 11] showed that patients believe physiotherapists should be role models for certain health behaviours such as keeping a healthy weight. HCPs’ counselling is strongly associated with their health practices and some of them reported difficulty in advising patients about a health behaviour they were also finding hard to follow [12,13]. Providing strategies to HCPs so they can improve their health behaviours may not only have benefits for their own health but also for their patient’s health [14] and therefore impact the health of the wider public.
There is a lack of evidence that explores the HCPs’ perceptions, especially physiotherapists, of the influence of their lifestyle behaviours on their patients' advice concerning the adoption of healthy behaviours and most evidence available about this topic was performed in physicians and nurses [9,13]. To fill this literature gap, this research aims to explore the physiotherapists' perceptions of the influence of their health behaviours on their counselling of patients.
## Materials and methods
Design The study design was based on the interpretivism paradigm [15]. It aimed to understand the physiotherapists' perceptions of the influence of their health behaviours on their counselling to patients. To promote a natural and genuine interaction between the participants and the main researcher, virtual semi-structured interviews were the selected design for this project [15].
Participants To determine the most appropriate sample size, the researchers needed to do a balance between a small sample that would allow them to cope with the amount of data for analysis during a specific timeline to complete the project and a sample that would allow enough in-depth information to answer the research question. The researchers agreed on 15 interviews. The sample size included 15 MSK physiotherapists from a UK private company. To participate in this research, the physiotherapists needed to be registered and have direct contact with patients virtually or in a clinical setting. Those who did not have direct contact with patients or had been on a career break longer than 12 months were excluded.
Ethical considerations All participants provided written informed consent before participating in this study. It was approved by the private company through its own internal ethics board as well as the Coventry University Ethical Approval (CUEA). The principles of respect for autonomy and confidentiality, beneficence, dignity and justice were taken into consideration to guide the main researcher's actions when collecting data from the participants [16].
Recruitment After ethical approval, an advertisement post was published in the private company’s weekly newsletter and in some of the company’s Microsoft Team (MT) groups, to recruit volunteers, with information that included the study title, the sample size needed, the study design and the main researcher's contacts. Those who contacted the main researcher and met the inclusion criteria received the “Participant Informative Sheet” (PIS) and the “Consent form” by email and those who agreed to take part in this project received details to arrange an interview date and time. Participant 12 decided not to take part in the interview after receiving the PIS. Due to a numbering error, there was no participant 8. The first 15 participants who met the inclusion criteria and demonstrated interest in participating in this research project were selected for a virtual interview and were anonymised by number allocation.
Data collection Taking the research question into consideration the researchers discussed and agreed on what specific and probing questions the participants would be asked. Some questions were based on a topic guide and two questionnaires from existing qualitative and quantitative literature [6,17,18]. A pilot interview was undertaken prior to the start of the study to ensure the data collection methods and the structured interview questions were appropriate [15]. Following a review of the pilot, some closed questions were replaced with more open ones. 15 semi-structured interviews were performed through MT between November and December 2021. Each interview was recorded with the participants' permission. An initial transcription was performed by MT. The main researcher listened to the recordings and made the necessary adjustments in each transcription. All of them were reviewed twice to improve their accuracy. During each interview, the main researcher did not provide her personal views concerning the topics discussed to prevent influencing the information given by each participant [15]. However, rephrasing some questions as well as prompting was necessary for some situations where participants did not understand the open questions and/or only provided a brief answer. Although leading questions were avoided during the interviews, these were needed on rare occasions when participants still misunderstood the rephrased question.
Data analysis The thematic analysis was based on the phases described by Braun and Clarke [19]. To improve the study’s credibility and dependability the themes, sub-themes and quotations were reviewed by the second researcher [16]. Reflexivity was also considered in our study to allow the main researcher to understand the level of influence she might have over the participants [15]. A reflective diary explaining how the research project was going, describing incidents that occurred during the interviews and data analysis was written as a strategy to overcome the researcher’s positionality [15].
## Results
Participants’ information can be found in Tables 1, 2. The interviews' length lasted between 32 and 86 minutes. Table 1 describes some of the participants' demographic information and Table 2 describes participants' lifestyle behaviours.
After thematic analysis, four themes and ten sub-themes emerged from the data. Participants’ quotations were used to illustrate the themes.
Drivers to health promotion *Being a* Role Model Some physiotherapists believed that they needed to set a good example when counselling patients about healthy lifestyles.
However, one participant believed that patients expect them to be role models and if they believe they are not, then this could create some barriers when discussing health behaviours with them.
Understand the Importance of Health Promotion All participants had at least basic knowledge concerning the health effects of nutrition, smoking and alcohol intake.
Participants also understood the importance of addressing lifestyle risk factor behaviours when treating patients and seven of them talked about the importance of adopting a holistic approach when treating patients.
Feeling Confident When Discussing Certain Lifestyle Risk Factors Some physiotherapists felt confident in discussing certain lifestyle behaviours with patients which came from either their knowledge or their own experiences.
Furthermore, all participants felt confident in discussing PA levels compared to other lifestyle behaviours, because they had more knowledge about this health behaviour.
Despite this, it can be noticed that not all physiotherapists felt confident in discussing these lifestyle behaviours and some of them reported a lack of confidence when discussing nutrition and feeling “Awkward” (Participant 3) when discussing alcohol with their patients.
Barriers to health promotion Appointments’ Short Duration The short duration of the appointments was one of the main common barriers between participants in discussing lifestyle behaviours with patients.
Physiotherapists’ Scope of Practice for Health Promotion There were some inconsistencies among participants as to whether discussing different lifestyle risk factors was within their scope of practice. For some, it was not, but for others discussing these subjects should be part of a physiotherapist’s job leading to some confusion about what the exact role of a physiotherapist should be. This resulted in an internal conflict among some participants. As an example, Participant 1 believed that physiotherapists “…don’t have the expertise…” and discussing nutrition is out of a physiotherapist’s “…scope of practice.”. However, he believed that discussing this subject “…should really be a part of our job…”.
Participant 4 believed that discussing nutrition was part of the physiotherapists' scope of practice, but she had some difficulties raising this subject with her patients due to knowledge not being provided by the universities.
Perception of Patient’s Reaction to Health Promotion Questions and Relevance of the Questions Another common barrier between participants was believing that patients would respond negatively to questions about certain lifestyle behaviours. Discussions about these behaviours were often seen as a sensitive topic.
Furthermore, participants stated that patients may also respond negatively to questions not expected from a physiotherapist.
Some physiotherapists did not consider it relevant to ask about certain lifestyle behaviours even though they knew their importance to a person's health. As an example, Participant 1 understood that alcohol could impact people's relationships, their “organizational…skills, their mental health.” and alcohol could increase people’s weight. He believed that having this knowledge was important so physiotherapists “… can pass it onto individuals who… wasn’t aware that it was affecting their balance…” or their health. However, he normally does not talk about this subject with his patients.
It was also observed that some participants consciously decide which lifestyle behaviours they would discuss with which patient. Normally, these discussions would only take place if these behaviours were relevant to the patient’s MSK condition that the physiotherapist was treating or if the physiotherapist suspected that the patient may have an alcohol problem.
Lack of Knowledge in Relation to Certain Lifestyle Behaviours Can Impact Their Counselling Participants believed that their lack of knowledge concerning certain lifestyle risk factors, such as nutrition, alcohol and smoking was another barrier when counselling patients.
What became apparent from reviewing the results on “Barriers for Health Promotion” theme was that some participants identified the relevance of assessing lifestyle risk factors, but for all the reasons described above, this generally does not take place.
Knowledge and personal experiences Physiotherapists’ Lifestyle Behaviours and Counselling From analysing the interviews, there were multiple factors that influenced the participants’ counselling and guidance to their patients. Two participants found that their counselling was influenced by their knowledge, whereas other participants found that their guidance was directed by their own personal health decisions. However, for the majority, the participants found that their health decisions impacted positively on their counselling. Participant 2, would discuss certain lifestyle behaviours with patients, but would avoid discussing others. As an example, concerning smoking, she could share her personal experiences when counselling patients, but she avoided discussing nutrition because she felt she was not a good role model.
Internal Conflict An internal conflict of participants is demonstrated in the quotes below. The benefits of adopting healthy lifestyle behaviours were identified, but not put into practice due to their preferences.
Solutions for health promotion Improvements in Communication Skills, Discussions, Sharing Evidence Between Peers and Informative Leaflets During the interviews, the participants presented some solutions for health promotion concerning all lifestyle behaviours analysed. The most common solutions were training to improve their communication skills, discussions and sharing evidence with their peers and having some informative leaflets they could share with their patients.
## Discussion
This study aimed to explore the physiotherapists' perceptions of the influence of their health behaviours on their patients’ advice. The results showed that 13 participants reported their lifestyle behaviours impacted what they advise patients while for the other two, their counselling was based more on their knowledge. Of those 13, most of them believed their lifestyle behaviours impacted positively their counselling, while one participant stated avoiding discussing certain lifestyle behaviours that she was also struggling to follow. This is consistent with a cross-sectional study made by Vickers et al. [ 13], which showed that those primary care providers who had healthier behaviours were more likely to advocate them to patients and participants who were struggling to follow a specific health behaviour found counselling their patients on this challenging.
Providers who practice healthy lifestyle behaviours play an important role in helping patients also change their lifestyles to reduce their risk of chronic diseases [14] and those who share their personal experiences and serve as role models are perceived as being more motivating and credible [12,20]. Role modelling was seen as a driver for health promotion in our study, which is consistent with other quantitative and qualitative studies of physiotherapists [6,17]. In previous quantitative studies, patients, as well as physiotherapists, agreed that physiotherapists should act as role models concerning maintaining a healthy weight, practising regular PA, and avoiding smoking [11,17]. However, the majority of Black et al. ’s [17] participants, who were physiotherapists, had healthy behaviours and the data collection was made through a questionnaire which led to self-reported data and social desirability bias. It would be interesting to understand, in future studies, the perceptions of physiotherapists who did not adopt these healthy behaviours concerning their role as role models. Practising what one preaches may have an important effect in helping patients adhere to healthier lifestyles [21]. Frank et al. and Lobelo [12,20] showed that patients had a higher motivation to improve their lifestyle if their health practitioner or other HCPs (physiotherapists not included in these quantitative studies) also engaged in healthy behaviours. For the majority of participants in our study, counselling of patients was largely influenced by their own health behaviours, and for some, being a role model was an important factor. Despite this, it is imperative to state that all participants must engage in evidence-based practice when treating patients and follow the code of conduct, performance and ethics from the Health and Care Professions Council (HCPC) [22].
All participants stated being healthy. However, despite none of them being smokers, four participants stated being overweight, another three reported they could/want to lose weight and one stated not being happy with her weight. According to Kyle et al. [ 10], there is a high prevalence of obesity in the UK among different HCPs, including physiotherapists. However, it is unknown the exact percentage of physiotherapists who were overweight in this study data was not collected to ascertain this [10]. Despite this limitation, the study’s results are important because it is known that obesity is a risk factor for the development of different NCDs and excess weight in HCPs can affect the effectiveness of health promotion messages delivered to their patients [3,10]. This point is highlighted in our study by one participant who stated being overweight and reported avoiding discussing nutrition with the patients. Concerning PA, seven participants of our study did not follow the recommended PA guidelines [23] and one of the main barriers reported was lack of time. According to a cross-sectional study by Lowe et al. [ 18], that assessed PA promotion in 522 UK physiotherapists’ routine practice, only $38\%$ of them followed the recommended guidelines concerning PA levels. The reasons why most of these HCPs did not follow the guidelines were not explored in this study, which is a limitation, but their PA levels were not associated with their PA promotion activity [18]. These results were replicated in our study where all physiotherapists were confident in discussing PA with their patients despite seven of them not following the guidelines [23]. Concerning the participants of our study there is space for improvements in relation to their health. Another internal conflict was also seen between some participants’ knowledge and own health behaviours and this discordance between what they know and what they do may be related to a lack of self-efficacy and motivation, as well as costs and access to food/services/facilities that promote healthier behaviours [24]. It would be important that organisations that employ physiotherapists put in place strategies that can assist them to adopt healthier lifestyles, such as maintaining a healthy weight and improving their PA levels. This may not only improve their health, but it can also impact their health promotion counselling and, consequently, their patient’s health, through role modelling, as previously discussed [14].
Multiple barriers to health promotion were found in our study which included limited clinical time. This is consistent with other quantitative cross-sectional studies [9,24]. Furthermore, expecting higher contact time with patients may be unrealistic, so it would be important to change the perception of physiotherapists about their role in health promotion [6]. These HCPs have the advantage of having multiple appointments over a period of time with each patient when compared to other HCPs, which allows them to create a good rapport and trust between each other and provides them with the chance of multiple teachable moments that may potentially lead to patients’ behaviour change [25]. Furthermore, there was some confusion about what is the physiotherapists’ scope of practice which resonates with other quantitative and qualitative studies [6,9]. According to the Chartered Society of Physiotherapists (CSP), physiotherapists should provide brief interventions or advice concerning PA, tobacco use, poor diet and obesity as well as the risk of alcohol misuse [26]. Brief interventions or advice in these areas are known to be effective tools in preventative measures [26]. Other barriers to health promotion present in our study included the perceived patients’ reaction concerning being asked about certain lifestyle behaviours, as well as the relevance of those questions in relation to the patient’s current symptoms, which is comparable with other quantitative and qualitative publications [6,27]. However, a cross-sectional study by Black et al. [ 11] which aimed to understand patients’ opinions regarding physiotherapists discussing certain lifestyle behaviours showed that $91.3\%$, $73\%$ and $51.3\%$ agreed that these HCPs should discuss with them PA, maintaining a healthy weight and tobacco cessation, respectively. Nevertheless, this study only included 230 participants, so its results have limited generalizability [11]. Future studies with larger samples to understand how patients perceive the importance of behaviour change interventions provided by physiotherapists would be valuable.
Although some participants had confidence discussing certain lifestyle behaviours and all were confident in discussing PA with patients, there was a lack of knowledge and confidence in counselling about nutrition, smoking and alcohol which is in line with other quantitative studies of physiotherapists [9,24,27]. One of the reasons for this may be the fact that lifestyle behaviours are not systemically and consistently taught across international physiotherapy educational programs when compared to other competencies such as MSK and neurological areas [28,29]. The proportion of health promotion content in international physiotherapy curricula was not well studied and the available evidence only included the curricula from six English-speaking high-income countries, including the UK, so its results cannot be globally generalized [28]. According to this study, there was a small proportion of content in different physiotherapy educational programs related to primary and secondary disease prevention [28]. It would be important that physiotherapy accreditation bodies, academic institutions and professional bodies create universal standards and guidelines regarding health promotion, as well as assessment and management of lifestyle risk factors as clinical competencies for physiotherapists [28]. This would help the physiotherapists to prioritize the public’s health and well-being and holistically manage a patient’s presenting condition considering the individual's overall lifestyle-related behaviours and, on some occasions, their chronic diseases [5].
Strategies proposed by some of the participants to help them improve their health promotion skills were sharing leaflets with patients, peer discussions and sharing information and communication skills training. According to the results of a systematic review by Alexander et al. [ 30] that aimed to identify the types of educational content used by physiotherapists, the distribution of handouts/brochures was a common strategy that concurs with the results of our study. However, providing information alone, although it can be convenient, is an inadequate strategy to promote behaviour change, which is very complex [21,30], so other strategies (or a combination of strategies) may be warranted. More than communication skills training alone, it would be valuable to provide physiotherapists with the opportunity to undertake training on lifestyle behaviours (e.g., smoking cessation, alcohol misuse, basic nutrition counselling and sleep/stress management), patients’ readiness for change, motivational interviewing and knowledge about behaviour change models and theories so they can understand the reasons why patients sustain a positive or negative health behaviour [5,21,24]. This may also help break the “knowledge translation gap” where some HCPs understand the relationship between people's lifestyles and the development of NCDs, but they do not apply their knowledge by helping patients change their lifestyle-related health behaviours [29]. This point can be observed in some participants of our study where they understood how lifestyle behaviours could impact patients’ health, but they would not ask about or discuss these behaviours with them. Furthermore, employers need to make lifestyle learning materials accessible to HCPs [24]. Another strategy that could also be considered to help physiotherapists start the conversation concerning different health behaviours is the introduction of key questions on the assessment forms they need to fill out. For example, although Chow’s study was performed with physiotherapy students, it showed that when questions concerning smoking status and PA levels were added to the assessment form, they facilitated and prompted them to ask their patients about these behaviours [27].
If all the recommendations described above take place, then physiotherapists will have better knowledge and may adopt healthier lifestyle behaviours which may improve their health promotion messages to their patients and serve as role models which ultimately may have a positive public health impact. If the general population’s health could be improved through lifestyle changes, this might have a positive impact concerning the prevalence of NCDs and the mortality rates caused by these diseases in the UK and globally.
Strengths and limitations One of the strengths of this study is the fact it addresses the main modifiable lifestyle risk behaviours that can lead to the development of NCDs. To the researchers’ knowledge is the first study that explores the drivers and barriers MSK physiotherapists (working in the UK) experience when counselling these behaviours as well as the impact of their lifestyle behaviours when advising patients. It also included physiotherapists originally from different countries, with different ages and years of experience that were living in different parts of the UK, which can impact the study’s transferability as each individual has their own perceptions and experiences. However, this study cannot be transferred to other physiotherapy specialities or other HCPs because the sample size only included MSK physiotherapists.
Despite our study including 15 interviews, it cannot be clear if data saturation was reached, so it is unknown if further insights would have emerged if the sample size had been greater [15,16]. All participants took part in the study voluntarily and some of them knew the main researcher which may indicate that all of them had an interest in the research topic and/or an interest in helping a work colleague and maybe felt confident in discussing health behaviours, leading to selection bias. This familiarity could establish a better rapport during some interviews, but at the same time could have impacted the participants’ answers which could have led to social desirability bias. Furthermore, on a few occasions, the main researcher needed to prompt the participants or use leading questions when they misunderstood certain open questions even after rephrasing, which could impact their answers, which is another limitation. Additionally, the fact participants work in the same company, can be another limitation as they will follow the company's culture and values. If similar studies are conducted in the future, it will be important that the researchers do not have this familiarity with their participants, to reduce the risk of influencing their answers. However, even if this occurs, social desirability bias may still take place. A larger sample size, including participants working in different organisations, should also be considered. Finally, to allow a richer exploration of this topic a mixed-method study where, through a questionnaire, researchers could identify participants with different lifestyle behaviours and select some of them, through purposive sampling, to perform an interview to understand if their views about their health behaviours and patient counselling are different should be considered.
## Conclusions
In this study, the majority of participants believed their lifestyle impacts their counselling to patients. Drivers for health promotion included being a role model, having some knowledge concerning certain lifestyle behaviours and understanding their importance in health. However, barriers to health promotion were also present. These included a lack of time and knowledge about certain lifestyle behaviours, some confusion if discussing some of these behaviours is included in their scope of practice, perception of patients’ reactions to certain questions and their relevance to the MSK condition the patient was experiencing.
The results of this study demonstrate the need for training physiotherapists about certain lifestyle behaviours, including smoking cessation, alcohol misuse and basic nutrition counselling which may improve their health promotion messages, potentially leading to patient behaviour change which ultimately may have a positive public health impact. Furthermore, physiotherapists should recognise that they have a crucial role in health promotion and brief discussions about different lifestyle behaviours are within their scope of practice.
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|
---
title: 'HbA1c-based rather than fasting plasma glucose-based definitions of prediabetes
identifies high-risk patients with angiographic coronary intermediate lesions: a
prospective cohort study'
authors:
- Chenxi Song
- Sheng Yuan
- Kongyong Cui
- Zhongxing Cai
- Rui Zhang
- Jining He
- Zheng Qiao
- Xiaohui Bian
- Shaoyu Wu
- Haoyu Wang
- Boqun Shi
- Zhangyu Lin
- Rui Fu
- Chunyue Wang
- Qianqian Liu
- Lei Jia
- Qiuting Dong
- Kefei Dou
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10040102
doi: 10.1186/s12933-023-01750-6
license: CC BY 4.0
---
# HbA1c-based rather than fasting plasma glucose-based definitions of prediabetes identifies high-risk patients with angiographic coronary intermediate lesions: a prospective cohort study
## Abstract
### Background
Prediabetes is common and associated with poor prognosis in patients with acute coronary syndrome and those undergoing revascularization. However, the impact of prediabetes on prognosis in patients with coronary intermediate lesions remains unclear. The objective of the current study is to explore the impact of prediabetes and compare the prognostic value of the different definitions of prediabetes in patients with coronary intermediate lesions.
### Methods
A total of 1532 patients attending Fuwai hospital (Beijing, China), with intermediate angiographic coronary lesions, not undergoing revascularization, were followed-up from 2013 to 2021. Patients were classified as normal glucose tolerance (NGT), prediabetes and diabetes according to various definitions based on HbA1c or admission fasting plasma glucose (FPG). The primary endpoint was defined as major adverse cardiovascular events (MACE), the composite endpoint of all-cause death, non-fatal myocardial infarction and repeated revascularization therapy. Multivariate cox regression model was used to explore the association between categories of abnormal glucose category and MACE risk.
### Results
The proportion of patients defined as prediabetes ranged from $3.92\%$ to $47.06\%$ depending on the definition used. A total of 197 MACE occurred during a median follow-up time of 6.1 years. Multivariate cox analysis showed that prediabetes according to the International Expert Committee (IEC) guideline (6.0 ≤ HbA1c < $6.5\%$) was associated with increased risk of MACE compared with NGT (hazard ratio [HR]: 1.705, $95\%$ confidence interval [CI] 1.143–2.543) and after confounding adjustment (HR: 1.513, $95\%$CI 1.005–2.277). Consistently, the best cut-off point of glycated haemoglobin (HbA1c) identified based on the Youden’s index was also $6\%$. Restricted cubic spline analysis delineated a linear positive relationship between baseline HbA1c and MACE risk. Globally, FPG or FPG-based definition of prediabetes was not associated with patients’ outcome.
### Conclusions
In this cohort of patients with intermediate coronary lesions not undergoing revascularization therapy, prediabetes based on the IEC-HbA1c definition was associated with increased MACE risk compared with NGT, and may assist in identifying high-risk patients who can benefit from early lifestyle intervention.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01750-6.
## Introduction
Prediabetes refers to the intermediate stage between normal glycemia and diabetes mellitus (DM), which is defined by glycemic variables that are higher than normal but lower than the thresholds for diabetes [1]. International Diabetes Federation (IDF) projections estimated that by 2045, the number of adults with prediabetes would be 548 million, corresponding to $8.4\%$ of the world’s adult population [2]. Prediabetes is also common in patients hospitalized for coronary artery disease (CAD) without previous known diabetes mellitus history, in whom over $30\%$ had newly detected prediabetes detected by oral glucose tolerance test (OGTT) [3, 4].
Growing evidence suggested that prediabetes was associated with poor prognosis in patients with coronary heart disease [1, 5, 6], and majority of previous studies enrolled patients with acute coronary syndrome or those who received revascularization therapy. However, the association between prediabetes and outcome in patients with coronary intermediated lesions remains unclear. In addition, there are currently five widely used definitions of prediabetes, and consensus is lacking as to the optimal definition to identify those at high risk of major adverse cardiovascular events (MACE). A better understanding of the prognostic significance of prediabetes, and which definition if any, may by most useful in the setting of coronary intermediate lesions would provide an opportunity for lifestyle modification or pharmacologic interventions to improve patients’ outcome.
The objective of the current study is therefore to examine the impact of prediabetes on outcome in patients with intermediate coronary lesions, and to compare the prognostic value of the different definitions of prediabetes.
## Study population
Consecutive patients who underwent coronary angiography due to suspected cardiac ischemia symptoms in year 2013 were prospectively enrolled from Fuwai hospital, which locates in Beijing, China. Eligible patients had at least one lesion with angiographic stenosis of 50–$70\%$. We excluded patients who had lesions with stenosis greater than $70\%$, with history of percutaneous coronary intervention (PCI) or coronary artery bypass graft surgery (CABG), underwent PCI or CABG revascularization during hospitalization, or without available data on glycemic status. The study protocol complied with the principles of the Declaration of Helsinki and was approved by the Review Board of Fuwai Hospital. Written informed consent was obtained from each participant.
## Definition of glycemic status
Patients were categorized into three groups according to prior history of diabetes, admission fasting glucose and HbA1c level. Patients were classified as diabetes mellitus, either known diabetes mellitus, defined as medical history of physician-diagnosed diabetes mellitus or taking hypoglycemic medication, or newly diagnosed diabetes, defined as the absence of known diabetes and had fasting plasma glucose (FPG) ≥ 7.0 mmol/L or HbA1c ≥ $6.5\%$. Prediabetes was defined as impaired fasting glucose according to World Health Organization (WHO) criteria (WHO FPG-based: 6.1− < 7 mmol/L) [7] or the American Diabetes Association (ADA) definition (ADA FPG-based: 5.6− < 7 mmol/L) [8], or raised HbA1c according to ADA criteria (ADA HbA1c-based: 5.7− < $6.5\%$) [8] or International Expert Committee (IEC) (IEC HbA1c-based: 6.0− < $6.5\%$) [9]. The corresponding definition for normal glycaemia are shown in Table 1.Table 1Definitions of prediabetes according to different guidelinesNormal glycaemiaPrediabetesIEC HbA1c-based definitionHbA1c < $6.0\%$6.0 ≤ HbA1c < $6.5\%$ADA HbA1c-based definitionHbA1c < $5.7\%$5.7 ≤ HbA1c < $6.5\%$ADA FPG-based definitionFPG < 5.6 mmol/L5.6 ≤ FPG < 7 mmol/LWHO FPG-based definitionFPG < 6.1 mmol/L6.1 ≤ FPG < 7 mmol/LWHO World Health Organization, ADA american diabetes association, FPG fasting plasma glucose, HbA1c glycated haemoglobin, IEC international expert committee
## Outcome
The primary outcome was defined as MACE, which was a composite endpoint of all-cause death, non-fatal myocardial infarction and repeated ischemia-driven revascularization. Follow-up was performed by trained cardiologists via telephone call or clinical visit at approximately 5 year post discharge. All events were carefully adjudicated by two independent clinical cardiologists, and discrepancies were dissolved by a consensus discussion with a third cardiologist. Primary outcome was defined as MACE, which was a composite endpoint of all-cause death, non-fatal myocardial infarction and revascularization.
## Laboratory analysis
Fasting blood sample was collected within 24 h on admission prior to angiography. The blood samples were collected into EDTA-anticoagulant tubes and centrifuged to obtain the plasma. Enzymatic hexokinase method was used to measure the concentrations of blood glucose. Tosoh Automated Glycohemoglobin Analyzer (HLC-723G8) was used to measure the HbA1c levels. All other laboratory measurements were performed at the biochemistry center of Fuwai Hospital by standard biochemical techniques.
## Statistical analysis
Continuous data were presented as mean ± SD or median (interquartile), and compared by using analysis of variance or the Mann–Whitney U test. Categorical variables were presented as frequency (percentage) and compared with chi-square test or Fisher’s exact test as appropriate. Restricted cubic spline was used to flexibly model and characterize the relationship between each individual glycaemic index (HbA1c and fasting glucose) and MACE, and P value for non-linearity was determined. Survival distributions were presented by Kaplan–Meier curves and compared by log-rank test. The best cutoff value in the prediction of MACE risk was defined as the cutoff point having the highest Youden index (sensitivity + specificity − 1). Univariate cox proportional hazard regression was performed to explore the association between each baseline variable and outcome, and the hazard ratio (HR) ($95\%$ confidence interval [CI]) was calculated for each variable. Multivariate cox proportional hazard regression model was used to explore the association between glycaemic status (i.e. normal glycaemia, prediabetes, DM) and outcome after the adjustment of confounding variables. Covariates are selected based on statistical and clinical significance, which included the variables with P value less than 0.05 in baseline comparison across groups and univariate analysis (Additional file 1: Table S1), as well as those clinically judged as important prognostic factors in the setting of CAD. A total of two models was used: Model 1 (the base model) adjusted for age, sex; Model 2 (fully-adjusted model) adjusted for the variables in model 1 plus medical history of hypertension, hyperlipidemia, smoking status, alcoholic consumption, body mass index (BMI), heart rate, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), high-sensitivity C-reactive protein (hsCRP), D-Dimer and triple vessel disease. The same univariate and multivariate Cox regression analysis was performed when HbA1c and fasting glucose was modelled as a continuous variable. Spearman’s rank correlation analysis was performed to explore the association between HbA1c/fasting glucose level and hsCRP, LDL, HDL and total cholesterol. Subgroup analysis was performed to investigate whether the association between glycemic parameters and MACE differed by subgroup according to age, sex, smoking status and medical history of hypertension, and the P value for interaction test was determined. The statistical analysis was performed by SAS software Version (SAS Institute, USA) and figures were generated by GraphPad Prism version 7.0.0 for windows (GraphPad Software, San Diego, California USA).
## Categories of abnormal glucose metabolism according to different definitions
From January 2013 to February 2013, a total of 1725 consecutive patients who.
had coronary angiographically confirmed intermediate lesions were admitted to Fuwai Hospital. We excluded a total of 50 patients with missing data on glycemic status and 143 patients who did not response to our follow-up invitation, and finally included a total of 1532 patients (Fig. 1).Fig. 1Study flow chart. A total of 1725 consecutive patients with angiographically confirmed intermediate coronary lesions were enrolled in year 2013. After excluding 50 patients with missing data on glycemic status and 143 patients who did not response to our follow-up invitation, the current study included a total of 1532 patients The percentage of patients according to categories of abnormal glucose metabolism based on various definition are shown in Fig. 2. The number (proportion) of patients who NGT according to the IEC HbA1c-, ADA HbA1c -, ADA FPG- and WHO FPG- based definition were 527 ($34.40\%$), 225 ($14.69\%$), 788 ($51.44\%$), and 886 ($57.83\%$) respectively. The number (proportion) of patients who had prediabetes according to the IEC HbA1c-, ADA HbA1c—ADA FPG- and WHO FPG-based definition 419 ($27.35\%$), 721 ($47.06\%$), 158 ($10.31\%$), 60 ($3.92\%$) respectively. Fig. 2The percentage of patients according to categories of abnormal glucose metabolism by various definitions
## Baseline characteristics according to categories of abnormal glucose metabolism
Baseline characteristics according to the IEC HbA1c-based definition is shown in. Table 2. Compared with those with abnormal glucose metabolism, patients with normal glucose tolerance were younger, had lower BMI and heart rate. The proportion of patients with hypertension and hyperlipidemia were lower in the NGT group compared with abnormal glucose metabolism groups. Patients who had NGT had lower hsCRP, D-Dimer. No significant difference in smoking and alcohol status, as well as the proportion of triple vessel disease were found across groups. Baseline characteristics according to other abnormal glucose metabolism definitions are shown in Additional file 1: Tables S2, S3, S4.Table 2Baseline characteristics according to categories of abnormal glucose metabolism based on IEC HbA1c-based definitionVariablesNGTN = 527PrediabetesN = 419DMN = 586P valueAge (years)56.68 ± 9.9360.63 ± 9.4960.47 ± 9.14 < 0.0001Female (%)$\frac{150}{527}$ (28.46)$\frac{129}{419}$ (30.79)$\frac{209}{586}$ (35.67)0.0312Hypertension (%)$\frac{304}{527}$ (57.69)$\frac{272}{419}$ (64.92)$\frac{437}{586}$ (74.57) < 0.0001Hyperlipidemia (%)$\frac{270}{527}$ (51.23)$\frac{263}{419}$ (62.77)$\frac{382}{586}$ (65.19) < 0.0001Smoke (%)$\frac{233}{527}$ (44.21)$\frac{170}{419}$ (40.57)$\frac{265}{586}$ (45.22)0.3216Alcohol (%)$\frac{254}{527}$ (48.20)$\frac{190}{419}$ (45.35)$\frac{252}{586}$ (43.00)0.2208BMI (kg/m2)25.18 ± 3.0225.73 ± 3.0726.22 ± 3.26 < 0.0001HR (bpm)68.66 ± 9.6969.62 ± 10.3871.30 ± 12.150.0002SBP (mmHg)127.09 ± 16.24126.45 ± 16.40129.03 ± 16.370.0624LVEF (%)64.77 ± 5.8865.10 ± 6.6864.45 ± 6.500.4097NT-proBNP (pmol/L)537.40 (427.40, 693.90)550.95 (441.90, 703.20)547.15 (432.75, 737.35)0.4443hsCRP (mg/L)1.01 (0.54, 1.88)1.36 (0.67, 2.64)1.56 (0.80, 3.08) < 0.0001Cr (umol/L)72.94 (63.17, 82.35)73.25 (64.00, 80.95)72.60 (62.62, 82.38)0.8906D_Dimer (ug/ml)0.26 (0.18, 0.37)0.28 (0.20, 0.39)0.29 (0.20, 0.41)0.0005TC (mmol/L)4.22 (3.56, 4.87)4.14 (3.48, 4.86)4.04 (3.38, 4.78)0.1502LDL (mmol/L)2.46 (1.93, 3.10)2.42 (1.80, 3.08)2.32 (1.84, 3.00)0.0953HDL (mmol/L)1.10 (0.90, 1.30)1.08 (0.90, 1.28)1.02 (0.86, 1.20) < 0.0001Lpa (mg/L)162.36 (65.18, 320.46)150.19 (64.42, 334.84)145.11 (53.53, 358.73)0.5735Fasting glucose (mmol/L)5.03 ± 0.555.26 ± 0.577.12 ± 2.60 < 0.0001HbA1c (%)5.64 ± 0.246.16 ± 0.137.26 ± 1.23 < 0.0001Angiographic characteristics LM (%)$\frac{21}{527}$ (3.98)$\frac{17}{419}$ (4.06)$\frac{16}{586}$ (2.73)0.4138 RCA (%)$\frac{119}{527}$ (22.58)$\frac{82}{419}$ (19.57)$\frac{146}{586}$ (24.91)0.1364 LAD (%)$\frac{349}{527}$ (66.22)$\frac{254}{419}$ (60.62)$\frac{388}{586}$ (66.21)0.1240 LCX (%)$\frac{122}{527}$ (23.15)$\frac{107}{419}$ (25.54)$\frac{196}{586}$ (33.45)0.0003 Triple-vessel disease$\frac{23}{527}$ (4.36)$\frac{9}{419}$ (2.15)$\frac{30}{586}$ (5.12)0.0560NGT normal glucose tolerance, DM diabetes mellitus, BMI body mass index, HR heart rate, SBP systolic blood pressure, LVEF left ventricular ejection fraction, NT-proBNP N-Terminal Pro–B-Type Natriuretic Peptide, hsCRP high-sensitivity C-reactive protein, Cr Creatinine, TC Total cholesterol, LDL low-density lipoprotein, HDL high-density lipoprotein, Lpa Lipoprotein (a), HbA1c glycated hemoglobin, LM left main; RCA right coronary artery, LAD left anterior descending, LCX left circumflex artery
## Association between abnormal glucose metabolism and long-term outcome
A total of 197 MACE occurred during a median follow-up time of 6.1 years, and. Included 62 deaths, 31 MI and 125 revascularization (a total of 21 patients suffered from both MI and revascularization). According to the IEC HbA1c-based definition, a total of 41 ($7.78\%$) events occurred in the NGT group, 58 ($13.84\%$) events occurred in the prediabetes group, 98 ($16.72\%$) events occurred in the DM group (Table 3). Compared with normal glucose metabolism, each category of abnormal glucose metabolism was associated with higher risk of MACE. Compared with the NGT group, the HR ($95\%$ CI) of MACE was 1.705 (1.143, 2.543) for the prediabetes group, 2.173 (1.509, 3.129) for the DM group. The Kaplan–Meier curves showing the survival freedom from the MACE across groups are shown in Fig. 3A. The multivariate-adjusted HR ($95\%$ CI) for MACE was 1.513 (1.005, 2.277) for the prediabetes 1.870 (1.273, 2.745) for the DM group. The number and proportion of events according to categories of abnormal glucose metabolism based on other definitions are shown in Additional file 1: Tables S5, S6, S7. *In* general, prediabetes was not significantly associated with MACE risk, and DM was associated with increased MACE risk according to the ADA HbA1c—ADA FPG-, and WHO FPG-based definition. The Kaplan–Meier curves showing the survival freedom from the MACE across groups are shown in Fig. 3B, D.Table 3Adjusted HR for MACE during 6-year follow-up according to baseline categories of abnormal glucose metabolism by IEC HbA1c-based definitionEvent/Total (%)HR ($95\%$ CI)P valueModel 1 NGT$\frac{41}{527}$ (7.78)1 (reference)1 (reference) Prediabetes$\frac{58}{419}$ (13.84)1.705 (1.143, 2.543)0.0090 DM$\frac{98}{586}$ (16.72)2.173 (1.509, 3.129) <.0001Model 2 NGT$\frac{41}{527}$ (7.78)1 (reference)1 (reference) Prediabetes$\frac{58}{419}$ (13.84)1.514 (1.011, 2.267)0.0440 DM$\frac{98}{586}$ (16.72)2.027 (1.403, 2.927)0.0002Model 3 NGT$\frac{41}{527}$ (7.78)1 (reference)1 (reference) Prediabetes$\frac{58}{419}$ (13.84)1.513 (1.005, 2.277)0.0471 DM$\frac{98}{586}$ (16.72)1.870 (1.273, 2.745)0.0014Model 1 is univariate analysisModel 2 adjusted for age and sexModel 3 adjusted for model 2 plus medical history of hypertension, hyperlipidemia, smoking status, alcoholic consumption, body mass index, heart rate, total cholesterol, LDL, HDL, hsCRP, D-Dimer and triple vessel disease;MACE major adverse cardiovascular events, IEC international expert committee, HbA1C glycated haemoglobin, NGT normal glucose tolerance, DM diabetes mellitus, HR hazard ratio, CI confidence intervaFig. 3Kaplan–Meier curve showing survival free of MACE for different categories of abnormal glycemic metabolism according to IEC HbA1c-(A), ADA HbA1c- (B), ADA FPG- (C) and WHO FPG- (D) based definition
## The association between glycemic parameters and MACE
We next investigated the relationship between glycemic parameters (HbA1c and admission fasting glucose) as a continuous variable and outcome. Restricted cubic spline showed that HbA1c presented a linear relationship with the risk of MACE (p for non-linearity = 0.2119), and the risk of MACE increased along with HbA1c levels (Additional file 1: Fig. S1A). Admission fasting glucose also presented a linear relationship with the risk of MACE (p for non-linearity = 0.4014), but a significant increased risk along with fasting glucose level was not observed (Additional file 1: Fig. S1B). When glycemic parameters were modelled as a continuous variable, the HR ($95\%$ CI) for MACE was 2.150 (1.124, 4.115) per doubling increase in HbA1c (Additional file 1: Table S8), and 1.021 (0.952, 1.094) per unit increase in admission fasting glucose in the fully adjusted model (Additional file 1: Table S9). The best cut-off point of HbA1c based on Youden’s index was $6.0\%$ in predicting MACE in patients without known diabetes, with sensitivity of 0.667 and specificity of 0.493 (Additional file 1: Fig. S2).
The correlation between HbA1c and hsCRP, N-Terminal Pro–B-Type Natriuretic Peptide (NT-proBNP), LDL, triglyceride, total cholesterol, HDL and fasting plasma glucose are shown in Additional file 1: Fig. S3. HbA1c level was positively associated with hsCRP (R2 = 0.1917, $p \leq 0.0001$), triglycerides (R2 = 0.0927, $$p \leq 0.0003$$), FPG (R2 = 0.5434, $p \leq 0.0001$) and negatively associated with HDL (R2 = − 0.0862, $$P \leq 0.0009$$). No significant association between HbA1c and NT-proBNP, LDL, and total cholesterol was observed. Of note, the correlation coefficient was weak despite statistically significant, and may not be able to provide sufficient clinical significance.
## Subgroup analysis of the association between abnormal glucose metabolism and long-term outcome
Subgroup analysis of the association between abnormal glucose metabolism based on IEC HbA1c-based definition with MACE according to age, sex, smoking status and medical history of hypertension are shown in Additional file 1: Table S10. P value for interaction was greater than 0.05 across all subgroup analyses, indicating that the effect of categories of abnormal remains consistent patients according to age (age ≥ 65 years or age < 65 years), sex (female or male subgroup), smoking status (current smokers or nonsmokers) and medical history of hypertension (with or without medical history of hypertension).
## Major findings
By investigating the association between categories of abnormal glucose.
metabolism based on various definition and MACE in patients with intermediate lesions, the current study found that prediabetes based on IEC HbA1c-based definition (6.0 ≤ HbA1c < $6.5\%$) was associated with significant increased MACE risk compared with NGT, which was consistent with the best cut-off point identified based on the Youden’s index. Newly diagnosed diabetes was associated with increased MACE risk compared with normal glycemia based on all the currently widely used definitions. Globally, FPG or FPG-based definition of prediabetes was not associated with patients’ outcome. The current study supported the use of IEC HbA1c-based definition to identify high-risk patients of MACE, who may benefit from early lifestyle interventions.
## Reasons for selecting patients with intermediate lesions
The current study enrolled patients with angiographically confirmed coronary.
intermediate lesions to represent patients with stable coronary heart disease for the following two reasons: On the one hand, patients with coronary intermediate lesions had similar degree of coronary stenoses (DS% of 50–$70\%$), and thus the effect of lesion stenosis severity on prognosis may be reduced. On the other hand, patients with coronary intermediate lesions various significantly in short-term prognosis. In patients without functional significant lesions and deferred from revascularization therapy, MACE occurred in approximately $4\%$ of the population in one-year follow-up [10], suggesting that further investigation of prognostic factors will assist in risk stratification and outcome improvement.
## Explanations for the superiority of HbA1c over FPG
Our findings showed that prediabetes defined based on HbA1c, but not fasting plasma glucose, identified a group of patients at high-risk of MACE. Explanations for the superiority of HbA1c over fasting glucose to identify patients at risk for MACE are proposed as follows: Glycated hemoglobin values reflect the three-month average endogenous exposure to glucose, including postprandial spikes, and show low intraindividual variability, particularly in people without diabetes [11]. In addition, HbA1c is a useful marker for other glycated molecules, such as advanced glycation end-products, which are likely drivers of vascular inflammation and subsequent plaque progression and rupture, leading to major adverse events in patients [12]. These features support the role of HbA1c as a novel biomarker in risk stratification.
## Comparison with previous studies
Growing number of studies explored the association between prediabetes defined based on HbA1c value and MACE in the setting of CAD [1]. However, most studies enrolled patients with acute coronary syndrome [13, 14] or those who received revascularization [14–16], and the current study add new data in this field by examining this association in patients with stable CAD and not undergoing revascularization. Our study found that prediabetes defined according to IEC HbA1c-based definition was associated with increased risk of MACE. Our findings are in consistent with previous studies showing that prediabetes was associated with poorer prognosis in patients who underwent PCI and treated with contemporary drug eluting stents (DES) [16, 17]. In contrast, some previous studies reported no significant association between HbA1c-defined prediabetes and prognosis [15, 18]. The contradictory results may be explained by the difference in prediabetes definition and endpoint. In the above two studies, prediabetes was defined by HbA1c-ADA definition, which is HbA1c of 5.7–$6.4\%$. Similarly, when prediabetes was defined based on HbA1c-ADA definition in our study, approximately half of the study population were classified as prediabetes, and only $15\%$ of the study population were classified as normal glucose metabolism. This may explain the non-significant association between prediabetes and outcome.
## Possible underlying mechanisms
Several plausible biological mechanisms have been proposed to explain a possible direct relationship between chronically elevated blood glucose levels and coronary heart disease (CHD) [24]. Glucose can react with many different proteins, creating advanced glycation end products (AGE), which contribute to long-term complications in diabetes as well as to endothelial dysfunction, plaque formation and progression.
[19] reported that circulating AGEs and soluble receptor for AGE (RAGE) isoforms in patients with type 2 diabetes as predictors of MACE and all-cause mortality [19]. In addition, AGEs can be estimated by the non-invasive skin autofluorescence, and provided additional prognostic information in patients with both type 1 [20] and type 2 diabetes [21]. In addition to the direct effect of elevated glucose on atherosclerosis, chronically elevated blood glucose levels, as reflected by greater HbA1c level, is also related to increased risk of other CHD risk factors including diabetic dyslipidemia [22], hypertension [23], which together accelerate vascular injury and cardiovascular disease risk [24]. Diabetes has been proposed to accelerate atherosclerosis via oxidative stress, and increased inflammation [25].
## Clinical significance
Our study suggested that prediabetes based on the IEC HbA1c-based definition predicts MACE risk in patients with stable CAD. As discussed above, HbA1c reflects the average endogenous exposure to glucose and have low variability compared with fasting glucose. In addition, it less time-consuming compared with OGTT. These characteristics may contribute to the superiority of glycated hemoglobin over other diagnostic methods for long-term risk stratification. Of note, newly diagnosed diabetes across all the four definitions was associated with a significant increased MACE risk compared with normal glycemic metabolism. Since patients with prediabetes have a significant higher risk of progression to diabetes, efforts including dietary and exercise intervention should be made in all patients with CAD and abnormal glucose metabolism [26]. Regarding pharmacologic interventions, no pharmacologic agent has been approved currently by the U.S. Food and Drug administration specifically for diabetes prevention or the treatment for prediabetes [27]. However, recent cardiovascular outcomes trials indicated cardiovascular benefits of novel glucose-lowering drugs, which included sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists. Therefore, these drugs may be recommended in patients with prediabetes to prevent or delay the onset of diabetes, which requires validation in future studies [28].
## Limitations
Our study has several limitations: Firstly, OGTT tests were not performed in the majority of patients, and the relationship between post-load glucose value, and impaired glucose tolerance, another form of prediabetes defined by the 2 h-postload glucose level was not evaluated. Secondly, only baseline HbA1c was collected, while the association between variations in HbA1c during follow-up was not assessed. Finally, the current study was a single center study with moderate size, and unmeasured confounders could not be excluded. Our findings need further validation in large-scale prospective cohort in future studies.
## Conclusions
The prevalence of prediabetes varies significantly according to different definitions, and a high proportion of patients with coronary intermediate lesions without previously known history of diabetes have abnormal glycemic metabolism, suggesting the importance of screening for diabetes in this population. In our study cohort, prediabetes according to IEC HbA1c-based definition was associated with significant increased MACE risk compared with NGT, and newly diagnosed diabetes was associated with increased MACE risk based on all the currently widely used definitions. The current study supported the use of IEC HbA1c-based definition to identify high-risk patients of MACE, who may benefit from early lifestyle interventions, and these findings require further validation in future studies.
## Supplementary Information
Additional file 1: Table S1. MACE risk according to baseline variables. Table S2. Baseline characteristics according to categories of abnormal glucose metabolism based on ADA HbA1c-based definition. Table S3. Baseline characteristics according to categories of abnormal glucose metabolism based on ADA FPG-based definition. Table S4. Baseline characteristics according to categories of abnormal glucose metabolism based on WHO FPG-based definition. Table S5. Adjusted HR for MACE during 6-year follow-up according to baseline categories of abnormal glucose metabolism by ADA HbA1c-based definition. Table S6. Adjusted HR for MACE during 6 year follow-up according to baseline categories of abnormal glucose metabolism by WHO FPG-based definition. Table S7. Adjusted HR for MACE during 6 year follow-up according to baseline categories of abnormal glucose metabolism by ADA FPG-based definition. Table S8. Adjusted HR for MACE during 6 year follow-up according to baseline HbA1c level as a continuous variable (log2 transformed). Table S9. Adjusted HR for MACE during 6 year follow-up according to baseline glucose level as a continuous variabl. Table S10. Subgroup analysis of the association between categories of abnormal glucose metabolism and MACE. Figure S1. Restricted cubic spline analysis of the association between baseline HbA1c level (A) and admission fasting glucose (B) and major cardiovascular event (MACE) risk. Baseline HbA1c and admission fasting glucose level presented a linear relationship with the risk of MACE (p for non-linearity 0.2119 and 0.4014 respectively). The curves are presented with $95\%$ confidence interval. Figure S2. ROC Curve of HbA1c in Predicting MACE. The c-index on the basis of the AUC for HbA1c in predicting ischemic stroke was 0.5927. The best cutoff value of HbA1c based on the highest Youden’s index was $6\%$ with sensitivity of 0.667 and specificity of 0.493. Figure S3. Correlation analysis of the relationship between HbA1c and hsCRP (A), NT-proBNP (B), LDL (C), triglyceride (D), total cholesterol (E), HDL (F) and fasting glucose (G).
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|
---
title: Elevated urine albumin-to-creatinine ratio increases the risk of new-onset
heart failure in patients with type 2 diabetes
authors:
- Jie Tao
- Dasen Sang
- Libo Zhen
- Xinxin Zhang
- Yuejun Li
- Guodong Wang
- Shuohua Chen
- Shouling Wu
- Wenjuan Zhang
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10040119
doi: 10.1186/s12933-023-01796-6
license: CC BY 4.0
---
# Elevated urine albumin-to-creatinine ratio increases the risk of new-onset heart failure in patients with type 2 diabetes
## Abstract
### Background
Although albuminuria has been linked to heart failure in the general population, the relationship between urine albumin-to-creatinine ratio (uACR) and heart failure in type 2 diabetes patients is not well understood. We aimed to investigate the relationship between uACR and new-onset heart failure (HF) in type 2 diabetics.
### Methods
We included 9287 Chinese participants with type 2 diabetes (T2D) but no heart failure (HF) who were assessed with uACR between 2014 and 2016. The participants were divided into three groups based on their baseline uACR: normal (< 3 mg/mmol), microalbuminuria (3–30 mg/mmol), and macroalbuminuria (≥ 30 mg/mmol). The relationship between uACR and new-onset HF was studied using Cox proportional hazard models and restricted cubic spline. The area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to see if incorporating uACR into existing models could improve performance.
### Results
216 new-onset HF cases ($2.33\%$) were recorded after a median follow-up of 4.05 years. When compared to normal uACR, elevated uACR was associated with a progressively increased risk of new-onset HF, ranging from microalbuminuria (adjusted HR, 2.21; $95\%$ CI 1.59–3.06) to macroalbuminuria (adjusted HR, 6.02; $95\%$ CI 4.11–8.80), and 1 standard deviation (SD) in ln (uACR) (adjusted HR, 1.89; $95\%$ CI 1.68–2.13). The results were consistent across sex, estimated glomerular filtration rate, systolic blood pressure, and glycosylated hemoglobin subgroups. The addition of uACR to established HF risk models improved the HF risk prediction efficacy.
### Conclusions
Increasing uACR, even below the normal range, is an independent risk factor for new-onset HF in a type 2 diabetic population. Furthermore, uACR may improve HF risk prediction in community-based T2D patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01796-6.
## Introduction
Heart failure (HF) is viewed as the chronic, terminal stage of various cardiovascular diseases. The global prevalence of HF is increasing over time because of the aging population and improvement in diagnostic and treatment methods for coronary heart disease (CHD) and valvular heart disease. Epidemiological data have shown that the prevalence of HF is estimated at 1–$2\%$ of the general adult population in developed countries and as high as $10\%$ in the population aged > 70 years [1, 2]. The prevalence of HF in Chinese adults was $0.9\%$ and $1.3\%$ in 2003 and 2012–2015, showing an increase of nearly 5 million [3–5] patients when compared with 2003 data. HF patients display a high rehospitalization rate and a similar 5 year survival rate as the patients with malignant tumors, estimated at $50\%$.
The prevalence of HF in diabetic patients is 2.5–3 times higher than that of the general population [6]. Although diabetic patients have an increased risk of atherosclerosis which might lead to HF through coronary atherosclerosis, the high risk of HF cannot be fully explained by this association. Diabetic microangiopathy is associated with an increased risk of HF and can be assessed by using the urine albumin-to-urine creatinine ratio (uACR). It has been demonstrated that albuminuria was closely related to the occurrence, development, and prognosis of CHD and HF [7–10]. A community-based study also confirmed that the risk of HF was increased by 54–$91\%$ with a mild increase in uACR [11].
However, none of the studies mentioned above considered how uACR (spot urine albumin indexed to creatine) affects new-onset HF in type 2 diabetic population. Therefore, this study aimed to analyze the impact of uACR on new-onset HF in patients with T2D and evaluate whether adding uACR to established HF risk models can improve the prediction efficacy of HF risk.
## Study cohort
This prospective cohort study comprised in-service and retired Kailuan employees of the Kailuan Group, who participated in the health examination conducted every 2 years in 11 hospitals (Kailuan General Hospital and the affiliated hospitals) from June 2006 to October 2007. The follow-up included an evaluation of HF and death. As urine albumin and creatinine tests were added during the physical examinations in 2014 (5th) and 2016 (6th), diabetic patients who underwent these tests and participated in the 5th and 6th physical examinations were enrolled.
The inclusion criteria were: [1] Patients who participated in the 2014 or 2016 health examination; [2] Participants who met the diagnostic criteria for type 2 diabetes; [3] those who had complete urine albumin and creatinine data, and [4] those patients who agreed for participation and signed informed consent.
The exclusion criteria were: [1] Patients having a history of HF before the physical examination; [2] Patients suffering from valvular and congenital heart diseases.
A total of 1820 T2D patients participated in the 5th physical examination, which included urine albumin and creatinine tests; 8827 patients participated in the 6th physical examination, which included urine albumin and creatinine tests. However, 9642 patients were included in the study after excluding 167 and 188 patients who had incomplete urine albumin and creatinine data and a history of HF before the physical examination, respectively. Subsequently, 9287 patients with T2D were finally included in the statistical analysis (Fig. 1).Fig. 1Flowchart of the current study
## Collection of general clinical data and laboratory investigations
All participants completed a questionnaire documenting their sociodemographic status (e.g., age, sex), personal and family health history (e.g., hypertension, diabetes), and lifestyle habits during the on-site visit. Height, weight, and blood pressure measurements, as well as the methods and criteria for determining relevant biochemical parameters, are all described in greater detail elsewhere [12]. Smokers were defined as having smoked at least one cigarette per day on average for the past year, and those who had quit smoking for < 1 year were defined as smokers too. Body mass index (BMI) was calculated as BMI = body weight/height2 (kg/m2). The estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [13].
The study was carried out in accordance with the Declaration of Helsinki and was approved by the ethics committee of our hospital. Each participant provided written informed consent.
## Urine albumin and urine creatinine determination and grouping
After an overnight fast, a single random midstream morning urine sample was collected. All participants’ morning urine samples were centrifuged at 600 g for 5 min and stored at − 80 °C until tested. A urine analyzer was used to measure all of the urine samples (N-600, Dirui, Changchun, China). Jaffe’s kinetic method was used to measure urinary creatinine. Turbidimetry was used to measure urinary albumin (DAKO kit, Denmark).
We looked at uACR as a continuous and categorical variable, with normal (uACR < 3 mg/mmol), microalbuminuria (3–30 mg/mmol), and macroalbuminuria (≥ 30 mg/mmol) categories [14, 15].
## Diagnostic criteria
Type 2 diabetes: The American Diabetes Association (ADA) Criteria for Diagnosis of Diabetes [2010] was referred [16].1) History of type 2 diabetes;Or 2) Fasting blood glucose (FBG) ≥ 7.0 mmol/L;Or 3) Two-hour blood glucose of ≥ 11.1 mmol/L in random plasma glucose test or oral glucose tolerance test;Or 4) Hemoglobin A1c (HbA1c) ≥ $6.5\%$ (47.5 mmol/mol).
HF: Chinese Guidelines for the Diagnosis and Treatment of Chronic Heart Failure [2018] was referred [17].1) Symptoms and signs of HF, manifested as shortness of breath, fatigue, palpitations, fluid retention, as well as New York Heart Association (NYHA) heart function grade II and above;2) Modified Simpson’s method: the left ventricular ejection fraction < $50\%$ measured by echocardiography;3) Plasma N-terminal pro-B-type natriuretic peptide ≥ 125 ng/L.
The diagnosis must meet conditions [1] as well as at least one of conditions [2] and [3].
If the time of the first hospitalization for heart failure was earlier than the 5th or 6th physical examination, the patient was considered to have a history of heart failure.
## Follow-up and endpoint events
After the completion of the 5th or 6th health examination, that is, the starting point of follow-up, trained medical staff reviewed the inpatient diagnosis and recorded the end-point events of the participants in the Affiliated Hospitals of Kailuan Group and the Designated Hospitals for Medical and Health Insurance of China every year. The end-point events ware defined as HF during the follow-up. The time of the first event was considered as the end-point for those with > 2 events, and the final follow-up date for those without HF was December 31, 2020. All diagnoses were confirmed by professional physicians according to the inpatient medical records.
## Statistical analysis
Normally distributed measurement data were expressed as mean + sd. Multiple pairwise-comparison between different groups was conducted using a one-way analysis of variance. The least significant difference (LSD) test and Dunnett’s T3 test were used for evaluating the homogeneity of variance and heterogeneity of variance, respectively. Non-normally distributed data were presented as median and centiles (25th and 75th), while the comparison between the groups was performed using the Kruskal–Wallis rank sum test. Enumeration data were presented as frequency and percentage (n, %), and comparisons between groups were performed by the chi-square test. The Kaplan–Meier method was used to calculate the incidence of HF events in each group and the overall population, and a log-rank test was adopted to compare the difference in the incidence of HF.
The uACR was assessed as a categorical and continuous variable. Given a non-normal distribution, uACR was ln-transformed for the continuous model. The effect of different uACR groups and each 1-standard deviation (SD) increase in ln (uACR) on new-onset HF was studied using a multivariate Cox stepwise regression model. Model 1 unadjusted. Model 2 was adjusted for age and gender. Model 3 was further adjusted for SBP, BMI, total cholesterol, HbA1c, eGFR, hemoglobin, smoking, anti-diabetic treatment, antihypertensive treatment, CHD, and atrial fibrillation.
In addition, based on Model 2 (age, gender), Model 4 (WATCH-DM risk score: age, BMI, SBP, DBP, FPG, serum creatinine, HDL cholesterol, CHD) and Model 5 (Williams et al. study model: age, SBP, CHD, Atrial fibrillation, HbA1c, Albumin, BUN, eGFR, smoking), the receiver operating characteristic (ROC) area under the curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to assess the ability of uACR to improve HF prediction models, respectively.
A spline function curve was plotted to see if there was a linear correlation between uACR and new-onset HF. The multivariable adjusted model include age, gender, SBP, BMI, total cholesterol, HbA1c, eGFR, hemoglobin, smoking, anti-diabetic treatment, antihypertensive treatment, CHD, and atrial fibrillation.
Considering the impact of death on HF during follow-up, a competing risk model for mortality was constructed for the overall population. Furthermore, in order to avoid the influence of CHD, hypertension, and antihypertensive drugs on HF, sensitivity analysis was performed after excluding the above population.
SAS version 9.4 was used for the analysis (SAS Institute, Cary, NC, USA). All statistical analyses were double-tailed, with statistical significance set at $P \leq 0.05.$
## Baseline characteristics
The observed patients’ baseline age was 61.10 ± 9.97 years and included 6815 ($73.37\%$) males and 2472 females ($26.63\%$). The systolic blood pressure (SBP) was 146.70 ± 20.69 mmHg, HbA1c was 7.60 ± $1.65\%$ (57.36 ± 18.21 mmol/mol), and uACR was 1.67 (0.80, 4.61) mg/mmol; $65.9\%$ of the overall population had uACR in the normal range ($$n = 6120$$), and $28.1\%$ and $6.0\%$ of them had microalbuminuria and macroalbuminuria, respectively. When compared with the normal uACR, the patients with microalbuminuria and macroalbuminuria exhibited higher SBP, total cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, HbA1c, BMI, high sensitivity C-reactive protein (hs-CRP), heart rate, hypertension prevalence, and HF prevalence as well as a lower eGFR level (Table 1).Table 1Baseline characteristics overall and by uACR categories in participantsOverall 9287 < 3 mg/mmol 61203–30 mg/mmol 2611 ≥ 30 mg/mmol 556P-valueHeart failure219 ($2.33\%$)72 (1.18)84 (3.22)60 (10.79) < 0.001Male, n(%)6815 (72.37)4452 (72.75)1887 (72.27)415 (74.64)0.513Age, year61.10 ± 0.9760.42 ± 9.7862.17 ± 10.0563.57 ± 10.09 < 0.001uACRa, mg/mmol1.67 (0.80 4.61)1.01 (0.63–1.64)6.18 (4.13–11.29)61.66 (42.68–114.64) < 0.001SBP, mmHg146.70 ± 20.69143.56 ± 19.72151.82 ± 20.74157.01 ± 22.30 < 0.001DBP, mmHg82.92 ± 11.0381.95 ± 10.5284.57 ± 11.6486.01 ± 12.04 < 0.001Heart rate, beats/min77.48 ± 12.8276.58 ± 12.4378.99 ± 13.2280.39 ± 13.58 < 0.001Waist circumference, cm90.72 ± 9.6290.32 ± 9.5791.38 ± 9.6991.96 ± 9.63 < 0.001BMI, kg/m225.81 ± 3.4325.58 ± 3.3226.14 ± 3.5826.54 ± 3.64 < 0.001Triglyceridesa, mmol/L1.53 (1.05–2.30)1.45 (1.00–2.18)1.68 (1.15–2.60)1.85 (1.26–2.83)0.008Total cholesterol, mmol/L5.48 ± 1.175.42 ± 1.135.58 ± 1.215.81 ± 1.38 < 0.001HDL cholesterola, mmol/L1.37 (1.18–1.62)1.39 (1.19–1.64)1.34 (1.16–1.58)1.32 (1.12–1.57)0.505LDL cholesterol, mmol/L3.24 ± 0.953.20 ± 0.923.30 ± 0.993.42 ± 1.050.001FBG, mmol/L9.10 ± 3.268.54 ± 2.9510.04 ± 3.4810.77 ± 3.86 < 0.001HbA1c, %(mmol/mol)7.60 ± 1.65, 57.36 ± 18.217.32 ± 1.54, 56.50 ± 16.798.10 ± 1.74, 65.05 ± 19.408.40 ± 1.84, 68.31 ± 19.95 < 0.001Hemoglobin, g/L150.65 ± 14.51150.66 ± 14.16151.00 ± 14.61148.89 ± 17.470.019Albumin, g/L44.4 ± 9.0744.56 ± 8.7344.33 ± 9.7743.15 ± 9.210.002BUN, mmol/L6.08 ± 2.155.99 ± 2.066.08 ± 2.037.05 ± 3.23 < 0.001Hs-CRPa,mg/L1.10 (0.34–2.76)0.93 (0.29–2.40)1.41 (0.47–3.39)1.68 (0.68–3.65)0.264eGFR, mL/min/1.73 m290.95 ± 16.9291.93 ± 16.0990.30 ± 16.7083.29 ± 23.25 < 0.001smoking3123 (33.63)2092 (34.18)860 (32.94)171 (30.76)0.177Hypertension, n (%)5272 (56.77)3161 (51.65)1719 (65.84)392 (70.50) < 0.001Atrial fibrillation, n (%)100 (1.09)55 (0.90)38 (1.46)7 (1.28)0.063CHD, n (%)493 (5.31)309 (5.05)146 (5.59)38 (6.83)0.148Anti-diabetic treatment, n (%)3947 (41.91)2294 (37.46)1269 (48.60)338 (60.79) < 0.001Insulin, n (%)1678 (17.82)906 (14.80)555 (21.26)191 (34.35) < 0.001Oral medicine, n (%)2274 (24.15)1391 (22.73)715(27.38)148(26.62) < 0.001Antihypertensive treatment, n (%)3899 (41.98)2461 (40.21)1136 (43.51)302 (54.32) < 0.001ACEI or ARB, n (%)923 (9.94)501 (8.19)311 (11.91)111 (19.96) < 0.001Beta-blocker, n (%)471 (5.07)267 (4.36)155 (5.94)49 (8.81)0.710Calcium channel blocker, n (%)869(9.36)434 (7.09)309 (11.83)126 (22.66) < 0.001Diuretic, n (%)280 (3.01)121 (1.98)104 (3.98)55 (9.89)0.001Others, n (%)2460 (26.49)1682 (27.48)643(24.63)135 (24.28)0.001aExpressed in M (Q1–Q3)
## Cumulative incidence of HF events in each uACR group
Following a median follow-up time of 4.05 (3.55, 4 0.48) years, 216 patients ($2.33\%$) developed HF, and 496 patients ($5.3\%$) died of all-cause mortality, respectively.
The cumulative incidence of HF in all three groups was $1.55\%$, $3.37\%$, and $15.07\%$, respectively. A log-rank test showed a significant difference in the cumulative incidence between the three groups (Fig. 2).Fig. 2Incidence of heart failure by albuminuria category: albuminuria categories were based on urinary albumin-creatinine ratios (uACR) as macroalbuminuria (uACR ≥ 30 mg/mmol), microalbuminuria (uACR < 30 to ≥ 3 mg/mmol), and normal (uACR < 3 mg/mmol). $P \leq 0.0001$ for differences among curves using the log-rank test
## Multivariate Cox regression analysis of the relationship between uACR and new-onset HF
With the presence or absence of HF as the dependent variable and uACR groups or per 1-SD increase in ln (uACR) as the independent variable, and after adjustment for covariates, the risk of new-onset HF was 2.21 fold ($95\%$ CI 1.59–3.06) and 6.02 fold ($95\%$ CI 4.11–8.80) higher in the patients with microalbuminuria and macroalbuminuria than in the patients with normal uACR, respectively; the risk of new-onset HF increased by $89\%$ ($95\%$ CI 68–$113\%$) per 1-SD increase in ln (uACR) (Table 2).Table 2Hazard ratios (HR) and $95\%$ confidence intervals of uACR for heart failureuACR categoryNoMedian follow-up (years)Incident heart failure (%)Incidence rate (/1000 person-years)Model1Model 2Model 3 < 3 mg/mmol61204.0472 (1.18)2.891113–30 mg/mmol26114.0684 (3.22)8.102.81 (2.05, 3.84)2.48 (1.81, 3.41)2.21 (1.59, 3.06) ≥ 30 mg/mmol5564.0860 (10.79)29.2910.13 (7.19, 14.27)8.39 (5.94, 11.86)6.02 (4.11, 8.80)ln(uACR), Per 1 SD92874.05216 (2.13)5.792.15 (1.94, 2.37)2.06 (1.85, 2.28)1.89 (1.68, 2.13)Model 1: unadjusted; Model 2: adjusted for age and sex; Model 3: adjusted for age, sex, SBP, BMI, total cholesterol, HbA1c, eGFR, hemoglobin, smoking, anti-diabetic treatment, antihypertensive treatment, CHD and atrial fibrillation Additionally, we constructed a competing risk model for mortality for the overall population to eliminate the impact of all-cause mortality events on the outcome during follow-up and obtained consistent results (Additional file 1: Table S1).
## Restrictive cubic spline Cox proportional hazards model was used to analyze the relationship between uACR and the risk of new-onset HF
The overall and nonlinear associations between uACR and new-onset HF were statistically significant ($p \leq 0.001$). The results of the restrictive cubic spline Cox proportional hazards model indicated that the risk of HF gradually increased with an increase in uACR after adjustment for covariates (Fig. 3).Fig. 3Adjusted relative hazard of heart failure by the continuous level of urinary albumin-to-creatinine ratios (uACR). The reference point is uACR of 3 mg/mmol. The solid lines represent the hazard ratios across the spectrum of uACR. The dashed lines represent the upper and lower bounds of the $95\%$ confidence interval. P-values reflect adjusted trends (accounting for age, sex, SBP, BMI, Total cholesterol, HbA1c, eGFR, hemoglobin, smoking, Anti-diabetic treatment, Antihypertensive treatment, CHD, and atrial fibrillation)
## Additional predictive value of uACR for established HF risk models
In order to investigate whether the addition of uACR to known HF risk assessment models can improve the predictivity of HF risk, it was added to model 1 as well as the WATCH-DM [18] and Williams et al. [ 19] study models, respectively. As shown in Table 3, the addition of uACR to the known models improved the predictivity of HF risk ($p \leq 0.001$).Table 3The additional predictive value of uACR for heart failureAUCP-valueNRIP-valueIDIP-valueModel 20.680 (0.665, 0.715)–Ref.–Ref.–Model 2 + ln(uACR)0.783 (0.754, 0.813) < 0.0010.330 (0.237, 0.404) < 0.0010.034 (0.022, 0.052) < 0.001Model 2 + uACR categories0.768 (0.737, 0.799) < 0.0010.362 (0.296, 0.424) < 0.0010.024 (0.014, 0.039) < 0.001Model 40.744 (0.710, 0.776)–Ref.–Ref.–Model 4 + ln(uACR)0.802 (0.773, 0.832) < 0.0010.291 (0.200, 0.369) < 0.0010.037 (0.021, 0.061) < 0.001Model 4 + uACR categories0.793 (0.763, 0.823) < 0.0010.284 (0.209, 0.358) < 0.0010.025 (0.012, 0.042) < 0.001Model 50.755 (0.721, 0.788)–Ref.–Ref.–Model 5 + ln(uACR)0.807 (0.777, 0.837) < 0.0010.285 (0.182, 0.347) < 0.0010.039 (0.021, 0.060) < 0.001Model 5 + uACR categories0.798 (0.767, 0.830) < 0.0010.303 (0.231, 0.375) < 0.0010.025 (0.012, 0.042) < 0.001Model 2: age, sex; Model 4 (WATCH-DM risk score): age, BMI, SBP, DBP, FPG, serum creatinine, HDL cholesterol, CHD; Model 5(Williams et al. study model): age, SBP, CHD, Atrial fibrillation, HbA1c, Albumin, BUN, eGFR, smoking;uACR urine albumin-to-creatinine ratio, AUC the area under the receiver operating characteristic curve, NRI net reclassification improvement, IDI integrated discrimination improvement
## Multivariate Cox regression subgroup analysis of uACR effect on new-onset HF
Among the factors influencing HF, uACR was not significantly interactive with sex, SBP, and HbA1c ($p \leq 0.05$) but was interactive with eGFR ($p \leq 0.05$). Multivariate Cox regression analysis was performed in gender, renal function (assessing eGFR levels), SBP, and HbA1c subgroups, respectively. Our results revealed that the incidence and risk of HF in each population increased with an increase in uACR and were consistent in the overall population (Table 4).Table 4Hazard ratios (HR) and $95\%$ Confidence intervals of uACR for heart failure (subgroup analysis)No. Incident heart failure (%)Incidence rate (/1000 person-years) < 3 mg/mmol3–30 mg/mmol ≥ 30 mg/mmolInteraction p valueGender0.054 Male67541464.45Ref.2.50 (1.65, 3.77)7.79 (4.91, 12.36) Female2533705.60Ref.1.70 (1.00, 2.93)3.54 (1.73, 7.25)Baseline eGFR0.022 ≥ 903456613.92Ref.2.59 (1.49, 4.50)2.21 (1.01, 6.04) 60–904694975.37Ref.1.84 (1.14, 2.96)5.19 (2.94, 9.16) < 6011375814.51Ref.2.74 (1.21, 6.20)15.74 (7.21, 34.36)Baseline SBP0.652 SBP < 1403582644.41Ref.2.01 (1.18, 3.61)2.78 (1.25, 6.19) 140 ≤ SBP < 1603424755.59Ref.2.49 (1.42, 4.39)10.37 (5.62, 19.15) SBP ≥ 1602281778.30Ref.2.14 (1.19, 3.84)5.81 (3.06, 11.04)Baseline HbA1c0.186 HbA1c < $7.0\%$(53.01 mmol/mol)3857624.41Ref.3.65 (1.99, 6.68)13.99 (6.96, 28.16) HbA1c ≥ $7.0\%$(53.01 mmol/mol)54301546.76Ref.1.78 (1.21, 2.62)4.47 (2.87, 6.97)Adjusted for age, sex, SBP, BMI, Total cholesterol, HbA1c, eGFR, hemoglobin, smoking, Anti-diabetic treatment, Antihypertensive treatment, CHD, and atrial fibrillation
## Sensitivity analysis
Even after adjusting for covariates and excluding all participants on anti-hypertension medication or with hypertension at baseline, uACR was still significantly associated with incident HF (all $p \leq 0.001$). Even after excluding individuals with CHD before baseline or during follow-up ($p \leq 0.001$), the relationship persisted (Additional file 1: Table S2). Also, there was no significant change in the primary result when waist circumference, but not BMI, was included in the Cox regression model (Additional file 1: Table S3).
## Discussion
Our results confirmed that elevated uACR is an independent risk factor for new-onset HF in patients with T2D, irrespective of sex, renal function strata, SBP strata, HbA1c strata, and the presence of hypertension or CAD. Furthermore, uACR was also associated with the risk of HF in a dose–response manner. Additionally, our results proved that adding uACR to established HF risk models can improve their predictive ability for HF risk.
Our important finding revealed that elevated uACR is a significant risk factor for HF in type 2 diabetic patients; when uACR is mildly elevated (3–30 mg/mmol), the risk of HF increases by 2.21 fold, and when uACR ≥ 30 mg/mmol, the risk of HF increases by 6.02 fold. Previous research [20, 21] has demonstrated that the risk of HF in diabetic patients increased with the increase in urinary albumin excretion at 24 h. Current guidelines recommend measuring uACR in spot urine samples, which has a comparable diagnostic value to the urinary protein quantification at 24 h [22, 23]. To our knowledge, this is the first study to demonstrate that elevated uACR is an independent risk factor for new-onset HF in patients with T2D. Although few previous studies had similar results, the ARIC [11] and SPRINT [24] studies demonstrated a 2.49–2.75 fold and 3.47–4.76 fold higher risk of HF in people with microalbuminuria and macroalbuminuria than in those without albuminuria in the general population, respectively.
We not only verified uACR elevation as an independent risk factor for HF in patients with T2D but also found a dose–response relationship between uACR and HF risk. In type 2 diabetic patients, the risk of HF increased significantly with an increase in uACR even below the clinically defined microalbuminuria threshold (3 mg/mmol), while the HF risk increased relatively slowly when uACR elevated to about 25 mg/mmol. ARIC study [11] also demonstrated that the HF risk increased when uACR was at a high normal value (about 1–3 mg/mmol) in the general population, while the HF risk increased relatively slowly after uACR exceeded about 30 mg/mmol.
Although eGFR and uACR are both sensitive markers for renal function and independent risk factors for HF [25], our results revealed that uACR and eGFR were two interactive factors affecting HF. However, the subgroup analysis by eGFR category showed that increasing uACR increased the risk of HF more significantly as eGFR decreased; the risk of HF increased by 15.74 fold in people with uACR ≥ 30 mg/mmol and eGFR < 60 mL/min/1.73 m2. These risk values were consistent with that of the general population, but they were significantly higher in diabetic patients than in the general population [11, 24]. Additionally, the elevation of uACR had the strongest increasing effect on HF risk in people with a baseline SBP of 140–160 mmHg or HbA1c < $7\%$ (53.01 mmol/mol), which might be because these people received more intensive antihypertensive or hypoglycemic therapies in clinical practice.
In recent years, many epidemiological surveys and clinical studies on HF risk factors have shown that in addition to traditional risk factors such as age, CHD, hypertension, hyperglycemia, various risk factors closely related to the pathogenesis of HF need to be further studied and confirmed. In this study, we added uACR to the WATCH-DM risk score [18] from the ACCORD test and Williams et al. [ 19] HF risk prediction model in diabetic patients. Our results confirmed that the addition of uACR in validated models could improve the prediction efficacy of HF risk in patients with T2D, which was consistent with the findings of Nowak et al. [ 14] using the ARIC HF prediction model in the general population. Our results suggest that uACR can provide a predictive value beyond the traditional risk factors for HF in patients with T2D, so uACR should be monitored regularly in the early stages of diabetes.
The possible mechanisms underlying the high risk of HF in diabetic patients include both macroangiopathy and microangiopathy. Firstly, diabetes mellitus acts as a risk factor for coronary atherosclerosis [26] and can lead to HF through CHD. Secondly, the myocardial damage caused by diabetes mellitus mainly involves small and medium-sized microvessels and plays a vital role in vascular endothelial function, including endothelial proliferation, subendothelial fibrosis; thus, decreasing the reactivity of myocardial small vessels to vasoactive substances and causing coronary small vessel hypoperfusion [27]. We found that the HF risk increased more significantly with increased uACR after excluding patients with baseline CHD and new-onset CHD during follow-up, with a 2.40 (1.64–3.50) fold and 6.61 (4.28–10.20) fold higher risk of HF in people with microalbuminuria and macroalbuminuria than in people with normal uACR. These results indicate that the high risk of HF in diabetic patients cannot be entirely explained by coronary atherosclerosis, and microcirculatory disturbance might be present in their myocardium. Thus, an increase in uACR may reflect cardiac microangiopathy of the myocardium in the absence of coronary artery disease, subsequent pathological left ventricular hypertrophy, and myocardial remodeling [28–30].
Several studies have confirmed a significant clustering between elevated uACR and traditional risk factors for HF, which include insulin resistance [31], inflammatory response [32], and renin-angiotensin- aldosterone system (RAAS) activation [33].
Considering the higher mortality rate in patients with T2D than in the general population, impending death may generate a competing risk, so we established a competing risk model for mortality in the overall population and obtained consistent and reliable results with the main model. However, there are no studies on the effect of uACR on new-onset HF analyzed by a competing risk model for mortality to date.
Our research had some limitations. At first, all HF events were hospitalized and relied on hospital diagnostic coding. This outcome may have excluded HF patients who were never admitted to the hospital. While we had information on HF hospitalizations, there was no echocardiographic data, we could not distinguish HF with preserved ejection fraction from HF with reduced ejection fraction. A previous clinical trial found that proteinuria increases the hospitalization rate of HF patients [9]. Our findings may be useful in the prediction, evaluation, and treatment decision-making of high-risk HF populations with clinically detected proteinuria. Second, the proportion of each type of antihypertensive drug counted in this study was low, to avoid the impact of this deficiency on our results, we adjusted for antihypertensive drugs (yes or no) in the regression model. While, after adjusting for the use of angiotensin-converting enzyme inhibitors (ACEI) and/or angiotensin II receptor blockers (ARB), β-blockers, diuretics, and the exclusion of individuals taking antihypertensive medications, sensitivity analyses produced results that were consistent with our primary analyses. Furthermore, The proportion of participants receiving anti-diabetic treatment was low in this study, which may affect the endpoints of the study, particularly HF. Finally, because the study participants were mostly male Kailuan Group employees, the extrapolation of results may be limited. However, the results in the male and female populations were both consistent with those in the overall population after gender subgrouping.
## Conclusion
This study confirmed the independent predictive value of elevated uACR in T2D patients for an increased risk of HF, which can help to explain the high risk of HF in T2D patients and provide a useful reference for screening high-risk HF populations and assessing HF risk in T2D patients.
## Supplementary Information
Additional file 1: Table S1. Cox proportional-hazards model (death competitive risk model) affecting HF. Table S2. Hazard ratios (HR) and $95\%$ Confidence intervals of uACR for heart failure (sensitivity analysis). Table S3. Hazard ratios (HR) and $95\%$ Confidence intervals of uACR for heart failure (sensitivity analysis).
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|
---
title: Using random-forest multiple imputation to address bias of self-reported anthropometric
measures, hypertension and hypercholesterolemia in the Belgian health interview
survey
authors:
- Ingrid Pelgrims
- Brecht Devleesschauwer
- Stefanie Vandevijvere
- Eva M. De Clercq
- Stijn Vansteelandt
- Vanessa Gorasso
- Johan Van der Heyden
journal: BMC Medical Research Methodology
year: 2023
pmcid: PMC10040120
doi: 10.1186/s12874-023-01892-x
license: CC BY 4.0
---
# Using random-forest multiple imputation to address bias of self-reported anthropometric measures, hypertension and hypercholesterolemia in the Belgian health interview survey
## Abstract
### Background
In many countries, the prevalence of non-communicable diseases risk factors is commonly assessed through self-reported information from health interview surveys. It has been shown, however, that self-reported instead of objective data lead to an underestimation of the prevalence of obesity, hypertension and hypercholesterolemia. This study aimed to assess the agreement between self-reported and measured height, weight, hypertension and hypercholesterolemia and to identify an adequate approach for valid measurement error correction.
### Methods
Nine thousand four hundred thirty-nine participants of the 2018 Belgian health interview survey (BHIS) older than 18 years, of which 1184 participated in the 2018 Belgian health examination survey (BELHES), were included in the analysis. Regression calibration was compared with multiple imputation by chained equations based on parametric and non-parametric techniques.
### Results
This study confirmed the underestimation of risk factor prevalence based on self-reported data. With both regression calibration and multiple imputation, adjusted estimation of these variables in the BHIS allowed to generate national prevalence estimates that were closer to their BELHES clinical counterparts. For overweight, obesity and hypertension, all methods provided smaller standard errors than those obtained with clinical data. However, for hypercholesterolemia, for which the regression model’s accuracy was poor, multiple imputation was the only approach which provided smaller standard errors than those based on clinical data.
### Conclusions
The random-forest multiple imputation proves to be the method of choice to correct the bias related to self-reported data in the BHIS. This method is particularly useful to enable improved secondary analysis of self-reported data by using information included in the BELHES. Whenever feasible, combined information from HIS and objective measurements should be used in risk factor monitoring.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12874-023-01892-x.
## Background
Worldwide, $63\%$ of deaths are caused by non-communicable diseases (NCDs). A high proportion of NCDs are preventable by addressing their main physiological risk factors, such as high blood pressure, obesity and hypercholesterolemia [1]. Accurate data on the prevalence of these risk factors is therefore essential to build evidence-based prevention programs and policies [2]. In many countries, the prevalence of NCDs risk factors is commonly assessed through self-reported information from health interview surveys. It has been shown, however, that relying on self-reported data lead to an underestimation of the prevalence of overweight and obesity [3–6], hypertension [7–10] and hypercholesterolemia [11–16]. Social desirability or lack of knowledge may explain the overall validity problem. In addition to biased prevalence estimates, the measurement error related to self-reported data can also bias the estimated association between exposure and disease [17, 18]. In particular, exposure-disease associations are often attenuated when based on self-reported exposures [19, 20]. Although a large body of literature already exists on methods to obtain more accurate surveillance data by correcting for measurement error related to self-reported data, few epidemiologic studies use them in practice [21, 22].
Promising methods to correct for measurement error are based on validation sampling, whereby an accurate measure is collected for a random subset of units of a large study, which are sampled according to a pre-specified design [20]. Regression calibration is the most commonly applied method to correct for measurement error related to self-reported data [22]. This method uses the information that maps objective clinical values to self-reported values by fitting a regression model for the clinical values [23]. All self-reported values in the regression model of interest are then replaced by the predicted clinical values. This method is popular because of its simplicity, but known to leave residual bias; standard error calculations are moreover complicated by the fact that they must acknowledge uncertainty in the predicted clinical values [24, 25]. For the correction of BMI, in particular, it has been shown that the characteristic pattern of error associated with self-reported BMI is practically impossible to correct by the use of linear regression models [26]. Although regression calibration may be an adequate tool to produce valid prevalence estimates of obesity, hypertension or hypercholesterolemia in a population, this method is limited if researchers are interested in using these adjusted factors as predictor variables for modelling disease. A previous study showed that both self-reported and corrected BMI from regression model resulted in biased estimates of association [27].
Some of these concerns can be overcome via multiple imputation, a well-established method of handling missing data that is also useful in dealing with exposure measurement error, known as MIME (Multiple Imputation for Measurement Error) [23, 25, 28–30]. It fills in the missing data with plausible clinical values, which are randomly drawn from a distribution of predicted values. This process is repeated multiple times, the final analysis is carried out on each filled-in dataset, and the results are pooled using Rubin's rules [31]. Multiple imputation has the advantage to produce unbiased estimates and valid inference for data that are correctly modelled and obey missingness at random (MAR) [32]. In the case of measurement error where a validation study is available, MAR is satisfied because the validation subset is chosen completely at random so that people with versus without error-prone measurements are comparable [18]. Within the multiple imputation by chained equations algorithm (MICE), imputed values for one variable are drawn from a predictive model based on all other variables. The process then cycles through each variable imputation until convergence. A misspecified imputation model may however give biased estimates and invalid inferences. Attention is therefore shifting towards the use of a machine-learning-based imputation technique, the random-forest algorithm [32–35]. Random-forest is an algorithm which combines the output of multiple decision trees to solve classification or regression problems. Besides their ability to handle data with complex interaction or non-linearity, those techniques do not require to specify an imputation model and allow the inclusion of a large number of predictors [25, 34, 36]. Furthermore, It has been demonstrated that, in complex settings, those methods produce more plausible imputations and more reliable inferences than standard regression imputation techniques [34, 37].
In Belgium, the prevalence of physiological risk factors is assessed on a regular basis via self-reported information from the Belgian Health Interview surveys (BHIS 1997–2018) [38]. Additionally, small-scale surveys such as the Food consumption surveys (FSC 2004, 2014) [39], the Belgian Health examination survey (BELHES 2018) [40] provide objective measurements, albeit on a smaller subset of the population. Since the BELHES 2018 was conducted on a sub-sample of the BHIS 2018, this joint dataset provides a unique opportunity to assess the validity of self-reported information on physiological risk factors.
The objective of this study is threefold: 1) to assess the agreement between self-reported and measured information on height, weight, hypertension and hypercholesterolemia in Belgian adults and examine how the use of self-reported data impacts the estimates of the prevalence of those risk factors, 2) to identify an adequate approach for valid measurement error correction by comparing regression calibration with MICE based on parametric and non-parametric techniques and 3) to enrich the BHIS 2018 dataset with imputed clinical values for height, weight hypertension and hypercholesterolemia allowing researchers to improve their analysis of self-reported data in the BHIS 2018.
## Study area, study population and data
The study area is the entire Belgian territory with a population of 11.4 million inhabitants.
The study sample consists of 9439 participants of the BHIS 2018 older than 18 years including a subset of 1184 participants who additionally participated to the BELHES 2018.
The BHIS is a national cross-sectional population survey carried out every five years by Sciensano, the Belgian institute of health, in partnership with Statbel, the Belgian statistical office. Data are collected through a stratified multistage, clustered sampling design (approximately 10,000 participants) and weighting procedures are applied to obtain results which are as representative as possible of the Belgian population. Data are obtained on socio-economic status, physical and mental health, lifestyle and use of health care [38, 40, 41].
In the BELHES, objective health information was collected among a random subsample of the BHIS participants. In the BELHES, objective health information was collected among a subsample of the BHIS participants. A random subsample of eligible BHIS participants (at least 18 years and having participated in the BHIS themselves) was invited to participate in the BELHES. Recruitment from among this subsample continued until a predefined number of participants was reached. Finally, 1184 individuals participated in the BELHES. The BELHES followed as much as possible the methodological guidelines provided in the framework of the European Health Examination Survey initiative [42].
Data were collected at the participant’s home by trained nurses. The BELHES included a short additional questionnaire, a physical examination and the collection of a blood sample. The physical examination consisted of the measurement of height, weight, waist circumference, blood pressure and for people aged 50 years and above a handgrip measurement. Laboratory blood analyses included the measurement of total and HDL serum cholesterol. Details on the data collection are available in the BELHES publication [40].
## Statistical analyses
In a first step, the merged BHIS/BELHES 2018 database was used ($$n = 1184$$) to assess the validity of the self-reported data related to height, weight, overweight, obesity, hypertension and hypercholesterolemia. The definitions of the variables for measured and self-reported data are given in Table 1. The difference in the prevalence of self-reported versus measured risk factors was assessed using the McNemar test for paired data. Confusion matrix and Kappa coefficients were used to assess the agreement between self-reported and measured hypertension, hypercholesterolemia and WHO BMI categories. Bland & Altman plots and Intra Class Correlation coefficients (ICC) were used to assess agreement between self-reported and measured height, weight and BMI [43]. The mean difference was assessed using the paired-t test. In Bland & Altman plots,Table 1Definition of indicators from the Belgian health interview survey (BHIS) and the Belgian health examination survey (BELHES)IndicatorVariable definition in the BHIS(SR data)Variable definition in the BELHES(Measured data)BMISR weight (kg)/(SR height (m)) 2Measured weight (kg)/(Measured height (m)) 2WeightSR weight (kg)Measured weight (kg)HeightSR height (cm)Measured height (cm)OverweightBMI, based on SR data ≥ 25 kg/m2BMI, based on measured data ≥ 25 kg/m2ObesityBMI, based on SR data ≥ 30 kg/m2BMI, based on measured data ≥ 30 kg/m2HypertensionHas answered “Yes” to question “Did you suffer from hypertension in the last 12 months?”Systolic blood pressure ≥ 140 mmHg or diastolic blood pressure > 90 mmHg or medication use for hypertensionHypercholesterolemiaHas answered “Yes” to question “Did you suffer from high cholesterol in the last 12 months?”*Total serum* cholesterol > 190 mg/dlSR Self-reported Horizontal lines are drawn at the mean difference, and at the limits of agreement, defined as the mean difference plus and minus 1.96 times the standard deviation of the differences. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for each risk factor (overweight, obesity, hypertension and hypercholesterolemia). The parameters of accuracy were stratified by age, gender and education level.
In a second step, different methods to correct for measurement error related to the self-reported risk factors were applied to the complete BHIS 2018 dataset ($$n = 9439$$). Prevalence of overweight, obesity, hypertension and hypercholesterolemia were compared using regression calibration, MICE based on parametric and non-parametric techniques.
To correct for measurement error with regression calibration, a regression model was fitted to predict the measured health condition based on the self-reported health condition, age, sex and highest educational level in the household of the participant. Interaction terms between the self-reported health condition and covariates were added in the model when they significantly improved the accuracy of the model at the $5\%$ significance level (Wald test). The sample was separated in a training ($70\%$) and a test dataset ($30\%$) to calculate the predictive power of each regression model, assessed by the means of the R2 for the linear regression model and by the means of the Area Under the Curve (AUC) for the logistic regression models. The self-reported values were then replaced by the predicted values obtained from the linear regression (for height and weight) and logistic regression (for hypertension and hypercholesterolemia) and the corrected BMI value was finally calculated based on the predicted values for height and weight.
To correct for measurement error with multiple imputation, the measurement error related to self-reported data was treated as a missing data problem. This means that all BHIS participants who were not included in the BELHES were considered with missing values for the measured height, weight, hypertension, and hypercholesterolemia. The missing data pattern of the variables of interest of the merged BELHES/BHIS 2018 dataset can be visualized in Additional file 1.
Unlike the regression calibration, the model was used to multiply impute the measured values for the BHIS. A MICE algorithm [25] was used to multiply impute the missing values of the measured height, weight, hypertension and hypercholesterolemia for every BHIS 2018 participant. Two multiple imputation techniques were compared: a parametric approach based on the predictive mean matching method and logistic regressions and a non-parametric random-forest approach. The imputation model included the same variables that were used for the regression calibration: main effects of age, sex, education level and the self-reported health condition [21]. In addition, variables related to the sample design, such as household size and province were also taken into account in the imputation model.
The number of imputations was limited to 10 to create a small number of completed datasets in public-use data for the convenience of analysts. The relative efficiency was computed to assess if an additional number of completed datasets could reduce the SE of the parameters. The relative efficiency above $99\%$ indicated that 10 completed dataset was sufficient. In particular, using infinitely many imputations would only reduce the variance of the estimators by $1\%$. The number of iterations of the MICE algorithm was 100. For the random-forest based imputation, the defined number of trees was set to 100. All missing values of the covariates included in the imputation models were imputed in the same process. The convergence of the algorithm was assessed by plotting the mean and standard deviation of the synthetic values against the iteration number for the imputed BHIS data.
Risk factor prevalence estimates were calculated in each completed dataset and results of the multiple analysis were pooled using the standard Rubin rules [31]. Corrected prevalence estimates were obtained by taking the survey weights relative to the sample design into account. Standard errors of the prevalence estimates were obtained as the square root of the total variance (taking into account the within and between imputation variance and a correction factor for using 10 imputations).
Sensitivity analyses were carried out using a wider set of self-reported variables that could potentially be related to the measured health conditions of interest such as socio-economic, lifestyle and health condition variables (the list of the wide set of variables is available in Additional file 2). Missing data of all variables included in the imputation model were imputed in the same process. Finally, the ability of the imputation model to predict valid national estimates for the previous BHIS waves was assessed by applying the random-forest multiple imputation model to the complete BHIS$\frac{2008}{2013}$/2018 dataset and adding the year in the imputation model.
All statistical analysis were performed by taking into account the survey weights, strata and clusters relative to the sample design. For multiple imputation, the variables used in the weighting procedure (province, number of persons by household, age and sex) were included in the imputation model. All analyses were fit and evaluated using the statistical software R [44], version 4.2.1 (R Development Core Team, 2006) and the “MICE” package [45].
## Data description
Summary statistics of all considered variables are displayed in Additional file 3.
## Height, weight and BMI
There was a high agreement between self-reported and measured data for height and weight (ICC for height: 0.95; $95\%$ CI [0.94;0.95], ICC for weight: 0.96; $95\%$ CI [0.95;0.97]). On average, people tended however to overestimate their height by 1.05 ($95\%$ CI [-0.83;1.29]) cm and underestimate their weight by 1.50 kg ($95\%$ CI [-1.81;-1.20]) (Table 2). This trend was more pronounced for women and older people. While the bias for height was higher among low educated people, the bias for weight was higher among high educated people. Bland–Altman plots illustrating the agreement between self-reported and measured height and weight stratified by age, gender and education level are available in Additional files 4, 5, 6, 7, 8 and 9.Table 2Estimates of the Bland–Altman plots for analysis of agreement between self-reported and measured height, weight and Body Mass Index (BMI) stratified by ageWeighted mean differenceLLOAULOABMI (kg/m2)[$95\%$ CI]Whole population-0.87 [-1;-0.74]-4.31 [-4.48;-4.14]2.63 [2.45;2.80]Age category$\frac{18}{24}$-0.59 [-0.95;-0.23]-3.41[-4.03;-2.79]2.23 [1.61;2.85]$\frac{25}{44}$-0.67 [-0.83;-0.52]-3.74 [-4.0;-3.48]2.40 [2.13;2.66]$\frac{45}{64}$-0.74 [-0.92;-0.55]-4.72 [-5.04;-4.4]3.25 [2.93;3.58]> 65-1.35 [-1.54;-1.16]-4.36 [-4.68;-4.04]1.64 [1.32;1.97]GenderMen-0.61 [-.077;-0.45]-4.24 [-4.50;-3.98]2.94 [2.68;3.02]Women-1.12 [-1.30;-0.94]-4.34 [-4.57;-4.11]2.31 [2.08; 2.34]Education levelNo diploma/ prim-1.27 [-2.11;-0.42]-7.34 [-8.80;-5.86]4.80 [3.34;6.25]Lower secondary-0.93 [-1.27;-0.58]-4.55 [-5.14;-3.95]2.69 [2.10;3.29]Higher secondary-0.87 [-1.09;-0.65]-5 [-5.40;-4.65]3.27 [2.87;3.64]Higher-0.7 [-0.86-;-0.66]-3.31 [-3.48;-3.13]1.78 [1.60;1.95]Height (cm)[$95\%$ CI]Whole population1.05 [0.83;1.29]-4.7 [-4.99;-4.41]6.71 [6.42;7]Age category$\frac{18}{240.38}$ [-0.27;1]-4.75 [-5.88;-3.63]5.51 [4.39;6.64]$\frac{25}{440.35}$ [0.10;0.60]-4.69 [-5.12;-4.25]5.40 [4.97;5.84]$\frac{45}{640.78}$ [0.53;1.03]-4.57 [-5.0;-4.14]6.14 [5.71;6.58]> 652.56 [2.16;2.96]-3.76 [-4.45;-3.03]8.89 [8.20;9.58]GenderMen0.69 [0.43;0.94]-4.37[-4.74;-3.99]5.99 [5.62;6.37]Women1.41 [1.06;1.75]-3.74 [-3.48;-4.0]7.31 [6.88;7.74]Education levelNo diploma/ prim2.32 [0.80;3.83]-8.55 [-11.55;-5.94]13.19 [10.6;15.79]Lower secondary1.35 [1.82;1.88]-4.13 [-5.03;-3.23]6.84 [5.93;7.75]Higher secondary1.30 [0.99;2.96]-4.49 [-5.01;-3.97]7.10 [6.57;7.63]Higher0.64 [0.43;0.82]-4.01 [-4.36;-3.72]5.32 [4.99;5.64]Weight (kg)[$95\%$ CI]Whole population-1.50 [-1.81;-1.20]-9.75 [-10.2;-10.3]6.89 [6.47;7.31]Age category$\frac{18}{24}$-1.42 [-2.46;-0.37]-9.64 [-11.44;-7.8]6.80 [5;8.60]$\frac{25}{44}$-1.62 [-2;-1.23]-9.20 [-9.54;-8.54]5.95 [5.30;6.61]$\frac{45}{64}$-1.33 [-1.83;-0.85]-11.4 [-12.2;-10.6]8.77 [7.96;9.59]> 65-1.76 [-1.71;-1.01]-6.87 [-7.47;-6.27]4.15 [3.15;4.74]GenderMen-1.26 [-1.72;-0.81]-11.1 [-11.9;-10.4]8.66 [7.93;9.38]Women-1.73 [-2.14;-1.32]-8.20 [-8.65;-7.74]4.95 [4.45;5.41]Education levelNo diploma/ prim-0.89 [-2.32;0.53]-11.13 [-13.59;-8.68]9.34 [6.88;11.79]Lower secondary-1.26 [-2.04;-0.48]-9.44 [-10.74;-8.06]6.88 [5.53;8.22]Higher secondary-1.29 [-1.87;-1.72]-12.1 [-13.15;-11.16]9.55 [8.57;10.54]Higher-1.62 [-1.88;-1.37]-7.91 [-8.35;-7.48]4.66 [4.23;5.1]LLOA Lower limits of agreement, ULOA Upper limits of agreement (mean difference ± 2 standard deviations) The agreement between self-reported and measured BMI was slightly lower than for height and weight (ICC: 0.92; $95\%$ CI [0.86;0.95]). Figure 1 illustrates the agreement between self-reported and measured BMI separately for men and women. The mean bias for the whole population was close to zero (-0.84 kg/m2) indicating a very good agreement at the population level. Fig. 1Bland–Altman plot for analysis of agreement between self-reported and measured Body Mass Index (BMI), for the whole population and by gender. A Whole study population, B: Men, C: Women. The solid line represents the mean difference. The dashed lines represent the upper and lower limits of agreement (mean difference ± 2 standard deviations) The lower limit of agreement (LLOA) and upper limit of agreement (ULOA) revealed however a wider variability at the individual level (Table 2). The plots in Fig. 1 show that people with overweight (> 25 kg/m2) were more likely to underestimate their BMI. The stratified analysis indicated a more pronounced misreporting bias among women, older and low educated people (Table 2). Bland–Altman plots for BMI stratified by age and education level are available in Additional files 10 and 11.
The agreement between self-reported and measured BMI categories was high with $82\%$ of the participants correctly classified (Kappa: $73\%$). The prevalence of obesity (BMI > 30 kg/m2) was however significantly underestimated when based on self-reported body weight and height (Table 3). Using self-reported BMI allowed us to detect only $78\%$ of BHIS participants with overweight and $69\%$ of BHIS participants with obesity (Table 3).Table 3Prevalence estimates of overweight, obesity, hypertension and hypercholesterolemia using self-reported data (BHIS 2018) and measured data (BHES 2018). Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) using self-reported dataOverweight (%)Obesity (%)TotalMenWomenTotalMenWomenPrevalence BELHES [$95\%$ CI]34 [31-38]39 [34-45]29 [25-34]22 [19-25]20 [16-24] 22 [18-27]Prevalence BHIS [$95\%$IC]34 [30-37]39 [34-44]29 [24-33]15 [13-18]14 [11-18] 16 [13-21]Sensitivity788073696965Specificity888689999999PPV788075949593NPV888685929291Hypertension (%)Hypercholesterolemia (%)TotalMenWomenTotal MenWomenPrevalence BELHES [$95\%$ CI]33 [29-36]33 [28-38]33 [28-38]47 [43- 51]46 [40-51]48 [43-53]Prevalence BHIS [$95\%$IC]16 [13-19]15 [12-19]17 [13-21]21 [18- 24]18 [14-2]22 [17-27]Sensitivity454151224118Specificity999798837887PPV908891564566NPV797483525451*CI Confidence interval. Significant underestimation of the prevalence estimates for obesity, hypertension and hypercholesterolemia in the BHIS ($P \leq 0.001$) By contrast, the high specificity rates indicates that self-reported BMI is a reliable indicator to rule out the existence of overweight and obesity. Prevalence estimates stratified by age and education level are available in Additional files 12 and 13.
## Hypertension and hypercholesterolemia
There was a moderate agreement between self-reported and measured hypertension, with a Kappa coefficient of 0.49 (Fig. 2). The agreement was slightly worse for men (Kappa: 0.43) than for women (Kappa: 0.56). The stratified analysis by age category and education level did not show any specific trend (Additional files 14 and 15). Using self-reported data, the prevalence of hypertension was significantly underestimated and only $45\%$ of the BHIS participants with a measured hypertension were detected (Table 3). The high specificity rates indicate however that self-reported hypertension is a reliable indicator to rule out the existence of hypertension. Fig. 2Confusion matrix comparing self-reported and measured high blood pressure and hypercholesterolemia, for the whole population and by gender. A and D: Whole study population, B and E: Man, C and F: Women For hypercholesterolemia, there was a very poor agreement between self-reported and measured data, with a Kappa coefficient of $5\%$ (Fig. 2). The agreement was slightly worse for men, older people and high educated people (Additional files 16 and 17). Using self-reported data allowed to detect only $22\%$ of the BHIS participants with a measured hypercholesterolemia. By contrast, the high specificity rates indicate that self-reported hypercholesterolemia is a reliable indicator to rule out the existence of hypercholesterolemia.
## Regression calibration
With regression models based on the self-reported health condition, age, sex and education level, the measured height and weight could be predicted with relatively good accuracy (R2: $93\%$ for height, R2: $95\%$ for weight). The accuracy of the model for hypertension was relatively moderate (AUC: $86\%$) and poor for the hypercholesterolemia model (AUC: $65\%$). Using predicted values instead of self-reported data yielded higher estimates of people suffering from overweight (+ $8\%$ relative increase), obesity (+ $12\%$), hypertension (+ $24\%$) and hypercholesterolemia (+ $36\%$). Forest plots of the estimates of the regression models for height, weight, hypertension and hypercholesterolemia are available in Additional file 18.
## Multiple imputation for measurement error (mime)
The missing data pattern of the variables (age, sex, education level and self-reported risk factors) of the merged BELHES/BHIS 2018 dataset is visualized in Additional file 1.
In Fig. 3, the prevalence estimates of overweight, obesity, hypertension and hypercholesterolemia were compared in the different datasets BHES 2018 and BHIS 2018 adjusted with the three correction methods. The convergence of the classic and random-forest multiple imputation is visualized in Additional file 19.Fig. 3Prevalence estimates of overweight, obesity, hypertension and hypercholesterolemia in Belgium using self-reported, measured and adjusted 2018 BHIS data. Classical MI: classical multiple imputation. RF MI: random-forest multiple imputation. Regression calibration and multiple imputation model included age, sex, education level and the self-reported health conditions. Error bars represent one standard deviation of uncertainty of the estimates Prevalence estimates based on the predicted values (regression calibration) and the multiply imputed clinical values (random-forest and classic multiple imputation) were closer to their BELHES clinical counterparts than were the BHIS estimates based on self-reported data. Furthermore, for overweight, obesity and hypertension, prevalence estimates based on the adjusted datasets (using regression calibration and multiple imputation) had smaller estimated standard errors than those based solely on the BELHES clinical data (Table 4). By contrast, for hypercholesterolemia, which had a poor model accuracy, regression calibration was less effective than multiple imputation (with standard errors larger than the one obtained in the BELHES measured data).Table 4Ratio of estimated standard errors: BELHES 2018 clinical data/adjusted BHIS 2018 dataRegression calibrationClassic multiple imputationRandom-forest multiple imputationOverweight1.772.102.10Obesity2.131.861.93Hypertension1.102.072.10Hypercholesterolemia0.321.711.73BHIS Belgian health interview survey, BELHES Belgian Health examination survey By looking at the distribution of BMI in the different adjusted datasets, it appears that the distribution of the imputed BMI (random-forest and classic imputation) is the best approximate of the distribution of the measured BMI (Fig. 4).Fig. 4BMI distribution using self-reported, measured and adjusted 2018 BHIS data. For the imputed BMI, only the first imputed dataset was represented for more visibility
## Sensitivity analyses
Sensitivity analyses included a wider set of variables in the imputation model such as income, smoking status, handicap or chronic disease (variables listed in Additional file 2). Prevalence estimates and standard errors obtained from the multiply imputed datasets using the wider set of variable were similar to the one obtained using the small set of variables. In addition, the random-forest multiple imputation model using the small set of variables was applied to the merged BHIS data from 2008, 2013 and 2018 ($$n = 27$$,536). The imputation model provided valid prevalence rates for the previous BHIS waves 2008 and 2013, assuming that the trend in the prevalence estimates remained approximately the same across the last ten years (Additional file 20). It is also interesting to note that the imputation-based analysis provided valid results for hypercholesterolemia including for the year 2008, for which the self-reported data on hypercholesterolemia was not available. Finally, applying the imputation model to the larger dataset including the three BHIS waves resulted in even smaller standard errors (Additional file 21).
## Main findings
Consistent with previous literature, this study showed an underestimation of the prevalence of obesity, hypertension and hypercholesterolemia based on self-reported data [3, 5–7, 10, 13, 14, 16, 46–50]. The observed under-reporting for weight and over-reporting for height, resulting in a underestimation of the BMI, is a general trend observed in many studies although the degree of the trend varies for men and women and the characteristics of the population being examined [6].
The self-reported prevalence of obesity was six percentage points lower for both men and women but did not show an underestimation of the prevalence of overweight, confirming a higher misreporting bias among people with obesity. In a literature review, Maukonen et al. reported similar results with an underestimation of obesity ranging from $0.7\%$ points to $13.4\%$ points [3] and highly heterogeneous results regarding overweight prevalence [3, 51]. The higher self-reporting bias observed in specific subgroups such as women, older people and participants with obesity has been observed in several previous studies [3, 52–56]. Social desirability, leading people to report values that are closed to their ideal, could partially explain this observation [57]. The over-reporting of height in older people may be due to the fact that people have their height measured over a decade ago but became shorter with age.
The weak validity of self-reported hypertension observed in our study is in line with several study results showing that approximately half of patients with hypertension would not be identified by self-reporting in epidemiological studies [7–10, 13, 16]. In the same trend, our study shows that the prevalence of elevated total serum cholesterol based on measurements was much higher ($47\%$) than the self-reported prevalence of hypercholesterolemia ($21\%$). Using self-reported data only, $78\%$ of the population suffering from hypercholesterolemia was missed compared with data from objective measurements. Similar results were obtained in previous studies [13, 42]. Specificity, by contrast, provided accurate results for all risk factors (> $99\%$ for obesity and hypertension and $83\%$ for hypercholesterolemia), which means that self-reported measures can be considered reliable to rule out the existence of the risk factors in the population.
The inaccuracy of self-reported hypertension and hypercholesterolemia could be explained by the fact that they are usually asymptomatic and remain therefore easily undetected. This might also be related to clinicians using different threshold values to identify and communicate the risk factor. For hypertension for example, it is worth noting that some medical staff may continue to use the old diagnosis criterion (which changed in 1999 from $\frac{160}{95}$ to $\frac{140}{90}$ mmHg) and thereby wrongly classify patients with hypertension as not having the disease. Regarding hypercholesterolemia, people may only report it if their physician told them they had a high cholesterol risk factor, which is in fact the ratio total cholesterol/HDL cholesterol. Even if an elevated total cholesterol (defined as total cholesterol > 190 mmol/l) is a WHO recommended indicator to monitor NCDs [58], it does not represent in itself a risk factor.
The frequency of physician visits, educational level, urban living and access to healthcare have been identified as factors associated with the accuracy of self-reporting hypertension and hypercholesterolemia [7]. The underestimation of self-reported hypertension and hypercholesterolemia demonstrates that screening for those cardiovascular risk factors needs to be strengthened and population awareness of early detection of those risk factors to be increased.
With both regression calibration and multiple imputation, adjusted estimation of height, weight, hypertension and hypercholesterolemia in the BHIS 2018 allowed to generate national prevalence rates that were closer to their BELHES clinical counterparts. For overweight, obesity and hypertension, all methods provided smaller standard errors than those obtained with clinical data alone. However, for hypercholesterolemia, for which the regression model’s accuracy was poor, MIME has better performance than regression calibration. This result should however be taken with caution because the random-forest MIME might potentially give underestimated standard errors as a result of ignoring the uncertainty in the imputation models fitted via random-forest [34]. One theoretical reason for expecting MIME to perform better than regression calibration is because MIME uses the measured risk factor when it is available, rather than imputing it, whereas regression calibration always predicts the measured risk factor from the self-reported risk factor [23]; another reason is that imputations obtained by MIME resemble real data better by acknowledging that real data vary around the (predicted) mean.
Sensitivity analyses demonstrated that the imputation model based on a small set of variables (self-reported health condition, age, sex and education) was largely sufficient to correct the measurement error in the BHIS data. Since the self-reported measure is such a strong predictor, additional variables in the imputation model had little influence and did not improve the efficiency of the imputations. Furthermore, by applying the imputation model to the complete BHIS dataset from 2008 to 2018, results shows that the method could correctly predict the missing clinical values for the previous BHIS waves 2008 and 2013.
## Regression calibration or multiple imputation?
While regression calibration is quite easy to implement, this method should however be used with caution because of its inherent limitations. First, this is so because predictive equations to correct for self-reporting bias will only work if the percentage of explained variance is very high [25]. Secondly, regression calibration does not take into account the uncertainty in the estimated prediction because the method is based on single predicted values, which may result in potentially biased standard errors [18]. Even if the method may be appropriate to model the population distribution of the risk factor, this method is not recommended if the researcher is interested in using the adjusted risk factor as a predictor variable for modelling disease [27]. Finally, with regard to the bias related to self-reported BMI, studies have shown that predictive equations were unsuitable as correction methods because they had a systematic downward bias [24, 26]. Because the relationship of self-reported BMI to measured BMI is characterized by a “flat slope syndrome” (over reporting of low values and underreporting of high values), the self-reported bias in BMI is highly correlated with measured BMI [26].
Among the different methods explored in this study, the random-forest multiple imputation proved to be preferred to correct the self-reported bias in the BHIS. Although this method is only applicable if a validation study is available (where a “true” exposure is measured in a subsample) [59], its offers numerous advantages. Unlike the regression calibration, the random-forest multiple imputation has the advantage to explicitly account for the uncertainty in the predicted clinical measurement and, hence, produce more reliable statistical inferences [25]. Additionally, MIME allows to easily handle the missing data problem of all covariates in the same process, which increases the statistical power when assessing the risk factor disease association in survey data. Finally, the random-forest MIME brings two additional benefits compared to classic multiple imputation. Unlike the standard imputation approach, the random forest-based imputation handles data with complex interactions or non-linearity and does not assume normality or require specification of parametric models. Secondly, because of this additional complexity, the random forest-based imputation does not suffer from the “congeniality” problem that it must obey the form of the final analysis model. This assumption required by the standard imputation approach may not be met if the goal is to allow researchers to use these imputations in subsequent analyses. In view of this, the random-forest MIME was the chosen method to impute 10 clinical values of the risk factors of interest for all BHIS participants from 2008 to 2018. While researchers are aware that measurement error related to self-reported data could affect the results of their studies, very few adjusted their analysis for the error. Furthermore, they often do not provide a complete discussion of the potential effects of measurement error on their results. By providing 10 imputed clinical values for height, weight, BMI, hypertension and hypercholesterolemia in the BHIS $\frac{2008}{2013}$/2018 we aimed to enable secondary analysts to improve their analysis of self-reported BHIS data by using information included in the BELHES. However, caution is needed when using the imputed clinical values. Those imputed values may be used to model an exposure-disease association or to provide prevalence estimates using the Rubin ‘s rule. They should not be used in combination with risk estimates based on unadjusted self-reported data only. For example, to calculate a population attributable fraction (PAF), the risk estimate should not be taken from the literature but rather computed from the adjusted BHIS data. The PAF is used to estimate the burden of a risk factor and is based on the risk estimate of the risk factor and the prevalence of the disease in the population. Calculating a PAF using a risk estimate based on self-reported data and a prevalence of the risk factor based on corrected data would be the same as comparing apples and oranges.
## Strengths and limitations
The main added value of this study resides in the novelty of the approach. To our knowledge, this study is the first to consider a random forest-based multiple imputation to correct the measurement error related to self-reported data in health interview surveys. This has been made possible thanks to the validation sample, the BELHES 2018, where data on self-reported medical conditions could be compared with objective measurements for the same individuals. Furthermore, this study is based on a nationwide, large scale population survey, using standardized methods, regarding the sampling, questionnaires and measurement protocols, which makes our results comparable across countries.
The findings of this study must nevertheless be seen in the light of some limitations. The definitions and selected cut-off values for the measured risk factors could be questioned, since according to the reference standards considered, results on the agreement with self-reported data may vary substantially. If WHO categories are widely used to determine obesity, a higher heterogeneity of gold standards was found to diagnose hypertension and hypercholesterolemia across studies. In our study, hypertension was defined as a systolic blood pressure ≥ 140 mmHg or a diastolic blood pressure > 90 mmHg or medication use for hypertension; and hypercholesterolemia as a total cholesterol level > 190 mg/dl (> 5 mmol/l). In other studies, hypertension was sometimes diagnosed using a $\frac{160}{90}$ cut-off and reference ranges for hypercholesterolemia varied from 5 to 6.5 mmol/l. Medication was furthermore not always taken into account in the definition of the risk factor [13]. In our definition of the measured hypercholesterolemia (Table 1), we decide to not include the use of medication because statins are often used as preventive treatment. Regardless of the selected cut-off, the prevalence of specific risk factors in health examination surveys could still be over- or under-estimated because measurements are taken on a single occasion while a medical diagnosis of hypertension or hypercholesterolemia is generally based on several subsequent measurements. In self-reported data, the format of questions may also impact the validity results. In our study, the self-reported health condition relied on the question: “Did you suffer from…in the last 12 months?”, but in some other studies, only diagnosed conditions or conditions that a health professional had ‘told’ about were enquired. The main challenge of a diagnostic method is to obtain a satisfactory balance between high sensitivity and high specificity, yielding a minimum of both false positives and false negatives. Sensitivity estimates related to self-reported obesity, hypertension and hypercholesterolemia are particularly important in health interview surveys, as they ensure identification of the largest number of people at risk of developing NCDs. A second limitation of our study is related to the underrepresentation of low educated people in the validation sample. Because of the second stage recruitment of the BELHES, this underrepresentation, which was already present in the BHIS, was reinforced. Unfortunately, educational level was not taken into account in the survey weights, because this information was not available. Thirdly, the imputed clinical values in the BHIS $\frac{2008}{2013}$/2018 were all based on the available validation sample BELHES 2018, which implies that our analysis assumes self-reporting bias not to change over time. This assumption may however not be met, since the awareness of one’s own condition may have increased due to the common use of digital devices at home for measuring blood pressure and the wider availability of blood glucose measurements in pharmacies. Subsequent BELHES data in the coming years, when available, should therefore be used to update the imputed clinical values in the following BHIS datasets.
Finally, it is important to have in mind that, in epidemiology, measurement error in confounders might be even more challenging than measurement error in exposure. Measurement error in confounders can lead to overestimation of exposure–disease associations whereas measurement error in exposures typically dilutes the associations. Future analysis could therefore be conducted to extend the MIME correction to other important self-reported risk factors or confounders such as smoking or diabetes in the BHIS data.
## Conclusions
Obesity, hypertension and hypercholesterolemia are leading biomedical risk factors of NCDs with surveillance often based on self-reported data. With a general increase in these risk factors rates in Belgium it is of paramount importance to obtain accurate prevalence data to correctly assess the effectiveness of NCD prevention programs. Results of this study confirm that using self-reported data alone leads to a severe underestimation of the prevalence of obesity, hypertension and hypercholesterolemia in Belgium. By exploring different approaches to correct for measurement error, this study shows how information from the BHIS and BELHES 2018 can be combined to provide a valid correction of those risk factors. Both regression calibration and MIME techniques generate accurate national prevalence rates of these risk factors, that could in turn be used by decision makers to allocate resources and set priorities in health. Our results suggest however that the random-forest multiple imputation is the most appropriate choice to correct the measurement error related to self-reported data in health interview surveys. Besides its ability to handle data with complex interaction or non-linearity, the technique has the advantage that it does not require to specify an imputation model which is particularly useful to allow secondary analysts to improve their analysis of self-reported data by using information included in the BELHES. Whenever feasible, combined information from health interview survey and measurements should be used in risk factor monitoring.
## Supplementary Information
Additional file 1. Missing data pattern of the merged Belgian health interview survey/Belgian health examination survey 2018 dataset. Additional file 2. List of variables from the wider set of variables included in the imputation model. Additional file 3. Description of the population. Additional file 4. Bland-Altman plot for analysis of agreement between self-reported and measured height (by gender).Additional file 5. Bland-Altman plot for analysis of agreement between self-reported and measured height (by age category).Additional file 6. Bland-Altman plot for analysis of agreement between self-reported and measured height (by education level).Additional file 7. Bland-Altman plot for analysis of agreement between self-reported and measured weight (by gender).Additional file 8. Bland-Altman plot for analysis of agreement between self-reported and measured weight (by age category). Additional file 9. Bland-Altman plot for analysis of agreement between self-reported and measured weight (by education level).Additional file 10. Bland-Altman plot for analysis of agreement between self-reported and measured BMI (by age category).Additional file 11. Bland-Altman plot for analysis of agreement between self-reported and measured BMI (by education level).Additional file 12. Prevalence of overweight, obesity, hypertension and hypercholesterolemia using self-reported and measured data (by age).Additional file 13. Prevalence of overweight, obesity, hypertension and hypercholesterolemia using self-reported and measured data (by education level).Additional file 14. Confusion matrix comparing self-reported and measured high blood pressure (by age category).Additional file 15. Confusion matrix comparing self-reported and measured high blood pressure (by education level).Additional file 16. Confusion matrix comparing self-reported and measured hypercholesterolemia (by age category).Additional file 17. Confusion matrix comparing self-reported and measured hypercholesterolemia (by education level).Additional file 18. Estimates of the regression models for height, weight, hypertension and hypercholesterolemia. Additional file 19. Mean and standard deviation of the synthetic values plotted against iteration number for the classic and Random-forest multiply imputed 2018 BHIS data. Additional file 20. Prevalence estimates of overweight, obesity, hypertension and hypercholesterolemia in Belgium using self-reported, measured and adjusted BHIS data for 2008, 2013, and 2018. Additional file 21. Ratio of estimated standard errors: BELHES 2018 clinical/adjusted BHIS 2008-2013-2018.
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|
---
title: BaoShenTongLuo formula protects against podocyte injury by regulating AMPK-mediated
mitochondrial biogenesis in diabetic kidney disease
authors:
- Yifan Guo
- Mengdi Wang
- Yufei Liu
- Yanyu Pang
- Lei Tian
- Jingwen Zhao
- Mengchao Liu
- Cun Shen
- Yuan Meng
- Yuefen Wang
- Zhen Cai
- Wenjing Zhao
journal: Chinese Medicine
year: 2023
pmcid: PMC10040124
doi: 10.1186/s13020-023-00738-4
license: CC BY 4.0
---
# BaoShenTongLuo formula protects against podocyte injury by regulating AMPK-mediated mitochondrial biogenesis in diabetic kidney disease
## Abstract
### Background
Mitochondrial dysfunction is considered to be an important contributor in podocyte injury under diabetic conditions. The BaoShenTongLuo (BSTL) formula has been shown to reduce podocyte damage and postpone the progression of diabetic kidney disease (DKD). The potential mechanisms underlying the effects of BSTL, however, have yet to be elucidated. In this study, we aimed to investigate whether the effects of BSTL are related to the regulation of mitochondrial biogenesis via the adenosine monophosphate-activated protein kinase (AMPK) pathway.
### Methods
High-Performance Liquid Chromatography Electrospray Ionization Mass Spectrometer (HPLC–ESI–MS) analysis was performed to investigate the characteristics of pure compounds in BSTL. db/db mice and mouse podocyte clone-5 (MPC5) cells were exposed to high glucose (HG) to induce DKD and podocyte damage. Body weight, random blood glucose, urinary albumin/creatinine ratio (UACR), indicators of renal function and renal histological lesions were measured. Markers of podocyte injury, mitochondrial morphology, mitochondrial deoxyribonucleic acid (mtDNA) content, mitochondrial respiratory chain complexes activities, reactive oxygen species (ROS) production, and mitochondrial membrane potential (MMP) levels were assessed. Protein expressions of AMPK, peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α), transcription factor A (TFAM), mitochondrial fusion protein 2 (MFN2) and dynamin-related protein 1 (DRP1) were also detected. MPC5 cells were transfected with AMPKα small interfering RNA (AMPKα siRNA) to determine the underlying mechanisms of BSTL improvement of mitochondrial function under diabetic conditions.
### Results
In vivo, treatment with BSTL reduced the UACR levels, reversed the histopathological changes in renal tissues, and alleviated the podocyte injury observed in db/db mice. After BSTL treatment, the decreased mtDNA content and mitochondrial respiratory chain complex I, III, and IV activities were significantly improved, and these effects were accompanied by maintenance of the protein expression of p-AMPKαT172, PGC-1α, TFAM and MFN2. The in vitro experiments also showed that BSTL reduced podocyte apoptosis, suppressed excessive cellular ROS production, and reversed the decreased in MMP that were observed under HG conditions. More importantly, the effects of BSTL in enhancing mitochondrial biogenesis and reducing podocyte apoptosis were inhibited in AMPKα siRNA-treated podocytes.
### Conclusion
BSTL plays a crucial role in protecting against podocyte injury by regulating the AMPK-mediated mitochondrial biogenesis in DKD.
## Introduction
DKD is the primary cause of end-stage renal disease (ESRD) worldwide [1]. Podocytes are essential in maintaining the glomerular filtration barrier's integrity and permselectivity [2]. Podocytes have limited capacities for proliferation and regeneration, and changes in these cells result in persistent and irreversible renal injury [3, 4]. Chronic hyperglycaemia triggers both morphological and functional abnormities in podocytes, which result in remodelling of the actin cytoskeleton, decreased protein expression of phenotype markers, hypertrophy, fusion or effacement of foot processes; these phenomena are followed by detachment, loss and apoptosis, thereby leading to proteinuria and subsequent damage [5]. Therefore, it is generally acknowledged that podocyte damage is crucial to the initiation and development of DKD.
Although multiple variables contribute to podocyte damage in DKD, the particular mechanisms are yet unknown. Recent studies have shown that mitochondria are essential for maintaining podocyte homeostasis [6]. Mitochondria, which are ‘power stations’ of cells, provide podocytes with adequate energy to maintain their specific structure and physiological functions [7], and abnormities in mitochondria results in podocyte injury [8]. Mitochondrial biogenesis, which is a complex cellular mechanism of self-protection that is initiated by pathological damage or insufficient energy supply, contributes to inner and outer mitochondrial membrane stability, new protein and lipid synthesis and mtDNA replication under the control of highly regulated transcriptional activities [9]. Recent studies have shown that the induction of mitochondrial biogenesis prevents oxidative damage and apoptosis in podocytes during DKD [10], indicating that mitochondrial biogenesis may play a protective role in podocytes. AMPK, which is a cellular energy sensor and regulator, is one of the most important molecules that positively regulates mitochondrial biogenesis [11]. Previous studies have shown that decreased AMPK activation disrupts mitochondrial biogenesis, eventually leading to impaired renal podocyte function and the development of albuminuria under diabetic conditions [12]. However, metformin and pioglitazone, which are AMPK agonists, ameliorate hyperglycaemia-induced ROS production and prevent renal damage by improving mitochondrial biogenesis [13]. Therefore, the AMPK pathway is a potential target for promoting mitochondrial biogenesis and repairing podocyte injury in DKD.
Increasing numbers of studies have shown that traditional Chinese medicines (TCMs) have been widely utilized to treat patients, and TCMs have been proved to be useful in the treatment of a number of renal diseases [14–19]. BSTL is a TCM prescription that is composed of *Astragalus membranaceus* (Huangqi), *Rehmannia glutinosa* (Dihuang), *Cuscuta chinensis* (Tusizi), Artemisia Anomala (Liujinu), *Euonymus alatus* (Guijianyu), Hirudo (Shuizhi) and Salvia Miltiorrhiza Bunge (Danshen). Previously, we demonstrated that BSTL significantly reduced 24 h urinary protein and serum creatinine (Scr) levels and improved renal function in patients with DKD [20, 21], and we further confirmed that BSTL inhibited podocyte apoptosis by regulating the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) pathway in kk-Ay mice [22]. However, it remains unclear whether BSTL protects against podocyte damage by promoting mitochondrial biogenesis via the AMPK pathway under diabetic conditions. In the present study, we explored the role of BSTL in db/db mice and HG-treated podocytes and further elucidated the underlying molecular mechanisms.
## HPLC–ESI–MS analysis
SCIEX ExionLC AD system (SCIEX, Foster City, CA, USA) equipped with a solvent delivery system, a degasser, an autosampler, a column oven, and a controller was used for HPLC–ESI–MS analysis. After decocting BSTL formula pieces, the liquid was diluted 100 times, filtered, and formed the sample compounds. The separation of the compounds was performed on a Waters ACQUITY UPLC HSS T3 (2.1 × 100 mm, 1.8 μm) at 40 °C, using water (A) and acetonitrile (B) containing $0.05\%$ formic acid as the mobile phase, with a flow-rate of 0.3 ml/min. The substances were ionized in the mass spectrometer's electrospray ionization (ESI) ion source and were detected in the selected ion recording (SIR) mode. The negative and positive ESI mode of mass spectra was acquired using the X500R Q-TOF system with a Twin Spray source (SCIEX, Foster City, CA, USA). The spectra for TOF–MS and TOF–MS/MS analysis covered the m/z ranges of 100-1,500 Da and 50-1,500 Da. SCIEX OS Software™ 2.0 (SCIEX, Foster City, CA, USA) was used to analyze the data.
## Animals and treatment
All animal experiments were carried out in accordance with the protocol authorized by the Ethics Committee of Beijing University of Chinese Medicine (BUCM-4-2020121804-4173). Male db/db mice and wild-type m/m mice (6 weeks of age) were purchased from Cavens Biogle Model Animal Research Co., Ltd. (certificate number: SCXK2016-0010). All mice were housed in Beijing University of Chinese Medicine's pathogen-free animal facility. The mice were housed in an environment with a $\frac{12}{12}$ h light cycle, a humidity level of $60\%$, a temperature range of 22–24 °C, and unrestricted access to food and drink. After 2 weeks of adaptive feeding, the blood glucose levels of the mice were randomly measured, and two consecutive readings over 16.7 mmol/L were considered to indicate the successful establishment of the model; db/db mice that met this criterion were used for further research. Then, db/db mice were randomly divided into the model group (db/db, $$n = 8$$) or BSTL group (db/db + B, $$n = 8$$) using the random number approach, and the m/m mice were placed in the nondiabetic control group (con, $$n = 8$$). The mice in the BSTL group were given BSTL by intragastric administration, and the BSTL extract was provided by the pharmacy of Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, as reported in our previous study [22]. The dosage of the crude BSTL drug that was administered to the mice was 16.5 g/kg/d; this dosage represented the regular dosage given to human adults. The mice in the control and model groups were given the same volume of distilled water. Body weight and random blood glucose levels in tail blood were tested every 2 weeks. In the case of water supplied only, the 8 h urine volume of mice was collected to evaluate the urinary albumin level every 4 weeks. The mice were killed after 12 weeks of treatment. Serum was collected, and kidneys were harvested for further analysis.
## Preparation of drug-containing serum
Forty male Sprague-Dawley (SD) rats (8 weeks of age and weighing 200 ± 30 g) were purchased from Beijing Huafukang Biotechnology Co., Ltd., and after one week of adaptive feeding, all the SD rats were randomly assigned to either the BSTL or blank groups, with 20 rats in each group. The rats in the BSTL group were given BSTL at a dosage of 36.6 g/kg/days and the rats in the blank group were given an equal volume of distilled water for 7 consecutive days. Then, the rats were anaesthetized with $1\%$ pentobarbital sodium at a dose of 40 mg/kg, and 5–8 ml of blood was collected from the abdominal aorta of each rat. Serum samples from the same group were pooled after being centrifuged at 3,000 rpm for 10 min. After incubation 30 min in a 56 °C water bath, the serum was filtered, rebottled into 1.5 ml sterile centrifuge tubes, and stored at − 20 °C.
## Cell culture and treatment
MPC5 cells were donated by Prof. Weijing Liu (Dongzhimen Hospital, Beijing University of Traditional Chinese Medicine, China). Podocytes were cultured at 33 °C in medium that consisted of Roswell Park Memorial Institute (RPMI) 1640 medium ($\frac{11879020}{11875093}$, Gibco, NY, USA) supplemented with $10\%$ foetal bovine serum (10099-141, Gibco), 100 μg/mL streptomycin, 100 U/mL penicillin G (V900929, Sigma, MO, USA), and 100 U/mL recombinant murine interferon (IFN)-γ (315-05-20, PeproTech, NJ, USA) to facilitate proliferation. Then, the podocytes were cultured at 37 °C for 10–14 days in RPMI 1640 medium without IFN-γ to facilitate cell differentiation. The podocytes were used for the in vitro experiment when the confluence was approximately $80\%$. The differentiated cells were stimulated for 48 h with normal glucose (NG, 5.5 mM), HG (HG, 30 mM), and BSTL (H + B, 30 mM glucose + BSTL drug-containing serum). All experimental results were verified in at least three independent podocyte cultures.
## Biochemical indicator measurements
A mouse albumin ELISA kit (ab108792, Abcam, OR, USA) and creatinine assay kit (C011-2-1) were used to measure the urinary albumin and urine creatinine levels, and then, the results were used to calculate the UACR. Scr, blood urea nitrogen (BUN), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) levels were measured with the creatinine assay kit (C011-2-1), urea assay kit (C013-1-1), ALT assay kit (C009-2-1) and AST assay kit (C010-2-1) from Nanjing Jiancheng Biotechnology Co., Ltd. (JiangSu, China) according to the manufacturer’s instructions.
## Renal histological examination
Kidney samples were fixed with $4\%$ paraformaldehyde and incubated at 4 °C for 72 h. The process of dehydration was carried out by a completely automated closed tissue dewatering machine according to standard procedures. Then, the samples were embedded in paraffin and cut into 2–3-μm-thick sections. Haematoxylin and eosin (HE), periodic acid-Schiff (PAS), and Masson staining were performed by the Department of Pathology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University. The slides were scanned and viewed with a Leica (Aperio CS2, Germany). At least ten randomly chosen fields for each mouse were evaluated under the microscope and analysed with Image-Pro Plus 6.0 software.
## Immunohistochemical (IHC) staining
Paraffin-embedded kidney sections were incubated at 60 °C for 60 min, deparaffined with xylene three times for 15 min each, hydrated with gradient ethanol solution for 5 min each, and finally immersed in deionized water. Following a 20 min incubation at 95 °C with an antigen retrieval solution for antigen retrieval, the sections were let to cool naturally to room temperature. The endogenous peroxidase activity was quenched by incubation with $3\%$ H2O2 at room temperature for 10 min. The sections were then blocked with goat serum (ZLI-9056, ZSBIO company, Beijing, China) at 37 °C for 30 min, followed by incubation with anti-nephrin (1:2000, ab216341, Abcam), anti-podocin (1:1000, ab50339, Abcam) and anti-cleaved caspase-3 (1:400, 19677-1-AP, Proteintech, Wuhan, China) antibodies overnight at 4 °C. The sections were then washed in phosphate-buffered saline (PBS), treated for 20 min at room temperature with horseradish peroxidase-conjugated anti-rabbit secondary antibody (PV-9001, ZSBIO business), and colour development was performed by incubating with diaminobenzidine (DAB, ZLI-9018, ZSBIO company). Finally, the nuclei were stained with haematoxylin. At least five randomly chosen fields for each sample were evaluated under the microscope and analysed with Image-Pro Plus 6.0 software.
## Transmission electron microscopy (TEM)
The mouse kidneys were cut into rectangular strips with a volume of approximately 1 mm3, fixed in $2.5\%$ glutaraldehyde solution at 4 °C for 4 h, and washed three times with 0.1 mol/L phosphate buffer for 15 min each. Then the samples were fixed with $1\%$ citrate solution for 2 h, washed three times with 0.1 mol/L phosphate buffer again, and dehydrated in a series of ethanol solutions (15 min each in $30\%$, $50\%$, $70\%$, $90\%$, $95\%$ and $100\%$ ethanol) followed by $100\%$ acetone before hardening with epoxy resin (Epon) for 9 h. The samples were cut into 50–70-nm-thick ultrathin sections by a Reichert-Jung Ultracut S Ultramicrotome (Leica EM UC7, Germany). Then, the sections were stained with $2\%$ uranyl acetate and lead citrate and observed with high-resolution TEM (JEM-1400 Plus, Japan).
## Immunofluorescence assay
Paraffin-embedded kidney sections were deparaffinized and hydrated, and antigens were retrieved as described for IHC staining. Then, the sections were permeabilized by incubation with $0.3\%$ phosphate-buffered solution (PBST) for 10 min, followed by incubation with $3\%$ donkey serum for 30 min at 37 °C. The sections were then incubated with a mixture of rabbit anti-phospho-adenosine monophosphate-activated protein kinase alpha (p-AMPKα, 1:100, AF3423, Affinity, NJ, USA) antibody and mouse anti-synaptopodin (1:200, sc515842, Santa Cruz Biotechnology, CA, USA) antibody overnight at 4 °C. After washing with PBS, the sections were stained with a mixture of Alexa Fluor 488-conjugated donkey anti-mouse IgG (1:2000, A21206, Invitrogen, PA, USA) and Alexa Fluor 594-conjugated donkey anti-rabbit IgG (1:2000, A32754, Invitrogen) as the secondary antibodies at 37 °C for 60 min. The nuclei were counterstained with 4’,6-diamidino-2-phenylindole (DAPI, ZL1-9557, ZSBIO company). In vitro, podocytes were cultured in 24-well plates and treated with different media (NG, HG, HG + B) for 48 h. After fixation with $4\%$ paraformaldehyde, the cells were permeabilized with $0.3\%$ Triton X-100 and blocked with $3\%$ donkey serum. Then, the cells were stained with a rabbit anti-p-AMPKα antibody or mouse anti-translocase of outer mitochondrial membrane 20 homologue antibody (TOM20, 1:200, sc17764, Santa Cruz Biotechnology) overnight at 4 °C. The podocyte slides were stained with a mixture of Alexa Fluor 488-conjguated donkey anti-mouse IgG (1:2000) or Alexa Fluor 594-conjguated donkey anti-rabbit IgG (1:2000) as secondary antibodies at 37 °C for 60 min, and then, the nuclei were counterstained with DAPI. A fluorescence microscope (A1 HAL 100, ZEISS Scope, Germany) was used to observe the slides and capture microscopic images, and Image-Pro Plus 6.0 software was used to analyse and quantify the data.
## Phalloidin staining
Podocytes were cultured, treated, fixed, permeabilized, and blocked as described for the immunofluorescence assay. Then, podocytes were incubated with phalloidin (1:5000, P5282, Sigma) in the dark for 40 min at 37 °C, and the nuclei were counterstained with DAPI. The cells were observed and microscopic images were recorded by fluorescence microscopy (A1 HAL 100, ZEISS Scope, Germany).
## MitoTracker staining
Podocytes were cultured and treated as described for the immunofluorescence assay. Subsequently, the cells were stained with MitoTracker red (1:2000, 8778P, CST, MA, USA) following the manufacturer’s instructions. Fluorescence images were captured with a confocal microscope (DCM-3D, Leica, Germany).
## Terminal Deoxynucleotidyl Transferase-Mediated dUTP-biotin Nick end Labelling (TUNEL) analysis
The DeadEnd™ Colorimetric TUNEL System (G7130/G7160, Promega, WI, USA) was used to identify apoptotic glomerular cells in vivo. Paraffin-embedded kidney sections were deparaffinized and hydrated as described for IHC staining. Then, the sections were treated with proteinase K for 15 min, washed with PBS, and fixed with $4\%$ paraformaldehyde, followed by equilibration for 10 min at room temperature. The sections were incubated with r-terminal deoxynucleotidyl transferase (rTdT) reaction mixture for 1 h at 37 °C and immersed in 2X saline sodium citrate (SSC) for 15 min at room temperature. After washing with PBS, the sections were incubated with $3\%$ hydrogen peroxide for 5 min at room temperature, incubated with streptavidin horseradish peroxidase (HRP) solution and visualized with DAB. Finally, the sections were blocked with $100\%$ glycerin. An in vitro in situ cell death detection kit (11684817910, Roche, BASEL, SWZ) was used to assess podocyte apoptosis following the manufacturer’s protocol. Podocytes were cultured in 6-well plates and treated with different media for 48 h. Then, they were incubated with the TUNEL reaction mixture for 1 h at 37 °C, washed with PBS, and counterstained with DAPI. The apoptotic cells in the kidney sections were observed with a Leica microscope (Aperio CS2, Germany), and the apoptotic podocytes were observed under a fluorescence microscope (CKX41, OLYMPUS). Image-Pro Plus 6.0 software was used to analyse and quantify the data.
## Mitochondrial respiratory chain complex measurement
Kidney tissues were minced, and the activities of mitochondrial respiratory chain complexes I, III, and IV were measured using mitochondrial respiratory chain complex I, III, and IV activity detection kits (BC0515/BC3245/BC0945, Solarbio, Beijing, China) according to the manufacturer’s instructions. Podocytes were cultured in 6-well plates and treated with different media for 48 h. After digestion with trypsin, the podocytes were collected and used for analysis following the instructions.
## Flow cytometry
Podocytes were cultured in 6-well plates and treated with different media for 48 h. The level of podocyte apoptosis was measured via an Annexin V-fluorescein isothiocyanate (FITC) apoptosis detection kit (556547, Becton Dickinson and Company, NY, USA). Briefly, the density of podocytes was adjusted to 1 × 106 cells/mL, and then, 5 μl Annexin V-FITC and 5 μl propidium iodide (PI) were added to 100 μl cell suspensions. After incubation at room temperature for 30 min in the dark, podocytes were centrifuged and resuspended in binding buffer. The MMP level was measured via a JC-1 assay kit (C2006, Beyotime Biotechnology, Shanghai, China). Briefly, 100 μl of cell suspensions were collected and incubated with JC-1 working solution at 37 °C in the dark for 20 min. Then, the podocytes were washed with JC-1 staining buffer (1X) and resuspended in binding buffer. *The* generation of ROS by podocytes was measured using the 2’,7’-dichlorodihydrofluorescein diacetate (DCFH-DA) activity assay kit (red) (MAK145, Sigma), which includes an intracellular ROS fluorescent probe. Briefly, the cell suspension was collected and centrifuged, and then podocytes were resuspended in ROS working solution and incubated at room temperature in the dark for 20 min. A flow cytometer (Calibur II, Becton, Dickinson and Company, USA) was used to analyse the podocytes from the different groups, and FlowJo software was used to analyse the levels of Annexin V-FITC/PI, MMP and ROS.
## AMPKα siRNA transfection
AMPKα siRNA (45313, Santa Cruz Biotechnology) was transfected into cells with the Lipofectamine® RNAiMAX transfection kit (13778150, Invitrogen) according to the manufacturer's protocol. Briefly, podocytes were cultured in 6-well plates for 24 h. RNAiMAX transfection reagent and AMPKα siRNA were added to the reaction mixture. Then, the podocytes were cultured with serum-free medium and reaction mixture for 24 h, and the medium was changed for 6–8 h. After the transfection was completed, the podocytes were treated for 48 h with various mediums. The protein level of AMPKα was measured by western blotting analysis to confirm transfection success. Podocytes were collected 24 h after transfection for the following experiments.
## Western blotting (WB) analysis
The renal cortex tissues and podocytes were lysed with radio immunoprecipitation assay lysis buffer (1:50, C1053, Applygen, Beijing, China) supplemented with protease inhibitors and protein phosphorylase inhibitors (1:100, P1260, Applygen). The renal cortex tissues were cut into pieces, and podocytes were harvested by scraping with a cell scraper; then, the samples were lysed via intermittent ultrasound for 3 min. After centrifugation at 15000 rpm at 4 °C for 15 min, the supernatants were harvested to measure the protein concentration at 562 nm with the bicinchoninic acid (BCA) protein quantification kit (P1511, Applygen). The protein extraction solution was diluted with loading buffer (5X), and the samples were incubated at 95 °C for 15 min, aliquoted, and stored at − 80 °C. WB analysis was performed using a standard protocol. Briefly, markers and samples were added to the designated wells of electrophoresis gels. The proteins were transferred to membranes after electrophoresis and then blocked. The anti-nephrin (1:1000, ab216341, Abcam), anti-podocin (1:1500, ab50339, Abcam), anti-cleaved caspase-3 (1:1000, 19677-1-AP, Proteintech), anti-PGC-1α (1:500, AF5395, Affinity), anti-TFAM (1:2000, ab131607, Abcam), anti-DRP1 (1:1000, ab184247, Abcam), anti-MFN2 (1:1000, 9482S, CST), anti-AMPKα (1:1000, 5831 T, CST), anti-p-AMPKα (1:1000, 2535S, CST) and glyceraldehyde phosphate dehydrogenase (GAPDH, 1:5000, 10494-1-AP, Proteintech) primary antibodies were added to the membranes and incubated at 4 °C overnight. After washing, the membranes were incubated with goat anti-rabbit IgG (1:5000, C1309, Applygen) secondary antibody at room temperature for 1 h, and excess secondary antibodies were removed with western washing buffer. The bands were visualized by enhanced chemiluminescence (ECL) hypersensitive luminescence solution in a dark room, and the densitometry values were measured with ImageJ.
## Quantification of mitochondrial DNA
The relative copy number of mtDNA was determined based on the ratio of mtDNA to nuclear DNA (nDNA) and measured by qPCR assay. Cytochrome b (Cyt B) and cytochrome c oxidase subunit II (COII) were used as controls for mtDNA, and GAPDH was used as a control for nDNA. The primer sequences (synthesized by Bao Biological Engineering Co., LTD, Dalian, China) are listed in Table 1. Total DNA was extracted from renal tissues and podocytes with a universal genomic DNA purification mini spin kit (D0063, Beyotime Biotechnology) according to the manufacturer's instructions. A Talent quantitative real-time PCR (qPCR) kit (RR003Q, Bao Biological Engineering Co., Ltd., Dalian, China) was used to perform the qPCRs (5 min denaturation step at 95 °C, then 40 cycles of 10 s at 95 °C, 30 s at 60 °C and 30 s at 70 °C) using a Fast Real-Time PCR system (Roche, Switzerland). The 2−ΔΔCt method was used to calculate the relative expression. Table 1Primer informationGene symbolPrimersSequence (5′ → 3′)Gene IDProduct Length (bps)COIIForwardACCTGGTGAACTACGACTGCTAGANC_005089.1184 bpReverseCCCTGGTCGGTTTGATGTTACTGTCyt BForwardTTCGCAGTCATAGCCACAGCATTNC_005089.1242 bpReverseTGGAGGAAGAGGAGGTGAACGATTGAPDHForwardGAAGGTGGTGAAGCAGGCATCTNC_000072.7116 bpReverseCGGCATCGAAGGTGGAAGAGTG
## Statistical analysis
Normally distributed data were presented as mean ± standard deviation. One-way analysis of variance (ANOVA) was used for multiple group comparisons, and the least significant difference (LSD) test was used for pairwise comparisons. While skewed distribution data were presented as the median and interquartile range (IQR) and compared using the nonparametric test. Wilcoxon Rank Sum test was used to compare the differences between the two groups. The data came from at least three separate tests. $P \leq 0.05$ was considered statistically significant. Statistical and data analyses were performed with International Business Machines Corporation Statistical Product and Service solutions (IBM SPSS) 26.0 software. The graphical results were analysed using GraphPad Prism 7.0, and composite figures were generated with Adobe Illustrator CC 2018.
## Characteristics of pure compounds in BSTL
HPLC–ESI–MS analysis is a modern, rapid, and sensitive method for drug analysis, which has been widely used in the analysis and identification of TCM components. In our study, BSTL analysis was conducted in both positive and negative ESI modes. As shown in Fig. 1, the positive base peak and negative base peak were displayed in the MS spectrum. There were 48 kinds of substances of BSTL identified by MS. The identified compounds were displayed in Tables 2 and 3, including salvianolic acid, tanshinones, astragalosides, and rehmanniosides. More importantly, many substances such as cryptotanshinone (number 21, Fig. 1A), tanshinone IIA (number 22, Fig. 1A), rosmarinic acid (number 17, Fig. 1B), salvianolic acid A (number 18, Fig. 1B) and apigenin (number 22, Fig. 1B) might present the biological activity of improving mitochondrial dysfunction [23–27]. Therefore, our research furture studied the role and mechanism of BSTL in mitochondrial biogenesis. Fig. 1On chromatograms of BSTL analyzed by HPLC–ESI–MS analysis. A Positive base peak mass spectrometry spectrum of BSTL. B Negative base peak mass spectrometry spectrum of BSTLTable 2Positive base peak chemical components of BSTL identified by HPLC-ESI/MSNoRetention timeFormulaExperimental massIdentification11.54C5H11NO2118.0859Betaine21.56C7H7NO2138.055Trigonelline31.64C7H13NO2144.1019Stachydrine42.33C5H5N5136.0615Adenine52.35C5H4N4O137.04586-Hydroxypurine62.35C6H5NO2124.0390Nicotinic acid72.70C6H13NO2132.1018Isoleucine83.52C10H13N5O3252.1089Cordycepin97.71C16H24NO5310.1645Sinapine107.80C17H20N4O6377.1455Vitamin B2118.11C20H24NO4342.1697Magnoflorine129.43C22H22O10447.1281Calycosin-7-O-glucoside139.59C15H10O7303.0497Quercetin149.59C21H20O12465.1025Hyperin1510.58C16H12O7317.0655Isorhamnetin1610.92C22H22O11463.1239Pratensein-7-O-glucoside1713.15C17H18O5303.1227Isomucronulatol1813.64C16H12O5285.0755Calycosin1922.69C18H14O3279.1013Dihydrotanshinone I2025.37C18H12O3277.0858Tanshinone I2125.44C19H20O3297.1483Cryptotanshinone2228.22C19H18O3295.1326Tanshinone IIATable 3Negative base peak chemical components of BSTL identified by HPLC-ESI/MSNoRetention timeFormulaExperimental massIdentification12.45C9H12N2O6243.0620Uridine23.36C27H42O20685.2193Rehmannioside D34.12C9H10O5197.0455Danshensu45.55C16H18O9353.0875Neochlorogenic acid55.78C11H12N2O2203.0824L-Tryptophan66.79C16H18O9353.0873Chlorogenic acid77.70C17H24O10.HCOOH433.1348Geniposide87.78C22H30O14517.1561Sibiricose A597.79C9H8O4179.0347Caffeic acid109.17C22H18O12473.0728Cichoric acid119.71C21H18O12461.0724Luteolin-7-O-Glucuronide1210.26C20H18O10417.0824Salvianolic acid D1310.67C36H30O16717.1455Salvianolic acid B1410.68C30H38O15637.2138Leucosceptoside A1510.79C21H20O10431.0988Genistin1610.83C21H18O11445.0773Baicalin1711.18C18H16O8359.0771Rosmarinic acid1811.81C26H22O10493.1138Salvianolic acid A1912.10C31H40O15651.2297Rehmannioside2012.71C31H40O15651.2298Epimeredinoside A2113.14C23H28O10463.1612Isomucronulatol-7-O-glucoside2215.05C15H10O5269.0453Apigenin2316.25C41H68O14.HCOOH829.4587Astragaloside IV2416.61C42H66O14793.4386Chikusetsusponin IVa2520.19C45H72O16.HCOOH913.4797Astragaloside I2624.02C48H76O19955.4906Ginsenoside-Ro
## BSTL reduced proteinuria and renal histological damage in db/db mice
As shown in Fig. 2A, db/db mice had higher body weight, random blood glucose levels, and UACR as well as lower BUN levels than the control mice; however, the Scr levels were not significantly different. BSTL treatment caused a significant decrease in the UACR, a mild decrease in body weight, and only a slight recovery of BUN levels, while there were no significant changes in blood glucose and Scr levels, compared with those in the untreated db/db mice at week 12. The levels of ALT and AST were significantly increased in db/db mice, indicating metabolism-related liver damage. However, BSTL slightly restored the levels of ALT and AST in db/db mice. Histological analysis revealed glomerular hypertrophy, thickening of the glomerular basement membrane, mesangial expansion with increased matrix and collagen deposition in glomeruli, particle and vacuole denaturation and lumen dilation in the tubules of db/db mice, and BSTL treatment significantly improved these pathological changes (Fig. 2B, C).Fig. 2Effect of BSTL on biochemical indicators and renal histological changes in db/db mice. A Quantitative assessment of body weight, random blood glucose, UACR, Scr, BUN, ALT and AST of mice. B Representative micrographs of HE-stained kidney sections (× 200), PAS-stained kidney sections (× 400), and Masson’s trichrome-stained kidney sections (× 400) from different groups. Scale bar, 30 µm. The arrows indicate representative pathological changes. C Quantitative assessment of mesangial matrix by PAS staining and collagen deposition in glomeruli. * $P \leq 0.05$ vs. con; **$P \leq 0.01$ vs. con; #$P \leq 0.05$ vs. db/db; ##$P \leq 0.01$ vs. db/db. con, control mice; db/db, db/db mice; db/db + B, db/db mice treated with BSTL
## BSTL protected against podocyte injury in db/db mice and HG-treated podocytes
TEM examination revealed podocyte injury in the glomeruli of db/db mice, which included podocyte foot process broadening and effacement; these phenomena were alleviated by BSTL treatment (Fig. 3A). According to phalloidin staining, HG-treated podocytes exhibited polygonal cellular shapes, which were related to a decrease in actin stress fibre content and accompanied by the formation of cortical F-actin rings in the cytoplasm. However, these structural changes in podocytes were reversed by BSTL treatment (Fig. 4A).Fig. 3Effect of BSTL on podocyte injury in vivo. A Representative TEM micrographs of the kidneys of mice from different groups (× 15 000, × 50 000). Scale bar, 1 µm. B Representative western blots and quantitative assessment of nephrin, podocin, and cleaved caspase-3 in the kidneys of mice. C IHC staining of renal sections and quantitative assessment for nephrin, podocin, and cleaved caspase-3 (× 400). Scale bar, 30 µm. D TUNEL staining and quantitative assessment in renal sections from different groups (× 400). Scale bar, 30 µm. * $P \leq 0.05$ vs. con; **$P \leq 0.01$ vs. con; #$P \leq 0.05$ vs. db/db; ##$P \leq 0.01$ vs. db/db. con, control mice; db/db, db/db mice; db/db + B, db/db mice treated with BSTLFig. 4Effect of BSTL on podocyte injury in vitro. A Representative micrographs of phalloidin stained cytoskeletal microfilaments of podocytes from different groups (× 400). Scale bar, 30 µm. B Representative western blots and quantitative assessment of nephrin, podocin, and cleaved caspase-3 in podocytes from different groups. C TUNEL staining and quantitative assessment in podocytes from different groups (× 200). Scale bar, 30 µm. * $P \leq 0.05$ vs. NG; **$P \leq 0.01$ vs. NG; #$P \leq 0.05$ vs. HG; ##$P \leq 0.01$ vs. HG. NG, normal glucose; HG, high glucose; HG + B, high glucose combined with BSTL drug-containing serum In parallel with the changes in podocyte structure, the expression of the protein components of filtration slits, namely, nephrin and podocin, was significantly reduced in diabetic kidneys, and this effect was reversed by BSTL treatment (Fig. 3B, C). The TUNEL assay showed that the number of apoptotic cells in the glomeruli was significantly increased in db/db mice compared with the control mice; WB and IHC staining revealed higher protein levels of cleaved caspase-3 in the kidneys of db/db mice than those of the control mice. BSTL markedly reduced the apoptotic cell numbers and cleaved caspase-3 expression (Fig. 3B–D). Similar to that in the kidneys of db/db mice, the expression of nephrin and podocin was also reduced in HG-treated podocytes. Consistently, HG significantly exacerbated apoptosis and increased the protein level of cleaved caspase-3 in podocytes, and these effects were nearly completely blocked by BSTL (Fig. 4B, C).
## BSTL improved mitochondrial biogenesis and dysfunction in db/db mice and HG-treated podocytes
As shown in Fig. 5A, TEM revealed mitochondrial damage in the podocytes of db/db mice, as shown by changes in mitochondrial shape, size, and organization of cristae. To determine the mitochondrial biogenesis capacity, we measured the mtDNA content and found a decreased mtDNA to nDNA ratio in the renal cortex tissues of db/db mice compared with the control mice. Subsequently, the activities of mitochondrial respiratory chain complexes I, III, and IV, which include proteins encoded by mtDNA, were significantly decreased in the kidneys of db/db mice. Effectively, BSTL treatment attenuated the decreased relative mtDNA content and rescued the activities of mitochondrial respiratory chain complexes I, III, and IV (Fig. 5B, C). At the molecular level, the protein expression levels of the mitochondrial biogenesis key protein PGC1-α, mtDNA replication/translation key protein TFAM, and mitochondrial fusion protein MFN2 were significantly decreased, while those of the mitochondrial fission protein DRP1 were increased in db/db mice compared with the control mice. BSTL significantly restored the protein expression levels of PGC1-α, TFAM, and MFN2; as for DRP1, BSTL reduced its expression level, however, with no statistically significant difference (Fig. 5D).Fig. 5Effect of BSTL on mitochondrial biogenesis and dysfunction in vivo. A TEM of mitochondria from different groups (× 50 000). Scale bar, 1 µm. Arrow means representative pathological changes. B Quantitation of mtDNA content in the kidneys of mice from different groups. C Quantitation of mitochondrial respiratory chain complexes I, III, and IV activities in the kidneys of mice. D Representative western blots and quantitative assessment of PGC-1α, MFN2, DRP1, and TFAM in the kidneys of mice. * $P \leq 0.05$ vs. con; **$P \leq 0.01$ vs. con; #$P \leq 0.05$ vs. db/db; ##$P \leq 0.01$ vs. db/db. con, the control mice; db/db, db/db mice; db/db + B, db/db mice treated with BSTL TOM20 and MitoTracker staining were used to label mitochondria, and this staining revealed that podocytes had a decreased number of mitochondria under HG conditions; BSTL treatment reversed the HG-induced mitochondrial abnormalities by maintaining mitochondrial quantity in podocytes (Fig. 6A, B). Our in vitro study further confirmed the effect of BSTL in improving mitochondrial biogenesis and podocyte dysfunction. Consistent with the findings of animal experiments, we found that HG significantly reduced mtDNA copy numbers, mitochondrial respiratory chain complexes I, III, and IV activities and PGC1-α, TFAM, and MFN-2 protein levels and increased DRP1 levels in podocytes; all of these effects, except for the increased DRP1 levels, were markedly reversed by BSTL treatment (Fig. 6C–E). Moreover, we found that the MMP was significantly decreased and ROS production was markedly increased in HG-treated podocytes compared with the control cells; however, BSTL restored the MMP and inhibited the excessive ROS production in HG-treated podocytes. These results suggest that BSTL effectively protects the mitochondria by maintaining mitochondrial biogenesis in DKD (Fig. 6F, G).Fig. 6Effect of BSTL on mitochondrial biogenesis and dysfunction in vitro. A Immunofluorescence results of TOM20 in podocytes from different groups (× 400). Scale bar, 30 µm. B Immunofluorescence results of MitoTracker in podocytes from different groups (× 400). Scale bar, 30 µm. C Quantitation of mtDNA content in podocytes from different groups. D Quantitation of mitochondrial respiratory chain complexes I, III, and IV activities in podocytes from different groups. E Representative western blots and quantitative assessment of PGC-1α, MFN2, DRP1, and TFAM in podocytes from different groups. F Representative flow cytometry analysis depicting the detection of MMP in podocytes with different treatments, and quantitative data expressing the overall percentage of cells apoptotic and necrotic (Q2 represented the ratio of red fluorescence, Q3 represented the ratio of green fluorescence, Q2/Q3 represented the MMP). G Representative flow cytometry scatter plots and quantitative assessment of ROS production in podocytes from different groups. * $P \leq 0.05$ vs. NG; **$P \leq 0.01$ vs. NG; #$P \leq 0.05$ vs. HG; ##$P \leq 0.01$ vs. HG. NG, normal glucose; HG, high glucose; HG + B, high glucose combined with BSTL drug-containing serum
## BSTL restored mitochondrial biogenesis in podocytes by regulating the AMPK pathway
To explore the molecular mechanisms by which BSTL induced mitochondrial biogenesis in podocytes, we next assessed the status of AMPKα under diabetic conditions. Our results revealed that the phosphorylation of AMPKα at T172 was inhibited in the kidneys of db/db mice (Fig. 7A). Consistently, the diabetic mice displayed a reduced level of p-AMPKαT172 in the glomeruli compared with the control mice, and this effect was significantly reversed by BSTL treatment (Fig. 7B). To further measure p-AMPKαT172 expression in diabetic podocytes, we detected the expression of synaptopodin and p-AMPKαT172 by using double immunofluorescence staining. As shown in Fig. 7C, db/db mice exhibited less colocalization of p-AMPKαT172 and synaptopodin in the glomeruli than the control mice, indicating that db/db mice had decreased expression of p-AMPKαT172 in podocytes. After BSTL treatment, p-AMPKαT172 expression in podocytes was significantly restored. Fig. 7Effect of BSTL on the expression of AMPK in vivo. A Representative western blots of p-AMPKαT172 and total AMPKα in the kidneys of mice and quantitation of these results. B IHC staining of renal sections for p-AMPKαT172 in different groups, then assessed quantitatively for them (× 400). Scale bar, 30 µm. C Representative images of double immunofluorescent staining of glomerular synaptopodin and p-AMPKαT172 in different groups and quantitation of these results (× 400). Scale bar, 30 µm. * $P \leq 0.05$ vs. con; **$P \leq 0.01$ vs. con; ##$P \leq 0.01$ vs. db/db. con, control mice; db/db, db/db mice; db/db + B, db/db mice treated with BSTL Finally, we determined the role of BSTL-regulated AMPK signalling pathway in promoting mitochondrial biogenesis and ameliorating podocyte toxicity under HG conditions. Our findings showed that the protein level of p-AMPKαT172 was significantly decreased in HG-treated podocytes, while BSTL significantly increased p-AMPKαT172 expression, which was consistent with the immunofluorescence results (Fig. 8A, B). Then, we silenced AMPKα in podocytes by transfection with a specific siRNA that targeted AMPKα, and endogenous AMPKα expression was markedly decreased, as shown in Fig. 8C. The renoprotective effect of BSTL on the protein expression levels of PGC-1α, MFN2, and TFAM was partially abolished in AMPKα siRNA-treated podocytes (Fig. 8D). Additionally, inhibition of AMPKα expression impaired the protective role of BSTL in HG-induced podocyte toxicity, as shown by the percentage of apoptotic cells. The results suggest that the BSTL-induced mitochondrial biogenesis in podocytes under HG conditions depends on the AMPK pathway, and this mechanism alleviates DKD podocyte injury (Fig. 8E).Fig. 8Effects of AMPKα siRNA on mitochondrial biogenesis and podocyte apoptosis in BSTL treated podocytes under HG conditions. A Representative western blots of p-AMPKαT172 and total AMPKα in podocytes and quantitation of these results. B Immunofluorescence staining of podocytes for p-AMPKαT172 in different groups, then assessed quantitatively for them (× 400, × 2000). Scale bar, 30 µm. C Representative western blots images and quantitation of siAMPKα expression in podocytes after the indicated treatments. D Representative western blots and quantitative assessment of PGC-1α, MFN2, and TFAM in podocytes after silenced AMPKα. E Representative flow cytometry analysis depicting the detection of apoptosis in podocytes with different treatments, and quantitative data expressing the overall percentage of podocyte apoptosis (Q2 represented the ratio of late apoptotic cells, Q3 represented the ratio of early apoptotic cells, Q2 + Q3 represented the total ratio of the apoptotic cells). * $P \leq 0.05$ vs. NG; **$P \leq 0.01$ vs. NG; #$P \leq 0.05$ vs. HG; ##$P \leq 0.01$ vs. HG; ▲$P \leq 0.05$ vs. HG + B; ▲▲$P \leq 0.01$ vs. HG + B. NG, normal glucose; HG, high glucose; HG + B, high glucose combined with BSTL drug-containing serum. siAMPKα + B, silenced AMPKα in HG condition and treated with BSTL drug-containing serum
## Discussion
BSTL is a compound formula which contains seven medical botanical drugs, according to the TCM theory, it has the effect of tonifying the kidney, activating blood, and dredging collaterals. Previous studies have identified its effect on DKD. Through the HPLC–ESI–MS analysis, we obtained the main active substances of the BSTL, including cryptotanshinone, rosmarinic acid, salvianolic acid A, tanshinone IIA, apigenin and astragaloside IV, etc. Among all 48 components, more than half of them have regulatory effects on mitochondria. For example, studies have shown that tanshinone IIA [27] and apigenin [23] can protect the mitochondria by inhibiting mitochondrial oxidative stress and improving mitochondrial dysfunction in many diseases. Cryptotanshinone [24], rosmarinic acid [25] and salvianolic acid A [26] also has been reported to be useful in promoting mitochondrial biogenesis by activating AMPK pathway. Moreover, astragaloside IV could protect podocytes from injury via ameliorating mitochondrial dysfunction in diabetic rats, as demonstrated in previous studies [28]. Therefore, we assume that these active compounds may be involved in the podocyte protection effect of BSTL by improving mitochondrial dysfunction in DKD. Podocytes that are exposed to hyperglycaemia often undergo a variety of pathological changes, including hypertrophy, dedifferentiation, foot process effacement, detachment, loss, and apoptosis, thus resulting in proteinuria and glomerulosclerosis, which are representative features of DKD [29–32]. Of note, podocyte damage often occurs in the early stage of DKD [33]. In our study, db/db diabetic mice developed overt proteinuria with major glomerular lesions, but the mice did not exhibit significant changes in the Scr levels; these results are consistent with the early manifestations of DKD. From the results, it is clear that BSTL significantly reduced the UACR and attenuated the renal histological lesions in db/db mice. Consistently, we demonstrated that BSTL prevented podocyte damage in both db/db diabetic mice and HG-treated podocytes. BSTL significantly improved podocyte foot process fusion and effacement in db/db mice and inhibited actin cytoskeleton rearrangement in HG-induced podocytes. Moreover, BSTL restored the protein expression of nephrin and podocin, which are key proteins of the glomerular filtration barrier, and decreased podocyte apoptosis in both in vivo and in vitro studies.
Podocytes are rich in mitochondria, which are double-membraned organelles with abundant cristae that provide sites for cellular respiration and adenosine triphosphate (ATP) production via oxidative phosphorylation (OXPHOS) [34]. Under diabetic or hyperglycaemic states, excessive glucose enters the tricarboxylic acid cycle, resulting in a greater number of protons entering the mitochondrial electron transport chain [35]; however, damaged respiratory chain complexes cause abnormally high proton leakage across the inner mitochondrial membrane to produce excessive ROS [36], which are primarily produced by redox-active components that are involved in the mitochondrial respiratory chain [37, 38]. Moreover, increased uncoupling of the respiratory chain resulting from a decreased MMP also leads to diminished ATP synthesis by mitochondria under diabetic conditions [39]. Previous studies described that mice with diabetes mellitus and podocytes treated with HG exhibited aberrant mitochondrial structure and function, such as fragmented morphology, decreased number of mitochondria, decreased MMP and excessive ROS production, which further damaged the podocytes [40]. Similarly, our study revealed mitochondrial abnormalities, including a decrease in mitochondrial number, abnormalities in mitochondrial morphology, and impaired activity of mitochondrial respiratory chain complexes I, III, and IV, in db/db mice and HG-treated podocytes. We also observed decreased MMP, which is another marker of apoptosis, and excessive ROS production in HG-treated podocytes. However, BSTL effectively improved these mitochondrial abnormalities, increased the activities of mitochondrial respiratory chain complexes I, III, and IV, restored the MMP, and inhibited excessive ROS production. These findings indicated that BSTL prevents the progression of DKD by positively regulating the mitochondrial function of podocytes.
Increasing numbers of publications have reported that impaired mitochondrial functions are involved in various diseases, such as diabetes, Parkinson's disease, and nonalcoholic fatty liver disease [41–44]. Mitochondrial biogenesis is the process by which new mitochondria are formed in tissues and cells, and this process is activated by different signals in response to internal and external environmental stimuli [45]. PGC-1α, which is an upstream transcriptional regulator of mitochondrial biogenesis [41], regulates TFAM to promote mitochondrial biogenesis, thus increasing mtDNA replication and transcription and ultimately leading to an increased number of mitochondria and enhanced OXPHOS function [46]. TFAM is also a major regulator of mtDNA copy number, and TFAM deficiency has been linked to lower production of mtDNA-encoded proteins and lower OXPHOS capability [47]. Studies have shown that chronic hyperglycaemia reduces the expression levels of PGC-1α and TFAM [48], while activation of PGC1-α and increased expression of TFAM protect against HG-mediated podocyte injury by promoting mitochondrial biogenesis [49]. Additionally, the normal process of mitochondrial biogenesis is inseparable from the dynamic balance of mitochondrial fusion and fission [50]. Mitochondria meet the metabolic and energy needs of cells by constantly changing and remodelling their shape [51]. When tissues and cells are stimulated by internal and external factors, DRP1 is transported from the cytoplasm to the outer mitochondrial membrane and assembled into a circular polyplex structure. Then, a guanosine triphosphatase (GTP)-dependent mechanism activates the downstream mitochondrial fission factors to drive mitochondrial breakage [52], and MFN2 is embedded in the outer mitochondrial membrane to regulate mitochondrial fusion [53]. Increasing evidence suggests that mitochondrial biosynthesis is reduced in HG environments [54], and HG increases the expression of mitochondrial fission proteins, while inhibits the expression of mitochondrial fusion proteins in renal tissues [55]. Our previous study found that mitochondrial autophagy impairment in podocytes under HG conditions, and BSTL treatment promotes mitochondrial autophagy to alleviate podocyte injury. In this study, we found that the decrease in the mtDNA content was accompanied by a reduction in the protein expression of PGC-1α and TFAM in db/db mice and HG-treated podocytes, while BSTL increased the mtDNA copy numbers and the expression of mitochondrial biogenesis-related proteins. Our study also showed increased DRP1 protein expression and decreased MFN2 protein expression in db/db mice and HG-treated podocytes. However, BSTL promoted MFN2 protein expression but had no significant effect on DRP1 levels. These results suggest that BSTL attenuated podocyte mitochondrial dysfunction by activating mitochondrial biogenesis.
Mechanistically, AMPK is a key regulator in the maintenance of cellular metabolism, and it is involved in cellular activities such as cell proliferation, cell cycle progression, and apoptosis [56]. Increasing numbers of studies have suggested that some agents, such as pyrroloquinoline, quinine, and nanomitochondria N-tert-butyl-α-phenylnitrone (MitoPBN), as well as natural products, such as catalpol and hydroxytyrosol, mitigate Parkinson's disease and diabetes by improving mitochondrial biogenesis via the AMPK signalling pathway [42, 57–59]. AMPK increases mitochondrial biogenesis by directly regulating PGC-1α expression, thus improving the OXPHOS capacity of tissues and cells [60]. Moreover, AMPK augments glucose metabolism, inhibits oxidative stress, and elevates MMP by regulating MFN2 to improve mitochondrial fusion and mitochondrial biogenesis function during mitochondrial energy crises [61]. Previous studies showed decreased p-AMPK levels in HG-treated podocytes [62]. Our study indicated that BSTL improved p-AMPKα expression in vitro and in vivo. In contrast, the renoprotective effects of BSTL on the protein expression of PGC-1α, MFN-2, and TFAM as well as podocyte apoptosis were partially abolished in AMPKα siRNA-treated podocytes. These results indicate that BSTL may protect against DKD podocyte injury by promoting mitochondrial biogenesis through the regulation of the AMPK signalling pathway.
However, our study has some limitations. Firstly, we did not perform AMPKα knockdown experiments in vivo. Secondly, mitochondrial oxygen consumption rates and ATP contents were not measured in our study. Thirdly, our results confirm the effect of BSTL on AMPK, but not enough to determine whether it is a direct or indirect target. In addition, the extraction and isolation of monomers of TCM compounds that protect mitochondria may provide a further base for the prevention and treatment of podocyte injury in DKD.
## Conclusions
We revealed that BSTL alleviated podocyte injury and repaired mitochondrial damage in db/db mice and HG-treated podocytes and that promoting mitochondrial biogenesis through AMPK regulation was one of the important potential underlying mechanisms. On the one hand, BSTL directly increased mitochondrial biosynthesis by activating AMPK; on the other hand, BSTL promoted mitochondrial biogenesis by regulating the mitochondrial fusion process. In summary, our study provides a feasible clinical application for the prevention and treatment of DKD and identifies a potential mechanism underlying this effective therapy. Our study has important significance for future research and treatment of DKD.
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|
---
title: CPT1A in AgRP neurons is required for sex-dependent regulation of feeding and
thirst
authors:
- Sebastián Zagmutt
- Paula Mera
- Ismael González-García
- Kevin Ibeas
- María del Mar Romero
- Arnaud Obri
- Beatriz Martin
- Anna Esteve-Codina
- M. Carmen Soler-Vázquez
- Marianela Bastias-Pérez
- Laia Cañes
- Elisabeth Augé
- Carme Pelegri
- Jordi Vilaplana
- Xavier Ariza
- Jordi García
- José Martinez-González
- Núria Casals
- Miguel López
- Richard Palmiter
- Elisenda Sanz
- Albert Quintana
- Laura Herrero
- Dolors Serra
journal: Biology of Sex Differences
year: 2023
pmcid: PMC10040140
doi: 10.1186/s13293-023-00498-8
license: CC BY 4.0
---
# CPT1A in AgRP neurons is required for sex-dependent regulation of feeding and thirst
## Abstract
### Background
Fatty acid metabolism in the hypothalamus has an important role in food intake, but its specific role in AgRP neurons is poorly understood. Here, we examined whether carnitinea palmitoyltransferase 1A (CPT1A), a key enzyme in mitochondrial fatty acid oxidation, affects energy balance.
### Methods
To obtain Cpt1aKO mice and their control littermates, Cpt1a(flox/flox) mice were crossed with tamoxifen-inducible AgRPCreERT2 mice. Food intake and body weight were analyzed weekly in both males and females. At 12 weeks of age, metabolic flexibility was determined by ghrelin-induced food intake and fasting–refeeding satiety tests. Energy expenditure was analyzed by calorimetric system and thermogenic activity of brown adipose tissue. To study fluid balance the analysis of urine and water intake volumes; osmolality of urine and plasma; as well as serum levels of angiotensin and components of RAAS (renin–angiotensin–aldosterone system) were measured. At the central level, changes in AgRP neurons were determined by: [1] analyzing specific AgRP gene expression in RiboTag–Cpt1aKO mice obtained by crossing Cpt1aKO mice with RiboTag mice; [2] measuring presynaptic terminal formation in the AgRP neurons with the injection of the AAV1-EF1a-DIO-synaptophysin-GFP in the arcuate nucleus of the hypothalamus; [3] analyzing AgRP neuronal viability and spine formations by the injection AAV9-EF1a-DIO-mCherry in the arcuate nucleus of the hypothalamus; [4] analyzing in situ the specific AgRP mitochondria in the ZsGreen-Cpt1aKO obtained by breeding ZsGreen mice with Cpt1aKO mice. Two-way ANOVA analyses were performed to determine the contributions of the effect of lack of CPT1A in AgRP neurons in the sex.
### Results
Changes in food intake were just seen in male Cpt1aKO mice while only female Cpt1aKO mice increased energy expenditure. The lack of Cpt1a in the AgRP neurons enhanced brown adipose tissue activity, mainly in females, and induced a substantial reduction in fat deposits and body weight. Strikingly, both male and female Cpt1aKO mice showed polydipsia and polyuria, with more reduced serum vasopressin levels in females and without osmolality alterations, indicating a direct involvement of Cpt1a in AgRP neurons in fluid balance. AgRP neurons from Cpt1aKO mice showed a sex-dependent gene expression pattern, reduced mitochondria and decreased presynaptic innervation to the paraventricular nucleus, without neuronal viability alterations.
### Conclusions
Our results highlight that fatty acid metabolism and CPT1A in AgRP neurons show marked sex differences and play a relevant role in the neuronal processes necessary for the maintenance of whole-body fluid and energy balance.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13293-023-00498-8.
The online version contains supplementary material available at 10.1186/s13293-023-00498-8.
## Highlights
Fatty acid metabolism and CPT1A in AgRP neurons show marked sex-dependent differences in the control of feeding. Cpt1a gene deletion in AgRP neurons increases energy expenditure in females but not in males. CPT1A in AgRP neurons is involved in the control of thirst and fluid homeostasis. Cpt1a gene deletion in AgRP neurons induces morphological, mitochondrial, and gene expression alterations in a sex-dependent manner.
## Background
Food intake and whole-body energy balance are regulated by the brain through a sophisticated neuronal network located mostly in the hypothalamus. In particular, the hypothalamic arcuate nucleus (ARC) is a fundamental sensor of the hormones and nutrients that indicate the energy status of the organism. The ARC contains two populations of neurons with opposite functions: anorexigenic proopiomelanocortin (POMC)-expressing neurons and orexigenic agouti-related protein (AgRP)-expressing neurons hypothalamus [1–4]. AgRP neurons promote a potent orexigenic response by opposing the actions of POMC neurons, in part through the release of the AgRP neuropeptide, a competitive inhibitor of melanocortin receptors [5, 6]. It also has an important effect on energy expenditure (EE) via modifying the activity of the sympathetic nervous system (SNS) [3]. The outflow onto peripheral tissues leads to coordination of overall body metabolism, including adipose tissues, liver and other tissues that respond to AgRP neuronal activity. For that reason, the action of AgRP neurons on energy balance should be studied beyond the acute control of food intake [7]. Although our understanding of the AgRP neuronal mechanism controlling feeding behavior has expanded immeasurably, few studies have explored sex-dependent regulatory mechanisms, especially considering the sex differences in calorie intake and body weight [8–10] and new studies are necessary to reveal sex differences in neuronal mechanisms that control energy balance.
In addition to food consumption, another physiological need essential for survival is thirst. Several brain areas involved in the control of fluid balance and thirst have been already elucidated [11–19], including the lamina terminalis, comprising the subfornical organ (SFO), organum vasculosum of the lamina terminalis (OVLT), and the median preoptic nucleus (MnPO). Neurons from these nuclei project to paraventricular nucleus of hypothalamus (PVH) where the neurons that produce vasopressin (VP, also known anti-diuretic hormone, ADH) are located [20]. The involvement of AgRP neurons in thirst behavior is poorly understood. It is known that AgRP activation induces water intake in the presence of food, but this activation is not sufficient to promote water intake independent of food consumption [21], suggesting that food and drink behavior are tightly coordinated.
Lipid metabolism has emerged as a key regulator of energy balance [22–24]. Although the oxidation of fatty acids (FAs) has been explored little in neurons, FAs undoubtedly have an important role through their structural role in biological membranes, their participation as a messenger in neuronal signaling pathways and their involvement in the energy supply [25, 26]. Here, we explore some key aspects of FA utilization in the control of energy balance and raise new questions on whether FAs could be also a signal for hypothalamic neurons. It is well documented that most enzymes in FA metabolic pathways are expressed in the hypothalamus, including AMP-activated protein kinase (AMPK), acetyl-CoA carboxylase (ACC) carnitine palmitoyltransferase 1 (CPT1), fatty acid synthase (FAS), and malonyl-CoA decarboxylase (MCD) [27, 28]. Of note, fasting stimulates hypothalamic AMPK activity and inhibits ACC and FAS activities, whereas re-feeding induces the opposite [26, 29–31]. Moreover, the pharmacological and genetic manipulation of some of these genes/proteins has a profound impact on food intake and whole-body energy homeostasis [32–34].
Here, we focused on CPT1A, a key regulatory enzyme localized in the outer mitochondrial membrane that is involved in the uptake of long-chain fatty acids (LCFAs) into the mitochondrial matrix for entry into the β-oxidation spiral [35]. Although this isoform is expressed mainly in the liver, there is evidence that its expression in the brain could play an important role in peripheral metabolism. Previous results from our laboratory showed that the long-term expression of a permanently activated CPT1A isoform in the hypothalamus triggers hyperphagia, leading to an overweight state [36]. Consistent with these results, the intracerebroventricular administration of 3-carboxy-4-alkyl-2-methylenebutyrolactone (C75), a synthetic inhibitor of fatty acid synthase (FAS) and CPT1A activity, has been reported to reduce body weight and food intake by altering the melanocortin system [37–39].
Most of the studies in this field lack cell type-specificity and it is still unclear whether lipid metabolism specifically regulates the physiological features modulated by AgRP neurons. To answer this question, we developed an adult transgenic mouse model lacking CPT1A in their AgRP neurons. Here, we report that AgRP neurons require CPT1A to regulate some aspects of energy balance, since female and male Cpt1aKO mice showed reduced body weight gain compared with their control littermates. Notably, this regulation varied depending on the sex. We also provide evidence that the metabolic changes induced by CPT1A deficiency in AgRP neurons modify the fluid balance. Our results suggest that CPT1A in AgRP neurons affect food intake and the peripheral energy balance in a sex-dependent manner, highlighting this enzyme as a possible target for therapeutic strategies that aim to decrease body weight and fight obesity.
## Mice
Mice were kept in the Unitat d’Experimentació Animal facilities of the Universitat de Barcelona under standard laboratory conditions with free access to standard chow diet (Harlan Ibérca, ref. 2014) and water at 22 ± 2 °C and $60\%$ humidity in a 12-h light/dark cycle. Mice were housed in groups to prevent isolation stress unless otherwise stated. Mice animal welfare was regularly monitored. Experiments were performed with 8- to 25-week-old male and female mice.
To evaluate the role of FA metabolism in AgRP neurons, we generated the time-dependent conditional Cpt1aKO mouse model. Cpt1a(flox/flox) mice [40] and tamoxifen-inducible AgRPCreERT2 mice [41] were bred to generate (Cpt1a(flox/flox); AgrpCreERT2) mice and their control littermates (Cpt1a(+/+); AgRPCreERT2), which were called Cpt1aKO and control mice, respectively. All animals were on the C57BL/6J background. To evaluate AgRP neuron-specific RNA expression, we took advantage of the RiboTag method [42]. We bred the RiboTag mouse line with Cpt1aKO mice to generate RiboTag;Cpt1a(flox/flox); AgRPCreERT2 (RiboTag-Cpt1aKO) mice and their RiboTag;Cpt1a(+/+); AgRPCre ERT2 control littermates. These transgenic mice expressed, in an inducible manner, epitope-tagged ribosomal protein L22-hemagglutinin (RPL22-HA) in only the AgRP neurons.
To study mitochondrial morphology specifically in the AgRP neurons, we generated a Rosa26-flox-stop-MLS-ZsGreen mice (ZsGreen mice). A Cre-dependent mitochondrial-ZsGreen-expressing mice that allows targeting of ZsGreen to the mitochondrial matrix. The mitochondrial translocation sequence (MLS) from the *Ndufs4* gene was fused in frame at the N-terminus of ZsGreen, which was then inserted 3′ of a floxed Pgk-neomycin resistance (Neo) gene (for positive selection) in a transfer plasmid. The MLS-ZsGreen-floxed Pgk-Neo region was excised and inserted into a targeting vector that has the CMV-chicken β-Actin promoter (CBAp) inserted at the transcription start site of the Gt(Rosa26)*Sor* gene with 7.7 kb of 5′ flanking and 4.1 kb of 3′ flanking Gt(Rosa26)Sor sequence and a Pgk-DTa gene for negative selection. This construct was linearized and electroporated into G4 embryonic stem (ES) cells. *Correct* gene targeting was determined by Southern blot of DNA digested with Nde1 using a probe that lies outside of the targeting vector; 24 of 84 clones analyzed were correctly targeted. ES cells from one correctly targeted clone were injected into blastocysts of C57BL/6 recipients and chimeric pups were bred with C57BL/6 mice. The action of Cre recombinase deletes Pgk-Neo and allows expression of ZsGreen that is targeted to the mitochondria. We bred ZsGreen mice with Cpt1aKO mice to generate ZsGreen; Cpt1a(flox/flox); AgRPCre ERT2 (ZsGreen-Cpt1aKO) mice and their ZsGreen-Cpt1a(+/+); AgRPCreERT2 control littermates.
To induce Cre-ERT2 expression and avoid the possible toxic effect of tamoxifen, adult mice (8 weeks old) were intraperitoneally (i.p.) injected with five doses of tamoxifen (150 mg/kg of body weight; Sigma, MO, USA) solved in corn oil. The first two injections were combined with 24 h of food deprivation (separated by 48 h) to enhance Agrp promoter activity. The last three injections were administered daily with ad libitum access to food and water. Control mice were also injected with tamoxifen solved in corn oil. All studies used age-matched littermates, randomly assigned to experimental groups.
Mice were genotyped by polymerase chain reaction (PCR) using the primer listed in Additional file 7: Table S1. Genomic DNA was extracted from ear samples using the QuickExtract™ DNA Extraction solution, following the manufacturer’s instructions. Each polymerase chain reaction (PCR) was conducted in a 20-µl final volume containing 10 µl of REDExtracti-N-Amp PCR ReadyMix (Sigma-Aldrich, ref. R4775), which includes all the reagents needed for PCR amplification. For the Cpt1a flox gene, the Cpt1a HomArm forward and reverse primers were used (Additional file 7: Table S1). The DNA fragments obtained were 990 bp for the WT Cpt1a amplified allele, 1030 bp for the Cpt1a flox allele and 219 bp for the recombined genomic DNA. For the AgRPCreERT2 gene, the AgRP CRE-ERT2 forward, reverse and control primers were used (Additional file 7: Table S1). The obtained amplicon was 514 bp from the WT mice and 323 bp for the AgRPCreERT2 allele. For the RiboTag gene, the RiboTag loxP forward and reverse primers were used (Additional file 7: Table S1). The obtained amplicon was 260 bp from the WT mice and 290 bp for the RiboTag allele. For the ZsGreen gene, the ZsGreen forward and reverse primers were used (Additional file 7: Table S1). The amplicon obtained was 600 bp from the WT mice and 500 bp for the ZsGreen allele.
## Food intake and body weight analysis
All mice had ad libitum food supply and after the recombination the food was monitored weekly during the first month. The initial amount of compound pelleted fodder was weighed at the beginning of every week, using the same precision scale. At the following week, remaining pellets were measured again with the same scale, and food was added to give the same amount as in the previous week. The body weight was monitored weekly until killing using every week the same precision scale.
## Fasting–refeeding satiety test
This test was assessed in 12-week-old mice. Mice were housed in individual cages 2 days before the beginning of the experiment. Mice were fasted overnight (ON) for 12 h and then re-fed a pre-weighed meal. Food intake was measured at 30 min, 1 h, 2 h, 3 h and 4 h after refeeding. All measurements were weighed using the same precision scale [43].
## Ghrelin-induced food intake test
Mice at 12 weeks old were housed in individual cages two days before the beginning of the experiment. Mice were deprived of food for 2 h after the dark period and i.p. injected with ghrelin (0.4 μg/g of body weight; Merck Millipore, Cat# 494127-100G) in physiological saline solution (B. Braun; Cat# 12260029_1019) at 0 min and again at 30 min. We monitored the eating time and food intake for 1 h after the first injection [44].
## Glucose and insulin tolerance tests
For the glucose tolerance test (GTT), mice at 14 weeks old were fasted ON. The baseline blood glucose concentration from a tail snip was measured using a hand glucometer (Bayer, Contour XT, Cat# 83415194) and test strips (Bayer, Contour next, Cat# 84191389). A blood sample was collected in a capillary tube (Deltalab, Cat#7301) and kept in serum separator tubes (Sarstedt, Cat# 201.280) at 4 °C to measure the insulin level. Samples were allowed to clot on ice for 15 min and then centrifuged for 15 min at 5700 rpm. The supernatant was collected and frozen at − 20 °C. Mice were injected i.p. with $20\%$ glucose (Baxter, Cat# 2B0124P) at 1.5 mg/g of body weight. Blood glucose concentrations were measured at 0, 15, 30, 60, 90 and 120 min after the glucose injection. For the insulin tolerance test (ITT), mice were fasted for 6 h. Mice were i.p. injected with 1.5 U of insulin/kg of body weight. Blood glucose concentrations were then measured at 0, 15, 30, 60, 90 and 120 min after the insulin injection.
## Indirect calorimetry
Mice at 12 weeks old were analyzed for EE, respiratory quotient (RQ) (VCO2/VO2) and locomotor activity (LA) using a calorimetric system (LabMaster; TSE Systems; Bad Homburg, Germany). Briefly, mice were placed in a temperature-controlled (24 °C) cage with flowing air for 48 h for acclimation before starting the measurements. After calibrating the system with the reference gases ($20.9\%$ O2, $0.05\%$ CO2 and $79.05\%$ N2), the metabolic rate was measured for 3 days. After that, we extended the experiment for 12 h under fasting conditions and then 4 h under refeeding conditions. O2 consumption and CO2 production were recorded every 30 min to indirectly determine the RQ. EE, RQ, food intake and LA were measured during the dark and light phases. The LA was assessed using a multidimensional infrared light beam system with the parameters defined by the LabMaster system.
## Non-invasive measurement of blood pressure
Systolic, diastolic and mean blood pressures were measured in conscious mice using the tail-cuff plethysmography method (CODA® tail-cuff blood pressure system; Kent Scientific Corporation; Torrington, CT, USA). 12-week-old mice were trained for tail-cuff measurements over a period of 4 days. Blood pressure measurements were performed at the same time (between 9 a.m. and 12 a.m.) to avoid the influence of the circadian cycle. Blood pressure values were taken from at least ten consecutive measurements [45].
Heat production was visualized using a high-resolution infrared camera (FLIR T420; FLIR Systems, AB, Sweden). Infrared thermography images were taken from the upper half of the body to specifically analyze BAT activity. On the day before the experiment, mice were fasted ON and shaved in the interscapular area to minimize interference. Interscapular BAT temperature was analyzed within a fixed area (region of interest; ROI) using the Flir Tools software (version 4.1). For each image, the average temperature of the skin area was calculated as the average of 3 images/animal.
## Blood and urine collection
Mice were individually placed in metabolic cages that had a floor area of 370 cm2 and a length of 207 cm, a width of 267 cm and a height of 140 cm. The cages were adapted with a grid at the bottom to collect urine. 13-week-old mice were housed one day before the start of the experiment, with ad libitum access to food and water. On the day of the study, drinking water and urine volumes were measured for 24 h and urine specimens were taken for osmolality analysis. At the end of the experiment, blood samples were collected in heparinized tubes through the facial vein method [46]. Plasma was collected and frozen at − 20 °C until analysis of the plasma osmolality. At the end of the study, all mice were rehoused in their original cages. One week later, the mice were placed back in the metabolic cages to perform the same protocol under water restriction for 24 h. Urine volume was measured, and blood and urine osmolality was also analyzed. Water intake and urine volumes are expressed as values per gram of body weight.
## Stereotaxic procedure
Mice at 8 week old were anesthetized using 0.1 mg/g of ketamine (Richter Pharma Ag., Cat# 580393.7) and 0.01 mg/g of xylazine (Bayer, Cat#580393.7) before being placed in a stereotaxic apparatus (Kopf instrument, Model 900 Small Animal Stereotactic Instrument). Once the head was shaved with an electric trimmer, a sagittal incision was made through the skin along the midline of the head and a hole was drilled into the skull 1.5 mm posterior and 0.3 mm lateral (left and right) to the bregma (bregma point was found in the perpendicular intersection between the sagittal and coronal synarthroses). A 400 nl of solution containing either AAV9-EF1a-DIO-mCherry or AAV1-EF1a-DIO-synaptophysin-GFP were injected 50 nl/min using a Hamilton Neuros syringe (5 µl, Neuros Model 75 RN, point style 3, SYR, Cat# 65460-02) and a microinjection pump over 8 min into the ARC. The coordinates for the ARC were − 1.5 mm posterior, ± 0.3 mm lateral and − 5.8 mm ventral to the bregma [47]. Once the procedure was completed, a tissue adhesive (3 M Vetbond™, Cat# 1469Sb) was used to close the incision. Mice were caged with ad libitum access to food and post-surgical drug-supplemented water containing $10\%$ enrofloxacin (Bayer, Cat# 572126.2) and 0.3 g/400 ml of buprenorphine (Indivior, Cat# 679588) as the antibiotic and analgesic, respectively. Daily monitoring was used to follow up on the general state of the operated mice for one week. Three weeks after the adeno-associated viruses (AAVs) injection, mice were treated with tamoxifen to induce the recombination.
## Tissue collection
To analyze gene expression, blood metabolites, protein levels and the histological morphology of different tissues, mice were fasted ON and anesthetized with $4\%$ isoflurane (Piramal Healthcare, Cat# 60307-120-25) before being maintained at a surgical plane of anesthesia by the continuous inhalation of $2\%$ isoflurane using a calibrated anesthetic delivery machine (Combi-Vet® Rothacher Medical, Switzerland). Blood was rapidly collected in heparinized tubes (Fibrilin, Cat# 0318) from the descending aorta using a 25-gauge needle (BD Microlance™ 3, Cat# 300600). It was allowed to clot on ice for 15 min and then centrifuged for 15 min at 5700 rpm at 4 °C. The supernatant was collected and frozen at − 20 °C. Samples of liver, inguinal white adipose tissue (iWAT), gonadal white adipose tissue (gWAT), BAT, adrenal gland (AG), pancreas, testis, ovary, kidney, hypothalamus, cortex and hippocampus were collected and stored immediately at − 80 °C until processing. A piece of the tissues was fixed in formalin solution, neutral buffered, $10\%$ (Sigma, Cat# HT501128-4L) for 24 h and then transferred to 1X phosphate buffered saline (PBS) (Sigma, Cat# D1408-500ML) for histological analysis.
To obtain ARC samples, the brain was gently placed in a mouse brain matrix (Agnthos, Cat# 69-2175-1), which can be used to obtain coronal sections with a width starting from 1 mm. A 3-mm coronal section encompassing most of the ARC was obtained, taking as a reference the optical chiasm to establish them for dissection. Once the section was cut, the remaining brain was carefully removed from the matrix and the section was extracted and horizontally positioned. The area was dissected with a crosswise cut starting from the 3rd ventricle up to the base of the hypothalamus. The tissue was immediately stored at − 80 °C until processing.
To perform immunofluorescence assays, whole animal perfusion fixation was applied. Briefly, animals were anesthetized with an i.p. injection of ketamine (Richter Pharma Ag., Cat# 580393.7) and xylazine (Bayer, Cat#580393.7). The delivery dose was 0.1 mg/g of ketamine and 0.01 mg/g of xylazine. Once the animal reached the surgical plane of anesthesia, a 25-gauge blunt perfusion needle connected to a perfusion pump (Gilson, miniplus 3) was inserted into the left ventricle. An incision into the right atrium was performed to create as large an outlet as possible. 75 ml of cold 1X PBS were perfused to remove red blood cells. The PBS was then replaced with 50 ml of $4\%$ paraformaldehyde (PFA), pH 7.4 (Sigma-Aldrich, Cat# 158127). Once the perfusion was complete, the brain was extracted from the skull and fixed in $4\%$ PFA for 4 h at 4 °C. Brains were kept in $30\%$ sucrose (Panreach Applichem, Cat#131621) until they sank. Then, they were frozen in dry ice using pre-cooled 2-methylbutane (Merck, Cat# 277258) and stored at − 80 °C.
To isolate the mRNA from AgRP neurons in vivo, RiboTag-Cpt1aKO and control mice were fasted ON and killed by cervical dislocation. The brain was removed from the skull and gently placed in the mouse brain matrix. A 3-mm coronal section containing the ARC was obtained. The section was extracted and horizontally positioned, and the ARC was extracted with punches from the base of the hypothalamus. The tissue was immediately stored at − 80 °C until processing.
## Purification of genomic DNA
Genomic DNA from the different tissues was extracted using the proteinase K method. Briefly, 2.5 µl of proteinase K (0.0001 ng/µl in the reaction) (Thermo Fisher Scientific, Cat# AM2546) were used and the tissue was incubated in 500 µl of lysis buffer (12.2 g of Tris, 1.9 g of EDTA, 2 g of SDS and 11.7 g of NaCl made up to a 1-L solution, pH 8.5) at 55 °C for 4 h. Once digested, 10 µl of RNase A (10 mg/ml) (Sigma-Aldrich, Cat# 10109142001) were added to each tube and incubated at 37 °C for 1 h for RNA degradation. DNA was extracted with 700 µl of phenol:chloroform:isoamyl alcohol (25:24:1) (Merck, Cat# P3803-100ML) and precipitated by 700 µl of 2-propanol (Sigma-Aldrich, Cat# 190764-1L) and 15 µl of 5 M NaCl (Sigma-Aldrich, Cat# S7653-5 KG). The DNA pellet was washed by ethanol ($70\%$) (Panreac, Cat# 361086.1611). The genomic DNA pellet was resuspended in 100 µl of a 10-mM Tris–EDTA solution and stored at 4 °C at least ON before any further processing. The genomic DNA yield was quantified using a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific, ref. ND-1000).
## Total RNA extraction, cDNA synthesis and qRT-PCR
Depending on the sample, RNA was extracted using the Trizol reagent (Sigma-Aldrich, Cat# T9424) or with a specific kit. For fatty tissues or tissues with a high lipid content, RNA was extracted using the RNeasy Lipid Tissue Mini Kit (QIAGEN, #74804), following the manufacturer's instructions. For the other tissues, the Trizol reagent was used according to the manufacturer’s protocol. RNA samples were heated to 55–60 °C for 10–15 min using a thermoblock (JP Selecta EN, Temblock, Cat# 7462200), quantified using a NanoDrop ND-1000 spectrophotometer and stored at − 80 °C until processing. RNA was reverse transcribed into complementary DNA (cDNA) using TaqMan® Reverse Transcription reagents (Thermo Fisher Scientific, Cat# N808-0234), following the manufacturer’s instructions. The cDNA obtained was diluted in RNase-free water up to a concentration of 10 ng/µl.
Quantitative real-time polymerase chain reaction (qRT-PCR) was performed using the Power SYBR Green PCR Master Mix adapted for the LightCycler 480 system (Roche, Cat# 4887352001), according to the manufacturer’s indication for the LightCycler 480 instrument II (Roche, Cat# 05015243001). mRNA levels from liver tissues were normalized against the β-actin level, while those from BAT and WAT were normalized against the hypoxanthine–guanine phosphoribosyl-transferase (Hprt) or β-actin level. Finally, for the cDNA extracted from AgRP neurons, the housekeeping gene used was glyceraldehyde-3-phosphate dehydrogenase (Gapdh). All the forward and reverse primers used are described in Additional file 7: Table S1.
## Protein extracts and western blot analysis
Tissues were disrupted by adding 1 ml or 500 µl of a protein extraction buffer to 30–70 mg of non-fat tissues or 50–100 mg of fat tissues, respectively. The protein extraction buffer contained 30 mM HEPES, pH 7.4, 150 mM NaCl, $10\%$ glycerol, $1\%$ Triton X-100, $0.5\%$ sodium deoxycholate (DOC), a Mini Protease Inhibitor Tablet (Roche, Cat# 11836153001) and a PhosSTOP Phosphatase Inhibitor Tablet (Roche, Cat# 04906837001). To disrupt the tissue, the TissueLyser LT was used for 3 min at 50 Hz. Protein concentration of the samples were quantified using the Pierce™ BCA Protein Assay Kit (Thermo Fisher Scientific, Cat# 23227), following the manufacturer’s instructions. Protein lysates were separated in SDS-PAGE and transferred on to 0.45-µm nitrocellulose membranes (Bio-Rad Laboratories, Cat# 1620115), which were incubated ON with primary antibodies (Additional file 7: Table S2). Images were acquired with an ImageQuant LAS 4000 mini developer system (GE Healthcare Life Sciences). The images obtained were processed with Fiji ImageJ1.33 software (NIH; Bethesda, MD, USA) to quantify the average optical density of all the immunoreactive bands.
## Histopathology
Fixed tissues were dehydrated and paraffin-embedded. The resulting blocks were cut into 4-µm sections and stained with hematoxylin and eosin (H&E) to assess histology. Photomicrographs were obtained using a microscope camera (Leica MC 190 HD Camera) and microscope (Leica DM IL LED Tissue Culture Microscope). The images shown are representative of 10 biological replicates per condition.
## Assessment of urine and serum osmolality
Urine osmolarity (Uosm) and serum osmolality (Posm) were measured with 20-µl samples using the freezing-point depression technique involving an osmometer (3320 Micro-Osmometer, Advanced Instruments). A control (Clinitrol 290) and a set of calibration standards (50, 850 and 2000 mosm/kg H2O) were used before running each batch.
## Plasma and urine analyses
Plasma insulin (Insulin ELISA Kit, Alpco, Cat# 80-INSHU-E01.1), aldosterone (Aldosterone ELISA Kit, LSBio, Cat# LS-F28206-1), VP/ADH (Vasopressin ADH ELISA Kit, LSBio, Cat# LS-F7592-1), renin (Renin ELISA kit, Elabscience, Cat# E-EL-M0061), angiotensin (Angiotensin ELISA Kit, LSBio, Cat# LS-F67331-1), leptin (Leptin ELISA kit, Sigma-Aldrich, Cat# RAB0334), 17-β-estradiol (Estradiol ELISA kit, Cayman, Cat# 501890), testosterone (Testosterone ELISA kit, Arbor Assays, Cat# K080-H1), glucose (Monlab, Cat# MO-165086), TGs and non-esterified fatty acids (NEFAs) (NEFA-HR kit, Wako, Cat#434-91795, 436-91995, 270-77000) levels were all measured according to the manufacturer’s instructions.
## Immunohistochemistry
To determine the tissue distribution of the protein of interest, immunofluorescence for free-floating brain sections was performed. Frozen brains were embedded in OCT (Tissue-Tek, Cat#4583) and 30-μm slices were obtained with a microtome (Leica SM2000R). Tissue slices were stored at 4 °C in a cryoprotectant solution [$30\%$ (w/v) sucrose, $30\%$ (v/v) ethylene glycol and 250 ml of PBS] until the immunostaining was performed. Tissue slices were washed 3 times for 5 min in 1X PBS to remove the cryoprotectant solution. All steps were performed under gentle agitation in a shaker (Thermo-Shaker, PST-100HL). Slices were permeabilized in potassium phosphate buffered saline (KPBS) [$0.9\%$ (p/v) NaCl, 52 mM potassium phosphate dibasic and 9.6 mM potassium dihydrogen phosphate] containing $0.1\%$ Triton X-100, for 10 min and blocked for 1 h with blocking solution (KPBS containing $0.1\%$ (v/v) Triton X-100, $3\%$ (w/v) BSA and $2\%$ (v/v) goat serum (Sigma-Aldrich, Cat# G9023). The slices were incubated with the primary antibody in blocking solution for 1 h at room temperature and ON at 4 °C. They were then washed 3 times for 10 min with KPBS containing $0.1\%$ Triton X-100 before being incubated with the secondary antibody in blocking solution for 2 h at room temperature and protected from the light. Finally, the samples were washed 3 times for 10 min with KPBS containing $0.1\%$ Triton X-100 and mounted onto SuperFrost Plus slides (Thermo Fisher Scientific, Cat# J7800AMNT) with fluoromount-G containing DAPI (Thermo Fisher Scientific, Cat# 00-4959-52) and cover slipped. The primary and secondary antibodies are listed in Additional file 7: Table S2 showing the dilutions used in each incubation. Fos-positive cells and synaptophysin fluorescence in PVN sections were quantified using the Fiji ImageJ 1.33 (NIH; Bethesda, MD, USA), consistent with previous studies [48].
## Dendritic spine analysis
For detailed morphological analyses of dendritic spines, samples were imaged with a Zeiss LSM 800 confocal microscope using a 1003 Plan Apo TIRF DIC-oil immersion objective (total magnification of 63×). To visualize the mCherry protein in AgRP neurons, samples were excited with a 587-nm laser and the fluorophore emission was captured by a 610 band-pass filter. A Z-stack was obtained for each dendrite extending from the apex of the cell soma. For each animal, 20 dendrites (segmented by 50 µm) were analyzed. Z-stacks were used for three-dimensional reconstruction. The dendritic spine density in each dendritic segment was quantified using the Fiji ImageJ 1.33 (NIH; Bethesda, MD, USA) and is expressed as the dendrite number/50 µm.
## Adipocyte area measurement
To quantify the adipocyte area, three representative images from each adipose tissue section were taken with a 20× objective using a high-sensitivity camera (Leica MC 190 HD Camera). Images were analyzed with the Adiposoft software, a fully automated open-source program for the analysis of WAT cellularity in histological sections [49]. Each image was calibrated to 4.65 pixels per micron using a 20× objective and a Leica microscope. Any adipocyte with visible alterations in the membranes was “closed” digitally prior to continuing with the automated quantification. To quantify the adipocyte area, images were analyzed and adipocytes were highlighted if they met the following criteria: [1] the boundaries for sizing of the cell were 40–40,000; [2] the adipocyte had a shape factor of 0.35–1 (a shape factor of 0 indicated a straight line, while a shape factor of 1 indicated a perfect circle); and [3] the adipocyte did not border the image frame. The results are represented as the frequency distribution and the average of total area counted.
## Mitochondrial content
Sections containing the ARC from ZsGreen mice at 12 weeks old fasted ON were scanned with (Multiphoton Microscope Leica TCS SP8 MP). The LAXZ software was used to obtain high-quality images. Images were acquired sequentially, using 405- and 488-nm laser lines, with a 63× oil immersion objective. The confocal pinhole was set at 1 Airy unit. The format was 1024 × 1024 pixel. Specific settings for the frame averaging (6 frames per image) and laser gain (900 for the 488-nm laser and 800 for the 405-nm laser) were selected for all the images to improve quality. For analysis, Z-stacks over the diameter of AgRP neurons were performed with a zoom of 4×. The analysis was performed using the Fiji ImageJ1.33 software (NIH; Bethesda, MD, USA).
## Transcriptomic analysis with the RiboTag technique
Punches containing the ARC from mice at 12 weeks old fasted ON were homogenized in 350 µl of homogenization buffer (50 mM Tris, 100 mM KCl, 12 mM MgCl2, $1\%$ Nonidet P-40, 1 mM DTT, 200 U/ml Promega RNasin, 1 mg/ml heparin, 100 μg/ml cycloheximide, Sigma protease inhibitor mixture at pH 7.5) with a 30G needle. After clearing, 30 μl was separated as input and stored at − 80 °C until further processing. 3 µl of the anti-HA antibody (BioLegend, Cat# MMS-101R) were added to the remaining supernatant and samples were incubated in a cold-room spinner for 2 h. After incubation, 200 μl of Dynabeads protein G magnetic beads (Thermo Fisher Scientific, Cat# 10004D) were added and incubated for 2 h at 4 °C with rotation. Next, beads were washed 3 times for 10 min with gentle rotation at 4 °C in high-salt buffer (50 mM Tris, 300 mM KCl, 12 mM MgCl2, $1\%$ Nonidet P-40, 1 mM DTT, 100 μg/ml cycloheximide at pH 7.5). After the final wash, beads were incubated with 350 µl of the RLT buffer (QIAGEN, Cat# 74034). Total RNA was prepared according to the manufacturer’s instructions using the RNeasy-plus Mini kit (QIAGEN, Cat# 74034). RNA was quantified with the Quant-iT RiboGreen RNA (Thermo Fisher Scientific, Cat# R11490). cDNA was generated using the SuperScript IV reverse transcriptase (Thermo Fisher Scientific, Cat# 11750150) following the manufacturer’s instructions. Briefly, 0.27 ng of RNA from the samples and the input was incubated with 5X SuperScript IV RT buffer, 100 mM DTT, the RNaseOUT Recombinant RNase inhibitor and the SuperScript IV reverse transcriptase (200 U/µl). The samples were incubated at 23 °C for 10 min, 50 °C for 10 min and 80 °C for 10 min. They were then stored at − 20 °C until the qRT-PCR was performed. qRT-PCR was performed with the Taqman probes for Agouti-related peptide (Agrp): Mm00475829_g1; aldehyde dehydrogenase 1 family member L1 (Aldh$\frac{1}{1}$): Mm03048957_m1; Discs large MAGUK scaffold protein 4(Dlg4): Mm00492193_m1; Glutamic acid decarboxylase 1 (Gad1): Mm04207432_g1; Neuropeptide y (Npy): 00445771m1; Proopiomelanocortin (Pomc): Mm00435874_m1; Solute carrier family 32 member 1 (Slc32a1): Mm00494138_m1; Solute carrier family 17 member 6 also called Vesicular glutamate transporter 2 (Slc17a6): Mm 00499876m1 (Thermo Fisher Scientific).
## Quantification and statistical analysis
Statistical significance was determined using the Prism 8.0 software from GraphPad (GraphPad Software, La Jolla, CA, USA). Two-way ANOVA followed by post hoc analysis with the Sidak and Bonferroni test in the multiple comparison analysis were applied when more than two groups were compared. Student’s t-test was performed when two groups were compared. Data are expressed as the mean ± SEM. A p value lower than 0.05 was considered significant. Fiji ImageJ 1.33 (NIH; Bethesda, MD, USA) was used to determine the levels of Fos-positive cells, GFP-syn, mitochondria and dendritic spines as well as for western blot analyses. The numbers of animals used in each experimental setting and analysis are specified in each figure legend.
## CPT1A in AgRP neurons is involved in energy balance sexual-dimorphic phenotype
*We* generated mice with a selective deletion of Cpt1a in their AgRP neurons at adult stage (Cpt1aKO mice). Mice expressing tamoxifen-inducible Cre recombinase (CreERT2) in their AgRP neurons (AgRPCreERT2) [41] were crossed with mice harboring conditional alleles of Cpt1a (Cpt1aflox/flox mice) obtained in our laboratory [40]. Tamoxifen injections were done at 8-week-old mice and they were all studied 4 weeks after the injection. To confirm Cre expression in the AgRP neurons, we injected AAV9-EF1a-DIO-mCherry into the ARC in Cpt1aKO mice and we observed that AgRP neurons showed red fluorescence (Additional file 1: Fig. S1a–c). To corroborate the deletion of Cpt1a exon 4 in the AgRP neurons we isolated genomic DNA from the ARC and several other tissues from both Cpt1aKO and control mice. In order to distinguish the recombined Cpt1a gene lacking exon 4 from WT Cpt1a gene, which is also expressed in other cell types, we firstly digested the genomic DNA from all studied tissues with the restriction enzymes PstI and AatII. The restriction sites in those two enzymes are only present in the WT Cpt1a gene but not in the recombined Cpt1a gene lacking exon 4 (Additional file 1: Fig. S1d, e). Next, we confirmed the Cpt1a recombination by using PCR amplification of the loxP-flanked region containing Cpt1a exon 4. The resulting recombined Cpt1a amplification band was 219 bp and it was only detected in the genomic DNA from ARC. This amplicon was also sequenced and our results confirmed that Cpt1a exon 4 was only removed in the genomic DNA from ARC of Cpt1aKO mice (Additional file 1: Fig. S1g). The deletion of exon 4 has been shown previously by our group to result in a reading frame change and an appearance of a stop codon in exon 5 of the recombined Cpt1a gene [40]. Since AgRP is also expressed by chromaffin cells of the adrenal medulla [50], we analyzed adrenal gland histology, weight, Tyrosine hydroxylase (Th) mRNA levels (indicative of adrenal gland integrity). No changes in histology, adrenal gland weight and Th mRNA levels were observed in both female and male Cpt1aKO mice with respect to their control littermates (Additional file 2: Fig. S2a–e).
When we assessed the study of the phenotype we observed that female and male Cpt1aKO mice gained less weight than their control littermates when fed a normal chow diet ad libitum (Fig. 1a, b and Additional file 3: Fig. S3a). However, only the males showed a reduction in food intake (Fig. 1c, d and Additional file 1: Fig. S3b, c). This was confirmed by the food intake analysis during the dark phase (Fig. 1e and f), indicating sex differences in the function of AgRP neurons. To gain more insight into this difference in food intake behavior, we analyzed feeding patterns after an ON fast or after the i.p. ghrelin administration. Both female and male Cpt1aKO mice showed an impaired feeding response after an ON fast compared to the control mice (Fig. 1g and h). However, the food intake reduction was greater in male Cpt1aKO mice. Interestingly, ghrelin administration induced greater food ingestion in control than in Cpt1aKO female mice. This was also observed in male mice. However, the food intake observed in the females was 3 times greater than that observed in the males, indicating that the females were more sensitive to ghrelin than the males (Fig. 1i). Consistent with these results, male Cpt1aKO mice spent less time eating food than the female mice (Fig. 1j). Altogether, these results suggested a different role of CPT1A in males and females in the AgRP neurons under ad libitum conditions and after ghrelin administration. Fig. 1Deletion of Cpt1a in AgRP neurons affects feeding behavior in a sex-dependent manner. a, b Body weight in Cpt1aKO female in red (a, $$n = 8$$) and male mice in blue (b, $$n = 6$$) vs their control littermates (female $$n = 10$$, and male $$n = 9$$). c, d Cumulative food intake in female (c, $$n = 8$$–10) and male (d, $$n = 6$$–9) mice measured during 1 month after tamoxifen induction. e, f Analysis of food intake measured by the TSE system during light and dark phases in female (e, $$n = 6$$–6) and male mice during 3 consecutive days (f, $$n = 6$$–6). g, h Response to fasting–refeeding in female (g, $$n = 8$$–10) and male mice (h, $$n = 10$$–8). ( i) Cumulative food intake measured 1 h after intraperitoneal (i.p.) ghrelin administration in female ($$n = 7$$–7) and male mice ($$n = 6$$–6). j Time that female ($$n = 7$$–7) and male mice ($$n = 6$$–6) spent eating after the ghrelin injection. Data are expressed as the mean ± SEM. In a–d, *$p \leq 0.05$, **$p \leq 0.01$, ****$p \leq 0.0001$, using two-way repeated-measures ANOVA followed by Šidák’s post hoc test. In e and f, * $p \leq 0.05$, using Student’s t-test. In g and h, *$p \leq 0.05$ using two-way repeated-measures ANOVA followed by Šidák’s post hoc test. In i–j, *$p \leq 0.05$, **$p \leq 0.01$, using Student’s t-test. In i–j *$p \leq 0.05$, ****$p \leq 0.0001$, using two-way ANOVA followed by Turkey post hoc test
## Cpt1a deletion in AgRP neurons increases energy expenditure (EE)
Analysis of metabolic parameters revealed differences in EE between the sexes. Cpt1aKO female mice showed increased EE compared to the control female mice. These changes in EE were only observed in Cpt1aKO female mice but not in male mice (Fig. 2a and b and Additional file 3: Fig. S3d–f). No changes were observed in the RQ (Additional file 3: Fig. S3g, h) or in locomotor activity (Additional file 3: Fig. S3i, j) in both sexes. However, only Cpt1aKO female mice increased their RQ to 0.95 in refeeding conditions after an ON fast (Additional file 3: Fig. S3g).Fig. 2Cpt1a ablation in AgRP neurons increases brown adipose tissue activity. a, b EE profile normalized against the lean body mass in female (a, $$n = 6$$–6) and male mice (b, $$n = 6$$–6). c Representative infrared thermal images of female (top panel, $$n = 11$$–9) and male mice (bottom panel, $$n = 8$$–7). d Quantification of the interscapular temperature adjacent to the brown adipose tissue (BAT) of female ($$n = 11$$–9) and male mice ($$n = 8$$–7). e, f Weight of BAT normalized against the body weight of female (e, $$n = 6$$–9) and male mice (f, $$n = 6$$–7). g Representative images of BAT sections dyed with the H&E stain in female (top panel) and male mice (bottom panel). Scale bar, 100 μm (×magnification 20). h, i Lipid droplet quantification using ImageJ in female (h, $$n = 4$$–4) and male mice (i, $$n = 4$$–4). j, k Analysis by qRT-PCR of the mRNA levels of Pnpla2, Lipe, Cpt1b, Slc2a4 Ucp1, Cidea, Mmp2, Leptin, Adiponectin and Resistin in female (j, $$n = 7$$–7) and male mice (k, $$n = 5$$–6). l, m Representative western blot of UCP1 protein levels in BAT (30 μg) from female (l, $$n = 6$$–8) and male mice (m, $$n = 6$$–8) normalized against β-actin levels. Data are expressed as the mean ± SEM. In d * $p \leq 0.05$, ***$p \leq 0.001$, using two-way ANOVA followed by Turkey post hoc test. In a, b, e, f, h, I, j–m, *$p \leq 0.05$, **$p \leq 0.01$, using Student’s t-test To determine whether the Cpt1a ablation from AgRP neurons enhanced EE in female mice, we measured the interscapular BAT temperature as an indicator of activated BAT thermogenesis. Cpt1aKO female mice showed a substantial increase in the BAT temperature compared to the control female mice (Fig. 2c and d). Consistent with this, Cpt1aKO female mice showed a significant reduction in BAT weight and lipid droplet (LD) area compared to their control littermates and the male mice (Fig. 2e–i). At the molecular level, the UCP1 protein concentration and mRNA levels of thermogenesis-related genes (Cidea and Mmp2), lipolytic genes (Pnpla2 and Lipe) and FAO markers (Cpt1b) were also elevated, confirming an enhanced activation of thermogenesis in Cpt1aKO female mice compared to their control littermates (Fig. 2j). All this thermogenic activity seemed to respond to an activated sympathetic tone. The thermogenic response and LD reduction were much lower in the Cpt1aKO male mice than in the Cpt1aKO female mice (Fig. 2d, j–m), in accordance with the higher EE observed in the Cpt1aKO female mice. In addition, we analyzed the mRNA levels of leptin, adiponectin and resistin. We only observed an increase in the resistin mRNA levels in Cpt1aKO male mice.
We also analyzed the effects of Cpt1a ablation in AgRP neurons on different tissues. An important reduction was observed in the selected WAT deposits in Cpt1aKO mice compared to the control mice (Fig. 3a and b and Additional file 4: Fig. S4a and b), which was consistent with the observed reduction in body weight. Gonadal and inguinal WAT (gWAT and iWAT, respectively) of Cpt1aKO mice of both sexes showed a reduced adipocyte size with respect to their control littermates (Fig. 3c, g, j and n). Interestingly, while Cpt1aKO female mice showed a major reduction in iWAT ($76\%$ iWAT reduction vs $45.5\%$ gWAT reduction), gWAT was the most affected tissue in Cpt1aKO male mice ($70\%$ gWAT reduction vs $56.7\%$ iWAT reduction) (Fig. 3c, g, j and n). These results were consistent with the analysis of the frequency distribution of the adipocyte area, since a higher frequency of smaller adipocytes (< 500 μm2) in the iWAT of Cpt1aKO female mice corresponded to $80\%$ of the tissue (Fig. 3g). In addition, we observed an enhanced browning in the iWAT of both sexes (Fig. 3h and o), consistent with the increased Ucp1 mRNA levels (Fig. 3i and p). We also measured serum leptin levels (Additional file 4: Fig. S4c and S4d) but no significant changes were observed between controls and Cpt1aKO mice. Furthermore, we measured serum TG and NEFAs levels in both sexes. A reduction in serum TGs and NEFAs levels was observed in Cpt1aKO female and male mice (Fig. 3e, f, l and m). These results are suggestive that deletion of Cpt1a from AgRP neurons activated the sympathetic nervous system, leading to enhanced thermogenesis mainly in Cpt1aKO female mice, an increased browning of the iWAT in both sexes as well as considerably reduced fat deposits consistent with the reduction in body weight. Fig. 3Cpt1a ablation in AgRP neurons reduces lipid content in white adipose tissue. a, b Representative image of gonadal white adipose tissue (gWAT) (a) and inguinal white adipose tissue (iWAT) (b) of female and male mice vs their control littermates. c, j Average adipocyte area of gWAT in female (c, $$n = 10$$–10) and male mice (j, $$n = 10$$–15). d, k *Morphometric analysis* of the adipocyte area distribution in the gWAT of female (d, $$n = 5$$–5) and male mice (k, $$n = 5$$–5). g, n Average adipocyte area of iWAT in female (g, $$n = 10$$–10) and male mice (n, $$n = 8$$–8). h, o *Morphometric analysis* of adipocyte area distribution in the iWAT of female (h, $$n = 5$$–5) and male mice (o, $$n = 5$$–5). e, f TG and NEFAs measurement in female ($$n = 5$$–5) and male mice ($$n = 5$$–5) (l–m). i, p Analysis by qRT-PCR of the mRNA levels of Pnpla2, Leptin, Ucp1, Cpt1a, Il6, Adiponectin and Resistin in the iWAT of female (i, $$n = 8$$–7) and male mice (p, $$n = 6$$–6). Data are expressed as the mean ± SEM. In d, h, k, o, *$p \leq 0.05$, **$p \leq 0.001$, ****i < 0.0001, using two-way repeated-measures ANOVA followed by Šidák’s post hoc test. In c, e, f, g, i, j, l, m, n, p, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001$ using Student’s t-test We also analyzed other tissues such as the liver, pancreas, testicles, and ovaries. No morphological changes were observed in these tissues (Additional file 5: Fig. S5). Moreover, no changes were observed in the liver weight in both sexes (Additional file 5: Fig. S5b and d). However, when we assessed the mRNA levels of the marker genes involved in glucose and FA metabolism, we observed that Cpt1aKO male mice showed increased mRNA levels of the gluconeogenic genes encoding phosphoenolpyruvate carboxykinase (Pepck) and glucose 6-phosphatase (G6pc) (Additional file 5: Fig. S5c and e). These changes were not observed in the female mice. Expression Cpt1a was increased in both sexes, but the expression of Ucp2 was increased only in the Cpt1aKO male mice. These results suggested a sex-dependent difference in the liver metabolic adaptation during fasting to the deletion of Cpt1a in AgRP neurons. Although we did not observe morphological changes in ovaries and testis, we measured the plasma levels of 17-β-estradiol in females and testosterone in males (Additional file 5: Fig. S5g and h). No changes were observed in circulating levels of 17β-estradiol in females. However, we observed a significative reduction of plasma testosterone levels in Cpt1aKO male mice.
## CPT1A in AgRP neurons is involved in thirst control
One week after tamoxifen administration and Cre induction, we observed that female and male Cpt1aKO mice resulted in polyuria. Thus, mice were housed in metabolic cages for 24 h to study their water balance. Ad libitum water intake in both female and male Cpt1aKO mice led to the excretion of an abnormally large volume of urine each day, which was more prominent in the female mice (Fig. 4a and e). This was accompanied by low urine osmolality, indicating high urine dilution (Fig. 4b and f). As expected, given their polyuria, female and male Cpt1aKO mice showed water consumption that was twice that of their control littermates (Fig. 4c and g). Interestingly, no significant changes were observed in serum osmolality, suggesting that the excretion of very diluted urine was compensated by the high intake of water (Fig. 4d and h).Fig. 4Cpt1aKO mice display changes in thirst behavior. a, e 24 h of urine collection in female (a, $$n = 6$$–5) and male mice (e, $$n = 6$$–7). b, f Analysis of urine osmolality in female (b, $$n = 11$$–8) and male mice (f, $$n = 16$$–9) under 24 h of water restriction or ad libitum access to water; ND, not detected. c, g Total amount of water intake for 24 h in female (c, $$n = 6$$–7) and male mice (g, $$n = 6$$–5). d, h Analysis of serum osmolality in female (d, $$n = 10$$–5) and male mice (h, $$n = 9$$–6) under 24 h of water restriction or ad libitum access to water. i Plasma level of the vasopressin hormone in female ($$n = 8$$–8) and male mice ($$n = 9$$–6). j, n Plasma levels of renin in female (j, $$n = 5$$) and male mice (n, $$n = 7$$–9). k, o Plasma levels of angiotensin II in female (k, $$n = 10$$–8) and male mice (o, $$n = 9$$–6). l, p Plasma levels of aldosterone in female (l, $$n = 8$$) and male mice (p, $$n = 8$$–10). m, q Blood pressure in female (m, $$n = 5$$–5) and male mice (q, $$n = 8$$–8). Data are expressed as the mean ± SEM. In b, d, f, h, i, **$p \leq 0.01$, ****$p \leq 0.0001$ using two-way repeated-measures ANOVA followed by the post hoc Bonferroni test. In a, c, e, g, j–o, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, using Student’s t-test To distinguish the Cpt1aKO polyuria from a diabetic condition, GTT and ITT were performed. Cpt1aKO male mice showed significantly increased glucose tolerance compared to their control littermates (Additional file 6: Fig. S6f and g). Despite females having a slight change in the first GTT timepoint, the overall glucose sensitivity measured as AUC was not improved (Additional file 6: Fig. S6a, b). No differences were observed in the ITT in both sexes (Additional file 6: Fig. S6c and h). Next, we measured serum insulin levels and urine glucose content. No alterations were observed in these two parameters in the Cpt1aKO mice with respect to the control mice (Additional file 6: Fig. S6d, e, i, j). In addition, no changes were observed in the morphology of the kidneys in both sexes, excluding an intrinsic renal disease (Additional file 6: Fig. S6k). These data emphasized that Cpt1aKO mice did not suffer type II diabetes, but they did have disrupted water intake.
To further understand the mechanisms that caused polydipsia, mice were deprived of water for 24 h in the metabolic cages. During dehydration, male Cpt1aKO mice showed a significantly increased urine osmolality, as well as their control littermates (Fig. 4f). Unfortunately, we were not able to collect enough urine volume to perform this analysis in the female mice under water deprivation (Fig. 4b). Despite this, no differences in serum osmolality were observed in response to water restriction (Fig. 4d and h). These data indicated that Cpt1aKO mice partially conserved their ability to respond to water deprivation. We then analyzed plasma levels of vasopressin (VP, also known as anti-diuretic hormone, ADH) an important regulator of fluid balance and is synthesized in magnocellular neuronal cell bodies of the PVN and SON [51]. We observed reduced circulating levels of VP/ADH in both sexes. However, the effect was more prominent in female Cpt1aKO mice (Fig. 4i). Another critical regulator of fluid balance and blood pressure is the renin–angiotensin–aldosterone system (RAAS). Typically, the RAAS is activated when there is a drop in blood volume to increase water and electrolyte reabsorption in the kidneys. Thus, we determined the levels of renin, angiotensin II (AgII) and aldosterone as important integrators of the RAAS and blood pressure. Both female and male Cpt1aKO mice showed increased circulating levels of renin, AgII and aldosterone (Fig. 4j–l and n–p), which were accompanied by a mild increase in the systolic blood pressure (Fig. 4m and q). Taken together, these data suggest that lipid metabolism in AgRP neurons is involved in the regulation of fluid homeostasis.
## CPT1A is required in AgRP neurons for spine and presynaptic terminal formation
Considering that the PVN is an important target of AgRP neuronal projections and since VP/ADH is released from the axonal projections of paraventricular magnocellular cells into different brain areas (the rostro lateral medulla, nucleus tractus solitarius and intermediolateral column of the spinal cord) that are responsible for the integration of the peripheral sympathetic and vagal outflow [52, 53], we wanted to know whether Cpt1a deletion from AgRP neurons affected the activity of PVN neurons. Immunofluorescence assays of Fos were used to detect the pattern of PVN activity under ad libitum access to water or in response to 24 h of water restriction. No statistical differences were observed in Fos activation in the PVN under ad libitum access to water in both female and male mice. This was interesting since male and female Cpt1aKO mice showed an increase in water intake with no sign of Fos activation in the PVN during free access to water (Fig. 5a–c). However, we observed a significant reduction in Fos activation in both female and male Cpt1aKO mice compared with their control littermates during water deprivation (Fig. 5a–c). Thus, these results suggested that the AgRP neurons in Cpt1aKO mice reduced their neuronal connections to the PVN.Fig. 5Deletion of Cpt1a in AgRP neurons impairs PVN activation during water restriction. a Representative images of Fos activation in the paraventricular nucleus (PVN) of female (left panel) and male mice (right panel) under conditions of ad libitum access to water or 24 h of water restriction. Dashed lines indicate the PVN area analyzed. b, c Quantification of Fos-positive cells per PVN section in female (b, $$n = 4$$–4) and male (c, $$n = 4$$–4) mice. Data are expressed as the mean ± SEM. In b and a, *$p \leq 0.05$, **$p \leq 0.001$, using two-way ANOVA followed by the post hoc Bonferroni test Next, we studied the projections from AgRP neurons to the PVN in female Cpt1aKO mice under fasting conditions. To study this, we injected AAVs that conditionally expressed synaptophysin-GFP (AAV1-EF1a-DIO-synaptophysin-GFP) under Cre recombinase activation into the ARC. GFP fluorescence was analyzed in the presynaptic area of AgRP neurons in the PVN sections (Fig. 6a and b). We observed a reduction in the GFP fluorescence in the PVN section from Cpt1aKO female mice compared to their control littermates. This reduction was less observed in Cpt1aKO male mice. Altogether, this suggested that Cpt1a deletion in AgRP neurons reduced the projections from AgRP neurons to the PVN in female mice. Fig. 6AgRP neurons lacking Cpt1a show reduced dendritic spines and projections to the PVN. a Scheme of AAV1-EF1a-DIO-synaptophysin-GFP administration into the ARC and the projection to the PVN. b Representative fluorescence microphotograph of AgRP-syn projection to the PVN in female and male mice. Scale bar, 10 μm. Syn-GFP intensity level was quantified with ImageJ using 6 slices ($$n = 3$$–4). c Representative images of AgRP neurons along the anterior–posterior axis of the ARC. d Quantification of AgRP neurons in the anterior, medial and posterior section of the ARC in female mice (d, $$n = 3$$–4). Scale bar, 25 μm. e Representative microphotograph of the anterior ARC section in female mice. Scale bar, 100 μm. f, g Representative fluorescence microphotograph of the dendrites of AgRP neurons. Scale bar, 100 μm. Quantification of the number of dendritic spines per 50 μm of dendrites in $\frac{20}{25}$ axons of AgRP neurons in female mice (g, $$n = 3$$). h Scheme of mitochondrial labeling in the ZsGreen Cpt1aKO mice. i Representative fluorescence microphotograph of mitochondria from ZsGreen mice. Scale bar, 10 μm. j, l Quantification of the number of mitochondria per cell. k, m The average mitochondrial area in AgRP neurons ($$n = 3$$, 20–28 neurons per genotype). Data are expressed as the mean ± SEM. In d, g, j–o, *$p \leq 0.05$, **$p \leq 0.01$, ****$p \leq 0.001$ using Student’s t-test To explore if these reduced synapses were a result of reduced AgRP neuron viability, we analyzed the number of AgRP neurons along the anterior–posterior axis of the ARC. To accomplish this, we injected AAVs that conditionally expressed mCherry (AAV9-EF1a-DIO-mCherry) in the presence of Cre recombinase into the ARC of control mice or mice lacking Cpt1a in their AgRP neurons. Female mice were analyzed 1 month after tamoxifen induction. Importantly, no differences in the number of AgRP neurons were observed in the sections selected from the anterior, medial and posterior ARC (Fig. 6c and d). When we analyzed the morphology of AgRP neurons by confocal microscopy, we observed an alteration in the dendritic morphology of AgRP neurons lacking Cpt1a (Fig. 6e and g), which showed a reduced number of dendritic spines per 50 µm of dendrons compared to the AgRP neurons from their control littermates.
## Cpt1aKO mice showed altered mitochondria density and gene expression in AgRP neurons
To further investigate the mechanisms reducing the dendritic spines and presynaptic terminals, we first focused our attention on mitochondria. It has been reported that mitochondria play a key role in the number and size of dendritic spines [54]. We analyzed the effect of Cpt1a deletion in AgRP neurons on mitochondrial morphology in in vivo conditions. We first generated ZsGreen mice. Cpt1aKO mice were then crossed with ZsGreen mice that expressed the green fluorescent protein ZSGreen in the mitochondria of AgRP neurons after tamoxifen induction. Brain sections from Cpt1aKO-ZsGreen mice and their control littermates were obtained and analyzed by confocal microscopy. We observed an increased number and size of the mitochondria per cell of female with respect to the mitochondria per cell of males. Both Cpt1aKO-ZsGreen mice sexes showed a reduced number of mitochondria per cell as well as a reduced mitochondrial area (Fig. 6h–m). Although the lack of CPT1A reduced the number of mitochondria, there seemed to be enough mitochondria to support neuronal survival.
To determine if this reduced pool of mitochondria and synapses affected the synthesis of the NPY and AgRP neuropeptides and neurotransmitters (GABA and glutamate) by AgRP neurons, we performed an in vivo transcriptomic analysis using the RiboTag technique [42]. We crossed Cpt1aKO mice with RiboTag homozygous mice. This allowed us to isolate AgRP neuron-specific ribosome-associated mRNAs from the ARC of RiboTag-Cpt1aKO mice by immunoprecipitating the actively translating polyribosomes tagged with the HA epitope (Fig. 7a). To confirm the correct labeling of the AgRP ribosomes, brain sections from RiboTag Cpt1aKO mice were obtained and we assessed the HA epitope by immunofluorescence (Fig. 7b). To determine the specificity of the immunoprecipitation for AgRP ribosomes, we evaluated the transcript levels of Agrp, Pomc and Adhl1 as markers of AgRP neurons, POMC neurons and astrocytes, respectively, in the input and immunoprecipitate by qRT-PCR. We observed an increase in Agrp mRNA levels only in the immunoprecipitate, indicating the specific isolation of AgRP ribosomes (Fig. 7c). Gene expression analysis of the immunoprecipitate from RiboTag Cpt1aKO mice and their control littermates showed a reduction of mRNA levels of three genes, Agrp,Slc17a6 and Dgl4 in the RiboTag Cpt1aKO female mice (Fig. 7d). Slc17a6 codifies for the protein of vesicular glutamate transporter (VGLU2) in presynaptic terminal and, while Dgl4 codifies for a postsynaptic scaffolding protein (PSD-95) that plays a critical role in synaptogenesis and synaptic plasticity by providing a platform for the postsynaptic clustering of crucial synaptic proteins. No changes were observed in the gene expression of Slc32a1 that codifies for vesicular transporter of GABA (VGAT1) or Gad1 (glutamate descarboxylase), an enzyme involved in the synthesis of GABA (Fig. 7d). These changes were not observed in the males (Fig. 7e). All these results indicated that FA metabolism and CPT1A are involved in the neuronal processes related to neurotransmission and neuropeptide expression in AgRP neurons in fasting conditions and play different roles depending on sex. Fig. 7Validation of the RiboTagCpt1aKO mouse model and gene expression analysis. a Scheme of the experimental strategy to isolate AgRP neuron-specific RNAs from Cpt1aKO–RiboTag mice. b Representative images of AgRP neurons tagged with the anti-HA antibody. Scale bar, 100 µm. c Analysis by qRT-PCR of mRNA levels of Agrp, Pomc and Adh1 was performed in the input and immunoprecipitated HA samples. d, e Analysis by qRT-PCR of the mRNA levels of Agrp, Npy, Slc17a6, Slc32a1, Gad1, Dlg4 in female mice ($$n = 4$$–6) and male mice ($$n = 5$$–6). The reference gene was Gapdh for all the samples analyzed by qRT-PCR. Data are expressed as the mean ± SEM. * $p \leq 0.05$, ****$p \leq 0.0001$, using Student’s t-test
## Discussion
In this study, we showed that CPT1A, an enzyme that regulates FAO, is relevant for orexigenic AgRP neuronal function, energy homeostasis, and fluid balance in a sex-dependent way (Table 1). Conditional Cpt1a deletion in AgRP neurons in adult mice led to a clear reduction in body weight gain in Cpt1aKO male mice, which was less pronounced in the female mice compared to their control littermates. These sex-dependent differences were clearer in the feeding behavior, since Cpt1aKO female mice did not show any change in food intake under ad libitum conditions. Sex-based differences in feeding behavior in mammals have been attributed exclusively to the effects of gonadal hormones, especially estrogens and androgens, which regulate food intake and energy metabolism by acting on the brain and diverse peripheral tissues [55–57]. It has been shown that in gonadal-intact mice, total food intake is higher in male than in female mice during dark phases, with female mice eating more than the males during light phases. Interestingly, these differences were blunted in gonadectomized animals, since no differences were observed between the sexes [58], thus suggesting a complex interaction between the gonadal hormones in daily feeding rhythms and revealing sex differences depending on gonadal hormones. In our model, Cpt1a deletion in AgRP neurons affected food intake during the dark phase in male Cpt1aKO mice, which dropped by half. Table 1Summary of metabolic phenotypes between males and females and sexual dimorphismParameterControl FemaleCpt1aKO FemaleControl MaleCpt1aKO MaleSexual dimorphismBody weight gain–↓–↓Cumulative food intake–––↓*Food intake induced by fast–↓–↓Food intake induced by ghrelin–↓↓↓↓*Energy expenditure–↑––*BAT temperature–↑↑–*BAT tissue–↓–↓Lipid droplet area–↓–↓*Thermogenic* gene expression in BAT–↑–↑UCP1 protein–↑–↑gWAT tissue–↓–↓adipocyte size of gWAT–↓–↓adipocyte size of iWAT–↓–↓TG and NEFAs–↓–↓Leptin––––UCP1 gene expression in iWAT–↑↑–↑Urine output–↑–↑Urine osmolality under water ad libitum–nd–↓Water intake–↑–↑Plasma osmolality––––Vasopressin–↓↑↓*Renin–↑–↑Angiotensin II–↑–↑Aldosterone–↑–↑Systolic blood pressure–↑–↑GTT (AUC)–––↓*ITT––––Fasted insulin––––Urine glucose––––Liver weight––––Cpt1a liver expression–↑–↑Ucp2, Pepck and G6pc–––↑*Mitochondrial content–↓–↓Agrp, Slc17ab and *Dlg4* gene expresion in AgRP neurons–↓––*17β-Estradiol––Testosterone–↓*Indicates sexual dimorphism considering control females as reference. nd, not detected It has been reported that the fasting-mediated activation of AgRP neurons promotes food-seeking behavior and energy conservation [59]. In Cpt1aKO mice, we observed an important reduction in food intake in challenging conditions, such as during fasting-induced refeeding and ghrelin administration, in both sexes. These results suggested that neurons lacking CPT1A suffer metabolic inflexibility. Thus, AgRP neurons cannot cope with the transition between fasting and refeeding. These findings agree with those of two previous studies [60, 61]. In agreement with these findings, our results suggested that CPT1A and FA metabolism are imperative in the feeding behavior response after the activation of AgRP neurons during fasting. In addition, it has been observed that ghrelin, a hormone that stimulates appetite, binds to its receptor [62–64] and promotes feeding through the activation of the AMPK–CPT1A–UCP2 axis in hypothalamic neurons [65–67]. Although the effects of ghrelin on AgRP neurons are well established, it is not clear which molecular mechanisms and pathways are involved in AgRP neuron activation. Our finding reinforces the importance of CPT1A downstream of the ghrelin receptor in AgRP neurons for the induction of food-seeking behavior, since both female and male mice lacking CPT1A in their AgRP neurons reduced food intake after ghrelin administration.
Although the reduction in body weight gain triggered by the Cpt1a deletion in AgRP neurons was independent of sex, our findings provide evidence that males and females are hardwired differently in the regulation of their energy balance. At the physiological level, Cpt1aKO male mice reduced their body weight gain through dietary restriction more efficiently than the female mice, while Cpt1aKO female mice increased EE that exceeded the energy ingested. A change in food consumption affects EE primarily through its effect on diet-induced EE [68]. Thus, a decrease in food intake decreases EE and vice versa. Accordingly, the drop in the food intake observed in Cpt1aKO male mice probably blunted the increase of the EE observed in Cpt1aKO female mice. This increased EE in Cpt1aKO female mice correlated with an exacerbated thermogenesis in their BAT, an enhanced browning of their subcutaneous adipose tissue (iWAT) and increased lipolytic activity in their adipose tissues, resulting in an important reduction in their fat mass. The connection between AgRP neurons and BAT has been well established, anatomically and functionally [69–71]. The results from our model are suggestive that Cpt1a deletion in AgRP neurons triggers an increase in the sympathetic outflow to BAT. Cpt1aKO mice presented enhanced BAT activity, showing an increased temperature of the suprascapular area mainly in the females. This was confirmed by measuring UCP1 protein levels in BAT and the expression of the genes encoding lipolytic enzymes. In addition, an enhanced browning of iWAT and an important reduction in the fat pads, mainly in the females, were consistent with the physiological role of AgRP neurons in the regulation of peripheral nutrient utilization [7, 72–74].
The liver is also an essential target of AgRP neurons in the maintenance of energy homeostasis [61, 75]. This agrees with our findings from the gene expression analysis of the livers of Cpt1aKO mice. Both male and female Cpt1aKO mice showed a significant increase in Cpt1a gene expression in the liver, suggestive of increased β-oxidation of FA. We also observed a significant increase in the expression of Pepck and G6pase in the male but not female mice under fasting conditions, suggesting a new sex-dependent difference in coping with challenging fasting conditions.
Most studies on AgRP function have focused on the molecular mechanisms underlying food intake, energy metabolism and body weight changes. However, little is known about the implication of AgRP in water consumption. The polydipsia and polyuria observed in the Cpt1aKO mice were consistent with the reduced blood levels of VP/ADH in Cpt1aKO mice compared to their control littermates. Considering that the PVN is an important nucleus for VP/ADH production and since AgRP neurons project to the PVN, the observed VP/ADH reduction could be due to an impaired ability of AgRP neurons to activate the PVN. We also investigated the RAAS, which is a critical regulator of fluid balance and blood pressure. Typically, the RAAS is activated when there is a drop in the blood volume to increase water and electrolyte reabsorption in the kidneys. We measured the plasma levels of AgII to determine whether the polyuria activated the RAAS. The increased blood levels of AgII observed in Cpt1aKO mice indicated that the normal renal parenchyma was able to detect the drop in water and electrolyte reabsorption, triggering RAAS activation. Thus, this cascade emerges as a compensatory mechanism for water loss. Our finding was supported by those of studies evaluating the effect of AngII on diuresis, since the intravenous infusion of angiotensin inhibited polyuria [76]. It is also plausible to speculate a direct activation of renin release from the juxtaglomerular cells of the kidneys. It is well stablished that the SNS triggers renin release for the generation of angiotensin I, which is then converted to Ang II [77]. More recently, direct multifiber recording of sympathetic nerve activity subserving the kidney was performed followed by a chemogenetic activation of AgRP neurons. The authors showed a rapid decline of sympathetic nerve activity upon AgRP activation [78]. Based on the experiments reported here, the lack of CPT1A in AgRP neurons is expected to upregulate sympathetic nerve activity to the kidney. This could also account for the activation of RAAS in our model. AgII raises blood pressure through different mechanisms, the most important ones being vasoconstriction, sympathetic nervous stimulation, increased aldosterone biosynthesis and renal activities. Here, we observed that both systolic blood pressure and aldosterone levels were increased in Cpt1aKO mice, strongly reinforcing RAAS activation. AgII has also been reported to exert effects on the brain. It can bind to the hypothalamus, stimulating thirst and the release of VP/ADH by the posterior pituitary. However, the reduced level of this hormone in Cpt1aKO mice indicated that AgII requires neuronal stimuli to maintain VP/ADH levels under physiological conditions. Altogether, our results suggested that the normal function of AgRP neurons is necessary to maintain fluid balance.
The drop in the activation of PVN neurons suggested reduced AgRP presynaptic innervation or neuronal viability. We explored both and did not find differences in the number of AgRP neurons in the ARC sections analyzed. However, the number of dendritic spines on AgRP neurons were reduced, as in the studies reporting a lack of NMDAR signaling components in spines [8, 60]. The analysis of AgRP presynaptic terminals in the PVN using labeled synaptophysin specifically expressed in AgRP neurons confirmed that female Cpt1aKO mice substantially reduced their presynaptic activity, which occurred to a lesser extent in the males. This suggested that the female mice were very susceptible to the lack of the CPT1A enzyme. One limitation of this study is to understand which specific neurons of PNV or other hypothalamic nucleus could be affected by the reduced AgRP presynaptic activity in both sexes. This could potentially explain the physiological sex differences observed not only in the maintenance of the fluid balance, but also in the control of food intake and body weight.
Mitochondria play important roles in neurons. In addition to energy production via the synthesis of ATP, this dynamic organelle is involved in cellular metabolism, innate and adaptative immune responses, and cell death (revised in [79]). Since CPT1A is a key enzyme in FAO, we speculated that the lack of CPT1A enzyme in AgRP neurons may produce metabolic shifts that could affect spine formation and synapses. Interestingly, we have found sex differences in the size and quantity of mitochondria per neuron in control mice. These findings highlight the possibility that females could be more protected to cope with stressful conditions [80, 81]. We also observed a reduced size and number of mitochondria per neuron in mice lacking CPT1A, mainly in females, which could reduce the energy production necessary for many neuronal processes. In line with this, in culture of embryonic primary cortical neurons we have observed that the lack of CPT1A reduces GABA release [40]. Another limitation of our study is the lack of analysis of the functionality that reduced mitochondria have on neuronal processes in adult mice of both sexes in vivo. In order to do this, the AgRP neuronal isolation and culture from adult mice would be needed, but currently primary hypothalamic cultures are only feasible in the embryonic stage. In a recent study in embryonic hypothalamic culture from mice with a AgRP-selective deletion of Dnm1, which codifies for a mediator of mitochondrial fission protein (DRP1), has been shown an attenuated mitochondrial respiration that could affect AgRP function [82]. Future studies assessing the mitochondrial functionality and subcellular distribution in both sexes are necessary to clarify the mitochondrial role in the AgRP neuronal function.
RiboTag analysis also showed important differences in gene expression between the females and males. In fasting conditions, female mice showed reduced transcript levels of key genes involved in neurotransmission and neuropeptide production, which was not observed in the males. The decrease in AgRP neuropeptide levels was consistent with the increased EE observed in Cpt1aKO female mice. Neurons in the PVN expressing thyroid-releasing hormone (TRH), oxytocin (OT), and corticotropin-releasing hormone (CRH) all express MC4R [83]. The binding of α-MSH secreted by POMC neurons to MC4R on these neurons has a positive effect on the hypothalamic–pituitary–thyroid (HPT) axis and the hypothalamic–corticotropic axis (HPA) [84]. Since AgRP acts as an inverse agonist of α-MSH in the PVN in fasting conditions [85–88] its reduced expression in Cpt1aKO female mice could stimulate both the HPT and HPA axes, resulting in a positive enhancement of thermogenesis and EE. However, other brain regions and neuronal mechanisms could be involved in the enhancement of thermogenic activity, EE and the control of body weight, since specific groups of AgRP neurons have been reported to project to the dorsal lateral part of the dorsal raphe nucleus [89] and the parabrachial nucleus [90]. We also observed reduced Slc17a6 mRNA levels in Cpt1aKO female mice, which could partly explain the reduced activity of vasopressin neurons. Glutamate and GABA are the main neurotransmitters in the PVN and SON that are involved in the synaptic regulation of vasopressin neurons [91]. A decrease in glutamatergic inputs is partly consistent with the polydipsia and polyuria observed in our Cpt1aKO mouse model. Although there were no changes in VGAT gene expression in fasting conditions between control and Cpt1aKO mice, we cannot discard the effect of GABA in polydipsia in conditions other than fasting [90]. Dgl4 mRNA levels were also decreased in female Cpt1aKO mice. Since PSD-95, the protein codified by *Dgl4* gene, is required for the stabilization of spines, the maturation of excitatory synapses and the synaptic function [92], we propose that this reduction on Dgl4 mRNA levels could result in a partial reduction of the postsynaptic plasticity of AgRP neurons in fasting conditions. The decrease of both Scl17a6 and Dgl4 mRNA levels suggest that Cpt1aKO female mice reduce AgRP neuronal synaptic processes in the fasting conditions, which is mildly observed in Cpt1aKO male mice. This is consistent with the reduced number of mitochondria, presynaptic innervation to PVN observed in females in respect to the males. We propose that this reduction in the neuropeptide AgRP and proteins involved synaptic processes could reduce the signaling to the PVN enhancing HPA axis and sympathetic tone in fasting conditions.
Sexual hormones could be affected by AgRP levels. It has been shown that starvation-activated agouti-related peptide (AgRP) neurons can inhibit the reproductive neuroendocrine circuit, mainly in females [93]. No significant changes were observed in 17β-estradiol in Cpt1aKO female mice, in contrast, Cpt1aKO male mice showed a decrease of testosterone. Since levels of testosterone are mediated by the hypothalamic–pituitary–gonadal axis we cannot rule out that a drop in the activation of the PVN neurons from AgRP neurons could modulate the hypothalamic releases of gonadotropin-releasing hormone (GnRH) and further reducing the secretion of follicle-stimulating hormone (FSH) and luteinizing hormone (LH) by the anterior pituitary gland and consequently leading a reduction of testosterone production. All these results highlight the different role of AgRP neurons in males and females.
## Perspectives and significance
Our study provided insight into understanding how fatty acid metabolism and, in particular, CPT1A in AgRP neurons play role in the control of food intake, energy expenditure and body weight. We suggest that CPT1A should be considered as a new target against obesity. Additionally, we provide evidence that AgRP neurons are involved in the control of thirst and fluid balance, suggesting that AgRP neurons are relevant components of the neural circuits underlying thirst and fluid homeostasis. Further analysis of AgRP connections with other centers of the hypothalamus and the brain that are involved in the control of thirst could help to understand the circuitry and the mechanisms that underlie drinking behavior.
Our study demonstrates that there are sex-specific differences in the effect of CPT1A deletion, a key enzyme in fatty acid metabolism, on specific neurons, emphasizing the need to analyze both females and males when describing the mouse phenotypes. Future studies will be needed to specifically address the relevance of fatty metabolism in mitochondrial function and consequently in neuronal processes.
## Supplementary Information
Additional file 1: Figure S1. Validation of Cre-mediated recombination in AgRP neurons. a Scheme of the time-course of the experiment. b Bilateral injection of 400 nl of AAV9-EF1a-DIO-mCherry at a dose of 1.23 × 1013 gc/ml into the ARC of AgRP-Cre-ERT2 mice. c Representative histological slice of Cre-dependent mCherry expression in the ARC. Scale bar, 500 μm (top image) and 200 μm (bottom image). d Schematic of the Cre-mediated recombination product showing the floxed band (1030 bp) containing the LoxP sequences surrounding exon 4 of the Cpt1a gene. After Cre recombination, the product resulted in a 219-bp DNA fragment. e Scheme of the restriction sites of the restriction enzymes PstI and AatII that were used to separate Cpt1a amplicons from unrecombined genomic DNA. f Representative PCR analysis of genomic DNA from the liver, gonadal white adipose tissue (gWAT), brown adipose tissue (BAT), adrenal gland (AG), cortex, hippocampus (Hyp) and arcuate nucleus (ARC) treated with PstI and AatII enzymes in Cpt1aKO mice and control mice (g) FASTA analysis of the sequenced 219 bp DNA fragment extracted from the gel. Additional file 2: Figure S2. Analysis of the adrenal gland after Cpt1a deletion in AgRP neurons. a Representative H&E staining of female (left panel) and male adrenal gland (right panel). Scale bar, 500 μm (magnification 4×) and 100 μm (magnification 20×). b and c Weight of the left and right adrenal glands in female (b, $$n = 5$$–5) and male mice (c, $$n = 5$$–5). ( d and e) Analysis by qRT-PCR of the mRNA levels of Th in the adrenal gland of female (D, $$n = 5$$–9) and male mice (E, $$n = 9$$–7).Additional file 3: Figure S3. Effect of Cpt1a deletion on EE. a Representative image of Cpt1aKO female (left image) and male mice (right image) compared with their control littermates 3 months after tamoxifen induction. b and c 24 h of food consumption in female (b, $$n = 8$$–10) and male mice (c, $$n = 6$$–9). d and e EE in light and dark cycles and during fasting and refeeding in female (d, $$n = 6$$–6) and male mice (e, $$n = 6$$–6). f EE in light and dark cycles comparing control littermates and Cpt1aKO animals (f, $$n = 6$$–6). g and h Respiratory quotient (RQ) registered by the TSE system in female (f, $$n = 6$$–6) and male mice (g, $$n = 6$$–6). h and i Locomotor activity (LA) registered by the TSE system in female (h, $$n = 6$$–6) and male mice (i, $$n = 6$$–6). Data are expressed as the mean ± SEM. In b–i, * $p \leq 0.05$, using Student’s t-test. Additional file 4: Figure S4. Deletion of Cpt1a in AgRP neurons reduces the fat mass in male and female mice. a and b Gonadal white adipose tissue (gWAT) weight in female (a, $$n = 6$$) and male mice (b, $$n = 6$$). ( c and d) (c and d) Plasma level of leptin in female (c, $$n = 4$$–5) and male mice (d, $$n = 4$$–7). e and f Analysis by qRT-PCR of Pnpla2, Vegfa, Il6, Leptin, Mmp2, Adiponectin and Resistin mRNA levels in the gWAT of female (e, $$n = 8$$–6) and male mice (f, $$n = 9$$–6). Data are expressed as the mean ± SEM. In a–d, * $p \leq 0.05$, using Student’s t-test. Additional file 5: Figure S5. Deletion of Cpt1a in AgRP neurons upregulates Cpt1a expression in the liver. a Representative hematoxylin and eosin (H&E) staining of the livers of male and female mice. Scale bar, 50 µm (magnification 20×). b and d Liver weight of female (b, $$n = 6$$–8) and male mice (d, $$n = 6$$–8). ( c and e) Analysis by qRT-PCR of Cpt1a, Cd36, Ucp2, Hmgcs2, Pepck and G6pc mRNA levels in female (c, $$n = 8$$) and male mice (e, $$n = 9$$–8). f Representative H&E staining of the pancreas and testes from male mice (upper panel) and the pancreas and ovaries from female mice (lower panel). g Plasma level of 17β-estradiol in female mice ($$n = 8$$–8) and h testosterone level in male mice ($$n = 9$$–10). Scale bar, 50 µm (magnification 20×). Data are expressed as the mean ± SEM. In b–e, * $p \leq 0.05$, using Student’s t-test. Additional file 6: Figure S6. Cpt1a deletion in AgRP neurons does not induce a diabetic state. a and f Glucose tolerance test (GTT) in female (a, $$n = 10$$–8) and male mice (f, $$n = 10$$–8). b and g area under the curve (AUC) quantification in female (b, $$n = 10$$–8) and male mice (g, $$n = 10$$–8). c and h Insulin tolerance test (ITT) in female (c, $$n = 7$$–8) and male mice (h, $$n = 10$$–9). d and i Fasting insulin levels in female (d, $$n = 8$$–6) and male mice (i, $$n = 7$$–4). e and j Urinary glucose levels in female (e, $$n = 5$$–7) and male mice (j, $$n = 9$$–6). k Representative hematoxylin and eosin (H&E) staining of the cortex (left panel) and medulla (right panel) of the kidneys from female and male mice. Scale bar, 50 µm (magnification 20×). Data are expressed as the mean ± SEM. In a, f, c, h, * $p \leq 0.05$, using two-way repeated-measures ANOVA followed by Šidák’s post hoc test. In b, g, d–j, *** $p \leq 0.001$, using Student’s t-test. Additional file 7. Table S1: Forward and reverse primers used in the PCR analysis. Table S2: Antibodies used in the study.
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|
---
title: ceRNA network construction and identification of hub genes as novel therapeutic
targets for age-related cataracts using bioinformatics
authors:
- Yingying Hong
- Jiawen Wu
- Yang Sun
- Shenghai Zhang
- Yi Lu
- Yinghong Ji
journal: PeerJ
year: 2023
pmcid: PMC10040182
doi: 10.7717/peerj.15054
license: CC BY 4.0
---
# ceRNA network construction and identification of hub genes as novel therapeutic targets for age-related cataracts using bioinformatics
## Abstract
### Background
The aim of this study is to investigate the genetic and epigenetic mechanisms involved in the pathogenesis of age-related cataract (ARC).
### Methods
We obtained the transcriptome datafile of th ree ARC samples and three healthy, age-matched samples and used differential expression analyses to identify the differentially expressed genes (DEGs). The differential lncRNA-associated competing endogenous (ceRNA) network, and the protein-protein network (PPI) were constructed using Cytoscape and STRING. Cluster analyses were performed to identify the underlying molecular mechanisms of the hub genes affecting ARC progression. To verify the immune status of the ARC patients, immune-associated analyses were also conducted.
### Results
The PPI network identified the FOXO1 gene as the hub gene with the highest score, as calculated by the Maximal Clique Centrality (MCC) algorithm. The ceRNA network identified lncRNAs H19, XIST, TTTY14, and MEG3 and hub genes FOXO1, NOTCH3, CDK6, SPRY2, and CA2 as playing key roles in regulating the pathogenesis of ARC. Additionally, the identified hub genes showed no significant correlation with an immune response but were highly correlated with cell metabolism, including cysteine, methionine, and galactose.
### Discussion
The findings of this study may provide clues toward ARC pathogenic mechanisms and may be of significance for future therapeutic research.
## Introduction
As population age around the world trends upward, age-related cataracts (ARC) continue to be the primary cause of reversible severe vision impairment and blindness worldwide, with incidence increasing. The World Health Organization estimated that in 2014, there were 95 million people worldwide who were visually impaired due to cataracts (WHO, 2014), and a 2018 meta-analysis projected that the number of people affected by ARC would increase to 187.26 million (Song et al., 2018). The primary disease mechanisms of ARC formation are metabolic abnormalities and oxidative stress, mainly caused by ageing (Tang et al., 2015). There are also several risk factors for ARC formation including ultraviolet B exposure, some systematic disease factors (diabetes and hypertension), and some lifestyle factors such as smoking, drinking, and malnutrition (Liu et al., 2017). Currently, the only effective treatment method for ARC is the surgical removal of the onset lens and replacement of the intraocular lens. This procedure has a high success rate, but is also expensive. There are currently no effective therapeutic drugs for cataracts. With expensive surgery as the only treatment option available, cataracts are a significant medical and financial burden both at the individual and population levels. Further characterization of the pathogenesis of ARC is essential for developing new therapies.
Non-coding RNA, such as long non-coding RNA, circular RNA, microRNA, and pseudogenes, lack open-reading frames so they cannot be translated into proteins. Long non-coding RNAs (lncRNAs) are RNA molecules that are longer than 200 nucleotides. There are seven main types of lncRNA, including antisense lncRNAs, intronic lncRNAs, bidirectional lncRNAs, intergenic lncRNAs (lincRNAs), enhancer RNAs (eRNAs), and circular RNAs (circRNAs; Beermann et al., 2016). Recent studies have found that lncRNA can interact with proteins, DNA, and RNA, contributing to the pathogenesis of ocular disease (Chen et al., 2022; Zhang et al., 2019). Moreover, lncRNAs have been shown to be differentially expressed in ocular tissue, which may also play a vital role in the pathogenesis of ophthalmic diseases such as corneal disease, glaucoma, cataracts, retinopathy, and ocular tumors (Liu & Qu, 2021; Zhang et al., 2019). The modification of lncRNA is critically involved in cellular senescence and the ageing process, which contribute to the pathological mechanisms of ARC through cell proliferation, apoptosis, migration, and epithelial-mesenchymal transition (EMT; Chen et al., 2022; Jiapaer et al., 2022). For example, previous studies have shown that lncRNA 1-phosphatidylinositol-4,5-bisphosphate phosphodiesterase delta 3-sence RNA 1 (PLCD3-OT1; Xiang et al., 2019a), H19 (Cheng et al., 2019; Liu et al., 2018), myocardial infarction associated transcript (MIAT; Ling et al., 2020; Shen et al., 2016), maternally expressed gene 3 (MEG3; Tu et al., 2020), taurine up-regulated 1 (TUG1; Li et al., 2017a; Shen & Zhou, 2021), glutathione peroxidase 3 (GPX3)-antisense (AS; Tu et al., 2019), NONHSAT143692.2 (Zhou et al., 2020), and antisense non-coding RNA in the INK4 locus (ANRIL; Qi et al., 2019) are all involved in the progression of ARC. The ceRNA hypothesis, introduced by Salmena et al., hypothesizes that lncRNAs could bind to miRNAs competitively, then regulate gene expression at the post-transcriptional level (Salmena et al., 2011). Xiang et al. ( 2019b) using RNA-sequencing, found that the PLCD3-OT1 lncRNA acts as a ceRNA, preventing ARC by sponging miR-224-5p and regulating PLCD3 expression. However, more research is required to determine the expression patterns and mechanisms of specific ceRNA networks in ARC patients.
In this study, we aimed to identify differentially expressed lncRNAs and the correlated hub genes to construct a differential lncRNA-associated competing endogenous (ceRNA) network and explore the underlying molecular mechanisms and therapeutic targets of ARC patients.
## Patient tissue sample collection
ARC patients undergoing uncomplicated cataract surgery by one surgeon (Y.H.J.) at the Department of Ophthalmology of the Eye & ENT hospital (Shanghai, China) in 2014 were included in this study. Patients with other ocular and systemic diseases affecting vision, such as high myopia, uveitis, ocular trauma, retinopathy, diabetes, and hypertension, were excluded. During the capsulotomy procedure, the anterior lens capsules (ALCs) of the ARC patients included in the study were collected in an RNase-free tube and frozen with liquid nitrogen. Three ALCs were mixed in each tube as one sample to reach enough RNA concentration to be able to conduct the study. The control anterior lens capsule specimens, age-matched to ARC patients, were obtained from the Shanghai Red Cross Eye Bank. This study fully complied with the Declaration of Helsinki and received ethics approval from the Fudan University-affiliated Eye & ENT Hospital (Shanghai, China; IRB number: KJ2011-25). Informed consent was obtained from all participants.
## Data collection
At least 500 ng of total RNA was extracted from the sample tissue with Trizol reagent, then the cDNA and biotinylated cRNA were prepared with illumina totalPrep RNA amplification kits (Cat#IL1791). An Illumina BeadChip (HumanHT-12_V4) experiment was conducted to obtain the text data file following the cRNA quality examination. Illumina BeadStudio Gene Expression Module v1.0 normalized the gene expression profiles (GSE213546). The FunRich software (downloaded from http://www.funrich.org) was used in this study to conduct the miRNA-related functions enrichment analysis.
## Differential Expression Genes (DEGs)
The lncRNA expression profiles were retrieved from the transcriptome sequencing data using the Gencode annotation file (https://www.gencodegenes.org/) from Perl software (version 30). The differentially expressed messenger RNA (mRNA) and lncRNA were obtained using the “limma” packages for R (version 4.2; R Core Team, 2022) and R studio (version 2022.02.2; R Core Team, 2022; RStudio Team, 2013) by comparing the normal and ARC groups with an adjusted p-value <0.05 after filtering the data with standard —log2 Fold Change—>1. The differently expressed genes were then used to explore the molecular mechanisms of ARC pathogenesis and development and the volcano map and correlation plot of the DEGs were drawn.
## Functional enrichment analysis
In order to annotate specific genes and to identify the biological function and signaling pathways of the DEGs associated with the pathogenesis of ARC, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed using the “ClusterProfiler” R package (based on p-value <0.05).
## Immune infiltration-related analysis
The relative infiltration proportions of 22 immune cells in the ARC patients were estimated using the CIBERSORTx algorithm (https://cibersortx.stanford.edu/). The correlation and influence of these immune cell proportions were then analyzed using the “corrplot” R package and the relative percentage of the immune cells were compared by Wilcoxon test.
The relationship between the expression levels of the hub genes (mentioned below) and the number of immune cells was analyzed using the Spearman method with $p \leq 0.05$ considered significant.
The relative infiltration proportions of 22 immune cells in the ARC patients were obtained using the CIBERSORTx algorithm shown in Fig. 4A. The correlation and interaction influence of these immune cell proportions are shown in Fig. 4B. However, the box plot (shown in Fig. 4C) compared the relative number of immune cells between the control group and the ARC group and showed no significant difference.
**Figure 4:** *Immune status in the sample analyzed with the CibersortX program.(A) The bar-plot shows the distribution of the 22 types of immune cells in each sample; (B) the correlation plot between the 22 types of immune cells; (C) the box plot comparison (Wilcoxon test) between the control group and ARC group.*
## The construction of the ceRNA network and PPI network
The potential microRNAs (miRNAs) that interact with the differential lncRNAs were predicted using the Perl software combined with the miRcode file (http://www.mircode.org/, the public transcriptome database). The miRDB (http://www.mirdb.org), mirtarbase (https://mirtarbase.cuhk.edu.cn/ miRTarBase/miRTarBase_2022/php/index.php), and TargetScan (http://www.targetscan.org) databases were used in combination to predict the target mRNA of the miRNA. Next, the intersection of miRNA-predicted-mRNAs and differential-mRNAs (co-identified target genes) was identified and used to construct the ceRNA and protein-protein interaction (PPI) network. Next, the five hub genes with the highest scores were identified using the Maximal Clique Centrality (MCC) algorithm (Chin et al., 2014); a topological analysis method introduced by Chin et al. that is good at predicting the proteins that form the yeast PPI network) from the Cytohubba plugin in Cytoscape (version 3.9.1).
We used the miRcode database to predict the target miRNA of the four identified differentially expressed lncRNAs. We obtained 202 target miRNAs and 483 mRNA-miRNA pairs. A total of 1,911 target mRNAs and 3,123 miRNA-mRNA pairs were identified by all three databases used (miRDB, mirtarbase, and TargetScan). A total of 24 mRNA were then acquired from the intersection of miRNA-predicted-mRNAs and differentially expressed mRNAs and identified as co-identified target genes. Finally, 101 lncRNA-miRNA-mRNA pairs, including four lncRNAs, 17 miRNAs (hsa-miR-3619-5p, hsa-miR-10a-5p, hsa-miR-17-5p, hsa-miR-24-3p, hsa-miR-20b-5p, hsa-miR-129-5p, hsa-miR-507, hsa-miR-23b-3p, hsa-miR-216b-5p, hsa-miR-107, hsa-miR-135a-5p,hsa-miR-449c-5p, hsa-miR-206, hsa-miR-761, hsa-miR-363-3p, hsa-miR-27a-3p, hsa-miR-140-5p) and 24 mRNA (CDK6, CHL1, ARSJ, TFAP2C, MLEC, ARHGEF3, CA2, NRIP3, COL4A4, CYP27C1, SNCG, NPAS2, FOXO1, PANK3, NOTCH3, PGD, GFPT2, FZD4, SH3PXD2A, RPUSD2, BAMBI, SPRY2, MT1M, ADAMTS5) were obtained and the ceRNA network was visualized using Cytoscape (shown in Fig. 5A). The most enriched BP in the GO analysis was a cellular response to leukemia inhibitory factor, the most enriched CC was actin cytoskeleton, and the most enriched MF was protease binding (Fig. 5C).
**Figure 5:** *The differentially expressed long non-coding RNA (lncRNA) associated competing endogenous (ceRNA) network, the protein–protein interaction (PPI) network and the functional enrichment of the co-target mRNA.(A) The ceRNA network of 101 pairs of lncRNA-miRNA-mRNA relationships, visualized by Cytoscape; (B) the PPI network of the 24 co-identified target genes, acquired from the intersection of miRNA-predicted-mRNAs and differentially expressed mRNAs, and visualized by the String database (https://cn.string-db.org); (C) the GO analysis and (D) the KEGG analysis of the co-identified target genes.*
The KEGG results (Fig. 5D) showed that human papillomavirus infection, breast cancer, and microRNAs in cancer were the top three enriched pathways. The PPI network of the 24 co-identified target genes was generated and visualized using the STRING database and Cytoscape (Fig. 5B). The hub genes, including forkhead box O1 (FOXO1), NOTCH3, Cyclin-dependent kinase (CDK6), sprouty 2 (SPRY2), and Carbonic anhydrase 2 (CA2), were then tagged with the top five MCC scores calculated by the Cytohubba plugin in Cytoscape and selected as potential ARC therapeutic targets. The FunRich software was used to perform a GO enrichment analysis of the nine predicted miRNAs (miR-107, miR-129-5p, miR-135a-5p, miR-206, miR-23b-3p, miR-27a-3p, miR-3619-5p, miR-449c-5p, and miR-761) related to the hub genes. The most enriched BP in the GO analysis was signal transduction (Fig. 6A), the most enriched CC was nucleus (Fig. 6B), and the most enriched MF was transcription factor activity (Fig. 6C). The correlation plot of the expression of the five hub genes is shown in Fig. 6D.
**Figure 6:** *The gene ontology analysis of the predicted miRNA and the correlation of the hub genes.(A) Top 10 biological processes of predicted miRNA. (B) Top 10 cellular components of predicted miRNA. (C) Top 10 molecular functions of predicted miRNA. (D) The correlation plot of the top five hub genes (FOXO1, NOTCH3, CDK6, SPRY2, and CA2) selected by the MCC algorithm of the Cytoscape plugin, Cytohubba.*
## Gene set enrichment analysis (GSEA) and construction of drug network on hub genes
The expression of each hub gene between the normal and ARC groups were compared and the correlations analyzed. The GSEA of each core gene was also performed to explore the biological signaling pathways (based on p-value <0.05). Additionally, protein-drug interaction data were retrieved from the DGIdb database (http://www.dgidb.org) and visualized using Cytoscape to predict potential therapeutic agents for ARC patients.
## Differentially Expressed Genes (DEGs)
The process for identifying potential therapeutic targets for ARC is shown in Fig. 1. After annotating with the Gencode annotation file, 25,023 protein-coding RNAs and 143 lncRNAs were retrieved from the output expression files and used in the differential analysis. With the threshold of adjusted —log2(FC)—>1 and a p- value of 0.05, we identified a total of 187 differentially expressed mRNAs, including 56 up-regulated and 131 down-regulated genes. Additionally, four differentially expressed lncRNAs were identified, including two up-regulated—H19 and X-inactive specific transcript (XIST)—and two down-regulated—testis-specific transcript Y-linked 14 (TTTY14) and MEG3. Figures 2A–2D shows the volcano and heatmap plots of the differentially expressed mRNAs (A, B) and lncRNAs (C, D).
**Figure 1:** *A Flowchart for the identification of potential ARC therapeutic targets.RC, age-related cataract; mRNA, messenger RNA; miRNA, microRNA; lncRNA, long non-coding RNA; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; ceRNA, competing endogenous RNA; PPI, protein-protein interaction.* **Figure 2:** *The volcano plots and heatmaps for the differentially expressed genes between the normal group and ARC group (adjusted p-value < 0.05 after filtering the data with standard —log2 Fold Change— > 1).(A) The volcano plots of the 187 identified differentially expressed mRNAs, including 56 up-regulated genes (red dots) and 131 down-regulated genes (blue dots); (B) the volcano plots of the four identified differentially expressed lncRNA, including two up-regulated (red dot) and two down-regulated (blue dot) lncRNAs; (C) the heatmap of the differentially expressed mRNAs; (D) the heatmap of the differentially expressed lncRNAs.*
## Functional enrichment of DEGs
The GO and KEGG pathway enrichment analyses were conducted on the 187 differentially expressed mRNAs that were identified. The GO analysis identified 559 enriched pathways of significance in the biological processes (BP) category, 55 in the cellular components (CC) category, and 64 in the molecular functions (MF) category. The most significantly enriched terms in each of the categories were then identified (Fig. 3A). The most significantly enriched BP were: extracellular matrix organization, extracellular structure organization, and external encapsulating structure organization. The most significantly enriched CC were the collagen–containing extracellular matrix, endoplasmic reticulum lumen, and basement membrane. The most significantly enriched MF were: the extracellular matrix structural constituent, extracellular matrix binding, and peroxidase activity. The KEGG results identified 25 enriched pathways. The five psathways with the greatest enrichment were: human papillomavirus infection, breast cancer, the Wnt signaling pathway, glutamatergic synapse, and the TGF −beta signaling pathway (Fig. 3B).
**Figure 3:** *The bubble plot of the enriched functional pathways of the differentially expressed mRNAs between the normal group and ARC group (based on p-value < 0.05).(A) The Gene Ontology (GO) analysis shows that the differentially expressed genes are mainly enriched in the biological pathways of extracellular matrix organization and extracellular structure organization; (B) the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that the differentially expressed genes are mostly enriched in human papillomavirus infection, breast cancer, and the Wnt signaling pathway.*
## Gene set enrichment analysis (GSEA)
The expression of the screened target genes were analyzed between the normal and ARC groups, as shown in Fig. 7 (A, C, E, G, I, ***<0.001, **<0.01, *<0.05). The GSEA of the top six enriched genes—FOXO1 (ABC Transporters, Galactose Metabolism, and Glycosylphosphatidylinositol GPI Anchor Biosynthesis), NOTCH3 (Aminoacyl-tRNA Biosynthesis, Huntingtins Disease, and Neuroactive Ligand Receptor Interaction), CDK6 (ABC Transporters, Galactose Metabolism, and Glycosylphosphatidylinositol GPI Anchor Biosynthesis), SPRY2 (Aldosterone Regulated Sodium Reabsorption, Biosynthesis of Unsaturated Fatty Acid and Neuroactive Ligand Receptor Interaction), and CA2 (ABC Transporters, Cysteine and Methionine Metabolism, and Galactose Metabolism)—was then performed (shown in Figs. 7B, 7D, 7F, 7H, 7J).
**Figure 7:** *The comparison of the expression of each hub gene between the control group and the ARC group (A, C, E, G, I, t-test, *** <0.001 ** <0.01 * <0.05), and the Gene Set Enrichment Analysis (GSEA; based on p-value <0.05) of each hub gene (B, D, F, H, J).*
## Construction of ceRNA network and drug network on hub genes
The core ceRNA network was constructed with five hub genes, the nine miRNAs that interacted with the hub genes, and the four miRNA-targeted lncRNAs (shown in Fig. 8A). Figure 8B shows no significant correlation between immune cells and the five hub genes. A total of 26 drugs that interact with the identified ARC hub genes were acquired from the DGIdb database. Of the top five drugs identified with the greatest interaction score, one (Tarextumab) targets NOTCH3, and four (Dorzolamide, Brinzolamide, Ethinamate, and Sulthiame) target CA2. Four hub genes with drug interactions constitute the drug network visualized by Cytoscape shown in Fig. 9.
**Figure 8:** *The ceRNA network and Gene Set Enrichment Analysis (GSEA) of the hub genes.(A) The core ceRNA network with four lncRNA, nine mi-RNA, and five hub genes; (B) the correlation between immune cells and the five hub genes.* **Figure 9:** *The drug network of the hub genes retrieved by the DGIdb database (https://www.dgidb.org) and visualized by cytoscape; red: up-regulated gene, orange: down-regulated gene, blue: drug.*
## Discussion
In this study, we analyzed the differentially expressed lncRNA in the anterior lens capsules of ARC patients and the control group using a differential expression analysis, functional enrichment analysis, and an immune infiltration-related analysis. We then constructed a ceRNA network and identified five hub genes to serve as possible therapeutic targets for ARC progression.
The GO enrichment of the DEGs in the BP were mainly enriched in extracellular matrix (ECM) organization. Because of the importance of the interactions between the ECM of the lens capsular cells during lens development, the disruption of the ECM or a change in cell signaling, especially transduced by integrins, may stunt lens growth and promote cataracts (Wederell & De Iongh, 2006). The KEGG pathway analysis of the DEGs identified the Wnt and TGF–beta signaling pathways, which is in accordance with the results of previous studies (Chen et al., 2021; Shi & Yang, 2021).
Our analysis showed no statistical differences in the percentage of immune cells between the normal and ARC groups. Our study used an estimated prediction of the abundance of different types of immune cells, and it is important to note that this finding was not experimentally verified and the data was not derived from a single-cell RNAseq experiment. Although a group of resident immune cells of the lens was found to be established during embryonic development and maintained in adulthood, the immune cells surveilling the lens are usually activated during eye injury, lens degeneration, and lens wounding, especially post cataract surgery (Stepp & Menko, 2021).
Competing endogenous RNA (ceRNA) has a complex regulatory network with rich biological functions, which has attracted extensive attention in the academic community. We constructed the ceRNA network using the differentially expressed lncRNA and the intersection of miRNA-predicted-mRNAs and differentially expressed mRNAs. Subsequently, the five hub genes with the highest scores, as calculated by the MCC method, were selected from the co-identified target mRNA of the PPI network. There was no significant correlation between the hub genes and the proportion of immune cells, which was consistent with the results of the immune analysis. Conversely, the GSEA demonstrated that the hub genes were highly correlated with cell metabolism, in line with previous research (Chen et al., 2014; Duncan & Wormstone, 1999; Stambolian, 1988; Truscott, 2005). Finally, the drug network was constructed to show the interaction between the identified proteins and possible drug therapies.
There were four differentially expressed lncRNA identified in this study: H19 and XIST were up-regulated, and MEG3 and TTTY14 were down-regulated in the ARC patients. The H19 lncRNA was also found to be up-regulated in ARC patients in Cheng et al. [ 2019] and Liu et al. [ 2018], which is consistent with our study. These studies also found that the protective role of H19 lncRNA was associated with cell proliferation and apoptosis by regulating the miR-29a–TDG axis and miR-675-CRYAA axis. Fibrosis in the lens (EMT process, including the loss of epithelial cell integrity with abnormal proliferation, migration, and the cell morphology changing into more mesodermal-derived mesenchymal-like cells), increasing apoptosis resistance, and exaggerated ECM components production, is featured by the accumulation of excess connective tissue that destroys the normal structure and function of the lens (Lovicu, Shin & McAvoy, 2016).
In an in vitro study, Xiong et al. [ 2022] indicated the novel role of H19 lncRNA in inhibiting TGF- β2-induced EMT to prevent lens fibrosis. Previous studies have also demonstrated that Xist lncRNA can play a role in regulating X chromosome inactivation (XCI) and lead to the inheritable silencing of one of the X-chromosomes during female cell development. Xist lncRNA is also involved in tumor development and the progression of other diseases by acting as a ceRNA (Wang et al., 2021). Our study’s functional enrichment results showed no significant enrichment in the X-chromosome, indicating that the ARC pathogenesis may be independent of the X-chromosome. Another study found that Xist lncRNA plays a protective role in diabetic cataracts by promoting cell proliferation and decreasing apoptosis through the miR-34a/SMAD2 (Wang, Zhao & Zhang, 2022). It is unclear in our study whether the Xist up-regulation that was observed was a causative or reactive protective factor in ARC. Another study showed that MEG3 lncRNA is a cataractogenesis molecule through the up-regulation of TP53INP1 in ARC patients (Tu et al., 2020). Though TTTY14 lncRNA has not been found in any cataract pathogenesis, previous studies have shown that it is associated with the progression of cancers (Gong et al., 2020; Kopczyńska et al., 2020; Li et al., 2017b), endometriosis (Bhat et al., 2019), and COVID-19 (Askari, Hadizadeh & Rashidifar, 2022), suggesting the critical role of TTTY14 in biological processes. Therefore, the molecular function of TTTY14 in ARC patients should be further studied.
MicroRNAs (miRNAs) are a class of endogenous short non-coding RNAs (containing ∼22 nucleotides) that are part of the epigenome and post-transcriptional control gene expression through translational repression or mRNA degradation (Cai et al., 2009). The predicted target miRNAs of the hub genes associated the ceRNA network include miR-107, miR-129-5p, miR-135a-5p, miR-206, miR-23b-3p, miR-27a-3p, miR-3619-5p, miR-449c-5p, and miR-761. For example, the upregulated miR-23b-3p, which involved in apoptosis (Liu et al., 2022), autophagy (Zhou et al., 2019), and resistance to oxidative damage (Liang et al., 2020) in ARC via translational repression of key molecules, such as homeodomain interacting protein kinase 3 (HIPK3) and silent information regulator 1 (SIRT1). Importantly, the miRNAs were predicted by miRNA related database which need further experiment validation.
FOXO1, one of the members of the FOXO subfamily of Forkhead transcription factors, is known as a cell response regulator to oxidative stress. Up-regulation of FOXO1 has been reported in high glucose-treated human lens epithelium cell (HLEC) lines contributing to cataractogenesis as a cell death-related gene and serving as the target of *Lycium barbarum* polysaccharide treatment (Yao et al., 2020). Conversely, another study (Zhu et al., 2018) with different glucose concentrations showed that up-regulating FOXO1 can prevent HLECs from oxidative damage induced by high glucose via beta-casomorphin-7 treatment. FOXO1 is also associated with choroidal neovascularization, and retina vein occlusions have also been reported (Chen et al., 2019; Zhou et al., 2021b). However, there is a lack of research on the role of up-regulated FOXO1 in ARC pathogenesis.
Notch proteins (NOTCH1-4) are a family of transmembrane receptors that play a vital role in both developmental and cell fate decisions (Artavanis-Tsakonas, Rand & Lake, 1999). The dysregulation of Notch proteins is involved in many diseases, such as cancer, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, and pulmonary hypertension (Hosseini-Alghaderi & Baron, 2020). Additionally, Zhou et al. ( 2021a) proposed that the down-regulation of NOTCH3/Hes1 was related to the apoptosis of lens epithelial cells under cold stimulation, which supports the decreased NOTCH3 expression observed in ARC patients in our study. However, another study suggested that NOTCH3 can be directedly regulated by the toll-like receptor (TLR)-3, contributing to the process of EMT, triggering fibrotic cataracts (Xie et al., 2022). Thus, further studies of the NOTCH3 inactivation mechanism in ARC patients are needed.
It is universally accepted that Sprouty proteins with four mammalian orthologs (SPRY1-4) function as antagonists of receptor tyrosine kinase-induced signal transduction in organisms, which is essential to growth and development (Cabrita & Christofori, 2008). The down-regulation of SPRY2 in patients with ARC has been observed in a number of studies. Previous studies (Lovicu, Shin & McAvoy, 2016; Shin et al., 2012) have shown that SPRY2 is a protective factor as a negative regulator of transforming growth factor β-induced EMT and cataract formation, likely by regulating ERK$\frac{1}{2}$ and Smad2 (Tan et al., 2016; Zhao et al., 2022). Studies have also shown that SPRY2 interacts with microRNA (Liu et al., 2019; Liu et al., 2021), suggesting that it may play a crucial role in the EMT of lens epithelial cells in ARC patients.
The down-regulation of CDK6 and CA2 were also identified in our study. CDK6, which is highly correlated with the cell cycle, constitutes a complex with cyclin D and CDK inhibitor to control the G1 checkpoint through the phosphorylation of the retinoblastoma protein (pRb; Lukas, Bartkova & Bartek, 1996). A study exploring the expression and activity of CDK cells during lens differentiation (developing rat lenses) showed that Cdk6 was not expressed in lens fiber cells or epithelial cells during lens differentiation (Gao et al., 1999), but there has been little study on the relationship between CDK6 and ARC.
There are 15 known human isoforms of carbonic anhydrase (CA) with different functions and distributions, which belong to a class of metalloenzymes that catalyze carbon dioxide into bicarbonate. Human CA variants have been linked to glaucoma, macular edema, ulcers, obesity, and cancer (Cabaleiro-Lago & Lundqvist, 2020). A study (Wistrand, 1999) investigating human lens CA demonstrated that the CA activity originates from CA 1, 2 and 3 in the cytoplasm, and from CA4 in the plasma membranes of lens epithelium and fibers in normal patients, and there was no CA activity observed in the supernatant of senile cataract lenses. As studies (Wistrand, 1999; Wistrand, 2000) have shown that the chronic intake of CA inhibitor does not seem to induce lens opacification (signs of cataract), we hypothesize that the down-regulation of CA2 observed in our study may be a protective response to oxidative stress.
Some limitations in this study should be recognized. First, our results require further validation through cell and animal experiments. Second, the samples are limited and lack clinical signatures (such as postoperative vision, surgery complication, and visual quality), which needs further study by enlarging the samples combined with clinical information.
## Conclusion
The differentially expressed lncRNAs and hub genes identified in this study have the potential to serve as therapeutic targets for ARC patients based on cell metabolism.
## RNA-seq power calculation
Not applicable. This is study used an Illumina BeadChip microarray of transcriptome sequencing with three biological samples as minimum replicates to conduct comparative gene expression analyses (Conesa et al., 2016).
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|
---
title: Maternal serum preptin levels in the pathogenesis and diagnosis of Gestational
diabetes mellitus
authors:
- Utku Irem Kıraç
- Esra Demır
- Hanişe Ozkan
- Berrak Sahtıyancı
- Hafize Uzun
- Iskender Ekıncı
- Mitat Buyukkaba
- Sinem Durmus
- Murat Akarsu
- Remise Gelisgen
- Omur Tabak
journal: Journal of Medical Biochemistry
year: 2023
pmcid: PMC10040191
doi: 10.5937/jomb0-36287
license: CC BY 4.0
---
# Maternal serum preptin levels in the pathogenesis and diagnosis of Gestational diabetes mellitus
## Abstract
### Background
Gestational diabetes mellitus (GDM) is a metabolic disorder that occurs during pregnancy that increases both maternal and fetal mortality and morbidity. It was investigated whether there is a change in circulating levels of preptin, a new peptide secreted from pancreatic beta cells, due to GDM in pregnant women. The relationship between serum preptin levels with insulin and other metabolic parameters was also evaluated in these subjects.
### Methods
Eighty-five patients diagnosed as GDM and 89 healthy pregnant women with 75 mg oral glucose tolerance test (OGTT) was assessed in terms of serum preptin levels.
### Results
The serum preptin levels of the GDM group were significantly higher than those of the control group ($$p \leq 0.001$$; $p \leq 0.01$). For the cutoff value of preptin measurement of 335.3 ng/L, the sensitivity was $97.65\%$, specificity was $87.64\%$, positive predictive value was $88.3\%$ and negative predictive value was $97.5\%$. The risk of developing the disease is 294.273 times higher in patients with preptin level of 335.3 and above.
### Conclusions
We think that the reason for the increase in serum preptin levels in GDM is probably the response to glucose. The current results indicate that preptin plays an important role in elucidating the pathology of GDM. In addition, the search for a practical marker for the diagnosis of GDM suggests that the measurement of preptin level is promising.
## Uvod
Gestacijski dijabetes melitus (GDM) je metabolički poremećaj koji se javlja tokom trudnoće i povećava smrtnost i morbiditet i majke i fetusa. Ispitivano je da li po stoji promena u cirkulišućim nivoima preptina, novog peptida izlučenog iz beta ćelija pankreasa, usled GDM kod trudnica. Odnos između nivoa preptina u serumu sa insulinom i drugih metaboličkih parametara je takođe procenjen kod ovih ispitanika.
## Metode
Osamdeset pet pacijenata sa dijagnozom GDM i 89 zdravih trudnica sa 75 mg oralnim testom tolerancije glukoze (OGTT) je procenjeno u smislu nivoa preptina u serumu.
## Rezultati
Nivoi preptina u serumu GDM grupe bili su značajno viši od onih u kontrolnoj grupi ($$p \leq 0$$,001; $p \leq 0$,01). Za graničnu vrednost merenja preptina od 335,3 ng/L, osetljivost je bila 97,$65\%$, specifičnost je bila 87,$64\%$, pozitivna prediktivna vrednost je bila 88,$3\%$ i negativna prediktivna vrednost je bila 97,$5\%$. Rizik od razvoja bolesti je 294.273 puta veći kod pacijenata sa nivoom preptina od 335,3 i više.
## Zaključak
Smatramo da je razlog za povećanje nivoa serumskog preptina u GDM verovatno odgovor na glukozu. Sadašnji rezultati pokazuju da preptin igra važnu ulogu u rasvetljavanju patologije GDM. Pored toga, potraga za praktičnim markerom za dijagnozu GDM-a sugeriše da je merenje nivoa preptina obećavajuće.
## Introduction
Gestational diabetes mellitus (GDM) is glucose intolerance of varying degrees that begins during pregnancy. Although the incidence of GDM in the population varies from $2\%$ to $38\%$, the prevalence is about $17\%$ worldwide [1]. It is associated with an increased risk of short- and long-term complications for both the baby and the mother during and after pregnancy [2]. Risk factors for GDM include: impaired glucose tolerance in history, GDM in previous pregnancy, family history of diabetes, high BMI, advanced maternal age, and birth of a macrosomic baby in history [3] [4].
There is no complete international consensus on the approach that should be used in the diagnosis and management of GDM. There continues to be debate about whether screening should be done in general or high-risk groups, the timing of screening, the test to be used, and the thresholds for assessment. For screening and diagnosis of gestational diabetes, obstetricians in the United States tend to use the two-step approach regardless of gestational age [5], whereas other countries, including Turkey, generally tend to use a one-step approach [75 mg oral glucose tolerance test (OGGT)] [6]. Because multiple samples must be collected when using the OGTT, there are problems with its use if samples are not given in a timely manner or are left before the process is completed. In addition, nausea and vomiting are commonly observed limitations in the use of the OGGT. Studies highlight the need to develop new and simpler diagnostic methods [7].
A relation was reported between GDM and the circulating levels of some peptide hormones as ghrelin, obestatin, visfatin, preptin. Preptin is a peptide composed of 34 amino acids that is secreted by pancreatic beta cells along with insulin and amylin [8]. Experimental studies have also shown the insulin-releasing effect of preptin. This peptide, which is closely related to insulin resistance, may be important in explaining the pathophysiology of GDM. In the literature, DM has been associated with metabolic syndrome, obesity, polycystic ovary syndrome, hypertension, thyroid disease, and cardiovascular disease [9] [10] [11] [12]. In a small number of studies with limited patient populations, maternal serum and cord blood preptin levels have also been examined and found to be significantly associated with GDM [13].
In our study, it was investigated the relationship between serum preptin level and GDM. In this way, we investigated the role of preptin in the pathophysiology of GDM and whether it could be used as a marker in the diagnosis of GDM in the future.
## Materials and methods
All pregnant subjects who participated in the study gave informed consent and the study was approved by the ethics committee of our hospital (approval number: KAEK/2021.06.213). This study was conducted in accordance with the Declaration of Helsinki.
Our study was conducted on 174 female subjects who presented to Kanuni Sultan Suleyman Training and Research Hospital between August and November 2021. A total of 174 participants, 85 patients and 89 volunteers, were enrolled in the study. The study participants were divided into two groups to assess relevant variables: the GDM patient group and the control group. Pregnant women aged 24-28 weeks who presented to the Internal Medicine Outpatient Department of Kanuni Sultan Suleyman Training and Research Hospital and were diagnosed with GDM by routine OGGT test according to the American Diabetes *Association criteria* (ADA) were selected as the GDM patient group [6]. The control group consisted of healthy pregnant women aged 24 to 28 weeks who presented to the outpatient clinic for routine examination and were not suffering from any disease. Patients were included in the study if they were over 18 years of age, were between 24 and 28 weeks pregnant, had no previous diagnosis of diabetes, knew of no chronic diseases, and were not taking any medications. Patients with known overt diabetes, hypertension, preeclampsia, active infection, malignant diseases, chronic renal failure (creatinine > 1.5), severe malnutrition, advanced-stage heart failure (stage 3±4), chronic inflammatory diseases, chronic lung diseases (such as COPD, bronchiectasis, asthma, pulmonary hypertension) and thyroid dysfunction were excluded from the study.
The diagnosis of GDM was made by a 75 g OGTT performed between the 24th and 28th week of gestation and in accordance with the American Diabetes Association guidelines (ADA) [6].
Fasting blood glucose was measured after 8 hours of fasting followed by a 75 g OGGT test. GDM was diagnosed based on the assessment of the following fasting blood glucose levels: 5.11 mmol/L, first-hour plasma glucose: 9.99 mmol/L, second-hour plasma glucose: 8.49 mmol/L.
Age, gender, smoking status, height, weight, BMI, waist circumference, hip circumference, medical disease history and medication of the subjects were recorded. Among the comorbid diseases, diabetes mellitus type 2 (T2DM), pre-diabetes, hyper tension, hyperlipidemia and hypothyroidism were specifically queried and recorded. Medications taken by the patients with comorbid diseases were also recorded. Blood pressure was measured in all participants. For determination of preptin level, blood was drawn from the participants and serum samples were stored at -80°C after centrifugation at 3000 rpm (revolutions per minute). Plasma glucose, lipid profiles, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), and uric acid levels were measured spectrophotometrically using the Abbot Aeroset 2.0 (Abbott Diagnostic, Abbott Park, IL USA). HbA1c (%) was measured using the Variant 2 Turbo (Biorad, Hercules, CA, USA), which uses glycation-specific binding of boron affinity to detect all glycated Hb species present. Complete blood count (CBC) was measured with the Sysmex XN 9000 (Sysmex Europe GmbH, Norderstedt, Germany) hematology analyzer, and insulin levels were measured with the Cobas 8000 C702 (Roche Diagnostics, Indianapolis, IN, USA) chemistry analyzer. Electrolytes were measured using the Cobas 8000 C702 (Roche Diagnostics) chemistry analyzer (Biomolecules 2019, 9, 24 3 of 8). The homeostatic model score for insulin resistance (HOMA-IR) was calculated using the following formula: HOMA-IR = fasting insulin (mIU/mL) x fasting glucose (mmol/L)/22.5.
Serum preptin levels were measured using the Human Preptin ELISA Kit (catalog number: E1448Hu, BT-LAB). The coefficients of intra and inter assay variation were $4.6\%$ ($$n = 20$$) and $5.7\%$ ($$n = 20$$), respectively.
## Statistical analysis
Using the »comparison of two ratios« formula to determine the sample size and assuming an alpha error of $5\%$, it was concluded that at least 172 patients should be included to achieve a study power of $80\%$. A total of 174 participants, 85 patients and 89 volunteers, were included in the study. The Number Cruncher Statistical System (NCSS) 2007 program (Kaysville, Utah, USA) was used for statistical analysis. Descriptive statistical methods (mean, standard deviation, median, frequency, percentage, minimum, maximum) were used to analyze the study data.
Agreement of quantitative data with normal distribution was tested using Shapiro-Wilk test and graphical tests. Independent groups t-test compared normally distributed quantitative variables between two groups and Mann-Whitney U test compared non-normally distributed quantitative variables between two groups. Pearson’s chi-square test and Fisher’s exact test were used to comparing qualitative data. Spearman correlation analysis evaluated the relationships between quantitative variables. Diagnostic screening tests (sensitivity, specificity, PPV, NPV) and ROC analysis were performed to determine the cut-off value for the parameters. Statistical significance was accepted as $p \leq 0.05.$
## Results
Age and number of pregnancies of participants in the GDM patient group were not statistically significantly different between groups ($p \leq 0.05$). The incidence of GDM in the history was statistically significantly higher among patients in the GDM patient group than in the control group ($$p \leq 0.035$$). The BMI values of the participants in the GDM patient group were statistically significantly higher than those of the control group ($$p \leq 0.027$$) (Table 1).
**Table 1**
| Unnamed: 0 | Unnamed: 1 | GDM group<br>(n=85) | Healthy group<br>(n=89) | P |
| --- | --- | --- | --- | --- |
| Age<br>(Year) | Mean<br>±SD | 30.2±4.9 | 31.3±6.4 | 0.169a |
| BMI (kg/m2) | Mean<br>±SD | 29.37±4.64 | 27.85±4.33 | 0.027a* |
| Number of pregnancies | Mean<br>±SD | 2.66±1.38 | 2.4±1.25 | 0.229b |
| History of GDM | yes | 75 (88.2) | 86 (96.6) | 0.035c* |
| History of GDM | no | 10 (11.8) | 3 (3.4) | |
The presence of HT, smoking and family history distribution showed no statistically significant difference between groups ($p \leq 0.05$) (Table 2).
**Table 2**
| Unnamed: 0 | Unnamed: 1 | GDM group<br>(n=85) | Healthy<br>group<br>(n=89) | P |
| --- | --- | --- | --- | --- |
| | | n (%) | n (%) | |
| Hypertension | no | 83 (97.6) | 88 (98.9) | 0.614d |
| Hypertension | yes | 2 (2.4) | 1 (1.1) | |
| Smoking | no | 71 (83.5) | 75 (84.3) | 1.000c |
| Smoking | yes | 14 (16.5) | 14 (15.7) | |
| Family History of GDM | no | 55 (64.7) | 46 (51.7) | 0.082c |
| Family History of GDM | yes | 30 (35.3) | 43 (48.3) | |
The urea, creatinine, AST, LDL, HDL, TG, total cholesterol, uric acid, GGT, HG, WBC, PLT, MPV measurements of the patients showed no statistically significant difference between the groups ($p \leq 0.05$). ALT values of the GDM patient group were found to be statistically significantly higher than those of the control group ($$p \leq 0.009$$; $p \leq 0.01$) (Table 3).
**Table 3**
| Unnamed: 0 | Unnamed: 1 | GDM group<br>(n=85) | Healthy group<br>(n=89) | p |
| --- | --- | --- | --- | --- |
| Urea (mmol/L) | Ort±SD | 5.22±1.42 | 5.00±1.33 | 0.283 a |
| Creatinine (mmol/L) | Mean ±SD | 39.78±7.96 | 38.01±7.10 | 0.213b |
| Insulin (mU/L) | Ort±SD | 11.89±4.59 | 12.33±5.24 | 0.832b |
| AST (U/L) | Mean ±SD | 17.91±5.95 | 16.73±4.94 | 0.161b |
| ALT (U/L) | Mean ±SD | 14.33±6.13 | 12.55±5.93 | 0.009b** |
| LDL (mmol/L) | Mean ±SD | 2.95±1.30 | 2.90±0.99 | 0.699b |
| HDL (mmol/L) | Mean ±SD | 1.71±0.43 | 1.75±0.39 | 0.478a |
| TG (mmol/L) | Mean ±SD | 2.33±0.80 | 2.22±0.85 | 0.399a |
| T. Cholesterol (mmol/L) | Mean ±SD | 5.67±1.55 | 5.66±1.22 | 0.951a |
| Uric acid (μmol/L) | Mean ±SD | 195±49 | 194±35 | 0.929a |
| GGT (IU/L) | Mean ±SD | 90.00±54.50 | 83.70±49.30 | 0.436b |
| Hgb (g/L) | Mean ±SD | 112.10±9.60 | 110.90±13.30 | 0.737b |
| WBC (x103) | Mean ±SD | 10.25±2.18 | 9.65±2.28 | 0.079a |
| PLT (x109) | Mean ±SD | 237.71±62.2 | 241.31±65.33 | 0.710a |
| MPV (fL) | Mean ±SD | 0.9±0.91 | 10.83±1.27 | 0.672a |
| Potasium (mmol/L) | Mean ±SD | 4.13±0.3 | 4.04±0.27a | 0.046* |
| Sodium (mmol/L) | Mean ±SD | 137.24±1.74 | 37.46±1.36 | 0.684b |
It was determined that the serum preptin levels of participants in the GDM patient group were statistically significantly higher than those of the control group ($$p \leq 0.001$$; $p \leq 0.01$) (Table 4).
**Table 4**
| Unnamed: 0 | Unnamed: 1 | GDM group<br>(n=85) | Healthy group<br>(n=89) | p |
| --- | --- | --- | --- | --- |
| Glucose (mmol/L) | Mean ±SD<br>Median (Min-Maks) | 4.71±0.80<br>4.66 (3.05–8.55) | 4.05±0.57<br>4.05 (2.72–5.72) | 0.001 a** |
| OGTT 0. (mmol/L) | Mean ±SD<br>Medyan (Min-Maks) | 5.22±0.64<br>5.11 (3.05–8.44) | 4.54±0.30<br>4.61 (3.89–5.05) | 0.001b** |
| OGTT 1. (mmol/L) | Ort±SD<br>Medyan (Min-Maks) | 9.45±2.07<br>9.71 (4.44–14.38) | 7.24±1.30<br>7.27 (4.55–9.94) | 0.001a** |
| OGTT 2. (mmol/L) | Mean ±SD<br>Medyan (Min-Maks) | 8.03±1.70<br>7.99 (1.70–13.26) | 5.03±1.04<br>5.77 (3.39–8.21) | 0.001a** |
Based on this significance, it was considered to calculate the cut-off point for preptin. ROC analysis and diagnostic screening tests were used to determine the cut-off point for each group. The cut-off point for preptin measurements was set at 335.3 and above for the study and control groups. For the cut-off value of preptin measurement of 335.3, the sensitivity was $97.65\%$, specificity was $87.64\%$, positive predictive value was $88.3\%$ and negative predictive value was $97.5\%$ (Table 4).
The area under the ROC curve obtained was $92.6\%$ with a standard error of $2.3\%$ (Figure 1). The Odds Ratio (OR) for preptin was 294.273 ($95\%$ CI: 63.212–1369.937) (Table 5). A statistically significant correlation was found between the groups and the cut-off value of the preptin level of 335.3 ($$p \leq 0.001$$). We can say that the risk of developing the disease is 294.273 times higher in patients with preptin level of 335.3 and above. Table 6 **Figure 1:** *The distribution of thyroid stimulating hormone (TSH) with age. Plots showing the median TSH for each of the changes according to different age group.* TABLE_PLACEHOLDER:Table 5 TABLE_PLACEHOLDER:Table 6
## Discussion
Although GDM is a condition that resolves immediately with termination of pregnancy, there is no clear data on how it develops during pregnancy. Peptides are thought to play a role in pathogenesis. Preptin is a 34 amino acid peptide that is secreted by pancreatic beta cells along with insulin and amylin.
In a study by Buchanan et al. [ 14] in a rat pancreas model, preptin was shown to play a direct role in glucose-mediated insulin secretion. In the same study, it was found that glucose-dependent insulin secretion increased by $30\%$ after preptin infusion into rat pancreas. Yang et al. [ 9] was reported that preptin may play a role in the development of type 2 DM, and it was indicated that preptin levels were significantly higher in the DM group. In current study, we considered the similarities in the pathogenesis of type 2 DM and GDM and hypothesized that preptin might also play a role in the development of GDM. There are few studies in the literature investigating preptin levels in serum, cord blood and colostrum of GDM patients. In a study conducted by Aydin et al. [ 15] in 36 patients, 12 of whom had GDM, it was found that preptin levels in plasma and colostrum were significantly higher in the GDM group. In another study conducted by Aslan et al. [ 13] in 62 patients, 31 of whom had been diagnosed with GDM, it was revealed that preptin levels in maternal plasma and fetal cord blood were significantly higher in the GDM group. On the other hand, in a study by Ersahin et al. [ 16] difference was not found between the preptin levels in plasma of GDM patients and healthy pregnant women with the same BMI [16].
In current study, serum preptin level was significantly higher in the GDM patient group than in the control group. This increase may be due to either increased secretion of preptin or decreased metabolism. Again, we detected that the cut-off point for preptin measurements in the GDM patient and control groups was 335.3 and above. We can say that the risk of disease is 294.273 times higher at preptin level of 335.3 and above. This result gave us the idea that measuring preptin level could be a practical indicator for diagnosing GDM in the future. We did not find similar data in the literature. The OGGT test is routinely performed for the diagnosis of GDM. However, there are problems such as not being able to finish the drink completely, not being able to continue the test, not giving the blood sample at the right time, and ending the test due to nausea and vomiting [17].
It may be beneficial to have a marker in the blood that can be tested immediately once. In this regard, it is recommended that testing be performed in larger groups of patients. Furthermore, no correlation was found between preptin levels and BMI in our study’s GDM patient and control groups. This suggested that a significant increase in preptin level in the GDM population was not associated with increased BMI. Baykus et al. [ 18] found that A positive correlation was established between desacylated ghrelin and acylated ghrelin, desacylated ghrelin and preptin and preptin and insulin in the GDM group during pregnancy. Preptin is reported to be secreted together with insulin in response to glucose [19].
We think that the reason for the increase in serum preptin levels in GDM is probably the response to glucose. In future studies, measuring serial changes in serum preptin levels from the beginning of pregnancy may provide valuable clues to elucidate the role of preptin in the pathogenesis of GDM. Further studies are needed to investigate the role of this peptide in the pathogenesis of GDM. It could be used in the diagnosis of GDM in the future.
## Acknowledgments
This research did not receive any specific grant from any funding agency in the public commercial or a non-profit section.
## Conflict of interest statement
All the authors declare that they have no conflict of interest in this work.
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|
---
title: Serum fetuin-A and RANKL levels in patients with early stage breast cancer
authors:
- Cigdem Usul Afsar
- Hale Aral
- Orçun Can
- Trabulus Didem Can
- Didem Karacetin
- Nazlı Mehmet Ali
- Gursu Rıza Umar
- Senem Karabulut
journal: Journal of Medical Biochemistry
year: 2023
pmcid: PMC10040193
doi: 10.5937/jomb0-37386
license: CC BY 4.0
---
# Serum fetuin-A and RANKL levels in patients with early stage breast cancer
## Abstract
### Background
Breast cancer (BC) is the primary cause of mortality due to cancer in females around the world. Fetuin-A is known to increase metastases over signals and peroxisomes related with growing. Receptor activator of nuclear factor-kB ligand (RANKL) takes part in cell adhesion, and RANKL inhibition is used in the management of cancer. We aimed to examine the relationship between serum fetuin-A, RANKL levels, other laboratory parameters and clinical findings in women diagnosed with early stage BC, in our population.
### Methods
Women having early stage BC ($$n = 117$$) met our study inclusion criteria as they had no any anti-cancer therapy before. Thirty-seven healthy women controls were also confirmed with breast examination and ultrasonography and/or mammography according to their ages. Serum samples were stored at -80°C and analysed via ELISA.
### Results
Median age of the patients was 53 (range: 57-86) while it was 47 (range: 23-74) in the healthy group. Patients had lower high-density lipoprotein levels ($$p \leq 0.002$$) and higher neutrophil counts ($$p \leq 0.014$$). Fetuin-A and RANKL levels did not differ between the groups ($$p \leq 0.116$$ and $$p \leq 0.439$$, respectively) but RANKL leves were found to be lower in the favorable histological subtypes ($$p \leq 0.04$$).
### Conclusions
In this study, we found no correlation between serum fetuin-A levels and clinical findings in patients diagnosed with early stage BC. However, RANKL levels are found to be lower in subgroups with favorable histopathologic subtypes such as tubular, papillary and mucinous BC and there was statistically significant difference.
## Uvod
Rak dojke (BC) je primarni uzrok smrtnosti od raka kod žena širom sveta. Poznato je da fetuin-A povećava metastaze u odnosu na signale i peroksizome povezane sa rastom. Receptorski aktivator liganda nuklearnog faktora-kB (RANKL) učestvuje u ćelijskoj adheziji, a RANKL inhibicija se koristi u lečenju kancera. Cilj NAM je bio da ispitamo odnos između serumskog fetuina-A, nivoa RANKL, drugih laboratorijskih parametara i kliničkih nalaza kod žena sa dijagnozom ranog stadijuma BC, u našoj populaciji.
## Metode
Žene sa ranim stadijumom BC ($$n = 117$$) ispunjavale su kriterijume za uključivanje u našu studiju pošto ranije nisu imale nikakvu terapiju protiv raka. Kontrola 37 zdravih žena je takođe potvrđena pregledom dojki i ultrasonografijom i/ili mamografijom u skladu sa njihovim godinama. Uzorci seruma su čuvani na -80 °C i analizirani pomoću ELISA.
## Rezultati
Srednja starost pacijenata bila je 53 godine (raspon: 57-86) dok je u zdravoj grupi bila 47 (raspon: 23-74). Pacijenti su imali niži nivo lipoproteina visoke gustine ($$p \leq 0$$,002) i veći broj neutrofila ($$p \leq 0$$,014). Nivoi fetuina-A i RANKL nisu se razlikovali između grupa ($$p \leq 0$$,116 i $$p \leq 0$$,439, respektivno), ali je utvrđeno da su nivoi RANKL niži kod povoljnih histoloških podtipova ($$p \leq 0$$,04).
## Zaključak
U ovoj studiji nismo pronašli korelaciju između nivoa fetuina-A u serumu i kliničkih nalaza kod pacijenata sa dijagnozom ranog stadijuma BC. Međutim, utvrđeno je da su nivoi RANKL niži u podgrupama sa povoljnim histopatološkim podtipovima kao što su tubularni, papilarni i mucinozni BC i postojala je statistički značajna razlika.
## Introduction
Breast cancer (BC) is the second most frequently diagnosed malignancy just behind lung cancer and also the primary cause of mortality due to cancer in female around the world. Over 1.5 million women ($25\%$ of all women with cancer) are diagnosed with BC every year throughout the world [1]. Today, the total number of BC patients have increased in response to exposure to several risk factors such as abnormal levels of estrogen, smoking, alcohol and obesity [2]. Nevertheless, we know that early BC detection could reduce BC death rates significantly in the long-term [3].
Therefore, specific screening methods with the use of the correct biomarkers are important in the detection of early stage BC [4]. Specially, blood, saliva, and urine were considered as ideal origin in which to assign the presence of cancer biomarkers such as annexin, peroxiredoxin and calreticulin [5].
Fetuin-A, also called Alpha 2-Heremans Schmid Glycoprotein (AHSG), is a serum glycoprotein synthesized by the liver and secreted into the blood stream [6]. Its founded principal role is the inhibition of ectopic calcification, but mounting evidence suggests that it is a multifunctional protein capable of modulating a number of critical signaling pathways and it has roles in disease processes such as diabetes mellitus and kidney disease [7]. In particular, high fetuin-A concentrations are found to be associated with atherogenic lipid profile and metabolic syndrome, low fetuin-A levels are related to vascular calcifications and inflammation [8]. In addition, there are few suggesting increased fetuin-A level may be a new serum biomarker in early BC [9].
Receptor activator of nuclear factor-kappa B ligand (RANKL) appertain to tumour necrosis factor superfamily which is a group of proteins that act as bidirectional signalling molecules. RANKL with osteocyte origin induces bone destruction by stimulating osteoclasts, while RANKL released from osteoblasts functions in the reverse effect [10]. At the same time, available evidence suggests that the RANKL signaling system is associated with in almost all steps in BC development, from primary oncogenesis to the establishment of secondary tumors in the bone [10].
The potential role of tumor markers is to improve early cancer determination and they significantly provide an unmatched opportunity to understand the disease’s biology, improve diagnosis and enhance treatment. In the present study; we aimed to investigate the the serum levels of fetuin-A and RANKL and their relationship with clinical parameters in patients with early stage BC.
## Demographic features of patients
A total of 117 female patients with early stage BC between the ages of 27–86 years (median: 53) were included in the study. Our control group was 37 healthy women between the ages of 23–74 (median: 47) years. BC was diagnosed according to the ultrasonography and histopathologic findings of the patients. Healthy controls were also confirmed with breast examination and ultrasonography and/or mammography according to their ages. The patients were asked for their medical history (type 2 diabetes, hypertension, smoking and medications) and measured for their glomerular filtration rate (GFR) and other biochemical parameters. Exclusion criteria included trauma history, major surgical history, chronic kidney disease with a creatinine clearance under 15 mL/min, hepatitis (alcoholic, toxic hepatitis, chronic autoimmune), fatty liver, alcoholic and primer biliary cirrhosis, chronic inflammatory disease, acute infection, known lung or liver disease, known rheumatic heart valve disease, congenital heart disease including bicuspid aorta, dilated cardiomyopathy and known osteoporosis.
## Serum preparation
Blood was drawn after 12 hours of fasting in the morning. Serum was obtained after at least 30 minutes of clotting by centrifugation at 2500xg for 15 minutes. Serum was stored at –80°C until assayed. All icteric or haemolytic blood samples were discarded. All parameters were analyzed in all samples together in a single batch at the termination of the experimental protocol (control and patient samples were analysed in the same batch).
## Measurement of serum fetuin-A and RANKL levels
Serum fetuin-A and RANKL levels of patients with BC and of the control group were measured in the venous blood. A commercial kit (Assaypro, USA, cat no: EG 63501-1), based on a quantitative sandwich ELISA, was used and results were determined with ELX 800 UV version ELISA reader and calculated in grams per liter. Mean intra-assay and inter-assay coefficients of variation were less than $4.9\%$ (n:10) and $6.7\%$ (n:10).
## Measurement of other biochemical parameters
Levels of serum glucose, urea, creatinine, total cholesterol, HDL, LDL, AST, ALT, GGT, LDH, ALP, total protein, albumin, parathyroid hormone (PTH), calcium, phosphorus, magnesium, CRP, complete blood count (CBC), CEA, CA 15.3 and erythrocyte sedimentation rate (ESR) were measured in the patient and control groups using the same biochemistry laboratory in our hospital.
## Statistical analysis
Statistical analyses (Mann–Whitney U-test, Student t test) were performed with SPSS 19 (Statistical Package for Social Sciences). The difference in various parameters were analyzed by the Chi-square test. Pearson correlation test was used for correlating fetuin-A, RANKL and the different biochemical parameters. Multivariate logistic regression model was performed to determine the effect of independent risk factors for BC. P-values < 0.05 were considered significant.
## Results
Median age of the patients was 53 (range: 57–86) while it was 47 (range: 23–74) in the healthy group. Patients were $56\%$ postmenopausal, $40\%$ premenopausal and $4\%$ perimenopausal. Twenty-four ($20.5\%$) of the patients had cerbb2 3 positive or cerb2 2 positive and SISH/FISH positive disease. Grade 2 disease was found in 60 ($52.6\%$) patients and 47 patients ($41.2\%$) had grade 3 disease. Seventy-four ($63.2\%$) patients had invasive ductal carcinoma (IDC), 15 ($12.8\%$) had invasive lobular carcinoma (ILC), 6 ($5.1\%$) had mixed (IDC+ILC) carcinoma, 1 had metaplastic cancer and 21 ($17.9\%$) had other favorable (tubular, apocrine, papillary and mucinous) types. Twenty-nine patients ($24.7\%$) had stage I, thirty ($25.6\%$) had stage II and fifty-eight ($49.5\%$) patients had stage III BC.
There was no statistically significant difference between ER status, PR status, cerbb2 status, grade, lymphovascular invasion, perineural invasion, stage, menopausal status and serum parameters. Patients had lower high-density lipoprotein levels ($$p \leq 0.002$$) and higher neutrophil counts ($$p \leq 0.014$$) rather than the control group (Table 1 and Table 2). Table 3 Fetuin-A and RANKL levels did not differ between the patients and control groups ($$p \leq 0.116$$ and $$p \leq 0.439$$, respectively) (Table 4). There was no statistically significant difference in fetuin-A levels according to various clinical/laboratory parameters (Table 5). However, patients with favorable histopathologies such as tubular, apocrine, papillary and mucinous subtypes ($$n = 24$$) had lower RANKL values and it was found to be significant ($$p \leq 0.04$$) (Table 6).
## Discussion
For BC, different serum markers were evaluated up to now and some of them are found to be prognostic, some are diagnostic and/or predictive [11]. Fetuin-A was originally discovered to be an inhibitor of vascular calcification. Furthermore it is demonstrated that it plays an important role in free fatty acid induced insulin resistance in the liver [12] [13]. Increased fe tuin-A had been also been linked to increased occurrence of non-alcoholic fatty liver disease and cardiovascular events, believed to be due to its pro inflammatory effects. Thus, in contrast it has some anti-inflammatory properties. It is a negative acute-phase reactant in sepsis, promotes wound healing, and is neuroprotective [14]. The potential role of fetuin-A in tumor progression stemmed from earlier studies that suggested that it was the cell attachment factor in serum [15]. In head and neck squamous cell carcinoma (HNSCC), there was an increased expression of a higher molecular weight fetuin-A [16]. There is ectopic synthesis of fetuin-A by divergent cancer cell lines [17]. Patients with high ectopic expression of fetuin-A in lung cancer and gastric cancer tend to have lower survival [18] [19]. Fetuin-A is an important marker in the tumor microenvironment, for cancer stem cells and for matrix metalloproteinases [20] [21].
Fetuin-A is found to be a serum biomarker for colorectal cancer patients [22]. It is found to be increased in malignant pleural effusion of lung cancer patients [23]. Furthermore, in a study done in Mexican BC population, the presence of serum autoantibodies against fetuin-A protein found to be useful as serum biomarkers for early-stage BC screening [9]. Fetuin-A seems to be a serum chemo-attractant protein that also promotes invasion of BC tumor cells [24].
In our study, we found no association of serum fetuin-A levels for BC patients with other laboratory parameters and with control subjects. This may be a result of exploring only early staged patients. In the Mexican BC population [9], there were 36 patients (30 with ductal and 6 lobular carcinoma) but they used an immune proteomic approach, combining two-dimensional (2D) electrophoresis, Western blot, and matrix-associated laser desorption/ionization mass spectrometry (MALDI-MS) methods. We used one method which was the ELISA method for the detection of fetuin-A levels. We performed this study in 117 patients with invasive ductal, lobular, tubular, papillary and mucinous cancers. However, in our study, there was a trend to be lower for fetuin-A levels for more favorable histologic subtypes. It is very well known that BC has different histologic subtypes as well as its diffrenet molecular characteristics. In the Mexican study [9], there is no data about the tumors’ molecular characteristics such as ER, PR and cerbb2 status. In our study; there are older patients than the other study. Taken together all these discrepancies, in our study which was done in Turkish BC patients, fetuin-A levels did not differ.
RANKL/RANK system is seen as a downstream mediator of progesterone-driven mammary epithelial cells proliferation, BC initiation and progression. Expression of RANKL, RANK has been detected in BC cell lines and in human primary BCs. To date, dysregulation of RANKL/RANK at the skeletal level has been widely documented in the context of metastatic bone disease [25]. The interference with the RANK/RANKL system could therefore serve as a potential target for prevention and treatment of BC [26] [27]. For metastatic BC patients, specifically for patients with bone metastasis, RANKL levels were found to be diagnostic and somewhat predictive for therapy [26]. In our study; for early staged BC patients, RANKL levels were found to be lower in the favorable histological subtypes of BC. This is a new topic for early stage BC patients.
## Conclusion
We found a correlation between serum RANKL levels and favorable histological subtypes of BC.However, there was no significance between fetuin-A levels and other clinical/laboratory parameters. Further and detailed studies can enlighten the role of these cell adhesion markers better for BC patients.
## Conflict of interest statement
All the authors declare that they have no conflict of interest in this work.
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|
---
title: Malondialdehyde as an independent predictor of body mass index in adolescent
girls
authors:
- Aleksandra Klisic
- Maja Malenica
- Jelena Kostadinovic
- Gordana Kocic
- Ana Ninic
journal: Journal of Medical Biochemistry
year: 2023
pmcid: PMC10040194
doi: 10.5937/jomb0-39044
license: CC BY 4.0
---
# Malondialdehyde as an independent predictor of body mass index in adolescent girls
## Abstract
### Background
Given the fact that the studies that examined oxidative stress in relation to obesity that included late adolescents are scarce and show inconclusive results we aimed to investigate a wide spectrum of nitro-oxidative stress biomarkers i.e., malondialdehyde (MDA), xanthine oxidase (XO), xanthine oxidoreductase (XOD), xanthine dehydrogenase (XDH), advanced oxidation protein products (AOPP) and nitric oxide products (NOx), as well as an antioxidative enzyme, i.e., catalase (CAT) in relation with obesity in the cohort of adolescent girls ages between 16 and 19 years old.
### Methods
A total of 59 teenage girls were included in this cross-sectional study. Binary logistic regression analysis was performed to examine possible associations between biochemical and nitro-oxidative stress markers and body mass index (BMI).
### Results
There were not significant differences between oxidative stress markers between normal weight and overweight/obese girls (i.e., AOPP, XOD, XO, XDH) and CAT, except for MDA ($p \leq 0.001$) and NOx ($$p \leq 0.010$$) concentrations which were significantly higher in overweight/obese adolescent girls. Positive associations were evident between BMI and high sensitivity C-reactive protein (hsCRP) (OR=2.495), BMI and uric acid (OR=1.024) and BMI and MDA (OR=1.062). Multivariable binary regression analysis demonstrated significant independent associations of BMI and hsCRP (OR=2.150) and BMI and MDA (OR=1.105). Even $76.3\%$ of the variation in BMI could be explained with this Model.
### Conclusions
Inflammation (as measured with hsCRP) and oxidative stress (as determined with MDA) independently correlated with BMI in teenage girls.
## Uvod
Imajući u vidu činjenicu da je malo studija koje su ispitivale povezanost oksidativnog stresa i gojaznosti kod adolescenata i da iste pokazuju oprečne rezultate, cilj istraživanja je bio da se ispita povezanost širokog spektra biomarkera nitro-oksidativnog stresa tj. malondialdehida (MDA), ksantin oksidaze (XO), ksantin oksidoreduktaze (XOD), ksantin dehidrogenaze (XDH), produkata uznapredovale oksidacije proteina (AOPP) i produkata azot-monoksida (NOx), kao i enzima antioksidativne zaštite, tj. katalaze (CAT) i gojaznosti u kohorti adolescentkinja starosne dobi izme|u 16 i 19 godina.
## Metode
Ukupno 59 tinejdžerki je uključeno u ovu studiju preseka. Binarna logistička regresija je primenjena u cilju ispitivanja potencijalne povezanosti između biohemijskih markera i markera nitro-oksidativnog stresa i indeksa telesne mase (ITM).
## Rezultati
Nije uočena razlika u biomarkerima oksidativnog stresa između normalno uhranjenih i predgojaznih/gojaznih adolescentkinja (odnosno AOPP, XOD, XO, XDH) i CAT, osim u vrednostima MDA ($p \leq 0$,001) i NOx ($$p \leq 0$$,010) koje su bile značajno veće kod predgojaznih/gojaznih adolescentkinja. Pozitivna korelacija je utvrđena izme|u ITM-a i visokosenzitivnog C-reaktivnog proteina (hsCRP) (OR=2,495), ITM-a i mokraćne kiseline (OR=1,024) i ITM-a i MDA (OR=1,062). Multivarijanta binarna regresija je pokazala nezavisnu povezanost ITM-a i hsCRP (OR=2,150), kao i ITM-a i MDA (OR=1,105). Čak 76,$3\%$ varijabiliteta ITM-a može biti objašnjeno ovim modelom.
## Zaključak
Inflamacija (merena hsCRP-om) i oksidativni stres (meren malondialdehidom) nezavisno koreliraju sa ITM kod adolescentkinja.
## Introduction
The prevalence of obesity has reached high prevalence both in young and adult populations. Recent reports indicate that the prevalence of obesity in women has increased 2.5-fold (i.e., from $6\%$ to $15\%$) in the last 40 years [1]. Also, a higher percentage of body fat was shown in women compared to men, which indicates that pathophysiological processes that underly obese state, such as in flammation and oxidative stress may have a stronger impact on obesity related disorders in women compared to men [2].
Obesity in adolescents often tracks into the adult period and is regarded as one of the most serious public health concerns nowadays. Almost $17\%$ of children are with obesity in the USA, whereas one out of three children is overweight/obese in Europe. Similar results are shown in Africa, where obesity in female adolescents ($36.1\%$) is more prevalent than in males [3] [4].
These metabolic changes are mainly attributed to sedentary lifestyle and unhealthy dietary pattern (e.g., easy access to fast-food and sugar-sweetened beverages) [5].
Obesity leads to many metabolic disturbances, such as polycystic ovary syndrome (PCOS), fatty liver disease, diabetes mellitus type 2 and cardiovascular disease [6] [7] [8] [9] [10].
Oxidative stress and inflammation are the main pathophysiological features of the obesity-related disorders [5] [10]. The over production of reactive nitrogen or oxygen species (RNS/ROS) (i.e., prooxidants) over the antioxidant defence systems (i.e., antioxidants) results in oxidative stress. To date, the precise mechanism by which prooxidants and antioxidants influence metabolic disturbances are not fully enlightened [5].
Despite the large number of studies that explored different oxidative stress biomarkers, the results are still inconclusive and the biomarker that best reflects oxidative stress in obesity has not been found yet, given the fact that RNS/ROS are not easily measured due to their short half-life and their low levels [10] [11].
Therefore, the determination of lipid and protein oxidation products, as well as by-products of DNA modification take their place in clinical settings [11].
Malondialdehyde (MDA) is among the most common used biomarker that reflects lipid peroxidation. The key targets of this process are polyunsaturated fatty acids, mostly arachidonic and linoleic acid. During the reaction of these molecules with ROS the autocatalytic reaction of lipid peroxidation occurs, with consequent formation of secondary by-products, such as MDA, isoprostanes and trans-4-hydroxy-2-nonenal [11].
The commonly used biomarkers of protein oxidative modification are advanced oxidation protein products (AOPP) [11].
Nitric oxide (NO) is one of the biomarkers of nitro-oxidative stress and the major determinant of vascular tone and energy metabolism [12]. NO also favors oxidative stress by regulating lipid peroxidation and favors the generation of MDA [13].
Xanthine oxidase (XO) represents an oxidant form of the enzyme xanthine oxidoreductase (XOD). XO is the main culprit for liberation of ROS in circulation. XOD is responsible for the conversion of purine bases to uric acid and is presented as xanthine dehydrogenase (XDH) under physiological conditions [14]. In obesity-metabolic disorders, when antioxidative defense enzymes are depleted [such as superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx)] [11], XOD is being converted into XO [14].
Given the fact that the prevalence of overweight/obesity among teenage girls is inconsistently reported due to the different ages of adolescents included in the research [15] and since the studies that examined oxidative stress in late adolescents are scarce and show inconclusive results we aimed to investigate a wide spectrum of nitro-oxidative stress biomarkers [i.e., MDA, AOPP, XOD, XO, XDH) and nitric oxide products (NOx=nitrates and nitrites), as well as antioxidative enzyme-CAT] in relation with obesity in the cohort of adolescent girls ages between 16 and 19 years old.
## Study population
This case-control cross-sectional study included 59 adolescent girls who were reluctant to participate in the research. After obtaining the approval of the Institutional Ethics Committee, girls between the ages 16-19 years (i.e. from the last two grades of the secondary schools in Podgorica, Montenegro) were consecutively included. Each girl provided written informed consent. The parental informed consent was also provided for girls younger than 18 years. The exclusion criteria were any sign/symptom of inflammatory disease, thyroid diseases, diabetes, autoimmune diseases, any medication use, cigarette smoking, alcohol consumption, pregnancy, irregular menstrual cycle. Also, girls who did not keep their body weight stable in the last three months were excluded from the study, as well as girls that exhibited high sensitivity C-reactive protein (hsCRP) 10 mg/L to minimize the confounding factors and sources of inflammation and oxidative stress other than obesity.
## Biochemical analyses
Blood sampling was performed after an overnight fast of at least 8 hours, between 7:00 h and 10:00 h, a.m. the same morning when anthropometric measurements were obtained.
The samples were provided in the tube with a serum separator and clot activator and after clotting within 30 minutes, the samples were centrifuged for 10 minutes at 3000xg (at room temperature). Serum levels of triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), total cholesterol (TC), creatinine, uric acid, glucose and hsCRP were measured immediately after the centrifugation of samples on the biochemistry analyzer Roche Cobas c501 (Roche Diagnostics GmbH, Mannheim, Germany). The aliquots of sera were frozen at -80°C until the analyses of the oxidative stress biomarkers.
The oxidative stress parameters were measured as previously described [8] [14] [16]. Briefly, serum AOPP levels were measured by reaction with potassium iodide and glacial acetic acid method [17]. The measurement of serum XOD and XO was related to the liberation of uric acid, whereas xanthine was used as a substrate in the presence of NADH (for XOD) or the absence of NADH (for XO) when molecular oxygen was the electron acceptor [18]. The XDH activity was calculated as follows: XOD - XO activity. Serum NO production is determined after the reduction of nitrate (NO3 -) into nitrite (NO2 -) with cadmium. The measurement of nitrates and nitrites (NO3 - + NO2), commonly named as NOx, is used as an indicator of NO production [19]. Serum MDA levels (i.e., an end degradation product of lipid hydroperoxides) were determined as a thiobarbituric acid reactive substance [20]. CAT test is related to the breaking down of oxygen from harmful hydrogen peroxide (H2O2), based on the formation of its complex with ammonium molybdate [21].
## Anthropometric measurements
Anthropometric measurements were obtained as described elsewhere [22]. Body mass index (BMI) was calculated as body weight (kg) divided by body height in meters squared (kg/m2). Girls with BMI <25 kg/m2 were regarded to be normal weight, whereas girls with BMI 25 kg/m2 were regarded as overweight/obese.
## Statistical analysis
Statistical analysis was conducted using SPSS for Windows version 21.0 (SPSS Inc., Chicago, IL, USA). The normality of data distribution was evaluated using the Shapiro Wilk test. Values are expressed as mean ± standard deviation for normally distributed data and median (interquartile range) for data with skewed distribution. Differences among groups were assessed by Student t-test and Mann-Whitney U test depending on data distribution. Correlations between variables were analysed by calculating Spearman's correlation coefficients (ρ). Binary logistic regression analysis was performed to examine possible associations between biochemical and oxidative stress markers and BMI. Biochemical markers with significant Spearman's correlation coefficients were applied in a multivariable binary regression model to search for possible independent association with BMI. Data from these analyses were given as Odds Ratio (OR) and $95\%$ Confidence interval (CI). The explained variation in BMI was given by Nagelkerke R2 value. A P value less than 0.05 was considered significant.
## Results
The anthropometric and biochemical values of the study participants according to their BMI, are shown in Table 1. The groups were similar ages and height. There were no differences in glucose, TC, LDL-c, TG levels and creatinine. Nevertheless, HDL-c was significantly lower in overweight/obese, while hsCRP and uric acid levels were significantly higher compared to normal weight adolescent girls.
**Table 1**
| Unnamed: 0 | BMI ≥ 25 kg/m2 Normal weight | BMI ≥ 25 kg/m2 Overweight/obese | P |
| --- | --- | --- | --- |
| Adolescents No. | 30 | 29 | |
| Age, years* | 18 (17919) | 18 (16–19) | 0.287 |
| Weight, kg* | 59 (55-65) | 75 (71.5-89) | < 0.001 |
| Height, cm* | 167.0 (165.0-171.0) | 167.0 (165.5–169.5) | 0.303 |
| Glucose, mmol/L | 4.94±0.36 | 5.07±0.58 | 0.463 |
| TC, mmol/L | 4.21±0.76 | 4.34±0.43 | 0.374 |
| HDL-c, mmol/L | 1.66±0.32 | 1.38±0.41 | 0.021 |
| LDL-c, mmol/L | 2.16±0.61 | 2.47±0.39 | 0.056 |
| TG, mmol/L* | 0.81 (0.66–0.98) | 0.87 (0.73–1.32) | 0.217 |
| Creatinine, μmol/L | 57±6 | 58±6 | 0.791 |
| hsCRP, mg/L* | 0.30 (0.30–0.53) | 2.00 (0.33–3.70) | 0.001 |
| Uric acid, μmol/L | 209±37 | 256±50 | 0.001 |
In Table 2 we presented oxidative stress and antioxidative defense markers in examined girls. There were no significant differences between these markers in tested groups except for MDA and NOx concentrations which were significantly higher in overweight/obese adolescent girls.
**Table 2**
| Unnamed: 0 | BMI <25 kg/m2 | BMI ≥25 kg/m2 | P |
| --- | --- | --- | --- |
| MDA, μmol/L* | 46.7 (42.4–71.9) | 94.6 (74.1–103.5) | <0.001 |
| AOPP, T/L | 141.1±34.2 | 153.5±23.5 | 0.130 |
| CAT, U/L | 60.1±29.4 | 57.8±26.6 | 0.649 |
| XOD, U/L | 285±66.5 | 289±57.6 | 0.941 |
| XO, U/L | 144.0±47.2 | 173.8±52.1 | 0.114 |
| XDH, U/L* | 118 (100–189) | 105 (99–116) | 0.085 |
| NOx*, μmol/L | 51.1 (36.7–68.2) | 72.9 (64.7–80.8) | 0.010 |
Further, we conducted correlation analyses for all tested markers and BMI in all participants. We found that BMI was positively related to weight, LDLc, hsCRP, uric acid, MDA and AOPP (Table 3).
**Table 3**
| Variable | ρ | P |
| --- | --- | --- |
| Age, years | -0.084 | 0.557 |
| Weight, kg | 0.915 | <0.001 |
| Height, cm | -0.159 | 0.293 |
| Glucose, mmol/L | 0.141 | 0.348 |
| TC, mmol/L | 0.123 | 0.414 |
| HDL-c, mmol/L | -0.289 | 0.051 |
| LDL-c, mmol/L | 0.301 | 0.042 |
| TG, mmol/L | 0.213 | 0.155 |
| Creatinine, μmol/L | 0.014 | 0.926 |
| hsCRP, mg/L | 0.485 | 0.001 |
| Uric acid, μmol/L | 0.458 | 0.001 |
| MDA, μmol/L | 0.499 | <0.001 |
| AOPP, T/L | 0.31 | 0.036 |
| CAT, U/L | -0.071 | 0.651 |
| XOD, U/L | -0.073 | 0.631 |
| XO, U/L | 0.019 | 0.899 |
| XDH, U/L | -0.176 | 0.248 |
| NOx, μmol/L | 0.28 | 0.063 |
We performed binary regression analysis to determine in-depth associations of BMI and other markers with a significant Spearman's correlation coefficient (Table 4). Positive associations were evident between BMI and hsCRP (OR=2.495), BMI and uric acid (OR=1.024) and BMI and MDA (OR=1.062). Multivariable binary regression analysis demonstrated significant independent associations of BMI and hsCRP (OR=2.150) and BMI and MDA (OR=1.105). Adjusted R2 for the Model was 0.763, which means that even $76.3\%$ of the variation in BMI could be explained with this Model. AOPP was not tested in multivariable analysis because its concentrations were not significantly associated with BMI in univariate analysis.
**Table 4**
| Predictors | Unadjusted OR (95% CI) | P | R2 |
| --- | --- | --- | --- |
| LDL-c, mmol/L | 3.094 (0.935–10.235) | 0.064 | 0.110 |
| hsCRP, mg/L | 2.495 (1.253–4.967) | 0.009 | 0.347 |
| Uric acid, μmol/L | 1.024 (1.008–1.040) | 0.009 | 0.190 |
| MDA, μmol/L | 1.062 (1.024–1.102) | 0.001 | 0.467 |
| AOPP, T/L | 1.067 (1.010–1.127) | 0.134 | 0.072 |
| Model | Adjusted<br>OR (95% CI) | P | R2 |
| LDL-c, mmol/L | 11.766 (0.882–157.050) | 0.062 | 0.763 |
| hsCRP, mg/L | 2.150 (1.139–4.057) | 0.018 | |
| Uric acid, μmol/L | 1.004 (0.978–1.032) | 0.754 | |
| MDA, μmol/L | 1.105 (1.028–1.187) | 0.004 | |
## Discussion
This is the first study that investigated a broad spectrum of oxidative stress biomarkers in adolescent girls of a narrow age range (i.e. between 16 and 19 years old). Namely, previous studies included a wide age range of participants and the majority of them included both, children and adolescents. In order to elimi nate hormonal changes during puberty, as well as to minimize the other bias factors that might influence oxidative stress and inflammation, such as co-morbi dities, we have included only normal weight and overweight/obese girls who have a regular menstrual cycle.
The results of the current study showed that there was no difference in routine biochemical parameters, such as fasting glucose, creatinine and lipid status (except for HDL-c) between normal weight and overweight/obese girls. However, overweight/obese girls exhibited higher inflammation (i.e., hsCRP) and higher oxidative stress level (i.e., uric acid, MDA and NOx). Moreover, hsCRP and MDA independently correlated with BMI.
We have previously reported that hsCRP correlated with surrogate markers of insulin resistance [i.e., HOMA-IR [22] and HDL-c/TG ratio [23]] and cardiovascular risk [9] which is in line with the findings of higher inflammation in obesity-related cardiometabolic disorders [8] [16] [24] [25].
Although many previous studies examined the influence of obesity and obesity-related disorders on oxidative stress, no universal biomarker was established. Also, when MDA is concerned discrepant results were reported between studies. Higher serum MDA levels were shown in some studies [26] [27] [28] [29] [30], whereas no difference between study groups was found in the others [31] [32] [33] [34] [35] [36].
Possible explanations for these discrepancies may be attributed to the different sample sizes, variations in age of the examined participants, comorbidities, as well as variations in duration and the extent of obesity.
The small sample size is one of the limitations of this study. However, previous studies also included a relatively small number of participants [26] [35] [36]. Aztatzi-Aguilar et al. [ 36] included an even smaller number of participants, i.e., 35 students with a median age of 16 years. Of them, 12 were normal weight and 23 were overweight/obese. Unlike our findings, they reported neither difference in MDA, nor in uric acid levels. Mizgier et al. [ 35] included a total of 37 normal weight and 22 overweight/obese girls with PCOS. Neither did they find the difference in MDA nor in CRP. A total of 62 children were encompassed (of the 32 with obesity) in a study of Dokumacioglu et al. [ 26] Serum MDA values were higher than those of the control group which is consistent with our results. The study of Zalewska et al. [ 27] included 40 normal weight (of them 20 teenagers), 20 overweight (of them 10 teenagers) and 20 obese (of them 10 teenagers) adolescents aged 11-18 years and showed higher MDA and uric acid in overweight/obese participants compared to normal weight counterparts.
Also, discrepant results in antioxidant enzyme activity was reported. Low SOD, higher GPx, but no difference in CAT activity and MDA was shown in over weight ($$n = 36$$) and obese ($$n = 33$$) adult participants, compared with normal weight ($$n = 23$$) counter parts. However, visceral abdominal fat positively correlated with lipoperoxides (i.e., MDA is its main product) [31].
Similarly to our results, Monserrat-Mesquida et al. [ 28] showed higher MDA, and no difference in CAT in women with metabolic syndrome ($$n = 40$$), compared to those without metabolic syndrome ($$n = 40$$). Adenan et al. [ 33] examined a total of 80 female adults [normal weight ($$n = 23$$), overweight ($$n = 28$$) and obese ($$n = 29$$)], and found a higher CAT activity in the females with obesity compared with normal weight women, although no differences in MDA levels were shown. They explained such results by increased removal of ROS by enhanced CAT activity or the possibility that MDA increase in the obese group was at low level that cannot be determined by the assays. A positive correlation was observed between MDA and BMI in women with PCOS [30].
Similarly, an animal experimental study that included a total of 28 Wistar-Bratislava white male rats showed that dyslipidemia, hypertension, and diabetes mellitus were associated with an increase in serum MDA levels [37]. Another experimental study [38] showed that isolated adipocytes from adipose tissue from mice fed on a diet enriched with fat were characterized by insulin resistance and a twofold increase in the production of ROS which led to metabolic syndrome. Moreover, obese mice with type 2 diabetes mellitus exhibited increase in MDA levels in plasma and white adipose tissue, and lower antioxidant enzyme activity as compared with nonmetabolic syndrome mice [39].
Oxidative stress can lead to reduced activity of phosphatidylinositol 3-kinase (PI3K). The letter represents the key enzyme responsible for insulindependent signaling and takes part in the ROSinduced insulin resistance formation. At the same time, the activity of protein kinase Cδ (PKC-δ) and the activity of janus kinase (JAK) in adipocytes are increased. All these pathophysiological processes may contibute to obesity-related metabolic disorders [40].
As previously stated, we have also found higher serum NOx levels in overweight/obese girls, as compared to normal weight peers. This is contrary to our previous finding where there was no difference in this biomarker in the adult population with and without metabolic syndrome. This may be attributed to confounding factors, such as medication use, smoking habits and other co-morbidities in the latter study [16]. Stable end metabolites, i.e., inorganic nitrates and nitrites (NOx) represent the NO production as one of the biomarkers of nitro-oxidative stress and the key indicator of energy metabolism and vascular tone [12]. It is synthesized from L-arginine under the control of NO synthase (NOS), an enzyme that is presented in 3 isoforms (i.e., inducible, neuronal and endothelial) with inducible NOS with the best capacity for NO formation in the state of oxidative stress [12] [16].
On the contrary, we found no difference in AOPP between examined groups, although positive correlation between BMI and AOPP in the whole group of participants was shown. AOPP is a biomarker that reflects the proteins' oxidative damage and was reported to be higher in some other metabolic disorders [8] [16] [41].
We also did not observe any difference between XOD, XO and XDH. XO takes part in the differentiation of adipocytes by controlling the activity of the nuclear receptor peroxisome proliferator-activated receptor (PPAR) [42]. Although previous studies reported higher XO activity in obesity, these included a smaller and younger group of participants than we did [43] [44]. Tam et al. [ 43] evaluated 22 normal weight and 20 obese children and adolescents (mean age, 12±3 years) and showed a 3.8-fold increase in plasma XO activity in obese, compared to normal weight counterparts. Similarly, Chiney et al. [ 44] included even younger participants, i.e., 9 obese prepubertal children and 16 normal weight between ages 6-10 years. However, in our study that included 118 overweight/obese adults, we have confirmed the association between XO and BMI [14].
Besides the small sample-size as mentioned above, this study cannot confirm causality due to its cross-sectional design. On the other hand, the strength of the current study lies in the fact that we included only late-adolescent girls who were nonsmokers, without co-morbidities and without any medication use. Thus, we put an effort into minimizing bias factors that might affect oxidative stress level. Moreover, we investigated a broad spectrum of nitro-oxidative stress biomarkers to gain deeper insight into the relationship between patho physiolo gical traits of obesity and nitro-oxidative stress.
## Conclusion
The increased level of nitro-oxidative stress was observed in late-adolescent girls. Although not all biomarkers of oxidative stress differed between normal weight and overweight/obese girls (i.e., AOPP, XOD, XDH, XO), neither difference in CAT activity between those groups was shown, the hsCRP and MDA independently correlated with BMI. Since obesity in adolescents often tracks into the adult period, leading to many obesity-related disorders, more studies with larger sample size and with longitudinal design are needed to confirm the causal link between obesity and oxidative stress and to find the best therapeutic approach to this issue.
## Acknowledgement
This research was partially funded by a grantfrom the Ministry of Science, Montenegro and the Ministry of Education, Science and Technological Development, Republic of Serbia through Grant Agreement with University of Belgrade-Faculty of Pharmacy No: 451-03-$\frac{68}{2022}$-$\frac{14}{200161.}$
## Conflict of interest statement
All the authors declare that they have no conflictof interest in this work.
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|
---
title: MiRNA-200b level in peripheral blood predicts renal interstitial injury in
patients with diabetic nephropathy
authors:
- Tingfang Chen
- Zhenzhen Jiang
- Haiying Zhang
- Ruifeng Yang
- Yan Wu
- Yongping Guo
journal: Journal of Medical Biochemistry
year: 2023
pmcid: PMC10040195
doi: 10.5937/jomb0-40379
license: CC BY 4.0
---
# MiRNA-200b level in peripheral blood predicts renal interstitial injury in patients with diabetic nephropathy
## Abstract
### Background
To uncover the diagnostic potential of peripheral blood microRNA-200b (miRNA-200b) in renal interstitial injury in diabetic nephropathy (DN) patients.
### Methods
A total of 50 diabetes subjects, 50 mild DN subjects, 50 moderate-severe DN subjects and 50 healthy subjects were included. Peripheral blood level of miRNA-200b in every subject was detected by reverse transcriptase-polymerase chain reaction (RT-PCR). Serum levels of renal function indicators were determined by enzyme-linked immunosorbent assay (ELISA). Meanwhile, relative levels of fibrosis damage indicators were examined by chemiluminescent immunoassay. Diagnostic potentials of miRNA200b in diabetes, mild DN and moderate-severe DN were assessed by depicting receiver operating characteristic (ROC) curves.
### Results
Peripheral blood level of miRNA-200b was higher in DN subjects than diabetes subjects without vascular complications, especially moderate-severe DN patients. Peripheral blood level of miRNA-200b in DN subjects was negatively correlated to relative levels of serum creatinine, urinary nitrogen, cystatin, TGF-b, CIV and PCIII. ROC curves demonstrated diagnostic potentials of miRNA-200b in mild and moderate-severe DN.
### Conclusions
Peripheral blood level of miRNA-200b is closely linked to the degree of renal interstitial injury in DN patients. MiRNA-200b may be a vital indicator in predicting the development of DN.
## Uvod
Cilj je bio da se otkrije dijagnostički potencijal mikroRNA-200b periferne krvi (miRNA-200b) kod intersticijalne povrede bubrega kod pacijenata sa dijabetičkom nefropatijom (DN).
## Metode
Uključeno je ukupno 50 ispitanika sa dijabetesom, 50 ispitanika sa blagim DN, 50 umereno-teškim i 50 zdravih ispitanika. Nivo miRNA-200b u perifernoj krvi kod svakog ispitanika je detektovan lančanom reakcijom reverzne transkriptaze-polimeraze (RT-PCR). Serumski nivoi indikatora bubrežne funkcije određivani su enzimskim imunosorbentnim testom (ELISA). U međuvremenu, relativni nivoi indikatora oštećenja fibroze su ispitani hemiluminiscentnim imunotestom. Dijagnostički potencijali miRNA-200b kod dijabetesa, blage DN i umereno-teške DN procenjeni su prikazom krive operativnih karakteristika prijemnika (ROC).
## Rezultati
Nivo miRNA-200b u perifernoj krvi bio je viši kod ispitanika sa DN nego kod ispitanika sa dijabetesom bez vaskularnih komplikacija, posebno kod pacijenata sa umereno-teškim DN. Nivo miRNA-200b u perifernoj krvi kod DN subjekata je bio u negativnoj korelaciji sa relativnim nivoima serumskog kreatinina, azota u urinu, cistatina, TGF-b, CIV i PCIII. ROC krive su pokazale dijagnostičke potencijale miRNA-200b u blagom i srednje teškom DN.
## Zaključak
Nivo miRNA-200b u perifernoj krvi je bio usko povezan sa stepenom intersticijalnog oštećenja bubrega kod pacijenata sa DN. MiRNA-200b može biti vitalni indikator u predviđanju razvoja DN.
## Introduction
Diabetic nephropathy (DN) is an important microvascular complication of diabetes, which is the most common cause of end-stage renal failure (ESRD) [1]. It is estimated that by 2045, the number of diabetes patients worldwide will reach 693 million [2]. Sustained hyperglycemia results in extensive vascular damage to eyes, kidneys, heart, and nerves. About $40\%$ of diabetic patients are susceptible to DN [3]. At present, renal biopsy and urine microalbumin detection are the major approaches to diagnose and monitor DN. However, renal biopsy is an invasive examination that is not acceptable to every DN patients and it fails to reflect the severity of DN [4]. It is of significance to develop effective and specific biomarkers of DN.
MicroRNAs (miRNAs) are endogenous, single-stranded RNAs containing 21–25 nucleotides [5]. They are tissue- and time-specific. Through inducing mRNA degradation and blocking protein translation, miRNAs exert post-transcriptional regulations [6]. Functionally, miRNAs are extensively involved in early embryonic development, gene expressions, cell phenotypes, etc. [ 7] [8]. They also display a certain role in the development of kidney diseases [9]. Detection of miRNA levels in blood or urine may contribute to early screening and disease monitoring of DN.
MiRNA-200 family is a cluster of epithelial-mesenchymal transition (EMT)-associated miRNAs. In particular, miRNA-200b is considered as a negative regulator in tumor metastasis [10]. It is reported that miRNA-200b initiates EMT by interacting with ZEB$\frac{1}{2}$ [11]. Intercellular TAMs actively participate in tumor neovascularization by regulating EMT and enhancing tumor microvessel density [12]. A relevant study showed that miRNA-200b protects diabetic retinopathy by downregulating VEGFA [13]. Our study aims to uncover the role of miRNA-200b in the development of DN and its diagnostic potential.
## Baseline characteristics
This study was performed after obtaining the approval of The Ethic Committee of Shanghai Sixth People's Hospital and the informed consent from the subjects. A total of 50 diabetes subjects without any vascular complications, 50 mild DN subjects (Mogensen II) and 50 moderate-severe DN subjects (Mogensen III-IV) were included. During the same period, 50 healthy subjects undergoing healthy examinations were included. Diabetes and DN were diagnosed based on the standard criteria [14] and kidney biopsy, respectively. Inclusion criteria were: [1] Diagnosis as type 2 diabetes mellitus; [2] DN in Mogensen II-IV; and [3] BMI: 18.5-27 kg/m2. Exclusion criteria were: [1] Subjects with urinary calculi, cysts or other occupying lesions; [2] Cerebral infarction; [3] Defect of immune system; [4] Hormone drugs or immunomodulators used in the past 6 months and [5] allergic constitution or history of allergies.
## Blood sample collection
5 mL of venous blood was extracted in each subject under the fasting state in the morning. Blood was centrifuged at 3,000 r/min for 10 min, and the serum was collected and stored at -80°C.
## Reverse transcriptase-polymerase chain reaction (RT-PCR)
TRIzol method (Invitrogen, Carlsbad, CA, USA) was applied for isolating RNAs from serum samples. Through reverse transcription of RNA, the extracted complementary deoxyribose nucleic acid (cDNA) was used for PCR detection by SYBR Green method (TaKaRa, Tokyo, Japan). Primer sequences were listed as follows. MiRNA-200b, F: 5'-GCGGCTAATACTGCCTGGTAA-3', R: 5'-GTGCAGGGTCCGAGGT-3'; and U6, F: 5'-CGCTTCGGCAGCACATATA-3', R: 5'-TTCACGAATTTGCGTGTCAT-3'. The primers were designed based on a previous literature [15].
## Determination of serum markers
Renal function indicators, including serum creatinine, urinary nitrogen, uric acid, and cystatin C were measured through enzyme-linked immunosorbent assay (ELISA) (R&D Systems, Minneapolis, MN, USA) by sarcosine oxidase method, immunoturbidimetry method, enzymatic method and immunoturbidimetry, respectively. Fibrosis damage indicators were examined by chemiluminescent immunoassay.
## Statistical analyses
Statistical Product and Service Solutions (SPSS) 20.0 (IBM, Armonk, NY, USA) was used for all statistical analysis. Data were expressed as mean ± SD (standard deviation). Differences between two groups were analyzed by using the Student's t-test. Comparison between multiple groups was done using One-way ANOVA test followed by Post Hoc Test (Least Significant Difference). Pearson correlation test was conducted for assessing the relationship between miRNA-200b level and serum markers. Receiver operating characteristic (ROC) curves were depicted for evaluating diagnosis potentials of miRNA-200b. $P \leq 0.05$ indicated the significant difference.
## Baseline characteristics of subjects
Among 50 healthy subjects, there were 24 males and 26 females, with the age of 32–65 years (mean: 44.18±6.75 years). Their mean BMI and HbA1c were 23.15±3.32 kg/m2 and 5.85±$0.74\%$, respectively. Among 50 diabetes subjects, there were 26 males and 24 females, with the age of 34–69 years (mean: 46.23±7.85 years). Their mean BMI and HbA1c were 24.73±3.11 kg/m2 and 6.85±$0.93\%$, respectively. Among 50 mild DN subjects, there were 22 males and 28 females, with the age of 31–69 years (mean: 44.91±6.08 years). Their mean BMI and HbA1c were 23.01±3.65 kg/m2 and 7.31±$0.86\%$, respectively. Among 50 moderate-severe DN subjects, there were 23 males and 27 females, with the age of 36–60 years (mean: 45.21±5.45 years). Their mean BMI and HbA1c were 23.23±3.24 kg/m2 and 7.85±$0.93\%$, respectively. No significant differences in age, gender and BMI were identified among the four groups (Table 1).
**Table 1**
| Groups | Age | Sex<br>(male/female) | BMI<br>(kg/m2) | HbA1c <br> (%) |
| --- | --- | --- | --- | --- |
| Controls | 44.18±6.75 | 24/26 | 23.15±3.32 | 5. 85±0.74 |
| Diabetes | 46.23±7.85 | 26/24 | 24.73±3.11 | 6. 85±0.93a |
| Mild DN | 44.91±6.08 | 22/28 | 23.01±3.65 | 7. 31±0.86ab |
| Moderate-severe DN | 45.21±5.45 | 23/27 | 23.23±3.24 | 7. 85±0.93abc |
| F/χ2 | 0.148 | 0.702 | 3.216 | 53.626 |
| P | 0.931 | 0.873 | 0.024 | <0.001 |
## Peripheral blood level of miRNA-200b
RT-PCR data showed that peripheral blood level of miRNA-200b was higher in healthy subjects than diabetes and DN subjects. In particular, miRNA-200b level was lower in DN subjects than diabetes subjects, especially moderate-severe DN subjects (Table 2). It is indicated that miRNA-200b may be favorable to prevent DN development.
**Table 2**
| Groups | n | Relative expression<br>of miR-200b |
| --- | --- | --- |
| Controls | 50 | 1.885±0.647 |
| Diabetes | 50 | 1.351±0.477a |
| Mild DN | 50 | 0.917±0.328ab |
| Moderate-severe DN | 50 | 0.792±0.204ab |
| F | 54.131 | |
| P | <0.001 | |
## Renal function indicators
Relative levels of serum creatinine, urinary nitrogen, uric acid and cystatin were lower in healthy subjects than diabetes and DN subjects. Notably, the highest levels of renal function indicators were found in moderate-severe DN subjects, followed by mild DN subjects and diabetes subjects (Table 3). We believed that renal function indicators contribute to assess the severity of DN.
**Table 3**
| Groups | Serum creatinine<br>(μmol/L) | Urinary nitrogen<br>(mmol/L) | Uric acid<br>(μmol/L) | Cystatin<br>(mg/L) |
| --- | --- | --- | --- | --- |
| Controls | 60.22±9.33 | 4.97±0.51 | 210.22±22.53 | 0.89±0.14 |
| Diabetes | 75.31±11.6a | 8.16±1.25a | 258.84±38.62a | 1.23±0.25a |
| Mild DN | 110.5±15.21ab | 12.83±1.96ab | 345.94±45.17ab | 1.56±0.30ab |
| Moderate-severe DN | 152.15±20.62abc | 15.62±2.13abc | 489.25±56.38abc | 1.81±0.42abc |
| F | 570.01 | 523.098 | 426.361 | 101.1 |
| P | <0.001 | <0.001 | <0.001 | <0.001 |
## Serum markers of fibrosis damage
Serum markers of fibrosis damage, including TGF-β, HA, CIV and PCIII were examined in each subject. Relative levels of fibrosis damage indicators were lower in healthy subjects than diabetes and DN subjects. The highest levels were seen in moderate-severe DN subjects (Table 4). Therefore, serum markers of fibrosis damage may also be used to assess the severity of DN.
**Table 4**
| Groups | TGF-β (μg/L) | HA (μg/L) | CIV (μg/L) | PCIII (μg/L) |
| --- | --- | --- | --- | --- |
| Controls | 5.28±0.8 | 30.85±4.69 | 41.29±6.33 | 23.69±2.69 |
| Diabetes | 8.25±0.91a | 42.68±5.24a | 59.14±6.85a | 32.75±3.22a |
| Mild DN | 12.24±1.36ab | 58.33±6.96ab | 67.32±7.17ab | 44.23±4.16ab |
| Moderate-severe DN | 15.21±1.98abc | 78.91±9.51abc | 89.53±9.24abc | 55.98±5.96abc |
| F | 621.003 | 524.517 | 280.321 | 646.206 |
| P | <0.001 | <0.001 | <0.001 | <0.001 |
## Pearson correlation test on miRNA-200b level and serum markers
We have proven that relative levels of miRNA-200b, renal function indicators, and serum markers of fibrosis damage were different in diabetes and DN subjects. Subsequently, Pearson correlation test showed that peripheral blood level of miRNA-200b was negatively correlated to serum creatinine, urinary nitrogen, cystatin, TGF-β, CIV and PCIII (r = -0.521, -0.683, -0.683, -0.811, -0.588 and -0.721, respectively) in DN subjects (Table 5).
**Table 5**
| Serum marker | Controls | Controls.1 | Diabetes | Diabetes.1 | DN | DN.1 |
| --- | --- | --- | --- | --- | --- | --- |
| Serum marker | r | P | r | P | r | P |
| Serum creatinine | -0.256 | 0.095 | -0.512 | 0.064 | -0.521 | 0.002* |
| Urinary nitrogen | -0.239 | 0.06 | -0.115 | 0.157 | -0.683 | 0.018* |
| Uric acid | -0.125 | 0.335 | -0.442 | 0.095 | -0.522 | 0.071 |
| Cystatin | -0.557 | 0.497 | -0.109 | 0.415 | -0.683 | 0.029* |
| TGF-β | 0.467 | 0.082 | -0.254 | 0.155 | -0.811 | <0.001* |
| HA | -0.425 | 0.466 | -0.328 | 0.261 | -0.462 | 0.627 |
| CIV | -0.267 | 0.185 | -0.612 | 0.32 | -0.588 | 0.026* |
| PCIII | 0.359 | 0.447 | -0.324 | 0.054 | -0.721 | 0.005* |
| HBA1c | -0.305 | 0.064 | -0.287 | 0.981 | -0.253 | 0.143 |
## Diagnostic potentials of miRNA-200b in DN
ROC curves were depicted for assessing diagnostic potentials of miRNA-200b in DN. Sensitivity and specificity of miRNA-200b in diagnosing diabetes were $76\%$ and $72\%$, respectively (AUC=0.7992, $p \leq 0.001$, cut-off value=1.636) (Figure 1A). MiRNA-200b was able to diagnose mild DN (sensitivity=$90\%$, specificity=$86\%$, AUC=0.9332, $P \leq 0.001$, cut-off value=1.294) (Figure 1B). Sensitivity and specificity of miRNA-200b in diagnosing moderate-severe DN were $88\%$ and $90\%$, respectively (AUC=0.9516, $P \leq 0.001$, cut-off value=1.092) (Figure 1C). It is concluded that miRNA-200b was able to diagnose diabetes, mild DN and moderate-severe DN.
**Figure 1:** *Diagnostic potentials of miRNA-200b in diabetes, mild DN and moderate-severe DN. (A) Diagnostic potential of miRNA-200b in diabetes (AUC=0.7992, P<0.001); (B) Diagnostic potential of miRNA-200b in mild DN (AUC=0.9332, P<0.001); (C) Diagnostic potential of miRNA-200b in moderate-severe DN (AUC=0.9516, P<0.001)*
## Discussion
It is estimated that by 2030, $7.7\%$ of people aging 20–79 years suffer from diabetes [16]. DN is a severe complication of diabetes. Uncontrolled DN will deteriorate into ESRD that is difficult to be treated. Current therapeutic strategies of DN aim to control blood glucose, blood pressure and lipids [17]. Nevertheless, the development of DN cannot be reversed or blocked. Prevention and intervention of DN in the early stage are of significance.
A single miRNA can bind several target genes, thereafter influencing gene expressions and functions [18]. Differentially expressed miRNAs in kidney tissues of DN patients are able to reflect the disease condition [19]. MiRNAs are stably expressed in serum, and detection of serum miRNAs is sensitive and specific [20]. It is reported that miRNAs are involved in thickening of the glomerular basement membrane, podocyte apoptosis, deposition of extracellular matrix, cell fibrosis, etc., and eventually lead to the development of DN [21]. MiRNAs are believed as promising biomarkers in diagnosis and monitoring of DN. Bai et al. [ 22] proposed that miRNA-130b is downregulated in kidney tissues of DN patients. MiRNA-130b alleviates EMT-induced fibrosis in rat renal tubular epithelial cells through downregulating Snail. A prospective study conducted in Europe involving 455 type 1 diabetes mellitus patients uncovered that serum level of miRNA-126 is negatively linked to susceptibilities to diabetic vascular complications, especially proliferative kidney diseases [23].
In this trial, we found out that miRNA-200b level was downregulated in peripheral blood of DN subjects, especially moderate-severe DN subjects. Subsequently, potential relationship between miRNA-200b level and renal function and fibrosis damage indicators was analyzed. Pearson correlation test showed that peripheral blood level of miRNA-200b was negatively correlated to serum creatinine, urinary nitrogen, cystatin, TGF-β, CIV and PCIII in DN patients. Such a correlation was not identified in diabetes patients without vascular complications, suggesting that renal function may be normal in diabetes patients. ROC curves analyses further demonstrated the diagnostic potentials of miRNA-200b in mild and moderate-severe DN. However, there are still two shortcomings in this study. Firstly, evaluation indicators of renal interstitial fibrosis lack organ specificity. Secondly, the role of miRNA-200b may be varied in DN with different pathological stages. Our results should be validated in future explorations.
Several previous studies demonstrated that miR-200 family may be involved in the development of diabetic nephropathy, but most of these studies focused on molecular mechanisms rather than directly analyzing clinical data through peripheral blood samples of patients [24] [25]. Compared with previous studies, the most significant innovation of this study is that it is the first study to focus on the expression level of miR-200b in peripheral blood of patients with diabetic nephropathy and its clinical value.
## Conclusions
Peripheral blood level of miRNA-200b is closely linked to the degree of renal interstitial injury in DN patients. MiRNA-200b may be a vital indicator in predicting the development of DN.
## Financial Disclosure
The authors declared that this study has received no financial support.
## Conflict of interest statement
All the authors declare that they have no conflict of interest in this work.
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|
---
title: 'False negative effect of high triglycerides concentration on vitamin D levels:
A big data study'
authors:
- Murat Çağlayan
- Ataman Gonel
- Tugba Songul Tat
- Osman Celik
- Fidanci Ali Aykut
- Ayvali Mustafa Okan
- Ulgu Mustafa Mahir
- Naim Ata
- Suayip Birinci
journal: Journal of Medical Biochemistry
year: 2023
pmcid: PMC10040198
doi: 10.5937/jomb0-40106
license: CC BY 4.0
---
# False negative effect of high triglycerides concentration on vitamin D levels: A big data study
## Abstract
### Background
Inaccurate test results may be a reason why vitamin D deficiency is seen as a common problem worldwide. Interferences from the sample matrix during testing are the most important factors in measurement errors. In this study, the relationship between triglycerides and total cholesterol levels and vitamin D levels in Turkey was investigated.
### Methods
The 25-hydroxyvitamin D test results and lipid test results studied in Turkey in 2021 were compared. Data were obtained from the Ministry of Health National Health Database. Simultaneously, 25-hydroxyvitamin D, triglyceride, and total cholesterol levels were studied, and 1,135,644 test results were taken as the basis.
### Results
In the group of patients with total cholesterol levels between 0-10.33 mmol/L, the proportion of patients below 20 mg/L ranged from $56.8\%$ to $61.8\%$. In the patient group with cholesterol between 10.36-259 mmol/L, the rate of patients with less than 20 mg/L was between 70.8-$100\%$, while the rate of patients with cholesterol above 100 mg/L was $0\%$. The mean 25-hydroxyvitamin D level was 20.1 mg/L in the patient group with a total cholesterol level between 0-10.33 mmol/L, and 16 mg/L in the patient group with a cholesterol level above 10.36 mmol/L. The mean 25-hydroxyvitamin D level was 20.11 mg/L in the patient group with triglycerides 0-10.16 mmol/L, and the 25-hydroxyvitamin D level was 12.28 mg/L in the patient group with triglycerides 10.17-113 mmol/L. The proportion of patients with vitamin D levels above 100 mg/L was found to be $0\%$ in the group of patients with triglycerides above 10.17-113 mmol/L.
### Conclusions
According to this study, there is a risk of toxicity when administering vitamin D therapy in patients with high cholesterol and triglycerides levels. This study is the first of this size in the literature. High triglycerides and cholesterol levels can cause inaccurate measurement of vitamin D levels, so care should be taken when evaluating these tests.
## Uvod
Netačni rezultati testova mogu biti razlog zašto se nedostatak vitamina D smatra uobičajenim problemom širom sveta. Interferencije iz matrice uzorka tokom testiranja su najvažniji faktori grešaka u merenju. U ovoj studiji istražen je odnos između nivoa triglicerida i ukupnog holesterola i nivoa vitamina D u Turskoj.
## Metode
Upoređeni su rezultati testa 25-hidroksivitamina D i rezultati testa lipida koji su proučavani u Turskoj 2021. godine. Podaci su dobijeni iz Nacionalne zdravstvene baze podataka Ministarstva zdravlja. Istovremeno su proučavani nivoi 25-hidroksivitamina D, triglicerida i ukupnog holesterola, a za osnovu su uzeta 1.135.644 rezultata testova.
## Rezultati
U grupi pacijenata sa nivoom ukupnog holesterola između 0-10,33 mmol/L, udeo pacijenata ispod 20 mg/L kretao se od 56,$8\%$ do 61,$8\%$. U grupi pacijenata sa holesterolom između 10,36-259 mmol/L, stopa pacijenata sa manje od 20 mg/L bila je između 70,8-$100\%$, dok je stopa pacijenata sa holesterolom iznad 100 mg/L bila $0\%$. Prosečan nivo 25-hidroksivitamina D bio je 20,1 mg/L u grupi pacijenata sa nivoom ukupnog holesterola između 0-10,33 mmol/L i 16 mg/L u grupi pacijenata sa nivoom holesterola iznad 10,36 mmol/L. Prosečan nivo 25-hidroksi vitamina D bio je 20,11 mg/L u grupi pacijenata sa trigliceridima 0-10,16 mmol/L, a nivo 25-hidroksivitamina D je bio 12,28 mg/L u grupi pacijenata sa trigliceridima 10,17-113 mmol. Utvrđeno je da je procenat pacijenata sa nivoom vitamina D iznad 100 mg/L $0\%$ u grupi pacijenata sa trigliceridima iznad 10,17-113 mmol/L.
## Zaključak
Prema ovoj studiji, postoji rizik od toksičnosti pri primeni terapije vitaminom D kod pacijenata sa visokim nivoom holesterola i triglicerida. Ova studija je prva ove veličine u literaturi. Visoki nivoi triglicerida i holesterola mogu uzrokovati netačno merenje nivoa vitamina D, tako da treba biti oprezan prilikom procene ovih testova.
## Introduction
Vitamin D deficiency is a very common problem in both developed and developing countries [1] [2] [3] [4]. It is reported that 1 billion people worldwide are exposed to vitamin D deficiency or insufficiency [1]. The fact that vitamin D deficiency is quite common even in regions such as the Middle East, Asia, and South America that are exposed to the sun, and the fact that vitamin D deficiency is seen in many people, suggests that there may be erroneous 25-hydroxyvitamin D (25-OHD) test results [4]. The existence of clinically inconsistent results supports this finding. The fact that some kit manufacturers have changed their vitamin D kits for several generations may be due to their inability to optimize the kits sufficiently. Matrix-induced interferences in the sample during measurement are the most important factors in measurement error [5]. Lipemia has been found to cause erroneous laboratory measurements and to cause negative interference with vitamin D [5] [6] [7] [8]. This negative interference caused by lipemia may hide intoxication levels in patients receiving vitamin D replacement therapy [6]. The aim of this study was to investigate the relationship between triglyceride and total cholesterol levels and vitamin D levels in Turkey.
## Materials and methods
The results of the 25-OHD tests studied in laboratories in Turkey in 2021 were compared with the results of simultaneous blood lipids. Laboratory test results were obtained from the Turkish Ministry of Health National Health Database (THND). To encourage data sharing with the scientific community and scientific research, laboratory results were presented to the author group under the supervision of the Ministry of Health, and various publications were made [9] [10]. The National Health Database includes laboratory service information and test process information in terms of laboratory tests. Test process information consists of the test name, test result, test unit, and reference range. Laboratory service information includes the demographic information of individuals. In the 25-OHD tests studied in Turkey, chromatographic methods were generally used, especially the immunoassay method (approximately $95\%$). Triglyceride and total cholesterol tests were obtained using various biochemistry auto analysers, and the test results were transferred to the database after they were approved in the laboratory. In this study, synchronized 25-OHD, triglycerides, and total cholesterol levels were studied, and 1,135,644 laboratory test results were taken as the basis in 2021. Samples from each application had the same run number, and the samples were considered to have the same serum properties.
This study was conducted under the Declaration of Helsinki and received approval from the Turkish Ministry of Health with the waiver of informed consent for retrospective data analysis (95741342-$\frac{020}{27112019}$). The analyses were completed by transferring the study data to the IBM SPSS Statistics (version 26) program. Descriptive statistics (average, standard deviation) were given for the numerical data. Whether there was a difference between the two independent groups was checked with the independent t-test. The results were interpreted by comparing the test statistical results with α=0.05.
## Results
Data from a sample of 1,135,644 patients were included in the study, and patient results performed simultaneously with 25-OHD, triglycerides, and total cholesterol were used. The levels of triglycerides and 25-OHD in the patient samples, and total cholesterol and 25-OHD levels were compared. Total cholesterol levels divided into 10 groups in the range of 0-25.9 mmol/L in 2.59 mmol/L increments, and one group in the range of 25.9-259 mmol/L, for a total of 11 groups. Mean values between 0-10.33 μmol/L (mean±SD) were respectively; 19±13.04 μg/L, 19.51±12.45 μg/L, 20.86±13.37 μg/L, and 20.89±14.22 μg/L, respectively (Table 1). It was observed that 25-OHD levels were lower in groups with total cholesterol levels above 10.36 mmol/L. The 25-OHD level (mean±SD) in the group with total cholesterol levels of 10.36-12.92 mmol/L was 16.76±12.86 μg/L. In the 12.95-15.51 mmol/L group, the 25-OHD level (mean±SD) was 12.89±12.36 μg/L that can be considered as lower. The lowest 25-OHD levels were 8.08±4.54, 3±0 μg/L in the 20.72-23.28 mmol/L and 23.31-25.87 mmol/L groups (Table 1). 25-OHD levels corresponding to total cholesterol levels were 20.1±12.9 μg/L in the range of 0-10.33 mmol/L (mean±SD) and in the range of 10.36-259 mmol/L the levels were 16±12.93 μg/L (mean±SD). The mean 25-OHD level in the range of 10.36-259 mmol/L was found to be quite low (Table 2). In the four groups in the range of 0-10.36 mmol/L, there were 6, 713, 771 and 48 patient samples respectively. There were 1538 patient samples in total, and no intoxication was observed in the range of 10.36-259 mmol/L (Table 2).
Triglycerides levels in the range of 0-45.2 mmol/L were divided into 40 groups with 1.12 mmol/L increments, and one group in the range of 45.2-113 mmol/L, for a total of 41 groups. The number of patients with triglycerides levels between 0-1.12 mmol/L was 419424, the number of patients between 1.13-2.25 mmol/L was 510416, and the number of patients between 2.26-3.38 mmol/L was 143196. The number of patients in the other groups varied between 0-40245. The 25-OHD concentrations of the patients varied between 6-20.79 μg/L. In the patient group with the lowest triglycerides level (0-1.12 mmol/L), the lowest rate of patients with less than 20 mg/L was determined as $55.7\%$. The proportion of patients below 20 μg/L gradually increased up to the triglycerides 12.43-13.55 mmol/L group. Except for one group, the rate of patients below 20 μg/L was found to be between $75\%$ and $100\%$ in all the other groups. The proportion of patients with 25-OHD levels above 100 μg/L was between $0.1\%$-$0.3\%$, up to the patient group with 9.04-10.16 mmol/L. It was determined to be $0\%$ in the other groups (Table 3).
**Table 3**
| Triglycerides <br>(mmol/L) | Number<br>of tests | Mean<br>(μg/L) | SD | Number of results with a 25-OHD level below 20 μg/L | Number of results with 25-OHD levels above 100 μg/L | Rate of results with 25-OHD level below 20 μg/L (%) | Rate of results with 25-OHD levels above 100 μg/L (%) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 0–1.12 | 419424.0 | 20.79 | 13.35 | 233754.0 | 649.0 | 55.7 | 0.2 |
| 1.13–2.25 | 510416.0 | 20.24 | 12.91 | 294744.0 | 694.0 | 57.7 | 0.1 |
| 2.26–3.38 | 143196.0 | 18.82 | 11.94 | 90145.0 | 148.0 | 63.0 | 0.1 |
| 3.39–4.51 | 40245.0 | 17.68 | 11.31 | 27173.0 | 30.0 | 67.5 | 0.1 |
| 4.52–5.64 | 11725.0 | 16.99 | 11.07 | 8242.0 | 10.0 | 70.3 | 0.1 |
| 5.65–6.77 | 4679.0 | 15.98 | 10.08 | 3475.0 | 1.0 | 74.3 | 0.1 |
| 6.78–7.9 | 2259.0 | 15.86 | 10.6 | 1696.0 | 2.0 | 75.1 | 0.1 |
| 7.91–9.03 | 1211.0 | 15.25 | 11.71 | 947.0 | 3.0 | 78.2 | 0.3 |
| 9.04–10.16 | 713.0 | 14.8 | 11.16 | 576.0 | 1.0 | 80.8 | 0.1 |
| 10.17–11.29 | 403.0 | 13.6 | 8.82 | 332.0 | 0.0 | 82.4 | 0.0 |
| 11.3–12.42 | 283.0 | 12.53 | 8.76 | 253.0 | 0.0 | 89.4 | 0.0 |
| 12.43–13.55 | 253.0 | 11.37 | 7.14 | 227.0 | 0.0 | 89.7 | 0.0 |
| 13.56-14.68 | 149.0 | 12.12 | 8.33 | 218.0 | 0.0 | 87.6 | 0.0 |
| 14.69–15.81 | 140.0 | 12.12 | 8.34 | 121.0 | 0.0 | 86.4 | 0.0 |
| 15.82–16.94 | 115.0 | 12.39 | 7.97 | 102.0 | 0.0 | 88.7 | 0.0 |
| 16.95–18.07 | 69.0 | 10.86 | 6.91 | 61.0 | 0.0 | 88.4 | 0.0 |
| 18.08-19.2 | 53.0 | 11.96 | 7.79 | 45.0 | 0.0 | 84.9 | 0.0 |
| 19.21–20.33 | 41.0 | 12.39 | 8.43 | 34.0 | 0.0 | 82.9 | 0.0 |
| 20.34–21.46 | 27.0 | 9.81 | 5.22 | 26.0 | 0.0 | 96.3 | 0.0 |
| 21.47–22.59 | 21.0 | 12.05 | 12.29 | 17.0 | 0.0 | 81.0 | 0.0 |
| 22.6–23.72 | 9.0 | 12.25 | 5.59 | 8.0 | 0.0 | 88.9 | 0.0 |
| 23.73–24.85 | 10.0 | 11.95 | 4.79 | 9.0 | 0.0 | 90.0 | 0.0 |
| 25.86–25.98 | 10.0 | 8.66 | 5.01 | 10.0 | 0.0 | 100.0 | 0.0 |
| 25.99–27.11 | 10.0 | 15.14 | 8.66 | 6.0 | 0.0 | 60.0 | 0.0 |
| 27.12–28.24 | 9.0 | 12.41 | 10.79 | 8.0 | 0.0 | 88.9 | 0.0 |
| 28.25–29.37 | 10.0 | 13.56 | 6.23 | 8.0 | 0.0 | 80.0 | 0.0 |
| 29.38–30.5 | 8.0 | 10.55 | 4.28 | 8.0 | 0.0 | 100.0 | 0.0 |
| 30.51–31.63 | 4.0 | 8.39 | 4.35 | 4.0 | 0.0 | 100.0 | 0.0 |
| 31.64–32.76 | 6.0 | 9.94 | 4.59 | 6.0 | 0.0 | 100.0 | 0.0 |
| 32.77–33.89 | 2.0 | 7.77 | 0.27 | 2.0 | 0.0 | 100.0 | 0.0 |
| 33.9–35.02 | 4.0 | 9.36 | 1.28 | 4.0 | 0.0 | 100.0 | 0.0 |
| 35.03–36.15 | 4.0 | 20.47 | 8.51 | 3.0 | 0.0 | 75.0 | 0.0 |
| 36.16–37.28 | 4.0 | 9.37 | 4.88 | 4.0 | 0.0 | 100.0 | 0.0 |
| 37.29–38.41 | 2.0 | 6.0 | 3.0 | 2.0 | 0.0 | 100.0 | 0.0 |
| 38.42–39.54 | 3.0 | 8.69 | 4.59 | 3.0 | 0.0 | 100.0 | 0.0 |
| 39.55–40.67 | 3.0 | 7.29 | 1.96 | 3.0 | 0.0 | 100.0 | 0.0 |
| 40.68–41.8 | | | | | | | |
| 41.81–42.93 | | | | | | | |
| 42.94–44.06 | 1.0 | 18.27 | 0.0 | 1.0 | 0.0 | 100.0 | 0.0 |
| 44.07–45.19 | 2.0 | 14.35 | 2.35 | 2.0 | 0.0 | 100.0 | 0.0 |
| 45.2–113 | 15.0 | 6.5 | 4.38 | 15.0 | 0.0 | 100.0 | 0.0 |
According to Table 4, the number of patients between 0-10.16 mmol/L triglycerides levels was 1,133,868. The number of patients between 10.17-113 mmol/L was determined as 1771. There was a significant difference in vitamin D levels between the two groups ($p \leq 0.001$). The proportion of patients with vitamin D below 20 μg/L was calculated as $58.3\%$ and $87.1\%$ respectively. The proportion of patients with vitamin D above 100 μg/L was calculated as $0.1\%$ and $0\%$ respectively.
**Table 4**
| Triglycerides <br>(mmol/L) | Number<br>of tests | Mean<br>(μg/L) | SD | Number of results with a 25-OHD level below 20 mg/L | Number of results with 25-OHD levels above 100 μg/L | Rate of results with 25-OHD level below 20 μg/L (%) | Rate of results with 25-OHD levels above 100 μg/L (%) | p<br>value |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0-10.16 | 1133868 | 20.11 | 12.9 | 660752 | 1538 | 58.3 | 0.1 | <0.001 |
| 10.17-113 | 1771 | 12.28 | 8.25 | 1542 | 0 | 87.1 | 0.0 | <0.001 |
As a result of the Pearson correlation test applied in the group with a triglyceride value less than 10.17 mmol/L, a negative significant relationship was found between 25-OHD and triglyceride ($p \leq 0.05$). As a result of the Pearson correlation test applied for the group with a triglyceride value of 10.17 mmol/L or more, it was determined that there was no significant relationship between 25-OHD and triglyceride ($p \leq 0$,05) (Table 5). In the Pearson correlation analysis applied between 25-OHD and total cholesterol; a significant positive correlation was found in those with a total cholesterol value below 10.36 mmol/L; significant negative correlation was found in the group above 10.36 mmol/L ($p \leq 0.05$) (Table 6).
## Discussion
Vitamin D deficiency has a high prevalence and is associated with musculoskeletal functions as well as many clinical conditions, such as certain malignancies, cardiovascular diseases, infections, obesity, metabolic syndrome, diabetes mellitus, and autoimmune diseases. This has led to a large increase in vitamin D testing worldwide [9] [11]. 25-OHD is the most abundant form of vitamin D in circulation and occurs in the liver. It is used to determine a patient's vitamin D status [1]. There are two types of 25-OHD in circulation: 25-OHD3 (cholecalciferol) which is $95\%$ of 25-OHD, and is of endogenous origin. The other type is 25-OHD2 (ergocalciferol), obtained from plants and fish, which comprises a very small rate in circulation unless vitamin D supplementation is taken [11] [12]. For 25-OHD measurement, immunoassay, HPLC, and liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods were used [13]. Chromatographic methods can effectively distinguish the 25-OHD3 and 25-OHD2 forms that make up 25-OHD, but immunoassay methods are not capable of distinguishing these forms [12] [14]. The most important disadvantage of immunoassays used in vitamin D measurement is that they are affected by many parameters such as endogenous antibodies and xenobiotics in the blood; therefore, and therefore some laboratories prefer to use the LC-MS/MS method [11] [15]. The National Health and Nutritional Examination Survey (NHANES) has recommended LC-MS/MS as the best method for measuring vitamin D metabolites due to its improved sensitivity, accuracy, reproducibility, and high sensitivity [16]. Immunochemical methods are simpler, faster and more cost-effective compared to LC-MS/MS systems [11] [16]. However, with the effect of lipemia and other factors (such as carbohydrates, phospholipids, bile salts, xenobiotics and proteins) that increase the turbidity of the measurement material in the LC-MS/MS technique, ionization may be impaired, and the results may be affected [15]. In immunoassay tests, lipoproteins that block the binding sites on antibodies can affect the antigen-antibody reaction, resulting in false high or false low results [17] [18]. In a case report by Gönel et al. [ 19], an erroneous low vitamin D result was found in a case in which 25-OHD was measured using the LC-MS/MS method. While the triglycerides level of the patient who was hyperlipidemic was 27.12 mmol/L, vitamin D was measured as 6.4 μg/L and after the dilution study, the level was found to be 188 μg/L. In the same case, it was reported that hypercalcemia and nephrolithiasis developed due to vitamin D replacement at levels higher than needed. When replacement therapy was discontinued, high calcium levels were found to return to normal [19]. In the study by Agarwal et al. [ 8] on pediatric patients using the immunoassay method, the average of 25-OHD levels to lipemia levels was taken. According to the lipemia levels, the 25-OHD averages are as follows: mild lipemia was 24.7±2.8 μg/L, moderate lipemia was 22.4±3.2 μg/L, and severe lipemia was 20.4±2.7 μg/L, and 25-OHD levels were found to decrease depending on the increase in lipid levels [8]. In a study by Gönel et al. [ 19] with 100 patients, the 25-OHD3 concentrations of 21 patients were found to be falsely low. The mean 25-OHD level of the initial results of these 21 patients was 9.94 ± 7.85 μg/L. The level after repeated study with dilution was 39.23±18.13 μg/L [15]. Similar results were obtained in our study as well. As total cholesterol and triglyceride levels increased, vitamin D levels were found to be lower. The lowest mean 25-OHD level of the groups with total cholesterol between 0-10.36 mmol/L was 19 μg/L and the mean value was higher. The mean 25-OHD level of the groups with total cholesterol over 10.36 mmol/L was lower, and the highest mean level was found to be 16.76 μg/L (Table 1). It was observed that there was a statistically significant difference between the two groups formed according to values below and above 10.36 mmol/L ($p \leq 0.01$) (Table 2). In patients with total cholesterol levels below 10.36 mmol/L, the rate of patients with 25-OHD levels below 20 μg/L is $61.8\%$, while above 10.36 mmol/L, $70.8\%$ and above 20.72 mmol/L, it was $100\%$. This demonstrates that as the total cholesterol level increases, the rate of vitamin D deficiency increases. In the patient group with a total cholesterol level below 10.36 mmol/L, 1538 patient results showed vitamin D intoxication, while no intoxication was observed above 10.36 mmol/L (Table 2). This indicates that our study supports the case report study of Gönel et al. [ 19].
The relevance between the effect of lipemia and total cholesterol levels was determined in this study. It has been shown that total cholesterol values above certain levels affect 25-OHD levels. Among the groups formed according to triglycerides levels, the mean levels of 25-OHD were found to be higher in the groups in the 0-10.16 mmol/L range, and lower in the 10.17-113 mmol/L range (Table 3). In the comparison between the two groups, the difference is found to be statistically significant (Table 4) ($p \leq 0.001$). When comparing triglycerides levels with 25-OHD levels, a total of 41 groups were formed: 40 groups with triglycerides levels between 0-45.19 mmol/L with 1.13 mmol/L increments, and one group between 45.2-113 mmol/L (Table 3). When the 25-OHD mean (mean±SD) of the groups with triglycerides levels between 0-10.16 mmol/L was evaluated, the highest value (mean±SD) in the group with triglycerides levels between 0-1.12 mmol/L was 20.79±13.35 μg/L. Depending on the increase in triglycerides levels, 25-OHD levels decreased regularly. Groups with higher triglycerides levels (>10.17 mmol/L) had lower average 25-OHD levels in general and were found to decrease further with an increase in triglycerides levels (Table 3). Considering the change in 25-OHD levels, the 25-OHD levels of patients with triglycerides levels below and above 10.17 mmol/L were compared (Table 4). The mean of 25-OHD (mean±SD) in patient samples with triglycerides levels in the range of 0-10.16 mmol/L was 20.11±12.9 μg/L, and 12.28±8.25 μg/L in the range of 10.17-113 mmol/L (Table 4). Samples of patients with 25-OHD levels above 100 μg/L (considered vitamin D intoxication) were seen only in groups with a total cholesterol level of 10.33 mmol/L and below. In the range of 0-10.33 mmol/L, there was a total of 1538 patient samples. No intoxication was detected in the range of 10.36-259 mmol/L (Table 3).
The pre-analytical stage is vital to the accuracy of laboratory test results. At the pre-analytical stage, the overall proportion of lipemic samples among all samples, whether inpatient or outpatient, varied between 0.5-$2.5\%$ depending on the type of hospital [18]. The most common preanalytical cause of lipemia was the short time between eating and drawing blood. Since it was not possible to adjust this time in patients admitted to the emergency department, the rate of lipemic samples was high. Ambulatory patients need to make appropriate preparations, including fasting, before sampling [18].
It is showed that vitamin D supplementation has a beneficial effect on reducing serum total cholesterol, low density lipoprotein (LDL) cholesterol, and triglycerides levels, but not high density lipoprotein (HDL) cholesterol levels in a review and meta-analys [20]. However according to our study, when vitamin D therapy is to be given, care should be taken in terms of toxicity in patients with high cholesterol and triglycerides.
The subject discussed in this article is to investigate the effect of the presence of cholesterol on the measurement of 25-OHD vitamins. However, it has been suggested that vitamin D may affect endogenous cholesterol synthesis and vice versa. A recovery study is required to say that cholesterol causes this by interacting with 25-OHD at the molecular level or by interfering with the measurement method. According to previously published case report data, it is likelier to say that 25-OHD is affected by high triglycerides as opposed to high cholesterol [19]. The limitations of this study are that the effect of endogenous cholesterol synthesis on the measurement of vitamin D could not be evaluated, and patients who took vitamin D supplements could not be excluded.
## Conclusion
The strength of this study is that it is the first of this size in the literature. Another advantage is that it draws attention to the possibility of intoxication in cases of high cholesterol and triglycerides. Knowing that vitamin D levels may be wrong in the presence of high cholesterol and triglycerides will save time and money. We also conclude from this study that knowing that there may be toxicity, it is necessary to be careful when prescribing vitamin D. A limitation of our study is the absence of dilution measurements.
As a result, high triglycerides and cholesterol levels can cause inaccurate measurement of vitamin D levels, so care should be taken when evaluating these tests.
## Conflict of interest statement
All the authors declare that they have no conflictof interest in this work.
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|
---
title: Clinical meaning of serum trimethylamine oxide, N-terminal-pro-brain natriuretic
peptide, hypoxia-inducible factor-1a and left ventricular function and pregnancy
outcome in patients with pregnancy-induced hypertension
authors:
- Ying Wu
- Yue Wu
- Lihong Duan
- Chunhui Xiao
- Zeya Ren
- Yuntai Liang
journal: Journal of Medical Biochemistry
year: 2023
pmcid: PMC10040199
doi: 10.5937/jomb0-37030
license: CC BY 4.0
---
# Clinical meaning of serum trimethylamine oxide, N-terminal-pro-brain natriuretic peptide, hypoxia-inducible factor-1a and left ventricular function and pregnancy outcome in patients with pregnancy-induced hypertension
## Abstract
### Background
To figure out the clinical meaning of serum trimethylamine oxide (TMAO), N-terminal-pro-brain natriuretic peptide (NT-proBNP) and hypoxia-inducible factor-1a (HIF-1a) with left ventricular function and pregnancy outcome in patients with pregnancy-induced hypertension.
### Methods
From January 2018 to October 2020, 117 patients with gestational hypertension were taken as the research objects and grouped into the gestational hypertension (pregnancy-induced hypertension, 55 cases), mild preeclampsia (mild PE, 43 cases) and severe preeclampsia (severe PE, 19 cases) in the light of the severity of the disease. Analysis of the relation of serum TMAO, NT-proBNP and HIF-1a with the severity of disease and cardiac function indexes in patients with gestational hypertension was conducted. All patients were followed up to the end of pregnancy, and the predictive value of serum TMAO, NT-proBNP and HIF-1a on pregnancy outcome in patients was analyzed.
### Results
Serum TMAO and NT-proBNP of patients were elevated, while HIF-1a was reduced with the severity of the disease ($P \leq 0.05$). Serum TMAO and NT-proBNP in patients with gestational hypertension were positively correlated but HIF-1a was negatively correlated with the severity of the disease ($P \leq 0.05$). Left ventricular end-diastolic volume (LVEDV) and left ventricular end-systolic volume (LVESV) were elevated in gestational hypertension patients, while ejection fraction (LVEF) was reduced with the severity of disease ($P \leq 0.05$). Serum TMAO, NT-proBNP and HIF1a were associated with LVEDV, LVESV and LVEF values in patients with gestational hypertension ($P \leq 0.05$). Serum TMAO and NT-proBNP were elevated but HIF-1a was reduced in patients with a poor pregnancy outcome ($P \leq 0.05$). The AUC of the combined detection of serum TMAO, NT-proBNP and HIF-1a on pregnancy outcome was greater ($P \leq 0.05$).
### Conclusions
Serum TMAO, NT-proBNP and HIF-1a in patients with gestational hypertension are associated with disease severity and cardiac function, and have predictive and evaluative values for disease severity and pregnancy outcome.
## Uvod
Cilj je bio da se otkrije kliničko značenje serumskog trimetilamin oksida (TMAO), N-terminalnog pro-moždanog natriuretskog peptida (NT-proBNP) i faktora indukovanog hipoksijom-1a (HIF-1a) sa funkcijom leve komore i ishodom trudnoće u pacijenti sa hipertenzijom izazvanom trudnoćom.
## Metode
Od januara 2018. do oktobra 2020. godine, 117 pacijentkinja sa gestacijskom hipertenzijom uzeto je kao predmet istraživanja i grupisano u gestacijsku hipertenziju (hipertenzija izazvana trudnoćom, 55 slučajeva), blagu preeklampsiju (blaga PE, 43 slučaja) i tešku preeklampsiju (teška PE, 19 slučajeva) u svetlu težine bolesti. Urađena je analiza odnosa serumskih TMAO, NT-proBNP i HIF-1a sa težinom bolesti i indeksima srčane funkcije kod pacijenata sa gestacionom hipertenzijom. Sve pacijentkinje su praćene do kraja trudnoće i analizirana je prediktivna vrednost serumskih TMAO, NT-proBNP i HIF-1a na ishod trudnoće kod pacijenata.
## Rezultati
TMAO i NT-proBNP u serumu pacijenata su bili povišeni, dok je HIF-1a smanjen sa težinom bolesti ($P \leq 0$,05). TMAO i NT-proBNP u serumu kod pacijenata sa gestacionom hipertenzijom bili su u pozitivnoj korelaciji, ali je HIF-1a bio u negativnoj korelaciji sa težinom bolesti ($P \leq 0$,05). Krajnji dijastolni volumen leve komore (LVEDV) i end-sistolni volumen leve komore (LVESV) bili su povišeni kod pacijenata sa gestacionom hipertenzijom, dok je ejekciona frakcija (LVEF) smanjena sa težinom bolesti ($P \leq 0$,05). TMAO, NT-proBNP i HIF-1a u serumu su bili povezani sa vrednostima LVEDV, LVESV i LVEF kod pacijenata sa gestacionom hipertenzijom ($P \leq 0$,05). TMAO i NT-proBNP u serumu su bili povišeni, ali je HIF-1a smanjen kod pacijenata sa lošim ishodom trudnoće ($P \leq 0$,05). AUC kombinovane detekcije TMAO, NT-proBNP i HIF-1a u serumu na ishod trudnoće bila je veća ($P \leq 0$,05).
## Zaključak
Serumski TMAO, NT-proBNP i HIF-1a kod pacijenata sa gestacionom hipertenzijom povezani su sa težinom bolesti i srčanom funkcijom i imaju prediktivne i evaluativne vrednosti za težinu bolesti i ishod trudnoće.
## Introduction
Gestational hypertension causes proteinuria, edema, hypertension and even eclampsia in pregnant women during pregnancy. It often takes place after 20 weeks of pregnancy. In severe cases, it can endanger the health of mothers and babies, and even result in death. Systemic arteriolar spasm in patients with gestational hypertension results in hypoxic metabolism in systemic tissues, and the body is in a hypoxic environment [1]. Hypoxia-inducible factor-1α (HIF-1α) is a regulator of cells and tissues adapting to a hypoxic environment. It can take part in the formation of inflammatory response, and take on a momentous function in the formation of endothelial factor and the proliferation of smooth muscle cells in a hypoxic environment [2] [3]. Trimethylamine-N-oxide (TMAO) is a metabolite of intestinal flora, and its abnormality can motivate vascular inflammation and oxidative stress. TMAO is a latent risk for atherosclerosis and cardiometabolic diseases [4]. N-terminal pro brain natriuretic peptide (NT-proBNP) is a crucial serum factor in the vascular system. It is an N-terminal precursor fragment after the cleavage of brain natriuretic peptide premonomer. When the vascular wall pressure is overloaded, it can motivate the synthesis and release of BNP precursor [5]. It is clinically believed that NT-proBNP is closely linked with the progression of cardiovascular disease. However, no report clearly points out its association with serum TMAO and HIF-1α and cardiac function and pregnancy outcome in patients with gestational hypertension. Therefore, this research aims to figure out the clinical meaning of serum TMAO, NT-proBNP, HIF-1α, left ventricular function and pregnancy outcome in patients with pregnancy-induced hypertension, offering a reference for the clinical evaluation of the disease.
## Clinical data
From January 2018 to October 2020, 117 patients with gestational hypertension were selected as the research objects and grouped into the gestational hypertension (the PIH, 55 cases), mild preeclampsia (the mild PE, 43 cases), and severe preeclampsia (the severe PE, 19 cases) in the light of the severity of gestational hypertension according to the seventh edition of Obstetrics and Gynecology [6]. Inclusion criteria: Meeting the diagnostic criteria for gestational hypertension in the Guidelines for the Diagnosis and Treatment of Hypertensive Diseases in Pregnancy [2015] [7]; Patients with complete clinical data; Patients with singleton pregnancy. Exclusion criteria: Patients combined with abnormal liver and kidney function; Prone to hypertension before pregnancy; combined obstetric complications; combined gestational diabetes mellitus; combined inflammatory diseases; multiple pregnancy. No clear difference exhibited in general data among the three groups ($P \leq 0.05$, Table 1).
**Table 1**
| Groups | the PIH<br>(n=55) | the mild PE<br>(n=43) | the severe PE<br>(n=19) | χ2/F | P |
| --- | --- | --- | --- | --- | --- |
| Age (years) | 31.62±3.10 | 32.08±3.29 | 31.19±3.37 | 0.569 | >0.05 |
| Gestational Week (Weeks) | 33.19±2.57 | 32.74±2.85 | 32.23±3.01 | 0.961 | >0.05 |
| Pregnancy (times) | 1.82±0.23 | 1.75±0.21 | 1.72±0.22 | 2.041 | >0.05 |
| Parity (times) | 1.27±0.25 | 1.33±0.22 | 1.42±0.27 | 2.875 | >0.05 |
| Body mass index (kg/m2) | 23.19±2.75 | 23.58±2.89 | 24.01±2.94 | 0.663 | >0.05 |
| Total cholesterol (mmol/L) | 4.71±0.78 | 4.92±0.85 | 4.69±0.76 | 1.005 | >0.05 |
| Triglycerides (mmol/L) | 1.38±0.21 | 1.45±0.19 | 1.32±0.24 | 2.955 | >0.05 |
| History of drinking (cases) | 5 | 3 | 2 | 0.251 | >0.05 |
| Smoking history (cases) | 1 | 1 | 1 | 0.692 | >0.05 |
| Family history of hypertension (cases) | 3 | 2 | 1 | 0.031 | >0.05 |
| Education (cases) | | | | | |
| Senior high school and above | 34 | 24 | 11 | 0.373 | >0.05 |
| Junior high school and below | 21 | 19 | 8 | | |
## Methods
Detection of serum TMAO, NT-proBNP and HIF-1α: 3 mL of venous blood was collected when patients woke up in the morning within 24 h of enrollment and serum was separated. Mitsubishi PATH-FAST MITSUBISHI chemical spectrophotometer was applied for detecting serum NT-proBNP with chemiluminescence immunoassay in patients. ELISA method was utilized to test TMAO and HIF-1α in patients, and the kit was offered by Shanghai Jinma Experimental Equipment Co., Ltd.
Detection of cardiac function indicators: Siemens SC2000 color ultrasound diagnostic apparatus (4V1c/4Z1c probe, frequency of 1-4MHz) was used. Patients were conventionally connected to lead ECG, and conventional two-dimensional echocardiography and RT-3DE examination were performed in appropriate posture. The patient was in the left lateral decubitus position, the 4Z1c probe and the apical four-chamber view were selected, and the display effect was adjusted to clearly show the left ventricular wall. The full-volume three-dimensional images of three consecutive cardiac cycles were collected and processed by SC2000WP workstation. The in-machine analysis software eSie LVA was applied to measure left ventricular end-diastolic volume (LVEDV), left ventricular end systolic volume (LVESV) and left ventricular ejection fraction (LVEF).
Pregnancy outcome grouping: All patients were followed up to the end of pregnancy and divided into a better pregnancy outcome group and a worse pregnancy outcome group according to the pregnancy outcome. Adverse pregnancy outcomes mainly included stillbirth, miscarriage, and preterm birth.
## Observation indicators
The serum TMAO, NT-proBNP and HIF-1α of PIH, mild PE and severe PE were compared, and their correlation with disease severity and evaluation value of disease severity were analyzed. The serum TMAO, NT-proBNP and HIF-1α in patients with better pregnancy outcome and poor pregnancy outcome were compared, and the predictive value of combined detection on pregnancy outcome was analyzed.
## Statistical processing
SPSS22.0 software was applied for processing data, and enumeration data were shown in %, and compared by χ2 test; *Measurement data* were illustrated by (x̅ ± s) after normality test, and comparison of differences between two groups was done by t test. One-way analysis of variance was applied for comparison of the differences of multiple groups. Spearman test was applied to analyze the correlation between serum TMAO, NT-proBNP, HIF-1α and the severity of gestational hypertension. ROC curve analysis was employed for combined detection of estimates of the severity of gestational hypertension. $P \leq 0.05$ emphasized obvious statistical meaning.
## Comparison of serum TMAO, NT-proBNP and HIF-1α in patients with different severity
Serum TMAO and NT-proBNP were positively associated with disease severity in patients with gestational hypertension, but HIF-1α was negatively correlated with disease severity ($P \leq 0.05$, Figure 1).
**Figure 1:** *Comparison of serum TMAO, NT-proBNP and HIF-1α in patients with different severity. Comparison of TMAO, NT-proBNP and HIF-1α among the three groups, F=56.834, 2456.007, 93.805, P < 0.05*
## Correlation analysis of serum TMAO, NT-proBNP, HIF-1α and severity of gestational hypertension
Serum TMAO and NT-proBNP were positively associated with disease severity in patients with gestational hypertension, but HIF-1α was negatively correlated with disease severity ($P \leq 0.05$, Figure 2).
**Figure 2:** *Correlation analysis of serum TMAO, NT-proBNP, HIF-1α and severity of gestational hypertension*
## Comparison of left ventricular function indexes in patients with different severity
The LVEDV and LVESV values of gestational hypertension patients were elevated with disease aggravation, but LVEF value was reduced ($P \leq 0.05$, Figure 3).
**Figure 3:** *Comparison of left ventricular function indexes in patients with different severity. Comparison of LVEDV, LVESV and LVEF among the three groups, F=35.027, 35.301, 53.556, P < 0.05; vs. the mild PE and the severe PE, *P < 0.05.*
## Correlation analysis of serum TMAO, NT-proBNP, HIF-1α and left ventricular function indexes
The serum TMAO, NT-proBNP, and HIF-1α were associated with LVEDV, LVESV, and LVEF values in patients with gestational hypertension ($P \leq 0.05$, Figure 4).
**Figure 4:** *Correlation analysis between serum TMAO, NT-proBNP, HIF-1α and left heart function indexes*
## Comparison of serum TMAO, NT-proBNP and HIF-1α between the good pregnancy outcome group and the poor pregnancy outcome
The serum TMAO and NT-proBNP were elevated but HIF-1α was reduced in patients with a poor pregnancy outcome compared with those with a good pregnancy outcome ($P \leq 0.05$, Figure 5).
**Figure 5:** *Comparison of serum TMAO, NT-probNP and HIF-1α between the better pregnancy outcome and the poor pregnancy outcome Vs. the better pregnancy outcome, *P < 0.05.*
## Analysis of the predictive value of serum TMAO, NT-proBNP and HIF-1α on pregnancy outcome
The AUC of combined detection of serum TMAO, NT-proBNP and HIF-1α on prediction of pregnancy outcome was greater than that of detection of each indicator ($P \leq 0.05$, Table 2 and Figure 6).
## Discussion
Gestational hypertension mostly takes place after 20 weeks of pregnancy and 6 weeks after delivery. Its pathological changes are manifested as spasm of small blood vessels, which increases peripheral vascular resistance, resulting in decreased uteroplacental blood supply and placental function, which in turn causes fetal intrauterine growth retardation, which can lead to fetal death [8] [9]. At present, it is believed that the formation of hypertension is closely related to the formation of atherosclerosis, which can stimulate the thickness of the intima of the blood vessel, and the amount of oxygen entering the intima gradually reduces, which in turn leads to a reduction in serum HIF-1α [10]. Recent studies have clarified that the intestinal microbial metabolite TMAO can take part in the occurrence and development of atherosclerosis. TMAO can activate the NLRP3 inflammasome and motivate inflammation, which results in the occurrence of endothelial dysfunction [11] [12]. TMAO participates in oxidative stress, inflammatory response and the occurrence of atherosclerosis [13]. The prototype structure of NT-proBNP, brain natriuretic peptide, is the active precursor of polypeptides. Once the cardiomyocytes in the human heart are stimulated, they can be decomposed into NT-proBNP and amino acids under the action of activating enzymes. Plasma brain natriuretic peptide has no significant changes in different stages of gestational hypertension, which is difficult to apply to disease assessment, while NT-proBNP can be used to monitor cardiac function in patients due to its low plasma clearance rate. Studies suggest that it may be linked with disease progression in patients with gestational hypertension [13] [14]. This study discovered that serum TMAO, NT-proBNP were positively associated with the disease severity in patients with gestational hypertension, but HIF-1α was negatively associated. The reason is that the more severe the condition of patients with gestational hypertension, the heavier the heart load, the more likely to have cardiac insufficiency, resulting in the increase of serum NT-proBNP.
Hypertensive disorders of pregnancy can produce systemic small vasospasm, increase blood pressure, and increase cardiac load, resulting in a state of low output and high resistance, which in turn leads to a decrease in left ventricular diastolic function. Meanwhile, coronary artery spasm can result in myocardial ischemia interstitial edema in patients, leading to heart failure in severe cases [15] [16]. Relevant reports point out that the progression of the disease in patients with gestational hypertension is implicated in cardiac function [17]. This study discovered that LVEDV and LVESV values of patients with gestational hypertension were elevated with the severity of the disease, but the LVEF value was reduced, indicating that the cardiac function of patients with gestational hypertension is linked with the severity of hypertension, which is consistent with the results of a former study [18]. The results of this study clarified that serum TMAO, NT-proBNP, and HIF-1α in patients with gestational hypertension were linked with LVEDV, LVESV, and LVEF values, suggesting that serum TMAO, NT-proBNP, and HIF-1α were implicated in cardiac function in patients. This is because cardiac insufficiency leads to increased cardiac volume load and pressure, stretching of ventricular muscle fibers, which increases serum NT-proBNP. Increased cardiac load and blood flow resistance can increase myocardial oxygen consumption, cause tissue hypoxia, and lead to a decrease in HIF-1α. TMAO can activate inflammatory pathways, induce a variety of endothelial-related factors, and at the same time enhance the adhesion of macrophages, promote the occurrence of atherosclerosis, and ultimately affect hyperthe left ventricular function of patients [19] [20].
The main clinical manifestations of gestational hypertension are hypertension, proteinuria and edema. Patients are often accompanied by systemic multiple organ damage, and the disease will worsen with the progress of pregnancy. In severe cases, it will threaten the safety of the mother and the fetus [21] [22]. Some scholars have found that changes in cardiac function in patients with gestational hypertension are closely linked with pregnancy outcomes [23]. This study confirmed that the serum NT-proBNP of patients is related to the cardiac function of the patients, and it may be related to the pregnancy outcomes of the patients. Further research discovered that serum TMAO and NT-proBNP were promoted and HIF-1α was reduced in patients with a poor pregnancy outcome compared with those with a better pregnancy outcome. Moreover, the results of the research clarified that the AUC of combined detection of serum TMAO, NT-proBNP and HIF-1α on prediction of pregnancy outcome was greater than that of alone detection of each indicator, illustrating that combined detection had a predictive value for pregnancy outcome of patients with gestational hypertension.
All in all, serum TMAO, NT-proBNP and HIF-1α in patients with gestational hypertension are linked with disease severity, and have predictive and evaluative values for disease severity and pregnancy outcome.
## Acknowledgments
Not applicable.
## Funding
Not applicable.
## Conflict of interest statement
All the authors declare that they have no conflict of interest in this work.
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|
---
title: Diagnostic significance of hsa_circ_0000146 and hsa_circ_0000072 biomarkers
for Diabetic Kidney Disease in patients with type 2 diabetes mellitus
authors:
- Amul M. Badr
- Omayma Elkholy
- Mona Said
- Sally A. Fahim
- Mohamed El-Khatib
- Dina Sabry
- Radwa M. Gaber
journal: Journal of Medical Biochemistry
year: 2023
pmcid: PMC10040202
doi: 10.5937/jomb0-39361
license: CC BY 4.0
---
# Diagnostic significance of hsa_circ_0000146 and hsa_circ_0000072 biomarkers for Diabetic Kidney Disease in patients with type 2 diabetes mellitus
## Abstract
### Background
Diabetic Kidney Disease (DKD) is a significant challenge in healthcare. However, there are currently no reliable biomarkers for renal impairment diagnosis, prognosis, or staging in DKD patients. CircRNAs and microRNAs have emerged as noninvasive and efficient biomarkers.
### Methods
We explored Cannabinoid receptor 1 (CNR1), C reactive protein (CRP), hsa_circ_ 0000146 and 0000072, and hsa-miR-21 and 495 as diagnostic biomarkers in DKD. The serum concentrations of CRP and CNR1 were measured using ELISA. Rt-qPCR was used to evaluate the expression levels of CNR1, circRNAs, and miRNAs in 55 controls, 55 type 2 diabetes mellitus patients, and 55 DKD patients. Their diagnostic value was determined by their ROC curve. KEGG pathway was used to predict the functional mechanism of the circRNA's target genes.
### Results
DKD patients exhibited a significant increase in CRP and CNR1 levels and the expression of miR-21 and 495. The expression levels of circ_0000146 and 0000072 decreased in DKD patients. ROC analysis revealed that circRNAs and miRNAs alone or CNR1 and CRP have significant diagnostic potential. The functional prediction results showed the involvement of hsa_circ_0000146 and 0000072 in various pathways that regulate DKD.
### Conclusions
Therefore, the examined circRNAs and miRNAs may represent a novel noninvasive biomarker for diagnosing and staging DKD.
## Uvod
Dibetesna bolest bubrega (DKD) predstavlja značajan izazov u zdravstvu. Međutim, trenutno ne postoje pouzdani biomarkeri za dijagnozu, prognozu ili određivanje stadijuma oštećenja bubrega kod pacijenata sa DKD. CircRNA i mikroRNA su se pojavili kao neinvazivni i efikasni biomarkeri.
## Metode
Istražili smo kanabinoidni receptor 1 (CNR1), C reaktivni protein (CRP), hsa_circ_ 0000146 i 0000072, i hsa-miR-21 i 495 kao dijagnostičke biomarkere za DKD. Koncentracije CRP i CNR1 u serumu su merene korišćenjem ELISA. Rt-kPCR je korišćen za procenu nivoa ekspresije CNR1, circRNA i miRNA kod 55 kontrolnih pacijenata, 55 pacijenata sa dijabetes melitusom tipa 2 i 55 pacijenata sa DKD. Njihova dijagnostička vrednost određena je njihovom ROC krivom. Za predviđanje funkcionalnog mehanizma ciljnih gena circRNA korišćena je KEGG mapa puteva.
## Rezultati
Pacijenti sa DKD su pokazali značajno povećanje nivoa CRP i CNR1 i ekspresije miR-21 i 495. Nivoi ekspresije circ_0000146 i 0000072 su se smanjili kod pacijenata sa DKD. ROC analiza je otkrila da samo circRNA i miRNA ili CNR1 i CRP imaju značajan dijagnostički potencijal. Rezultati funkcionalnog predviđanja pokazali su učešće hsa_circ_0000146 i 0000072 u različitim putevima koji regulišu DKD.
## Zaključak
Stoga, ispitivani circRNA i miRNA mogu predstavljati novi neinvazivni biomarker za dijagnostikovanje i određivanje stadijuma DKD.
## Introduction
Diabetic kidney disease (DKD) is a significant condition that affects up to $50\%$ of people with diabetes. It develops into end-stage kidney disease (ESKD) [1]. The gold standard for diagnosing DKD is microalbuminuria, glomerular filtration rate (GFR) based on creatinine or cystatin C, and kidney histology [2]. These diagnostic markers are unreliable, insensitive, costly, and invasive. The lack of cost-effective, reproducible, and noninvasive biomarkers for DKD is the leading cause of delayed diagnosis and treatment. Consequently, there is growing interest in developing alternative prognostic or predictive biomarkers.
The endocannabinoid system comprises type 1 (CNR1) and type 2 (CNR2) cannabinoid receptors and ligands that are dominant in the kidney [3]. In DKD, CNR1 signaling contributes to the formation of inflammation and fibrosis [4]. C reactive protein (CRP) is an acute-phase inflammatory protein linked to microalbuminuria and renal impairment in T2DM patients [5].
Noncoding RNAs (ncRNAs), miRNAs, and circRNAs are epigenetic regulators. miRNAs are single-stranded, small (19–25 nucleotides) noncoding RNAs that have recently gained prominence as vital regulators of gene expression [6]. hsa-miR-495 has been linked to several immunological and inflammatory processes, cancer cell proliferation, metastasis, and treatment resistance [7]. *Multiple* gene regulatory functions of hsa-miR-21 are associated with complications of T2DM, and its silencing ameliorates DKD [8]. CircRNAs are covalently closed RNA loop products generated by back-splicing during transcription. They act by sponging miRNAs and proteins that regulate their expression [9]. CircRNAs are additionally distinguished by being structurally stable and tissue- specific [10]. Intriguingly, studies hypothesize that circRNAs regulate the inflammation and fibrosis of the proximal tubules caused by high glucose levels [11]. Due to the paucity of information on circRNAs and DKD, bioinformatics analyses were used to select circ_ 0000146 and 0000072, which serve as sponges for miR-21 and miR-495, respectively.
Therefore, this study examines expression profiles for CNR1, CRP, hsa_circ_0000146 and 0000072, and hsa-miR-21 and 495 as potential noninvasive biomarkers for the diagnosis of DKD.
## Subjects
A case-control study (approval number: MD-83-2020) was conducted at the Medical Biochemistry and Molecular Biology Department of Cairo University’s Faculty of Medicine. All participants gave their informed consent for this study, which was carried out in conformity with the Declaration of Helsinki of the World Medical Association.
The patients were enrolled at the outpatient clinic of the Internal Medicine and Nephrology department at the Faculty of Medicine, Cairo University. The study included 110 Egyptian patients, including 55 with type 2 diabetes (T2DM) and 55 with DKD diagnosed, according to the American Diabetes Association and the American Society of Nephrology. Patients recruited had to meet the following inclusion criteria: age >18 years, fasting plasma glucose (FPG) more than 7 mmol/L, postprandial glucose (PPG) exceeding 11.1 mmol/L, HbA1c ≥ $6.5\%$. DKD patients have an albumin-to-creatinine ratio (ACR) of more than 30 mg/g and a reduction in GFR ≤ 60 mL/min per 1.73 m2. ESRD stage 5 (G5) showed a further reduction in GFR <15 mL/min/1.73 m2.
The trial excluded patients with nephropathy due to other causes, autoimmune diseases, concurrent urinary tract infection, hepatitis, HIV positivity, glucocorticoid treatment, kidney transplantation, and cancer. Pregnancy and breastfeeding were also exclusion factors. After examining the inclusion and exclusion criteria, all participants underwent a clinical evaluation consisting of a comprehensive medical history and laboratory tests. Fifty-five healthy volunteers of the same age and gender with no history, clinical symptoms, or test results of diabetes mellitus participated in the study.
## Blood sample collection and laboratory assays
The sample size was estimated as 53 patients for each group using the G*Power v3 software with a significance level of 0.05, an effect size of 0.25, a power of 0.8, and a correlation of 0.8. Based on this assumption, a total of 165 participants in the three studied groups were considered adequate.
After 8 h fasting and 2 h following a meal, trained laboratory personnel extracted 5 mL of peripheral venous blood from each participant. The blood was collected in EDTA tubes for measuring glycosylated hemoglobin (HbA1c), frozen at -80°C until RNA extraction for circ_0000146, circ_0000072, miR-21, and miR-495 quantification, or centrifuged for 15 min at 1000 × g for plasma separation and measurement of FPG and PPG. GFR was calculated using the diet modified-kidney disease equation. Another portion of blood was kept in plain tubes and left to clot for 15 min before centrifugation at 4000 × g to collect serum. Kidney function tests (serum urea and creatinine levels), albumin, CRP, and CNR1 were determined using the serum. In addition, the ACR was calculated using two morning-collected urine samples.
## RNA extraction
Total RNA was extracted using the miRNeasy Mini Kit (Qiagen, Catalog Number: 217004, Frank furt,Germany). Quantifying and analyzing the purity of RNA samples using the NanoDrop® (ND)-1000 spectrophotometer (NanoDrop Technologies, Inc., Wilmington, USA). The total RNA yield was calculated at 260 and 280 nm using a Beckman dual spectrophotometer.
## Circ_0000146, circ_0000072, miR-21, miR-495 expression by RT-qPCR
For RT-qPCR, the TransScript® Green One-Step RT-qPCR SuperMix kit (Transgen Biotech, Cat. # AQ211-01, Beijing, China) was utilized. The protocol for determining circ_0000146 and circ_0000072 consisted of 15 min at 45°C followed by 5 min at 95°C. Subsequently, 40 cycles of PCR amplification were performed, with 15s at 95°C, 20 s at 55°C, and 30s at 72°C. Regarding miR-21 and miR-495, the following modifications were made: 94°C for 30s, then 40 cycles of 94°C for 5s, and 60°C for 30 s. Using the 2−ΔΔCt method, the circRNAs and miRNAs were normalized relative to the mean Ct values of the GAPDH and RUN U6B housekeeping genes, respectively. An RT-qPCR system (StepOne, version 2.1, Applied Biosystems, Foster City, USA) was used for the analysis. The sequences of the primers are shown in Table 1.
**Table 1**
| Gene | Primer sequence | Accession # |
| --- | --- | --- |
| CNR1 | F: 5’-GGCAGTGAAGAACCGATACA-3’<br>R:5’-CCCAAACCTACCAAGACAGAG-3’ | NM_001160226.3 |
| hsa_circ_0000146 | F: 5’-CCACAAGCAAACCACAGTCA-3’<br>R: 5’-AATGACCACAGCCACAATGA-3’ | NM_021642 |
| hsa_circ_0000072 | F: 5’-TCATGGCAATCGAGTTGAGT-3’<br>R: 5’-CAAACCAAGGAATAGCTTCCA-3’ | NM_145243 |
| hsa-miR-21 | F: 5’-GTGCAGGGTCCGAGGT-3’ | MIMAT000449 |
| hsa-miR-495 | F: 5’-GTGCAGGGTCCGAGGT-3’ | MIMAT0002817 |
| GAPDH | F: 5’-ACCCACTCCTCCACCTTTGA-3’<br>R: 5’-CTGTTGCTGTAGCCAAATTCGT-3’ | NM_001357943.2 |
| RUN U6B | F: 5’GGCAGCACATATACTAAAATTGGAA-3’ | M14486.1 |
| Universal reverse primer | R: 5’-GTGCAGGGTCCGAGGT-3’ | |
## Quantitative determination of serum CRP and CNR1
The quantitative determination of CRP and CNR1 was performed using a commercially available ELISA Kit (SunLong Biotech Co., LTD, Cat. # SL0535Hu, Zhejiang, China) and (SunLong BiotechCo., LTD, Cat. # SL3387Hu, Zhejiang, China), respectively.
## Statistical analysis
The Kolmogorov-Smirnov normality test was used. We employed nonparametric tests (the Kruskal–Wallis test and the Mann–Whitney U test, $P \leq 0.05$) and one-way ANOVA (parametric test, $P \leq 0.05$). The chi-square (x2) test was used to analyze categorical variables. Mean and standard deviation are employed to characterize the continuous variables. Spearman’s rank correlation was employed to determine the correlation between continuous variables. IBM SPSS Statistics software version 26 was used for statistical analysis. As the cutoff value for statistical significance, a P-value of 0.05 was used. GraphPad Prism Software version 9.0.0 was used to generate the graphs.
## Bioinformatics analysis and rationale of circRNAs and miRNAs selection
Due to the lack of information regarding circRNAs and DKD, circRNAs of interest were selected using an integrated bioinformatics approach. This network was acquired in three main steps. Initially, the Gene Atlas (http://genatlas.medecine.univ-paris5.fr/) was used to identify the relevant protein-coding gene CNR1 (6q15) implicated in the pathogenesis of DKD. Second, miR-21 and miR-495 were selected because they have been identified as epigenetic regulators of the CNR1 gene (Diana tools, http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=tarbasev$8\%$2Findex and TargetScan, https://targetscan.org/vert71/). HMDD v3.2 (https://www.cuilab.cn/hmddcirc_0000146) was used to examine the causality of the miRNAs and DKD. Using the CircInteractome database (https://circinteractome.nia.nih.gov/), the cirRNAs circ_0000146 and circ_0000072 were determined to be sponges for miR-21 and miR-495, respectively. Lastly, the circRNAs were examined using KEGG pathway analyses to identify target genes and likely signaling pathways associated with DKD.
## Demographic and biochemical parameters of the studied groups
No statistically significant difference was found in age or gender between the examined groups, as shown in Table 2 ($P \leq 0.05$). The BMI of diabetic and DKD patients was significantly higher than that of the control group ($P \leq 0.0001$). Except for calcium and phosphorus, all laboratory values were significantly higher in the DKD group than in the diabetic and control groups ($P \leq 0.0001$).
**Table 2**
| Variables | Control<br>(n=55) | Diabetic patients<br>(n=55) | DKD patients<br>(n=55) | P-value |
| --- | --- | --- | --- | --- |
| Age (years) | 51.2 (5.07) | 53 (6.1) | 51.2 (6.7) | 0.16 |
| Gender<br>Male<br>Female | <br>28 (50.9%)<br>27 (49.1%) | <br>19 (34.6%)<br>36 (65.4%) | <br>25(45.4%)<br>30(54.6%) | 0.21 |
| BMI | 22.7 (1.56) | 32.9 (5.47) a*** | 29.1 (4.71) a***b* | < 0.0001 |
| FPG (mmol/L) | 5.04 (0.31) | 11.51 (3.16) a*** | 14.46 (2.8) a***b* | < 0.0001 |
| PPG (mmol/L) | 6.74 (0.43) | 16.83 (3.95) a*** | 20.1 (3.95) a***b* | < 0.0001 |
| HbA1c (%) | 4.8 (0.25) | 6.9 (0.35) a*** | 7.08 (0.52) a*** | <0.0001 |
| Albumin (g/L) | 41.78 (3.22) | 38.39 (2.3) a*** | 36.74 (1.92) a***b** | <0.0001 |
| Urea (mmol/L) | 6.4 (1.3) | 6.8 (2.15) | 18.6 (3.02) a***b*** | <0.0001 |
| Creatinine (μmol/L) | 71.5 (12.7) | 80.9 (31.2) | 289.7 (63.9) a***b*** | <0.0001 |
| GFR (mL/min/1.73 m2) | 97.8 (17.7) | 88.8 (15.6) a* | 20.12 (5.8) a***b*** | <0.0001 |
| ACR (mg/g) | 7.8 (2.18) | 20.4 (6.78) a*** | 92.5 (72.46) a***b*** | <0.0001 |
| Calcium (mmol/L) | 2.2 (0.07) | 2.2 (0.12) | 2.3 (0.21) | 0.07 |
| Phosphorus (mmol/L) | 1.13 (0.16) | 1.12 (0.17) | 1.13 (0.14) | 0.94 |
## CRP and CNR1 levels among the studied groups
The serum CRP and CNR1 protein levels are depicted in Figure 1A and Figure 1B, respectively. CRP and CNR1 were significantly higher in DKD patients than in diabetic patients and healthy controls ($P \leq 0.0001$). Moreover, diabetic patients had significantly elevated levels of CRP and CNR1 than the control group ($P \leq 0.0001$).
**Figure 1:** *Serum CRP (A) and CNR1 (B) protein levels among studied groups.Data expressed as mean ± SD. CNR1; cannabinoid receptor protein, CRP; C-reactive protein *Significant at <0.05, ***significant at P<0.0001*
## The expression levels of CNR1, circ_0000146, and 0000072, as well as miR-21 and 495 within the groups being studied
Following the ELISA results, CNR1 expression was significantly higher in DKD patients compared to both diabetic patients and the control group ($P \leq 0.0001$). Moreover, CNR1 expression was significantly higher in diabetic patients compared to the control group at $P \leq 0.05$ (Figure 2A).
**Figure 2:** *Circ_0000146 (A), circ_0000072 (B), miR-21 (C), miR-495 (D), CNR1 (E) and circ_0000146 staging (F) relative gene expression among the studied groups.Data expressed as mean ± SD. *significant at P<0.05, **significant at p<0.001, ***significant at P<0.0001*
Figure 2B, Figure 2C, Figure 2D and Figure 2E illustrated that the expression levels of circ_0000146 and 0000072 were significantly lower in DKD patients compared to patients with diabetes and the control group. While circ_0000146 and circ_0000072 demonstrated an insignificant reduction in patients with diabetes compared to the control group ($P \leq 0.05$). In DKD patients, the expression levels of miR-21 and 495 are 1.56- and 2.3-fold higher in DKD patients compared to diabetic patients at $P \leq 0.05.$ Moreover, compared to the control group, DKD patients displayed a 2, 3- and 3, 88-fold increase in miR-21 and 495 expression levels, respectively ($P \leq 0.0001$). In addition, both miR-21 and 495 expression levels were elevated in patients with diabetes relative to the control group at $P \leq 0.05.$
In addition, compared with patients with mild renal impairment, those with advanced renal impairment (G5) demonstrated a significant 1.9-fold increase in circ_0000146 (Figure 2F).
## Spearman correlations for all investigated circRNAs and miRNAs
The relationship between circ_0000146 and 0000072 with miR-21 and 495, CNR1 the inflammatory biomarker; CRP, glucose indicators; PPG, FPG, and HbA1c, and renal function predictors; ACR and GFR is provided in Table 3. circ_0000146 and 0000072 demonstrated a negative association with all estimated laboratory tests, biomarkers, and their respective target miRNAs. Circ_0000146 and 0000072 were correlated negatively with miR-21 (circ_ 0000146: r =-0.143, $$P \leq 0.04$$, circ_0000072:r =-0.15, $$P \leq 0.04$$) and miR-495 (circ_ 0000146: $r = 0.03$, $$P \leq 0.7$$, circ_0000072: r=-0.43, $P \leq 0.0001$), CNR1 (circ_0000146: r =-0.19, $$P \leq 0.014$$, circ_0000072: r=-0.2, $P \leq 0.01$), the inflammatory biomarker; CRP (circ_0000146: r=-0.19, $$P \leq 0.13$$, circ_0000072: r =-0.32, $P \leq 0.0001$), blood glucose indicators; PPG (circ_0000146: r=-0.17, $$P \leq 0.02$$, circ_0000072: r=-0.26, P 0.001), FPG (circ_0000146: r=-0.19, $$P \leq 0.01$$, circ_0000072: r=-0.29, $P \leq 0.0001$) and HbA1c (circ_0000146: r=-0.21, $$P \leq 0.008$$, circ_0000072: r=-0.27, $P \leq 0.0001$), and the renal function predictors; ACR (circ_0000146: r=-0.21, $$P \leq 0.009$$, circ_0000072: r=-0.35, $P \leq 0.0001$) and GFR (circ_0000146: $r = 0.2$, $$P \leq 0.009$$, circ_0000072: r=-0.29, $P \leq 0.0001$).
**Table 3**
| Unnamed: 0 | circ_0000146 | circ_0000146.1 | circ_0000072 | circ_0000072.1 |
| --- | --- | --- | --- | --- |
| | r | P-value | r | P-value |
| miR-21 | -0.14 | 0.04 | -0.15 | 0.04 |
| miR-495 | -0.03 | 0.7 | -0.43 | <0.0001 |
| CNR1 | -0.19 | 0.014 | -0.2 | <0.01 |
| CRP | -0.19 | 0.013 | -0.32 | <0.0001 |
| PPG | -0.17 | 0.02 | -0.26 | 0.001 |
| FPG | -0.19 | 0.01 | -0.29 | <0.0001 |
| HbA1c | -0.21 | 0.008 | -0.27 | <0.0001 |
| ACR | -0.21 | 0.009 | -0.35 | <0.0001 |
| GFR | 0.2 | 0.009 | 0.29 | <0.0001 |
## Potential diagnostic values of circ_0000146 and 0000072, hsa-miR-21, and 495 in DKD
ROC curves were analyzed to evaluate the diagnostic performance of all molecules with significant differential expression. We calculated the potential diagnostic value of CNR1, CRP, circ_0000146 and 0000072, miR-495, and 21 to distinguish between patients with T2DM and DKD (Figure 3A-Figure 3D).
**Figure 3:** *Diagnostic accuracy of circ_0000146 (A), circ_0000072 (B), miR-495 (C), has-miR-21 (D) and their combination with the DKD biomarkers; CRP and CNR1 to distinguish DKD patients and for renal staging (E) by ROC curve.*
Circ_0000072 had an AUC value of 0.72 (sensitivity: $71\%$, specificity: $64\%$), whereas circ_0000146 had an AUC value of 0.64 (sensitivity: $71\%$, specificity: $64\%$). ( AUC; 0.65, sensitivity; 63 %, specificity;$62\%$) Compared with miR-21, miR-495 had a higher AUC value of 0.83 (sensitivity: $73\%$, specificity: $69\%$). ( AUC; 0.66, sensitivity; $60\%$, specificity; $61\%$). circ_0000072 (CRP + circ_0000072: AUC;0.91, sensitivity; 91 %, specificity; $81\%$, CNR1 + circ_0000072: AUC;0.89, sensitivity; $87\%$, specificity; $81\%$), circ_0000146 (CRP + circ_0000146: AUC;0.88, sensitivity; $96\%$, specificity; $80\%$, CNR1 + circ_0000146: AUC; 0.85, sensitivity; $82\%$, specificity; $71\%$), miR-495 (CRP + miR-495: AUC; 0.94, sensitivity; $98\%$, specificity; $70\%$, CNR1 + miR-0000 and miR-21 (CRP + miR-21: AUC=0.9, sensitivity=$98\%$, specificity=$72\%$; CNR1 + miR-21: AUC=0.87, sensitivity=$80\%$, specificity=$76\%$). In addition, the discriminating power of circ_0000146 to distinguish between G3/G4 and G5 stages had an AUC of 0.69 (sensitivity: $73\%$, specificity: $72.5\%$, $$P \leq 0.03$$).
## Prediction of target miRNAs of circ_0000146 and circ_0000072, and their pathway prediction analysis
Predictive software such as the CircInteractome and TargetScan databases was used to identify potential target miRNAs and genes for circ_0000146 and circ_0000072.
According to the KEGG database, the target genes of circ_0000146 were significantly involved in mitogen-activated protein kinase (MAPK), RAS, and transforming growth factor (TGF-β) signaling pathways at $P \leq 0.001.$ Alternatively, circ_0000072-targeted genes were connected to the TGF-β AMP-activated protein kinase (AMPK) and Wnt signaling pathways (Table 4).
**Table 4**
| KEGG signaling pathway | Genes<br>n (%) | P-value |
| --- | --- | --- |
| circ_0000146 | | |
| MAPK signaling pathway | 21 (5.8%) | 5.4 × 10-7 |
| RAS signaling pathway | 16 (4.4 %) | 2.8 × 10-5 |
| TGF-β signaling pathway | 8 (2.2 %) | 1.9 × 10-3 |
| circ_0000072 | | |
| TGF-β signaling pathway | 14 (1.7 %) | 5 × 10-5 |
| AMPK signaling pathway | 13 (1.6%) | 1.9 × 10-3 |
| Wnt signaling pathway | 15 (1.8%) | 4.4 × 10-3 |
## Discussion
DKD is a chronic vascular complication of T2DM leading to ESKD [12]. DKD is becoming more prevalent in developing countries and is now recognized as a worldwide health concern [13]. Consequently, the identification of noninvasive potential diagnostic and prognostic biomarkers is crucial.
In this study, CNR1 was chosen because it plays a crucial role in various pathophysiological processes that promote DKD as oxidative stress, inflammation, and fibrogenesis [14]. In the present study, the levels of CNR1 gene and protein expression were significantly higher in DKD patients in relation to others. It was reported that exposing podocytes to an increased glucose level for 48 hours caused podocyte damage and CNR1 expression [15]. CNR1 blockers were reported to normalize kidney functions and tubular injury in mice by reducing lipocalin 2, clusterin, cystatin C, and TNF expression [16]. Two neutral CNR1 receptor antagonists, AM6545 and AM4113, were previously reported to have renoprotective properties and lower kidney TGF levels [17]. CRP, an acute phase inflammatory protein, is associated with an increase in microalbuminuria and renal impairment in diabetic patients, suggesting a connection between CRP and DKD progression [5] [18]. In our study, DKD patients had significantly higher CRP protein concentrations than other participants. Dawood et al. [ 19] previously reported the impact of elevated serum CRP levels on the development of DKD.
CircRNAs have been reported to be associated with the onset and progression of renal diseases, including diabetic glomerular injury [9]. RT-qPCR analysis of the gene expression levels of hsa_circ_0000146 and hsa_circ_0000072 in DKD patients was exclusive to our study. We discovered that DKD patients had significantly lower hsa_circ_0000146 and hsa_circ_0000072 gene expression than diabetes patients and controls. The mechanism of action of circ_0000146 and circ_0000072 was evaluated using bioinformatics to predict their target miRNAs and genes from the CircInteractome and TargetScan databases. The predicted miRNA targets for circ_0000146 were miR-21, miR-136, miR-145, miR-217, and miR-346. The predicted targets of circ_0000072 were miR-495, miR-146, miR-136, miR-145, and miR-638. The KEGG pathway results indicated that circ 0000146’s target genes are significantly involved in the MAPK, RAS, and TGF-β signaling pathways. Alternatively, circ_0000072-targeted genes were associated with TGF-β, AMPK, and Wnt signaling pathways. MAPK signaling pathway is involved in cell signal transduction that contributes to insulin signaling and glucose transporter 4 expression levels, which are associated with insulin resistance in T2DM [20] [21]. In addition, at high glucose concentrations, p38MAPK promotes cell proliferation, protein accumulation, and TGF-β, which ultimately results in DKD [22] [23]. In addition, the Wnt signaling cascade appears to play a crucial role in regulating the development of DKD in podocyte and mesangial cell damage and kidney fibrosis [24]. Angiotensin II (AngII), the principal peptide of RAS, promotes podocyte injury and reactive oxygen species production. Its blockers can reduce progressive glomerulosclerosis [25]. MiRNAs contribute to the progression of several glomerular basement membranes and extracellular matrix alterations associated with renal tissue fibrosis [26]. Compared to diabetic patients and healthy controls, the level of miR-21 expression in DKD patients was higher. miR-21 is involved in T2DM complications due to its diverse gene regulatory functions, and its silencing ameliorates DKD [8]. MiR-21 has been reported to be overexpressed in the blood and kidney tissues of DKD patients and was correlated with ACR [27] [28]. Intriguingly, mice lacking miR-21 had lower concentrations of mesangial extension, albumin in their urine, fibrotic biomarkers, macrophage infiltration, and podocyte damage [29]. In our study, we discovered that the expression of the miR-495 gene is significantly greater in DKD patients than in diabetic patients and healthy controls. Our results concur with a previous study finding that mice injected with streptozotocin had a higher miR-495 gene expression level than their corresponding controls [30]. Moreover, miR-495 was markedly up-regulated in retinal ganglion cells treated with high glucose, increasing their apoptosis [31]. In contrast, a different study found that serum miR-495-3p levels were lower in diabetic patients with retinopathy than those without retinopathy [32].
Moreover, circ_0000146 and circ_0000072 were found to negatively correlate with miR-21 and miR-495, respectively. In addition, both circ_0000146 and circ_0000072 were negatively correlated with FPG, PPG, and glycosylated HbA1c levels. Previously, circRNA_0054633 was observed in pregnant women and associated with PPG and glycosylated hemoglobin [33]. There was also a significant negative correlation between circ_0000146 and circ_0000072 and ACR, CRP, and CNR1.
The results of the ROC curve indicated that the sensitivity and specificity of circ_0000146 and circ_0000072 alone or in combination with CRP or CNR1 were within an acceptable range and could be considered as new diagnostic biomarkers for DKD. Consequently, we may deduce that hsa_circ_0000146 and hsa_circ _0000072 may play a role in the pathogenesis of DKD, may serve as potent, novel, noninvasive biomarkers, and maybe a promising therapeutic target in DKD.
## Compliance with Ethical Standards
This study was conducted in-house in accordance with the ethical standards of the World Medical Association’s Declaration of Helsinki and with an approval number: MD-83-2020. All participants gave their informed consent for this study.
## Funding
The research was funded by Al Kasr el Aini, Faculty of Medicine, Cairo university.
## Informed consent
All participants gave their informed consent for this study.
## Conflict of interest statement
All the authors declare that they have no conflict of interest in this work.
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|
---
title: Thirty-Day Morbidity and Mortality After Total Knee Replacement in a Tertiary
Care Hospital in Pakistan
journal: Cureus
year: 2023
pmcid: PMC10040218
doi: 10.7759/cureus.35409
license: CC BY 3.0
---
# Thirty-Day Morbidity and Mortality After Total Knee Replacement in a Tertiary Care Hospital in Pakistan
## Abstract
Background Total knee arthroplasty has become very popular globally as a safe surgical modality for relieving pain and improving functional outcomes in patients who fail to respond to conservative treatments; however, it may be associated with postoperative complications.
The aim of this study is to determine the incidence of postoperative complications occurring within the first 30 days after total knee replacement (TKR).
Materials and methods *This is* a prospective cross-sectional study. All consecutive patients who underwent primary unilateral or bilateral total knee arthroplasty between November 2020 and July 2021 were included in the study. Patients were followed for a period of 30 days, and postoperative complications (if any) were documented. Continuous variables were expressed as means ± standard deviations. Categorical variables were expressed as frequency and percentages, and chi-square test was used to compare the qualitative variables. Univariate and multiple logistic regression analyses were done to analyze the magnitude of associations of the complication with other predictor variables keeping a level of significance of <0.05.
Results The overall complication rate within the 30-day window was $7.0\%$. Postoperative surgical site infections (SSI) were noted in three patients ($2.6\%$). Thromboembolic complications were seen in only one patient ($0.9\%$). One patient ($0.9\%$) was readmitted within the one-month period after initial discharge, and one patient ($0.9\%$) expired within 12 hours postoperatively.
Conclusion TKR renders satisfactory results with a low incidence of complications in general; however, wound infections, thromboembolic complications, and cardiovascular complications do occur postoperatively. Male gender, obesity, and bilateral TKRs remain the notable risk factors for the development of complications post-procedure.
## Introduction
Total knee arthroplasty (TKA) has become a popular surgical option for patients who fail to respond to conservative treatments. TKA is a cost-effective procedure and is growing globally at a considerable rate [1]. Majority of patients who undergo TKA report pain relief and improved functional outcomes [2-4]. TKA is generally a safe procedure; however, it may be associated with postoperative complications that result in suboptimal clinical outcomes, increased financial burden, disability, and mortality [5,6]. The majority of postoperative complications following total joint replacements in the lower extremities occur during the hospital stay [5].
The risk of mortality is increased in the setting of cardiovascular disease, old age, simultaneous bilateral arthroplasty, and the use of cemented implants [7]. The risk of mortality associated with TKA is low, ranging from $0.1\%$ to $0.8\%$, and is on a declining trajectory over the last decade [7,8]. This declining trend in postoperative mortality is likely due to improvements in patient selection and better perioperative care [8]. Perioperative morbidity after TKA is mainly related to infection and thromboembolism. Postoperative infection is an important cause of implant failure and revision arthroplasty [7,9]. The rate of postoperative infection has reduced from $9.1\%$ in the 1980s to $1\%$-$2\%$ in the last decade [7,9,10]. The causative microorganisms are Staphylococcus aureus, Staphylococcus epidermidis, Group B Streptococcus, and *Pseudomonas aeruginosa* [7,11]. Periprosthetic infection is usually managed with one or more antibiotics, washout, debridement, revision, and arthrodesis. The rate of deep-seated infection is generally lower [12]. Patients with deep infections are treated with the removal of implants. TKA is associated with a low risk of thromboembolic ranging from $2\%$ to $3\%$ and may require prolonged use of anticoagulants.
The statistics on mortality and morbidity associated with TKA procedures are readily available from western nations, whereas locoregional data on this subject from countries like *Pakistan is* scarce. This study aims to determine the incidence rates of mortality and morbidity after TKA as well as identify risk factors and leading causes of complications in our region.
## Materials and methods
This prospective, cross-sectional, single-center study was conducted after approval from the Institutional Ethical Review Committee at the Section of Orthopedics, Department of Surgery, Aga Khan University Hospital, Karachi, Pakistan, and the approval number is 2022-5371-14526.
All consecutive patients enlisted for primary unilateral or bilateral TKA between November 2020 and July 2021 were included in the study. Indications of surgery were osteoarthritis and rheumatoid arthritis. Surgeries were performed by five different surgeons, all of whom had more than 10 years of experience in arthroplasty. Patients with revision total knee and those who lost to follow-up were excluded.
All patients underwent cemented TKA implants, and tourniquets were used in all cases. A standard midline medial parapatellar approach was used in all procedures. Low molecular weight heparin (enoxaparin) was administered for thromboprophylaxis 12 hours before surgery, and Ascard or enoxaparin was then continued until two weeks after surgery. Perioperative antibiotics (cefazolin or ciprofloxacin) were used prophylactically for 48-72 hours postoperatively.
All patients were followed in routine clinical visits for a period of two weeks and one month postoperatively and thereafter. Data items collected for each patient included demographics; pre-procedural comorbidities; length of hospital stay; any complications such as infection; thromboembolic events including deep venous thrombosis, pulmonary embolism, and cardiac event; and readmission within the 30-day interval following TKA.
Data analysis was done on Statistical Package for the Social Sciences (SPSS) version 23 (IBM Corp., Armonk, NY). Continuous variables were expressed as means + standard deviations. Categorical variables were expressed as frequency and percentages, and chi-square test was used to compare the qualitative variables. Univariate and multiple logistic regression analyses were done to analyze the magnitude of associations of the complication with other predictor variables keeping a level of significance of <0.05.
## Results
A total of 114 patients were included in the final dataset with a mean age of 62.8 ± 8.99 years (range: 24-82 years). Majority of the patients were females, accounting for $76.3\%$, while $23.7\%$ were males. Eighty five out of the total 114 patients had prior comorbid conditions. Among them, $66.7\%$, $36.8\%$, and $25.7\%$ had hypertension, obesity, and diabetes mellitus, respectively (Table 1).
**Table 1**
| Age (in years) | 62.8 ± 8.99 |
| --- | --- |
| Gender | Gender |
| Male | 23.7% |
| Female | 76.3% |
| Comorbidities | Comorbidities |
| Hypertension | 66.7% |
| Diabetes mellitus | 25.7% |
| Obesity | 36.8% |
In total, 39 patients underwent unilateral TKA, and 75 patients underwent one-stage bilateral TKA. Majority of the patients constituting $74.6\%$ had ASA (American Society of Anesthesiologists) level 2; $14\%$ and $11.4\%$ of patients had ASA levels 3 and 1, respectively. Out of the total 114 patients, 101 underwent TKA under general anesthesia and 13 under spinal anesthesia (Table 2).
**Table 2**
| Laterality | Laterality.1 |
| --- | --- |
| Unilateral | 34.2% |
| Bilateral | 65.7% |
| ASA status | ASA status |
| I | 11.4% |
| II | 74.5% |
| III | 14% |
| Anesthesia | Anesthesia |
| General | 88.59% |
| Spinal | 11.4% |
The overall complication rate within the 30-day window was $7.0\%$. Postoperative surgical site infections (SSI) were noted in three patients ($2.6\%$). Among these, two patients had superficial infections, and one patient had deep-seated infections. Thromboembolic complications were seen in only one patient ($0.9\%$) who was diagnosed with pulmonary embolism one week after the surgery. Cardiopulmonary complications were seen in two patients accounting for $1.8\%$. One patient ($0.9\%$) was readmitted within the one-month period after initial discharge, and one patient ($0.9\%$) expired within the 12 hours postoperative period due to sudden cardiac death (Table 3).
**Table 3**
| Classification | Complication | Number | Percentage |
| --- | --- | --- | --- |
| Systemic complications | Thromboembolic complications | 1 | 0.9% |
| Systemic complications | Cardiopulmonary complications | 2 | 1.8% |
| Local/regional complications | Superficial surgical site infection | 2 | 1.8% |
| Local/regional complications | Deep wound infection/organ space infection | 1 | 0.9% |
| | Readmission | 1 | 0.9% |
| Others | Death | 1 | 0.9% |
| Total | Total | 8 | 7% |
The complication rate was $33.3\%$ in males and $14.4\%$ in females with a p-value of 0.043, which is statistically significant. The complication rate was $9.5\%$ for unilateral TKA and $23.6\%$ for bilateral TKA, and the differences were statistically significant (p-value = 0.080). There was no significant difference in the frequency of complications in the study population based on the type of anesthesia received during the surgery (p-value = 0.37). Patients with comorbidities had a slightly higher rate of complications of $19.4\%$ versus $12.5\%$ in the subgroup without comorbidities, which was not statistically significant (p-value = 0.732). No significant differences were observed in the patient subgroups based on their ASA scores (p-value = 0.75).
Univariate logistic regression analysis was performed for separate complications, i.e., deep vein thrombosis (DVT), pulmonary embolism (PE), SSI, and cardiac complications. Interestingly, the odds of the postoperative length of hospital stay of less than three days and more than seven days among patients with SSI were 21 times and 34 times more as compared to those who did not have SSI ($$p \leq 0.037$$). The odds of obesity among patients with SSI were 3.5 times more as compared to patients who did not have SSI ($$p \leq 0.048$$). A multiple logistic regression model was then created, which showed the odds of obesity are higher among patients with SSI as compared to those who did not have SSI keeping all other variables constant (OR = 3.6, CI: 1.9-6.5). In the same model, we identified that the odds of patients with longer length of stay (>7 days) or decreased length of stay (<3 days) are higher among the patients with SSI as compared to those who did not have SSI (OR = 31 and 23, CI: 13.8-48 and 8.3-40).
## Discussion
In our study, the incidence of 30-day postoperative complications rate is $7\%$ including local and systemic complications. Previous literature suggests that although the complications after TKA seldom occur, they are dreaded [2,3]. According to a study by Belmont et al., $1.83\%$ of the total subjects had to experience major systemic complications including events that were cardiovascular in origin, and $3.20\%$ of patients had minor systemic complications including urinary tract infections and deep venous thrombosis, while $1.43\%$ of the patients experienced local complications [2]. The most common postoperative complication encountered in our data set is SSI. This was seen in $2.7\%$ of the patient population, with superficial site infection and deep site infection accounting for $1.8\%$ and $0.9\%$, respectively. Belmont et al. reported that out of all the study subjects, $0.79\%$ of the patients had superficial site infection and $0.30\%$ of the patients experienced deep wound infection [2]. In a study by Seah et al., out of 2219 patients, SSI was reported to be $1.8\%$, out of which $1.44\%$ of the subjects had superficial site infection, and $0.36\%$ of the patients had deep wound infection [7]. Feng et al. reported the incidence of 30 days postoperative wound infections as $0.8\%$, with deep and superficial infection accounting for $0.1\%$ and $0.7\%$, respectively [13].
The present study further discovered that male gender, BMI ≥ 30.0 kg/m2, and bilateral procedures are significant risk factors for the development of complications within 30 days following TKA, which is consistent with the previous literature [2,5,13]. According to the results of our study, the complication rate was higher in males than females accounting for $33.3\%$ and $14.4\%$, respectively, with a p-value of 0.043. Singh et al. also reported similar results with the postoperative complications being significantly higher in males ($6.18\%$) than females ($5.26\%$) with a p-value of 0.010 [14].
Thromboembolic phenomena are a recognized complication in the postoperative period after lower limb arthroplasty mostly occurring in the period of 5-36 days, eventually resulting in increased morbidity and mortality. In our study, none of the patients were reported to develop DVT within the first 30 days post-TKA; however, one patient ($0.9\%$) was diagnosed with PE on imaging studies after one week of TKA. This rate is quite similar to the previous literature. Feng et al. in their study reported that PE was seen in $0.3\%$ of cases [13]. Similarly, in a study by Belmont et al., the incidence rate of PE was reported as $0.78\%$ [2]. Aggressive VTE prophylaxis, both pharmacological and mechanical, remains the only solution to prevent the occurrence of DVT and PE [15]. A meta-analysis by Lee et al. reported that the incidence of PE in the Asian population was quite low accounting to be $0.01\%$ [16].
There is a considerable risk of postoperative mortality following major surgeries; however, TKA remains an optimal procedure with a rare incidence of postoperative mortality [11,17]. Belmont et al. and Seah et al. reported the 30-day mortality rate after total knee replacement (TKR) as $0.1\%$ and $0.27\%$, respectively [2,7]. Parvizi et al. reviewed 22,540 patients in the Mayo Clinic and reported a 30-day mortality of $0.24\%$, and the decreasing trends of mortality rates have been recorded in the past three decades [5]. In our study, there was only one mortality out of the total 114 subjects accounting for $0.9\%$, which occurred due to sudden cardiac arrest in the first 24 hours after the surgery. The findings of our study support the previous literature, which suggests that cardiovascular diseases are one of the major causes of mortality after TKA. Cardiac health concerns must be addressed, and the patients should be optimized before proceeding with surgery. In a study by Smith et al., the postoperative mortality rate was found to be $0.08\%$ with myocardial infarction being the most common cause of death after TKA [18]. Chan et al. in their study reported that $50\%$ of the deaths in their dataset occurred due to cardiovascular diseases [19]. The existing literature also emphasizes the fact that there is a declining trend of postoperative mortality over the past years. Harris et al. in their study found that the 30-day mortality was reduced from $0.17\%$ in 2003 to $0.08\%$ in 2017 [20].
A strong positive association was found between SSI and patients with higher BMI on the multiple logistic regression model (OR = 3.6, CI: 1.9-6.5); this finding is consistent with the pre-existing literature. In a study by Wallace et al., the results of multivariate analysis showed a positive association between wound infection and patients with a BMI of 30 kg/m2 and over (30-35 kg/m2 adjusted OR = 1.23; $95\%$ CI: 1.01-1.50; $$P \leq 0.04$$; >35 kg/m2 adjusted OR = 1.39; $95\%$ CI: 1.11-1.72; $P \leq 0.01$) [21].
According to the previous literature, TKA is regarded to be one of the safest and most cost-effective procedures for patients with osteoarthritis, and it yields clinically successful outcomes with patients returning to their routine activities. This study has a few limitations. The results of our study are not generalizable to the entire population as it is a single-center study, and a greater number of TKA procedures are performed in more affordable healthcare settings in Pakistan. At our institution, the number of yearly procedures performed is much lower due to a relatively higher financial cost burden. We aim to collaborate with other tertiary care hospitals and publish results from multi-institutional analysis in a future attempt.
## Conclusions
To conclude, TKR renders satisfactory results with a low incidence of complications in general; however, wound infections, thromboembolic complications, and cardiovascular complications do occur postoperatively. Male gender, obesity, and bilateral TKRs remain the notable risk factors for the development of complications post-procedure. The incidence of postoperative complications can be reduced to a considerable extent if the patients are optimized preoperatively and sterile measures are followed properly during the surgery.
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|
---
title: The Association Between Depression and Obesity Among Adults in Jeddah, Saudi
Arabia, in 2022
journal: Cureus
year: 2023
pmcid: PMC10040245
doi: 10.7759/cureus.35428
license: CC BY 3.0
---
# The Association Between Depression and Obesity Among Adults in Jeddah, Saudi Arabia, in 2022
## Abstract
Background Depression has emerged as a significant contributor to the worldwide loss of disability-adjusted life years. Simultaneously, obesity is regarded as a substantial global health issue. The co-existence of depression and obesity can further exacerbate negative health outcomes.
Objective The objective of this study was to investigate the relationship between depression and obesity in adult populations in Jeddah, Saudi Arabia. Further, the study aimed to examine the impact of confounding variables and their association with depression and obesity.
Methods This analytical cross-sectional study utilized an interviewer-assisted questionnaire to collect data from adult participants aged 18 y/o or older attending primary healthcare centers at the Ministry of Health in Jeddah. The study was conducted at primary healthcare centers in Jeddah city, Saudi Arabia. The questionnaire included information on demographic characteristics, comorbidities, weight and height, and the Patient Health Questionnaire-9 (PHQ-9) tool for assessing the incidence of depression.
Results A total of 397 individuals were included in the study with more than $50\%$ of the participants between 26 and 45 years. The majority of the participants were males in the study ($56.9\%$). The self-reported chronic diseases by the participants included diabetes mellites ($25.9\%$), hypertension ($23.7\%$), and dyslipidemia ($19.9\%$). The study found that $12.8\%$ of respondents had depression, $11.1\%$ had anxiety, and $2.5\%$ had obsessive-compulsive disorder. A total of $29.7\%$ of participants had a PHQ-9 score of 10 or more. A significant negative linear correlation was found between the PHQ-9 score of the participants and their body mass index (BMI) results. However, this association did not remain significant when the chi-square test was used. Moreover, diabetes mellites and hypertension among the study sample were significantly associated with moderate to severe depression (p-values =.006 and =.005, respectively). The PHQ-9 score was negatively correlated with the participants' BMI, with a coefficient of -.190 (p-value <.001).
Conclusion In the current study, the majority of obese participants displayed symptoms of depression ranging from mild to moderate. However, no significant correlation was established between depression and BMI.
## Introduction
Overweight and obesity are medical conditions that point to an excessive buildup of body fat and have detrimental effects on one's health. Obesity and overweight have been recognized as risk factors for a number of diseases, including diabetes, different cancers, cardiovascular disorders, and hypertension by epidemiological research. The health of people is at risk in many regions owing to the rising incidence of high body mass index (BMI) and the mortality that follows. It also has detrimental impacts on health and puts a financial burden on individuals and society [1].
Obesity has raised a major global public health issue [2]. In both developed countries and developing countries, the incidence of obesity has dramatically increased over the last several decades [3], approximately doubling between 1980 and 2008 [4,5]. The rate of adult obesity in the Middle East, which encompasses parts of North Africa and western Asia, is alarmingly high ($24.5\%$), matching those from other European nations like the United Kingdom ($22.9\%$) and Germany ($26.3\%$). Also in Middle Eastern countries, in particular, obesity has been far more prevalent among women ($30.6\%$) than men ($16.6\%$). The primary causes of the high incidence of obesity and its comorbidities in the Middle East have been recognized as the major behavioral transformation highlighted by the increasingly sedentary lifestyle, the replacement of regular diet with a westernized food pattern, and the lack of exercise as well as other social and cultural factors [6].
A recent study undertaken in nine Middle Eastern countries established a correlation between the region's rising non-communicable disease occurrence and the rise in obesity, including diabetes, cardiovascular disease, and cancer [7]. Obesity may have a significant psychological burden in addition to exerting a negative impact on physical health [8]. In fact, studies on the general population in Western nations have focused more and more on the relationship between obesity and depression. In the general adult population, obesity has a strong positive association with depression according to a recent meta-analytic assessment of cross-sectional community-based research [9]. Furthermore, women have been more strongly associated with this relationship than men [9]. According to findings from another statistical analysis of 15 cross-sectional studies, depression in the general public is positively associated with abdominal obesity [10]. Additionally, a recent systematic review and meta-analysis of 15 longitudinal studies revealed a causal relationship between depression and obesity [11]. Since studies from Western populations are the only ones used to draw these conclusions on the link between fat and depression. It is important to look into the connection between these two variables in these communities considering the potential psychological burden caused by the rising obesity rates among adults in the Middle East. Health policymakers must prioritize interventions to prevent and control weight gain and obesity based on the most recent data on the prevalence and trends of these conditions [12].
As established in the literature, being overweight and obese are substantial issues in Saudi Arabia. Moreover, these disorders are associated with multiple health problems such as depression, which may decrease the individual quality of life significantly. Thus, this study aims to investigate the relationship between probable associated factors and depression among adults in Jeddah, Saudi Arabia.
## Materials and methods
This is an analytical cross-sectional study that utilized an interviewer-assisted questionnaire to collect data from participants. The study will be carried out at primary healthcare centers (PHCCs), belonging to the Ministry of Health in Jeddah city, which is the largest city located in the western region of the Kingdom of Saudi Arabia.
Adults residing in Jeddah city in the Kingdom of Saudi Arabia at the time of the study were invited. The sample size was estimated at 385 using a web-based sample size calculator (www.raosoft.com), with a $5\%$ margin of error, $95\%$ confidence level, and $50\%$ response distribution. However, a total of 397 respondents were included in this study to overcome missing values.
Data collection technique and questionnaire This study used a convenient sampling technique to include individuals from the target population attending PHCCs in Jeddah city, and the technique was the most appropriate in the settings to include a representative sample in number. The questionnaire was administered by the trained data collectors who are administrative workers at the PHCCs where they interviewed the study participants. The inclusion criteria included adults aged 18 y/o or older who attend PHCCs belonging to the Ministry of Health (MOH) in Jeddah city. The validated questionnaire was adapted from a previous study that was conducted in similar circumstances and included a comparative population to the current study target population [13]. The questionnaire included the following variables: demographic characteristics, comorbidities, and the Patient Health Questionnaire-9 (PHQ-9) tool. In addition, participants' weight and height were measured at the PHCCs to calculate BMI measures. The PHQ-9 tool is a validated tool to screen for depression, which is composed of nine items. The tool score ranges from 0 to 27, and based on the patient's score, they are labeled from mild to severe depression. A cut-off score of 10 or more is sensitive and specific for moderate to severe depression.
Statistical analysis Data were analyzed using the Statistical Package for the Social Sciences (SPSS, version 29.0; IBM Corp., Armonk, NY). Proportions and frequencies were used to summarize the data. Moreover, inferential statistics using the chi-square test (χ2), the extension of the Fisher-Freeman-Halton exact test, and Spearman's correlation test were applied, and p-values <.05 were considered statistically significant.
Ethical consideration Ethical approval was obtained from the research ethics committee of the Research and Studies department at the Directorate of Health Affairs in Jeddah city before data collection. A statement explaining the nature and the purpose of the study was included to gain the participants' consent before filling out the questionnaire. Participating in the study was voluntary and did not affect the care participants received. All data were handled anonymously, securely saved, and used for research purposes only.
## Results
A total of 397 individuals met the inclusion criteria and were included in the study analysis. More than $50\%$ of the participants were between the ages of 26 and 45 years. Male respondents represent slightly more than half of the total study sample ($56.9\%$), and $91.9\%$ of the total number were Saudi residents. Furthermore, $45.3\%$ hold a bachelor's degree, while $17.1\%$ of the included participants have pursued higher education degrees. Detailed demographic data such as marital status and socioeconomic status are illustrated in Table 1.
**Table 1**
| n = 397 | n = 397.1 | N | % |
| --- | --- | --- | --- |
| Age (y/o) | 18-25 | 67 | 16.9% |
| Age (y/o) | 26-35 | 90 | 22.7% |
| Age (y/o) | 36-45 | 117 | 29.5% |
| Age (y/o) | 46-55 | 63 | 15.9% |
| Age (y/o) | 56-65 | 47 | 11.8% |
| Age (y/o) | >65 | 13 | 3.3% |
| Gender | Male | 226 | 56.9% |
| Gender | Female | 171 | 43.1% |
| Nationality | Saudi | 365 | 91.9% |
| Nationality | Non-Saudi | 32 | 8.1% |
| Marital status | Single | 105 | 26.4% |
| Marital status | Married | 268 | 67.5% |
| Marital status | Divorced | 18 | 4.5% |
| Marital status | Widowed | 6 | 1.5% |
| Number of family dependents | 1 | 74 | 18.6% |
| Number of family dependents | 2 | 49 | 12.3% |
| Number of family dependents | 3 | 68 | 17.1% |
| Number of family dependents | >3 | 206 | 51.9% |
| Family income (SAR) | <5000 | 78 | 19.6% |
| Family income (SAR) | 5000-10,000 | 101 | 25.4% |
| Family income (SAR) | 10,001-15,000 | 79 | 19.9% |
| Family income (SAR) | >15,000 | 139 | 35.0% |
| Educational level | No official education | 2 | 0.5% |
| Educational level | Primary school | 6 | 1.5% |
| Educational level | Secondary school | 23 | 5.8% |
| Educational level | High school | 118 | 29.7% |
| Educational level | Bachelor | 180 | 45.3% |
| Educational level | Higher education | 68 | 17.1% |
The study participants self-reported any chronic diseases they were living with at the time of the study, and diabetes mellites ($25.9\%$), hypertension ($23.7\%$), and dyslipidemia ($19.9\%$) were the most prevalent chronic diseases among the study participants. Furthermore, $12.8\%$ of the respondents reported an established diagnosis of depression, anxiety ($11.1\%$), and obstructive-compulsive disorder ($2.5\%$). In addition, the study investigator measured the participants' weight and height to attain their BMI, and the results show that $65.5\%$ were obese, and $17.6\%$ were overweight. The study screened participating individuals for depression using PHQ-9, and $29.7\%$ had a total score of 10 points or more (Table 2).
**Table 2**
| n = 397 | n = 397.1 | N | % |
| --- | --- | --- | --- |
| PHQ-9 | No depression to mild depression | 279 | 70.3% |
| PHQ-9 | Moderate to severe depression (≥10) | 118 | 29.7% |
| BMI | Underweight | 5 | 1.3% |
| BMI | Healthy weight | 62 | 15.6% |
| BMI | Overweight | 70 | 17.6% |
| BMI | Obesity | 260 | 65.5% |
| Use of medications | | 156 | 39.3% |
| DM | | 103 | 25.9% |
| HTN | | 94 | 23.7% |
| Dyslipidemia | | 79 | 19.9% |
| Thyroid disorders | | 17 | 4.3% |
| Depression | | 51 | 12.8% |
| Anxiety | | 44 | 11.1% |
| OCD | | 10 | 2.5% |
| Bipolar | | 7 | 1.8% |
| Other psychiatric disorders | | 8 | 2.0% |
The current study investigated all demographic variables such as age, gender, marital status, educational level, and family income for the possible association with moderate to severe depression based on the PHQ-9 score. However, none of the demographic factors showed a statistically significant association with moderate to severe depression. Meanwhile, patients with suggested moderate to severe depression were more likely to use medication at the time of data collection (p-value <.001). Moreover, diabetes mellites among the study sample was associated with moderate to severe depression, and this association achieved statistical significance (p-value =.006). In addition, $33.1\%$ of participants with suggestive moderate to severe depression were diagnosed with hypertension, and this association was statistically significant (p-value =.005) as shown in Table 3. Moreover, the PHQ-9 score of the study participants was negatively correlated with their BMI results with a coefficient of.190 (p-value <.001).
**Table 3**
| n = 397 | n = 397.1 | PHQ-9 | PHQ-9.1 | PHQ-9.2 | PHQ-9.3 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| n = 397 | n = 397 | No depression to mild depression | No depression to mild depression | Moderate to severe depression (≥10) | Moderate to severe depression (≥10) | |
| n = 397 | n = 397 | N | % | N | % | p-values |
| Gender | Male | 165 | 59.1% | 61 | 51.7% | .184 |
| Gender | Female | 114 | 40.9% | 57 | 48.3% | |
| Age | 18-25 | 42 | 15.1% | 25 | 21.2% | .179* |
| Age | 26-35 | 65 | 23.3% | 25 | 21.2% | |
| Age | 36-45 | 81 | 29.0% | 36 | 30.5% | |
| Age | 46-55 | 45 | 16.1% | 18 | 15.3% | |
| Age | 56-65 | 33 | 11.8% | 14 | 11.9% | |
| Age | >65 | 13 | 4.7% | 0 | 0.0% | |
| Income (SAR) | <5000 | 51 | 18.3% | 27 | 22.9% | .588 |
| Income (SAR) | 5000-10,000 | 70 | 25.1% | 31 | 26.3% | |
| Income (SAR) | 10,001-15,000 | 55 | 19.7% | 24 | 20.3% | |
| Income (SAR) | >15,000 | 103 | 36.9% | 36 | 30.5% | |
| BMI | Underweight | 3 | 1.1% | 2 | 1.7% | .806* |
| BMI | Healthy weight | 45 | 16.1% | 17 | 14.4% | |
| BMI | Overweight | 47 | 16.8% | 23 | 19.5% | |
| BMI | Obesity | 184 | 65.9% | 76 | 64.4% | |
| Use of medications | No | 188 | 67.4% | 53 | 44.9% | |
| Use of medications | Yes | 91 | 32.6% | 65 | 55.1% | |
| DM | No | 218 | 78.1% | 76 | 64.4% | .006 |
| DM | Yes | 61 | 21.9% | 42 | 35.6% | |
| HTN | No | 224 | 80.3% | 79 | 66.9% | .005 |
| HTN | Yes | 55 | 19.7% | 39 | 33.1% | |
## Discussion
The co-occurrence of depression and obesity is common due to intersecting pathophysiology and shared biological pathways, often leading to negative health implications [14,15]. In the current study, we observed a high rate of obese and overweight participants in this study. Almost two-thirds of the participants were obese, which was a much higher percentage compared to previous studies [13,16,17]. Al-Rethaiaa et al., in their cross-sectional study, reported that $21.8\%$ of university students were overweight, and $15.7\%$ were obese [16]. In another study from Saudi Arabia, Alshahrani et al. reported a prevalence of $38.4\%$ obese individuals, whereas $44.2\%$ were overweight [17]. Our findings were much higher compared to a study from an Eastern province in Saudi Arabia [13]. Almarhoon et al. reported that $30.5\%$ of participants in their study met the criteria of overweight and $26.4\%$ of obese [13]. A national survey of 4709 participants from 13 regions in Saudi Arabia in 2020 reported a national prevalence of $24.7\%$ [18].
In the current study, we found a significant negative linear correlation between the PHQ-9 score of the participants and their BMI results. However, the association using the chi-square test was not significant. According to our PHQ-9 results, almost one-third of participants ($29.7\%$) had moderate to severe depression. The PHQ-9 has been extensively researched and has been found to be the most precise tool for the assessment of depression [19-21]. The PHQ-9 is a validated tool that is recommended to be incorporated as a fundamental aspect of a comprehensive screening methodology in a two-stage screening process for depression [22]. This was in line with Almarhoon et al. who observed moderate to severe depression in $34.8\%$ of their study participants [13]. However, our findings were much higher compared to those of Al-Qadhi et al., who reported that only $1\%$ of participants in their study had severe depression, whereas $13.4\%$ and $4.4\%$ had moderate and moderate-severe prevalence, respectively [23]. In our study, most cases of depression were seen among overweight and obese individuals ($19.5\%$ and $41.7\%$), respectively. Carey et al., in their investigations of obese participants from 12 *Australian* general practices, reported much lower percentages compared to our study [24]. They revealed that among overweight participants, $12\%$ had depression, whereas $23\%$ of depression was seen in obese participants [24]. A cross-sectional study by Garg et al. from India reported that $12\%$ of participants had moderate depression, whereas $54\%$ had mild depression [25]. Our results were supported by a cross-sectional study from Abha, Saudi Arabia, which found that $42.7\%$ of participants had moderate to severe depression [26]. They also had a similar percentage of obese individuals ($71\%$) in their study, which could explain the similarity in results [26].
In the current study, we observed a statistical significance between diabetes mellites, hypertension, and moderate to severe depression. These findings were supported by Stecker et al., who found a significant association between diabetes, obesity, depression, and hypertension ($p \leq .05$) [27]. We also found that a significantly higher number of participants were taking medications regularly at the time of the study ($p \leq .001$). Similar findings have been shared previously by Almarhoon et al., who reported that $18.3\%$ of participants were regularly taking any medications during their study period [13]. This phenomenon can be attributed to the adverse metabolic effects associated with the administration of antidepressant medications [28]. Our finding showed that a significant proportion of participants exhibiting moderate to severe levels of depression were found to have a BMI of 30 or greater. This can guide further future interventions to aim at mitigating obesity in an attempt to improve mental health.
Although the current study found a disproportional distribution of depression between individuals in different BMI categories, this variation did not achieve statistical significance, which may be attributed to the limitation of the sample size of the current study. Therefore, the authors of the present study suggest further research with the inclusion of a larger sample size that effectively represents the normal distribution of BMI in the general population. Moreover, a nationwide multicenter study is suggested as it may reveal more precise and informed recommendations.
## Conclusions
The co-occurrence of depression and obesity has emerged as a major public health concern. Currently, the awareness level is rudimentary concerning this topic among the general population. In light of this, the present study was undertaken to explore the relationship between depression and obesity in Saudi Arabia. We found a significant association between moderate to severe depression, hypertension, and diabetes mellitus. Our findings were in line with previous studies on this subject. However, we reported a much higher prevalence of obese participants in the current study. Although the majority of obese participants were depressed, the study did not find a statistical significance between BMI and depression. The findings of the current study indicate the urgency for public health initiatives focused on enhancing consciousness among the susceptible population.
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|
---
title: The role of the microbiome in the neurobiology of social behaviour
authors:
- Amar Sarkar
- Siobhán Harty
- Katerina V.-A. Johnson
- Andrew H. Moeller
- Rachel N. Carmody
- Soili M. Lehto
- Susan E. Erdman
- Robin I. M. Dunbar
- Philip W. J. Burnet
journal: Biological reviews of the Cambridge Philosophical Society
year: 2020
pmcid: PMC10040264
doi: 10.1111/brv.12603
license: CC BY 4.0
---
# The role of the microbiome in the neurobiology of social behaviour
## Abstract
Microbes colonise all multicellular life, and the gut microbiome has been shown to influence a range of host physiological and behavioural phenotypes. One of the most intriguing and least understood of these influences lies in the domain of the microbiome’s interactions with host social behaviour, with new evidence revealing that the gut microbiome makes important contributions to animal sociality. However, little is known about the biological processes through which the microbiome might influence host social behaviour. Here, we synthesise evidence of the gut microbiome’s interactions with various aspects of host sociality, including sociability, social cognition, social stress, and autism. We discuss evidence of microbial associations with the most likely physiological mediators of animal social interaction. These include the structure and function of regions of the ‘social’ brain (the amygdala, the prefrontal cortex, and the hippocampus) and the regulation of ‘social’ signalling molecules (glucocorticoids including corticosterone and cortisol, sex hormones including testosterone, oestrogens, and progestogens, neuropeptide hormones such as oxytocin and arginine vasopressin, and monoamine neurotransmitters such as serotonin and dopamine). We also discuss microbiome-associated host genetic and epigenetic processes relevant to social behaviour. We then review research on microbial interactions with olfaction in insects and mammals, which contribute to social signalling and communication. Following these discussions, we examine evidence of microbial associations with emotion and social behaviour in humans, focussing on psychobiotic studies, microbe–depression correlations, early human development, autism, and issues of statistical power, replication, and causality. We analyse how the putative physiological mediators of the microbiome–sociality connection may be investigated, and discuss issues relating to the interpretation of results. We also suggest that other candidate molecules should be studied, insofar as they exert effects on social behaviour and are known to interact with the microbiome. Finally, we consider different models of the sequence of microbial effects on host physiological development, and how these may contribute to host social behaviour.
## INTRODUCTION
All multicellular life hosts microbial life, and the relationships between microorganisms and host lineages appear to be stable over millions of years of host evolution (Moeller et al., 2016, 2019; Nishida & Ochman, 2018, 2019). In animals, the majority of these microbes reside in the intestinal tract, where they may number in the trillions. In mammals, microbial colonisation of the host begins during parturition, with the mother’s vaginal and faecal microbes being transmitted to, and subsequently becoming established within, the infant gut (Dominguez-Bello et al., 2010; Mueller et al., 2015; Ferretti et al., 2018; Sprockett, Fukami, & Relman, 2018). The infant microbial community then undergoes substantial reorganisation in response to changes in development, health, and the environment (Koenig et al., 2011), but also continues to be shaped by microbial transmission from the mother (Ferretti et al., 2018; Moeller et al., 2018). The gut microbiome refers to the community of microbes, microbial genes, and the environment they inhabit (Marchesi & Ravel, 2015).
A surge of investigations on the gut microbiome during the past two decades has revealed that these microbes make important contributions to numerous aspects of animal health and physiology across the lifespan (McFall-Ngai et al., 2013; Kundu et al., 2017; Rook et al., 2017). In particular, gut microbes contribute to the regulation of host metabolism, adiposity, and energy balance (Bäckhed et al., 2004; Turnbaugh et al., 2006; Nicholson et al., 2012), as well as appetite and nutrient intake (Perry et al., 2016), and the maturation and activity of the immune system (Fung, Olson, & Hsiao, 2017). More recently, gut microbes have been found to influence brain development and function (Diaz Heijtz et al., 2011; Braniste et al., 2014; Sampson & Mazmanian, 2015; Sharon et al., 2016; Vuong et al., 2017).
Alongside these effects on the host’s peripheral and central physiology, a growing body of evidence suggests that the microbiome influences host psychological processes such as emotion, learning, and memory (Diaz Heijtz et al., 2011; Cryan & Dinan, 2012; Foster & McVey Neufeld, 2013; Dinan et al., 2015; Vuong et al., 2017; Hoban et al., 2018; Sarkar et al., 2018). Several investigations in this area are beginning to reveal associations between the microbiome and animal sociality (Hsiao et al., 2013; Desbonnet et al., 2014; Arentsen et al., 2015; Tung et al., 2015; Buffington et al., 2016; Parashar & Udayabanu, 2016; Stilling et al., 2018), and researchers have begun developing hypotheses on the evolutionary and biological mechanisms underpinning microbiome–sociality associations (Montiel-Castro et al., 2013; Stilling et al., 2014; Archie & Tung, 2015; Münger et al., 2018). For an in-depth analysis of these hypotheses in terms of evolutionary theory, see Johnson & Foster [2018].
However, to date there is little evidence that elucidates which causal physiological pathways (at the systems, cellular, and molecular levels) mediate microbial contributions to host social behaviour. Here, we describe three potentially relevant mediators of the link between the microbiome and animal sociality (see Fig. 1). First, the microbiome affects the development and function of brain regions such as the amygdala, hippocampus, and prefrontal cortex (Sudo et al., 2004; Hoban et al., 2016; Luczynski et al., 2016) that are known to contribute to social cognition and social behaviour. Second, the microbiome is capable of generating or regulating the bioavailability of a large number of signalling molecules that influence animal social behaviour, including glucocorticoids, sex hormones, neuropeptides, and monoamines (Sudo et al., 2004; Wikoff et al., 2009; Markle et al., 2013; Poutahidis et al., 2013). Finally, the microbiome affects gene expression and epigenetic processes relevant to social behaviour. Although these investigations themselves often do not explicitly link changes in the brain, biochemicals, and gene expression to social behaviour, they do indicate the possible physiological pathways through which the microbiome may influence sociality. Our goal, therefore, is to connect these findings in the context of their relevance to animal social behaviour in order to elucidate some of the physiological mechanisms that may underpin the microbiome–sociality association. Although we focus mainly on the gut microbiome in this review, it should be noted that there are numerous microbiomes distributed across the host body, including the mouth, nose, vagina, and skin, all of which make contributions to host physiology (Dethlefsen, McFall-Ngai, & Relman, 2007; Costello et al., 2009; Grice & Segre, 2011).
We first provide a brief overview of the experimental methods used in this field, focussing on pharmacological manipulations, microbial transfers, and germ-free models (i.e. animals that are born and reared in sterile settings, and are therefore devoid of any microorganisms). Then, we adopt a top-down approach, beginning with an overview of experimental investigations of the microbiome–sociality relationship in animals. We synthesise laboratory evidence of the microbiome’s role in the regulation of brain circuitry and signalling molecules implicated in social behaviour. We describe microbial interactions with potential molecular genetic mechanisms underlying animal social behaviour. We consider the contributions of the microbiome to social olfactory signalling in insects and mammals. We also assimilate the emerging research on microbial associations with human emotion and social behaviour, and discuss issues of statistical power and replication. We then focus on the relationship between social behaviour and its underlying physiology and how the microbiome may affect this relationship. Specifically, although the microbiome influences numerous physiological substrates of social behaviour, there is little evidence for microbiome → host physiology → social behaviour pathways. Finally, we describe the importance of attempting to disentangle the order and nature of microbial effects on sociality.
## EXPERIMENTAL METHODS IN MICROBIOME–HOST INTERACTION RESEARCH
Three of the most common laboratory experimental techniques in investigating host–microbiome interactions are the use of pharmacological or exogenous manipulations (e.g. antibiotics, probiotics, and prebiotics), germ-free models, and microbiome transplants (via faecal transfers). As we and others have described these methods elsewhere (Sarkar et al., 2018), we cover them only briefly here (see Fig. 2).
## Exogenous manipulations
The microbial content of the gut can be exogenously manipulated using antibiotics, probiotics, prebiotics, and psychobiotics (which are a subset of probiotics and prebiotics).
## Antibiotics
The effect of antibiotics on gut bacteria depends on the type of antibiotic used and its mode of action. Since antibiotics can and often do ablate non-target microbial populations, they may exert a widespread and significant impact on the host microbiome. Furthermore, not all antibiotic effects necessarily occur via modulation of the microbiome (Forsythe, Kunze, & Bienenstock, 2016). For instance, some antibiotic molecules may exert physiological and psychological effects by directly interacting with microglia, enteric neurons, or by modulating enzymatic action (Forsythe et al., 2016). Since antibiotic administration studies do not always assess changes in microbial populations directly, it is possible that behvioural outcomes occur via antibiotic effects on non-microbial targets. Furthermore, even if researchers do measure changes in the microbiome that covary with a particular behaviour, it does not rule out the possibility that other, non-microbial changes in response to antibiotic exposure may also have contributed to any observed behavioural effects.
## Probiotics
Probiotics are exogenous live bacteria introduced into the host gut via direct ingestion or oral gavage (the latter in the case of animals). Bacteria from the Bifidobacterium and *Lactobacillus* genera are often used as probiotics. Once ingested, these microbes may then have opportunities to colonise the host (perhaps only transiently) and may influence the host’s physiology. However, the incoming probiotics face colonisation resistance in the gut, both from resident microbes (Zmora et al., 2018) and the chemical and physical environment of the gut itself (e.g. low pH, rapid effluent flow, secretion of bile, and antimicrobial peptides) (Walter & Ley, 2011). Further research is needed to determine the proportion of ingested probiotics that reach and colonise the gut, dose–response associations, the longevity of probiotic effects, and any possible long-term effects of probiotics on the microbiome (Sarkar et al., 2016).
## Prebiotics
Prebiotics are nutritive resources for microbes, such as indigestible oligosaccharides, that are introduced into the gut to support the growth of beneficial microorganisms. Bacterial fermentation of prebiotics often results in the production of short-chain fatty acids (SCFAs) which can exert a wide range of physiological effects, including on the immune system and metabolism, and the enteric and central nervous systems (Kao, Harty, & Burnet, 2016; Kimura et al., 2011; Koh et al., 2016). However, some prebiotics are able to exert physiological effects independent of their effects on microbial populations (Forsythe et al., 2016). For instance, oligosaccharides may bind directly to the immune system’s pattern-recognition receptors in the lumen or physically prevent these receptors from detecting microbes, with potential anti-inflammatory effects (Bode et al., 2004; Eiwegger et al., 2010).
## Psychobiotics
The collection of probiotics and prebiotics that exert psychological effects via the microbiome–gut–brain axis are defined as ‘psychobiotics’ (Dinan, Stanton, & Cryan, 2013; Sarkar et al., 2016), and researchers may also consider expanding the definition of psychobiotics to include other substances such as antibiotics or dietary components, if their psychological consequences are at least partially mediated by the microbiome (Sarkar et al., 2016). In particular, the microbiome is extremely sensitive to the host’s diet (Wu et al., 2011; David et al., 2014a, 2014b; Carmody et al., 2015, 2019; Sonnenburg et al., 2016), with diet-induced changes becoming detectable in the microbiome even a day later in some instances (Wu et al., 2011; David et al., 2014a). As such, we have suggested the possibility that diet could be the strongest source of psychobiotics (Sarkar et al., 2018).
## Germ-free models
Germ-free animals are born and raised in microbe-free environments, and are therefore important resources for understanding the influence of microbes on animal physiology. However, it should be noted that germ-free animals differ from conventional animals in terms of both their physiology and social behaviour, and therefore when these animals are colonised by bacteria (e.g. via probiotics or microbiome transplants), the results cannot necessarily be extrapolated to animals with normal microbiomes (Hanage, 2014). The most attention is paid to the gut microbiome, which forms the largest and most complex microbial community in the body. The gut microbiome reaches densities in the large intestine that exceed those of other body sites by several orders of magnitude, and the composition of this distal gut community can be inferred non-invasively through DNA sequencing of faecal samples. However, there are also distinct microbial communities associated with other body sites including the skin, mouth, lungs, vagina, and nose, and all of these microbial communities presumably contribute to host health and homeostasis. Germ-free animals lack all of these microbiomes simultaneously and thus it cannot necessarily be deduced that the differences observed in germ-free animals arise solely from the absence of the gut microbiome, given that conventionally colonised animals have numerous other microbiomes which may exert independent and interactive effects on host physiology.
## Microbial transfers
Gut microbes can be transmitted from one animal to another via the transfer of faecal matter, which can occur when co-housing animals (a process that is enhanced in coprophagic species), or more directly by transplanting faecal content from one animal to another.
## Co-housing
Merely housing animals in the same physical environment enables a degree of microbial transfer among individuals. The co-housing approach relies on the environmental and social transmission of microbes among animals (Ridaura et al., 2013). In some cases, microbes transferred via co-housing can alter phenotypes in recipient mice, including the induction of inflammation (Rehaume et al., 2014), as well as affecting other aspects of host physiology. A recent study showed that bacterial transfer via co-housing was sufficient to induce immunological changes associated with neurodevelopmental abnormalities in mice (Kim et al., 2017). Similarly, microbiome-related social deficits have in some cases been reversed by co-housing experimental and control mice (Buffington et al., 2016). These results demonstrate the efficacy of co-housing as a means of microbial transfer in mice, and justify the use of co-housing to at least partially homogenise microbiome composition in mouse experiments (Laukens et al., 2016). However, microbial ‘homogenisation’ (or mixing of the microbiomes of co-housed animals) does not consistently occur, and in some cases, microbes may only be transmitted unidirectionally between animals. Specifically, an important study found that mice carrying an ‘obese’ microbiome were sensitive to colonisation by microbes from co-housed mice carrying ‘lean’ microbiomes under specific dietary conditions, but the opposite was not the case (Ridaura et al., 2013).
## Transplantation of faecal microbes
A germ-free mouse can be administered a faecal transplant from a healthy mouse (conventionalisation). Researchers have recently established that these donated faecal microbes can survive in the recipient’s gut for at least 3 months (Li et al., 2016). However, microbes from one donor do not always coexist with the recipient’s microbiome to the same extent in different recipients, suggesting that host factors (e.g. host genetics, physiology, or the host’s microbiome itself) can influence the successful establishment of new microbes in the gut.
With appropriate controls, changes in host physiology and social behaviour that follow a faecal transplant can be attributed to the effects of the donor’s microbiome. Germ-free and normally colonised mice can also be colonised with disease-associated microbiomes, either from conspecifics or from humans. In these cases, the microbiome donor has a specific condition (e.g. obesity, anxiety, depression, autism). If microbes are sufficient to induce the physiological or behavioural features of the condition, then faecal transplants to rodents should result in a recapitulation of condition-relevant phenotypes in the recipients, assuming that the faecal microbiome accurately captures the total gut microbiome. While there is evidence that this is the case (Eckburg et al., 2005), recent research does suggest that the microbiome associated with the gut mucosa may have limited representation in stool samples (Zmora et al., 2018).
Overall, however, faecal transplants allow the inference that the microbiome makes at least some causal contribution to the condition of interest. It is important to keep in mind that these experiments do not necessarily reveal the mechanisms underlying microbial contributions to the condition, or which microbes are involved.
## MICROBIAL ASSOCIATIONS WITH SOCIAL STRESS AND SOCIAL BEHAVIOUR
We first focus on the association between microbes and host sociality, with an emphasis on social stress and social behaviour (see Fig. 3). We also consider autism, the key features of which include impairments in normal social behaviour. We focus on rodent studies, as most experimental research on microbiome–sociality relationships uses rodents as experimental models (although some studies also examine fish and insects). We illustrate the diversity, potential, and limitations of investigations of the microbiome–sociality relationship. Despite the opportunities that rodent models provide for discovering effects of the microbiome on host behavioural phenotypes, it is important to keep in mind that such findings may not necessarily be extrapolated to humans.
## Social stress in rodents
Stress and negative emotional states significantly alter social interactions, and form a core component of mood disorders and many other psychiatric conditions. In mice, the stress induced by social aggression and subordination to dominant conspecifics triggers changes in the gut microbiome and immune function (Bailey et al., 2011; Galley et al., 2014; Bharwani et al., 2016). Social disruption and social defeat (in which mice are forced to interact with aggressive conspecifics) can reduce gut bacterial diversity (Galley et al., 2014; Bharwani et al., 2016; Szyszkowicz et al., 2017), and can also alter the abundance of specific bacterial taxa. These changes include, for instance, decreases in the relative abundance of the Bacteroides and *Lactobacillus* genera (Bailey et al., 2011; Galley et al., 2014) and increases in the relative abundance of the *Clostridium genus* (Bailey et al., 2011). Moreover, some of these changes in bacterial populations occur as early as within 2 hours of exposure to the social stressor (Galley et al., 2014), and can last for at least 3 weeks (Szyszkowicz et al., 2017), suggesting that microbial responses to the social environment may be both rapid and long-lasting. These microbial changes may occur in parallel with elevations in peripheral proinflammatory cytokines such as interleukin-6 (Bailey et al., 2011; Bharwani et al., 2016), although this is not always the case (Szyszkowicz et al., 2017). Antibiotics have also been observed to attenuate stress-induced proinflammatory immunological activity, further suggesting that gut microbes may mediate the relationship between social stress and inflammation (Bailey et al., 2011).
Social stress can also be induced by isolation (Weiss et al., 2004). Postweaning separation of rats from conspecifics led to elevations in the Actinobacteria phylum, reductions in the Clostridia class, and an unexpected decrease in hippocampal interleukin-6 (Dunphy-Doherty et al., 2018). Insofar as maternal contact during infancy is a crucial form of early social interaction (Feldman, 2017), maternal separation may also be interpreted as a form of social isolation, and is frequently used as a method of inducing stress in young rodents (Meaney et al., 1996; Desbonnet et al., 2010). In this regard, maternal separation of rat pups affects gut bacterial composition, reducing the relative abundance of the Lactobacillus genus, and elevating concentrations of proinflammatory cytokines (Gareau et al., 2007; O’Mahony et al., 2009).
While social stress does appear to reliably alter microbial composition, it also seems that different forms of social stress – defeat and aggression (Bailey et al., 2011; Galley et al., 2014; Bharwani et al., 2016), and isolation and separation (Gareau et al., 2007; O’Mahony et al., 2009; Dunphy-Doherty et al., 2018) – trigger different types of changes, with inconsistencies across studies. Aside from the nature of the stressor, other factors that likely contribute to differing effects of social stress on microbial composition include the species, strain, and sex of the rodent, as well as the age at which the stressor is experienced (infancy in the case of maternal separation, adulthood in the case of social defeat and disruption).
Given the bidirectional communication between the gut microbiome and brain, it is possible that the animal’s microbiome can itself affect the stress response. For example, a recent study found that mice which were more resilient to social stress also had a higher prevalence of Bifidobacterium in the gut compared to susceptible individuals, suggesting that gut bacteria may buffer against stress (Yang et al., 2017). Similarly, social avoidance induced by social stress was found to be most extreme in mice with lower levels of Gram-positive *Firmicutes bacteria* (Oscillospira spp. and Turicibacter spp.) and higher levels of Gram-negative Bacteroidetes (Flavobacterium spp., Parapedobacter spp., and Porphyromonas spp.) ( Szyszkowicz et al., 2017). While these findings are of course correlational, they are at least suggestive of the possibility that certain bacteria may promote psychological resilience against social stress, and as such these potential protective effects warrant further investigation.
## Social behaviour in rodents
A widely used measure of rodent social behaviour is the three-chamber test (see Fig. 3), which provides an index of rodent sociability and social cognition (Nadler et al., 2004; Moy et al., 2007; Silverman et al., 2010; Yang, Silverman, & Crawley, 2011). The task involves two steps, following an initial habituation phase. First, the rodent is placed in the middle of three interconnected chambers. One of the adjacent chambers contains an unfamiliar conspecific, while the other contains a novel object (alternatively, this chamber may be empty). Normal rodent sociability is indexed by greater behavioural preference for the conspecific. The second step also involves three interconnected chambers. In this case, the adjacent chambers contain a familiar rodent (from the first step) and an unfamiliar rodent. Typical social cognition is indexed by greater behavioural preference for the unfamiliar conspecific. Disturbances in sociability and social cognition are reflected in reduced interest in the conspecific (step 1) and the unfamiliar conspecific (step 2), respectively.
This three-chamber test is frequently used to assess social behaviour in germ-free rodents in microbiome experiments. For instance, unlike their normally colonised counterparts, germ-free mice exhibit social impairments in the three-chamber test. In particular, they do not show the normal preference for interacting with other rodents (impaired sociability), nor a preference for interacting with an unfamiliar mouse over a familiar one (impaired social cognition) (Desbonnet et al., 2014; Buffington et al., 2016; Stilling et al., 2018). Microbial reconstitution attenuated the impairments in sociability, but did not ameliorate social cognition (Desbonnet et al., 2014; Stilling et al., 2018), suggesting that some – but not all – of the social deficits may be reversible. However, because both sociability and social cognition were each only tested once in these studies, it may also be the case that changes in social cognition occur more slowly than changes in sociability, and may therefore be apparent only in further testing sessions. Similar to germ-free mice, germ-free rats also show impairments in sociability in the early stages of a social interaction task (Crumeyrolle-Arias et al., 2014). Overall, these results provide causal evidence that some aspects of normal host sociality may require the presence of a microbiome.
However, there is one intriguing report that germ-free status increased sociability in mice, as observed in the three-chamber test (Arentsen et al., 2015). The mice used in this study were older than those used in some of the research that found that germ-free status decreased sociability (Desbonnet et al., 2014; Stilling et al., 2018), and this may account for the divergent effects of germ-free status on sociability. The hypothesis that an animal’s age may affect how its microbiome influences its social behaviour could be tested by systematically examining social interactions in germ-free mice of different ages.
## Associations with the gut microbiome in rodent models of autism
The microbiome has been implicated in autism, which is a complex condition defined by deficits in social communication and interaction, as well as rigid and repetitive behavioural patterns (Baron-Cohen & Belmonte, 2005; Happé, Ronald, & Plomin, 2006). Autism is often also associated with gastrointestinal and immunological disturbances (Horvath & Perman, 2002; Ashwood et al., 2011; Patterson, 2011; Onore, Careaga, & Ashwood, 2012; McElhanon et al., 2014). Gastrointestinal and immunological processes are in turn associated with the microbiome, and as such, the nature of microbial involvement in the multidirectional relationships between the gastrointestinal system and the immune system in autism are unclear, and are an important area of investigation (Azhari, Azizan, & Esposito, 2019).
A rapidly growing body of research is beginning to suggest ways in which the microbiome may be functionally involved in autism (Vuong et al., 2017; Vuong & Hsiao, 2017), raising the possibility that the microbiome may contribute to its aetiology. For instance, research in rodents shows that maternal experiences can disturb microbial composition in the offspring. These maternal experiences include exposure to antibiotics (Degroote et al., 2016), acute systemic inflammation (i.e. maternal immune activation; Hsiao et al., 2013; Kim et al., 2017; Lammert et al., 2018; Morais et al., 2018), or consumption of high-fat diets (Buffington et al., 2016), all of which alter the offspring’s microbiome. Crucially, these microbial perturbations are associated with behavioural profiles consistent with autistic traits, including reduced sociability and repetitive behaviour [assessed, for example, by excessive burying of marbles (Thomas et al., 2009; Malkova et al., 2012)].
In particular, a recent study (Buffington et al., 2016) found that pregnant mice that consumed high-fat diets gave birth to offspring that showed autistic-like phenotypes. When healthy mice engaged in regular social interactions, long-term potentiation occurred in the ventral tegmental area. In comparison, the autistic-type mice showed comparatively lower levels of long-term potentiation in the ventral tegmental area after social interactions, and also had fewer oxytocin-expressing neurons. The causal role of the microbiome was revealed using faecal transplants to transfer microbes from the autistic-type mice to the control mice: the recipients developed social behavioural deficits and showed impaired long-term potentiation in the ventral tegmental area, as well as reductions in oxytocin-expressing neurons. This suggests that the microbiome is able to induce autistic-like phenotypes in neurotypical recipients.
Perhaps most striking, however, is the finding that probiotic treatment with Lactobacillus reuteri and *Bacteroides fragilis* ameliorated some of the autistic-like phenotypes in mice (Hsiao et al., 2013; Buffington et al., 2016). While of course still very far from clinical application to humans, such rodent findings nonetheless provide early evidence that some of the behavioural features of complex neurodevelopmental conditions may be at least partially reversible in some cases through exogenous manipulation of the gut microbiome.
The specific pathways through which the microbiome may contribute to autistic-like behaviours are still largely unknown and in need of rigorous mechanistic elucidation. However, recent efforts using the maternal immune activation model of autism in rodents have begun to uncover microbiome–immune associations that affect the likelihood of developing autistic phenotypes in response to inflammation during pregnancy. In mice, elevations in maternal concentrations of the proinflammatory cytokine interleukin-17a produced by T helper 17 (TH17) cells may mediate the relationship between maternal infection during pregnancy and infant autistic phenotypes (Choi et al., 2016). Signalling by TH17 cells and interleukin-17a during pregnancy appears to rely on the presence of segmented filamentous bacteria in the maternal gut (Kim et al., 2017). Maternal immune activation in the absence of TH17-promoting segmented filamentous bacteria in the gut does not produce autistic-type offspring (Kim et al., 2017). However, when mice that were lacking segmented filamentous bacteria were then exposed to these bacteria, either directly or through interactions with other mice carrying these bacteria, maternal immune activation did trigger autistic phenotypes in the offspring via elevations of interleukin-17a (Kim et al., 2017; Lammert et al., 2018). These results suggest that maternal microbes may be acting as environmental risk factors for autism.
*The* genetic background of the host may also moderate the effects of environmental risk factors on the development of autism. *Host* genes are known to exert some influence on the composition of the microbiome (Goodrich et al., 2014), and therefore host genetic factors may also influence the microbiome–autism association. For instance, in a comparison of the effects of maternal immune activation on autistic traits between C57BL/6J mice and NIH Swiss mice, the latter were found to bury significantly more marbles than the former, although sociability was similarly impaired in both strains following the intervention (Morais et al., 2018).
Genetic research on autism in humans has implicated SHANK family genes in the aetiology of autism (Jiang & Ehlers, 2013). SHANK genes (SHANK1, SHANK2, and SHANK3) encode synaptic folding proteins, and genetic manipulations that alter the expression of these proteins have been used to model the effects of genetic risk factors of autism (Jiang & Ehlers, 2013). A recent gene-knockout study found that mice lacking Shank3 displayed autistic-like phenotypes (e.g. impaired sociability and repetitive behaviours) alongside several changes in gut bacterial composition and reductions in the expression of γ-aminobutyric acid (GABA) receptors in the hippocampus and prefrontal cortex (Tabouy et al., 2018). Crucially, treatment with the probiotic Lactobacillus reuteri attenuated the behavioural deficits and also increased expression of GABA receptors in the affected brain regions (Tabouy et al., 2018). Therefore, Lactobacillus reuteri appears to diminish autism-related phenotypes in two distinct murine models of autism (Buffington et al., 2016; Tabouy et al., 2018).
Indeed, researchers have now followed this lead to show explicitly that Lactobacillus reuteri appears to be effective in treating murine autism symptoms with diverse aetiologies (Sgritta et al., 2019). These include environmental models (maternal exposure to valproic acid), genetic models (Shank3 knockout), and idiopathic models (BTBR mice show autistic traits but there are no known genetic or environmental sources, and as such these mice are considered to represent idiopathic autism). In all cases, treatment with Lactobacillus reuteri ameliorated the social deficits associated with these conditions (i.e. increased time in social interactions, increased sociability, and increased preference for social novelty compared to untreated mice). Vagotomy (i.e. surgical removal of the vagus nerve) abolished probiotic benefits, suggesting that the behavioural benefits of Lactobacillus reuteri are mediated by the vagus nerve. Moreover, monoassociation of germ-free mice with Lactobacillus reuteri also rescued social functioning (Sgritta et al., 2019). These results suggest that this probiotic can exert its effects independent of other microbes and that it can rescue social impairments in diverse mouse models of autism.
Another recent study sought to examine the effects of transplanting gut microbes from autistic humans to mice (Sharon et al., 2019). Germ-free mice were colonised using faecal transplants from neurotypical or autistic donors, with the autistic donors for this study comprising 11 individuals with mild, moderate, and severe autism. This initial generation of colonised mice was then used to breed a second generation. In particular, each member of the second generation was bred from parents which had received microbiome transplants from the same human donor. The gnotobiotic conditions meant that vertical transmission of microbes could only include microbial populations derived from human donors, as those were the only microbes that had colonised the parents. This allowed for an examination of the causal contributions of the microbiome to autism in the offspring.
The researchers did not observe any differences between the mice carrying microbiomes derived from autistic donors compared to mice carrying microbiomes derived from neurotypical donors in the three-chamber test. However, the experimental mice did show reduced social engagement with conspecifics in a separate test investigating direct social interactions, and also buried significantly more marbles compared to the control group (although in this latter case, the effect was only apparent when excluding mice whose microbiomes were derived from donors diagnosed with mild autism). However, subsequent work by independent researchers suggested that there may have been software-associated technical issues in the original analysis that led to errors in the estimates of statistical significance in the results. In particular, researchers have suggested that the mouse data may have been analysed as if each mouse received microbes from independent donors, whereas in fact all of the mice were colonised by microbes from one of 11 donors (meaning that multiple mice received transfers from the same donor). It appears that correcting for this issue leads to a loss of statistical significance in the case of social interaction, although the differences in marble burying remained statistically significant. Overall, therefore, it will be crucial to replicate these results using a wider pool of autistic and neurotypical donors.
## Drawbacks to rodent models of autism and potential alternatives
*In* general, there is much debate over the utility of rodent models of autism, and there is as yet no universally accepted rodent model that is considered equivalent to the behavioural impairments associated with autism in humans. While of course atypical sociality and repetitive behaviour in mice provide an attractive resemblance to human autism, it is far from clear whether these behavioural impairments in rodent models are effective at genuinely capturing the vastly more complex phenotypes of human autism. Thus, while the results of microbiome–sociality studies in rodents are certainly provocative and conceptually interesting, the distance between rodent ‘autism’ and human autism poses a significant translational barrier. Initiating human clinical trials on the basis of only rodent results would be extremely resource intensive and may not yield any meaningful results, and, moreover, may unnecessarily subject young participants to discomfort or distress associated with the testing procedures. One solution that we have suggested previously is the use of primate models after preclinical rodent results have been established (Sarkar et al., 2018). In this regard, researchers have recently developed a macaque (Macaca fascicularis) model of autism with SHANK3 mutations using the CRISPR–Cas9 (clustered regularly interspaced short palindromic repeats–CRISPR-associated protein 9) gene-editing system (Zhou et al., 2019). Crucially, alongside disturbances in neurocircuitry, the macaques showed social impairments and repetitive behaviour reminiscent of the hallmark features of autism. As such, it may be worthwhile to consider, where feasible, how microbial interventions affect autism-relevant phenotypes in macaques prior to initiating human investigations. Though such primate studies would themselves be highly resource intensive, in the long run they would likely be more efficient if conducted as follow-ups to rodent studies and prior to human studies.
## MICROBIAL INFLUENCES ON THE SOCIAL BRAIN
Gut microbes make important contributions to brain development and function (see Fig. 4), including the amygdala and the prefrontal cortex, both of which are crucial nodes in the network comprising the ‘social’ brain. In addition, the microbiome has been found to affect the hippocampus, which also plays a role in social cognition. The microbiome also influences the hypothalamus, which regulates a range of signalling molecules that exert well-known social effects.
## Amygdala
The amygdala is a subcortical brain structure that plays an important role in processing social-affective information (Phelps & LeDoux, 2005), and mediates the experience of stress, fear, and anxiety (Roozendaal, McEwen, & Chattarji, 2009). On the other hand, reduced amygdalar activity during social perception tasks is hypothesised to be associated with autism and autistic-type traits in humans (Baron-Cohen et al., 1999, 2000). More recently, researchers have also observed ‘simulation’ neurons in the primate amygdala (Grabenhorst et al., 2019). Specifically, these neurons appear to facilitate the simulation of the mental states of a monkey’s social partners (Grabenhorst et al., 2019).
Several studies have revealed that the microbiome exerts effects on the structure and function of the amygdala (Cowan et al., 2018). For example, in germ-free mice, the lateral amygdala, the basolateral amygdala, and the central nucleus of the amygdala have a greater volume compared to normally colonised controls (Luczynski et al., 2016). Dendritic hypertrophy has also been observed in the basolateral amygdala of germ-free mice. In particular, the dendrites of aspiny interneurons of germ-free mice were both longer and had a greater number of branch points compared to normally colonised controls (Luczynski et al., 2016). The dendrites of pyramidal neurons in the basolateral amygdala of germ-free mice were also longer, with increased density of thin spines, stubby spines, and mushroom spines (Luczynski et al., 2016). In mice, ingestion of the probiotic *Lactobacillus rhamnosus* lowers amygdalar expression of GABAAα2 messenger ribonucleic acid (mRNA) (Bravo et al., 2011). The microbiome also affects other aspects of gene expression in the murine amygdala, which we discuss later (see Section VI).
There is also some evidence of a possible link between the gut microbiome and the human amygdala, although it is far less robust than findings in rodents. In particular, higher levels of intrinsic Prevotella spp. in healthy volunteers were associated with greater white matter connectivity between the amygdala and the caudate (Tillisch et al., 2017). Higher levels of Actinobacteria were also found to be positively correlated with fractional anisotropy of the amygdala (with higher fractional anisotropy in turn predicting better microstructural organisation) (Fernandez-Real et al., 2015). Researchers have also found preliminary evidence of an association between microbial diversity and the functional connectivity between the amygdala and the thalamus (Gao et al., 2019). However, it is important to note that since these are correlational studies, it may be that the relationship between the microbiome and the amygdala is mediated by stress, since stress can affect both the amygdala and microbiome composition.
Two reward-related networks, the amygdala–nucleus accumbens circuit and the amygdala–anterior insula circuit, have also recently been shown to be associated with microbially generated indole metabolites in humans (Osadchiy et al., 2018). In particular, the concentrations of different indole metabolites (indole, indoleacetic acid, and skatole) obtained from faecal samples were positively correlated with both anatomical and functional connectivity in the amygdala (Osadchiy et al., 2018). Moreover, consumption of probiotics (relative to controls) has been found to reduce activity in a brain network implicated in processing emotional information, including the amygdala, in a group of healthy female volunteers (Tillisch et al., 2013). Notably, studies have also failed to detect correlations between bacterial profiles and amygdalar volume in comparisons of healthy individuals and those diagnosed with irritable bowel syndrome (Labus et al., 2017; Tillisch et al., 2017). As such, the strength of the association between the microbiome and the amygdala remains to be clarified. *More* generally, though intriguing, these reports will need to be followed up with larger investigations in order to determine the nature of the microbiome–amygdala relationship with greater specificity and to test replicability.
## Prefrontal cortex
The prefrontal cortex is involved in high-level cognition and executive functions (Miller & Cohen, 2001), and also makes key contributions to social cognition, including impression formation (Mitchell, Macrae, & Banaji, 2005), learning social value (Behrens et al., 2008), and social and moral reasoning (Anderson et al., 1999). Furthermore, in humans, the prefrontal cortex is associated with social network size both volumetrically (Lewis et al., 2011) and functionally (Noonan et al., 2014, 2018), relationships that appear to be evident in other primates as well (Sallet et al., 2011).
Germ-free status in mice triggers morphological abnormalities in the prefrontal cortex, particularly enhanced thickness of the myelin sheath and an upregulation of genes associated with myelination and myelin plasticity (Hoban et al., 2016). Microbial transfers from stressed mice have also been found to trigger prefrontal demyelination and social avoidance in healthy recipients (Gacias et al., 2016), suggesting that the effects of stress on the brain may be at least partially mediated by the gut microbiome. Furthermore, given that social isolation in mice impairs adult prefrontal myelination (Liu et al., 2012) and that social isolation itself affects the microbiome (Gacias et al., 2016; Hoban et al., 2016), it is reasonable to hypothesise that some of the effects of social isolation on myelination of the prefrontal cortex may be microbially mediated. There is also evidence that the prefrontal cortex is sensitive to probiotics. In particular, mice that were treated with the probiotic *Lactobacillus rhamnosus* showed reduced expression of GABAAα2 mRNA in the prefrontal cortex (Bravo et al., 2011).
## Hippocampus
The hippocampus plays an essential role in the generation and maintenance of cognitive spatial maps (O’Keefe & Dostrovsky, 1971). Although often not considered within the typical network comprising the social brain, it is becoming increasingly apparent that the hippocampus plays an important role in mammalian social cognition. For example, the hippocampus contributes to social recognition and social memory (Kogan, Franklandand, & Silva, 2000). Analogous to its role in navigating physical space, researchers have also recently uncovered hippocampal contributions to navigating ‘social’ space in humans (Tavares et al., 2015). In particular, the hippocampus tracks others in this social space based on their degree of affiliation or closeness to the self and the social status they possess (Taveras et al., 2015). Importantly, hippocampal abnormalities, including cellular changes and volumetric reduction, have also been linked to depression (MacQueen et al., 2003; Hastings et al., 2004; Stockmeier et al., 2004; Videbech & Ravnkilde, 2004; Rosso et al., 2005). As such, it is worth considering the possibility that some of the relationships between the microbiome and depression could be mediated by changes in hippocampal structure and function.
The effects of the microbiome on the rodent hippocampus are some of the most consistent in the microbiome–gut–brain field. For example, germ-free mice show reduced levels of hippocampal brain-derived neurotrophic factor (BDNF) and BDNF mRNA (Clarke et al., 2013; Diaz Heijtz et al., 2011; Sudo et al., 2004), a protein involved in neuroplasticity and memory (Greenberg et al., 2009). Furthermore, both prebiotics and probiotics increase hippocampal BDNF levels (Desbonnet et al., 2008; Savignac et al., 2013; Burokas et al., 2017).
Relative to normally colonised controls, germ-free status in mice impacts several aspects of dendritic morphology in the hippocampus (as well as the amygdala), including reduced dendritic length and a smaller number of branch points (Luczynski et al., 2016). Overall hippocampal dendritic spine density is also lower in germ-free mice, a reduction accounted for by reduced densities of stubby spines and mushroom spines (Luczynski et al., 2016). At the same time, germ-free mice also show greater total volume of certain hippocampal regions, such as CA$\frac{2}{3}$ (Luczynski et al., 2016). Evidence is also emerging that the microbiome regulates adult neurogenesis in the hippocampus (Möhle et al., 2016; Ogbonnaya et al., 2015). In particular, germ-free status in mice elevates hippocampal neuroproliferation that is not reversible by colonisation with a normal microbiome (Ogbonnaya et al., 2015). However, antibiotic exposure in adult mice supresses hippocampal neurogenesis, but this can be reversed via treatment with probiotics (Möhle et al., 2016).
There is much less evidence of a hippocampal association with the microbiome in humans, but subgroup analysis from one small study suggests that individuals with high levels of Prevotella spp. may have lower hippocampal volume, and also show reduced hippocampal activity in response to negative emotional images (Tillisch et al., 2017). Since activity in the hippocampus has been associated with emotional regulation (Phelps, 2004), reduced Prevotella-associated hippocampal activation in response to negative emotional stimuli may be a risk factor for certain psychiatric conditions (Tillisch et al., 2017), although of course such an interpretation is highly speculative (the result itself should be subject to replication, and the causal contribution of Prevotella should be assessed).
## MICROBIAL REGULATION OF SOCIAL SIGNALLING MOLECULES
In addition to modulating brain anatomy and physiology, the microbiome may also affect the central nervous system via the generation and regulation of a range of ‘social’ signalling molecules including glucocorticoids, sex steroids, neuropeptides, and monoamines (see Fig. 5). Microbial communities regulate the biosynthesis and bioavailability of several neurotransmitters that play important roles in animal social interaction. There has also been a steadily growing interest in microbial endocrinology in terms of the relationship between microbes and host neuroendocrine function (Lyte, 2014), and such microbe–hormone interactions could be relevant to social behaviour. For instance, the microbiome affects several steroids regulated by the hypothalamus, including along the hypothalamic–pituitary–adrenal (HPA) axis and hypothalamic–pituitary–gonadal (HPG) axis.
There are at least three non-mutually exclusive pathways by which microbes regulate the biosynthesis and bioavailability of these signalling molecules. First, these molecules may be generated as by-products of bacterial metabolism. For instance, Lactobacillus and Bifidobacterium secrete GABA, Lactobacillus secretes acetylcholine, *Escherichia and* *Bacillus secrete* norepinephrine, and *Bacillus and* Serratia secrete dopamine (Lyte, 2011). Second, bacterial metabolites such as SCFAs and secondary bile acids can interact with host cells that regulate the production of signalling molecules. Third, signalling molecules can be converted into their active forms via bacterially mediated enzymatic deconjugation. In the examples that follow, we describe instances of all three processes.
These signalling molecules also vary in their brain-penetrant properties, with some readily able to cross the blood–brain barrier (e.g. glucocorticoids and sex steroids), while others are thought to be unable to do so (e.g. oxytocin). Overall, these molecules may exert their behavioural effects by entering the brain directly (if the molecule or its precursor can cross the blood–brain barrier), via effects on the immune system, or by modulating activity of the vagus nerve (Johnson & Foster, 2018). They may also perhaps exert their behavioural effects by modulating the activity of the proximal synapses of the enteric nervous system that innervates the gut, changes that may then be relayed to the brain (Sarkar et al., 2016; Johnson & Foster, 2018).
## Glucocorticoids
The gut microbiome influences concentrations of endogenous steroids, including glucocorticoids such as cortisol and corticosterone, which are the hormonal end-products of the HPA axis. The primary physiological function of glucocorticoids is glucose metabolism, a process that prepares the body for action by releasing energy. Importantly, once glucocorticoids are released into systemic circulation, they are also able to cross the blood–brain barrier, and can therefore interact directly with the central nervous system (Pardridge & Mietus, 1979). At the psychological level, glucocorticoid release is tightly coupled with the experience of fear and anxiety (Dickerson & Kemeny, 2004). The elevatation of glucocorticoids is considered to be one of the physiological hallmarks of stress. Hyperactivity of the HPA axis in humans predicts behaviours such as social avoidance (Roelofs et al., 2009), which have implications for social interaction. Similarly, pharmacologically elevating corticotropin-releasing factor in rodents enhances anxiety and supresses normal social interaction (Dunn & File, 1987).
The effect of the microbiome on the development of the HPA axis, and therefore its influence on the host’s stress response, has become an important area of investigation (de Weerth, 2017). For instance, germ-free rodents consistently show elevated corticosterone levels in response to stress compared with normally colonised animals (Sudo et al., 2004; Neufeld et al., 2011; Crumeyrolle-Arias et al., 2014). Ingestion of probiotics and prebiotics has been noted to reduce levels of circulating glucocorticoids in both humans and rodents, and is also associated with decreased anxiety (Bravo et al., 2011; Messaoudi et al., 2011; Schmidt et al., 2015; Allen et al., 2016; Burokas et al., 2017).
## Sex steroids
Gut microbes are also associated with the activity of host sex steroids such as androgens, oestrogens, and progestogens, the hormonal end-products of the HPG axis. Like glucocorticoids, sex steroids are capable of crossing the blood–brain barrier and can therefore bind directly to neurons in the brain (Pardridge & Mietus, 1979). It has been known for several decades that the microbiome regulates the bioavailability of endogenous steroids, as early studies found that germ-free rats produced very small quantities of steroids compared to normally colonised rats (Eriksson, Gustafsson, & Sjövall, 1969). Germ-free status was also found to interfere with normal reproduction in both males and females, and these effects were reversed by microbial colonisation (Shimizu et al., 1998). Technological advances have resulted in more fine-grained studies, and many of the microbial effects on these molecules have been investigated within the last decade.
## Androgens
Androgens are a major class of steroids that regulate male sexual development, exerting a variety of important physiological and psychological effects. They are also present in much smaller quantities in females, but their role in female biology and behaviour is generally less well understood compared to males. The primary androgen is testosterone, an end-product of the HPG axis. Others include androstenedione, dehydroepiandrosterone, and dihydrotestosterone. In males, rising testosterone levels associated with adolescence trigger sexual development, spermatogenesis, and the development of secondary sexual characteristics (Mooradian, Morley, & Korenman, 1987; Hau, 2007; Walker, 2011, 2009). From the perspective of animal sociality, testosterone controls mating and reproductive behaviour, especially in males, and is implicated in the motivation for status-seeking, including in humans (Mazur, 1985; Mazur & Booth, 1998; Archer, 2006; Eisenegger, Haushofer, & Fehr, 2011).
Male germ-free mice show markedly lower serum testosterone concentrations compared to normally colonised male conspecifics, while female germ-free mice show the opposite pattern (Markle et al., 2013). The transplantation of microbes from adult males into pre-adolescent female recipients (via faecal transfer) increases testosterone concentrations in the recipients (Markle et al., 2013). Similarly, researchers have found that germ-free status is associated with lower levels of both circulating gonadotropins and intratesticular testosterone concentrations, as well as reduced integrity of the blood–testis barrier, which protects the gonads from many peripheral influences such as proinflammatory factors (Al-Asmakh et al., 2014). The impairment in blood-testis barrier integrity in germ-free mice was associated with reduced expression of cell adhesion proteins, while colonisation with *Clostridium tyrobutyricum* ameliorated the expression of cell adhesion proteins and restored the integrity of the blood–testis barrier (Al-Asmakh et al., 2014). Furthermore, ageing mice fed Lactobacillus reuteri show higher concentrations of serum testosterone and enhanced rates of spermatogenesis (Poutahidis et al., 2014). Moreover, relative to controls, mice treated with Lactobacillus reuteri display both morpho-morphological changes (as measured by greater testis size) and cellular changes (as measured by a proliferation of testosterone-producing Leydig cells) (Poutahidis et al., 2014). Together, these findings point to a causal role of the gut microbiome in the biosynthesis or regulation of testosterone and testicular morphology and function across the lifespan, suggesting that the microbiome may therefore influence some aspects of reproduction and reproductive behaviour.
## Oestrogens
The microbiome also influences endogenous concentrations of oestrogens, which are an important group of ‘female’ reproductive steroids (they are also present in smaller quantities in males). They include oestradiol (the primary oestrogen), oestrone, and oestriol. Oestrogens regulate the maturation and maintenance of the female reproductive system (McCarthy, 2008; Colvin & Abdullatif, 2013). Compared to testosterone, much less research has been done on the social and behavioural correlates of oestrogens, although there is some evidence that oestradiol drives female competition and status-seeking behaviour in humans (Knight & Mehta, 2014; Stanton & Edelstein, 2009; Stanton & Schultheiss, 2007).
The microbiome plays an important role in the availability of oestrogens (Flores et al., 2012; Fuhrman et al., 2014), and researchers have developed the concept of the ‘estrobolome’, or the total collection of bacterial genes that encodes products capable of metabolising oestrogens (Plottel & Blaser, 2011). Disturbances in the estrobolome are thought to be associated with breast cancer (Kwa et al., 2016). A significant proportion of oestrogen molecules are hepatically conjugated with glucuronide or sulphate, rendering them inactive, and their resultant polarity allows for re-entry into the lumen and subsequent excretion (Kwa et al., 2016). This phenomenon potentially prevents a substantial quantity of oestrogens from exerting physiological effects. However, several bacteria intervene in this process. For example, some bacteria can influence the concentration of active oestrogen through their capacity to encode enzymes such as β-glucuronidase and β-glucosidase, which deconjugate oestrogen molecules (Dabek et al., 2008; Kwa et al., 2016). This deconjugation of oestrogen molecules into their active forms enables their intestinal reabsorption and return to circulation. Thus, gut microbes can enhance the bioavailability of oestrogens beyond the host’s intrinsic capacity. In humans, some early studies found that antibiotic treatment increased the presence of conjugated oestrogens in faeces, suggesting that antibiotics could suppress microbially mediated deconjugation in the gut, an effect observed in both females (Adlercreutz et al., 1975; Martin et al., 1975) and males (Hämäläinen, Korpela, & Adlercreutz, 1987). While of course these results could be attributable to off-target effects of antibiotics, the close association between the microbiome and host oestrogens does suggest that antibiotics may exert a potent effect on the bioavailability of oestrogens via loss of microbial enzymes necessary for the deconjugation of oestrogen molecules.
## Progestogens
Researchers have also recently detected microbiome–progestogen associations. Like oestrogens, progestogens are ‘female’ steroid hormones that contribute to female reproductive processes (Colvin & Abdullatif, 2013). However, like oestrogens, progestogens are also present in males in small quantities and contribute to male biology. The primary progestogen is progesterone, and others include 16α-hydroxyprogesterone, 3β-dihydroprogesterone, and 5α-dihydroprogesterone. Progesterone is involved in female reproduction and related processes, including regulation of the menstrual cycle, maintenance of pregnancy, inhibition of milk production during pregnancy, and breast development. At the behavioural level, it has been suggested that progesterone is involved in human social bonding and affiliation (Brown et al., 2009; Fleischman, Fessler, & Cholakians, 2015; Gangestad & Grebe, 2017; Schultheiss, Wirth, & Stanton, 2004; Wirth & Schultheiss, 2006).
The host microbiome changes continuously over the course of pregnancy, with particularly large differences between the first and third trimesters (Koren et al., 2012). Notably, recent work has found substantial progesterone-associated changes in the microbiomes of both humans and mice (Nuriel-Ohayon et al., 2019). Specifically, the relative abundance of Bifidobacterium spp. increases in the later stages of pregnancy (Nuriel-Ohayon et al., 2019). Subsequent analysis found that the presence of progesterone sharply elevated the relative abundance of Bifidobacterium spp. both in vivo and in vitro, suggesting that progesterone is able to alter microbial composition (Nuriel-Ohayon et al., 2019).
## Oxytocin
Oxytocin is a neuropeptide hormone produced mainly in the hypothalamus. It plays an evolutionarily conserved role in mating and reproductive behaviour (Garrison et al., 2012; Feldman, 2017). Oxytocin and oxytocin-like molecules perform these functions in animals ranging from invertebrates such as nematodes (Garrison et al., 2012; Elphick, Mirabeau, & Larhammar, 2018) to humans (Feldman, 2017). At the psychological level, oxytocin plays a prominent role in mammalian social attachment, beginning with the mother–infant bond, followed by bonds with other social partners as the mammal matures (Feldman, 2017). There has also been a great deal of interest in the prosocial effects of oxytocin, particularly following the finding that exogenously administered oxytocin promotes interpersonal trust (Kosfeld et al., 2005). However, subsequent studies have failed to replicate this result (Lane et al., 2015; Nave, Camerer, & McCullough, 2015), and at the very least, the oxytocin → trust relationship is not as straightforward as originally anticipated. Moreover, it is currently believed that it is not possible for peripheral oxytocin to cross the blood–brain barrier to exert effects on the central nervous system (Ermisch et al., 1985; Leng & Ludwig, 2016).
A range of studies suggests that the gut microbiome can influence oxytocin signalling (Erdman & Poutahidis, 2016). Antibiotic administration reduces hypothalamic oxytocin levels in mice, alongside depleting microbial populations (Desbonnet et al., 2015). As discussed earlier, the offspring of mice fed high-fat diets during pregnancy display significant social impairments and have fewer hypothalamic oxytocin-expressing neurons, attributable to maternal diet-induced differences in their gut bacteria (Buffington et al., 2016). Moreover, early ingestion of the probiotic Lactobacillus reuteri in the offspring restored the number of oxytocin-expressing neurons in the mice and attenuated the social deficits.
Treatment with Lactobacillus reuteri also increased the number of oxytocin-positive neurons and their oxytocin expression in the paraventricular nucleus of Shank3-knockout mice, which otherwise had fewer such neurons in this brain region (Sgritta et al., 2019). Furthermore, the social benefits of Lactobacillus reuteri are dependent on oxytocinergic signalling in the ventral tegmental area. Specifically, Shank3-knockout mice lacking oxytocin receptors in dopamine neurons did not show improvements in their impaired social behaviour, and also did not show normal levels of long-term potentiation in the ventral tegmental area following social interaction (Sgritta et al., 2019). As such, the capacity of this probiotic to exert effects on host social behaviour appears to depend on the integrity of the oxytocin signalling system. As mentioned earlier, vagotomy abolished the beneficial effects of Lactobacillus reuteri, suggesting that the vagus nerve mediates this relationship. Beyond these central effects, administration of Lactobacillus reuteri to mice has also been found to upregulate plasma oxytocin levels via the vagus nerve (Poutahidis et al., 2013).
Interestingly, Lactobacillus reuteri appears to increase both oxytocin and testosterone signalling, and also suppresses glucocorticoid signalling (Poutahidis et al., 2013; Buffington et al., 2016; Varian et al., 2017). The mechanism by which a single probiotic exerts effects on both neuropeptides and steroids remains unknown, although one possibility is that these effects occur via changes in the immune system. Also, given the involvement of the hypothalamus in these signalling pathways, and since the gut microbiome has been shown to affect the hypothalamus (Buffington et al., 2016), it is plausible that Lactobacillus reuteri produces these effects by modulating hypothalamic function.
## Arginine vasopressin
Arginine vasopressin (vasopressin) is a neuropeptide hormone that is structurally similar to oxytocin, and, like oxytocin, is produced mainly in the hypothalamus. Amongst the primary physiological functions of vasopressin are the control and regulation of the organism’s water balance and cardiovascular function (Share, 1988; Nielsen et al., 1995). Like oxytocin, systemic vasopressin is unable to cross the blood–brain barrier. At the psychological level, vasopressin has been implicated in maternal behaviour. For example, in rodents, vasopressin promotes maternal aggression towards intruders (Bosch & Neumann, 2010). Central vasopressin has also been found to be positively associated with sociability in monkeys, with some evidence of a similar association in humans as well (Parker et al., 2018). *In* general, the microbiome–vasopressin relationship has not received as much attention as the microbiome–oxytocin relationship. However, some interesting patterns have been observed that suggest this may be a worthwhile area of investigation. For instance, the administration of antibiotics to mice reduces hypothalamic vasopressin expression (Desbonnet et al., 2015). There is also recent, intriguing evidence from rats that deletion of the *Avp* gene (which controls vasopressin expression in the brain) leads to sex-specific changes in the composition of the microbiome, including an increase in Lactobacillus spp. in males (Fields et al., 2018).
## Serotonin
The indolamine serotonin (5-hydroxytryptamine) is a metabolite of the essential amino acid tryptophan. Serotonin regulates a variety of physiological processes in the host, including normal gastrointestinal, cardiovascular, and excretory functions (Berger, Gray, & Roth, 2009). In terms of host psychological processes, the serotonergic system is implicated in emotion regulation, social cognition, and social interaction (Young & Leyton, 2002; Canli & Lesch, 2007). Serotonergic signalling is also implicated in social dominance and aggression across the animal kingdom (Nelson & Chiavegatto, 2001). Serotonergic dysfunction has also been linked to psychiatric disorders such as depression (Owens & Nemeroff, 1994). However, researchers are discovering that the aetiology of depression extends well beyond serotonergic disruption, and there is increasing evidence that clinical depression is a highly heterogeneous disorder with multiple, intertwined aetiologies linked to alterations in brain plasticity and monoamine functions in general, as well as disturbances in the immune system and the HPA axis (Miller & Raison, 2016; Pariante, 2017; Levy et al., 2018).
There has been a great deal of interest in the association between the microbiome, tryptophan metabolism, and the regulation of host serotonergic signalling (O’Mahony et al., 2015). Compared to normally colonised mice, male germ-free mice were found to have substantially higher levels of plasma tryptophan, but substantially lower levels of plasma serotonin, suggesting that the absence of gut microbes impairs the peripheral conversion of tryptophan into serotonin (Wikoff et al., 2009; Clarke et al., 2013). Microbial transfer via faecal transplants from normally colonised mice to germ-free mice is sufficient to increase peripheral serotonin concentrations within a few days of colonisation (Hata et al., 2017). On the other hand, male (but not female) germ-free mice also have significantly increased concentrations of serotonin in the hippocampus (Clarke et al., 2013) and increased serotonin turnover in the striatum (Diaz Heijtz et al., 2011). This gives rise to an important conceptual puzzle: why – and through what mechanism – does the absence of gut bacteria increase central serotonin levels (Clarke et al., 2013) and its turnover (Diaz Heijtz et al., 2011), but decrease peripheral serotonin levels (Clarke et al., 2013; Wikoff et al., 2009)? Furthermore, are these changes related to one another, and do they occur via a compensatory mechanism? Two of these studies (Clarke et al., 2013; Wikoff et al., 2009) used Swiss Webster mice, and therefore species-level variations in genetic background are less likely to account for such differences between peripheral and central serotonin levels. One possibility relates to the potential role of serotonin in meeting the brain’s energy demands. In particular, researchers have recently hypothesised that one of the primary functions of serotonin in the brain is to support and regulate its energetic and metabolic requirements, including in the hippocampus (Andrews et al., 2015). If this is correct, then the enhanced hippocampal serotonin concentrations in germ-free mice (Clarke et al., 2013) might be attributable to central-level differences in energy demands between germ-free and normally colonised mice. One of the key roles of the microbiome is the regulation of host peripheral metabolism (Turnbaugh et al., 2006; Nicholson et al., 2012; Perry et al., 2016), and it could be plausible that the microbiome also influences metabolism in the central nervous system. At the very least, this hypothesis warrants experimental investigation.
The mechanisms underlying serotonin differences in germ-free and normally colonised animals are still under investigation. One possibility is that the microbes themselves generate a considerable quantity of serotonin. Indeed, bacteria including species of Candida, Enterococcus, Escherichia, and *Streptococcus are* capable of secreting serotonin directly (Lyte, 2011), although it is unknown whether, and to what extent, this occurs in the gut environment (Johnson & Foster, 2018). A recent investigation found that indigenous sporeforming bacteria (and particularly those from the genus Clostridium) can regulate the host’s gut-based serotonin biosynthesis (Yano et al., 2015). These bacteria produce metabolites such as SCFAs that promote serotonin production by the host’s enterochromaffin cells (Reigstad et al., 2015; Yano et al., 2015). Thus, it may be that the majority of bacterial contributions to host serotonin arise from bacterially derived metabolites regulating the production of serotonin by the host’s enterochromaffin cells, rather than from serotonin directly produced by the bacteria themselves. Furthermore, recent research also suggests that much of the luminal serotonin in germ-free mice is conjugated with glucuronide and is rendered biologically inactive (Hata et al., 2017). Bacterially derived enzymes deconjugate glucuronidated serotonin molecules, increasing the total amount of bioavailable serotonin in the lumen (Hata et al., 2017).
Importantly, as systemic serotonin is thought to be unable to cross the blood–brain barrier, it is currently unclear whether microbially derived peripheral serotonin is able to affect the activity of the central nervous system directly. *The* general implications of free luminal serotonin are presently unclear. However, a proportion of this serotonin may be used in bacterial metabolism. In particular, there is evidence that serotonin may promote the growth of some bacteria (Roshchina, 2016). If serotonin is able to stimulate the growth of particular bacterial taxa, then deconjugating serotonin molecules in the lumen into free serotonin might directly enhance the fitness of these bacteria by enhancing their growth and reproduction. A recent study has shown that enhancing serotonin levels using pharmacological and genetic manipulations substantially increases the abundance of Clostridia spp. and Turicibacter spp., suggesting that some gut bacteria are able to detect and respond to serotonin as a growth factor (Fung et al., 2019). In particular, *Turicibacter sanguinis* was found to possess genetic adaptations that enable serotonin importation. This helps the bacterium gain an advantage over other bacteria in colonising the gastrointestinal tracts of mice supplemented with serotonin. On the other hand, the drug fluoxetine (a selective serotonin reuptake inhibitor frequently used as an antidepressant) reduces the capacity of *Turicibacter sanguinis* to utilise host serotonin by inhibiting its serotonin-importer system, thereby reducing its competitive advantage. Interestingly, when mice were monoassociated with Turicibacter sanguinis, supplementation with serotonin did not enhance bacterial growth. This suggests that serotonin supplementation is only advantageous when *Turicibacter sanguinis* faces competition from other microbes in colonising the gut (Fung et al., 2019).
## Dopamine
Gut microbes have also been noted to influence the concentrations of the endogenous catecholamine dopamine (3,4-dihydroxyphenethylamine), which is synthesised from its precursor, the amino acid levodopa (l-3,-4-dihydroxyphenylalanine), which is itself synthesised from tyrosine (4-hydroxyphenylalanine) (Nagatsu, Levitt, & Udenfriend, 1964; Shiman, Akino, & Kaufman, 1971). Levodopa (which is also used pharmacologically to treat Parkinson’s disease) occurs naturally in the body. It is able to cross the blood–brain barrier, where it is converted into dopamine. Peripheral dopamine, however, cannot cross the blood–brain barrier.
The physiological roles of dopamine include motor control and coordination (Howe & Dombeck, 2016), as well as the regulation of cardiovascular and renal function (Goldberg, 1972). At the psychological level, dopamine is best known for its role in the reward system, where it plays a fundamental part in reward learning and prediction (Schultz, 2002). Central dopaminergic signalling in brain regions such as the striatum and ventral tegmental area is also thought to facilitate social bonding by enhancing the reward value of social interaction (Feldman, 2017), and some evidence from dopamine receptor genetics suggests a role for dopamine in the size and maintenance of human social networks (Pearce et al., 2017).
Comparisons between germ-free and normally colonised mice have found that germ-free specimens show reduced levels of luminal dopamine (Velagapudi et al., 2010; Asano et al., 2012). Moreover, a substantial proportion of luminal dopamine in germ-free mice was conjugated with glucuronide or sulphate and biologically inactive, whereas the reverse pattern was observed in normally colonised mice (Asano et al., 2012). Both conventionalisation with a normal microbiome and colonisation with Clostridium spp. increased levels of dopamine and β-glucuronidase. Moreover, comparisons between mice colonised with *Escherichia coli* (capable of secreting β-glucuronidase) and mice colonised with an *Escherichia coli* mutant (incapable of producing β-glucuronidase) revealed that luminal dopamine in mice colonised with the mutant strain (in which β-glucuronidase production was suppressed) was conjugated and biologically inactive, suggesting a causal role for bacterially derived β-glucuronidase activity in regulating luminal dopamine availability.
Another example of microbial metabolism influencing the availability of dopamine in the gut is a recent study reporting that gut bacteria metabolise exogenously administered levodopa into dopamine, and then convert dopamine into m-tyramine (Maini Rekdal et al., 2019). Specifically, *Enterococcus faecalis* converts levodopa into dopamine via a decarboxylation reaction, and *Eggerthella lenta* converts dopamine into m-tyramine via a dihydroxylation reaction (Maini Rekdal et al., 2019). The implications of this phenomenon for central dopamine levels are presently unclear. It may be that gut microbes such as *Enterococcus faecalis* convert naturally occurring levodopa to dopamine which might therefore lead to reduced central dopamine availability in the brain (since dopamine cannot cross the blood–brain barrier, unlike its precursor levodopa). Further research is necessary to examine whether the natural conversion of levodopa to dopamine by gut bacteria has a significant effect on dopamine levels in the brain.
Researchers have also observed direct relationships between the microbiome and central dopamine activity. For instance, studies have found an increased concentration of brain dopamine in germ-free mice (Matsumoto et al., 2013; Nishino et al., 2013). Compared with normally colonised conspecifics, germ-free mice also showed elevated hippocampal expression of mRNA encoding D1, a key dopamine receptor (Diaz Heijtz et al., 2011), as well as elevated levels of striatal dopaminergic turnover (Diaz Heijtz et al., 2011). However, enhanced central dopamine turnover has not consistently been observed. For instance, one study comparing germ-free and normally colonised rats found lower dopaminergic turnover in the frontal cortex, hippocampus, and striatum in germ-free specimens (Crumeyrolle-Arias et al., 2014).
Clearly, while additional studies are needed to clarify the role of the microbiome in dopaminergic turnover, the available data at least suggest that the microbiome can influence central dopaminergic signalling. Moreover, Sgritta et al. [ 2019] found that the probiotic-induced rescue of social deficits in autistic-type mice required the presence of oxytocin receptors on dopamine neurons in the ventral tegmental area. Thus, alongside oxytocinergic signalling, dopaminergic signalling also appears to be necessary for the benefits of Lactobacillus reuteri.
## MICROBIAL REGULATION OF GENE EXPRESSION AND EPIGENETIC PROCESSES IN THE SOCIAL BRAIN
There is growing interest in microbial contributions to social behaviour at the level of host gene expression and epigenetic mechanisms (Stilling et al., 2014). In particular, if the microbiome is affecting brain morphology and function and hormonal and neurotransmitter signalling, then it can be expected that the microbiome also influences host gene expression. In this regard, research is now revealing that the microbiome can modulate gene activity relevant to sociality.
## Neuroanatomical distribution of gene expression
Relative to normally colonised mice, germ-free mice show extensive dysregulation in networks of micro-ribonucleic acids (miRNAs) in the amygdala and prefrontal cortex, both key regions of the social brain (Hoban et al., 2017). Some, but not all, of these aberrations were attenuated by colonisation with a normal microbiome (Hoban et al., 2017). Furthermore, gene expression profiling in the amygdalas of germ-free mice (compared to normally colonised mice) revealed elevated expression of transcription factor genes (Fos, Egr2, Nr4a1) and the genes Arc and Homer1 that are indices of increased BDNF signalling and neuronal activation, respectively (Stilling et al., 2015).
The absence of microbes is associated with other pre- and post-transcriptional events including differential exon splicing and editing of mRNAs that ultimately sculpt changes in neuronal function (Hoban et al., 2017, 2018; Stilling et al., 2018). These studies found that the expression of transcription factor genes and genes involved in neuronal activity were elevated in the amygdalas of both germ-free and normally colonised mice that had recently engaged in social interactions, suggesting that social interaction rapidly affects amygdalar gene expression. Importantly, however, the amygdalar neurons of germ-free animals displayed higher rates of alternative splicing (Stilling et al., 2018), a process that expands the number of proteins that could otherwise be encoded by a given number of genes, ultimately increasing the range of biological functions those genes can perform. Furthermore, in germ-free mice, there is enhanced expression of genes regulating cholinergic and dopaminergic neurotransmission, which are associated with amygdalar learning (Hoban et al., 2018).
These changes in splicing and expression observed in germ-free animals likely represent aberrant alterations in host genetics as a result of the absence of microorganisms. For example, we might speculate that these variations in splicing and gene expression reflect compensatory processes initiated by the host, such that functions which would otherwise be supported by microbiome-related activity can be fulfilled in the absence of microbes. Another possibility is that this increased alternative splicing is maladaptive, and is kept in check by microbiome-related processes.
Overall, germ-free mice show deficits in social development (Desbonnet et al., 2014; Buffington et al., 2016; Stilling et al., 2018) and there is evidence of an association between aberrant gene expression and neuronal function in the amygdalas of germ-free animals in response to social challenges (Stilling et al., 2018). For instance, an enrichment of RNA-splicing genes – but not those involved in mitogen-activated protein kinase (MAPK) cell signalling pathways – was observed in germ-free mice following social interaction (Stilling et al., 2018).
Based on these observations, it is reasonable to speculate that perturbations of the microbiome in normally colonised animals may impact their social behaviour via changes in gene and protein expression. For example, antibiotic-induced dysbiosis in mice reduced both social recognition and hippocampal BDNF levels, but elevated the expression of the BDNF receptor, tropomyosin receptor kinase B (TrkB) (Guida et al., 2018). Furthermore, ingestion of the probiotic Lactobacillus casei normalised both central BDNF levels and social recognition memory, although TrkB densities remained elevated (Guida et al., 2018). However, it should be noted that the design of this study did not permit examination of the possibility that TrkB density simply requires longer to return to normal. The capacity of this single-strain probiotic to rescue deficits in social recognition from antibiotic-induced dysbiosis suggests the possibility that discrete changes in a complex microbial community may be able to affect brain function, although of course these effects may occur via other pathways, such as modulation of the immune system.
## Epigenetic effects of glucocorticoids
It remains unclear what causes changes in central gene expression. One possibility is that the peripheral neuroendocrine stress response, mediated by the HPA axis, is a key link between gut microbes and host behaviour (Cryan & Dinan, 2012; Foster & McVey Neufeld, 2013; de Weerth, 2017). Glucocorticoids (which are elevated in circulation during stressful events) enter the brain and bind to glucocorticoid receptors which are abundantly expressed in the hippocampus and amygdala. Within the cell nucleus, the ligand-bound glucocorticoid receptors can affect transcription by direct high-affinity binding to glucocorticoid response elements found either in the promoters or the intragenic regions of glucocorticoid target genes (Tan & Wahli, 2016). Therefore, a heightened stress response is likely to result in changes in activity of hippocampal and amygdalar neurocircuitry, with subsequent changes in social behaviour. Consistent with this supposition, the activation of hippocampal glucocorticoid receptors has been shown to enhance contextual fear memory via elevation of BDNF signalling (Revest et al., 2014). This result corroborates the observation that germ-free mice have increased amygdalar BDNF levels, which is accompanied by an exaggerated stress response (Sudo et al., 2004). Of course, the mechanisms via which the microbiome can regulate HPA-axis activity are still being elucidated, and potentially include microbiome interactions with the gut immune system and the enteric nervous system (e.g. through direct contact or neurotransmitters secreted by bacteria, as described earlier in Section V).
## Epigenetic effects of microbial metabolites
Some of the microbial effects on host physiology may also be orchestrated by the metabolites that the microbes generate from breaking down complex dietary carbohydrates in the host’s diet. For example, the fermentation of indigestible carbohydrates by the gut microbiome produces SCFAs (Koh et al., 2016; Sarma et al., 2017), which can then enter systemic circulation and modulate sympathetic nervous system activity (Kimura et al., 2011). *Microbially* generated SCFAs include acetate, butyrate, and propionate. Of these, most of the butyrate is readily absorbed by epithelial cells of the colon where it is utilised as an energy source, and promotes anti-inflammatory responses (Koh et al., 2016). While propionate and a proportion of the acetate bind to specific receptors in the gut and initiate the release of gut hormones (Koh et al., 2016), the majority of acetate is taken up into the vascular system and distributed throughout the organs, including the brain (Koh et al., 2016; Perry et al., 2016). Glial cells can use acetate as a source of energy, but more importantly, this SCFA can exert epigenetic effects through the inhibition of histone deacetylases (Rae et al., 2012). That is, acetate promotes the process that allows the transcription of genes to occur. More specifically, acetate is an inhibitor of histone deacetylases that remove acetate groups from genomic DNA and hinder the dissociation of the doublestranded molecule that must occur prior to gene transcription (Kasubuchi et al., 2015). Thus, in general, the inhibition of histone deacetylases increases gene expression. Like acetate, butyrate is also a potent histone deacetylase inhibitor, and accordingly may impact gene expression in the gut since this is where it is largely absorbed (Koh et al., 2016). However, since butyrate can cross the blood–brain barrier, its epigenetic effects may also extend to the brain.
In rats, both the oral administration of acetate and the intake of bifidogenic oligosaccharides (prebiotics) can increase the circulating concentrations of acetate, as well as the expression of genes encoding central glutamate N-methyl-D-aspartate (NMDA) receptor subunits and BDNF (Savignac et al., 2013; Gronier et al., 2018). Prebiotic feeding has also been shown to enhance the function of brain NMDA receptors, and improve cognitive flexibility in rats (Gronier et al., 2018). These findings are consistent with an earlier study showing that oral acetate supplementation rescued impairments in NMDA receptor function and, importantly, was associated with the inhibition of histone deacetylase activity (Singh et al., 2016). Therefore, one hypothesis is that acetate is a mediator of the procognitive effects of prebiotics, although this has yet to be formally tested. With regard to social behaviour, one investigation demonstrated that the inhibition of histone deacetylase activity in Syrian hamsters exacerbated behavioural responses to social stress, suggesting that epigenetic gene silencing may be favourable for the maintenance of normal social interactions (McCann et al., 2017). However, the inhibitor in this instance was sodium butyrate that was systemically or centrally administered at pharmacological doses (McCann et al., 2017), and therefore was not representative of the quantity and anatomical distribution of this SCFA when it is derived from the gut microbiome.
These findings collectively suggest the effects of the microbiome on host RNA biology and post-transcriptional processes, and provide evidence of potential microbial contributions to the genetic basis of social behaviour.
## THE MICROBIOME AND SOCIAL OLFACTORY SIGNALS
The olfactory system plays an important role in conveying and detecting social information across the animal kingdom (Steiger, Schmitt, & Schaefer, 2011). The olfactory system participates in a variety of social processes, including territorial marking, discriminating between social groups, kin recognition, and mate detection and attraction. As just one example, in spotted hyaenas (Crocuta crocuta) a subcaudal gland secretion known as hyaena ‘paste’ relays a range of social information used for intra-specific signalling and communication (Drea et al., 2002a, 2002b; Burgener et al., 2009), including as a marker of social rank (Burgener et al., 2009).
## The fermentation hypothesis
The fermentation hypothesis proposes that olfactory signals are the products of bacterial metabolism which the host exploits for chemical communication (Albone et al., 1974; Albone & Perry, 1976). These bacterially produced odourants may be generated in dedicated scent glands, and can be present in faeces, urine, or other secretions. Researchers are now finding, consistent with the fermentation hypothesis, that bacterial metabolism generates a range of odourants which communicate important social information, including sex, kinship, fertility, lactation status, health, and group membership via the olfactory system (Lizé, McKay, & Lewis, 2013; Ezenwa & Williams, 2014; Archie & Tung, 2015; Vuong et al., 2017; Bienenstock, Kunze, & Forsythe, 2018; Carthey, Gillings, & Blumstein, 2018).
## A microbiome–olfaction–behaviour pathway?
Researchers have recently suggested that the microbiome–gut–brain axis may entail an underappreciated olfactory component – in other words, a microbiome–olfaction–behaviour pathway (Bienenstock et al., 2018). This olfactory component comprises the system of olfactory receptors and odourants, the molecules that bind to them. Olfactory receptors are widely distributed in the body. They are encoded by extensive multigene families, and are evolutionarily conserved across the animal kingdom. For example, the olfactory receptor multigene family comprises approximately 100 genes in catfish (Ngai et al., 1993), over 900 genes in mice (Godfrey, Malnic, & Buck, 2004), and over 300 genes in humans (Malnic, Godfrey, & Buck, 2004). In addition to the classical odour receptors, two new types of receptors have also been found to be involved in olfaction: trace-amine associated receptors and formyl peptide receptors (Bienenstock et al., 2018).
Importantly, host-associated microbes are capable of generating a number of odourants that bind to these receptors (i.e. classical odourant receptors, trace-amine associated receptors, and formyl peptide receptors), thereby modulating host tissue (Bienenstock et al., 2018). As such, some of the behavioural effects of the microbiome may be mediated by this broadly expressed system of olfactory receptors (Bienenstock et al., 2018). This microbiome–olfaction coupling may make larger contributions to host social behaviour than currently appreciated.
## Insects
A relatively well-established body of research demonstrates that host-associated bacteria influence chemosignalling and communication between conspecifics by modulating odour profiles in insects. Bacterial effects on individual or colony-level chemical profiles, with subsequent effects on behaviour, have been observed across a range of insects, including ants (Acromyrmex echinatior; Dosmann, Bahet, & Gordon, 2016), cockroaches (Blattella germanica; Wada-Katsumata et al., 2015), fruit flies (Drosophila melanogaster; Sharon et al., 2010; Venu et al., 2014), locusts (Schistocerca gregaria; Dillon, Vennard, & Charnley, 2000, 2002), and termites (Hodotermes mossambicus; Minkley et al., 2006).
Bacteria facilitate the production of guaiacol, an insect aggregation pheromone that supports swarming behaviour, as observed in locusts (Schistocerca gregaria) (Dillon et al., 2000). Interestingly, more recent research suggests that swarming behaviour in the same locust species is also mediated by serotonin, which plays a role in the behavioural gregarisation that precedes swarming (Anstey et al., 2009). It remains unknown whether the bacteria-associated, guaiacol-mediated pathway underlying swarming is related to the serotonin-mediated pathway underlying gregarisation. Indirect evidence for such a connection derives from studies that investigate the effects of infection with the fungus Paranosema locustae, which both inhibits locust swarming behaviour via acidification of the hindgut and supresses seroton-inproducing bacteria (Shi et al., 2014). In this regard, investigating the possibility of a microbiome–guaiacol–serotonin system supporting gregarisation and swarming in locusts would be particularly interesting (Münger et al., 2018).
*Microbially* generated odours may also provide cues to recognise colony members. For instance, experimental alteration or disruption of the external microbiome of harvester ants (Pogonomyrmex barbatus; Dosmann et al., 2016) and the gut microbiome of lower termites (Reticulitermes speratus; Matsuura, 2001) interferes with nestmate recognition, leading to rejection of colony members. The microbiome may also contribute to insect reproductive behaviour. For example, it has been suggested that fruit flies (Drosophila melanogaster) show mating preferences for conspecifics with similar microbial compositions, a social cue attributed to *Lactobacillus plantarum* (Najarro et al., 2015; Sharon et al., 2010; but see Leftwich et al., 2017; see also Rosenberg et al., 2018).
## Non-human mammals
Host-associated microbial populations in the gut or other dedicated scent-producing structures also contribute to mammalian social olfaction. Early observations of this phenomenon were made in the anal scent pouches of mongooses (Herpestes auropunctatus) (Gorman, Nedwell, & Smith, 1974; Gorman, 1976) and red foxes (Vulpes vulpes) (Albone et al. 1974; Albone & Perry, 1976). More recent work has examined bacterially mediated social olfaction in hyaenas.
The compounds in the scent gland secretions (paste) of spotted hyaenas contain bacterially derived odourants that are associated with the signalling of important social information (Theis et al., 2013), including host sex, immigration status in males, and pregnancy and lactation status in females (Theis et al., 2013). Furthermore, social groups of hyaenas are distinguishable on the basis of these bacterially generated odour profiles (Theis, Schmidt, & Holekamp, 2012; Theis et al., 2013). Other mammals in which microbial composition appears to correlate with social olfaction include badgers (Meles meles) (Sin et al., 2012; Noonan et al., 2019), meerkats (Suricata suricatta) (Leclaire, Nielsen, & Drea, 2014; Leclaire et al., 2017), and elephants (*Loxodonta africana* and Elephas maximus) (Goodwin et al., 2012).
Experimental efforts in rodents have begun elucidating the odourant molecules that are sensitive to the presence of microbes. For instance, the murine microbiome generates trimethylamine, which acts as an attractive olfactory cue. Antibiotic treatment reduces trimethylamine production, causing mice to become less sexually attractive to conspecifics (Li et al., 2013). Moreover, the urine of germ-free rats appears to lack biochemicals that are involved in individual identification based on odour discrimination (Singh et al., 1990), although this reduction in microbially derived odourants may not be sufficient to inhibit reproductive behaviour consistently (Nielsen et al., 2019).
## Humans
At present, it is not known whether bacteria affect human social perception or social interaction via modulation of social olfaction. There is some evidence that the skin microbiome may contribute to human odour profiles, but overall, the association is weak at best and appears to be very sensitive to behaviours (e.g. bathing, deodorant use) and external factors (Xu et al., 2007). There is also evidence that the human skin microbiome produces compounds that act as attractants for mosquitoes, including the malaria mosquito *Anopheles gambiae* sensu stricto (Verhulst et al., 2010a, 2010b). These mosquitoes rely on odour profiles to target potential hosts, and both the composition of the skin microbiome and the compounds it produces can influence odour profiles and therefore the host’s attractiveness to mosquitoes (Verhulst et al., 2010a, 2010b, 2011). Overall, these results suggest that microbes affect human odour profiles. The finding that microbes contribute to human odour has implications for host health and infection, but it cannot necessarily be inferred that this microbial influence on odour profiles extends to human social perception or social interaction. While some studies do suggest a role for pheromones in human social interaction (Gildersleeve et al., 2012; Frumin et al., 2015), further research is required to determine the existence and effects of human pheromones (Wyatt, 2015).
## Psychobiotic studies
Compared to the relatively clearer psychobiotic effects on rodent behaviour, human research has not found consistent psychological benefits of probiotic consumption (Kelly et al., 2017). However, there are some important parallels with rodent findings. For example, consumption of psychobiotics lowers cortisol levels (Messaoudi et al., 2011; Schmidt et al., 2015; Allen et al., 2016) and is accompanied by self-reported reductions in negative mood (Messaoudi et al., 2011; Steenbergen et al., 2015). In a recent double-blind, randomised psychobiotic administration experiment, *Bifidobacterium longum* 1714 consumed over a four-week period was found to affect brain activity associated with the psychological stress induced by social exclusion, as measured by magnetoencephalography (Wang et al., 2019). Relative to participants treated with a placebo, those treated with the psychobiotic displayed increased resting-state θ power in the frontal and cingulate cortices, and reduced β2 power in the hippocampus, the fusiform gyrus, the temporal cortex, and the cerebellum (Wang et al., 2019). In response to the social task, participants treated with the psychobiotic (relative to the placebo) also showed increased power in the θ and α bands in several brain regions, including the inferior, medial, and superior frontal cortices, the anterior and middle cingulate cortices, and the supramarginal gyrus (Wang et al., 2019). While these results cannot necessarily be linked to a particular psychological state or experience, they do suggest that psychobiotics may be capable of modulating brain activity both at rest and in response to social experiences. However, as with most other human studies, the sample sizes were relatively small.
We have hypothesised that one mechanism underlying some of the psychological effects of psychobiotics may be a generalised decrease in social–emotional reactivity (Sarkar et al., 2018). For example, consuming probiotics has been found to reduce activity in a brain network associated with processing emotional information in response to facial stimuli (including the amygdala) (Tillisch et al., 2013), and another study showed that probiotic consumption reduced psychological reactivity to sadness (Steenbergen et al., 2015). There is also evidence that prebiotics can reduce waking cortisol levels and emotional attention to negative stimuli (Schmidt et al., 2015).
## Microbiome–depression associations
Disorders of emotion, such as depression, often exert profound effects on normal human social behaviour, and are characterised by a loss of interest in pleasurable activities (including social interactions) as well as social withdrawal and isolation. There is much interest in characterising emotional disorders in terms of consistent bacterial signatures. For instance, depression has recently been associated with changes in the relative abundance of numerous bacterial taxa. These include increases in the Firmicutes phylum, decreases in the Bacteroides phylum, and increases in the genera Prevotella, Klebsiella, *Streptococcus and* Clostridium (Lin et al., 2017). Others have found increases in the Enterobacteriaceae family and the *Alistipes genus* and decreases in the *Faecalibacterium genus* in depressed individuals relative to healthy controls (Jiang et al., 2015), or order-level increases in Bacteriodales and family-level increases in Lachnospiraceae (Naseribafrouei et al., 2014). Some studies have found increases in Actinobacteria and Proteobacteria in depressed individuals (Jiang et al., 2015; Zheng et al., 2016).
Comparing studies reveals some contrasting results, with studies reporting evidence of depressed individuals showing both higher (Naseribafrouei et al., 2014; Jiang et al., 2015) and lower (Zheng et al., 2016) levels of Bacteroidetes. In some cases, the abundance of particular bacterial taxa correlates with the severity of depression. For instance, a negative association was found between the relative abundance of Faecalibacterium spp. and the severity of depressive symptoms (Jiang et al., 2015). Following up on the need for larger studies, a recent metagenomic survey in two large European samples reported evidence that depression is associated with reduced levels of Coprococcus spp. and Dialister spp., even after controlling for antidepressant treatment (Valles-Colomer et al., 2019).
The psychological implications of variation in particular bacterial communities for emotional disorders remain rather unclear. At present, it is largely unknown how different bacterial communities might contribute to depression, although perhaps some cautious inferences can be drawn from specific functions of bacteria that have been examined in other contexts. For example, Alistipes spp. may be linked to increased inflammation (Naseribafrouei et al., 2014), which is often a prominent physiological marker of depression (Miller, Maletic, & Raison, 2009). Other recent research has found that several human-associated bacterial genera produce GABA or use it as a nutrient (Strandwitz et al., 2019). For instance, growth of the bacterial isolate KLE1738 appears to depend on GABA as a nutrient, which is produced by members of the *Bacteroides genus* under pH conditions similar to the human gut (Strandwitz et al., 2019). Moreover, in a small sample of clinically depressed individuals, the relative abundance of the genus Bacteroides was negatively correlated with brain signatures of depression (Strandwitz et al., 2019). Specifically, reduced Bacteroides abundance was linked to stronger functional connectivity between the left dorsolateral prefrontal cortex and the default mode network (Strandwitz et al., 2019), and such increased functional connectivity has previously been associated with depression.
## Infancy and early development
There is strong interest in the changes in microbiome composition during infancy and early development. The mammalian neonate’s microbiome is shaped by numerous environmental influences, one of the first of which is breast-milk (Allen-Blevins, Sela, & Hinde, 2015). Breastmilk provides, for instance, an important supply of prebiotic glycans (human milk oligosaccharides) to the infant gut (Charbonneau et al., 2016). In infants and young children, microbial composition has been shown to correlate with temperament and emotional regulation (Christian et al., 2015; Aatsinki et al., 2019), as well as cognitive development and linguistic skill (Carlson et al., 2018), which are beneficial for social interaction.
One particularly important area in microbiome research that may be relevant to the social–emotional development of humans is the effect of early antibiotic exposure, given the rising prevalence of antibiotic use (Blaser, 2016; Sonnenburg & Sonnenburg, 2019), especially among young children (Cox & Blaser, 2015). For example, murine studies have found that exposure to low doses of antibiotics during infancy can permanently alter the host’s gut microbiome and endocrine physiology (Cho et al., 2012). In addition, antibiotic treatment in young mice has been found to reduce the expression of neuroreceptors implicated in social and emotional behaviour, namely μ-opioid, oxytocin, and vasopressin receptors (K.V.-A. Johnson & P.W.J. Burnet, in preparation). In humans, antibiotic administration in early life has been associated with greater incidence of depressive symptoms in later childhood (Slykerman et al., 2015). Similarly, antibiotics administered in early life were associated with negative outcomes on measures of cognitive function even at 11 years of age, after adjusting for other variables such as probiotic exposure and breastfeeding (Slykerman et al., 2019). These studies add inductive support to the hypothesis that a healthy microbiome in early life is important for typical social–emotional development in humans, and that antibiotics may disrupt this development. However, these investigations (Slykerman et al., 2015, 2019) did not directly examine antibiotic effects on microbial composition. Therefore, while variations in subsequent psychological outcomes are certainly consistent with the possibility of antibiotic-induced microbial disturbances, they may also arise from modulation of non-microbial targets. For example, they may be associated with the many off-target effects of antibiotics, such as those described in Section II. Alternatively, because antibiotics are administered in response to infection, the observed increase in childhood depression may be attributable to elevated inflammation caused by the infection for which antibiotics were used in the first instance. This is plausible given that inflammation and depression are often robustly associated (Dowlati et al., 2010), and that childhood inflammation can predict future depression in young adults, even several years later (Khandaker et al., 2014). Therefore, the finding that antibiotic exposure predicted depressive symptoms may simply indicate that the infants suffered from illness (and inflammation), rather than the depressive symptoms occurring in response to antibiotic-induced microbial perturbations. Future studies of this type should also incorporate analyses of the microbiome following antibiotic administration, which will provide a clearer understanding of the relationship between the microbial and psychological changes associated with antibiotic use.
The infant microbiome may also be sensitive to prenatal stress during pregnancy. For example, in the first 110 postnatal days, higher levels of maternal prenatal stress were found to be associated with shifts in infant microbial composition that, in turn, were associated with greater levels of inflammation and poorer health outcomes (Zijlmans et al., 2015). These human results appear similar to murine findings. In particular, it is important to keep in mind that maternal prenatal stress alters the vaginal microbiome (Jašarević, Morrison, & Bale, 2016). The vaginal microbiome is important in this context because it is assumed to be the first microbial exposure for mammalian infants, with vaginal microbes colonising the infant gut microbiome during parturition (Dominguez-Bello et al., 2010; Mueller et al., 2015; Sprockett et al., 2018). A stress-associated vaginal microbiome in female mice can be transmitted to the infant during birth, which in turn can impact the developing infant’s health, metabolism, and stress response (Jašarević et al., 2015, 2018). While the actual vertical transmission of stress-associated microbes has not yet been observed in humans, researchers have detected stress-associated microbial changes in the maternal human gut during pregnancy (Hechler et al., 2019). In addition, it has been shown that prenatal stress in pregnant monkeys alters the microbial composition of the infant gut (Bailey, Lubach, & Coe, 2004). Thus, it is plausible that vertical transmission of stress-associated microbes to the infant during vaginal births may occur in humans as well, with neurodevelopmental implications for the infant. Conclusive evidence for this phenomenon would require longitudinal studies of both maternal and infant microbiomes over time, alongside tracking of maternal and infant stress.
## Potential prenatal microbial exposures
In humans (and mammals more generally) the conventional view is that the womb is a germ-free environment. For mammals, the earliest colonisation event is believed to occur during parturition. The mother’s vagina serves as the infant’s first source of microbes, and it is assumed that there is no microbial exposure in utero.
Some researchers have questioned this ‘sterile womb’ hypothesis, suggesting that microbial exposures also occur in utero (Funkhouser & Bordenstein, 2013). Researchers have already identified a microbe → maternal physiology → foetus pathway (i.e. indirect microbe–foetus contact, as studied, for instance, by Kim et al., 2017). However, the possibility of prenatal exposures dramatically changes the nature of microbial influence on the foetus, as it would imply a mother → microbe → foetus pathway (i.e. direct microbe–foetus contact), with the possibility of prenatal microbial colonisation. To this extent, researchers found what appeared to be a unique placental microbiome (Aagaard et al., 2014; Antony et al., 2015), which suggests that microbial populations might be able to reach and colonise the foetus. Others have taken this possibility further by proposing the existence of an amniotic microbiome and a foetal microbiome (Collado et al., 2016; Martinez II et al., 2018). Such prenatal microbial exposures, if they existed, could profoundly alter the current understanding of mammalian developmental biology.
However, these intriguing possibilities are challenged by findings that the presence of microbes may instead result from methodological artefacts such as reagent contamination (Lauder et al., 2016; Perez-Muñoz et al., 2017; Leon et al. ,2018; Lim, Rodriguez, & Holtz, 2018; de Goffau et al., 2019; Theis et al., 2019). Moreover, it is also the case that some potentially pathogenic microbes, such as Streptococcus agalactiae, may indeed be capable of infecting the placenta, with implications for neonatal health (de Goffau et al., 2019). However, the presence of potential pathogens in the placenta cannot be interpreted as evidence that there is also an intrinsic or typical placental microbiome (comprising mutualists, commensals, and pathobionts). Rather, *Streptococcus agalactiae* appears to occur in a minority of cases, and its presence is considered atypical and infectious (de Goffau et al., 2019).
Bushman [2019] provides a useful historical overview of the issues regarding the placental microbiome. At present, the existence of placental, amniotic, or foetal microbiomes, although intriguing, remains controversial and requires rigorous confirmatory evidence.
## Social behaviour and autism
Any effect of the microbiome on human sociality is expected to occur through the mechanisms inferred from studies using mammalian models. In practice, however, testing this association will be extremely challenging, not least because of a lack of adequate animal models of human social development and the necessary ethical limitations of experimentation in humans (although primate models of the kind described in Section III above may provide further insight). There are few observations of microbial effects on human social behaviour, though researchers are particularly interested in the microbiome–autism link, which entails analyses of social behaviour by definition.
At the observational level, a number of studies have attempted to differentiate between autistic and neurotypical children on the basis of microbiome composition. For example, surveys of autistic individuals have found decreased levels of Coprococcus, Prevotella, and Veillonellaceae compared to healthy controls (Kang et al., 2013), and elevations in Clostridium (Finegold et al., 2002; Song, Liu, & Finegold, 2004; Parracho et al., 2005) and Sutterella (Williams et al., 2011; Wang et al., 2013). At the same time, there is also considerable variation and discrepancy in identifying bacterial markers of autism. For example, the ratio of the Firmicutes to *Bacteroidetes phyla* in autistic compared to non-autistic children has been found to be elevated, reduced, or unchanged in different studies (Finegold et al., 2010; Williams et al., 2011; Kang et al., 2013; Son et al., 2015; Forsythe et al., 2016). A recent systematic review of 16 studies did find cross-study evidence of some consistent microbial differences in autistic individuals compared to neurotypical controls, including increased Bacteroides, Clostridium, Desulfovibrio, Lactobacillus, and Proteobacter, and decreased Bifidobacterium, Blautia, Dialister, Prevotella, Veillonella, and Turicibacter (Liu et al., 2019). At present, it is unclear how specific bacterial populations might contribute to the pathophysiology of autism, but researchers are attempting to characterise the physiological roles that these bacteria play (e.g. modulation of inflammation and metabolism). This may then help researchers infer how altered relative abundances in different bacterial populations may be used to characterise at least some of the features of autism. In addition, a recent study employing multiple regression analyses found that certain bacterial genera previously associated with autism are also significantly related to individual differences in sociability in neurotypical adults, and in the same direction as typically found in autistic individuals (Johnson, 2020). It has therefore been suggested that the gut microbiome may contribute to variation in social behaviour in the general population, as well as in autism (Johnson, 2020).
The possibility that autism may be associated with distinct microbial profiles in humans has led to a great deal of interest in modifying the microbiome in an attempt to target autism-associated behaviours. These approaches have yielded varying rates of success. For instance, one probiotic administration study that implemented a double-blind, crossover design failed to detect changes in behaviour in autistic participants, but did observe some differences in microbial composition (Parracho et al., 2010). In another intervention study, researchers found that antibiotic treatment with vancomycin over an eight-week period mitigated behavioural phenotypes in a small sample of autistic children (Sandler et al., 2000). However, these benefits were transient, and were mostly absent within just 2 weeks following vancomycin treatment, and were also absent at long-term follow-ups (Sandler et al., 2000). Furthermore, though some antibiotics may provide short-term benefits (e.g. Sandler et al., 2000), it is likely unfeasible to engage in chronic antibiotic treatment for autism, as there is presently no way of controlling the detrimental effects on the microbiome, as well as the inevitable development of antibiotic resistance that prolonged exposure would induce.
Recently, researchers have adopted a more direct approach to modifying the microbiome: an open-label investigation in a sample of 18 autistic participants investigated the efficacy of faecal transplants in treating gastrointestinal and behavioural symptoms (Kang et al., 2017). In order to deplete as many gut bacteria as possible, participants first underwent broad-spectrum antibiotic treatment using vancomycin for 2 weeks, and were then given a bowel cleanse to remove any remaining bacteria and vancomycin. They were also given an acid suppressant to reduce stomach acidity, which would facilitate survival of orally administered microbes. Following this, participants received faecal transplants from neurotypical donors over several weeks (first at a high initial dose that was delivered orally or rectally, followed by lower maintenance doses administered orally). More precisely, rather than transferring pure faecal matter, donor faeces were used to generate a standardised human gut microbiome, containing over $99\%$ bacteria (Hamilton et al., 2012). At the end of the treatment, participants showed substantial improvement in both gastrointestinal symptoms (e.g. diarrhoea and indigestion), and social deficits and other behavioural features (e.g. repetitive behaviours). Participants were also reported to have gained 1.4 years in developmental age on measures of adaptive behaviours (e.g. communication and living skills). These improvements were apparent 8 weeks following the cessation of treatment. In addition, the researchers detected elevations in Bifidobacterium, Desulfovibrio, and Prevotella which also remained 8 weeks after treatment (Kang et al., 2017). Even more striking were the results of follow-up assessments conducted on these participants 2 years following the completion of the microbial transplant: most of the gastrointestinal and behavioural improvements had persisted through the intervening period, and several of the autism-related symptoms had improved even further (Kang et al., 2019). Moreover, the elevations in Bifidobacterium and Prevotella remained (Kang et al., 2019). By showing that some of the microbial changes were preserved in the recipient gut even 2 years later, these results also extend earlier findings that transferred faecal microbes can survive in the recipient for at least a few months (Li et al., 2016).
These results suggest that the human microbiome may serve as a therapeutic target in the treatment of autism. However, placebo-controlled, double-blind, randomised trials with larger samples are required to better understand the therapeutic potential of microbial transfers. In an earlier article (Sarkar et al., 2018), we suggested that one reason that microbiome transplants may yield greater therapeutic efficacy for autism compared to psychobiotic or antibiotic routes is the difference in scale: the number of microbes that can be introduced into a new host via faecal transfers is many orders of magnitude greater than probiotic consumption. Typical probiotic doses can only introduce a comparatively small number of microbes into the gut, and, as discussed earlier, these are often unsuccessful in colonising the new host. Furthermore, in comparison to probiotic treatment, which typically involves the administration of only one or a few bacterial strains, a faecal transfer can introduce an entire bacterial community into the recipient’s gut.
Of course, while these results (Kang et al., 2017, 2019) are promising, the small initial sample size ($$n = 18$$), the open-label nature of the design, and the lack of a control group all pose substantial challenges to the generalisability and applicability of these results. For example, a small sample size combined with a high degree of variance can often result in an overestimation of the true effect size (Gelman & Carlin, 2014). Thus, it may be that even if this approach yields therapeutic benefits for autistic individuals, the average improvement may be smaller than that observed in this sample.
## Statistical power, replication, and causal evidence
As noted elsewhere (Forsythe et al., 2016; Sarkar et al., 2018), human research on the link between the microbiome and psychological processes is fraught with noise arising from variations in genetics, sex, age, diet, past and present environmental exposures, and use of medicines, all of which can be strictly controlled in laboratory-based rodent studies. While some of the stress- and emotion-related findings in humans resemble rodent findings in several respects, they have much lower statistical power. Moreover, some experiments and meta-analyses have not found consistent psychological effects of probiotic consumption (Romijn & Rucklidge, 2015; Kelly et al., 2017; Romijn et al., 2017). Overall, while many of these findings are promising, they must also be viewed as preliminary, and highlight the need to examine the psychological and social effects of intrinsic microbial variation and exogenous microbial manipulation in larger and more diverse samples.
*In* general, there is limited evidence that the results obtained in one study will be reliably replicated in subsequent studies. This is especially true for the neuroimaging research we have described here. Given the relatively small sample sizes in these reports, alongside the known prevalence of very low statistical power in cognitive neuroscience and brain-imaging research (Button et al., 2013; Szucs & Ioannidis, 2017), it may be that many of the most intriguing microbiome–brain associations in humans are false positives. Thus, until replications have been conducted, it would be prudent to be at most cautiously optimistic about these associations.
It should also be kept in mind that these findings are instances of correlation (in many cases with low statistical power to detect effects). While causal speculation is of course permissible for the generation of hypotheses and design of future studies (particularly in light of evidence from animal research), most of the human findings do not provide any direct evidence of causation.
## Two types of investigations
Investigations of microbiome-associated changes in the host that are relevant to host social behaviour can broadly be placed in one of two categories. The first category consists of studies that analyse the effect of the microbiome (e.g. via germ-free animals or antibiotic administration) on concentrations of molecules implicated in social behaviour, or the structure and function of relevant brain regions. However, social behaviour itself is frequently not measured in these studies. For instance, the pronounced influence of the microbiome on endogenous testosterone concentrations (Markle et al., 2013) was discovered in the context of autoimmunity, and the motivation for the research more broadly was the immunosuppressive – not the social – effect of testosterone. The second category comprises studies that investigate microbial effects on social behaviour, and also examine microbial effects on host physiology in parallel. However, in most cases, the relationships between the behavioural and physiological effects uncovered by these studies are correlational. Thus, it is often rather difficult to interpret the direction of causality, or which biological changes mediate the relationship between the microbiome and assays of host social behaviour.
## Connecting the microbiome to social behaviour
There is limited research that conclusively identifies a biological mediator of the relationship between the microbiome and host sociality, although of course such mediators must exist. While it is likely that the microbiome–sociality relationship is mediated, at least in part, by changes in the anatomy and function of regions in the social brain, or in the biosynthesis and bioavailability of social signalling molecules, there are few studies that have identified such underlying pathways from changes in the microbiome to changes at the behavioural level.
Consider the involvement of the microbiome in autism. In terms of the neurological basis of the microbiome–autism connection, our current knowledge is based on adjacent links in a chain. One link, supplied by microbiology and neuroscience, is the finding that the microbiome influences amygdalar structure and function (Luczynski et al., 2016; Hoban et al., 2018). The second link, from cognitive neuroscience and biological psychiatry, is the finding that variations in amygdala structure and function may be involved in autism (Baron-Cohen et al., 1999, 2000). However, these findings cannot automatically be connected to infer that the amygdala plays a role in the microbiome’s interactions with autism in humans (or even in mice, for that matter). Indeed, microbiome-associated changes in the amygdala may only share minimal overlap with autism-associated changes in the amygdala. Therefore, at best, the current set of findings permits the possibility that a microbiome → amygdala → autism connection may exist and could be subject to future investigation.
Similarly, consider the example of testosterone. One link in the chain is that the microbiome affects testosterone, and the adjacent link, supplied by behavioural endocrinology, is that testosterone affects animal social behaviour. In rodents, this would likely manifest as aggression. But there is as yet no report of a microbiome → testosterone → aggression connection in rodents. Until evidence of such a link is generated, we cannot know whether microbial effects on testosterone actually influence social behaviour. Even when the microbiome does influence testosterone bioavailability, the hormone may not necessarily affect behaviour, since there are many different physiological actions of testosterone, some of which may have no significant behavioural correlates. Furthermore, it is important to keep in mind that all of these molecules (neurotransmitters, steroids, and neuropeptides) perform numerous physiological functions for the host, and variations in their bioavailability cannot be assumed to exert psychological effects on the host.
## Linking microbes to social behaviour via a biological mediator
To further our understanding of the microbiome–sociality connection, explicit investigations are required into how the physiological changes induced by the microbiome influence behaviour. The investigation of the microbiome–autism connection by Kim et al. [ 2017] is an example in this regard. The researchers found that the presence of segmented filamentous bacteria in the maternal gut is necessary for maternal immune activation to trigger autistic-like traits in the offspring. These findings reveal mechanistic connections between the maternal microbiome and offspring social behaviour that are mediated by the action of interleukin-17a secreted by maternal TH17 cells.
Another example is the finding that Lactobacillus reuteri only ameliorates social deficits in mice with functioning oxytocin systems, as conditional deletion of oxytocin receptors in neurons in the ventral tegmental area prevented Lactobacillus reuteri treatment from rescuing social impairments (Sgritta et al., 2019). Therefore, this experiment provides valuable evidence of a bacterium → oxytocin → social behaviour relationship. This type of investigation helps connect bacteria to behaviour via a likely physiological mediator (oxytocin), thereby providing evidence of a causal pathway. Of course, the causal pathway itself will be substantially more complex than this, involving a number of other signalling molecules and components (e.g. the vagus nerve), but at the very least, we can begin to consider how a social signalling molecule plays a role in the microbiome–sociality relationship.
## Other signalling molecules
In this article, we have focussed mainly on a specific set of molecules that have well-documented effects on social behaviour. However, the microbiome regulates a wide range of other molecules and some of these may also influence animal social behaviour. For instance, researchers have recently found evidence suggesting that the proinflammatory cytokine interferon-γ may play a role in social behaviour across the animal kingdom (Filiano et al., 2016). They hypothesise that this link between interferon-γ and social behaviour may have arisen over evolutionary time during the transition to sociality since group living may have favoured a stronger immune response to protect organisms from pathogens transmitted by conspecifics (Filiano et al., 2016). Some probiotics are able to alter the concentrations of interferon-γ, as well as other proinflammatory cytokines (Desbonnet et al., 2008; Donato et al., 2010; Rodrigues et al., 2012). While it is presently unknown whether microbiome-related variations in cytokines can affect social behaviour, the discovery of central lymphatic vessels that could deliver immune molecules to the brain suggests that the connection between the immune system and the brain is more direct that previously thought (Louveau et al., 2015). Given the relationship between the microbiome and the immune system (Round & Mazmanian, 2009; Fung et al., 2017) as well as between the immune system and social behaviour (Eisenberger et al., 2017), it is at least conceivable that some of the microbiome–sociality connections may be mediated by immune molecules.
## UNDERSTANDING THE ORDER AND NATURE OF MICROBIAL EFFECTS ON HOST SOCIALITY
Since the specific mechanisms by which the microbiome influences host physiology remain poorly understood, an important and currently unresolved question is the order of microbial effects on host social development and behaviour (see Fig. 6). Germ-free status has been linked to many physiological impairments, supporting the claim that microbes are essential for normal development. Since animal life evolved in the presence of microbes, it can be expected that a total absence of microbes would alter normal physiology (McFall-Ngai et al., 2013). Indeed, it is difficult to overstate the extent of dysfunction in germ-free animals, including supressed angiogenesis (Stappenbeck, Hooper, & Gordon, 2002), abnormal stress reactions (Sudo et al., 2004), abnormal immune development (Olszak et al., 2012), abnormal development of the enteric nervous system (McVey Neufeld et al., 2013), excessive permeability of the blood–brain barrier (Braniste et al., 2014), and abnormal brain development (Diaz Heijtz et al., 2011; Hoban et al., 2016, 2017; Luczynski et al., 2016).
Despite the many deficits of germ-free animals, it is not known how the numerous microbial effects on host physiological, psychological, and social development are connected to one another. Are microbial contributions to social behaviour purely reflective of what may be considered their core contributions to metabolic and immunological development? Perhaps the changes in one function are in fact caused by changes in another function, and may in turn trigger further changes. Or is it that microbial effects on neurotransmitters, brain circuitry, the endocrine system, and the olfactory system, all of which play key roles in sociality, arise independently of microbial regulation of host metabolism and immunity? This latter proposition is unlikely, but may have some utility as a point of comparison for more probable models. Researchers face significant challenges – and opportunities – in establishing the causal order of bacterial contributions to host physiological development, and this in turn will allow for more precise examination of microbial contributions to social behaviour. For example, to investigate the overall influence of the microbiome on social behaviour, researchers could longitudinally administer a battery of physiological and social tests to germ-free mice colonised at different ages with microbiomes from healthy and socially atypical conspecifics. Researchers could also administer the same physiological and social tests to conventional mice treated with broad-spectrum antibiotics and compare the results to the control group with matched ages. Then the microbiomes of a subset of these antibiotic-treated mice might be ‘restored’ via transplants from socially normal versus socially atypical conspecifics to gain insight into the extent to which physiology and social behaviour are transmissible via the microbiome, given an initially healthy phenotype.
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360. Zijlmans MA, Korpela K, Riksen-Walraven JM, De Vos WM, De Weerth C. **Maternal prenatal stress is associated with the infant intestinal microbiota**. *Psychoneuroendocrinology* (2015) **53** 233-245. PMID: 25638481
361. Zmora N, Zilberman-Schapira G, Suez J, Mor U, Dori-Bachash M, Bashiardes S, Kotler E, Zur M, Regev-Lehavi D, Ben-Zeev Brik R, Federici S, Cohen Y, Linevsky R, Rothschild D, Moor AE. **Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features**. *Cell* (2018) **174** 1388-1405. PMID: 30193112
|
---
title: 'Aerosol generation during coughing: an observational study'
authors:
- E Sanmark
- L A H Oksanen
- N Rantanen
- M Lahelma
- V-J Anttila
- L Lehtonen
- A Hyvärinen
- A Geneid
journal: The Journal of Laryngology and Otology
year: 2023
pmcid: PMC10040286
doi: 10.1017/S0022215122001165
license: CC BY 4.0
---
# Aerosol generation during coughing: an observational study
## Abstract
### Objective
Coronavirus disease 2019 has highlighted the lack of knowledge on aerosol exposure during respiratory activity and aerosol-generating procedures. This study sought to determine the aerosol concentrations generated by coughing to better understand, and to set a standard for studying, aerosols generated in medical procedures.
### Methods
Aerosol exposure during coughing was measured in 37 healthy volunteers in the operating theatre with an optical particle sizer, from 40 cm, 70 cm and 100 cm distances.
### Results
Altogether, 306 volitional and 15 involuntary coughs were measured. No differences between groups were observed.
### Conclusion
Many medical procedures are expected to generate aerosols; it is unclear whether they are higher risk than normal respiratory activity. The measured aerosol exposure can be used to determine the risk for significant aerosol generation during medical procedures. Considerable variation of aerosol generation during cough was observed between individuals, but whether cough was volitional or involuntary made no difference to aerosol production.
## Introduction
Airborne transmission is recognised as an important transmission route of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), as well as for many other respiratory infections.1–4 Aerosol particles are generated during breathing, talking, singing and coughing. They are also presumably generated in higher amounts during certain medical procedures performed in the respiratory tract area, such as otorhinolaryngological and anaesthesiological procedures; these procedures are called aerosol-generating procedures.5–7 As the understanding of humans as aerosol generators during normal respiratory activities has increased, the term ‘aerosol-generating behaviours’ has been proposed to be used alongside ‘aerosol-generating procedures’; abandonment of the classification of medical procedures as aerosol-generating procedures has even been proposed.8 However, the aerosol-generating procedure classification has been widely used in hospitals globally. Data indicate that surgical procedures involving the mucous membranes and respiratory tract have been postponed during the coronavirus disease 2019 (Covid-19) pandemic for fear of infection.9 Thus, variables used for risk assessment in the hospital environment and the area of otorhinolaryngology are still needed.
When assessing the risk of infection, the infectious dose associated with the pathogen, human-related factors such as co-morbidities, the time of exposure and the number of pathogens should be considered.10 However, both the infectious dose of different airborne pathogens and the number of infectious pathogens contained in aerosol particles are still widely unknown, and require further investigation before they can reliably be used as part of risk assessment for airborne diseases.11,12 As all normal human respiratory activities produce aerosols, simply drawing a line between aerosol-generating and non-aerosol-generating procedures is not sufficient for evaluating the risks of different aerosol exposures in healthcare.10 Currently, coughing is assumed to produce a potentially infectious concentration of aerosols, and it has recently been used as a quantitative reference, especially for high-risk aerosol generation during surgery and other medical procedures.13–16 *In a* risk assessment, the amount of aerosol particles that healthcare workers are exposed to is a reasonable observed factor, as the aerosol concentration in an operating theatre dilutes rapidly, especially with highly effective ventilation, which can considerably lower the overall exposure. Therefore, this study aimed to determine the aerosol exposure produced by coughing and thus obtain a scale to compare the aerosol production of other medical procedures in an operating theatre. The comparison of volitional and involuntary coughing allows a broader understanding for cough exposures, as coughing is known to be a heterogeneous activity. The results can be used both to assess the independent risk posed by the cough and, importantly, to produce a reference to evaluate aerosol exposure during potentially aerosol-generating medical procedures such as anaesthesiological and otolaryngological procedures.
## Materials and methods
*Particle* generation during coughing was measured in 37 volunteers. In addition, involuntary coughs from 15 electively operated patients were measured during local anaesthesia procedures ($$n = 1$$) and when patients arose from general anaesthesia ($$n = 14$$). Measurements were conducted in the Helsinki University Hospital's ENT department between December 2020 and February 2021.
The measurements were performed with a TSI® Optical Particle Sizer model 3330, which measures particle size from 0.3 to 10 μm, with a flow rate of 1 litre per minute, and with a measuring interval of 10 seconds. The size range was evaluated to be comprehensive, as 80 per cent to 90 per cent of particles produced during human respiratory activities are smaller than 1 μm after evaporation, and these small aerosols tend to carry most of the pathogens.17–23 The operating theatres had a Recair 4C ventilation system with an H14 high-efficiency particulate absorbing filter and ultra-clean ventilation in the laminar area of 1210–1298 litres per second, generating 400–572.83 air changes per hour, meaning a change in total air volume in the operating theatre every 6–10 seconds.
Volitional and involuntary coughing were compared to ensure there was no significant difference between the generated aerosol concentrations, which allowed a more accurate quantitative assessment of volitional coughs. During volitional coughs, the optical particle sizer was positioned at 40 cm, 70 cm and 100 cm from the volunteers, reflecting the same distances and thus the same particle amounts to which medical staff are exposed within the operating theatre. Volunteers were asked to cough as hard as possible towards the optical particle sizer device three to five times from each distance. No additional collection methods, such as funnels, were used to measure the actual particle exposure in a certain spot, considering the rapid spread of aerosols over a wider space.
The recording was continuous, but separate timing of each cough was attempted, thus ensuring that the particles from previous coughs had time to clear from the operating theatre. The marked coughing points were extracted from the continuous measurement data during data analysis, after which they were analysed separately. Not all coughs could be timed as a single cough, considering the sudden, short-term aerosol generation of the cough. In order to ensure that all measurements were as proportional as possible, coughs are shown by cough episodes (i.e. one volunteer and three to five coughs at one distance) in Table 1. Table 1.Observed particle concentration during volitional coughing from different distancesParticle concentration parameterAll volitional coughs40 cm from source70 cm from source100 cm from sourceCough episodes (n)74371522Total particle concentration– Mean ± SD1.706 ± 10.8020.923 ± 5.0780.091 ± 0.1364.12 ± 18.771– Median0.0550.0550.0260.070– Range0.000−88.1570.013−30.9730.000−0.4660.016−88.157<1 μm particle concentration– Mean ± SD1.692 ± 10.7910.906 ± 5.0300.087 ± 0.1334.111 ± 18.770– Median0.0440.0430.0260.053– Range0.000−88.1410.011−30.6680.000−0.4540.011−88.1411−5 μm particle concentration– Mean ± SD0.013 ± 0.0350.017 ± 0.0490.004 ± 0.0040.013 ± 0.006– Median0.0080.0080.0020.013– Range0.000−0.3040.000−0.3040.000−0.0120.004−0.028>5 μm particle concentration– Mean ± SD0.001 ± 0.0010.001 ± 0.0010.000 ± 0.0000.001 ± 0.001– Median0.0000.0000.0000.001– Range0.000−0.0050.000−0.0050.0000.000−0.004Data represent particles per cubic centimetre, unless indicated otherwise. A cough episode was defined as three to five coughs by one volunteer at a certain distance. Not all volunteers coughed from all distances. Both mean and median values are shown given the large heterogeneity in the data. SD = standard deviation The involuntary cough measurements were continuous throughout the whole procedure. The times of the coughs were recorded, extracted and analysed. The optical particle sizer was positioned towards the patient, vertically at the patient's head level, at an average of 124 cm (range, 40–180 cm) from the patient, always as close as possible considering the treatment situation. No additional collection methods were used.
As this study combines aerosol physics and medicine, existing power calculators are not available. However, a similar design has been used in a previous study.15 As infection risk is related to cumulative aerosol exposure, the mean was calculated for each patient at each coughing distance as a statistical representative. The size-dependent aerosol concentrations measured with the optical particle sizer were normalised with respect to the sizing bin widths within 0.3–10 μm. The particle number size distributions and total particle concentrations per cubic centimetre were calculated. The particles were categorised as follows: smaller than 1 μm, 1–5 μm and larger than 5 μm.
The data were log10 normalised prior to the comparisons. Pairwise comparisons were calculated using the unpaired student's t-test with the Benjamini–Yekutieli procedure, with a 5 per cent false discovery rate. The analyses were performed using Excel 2016 spreadsheet software (Microsoft, Redmond, Washington, USA), and GraphPad Prism statistical software version 9.0.2 for Mac (GraphPad Software, San Diego, California, USA) or RStudio version 1.3.959 (R Foundation for Statistical Computing, Vienna, Austria). The minimum concentration in all size classes was 0.000. A p-value of less than 0.05 was considered significant.
All procedures that involved human participants were conducted in accordance with the ethical standards of the institutional research committee and the 1964 Declaration of Helsinki. The Ethics Committee of Helsinki University Hospital approved the study protocol (HUS/$\frac{1701}{2020}$). All participants provided written informed consent prior to their participation.
## Results
Out of 37 volunteers for volitional cough, 28 (76 per cent) were female. The mean age of the volunteers was 41 years (range, 23–61 years). Out of 15 patients examined for involuntary coughing, 8 (53 per cent) were female. The mean age of the patients was 45 years (range, 24–72 years). General anaesthesia was used in 14 procedures with coughing patients and local anaesthesia was used in one procedure.
A total of 306 coughs were measured from 37 healthy volunteers. Information on particle aerosol concentrations from different distances is presented in Table 1, and compared in Figures 1 and 2. Particle concentrations of 0.000 particles/cm3 were measured during 22 of 306 volitional coughs. This reflects not only large differences between individuals in terms of generated concentrations, but also in terms of the dilution and effects of air currents caused by differences in ventilation between the two operating theatres. Background concentrations were low (maximum mean total concentration of 0.0053 particles/cm3), which enabled accurate evaluation of particle concentrations generated during coughing. Fig. 1.Comparison of volitional coughing between volunteers and different operating rooms (OR), presented as a Tukey box and whiskers plot with outliers. Involuntary coughs could not be combined into the figure, as there was, on average, only one measured involuntary cough per volunteer. Fig. 2.Comparison of volitional coughing between different operating rooms (OR) and at different coughing distances, presented as a Tukey box and whiskers plot with outliers.
Mean particle concentration during involuntary coughs was: 0.140 ± 0.332 particles/cm3 (range, 0.006–1.308 particles/cm3) for particles smaller than 1 μm, 0.025 ± 0.068 particles/cm3 (range, 0.000–0.270 particles/cm3) for particles 1–5 μm, and 0.002 ± 0.006 particles/cm3 (range, 0.000–0.024 particles/cm3) for particles larger than 5 μm. There were no significant differences between volitional and involuntary coughing in any particle size category ($$p \leq 0.244$$–0.883) (Figure 3). Fig. 3.Comparison of volitional coughing versus involuntary coughing. ( a) Average aerosol size distributions, presented with background concentration distribution (dotted line), during volitional and involuntary (local anaesthesia) coughs expressed as mean (line) with 95 per cent confidence interval (shaded area). ( b) Total concentrations, and concentrations of less than 1 μm, 1–5 μm and more than 5 μm aerosols, during volitional and involuntary coughs, presented as median with interquartile range (box) and range (whiskers). Volitional coughing participants, $$n = 37$$ (coughs $$n = 306$$); involuntary coughing participants, $$n = 15$$ (coughs, $$n = 15$$). C = concentration; Dp = particle diameter; dN = number of particles; dN/dlogDp = particle size distribution
## Discussion
This study examined aerosol concentration at different distances in volitional and involuntary coughing within the operating theatre, to obtain a perspective on the amount and significance of aerosol generation during medical procedures performed in the operating theatre. We found that the intentionality of coughing did not have a significant effect on aerosol concentration. Rather, large heterogeneity in aerosol generation was observed between individuals. Our results provide systematically collected, distance-scaled, approximate numerical limit values for aerosol exposure encountered by operating theatre personnel that can be used in the risk assessment for aerosol-generating procedures.
During the Covid-19 pandemic, the amount of aerosol produced by medical procedures such as various anaesthetic procedures and otolaryngological surgical procedures has been extensively measured; however, the clinical significance of these procedures from the viewpoint of risk assessment remains unclear compared to aerosol amounts generated in human respiratory activities.7,15,24 Our results do not change the fact that there is no absolute quantitative limit for significant aerosol production that poses a risk of infection.10 However, in the absence of better understanding, coughing is still commonly used as a limit value for high-risk aerosol output during medical procedures.15,25,26 *Coughing is* an activity that, for example, in the case of ENT diseases, healthcare workers encounter in their work daily, but for a relatively short time.
Comparison of aerosol production in other procedures with coughing helps to categorise the concentration of aerosol generated, such as a lower risk compared to coughing, a similar risk compared to coughing or a higher risk compared to coughing (high-risk aerosol-generating procedure). With this scaling, aerosol production measured in various studies can be brought into a form that can be used and understood clinically: what measures truly exceed aerosol generation of aerosol-generating behaviours? What are the high-risk exposures? On the other hand, these results may aid our understanding regarding exposure to potentially infectious aerosols during epidemics of airborne pathogens and provide useful information to determine the necessary personal protective equipment.27 As information on the pathogens contained in aerosol particles – as well as infectious doses of airborne diseases – increases, these factors can later add to the risk assessment of aerosol generation.
The concentrations we measured are consistent with a recent systematic review.8 However, our study complements previous studies with exposure-based measurements in the operating theatre and systematic distance-dependent evaluation, which is one of the most significant factors in aerosol exposure.8,24 The large range and individual differences of the particle concentrations in our study are seen typically in respiratory activities and related to the heterogeneity of the individual's aerosol generation.28,29 However, considering that no difference between volitional and involuntary coughs was observed, and that coughs measured on different days and in different operating theatres are comparable (Figure 2), we conclude that the presented data are representative regarding the exposure of average aerosol concentration generated during coughing. A previous study showed that infected patients generated a greater number of particles when coughing than healthy individuals,30 suggesting that the particle concentrations seen in our study represent the minimum value to determine the limit for a high-risk aerosol-generating procedure. In addition, in a clinical context, most patients are not operated on during respiratory tract infection.
*In* general, distance from the source of infection is a significant measure of exposure. Therefore, we measured cough-produced aerosol concentrations from several different distances.10 According to the nature of aerosols, which can travel long distances with air currents, the aerosol concentrations do not necessarily decrease linearly from the source and are influenced by various environmental factors, even in highly ventilated spaces such as operating theatres. This was the case in our study, as the highest aerosol concentrations were observed at a 100 cm distance and the lowest at a 70 cm distance (Table 1, Figure 2). In addition to the nature of the aerosols, this finding can at least partly be attributed to the methodology. The flow rate of the optical particle sizer is 1 litre per minute. When measuring particles with high acceleration at close range, some particles bypass the device and are not recorded. When distance increases further, the acceleration of the particles is reduced, and the concentration is observed more accurately. Still, despite the limitations of the methodology, the optical particle sizer is currently the most suitable and frequently used measuring device in operating theatre conditions. Coronavirus disease 2019 highlighted lack of knowledge on aerosol exposure of healthcare personnel during normal patient respiratory activity and suspected aerosol-generating proceduresAerosol concentrations generated by volitional and involuntary coughing in the operating theatre were measuredThese concentrations can be used to determine aerosol generation risk during medical proceduresWhether the cough was intentional or unintentional had no statistically observable effect on aerosol productionThe results provide a reference for assessing and comparing aerosol generation risk during surgical procedures Other limitations of our study include the different individuals in volitional and involuntary groups, the variable location of the optical particle sizer device during involuntary cough measurements, and the lack of repetitions in the involuntary group.
## Conclusion
This study measured concentrations and size distributions of aerosol particles to which operating theatre personnel are exposed, from volitional and involuntary coughs, at distances typically associated with medical procedures performed in the operating theatre. Whether the cough was intentional or unintentional had no statistically observable effect on aerosol production. These results can be interpreted as a reference for assessing and comparing the risk of aerosol generation during surgical procedures.
## Competing interests
None declared
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13. Hamilton FW, Gregson FKA, Arnold DT, Sheikh S, Ward K, Brown J. **Aerosol emission from the respiratory tract: an analysis of aerosol generation from oxygen delivery systems**. *Thorax* (2022.0) **77** 276. PMID: 34737195
14. 14World Health Organization. Transmission of SARS-CoV-2: implications for infection prevention precautions. In: https://www.who.int/news-room/commentaries/detail/transmission-of-sars-cov-2-implications-for-infection-prevention-precautions [29 November 2022]
15. Brown J, Gregson FKA, Shrimpton A, Cook TM, Bzdek BR, Reid JP. **A quantitative evaluation of aerosol generation during tracheal intubation and extubation**. *Anaesthesia* (2021.0) **76** 174. PMID: 33022093
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|
---
title: 'Disparities in access to ear and hearing care in Cambodia: a mixed methods
study on patient experiences'
authors:
- C J Waterworth
- C T M Watters
- T Sokdavy
- P L Annear
- R Dowell
- C E Grimes
- M F Bhutta
journal: The Journal of Laryngology and Otology
year: 2023
pmcid: PMC10040287
doi: 10.1017/S0022215122001396
license: CC BY 4.0
---
# Disparities in access to ear and hearing care in Cambodia: a mixed methods study on patient experiences
## Abstract
### Objective
Chronic suppurative otitis media is a major global disease disproportionately affecting low- and middle-income countries, but few studies have explored access to care for those with ear and hearing disorders.
### Method
In a tertiary hospital in Cambodia providing specialist ear services, a mixed method study was undertaken. This study had three arms: [1] quantitative analysis of patients undergoing ear surgery, [2] a questionnaire survey and [3] semi-structured in-depth interviews.
### Results
Patients presented with advanced middle-ear disease and associated hearing loss at rates that are amongst the highest per capita levels globally. Patients reported several structural, financial and socio-cultural barriers to treatment. This study showed a significant burden of ear disease in Cambodia, which reflects a delay in receiving timely and effective treatment.
### Conclusion
This study highlights the opportunity to integrate effective ear and hearing care into primary care service provision, strengthening the package of activities delivered at government facilities.
## Introduction
Ear disease and hearing loss have widespread and significant implications on a person's quality of life, education, socio-economic opportunity and well-being. The economic cost of hearing loss on a global scale is enormous, with recent conservative estimates of nearly 1 trillion US dollars annually.1 *There is* a significant inequality in the distribution of hearing loss, with 80 per cent of the global burden impacting those residing in low- and middle-income countries.2 Chronic suppurative otitis media (CSOM) is a major global disease, disproportionately affecting those in low- and middle-income countries.3 CSOM is characterised by a perforation of the ear drum, with or without the presence of a cholesteatoma, intermittent or continuous ear discharge, and around two-thirds of patients experience a moderate or worse hearing loss.3 Medical treatment of ear discharge in CSOM typically includes topical antibiotic drops4 and monitoring, but long-term resolution often necessitates surgery via repair of the ear drum, termed tympanoplasty, which often improves hearing, or excision of the cholesteatoma, termed mastoidectomy, to safely remove the disease.
In many low- and middle-income countries, patients residing in rural and semi-rural settings frequently present with advanced disease or complications,5 which are compounded by the interplay between impoverishment and the inequitable distribution of ear and hearing care services.6 In fact, within low- and middle-income countries, chronic ear discharge is one of the most common reasons for seeking specialist ear care services.7 Improving access to healthcare in low- and middle-income countries has been a long-term and ongoing concern for researchers and policy makers, yet significant disparities continue to exist.8 *There is* a body of literature on minimising the barriers to accessing care in low- and middle-income countries9,10 and on the effectiveness of interventions designed to improve access to care.11 Interventions designed to expand healthcare access for poor and disadvantaged communities are evident in a number of South Asian countries12 and in Southeast Asia. In Cambodia for example, Liverani et al. [ 2017] highlighted the complex and contextual barriers to improving access to treatment for malaria in remote areas of Kampot.13 Southeast Asia has one of the highest prevalence rates of CSOM in the world,14 yet there is very little literature regarding the state of ear and hearing care in Cambodia. To date, there have been no nationally recognised prevalence studies on hearing loss or ear disease. A population-based, cross-sectional national survey conducted between 2011 and 2012 reported hearing loss as the most common impairment amongst children, with 6.53 per cent of children recorded as having a disability.15 In 2013, it was reported that less than 2000 of the estimated 51 000 profoundly deaf Cambodians had access to deaf services.16 More recently, in 2016, the World Health Organization described recurrent ear discharge as being normalised amongst rural children.17 Gaps in the literature remain in terms of understanding the magnitude and impact of ear disease in the country, the supply of ear and hearing care providers including otolaryngologists and audiologists, and the key challenges that Cambodian people face in accessing timely and appropriate health and rehabilitation services relative to the need. Exploring access to receiving ear care is of particular policy relevance in Cambodia, where there is a lack of evidence on the severity and impact of ear disease, a significant inequity in available services, especially in rural areas, and an ongoing challenge in incorporating ear care into government policy and planning.
Several studies have explored challenges to the provision of medical8 and surgical18 care in low- and middle-income countries, but very few have explored the experience of those with chronic ear disease in their journey to seek ear and hearing care services.
The purpose of this study was to examine the experience of a cohort of patients who presented at Cambodia's principal ear care hospital as they sought care for their ear conditions. We investigated the severity of their disease and its impact, and we made an analysis of their healthcare-seeking for diagnosis and treatment, including delays in accessing care, the severity of the disease, distance to care, and the nature of ear and hearing care service delivery.
Spend time building rapport with participant. Remind the participant (and parent or guardian) that this will be audio-recorded and translated into English (then switch on recorders).Today, I am interested in what it has been like for you/your child to experience an ear or hearing problem. I am also interested in hearing about your journey to finding help for your ear problem.
## Background
Cambodia, now classified as a lower middle-income country,19,20 has achieved strong economic growth and has pioneered social health protection programmes designed to improve access to healthcare for the poor. In the last 20 years, there has been a significant decrease in the official poverty rate to 12.9 per cent of the population by 2018.15,21 The effective level of poverty, however, is much higher, and significant inequalities in standards of living between rural and urban areas prevail.22 Under the government's health coverage plan, health facilities (hospitals and health centres) have been placed equally across the country according to uniform population catchment areas. However, access to quality care remains a challenge, particularly for impoverished Cambodians residing in rural areas.23 Concerns about financial support for diagnosis and management of non-communicable diseases remain a challenge and are currently a focus of national health planning.24,25 The Cambodian health system consists of public and private providers. The public sector has undergone dramatic change since 1996, when nominal user fees were introduced at government health facilities, to increase access to services and improve healthcare coverage.26 Over the last 20 years, the private sector has expanded but remains largely unregulated, with a mixture of both qualified providers working in health facilities and unqualified providers, such as traditional healers and merchants, selling medications and offering services.24,25 Approximately 60 per cent of total health expenditure consists of household out-of-pocket payments, which are directed principally to unregulated private providers.27 In rural areas, only 15 per cent of primary care occurs in the public sector, and private, non-medical (unqualified) providers account for half of all healthcare providers.28 The Ministry of Health has committed to universal health coverage and has endeavoured to achieve more equitable access to care through its consecutive health strategic plans, the most recent being the Third Health Strategic Plan 2016–2020.29 Currently, public health facilities deliver services through 24 provincial health departments, which operate a provincial hospital in each province and govern 81 operational districts.27 Within operational districts, a referral hospital delivers the complimentary package of activities covering secondary level care, and health centres provide the minimum package of activities covering prevention and basic treatment.27 However, very few government hospitals or health centres provide ear and hearing care services, which are mostly provided by non-government organisations with foreign connections, primarily in Phnom Penh with a few others scattered throughout the country.30 In Phnom Penh, some public and private hospitals have established ENT departments. Among the government hospitals are Preah Ang Duong Hospital, the Khmer–Soviet Friendship Hospital, the National Paediatric Hospital, Calmette Hospital and Preah Kossamak Hospital.
Among non-governmental organisation supported facilities, the Children's Surgical Centre in Phnom *Penh is* a major provider of ENT services. All Ears Cambodia, a non-governmental organisation that has five clinics across the country, works with government and non-government service providers to increase coverage and awareness of primary ear care and is building a local workforce of ear and hearing care health workers.15 Special education is available for a limited number of children with hearing loss, who can attend Krousar Thmey School (preparation year 12), a local non-governmental organisation school that was officially transferred to the Ministry of Education, Youth and Sports in 2019, and young adults (16 years or over) with profound hearing loss who can attend the non-governmental organisation supported Maryknoll Deaf Development Program, which provides vocational training, Cambodian sign language training and basic education.16,31 The Children's Surgical Centre, a non-governmental organisation charity hospital in Phnom Penh, has been the country's main provider of surgical ear care since 2014, following a sustained in-country training programme by UK-trained surgeons.32 The Children's Surgical Centre provides free surgery and treatment to impoverished adults and children, covering all in-hospital expenses except for food, transportation or accommodation outside of the hospital. The ENT department primarily focuses on treatment and rehabilitation of CSOM (including cholesteatoma), with approximately 250 tympanoplasty or mastoidectomy operations performed each year.
Firstly, can you tell me the story of your ear or hearing problem, all the events and experiences that were important for you, up to now?
Prompts: When/how did you first notice your ear problem?What have been your main ear/hearing symptoms?What do you think caused your problem?What do your fear most about your illness?What are the biggest difficulties that your ear problem has caused you? Has the ear problem meant you are unable to work effectively/go to school?How has this problem affected your life up until now? Why do you think it started when it did?How severe is your ear problem? How long do you expect it to last?
## Materials and methods
We set out to answer a number of key research questions: *What is* the extent of ear disease in Cambodia? What are the patterns of utilisation of ear care services? What challenges do people face in obtaining appropriate ear care, and how can these challenges be addressed? Measuring access to care has been deemed a challenging task, but it ultimately relies on the ability to assess whether the characteristics of services, providers and systems are aligned with people, households and community capabilities.33 Barriers to care may be structural, financial or socio-cultural,34 which Levesque et al. [ 2013] conceptualised into a framework that encapsulates the demand and supply determinants that disrupt or delay the healthcare-seeking journey.33 *This is* a widely accepted health system framework that incorporates both health providers’ and healthcare users’ perspectives on access and has been used previously to explore, assess and measure access in various healthcare services and settings.35 Structural barriers include location of facilities, transportation, childcare and long waiting times, socio-cultural barriers include lack of knowledge or acceptance among local communities, and financial barriers include a lack of, or inadequate, health protection schemes. We adopted the Levesque framework for assessing access to ear and hearing care.
We employed a mixed methods approach to provide a better understanding of the multi-faceted challenges to ear and hearing care access through the triangulation of the data. Three data collection strategies were adopted: a quantitative analysis of the Children's Surgical Centre patient database to establish the extent of ear disease presenting to the hospital; a patient survey to establish a broad overview of the main challenges experienced; and in-depth qualitative interviews of patients and providers to gain further insights into the important factors hampering access to ear and hearing care of those attending the hospital for treatment.
For the extraction of data on symptomology, markers of disease severity and correlation with markers of disease severity to distance from hospital, we obtained electronic records of patients who underwent tympanoplasty surgery (for CSOM) or mastoidectomy surgery (for cholesteatoma) at the Children's Surgical Centre between October 2014 and April 2019 (excluding records for second-side surgery).
We extracted data on markers of disease severity, specifically: [1] symptoms and their duration at presentation; [2] mean air conduction hearing thresholds in decibels in the worse ear on pre-operative pure tone audiometry at 500 Hz, 1000 Hz, 2000 Hz and 4000 Hz; and [3] grading of anatomical destruction of temporal bone structures according to data from operative records.
For patients undergoing tympanoplasty, we scored size of tympanic perforation as: less than 30 per cent of the tympanic membrane = 1 point; 30–60 per cent of the tympanic membrane = 2 points; and more than 60 per cent of the tympanic membrane = 3 points. We added 1 more point if erosion of ossicles was present. We classified patients as: 1 point = grade 1; 2 points = grade 2, and equal to or more than 3 points = grade 3.
For patients undergoing mastoidectomy, we summed the number of temporal bone structures eroded from the following list: malleus, incus, stapes, chorda tympani, external ear canal, facial canal, tegmen, bone over posterior fossa and lateral semi-circular canal. We classified patients: equal to or less than 2 structures eroded = grade 1; 3–5 structures eroded = grade 2, and equal to or more than 6 structures eroded = grade 3.
We used patient records to explore the relationship between travel distance to the hospital, delays in accessing care and the severity of ear disease. For each patient, we used Google Maps (Google, Mountains View, USA) to estimate distance (in kilometres) and travel time (in minutes) to the Children's Surgical Centre from their commune of residence. Patient data were collated, anonymised and exported to Minitab 19® statistical software. For mean pure tone threshold and symptom duration (continuous data), we performed Pearson linear correlation to distance travelled. For grade of anatomical destruction (ordinal data), we compared distance travelled in each group using Tukey pairwise comparison.
Over a six-month continuous period (March to September 2019), we invited adults or primary caregivers of children with ear or hearing symptoms attending the Children's Surgical Centre ENT Department to complete a questionnaire on their journey prior to attending the hospital (Appendices 1–3). Survey questions were translated into Khmer and translated back into English for verification prior to interviews. Because of the varying literacy of participants, questionnaires were completed by local nurses in Khmer language under the local supervision of the head of the ENT department and researcher (TS). Prior to formal data collection, the questionnaire was tested amongst a group of Khmer-speaking patients and staff members to establish its reliability and to check for ambiguity; this group predominantly, but not exclusively, suffered from CSOM. We asked about participant or patient experiences of disease and the journey leading them to hospital. A power analysis in G*Power 3.1.9.2 statistical power analysis software determined a target sample size of 114 participants (alpha, 0.05; power, 0.95) for detecting a correlation between two numerical variables of medium effect (r > 0.30).36 Through patient records and the questionnaire survey, we purposively selected 15 adults and 5 caregivers to participate in semi-structured interviews to gain more understanding into the lived experience of people with ear disease and hearing loss who were seeking out ear and hearing care services. Interviews were conducted in August and September 2019 in a private room at a café that was a short walking distance from the Children's Surgical Centre hospital by two English-language researchers assisted by two local Khmer interpreters. In order to ensure reflexivity, cultural competency and a healthy dialogue within the research team, pilot interviews were conducted to review the topic and discuss potential sources of bias and interpretation of data. We asked participants open-ended questions on topics related to health and ear health experiences, access, utilisation, demand for services, perception of quality of care and health beliefs. Each interview was audio recorded, transcribed verbatim (excluding Khmer), and content-coded and analysed using NVivo qualitative data analysis software (version 12, QSR International, Melbourne, Australia).
We used a deductive approach to analyse data, and categorised responses into the model devised by Levesque et al.33: approachability and ability to perceive need for care; acceptability and ability to seek care; availability and ability to reach care; affordability and ability to pay for care; and appropriateness and ability to engage with healthcare services. Codes were inductively derived from interview responses, compiled into categories and then merged into main determinants.
## Results
For the patient data, 693 records were identified in the study period of which 113 were excluded because of duplication or second-site surgery. We had data for 407 patients undergoing tympanoplasty (173 of 407 (43 per cent) male) of which 122 were children (age range, 6–17 years) and 285 were adults (age range, 18–61 years), and 173 patients undergoing mastoidectomy (85 of 147 (49 per cent) male) of which 44 were children (age range, 4–17 years) and 129 were adults (age range, 18–57 years).
Regarding symptoms at presentation (Table 1), in the tympanoplasty group the most common symptom was self-reported hearing loss (73 per cent), and in the mastoidectomy group it was otorrhoea (98 per cent), followed by self-reported hearing loss (78 per cent). Mean symptom duration prior to receiving formal ear care at the hospital was 13.5 years (± 11.7 standard deviation (SD)) for the tympanoplasty and 13.9 years (± 11.0 SD) for the mastoidectomy group, with no significant difference between groups (two-sample t-test, $$p \leq 0.697$$). Pre-operative pure tone audiometry results were available for 97 per cent (564 of 580) of patients. Mean thresholds in the worst ear were 46 dB HL (± 16.3 SD) for the tympanoplasty group and 60.2 dB HL (± 23.3 SD) for the mastoidectomy group, which was a significant difference (two-sample t-test, $p \leq 0.001$). Half of the tympanoplasty group (206 of 407 (51 per cent)) and 29 per cent (50 of 173) of the mastoidectomy group had an abnormal contralateral ear. Table 1.Recorded symptoms at presentation for tympanoplasty and mastoidectomy cases in quantitative analysisParameterTympanoplasty*(n (%))Mastoidectomy†(n (%))Otorrhoea185 [45]170 [98]Self-reported hearing loss296 [73]135 [78]Tinnitus101 [25]35 [20]Otalgia44 [11]30 [17]Vertigo11 [2]12 [7]*$$n = 407$$; †$$n = 173$$ Regarding anatomical markers of disease severity in the 407 patients undergoing tympanoplasty, 7 per cent [30] had ossicular erosion recorded, and 98 per cent [397] had a perforation, of which 11 per cent (43 of 397) had a small perforation (less than 30 per cent of the tympanic membrane), 48 per cent (191 of 397) had a medium perforation (30–60 per cent) and 41 per cent (163 of 397) had a large perforation (more than 60 per cent). Based on the grading system used, 47 tympanoplasty patients were grade 1, 190 were grade 2 and 166 were grade 3 (the most severe). In the 173 patients undergoing mastoidectomy, there was erosion of temporal bone structures in the following numbers: incus, 129 (75 per cent), stapes, 88 (51 per cent), malleus, 77 (45 per cent), chorda tympani, 39 (23 per cent), facial canal, 39 (23 per cent), lateral semi-circular canal, 16 (9 per cent), external ear canal, 14 (8 per cent), tegmen, 12 (7 per cent) and posterior fossa, 8 (5 per cent). Based on the grading system, 103 mastoidectomy patients were grade 1, 60 were grade 2 and 10 were grade 3.
A total of 114 patients participated in the questionnaire survey, including 89 adults (mean age ± SD, 37.6, ± 13.4 years; 45 of 89 (52 per cent) male) and 25 caregivers of children (mean age ± SD, 10.8 ± 3.6; 16 of 25 (64 per cent) male). Some questions were skipped by some participants because of time constraints. Table 2 summarises the challenges that participants experienced prior to attending the Children's Surgical Centre for ear care services. There was a relatively even distribution of supply and demand influences. Reasons for attending the Children's Surgical Centre were recorded in 114 responses, with the most common being worsening symptoms (111 of 114; 97 per cent) and recent knowledge of the services at the Children's Surgical Centre (105 of 114; 92 per cent). A high proportion (85 of 114; 75 per cent) also reported a lack of successful prior treatment as a reason to attend, and more than half (62 of 114; 54 per cent) attended because their ear condition was impeding their ability to work. Other reasons cited were because other hospitals do not treat ears (52 of 114; 46 per cent) or had too long a waiting time (25 of 114; 22 per cent), because they were referred by another health professional (14 of 114; 12 per cent) or because there was a change in circumstances (7 of 114; 6 per cent). Table 2.Difficulties experienced seeking ear care prior to the Children's Surgical Centre*Supply or demandReasonValue (n (%))Supply/demandService location too far from house81 [75]DemandFear of treatment68 [63]DemandNo awareness of health service information62 [57]DemandNo education on ear health54 [50]SupplyAccommodation unavailable47 [44]DemandFear of hospital44 [40]SupplyTreatment or service expenses43 [40]SupplyRoads too poor43 [40]SupplyTransport expenses38 [35]SupplyWaiting list long35 [32]DemandCould not miss work26 [24]SupplyFood expenses13 [12]*$$n = 108$.$ Values taken from questionnaire We also conducted semi-structured interviews with 15 adults (age range, 20–72 years, 8 male) and 5 caregivers of children (age range, 9–18 years, 2 male). Here we present the combined results of the questionnaire and interviews thematically, following the categories created by Levesque et al. [ 2013]; specific patient quotations and interview excerpts related to each theme are shown in Table 3. Table 3.Emergent themes from patient interviewsDimension of accessSub-themeExcerptApproachability/ability to perceive need for careLack of knowledge or recognition of symptoms‘So, it's because of her understanding. At that time, she thought [the ear problem] was not important. She can still go to school, move around, walk, can do everything … It's just smelling and discharge…’ (participant 4)Acceptance of traditional health beliefs‘When he was a young baby, he cried a lot, so the tear is drop into the ear and he got infected in there’ (participant 10)Preference for traditional treatments or self-treatment‘His son has a hearing problem, so he tried to find the treatment somewhere … people gave advice to get treatment from the traditional healer, but most of that treatment [did] not help much’ (caregiver 18)Practice of medical pluralism‘She went to the doctor, but also asked help from the tree [spirit-belief/Kru Khmer], like combined together … she believes more in the doctor, but she likes to combine [treatment] together’ (caregiver 2)Transiency of symptoms‘It [discharge] usually happens every one or two months, maybe five days each time, and then it gets better and then another month it goes away’ (participant 4)Acceptability/ability to seek careFear or lack of trust in provider‘Initially, she felt very, very, very scared. She felt not too confident with the hospital’ (participant 5)Fear of surgery‘His family worry about after the surgery … Cannot work and affect his life after the surgery’ (participant 17)Fear of anaesthesia‘He's scared about the operation because most Khmer people don't understand about the anaesthesia. They usually hear from other rumours, “the anaesthesia can make people die!”’ ( participant 11)Stigma‘She never told anybody. So scared. Scared to let everybody know her disease … She didn't want anybody to know she had the problem because the ear gets smell and dirty. The pus came out, so she tried to clean and keep secret. She doesn't want anybody to know her problem. In family is ok but [not] for everybody around’ (caregiver 8)Cultural or family influence on decision making‘Everyone in her family influence her, like push her, to find treatment. Some people that know the place to go for treatment, they tell her’ (participant 1)Collectivism‘It's still a problem [her mother's ear condition], so that's why her daughter stopped studying and helped her … she need to stop her dream to take care of her mother. Her dream is, she want to have cafe and bakery shop, but she need to stop everything to take care of her… Sometimes [older parents] try to keep this [information] by themselves. They don't tell anybody except husband or wife. They don't want the children to know. Cambodian children worry very much if they heard that their parents have any problem. If she saw her mum was sick, [her daughter would] decide to stop studying, [so] the parents decided to keep things [secret]’ (caregiver 8)Availability/ability to reach careLack of available ear and hearing care services close by‘The ear service around the commune, it's just simple general medical care, but no specialists’ (participant 7).‘They said they don't have the ENT Department in their hospital, so they tell her to go to find the treatment outside, like a private clinic’ (caregiver 3)Lack of ability to navigate to service‘His village is far away from the town [Phnom Penh]. He doesn't like to stay there and then it's difficult traffic and roads’ (participant 13)Occupational flexibility/seasonal factors‘Right now [in] his neighbourhood, they plan to come to hospital to check, but you know the farmers have to decide the time. They need to be free from their farm work’ (participant 13)Lack of childcare‘She has many children. It's hard to take time to bring the child with the problem to the hospital. She has a problem with money too. That's why [it's] difficult to bring the child to see the doctor’ (caregiver 15)Affordability/ability to pay for careDirect costs too high‘They have to spend each time at least 40 to 50 USD each consultation and the medication, but her daughter looks not better. So, he decide to bring her to ‘free’ hospital’ (participant 3)Indirect (opportunity) costs too high‘When he cannot hear well, it's difficult; it's hard to work … when he got sick, he difficult to earn money and then not enough money is difficult to find treatment’ (participant 12)‘*In his* family, just only him that earn the money… so he is very important in the family. He cannot spend the time to get operation’ (participant 16)Appropriateness/ability to engage with providersLack of patient centred care‘…some hospital outside when she has a question, they just only shout back. She went many times for medical treatment, and she tried to ask why [is there] no cure for her daughter. They said “It's the disease for follow up! Cannot get cure quick!”, but the answer is not polite’ (caregiver 3)Lack of belief in providers‘Before, he felt they don't believe in medical staff who work at the commune because they don't work with experience about ear care. And then may need to be charged money a lot! So, both problems – the money and the technique or experience to try to make [healthcare] work’ (participant 13)Lack of belief in the integrity of the medical system‘They (hospital providers) don't do the right way. They take the money and they want to take again and again. Not just one way. So that is the way that they are making money into the hospital. So, most of the patients, they don't like to go and see them’ (participant 11)Medical paternalism‘No doctor told him anything about what is the problem that child has … He just got medical treatment, like injection and perfusion, but they didn't know what it is’ (caregiver 18)USD = US dollars
## Approachability and ability to perceive need for care
An individual's ability to perceive the need for ear care services related to three main factors: awareness of the likely reasons for hearing loss, health beliefs and transiency of symptoms. Half the patients completing the questionnaire (54 of 108; 50 per cent) reported that their lack of knowledge of ear conditions was a barrier to obtaining prior care. Among the in-depth interviewees, several demonstrated misunderstanding about their ear condition and its severity. Hearing loss was sometimes interpreted as a mental health problem or lack of concentration rather than a condition of the ear. Some participants were stoic and perceived the problem as unimportant, trivialising symptoms. Health-seeking behaviour may be influenced by traditional health beliefs.37 Some participants believed their ear disease was caused by things such as tears falling into the ears, exposure to dirty water while swimming, excessive use of earphones or over-cleaning the ears. The transient and fluctuating nature of ear and hearing symptoms meant that participants’ perceived need for care changed over time. During periods with lighter symptom burden, patients would delay seeking healthcare, believing that symptoms could resolve without intervention.
## Acceptability and ability to seek care
Once participants became aware of their need for ear care, fear was a significant barrier in initially seeking care, with 63 per cent (68 of 108) of patients in the questionnaire reporting fear of the treatment or surgery itself, the hospital and local environment, the outcomes of surgery or stigma from their community. Some lacked trust in the integrity of healthcare providers because of prior experience. For example, some thought that private providers would ask patients to return multiple times for financial gain, without any apparent improvement in symptoms.
The local culture of communal living and collectivism both helped and hindered health-seeking behaviour. The majority of participants reported support from friends and family in attending the hospital, and most discovered the hospital through relatives or neighbours in their village who had previously had a positive experience. Families were very influential in a participant's consideration for seeking and accepting care. Collectivism is a characteristic reflected in Cambodia and other Asian societies, whereby adult children become primary caregivers of their aging parents, often attending medical appointments together. It can in turn become a barrier to seeking care, with some participants explaining they might hide symptoms from other family members in order to prevent worry or responsibility.
## Availability and ability to reach services
A lack of local ear care services was the most frequent difficulty experienced (81 of 108; 75 per cent), and at least half of participants responding to the questionnaire believed their lack of knowledge of service availability (62 of 108; 57 per cent) was a barrier to obtaining prior care. Thirteen participants (12 per cent) had no knowledge of local ear care services, and the same number had not previously attended any such services.
For participants living in rural or remote areas, accessing the Children's Surgical Centre (the main provider of free surgical ear care in the country) had also presented barriers, with some interviewees travelling up to eight hours via any transport method available to them (motorbike, taxi, minibus or public transport). Analysis of the quantitative data showed a large variation in distance and time travelled to the hospital (Figure 1), with a mean of 94 km (SD, 87.7; range, 0–463) and 124 minutes (SD, 89; range, 0–449) for the tympanoplasty group and 131 km (SD, 128.2; range 2–484) and 154 minutes (SD, 124; range 4–259) for the mastoidectomy group. Patients undergoing mastoidectomy travelled significantly further than those undergoing tympanoplasty (two-sample t-test for distance, $p \leq 0.001$; for time, $$p \leq 0.004$$). A total of 62 per cent (362 of 580) of patients lived within 100 km of the hospital. Correlation between distance travelled and markers of disease severity are shown in Figure 2; we found no significant correlation in any of these variables. Fig. 1.Distribution of Children's Surgical Centre patients across Cambodian provinces. Fig. 2.Distance to hospital in kilometres plotted against scatterplot of pre-operative hearing loss in cases of (a) tympanoplasty and (b) mastoidectomy cases. Scatterplot of duration of symptoms (years) in cases of (c) tympanoplasty and (d) mastoidectomy. Boxplot of grade of anatomical destruction in cases of (e) tympanoplasty and (f) mastoidectomy. CI = confidence interval Where an ear care service was available in the home district, a similar proportion of utilisation to availability was observed (Table 4), suggesting that where services are available and known about, in general they were utilised. For those who did seek available modern medical services, they visited pharmacies, private medical clinics, public health centres, district health centres, and out-patient departments in government or non-government hospitals. Nonetheless, the majority recounted numerous barriers, particularly the lack of ear health specific services and treatments or uncertainty about how to navigate the health system in order to receive relevant and specific care. Table 4.Patients’ perception of availability and utilisation of ear care services prior to attending the Children's Surgical Centre*Type of providerService availability in participant's home district(n (%))Service utilisation prior to Children's Surgical Centre(n (%))Pharmacy79 [70]65 [58]Private hospital/health centre40 [35]53 [47]Government hospital/health centre34 [30]32 [28]Specialist ear doctor3 [3]8 [7]Traditional healer5 [4]5 [4]No health service13 [12]13 [12]*$$n = 113$.$ Values taken from questionnaire Information about prior treatments was recorded for 113 questionnaire respondents, with the majority receiving oral medication (93 of 113; 82 per cent) or ear drops (70 of 113; 62 per cent). A smaller proportion had received prior ear surgery [9], used traditional medicine [3] or treated the ear in some manner themselves [16]. Eleven patients reported no prior treatment. Several participants reported previously using traditional home remedies, often passed down from older family members and widely accepted. This included placing various items into the ears, such as perfume, coconut oil, papaya oil, garlic, pepper, tobacco leaves and feathers. Many (from both urban and rural abodes) sought care from traditional healers (Kru Khmer) prior to attending the hospital.
Kru Khmer were employed to treat severe or chronic discharge, ear pain or hearing loss. For discharging ears, treatments included cleaning the ears with chicken feathers, pouring hot wax around the ears, chewing tree roots and spitting them into the ears, burning (cauterising) with burnt cotton buds or cigarettes, and smoking the ears with long pieces of wood inserted into the ear canal. For hearing loss, treatments included massaging or slapping the ears. In most cases, such treatments were reported to be unsuccessful, provide only temporary relief or sometimes cause additional issues. Some participants practised medical pluralism, using traditional and modern medicine in conjunction.
## Affordability and ability to pay
Most participants cited out-of-pocket expenditure as a barrier to accessing ear care, which was why many initially sought care from a local traditional healer or pharmacy prior to attending a formal healthcare facility. Mean out-of-pocket expense (for patients in the questionnaire) prior to attending the Children's Surgical Centre was US$614 for adults (range, $10–2000) and $575 for caregivers (range, $50–1000). For those accessing formal care, participants reported difficulties in raising funds; some were able to self-fund completely whereas most had to work extra hours, access savings, or borrow from relatives or friends. One participant recounted selling her jewellery to pay for her daughter's ear treatment, and another reported spending US$2000 throughout their ear care journey.
The inability to pay direct medical costs including hospital fees, surgical fees and medication expenses in addition to non-medical costs, such as transportation, food and accommodation, caused some participants to delay treatment. Indirect costs were represented by expenditures incurred beyond direct medical costs (such as transport), and income-earning or childcare roles prevented some from dedicating time to health needs (for example, farmers who were understandably reluctant to travel during the rice harvesting season given the potential impact on their livelihood).
## Appropriateness and ability to engage with providers
The respondents at the Children's Surgical Centre believed that they had previously received sub-optimal care, and this impacted their ability to engage fully with the service provider. Several participants reported concerns of a low-quality service at previous providers and a failure to be referred to specialist services. There were also reports of poor communication, lack of trust and paternalism from medical professionals. Many participants sought care at charitable organisations, such as the Children's Surgical Centre, when they became aware of this option and other treatments had not worked.
## Discussion
To our knowledge, there is no documented evidence on the prevalence and severity of ear disease or hearing loss in Cambodia. Although hearing loss has been ranked the fourth leading chronic disease globally, data are available across only 16.5 per cent of all geographic areas.38 Regarding CSOM specifically, many social factors reportedly contribute to the complexity of the disease, including impoverishment, marginalisation, malnutrition, a lack of quality health services, poor education and a lack of evidence-based treatment protocols.39 Compared with similar studies in other countries, our findings confirm that the degree of hearing loss and disease progression for the Children's Surgical Centre cohort is amongst the most severe reported globally for patients undergoing either tympanoplasty40–46 or mastoidectomy surgery.41–43,47–51 Patients presented to the Children's Surgical Centre on average 13–14 years after they initially became aware of their symptoms, a delay which likely contributed to the severity of the disease and degree of hearing loss. A high proportion of patients living geographically closer to the hospital experienced a similar severity of disease to those living remotely, which suggests that distance to services is not the most significant barrier to access but rather the general lack of availability of appropriate care.
The Children's Surgical Centre cohort experienced many of the typical supply- and demand-side challenges that affect access, including a lack of knowledge of ear disease or provider information, high direct and in-direct healthcare costs, opportunity costs and time lost because of long distances travelled (which our cohort ultimately overcame when accessing the Children's Surgical Centre). On the supply side, most participants reported a lack of ear and hearing care services at the primary care level and even fewer specialist services at the secondary or tertiary level, and they resorted instead to ‘simple medical care’. On the demand-side, the preference for private providers (qualified and informal) reflects a general belief that they provide better quality of care than government services.37 The common resort to self-treatment (visiting the local pharmacy, seeking advice from family and sometimes practising medical pluralism with Kru Khmer) mirrors findings from Rwanda, for example, where medical pluralism is more common in rural settings and where modern healthcare facilities may be unavailable.52 The lack of awareness of the causes of hearing problems and the lack of knowledge of ear and hearing care services, are likely to reflect the fact that services are simply not available locally, evidenced also in feelings of fear, stigma and a lack of trust in providers. This is also demonstrated by the fact that care was sought where it was locally available (demonstrated by prior treatments) and that patients were willing to pay high out-of-pocket costs (US$500–600 expenditure is high related to average incomes) to get care even in places where neither professional services nor quality care were available.
This preliminary scoping study shows a significant burden of ear disease in Cambodia that is related to the delay in receiving timely and effective treatment and that exceeds current workforce and infrastructural capacity in both public and private sectors. As healthcare facilities were generally unavailable in rural areas, people tended to ‘get by’, learning to live with their symptoms for a significant period of time. The lack of health facilities specialising in ear and hearing care, poor quality of care and ensuing worsening of their chronic symptoms, encouraged patients to continue their ‘health shopping' behaviour until they reached the Children's Surgical Centre hospital. Our findings reflect gaps in the knowledge of healthcare providers about causes and treatment of otitis media and a lack of co-ordination of care between providers within the health system.
The global evidence indicates that early treatment for CSOM involves inexpensive topical antibiotics and ear cleaning and has been reported to be managed by non-specialists in appropriately equipped local or regional health facilities.5 A model utilising community healthcare workers has been used to deliver ear and hearing care in other low resource settings,53–55 although the current structure to support such workers in *Cambodia is* somewhat fragmented.56 Community healthcare workers have been an essential link between health centres and the community and are seen as key for health education and promotion of prevention activities.57 Stigma and lack of understanding of disease requires education and cultural change, where again local or regional community engagement may be relevant as well as communication and policy change at the national level. Despite the reported high prevalence of ear disease in Southeast Asia, few studies have explored access to care for those with chronic suppurative otitis mediaCambodia has seen significant improvements in health outcomes since the introduction of health financing policies to improve service delivery amongst the most vulnerableFindings indicate the otological disease progression and associated hearing loss for this hospital cohort is amongst the most severe reported globallyPatients described numerous barriers to accessing ear careAdopting policies that integrate quality ear and hearing care into primary care provision can provide a continuum of care In order to improve accessibility in remote areas, the use of specialist satellite clinics in isolated areas can also be fruitful.34 For example, in the Pacific Islands, co-ordination between developmental organisations, government and local communities helped to establish ear and hearing services in this region by raising awareness and strengthening the collaboration between key stakeholders within the community.58 This must be in tandem with developing high quality and affordable services and providing protection against unaffordable and informal costs of accessing care (even within the government system), which remains a challenge in Cambodia despite progress in providing financial protection.59 *As this* was an exploratory study that targeted a selected group, the main limitation was that the experiences and stories collected cannot be seen as representative of the wider community. However, the strength of our study is that we triangulated data using three different methods, providing both breadth and depth in analysing patient care-seeking pathways. Our findings support the need for a rigorous population-based random sample study to further explore the prevalence and burden of ear disease and the regional supply- and demand-side barriers to care. Further analysis of the supply-side barriers to ear and hearing care service delivery would also provide opportunity to understand current clinical management practices and to work with service providers and stakeholders to improve service co-ordination, workforce planning, priority setting, and investment in the ear and hearing care sector.
## Conclusion
This study showed a significant burden of ear disease in Cambodia, made greater by the delay in receiving timely and effective treatment, that exceeds current workforce and infrastructural capacity. The adequate provision of integrated care for non-communicable diseases remains a challenge for the Cambodian health system.60 There are apparent gaps in the knowledge of healthcare providers about causes of and treatments for otitis media and a lack of co-ordination of care between providers within the health system. Currently, healthcare providers are unable to impart the information or the management required for appropriate diagnosis and intervention in a timely manner.61 The Cambodian Ministry of Health, which delivers government health services at health centres and hospitals, has an ambitious programme for strengthening the availability, quality and affordability of government health services, reflected in the consecutive health strategic plans. Even so, the challenges of integrating ear and hearing care services within existing service-delivery arrangements remain. Three priority ear and hearing care areas for policy makers as they set the agenda under future health strategic plans are: [1] integration of ear and hearing care service delivery into primary care, [2] infrastructure development and human resource training, and [3] sustainable financing and social protection mechanisms in support of ear and hearing care.
Adopting policies that integrate quality ear and hearing care into primary care provision can provide a continuum of care, covering aspects such as health promotion, disease prevention, diagnosis, treatment and disease management through appropriate referral pathways. The global evidence suggests that integrating ear and hearing care within primary care by running patient education campaigns, improving health insurance coverage, investing in provider training and encouraging the take up of mobile health technologies (such as hearing screening) can improve access in rural or remote locations.62 *This is* consistent with the recently published World Report on Hearing, which recommended prioritising a strategy of integrated, community-based and people-centred ear and hearing care to improve local access to interventions and to improve co-ordination of referral pathways.61 With regard to the ear and hearing care infrastructure, the improved provision of hearing care by government health facilities could be tackled by strengthening the minimum package of activities and the complementary package of activities delivered at government facilities. This would also serve the purpose of providing patient benefit through existing social health protection mechanisms, such as the health equity funds and newly proposed social insurance arrangements. Health system strengthening in Cambodia in recent years has laid the foundation for moving further in the direction of responding to the emerging burden of non-communicable diseases and in particular for addressing the need for improved hearing care. There is now an opportunity to address more effectively the apparent personal, healthcare and economic burden of hearing loss.
## Competing interests
None declared
## Set script (to be translated into Khmer)
As a patient attending the Children's Surgical Centre, you are invited to participate in a questionnaire to explore the barriers people experience in accessing ear and hearing care services in Cambodia. If you choose to participate, you will be asked to complete a 15-minute survey. There are 23 questions, 3 of which are short-answer; all others are multiple choice or tick the box.
Your participation in this research project will in no way affect the care you receive at the Children's Surgical Centre.
Your answers are confidential, and responses are anonymous. We may ask if you would like to be involved in a more in-depth interview if we believe your experience may help provide more information for this study.
Participation is completely voluntary, and you are free to stop at any time while completing the survey. Once you have completed the survey, it is no longer possible to withdraw from the study.
Do you have any questions so far?
(Read plain language statement) Would you like to participate in this study?
Please sign or provide your thumb print on the consent form which indicates that you agree for your responses to be used for research. Table 1.Patient perspective questionnairePart 1: demographics *What is* your gender? FemaleMaleOtherPrefer not to answer *What is* your age?Answer: *What is* your current place of residence? ( district, village or town)Answer: How many hours did you travel to reach the CSC?Answer: *What is* the highest education level that you have completed at school? Did not attend schoolPre-primary schoolPrimary schoolSecondary schoolPost-secondary/tertiary Employment Never workedCurrently workingNot currently working *What is* your occupation? Not currently workingDriverTechnicianFarmerRetiredTraderStudentCivil servantOther: *What is* your current marital status? Never marriedCurrently marriedSeparatedDivorcedWidowedCohabitingDo not want to respondPart 2: reason for attendance *What is* your reason for today's visit? First visit to CSC ENT DepartmentFollow-up visitEar surgeryPost-operative surgical visitRehabilitation (e.g. hearing aids)Other: How did you hear about CSC? Health professional referred (e.g. doctor, All Ears Cambodia, other hospital)Family memberFriendSocial media (e.g. Facebook or Instagram)RadioOther: Before coming to CSC, what had been your main ear/hearing symptoms? ( circle all that apply) Hearing lossEar painDischarging or leaking earTinnitus (ringing sound or noise in ears)Blocked earDizziness or balance problemOther: How long did you have these symptoms? ( circle closest answer) Less than a weekLess than a monthUp to 6 monthsUp to 1 year1–3 years3–6 years6–10 yearsMore than 10 years What has caused you to seek treatment now? ( choose all that apply) Worsening symptomsCould not work due to ear problemRecently heard about the services at CSCOther treatments did not workOn waiting list at other hospital for too longChange in circumstances (e.g. now living closer to hospital)Referred by health professionalOther:Part 3: well-being *What is* the MAJOR health problem that limits your activities? ( circle only one) DiabetesHypertension/high blood pressureHeart problemStroke problemArthritis/rheumatismBack or neck problemFractures, bone/joint injuryEye/vision problemEar/hearing problemLung/breathing problemCancerDepression/anxiety/emotional problemOther disability or problem (please specify): Of each of the following, in the last six months, what effect have your ear problems had on your life?a) You have difficulties hearing, even with a hearing aid YesNoDoes not apply Your ear problem affects your ability to work YesNoDoes not apply If the ear problem affects ability to work, has lack of money stopped you seeking help? YesNoDoes not apply Your ear problem affects your ability to enjoy life YesNoDoes not apply Your ear problem affects your ability to participate in social activities YesNoDoes not apply Your ear problem affects your ability to take care of your family/household YesNoDoes not apply Your ear problem affects your ability to keep good family relationships YesNoDoes not apply Your ear problem affects your ability to have a meaningful life YesNoDoes not apply Why do you think you have your ear problem?Please explain:Part 4: access to services What services for ear or hearing care are available in your district? ( circle all that apply) District or referral hospitalPrivate hospital or health centreQualified doctor or nursePharmacyKru Khmer (traditional healer/herbalist)MonkNo services availableOther: Where you usually live, what are the reasons you could not attend ear care services as much as you needed? ( circle all that apply)Accessibility No available doctor or service near homeNo other hospital would do surgeryIneffective referral for ear careHospital or service was too far awayRoads are in poor condition to reach hospital or serviceNo accommodation near hospital or serviceNo one could take care of childrenNot well enough for surgery at the timeOn waiting list for too long Knowledge Did not know any ear care services were availableDid not think the ear problem was serious enough to need helpDid not think anything could be done to help the ear problem Financial I could not miss workSurgery or hearing aid very expensiveTransport very expensiveFood needed very expensiveChildcare expensiveToo expensive to bring carer with you to hospital Acceptability Family did not want you to attend the services/facilitiesFamily has difficulty assisting you to access services/facilityBelief that the ear problem cannot be fixed by seeing a medical doctorWorry about the treatment or poor outcome of treatmentWorry about how people behave towards you at the hospitalNoneOther: Among all the reasons you have selected above, which one is the main reason for you?(Choose one option)Answer: What other ear care services did you seek before coming to CSC? ( circle all that apply) NoneDistrict or referral hospitalPrivate hospital or health centreQualified doctorSpecialist ear doctorPharmacyDrug shopMonkKru Khmer (traditional doctor/healer)Kru Boramei (fortune teller)Other: What other treatment for your ear problem have you received before coming to CSC? ( circle all that apply) NoneMedicine from pharmacyEar surgeryTraditional medicine (e.g. herbal remedy)Dietary recommendationMassageLaxatives (banh chos)Bath (toek saoy) or steam bath (chpong)Other (please list): 22. If you could not attend CSC, what other services would you use for your ear problem? ( circle all that apply) District or referral hospitalOther private hospital or health centreMedical doctorPharmacyDrug shopMonkKru Khmer (traditional doctor/healer)Kru Boramei (fortune teller)Don't knowOther: In your community, where do people usually go for their ear problems? ( circle all that apply) District or referral hospitalPrivate hospital or health centreQualified doctorPharmacyDrug shopMonkKru Khmer (traditional doctor/healer)Kru Boramei (fortune teller)Other:They do not seek help. If they do not seek help, why not?
CSC = Children's Surgical Centre You have completed this questionnaire. Thank you for participating in this research project.
As you have a child under your care who is a patient attending the Children's Surgical Centre, you are invited to participate in a short questionnaire to find out the problems people have when trying to find help for their ear problems in Cambodia.
If you choose to participate, you will be asked to complete a 15-minute survey. There are 21 questions, 3 of which are short-answer, all others are multiple choice.
Your participation in this research project will in no way affect the care your child will receive at the Children's Surgical Centre.
Your answers are confidential, and responses are anonymous. If you agree, we may contact you later to ask if you would like to come back for a longer interview if we believe your experience may help provide more information for this study.
Participation is completely voluntary, and you are free to stop at any time while completing the survey. Once you have completed the survey, it is no longer possible to withdraw from the study.
If you would like to participate, I will read the plain language statement to ensure you are happy to proceed. Do you have any questions so far?
(Read plain language statement) Would you like to participate in this study?
Please sign or provide your thumb print on the consent form which indicates that you agree for your responses to be used for research. Table 1.Patient perspective questionnaire: carerPart 1: demographics24. What is the child's gender? FemaleMaleOther b) Prefer not to answer25. What is the child's age?Answer:26. Where is the child's current place of residence? ( district, village or town)Answer:27. How many hours did you travel to reach CSC?Answer:28. What is the highest education level that you, as carer of the child, have completed at school? Did not attend schoolPre-primary schoolPrimary schoolSecondary schoolUniversityPrefer not to answer29. Your employment Never workedCurrently workingNot currently workingPrefer not to answer30. What is your occupation? Not currently workingSellerDriverFactory or construction workerTeacherFarmerRetiredTraderStudentWork for the governmentOther:Prefer not to answerPart 2: reason for attendance31. What is the reason for the child's visit today? First visitFollow-up consultationEar surgery in-patientPost-operative follow-up consultationRehabilitation (e.g. hearing aids)Other:32. How did you hear about CSC? Health professional referred (e.g. doctor, All Ears Cambodia, other hospital)Family memberFriendSocial media (e.g. Facebook or Instagram)RadioOther:33. Before attending CSC, what had been the child's main ear/hearing symptoms? ( circle all that apply) Hearing lossEar painDischarging earTinnitus (ringing sound or noise in ears)Blocked ear or ear fullnessDizziness or balance problemHeadacheCannot speakOther:34. How long did the child have these symptoms from when the problem first began? ( circle closest answer) Less than a weekLess than a monthUp to 6 monthsUp to 1 year1–3 years4–6 years7–10 years11–15 yearsMore than 15 years35. Why did you decide to bring the child to CSC now? ( choose all that apply) Worsening symptomsCould not go to school due to ear problemRecently heard about the services at CSCOther treatments did not workOther hospital does not treat ear problemsWait too long to have operation at other hospitalChange in circumstances (e.g. now living closer to hospital)Referred by health professionalOther reason:Part 3: wellbeing36. In the last six months, what effect have the child's ear problems had on their life?a) Difficulties hearing school teacher YesNoDoes not applyb) Does not attend school because of ear problem YesNoDoes not applyc) Difficulties talking in family conversations YesNoDoes not applyd) Difficulties talking with friends YesNoDoes not apply37. What do you think caused the child's ear problem?Please explain:Part 4: access to services38. What services for ear or hearing care are available in your district? ( circle all that apply) Government, district or referral hospital or health centrePrivate hospital or health centreQualified doctor or nursePharmacyKru Khmer (traditional healer/herbalist)No services availableOther:39. Do other people in your district use these services YesNo. If not, why?40. Why did you decide to bring the child to CSC now?Accessibilitya) Hospital or service was too far away YES / NOb) Roads are in poor condition to reach hospital or service YES / NOc) No one could take care of other children YES / NOd) On waiting list for too long YES / NOKnowledgee) Did not know any ear care services were available YES / NOf) Did not think the ear problem was serious enough to need help YES / NOFinancialg) You could not miss work YES / NOh) Treatment was too expensive YES / NOi) Transport very expensive YES / NOj) Food needed very expensive YES / NOk) Childcare expensive for other children YES / NOAcceptabilityl) Thinks that the ear problem cannot be treated YES / NOm) Felt scared about going to the hospital or service YES / NOIf yes, why?n) Any other reason:41. Among all the reasons you selected, which one do you believe is the main reason?(Choose one option)Answer:42. What other ear care services did you seek for the child before coming to CSC? ( circle all that apply) NoneGovernment, district or referral hospital or health centrePrivate hospital or health centreQualified doctorSpecialist ear doctorPharmacyDrug shopKru Khmer (traditional doctor/healer)Other:43. What previous treatment for the child's ear problem were sought before coming to CSC?(circle all that apply) NoneEar drops or topical medicineOral medicationEar surgeryTraditional medicine (eg. herbal remedy)Dietary recommendationOther (please list):44. Do you have any other comments or questions?
CSC = Children's Surgical Centre You have completed this questionnaire. Thank you for participating in this research project.
## Accessibility/structural
Tell me about what you have done about your ear problem?
Prompts: Where did you go? Are services for ear care available where you live? Tell me about them. Did you use them?How far did you travel?How long did you wait before seeking help for your problem? Why?What has caused you to seek help now?Have there been any problems in accessing ear care services? Tell me about them.
## Affordability/financial
Tell me about what costs you had in coming to find treatment.
Prompts: How were these costs met? E.g. Did you have to borrow money? Does treatment affect the family income? Did you have access to the health equity fund?Direct costs may include: surgery, transport, food, clothes, accommodation for yourself or caregiver, emergency care, informal payments e.g. cost of childcare).Indirect costs (e.g. loss of earnings during surgery/recovery).
## Acceptability/cultural
Tell me about the main concerns you had in coming to a place for care for your ear problem.
Prompts: Did you have some concerns about services in your district?How do other people view hearing problems and ear disease in Cambodia?Did others influence your decision in trying to find help for your ear problem? ( significant others, co-workers, teachers, class-mates).What kind of treatment do you think you should receive? What are the most important results you hope to receive from treatment?What do you expect to change into the future?
Thank you for sitting down for this chat with me. Is there anything you would like to add, or do you have any questions?
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|
---
title: Proteomic characteristics and diagnostic potential of exhaled breath particles
in patients with COVID-19
authors:
- Gabriel Hirdman
- Embla Bodén
- Sven Kjellström
- Carl-Johan Fraenkel
- Franziska Olm
- Oskar Hallgren
- Sandra Lindstedt
journal: Clinical Proteomics
year: 2023
pmcid: PMC10040313
doi: 10.1186/s12014-023-09403-2
license: CC BY 4.0
---
# Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19
## Abstract
### Background
SARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool.
### Methods
Patients with PCR-verified COVID-19 infection (COV-POS, $$n = 20$$), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, $$n = 16$$) and healthy controls (HCO, $$n = 12$$) were prospectively recruited. EBPs were collected using a “particles in exhaled air” (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92.
### Results
Significant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs.
### Conclusion
Our results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12014-023-09403-2.
## Introduction
In late December 2019, doctors in Wuhan, China, notified the world of a new cluster of patients with pneumonia of unknown origin [1]. A novel virus, originating from the betacoronavirus family was rapidly sequenced and identified and named severe acute respiratory coronavirus 2 (SARS-CoV-2) causative of the respiratory disease, coronavirus disease 2019 (COVID-19) [2]. The specifics of the pathophysiology of SARS-CoV-2 infection remain poorly understood. Individuals are primarily infected via the airways, where SARS-CoV-2 binds with host angiotensin-converting enzyme 2 (ACE2) via its receptor-binding domain on the spike protein resulting in internalization of the virus into host cells [3]. The subsequent imbalance between the protective and adverse axis of the RAS pathway causes decreased stability of the pulmonary endothelium, inflammatory and thrombotic processes causing respiratory distress [4].
The COVID-19 pandemic highlighted many of the diagnostical challenges of pulmonary disease. Common diagnostical techniques include RT-PCR swabs for viral detection, auscultation, blood work, chest x-ray and computer tomography scans (CT-scans) [5]. However, only bronchoalveolar lavage (BAL) performed during bronchoscopy under sedation can properly detect pathological changes in the otherwise unreachable small airways. Furthermore, all current diagnostical methods have their weaknesses regarding sensitivity, specificity, or potential harm to patients. Novel diagnostical methods in pulmonary medicine are therefore urgently needed.
Exhaled breath is a carrier of valuable information from the respiratory system and analysis of particles and biomarkers provides an attractive such approach. Samples are collected non-invasively and provide a localized sample of the most distal parts of human lungs. Currently two such approaches are actively being researched. Measurements of the volatile compounds in breath, an alcohol breath analyzer being a common example, or the detection and analysis of exhaled breath particles (EBP). Compared with volatile compounds, EBPs can offer more specific insights into disease processes because an array of molecules can be measured. EBPs originate from the respiratory tract lining fluid that covers the epithelial surface of the distal parts of the lung. EBPs are thought to be generated during opening and closing of the distal airways but can also be generated through shear stress [6]. The protein composition of EBPs closely resembles that of BAL fluid of which changes in the proteomic composition have been connected to different pulmonary diseases [7].
A few studies have investigated the proteomic characteristics and changes in COVID-19 patients in plasma, BAL, sputum and pulmonary tissue [8–11]. Yet, none have yet investigated the proteomic profile of COVID-19 in EBPs. Furthermore, the proteomic composition of EBPs and alterations in human disease are still poorly understood. We therefore investigated the proteomic composition of EBPs in healthy subjects, in patients with respiratory symptoms but with repeated negative PCR test for COVID-19 infection and in COVID-19 infected patients through high-performance liquid chromatography-mass spectrometry (HPLC–MS/MS) to identify potential biomarkers in exhaled breath for rapid, non-invasive diagnosis and evaluation of pulmonary disease status.
## Patients
Patients were recruited prospectively between the 14th of May and 14th of November 2020. A total of 48 patients participated in the study and split into two groups: PCR-verified COVID-19 infection (COV-POS, $$n = 20$$), repeat PCR-negative but COVID-19 symptomatic patients (COV-NEG, $$n = 16$$) and additionally healthy volunteers were included as controls (HCO, $$n = 12$$). Patients were recruited as either inpatients at the infectious disease wards or the emergency department at Skåne university hospital in Sweden. Mean age was 57 years (range 21–70). All patients signed an informed consent form before taking part in the study. The study was approved by the Swedish Ethical Review Authority EPN Dur $\frac{2018}{129}$, 2020–018640427 and registered at ClinicalTrials.gov with the trial register number NCT04503057.
## Particle collection
Particles were collected using a PExA 2.0 device (PExA, Gothenburg, Sweden). The instrument uses a two-way valve that allows participants to inhale particle-free air through a HEPA filter and exhale into the instrument. Particles are measured by their size and quantity by an optical particle counter and sized into 16 size bins and collected on a membrane by an inertial impactor within the device. The bin sizes averages ranges from 0.33 µm to 3.67 µm. Exhaled flow and volume are measured by an ultrasonic flow meter. A breathing maneuver, previously described, was used for the EBP collection until a goal amount of 120 ng of sampled particles had been collected [6, 12]. The particles are measured and expressed as number of particles per volume (PEV) and relative counts per particle size. All samples were immediately transferred after collection and stored at − 80 °C for later analysis. No participants reported any adverse events in connection to EBP sampling.
## Statistical analysis of particle data
All statistical test related with PEV were done using Graphpad Prism 9 (Graphpad Software, San Diego, CA). Descriptive statistics in the form of median and interquartile range was used for particle and patient data. Kruskal–Wallis test with Dunn’s post hoc test was used to compare PEV between groups. For statistical analysis between correlation of PEV to age the data were first transformed into its natural logarithms and then analyzed using Pearson parametric correlation coefficients and reported as R2. For comparison of PEV between sexes Mann–Whitney-U was used. For comparison of relative particle sizes between groups log transformed particle data was analyzed with a mixed effects model REML and Tukey’s multiple comparisons test. Statistical significance was defined as ****$p \leq 0.0001$, ***$p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$ and NS ($p \leq 0.05$).
## Sample preparation for LC–MS/MS
EBP samples were incubated in $2\%$ sodium dodecyl sulfate (SDS, Sigma-Aldrich, St. Louis, USA) in 50 mM Triethylammonium bicarbonate (TEAB, Thermo Fisher Scientific) at 37 °C for 2 h with subsequent addition of 400 mM dithiothreitol (Sigma-Aldrich) and further incubation for 45 min.. Alkylation was performed in the dark for 30 min with the addition of 800 mM iodacetamide (Sigma-Aldrich) after which $12\%$ aqueous phosphoric acid was added to a final concentration of $1.2\%$. Proteins were collected onto S-TRAP columns (Protifi, Farmingdale, USA) with a mixture of $90\%$ methanol and 100 mM TEAB. Digestion of proteins was performed with 1 µg of Lys-C (Lys-C, Mass Spec Grade, Promega, Fitchburg, USA) incubated at 37 °C for 2 h after which 1 µg of trypsin (Promega sequence grade) was added overnight with addition of 0.45 µg Trypsin after 12 h. Peptides were then eluted with 50 mM TEAB, $0.2\%$ formic acid (FA, Sigma-Aldrich) and $50\%$ acetonitrile (ACN, Sigma-Aldrich) with $0.2\%$ formic acid and dried by speedvac (Eppendorf, Hamburg, Germany) at 45 °C and re-dissolved in 20 uL of $0.1\%$ FA and $2\%$ ACN solution.
## LC–MS/MS
Digested peptides were separated with nanoflow reversed-phase chromatography with an Evosep One liquid chromatography (LC) system (Evosep One, Odense, Denmark) after loading the samples on Evosep tips. Separation was performed with the 60 SPD method (gradient length 21 min) using an 8 cm × 150 µm Evosep column packed with 1.5 μm ReproSil-Pur C18-AQ particles. The Evosep One was coupled to a captive source mounted on a timsTOF Pro mass spectrometer from Bruker Daltonics (Billerica, Massachusetts, USA). The instrument was operated in the DDA PASEF mode with 10 PASEF scans per acquisition cycle and accumulation and ramp times of 100 ms each. Singly charged precursors were excluded, the ‘target value’ was set to 20,000 and dynamic exclusion was activated and set to 0.4 min. The quadrupole isolation width was set to 2 Th for m/z < 700 and 3 Th for m/z > 800.
## LC–MS/MS data analysis
MaxQuant (v2.0.20, Max Planck institute of biochemistry, Munich, Germany) using the Andromeda database search algorithm was used to analyze raw MS data [13]. Spectra files were searched against the UniProt filtered and reviewed human protein database using the following parameters: Type: TIMS-DDA LFQ, Variable modifications: Oxidation (M), Acetyl (Protein N-term) and Fixed modifications: Carbamidomethyl (C). Digestion, Trypsin/P, Match between runs: False. FDR was set at $1\%$ for both protein and peptide levels. MS1 match tolerance was set as 20 ppm for the first search and 40 ppm for the main search. Missed cleavages allowed was set to 2. Subsequently the Spectra files were searched against the UniProt SARS-CoV-2 proteome database (Proteome ID: UP000464024) using the same parameters. Data was first normalized with NormalyzerDE using robust linear regression normalization [14]. Perseus (v2.0.5.0, Max Planck institute of biochemistry, Germany) and RStudio (v4.2.0, RStudio, Boston, MA, US) were used for downstream analysis of proteomics data. Proteins denoted as decoy hits, contaminants, only identified by site were removed. Next proteins identified in less than $45\%$ of samples in at least one group were removed. Significant differences in protein intensities between groups were determined with an ANOVA q-value of < 0.05 and post hoc Tukey’s test of the log2-transformed LFQ intensities. Differentially expressed proteins were determined using and s0 of 0.1 and FDR of 0.05. For the heatmap LFQ values were normalized with a Z-score and rendered in RStudio using the pheatmap package using euclidean clustering. Protein–protein interaction and Reactome Pathways were analyzed using STRING v11.5 using the stringApp within Cytoscape v3.9.1. Subcellular location determined with CellWhere v.1.1 [15]. Statistical significance was defined as ****$p \leq 0.0001$, ***$p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$ and NS ($p \leq 0.05$).
## Machine learning classification model
A diagnostic classification model was built using the R CARET package (version 6.0–93). For the machine learning analysis, missing values were first imputed in Perseus with a width of 0.3 and a down shift of 1.3. Independent feature selection was used within Perseus and based on ANOVA scores and least number of missing values. The top 11 proteins as well as each subject´s PEV count was determined to give the smallest error percentage. The following biomarker panel was selected: ORM1, IGHG1, CAPN1, CASP14, PEV, IGLC6, APOA1, TF, IGKC, EPPK1, SFTPB and IGHA1 and the data subsequently exported into R. The cohort was split randomly in a $\frac{60}{40}$ split for training ($$n = 22$$) and testing ($$n = 12$$) respectively with subjects classified as either positive (COV-POS, $$n = 12$$) or negative (COV-NEG and HCO, $$n = 22$$). A random forest model was trained on the training set with tenfold cross validation repeated 100 times and using 1000 trees. Receiver operating characteristic (ROC) was used to select the optimal number of randomly drawn candidate variables (mtry) and set at 2. The results of the model are based on application of the model on the test set and reported as accuracy, sensitivity and specificity and area under the ROC curve (AUC-ROC).
## Patient demographics
Median age and sex were similar between COV-POS and COV-NEG with median age being lower in HCO. COV-POS patients had a higher incidence of obesity and asthma in comparison to COV-NEG and HCO. Symptomatology were similar between COV-POS and COV-NEG regarding fever, throat pain, stomach pain and myalgia but differed significantly regarding dyspnea with $95\%$ of COV-POS patients reporting it as a symptom. No symptoms were reported in the HCO group. EBP measurements were on average sampled on day 7 post COVID-19 positive test but ranged between 1 and 9 days. A summary of participant information can be found in Table 1.Table 1Patient characteristicsCharacteristicsCOV-POSCOV-NEGHCONumber of participants201612Sex: Male10 ($50\%$)8 ($50\%$)4 ($44\%$)Age (Median)56 (IQR: 53–64)69 (IQR: (53–80)44 (IQR: 29–46)Days since symptom debut*8 (IQR: 3.75–10)2 (IQR: 1–4.75)0Clinical diagnosis Infectious etiology Viral20 ($100\%$)5 ($31.3\%$)0 ($0\%$) Bacterial0 ($0\%$)4 ($25\%$)0 ($0\%$) Unknown0 ($0\%$)5 (31,$3\%$)0 ($0\%$) Non-infectious respiratory symptoms0 ($0\%$)2 ($12.5\%$)0 ($0\%$) Comorbidities Asthma1 ($5\%$)0 ($0\%$)0 ($0\%$) COPD3 ($15\%$)1 ($6.25\%$)0 ($0\%$) Obesity10 ($50\%$)4 ($25\%$)1 ($8.3\%$) Symptoms Coughing14 ($70\%$)8 ($50\%$)0 ($0\%$) Fever11 ($55\%$)6 ($38\%$)0 ($0\%$) Throat pain2 ($10\%$)3 ($19\%$)0 ($0\%$) Stomach pain4 ($20\%$)4 ($25\%$)0 ($0\%$) Dyspnea19 ($95\%$)9 ($56\%$)0 ($0\%$) Myalgia3 ($15\%$)1 ($6\%$)0 ($0\%$) Hospitalized20 ($100\%$)7 ($44\%$)0 ($0\%$)Characteristics for patients with PCR-verified COVID-19 infection (COV-POS), COVID-19 PCR-negative patients with respiratory symptoms (COV-NEG) and healthy controls (HCO)IQR Interquartile range*Or days since seeking medical care if unknown. Descriptive statistics presented as number of patients and percentage
## Analysis of exhaled particle data
EBPs were collected and particles per exhaled volume (PEV) were measured over time, summed, and compared between groups. There was a significant increase in PEV in COV-POS and COV-NEG patients compared to HCO. COV-POS exhaled a median of 11,902 particles (Interquartile range (IQR): 6119–17,893) and COV-NEG a median of 8,159 (IQR: 5406–12,000) compared to a median of 3,622 (IQR: 2506–5790) in the HCO group. Figure 1A demonstrates this large intra-group variation in IQR range in PEV in COV-POS and COV-NEG. Furthermore, there was no correlation between PEV and age (r2 = 0.06954) or between sexes in PEV ($$p \leq 0.3254$$).Fig. 1Exhaled breath particle concentrations and particle size distributions differed significantly between symptomatic and healthy patients. Particles in exhaled air were measured using an optical particle counter. A Particles per exhaled volumes (PEV) for patients with PCR-verified COVID-19 infection (COV-POS), patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests for COVID-19 (COV-NEG) and healthy controls (HCO) Data shown as individual values (black dots) with lower and upper boundary of boxplots representing 25th and 75th percentile. Statistical significance was tested with Kruskal–Wallis test with Dunn’s multiple hypothesis testing correction. B Relative particle size counts per particle size bin for COV-POS, COV-NEG and HCO. Data are shown as mean ± standard error of mean. Statistical significance was tested using ANOVA with Tukey’s multiple comparisons correction and significance values are shown between COV-POS and HCO. Statistical significance was defined as ****$p \leq 0.0001$, ***$p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$ and NS ($p \leq 0.05$) Patients with respiratory symptoms (COV-POS and COV-NEG) skewed towards exhaling smaller particles in comparison to the HCO group. In these patients, particle bin size 1, accounting for particles with a median diameter of 0.33 µm constituted on average $33\%$ of total exhaled particles compared to just $18\%$ for the same particle bin size in HCO. The HCO group presented with a bimodal distribution of relative particle size distribution in comparison with the right skewed distribution in the symptomatic groups. Figure 1B presents particle size distributions between the three groups.
## LC–MS/MS based protein identification of exhaled particles
Patient samples with 100 ng or more collected particles were selected for LC–MS/MS protein identification yielding a total of 34 samples for further analysis. 12 samples each from the COV-POS and COV-NEG groups were analyzed and 10 samples from the HCO group. A flow chart summarizing sample exclusion can be seen in Additional file 1: Fig. S1. In total 267 unique proteins could be identified across all three groups after exclusion of potential contaminants. 146 proteins were present in $45\%$ of samples in at least one group, identifying immunoglobulin heavy constant gamma 3 (IGHG33) as the only unique protein found in the COV-POS group, identified in $50\%$ ($$n = 6$$) of all samples in the group. Mean number of proteins identified per sample was 110.1 (SD: 15.8). No viral SARS-CoV-2 proteins could reliably be detected in any of the samples.
## LC–MS/MS quantitative proteomics of exhaled particles
Subsequently, identified proteins were quantified with label free quantification (LFQ) of exhaled particles. In total 26 proteins were identified as significantly differentially expressed and summarized in Table 2. Significantly differentiated proteins were mainly extracellular proteins, as shown in Fig. 2, but included proteins localized to the cell membrane and intracellular proteins. Reactome pathway analysis revealed differentially expressed proteins related to, among other things, the innate immune system as well as neutrophil and platelet degranulation. Clustering analysis of significantly differentiated proteins among groups revealed three distinct groups, of which $67\%$ ($$n = 8$$) of COV-POS patients compromised one cluster as shown in Fig. 3. A second cluster was comprised of three COV-NEG samples and the last cluster of the remaining samples, including four COV-POS samples. Nine proteins were significantly upregulated in COV-POS patients in comparison to the COV-NEG and HCO groups and are shown in Fig. 4A, B. In comparing COV-NEG to HCO, eight proteins were found to be significantly downregulated as shown in Fig. 4C. The upregulated proteins included three immunoglobulins: *Immunoglobin kappa* constant (IGKC), Immunoglobulin heavy constant gamma 1 (IGHG1) and immunoglobin lambda constant 3 (IGLC3) as well as Epiplakin (EPPK1), a protein involved in wound healing. Figure 5 presents boxplots of proteins significantly differentially expressed of particular interest in COV-POS patients and include Serotransferrin (TF, F), Apolipoprotein A-I (APOA1, C), Caspase-14 (CASP14, B), Calpain-1 (CAPN1, D), and Alpha-1-acid glycoprotein 1 (ORM1 1, A), a modulator of the immune system during the acute-phase reaction. Pulmonary surfactant-associated protein B (SFTPB, E) was significantly downregulated in COV-POS and COV-NEG patients versus the HCO group. Table 2Significantly differentially expressed proteinsGene namesProtein namesANOVAq-valueMean differenceAndromeda scoreCOV-POSCOV-NEGHCOIGHG1Ig gamma-1 chain C region0.0044.4-3.0-4.4323IGKCIg kappa chain C region0.0113.1-3.12.2323ORM1Alpha-1-acid glycoprotein 10.0122.8− 2.8− 2.6165SFTPBPulmonary surfactant-associated protein B0.016− 2.7− 2.22.769TFSerotransferrin0.0212.9− 2.90.0323IGHA1Ig alpha-1 chain C region0.0221.3− 2.92.9323CASP14Caspase-140.027− 2.62.61.5323EPPK1Epiplakin0.0292.6− 1.4− 2.6292CAPN1Calpain-1 catalytic subunit0.033− 2.32.31.461IGLC6Ig lambda-6 chain C region0.0342.5− 2.51.4229APOA1Apolipoprotein A-I0.0362.4− 2.40.0308CATCatalase0.036− 1.42.2− 2.2323DSC3Desmocollin-30.037− 2.22.2− 1.7270VCLVinculin0.040− 1.8− 1.81.852PKP1Plakophilin-10.041− 2.12.10.0323TGM1Protein-glutamine gamma-glutamyltransferase K0.043− 1.81.8− 1.5261PSMA3Proteasome subunit alpha type-30.043− 2.12.11.788ZG16BZymogen granule protein 16 homolog B0.0430.0− 2.22.2323ARG1Arginase-10.044− 2.12.10.0323SERPINA1Alpha-1-antitrypsin0.0442.2− 2.20.0323ACTN4Alpha-actinin-40.0462.0− 1.3− 2.065S100A14Protein S100-A140.047− 1.91.90.0227TXNThioredoxin0.048− 1.3− 1.81.884PIGRPolymeric immunoglobulin receptor0.048− 1.6− 1.61.6109HPHaptoglobin0.0492.00.0− 2.0188PLBD1Phospholipase B-like 10.049− 1.91.90.076Summary of significantly differentially expressed proteins between PCR-verified COVID-19 infection (COV-POS), COVID-19 PCR-negative patients with respiratory symptoms (COV-NEG) and healthy controls (HCO) and their adjusted p-value (ANOVA q-value) and Andromeda score from the MaxQuant search engineFig. 2Schematic of protein–protein interaction network with subcellular location and Reactome Pathways for significantly differentiated proteins. Protein–protein interaction and Reactome Pathways created with STRING v11.5 inside Cytoscape v3.9.1 and subcellular location determined with CellWhere v1.1. Only significantly differentiated proteins found within the STRING database are mapped. Image created with biorenderFig. 3COVID-19 positive patients exhibited a clustered expression profile of exhaled breath proteins. Protein intensities of the 27 differentially expressed proteins were log10 transformed, normalized with a Z-score and displayed as colors ranging from blue to red with white boxes indicating missing values. Rows are clustered using Euclidean distance and cluster into three distinct expression profiles indicated by gap between rows. Samples are grouped into patients with PCR-verified COVID-19 infection (COV-POS), patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests for COVID-19 (COV-NEG) and healthy controls (HCO)Fig. 4COVID-19 positive patients showed statistically significant differentially expressed proteins in exhaled breath. X-axis show difference in intensities and y-axis negative log p-value calculated using a student’s t-test. Significantly differentially expressed upregulated proteins are highlighted in red and downregulated proteins are highlighted in blue. A Volcano plot of differentially expressed proteins between PCR-verified COVID-19 infection (COV-POS) and healthy controls (HCO). B Volcano plot of differentially expressed proteins between COV-POS and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests for COVID-19 (COV-NEG). C Volcano plot of differentially expressed proteins between patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests for COVID-19 (COV-NEG) and healthy controls (HCO)Fig. 5The six most abundant differentially expressed proteins between groups. Differences in protein expression between PCR-verified COVID-19 infection (COV-POS), patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests for COVID-19 (COV-NEG) and healthy controls (HCO). Boxplots of COV-POS (orange), COV-NEG (grey) and HCO (blue) for A Alpha-1-acid glycoprotein 1 (ORM1), B Caspase-14 (CASP14), C Apolipoprotein 1 (APOA1), D Calpain 1 (CAPN1), E Pulmonary surfactant associated protein B (SFTPB), and F Transferrin (TF). Data are presented as individual values (black dots). Line in boxplots represents mean and the lower and upper boundary of boxplots representing 25th and 75th percentile with whiskers below and above boxes representing 10th and 90th percentile, respectively. Statistical significance was tested with ANOVA and Tukey’s honest significance test and defined as ****$p \leq 0.0001$, ***$p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$ and NS ($p \leq 0.05$)
## Machine learning classification of samples
A machine learning (ML) random forest classification model was built using 11 proteins found in all groups and subjects PEV counts. For training, 22 samples were randomly selected, and variables ranked by the ML model according to importance (Fig. 6A). The ROC-AUC for the training data was determined to be 0.97 (CI 0.88–1.06). Next the model was tested on the remaining 12 samples and achieved an accuracy of 0.92 (CI 0.62–0.99), with only one COVID-19 positive sample misclassified as negative, in the testing cohort. The misclassified sample belonged to a 51-year-old female that had tested positive 8-days prior to particle collection and was subsequently discharged from the hospital the following day, possibly affecting the classification. Sensitivity for the model was determined as $75\%$ and specificity as $100\%$. AUC-ROC in the training data was 0.97 (CI 0.88–1.06) and AUC-ROC of the test data 0.81 (CI 0.52–1.1).Fig. 6Random forest machine learning model classification of EBP data to predict COVID-19 disease status. A Scaled variable importance for the classification model ranked by mean decrease in accuracy of the model. B Receiver operating characteristics of the random forest model in the training cohort. C Outcome of the model on the test cohort shown as predicted value for COVID-19 status with 1.0 as certain and < 0.5 as negative for COVID-19. Only one sample was misclassified by the model
## Discussion
This study presents a novel method for analyzing the proteome of exhaled breath particles for diagnosis and characterization of disease. Sampling of approximately 100 ng of exhaled particles allowed for detection of an average of 110 proteins per sample. This is in stark comparison to the commonly used exhaled breath condensate (EBC) analysis, where the low protein concentrations often require pooling of samples to identify similar numbers of proteins [16, 17]. We achieved a deep proteomic profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid (RTLF), among others. EBP sampling moreover allowed for the analysis of the respiratory tract health status in two-dimensions. Both in terms of the proteome of the exhaled particles as well as the particle concentrations and size distributions, which in turn have previously been implicated in respiratory disease [18].
In accordance with other published work, we identified an increase in particle production in patients with respiratory symptoms [18, 22, 23]. Particle production is thought to depend on the bulk rheological properties of RTLF. Studies have shown that modifications to the viscoelastic properties of RTLF, such as inhalation of isotonic saline, significantly change particle production, possibly explaining the increases in particle production found in our study [24]. COVID-19 patients exhibited a significant increase in particle production with a tendency towards the smaller particles. Similarly, COV-NEG patients, meaning patients with respiratory symptoms, likewise presented with a slightly lower increase in particle concentrations suggestive of a disease-dependent variation in surfactant composition. Thus, EBP collection is a promising new method for monitoring pulmonary health status over the course of an infection and has previously been investigated in other diseases [25, 26].
Proteins in the RTLF originate from various sources, including respiratory epithelial cells, resident inflammatory cells, and plasma proteins that leak from the capillary membrane. Proteins in the RTLF have broad mechanistic roles, including microbial defence, wound healing, maintaining the viscoelastic properties of the fluid, and nutrient transport, among others. Understanding and being able to monitor the proteomic changes would therefore be an attractive approach for diagnosis and disease monitoring directly from the infection or pathological focus. Proteomic analysis of BALF is one such approach and allows direct sampling of the RTLF, yet it is highly invasive and can only be performed on a limited scale in the clinic and for biomarker research. Previously reported overexpressed proteins in BALF in COVID-19 patients, correspond well to our findings, particularly for the six most abundant proteins in all samples [27]. Of particular interest in biomarker research for infectious diseases are acute phase proteins, which increase in expression in response to inflammation. Three acute phase proteins were significantly overexpressed in EBP in COVID-19 patients compared to COV-NEG and HCO. These proteins were ORM1, alpha 1 antitrypsin, and haptoglobin. Of these three, ORM1 was identified in almost all samples and significantly increased in the COV-POS group compared with both COV-NEG and HCO in EBP. ORM1 is mainly excreted from hepatic cells in response to various stress-related stimuli, but extrahepatic production has been reported, such as from alveolar type II cells upon lipopolysaccharide (LPS) induction in rats [28]. ORM1 has previously been of interest for pulmonary infections. Hamid et al. found that ORM1 plasma levels were a sensitive and specific biomarker for mortality prediction in children with pneumonia [29]. Plasma proteomic studies in COVID-19 patients, have similarly found increased expression levels, and correlations to disease severity have been reported [27]. Sampling of ORM1 from the RTLF using EPB collection, therefore, presents an opportunity for direct detection of stress-related changes in the lungs, possibly long before such changes can be seen in plasma or detected through physiological changes (see Additional file 2).
Of further interest in biomarker discovery in COVID-19 are stress response proteins. APOA1 is such a marker and was found to be significantly increased between COV-POS and COV-NEG. It has previously been implicated in the inflammatory response and immune regulation, including antioxidative and antiviral properties and is expressed in the lung epithelium [30–33]. Recently published plasma proteomic studies of COVID-19, in contrast, report finding decreased levels of APOA1 [9, 34]. However, in BAL, increases in concentrations have been reported correlating with lymphocyte concentrations or severity of lung injury [35, 36]. APOA1 might therefore be a highly specific diagnostic protein for lung injury with upregulation localized to the RTLF and, together with ORM1 forms a signature of an early response to pulmonary infection. Other stress response proteins include serotransferrin (TF). It is an iron-binding transported glycoprotein mainly synthesized by hepatocytes and, to a certain degree, in lymphocytes [37, 38]. In the human lung, TF is primarily synthesized and excreted by pulmonary epithelial cells and submucosal glands, and alveolar macrophages [39]. TF in BAL have been reported to be present in much higher concentrations in comparison with plasma, making it a particularly interesting protein in EBP research [40]. TF is mainly known for the iron-binding activity. However, new evidence points to its activity within the coagulation cascade, interfering with antithrombin/SERPINC1 and factor XIIa leading to increased coagulation indicating an increased tendency for procoagulant disorders in COVID-19 patients [41]. Increased levels of TF have been reported in BAL fluid in patients with ARDS and patients at risk of ARDS while simultaneously being downregulated in plasma, presenting it as an exciting biomarker candidate in EBP [42]. Furthermore, TF abundance was discordantly downregulated in COV-NEG patients in comparison to HCO, suggestive of a COVID-19 causative specific increase in EBP.
COVID-19 utilizes ACE2 receptors to access and infect pulmonary surfactant-producing alveolar type II (ATII) cells [43]. Subsequent viral-induced lysis and apoptosis of ATII cells and consequent loss of surfactant in COVID-19 patients are an important part of the pathology and are linked to diffuse alveolar damage, protein leakage and hyaline membrane formation [44]. In accordance, levels of SFTPB were significantly decreased in the EBP of diseased lungs, indicating that EBP collection and analysis could offer a simple and effective way of sampling the health status of the distal parts of the lungs, which has not been possible in the clinic before. Reduction of SFTPB levels in the alveolar space has been shown to precede the clinical development of ARDS and decrease the surface tension, perhaps an important mechanism for increased particle production in these individuals [45, 46]. Surfactant is mainly composed of Dipalmitoylphosphatidylcholine and has previously been studied in EBP, showing decreases in smokers' lungs [47]. Exogenous administrated surfactant has been shown to improve oxygenation in COVID-19 ARDS, and early administration could provide a benefit, showing the potential for EBP collection and analysis in rapidly aiding clinicians in driving therapeutic decisions. [ 48].
No viral proteins were identified in any of the samples by LC–MS/MS analysis. Previous attempts at detecting viral SARS-CoV-2 proteins using the more sensitive PCR analysis corroborate these results with detection of SARS-CoV-2 in only 3 of 25 samples using the standardize breathing maneuver [19]. Although attempts at identifying SARS-CoV-2 proteins by LC–MS/MS methods have been successful, for example in gargle solution and nasopharyngeal nose swaps, these represent samples from the upper respiratory tract, which may explain the lack of detection in the lower tract sampling method of EBP [20, 21].
In order to examine the diagnostic potential of EBP for lung diseases we composed an integrated proteomic biomarker panel with particle production counts for a machine learning algorithm. The classifier consequentially achieved an overall accuracy of $92\%$ in our test data illustrating the robust potential for future protein and particle production fingerprints in diagnosing pulmonary disease, rapidly and non-invasively with minimal patient discomfort.
While this study shows promising results for the use of EBP it includes a few limitations. Firstly, the study includes a relatively small sample size. Correct sensitivity and specificity values for the machine classifier are therefore difficult to accurately quantify and more differences in EBP expression could be undetected due to low power. Furthermore, days since symptom onset were unmatched between groups, possibly affecting PCR readout accuracy of COVID-19 and proteomic changes in EBP. All patients with negative COVID-19 PCR tests have therefore been reviewed for the presence of a positive COVID-19 tests in the days during the patients entire hospital stay in the days following EBP sampling. Future studies of EBP in COVID-19 and similar diseases will be needed to improve and further evaluate the diagnostical accuracy.
EBP collection allows for the detection of upregulated proteins localized to the lung milieu and enables clinicians to obtain direct insight into disease-related activity at the source. Our data show promising results to stratify protein expression patterns to distinguishing healthy RTLF from diseased. Together with particle production data, a complete picture of RTLF composition and viscoelastic function can be discerned and used to drive clinical decision-making.
## Conclusion
Mass-spectrometry-based proteomic analysis of exhaled breath particles enables exciting new possibilities for pulmonary diagnostics and biomarker discovery. Particle production is indicative of pulmonary disease status, and protein composition differs significantly between healthy and infected patients. Potential biomarkers in EBP include extracellular acute-phase proteins, decreases in surfactant-associated proteins, and intracellular proteins. Furthermore, we have shown promising potential for the use of an EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBP.
## Supplementary Information
Additional file 1: Figure S1. Flow chart of patient inclusion and sample exclusion. In total 48 subjects were recruited and split into three groups based on symptoms and COVID-19 PCR test results. Subsequently 13 samples were excluded due to insufficient particle collection (< 100 ng of sampled material). One sample in the Healthy control group further failed the mass spectrometry analysis due to technical reasons. The remaining samples where then used for training and testing a machine learning classifier. Additional file 2: Table S1. LC-MS/MS identified proteins with their statistical differences. Summary of all comparisons between PCR-verified COVID-19 infection (COV-POS), PCR-negative patients with respiratory symptoms (COV-NEG) and healthy controls (HCO) and their adjusted p-value (ANOVA q-value) and Andromeda score from the MaxQuant search engine.
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|
---
title: Multi-organ single-cell analysis reveals an on/off switch system with potential
for personalized treatment of immunological diseases
authors:
- Sandra Lilja
- Xinxiu Li
- Martin Smelik
- Eun Jung Lee
- Joseph Loscalzo
- Pratheek Bellur Marthanda
- Lang Hu
- Mattias Magnusson
- Oleg Sysoev
- Huan Zhang
- Yelin Zhao
- Christopher Sjöwall
- Danuta Gawel
- Hui Wang
- Mikael Benson
journal: Cell Reports Medicine
year: 2023
pmcid: PMC10040389
doi: 10.1016/j.xcrm.2023.100956
license: CC BY 4.0
---
# Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases
## Summary
Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro- and anti-inflammatory upstream regulators (URs) and downstream pathways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.
## Graphical abstract
## Highlights
•scRNA-seq of mouse arthritis shows organome-, cellulome-, and genome-wide changes•Those changes are switched on or off by pro- or anti-inflammatory regulators•A similar, but graded, on/off switch system is found in human immune diseases•Targeting regulators of this system may be exploited for personalized treatment
## Abstract
Lilja et al. report that treating immune diseases is complicated by involvement of thousands of genes. Combined analyses of mouse arthritis and human immune diseases show organome-, cellulome-, and genome-wide changes, which are switched on or off by pro- or anti-inflammatory upstream regulators (URs). Targeting URs may contribute to personalized medicine.
## Introduction
“I never feel completely well.” *This is* a common complaint from patients with immune-mediated inflammatory diseases (IMIDs), despite state-of-the art treatment. This sentiment reflects a general health care problem: according to the US Food and Drug Administration, medication is deemed ineffective in $40\%$–$70\%$ of patients with common diseases.1 Genome-wide analyses down to the single-cell level indicate that this limited responsiveness depends on both complexity and heterogeneity. Clinical studies have shown that predicting treatment response based on omics data from IMID patients is challenging.2 Each disease can involve thousands of genes across multiple cell types, which vary between patients with the same diagnosis, and even between the same patient at different time points.3,4 The clinical manifestations of IMIDs suggest an added layer of heterogeneity, namely, variable organ involvement in the same disease. IMIDs encompass more than 80 diseases, which include rheumatoid arthritis (RA), ulcerative colitis (UC), Crohn disease (CD), psoriasis (PSO), systemic lupus erythematosus (SLE), and many others.5 As an example of variable organ involvement, RA can affect not only joints but also the skin and many internal organs, including the kidney, heart, and spleen. Successful pharmacological treatment of such variable organome-wide disease manifestations would ideally require answering questions such as: How many organs are affected? How complex and heterogeneous are the underlying molecular changes? Can those changes be organized into an overriding structure, which permits systematic, and increasingly detailed, analysis? Is there a hierarchy in the structure? Can that hierarchy be exploited to prioritize diagnostic and therapeutic targets?
These questions have not been investigated on combined organome-, cellulome-, and genome-wide scales. This type of analysis would involve challenges that are close to, or beyond, the limits of current understanding of the architectural principles of disease-associated changes in the multidimensional genome, for example:1.Characterization of genome-, cellulome-, and organome-wide changes: This can be achieved by single-cell RNA sequencing (scRNA-seq), which allows creation of atlases of all cell types in all organs in healthy mice and humans.6,7 One reason as to why no similar effort has been made in disease states is that many organs are difficult or impossible to investigate in living human patients. Another reason is that internal organs may not give rise to specific symptoms. As an example, pathogenic mechanisms in lung have been proposed to have a primary role in RA,8 but clinical and research foci are on joints. It is thus possible that important disease mechanisms, biomarkers, and drug targets are missed. Another problem with focusing on only one organ is that all organs may interact through the hematological, lymphatic, or nervous systems. Thus, they should ideally be studied together, rather than as individual parts.2.Organization of organome-wide scRNA-seq data: We and others have previously described methods to organize scRNA-seq data from individual organs into multicellular disease models (MCDMs).9,10 MCDMs are network models that show directed molecular interactions between cell types based on differentially expressed genes (DEGs) in each cell type and their predicted upstream regulators (URs) in other cell types. However, organization of MCDMs on an organome-wide scale is an unresolved challenge.3.Prioritization of regulatory mechanisms in organome-wide scRNA-seq data: Because MCDMs have not been characterized on an organome-wide scale, their potential interactions have not been systematically investigated, nor is there any form of molecular or cellular hierarchy between organs. However, in a previous study, we found that interactions in an MCDM from one inflamed organ were multi-directional, without any evident hierarchy.10 This complicated prioritization of diagnostic and therapeutic targets, which emphasizes the need to search for overriding structure to systematically find and prioritize regulatory mechanisms.
Here, we performed multi-organ scRNA-seq of a mouse model of collagen-induced arthritis (CIA) to develop a systems-level strategy to define such structures, which could be validated in human IMIDs (Figure 1). Although disease mechanisms may differ between mouse models and human diseases, we reasoned that overriding structures would be comparable. In summary, we found complex and heterogeneous organome-wide changes in CIA.11 Those changes could be organized into a multi-organ MCDM (MO-MCDM) in which all organs interacted without evident hierarchy. However, despite the widespread molecular changes across all organs, only joints showed signs of inflammation. This contrast led to the question whether there could be an overriding structure in which complex mechanisms are required not only to activate but also to inhibit inflammation. If so, could that structure be systematically analyzed to prioritize such mechanisms and their URs? Combined analyses of multi-organ data from the mouse model and 10 human IMIDs supported that shared transcriptional programs were switched on or off by variable combinations of URs. Subsequent analyses of IMID patients who did or did not respond to treatment with anti-TNF (tumor necrosis factor), as well as more than 600 blood samples from SLE patients, supported that variable combinations of URs have the potential for personalized diagnostics and therapeutics in IMIDs. We propose prospective clinical studies to examine this potential and have made the data and methods freely available for such studies. Figure 1Overview of the study(A1) Single-cell RNA sequencing (scRNA-seq) of a mouse model of collagen-induced arthritis (CIA) showed thousands of differentially expressed genes (DEGs) across all organs despite only joints showing signs of inflammation. ( A2) Multicellular disease models (MCDMs) were constructed based on scRNA-seq data from the organs with the most DEGs. ( A3) Transcriptional programs were identified in joints and muscle. These were turned on or off by partially shared combinations of upstream regulators (URs).(B1) Meta-analysis of multiple immune-mediated inflammatory diseases (IMIDs) showed a similar on/off switch that was (B2) regulated by different UR combinations in different IMIDs. ( B3) Those URs have potential for personalized diagnostics and therapeutics, either using single-drug or combinatorial drug treatments.
## scRNA-seq shows highly diverse cellulome- and genome-wide expression changes in joints and multiple other organs in a mouse model of arthritis
In order to search for systems-level principles to organize and prioritize disease mechanisms on organome-, cellulome-, and genome-wide scales, we performed Seq-Well-based massively parallel scRNA-seq of six DBA1/J mice with CIA and four healthy control mice. Three of the CIA mice developed mild arthritis (per limb arthritis score: 1–3) and three severe arthritis (per limb arthritis score: 4). We first analyzed 10 different organs, namely, joint, blood, draining lymph nodes, lung, thymus, skin, limb muscle, spleen, liver, and kidney, from at least one mouse with severe arthritis and one healthy mouse (Data S1). Despite only joints showing macroscopic signs of disease, we found DEGs between sick and healthy mice in multiple organs. The highest numbers of DEGs were found in muscle, joint, lung, skin, and spleen (Figure S1A). We proceeded to analyze these five organs from all sick and control mice. After filtering and quality control, we recovered 2,230, 814, 4,565, 1,167, and 3,320 cells from joint, lung, muscle, skin, and spleen, respectively (see “method details” in STAR Methods; Data S1). Clustering and cell-type annotation revealed 13 cell types, namely, B cells, dendritic cells, endothelial cells, erythrocytes, fibroblasts, granulocytes, macrophages, monocytes, natural killer (NK) cells, T cells, myocytes, basal III cells, and neutrophils (see “method details” in STAR Methods; Figure 2A; Data S1). Cell-type proportions and DEGs varied greatly between organs (Figures 2B and 2C; Data S1). Of the total number of DEGs identified in macrophages and T cells, which were the only cell types identified in all five organs, $5\%$ and $4\%$, respectively, intersected over all organs (Data S2). Pathway analysis of the cell types in the different organs resulted in a total of 501 pathways being significantly enriched in at least one cell type, although they were variably upregulated/downregulated in the different organs and cell types in which the direction could be inferred (Figure S2A; Data S3).Figure 2Cellular composition, differential gene expression, and H&E analysis of healthy and CIA mice(A) UMAP of 12,096 cells from all samples, colored by cell type.(B) Proportional abundance of cell types per organ and disease state. Healthy mice = 4; CIA mice = 6.(C) Heatmap presenting the similarity of DEGs. Rows and columns represent different cell types in respective organs, and the color scale corresponds to the Jaccard index.(D) Representative H&E images of the joint and muscle from control and CIA mice shown at a magnification of 100× (scale bars, 100 μm). Red, black, and blue arrows indicate synovial hyperplasia, bone destruction, and synovial infiltration of inflammatory cells, respectively. BM, bone marrow; C, cartilage; S, synovial cavity.
The daunting complexity and heterogeneity of the molecular changes across multiple organs and cell types highlighted the overarching questions behind this study: how can disease-associated changes on organome-, cellulome-, and genome-wide scales be organized and prioritized? We reasoned that one straightforward way would be to focus on DEGs in organs with microscopic signs of inflammation, a key endophenotype in CIA.
## Histological analysis shows signs of inflammation in joints, but not in other organs
To investigate the inflammation status in multiple organs and cell types, we conducted microscopic analyses of different organs from independent mice with severe CIA (clinical scores >8) and control mice. The results showed inflammation only in joints: significant infiltration of leukocytes in cartilage and synovium, together with bone destruction and synovial hyperplasia (Figures 2D and S1B).
## MCDMs show multi-directional networks in each organ without evident hierarchies
The presence of macroscopic and microscopic signs of inflammation only in joints, despite cellulome- and genome-wide changes in all analyzed organs, suggested an overriding structure underlying the organization and prioritization of DEGs in this system: DEGs in joints activated inflammation, whereas DEGs in other organs suppressed inflammation. If so, this structure would hypothetically act as an on/off switch for inflammation, which could help to prioritize URs and downstream target genes for that switch. To test this hypothesis, we constructed MCDMs of each organ. The MCDMs described predicted molecular interactions between cell types in each organ. The interactions were bioinformatically inferred by linking the DEGs in each cell type with their predicted UR12 (Data S4). DEGs linked with URs were referred to as downstream targets.13 Because the interactions were directed, they could potentially be traced to prioritize an UR and cell type with a hierarchically superior role, as well as its downstream target genes in other cell types. We began by analyzing whether the joint MCDM had such a pro-inflammatory UR with an “on” role for the switch. The joint MCDM included URs of known pathogenic importance for both mouse CIA and human RA, such as Il1b and Tnf.13 However, the MCDM showed multi-directional interactions mediated by many other URs without evident hierarchy (Figures 3A and 3B). A similar, multi-directional organization was found in MCDMs from lung, spleen, muscle, and skin (Figures 3C–3F).Figure 3MCDMs of five organs from CIA mice(A and C–F) Chord charts of predicted molecular interactions between different cell types in (A) joint, (C) lung, (D) muscle, (E) skin, and (F) spleen. Outgoing interactions are shown to the left and ingoing interactions to the right of each chart. Each line represents one interaction between an UR in one cell type and its downstream target genes in another cell type.(B) Joint MCDM, showing URs and their predicted, directed interactions. Node size denotes the number of cells of each cell type, and the color of the edges denotes the cellular origin of each interaction.(G) URs ranked based on their predicted downstream effects. Red spectra indicate the total number of predicted downstream targets for each UR, within each cell type and organ; white indicates that no downstream targets were predicted.
The complex, heterogeneous, and apparently non-hierarchical changes across multiple organs and cell types led us to attempt to prioritize URs on a multi-organ scale.
## Ranking indicates that altered balance between pro-inflammatory and anti-inflammatory URs has an on/off function for inflammation
To prioritize URs, we ranked the URs based on the size of their predicted molecular and cellular effects across all analyzed organs (see “method details” in STAR Methods; Figure 3G). The disease relevance of the ranking system was supported by four of the top-ranking URs in joints, Il1b, Tnf, Dsc3, and Ltf, being either therapeutically, functionally, or genetically associated with RA and other IMIDs.13,14,15,16 In further support of the clinical relevance of those URs, the expression of their downstream targets differed significantly between mild and severe arthritis in several cell types in joints: Tnf in fibroblasts ($$p \leq 2.00$$ × 10−3), Il1b in T cells ($$p \leq 4.14$$ × 10−5), Ltf in T cells ($$p \leq 9.51$$ × 10−5) and macrophages ($$p \leq 1.57$$ × 10−6), Dsc3 in macrophages ($$p \leq 9.9$$ × 10−7), and Mpz in macrophages ($$p \leq 1.84$$ × 10−14) (Figure S3; Data S4). Although the increased activity of pro-inflammatory URs in joints was expected, another finding was not: both Il1b and/or Tnf were also differentially expressed and predicted URs in organs that did not show macroscopic and microscopic signs of inflammation. However, in contrast with joints, the expressions of those URs varied and were counter-balanced by anti-inflammatory URs. For example, in muscle, Il1b increased, whereas Tnf decreased, and the anti-inflammatory UR Tgfb increased (Figure S1C). By contrast, both Il1b and Tnf, but not Tgfb, increased in joints. Thus, the altered balance between pro- and anti-inflammatory URs could act as an on/off switch for inflammation. Such a switch would explain why only joints showed signs of inflammation, despite organome-wide expression changes. Another unexpected finding was that although Tnf was downregulated in muscle, its predicted downstream targets in the same organ were activated in monocytes ($$p \leq 6.9$$ × 10−4; Z score = 0.69), and in T cells ($$p \leq 1.02$$ × 10−3; Z score = 1.182). A potential explanation was that the downstream targets were regulated by TNF derived from other organs and transported via the blood. If so, inflammatory mechanisms in different organs are interconnected. We hypothesized that this concept could be developed by searching for molecular interactions between MCDMs, such that a MO-MCDM would be formed.
## MO-MCDMs connect inflammatory mechanisms in different organs into a multi-directional network
To investigate systematically if molecular interactions between MCDMs in different organs could be organized into an MO-MCDM, we used the same methods as for individual MCDMs. However, we included only URs predicted to be released into the blood (based on the Human Protein Atlas). We identified 1,966 of such inter-organ interactions, which were mediated by 48 URs (Figure 4; see “method details” in STAR Methods). The resulting MO-MCDM formed a multi-directional network in which all MCDMs were interconnected. Figure 4Multi-organ MCDM (MO-MCDM)The MO-MCDM is presented as a chord chart showing predicted molecular interactions between MCDMs in joints, spleen, lungs, skin, and muscle. Each section in the outer circle represents an organ. Sections in the inner circle represent cell types. Each line represents a ligand secreted by the source cell types (left side of the chart) that were predicted to regulate genes within target cell types (right side of the chart).
To validate that URs could mediate interconnectivity in the MO-MCDM, we performed protein analyses of high-ranking URs and interacting cytokines in sera from independent CIA mice (Figure S4). Because CIA may variably involve different organs, these analyses were performed at different time points during disease progression.17 In support of interconnectivity, all analyzed URs and interacting cytokines were found in sera (Figure S4). TNF increased at early time points, whereas it decreased to normal expression at later stages of the disease. For interleukin (IL)-1β, no systemic changes in protein expression level could be seen at different stages of disease progression. However, for IL-1α, a significant drop in protein expression level was seen at later stages of the disease. IL-6 and interferon γ (IFN-γ), which are known to interact with IL-1β and TNF,18,19,20,21 also showed variable changes in expression level at different time points of disease. Such variations could be consistent with dynamic changes in organ inflammation in CIA.22 We next analyzed whether the altered balance between pro- and anti-inflammatory URs would be associated with an altered balance between downstream pro- and anti-inflammatory pathways.
## Connective pathway analysis supported a graded switch system in CIA
To systematically test whether the altered balance between downstream pro- and anti-inflammatory mechanisms explained why joints, and not muscle, showed signs of inflammation, we performed pathway analysis of all DEGs in different cell types from these two organs. In total, we identified 428 significantly enriched pathways in at least one cell type (Data S3). The large number of pathways complicated systematic testing of our hypothesis. A potential solution was suggested by the fact that $64\%$ of all genes in the 428 pathways were shared by more than one pathway. This led us to hypothesize that a higher-order structure than pathways could be identified, namely, groups of pathways with partially shared genes (henceforth referred to as programs). If such programs were relevant for pathogenesis, they should [1] be enriched in genome-wide association study (GWAS) genes from human RA and [2] differ in activation profiles between joints and muscle. To find such programs, we developed a method called connective pathway analysis. This approach used the 1-Jaccard index as a distance metric for clustering of pathways (pathways that share many genes would then be more proximate to each other than those that do not and, therefore, be closer in the dendrogram; Figure 5A; see “method details” in STAR Methods).Figure 5Connective pathway analysis to systematically define and prioritize transcriptional programs in joint and muscle from the CIA mouse model(A) Outline of connective pathway analysis: [1] identification of genes that belong to a pathway; [2] mapping DEGs on that pathway; [3] pairwise comparison of pathway-associated DEGs; [4] assessment of pathway-associated DEGs overlap (Jaccard Index); [5] examples of two extreme situations, pathways X1 (green) and X2 (orange) have a high overlap of DEGs but not X1 (green) and X3 (purple); [6] hierarchical clustering using the 1-Jaccard index as distance matrix; and [7] dendrogram transformation into a tree-like structure.(B) Connective pathway analysis identified two main programs, CIA_P1 (purple) and CIA_P2 (green). Each pathway was labeled as “activated” (red), “inhibited” (blue), “unknown activation” (gray), or “not significant” (black).(C) Tree-like representation of CIA_P1 with subprograms indicated with different node colors. Color bars indicate main program CIA_P1 (purple) and subprogram CIA_SP1.6 (ochre). Node size represents the total number of cell types in which the pathway is significantly enriched.(D) Detail of CIA_SP1.6. Each node represents a pathway, and pie charts within nodes represent ratios of cell types in muscle and in joint (left and right part of the pie chart, respectively) for which the pathway was significantly enriched. Colors represent pathway activation profile.(E) Detail of selected pathways from CIA_SP1.6. Left (right) part of polar charts presents all cells in muscle (joint). Each pie sector represents one cell type. The degree to which the sector is filled with color represents enrichment −log10(p value), whereas the color shows pathway activation (blue for inhibition, red for activation, and gray for unknown activation).
We reasoned that cutting the dendrogram at different levels would provide a systematic approach to prioritizing the programs that differed most in activation profiles between joints and muscle and therefore would be most relevant for an on/off switch. At the highest level of the dendrogram, we found two main CIA-associated programs (CIA_P), CIA_P1 and CIA_P2. Both CIA_P1 and CIA_P2 were enriched for GWAS genes of human RA ($p \leq 0.006$; Data S5). We also tested whether similar programs would be found using Kyoto Encyclopedia of Genes and Genomes (KEGG) instead of Ingenuity Pathway Analysis (IPA), and we found significant overlap ($p \leq 0.0001$; Figure S2B). Analyses of these programs did not support the existence of a discrete on/off switch: both programs included activated pathways in the non-inflamed muscle, compared with healthy control mice. This finding suggested a graded switch system in which the non-inflamed state was an intermediate in a continuous spectrum, in which healthy and inflamed organs were extremes. We next focused on CIA_P1 because pathways were mainly activated in joints and inhibited in muscle (Figure 5B). To facilitate the identification of pathways that had the most opposing activation directions in CIA_P1 (i.e., being activated in one organ and inhibited in the other or being significantly enriched in one organ and not significant in the other; see “method details” in STAR Methods), we cut the dendrogram into 10 subprograms (CIA_SPs) (Figure 5C). Of these, CIA_SP1.3, CIA_SP_1.1, and CIA_SP1.6 showed the highest percentages of pathways with opposing activation directions ($79\%$, $61\%$, and $60\%$, respectively; Data S3). The highest GWAS enrichment was found in CIA_SP1.6 (Data S5). Further analysis of CIA_SP1.6 showed that $75\%$ of its pathways were related to human RA (Data S3). CIA_SP1.6 contained pro-inflammatory pathways such as “Acute Phase Response Signaling” and “IL-6 signaling,” as well as anti-inflammatory pathways such as “PPAR signaling” (Figure 5D). The pro-inflammatory pathways were mainly activated in joint and inhibited in muscle, whereas the anti-inflammatory pathways showed the opposite pattern (Figure 5E). Il1b and Tnf were predicted URs of CIA_SP1.6 in both joint and muscle (Data S6). However, in muscle, the downregulation of the pro-inflammatory URs Tnf and Apoe23 and upregulation of the anti-inflammatory Tgfb1 (Figure S1C) could explain why no signs of inflammation were found. By contrast, increased expression of Il1b in muscle was consistent with partial activation of some pro-inflammatory pathways. For example, the “Senescence Pathway” showed mixed activation or inhibition in different cell types in both joint and muscle (Figure 5D). This pathway has previously been implicated in IL-1- and TNF-induced tissue damage in RA.24,25 The mixed activation pattern of the “Senescence Pathway” in joints and muscle is thus consistent with mixed activation of IL-1 and TNF in these two organs. Taken together, these findings support a graded, rather than discrete, on/off system. We next examined whether such a system could be translated to human IMIDs.
## Meta-analysis of human IMIDs supported a graded switch system
To test the disease relevance of the graded switch system, we performed meta-analysis of 10 different IMIDs (Data S1): RA, UC, CD, PSO, Sjögren’s syndrome (SS), systemic sclerosis (SSc), atopic dermatitis (AD), juvenile myositis (JM), “at risk for” type 1 diabetes (T1D), as well as SLE. The SLE datasets included discoid lupus erythematosus (DLE), subacute cutaneous lupus erythematosus (SCLE), and lupus nephritis (LN). The meta-analysis was based on 32 bulk profiling datasets from human organ biopsies. The IMID biopsies were taken from inflamed and/or non-inflamed sites and compared with biopsies from healthy controls. Meta-analysis of DEGs from each IMID showed highly complex changes in inflamed and non-inflamed sites with 647 pathways that differed significantly compared with controls (Data S3). Similar to CIA, connective pathway analysis revealed two IMID-associated programs (IMID_P): IMID_P1 and IMID_P2 (Figure 6A).Figure 6Transcriptional programs in inflamed and non-inflamed organs from IMIDs(A) Hierarchical clustering of all significant pathways in inflamed and non-inflamed organs from IMIDs identified two programs, IMID_P1 (purple) and IMID_P2 (green). Each pathway was labeled as “activated” (red), “inhibited” (blue), “unknown” (gray), or not significant (black).(B) Tree-like representation of subprograms in IMID_P1. The subprograms are indicated by different colors and numbers, with each node representing a pathway.(C) Detail of IMID_SP1.6. Each node represents one pathway. Pie charts within nodes represent ratios of datasets in non-inflamed and inflamed organ groups (left and right part of the pie chart, respectively) for which a particular pathway was predicted to be “activated,” “inhibited,” “unknown” direction, and “not significant.” The disease relevance of both IMID_P1 and IMID_P2 was supported by significant enrichment for GWAS genes (Data S5). However, when we compared the pathway overlap between IMID_P1 and IMID_P2 with the corresponding programs from each individual IMID, we mainly found significant overlaps within IMID_P1 (Figure 7A; see “method details” in STAR Methods). This observation indicated that IMID_P1 contained pathways that were shared across IMIDs, whereas IMID_P2 had more disease-specific pathways. In IMID_P1, $32\%$ of pathways were activated in inflamed organ sites and inhibited, or not significant, in non-inflamed sites. The corresponding figure for IMID_P2 was $21\%$. Both IMID_P1 and IMID_P2 also included many activated pathways in non-inflamed sites. In agreement with a graded switch system across a spectrum between health and an inflamed phenotype, this more graduated response could predict an increased risk of a gradual shift in the balance between pro- and anti-inflammatory pathways toward an inflamed phenotype. Figure 7Relevance of programs/subprograms for different human IMIDs(A) Pathway overlaps between programs/subprograms from all analyzed IMIDs and each individual IMID. Significant overlap is denoted with blue color, and non-significant with red (see “method details” in STAR Methods). Node size corresponds to the −log10(p value). The programs and subprograms (rows) were ordered by increasing number of associated IMIDs.(B) GWAS enrichment of subprograms in each IMID.(C) The predicted activity of TNF corresponds to the known clinical effects of anti-TNF treatment. Red color corresponds to significant predicted activity, whereas gray denotes non-significant activity. Green checkmarks and red X denote whether the anti-TNF treatment is clinically effective or not, respectively.
We next cut the dendrograms of both programs into subprograms (IMID_SPs) in order to prioritize the subprogram that had the most pronounced on/off pattern and GWAS enrichment. This analysis led to prioritization of IMID_SP1.6, which had $49\%$ of pathways with opposing patterns between inflamed and non-inflamed organ sites, and GWAS enrichment in $72\%$ of the IMID datasets, with median (range) odds ratio (OR) among the significant = 5.34 (∼3.54–8.94) (Figures 6, 7A, and 7B; Data S3 and S5). We also found that IMID_SP1.6 was shared across inflamed organ sites in all IMIDs but PSO (Figure 7A).
The top-ranking pathways in IMID_SP1.6, in inflamed sites, were “Acute phase response signaling,” “B cell receptor signaling,” “Chemokine signaling,” and “IL-6 signaling.” By contrast, anti-inflammatory pathways, such as “PPAR signaling” and “PPARa/RXRa Activation,”26,27,28 were inhibited (Figure 6C). However, IMID_SP1.6 also included activated pro-inflammatory pathways in non-inflamed organ sites: “Leukocyte extravasation,” “Natural Killer Signaling,” and “MS-RON Signaling,” all of which can contribute to chronic inflammation, and thereby a switch from off to on.29 Analysis of the URs of IMID_SP1.6 agreed with the graded switch system being regulated by variable combinations of pro- and anti-inflammatory URs. This could have important basic and clinical implications, namely, that the stepwise characterization of programs and subprograms, as described above, could help to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients.
## Combinatorial regulation of the graded switch system has diagnostic and therapeutic implications
For all IMIDs combined, we found a total of 389 predicted URs (Data S6). Specifically, for each disease we found a median (range) of 79 (0–218) URs, of which only 8 were shared by all IMIDs (except SS), namely, AR, ER-β, Fas, IFN-γ, IL-1α, IL-1β, TLR3, and TNF. In agreement with the graded switch system depending on altered balance between pro- and anti-inflammatory URs, Fas, IFN-γ, IL-1α, IL-1β, TLR-3, and TNF are mainly pro-inflammatory, whereas AR and ES-β are anti-inflammatory.30,31,32,33,34,35 Unexpectedly, however, the predicted effects of these URs, based on Z score, contrasted with their measured fold changes (FCs; Figures S5A and 5B). For example, TNF was predicted to be activated in 25 datasets, whereas it was differentially expressed in only 13 datasets. This difference could be explained by URs, other than TNF, having redundant effects on the same downstream target genes. Thus, one or more URs could have “backup” functions if another UR, like TNF, was therapeutically inhibited. We tested this hypothesis in IMID patients treated with anti-TNF.
## Different combinations of URs with redundant functions may explain variable response to anti-TNF treatment
TNF was predicted to be a top-ranking UR of IMID_SP1.6 in inflamed states of both UC and CD, but not in SS (Figures S5A and 5B; Data S6). These predictions agree with the clinical experience that anti-TNF treatment is effective in the two former diseases, but not in the latter (Figure 7C). This led us to examine the effects of anti-TNF treatment on subprograms in UC and CD. The pathways analyses of DEGs after treatment between CD patients who responded to anti-TNF (GEO: GSE52746, 10 treated anti-TNF responders versus 7 untreated patients) showed significant enrichment of anti-TNF-targeted pathways among IMID_P1 and subprograms IMID_SP1.6, IMID_SP1.9, and IMID_SP2.5 (false discovery rate [FDR], <4.58 × 10−2) (Data S6). The corresponding analyses of DEGs from patients with UC (GEO: GSE92415; 29 treated anti-TNF responders versus 32 untreated patients) showed significant enrichment of anti-TNF-targeted pathways in the subprograms IMID_SP1.2 and IMID_SP2.7 (FDR, <1.99 × 10−2). The enriched subprograms are henceforth referred to as affected subprograms and the others as non-affected. The relevance of the affected subprograms was supported by TNF being predicted to regulate all of them in inflamed organs (Data S6). For subprogram IMID_SP1.6, which we in this study identified as highly relevant for IMIDs, six of seven pathways (where a |Z score| > 0 could be inferred) showed the opposite direction of activation as a result of treatment response compared with how they were affected by the disease (Figure S5C), indicating effective treatment response.
We next tested the hypothesis that the non-affected subprograms could be explained by URs whose downstream targets overlapped with TNF, having redundant, “backup,” functions. In UC, we found 14 URs predicted to co-regulate non-affected subprograms. For example, NR4A2 was predicted to co-regulate 8 out of 16 non-affected subprograms (Figure S6A). In support of this prediction, NR4A2 was significantly upregulated in inflamed samples of UC (FDR, 2.07 × 10−4; logFC = 1.31). By contrast, affected subprograms were not predicted to be regulated by NR4A2.31 We further tested the potential of other URs to take over the effect of TNF in UC patients who did not respond to anti-TNF treatment. The pathways enriched among the DEGs of responders, respectively non-responders versus controls, showed similar main and subprogram associations (Figure S6B). We identified 92 predicted URs for the DEGs of non-responders versus controls. By predicting the potential for each alternative UR predicted for non-responders to take over the downstream effect of TNF (see “method details” in STAR Methods), we identified TLR6 as a potential UR to take over the effect of TNF among the non-responders after treatment.
To verify that the downstream genes of TLR6 and TNF followed the expected FCs before and after treatment, we additionally analyzed the DEGs for responders and non-responders after treatment versus controls. As expected, we found a tendency for a higher |FC| of the TLR6 downstream genes among the non-responders compared with the responders, before and after treatment (Figure S6C). However, only CXCL8 showed a significant difference between responders and non-responders after treatment ($$p \leq 3.62$$ × 10−2) (Figure S6C). Furthermore, the TNF downstream genes showed smaller differences compared with control for the treated responders, compared with any of the untreated groups or treated non-responders (Figure S6D).
Because UC and CD primarily affect the intestine, we next analyzed whether different UR combinations were associated with variable organ involvement in another IMID that often shows multi-organ involvement, namely, SLE. The clinical relevance lies in that this could indicate the need for diagnostic and therapeutic targeting of different URs in patients with different forms of organ involvement.
## Different combinations of UR proteins in sera were associated with different subtypes of SLE, as well as with disease severity
We analyzed 18 predicted UR proteins of IMID_SP1.6 in sera from two clinical visits of 304 patients who had been prospectively seen by the same rheumatologist (C.S.) at the tertiary referral unit, Linköping University Hospital, according to standardized criteria.36 The American College of Rheumatology (ACR) criteria were used to define disease phenotype.37 We constructed regression models to estimate relationships of UR proteins with ACR criteria, as well as measures of disease activity and organ damage (SLE disease activity index-2000 [SLEDAI] and Systemic Lupus International Collaborating Clinics/ACR index [SDI], respectively) (Figure S7). Clinical variables, including results from physical examinations, laboratory values, age, gender, and duration of the disease, were collected and used in all our models. We also included the treatment information in regression models that predicted SLEDAI and SDI. We did not use treatment information in models predicting ACR criteria, because the treatment is dependent on the patient phenotype, e.g., patients with LN are widely treated by mycophenolate mofetil (MMF). We showed that different UR proteins were associated with different ACR phenotypes (Data S1). For example, cases classified with ACR-1 (malar rash) were positively associated with CD40-L and negatively associated with Fas. Cases classified with ACR-7 (renal disorder/LN) were positively associated with FAS but negatively associated with hepatocyte growth factor (HGF). TNF was positively associated with ACR-9 (hematological disorder) and ACR-10 (immunological disorder). By contrast with ACR-7 and ACR-9 (hematological disorder), TNF and HGF were significantly associated with ACR-6 (serositis), TNF negatively and HGF positively.
Next, we examined whether different combinations of potential URs were associated with disease activity in patients with ($$n = 80$$) and without ($$n = 224$$) LN. The regression models showed that different proteins were associated with the disease activity in the two groups (Data S1). IL-1α, IL-4, Fas, and oncostatin M (OSM) were associated with SLEDAI in patients with LN, whereas TNF, HGF, and CD40 were associated with SLEDAI in patients without LN. In agreement with our hypothesis, the URs that were negatively correlated with SLEDAI may have anti-inflammatory roles. However, testing whether altered expression of these URs inhibit or activate inflammation is complicated by their context-dependent pleiotropic roles. For example, OSM has an anti-inflammatory role on synovial cells: it reduces IL-1 and TNF expression, but it has a pro-inflammatory role on endothelial cells by inducing leukocyte recruitment and IL-6 production from endothelial cells.38,39 A similar pleiotropy has been described for HGF and IL-4. Focusing on organ damage (Data S1), we found that TNF, IL-27, OSM, and TGF-β1 were associated with SDI in patients with LN. In contrast, IL-6, IL-1α, IL-2, HGF, and CD40-L were associated with SDI in patients without LN.
## Discussion
The main problem behind this study is that many patients with IMIDs do not respond adequately to treatment.1 An important reason for this inadequate response is the daunting complexity and heterogeneity of the molecular changes in these diseases. scRNA-seq studies of IMIDs have shown altered expression of thousands of genes across multiple cell types in individual organs with phenotypic signs of disease.40,41,42 Despite detailed information about those changes, translation of the data to personalized medicine has proven difficult. Thus, there is a wide gap between the complexity of disease-associated changes and health care today. Bridging that gap will likely involve great challenges for researchers and clinicians.
Variable multi-organ involvement in each IMID adds to the complexity and heterogeneity. This indicates the need for characterization, organization, and prioritization of molecular changes on organome-, cellulome-, and genome-wide scales. Our multi-organ scRNA-seq analyses of a mouse model of an IMID, namely, CIA, showed extensive changes on all those scales. Although those changes could be organized into an MO-MCDM, this analytical approach showed no evident molecular or cellular hierarchy that would allow prioritization of molecular changes. An unexpected finding led us to a potential solution: despite the organome-wide changes, only joints showed signs of disease. That contrast led us to hypothesize that the expression changes were organized into an overarching structure designed to switch inflammation on or off. We developed an analytical strategy that supported such an on/off switch and showed that it depended on altered balance between pro- and anti-inflammatory URs. Such a switch has been previously suggested in inflammatory responses and validated by functional studies of individual genes and cell types.40,41,42 Given the complexity and heterogeneity of the organome-, cellulome-, and genome-wide changes, ranking of URs is crucial for understanding and prioritization of disease mechanisms. Our strategy provided a solution for systematic characterization and prioritization of URs, as well as their downstream target genes. We found that URs could be ranked based on the size of their effects on the downstream genes. In support of clinical relevance, the top-ranking URs included known therapeutic targets in IMIDs, including IL-1 and TNF. The downstream target genes could be organized into two main programs, and their subprograms, which permitted increasingly detailed analyses of pathways. In support of disease relevance, both programs were significantly enriched for genes identified by GWAS of human RA. However, large molecular changes in non-inflamed organs, including partially activated pathways, did not support a discrete on/off switch, in which non-inflamed organs corresponded to an “off” state. Instead, the activated pathways could increase the risk of a switch to an “on” state. This observation is consistent with a graded on/off switch, in which non-inflamed organs are intermediates on a spectrum, where healthy and inflamed organs represent extremes. Such an intermediate “risk” state could explain an important characteristic of both CIA and human IMIDs, namely, variable organ involvement during disease progression. A graded on/off switch has been previously described in model organisms and proposed to be generally applicable to biological systems. The relevance of molecular gradients in disease is supported by previous findings that variable expression or dysregulation of interconnected genes will define whether an organ is affected by disease.43 The translational relevance of the strategy was supported by meta-analyses of human IMIDs. These showed a similar organization as in CIA, with URs, programs, and subprograms that agreed with a graded on/off switch. High-ranking URs included known drug targets such as IL-1 and TNF. However, except for a core group of URs, these varied across diseases and organs. For example, TNF was a predicted UR in IBD, but not in SS, which agrees with the clinical experience that anti-TNF treatment is effective in the former, but not in the latter. Clinical implications may be that characterization and ranking of URs will be needed for successful treatment, on the levels of IMIDs, subgroups, or even individuals. Those implications were supported by our analyses of the effects of anti-TNF treatment in patients with IBD who did or did not respond to that treatment. We found that lack of response could be explained by overlapping downstream effects of the URs, such that the effect of inhibiting one UR could be diminished by one or more other, functionally redundant, “backup” URs. Prospective clinical studies are warranted to test whether predictive classifiers for treatment response can be developed based on high-ranking URs. To examine whether UR combinations would vary in patients with different forms of organ involvement, we focused on an IMID with highly variable multi-organ manifestations, SLE. Analyses of predicted UR proteins in more than 600 sera from SLE patients did show that different combinations of UR proteins correlated with different forms of organ involvement. Furthermore, different combinations of URs were associated with disease severity in SLE patients with and without renal involvement. A clinical implication may be that different URs should be targeted in these two subgroups. Moreover, some shared URs showed opposing associations. Thus, targeting of such URs could have curative or aggravating effects in different subgroups of patients with the same disease. Interestingly, we found that HGF was positively associated with the damage index but negatively associated with organ involvement in SLE patients without renal involvement. This supports our message that a systems-level strategy for prioritization of URs on an organome-wide scale is important. Similar to CIA, several subprograms in non-inflamed organs included activated pro-inflammatory pathways. The pathogenic relevance of those subprograms was supported by enrichment of genes identified by GWAS. In agreement with a graded switch system, these subprograms could enhance risk of altered balance between pro- and anti-inflammatory pathways, resulting in an inflammatory phenotype or amplification of that phenotype. A potential clinical implication is the development of combinatorial diagnostic and therapeutic targeting of such URs during disease remission to prevent a gradual switch to active disease.3,43 We propose that our strategy to organize and prioritize disease-associated changes on organome-, cellulome-, and genome-wide scales has significant potential for future studies aimed at personalized combinatorial diagnostics and therapeutics. We have made the methods and data freely available for such studies.
## Limitations of the study
The analytical strategy was derived from multi-organ analyses of a mouse model of CIA, which may not be representative of human disease. Moreover, the CIA model includes use of an adjuvant to enhance the inflammatory response, which could induce pro-inflammatory pathways in tissues other than joints. The construction of MCDMs and the predicted effects of combinations of URs on downstream genes were based on previously described or predicted protein interactions, which may be confounded by knowledge bias. Although the analyses of the CIA mouse model were based on scRNA-seq, the analyses of human IMIDs were performed on bulk RNA-seq. From a translational perspective, future experimental and clinical studies are warranted to examine the diagnostic and therapeutic implications of the study, in particular the effects of URs on downstream genes.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERBiological samplesOrgan samples for scRNA-seq and Histological analysisThis paperSupplementary filesera from SLE patients for measurement of the UR proteins44Supplementary fileCritical commercial assaysLuminex Assay for proteinBio-techne (R&D)Custom code: LXSAHM-17 Lot number: L143454EILSA for TGFB1Bio-techne (R&D)Catalog: DB100CDeposited datascRNA-seq data of CIA mouseThis paperGEO: GSE206659bulk-RNA data of treated UC patients45GEO: GSE92415bulk-RNA data of treated CD patients46GEO: GSE52746bulk-RNA of AD patients47GEO: GSE16161bulk-RNA of AD patients48GEO: GSE32924bulk-RNA of CD patients49GEO: GSE16879bulk-RNA of CD patients50GEO: GSE179285bulk-RNA of CD patients51GEO: GSE75214bulk-RNA of JM patients52GEO: GSE148810bulk-RNA of LN patients52GEO: GSE32591bulk-RNA of PSO patients53GEO: GSE14905bulk-RNA of PSO patients54GEO: GSE181318bulk-RNA of RA patients55GEO: GSE1919bulk-RNA of RA patients56GEO: GSE55235bulk-RNA of lupus patients57GEO: GSE112943bulk-RNA of SLE patients52GEO: GSE148810bulk-RNA of lupus patients58,59,60,61,62GEO: GSE81071bulk-RNA of SS patients63GEO: GSE176510bulk-RNA of SS patients64,65GEO: GSE40568bulk-RNA of SSc patients66GEO: GSE81292bulk-RNA of SSc patients67GEO: GSE95065bulk-RNA of T1D patients68GEO: GSE66413bulk-RNA of UC patients69GEO: GSE11223bulk-RNA of UC patients50GEO: GSE179285bulk-RNA of UC patients51GEO: GSE75214Experimental models: Organisms/strainsmodel organism: mouse model of arthritis Mouse: DBA1/JJordbruksverket (Stockholm, Sweden) and GemPharmatech Co., Ltd. (Nanjing, China)N/ASoftware and algorithmsFull analysis pipelineThis paperhttps://github.com/SDTC-CPMed/multi-organ_DigiTwin.MONOCLE70http://monocle-bio.sourceforge.net/.James Nemesh, McCarrol’s lab Drop-seq Core Computational Protocol v1.0.1http://mccarrolllab.comhttp://mccarrolllab.coDrop-Seq tools v1.12http://mccarrolllab.comhttp://mccarrolllab.combcl2fastq v2.19.1https://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.htmlhttps://emea.support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.htmlPicard software v2.9.071https://github.com/broadinstitute/picard.STAR software v2.5.372http://code.google.com/p/rna-star/.2019SingleR v1.0.673https://github.com/dviraran/SingleR.Deep count autoencoder (DCA) v0.2.374https://github.com/theislab/dca.Seurat v3.1.275https://cran.r-project.org/web/packages/Seurat/index.html.scVI v0.7.176https://github.com/romain-lopez/scVI-reproducibility.NicheNet v1.0.012https://github.com/saeyslab/nichenetrIngenuity Pathway Analysis vQ1 2021 and vQ4 202077https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/rstatix v0.7.0https://CRAN.R-project.org/package=rstatixhttps://CRAN.R-project.org/package=rstatix.GEO2R78https://www.ncbi.nlm.nih.gov/geo/geo2r/missForest v1.579,80https://CRAN.R-project.org/package=missForestMASS v7.3-5481https://cran.r-project.org/web/packages/MASS/index.html.DHARMa v0.4.582https://cran.r-project.org/web/packages/DHARMa/index.html.stats v4.0.4https://rdrr.io/r/stats/stats-package.htmlhttps://rdrr.io/r/stats/stats-package.htmlOtherThe indexed reference GRCm38 (June 2017, Ensembl)https://www.ensembl.org/index.htmlhttps://www.ensembl.org/index.htmlEnsembl genes GRCh38.p13, Downloaded June 6, 2020http://www.ensembl.org/biomart/martviewhttp://www.ensembl.org/biomart/martviewGWAS-associated genes downloaded from DisGeNET (data downloaded on February 9, 2021)https://www.disgenet.org/https://www.disgenet.org/
## Lead contact
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Mikael Benson. ( mikael.benson@ki.se).
## Materials availability
This study did not generate new unique reagents.
## Mouse model of arthritis
Male DBA1/J mice aged between 8-12 weeks were housed in the Linköping Animal Housing Unit of the Faculty of Health Sciences and kept under standard temperature and light conditions. Experiments were conducted according to the Swedish Animal Welfare Act and ethical permission was granted by the Ethical Committee Board, Norra Stockholms Djurförsöksetiska nämnd (permission number: $\frac{6798}{18}$). For the independent histology analysis, male DBA1/J mice were purchased from GemPharmatech (China) and were maintained in a specific pathogen-free animal facility. All animal studies were performed in accordance with protocols approved by the Animal Experimental Ethics Committee of Xuzhou Medical University (permission number: 202012A162).
## Human data
Samples were obtained from 304 patients (263 women, 41 men) classified with SLE according to the 1982 American College of Rheumatology and/or the Fries’s diagnostic principle83 (Data S1). All subjects had provided oral and written informed consent. The study protocol was approved by the Regional Ethics Review Board in Linköping (M75-$\frac{08}{2008}$). All subjects were included in the prospective and observational research program Clinical Lupus Register in North-Eastern Gothia at the Rheumatology Unit, Linköping University Hospital48. Patients were not involved in the design, conduct, reporting or dissemination plans of our research. Serum was available from each patient at two different time-points from which disease activity had been assessed by the clinical SLE disease activity index (SLEDAI) and damage accrual by the Systemic Lupus International Collaborating Clinics/ACR damage index (SDI).84,85 The recent treatment of the patients prescribed at the previous visit was included in the clinical information (Data S1).
## Study design
Our aims were to characterize, organize and prioritize disease-associated organome-, cellulome- and genome-wide changes in IMIDs (Figure 1). We combined single cell and bulk multi-organ profiling of mouse and human IMIDs. We found complex and heterogeneous organome-wide changes in a mouse model of CIA, which could be organized into a multi-organ multicellular disease model (MO-MCDM). In this MO-MCDM all organs interacted, without evident hierarchy. Despite the organome-wide molecular changes only joints showed signs of inflammation. This contrast led to the identification of an overriding structure in which shared transcriptional programs were switched on or off by variable combinations of URs. Analyses of IMID patients who did or did not respond to treatment with anti-TNF, as well as more than 600 blood samples from SLE patients, supported a graded on/off switch regulated by variable combinations of URs, which have the potential for personalized diagnostics and therapeutics.
## CIA mouse model generation
For scRNA-seq analysis, CIA was established following a previously described method.11 Six mice were immunized with 100 μg (50 μL) bovine collagen II (BC-II, Chondrex, USA) emulsified with 50 μL Complete Freund’s Adjuvant (CFA) (Sigma-Aldrich, USA) in 1:1 ratio, via intradermal injection near the base of the tail. A booster immunization was administered on day 20 with 100 μg of BC-II emulsion (prepared with 1:1 incomplete Freund’s adjuvant (IFA)) and injected at the base of the tail. 100 μL of Phosphate-Buffered Saline (PBS) was injected similarly to control mice. The severity of arthritic limbs was scored on a 0–4 scale, 0: normal; 1: swelling and redness in one digit; 2: swelling and redness in more than one digit or swelling and redness in one digit, wrist and ankle; 3: Swelling and redness presenting in paw and digits; 4: maximum inflammation of limb involving all joints and digits as described in the protocol by Brand et al.11 The arthritic score for each mouse was the sum of the scores of arthritic limbs. The mice were sacrificed when they achieved scores of 8–12 or after they had been immunized for 60 days under isoflurane anesthesia via cervical dislocation. The joint, blood, draining lymph nodes, lung, thymus, skin, limb muscle, spleen, liver, and kidney were collected for further analysis.
For the independent histology analysis, four 8-week male DBA1/J mice were immunized intradermally in the proximal tail with 100 μL of emulsified chicken type II collagen (2 mg/mL, Chondrex, USA)/CFA (1 mg/mL). The clinical arthritis score was evaluated for each limb from 0 to 4 with a maximal score of 16 for each mouse.86 The healthy control and severe CIA mice (clinical score >8) were sacrificed, and the knee joints, lungs, livers, kidneys, skin, and hindlimb muscles were collected after heart perfusion.17,87
## Histological analysis
Whole knee joints, lungs, livers, kidneys, skin, and hindlimb muscles from healthy control and CIA mice were fixed in $4\%$ formaldehyde. Joints were further decalcified with Decalcification Solution (ServiceBio, G1107, China) for 7 days. The specimens were then embedded in paraffin and sagittal sections (4 μm) were cut. The sections were stained with hematoxylin and eosin (H&E, Sigma-Aldrich, USA) for the histology analysis. Histological sections were assessed for infiltration of cells into the synovial cavity resulting in inflammation, proliferation of cells in the synovial layer, and bone erosion.
## Sample cryopreservation
All dissected organ samples were placed into suitable tubes with freezing solution ($10\%$ DMSO and $90\%$ FBS), placed into a CoolCell LX box (Corning, USA), and frozen with gradually decreasing temperature (1°C/min) to −80°C. The samples were then stored at −175°C until further analysis.
## Sample thawing
Before digesting all the organs and harvesting the single cells, the cryopreserved samples were thawed following88 with slight differences. Briefly, cryopreserved samples were quickly thawed in a 37°C water bath with continuous agitation, then transferred into 15 mL centrifuge tubes with 1 mL pre-warmed thaw solution ($90\%$ Hibernate-A and $10\%$ FBS) and incubated at room temperature for 1 minute. Next, 2 mL, 5 mL, and 5 mL thaw solutions were added into the centrifuge tube, separated by 1-minute incubation. The samples were then centrifuged at 350 × g for 5 minutes. Lastly, the samples were resuspended with 1 mL Hibernate-A, after which the supernatant was removed and incubated until the next step.
## Single cell suspension
The thymus, spleen, and lymph node were thawed as described above, and were passed through a 70 μm strainer to collect cells in a 50 mL centrifuge tube. After being centrifuged at 350 × g for 5 minutes, cells were resuspended with 5 mL red blood cell lysis buffer for 5 minutes. The lysis reaction was quenched by adding medium ($90\%$ RPMI-1640 with $10\%$ FBS). Cells were centrifuged at 350 × g for 5 minutes and washed thrice to remove the lysis buffer. Single cell suspensions were prepared with RPMI-1640 at a density of 1 × 10- cells/mL. Samples from different organs (whole knee joint, muscle, lung, skin, liver, and kidney) were quickly transferred into 75 mm dishes with 1 mL DMEM after thawing, and then minced into ∼1mm pieces with scissors. Next, pieces of organ samples were transferred into 15 mL centrifuge tubes containing 5 mL DMEM. Different organ samples were treated with different enzymes for different durations (Data S1). After dissociation, another 5 mL DMEM with $10\%$ FBS was added to the 15 mL centrifuge tubes. Dissociated cells were centrifuged at 350 × g for 5 minutes after passing through a 70 μm strainer, after which the cells were washed thrice with PBS. Single cells were resuspended into RPMI-1640 at a density of 1 × 105 cells/mL for cell loading. Peripheral blood mononuclear cells (PBMCs) were isolated as previously described.89 Briefly, 0.5 mL peripheral blood was diluted with an equal volume of PBS (calcium free), which was further loaded on the top of 1 mL Lymphoprep followed by centrifugation at 800 × g for 30 minutes at room temperature in a swinging bucket rotor with the brake off. PBMCs were retrieved and washed with PBS. Erythroid cells of different organs and peripheral blood of each mouse were removed using RBC Lysis Buffer (Bio Legend, USA) (Data S1). Single cell suspensions were prepared by resuspension of PBMCs with RPMI-1640 at a density of 1 × 105 cells/mL.
## scRNA-seq wet-lab protocol
All scRNA-seq experiments were performed using the Seq-Well technique.90 Briefly, prepared single cell suspensions were co-loaded with barcoded and functionalized oligo-dT beads (Chemgenes, USA; cat. no. MACOSKO-2011-10) on microwell arrays synthesized as described.90 For each sample, 20,000 live cells were loaded onto an array to bind with oligo-dT beads. The arrays, covered with plasma-treated polycarbonate membranes, were placed in a 37°C incubator for 30 minutes. Next, beads were collected to perform cell lysis, hybridization, reverse transcription, and whole transcriptome amplification. Libraries were then prepared for each sample using the Nextera XT DNA Library Preparation Kit (Illumina, USA; cat. no. FC-131-1096) according to the manufacturer’s instructions. Libraries from three samples were pooled together and sequenced using the NextSeq $\frac{500}{550}$ system, and sequencing results were analyzed as described below.
## Cytokine analyses in peripheral blood
Approximately 100 μL of blood were collected from ten healthy control DBA1/J mice, as well as CIA mice at week three (before symptom onset, $$n = 12$$), week eight (early stage after symptom onset, $$n = 12$$) and week 15 (later stage after symptom onset, $$n = 9$$) after CIA induction by retro-orbital bleeding. Twenty-five μL of serum were used for assaying inflammatory cytokines using LEGENDplex™ Mouse Inflammation Panel (13-plex) (CAT: 740446, BioLegend, USA), including IL-1α, IL-1β, IL-6, IL-10, IL-12p70, IL-17A, IL-23, IL-27, MCP-1, IFN-β, IFN-γ, TNF, and GM-CSF. The assay was performed according to the manufacturer’s protocol and the data were collected on a BD FACSAria III flow cytometer and analyzed by Flowjo. The mean fluorescence intensity of each cytokine of the standards was used for calculating the standard curve for each cytokine using a log-log curve fit. The difference in concentration of each cytokine between the different time points was calculated using the Wilcoxon rank sum test, as described above.
## The analysis of the UR protein expressions in SLE sera
We used the clinical variables, patient information, drug treatment and protein levels (Data S1) to estimate the association between the protein levels and the patient phenotype, organ, damage, and disease activity. To preprocess the data, we log-transformed the protein levels, and used random forest imputation79 to impute the missing values. We removed the observations where the response variable was not available, and we scaled the input variables to zero mean and unit variance. In case of treatment information, we only included drugs that were used to treat at least ten patients. As the distribution of the response variables differed, we fitted different regression models for each respective response variable. We used a logistic regression,91 negative binomial regression92 and zero inflated negative binomial regression93 to predict the patient phenotype, organ damage, and disease activity respectively. We also used Akaike Information Criteria (AIC)94 to perform variable selection. These models returned the coefficient estimates as well as a p value, which tested a hypothesis that the estimate is zero. A positive coefficient estimate reflects that the patients with high response variables had high protein levels, and we further referred to it as a positive association. In contrast, a negative coefficient estimate reflects that the patients with high response variables had low protein levels, and we further referred to it as a negative association. To assess whether the tested models follow their assumptions, we standardized the residuals between 0 and 1, then compared them against the assumed distribution and performed outlier and dispersion checks. We also plotted the standardized residuals against the rank transformed predicted variable where we expected a uniform distribution.95 R functions missForest,79 glm,79 glm.nb,96 were used to perform the analysis, and function simulateResiduals95 was used to investigate the residuals.
## Measurement of the UR proteins in SLE sera
We measured the protein levels of the 18 predicted UR proteins of most disease relevant IMID_SP in the serum of 304 patients at two separate phlebotomies using human magnetic multiplex beads assay (R&D, Bio-Techne, USA) according to the manufacturer’s instructions. The five-parameter logistic curve was used to generate the standard curve. The Limit of Detection (LOD) was defined as the standard point with the lowest concentration of an analyte that can reliably distinguish signal from background noise. The Lower Limit of Quantification (LLOQ) is defined as the standard with the lowest concentration. The Upper Limit of Quantification (ULOQ) was defined as the standard point with the highest concentration. If protein values lay outside of the interval between LLOD and ULOQ but higher than LOD, we used extrapolated data for further analysis. We excluded IL-17 and GM-CSF from the analysis as $86\%$ and $75\%$ of measurements respectively were below LOD.
## Organ prioritization
The relevance of the organs for CIA development was tested in a pilot study, in which at least one sample from each organ was sequenced (Data S1) and processed as described.10 In short, the data from each sample were extracted, and poor-quality cells were sorted out as described below. Sequencing data from two of the organs, liver and kidney, did not meet quality criteria (≤25 cells with 10,000 reads per cell) and were excluded from further analysis. The data from the remaining samples was knn-smoothed ($k = 12$), whereafter DEGs were identified between sick and healthy individuals for each organ separately using Monocle70,97 as described in.10 For comparative analysis between organs, 40 cells were bootstrapped from each group of healthy and sick individuals, for ten sampling rounds, for the differential expression analysis. The number of DEGs (Benjamini-Hochberg adjusted p value (FDR) < 0.05) for each sampling was compared and the five organs with the highest number of DEGs (joint, lung, muscle, skin, and spleen) were selected for downstream analysis (Data S1 and Figure S1A).
## scRNA-seq data processing
The single cell data from the different mice samples were processed into digital gene expression matrices following James Nemesh, McCarrol’s lab Drop-seq Core Computational Protocol (version 1.0.1, Drop-Seq tools v1.12) (http://mccarrolllab.com) using bcl2fastq (v2.19.1) conversion and Picard software (v2.9.0). The indexed reference for alignment of the reads was generated from GRCm38 (June 2017, Ensembl) using STAR software (v2.5.3).72 Only primary alignments towards the reference genome were considered during downstream analyses, according to the mapping quality using STAR software. The quality of cells was assessed by having a minimum of 10,000 reads, 400 transcripts, 200 genes and less than $20\%$ of mitochondrial genes per cell. The five organs with the highest number of DEGs based on the organ prioritization described above, namely, joint, lung, muscle, skin and spleen, were then analyzed together. Outliers were removed based on an overestimation of transcripts count (i.e., cells with more than 6,000 transcripts) due to the risk of duplicates in the library. For a gene to be included in the data, it needed to be identified in at least $10\%$ of the cells.
## Clustering and cell type identification
We used a reference-based approach to identify cell types. As a reference dataset, we used mouse bulk expression data of sorted cell populations available in the R package SingleR (v1.0.6).73 To preprocess the data for cell type identification, we only retained 6,395 shared genes for both the bulk expression data and the single cell data. The resulting single cell data was denoised by the deep count autoencoder (DCA, v0.2.3)74 with the default settings. This method has an in-built data normalization and outputs 1) a denoised dataset corrected for dropouts and varying library sizes, where each data value represents the expected (denoised) gene expression, and 2) a latent representation of the denoised data in a 32-dimensional latent space. Existing reference-based single cell type identification such as73 or98 uses correlation measures, either Spearman or Pearson, to match the single cell observations to the reference data. While Pearson correlation measures linear relationships, Spearman correlation is more general in the sense that it accounts for monotonic (i.e., non-linear) relationships. Accordingly, for each reference point, a single cell observation having the highest Spearman’s correlation to that reference was found, and then a Monotonic Regression (MR)99 was computed with the reference vector as the input variable and the natural logarithm of the denoised single cell expression of the selected cell as the output variable in the regression. The exponents of the predicted values from the MR were then treated as reference data expressions in the scale of the single cell data. This means that the rounded exponents of the fitted MR values for all reference data points were used as inputs in the DCA that was estimated in the data validation step, and the latent representations outputted by the autoencoder were used in the following step. The latent space observations obtained from both the single cell data and from the bulk data were clustered together by the Leiden’s algorithm.100 In the Leiden’s algorithm, we set the size of local neighborhood to 30 and the resolution parameter (which determines the number of clusters) was set in such a manner that $70\%$ of cells in each resulting cluster matched the same cell type. Briefly, we started with a resolution parameter of 0.5, resulting in a low number of clusters. For each single cell observation in each computed cluster, a match was computed by finding a reference point that belongs to that cluster and has the highest Spearman correlation to the denoised gene expression of the single cell. The purity of a cluster was determined by computing the proportion of single cell observations within that cluster that had the same reference type. The resolution was increased until each cluster had a satisfactory purity, which resulted in resolution = 1. Finally, all single cell data points within a cluster were labeled with the reference label corresponding to the dominant reference match within that cluster (Data S1). All clusters recognized as the same cell type were merged for further analysis, resulting in 13 groups of cells (Figure 2A). The cell types identified, as well as those unidentified due to a lack of cell types in the reference, were further validated, or identified, using marker genes as described below.
## Data normalization and DEG analysis
To calculate DEGs in the dataset, we used the single cell variational inference (scVI, v0.7.1) framework.76 First, the variational inference model was set-up based on the UMI-count data, reducing batch effect based on the input samples, i.e., individual mice and organs, after which the model was trained using default parameters. The DEGs were then identified between CIA and healthy mice for each cell type in each organ separately, using the ‘change’-mode. The significant DEGs were identified as those with ‘is_de_fdr_0.05 = True’ in the scVI differential expression output. To infer the direction of change, we used the mean log(fold change) (‘lfc_mean’) values produced, where a positive fold change (FC) indicated upregulation while a negative FC indicated downregulation in cells from CIA compared to healthy mice (Data S2). The data used for differential expression analysis were normalized by the scVI autoencoder, correcting for variation in sequencing depth. The normalized expression matrix and the latent space representation data were used for single cell downstream analyses as further described. To compare how much the lists of DEGs differed between cell types and organs, the Jaccard index was calculated for each pair of gene lists.
## Cell type identification using marker genes
*Marker* genes were used to validate the cell types identified by the Leiden algorithm, and to obtain the identity of clusters that were not represented by any reference data point. For all organs combined, the marker genes were calculated based on DEG analysis as described above. The marker genes were defined as those being significantly differentially expressed between each cell type or cluster of unknown identity (based on the previous identification) and all other cell types in the dataset (Data S1). We then searched for the known marker genes (Table S1) within the sets of cell type-specific marker genes. If marker genes of a certain cell type were enriched in a cluster, i.e., a positive FC, the cluster was identified accordingly.
For each cell type identified in any of the organs, the variation in cell type proportion over organs was calculated using ANOVA, adjusting the p values by Holm correction.
## Cell-cell interaction analysis
The interactions between cells were identified by analyzing the data using NicheNet (v1.0.0).12 NicheNet is an R package developed for identification of inter-cellular interactions based on lists of potential interacting genes and a database of known upstream regulators (URs) to target interactions. For these analyses, we used the lists of DEGs between CIA and healthy mice to find the interactions which change due to arthritis. As NicheNet requires human gene symbols as input, the mouse genes were translated into their human orthologs based on the *Ensembl* genes (http://www.ensembl.org/biomart/martview, GRCh38.p13, Downloaded June 6, 2020). These human orthologs were used for all downstream analyses based on these cell-cell interactions.
The cut-off to define the expressed genes in the data was set according to the author’s recommendation, to give 5,000 to 10,000 expressed genes for the sender and receiver cell population independently. First, the previous transformation of the scVI-normalized expression data inverse logarithm was applied [10-1]. Next, genes with a mean expression level ≥1 × 10−5 in the population of cells were defined as expressed. The interactions were then identified, based on the lists of DEGs, between each pair of cell types using the default analysis set-up. For each interaction identified, all of the potential target genes in the source cell type were identified using the get_weighted_ligand_target_links() function and its default settings. To focus our analyses on the strongest interactions of the networks, we included only those with a Pearson Correlation Score (PCC) > 0, meaning that the target genes of the interactions are enriched among the differentially expressed genes.
When analyzing inter-organ interactions, the UR-target interactions identified by NicheNet were curated only to include those that are biologically feasible between organs. For this aim, we used Ingenuity Pathway Analysis (IPA, Q4 2020, Qiagen, Germany) to specify the cellular location known for each potential UR (Data S6). The list of inter-organ interactions was then curated to include only URs located in extracellular space, as they have the potential to be transported through the blood.
## MCDM construction and UR prioritization
For each organ separately, a MCDM was constructed based on predicted molecular interactions between all cell types in the organ. The predictions were inferred using NicheNet12 as described above. The MCDM thus consists of cell types as nodes and unique interactions as lines. A unique interaction represents one cell type-UR-cell type combination, thus enabling multiple edges, based on different UR, between each pair of nodes. The MO-MCDM was created in the same way, but only based on inter-organ interactions as described above. Thus, the nodes represent cell types in each organ, and the lines represented interactions between cell types in different organ.
The URs were then prioritized based on their downstream effect. First, they were ranked based on the total number of predicted downstream target genes that they were predicted to regulate, in all cell types and organs combined. Secondly, they were ranked based on the number of downstream cell types and organs which they were predicted to target.
## Pathway enrichment analysis
Identification of pathways was performed with IPA. We used the core analysis in the IPA software to identify canonical pathways based on a list of DEGs (e.g., all the potential targets of all the URs in the different organs). When this analysis was completed for each cell type and organ separately, the Bayes factors from the differential expression analysis were included to define the direction of change due to arthritis.
IPA consists of a global network that is based on manual curation of a vast body of medical literature and biomedical databases, which is continuously updated.101 The core analysis in IPA (parameters: species = mouse) was used to identify pathways that were significantly enriched among the list of genes. Statistical analysis was performed using Fisher’s exact test, right tailed, within the IPA software (Q1 2021 and Q4 2020 version).77 All pathways with $p \leq 0.05$ were considered significantly enriched. Pathway activation direction was indicated by IPA activation z-score as activated (z-score >0) or inhibited (Z score <0).
## The association between UR-target expression and arthritis score
The association between UR-target expression and arthritis score was calculated by pairwise comparison of the target mean expression levels between cells from mouse joints with mild arthritis (score 1–3), or severe arthritis (score 4) (Data S1), as well as healthy control mice, using a Wilcoxon rank sum test. *The* gene names of the normalized data from scVI analysis were first translated to their human orthologs as previously described. Thereafter, the data were standardized, producing a mean expression of zero and standard deviation of one, over all genes within each cell. The mean expression level of all UR target genes was calculated for each cell. The differences in target expression were then computed, in a pair-wise manner between the groups using the wilcox_test() and add_significance() functions in the R package rstatix (v0.7.0), producing p values adjusted for multiple testing by the Holm correction.
## Connective pathway analysis
Pathway analysis was first conducted using IPA, as described above, on all DEGs for each dataset/cell type. To systematically assess similarities and differences between pathways in the different groups, (i.e. joint and muscle for CIA or inflamed and non-inflamed organ sites for IMIDs), we first clustered the pathways based on gene list similarities. To do so, the Jaccard-index was calculated, and values of 1-Jaccard-index used as distances for hierarchical clustering. Clustering was performed using the hclust() function in R with Ward’s method (i.e. parameter ward. D2). *Only* genes differentially expressed in at least one cell type (CIA)/dataset (IMIDs) within each group (active and inactive disease organ) were considered for clustering. The dendrogram from the hierarchical clustering was next transformed into a tree-like structure, in which each node represents one pathway. To prioritize clusters for downstream analyses further, each pathway was labelled as “activated,” “inhibited,” “no_direction,” or “not_significant” in each group separately. Labelling was based on the ratio of datasets showing a specific direction of change (indicated by the IPA activation z-score). For example, in the CIA data, the “Acute Phase Response Signaling” pathway was found significantly enriched (overlap $p \leq 0.05$) in six cell types out of 12 in muscle: activated (z-score >0) in two cell types and inhibited (z-score <0) in six cell types. Since the pathway was inhibited in a higher number of datasets, “Acute Phase Response Signaling” was labelled as “inactive” in muscle. In cases in which there was an equal number of cell types with a pathway that was activated and inhibited or if the direction could not be predicted (z-score = NA), the pathway was labelled “no_direction”, and if none of the cell types showed significant enrichment of the pathway, it was labelled “not_significant”. This oversimplification helps to achieve an overview of general behavior of pathways in the distinct groups, and to prioritize clusters of pathways for further analyses.
For further validation, KEGG pathway analysis was also conducted on all DEGs for each cell type in muscle and joint using “enrichKEGG” function from R package “clusterProfiler”. Then, connective pathway analysis was performed on the KEGG pathways with $p \leq 0.05.$ First, similarities and differences were assessed between pathways using the Jaccard-index. Clustering was performed as previously described and divided into two programs. To assess whether connective pathway analysis using KEGG pathways has comparable results as using IPA, similarities between programs from connective pathway analysis of KEGG and IPA were compared using enrichment analysis (two-sided Fisher’s exact test). Since all pathways in KEGG have different pathway names, pathways were not directly compared between KEGG and IPA; instead, the enrichment analyses were performed on gene content of each program using Fisher’s exact test. *All* genes enriched in KEGG pathways were used as background for the analyses.
## Prediction of URs regulating specific programs/subprograms in connective pathway analysis
To identify potential URs of each program/subprogram from the connective pathway analysis, we performed enrichment analyses. For each potential UR-program/subprogram pair we computed the enrichment of the URs downstream targets among the pathway-associated genes (right tail Fisher’s exact test). *All* genes in connective pathway analysis were used as background. Enrichment p values were next combined with Fisher’s method over all cell types in the joint and muscle separately. For each respective organ, the URs with significant combined p values, corrected for multiple testing with the Benjamini-Hochberg procedure (FDR <0.05), were ranked by the lowest combined FDR values (Data S6).
Similarly, URs were identified in the meta-analyses of IMIDs, where enrichment p values were combined over all datasets representative of inflamed organs and non-inflamed organs separately. To identify specific URs for (UC and CD, enrichment p values were combined over all datasets representative of the disease and condition (e.g., all datasets representative of inflamed organs in CD).
## Differential expression analysis of expression profiling data from immune-mediated inflammatory diseases
We systematically mined the Gene Expression Omnibus (GEO) database for expression profiling datasets from different IMIDs. Each dataset included samples from at least three patients and three healthy controls (Data S1). The search included 32 different datasets from ten different IMIDs, namely RA, UC, CD, PSO, SLE, systemic sclerosis (SSc), Sjögren’s syndrome (SS), atopic dermatitis (AD), juvenile myositis (JM), and type 1 diabetes (T1D).47,48,49,50,51,52,53,55,56,57,61,63,65,66,69,102,103,104,105 Using GEO2R81, we identified DEGs between patients and healthy control samples (detailed in Data S1). The data were annotated by the National Center for Biotechnology Information (NCBI) and adjusted for multiple testing using the Benjamini-Hochberg procedure. The dataset was next filtered to include only significant DEGs (FDR <0.05), with the associated gene symbol for further core analysis using the IPA software. If more than 5,000 DEGs were found between patients and healthy controls, the top 5,000 DEGs (lowest FDR value) were used for the core analysis using the IPA software (Q1 2021 and Q4 2020 version), owing to the computational limitation of upload allowance. The core analysis in IPA (parameters: species = human) was used to identify canonical pathways as described above. Enrichment analyses were performed by applying Fisher’s exact test (right tailed)). In case two or more datasets were representative of the same disease and condition (for example UC inflamed organs), enrichment p values were combined with Fisher’s method in all downstream analyses. To check for similarities between subprograms from connective pathway analysis of CIA and IMID data, we computed the enrichment of pathways between each subprogram of CIA and each subprogram of IMID. As a background for the analyses, all pathways included in the connective pathway analysis were used.
## Genome-wide association study (GWAS) enrichment analyses within programs and subprograms
Genome-wide association study (GWAS) gene enrichment analysis (Fisher exact test, right tailed) of programs and subprograms in connective pathway analysis in both CIA and IMIDs was performed for each cell type/dataset separately. All DEGs within that cell type/dataset were used as a background. The GWAS-associated genes were downloaded from DisGeNET (data downloaded on February 9, 2021). For the CIA data, GWAS genes associated with ‘Rheumatoid Arthritis’ were included, identifying 777 genes. For the IMIDs data, we included GWAS genes associated with, RA: ‘Rheumatoid Arthritis’ (777 genes), CD: 'Crohn disease' (515 genes), lupus (except lupus nephritis (LN)): 'Lupus Erythematosus', 'Lupus Erythematosus, Cutaneous', 'Lupus Erythematosus, Discoid', 'Lupus Erythematosus, Subacute Cutaneous', 'Lupus Erythematosus, Systemic', and 'Neuropsychiatric Systemic Lupus Erythematosus' (626 genes), LN: 'Lupus Nephritis' (53 genes), SS: "Primary Sjögren’s syndrome" and "Sjogren’s Syndrome" (49 genes), T1D: 'Diabetes Mellitus, Insulin-Dependent', 'Diabetes Mellitus, Ketosis-Prone', 'Diabetes, Autoimmune', and 'Neonatal insulin-dependent diabetes mellitus' (485 genes), AD: 'Dermatitis, Atopic' and 'Dermatitis, Atopic, 2' (143 genes), JM: 'Adult type dermatomyositis', 'Dermatomyositis', 'Dermatomyositis, Childhood Type', and 'Myositis' (46 genes), PSO: 'Psoriasis' (416 genes), SSc: 'Systemic Scleroderma' (171 genes), and UC: 'Ulcerative Colitis' (452 genes).
## Prioritization of URs in IMIDs
The upstream analysis of the core analysis in IPA (parameters: species = human, Q1 2021 and Q4 2020 version) was used to predict URs for all IMIDs separately based on DEGs. If more than 5,000 DEGs were found between patients and healthy controls, the top 5,000 DEGs (lowest FDR value) were used for the upstream analysis. We focused on URs that belonged to one of the following categories: "G-protein coupled receptor", or "cytokine", or "growth factor", or "ligand-dependent nuclear receptor", or "transmembrane receptor” and whose downstream targets were significantly enriched among DEGs (FDR <0.05; Fisher exact test right-tailed; IPA). To predict further the URs regulating the specific programs/subprograms in IMIDs, we performed enrichment analysis as described above.
## Overlap between programs from all analyzed IMIDs and individual IMIDs
To test if the programs (IMID_P1 and IMID_P2) derived from all analyzed IMIDs overlapped with programs from individual IMIDs in inflamed and non-inflamed organ sites, separately, we performed Fisher’s exact tests (right tailed), using all pathways in connective pathway analysis as a background, followed by correction for multiple testing using the Benjamini-Hochberg procedure. These analyses were repeated for IMID_ subprograms (IMID_SPs) and subprograms from individual IMIDs. Disease pathways were defined as all pathways significantly enriched in a particular disease and inflammation state (IPA, $p \leq 0.05$). Enriched pathways were considered those whose combined $p \leq 0.05.$
## Treatment effect on IMIDs
We mined the Gene Expression Omnibus (GEO) database for the analysis of anti-TNF treatment effects on any of the previously analyzed IMIDs. Each dataset included samples from at least three patients (before and after treatment) and three healthy controls. The search included two different datasets, one from treated UC (GSE92415)45 and one from treated CD (GSE52746).46 We identified DEGs between treated responders and untreated patients, as well as between untreated patients and healthy control samples. Pathway enrichment analyses were performed as described above. We then performed enrichment analysis of the significantly enriched pathways among the pathways of the program/subprograms from connective pathway analysis, as described above.
Subsequently, DEGs were identified for responders and non-responders separately, between untreated patients vs. control and treated patients vs. control, as well as between treated non-responders vs. treated responders, as described above. Enrichment analyses of the significantly enriched pathways of the program/subprograms were performed for the untreated patients vs. control, as described above. The URs for non-responders and responders before treatment were predicted using IPA, as described above. Focusing on URs of molecule types, “G-protein coupled receptor”, “cytokine”, “growth factor”, “ligand-dependent nuclear receptor”, and “transmembrane receptor”, we next assessed how the downstream targets of each UR predicted for the non-responders overlapped with the downstream targets of TNF for each program/subprogram separately using enrichment analysis as described above. Among those URs whose downstream targets were predicted to be enriched in at least one program/subprogram, we prioritized them that showed the potential to take over the effect of TNF based on the predicted effect of any potential activators or inhibitors. The following criteria were used: 1) the activation z-score from IPA being similarly positive or negative in both responders and non-responders; 2) the FC direction being higher (if positive z-score) or lower (if negative z-score) in non-responders vs. control compared to responders vs. control; and 3) a significant positive (if positive z-score) or negative (if negative z-score) FC in non-responders vs. responders after treatment.
To test the potential of other URs to take over the effect of TNF, we analyzed data from GSE92415. The URs and enriched pathways were first inferred using IPA, based on the top 5,000 (lowest p value) of the 10,145 DEGs identified between anti-TNF responders before treatment (I.e., week 0, $$n = 32$$) and healthy control ($$n = 21$$), and the 11,351 DEGs between anti-TNF non-responders before treatment ($$n = 27$$) and healthy control ($$n = 21$$). We only focused on URs of molecule types, “G-protein coupled receptor”, “cytokine”, “growth factor”, “ligand-dependent nuclear receptor”, and “transmembrane receptor. First, we tested if the downstream genes of each alternative UR were enriched among the TNF downstream genes, for each main- and subprogram separately (Figure S6E). For each UR, whose downstream genes were predicted to be enriched in at least one main- or subprogram, we further checked if their z-scores and FCs in responders and non-responders followed the expectations for overtaking the effect of TNF among the non-responders, but not for the responders, after treatment. Such assumptions can be made, since TNF is known to be an activator, showing a positive z-score and FC among both responders and non-responders before treatment compared to controls, and since it is assumed that any UR taking over the effect of TNF will share many of its downstream genes. Specifically, we assumed that if the UR is an activator, its z-score should be positive in both responders and non-responders, while the direction of its FC should be higher in non-responders vs. control compared to responders vs. control (for example, if the FC is negative in responders vs. control, it should be zero or positive in non-responders vs. control) in order to take over the downstream effect of TNF. If the UR is an inhibitor, we instead expected a negative z-score among both responders and non-responders, and that the direction of the FC should be lower in non-responders vs. control compared to responders vs. control. We additionally assumed that an UR which has taken over the effect of TNF should be significantly upregulated (activator), or downregulated (inhibitor), in treated non-responders compared to treated responders, between which a total of 2,922 DEGs were identified.
## Supplemental information
Document S1. Figures S1–S7 and Table S1 Data S1. Data information including scRNA-seq, immune-mediated inflammatory diseases (IMIDs), and systemic lupus erythematosus (SLE), related to Figure 2 and STAR Methods Data S2. Significant DEGs calculated between arthritic versus healthy mice, related to STAR Methods Data S3. Pathways in collagen-induced arthritis (CIA) and immune-mediated inflammatory diseases (IMIDs) and the pathways in CIA_SP1.6 and its relation to human RA, related to Figures 5 and 6 Data S4. Lists of inter- and intra-organ interactions (with a positive Pearson correlation score [PCC]) and the predicted target genes, based on NicheNet analyses, related to Figures 3 and 4 Data S5. GWAS enrichment analysis of the genes involved in pathways of the different programs and subprograms of pathways associated to the different cell types (samples) in CIA (and IMIDs), related to Figure 7 Data S6. UR prediction information for the different main and subprograms and anti-TNF treatment effects on the different main and subprograms, related to STAR Methods Document S2. Article plus supplemental information
## Data and code availability
•Single-cell RNA-seq data have been deposited at GEO and are publicly available as of the date of publication. Accession numbers are listed in the key resources table.•All original code has been deposited at GitHub and is publicly available as of the date of publication. DOIs are listed in the key resources table.•Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
## Author contributions
Conceptualization, S.L., X.L., M.S., and M.B.; methodology, S.L., X.L., M.S., E.J.L., P.B.M., L.H., M.M., H.Z., Y.Z., C.S., D.G., and H.W.; writing – original draft, S.L., X.L., M.S., Y.Z., D.G., and M.B.; writing – review & editing, S.L., X.L., M.S., Y.Z., J.L., D.G., and M.B.; visualization, S.L., X.L., M.S., and D.G.; supervision, D.G., H.W., and M.B.; project administration, S.L., X.L., and M.S.
## Declaration of interests
M.B. is scientific founder of Mavatar, Inc. J.L. is co-scientific founder of Scipher Medicine, Inc.
## Inclusion and diversity
We support inclusive, diverse, and equitable conduct of research.
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|
---
title: Visualization of cardiac uptake of bone marrow mesenchymal stem cell‐derived
extracellular vesicles after intramyocardial or intravenous injection in murine
myocardial infarction
authors:
- Cynthia M. Xu
- Sharif A. Sabe
- Rayane Brinck‐Teixeira
- Mohamed Sabra
- Frank W. Sellke
- M. Ruhul Abid
journal: Physiological Reports
year: 2023
pmcid: PMC10040402
doi: 10.14814/phy2.15568
license: CC BY 4.0
---
# Visualization of cardiac uptake of bone marrow mesenchymal stem cell‐derived extracellular vesicles after intramyocardial or intravenous injection in murine myocardial infarction
## Abstract
In animal models, human bone marrow mesenchymal stem cell‐derived extracellular vesicles (MSC‐EV) have been found to have beneficial effects in cardiovascular disease, but only when administered via intramyocardial injection. The biodistribution of either intravenous or intramyocardial injection of MSC‐EV in the presence of myocardial injury is uncharacterized at this time. We hypothesized that intramyocardial injection will ensure delivery of MSC‐EV to the ischemic myocardium, while intravenous injection will not. Human bone marrow mesenchymal stem cells were cultured and the MSC‐EV were isolated and characterized. The MSC‐EVs were then labeled with DiD lipid dye. FVB mice with normal cardiac function underwent left coronary artery ligation followed by either peri‐infarct intramyocardial or tail vein injection of 3*106 or 2*109 particles of DiD‐labeled MSC‐EV or a DiD‐saline control. The heart, lungs, liver, spleen and kidneys were harvested 2 h post‐injection and were submitted for fluorescent molecular tomography imaging. Myocardial uptake of MSC‐EV was only visualized after intramyocardial injection of 2*109 MSC‐EV particles ($$p \leq 0.01$$) compared to control, and there were no differences in cardiac fluorescence after tail vein injection of MSC‐EV ($$p \leq 0.5$$). There was no significantly detectable MSC‐EV uptake in other organs after intramyocardial injection. After tail vein injection of 2*109 particles of MSC‐EV, the liver ($$p \leq 0.02$$) and spleen ($$p \leq 0.04$$) appeared to have diffuse MSC‐EV uptake compared to controls. Even in the presence of myocardial injury, only intramyocardial but not intravenous administration resulted in detectable levels of MSC‐EV in the ischemic myocardium. This study confirms the role for intramyocardial injection in maximal and effective delivery of MSC‐EV. Our ongoing studies aimed at developing bioengineered MSC‐EV for targeted delivery to the heart may render MSC‐EV clinically applicable for cardiovascular disease.
Intramyocardial delivery appears to be an efficient method of extracellular vesicle (EV) delivery to ischemic myocardium while intravenous injection is not.
## INTRODUCTION
The role of stem cell‐derived extracellular vesicles (EV) as a therapeutic in cardiovascular disease has been studied for the past decade, and they have been largely found to have beneficial effects on cardiac function in both rodent and large animal models of ischemic disease and heart failure (Guo et al., 2022; La Mantia et al., 2022; Potz et al., 2018; Scrimgeour et al., 2019; Spannbauer et al., 2020; Xiao et al., 2022). Under ischemic conditions such as in myocardial infarction, EVs can prevent delayed injury, promote angiogenesis and aid in tissue remodeling and function through various mechanisms (Zheng et al., 2021). However, a significant barrier to the use of EVs in a clinical setting is the method of delivery. As of now, the most effective and reliable mode of delivery is via intramyocardial injection, which would require thoracotomy in a patient population that may not be able to tolerate or achieve net benefit from an operation. Less invasive methods of delivery, such as intravenous, may not confer meaningful benefits in cardiovascular disease (Chen et al., 2021; Scrimgeour et al., 2020). However, it is not known if there are any significant differences in EV biodistribution depending on the route of administration in animal models of cardiovascular disease. This is the first study to attempt to compare the biodistribution of human bone marrow mesenchymal stem cell‐derived extracellular vesicles (MSC‐EV) after intramyocardial versus intravenous injection in an animal model of myocardial ischemia.
EVs are secreted by almost all cell types, and contain many bioactive molecules, including proteins and nucleic acids. EVs appear to be taken up by cells through endocytic routes and have key roles in cell‐to‐cell communication which affect recipient cells by influencing gene expression, signaling pathways, and cellular phenotype/behavior (Fu et al., 2020; Mulcahy et al., 2014). MSC‐EVs, as well as EVs derived from other progenitor sources, have regenerative and immunomodulatory properties, and been delivered via intramyocardial, intravenous, intracoronary, and intrapericardial routes (Dabrowska et al., 2021; Spannbauer et al., 2020). Intravenous and intracoronary injections have had mixed results; intrapericardial is not as well studied (Gallet et al., 2017; López et al., 2020; Scrimgeour et al., 2020; Spannbauer et al., 2020; Wang et al., 2021; Zhu et al., 2021). Additionally, when EVs are injected intravenously (either tail vein or retro‐orbital), they have a circulation half‐life well within 60 minutes, and are readily taken up by macrophages, which congregate in the liver or spleen (Kooijmans et al., 2016; Mentkowski & Lang, 2019; Parada et al., 2021; Wen et al., 2019). Other factors to consider in predicting the location of EV uptake are the origin of the progenitor cell from which the EVs were isolated, and the presence of cellular damage as injured cells more readily take up EVs than healthy cells (Stik et al., 2017; Wen et al., 2019).
Previous biodistribution experiments have investigated the uptake of EVs in mouse models and detected no cardiac uptake with intravenous injection—these models included MSC‐EVs delivered via tail vein injection in a murine radiation model and cardiosphere‐derived EVs delivered via retro‐orbital injection in wild‐type non‐infarcted mice (Mentkowski & Lang, 2019; Wen et al., 2019). Only with intramyocardial injection were cardiosphere‐derived EVs localized to the heart in the non‐infarcted mice (Mentkowski & Lang, 2019). No cardiac injury was present in these models. Therefore, the aim of this study is to investigate whether the routes of administration, intramyocardial versus intravenous, affect MSC‐EV uptake in the presence of myocardial injury.
## Human bone marrow mesenchymal stem cell (HBMSC) culture
HBMSC were purchased from Lonza (PT‐2501), grown in T175 cm2 flasks to passage 6 with 30 ml Mesenchymal Stem Cell Growth Medium BulletKit (MSCGM) (Lonza, PT‐3001), and cultured per the manufacturer's instructions. At passage 7 (consistent with previously used protocols from our group), the cells were split into 100‐mm dishes and cultured with 10 ml of MSCGM. The cells were placed in a humidified incubator at 37°C with $5\%$ CO2.
## MSC‐EV isolation
At passage 7 and $80\%$–$90\%$ confluency (approximately 6.5–7.2 million cells), the MSCGM was removed and was replaced with 7 ml fresh MSCGM. The cells were then placed in an airtight humidified hypoxia chamber (Billups‐Rothenberg, MIC‐101) containing $95\%$ N2 and $5\%$ CO2. Hypoxia was induced by connecting the chamber's inflow cannula to a gas tank containing $95\%$ N2 and $5\%$ CO2 with a flow rate of 20 L/min with the outflow cannula open for 7 min to wash out O2. After 7 min, the outflow cannula was clamped shut first, then the inflow cannula was clamped and the gas flow was turned off. The chamber was then placed at 37°C for 24 h. Afterwards, the hypoxia chambers were opened and the media was collected, which was then centrifuged at 2000× g to remove the cell debris. The media then underwent ultracentrifugation (WX Ultra Centrifuge with Sorvall AH‐629 rotor) at 100,000× g for 70 min to isolate the MSC‐EV pellet. The MSC‐EV were then washed with Dulbecco's Phosphate Buffered Saline (PBS) and centrifuged at 100,000× g for another 70 min. The MSC‐EV were re‐suspended in PBS with $1\%$ dimethylsulfoxide, and stored at −80°C (Potz et al., 2018; Wen et al., 2019).
## MSC‐EV characterization studies
The MSC‐EV were evaluated by electron microscopy (FEI Morgagni 268) after fixation in $2\%$ paraformaldehyde for 20 min. The MSC‐EV were washed with PBS, fixed with $1\%$ glutaraldehyde and contrasted in $4\%$ uranyl acetate. With the NanoSight NS500 (Malvern Instruments), the size, number and distribution of the MSC‐EVs were determined. The following MSC‐EV markers were evaluated via western blot: CD81 (Cell Signaling, 52892S), CD9 (Cell Signaling, 13403S), Alix (Cell Signaling, 92880S), GAPDH (Cell Signaling, 97166S), heat shock protein 70 (HSP70) (Cell Signaling, 4872T), and albumin (Cell Signaling, 4929S).
## Fluorescent labeling of MSC‐EV
Fluorescent‐labeled MSC‐EVs as well as a negative control were prepared. The MSC‐EV were thawed on ice. PBS was added to the MSC‐EV to make a total volume of 1 ml, and to this 5 ul of Vybrant DiD Cell‐Labeling Solution (Invitrogen, V22887) was added (Wen et al., 2019). For the negative control (DiD‐saline), 5 ul of the labeling solution was added to 1 ml of PBS. Light exposure was minimized during this entire process. These solutions were incubated at 37°C for 30 min. The solutions were then transferred to 2 separate ultracentrifuge tubes with an additional 30 ml PBS each. The solutions were washed two times with 30 ml PBS and underwent two ultracentrifuge cycles at 100,000× g for 1 h each. After the final wash, the DiD‐labeled MSC‐EV and negative control were re‐suspended with PBS, and aliquoted for intramyocardial or tail vein injection.
## Animals
Female and male FVB/NCrl mice (6–8 weeks old) from Charles River (Stock No. 207) were used in this study, with an $$n = 5$$ per experimental group. The animals were housed at the Coro Building Barrier facility, acclimatized appropriately and fed a normal diet. All experimental procedures carried out in accordance with the protocol approved by Institutional Animal Care and Use Committee (Protocol 1844667/CMTT# 5017‐22). The experiments were carried out over several weeks by a single experienced surgeon with freshly prepared fluorescent‐labeled MSC‐EVs or DiD‐saline.
## Echocardiogram
The mice underwent pre‐operative echocardiogram (Vevo 2100, FUJIFILM VisualSonic Inc.). Under $2\%$ isoflurane anesthesia, normothermia, and heart rate maintenance between 400–600 beats per minute, left heart systolic function was evaluated via two‐dimensional parasternal long axis views with left ventricular trace measurements to determine the left ventricular ejection fraction (LVEF).
## Surgical procedure: Left anterior descending coronary artery (LAD) ligation and injection
Anesthesia was induced in the mice with $3\%$ isoflurane and ketamine (100 mg/kg), and the mice were intubated and ventilated (MiniVent Type 845, Harvard Apparatus). Buprenorphine SR (1 mg/kg) was administered subcutaneously in the dorsal fat pad. Isoflurane was then maintained at $2\%$. The heart was exposed with a left thoracotomy, and the LAD was ligated with an 8–0 nylon suture 2–3 mm below the left atrial appendage (Reichert et al., 2017). Successful ligation was confirmed with subsequent blanching and dyskinesia.
The mice were then allocated to one of six groups – [1] intramyocardial injection with DiD‐saline ($$n = 5$$), [2] intramyocardial injection with 3*106 particles DiD‐labeled MSC‐EV ($$n = 4$$), [3] intramyocardial injection with 2*109 particles DiD‐labeled MSC‐EV ($$n = 5$$), [4] tail vein injection with DiD‐saline ($$n = 5$$), [5] tail vein injection with 3*106 particles DiD‐labeled MSC‐EV ($$n = 4$$), and [6] tail vein injection with 2*109 particles DiD‐labeled MSC‐EV ($$n = 5$$). The MSC‐EV particles were quantified by nanoparticle‐tracking analysis.
Immediately after LAD ligation, the injection was performed prior to closing the thoracotomy. To perform the intramyocardial injection, a Neuros Syringe (Hamilton, 1183U32) was used to inject 5 μl of DiD‐labeled MSC‐EV or DiD‐saline into the peri‐infarct area. To perform the tail vein injection, a 0.5 ml insulin syringe was used to inject 200 μl of DiD‐labeled MSC‐EV or DiD‐saline.
The thoracotomy was then closed with 6–0 vicryl suture and the pneumothorax was evacuated. The skin was closed with absorbable sutures. The mice were successfully weaned from anesthesia and extubated.
## Organ harvest and fluorescent molecular tomography (FMT) imaging
Two hours post‐injection, the mice were euthanized via carbon dioxide inhalation and cervical dislocation. The heart, lungs, liver, kidneys and spleen were then dissected and transferred into separate Eppendorf tubes with cold PBS. The organs were imaged with the FMT 4000 imaging system (PerkinElmer) to obtain fluorescence reflectance images. The fluorescence was quantified with TrueQuant v3.0 (PerkinElmer) by measuring the counts/energy, normalized by the geometric size captured of the organ.
## Statistical analysis
For the statistical analysis of the LVEF obtained via echocardiography, the Shapiro–Wilk and Kruskal–Wallis H tests were performed. For the analysis of the organ fluorescence quantification, the Shapiro–Wilk test, Kruskal–Wallis H test and post hoc Dunn's Multiple Comparison test were used.
## MSC‐EV characterization
The MSC‐EVs were visualized via electron microscopy (Figure 1a), and their size and concentrations were quantified via nanoparticle‐tracking analysis (mean EV size was 217.3 nm ± 10.9 nm; Figure 1b). Western blot analysis confirmed the presence of the following MSC‐EV markers: the transmembrane proteins CD81 and CD9 (Figure 1c); and the cytosolic proteins Alix and GAPDH (Théry et al., 2018; Wen et al., 2019). Neither HSP70, which has promiscuous incorporation in cytosolic protein content, nor albumin, a marker of contamination, were identified in the MSC‐EV lysates. The lack of albumin confirmed purity of the isolated MSC‐EVs.
**FIGURE 1:** *Human bone marrow mesenchymal extracellular vesicle (MSC‐EV) characterizations. (a). Electron microscopy image of MSC‐EV (scale bar = 200 nm; magnification 54,800x). (b). MSC‐EV fractions determined by nanoparticle‐tracking analysis, demonstrating mean particle size to be 217.3 nm ± 10.9 nm. c. Western blot images of CD81, CD9, Alix, GAPDH, HSP70, and albumin of MSC‐EV lysates and human bone mesenchymal stem cell (HBMSC) lysates serve as qualitative measures of protein marker presence. CD81, CD9, Alix, and GAPDH were identified in the MSC‐EV. HSP70, a promiscuous cytosolic protein, and albumin, a marker of contamination, were not identified in the MSC‐EV lysates. The absence of albumin confirmed the purity of the isolated MSC‐EVs. MSC‐EV and HBMSC bands are shown in separate images given the need for vastly different exposure times during imaging. See Supplemental Figures for full western blot images.*
## Normal pre‐operative systolic function was confirmed by echocardiogram
Normal left heart systolic function was confirmed via echocardiography, with LVEF identified within normal parameters. There were no significant differences in LVEF between the three MSC‐EV dosage animal groups (negative control, 3*106 MSC‐EV, 2*109 MSC‐EV) ($$p \leq 0.9$$). The mean LVEF for the three dosage groups (negative control, 3*106 MSC‐EV, 2*109 MSC‐EV) were $67\%$ ± $4.5\%$, $68\%$ ± $3.3\%$, and $67\%$ ± $3.2\%$, respectively.
## Organ fluorescence detection showed cardiac uptake of MSC‐EV only after intramyocardial injection
In the intramyocardial injection groups, myocardial MSC‐EV uptake 2 h post‐injection was seen only with the injection of 2*109 particles, with uptake significantly increased from control ($$p \leq 0.01$$). Intramyocardial injection of 3*106 particles of DiD‐labeled MSC‐EV did not demonstrate fluorescent uptake compared to control ($p \leq 0.9$; Figure 2). No significant fluorescence was detected in any other organs (lungs, liver, kidneys and spleen) 2 h post‐intramyocardial injection using 3*106 or 2*109 particles ($$p \leq 0.5$$, $$p \leq 0.5$$, $$p \leq 0.6$$, $$p \leq 0.9$$, respectively; Figure 3a).
**FIGURE 2:** *Fluorescence molecular tomography of the heart after intramyocardial injection or tail vein injection of either a negative DiD‐saline control or DiD‐labeled human bone mesenchymal stem cell‐derived extracellular vesicles (MSC‐EV). (a) Uptake of MSC‐EV was only detected after intramyocardial (upper panels) injection of 2*109 particles. No fluorescence was detected in the heart after tail vein (lower panels) injection of either 3*106 or 2*109 particles MSC‐EV. (b) Significantly higher fluorescence was detected only after intramyocardial injection of 2*109 DiD‐labeled MSC‐EV compared to control (p = 0.01). No significant fluorescence uptake was seen after tail vein injection of either low (3*106 particles) or high dose (2*109 particles) of MSC‐EV (p = 0.5). Thus, intramyocardial injection more reliably delivers MSC‐EV to the ischemic heart. The Shapiro–Wilk test, Kruskal–Wallis H test and post hoc Dunn's multiple comparisons test were used for statistical analysis.* **FIGURE 3:** *Organs with no detectable immunofluorescence after (a) intramyocardial injection of DiD‐labeled human bone marrow mesenchymal stem cell‐derived extracellular vesicles (MSC‐EV) and (b) tail vein injection of MSC‐EV (see Figure 2 for heart). After intramyocardial injection, no MSC‐EV uptake was visualized in the lungs, liver, kidneys or spleen (p = 0.5, p = 0.5, p – 0.9 and p = 0.6, respectively). After tail vein injection, no MSC‐EV uptake was visualized in the heart (see Figure 2), lungs or kidneys (p = 0.9, p = 0.6, respectively). Thus, intramyocardial injection did not result in systemic delivery of MSC‐EV at 2 h, and the MSC‐EVs were not detected in the heart, lungs or kidneys 2 h after intravenous injection. The Shapiro–Wilk test and Kruskal–Wallis H test were used for analysis.*
Following tail vein injection of MSC‐EV, no myocardial uptake was seen ($$p \leq 0.5$$; Figure 2). As expected, there was increased fluorescence detected in the liver ($$p \leq 0.02$$) and spleen ($$p \leq 0.04$$) after injection of 2*109 particles MSC‐EV compared to control. No increased fluorescence was detected in the liver or spleen after the tail vein injection of 3*106 particles MSC‐EV ($p \leq 0.9$, $p \leq 0.9$, respectively; Figure 4). No significant fluorescence was detected in the lungs or kidneys after tail vein injection of MSC‐EVs using 3*106 or 2*109 particles ($$p \leq 0.9$$, $$p \leq 0.6$$, respectively; Figure 3b).
**FIGURE 4:** *Immunofluorescence detected in the liver and spleen after tail vein injection of human bone marrow mesenchymal stem cell‐derived extracellular vesicles (MSC‐EV). (a) Representative images of the liver (upper panels) and spleen (lower panels) depicting increased organ fluorescence after the injection of 2*109 particles of DiD‐labeled MSC‐EV but not with 3*106 particles of MSC‐EV. (b) Quantification of immunofluorescence in the liver and spleen, which showed significantly increased levels of fluorescence after the MSC‐EV injection of 2*109 particles, compared to control (p = 0.02, p = 0.04, respectively). Thus, intravenous injection of MSC‐EV results in delivery to the liver and spleen. The Shapiro–Wilk test, Kruskal–Wallis H test and post hoc Dunn's multiple comparisons test were used for statistical analysis.*
## DISCUSSION
To the best of our knowledge, prior to this study, the biodistribution of MSC‐EVs after intramyocardial versus intravenous injection in a myocardial ischemia model had not been investigated before. Here we demonstrated that intramyocardial injection most effectively delivered the MSC‐EV dose to the ischemic myocardium, and that tail vein injection did not result in detectable levels of MSC‐EV in the heart despite the presence of myocardial injury.
Previous studies have shown that cellular injury can increase the uptake of EVs. For example, in murine models of glycerol‐induced acute kidney injury and radiation injury, MSC‐EVs had detectable heightened accumulation in the kidneys and hematopoietic organs, respectively, after tail vein injection (Grange et al., 2014; Wen et al., 2019). Unfortunately, our model does not demonstrate increased uptake in the heart after acute myocardial ischemia and immediate intravenous injection. Numerous studies have shown optimal EV therapeutic benefits in ischemic cardiovascular disease consistently with only intramyocardial injection but not intravenous injection (though this route may have some effects with very high dosing) (Chen et al., 2021; Gallet et al., 2017; Potz et al., 2018; Scrimgeour et al., 2020; Vandergriff et al., 2015). One explanation could be that in the presence of ischemia, EVs administered systemically are not able to reach their destination due to impaired blood flow. Additionally, the coronary capillaries, unlike the sinusoidal hepatic and splenic capillaries, have tight junctions that may limit EV uptake by the myocardium. Another factor to consider is the timing of the MSC‐EV administration – it is unclear when the cardiomyocytes and cardiac endothelial cells release injury signals that could potentially attract the EVs with subsequent increased uptake into the myocardium. Future studies could explore different time points of EV injection, or ischemia/reperfusion models with resulting increased endothelial permeability.
As expected, hepatic and splenic uptake was detected after tail vein injection of MSC‐EV – this finding is consistent with many previous experiments that demonstrated increased fluorescence of the liver and spleen after intravenous injection, which is due to the macrophage uptake of the EVs and subsequent macrophage accumulation in the liver and spleen. Lung uptake was not detected 2 h after injection, but previous studies show that most of the lung signal rapidly decreases after 1 h (Kang et al., 2021; Wen et al., 2019).
Limitations of this study include the lack of evaluation of the consequences of intramyocardial versus tail vein MSC‐EV administration in this myocardial ischemia model beyond 2 h—future experiments could examine post‐operative cardiac function and the effects on infarct size and angiogenesis. Also, the EV dosages administered in fluorescence uptake studies are markedly supra‐therapeutic given the limits of the detection devices—thus the lack of immunofluorescence may not equate to the absence of EVs. However, we can still conclude that intramyocardial injection results in the maximal dose delivered to the heart.
In conclusion, intramyocardial injection appears to be the optimal mode of delivery of MSC‐EV to ischemic myocardium. This study will help future experiments treat myocardial ischemia with MSC‐EVs by optimization of the MSC‐EV route of administration, and the murine model used in this study can be developed to be a high throughput vehicle for testing the efficacy of various kinds of MSC‐EVs, as well as testing EVs bioengineered to increase cardiac uptake.
## AUTHOR CONTRIBUTIONS
C.X. conceived the idea along with experimental design, conducted the majority of the experiments (cell culture, extracellular vesicle isolation and labeling, extracellular vesicle characterization studies, echocardiogram, surgeries, fluorescent imaging and organ harvest), performed the data analysis, and drafted the manuscript. S.S. helped perform experiments (cell culture, extracellular vesicle isolation and fluorescent imaging) and edited the manuscript. R.B.T. helped with the experiment conception, animal experiments (surgeries), and edited the manuscript. M.S. helped perform experiments (extracellular vesicle isolation and organ harvest) and reviewed the manuscript. F.W.S. contributed to the conception of the experiment and reviewed the manuscript. M.R.A. contributed to the conception of the experiment, supervised the project including experimental planning and data collection/analysis, reviewed the manuscript and provided critical feedback.
## FUNDING INFORMATION
Funding for this research was provided by the National Heart, Lung, and Blood Institute (NHLBI) 1R01HL133624 and 2R56HL133624‐05 (M.R.A.); R01HL46716 and R01HL128831‐01A1 (F.W.S.); T32 GM065085‐10 (J.A.).
## DISCLOSURES
None.
## ETHICS STATEMENT
This study was approved by the Institutional Animal Use and Care Committee at Rhode Island Hospital (ref. no. $\frac{501722}{2022}$).
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|
---
title: Beta‐adrenergic agonist induces unique transcriptomic signature in inguinal
white adipose tissue
authors:
- Henry A. Paz
- Anna‐Claire Pilkington
- Hannah D. Loy
- Ying Zhong
- Kartik Shankar
- Umesh D. Wankhade
journal: Physiological Reports
year: 2023
pmcid: PMC10040403
doi: 10.14814/phy2.15646
license: CC BY 4.0
---
# Beta‐adrenergic agonist induces unique transcriptomic signature in inguinal white adipose tissue
## Abstract
Activation of thermogenic adipose tissue depots has been linked to improved metabolism and weight loss. To study the molecular regulation of adipocyte thermogenesis, we performed RNA‐Seq on brown adipose tissue (BAT), gonadal white adipose tissue (gWAT), and inguinal white adipose tissue (iWAT) from mice treated with β3‐adrenoreceptor agonist CL316,243 (CL). Our analysis revealed diverse transcriptional profile and identified pathways in response to CL treatment. Differentially expressed genes (DEGs) in iWATCL were associated with the upregulation of pathways involved in cellular immune responses and with the upregulation of the browning program. We identified 39 DEGs in beige adipose which included certain heat shock proteins (Hspa1a and Hspa1b), and others suggesting potential associations with browning. Our results highlight transcriptional heterogeneity across adipose tissues and reveal genes specifically regulated in beige adipose, potentially aiding in identifying novel browning pathways.
Activation of thermogenic adipose tissue depots has been linked to improved metabolism and weight loss. To study the molecular regulation of adipocyte thermogenesis, we performed RNA‐Seq on brown adipose tissue (BAT), gonadal white adipose tissue (gWAT), and inguinal white adipose tissue (iWAT) from mice treated with β3‐adrenoreceptor agonist CL316,243 (CL). Our results highlight transcriptional heterogeneity across adipose tissues and reveal genes specifically regulated in beige adipose, potentially aiding in identifying novel browning pathways.
## INTRODUCTION
Adipose tissue is an important indicator of the systemic energy status. Adipose tissue dysfunction during obesity and metabolic syndrome is well‐documented (Blüher, 2009). Of the forms of adipose depots, brown adipose tissue (BAT), which burns energy when active, is recognized as thermogenic tissue, and white adipose tissue (WAT) as a storage depot of lipids. In addition to major differences in their developmental origins, WAT is characterized by a large unilocular lipid droplet, whereas BAT has smaller, multilocular lipid droplets. Beige adipocytes are a type of fat cell that is capable of thermogenesis mimicking a BAT phenotype (Wu et al., 2012). Ucp1‐expressing, mitochondrial‐rich beige adipocytes are found in WAT depots of mice and develop in response to cold temperatures or pharmacological stimulation of β‐adrenergic signaling through agonists such as CL316,243 (CL) (Labbé et al., 2016; McMillan & White, 2015). Similar to BAT, beige adipocytes can generate heat via the mitochondrial protein Ucp1 that uncouples mitochondrial respiration from ATP synthesis. The discovery of metabolically active BAT in humans has led to increased interest in the thermogenic fat cell's potential role in the counteracting increased weight gain and related metabolic disorders such as type 2 diabetes, and metabolic syndrome (Yoneshiro et al., 2013).
Brown and beige adipose tissue are sites of adaptive thermogenesis in mice, and their activity contributes significantly to total energy expenditure. The previously held belief that adult humans do not have BAT was debunked in the last decade when three different groups demonstrated that adult humans have active BAT in response to environmental stimuli such as cold ambient temperature (Cypess et al., 2009; van Marken Lichtenbelt et al., 2009; Virtanen et al., 2009). Transcript profiling of active BAT depots in humans revealed that this thermogenic tissue express Ucp1, implying that certain human depots have a molecular profile similar to rodent BAT, while others have a beige fat‐like profile (Ikeda et al., 2018). Thermogenic fat activity in humans correlates with lean mass composition, indicating a possible role for brown and beige fat in adult human metabolism (Kiefer, 2017; van Marken Lichtenbelt et al., 2009). Pharmacological interventions can activate BAT and increase energy expenditure in humans. However, the relatively small volume of BAT may not contribute significantly to overall body fat reduction. Transforming WAT into BAT‐like or beige fat is a more conceivable possibility, as it has been shown to be possible in experimental animals. However, the evidence for transformation of WAT to beige/BAT‐like fat in humans is less solid.
Growing efforts of the scientific community are focused on unraveling the complexities of transcriptional regulation of brown and beige fat formation. Understanding cell‐specific development and function can be aided by defining each adipose tissue type's specific gene expression signature. This study sought to understand more about the transcript profiles of WAT, BAT, and beige adipose tissue in mice. We performed transcriptional profiling of various adipose tissue depots, namely inguinal WAT (iWAT), gonadal WAT (gWAT), and BAT. We used iWAT from CL treated mice as beige fat (iWATCL). RNA‐Seq permits the digital quantification of gene expression data, allowing for the assessment of relative abundance of genes within and between samples. We aim to identify genes that are specific to different adipose tissue types, especially beige, in order to improve our overall understanding of adipose tissue function and development. We demonstrate that the transcriptional profile of beige adipose tissue differs from that of WAT and BAT. Beige cells have a distinct transcriptional profile, which we believe is responsible, if not critical, for the browning process. An in‐depth examination of the transcriptional profile and the biological pathways that are associated with it provides comprehensive and unique information on beige cells.
## Experimental design
Male C57BL6/J mice were individually housed in an AAALAC‐approved animal facility in a temperature (22°C) and light controlled room (12 h light‐12 h dark cycle). To avoid the potential distortion of the baseline for measuring transcriptional changes related to adipose thermogenesis, mice were acclimated to the housing conditions for several weeks prior to the start of the experiment. Additionally, all mice were subjected to the same housing conditions throughout the experiment, which allowed us to compare the differential response. The Institutional Animal Care and Use Committee at the University of Arkansas for Medical Sciences approved all experimental protocols. Starting at 5 weeks of age, mice were given ad libitum access to control diet ($17\%$ fat Harlan Teklad, TD95095) for 15 weeks. At 20 weeks of age, mice were treated with a daily intraperitoneal injection of β3‐adrenergic agonist CL ($$n = 5$$) (1 mg/kg body weight) or vehicle (saline solution; $$n = 5$$) for 7 days. On the morning of the 8th day, mice were euthanized by carbon dioxide asphyxiation and the interscapular BAT, gWAT, and iWAT were dissected carefully to avoid contamination with adjacent tissues such as muscle, weighed and fixed in $10\%$ formalin to perform histological examination. Pieces of tissues were flash‐frozen in liquid nitrogen and stored at −80°C until further analysis. For histomorphometric analyses 3–4 mm pieces of adipose tissue from the inguinal fat depot were fixed in buffered alcoholic formalin for 4 days and embedded in paraffin using routine histological procedures. Sections (6 μm thick) were stained with hematoxylin and eosin.
## RNA extraction and RNA‐Seq library preparation
Total RNA was isolated from 50 mg of adipose tissue using a combination of TRI reagent and RNeasy‐mini columns (Qiagen), including on‐column DNase digestion. RNA quality and integrity was confirmed spectrophotometrically (A260/A280 ratio > 1.9) and via visualization using Experion RNA Std‐Sens chips (BioRad). Equal amounts of total RNA from 1–2 mice were pooled, to generate three biologically distinct replicates per group representing all animals ($$n = 5$$). Poly‐A RNA was isolated from 5 μg of total RNA using Dynabeads® mRNA‐Direct kit (Invitrogen) and procedures described previously (Wankhade et al., 2017). Briefly, poly‐A RNA was captured by addition of 100 μL of Oligo‐(dt)25 Dynabeads in 150 μL of lysis buffer. The mixture was incubated on a rotary shaker for 20 min at room temperature. mRNA‐bead complexes were washed twice with 100 μL of wash buffer A (10 mM Tris–HCl, pH 7.5, 0.15 M LiCl, 1 mM EDTA, $0.1\%$ LiDS), followed by two washes (100 μL each) with wash buffer B (10 mM Tris–HCl, pH 7.5, 0.15 M LiCl, 1 mM EDTA). RNA was eluted from the beads in 11 μL of nuclease free water by heating to 65°C for 5 min. Stranded mRNA‐Seq library construction was carried out using NEB‐Next Ultra reagents (New England Biolabs). First and second strand cDNA synthesis, end‐filling using Klenow fragment, and dA‐tailing were carried out using manufacturer's recommendations. Ligation with Illumina's paired‐end adapters for multiplexed sequencing was performed with 1 μL of T4 DNA ligase, 0.3 μM of annealed adapters, in a 50 μL reaction volume for 30 min at room temperature. Ligated products were separated using a high‐resolution $2\%$ agarose gel, and products around 200 bp (±50 bp) were excised and purified using Qiagen gel extraction kit (Qiagen). Size‐selected cDNA libraries were amplified using indexed primers. PCR was carried out for 12–14 cycles using 29 μL of template, 1 μL of forward and reverse primers (25 μM), and 1 U Phusion high‐fidelity DNA polymerase (New England Biolabs). PCR products were purified using Qiaquick PCR purification columns (Qiagen) and eluted in 30 μL final volume. A small aliquot (~1 μL) was evaluated using DNA1K chip (Experion, Bio‐Rad) to confirm the absence of primer‐dimers and other spurious products. Quantification of the RNA‐seq libraries was done via Qubit dsDNA HS Assay kit.
## RNA‐Seq analysis
High‐quality reads were mapped against the mouse reference genome (GRCm38_v100) using TopHat (Trapnell et al., 2009) and resulting BAM files were used in SeqMonk v1.47.2 for transcript quantitation. Raw counts were normalized to log2 RPM values and only genes with log2 RPM > 0 were considered for analysis to minimize biological noise. Filtered data were transformed back to raw counts and differentially expressed genes (DEGs) were detected using the DESeq2 algorithm (Love et al., 2014) with gWAT from vehicle treated group as the control. A gene was considered differentially expressed when the false discovery rate (FDR) corrected p‐value was ≤0.05 and the absolute value of the log2 fold change was ≥2.
## Gene ontology analysis
The list of DEGs were analyzed using the Ingenuity Pathway Analysis (IPA, version 73,620,684) core analysis (Krämer et al., 2014). IPA uses the right‐tailed Fisher's Exact test to calculate significance where the p‐value is the probability of overlap between the treatment gene set and the IPA Ingenuity Knowledge Base reference gene set. To predict pathway activity, IPA uses a separate Z‐score test. To determine transcription regulators, the Upstream Analysis from IPA was used. For Canonical Pathway, Diseases and Functions, and Upstream Analyses, the significance threshold was considered at ‐log(p‐value) ≥ 1.3 and significant inhibition or activation were defined at Z‐score ≤ −2 or Z‐score ≥ 2, respectively. Analyses of pathway enrichment from the distinctive upregulated or downregulated gene sets of iWATCL were done using EnrichR (Kuleshov et al., 2016).
## Statistical analysis and data representation
Unconstrained ordination was computed, and figures were generated using R v4.2.0 (R Core Team, 2022). A principal coordinate analysis (PCoA) using Bray‐Curtis dissimilarities was conducted and differences in transcriptome profile among samples were visualized in a two‐dimensional PCoA plot. To further evaluate transcriptome profiles, a dendrogram was constructed using Euclidean distances and the Ward's method for hierarchical clustering. The profiling of fat tissue types (ProFAT) pipeline (http://ido.helmholtz‐muenchen.de/profat/) was used to determine the browning capacity among samples (Cheng et al., 2018). This computational tool uses robust gene signatures from white and brown adipocytes to predict thermogenic potential. Volcano plots were used to show the number of upregulated and downregulated DEGs from each adipose depot. Venn diagrams were used to identify distinctive DEGs within each adipose tissue or those shared among adipose tissues. Interactome networks were generated using the STRING database (Search Tool for Retrieval of Interacting Genes/Proteins) (Szklarczyk et al., 2019) where nodes are proteins and edges represent predicted functional associations.
## Adipose tissue transcriptome clustered together based on adipose tissue type
A week‐long stimulation of β‐adrenergic pathway via CL did not induce weight changes in mice in comparison to vehicle‐treated mice. ( Figure 1a). Likewise, weights of adipose tissues were similar between treatments (Figure 1b,c). As expected, treatment with CL promoted the appearance of multilocular brown‐like adipocytes in iWAT (Figure 1d). RNA transcriptome analysis of gWAT, iWAT, BAT (vehicle‐treated mice), and iWATCL (CL‐treated mice), included ~396 million reads (~33 million/sample) covering 12 biological replicates. Alignment of high‐quality reads showed that mapping to exons ranged from $91.2\%$ to $94.5\%$ across samples with no reads mapping to mitochondrial or ribosomal RNA (Table S1). To assess the global transcriptome profile of adipose tissues, a PCoA was conducted including all annotated genes. We found distinct profiles in adipose tissue depots (PERMANOVA = 0.001) based on type. In the PCoA plot (Figure 2a), samples clustered by location, with WAT depot samples being more similar to each other than BAT samples. The results of further analysis of the transcriptomes using hierarchical clustering (Figure 2b) were consistent with the PCoA. Transcriptome‐based meta‐analyses recently discovered distinct molecular signatures between brown and white adipocyte phenotypes and identified particular, basic marker‐genes for phenotype prediction. *Using* gene‐expression data from these markers, adipocyte thermogenic capacity could be estimated (Kuleshov et al., 2016). Analysis using ProFAT showed that gene expression levels of BAT marker‐genes were higher in iWATCL than in iWAT and gWAT, as expected (Figure 2c). Accordingly, iWAT from CL‐treated mice had a greater browning capacity than iWAT and gWAT from vehicle‐treated mice (Figure 2d).
**FIGURE 1:** *Body and adipose depot weights from vehicle‐ and CL316,243‐treated mice. (a) Body weights, (b) adipose depot weights, (c) adipose depot weights relative to body weight and (d) hematoxylin and eosin (H&E) stained sections of adipose depots.* **FIGURE 2:** *Transcriptome profile and browning potential of different adipose tissue depots. Clustering of samples by adipose tissue depot shown in a (a) two‐dimensional principal coordinate analysis (PCoA) plot based on Bray‐Curtis dissimilarities and in a (b) dendrogram constructed using Euclidean distances and the Ward's method for hierarchical clustering. (c) Heatmap showing the relative gene expression of marker genes from brown and white adipose tissues and (d) predicted browning capacity for each sample based on the profiling of fat tissue types (ProFAT) pipeline.*
## Browning and immune‐related pathways are increased during β‐adrenergic stimulation in iWAT
Genes with log2 RPM values >0 which amounted to nearly $41\%$ of total identified transcripts were considered for further analysis. We identified DEGs in BAT, iWAT, and iWATCL using gWAT from vehicle‐treated group as the control. The number of DEGs were greater in BAT [1130] followed by iWAT [653] and iWATCL [606] (Figure 3). Evaluation of the top five upregulated and downregulated DEGs (i.e., genes exhibiting greater fold change) showed that iWATCL shared more top upregulated genes with BAT (Atp2a1, Myh4, Tnnt3, and Xirp2) and more top downregulated genes with iWAT (Arx, Tcf21, and Upk3b). DEGs were then subjected to IPA to identify significantly enriched canonical pathways. Complete results of the core analysis among adipose tissues are presented in Table S2. Canonical pathways were sorted by p‐value and Z‐score and the top 10 significantly upregulated and downregulated pathways were identified (Figure 3). In BAT, top activated pathways included the browning pathway and pathways involved in the generation of energy such as the TCA cycle, glycolysis and oxidative phosphorylation, whereas top downregulated pathways were involved in degradation, and xenobiotic metabolism (Figure 3a). In iWAT, the top activated pathways included pathways involved in cellular immune responses such as the Th1 pathway and T cell receptor signaling, while PPAR signaling was among the top downregulated pathways (Figure 3b). The profile of top activated pathways in iWATCL was similar to iWAT; however, like BAT, the browning pathway was significantly activated (Table S2) and the dilated cardiomyopathy signaling pathway was among the top downregulated pathways (Figure 3c).
**FIGURE 3:** *Differentially expressed genes and associated canonical pathways among adipose depots. Blue, black and orange dots in volcano plots represent downregulated, not significant and upregulated genes; respectively, in (a) BAT, (b) iWAT, and (c) iWATCL. DEGs were considered when FDR adjusted p‐value ≤ 0.05 and log2 |fold change| ≥ 2. Divergent bar charts displaying significant enriched canonical pathways where inhibition or activation were defined at Z‐score ≤ −2 or Z‐score ≥ 2, respectively.*
Further assessment of biological processes associated with DEGs among adipose tissues was done using the diseases and functions analysis from IPA. Annotations were sorted by p‐value and Z‐score and the top 10 significantly upregulated and downregulated functions are shown in Figure S1. In BAT, thermogenesis, energy homeostasis and muscular associated functions were activated, while functions related to muscular disorders were downregulated. Consistent with the canonical pathway analysis, iWAT and iWATCL had a similar profile of upregulated functions which were mainly related to the general category of lymphoid tissue structure and development. The profiles of downregulated functions for iWAT and iWATCL were mainly related to immune signaling pathways.
## Heat shock proteins are downregulated in iWATCL
Identification of DEGs that were distinctive to each adipose depot was performed using Venn diagrams (Figure 4). Distinctive DEGs in iWATCL were of particular interest as these genes could represent potential genetic markers and could also aid to identify molecular mechanisms that differentiate this adipose tissue. For upregulated DEGs, the Venn diagram indicated that 343, 230 and 14 genes were selective to BAT, iWAT, and iWATCL, respectively (Figure 4a). For DEGS distinctive to iWATCL, evaluation of the expression levels showed that Cacng5, Dusp15, Mup3 and Nap1l5 were greater ($p \leq 0.05$) in iWATCL compared to both BAT and iWAT. The expression level of other DEGs such as Bcas1, Fa2h, and Serpina1a was greater in iWATCL compared to BAT but similar to iWAT. *Distinctive* genes in iWATCL were associated with molecular functions such as glycogen binding and voltage‐gated calcium channel activity (Figure 5a). Interactome analysis revealed that Igfn1 had the highest number of interactions with other upregulated DEGs in iWATCL and was associated with a muscle contraction regulation network (Figure 5b). For downregulate DEGs, the Venn diagram indicated that 452, 10 and 25 genes were selective to BAT, iWAT, and iWATCL, respectively (Figure 4b). For downregulated DEGS distinctive to iWATCL, expression levels of Atp1a3, Ccdc162, Hspa1a, Hspa1b, Mt2, Orm3, Scd1, and Thbs1 were lower ($p \leq 0.05$) in iWATCL compared to BAT. The expression levels for Hspa1b, Scd1 and Thbs1 were also lower ($p \leq 0.05$) in iWATCL compared to iWAT. These distinctive downregulated DEGs were associated with functions of ATPase activity such as ATP binding and hydrolysis (Figure 5c). Interactome analysis showed Hspa1a and Hspa1b being important genes in a protein folding chaperone network (Figure 5d).
**FIGURE 4:** *Distinctive and shared differentially expressed genes among adipose depots. (a) Upregulated and (b) downregulated DEGs and expression levels of unique DEGs in iWATCL. *p < 0.05, **p < 0.01, ***p < 0.001, one‐way ANOVA with Tukey honest significant differences (HSD) post hoc test.* **FIGURE 5:** *Functions associated with distinctive differentially expressed genes in iWAT from mice stimulated with CL316,243. Enriched molecular functions and interaction network of upregulated genes (a, b) and of downregulated genes (c, d).*
## Potential transcription factors that regulate browning in iWAT
Transcription factors are proteins that coordinate gene expression in specific cell types in a spatial and temporal manner. These proteins play a role in transcriptional regulation, influencing the expression of key genes involved in critical physiological functions. The Upstream Analysis from IPA was used to determine transcription factors associated with DEGs impacted by CL stimulation (Figure 6). From the top activated transcription factors, iWATCL shared two transcription factors with BAT (MEF2C and MYOD1) and none with iWAT. This suggests differences in the processes regulating browning in iWAT compared to BAT. Activated transcription factors in iWATCL were mainly immune (TCF3, STAT5B, ETS1) or muscle related (TBX5, GATA4, MYOCD). Whereas from the top downregulated transcription factors, iWATCL shared three with BAT (KDM5A, NRIPL, and SIX1) and three with iWAT (COMMD3‐BMI1, FOXA3, and GFI1).
**FIGURE 6:** *Top 10 upregulated and downregulated transcription regulators associated with differentially expressed genes among adipose depots. Inhibition or activation were defined at Z‐score ≤ −2 or Z‐score ≥ 2, respectively.*
## DISCUSSION
The study investigated the effects of β3‐adrenergic stimulation via CL on the browning of white adipose tissue in mice. The largest mouse adipose‐centric gene expression atlas, ProFAT, was used to assess browning potential and it was found that iWAT had the most potential for beiging. Differentially expressed genes in iWATCL included heat shock proteins (Hsp), Pif1, Ppp1r3c, Cyp2b10, Hpn, Sfrp1, and Thbs1. Hsp were found to have an association with the browning process in iWAT, with some Hsp being upregulated in BAT at 22°C while others were downregulated in iWAT after CL stimulation. Transcription factors such as KLF3, MEF2C, and MYOD1 were also found to be differentially expressed in iWATCL, potentially playing a role in the browning of WAT. The results suggest a potential link between Hsp and the browning process and the involvement of several transcription factors in the development of beige adipose tissue.
β‐3‐adrenergic stimulation with CL modulates the concentrations of metabolites related to glucose metabolism in fat depots along with emergence of beige adipocytes in iWAT and increased inflammatory markers in gWAT (Fujimoto et al., 2019; Granneman et al., 2005). In our study, DEGs in iWAT and gWAT showed upregulation of inflammatory pathways along with increased BAT specific genes in response to CL in iWAT. One of the upregulated genes in beige tissue was Pif1, which was previously shown to be critical player in metabolism, lack of which drove weight gain and decreased the exercise drive in mice (Belmonte et al., 2019). Other upregulated genes included Ppp1r3c and Cyp2b10. Ppp1r3c is enriched in adipocytes and is noteworthy that it increases during the browning process (Keinan et al., 2021) and Cyp2b10 is known to be regulated by nuclear receptors like PXR, repression of which leads to obesity and exacerbation of metabolic disease by disrupting metabolism of fatty acids (Chen et al., 2021). Among the downregulated transcripts in beige tissue, Hpn (hepsin) was previously reported to enhance liver metabolism and inhibit adipocyte browning in mice (Li et al., 2020; Li et al., 2021). Hepsin‐deficient mice are resistant to obesity, hyperglycemia, and hyperlipidemia (Li et al., 2020). Another downregulated gene, Sfrp1 is known to express in mature adipocytes and modulates the paracrine regulation of adipogenesis via Wnt/β‐catenin signaling (Lagathu et al., 2010). Thbs1 was also downregulated in our dataset, and it has previously been reported to express highly in visceral adipose tissue, loss of which makes mice resistant to diet‐induced weight gain (Inoue et al., 2013). In addition, Thbs1 is elevated in insulin resistant and obese humans (Varma et al., 2008). The differentially expressed genes observed in CL‐treated iWAT are likely derived from beige cells, which are formed from white adipocytes through recruitment or transformation. The comparison to gWAT as a control led to the identification of DEGs. Our interpretation is supported by the current literature and single‐cell RNA sequencing studies on various adipose tissue cell types. Specifically, we propose that the observed changes in genetic signature may contribute to the transformation of white adipocytes into beige cells (Burl et al., 2018; Ramirez et al., 2020).
Heat shock proteins are produced in response to multiple stressors and conduct chaperone roles such as protein folding (Park & Seo, 2015). Hsp are commonly named based on their molecular size. Emerging evidence suggests a greater involvement of Hsp on browning of adipose tissue. One study reported that in Hsp20 knockout mice, the thermogenic capacity of WAT increased compared to the wild type mice and that this response was mediated through the regulation of PPARγ (Peng et al., 2018). Another study showed that the deficiency of Hspa12a, member of the Hsp70 family, promoted greater browning in iWAT of knockout mice compared to wild type during cold exposure and suggested this effect could be mediated through paracrine mechanisms (Cheng et al., 2019). In contrast to the above, Kim et al found that Hsph1, Hsp90aa1, and Hspa8 are upregulated in BAT of young and old mice exposed to acute cold exposure (Kim et al., 2021). In the current study, the expression of Hspa1a and Hspa1b were lower in iWAT compared to BAT and β3‐adrenergic stimulation through CL further decreased their expression. These results although not conclusive, suggest associations between Hsp and the browning process in WAT. Additionally, some of the non‐discussed genes here (differentially and uniquely expressed in CL treated iWAT) are mainly implicated in nervous system‐related process, while they are not reported to be involved in adipose tissue‐related processes, it could be a potential area of exploration.
Adipocyte development is a highly orchestrated process that can vary between different fat depots. To date, many transcription factors have been found to play important roles in this development by binding to key genes and influencing their expression. In the current dataset, when differentially expressed genes were probed for their transcription factor associations, several interesting targets were revealed. KLF3, MEF2C, MYOD1, SRF, KDM5A, TCF3, and FOXA3 are some of the transcription factors that were differentially expressed in the data. KLF3, for example, has been established to play a role in adipogenesis and KLF3 knockout mice were found to have fewer adipogenic cells, indicating that it may have a key role in beige adipose tissue development (Sue et al., 2008). MEF2C, on the other hand, stimulates miR222 that inhibits SCD5 and thus decreases fat deposition. MYOD1 interacts with glucocorticoid receptors to regulate the size of myofibers and may have a role in the browning process (Ren et al., 2020) and it was upregulated in iWATCL in our dataset. SRF regulates MRTF‐A and regulates adipogenesis via actin dynamics (Mikkelsen et al., 2010), while KDM5A may play a role in adipocyte differentiation. KDM5A is a histone demethylase that is a transcriptional corepressor and that was downregulated in both BAT and iWATCL. Research shows that when KDM5A expression is decreased, the Wnt/beta‐catenin pathway that leads to preadipocyte differentiation is also decreased (Guo et al., 2019). TCF3 may be essential for white adipose development (Guo et al., 2012), while FOXA3 inhibits PGC1α and CREB binding (Ma et al., 2014), which could indicate its role in metabolic pathway activation and the control of thermogenesis. These findings could provide valuable future research targets.
The present study provides a detailed transcriptome profile of various adipose tissues, including beige adipose tissue. However, it is crucial to acknowledge that whole adipose tissue contains multiple cell types, such as preadipocytes, stromal vascular cells, immune cells, and adipocytes, among others, and therefore, caution should be exercised when interpreting the transcriptome readout from whole tissue. In this study, CL was used as a browning stimulant, although other factors such as cold ambient temperatures, exercise, and pharmacological agents can also induce browning and may result in different transcriptomic profiles of beige adipose tissue. Our study employed C57B6 mice, which are a commonly used strain for metabolic studies. However, it should be noted that the metabolic profile of other strains may differ from that of C57B6 mice. For example, SV129 mice are known to be more resistant to weight gain on a high‐fat diet due to their higher amount of brown adipose tissue, while C57B6JN mice have a mitochondrial metabolism impairment due to a Nicotinamide nucleotide transhydrogenase gene mutation (Ferrannini et al., 2016; Nicholson et al., 2010). Therefore, our findings may not be generalizable to other strains with different metabolic profiles.
In conclusion, our genome‐wide transcriptome analysis of various adipose tissue depots reveals that beige adipose tissue has a distinct set of genes and a unique transcriptional profile compared to WAT and BAT. β3‐adrenergic receptor agonist leads to differential regulation of specific genes in beige adipose tissue, with many of the differentially regulated genes being linked to metabolism and obesity. These findings deepen our understanding of the adipose tissue transcriptome and could lead to the discovery of novel pathways that control adipose tissue physiology and may be disrupted in obesity. Overall, this study opens new avenues for research into the functions of genes and transcriptional factors in adipose tissue development and pathology.
## AUTHOR CONTRIBUTIONs
Umesh D. Wankhade conceptualized the study; Umesh D. Wankhade, Kartik Shankar, Henry A. Paz, Y.Z., Anna‐Claire Pilkington, and Hannah D. Loy conducted the experiments; Henry A. Paz, Umesh D. Wankhade, and Kartik Shankar performed the data analysis; Henry A. Paz, Umesh D. Wankhade, Anna‐Claire Pilkington, and Hannah D. Loy wrote the manuscript. All authors have read and agreed to the published version of the manuscript.
## FUNDING INFORMATION
This research was funded in part by the United States Department of Agriculture‐Agricultural Research Service Project 6026–51000‐010‐05S and National Institute of Diabetes and Digestive and Kidney Diseases Grant R01‐DK‐084225 (to K. Shankar). K.S. is supported in part by grants from the NIH (5 P30DK048520–27 and 1 R01HD102726‐01A1) and funds from the Department of Pediatrics, University of Colorado Anschutz Medical Campus and the Anschutz Health and Wellness Center. U.W. is also supported by the Arkansas Children's Research Institute, the Arkansas Biosciences Institute, and the Center for Childhood Obesity Prevention funded under the National Institutes of Health (P20GM109096). Research reported in this publication was supported by the National Center For Advancing Translational Sciences of the National Institutes of Health under award number UL1 TR003107. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
## CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
## ETHICS STATEMENT
The reported research here was conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee (IACUC) and the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals. The experimental procedures were designed to minimize animal suffering and ensure the welfare of the animals used in this study. This is authors' own original work, which has not been previously published elsewhere. The paper reflects the authors' own research and analysis in a truthful and complete manner.
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|
---
title: Bone marrow-derived mesenchymal stem cell-conditioned medium ameliorates diabetic
foot ulcers in rats
authors:
- Yi-Feng Xu
- Yan-Xiang Wu
- Hong-Mei Wang
- Cui-Hua Gao
- Yang-Yang Xu
- Yang Yan
journal: Clinics
year: 2023
pmcid: PMC10040509
doi: 10.1016/j.clinsp.2023.100181
license: CC BY 4.0
---
# Bone marrow-derived mesenchymal stem cell-conditioned medium ameliorates diabetic foot ulcers in rats
## Highlights
•BMMSC-CM therapy on rats with DFUs enhanced the wound healing process.•It accelerated wound closure and promoted cell proliferation and angiogenesis.•It enhancd cell autophagy and reduced cell pyroptosis in ulcers.
## Abstract
### Objectives
This study aimed to explore the effects of bone marrow-derived Mesenchymal Stem Cell-Conditioned Medium (MSC-CM) treating diabetic foot ulcers in rats.
### Methods
Models of T2DM rats were induced by a high-fat diet and intraperitoneal injection of STZ in SD rats. Models of Diabetic Foot Ulcers (DFUs) were made by operation on hind limbs in diabetic rats. Rats were divided into four groups ($$n = 6$$ for each group), i.e., Normal Control group (NC), Diabetes Control group (DM-C), MSC-CM group and Mesenchymal Stem Cells group (MSCs). MSC-CM group was treated with an injection of conditioned medium derived from preconditioned rats' bone marrow MSCs around ulcers. MSCs group were treated with an injection of rats' bone marrow MSCs. The other two groups were treated with an injection of PBS. After the treatment, wound closure, re-epithelialization (thickness of the stratum granulosums of the skin, by H&E staining), cell proliferation (Ki67, by IHC), angiogenesis (CD31, by IFC), autophagy (LC3B, by IFC and WB; autolysosome, by EM) and pyroptosis (IL-1β, NLRP3, Caspase-1, GSDMD and GSDMD-N, by WB) in ulcers were evaluated.
### Results
After the treatment wound area rate, IL-1β by ELISA, and IL-1β, Caspase-1, GSDMD and GSDMD-N by WB of MSC-CM group were less than those of DM group. The thickness of the stratum granulosums of the skin, proliferation index of Ki67, mean optic density of CD31 and LC3B by IFC, and LC3B by WB of MSC-CM group were more than those of DM group. The present analysis demonstrated that the injection of MSC-CM into rats with DFUs enhanced the wound-healing process by accelerating wound closure, promoting cell proliferation and angiogenesis, enhancing cell autophagy, and reducing cell pyroptosis in ulcers.
### Conclusions
Studies conducted indicate that MSC-CM administration could be a novel cell-free therapeutic approach to treat DFUs accelerating the wound healing process and avoiding the risk of living cells therapy.
## Introduction
In 2021 there are 537 million people living with diabetes. It is predicted that by 2045, 700 million people will suffer from this disease worldwide.1 The pooled estimate of the global prevalence of Diabetic Foot Ulcers (DFUs) is approximately $3\%$ in community-based cohorts with a wide variation in rates of major amputation across the world.2,3 DFUs is one of the most severe chronic complications of diabetes with high treatment costs, which can lead to amputation and death. One estimate suggests that between one-third to one-fifth of patients with DM will develop a chronic non-healing wound such as a Diabetic Foot Ucer (DFU) in their lifetime, with an alarming recurrence rate ($40\%$ within one year and $65\%$ within five years) and there is no reliable way to predict its occurrence.4,5 The lifetime incidence of foot ulcers in people with diabetes can be as high as $19\%$ to $34\%$.6 Therefore, a large proportion of patients require amputation and expensive treatment, affecting the quality of life of patients. With the DFU market alone estimated to grow from US $7.03 billion in 2019 to US $11.05 billion in 2027, more effective diagnostic and therapeutic strategies must be developed to combat this debilitating disease.7,8 In recent years, stem cell therapy technology has developed rapidly. Mesenchymal Stem Cells (MSCs) have a high self-renewal ability. It's convenient to be collected, and easy to be isolated and cultured for transplantation. MSCs therapy can promote wound healing by reducing inflammation, promoting angiogenesis and granulation tissue formation, and accelerating epithelialization. But some limitation restricts the wide application of MSCs therapy. The main therapeutic mechanism associated with MSCs administration is thought to be the paracrine secretion of a broad spectrum of bioactive factors and extracellular vesicles, commonly referred to as MSC-Conditioned Medium (MSC-CM).9 Therefore, the authors performed this study of MSC-CM treating DFUs in rats. The aims of this study were to determine the therapeutic effect of BMMSC-CM treatment on DFUs in rats, and to investigate the possible mechanism of the treatment.
## Animal models and groups
Experimental protocols and methods in the current study have been approved by Institutional Animal Care and Use Committee (IACUC) of China Medical University (IACUC Issue n° CMU2022711) and were performed in accordance with the ARRIVE guidelines 2.0.10 Male Sprague Dawley (SD) rats weighing 90–100g aged four weeks were obtained from SPF (Beijing) Biotechnology Co.Ltd (SCXK2019-0010). All rats were housed at 25 ± 1°C on a 12h light/dark cycle and fed ad libitum for 1 week before study inception. Animals for diabetes models were then fed with a high-fat diet ($40\%$ fat, $40\%$ carbohydrate, and $20\%$ protein) for 8 weeks, and diabetic rat models were generated through a single intraperitoneal injection of streptozotocin (STZ, Sigma, USA) at 30 mg/kg body weight in sodium citrate buffer (pH4.2) after overnight fasting for 15 hours. Blood Glucose (BG) concentration was measured using a drop of tail capillary blood by a glucometer. Fasting Blood Glucose (FBG) ≥ 8.3 mmoL/L after 3 days for 7 days, was indicative of the successful establishment of the T2DM rat model.11,12 DFUs models were created by removing full-thickness skin of 3 × 7 mm from the right hind limb of the diabetic rats, then they were randomly divided into three groups as follows: MSC-CM therapy group (MSC-CM, $$n = 6$$), MSCs therapy group (MSCs, $$n = 6$$), and diabetes control group (DM-C, $$n = 6$$). Normal rats also received an operation for ulcers in the same way and were set as the normal control group (NC, $$n = 6$$).
## Cell culture, MSC-CM therapy
Isolation, culture, and identification of MSCs: Bone marrow was collected from both lateral femurs and tibias of one 4-week-old male SD rat weighing 150g. A complete culture medium was prepared, consisting of high glucose Dulbecco's Modified Eagle's Medium (DMEM) with $10\%$ Fetal Bovine Serum (FBS). Cut off the epiphysis at both ends of the femurs and tibias from the joint of the rat with ophthalmic scissors to expose the bone marrow cavity. Flushed the bone marrow out from one end of the bone marrow cavity and then flushed the bone marrow out in the opposite direction from the other end of the bone marrow cavity with culture medium in a 1ml syringe repeatedly, until flushing fluid from the bone marrow cavity became clear. Bone marrow cells were collected by Ficoll-Hypaque density gradient centrifugation. These cells were cultured at 37°C with $5\%$ CO2 in a complete culture medium. Nonadherent cells were removed, and a fresh medium was added after 48h of incubation. The medium was changed every 48h or 72h and further propagated the adherent spindle-shaped cells for three passages. BM-MSCs were harvested and identified by flow cytometry as CD73+ CD90+ CD105+ CD34− CD45− HLA-DR− for the expression of MSC markers.13 Preparation of MSC-CM: When the confluency of MSCs in 3rd generation reached $80\%$‒$90\%$, MSCs were cultured in L-DMEM without FBS and penicpstreptomycin for 24h. Then the supernatant was collected, and the dead cells were removed by centrifugation. The medium obtained was concentrated for about 25 times by ultrafiltration, filtered with 0.22 µm microporous membrane filter to remove bacteria. The concentrated conditioned media were frozen and stored in a refrigerator at -80°C until use.
MSC-CM therapy: When the models were created, MSC-CM were injected into four sites around the ulcer of each rat in MSC-CM group, totally 100 µL for each rat. 106 of MSCs were injected into the ulcer of each rat in MSCs group. DM-C group and NC group were injected with the same amount of PBS in the same way.
## Measurement of body weight, wound area and blood glucose level
Digital photographs of wounds were taken on days 0, 3, 7, 10, and 14. Body weight and fasting blood glucose level were determined at day 0, 7 and 14. The wound area was measured using Image-pro Plus 6.0 analysis software (IPP, Media Cybernetics, Inc.) by tracing the wound margin. The wound area rate was calculated as follows: *Wound area* (%) = ([area of actual wound] / [area of original wound]) × 100.
Histological assessment: At day 14 after therapy, the SD rats were killed and the wound samples (including 2 mm of the surrounding skin of the ulcers) were harvested for histological analysis.
H&E staining: The sections of the wound tissue were stained with Hematoxylin and Eosin (H&E) and the thickness of the stratum granulosums of the skin was measured by Caseviewer Software 2.4 (3DHISTECH Ltd) to detect the hyperblastosis of tissue formation.
ELISA (enzyme-linked immunosorbent assay): The levels of inflammatory factors Interleukin-1β (IL-1β) in ulcers were detected by ELISA kit (Product # abs104566; Absin).
Immunohistochemistry (IHC) and Immunofluorescence Colony (IFC) Staining: The anti-Ki67 antibody (1:300; Product # A16919; ABclonal) IHC, anti-CD31 antibody (1:300; Product # ab182981; ABcam) and the anti-LC3B antibody (1:200; Product # ABS82; Sigma-Aldrich) IFC were performed. Ki67 in ulcers was detected by immunohistochemistry and Proliferation Index (PI) was calculated. PI was calculated as follows: PI = Number of proliferative cells / (Number of proliferative cells + Number of normal cells). CD31 and LC3B were detected by immunofluorescence. IPP software was used to count positively stained cells in immunofluorescence sections, and the Integrated Optical Density (IOD) of positive staining for CD31 or LC3B was calculated and analyzed. Mean Optical Density (MOD) was calculated (MOD = IOD SUM/area) and compared.
Electron microscopy: The ulcer tissue samples were obtained from each group (three samples per group) and cut into small cubes (1 × 1 × 1 mm3). Samples were rinsed with Phosphate Buffered Saline (PBS), fixated in $2.5\%$ glutaraldehyde and dehydrated, and sectioned with an ultrathin microtome (Leica, Witzla, Germany), stained with saturated uranyl acetate. Autophagosomes were observed by transmission electron microscope (TEM, H-7650, Hitachi, Osaka, Japan).
Western blot analysis: Total protein was extracted from samples of the wound by Total Protein Extraction Kit (Beyotime Institute of Biotechnology, Shanghai, China) at day 14 posttreatment. Equal amounts of total protein were separated on $10\%$ SDS-PAGE and transferred to nitrocellulose membranes. Membranes were incubated overnight at 4°C with monoclonal antibodies against IL-1β (Product # A1112; Abclonal), LC3B(Product # 14600-1-AP; Proteintech), NLRP3 (Product # A5652; Abclonal), Caspase-1 (Product # A0964; Abclonal), GSDMD (Product # 66387-1-Ig; Proteintech), GSDMD-N (Product # ab215203, Abcam) and GAPDH (Product # 10494-1-AP; Proteintech) (all 1:1000). Then, the membranes were incubated with HRP-conjugated anti-rabbit (1:5000; Product # S0001; Affinity).
## Statistical analysis
Data are shown as means ± Standard Deviation (SD). Before analysis, the data were tested for normality of distribution using the Kolmogorov-Smirnov test. For normally distributed data, differences between groups were analyzed using the Least-Significant Difference test (LSD) and repeated measurement analysis. A value of $p \leq 0.05$ was considered significant. SPSS 22.0 (IBM) was used for statistical analyses.
## Identification of BM-MSCs characteristics
Isolated cells were plastic-adherent in culture and displayed a typical fibroblast morphology. Flow cytometry analysis showed that the BM-MSCs slightly expressed hematopoietic CD markers CD34 ($0.04\%$), CD45 ($0.11\%$) and HLA-DR ($0.22\%$), and completely expressed mesenchymal CD markers CD73, CD90, and CD105 ($100\%$), indicating that the cultured cells possessed the MSCs characteristics (Fig. 1).Fig. 1Characterization of rat BM-MSCs. Cell surface markers of MSCs were assessed using flow cytometry. MSCs expressed CD73, CD90 and CD105, but not CD34, CD45 or HLA-DR.Fig 1
## Measurement of body weight, wound area, and blood glucose levels
The body weight of DFUs was higher than that of NC group. There were no differences in body weight among DM-C, MSC-CM, and MSCs groups (Table 1)Table 1Body weight of the rats before and after therapy. Table 1Group0 d (g)7 d (g)14 d (g)NC352±29a381±36a391±42aDM-C401±25407±27416±24MSC-CM403±19411±22420±23MSCs398±26406±23414±18ap < 0.05 compared with the other three groups.
Both MSC-CM and MSCs therapy enhanced wound healing. Wounds of MSC-CM and MSCs groups exhibited accelerated wound closure compared with wounds of DM-C group on day 3, day 7 and day 10 ($p \leq 0.05$). There were no significant differences in the wound area between MSC-CM and MSCs groups (Fig. 2 A‒B; Table 2).Fig. 2Wound area and fasting blood glucose levels. ( A) Effect on DFUs in rats after treatment (2 mm). ( B) *Wound area* rate. At day 3, 7 and 10, wound area rate of DM-C group was higher than those of the other three groups. (* $p \leq 0.05$). ( C) Blood glucose levels. There were no significant differences in fasting blood glucose levels among DM-C, MSC-CM and MSCs group. Fig 2Table 2Wound area of the rats before and after therapy. Table 2Group0 d (%)3 d (%)7 d (%)10 d (%)14 d (%)NC10051±431±518±44±1DM-C10070±6a58±6a32±5a15±4aMSC-CM10055±638±722±48±3MSCs10054±534±621±57±2ap < 0.05 compared with the other three groups.
Fasting blood glucose levels of DM-C, MSC-CM, and MSCs group was higher than that of NC group. There were no significant differences in blood glucose levels among DM-C, MSC-CM, and MSCs groups (Fig. 2C; Table 3).Table 3FBG of the rats before and after therapy. Table 3Group0 d (mmoL/L)7 d (mmoL/L)14 d (mmoL/L)NC4.88±0.55a5.12±0.46a5.07±0.62aDM-C10.43±2.7511.47±2.3310.84±2.54MSC-CM11.32±2.8810.57±2.349.97±2.18MSCs10.68±2.2310.12±2.599.65±2.37ap < 0.05 compared with the other three groups.
## Histological assessment
H&E staining: The thickness of the stratum granulosums of the skin in MSC-CM or MSCs group was thicker than that in DM-C group ($p \leq 0.05$). There were no significant differences in the thickness of the stratum granulosums between MSC-CM and MSCs groups (Fig. 3 A‒F, Table 4).Fig. 3Histological assessment of the skin of ulcer specimens from rats at day 14 after therapy. ( A) H&E-stained sections (50 µm). ( B) IHC of Ki67 in the skin of ulcer specimens (50 µm). ( C) IFC of CD31 in the skin of ulcer specimens (500 µm). ( D) IFC of LC3B in the skin of ulcer specimens (500 µm). ( E) TEM of the skin of ulcer specimens (2 µm). Autophagosomes (arrow) could be seen in MSC-CM and MSCs group but could hardly be found in DM-C group. ( F) The thickness of the stratum granulosums of the skin. The thickness of the stratum granulosums of the skin in MSC-CM or MSCs group was thicker than that in DM-C group (*$p \leq 0.05$). ( G) PI from ki67 in ulcers. PI of MSC-CM or MSCs group was more than that of DM group (*$p \leq 0.05$). ( H) MOD from CD31 in ulcers. MOD from CD31 of MSC-CM or MSCs group was higher than that of DM group (*$p \leq 0.05$). ( I) MOD from LC3B in ulcers. MOD from LC3B of MSC-CM or MSCs group was higher than that of DM group (*$p \leq 0.05$). ( J) IL-1β levels in ulcers. IL-1β level in ulcers of MSC-CM or MSCs group was lower than that of DM-C group (*$p \leq 0.05$).Fig 3Table 4Histology parameters of wound at day 14.Table 4GroupThickness of stratum granulosums (µm)PI (%)CD31LC3BIL-1β (pg/mL)NC22.5±3.45.4±1.20.11±0.040.08±0.0217.1±2.5DM-C15.4±3.8a1.5±0.3a0.02±0.01a0.01±0.002a34.3±5.8aMSC-CM20.9±3.23.1±0.60.05±0.020.05±0.0122.7±2.8MSCs20.4±3.52.8±0.50.04±0.010.04±0.0121.3±2.5ap < 0.05 compared with the other three groups.
ELISA: IL-1β level in ulcers of MSC-CM or MSCs group was lower than that of DM-C group ($p \leq 0.05$). There were no significant differences in IL-1β levels of ulcers between MSC-CM and MSCs groups (Fig. 3J, Table 4).
IHC and IFC: PI from ki67 in ulcers of MSC-CM or MSCs group was more than that of DM group ($p \leq 0.05$). There were no significant differences with PI in ulcers between MSC-CM and MSCs groups (Fig. 3 B and G). MOD from CD31 in ulcers of MSC-CM or MSCs group was higher than that of DM group ($p \leq 0.05$). There were no significant differences with CD31 in ulcers between MSC-CM and MSCs groups (Fig. 3 C and H). MOD from LC3B in ulcers of MSC-CM or MSCs group was higher than that of DM group ($p \leq 0.05$). There were no significant differences with LC3B in ulcers between MSC-CM and MSCs groups (Fig. 3 D and I, Table 4).
Electron microscopy: Treatment with MSC-CM or MSCs induced the appearance of autophagosomes in the cells. Autophagosomes could hardly be found in DM-C group (Fig. 3E).
Western blot analysis: The relative expressions of protein of NLRP3, GSDMD, GSDMD-N, proCaspase-1 and pro-IL-1β in MSC-CM or MSCs group decreased obviously compared with those in DM-C group. The expressions of NLRP3, proCaspase-1 and pro-IL-1β in MSC-CM group were less than those in MSCs group. The relative expressions of protein of LC3B in MSC-CM group were higher than those in MSCs or DM-C group (Fig. 4).Fig. 4Western blot analysis. ( A) Western blot analysis of NLRP3, GSDMD, GSDMD-N, proCaspase-1, pro-IL-1β, LC3B and GAPDH expression in wound site at day 14 of four groups. ( B) The quantification of relative expressions of protein of NLRP3, GSDMD, GSDMD-N, proCaspase-1, pro-IL-1β and LC3B by Western blot. The expressions of NLRP3, GSDMD, GSDMD-N, proCaspase-1 and pro-IL-1β in MSC-CM or MSCs group decreased obviously compared with those in DM-C group. The expressions of LC3B in MSC-CM group were higher than those in MSCs or DM-C group. (* $p \leq 0.05$, **$p \leq 0.01$).Fig 4
## Discussion
Stem cell therapy for the treatment of DFUs has been a topic of much interest recently. Murine models of diabetes have found that stem cells derived from umbilical, adipose, smooth muscle, and bone marrow or in combination therapies with MSCs accelerated wound healing.14, 15, 16, 17, 18 BM-MSCs transplantation is a therapeutic way for DFUs, and intramuscular transplantation has been proven to have the probably best efficacy.19 However, currently, there are some limitations that hinder the widespread use of MSCs, such as spontaneous changes in properties and behavior, formation of malignant tumors, transmission of infectious diseases20,21 and so on.
Recent studies have shown that engrafted MSCs do not survive for the long term, suggesting that the benefits of MSC therapy might be attributable to their secreted factors. The function of mesenchymal stem cells to secrete protective factors was first discovered by Gnecchi et al.22 At present, many studies have confirmed that the paracrine effect is the main mechanism of MSCs therapy.23, 24, 25, 26 CM represents a fully regenerated milieu and the vesicular component of the cell-derived secretome. A growing body of literature recently has drawn attention to the plethora of bioactive factors produced by MSCs, including growth factors, cytokines, microRNAs, exosomes, and proteasomes, which may play important roles in the regulation of many physiological processes. The use of CM may have considerable potential advantages over living cells in terms of manufacturing, handling, storage, product shelf life, and their potential as ready-to-use biotherapeutics.27,28 It has been demonstrated that MSC-CM is sufficient to improve multiple pathophysiological biomarkers significantly and to be effective in the transplantation of the corresponding MSCs in many different animal models. BMMSC-CM has been used to treat many diseases such as spinal cord injury, cerebrovascular disease, lung injury, and so on.29, 30, 31 *Pyroptosis is* the process of inflammatory cell death. There are two major pathways for pyroptosis: canonical and noncanonical pyroptosis. In the canonical pyroptosis pathway, activated Caspase-1 cleans GSDMD protein, and the cleaved GSDMD produces an independent domain fragment as the N-terminal. GSDMD-N binds to the cell membrane, forms pores, and the cytoplasmic membrane is destroyed, resulting in pyroptosis and inducing inflammatory cell death.32 At the same time, activated caspase-1 cleaves the precursor of IL-1β to form active IL-1β, which is released to the outside of the cell through the pores and causes an inflammatory response. In vivo autophagy is a protective response that inhibits intracellular signaling and regulates the activation of inflammasomes by removing dysfunctional mitochondria.33 Impaired autophagy can activate NLRP3 inflammasome to trigger canonical pyroptosis34,35 and expand the inflammatory effect. Studies have suggested that pyroptosis is associated with the onset of diabetes and its complications.36,37 So reducing pyroptosis may have therapeutic effects on diabetic complications.
Some studies conducted about MSC-CM treating skin wounds. One study showed that the concentrated hypoxia-preconditioned adipose mesenchymal stem cell-conditioned medium could accelerate the skin wound healing in a rat full-thickness skin defect model, however, this study did not involve the mechanism of the treatment.38 A study in vitro showed that BMMSC-CM of rats could improve the proliferation and migration of keratinocytes in a diabetes-like microenvironment by decreasing High Glucose (HG) and/or Lipopolysaccharide (LPS) induced Reactive Oxygen Species (ROS) overproduction and reversing the downregulation of phosphorylation of MEK $\frac{1}{2}$ and Erk $\frac{1}{2.39}$ A recent study showed that adipose-derived stem cell CM could accelerate wound healing and hair growth in SD rats with burn wounds on the dorsal, but this study also did not reveal the mechanism of the treatment.40 In the present study, HE and Ki67 staining suggested that the treatment of MSC-CM promoted the proliferation of skin tissue, CD31 staining suggested that the treatment promoted the proliferation of blood vessels and increased the local blood supply. Electron microscopy showed that the treatment promoted cell autophagy. Autophagy was enhanced by promoting the expression of LC3B. The inflammatory state was improved by reducing the levels of NLRP3 and IL-1β. Caspase-1 was inhibited, and the expression of GSDMD-N was reduced, thereby cell pyroptosis was inhibited. The curative efficacy of MSC-CM therapy was similar to that of MSCs.
MSC-CM therapy, namely the use of cell-free therapy, has considerable advantages over cell-based applications. MSC-CM therapy resolves several safety concerns that may be associated with living cell transplantation including tumorigenicity, embolism, immune compatibility, and spread of infections. MSC-CM can be stored for long periods of time without losing much product potency.41,42 MSC-CM therapy does not require invasive cell collection procedures, and it is more economical, practical, and suitable for clinical applications.43 MSC-CM can be used in specific laboratory conditions, and produced in large quantities to provide controlled bioactive factors.
Factors secreted by different MSCs may be different, such as Adipose-Derived Stem Cells-CM (ADSC-CM) expresses Vascular Endothelial Growth Factor (VEGF), Nerve Growth Factor (NGF), Stem Cell Factor (SCF), and Hepatocyte Growth Factor (HGF), while human Umbilical Cord Perivascular Cell-CM (hUCPVC-CM) expressed no SCF or HGF.44,45 There were also differences between the composition of ADSC-CM and BMMSC-CM.46 In the present study, the authorsdid not detect the components of the MSC-CM. In order to standardize the production of CM from each MSC type, further studies on culture conditions, culture duration, culture medium, and supplements, and the criteria for the composition of MSC-CM are required.
## Conclusion
BMMSC-CM is effective in the treatment of DFUs in type 2 diabetic rats. BMMSC-CM can promote the healing of DFUs by inhibiting inflammation, enhancing autophagy, and reducing pyroptosis. These findings highlight a potential therapeutic method of BMMSC-CM for the treatment of DFUs, avoiding the risk of living cell therapy.
## Authors' contributions
All authors contributed to the conception of the work. Yi-Feng Xu contributed to study design, experiment performing, data analysis and wrote the manuscript. Yan-Xiang Wu and Hong-Mei Wang contributed to review & editing. Cui-Hua Gao, Yang-Yang Xu, and Yang Yan contributed to experiment performing and the data acquisition. Yi-Feng Xu and Yan-Xiang Wu contributed equally to this work. All of the authors have given final approval and agree to be responsible for all aspects of the work, ensuring accuracy and precision.
## Conflicts of interest
The authors declare no conflicts of interest.
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|
---
title: Exploring the dominant features and data-driven detection of polycystic ovary
syndrome through modified stacking ensemble machine learning technique
authors:
- Sayma Alam Suha
- Muhammad Nazrul Islam
journal: Heliyon
year: 2023
pmcid: PMC10040521
doi: 10.1016/j.heliyon.2023.e14518
license: CC BY 4.0
---
# Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique
## Abstract
Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with ‘Gradient Boosting’ classifier as meta learner outperforms others with $95.7\%$ accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.
## Highlights
•*Proposed a* modified stacking ensemble ML classifier to detect PCOS from patient symptoms.•Applied 3 feature selection methods to extract sets with varying numbers & mixtures of attributes.•Proposed methodology & existing ML approaches are trained & tested with different feature sets.•Examined the essential attributes and efficacy of the suggested ensemble methodology.•The proposed classifier outperforms with top 25 dominant features picked as per PCA technique.
## Introduction
Polycystic ovary syndrome (PCOS) is amongst the most prevalent endocrinological disorders [1], [2] which is typically caused by an abnormal increase of male hormone known as androgen hormone in female body, producing a long-term disturbance in hormonal levels and, as a result, impacting negatively in normal ovarian processes, leading to formation of many cysts inside the ovary [3]. It is a diverse and heterogeneous condition which can be predicted through observing various signs in female body such as hyperandrogenism with acne, hirsutism, and alopecia; anovulation with menstrual irregularities, oligomenorrhea, amenorrhea; polycystic ovarian morphology, among many others [4], [5]. According to epidemiological research [6], PCOS is found to yield a number of detrimental life-threatning impacts that are prevalent in PCOS patients, with 44–$70\%$ women suffering from various critical side effects as well as affecting one in every ten premenopausal reproductive female throughout the world. This condition has been linked to a variety of metabolic and psychological illnesses that reduces the quality of a healthier lifestyle; including the principal cause of anovulation and irregular menstrual cycles, hormonal imbalance, type 2 diabetes, hyperandrogenism (excessive presence of the male sex hormones), insulin resistance, sudden obesity, thyroid irregularities, increased mental breakdown, sexual frustration, and so on [7], [8], [9]. Women with PCOS are more likely to develop endometrium and ovarian cancer, both of which can be fatal if not diagnosed in time [10], [11]. Thus, the most widespread endocrinological disorder PCOS is associated with a wide spectrum of symptoms and comorbidities. Recent studies indicate that if a well-standardized diagnosis technique can be utilized to detect PCOS early on, the disease can be treated with a healthy diet as well as suitable, symptom-oriented, long-term, and dynamic therapies [12].
However, due to the wide range of symptoms associated with PCOS and the existence of a variety of concomitant gynecological problems, PCOS becomes extremely difficult for physicians to accurately identify at an early phase [13]. Also, the effective identification of PCOS necessitates a lot of clinical test evaluations by qualified healthcare providers, which is sometimes unattainable in areas where expert physicians and resources are scarce. As a result, numerous young women go unnoticed and untreated as they do not have any easier way to detect their condition other than visiting expert clinicians; subsequently experience the adverse consequences of this devastating ailment, specially in the rural areas of developing and least developed countries. Thus, to address this challenge, a variety of computational algorithms have been suggested to forecast PCOS in patients intelligently based on their symptoms and test result. But, the conventional machine learning algorithms which despite their major triumphs, may fail to generate satisfactory results when working with too many attributes and underlying mechanism of complicated data, such as unbalanced, high-dimensional, noisy data, and so on [14]. In such cases, the ensemble machine learning method can be a promising state-of-the-art solution that combines multiple typical machine learning techniques to generate weak predictive results based on attributes retrieved through a variety of data projections from the dataset, and then integrates those results with diverse mechanisms to achieve better forecasting results than any individual algorithm [15]. A method of ensemble learning known as stacking ensemble takes into account diverse weak learners, trains them concurrently, and then combines them by training a meta-model to produce a forecast based on the results of a variety of weaker models [16]. However, it has been observed that using stacking ensemble methodologies to anticipate various disease outbreaks or predictions is superior to using traditional techniques, but rarely this technique has been explored to predict PCOS. Moreover, few studies have focus on exploring the minimal yet optimal features to predict PCOS effectively using various feature engineering techniques.
Therefore, the purpose of this research is to explore several traditional as well as ensemble types of machine learning classifiers to predict PCOS and also to propose an ensemble machine learning classifier based on stacking approach that employs the minimal and optimal amount of prioritized features for more efficiently detecting PCOS through patients' symptoms and test result dataset. The key contributions which has been done to acquire the objective of this research work are listed hereafter.•An ensemble machine learning classifier based on the stacking state-of-the-art technique has been proposed, trained and tested where five types of traditional machine learning classifiers (Logistic Regression, Support Vector Machine, Decision Tree, K-Nearest Neighbour and Naive Bayes) have been used as the weak learners with one strong meta learner to classify the dataset between PCOS and non-PCOS criteria. One from five different kinds of boosting or bagging ensemble classifier (Adaptive Boosting, Categorical Boosting, eXtreme Gradient Boosting, Gradient Boosting, Random Forest Classifier) have been employed and evaluated as the meta learner of the model with an aim to explore the best performing stacked ensemble model in this scenario.•For exploring the dominant features required for predicting PCOS, three different types of feature selection techniques (Chi-Square, Principal Component Analysis, Recursive Feature Elimination) have been employed here. Each feature selection techniques select the different sets of features with different numbers and combinations of attributes from the dataset employing their own feature prioritization methods which are then applied to the machine learning classifiers to detect PCOS.•To validate the efficacy and potency of the proposed technique, other ten types of classifiers are also employed to attain the same objective which include five types of conventional classifiers (Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour, Naive Bayes), one bagging ensemble classifier (Random Forest) and four types boosting ensemble classifiers (Gradient Boosting, eXtreme Gradient Boosting, Adaptive Boosting, Categorical Boosting). Then a meticulous comparative performance analysis has been conducted between the traditional classifiers, bagging and boosting ensemble classifiers and the proposed stacking ensemble models through different performance parameters utilizing different sets of features obtained from feature selection techniques.
The remaining sections of the article are structured as follows: Section 2 presents the background study; the materials and methodology that have been employed in this study are demonstrated in Section 3; the result analysis with comparative findings is discussed in Section 4; and lastly, Section 5 and Section 6 contain discussion and conclusion that highlights the study's key findings along with its comparison with previous works, benefits, limitations, and future goals.
## Background study
PCOS has been linked to a number of disorders resulting in diverse symptoms in patients' bodies compared to normal ovulatory women, including type-2 diabetes, cardiovascular anomalies, hypertension, dyslipidemia, insulin resistance, increased Endometrium thickness and so on [17], [18], [19]. Furthermore, PCOS also causes a variation in the range of hormonal secretion such as luteinizing hormone (LH), Follicle-stimulating hormone (FSH), Anti-Müllerian Hormone (AMH) etc. [ 20], [21]. Additionally, some more indicators are identified to be strongly associated with PCOS including undesirable facial/body hair, accelerated hair loss, dark spots on the skin, higher BMI, obesity and abdominal obesity with increased hip ratio, dietary habits with excessive fast food intake etc. [ 22], [23], [24]. As a result, the standard clinical detection approach for PCOS is very critical, and also the accuracy as well as reliability of this anomaly identification and interpretations is heavily reliant on the physician's competence in this context [25].
Thus, number of studies have been conducted to investigate computer-assisted PCOS detection techniques, which offer substantial advantages such as rapid identification of the condition in the shortest time frame with the least amount of diagnostic error and human effort. [ 26]. With the massive expansion of healthcare data and utilization of information technology, machine learning techniques are being one of the most widely used, efficient, and promising predictive strategies, which can analyze and retrieve key information from immense amounts of heterogeneous clinical data in order to detect diseases intelligently [27], [28]. Recently researchers also have applied various machine learning techniques in this context to detect PCOS condition from patient's symptom dataset.
For example, to categorize between PCOS and non-PCOS criteria, Danaei et al. [ 29] employed Extra Tree, Adaptive Boosting (AdaBoost), Bagging Ensemble with Random Forest and Multi-Layer Perceptron (MLP) classification models which were then evaluated through performance parameters using the reduced subgroups of features obtained by filter, embedded, and wrapper feature extraction techniques. For feature selection, Nasim et al. [ 30] presented an improved chi-squared (CS-PCOS) mechanism and they then conducted a performance comparison analysis of ten hyper-parametrized machine learning models for PCOS prediction. Another work in this domain had been proposed by Agrawal et al. [ 31], where the top 30 features from the data were determined using the Chi-square technique, and the underlying state of PCOS was predicted using Random Forest, SVM, Logistic Regression, Gaussian Naive Bayes, and K Neighbors utilizing this reduced feature vector. Moreover, seven types of classifiers were used in the diagnostic model that Hdaib et al. [ 32] proposed using MATLAB to detect PCOS, and the findings showed that the Linear Discriminant classifier performs the best. In another work, proposed by Reka et al. [ 33] the follicular fluid sample from 100 women had been extracted and the obtained data set is then preprocessed using *Raman spectra* and effective feature selection techniques to be utilized for machine learning classification; which were classified using Random Forest, Multilayer Perceptron, Ada Boost and decision tree classification models for detecting PCOS. Again, Boomidevi et al. [ 34] suggested an artificial Neural network (ANN) model for detecting PCOS at an early stage where a comparative performance analysis had been conducted using different neural network optimizer to explore the best performing ANN design for classifying dataset into two classes: PCOS and Non-PCOS. Another related work in this field has been conducted by Prapty et al. [ 35], in which they investigated four different machine learning classifiers to categorize PCOS and non-PCOS records and compared their results where the Random Forest classifier outperformed the others; and then employing that Random Forest classifier a decision tree is developed to identify the top features responsible for PCOS. Denny et al. [ 36] also proposed a framework named ‘i-Hope’ as a paradigm for early identification and prediction of PCOS based on optimum yet promising indicators; here they used a patient survey of 541 records to design the proposed framework, in which 8 potential features from diagnostic and metabolic test results were selected using SPSS and the Principal Component Analysis (PCA) method based on their importance, and then applied to seven types of traditional ML classifiers to find the best performing model.
Since PCOS is associated with a wide range of symptoms as features, a few studies have emphasized on employing various feature reduction approaches before using machine learning models to accelerate the training process. For example, Inan et al. [ 37] suggested a strong sampling technique that includes both oversampling and undersampling procedures to boost minority samples; then applied two types of feature selection techniques: Chi- Square test for categorical and Analysis of Variance (ANOVA) test for numerical attribute selection; and then applied six types of machine learning classifiers where XGBoost classification model outperformed others. In another relevant article in this domain, Nandipati et al. [ 38] used RFE-LR, RFI-ECT, SelectKBest/Chi2 and Forward Backward propagation techniques to find the top 10 and 24 features from all 42 features in the dataset, and then applied seven types of traditional ML classifiers in two different types of implementation platforms: Python-Scikit Learn package and RapidMiner; in addition, performance comparisons between various classifiers were assessed utilizing complete (40 features) and selected features (10 and 24 features) to find the best performing classifier. Munjal et al. [ 39] used a genetic algorithm and WEKA (Waikato Environment for Knowledge Analysis) software to identify the nine primary features associated in (PCOS) illness development and then utilized those reduced set of features over three types of ML classifiers in PyCaret platform to predict the disease using minimal attributes. While, for extracting the most significant attributes from a dataset comprising 26 attributes of 303 instances, Meena et al. [ 40] suggested an approach based on Neural Fuzzy Rough Set (NFRS) and Artificial Neural Network (ANN) techniques; and then applied those reduced set of features in four different types of classification models to detect PCOS where the performances enhanced in comparison to other five types of traditional feature selection methods.
Now, a potential state-of-the-art approach for several machine learning challenges is ensemble methodologies, as they can significantly improve the performance of a single model's forecasting by training multiple models and combining those results [41]. Recently, a few scholars have used ensemble machine learning methods to generate accurate predictions in various healthcare domains. For example, Jabbar et al. [ 42] presented an ensemble learning approach to address the problem of categorizing breast cancer data.; Suha et al. [ 14] applied a hybrid model with CNN and stacked ensemble technique to classify PCOS ultrasound images; Kaushik et al. [ 43] developed an ensemble of multi-headed ML architectures to forecast the average weekly expenditures on two pain drugs taken by patients etc. A few studies have been found where ensemble techniques had been employed in PCOS identification, for example Gupta et al. [ 44] applied four types of Boosting ensemble techniques (Adaptive Boost, Gradient Boost, XGBoost and CatBoost) without applying any feature engineering techniques to classify PCOS. Again, Bharati et al. [ 45] applied hard and soft voting ensemble classifier employing ExtraTree, Random Forest, Gaussian Naive Bayes, LightGBM and eXtreme Gradien Boosting models with reduced set of features selected via recursive feature elimination and univariate feature selection techniques. However, From the prior studies, it can be demonstrated that even though many academics from around the world have suggested contributions where various machine learning strategies have been used to diagnose PCOS; seldom has a researcher looked into the viability and effectiveness of using several ensemble machine learning approaches (bagging, boosting, and stacking) in this circumstance.
From the previous related works it is also observable that, most of the studies have picked a specific reduced subset of features from the existing dataset through applying feature reduction techniques and then performed machine learning classification using that reduced feature subset. But, hardly any studies have explored multiple reduced feature subsets with different numbers and combinations of attributes from the complete dataset. Also, rarely they have investigated how the performances of various machine learning classification techniques might alter when different feature subsets with various combinations and numbers of attributes extracted from multiple feature reduction methods are being used. Furthermore, less attention has been made on investigating and validating whether the retrieved decreased features are genuinely important or not in terms of real-life clinical diagnosis of PCOS via a cross-check involving relevant healthcare specialists.
Thus, this research focuses on addressing these research gaps in this area with the goal of detecting PCOS more effectively and efficiently utilizing the optimum numbers of features. Therefore, a stacking ensemble classifier has been designed, trained, and evaluated as well as the performances of various forms of ensemble and conventional machine learning approaches have been investigated in this study, employing different sets of attributes acquired from feature selection methods.
## Materials and methods
An extended ensemble ML classifier has been proposed, trained and tested using patient's most significant set of symptom data to differentiate between PCOS & non-PCOS patients in this study. A framework of the research methodology is presented in Fig. 1, which has been thoroughly explained in the following subsections. The research has been conducted through several phases to investigate and prioritize the optimum collection of features essential for predicting PCOS as well as to discover the best performing classification model for PCOS detection using those features. The classification models' inputs are a collection of patient's symptom attributes, and the outputs would be binary responses indicating whether a patient has PCOS or not. After retrieving the data from the repository, the dataset has been analyzed using various visualization approaches to gain a detailed understanding of it and then, it has been meticulously pre-processed to transform it into a clean and suitable dataset that can be used for machine learning. Following that, several sets of reduced features with varied numbers of most significant attributes have been extracted using three different feature selection strategies. Then, for exploring different machine learning techniques several traditional as well as ensemble ML models have been trained and tested employing various sets of features and also a stacked ensemble model has been proposed. A comparative study of several classification models and feature prioritizing techniques has also been performed through different performance matrices to evaluate the efficacy of the classifiers. The methodological procedures that have been used in this research are detailed in the following subsections. Figure 1Framework of research methodology. Figure 1
## Data acquisition, analysis and visualization
A dataset containing the symptoms along with the PCOS diagnosis findings of patients has been utilized here as the training data for supervised machine learning models, for which a publicly available data collection of PCOS patients from ‘Kaggle’ [46] has been selected. The dataset has been thoroughly investigated for better understanding before employing them for training purpose. The primary analysis of the PCOS records shows that, the dataset comprises a total of 541 records of female patient's data with 45 columns containing various types of clinical information related to PCOS anomaly. One of the columns named ‘PCOS(Y/N)’ has a PCOS diagnosis outcome with ‘Yes’ and ‘No’ values indicating whether or not the patient has PCOS. This feature column has been considered as the target column for the training in this study. When the values of this column are counted, it has been observed that there is an uneven distribution of positive and negative outcomes, as there are 364 entries with ‘No’ indicating ‘NO PCOS’ and 177 entries with ‘Yes’ indicating ‘PCOS’.
Again, the relationship between target column with other attributes has been examined using various visualization approaches. For example, the Fig. 2(A) shows the age distribution of the patient records in the dataset which depicts that the records comprise information on women aged 20 to 50 years old and the Fig. 2(B) shows a violin plot of ‘PCOS(Y/N) vs. Age (yrs)’ which depicts the age range of women with and without PCOS in the dataset. Another example of data visualization has been illustrated in Fig. 3 (A) where the number of follicles in the left ovary vs. right ovary in relation to the goal attribute ‘PCOS(Y/N)’ has been plotted, demonstrating that a larger number of follicles in both the left and right ovary yields the most positive PCOS outcomes. Furthermore, correlation study between the different attributes has been performed using a correlation heatmap to statistically analyze the strength of the relationship between the features, as an example illustrated in Fig. 3 (B). The correlation value ranges from 0 to 1, where with a greater correlation value indicating that the features are highly correlated to each other. Figure 2(a) Distribution of Age in Dataset; (b) Relationship of ‘Age’ attribute with target column. Figure 2Figure 3(A) Scatter plot of Left ovary vs Right ovary Follicle numbers with respect to target attribute; (B) Correlation heatmap of some attributes. Figure 3
## Data preprocessing
In this work, the dataset has been critically analyzed and preprocessed before using them into machine learning models to address the flaws and irregularities in the datasets such as missing or contradictory data samples, inconsistencies, noise, and other issues. The following steps have been employed for preprocessing the dataset.
Firstly, for data preprocessing the null values have been handled. Features with too many null or missing values have been completely removed from the dataset because they don't give any useful information; for example, the feature ‘Unnamed’ in the dataset contains 539 null values for which it has been eliminated. The features comprising a few null values have been substituted with other relevant values; for example, ‘Marriage Status (Yrs)’ and ‘Fast Food (Y/N)’ contains a few null values which have been replaced with 0.
Secondly, the data balancing has been done for making the classes equally distributed for training, as the dataset has imbalanced target attribute with 364 records of non-PCOS and 177 records of PCOS patients. Therefore, the dataset has been over-sampled using ‘Synthetic Minority Oversampling Technique (SMOTE)’ method that generates a synthetic sample of a minority class to eliminate the imbalance in the target attribute values [47]. The mathematical formula followed for SMOTE method has been shown in Equation [1], where xsample is the sample generated from minority class value x and xrandom is a randomly chosen value among the nearest neighbors of x with 0≤η≤1. As a result of SMOTE, the dataset instances here have increased to 728 records, including 364 positive and negative PCOS diagnostic results.[1]xsample=x+η(xrandom−x) The third step has been to Drop Unnecessary Columns. At this step, superfluous or duplicated columns have been removed in order to improve forecasting accuracy. One of two columns giving the same information has been kept, while the other has been deleted from the dataset. For example, ‘I beta-HCG(mIU/mL)’ numerical column and ‘II beta-HCG(mIU/mL)’ categorical attribute provide same information from which ‘II beta-HCG(mIU/mL)’ has been discarded. Also the unnecessary columns ‘Sl. No.’ and ‘Patient File No.’ have been discarded from the dataset as they contain simply the serial numbers, patient's file no which can be ignored for further analysis.
The next step is data normalization in which the values of the dataset are normalized using the MinMax Scalar approach to reduce the influence of variance in measurement units of different features and eliminate attribute bias with sensitivities [48]. The MinMax scaller follows the Equation [2] for rescaling the values of the feature range between 0 to 1. In the Equation [2], xscaled is the rescaled value generated from the original vale x where xmin and xmax are the minimum and maximum values of that attribute Ai.[2]xscaled=(x−xmin)÷(xmax−xmin) Finally, as the last step of data preprocessing, the dataset has been divided into train and test datasets for applying them to classification models of machine learning, with $30\%$ of the instances randomly assigned to the test dataset and the remaining $70\%$ assigned to the train dataset.
## Feature selection
Feature selection is an efficient method for picking the most significant attributes and avoiding unimportant features to improve the prediction capacity and accuracy of machine learning algorithms [49]. It is the process of exploring the best subset(s) of features to assure the finest potential data description. In this study, the dataset contains 40 attributes after preprocessing, which may lower the accuracy of the classifier if all of the less significant ones are taken into account. Thus, the features in this context have been prioritized and selected rigorously using three types of feature selection techniques to find out the optimal set of features from the PCOS data set. The techniques have been described hereafter:•Chi-Square Technique: Chi-square feature selection technique is one of the most frequent and helpful feature selection strategies used in machine learning [50]. It conducts a numerical test that calculates deviation from the anticipated distribution when the feature event is independent to the class value and prioritizes features by examining the relationship between them [51]. The formula for the chi-square feature selection has been shown in Equation [3]. In the equation, the real number of observations in the dataset that fit into a particular feature i are the observed values and the number of observations which are anticipated to occur is represented by the expected values. Here, the prioritized features are chosen according to the best scores of χ2. In case of implementation, the python ‘SelectKBest’ function has been utilized, which implemented the chi-square numeric test with k=n, where k is the number of features that will be selected by the algorithm and then picked n features from the dataset's 40 features based on the highest scores.[3]χ2=∑$i = 1$n(Observed Valuei−ExpectedValuei)2ExpectedValuei•Principal component analysis (PCA) Technique: The second type of technique that has been used for feature selection in this study is the Principal component analysis (PCA) method, which is an efficient dimension reduction tool for feature prioritization utilizing numerical analysis which is accomplished by assessing the correlation between characteristics in order to determine the most important or principal components [52], [53]. PCA maps and reconstructs the original n-dimensional features to the required k-dimensional features (k<n), where the k-dimensional features are new orthogonal attributes termed as principle components that minimize data redundancy to accomplish the dimension reduction goal [54]. In this scenario, the python ‘PCA’ function from Scikit-learn has been utilized with the PCA variance, to determine the most important n features.•Recursive Feature Elimination (RFE) Technique: Recursive Feature Elimination, or RFE, is an efficient wrapper-type strategy that has been utilized in this study for removing features from a training sample for feature selection which ranks the set of attributes and eliminates them at the bottom that contribute the lowest to the categorization [55]. This approach is basically a recursive process that employs several machine learning techniques at its foundation, wrapped in the RFE methodology, and therefore feature importances are calculated at each iteration, with the least relevant one being eliminated to pick the prioritized features [56], [57]. The RFE function from the RFE class provided by the scikit-learn Python machine learning library has been employed here for implementation To explore the highly significant attributes that would yield the best performing accuracy when used in machine learning models, each type of feature selection approach selects the top 35, 30, 25, and 20 features from the PCOS dataset of 40 features. The algorithm followed for extracting the reduced set of features from the dataset in this study is shown in Algorithm 1. Then employing those different sets of features the machine learning classifiers are trained, tested and evaluated through different performance metrics. Algorithm 1Pseudo Code for Feature Selection. Algorithm 1
## Machine learning models
Classification is a machine learning technique that uses a model learned from training data to forecast the category of samples and therefore maps or classifies data instances into the associated class labels which have been predefined in the provided dataset [58]. In this study, for training the machine learning models with an aim to categorize between PCOS & non-PCOS classes from their symptom data, four types of techniques have been employed (see Fig. 1). The predictive models have been trained, tested and evaluated using different sets of features from the dataset. The machine learning techniques have been discussed briefly below.
## Existing machine learning techniques
•Traditional ML Classifiers: Although for conducting predictive analytics, a number of classification strategies with the ability to predict outcomes are employed, certain traditional machine learning classification methods have been widely employed to estimate a variety of clinical anomalies in numerous research. Here, technique 1 employs five kinds of well-known and widely utilized traditional machine learning classification techniques with fundamental algorithmic structure which are appropriate to this target area. The models are Logistic Regression classifier, Support Vector Machine classifier, Decision Tree classifier, K-Nearest Neighbour classifier and Naive Bayes classifier. These machine learning classifiers have been applied extensively in a variety of healthcare-related predictive studies. Table 1 shows a summary of these traditional machine learning models used in various clinical prediction related studies. Table 1A summary of traditional machine learning classifiers used for different healthcare predictive studies. Table 1ClassifiersBrief DescriptionExamples of healthcare predictionsReferencesLogistic RegressionA probabilistic-based statistical model in which the classifier assesses the association between the dependent variable as target class and independent variables or features for a given dataset using a logistic function [59]Chronic disease prediction, ovarian cancer classification, Alzheimer's disease detection etc. Nusinovici et al. [ 60], Octaviani et al. [ 61], Xiao et al. [ 62] Support Vector MachineA hyperplane is chosen, which is a line that can discover the coefficients, separate samples in the variable space with the best detachment of the classes [63]PCOS detection, heart disease diagnosis, cervical cancer detection etc. Sengur et al. [ 64], Bharati et al. [ 65], Zhang et al. [ 66] Decision TreeEstimates entropy and information gain for each attribute over a provided training sample and analyzes each feature at each node of a top-down tree for classification [67]Parkinson's disease identification, COVID-19 diagnosis, coronary artery disease diagnosis etc. Syapariyah et al. [ 68], Yoo et al. [ 69], Ghiasi et al. [ 70] K-Nearest NeighbourIt's a instance-based learning that considers local approximation presuming that similar data are close together & computation is conducted until classification [71]Diabetes detection, chronic kidney disease prediction, Ovarian cancer classification etc. Suyanto et al. [ 72], Devika et al. [ 73], Alqudah et al. [ 74] Naive BayesA fundamental probabilistic based classification strategy for predicting class membership probability by computing the likelihood of membership for each category [75]Breast cancer detection, brain tumor detection, thyroid detection etc. Kharya et al. [ 76], Zaw et al. [ 77], Chandel et al. [ 78]•Bagging Ensemble ML classifiers:A bagging classifier or bootstrap aggregation classifier is an ensemble method that fits multiple base classification models on randomized subsets of the dataset with the same weights given to each model and then aggregates their individual predictions to generate a final result [99]. In this study, Random Forest classifier has been used for classification as a type of bagging classifier which is created based on the aggregation of numerous decision tree base classifiers. During the evolution of a decision tree, Random Forest employs random subset or feature projection which means rather than using all of the parameters in one tree, each decision tree in Random Forest selects only a subset of variables at every prospective splits [100]. A number of researchers have used random forest classifier successfully to the various domains of healthcare predictive analysis. A brief summary in this regard has been shown in Table 2.Table 2A summary of bagging and boosting ensemble machine learning classifiers used for different healthcare predictive studies. Table 2TypeML ClassifiersBrief DescriptionExamples of healthcare predictionsReferencesBagging ensembleRandom Forest classifierIntegrates bootstrap aggregation (bagging) and random feature selection to create a set of decision trees with controlled variation that can anticipate the corresponding output activity class [79]PCOS detection, lymph disease diagnosis, thyroid disorder analysis etc. Tiwari et al. [ 80], Azar et al. [ 81], Mishra et al. [ 82] Boosting ensembleGradient Boosting classifierIt is an ensemble forward learning model which eliminates all weaker predictors in favor of a stronger one using an upgraded version of the decision tree, in which each successor is selected using the refined structure score, gain computation, and advanced approximations [83]Lung cancer detection, diabetes diagnosis, Leukemia prediction etc. Chandrasekar et al. [ 84], Bahad et al. [ 85], Deif et al. [ 86]eXtreme Gradient (XG) BoostingThis approach is scalable and efficient form of gradient boosting that improves on two fronts: tree construction speed and a novel distributed algorithm for tree searches [87]Heart disease detection, chronic kidney disease diagnosis, breast cancer detection etc. Ashish et al. [ 88], Ogunleye et al. [ 89], Inan et al. [ 90]Adaptive Boosting classifierIt's an adaptive classifier that leverages the results of various weak learning algorithms to substantially enhance performance and provide an effective predictor for the boosted classifier's final output [91]Endometrial cancer prediction, Hepatitis disease detection, cancer classification etc. Wang et al. [ 92], Akbar et al. [ 93], Lu et al. [ 94]Categorical Gradient (CAT) BoostingIt is an implementation of Gradient Boost classifier that employs ordered boosting with categorical features and uses binary decision trees as underlying predictors [95]Parkinson's disease prediction, COVID-19 detection from blood samples, diabetes risk prediction etc. Al et al. [ 96], Abayomi et al. [ 97], Kumar et al. [ 98]•Boosting Ensemble ML classifiers:*Boosting is* an ensemble machine learning approach in which a random sample data is chosen, fitted with a model, and then trained in a sequential manner, combining a set of weak learners into a strong learner with an aim to minimize training errors, with every model attempting to compensate for the shortcomings of the previous model [101]. Based on the different ways of producing and aggregating weak learners during the sequential approach, boosting algorithms can be categorized into different types. In this study, four types of widely utilized variations of boosting ensemble technique have been employed which are: Gradient (Grad) Boosting classifier, Adaptive (Ada) Boosting Classifier, eXtreme Gradient (XG) Boosting classifier and CAT Boosting classifier. These classifiers have been considered here because they have been successfully applied to a range of challenges in the field of healthcare predictive modeling, as a brief summary shown in Table 2.
## Proposed machine learning classifiers
To achieve greater forecasting performance than a single classifier, ensemble learning employs multiple classifiers; where Stacking ensemble learning is the technique that use a meta-classifier to aggregate various weak classifiers. The likelihood of belonging to a class is returned by the first layer's classifiers as a meta-feature; than these meta-features with the dataset are the input for the meta-classifier in the second level. Finally, the classifier's output can be either 1 or 0 [102]. A stacking ensemble based ML classification approach has been proposed for predicting the PCOS or non-PCOS criteria in this study that differs from bagging and boosting approaches in the following perspectives: (a) it evaluates diverse weak classifiers and simultaneously trains them.; ( b) then aggregates them by training a meta-learner to generate a forecast relying upon every weak learner's individualized predictions; and (c) hence, it reduces variance and improves the learning process' predictive power. [ 103]. The basic framework of the proposed stacking ensemble machine learning technique has been illustrated in Fig. 4.Figure 4Basic Framework of the Proposed Stacking Ensemble Technique. Figure 4 The proposed model is a multi-level stacked ensemble model where after preprocessing the raw data sample, it is being divided into train and test data and then initially sent to the base learners of level 0. At this phase, the five types of widely utilized traditional machine learning classifiers have been considered to be the weak learners or base classifiers at level 0 of the stacked model, which are: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN) and Gaussian Naive Bayes (NB) classifiers (see Table 1). These base models are each independently trained by employing their own prediction models, producing forecasts denoted by the letters p1, p2, p3, p4, and p5 in Fig. 4. The level 1 models are therefore given the level 0 forecasts, and a single classification algorithm, or meta-learner, learns to produce the final prediction from all of these. At level 1, the meta-learner is built using a stronger machine learning classifier. Five different classifier types have been investigated here as meta learners at level 1 while maintaining the same basis models at level 0, which has produced five different versions of the suggested model in an effort to find the one performing most effectively. The meta learner is one of the five types of bagging or boosting classifiers described previously in Table 2, which is eventually trained on top of level 0 to provide the final output depending on the forecasts provided by the base models. The output of the level 0 classification models serves as the input for the level 1 algorithms rather than features of the raw data. As a result, a stacked ensemble machine learning classifier has been suggested that incorporates five different classic classifiers as base models with one boosting or bagging type of classifier as meta learner, in order to distinguish between patients with PCOS and those who do not have PCOS.
## Results and findings
In this study, to evaluate and compare the efficacy of the predictive models for PCOS detection, total four types of ML techniques have been performed employing fifteen varieties of ML classifiers including the traditional (five models), bagging (one model), boosting (four models) and proposed (five models) techniques. All the experiments have been simulated using patient symptom dataset for classifying the records into PCOS and non-PCOS criteria. Furthermore, to explore the optimum and most significant attributes from the dataset, three types of feature selection methods (Chi-square, PCA and RFE) have been employed which picked the top 35, 30, 25 and 20 features out of the 40 features of the dataset. Each ML model's performance has then been evaluated employing these different sets of features acquired from feature selection techniques.
The performance of different varieties of machine learning algorithms utilizing different sorts of feature sets is analyzed using four performance measures, which are Accuracy, Precision, Sensitivity (recall), and F1 score, to investigate the efficacy of the prediction analysis [104]. The performance metrics are primarily based on a comparison of anticipated and actual values that investigates number of correct and incorrect predictions from the training sample, which is divided into four categories: True Positive (TP) that is both the true and predicted values are positive; True Negative (TN) in which both the original and the anticipated values are negative.; False Positive (FP) a where the actual value is negative but the anticipated result is positive and lastly False Negative (FN) where the actual value is positive, but the predicted result is negative. Based on these evaluations, the performance measures utilized here can be stated as Equations [4], [5], [6], and [7]:[4]Accuracy=(TP+TN)÷(TP+TN+FP+FN)[5]Precision=TP÷(TP+FP)[6]Recall(Sensitivity)=TP÷(TP+FN)[7]F1−score=2⁎(Precision⁎Sensitivity)÷(Precision+Sensitivity) The findings of this rigorous evaluation process have been shown in Table 3, Table 4, Table 5, Table 6; where Table 3 shows the accuracy, Table 4 shows the precision, Table 5 shows the recall and Table 6 shows the F1-score of different models using different sets of features. The best performance results from each column have been highlighted in the tables. Table 3Accuracy Comparison of ML models using different set of features. Table 3TypeClassification Models40 Feat.35 Feat. Accuracy30 Feat. Accuracy25 Feat. Accuracy20 Feat. AccuracyChi2PCARFEChi2PCARFEChi2PCARFEChi2PCARFETrad TechSVM0.5070.890.5110.5340.8710.9110.8930.7080.9210.6790.6090.5830.611Log. Reg0.8720.890.8860.8950.920.9110.8930.7020.9210.6880.7140.6110.711DecisionTree0.8360.8220.8680.8630.8040.780.8790.7160.8640.7130.6530.6640.65KNN0.6850.8160.6670.6850.8160.8640.8130.6220.8790.7360.6960.7160.707NaiveBayes0.8680.840.8630.8630.8590.7660.8640.740.8080.760.5650.5850.564 Bag. TechRandomForest0.8890.890.9060.9020.890.9070.9020.8510.9160.860.7080.7930.8 Boosting TechGradBoosting0.8720.8770.8830.8930.890.8880.930.8530.8740.8320.7770.7150.706XG Boosting0.890.8770.8970.8640.8810.9070.8970.8530.850.8640.6890.6750.715AdaBoosting0.8860.8960.8690.8830.8530.9020.9020.8710.8640.8690.7560.80.686CATBoosting0.90.8630.8410.8320.90.9160.9160.8650.8790.850.7890.7990.725 Proposed Tech (stacking)Meta learner-Grad Boost0.9270.9220.9320.9180.9260.9530.9430.883Image 10.9110.8530.8320.86Meta learner-XGBoost0.9130.9180.9180.9220.9260.9350.9240.8590.9420.8930.8710.8220.897Meta learner-AdaBoost0.9220.9340.9270.9360.9460.9310.9380.8830.9420.9070.8080.8020.893Meta learner-CATBoost0.9130.890.9320.9180.9330.9430.9330.9080.9470.8930.8020.8250.802Meta learner-RandForest0.9090.8830.9130.9270.9240.9180.9250.890.9250.9160.8020.8120.807Table 4Precision Comparison of ML models using different set of features. Table 4TypeClassification Models40 Feat.35 Feat. Precision30 Feat. Precision25 Feat. Precision20 Feat. PrecisionChi2PCARFEChi2PCARFEChi2PCARFEChi2PCARFETrad TechSVM0.650.8740.5140.5410.8550.9120.8940.7990.9210.680.610.5820.613Log. Reg0.8720.8780.8870.8970.910.9120.8930.7910.9210.690.7150.6210.711DecisionTree0.8370.8020.8680.8630.7830.7850.8790.7150.8650.7150.6540.660.651KNN0.6970.7980.6670.6850.7950.8650.8160.6020.8820.7360.6980.7150.71NaiveBayes0.8740.8410.8690.8650.8730.8050.8650.7410.8290.760.5650.5860.565 Bag. TechRandomForest0.8880.8780.9070.9020.8780.9120.9020.850.9170.8610.7080.7920.802 GradBoosting0.8730.8660.8850.8940.880.8880.930.8470.8760.8360.7750.7160.706Boosting TechXG Boosting0.8910.8790.8980.8650.8820.9090.8970.8860.8520.8650.6880.670.715AdaBoosting0.8860.8860.870.8830.8370.9020.9020.8640.8660.8690.7550.8220.67CATBoosting0.9010.8720.8430.8360.890.920.9170.850.880.8540.780.7960.724 Proposed Tech (stacking)Meta learner Grad Boost0.9270.9210.9310.9180.9250.9520.9450.868Image 20.9120.850.8350.862Meta learner XGBoost0.9140.8950.9180.9230.9220.9340.9250.8530.9420.8930.8670.8230.895Meta learner AdaBoost0.9220.9250.9270.9360.9480.9420.9370.8720.9310.9080.810.8020.893Meta learner CATBoost0.9140.8640.9320.9180.9350.9440.9330.8970.9470.8930.8010.8260.806Meta learner RandForest0.9090.8680.9140.9270.9250.9190.9240.8760.9260.9150.8020.8110.807Table 5Recall Comparison of ML models using different set of features. Table 5TypeClassification Models40 Feat.35 Feat. Recall30 Feat. Recall25 Feat. Recall20 Feat. RecallChi2PCARFEChi2PCARFEChi2PCARFEChi2PCARFESVM0.5130.8860.5060.5310.8640.9110.8930.790.9220.670.6120.5830.622TradLog. Reg0.8720.8780.8860.8960.9140.9110.8930.790.9210.680.7250.620.71TechDecisionTree0.8350.8050.8670.8630.7780.780.8790.80.8670.730.6550.660.655KNN0.6870.8220.6670.6850.8090.8640.8130.610.8870.740.6980.7250.711NaiveBayes0.8680.7980.8640.8630.8120.7660.8640.780.8150.860.560.5960.565 Bag. TechRandomForest0.8880.8780.9060.9020.8780.9070.9020.850.9220.860.7090.7930.803 Boosting TechGradBoosting0.8730.860.8830.8930.8730.8880.930.820.8710.830.7770.7170.71XG Boosting0.890.8780.8970.8640.8810.9070.8970.790.8530.860.6890.6730.712AdaBoosting0.8890.8820.8690.8830.8370.9020.9020.850.8620.880.7540.8230.671CATBoosting0.90.8690.8410.8320.8770.9160.9160.850.8860.850.7810.7940.724 Proposed Tech (stacking)Meta learner Grad Boost0.9270.920.9320.9180.9290.9520.9430.87Image 10.910.8510.8220.860Meta learner XGBoost0.9140.9040.9180.9230.9210.9340.9240.830.9420.890.8770.8260.894Meta learner AdaBoost0.9220.9250.9270.9360.9480.9420.9380.870.9340.910.8120.8050.893Meta learner CATBoost0.9140.8640.9330.9180.9340.9440.9330.910.9440.890.8040.8270.807Meta learner Rand Forest0.9090.8770.9140.9270.9250.9190.9230.880.9220.9200.8020.8150.81Table 6F1-Score Comparison of ML models using different set of features. Table 6TypeClassification Models40 Feat.35 Feat. F1-Score30 Feat. F1-Score25 Feat. F1-Score20 Feat. F1-ScoreChi2PCARFEChi2PCARFEChi2PCARFEChi2PCARFETrad TechSVM0.3670.880.4270.50.8640.9110.8930.790.9220.690.6910.6740.683Log Reg0.8720.8780.8860.8950.9140.9110.8930.790.9210.680.7120.6780.711DecisionTree0.8350.8040.8680.8630.7780.780.8790.790.8660.710.6230.620.664KNN0.6810.8050.6670.6850.8090.8640.8130.610.8780.740.6840.7860.716Naive Bayes0.8670.8120.8630.8630.8120.7660.8640.710.8140.860.5430.5550.585 Bag. TechRandomForest0.8890.8780.9120.9020.8780.9070.9020.850.910.860.7810.6850.853 Boosting TechGradBoosting0.8720.8630.8830.8920.8730.8880.930.830.8700.810.7930.7690.731XG Boosting0.890.8780.8970.8640.8810.9070.8970.820.8510.860.6830.6410.72AdaBoosting0.8870.08840.8690.8830.8370.9020.9020.850.8630.870.7830.8640.602CATBoosting0.90.870.8410.8310.8770.9160.9160.850.8770.850.7850.7690.725 Proposed Tech (stacking)Meta learner Grad Boost0.9270.920.9320.9180.9290.9500.9430.87Image 30.910.8910.8850.82Meta learner XGBoost0.9130.8990.9180.9220.9210.9340.9240.840.9230.890.8820.8690.869Meta learner AdaBoost0.9220.9250.9270.9360.9480.9420.9380.870.9330.910.8140.8010.881Meta learner CATBoost0.9130.8640.9320.9180.9340.9440.9330.890.9430.890.8090.8240.81Meta learner Random Forest0.9090.8720.9130.9270.9250.9190.9230.870.9230.9210.8110.8290.818
## Comparative performance analysis of the proposed technique with other ML techniques
Analyzing the evaluation results from Table 3, Table 4, Table 5, Table 6, it can be observed that, the performances of the classifiers enhance significantly using the proposed stacked ensemble techniques. For example, incorporating all the 40 features the best performance has been achieved using the proposed stacked ensemble classifier with Gradient boosting model as meta learner attaining $92.7\%$ accuracy, $92.7\%$ precision, $92.7\%$ recall and $92.7\%$ F1 score. Also it is noticiable that, each of the stacked ensemble models has acquired accuracy performance over $90\%$ using the proposed technique with all features whereas the other models typically have less than or equal to $90\%$ accuracy. Similar findings are also observed in case of using all the reduced set of features (set of 35,30,25 and 20 features) acquired from feature selection techniques, where the five varieties of proposed ML models outperform the other types of models in terms of all the performance metrics.
Fig. 5 graphically illustrates the comparative analysis of the accuracy of different ML models incorporating different feature sets where Fig. 5 (A) shows the accuracy of the models with features selected using chi-square method, Fig. 5 (B) shows accuracy with PCA features and Fig. 5 (C) shows comparative accuracy with RFE features. Each of the graphical representation compares the accuracy performances of four techniques with different classification models employed in this study using 40 features, 35 features, 30 features, 25 features and 20 features selected using chi-square (see 5 (A)), PCA (see 5 (B)) and RFE (see 5 (C)) feature selection method. From the graphical representation it is clearly visible that the performances of the proposed stacking ensemble models are comparatively higher than the other models in case of all types of feature selection methods. Thus, the results acquired from evaluating the ML techniques with different performance metrics clearly indicate that, the proposed stacking ensemble techniques provide a better performance for classifying the dataset into PCOS and non-PCOS classes. Figure 5Comparative accuracy analysis of ML models with different sets of (A)Chi-Square, (B)PCA and (C)RFE features. Figure 5
## Results of feature selection with different ML techniques
The different feature selection methods utilized here have selected different sets of attributes employing their own methodologies. A list has been given in Table 7 which shows the top 25 features that have been picked using three types of feature selection methods. It is apparent from the Table 7 that the most important attributes of the three techniques are evidently nonidentical. From the table it is observable that, the three set of top 25 features differs from each other such as both PCA and RFE has considered ‘Endrometrium’ (endrometrium thickness of follicles) as a significant feature but Chi-square technique has not selected it; on the other hand chi-square technique has selected ‘Marriage Status (Yrs)’ as an important feature but PCA technique has not prioritized it; and so on. These results indicate that different feature selection techniques pick different combinations of features from the dataset and thus it is necessary to investigate which set of feature provides the best performance. Table 7Top 25 dominant features prioritized by three types of feature selection methods. Table 7Chi-SquarePCARFE1Age (yrs)Weight (Kg)Weight (Kg)2Weight (Kg)BMIHeight (Cm)3BMIWeightGain Y/NBMI4Cycle (R/I)Waist (inch)Marraige Sta (yr)5Cycle lengthHip (inch)Cycle (R/I)6Marraige Sta. ( yr)hair growth-Y/NEndometrium7Pregnant (Y/N)Follicle No. ( L)Pregnant (Y/N)8No. of abortionsFast food (Y/N)Pulse rate (bpm)9LH (mIU/mL)Skin dark (Y/N)FSH (mIU/mL)10FSH (mIU/mL)Follicle No. ( R)LH (mIU/mL)11Hip (inch)Avg. F size (L)TSH (mIU/L)12Waist (inch)Avg. F size (R)PRG (ng/mL)13AMH (ng/mL)Cycle (R/I)No. of abortions14Vit D3 (ng/mL)Pimples (Y/N)WeightGain15PRG (ng/mL)Hair loss (Y/N)hair growth-Y/N16WeightGain-Y/NHeight (Cm)Skin dark (Y/N)17hair growth-Y/NAMH (ng/mL)Hair loss (Y/N)18Skin dark (Y/N)EndometriumPimples (Y/N)19Hair loss (Y/N)FSH/LHFast food (Y/N)20Pimples (Y/N)Cycle lengthFollicle No. ( R)21Fast food (Y/N)Hb (g/dl)Follicle No. ( L)22Reg. Exer.-Y/NVit D3 (ng/mL)Cycle length23Follicle No. ( L)RBS (mg/dl)Avg. F size (L)24Follicle No. ( R)Age (yrs)Reg. Exer.-Y/N25Avg. F size (L)BP SystolicRR (breaths/min) From the comparative evaluation with graphical representation in Fig. 5, another significant finding is that, the accuracy of the models employing Chi-square and RFE feature selection methods gradually enhances when the number of features have been reduced from 40 features to 30 selected features; but then the performances start decreasing for the selected 25 and 20 features for almost all the models. The highest accuracy for most of the models employing chi-square and RFE feature selection method has been acquired with top 30 selected features. Here, the highest accuracy with Chi-square feature selection method has been achieved using stacking ensemble classifier with ‘AdaBoost’ model as meta learner which is $94.6\%$ using top 30 features; and the highest accuracy with RFE feature selection method has been achieved using stacking ensemble classifier with ‘GradBoost’ model as meta learner which is $94.3\%$.
However, when using the PCA feature selection approach, most of the models' accuracy consistently improves with reduced features and has reached its peak with the top 25 features. Fig. 6 graphically displays the relative importance of all the features of the dataset based on PCA technique. Using the top 25 features selected via the PCA approach, the maximum accuracy being $95.7\%$ has been achieved in this context with a stacking ensemble classifier with the ‘GradBoost’ model as the meta learner. The most significant 25 attributes providing the best performance that has been explored using PCA technique are shown in Table 8. In this table the top selected features are further grouped based on the real-time clinical feature categories under the supervision of three expert clinicians in this relevant field. Furthermore, the identified 25 features of Table 8 have been discussed with three healthcare specialists and according to them, the selected criteria have been rightly regarded to be the crucial predictive attributes in terms of practical PCOS identification. This investigation shows that the PCA technique's minimal yet optimal number of features can not only be used to deliver the best performance with ML classifiers, but can also be effectively utilized to implement a real-time autonomous PCOS detection model in the future. Figure 6Relative importance of features based on PCA technique. Figure 6Table 8Categorization of top 25 dominant features based on PCA technique. Table 8Feature CategoriesFeaturesDemographicsAge (yrs) Vital SignsWeight GainCycle (R/I)Body hair growthPimplesSkin darkeningHair loss Patient HistoryBMIWaist (inch)Cycle lengthHip (inch)Weight (Kg)Height (Cm)BP Systolic Laboratory Diagnosis OutcomesFollicle No (L)Follicle No (R)Avg. F size (L)Avg. F size (R)Endometrium Thickness ComorbiditiesHb (g/dl)AMH (ng/mL)FSH/LHRBS (mg/dl)Vit D3 (ng/mL) Therefore, from the overall performance analysis, it is observable that, the traditional machine learning models, are explored as being weak classifiers in the context of this dataset and produce the weaker performances which eventually gives a bit better result through bagging and boosting type of ensemble classification models. On the other hand, as a result of the proposed stacked ensemble models' robust formulation, which incorporates the predictive analytics of several classifiers, the results show that each version of the suggested stacked ensemble approaches yields superior outcomes. Also, in terms of feature engineering, the selected features through PCA technique provide better results employing the classification models while chi-square technique provides least performances. Thus, the results of performance analysis indicate that, the machine learning model employing the proposed stacking ensemble method with five classifiers (SVM, LR,DT, KNN,NB) as base models and GradBoost classifier as meta learner; utilizing the top 25 attributes from the dataset selected through PCA feature selection technique has been explored to be the highest performing classification model with $95.7\%$ accuracy, $95.2\%$ precision, $95.2\%$ recall and $95.0\%$ F1-score that outperforms all other models to classify PCOS and non-PCOS criteria.
## Discussion
In this article, three types of ensemble machine learning strategies (bagging, boosting and stacking) with multiple classifiers have been explored, trained and tested along with traditional machine learning techniques to classify PCOS and non-PCOS data. Most of the previous studies in this area were based on traditional ML classifiers. However, recently a few researchers have focused on applying ensemble techniques in PCOS detection, but their exploration techniques are based on typical bagging, boosting or voting type of ensemble models [29], [45]. To the best of our knowledge, the proposed technique based on stacking ensemble classification approach where both traditional as well as boosting or bagging ensemble models are aggregated to provide a stronger prediction is a unique solution in this domain. Here in the stacked ensemble architecture, five types of weak traditional ML classifiers are used as the base models and then their predictions are integrated in a stronger meta-learner classification model to provide the final prediction. One from five types of boosting or bagging classifier has been used as the meta learner in the proposed stacked ensemble model to explore the best performing model where the highest performance has been acquired with $95.7\%$ accuracy which is also higher than previous studies employing identical dataset. For instance, Bharti et al. [ 45] had acquired the best accuracy of $91.12\%$ with voting ensemble technique, Nandipati et al. [ 38] showed $93.12\%$ accuracy with Random Forest classifier, Prapty et al. [ 35] acquired $93.5\%$ accuracy employing Random Forest classifier and so on.
Furthermore, using feature selection strategies, the majority of previous studies randomly picked a specified number of features. For example, Bharti et al. [ 65] applied ML classifiers with ten statistically significant features based on p-values, Inan et al. [ 37] proposed to use most significant top twelve features, Danaei et al. [ 29] had acquired best accuracy employing 28 features selected using Random Forest embedded feature selection technique and so on. However, hardly any study has investigated at how changing the numbers and combinations of features selected using that same feature selection method can affect the prediction result. Therefore, in this study, three distinct types of feature selection techniques (Chi-square, PCA and RFE) have been applied to identify the optimum features that are required for effective forecasting from the dataset's 40 attributes. Each of the feature selection techniques have been used to select different feature sets with top 35,30,25 and 20 attributes. And then the performances of the proposed stacking ensemble models as well as other traditional, bagging and boosting ensemble models are evaluated using those vast varieties of selected feature sets through performance metrics (accuracy, precision, recall and F1-scores). As per the findings of the comparative analysis, it has been observed that, the accuracy of most models using the feature set selected via Chi-square and RFE strategies improves up to the top 30 features and thereafter gradually diminishes, whereas in case of PCA feature selection approach the accuracy enhances upto top 25 features and then decreases. Therefore, comparing the performances of all the classifiers to categorize PCOS and non-PCOS patients, the result indicates that, the stacking ensemble model with ‘Gradient Boosting’ classifier as meta learner has outperformed other models utilizing the feature set of top 25 attributes picked using PCA technique. Furthermore, under the observation of expert clinicians, the highly prioritized 25 features selected using the PCA technique were sorted into real-time clinical categories.
## Implications of the study
The methodology presented in this study can be a pioneer in effectively detecting PCOS from patients symptoms and test results through machine learning strategies and thereby can play a potentially beneficial role in improving the reproductive health of thousands of women. The findings of this study can be significantly beneficial towards both patients and healthcare providers in identifying PCOS quickly and efficiently combining the advantages of multiple machine learning classifiers ensembled in one robust model employing minimal number of attributes and thus it is anticipated to be widely used in the real-world clinical practices. The study's outcome can be effectively helpful for the physicians in the arduous task evaluating patients by simplifying the complex diagnostic procedure of PCOS. This computational technique can be deployed in the healthcare facilities of rural areas to detect PCOS autonomously where there is scarcity of expert physicians and resources.
## Limitations and future work
Yet, owing to a lack of vast dataset, one of the study's flaws was that it only used machine learning algorithms on a small number of patient data. Real-time data couldn't have been acquired; the dataset was taken from an open source resource. Also, the five traditional ML model categories that have been used as base classifiers in the proposed stacked ensemble model were chosen based on their prominence in this field in earlier studies. The performance might have been different if other types of Ml classifiers had been utilized here. Moreover, the varied number of reduced set of features [35, 30, 25, 20] explored by the feature selection technique have been chosen randomly for this study. In addition, for intelligent clinical applications, explainable AI plays an important role in providing an explanation alongside sufficient justification of AI system predictions, which has not been included in this study. Therefore, the authors hope to investigate more about PCOS detection using larger datasets as well as more types of feature selection techniques in the future incorporating the techniques of eXplainable AI (XAI) with the current study, as well as implement the proposed methodology in other fields of clinical illness predictions.
## Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
## CRediT authorship contribution statement
Sayma Alam Suha - Conceived and designed the experiments; Performed the experiments.
Muhammad Nazrul Islam, Sayma Alam Suha - Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
## Declaration of Competing Interest
The authors declare no conflict of interest.
## Data availability
Data included in article/supplementary material/referenced in article.
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|
---
title: 'Prevalence of food insecurity and its association with depressive and anxiety
symptoms in older adults during the COVID-19 pandemic in Mexico: A secondary analysis
of ENCOVID-19 survey'
authors:
- De la Vega Martínez Alán
- Rosas-Carrasco Oscar
- Gaitán-Rossi Pablo
- Ancira-Moreno Mónica
- López-Teros Miriam
journal: Frontiers in Medicine
year: 2023
pmcid: PMC10040530
doi: 10.3389/fmed.2023.1110584
license: CC BY 4.0
---
# Prevalence of food insecurity and its association with depressive and anxiety symptoms in older adults during the COVID-19 pandemic in Mexico: A secondary analysis of ENCOVID-19 survey
## Abstract
### Introduction
COVID-19 infection has caused high rates of morbi-mortality in older adults (OAs). In addition, conditions such as depression, anxiety, unemployment, and poverty frequently contribute to this population being at higher risk of food insecurity (FI) during the COVID-19 pandemic.
### Objective
This study aimed to analyze the prevalence of FI and its association with depressive and anxiety symptoms in Mexican OAs during the COVID-19 pandemic.
### Methods
This study involved a secondary analysis of the National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19), a series of cross-sectional telephone surveys conducted between April and October 2020. The OA subsample was 1,065. FI was measured by using the Latin American and Caribbean Food Security Scale (ELCSA), and depression and anxiety symptoms were measured by using the Depression Scale of the Center for Epidemiological Studies (CESD-7) and the Generalized Anxiety Disorder Scale (GAD-2), respectively. Socioeconomic status (SES), occupation, schooling, and pension were also evaluated. ANOVA was used to compare the variables between the different FI groups, and logistic regression was used to analyze the risk between FI and the anxiety and depression variables.
### Results
The mean age of the participants was 67.31 ± 6.4 years, and FI was classified as mild, moderate, and severe, with prevalences of 38.6, 15.04, and $8.16\%$, respectively. Overall, $28.01\%$ of the OAs presented symptoms of anxiety and $39.09\%$ of depression. In the comparison between groups, a higher prevalence of depressive symptoms was observed with a higher degree of FI, with $65.75\%$ in moderate-to-severe, $10.39\%$ in mild, and $9.40\%$ without FI, p ≤ 0.000. Regarding anxiety symptoms, $48\%$ of the OAs showed moderate-to-severe, $30.05\%$ showed mild, and $15.38\%$ were without FI, p ≤ 0.000. Using multiple logistic regression, an OR of 5.50 ($95\%$ CI 2.74–11.04) was observed for depressive symptoms when moderate-to-severe FI is present. In the case of the risk of anxiety symptoms, it was significant in all degrees of FI, in mild (OR = 2.43, $95\%$ CI 1.66–3.59) and in moderate-to-severe (OR = 5.32, $95\%$ CI 3.45–8.19).
### Conclusion
There is a high prevalence of FI in Mexican OAs during the COVID-19 pandemic. FI increases the risk of other conditions such as depression and anxiety. It is important to design and implement programs aimed at OAs with these conditions to reduce or prevent FI.
## Introduction
The pandemic caused by the COVID-19 virus has caused the death of almost 6 million people in the world until February 2022 [1]. Several studies have shown that age is an important predictor of adverse outcomes among patients with COVID-19 (2–4). A study published in Mexico in 2021 showed that the main predictors of severity and mortality in 220,804 confirmed cases of COVID-19 were age (adults 60 years of age or older) and high social lag indexes [2]. In addition to comorbidities, high social lag indexes were identified as a predictor of mortality from COVID-19 adjusted for age and sex (HR 1.13, $95\%$ CI 1.054–1.21).
In many countries of the world, social determinants of health have been observed to influence morbidity and mortality from COVID-19. These include poverty, physical environment, race, or ethnicity [5]. OAs are usually a population with multiple risk factors such as chronic morbidity, lack of social support, living without company, and presenting greater effects on mental health such as anxiety or depression, reaching a range of 8.3 to $49.7\%$ and 14.6 to $47.2\%$, respectively, according to a review by Sepulveda-Loyola et al. that included 42 articles with a total sample of 20,069 OAs from Asia, Europe, and America [6].
Gaitán-Rossi et al. through data from the National Survey on the Effects of COVID-19 on the Wellbeing of Mexican Households (ENCOVID-19) and the National Health and Nutrition Survey (ENSANUT 2018) in people over 18 years of age showed that the pandemic was also associated with a reduction in food security (households that did not report concerns or difficulties in accessing food), decreasing from $38.9\%$ in 2018 to $24.9\%$ in 2020 in Mexican households. At the lowest level of socioeconomic status, moderate-to-severe FI reached its highest prevalence, with 28.9 and $20.9\%$, respectively. Anxiety was also associated with higher FI scores; for example, $57.1\%$ of people living with severe FI reported symptoms of anxiety [7].
Pourmotabbed et al. in a meta-analysis included 19 studies with 372,143 participants from 10 countries. The results showed that there was a positive relationship between FI and the risk of depression (OR = 1.40; $95\%$ CI: 1.30–1.58) and stress (OR = 1.34; $95\%$ CI: 1.24–1.44), but not anxiety. A subgroup analysis by age showed that adults older than 65 years had a higher risk of depression (OR = 175; $95\%$ CI: 120, 256) than younger participants (OR = 1.34; CI $95\%$: 1.20–1.50). This study shows the relationship between FI and mental health status, such as depression and anxiety, and as previously stated, these conditions increased during the COVID-19 pandemic [8].
It is important to know the effects of the pandemic on the Mexican population, mainly in vulnerable populations such as OAs in a situation of poverty, high social lag indexes, greater comorbidity, and FI. There are still few published studies on FI in OAs in Mexico during the pandemic and the factors that could be associated with it. Therefore, the objective of this study is to analyze the prevalence of FI and its association with depressive and anxiety symptoms in Mexican OAs during the COVID-19 pandemic.
## Study population and data sources
Secondary analysis of ENCOVID-19, which is a series of cross-sectional telephone surveys, is a national representative of people over 18 years of age who have a mobile phone. This survey provides data in four main domains: work, income, mental health, and food security, and began in April 2020 and continued monthly until October 2021, after which it was extended in frequency [9, 10].
The monthly surveys were compiled with probabilistic samples of mobile phone numbers using the national numbering plan, which is publicly available, as a sampling frame. To correct for slight deviations in the demographic composition of the ENCOVID-19 sample, post-stratification sample weights were used. Weights were calculated using data from the 2015 INEGI census to adjust the sample for geographic distribution (state), gender, age, and socioeconomic status (SES) [11].
For the present study, the rounds from April to October 2020 were used, including only people aged 60 years or older and who had data from the FI, depression, anxiety, and scales. A final sample of 1,065 participants was obtained.
## Measurements
Household FI was measured with the eight-item adult version of the Latin American and Caribbean Food Security Scale (ELCSA) [12], an instrument that has been validated for its use in Mexico [13]. The adapted version of the scale (telephone) was used, which proved to be reliable and valid, and the alpha coefficients in the April, May, and June rounds varied between 0.87 and 0.89. Correlations between items were above the cutoff point of 0.60 in all surveys. Furthermore, the Rasch models showed that the high reliability of the telephone version of the scale was comparable to the face-to-face application in ENSANUT 2018 [10].
The ELCSA asks if, in the last 3 months, due to lack of money or other resources, the respondent or any other adult in the household: (i) worried that they might run out of food (worried); (ii) they were unable to eat healthy and balanced and nutritious (healthy) food; (iii) ate only a few types of food (few foods); (iv) skipped breakfast, lunch, or dinner (omitted); (v) ate less than he thought he should have eaten (without eating); (vi) the food is over (it is over); (vii) were hungry but did not eat (hungry); and (viii) went without eating for a whole day. Responses to all items are dichotomous (Yes/No).
After calculating the total summative score for the eight items, FI was classified into four levels: food secure (total score = 0), mildly food insecure (1–3), moderately food insecure (4–6), and food insecure severe [7, 8]. For purposes of regression analysis, food insecurity was coded as no FI [0], mild FI [1], and moderate-to-severe FI [2]. This is due to the number of severe OAs FI, which was only 75, so it was decided to join these two categories.
Anxiety symptoms. Anxiety was measured with the two-item generalized anxiety disorder scale (GAD-2) (14–16) which asks about how often the respondent felt during the last 2 weeks: (i) nervous, anxious, or borderline and (ii) not being able to stop or control the worry. Response options are “Never”; “several days”; “More than half the days”; and “almost daily.” The scale was validated in Spanish with the neuropsychiatric interview and was found to be reliable (alpha = 0.93) and with predictive validity with a sensitivity of 0.91, a specificity of 0.85, a positive predictive value of $86.6\%$, a negative predictive value of $91\%$, and an area under the curve of 0.937 (14–16).
Depression symptoms. The depression scale of the Center for Epidemiological Studies, abbreviated version (CESD-7) [17] was used. It consists of seven items that indicate the probable presence of depressive symptoms during the last week in which they were presented: (i) rarely or never (less than 1 day), (ii) rarely or sometimes (1–2 days), (iii) a considerable number of times (3–4 days), and (iv) all or most of the time (5–7 days). It has a minimum score of 0 and a maximum of 21 points, without symptoms <5 points and with the presence of depressive symptoms ≥5 points.
Socioeconomic status. The SES of the household was measured with the Mexican Association of Market Intelligence and Opinion Agencies (AMAI) index [18]. It combines six indicators from the National Household Income and Expenditure Survey [2]: (i) educational level of the household head; (ii) number of complete bathrooms; (iii) number of cars or vans; (iv) have an internet connection; (v) number of household members 14 years or older who are working; and (vi) number of bedrooms. Based on a summative score and standard cutoff points, the socioeconomic level is classified into seven mutually exclusive categories, ranging from “A/B” to “E,” where E represents the lowest value.
Other sociodemographic variables were also included, such as sex (male and female), age (years), level of schooling (basic (≤6 years), upper secondary, higher and postgraduate (>6 years), and no education (0 years)), occupation (active or not economically), welfare pension, and consumption of food groups (fresh fruits, vegetables, milk, eggs, meat, and beans), where the number of servings consumed per day is asked.
## Statistical analysis
A descriptive analysis of the characteristics of the population, presented with means±SD and absolute and relative frequencies, was carried out. Similarly, an ANOVA was performed to compare the study variables between the FI categories. In a third analysis, logistic regression models were adjusted according to the variables that were significant in the ANOVA and previously reported in the literature associated with FI, depression, and anxiety (sex, age, SES, and schooling) to identify the variables associated with terms of measures of association with odds ratios (ORs). Statistical power was calculated based on the prevalence of depression by FI level, giving a power greater than 80. The models were evaluated checking that there was no collinearity or interaction. Statistical significance was verified through the construction of $95\%$ confidence intervals ($95\%$ CI). The statistical software used was Stata/SE, version 15.0 (Stata Corp., TX, United States).
## Results
The average age of the participants was 67.31 ± 6.4 years (60–99), $47.75\%$ were women, and most of the participants had basic level education (≤6 years) ($54.14\%$). The prevalence of FI in mild, moderate, and severe was 38.56, 15.04, and $8.16\%$, respectively, and $38.24\%$ with food security. In relation to the consumption of different food groups, $23.79\%$ reported that they stopped consuming fresh fruits, $21.52\%$ vegetables, $32.79\%$ meats, $24.12\%$ dairy products, $12.66\%$ eggs, and $6.11\%$ beans. In relation to anxiety symptoms, a prevalence of 25.52 and $39.02\%$ of depression symptoms was shown. Regarding the economic variables, $65.9\%$ were within the economically inactive population, and when the participants were classified by SES, the levels with the highest prevalence were D and E ($45.92\%$) and C, C−, and D+ ($38.59\%$) (see Table 1).
**Table 1**
| Characteristics of participants | Means ± SD or n (%) | N |
| --- | --- | --- |
| Sociodemographic | Sociodemographic | |
| Age | 67.31 ± 6.4 | 1065.0 |
| Gender (Women) | 521 (47.75%) | 511.0 |
| (Men) | 570 (52.25%) | 554.0 |
| Schooling (No education) | 43 (7.73%) | 556.0 |
| (≤ 6 years) | 301 (54.14%) | 556.0 |
| (> 6 years) | 212 (38.13%) | 556.0 |
| Food insecurity | Food insecurity | |
| Food insecurity scale (ELCSA-8) | 2.01 ± 2.07 | 918.0 |
| ELCSA with food safety | 351 (38.24%) | 918.0 |
| ELCSA mild FI | 356 (38.56%) | 918.0 |
| ELCSA moderate FI | 136 (15.04%) | 918.0 |
| ELCSA severe FI | 75 (8.16%) | 918.0 |
| Food consumption | | |
| (Fruit) | 3.03 ± 1.09 | 720.0 |
| (Vegetable) | 3.24 ± 2.0 | 720.0 |
| (Meat or egg) | 3.67 ± 2.5 | 720.0 |
| (Milk and dairy products) | 3.68 ± 2.9 | 720.0 |
| Less food consumption | | |
| (Fresh fruit) | 113 (23.79%) | 473.0 |
| (Vegetables) | 61 (21.52%) | 473.0 |
| (Meats) | 75 (32.79%) | 473.0 |
| (Dairy products) | 55 (24.12%) | 473.0 |
| (Eggs) | 29 (12.66%) | 473.0 |
| (Beans) | 14 (6.11%) | 473.0 |
| Mental health | Mental health | |
| Anxiety symptoms (GAD-2 ≥ 3) | 306 (25.52) | 1056.0 |
| Depressive symptoms (CESD-7 ≥ 5) | 119 (39.02) | 303.0 |
| Economy and occupation | | |
| Occupation (Economically active) | 160 (32.19) | 497.0 |
| (Economically inactive) | 236 (65.9) | 497.0 |
| Pension | 272 (36.41) | 272.0 |
| SES | | |
| 1 (A/B and C+) | 165(15.49) | 1065.0 |
| 2 (C, C− and D +) | 411(38.59) | 1065.0 |
| 3 (D and E) | 489 (45.92) | 1065.0 |
In the comparison of the variables between the degrees of (in) food security, a higher prevalence of depression symptoms was observed at a higher degree of FI, $65.75\%$ in moderate-to-severe FA, $10.39\%$ in mild FI, and $9.40\%$ without FI, $$p \leq 0.000.$$ Regarding anxiety symptoms, $48\%$ showed moderate-to-severe FI, $30.05\%$ in mild FI, and $15.38\%$ without FI, $$p \leq 0.000.$$ Regarding the socioeconomic level, there was a higher prevalence of moderate-to-severe FI in the lowest status of SES, level D with $51.54\%$ in severe FI, $40.84\%$ in moderate FI, $37.91\%$ in mild FI, and $25.48\%$ without FI, level E with $19.49\%$ in severe FI, $19.71\%$ in moderate FI, $10.16\%$ in mild FI, and $3.32\%$ without FI ($$p \leq 0.000$$) (see Table 2).
**Table 2**
| Characteristics of participants | Food safety 351 n (%) | Mild FI 356 n (%) | Moderate-to-severe FI 211 n (%) | p-value |
| --- | --- | --- | --- | --- |
| Gender (Women) | 136 (38.75) | 185 (51.97) | 124 (58.77) | 0.0 |
| (Men) | 215 (61.25) | 171 (48.03) | 87 (41.23) | 0.0 |
| Schooling (No education) | 7 (1.99) | 14 (3.93) | 19 (14.84) | 0.0 |
| (≤ 6 years) | 88 (25.07) | 122 (34.26) | 84 (65.63) | 0.0 |
| (> 6 years) | 209 (59.54) | 70 (19.66) | 25 (19.53) | 0.0 |
| Anxiety symptoms (GAD-2 ≥ 3 points) | 54 (15.38) | 107 (30.05) | 100 (48.08) | 0.0 |
| Depression symptoms (CESD-7 ≥ 5 points) | 33 (9.40) | 37 (10.39) | 48 (65.75) | 0.0 |
| Occupation (Economically active) | 84 (39.44) | 70 (32.86) | 59 (27.70) | 0.078 |
| (Economically inactive) | 118 (38.06) | 129 (41.61) | 63 (20.32) | 0.078 |
| Pension (Yes) | 85 (39.53) | 93 (43.36) | 37 (27.82) | 0.0586 |
| Socioeconomic status | | | | |
| 1 (A/B and C+) | 38 (69.09) | 14 (25.46) | 9 (4.27) | 0.0 |
| 2 (C, C− and D +) | 60 (65.22) | 26 (28.26) | 58 (27.49) | 0.0 |
| 3 (D and E) | 61 (57.55) | 33 (31.33%) | 144 (68.25) | 0.0 |
In the final adjusted regression model, a significant association was observed for depressive symptoms with moderate-to-severe FI (OR = 5.50, $95\%$ CI 2.74–11.04). In the case of the risk of anxiety symptoms, it was significant in all degrees of FI, in mild FI, an OR of 2.43 ($95\%$ CI 1.66–3.59) was observed, and in moderate-to-severe FI, an OR of 5.32 ($95\%$ CI 3.45–0.199) was observed.
## Discussion
The objective of this study was to analyze the prevalence of FI and its association with depression and anxiety symptoms in OAs in Mexico. With the results, we can observe that more than $60\%$ of the participants are under some degree of FI, these data were similar to those reported by the different ENCOVID-19 surveys, for example, Gaitán-Rossi et al. reported that food security was $24.9\%$ in Mexican households where a child lived, that is, $75\%$ had an FI degree [7]. Ponce-Alcala et al. according to data from the ENSANUT MC [2016] reported in 5456 adults aged 20 to 59 years that $70.8\%$ had some degree of food insecurity at home [19].
In relation to the prevalences reported in other countries during the pandemic, Giacoman et al. through a longitudinal study based on two population-based surveys in Chile (CASEN 2017 and COVID 2020) found that FI levels went up significantly ($p \leq 0.001$) between 2017 ($30\%$) and 2020 ($49\%$) mainly in those with economically dependent people (that is, children, adolescents, and older adults). In this last population group, it was found that mild FI went from $12.7\%$ in 2017 to $16.3\%$ in 2020 and moderate-to-severe FI from 14 to $20.6\%$ [20].
In the present study, it was shown that during the COVID-19 pandemic, at a higher degree of FI, there is a greater risk that OAs present anxiety and depression symptoms. This can be explained given the situation of the OAs since they were the ones who were in the greatest confinement due to their high risk of morbidity and mortality. In addition to these adverse effects associated with the pandemic, there was an increase in those OAs with the highest rate of social backwardness [3].
Few studies have analyzed the impact that the pandemic had on the mental health of OAs. Gaitán-Rossi et al. also found that anxiety was associated with higher FI scores during the pandemic, for example, symptoms of anxiety reported in people living in households with FI were $19.3\%$ while in people living in households with severe FI were $57.1\%$ [14]. Sepúlveda-Loyola et al. [ 21] showed through a review that included 20,069 OAs, from Asia, Europe, and America during isolation due to the pandemic, presented a high prevalence of anxiety and depression, with a range of 8.3 to $49.7\%$ and 14.6 to $47.2\%$, respectively. These results confirm the expected psychoemotional impact and the complex syndemic interaction of mental health and the FI experience during the pandemic (Table 3).
**Table 3**
| Depression symptoms | Depression symptoms.1 | Depression symptoms.2 | Depression symptoms.3 | Depression symptoms.4 | Depression symptoms.5 | Depression symptoms.6 | Depression symptoms.7 | Depression symptoms.8 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | No adjustment | No adjustment | No adjustment | No adjustment | Adjusted | Adjusted | Adjusted | Adjusted |
| | OR | CI 95% | CI 95% | P | OR | CI 95% | CI 95% | P |
| FI mild | 1.67 | 0.92 | 8.40 | 0.092 | 1.67 | 0.92 | 3.05 | 0.092 |
| FI moderate-to-severe SES | 9.34 | 3.09 | 28.23 | 0.000 | 5.50 | 2.74 | 11.04 | 0.000 |
| 2 (C, C− and D +) | 1.09 | 0.31 | 3.78 | 0.886 | 0.98 | 0.40 | 2.35 | 0.967 |
| 3 (D and E) | 1.92 | 0.55 | 6.69 | 0.304 | 0.640 | 0.19 | 2.16 | 0.479 |
| Schooling | Schooling | Schooling | Schooling | Schooling | Schooling | Schooling | Schooling | Schooling |
| ≤6 years | 1.14 | 0.47 | 2.77 | 0.757 | 1.36 | 0.49 | 3.80 | 0.570 |
| >6 years | 0.50 | 0.19 | 1.25 | 0.141 | 0.64 | 0.19 | 2.16 | 0.479 |
| Woman | 1.41 | 0.85 | 2.52 | 0.178 | 1.52 | 0.91 | 2.53 | 0.107 |
| Age, years | 0.97 | 0.35 | 5.08 | 0.255 | 0.97 | 0.94 | 1.01 | 0.292 |
Moreover, in the descriptive analysis, we can observe that $20\%$ of the population reported having stopped consuming fruits, vegetables, and dairy products and more than $30\%$ reported having stopped eating meat, the latter being higher than that reported by Federik et al. [ 22], who reported less meat consumption in only $11.5\%$ of young adults during the pandemic (Table 4).
**Table 4**
| Anxiety symptoms | Anxiety symptoms.1 | Anxiety symptoms.2 | Anxiety symptoms.3 | Anxiety symptoms.4 | Anxiety symptoms.5 | Anxiety symptoms.6 | Anxiety symptoms.7 | Anxiety symptoms.8 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | No adjustment | No adjustment | No adjustment | No adjustment | Adjusted | Adjusted | Adjusted | Adjusted |
| | OR | CI 95% | CI 95% | p | OR | CI 95% | CI 95% | p |
| FI mild | 2.437 | 1.66 | 3.59 | 0.008 | 2.43 | 1.66 | 3.59 | 0.000 |
| FI moderate-to-severe SES | 5.323 | 3.45 | 8.19 | 0.000 | 5.32 | 3.45 | 8.19 | 0.000 |
| 2 (C, C− and D +) | 0.961 | 0.44 | 2.08 | 0.922 | 0.88 | 0.55 | 1.41 | 0.609 |
| 3 (D and E) | 0.685 | 0.28 | 1.67 | 0.407 | 0.67 | 0.49 | 1.09 | 0.115 |
| Schooling | Schooling | Schooling | Schooling | Schooling | Schooling | Schooling | Schooling | Schooling |
| ≤6 years | 0.754 | 0.32 | 1.79 | 0.376 | 0.70 | 0.32 | 1.52 | 0.376 |
| >6 years | 0.963 | 0.27 | 2.07 | 0.586 | 0.70 | 0.24 | 2.07 | 0.456 |
| Woman | 1.82 | 1.25 | 3.45 | 0.030 | 1.60 | 1.18 | 2.18 | 0.020 |
| Age,years | 1.850 | 1.02 | 1.35 | 0.560 | 0.99 | 0.96 | 1.01 | 0.421 |
The present study has the strength of having used the ENCOVID-19 data source, which has a representative sample of OAs from the 32 states of the Mexican Republic and the survey was carried out month-by-month. However, a limitation of ENCOVID-19 was the insufficient inclusion of people living in rural and isolated locations due to lower mobile phone coverage [9, 10].
Another variable studied was socioeconomic status, which was measured through a reliable asset-based scale suitable for implementation in brief telephone surveys. It has previously been shown, in face-to-face interviews, to be highly associated with income deciles across all states in Mexico and across localities with different population sizes [13]. One limitation, however, is that this scale cannot capture changes in economic circumstances and only reflects pre-pandemic SES. However, in the present analysis, there was a higher prevalence of severe FI in households with lower levels of SES and it was associated with a higher risk of FI.
For future analyses, it is important to monitor the interaction of these factors (depression, anxiety, and SES) over time on the effects of FI and health in the older population during the pandemic. Furthermore, comparing these associations with pre-pandemic databases, we were able to measure the impact and incorporate other variables such as functional status, nutritional status, such as diet quality, and anthropometric and health data, such as comorbidity, and access to health services.
## Conclusion
There is a high prevalence of food insecurity during the COVID-19 pandemic, occurring in particularly vulnerable populations, such as older adults, in whom being food insecure has a higher risk of anxiety and depression symptoms. Interventions to increase access to healthy foods, especially among minorities and low-income people, and mitigate the socio-emotional effects are crucial to alleviating the economic stress of this pandemic.
## 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 reviewed and approved by the Universidad Iberoamericana Research Ethics Committee (CONBIOETHICS-09 —CEI-008-2016060). Verbal Informed consent was obtained from all participants.
## Author contributions
DA contributed to data and analysis, collection and manuscript writing. L-TM contributed to statistic analysis and manuscript writing. R-CO collaborated in statistic analysis and manuscript review. G-RP contributed to database and manuscript review. A-MM manuscript review. All authors contributed to the article and approved the submitted version.
## Funding
This article was produced with the support of the Research Institute for Equitable Development with ENCOVID-19 funds, EQUIDE and Department of Health, University Iberoamericana, Mexico, Mexico City.
## 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 handling editor MA-B declared a past collaboration with the author L-TM.
## 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.
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---
title: Effects of milk, milk replacer, and milk replacer plus ethoxyquin on the growth
performance, weaning stress, and the fecal microbiota of Holstein dairy calves
authors:
- Xiaoshi Wei
- Jifu Zou
- Yiwei Zhang
- Jinyong Yang
- Junhong Wang
- Yanming Wang
- Chong Wang
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10040532
doi: 10.3389/fmicb.2023.1113518
license: CC BY 4.0
---
# Effects of milk, milk replacer, and milk replacer plus ethoxyquin on the growth performance, weaning stress, and the fecal microbiota of Holstein dairy calves
## Abstract
The growth and health statuses of calves during the early stages of development have a significant effect on milk production during their first lactation period. Using appropriate milk replacers helps meet the long-term targets of dairy farmers. This study aimed to examine the effects of milk, milk replacer, and milk replacer plus ethoxyquin on growth performance, antioxidant status, immune function, and the gut microbiota of Holstein dairy calves. A total of 36 neonatal dairy calves were randomly divided into three groups and fed different diets: one group was fed milk, another group was fed milk replacer, and the third group was given milk replacer plus ethoxyquin. The supplementation with ethoxyquin was started on day 35 of the feeding period. The calves were weaned on day 45, and the experiment was conducted until day 49. The blood and fecal samples were collected at the end of the animal experiment. The results showed that milk replacers induced poor growth performance (body weight and average daily gain). However, milk replacer plus ethoxyquin aided in growth performance, enhanced the starter intake and blood antioxidative ability, and elevated the concentration of fecal valeric acid. Moreover, fecal fermentation and 16S rRNA analyses showed that milk replacer plus ethoxyquin altered the microbial composition (reducing Alistipes and Ruminococcaceae and increasing Bacteroides and Alloprevotella). Pearson's correlation assays showed that alterations in fecal microbiota strongly correlated with average daily gain and antioxidative ability. The results indicated the potential of milk replacer plus ethoxyquin in modulating the growth of dairy calves and in enhancing their ability to combat stress.
## Introduction
The growth and health statuses of calves during their first lactation period have a significant effect on milk production (Chester-Jones et al., 2017). Feeding ad-lib quantities of milk to dairy calves has been shown to cause higher growth rates at an early age (Khan et al., 2011; Iqbal et al., 2014). With the increasing demand for high production performance, milk replacer is an alternative that can boost growth performance but reduce the pre-weaning feeding cost.
Milk replacer is a type of artificial milk made of non-milk protein sources and is designed to meet the nutritional requirements of breast milk as per established standards. Compared to whole milk, the quality of milk replacer is not easily affected by external factors such as diet and season (Bernabucci et al., 2015; Toral et al., 2015). Calves fed milk replacers had a higher starter intake and longer-lasting effects on the rumen environment compared to those fed whole milk or pasteurized waste milk (Zhang et al., 2019). Exiting evidence also showed that milk replacers increase the survival rate of the Awassi lamb (Emsen et al., 2004). During the early stages of development, the gut microbiota is important to the host's health, as a stable gut bacterial community is a prerequisite for the host to perform normal physiological functions, metabolism, and immune functions (Gensollen et al., 2016; Li et al., 2021), while an imbalance may result in gastrointestinal diseases (Wang et al., 2019). Whether the gut bacterial community changes with the milk replacer is unknown.
Abrupt weaning, usually done at 6 weeks of age, is a source of stress for young animals and may lead to a reduction in body weight (de Passillé et al., 2011; Ungerfeld et al., 2011), diarrhea (Khan et al., 2007), and compromised intestinal barrier function (Li et al., 2018). Abrupt weaning makes young animals particularly vulnerable to infectious diseases as the immune system is not yet fully developed (Godbout and Glaser, 2006). Proper additives are a promising approach to protecting an animal from weaning stress (Kim et al., 2020; Mattioli et al., 2020). Ethoxyquin is widely used in animal feeds to protect against lipid peroxidation (Błaszczyk et al., 2013). Previous studies have demonstrated that feeding ethoxyquin can improve cows' lactation performance and antioxidant status, as well as partially mitigate the negative effects of feeding oxidized fat (Váquez-Añón et al., 2008; Boerman et al., 2014). Whether feeding ethoxyquin during the weaning period could help combat weaning stress is worth exploring.
We hypothesized that [1] a milk replacer could replace whole milk in dairy calves and [2] feeding ethoxyquin could mitigate negative effects during the weaning period. The objectives of this study were not only to determine the effects of milk, milk replacer, and milk replacer plus ethoxyquin on the growth performance and weaning stress of dairy calves but also to profile the changes in the gut microbiota.
## Experimental design and animal management
A total of 36 male Holstein calves were enrolled in this experiment. They were paired into 12 blocks based on their body weight and the date of their birth before being randomly assigned to one of three treatment groups within each block. The three treatments were control group (C, fed fresh milk), milk replacer group (MR, fed the milk replacer), and milk replacer plus ethoxyquin group (MRE, fed the milk replacer plus ethoxyquin). The ethoxyquin (Endox®5) was purchased from Kemin (China) Technologies Co. Ltd., Zhuhai. The dose of ethoxyquin used was 350 mg/kg of starter intake, and the feeding commenced on day 35.
The calves were given a total of 6 L of colostrum, with 4 L administered within the first hour after birth, and the remaining 2 L administered 5 h later. The calves were removed from their dam within 3 h of birth. The nutrient content of the milk replacer (Swot Technology Co., Ltd., Hangzhou) is presented in Table 1. Before feeding, the milk replacer was reconstituted with warm water (39°C) to $12.5\%$ solids. The amount of milk or milk replacer fed to the calves was $12\%$ v/w of their body weight, and the liquid feed was offered to them three times a day. They were fed from bottles at first but were then trained to drink from buckets. The day of birth was considered 1 day of age (DOA). After 38 days, the allowance of liquid feed was reduced by $50\%$ each day, and the frequency of feeding was reduced to two times per day. At 42 days of age, the liquid feed was given one time per day. Weaning ended on day 45.
**Table 1**
| Itemsa | Contents |
| --- | --- |
| Dry matter, % | 94.3 |
| Protein, % | 22.4 |
| Fat, % | 12.5 |
| Vitamin A, 104 IU/kg | 3.15 |
| Vitamin D, 104 IU/kg | 0.65 |
| Vitamin E, IU/kg | 70.0 |
| Calcium, % | 0.75 |
| Phosphorus, % | 0.6 |
The pellets of a starter and alfalfa hay were offered ad libitum to the calves in individual buckets beginning at 7 DOA and 10 DOA, respectively. The amount of starter pellets intake was measured weekly to calculate the dose of ethoxyquin used. The chemical composition of the starter and alfalfa hay is shown in Table 2. All the calves were housed in individual hutches and managed similarly, with used sand as the bedding material. The sand was replaced one time a week to keep the bedding material clean. During the experiment, the windows of the hutches were opened for ventilation. The overall timeline of the experimental protocol is summarized and presented in Figure 1.
## Intake and growth measures
The calves were weighed immediately after birth and weekly thereafter. The intake of starter pellets was recorded for each calf weekly.
## Blood sampling and analysis
Blood samples were collected at approximately 10 a.m. via the jugular venipuncture and divided into two tubes. One of the tubes contained K2-EDTA, and the samples were centrifuged at 3,500 g and 4°C for 15 min to obtain plasma. The sample in the other tube was allowed to clot at room temperature for 30 min to obtain serum. After centrifugation, all of the supernatants were then stored at −80°C for further analyses.
The plasma was analyzed for glucose, non-esterified fatty acids (NEFA), urea nitrogen (BUN), total protein (TP), and albumin (ALB) using commercial kits (Jiancheng Bioengineering Institute, Nanjing, China). The serum was used for analyzing the antioxidative status and immunity parameters. The total antioxidant capacity (T-AOC) was analyzed using the ferric antioxidant reducing power (FRAP) (Wang and Zuo, 2015). The activity of glutathione peroxidase (GSH-PX), as well as the malondialdehyde (MDA) concentration, were measured using commercial kits (Jiancheng Bioengineering Institute, Nanjing, China) following the manufacturer's instructions. The catalase (CAT) activity was determined using a commercial kit (Jiancheng Bioengineering Institute) based on the decomposition of hydrogen peroxide (H2O2). ELISA was used to determine the IgA, IgG, and IgM concentrations in serum (Cow IgA ELISA kit, catalog no. H108; Cow IgG ELISA kit, catalog no. H106; Cow IgM ELISA kit, catalog no. H109; and Jiancheng Bioengineering Institute, Nanjing, China).
## Feces collection and volatile fatty acid analysis
The feces were sampled in the last three consecutive days of the experiment so that the samples were represented every 3 h in a 24-h feeding cycle. After sampling, the fecal samples (about 200 g for each calf) were stored in liquid nitrogen immediately.
Before the analysis, all the fecal samples were pooled, mixed, and homogenized using a sterile slap homogenizer. Approximately 4 g of the samples was mixed in 4 mL of distilled water for volatile fatty acid (VFA) extraction and analysis. The concentrations and proportions of VFA (including acetic acid, propionic acid, butyric acid, valeric acid, isobutyric acid, isovaleric acid, and isoacids) were analyzed using gas chromatography (Agilent Technologies 7820A GC system, Santa Clara, USA) according to previously described methods (Li et al., 2019).
## DNA extraction and PCR amplification
Five samples from the treatment group were used for microbiological analysis. Approximately 1 g of the subsample was used for metagenomic DNA extraction. Microbial DNA was extracted from the fecal samples using the E.Z.N.A.® soil DNA kit (Omega Bio-tek, Norcross, GA, U.S.) according to the manufacturer's protocols. The final DNA concentration and purification were determined using a NanoDrop 2,000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA), and DNA quality was checked using $1\%$ agarose gel electrophoresis. The V3-V4 hypervariable regions of the bacterial 16S rRNA were amplified with primers 338F (ACTCCTACGGGAGGCAGCAG) and 806R (GGACTACHVGGGTWTCTAAT) by a thermocycler PCR system (GeneAmp 9700, ABI, USA). The PCR reactions were conducted using the following program: 3 min of denaturation at 95°C, 28 cycles of 30 s at 95°C, 30 s for annealing at 55°C, and 45 s for elongation at 72°C, followed by a final extension at 72°C for 10 min. PCR reactions were performed in triplicates in a 20-μL mixture containing 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase, and 10 ng of template DNA. The resulting PCR products were extracted from a $2\%$ agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, USA) according to the manufacturer's instructions.
## Illumina MiSeq sequencing and processing
Purified amplicons were pooled in equimolar amounts and paired-end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, San Diego, USA) according to the standard protocols by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Raw FASTQ files were quality-filtered using Trimmomatic and merged using FLASH in accordance with the following criteria: [1] The reads were truncated at any site receiving an average quality score of < 20 over a 50-bp sliding window; [2] sequences whose overlap was longer than 10 bp were merged according to their overlap with a mismatch of no more than 2 bp; [3] sequences of each sample were separated according to barcodes (exactly matching), primers (allowing two nucleotide mismatches); and reads containing ambiguous bases were removed. Operational taxonomic units (OTUs) were clustered with a $97\%$ similarity cutoff using UPARSE (version 7.1 http://drive5.com/uparse/) with a novel “greedy” algorithm that performs chimera filtering and OTU clustering simultaneously. The taxonomy of each 16S rRNA gene sequence was analyzed using the RDP Classifier algorithm (http://rdp.cme.msu.edu/) against the database using a confidence threshold of $70\%$.
The α diversity was analyzed using Mothur1.30.2 (https://www.mothur.org/wiki/Download_mothur). The β diversity analysis was based on the unweighted UniFrac distance and was performed using QIIME1.9.1. The microbiota composition at different levels was determined based on tax_summary and R package version 3.3.1, and the difference between the groups was analyzed using a one-way ANOVA and Tukey's test. The LDA effect size analysis (LEfSe) was conducted to screen differentially abundant bacterial taxa with an LDA score of >3.0.
## Statistical analysis
All the gathered data were analyzed using the MIXED procedure of SAS version 9.1 (SAS Institute Inc., Cary, NC). The repeated measures were used for the body weight (BW), average daily gain (ADG), and starter intake, and the model contained the effects of treatment, time, and the interaction of treatment and time. The initial BW was used for a covariate analysis. The linear model was used for fecal VFA concentrations and blood parameters. The results were expressed as least-squares means and standard errors of the mean. The correlations between fecal microbiota and performance, rumen fermentation, and blood parameters were calculated using Spearman's correlation coefficient. A heatmap diagram was drawn to visualize the data and identify the relationships between the variables. Statistical significance was determined for the treatment difference with a P ≤ 0.05, while a P-value of 0.05 < P ≤ 0.10 was considered indicative of a trend.
## Results
In total, five calves (1 in the C group, 3 in the MR group, and 1 in the MRE group) developed abomasal bloating. Consequently, they were removed from the experiment.
## BW, ADG, and starter intake of the calves
The overall effects on BW, ADG, and starter intake are shown in Table 3. The results indicated a decrease in BW with MR treatment ($$P \leq 0.02$$), while MRE treatment led to an increase in starter intake ($P \leq 0.01$). Significant time effects were observed for both BW and starter intake ($P \leq 0.01$). The interaction between week × treatment was observed for both BW and starter intake ($P \leq 0.01$), and we detected a trend for the week × treatment interaction for ADG ($$P \leq 0.09$$). Compared to the C group, the calves in the MR group showed a decrease in BW at weeks 4 and 7, while both the MR and MRE groups showed a decrease in BW at weeks 5 and 6 (Figure 2, $P \leq 0.05$). In particular, the BW of the MR group at week 7 was significantly lower than that of both the C and MRE groups ($P \leq 0.05$). Additionally, at weeks 5 and 6, the starter intake was higher in the MRE group compared to the C and MR groups ($P \leq 0.05$).
## Blood antioxidant ability and immunity parameters
The blood antioxidant ability and immunity parameters are shown in Table 4. The NEFA concentration was higher in the MRE group compared to the C and MR groups ($P \leq 0.05$), and the TP concentration tended to elevate ($$P \leq 0.06$$). There were no changes in the concentrations of glucose, BUN, or ALB. For antioxidant ability, the T-AOC was higher in the MRE group than in both the C and MR groups ($P \leq 0.05$). GSH-PX, CAT, and MDA also tended to increase ($$P \leq 0.07$$, 0.07, and 0.09, respectively). No difference was found in the immunity parameters (IgA, IgG, and IgM).
**Table 4**
| Items2 | Treatments 1 | Treatments 1.1 | Treatments 1.2 | SEM | P-value |
| --- | --- | --- | --- | --- | --- |
| | C | MR | MRE | | |
| Blood parameters | Blood parameters | Blood parameters | Blood parameters | Blood parameters | Blood parameters |
| Glucose, mmol/L | 6.82 | 7.03 | 6.87 | 0.224 | 0.93 |
| NEFA, mmol/L | 0.127b | 0.126b | 0.172a | 0.0085 | 0.03 |
| BUN, mmol/L | 6.78 | 5.28 | 5.50 | 0.418 | 0.30 |
| TP, g/L | 72.71 | 71.94 | 82.92 | 2.187 | 0.06 |
| ALB, g/L | 35.21 | 35.11 | 33.62 | 0.548 | 0.44 |
| Antioxidant ability | Antioxidant ability | Antioxidant ability | Antioxidant ability | Antioxidant ability | Antioxidant ability |
| T-AOC, mmol/L | 0.23b | 0.24b | 0.33a | 0.016 | 0.01 |
| GSH-PX, U/mL | 115.26 | 126.43 | 157.52 | 7.857 | 0.07 |
| CAT, U/mL | 8.40 | 8.75 | 12.72 | 0.878 | 0.07 |
| MDA, μmol/mL | 2.55 | 3.06 | 4.04 | 0.285 | 0.09 |
| Immunity | Immunity | Immunity | Immunity | Immunity | Immunity |
| IgA, mg/mL | 2.81 | 3.52 | 3.39 | 0.155 | 0.14 |
| IgG, mg/mL | 12.59 | 13.40 | 12.14 | 0.393 | 0.44 |
| IgM, mg/mL | 4.07 | 4.73 | 4.28 | 0.284 | 0.65 |
## Fermentation parameters
As shown in Table 5, the concentration of acetic acid tended to decrease, and the concentration of valeric acid was increased in the MRE group compared to the C and MR groups ($$P \leq 0.08$$ and 0.05, respectively). Moreover, the ruminal isoacids (expressed as the percentage of total VFA) tended to increase ($$P \leq 0.07$$).
**Table 5**
| Items2 | Treatments 1 | Treatments 1.1 | Treatments 1.2 | SEM | P-value |
| --- | --- | --- | --- | --- | --- |
| | C | MR | MRE | | |
| VFA, μM | VFA, μM | VFA, μM | VFA, μM | VFA, μM | VFA, μM |
| Acetic acid (A) | 179.0 | 170.6 | 160.9 | 3.40 | 0.08 |
| Propionic acid (P) | 83.9 | 85.2 | 93.0 | 4.25 | 0.63 |
| Isobutyric acid | 8.02 | 11.18 | 10.72 | 0.65 | 0.12 |
| Butyric acid | 60.1 | 51.8 | 46.6 | 3.54 | 0.30 |
| Isovaleric acid | 8.40 | 13.58 | 14.86 | 1.30 | 0.16 |
| Valeratic acid | 8.94b | 9.14b | 11.18a | 0.43 | 0.05 |
| Isoacids | 25.4 | 31.4 | 33.9 | 1.85 | 0.19 |
| Total VFA | 349.1 | 355.3 | 335.4 | 9.48 | 0.70 |
| VFA proportions, % | VFA proportions, % | VFA proportions, % | VFA proportions, % | VFA proportions, % | VFA proportions, % |
| Acetic acid | 51.2 | 50.3 | 45.2 | 1.40 | 0.16 |
| Propionic acid | 23.4 | 25.1 | 27.7 | 0.81 | 0.10 |
| Isobutyric acid | 2.64 | 3.30 | 3.16 | 0.21 | 0.59 |
| Butyric acid | 14.7 | 14.8 | 15.3 | 0.86 | 0.95 |
| Isovaleric acid | 2.93 | 3.97 | 4.13 | 0.38 | 0.51 |
| Valerate | 3.06 | 3.07 | 3.75 | 0.21 | 0.30 |
| Isoacids | 7.42 | 10.17 | 9.79 | 0.47 | 0.07 |
| A:P ratio | 2.18 | 2.12 | 1.69 | 0.11 | 0.13 |
## Microbial community composition
The alterations in the fecal microbiota were investigated. The coverage for each sample was >$99\%$, indicating sufficient sequencing depth to detect most of the fecal bacteria of the calves in this study. The total OTUs in the C, MR, and MRE groups were 958, 955, and 1,061, respectively (Figure 3A). Four indicators were used to reflect the microflora's richness (Chao, ACE) and diversity (Shannon, Simpson). As shown in Table 6, the richness was decreased in the MRE group compared to the C group ($P \leq 0.05$), with no difference in diversity. The β diversity was displayed in a PCA scatterplot and shown in Figure 3B, indicating a clear shift between the C and MRE groups.
**Figure 3:** *Analysis of the diversity, composition, and taxonomic biomarkers of fecal microbiota. C, calves fed fresh milk; MR, calves fed milk replacer; MRE, calves fed milk replacer plus ethoxyquin. $$n = 5$$ in each group. (A) Venn diagram presenting the operational taxonomic units (OTUs) from each group. (B) β diversity shown in a Principal component analysis (PCA) scatterplot. (C) A bar graph of microbial composition at both the phylum and genus levels. (D) A box plot of the significant phylum among groups. (E) A box plot of the significant genera among groups. (F) Histogram of Linear discriminant analysis (LDA) scores representing the taxonomic biomarkers by LDA effect size (LEfSe) analysis. LDA score (log10) >2 suggests the enriched taxa in cases. The data were analyzed by one-way ANOVA and Tukey's test. *$P \leq 0.05$, **$P \leq 0.01.$* TABLE_PLACEHOLDER:Table 6 The differences in microbial compositions at the phylum and genus levels are shown in Figure 3C. At the phylum level, the abundance of Firmicutes was decreased and that of Bacteroidota was increased in the MRE group compared to the C group ($P \leq 0.05$, Figure 3D), and the ratio of Firmicutes to Bacteroidota was lower in both the MR and MRE groups. The abundance of Jeotgalicoccus was significantly reduced in both the MR and MRE groups, and the abundance in the MRE group was the lowest (Figure 3E, $P \leq 0.05$). The abundance of Alistipes was found to be decreased, while the abundance of Alloprevotella was found to be increased in the MRE group compared to the C group ($P \leq 0.01$). The abundance of Bacteroides was increased in both the MR and MRE groups compared to the C group ($P \leq 0.05$ and $P \leq 0.01$, respectively). Moreover, the abundance of Turicibacter was decreased in both the MR and MRE groups ($P \leq 0.05$ and $P \leq 0.01$, respectively). The MRE significantly reduced the abundance of Ruminococcaceae compared with the C and MR groups ($P \leq 0.05$).
As shown in Figure 3F, the taxonomic biomarkers were Jeotgalicoccus, Christensenellaceae_R-7_group, Jeotgalibaca, Alistipes, Dorea, Clostridium_sensu_stricto_1, Facklamia, and Ruminococcaceae in the C group. In the MRE group, the predominant bacteria were Bacteroides, Prevotella, Collinsella, Ruminococcus_gauvreauii_group, Syntrophococcus, Subdoligranulum, and Erysipelotrichaceae_UCG-003. Additionally, no taxonomic biomarkers were found in the MR group under this condition.
## Relationship between bacterial and phenotypic variables
The correlations between fecal microbes, performance, and blood parameters were examined to further identify the underlying mechanisms. As shown in Figure 4, the concentration of MDA was positively correlated with Bacteroides and Prevotella. Alistipes showed a negative correlation with GSH-PX. Moreover, Succiniclasticum showed a positive correlation with CAT, T-AOC, NEFA, and isoacid proportions, while Ruminococcus was positively correlated with only isoacid proportions. Lachnospiraceae and Ruminococcaceae were positively linked with SOD. The results showed that the genera Psychrobacter, Atopostipes, Jeotgalibaca, Corynebacterium, Aerococcaceae, Bifidobacterium, Coprococcus, and Facklamia were positively related to the ADG.
**Figure 4:** *Heatmap diagram of correlations between fecal bacterial and performance and blood parameters at the genus level. Red was positively correlated and blue was negatively correlated. C, calves fed fresh milk; MR, calves fed milk replacer; MRE, calves fed milk replacer plus ethoxyquin. N = 5 in each group. Correlation significance P-value was indicated by “*”. *P < 0.05, **P < 0.01. ADG: average daily gain; SOD, superoxide dismutase; NEFA, nonesterified fatty acid; GSH-PX, glutathione peroxidase; MDA, malonaldehyde; T-AOC, total antioxidant capacity; CAT, catalase.*
## Discussion
This experiment evaluated the effects of milk, milk replacer, and ethoxyquin on growth performance, weaning stress, and fecal microbiota in dairy calves.
In this study, we found that the calves fed milk demonstrated superior growth performance and higher weaning weights.
The milk-fed calves showed better overall BW than those fed milk replacers (Zhang et al., 2019; Qadeer et al., 2021; Wang et al., 2022). The authors believe that this change might be due to the lower fat and protein contents and poor utilization of non-milk proteins. Moreover, milk might have better bioavailability of protein and energy along with minerals, enzymes, and growth factors (Lee et al., 2009). At the beginning and first few weeks, the BW of the dairy calves was not changed, while the milk replacer decreased the BW during weeks 4–7. A similar result was found in the study by Zhang et al. [ 2019], in which the BW of calves fed MR was significantly lower than that of those fed milk at 58 days of age. The nutrient contents of milk replacers were different in this study and that by Zhang et al. [ 2019]. Previous studies demonstrated that the nutritional composition and the number of milk replacers provided significantly affect the BW of calves (Geiger et al., 2014; Chapman et al., 2016; Qadeer et al., 2021), suggesting that these factors are crucial.
The starter intake in the first 2 months was significantly associated with milk, fat, and protein production in the first lactation and lifetime production (Heinrichs and Heinrichs, 2011). Starter intake has been reported to be higher in milk-fed calves than those fed milk replacers, thus contributing to a higher growth rate (Qadeer et al., 2021). However, there was no difference in starter intake between the calves fed milk and those fed milk replacers in this study. The ethoxyquin helped to narrow the differences in BW and ADG between the calves fed milk and milk replacers. It might be due to the increased starter intake, as it could result in the compensation of nutrients to meet growth requirements. Feeding cows with ethoxyquin increased their DMI during mid and late lactation (Váquez-Añón et al., 2008).
Moreover, the higher consumption of starter intake may improve early rumen microbial development, leading to greater rumen capacity and metabolic activity (Anderson et al., 1987; Khan et al., 2011). Smith et al. [ 2003] reported that feeding antioxidants mixed with ethoxyquin improved the organic matter's digestibility. We speculated that the digestibility of DM and CP would be increased, and more research is needed to verify this fact.
In this study, the milk replacer did not change the blood metabolites, while the milk replacer plus ethoxyquin elevated the plasma concentration of NEFA. Feeding branched-chain VFA could decrease NEFA concentration in dairy cows (Liu et al., 2009), and a greater concentration of ruminal isovalerate might induce lower NEFA in calves (Zhang et al., 2019). Ethoxyquin increased the starter intake in this study, indicating that the rumen fermentation and bacterial community would be altered (Zhang et al., 2019). Moreover, the plasma NEFA is usually derived from fat stores as a response to energy mobilization; however, in this study, the BW and starter intake were increased in the MRE group compared to the MR group, indicating that an increase in NEFA by ethoxyquin would be due to the rumen metabolism but not the fat mobilization. A previous study demonstrated that antioxidants, such as vitamin E, can potentially prevent the “trans-10 shift” during biohydrogenation (Pottier et al., 2006). The ethoxyquin supplementation would preserve the oxidation of unsaturated fatty acids before absorption (Andrews et al., 2006) and increase the cis-18:1 in milk (Váquez-Añón et al., 2008), suggesting lower ruminal hydrogenation and trans-isomerization of 18:1 in some cases. The question of whether and how the ethoxyquin supplementation altered the rumen fermentation and microbiota profile will be evaluated in future studies.
Weaning is a potent stressor for dairy calves because of the extreme dietary shift, which induces elevated blood reactive oxygen species (ROS) production (Bordignon et al., 2019). Maintaining a balance between the defensive ability (enzymatic system and non-enzymatic antioxidants) and ROS production is important. Any disruption in this balance can lead to oxidative stress (Lykkesfeldt and Svendsen, 2007). The observed increase in antioxidant ability was expected, as ethoxyquin is one of the well-known feed antioxidant for both domestic animals and fish. It is widely used in animal feed due to its high antioxidant capacity and low production costs (Błaszczyk et al., 2013). Greater SOD and GSH-PX activities resulted from ethoxyquin supplementation in lactating primiparous cows (Váquez-Añón et al., 2008). As suggested by the authors, it is possible that ethoxyquin reduced a load of peroxides by removing reactive oxygen molecules, thereby relieving the endogenous antioxidant defense system. The elevated antioxidative ability we found in this study may have helped to mitigate the weaning impact on the calves.
In this study, the concentration of acetic acid was increased in the MR and MRE groups. The isoacids are the sum of isobutyric acid, isovaleric acid, and valeric acid. Branched-chain VFAs are markers of protein fermentation and are primarily derived from the fermentation of branched-chain amino acids such as valine and leucine (Smith and Macfarlane, 1998). In a study by Kumar et al. [ 2021], a higher concentration of milk replacer induced lower acetic acid and valeric acid proportions. Thus, we presumed that the higher concentrations of milk replacer-derived proteins and peptides would reach the intestine.
Despite the fecal fermentation, the microbiota was also evaluated in this study. The composition and balance of the microbiota are closely related to the nutritional and physiological functions of the host. Milk replacer has been shown to increase the diversity and richness of the microflora in the ileum (Wang et al., 2021). The results of Badman et al. [ 2019] proved that the differences in the nutritional composition of bovine milk replacers had a major impact on microbiota composition, diversity, and succession in pre-weaned dairy calves, further influencing the health of the gut and the whole animal. In this study, milk replacers did not affect the richness of the microflora, and milk replacer plus ethoxyquin decreased the richness with no difference in diversity. Combining the heatmap diagram and PCA, the results suggested that ethoxyquin might alter the composition of a milk replacer containing less widely utilized substrates for microbial fermentation.
Despite the genus mentioned above, Firmicutes, Bacteroidetes, Proteobacteria, and Actinomycetes usually account for more than $90\%$ of gut microbes (Sankar et al., 2015). Firmicutes, an important indicator of intestinal microflora's composition, can be converted into short-chain fatty acids by fermenting polysaccharides to provide energy (Mariat et al., 2009). Firmicutes were also shown to promote energy absorption and fat deposition (Turnbaugh et al., 2006). We found that the abundance of firmicutes decreased in the MRE group compared to the C group, which suggested that the energy absorption and fat deposition would be lower, further favoring the elevated NEFA concentration. A previous study showed an increase in the abundance of Bacteroidetes in obese animals fed high-fiber diets (de Wit et al., 2012). The children who consumed a diet rich in fiber had higher proportions of Bacteroidetes and fewer Firmicutes than those fed a diet that included large amounts of protein, fat, sugar, and starch (De Filippo et al., 2010). However, we are unsure of why ethoxyquin can cause these changes.
Moreover, the ratio of Firmicutes/Bacteroidetes was significantly decreased. A previous meta-analysis revealed that a higher Firmicutes/Bacteroidetes ratio suggested more energy extraction from food by the microbiota (Suzuki and Worobey, 2014). Thus, we speculated that milk replacer and milk replacer plus ethoxyquin might alter the energy extraction from feed through microbiota, which could, in some cases, explain the decreased BW and ADG in this study. Moreover, we found that the abundance of Bifidobacterium was strongly correlated with ADG. Bifidobacterium is often among the first colonizers of gut environments (Malmuthuge et al., 2015) and is known to be beneficial to physiological conditions within the gut, aiding intestinal development and preventing intestinal dysbiosis (Hidalgo-Cantabrana et al., 2017).
The VFAs are important metabolites of the microbiota. Generally, acetic acid, propionic acid, and butyric acid are the primary fermentation products of Ruminococcus albus, Prevotella ruminicola, and Butyrivibrio fibrisolvens, respectively (Emerson and Weimer, 2017; Liu et al., 2017). The lower abundance of Ruminococcaceae in the MRE group tend to decrease the concentration of acetic acid. A previous study showed that the fermentation of branched-chain amino acids is mainly carried out by members of the genera Clostridium, Peptostreptococcus, and Bacteroides (Smith and Macfarlane, 1998), and the abundance of Bacteroides was found elevated in the calves fed milk replacer plus ethoxyquin in this study. Both *Ruminococcus albus* and Butyrivibrio fibrisolvens species are the main consumers of branched-chain VFAs (Feng, 2004). We also found that the abundance of Ruminococcaceae was lower in the MRE group, and the abundance of Ruminococcus, Succiniciasticum, and Parabacteroides were positively related to the isoacids proportion. Therefore, the decreased abundance of these bacteria might explain the greater proportion of isoacids. Moreover, previous studies demonstrated that isoacids could increase the number of cellulolytic bacteria (fibrobacter succinogenes, Ruminococcus flavefaciens, and so on) (Bryant and Doetsch, 1954; Dehority et al., 1967). Some in vitro studies have shown that isoacids can accelerate the degradation of DM and NDF (Soofi et al., 1982; Roman-Garcia et al., 2021). They tended to increase the proportion of isoacids, further suggesting that milk replacer and milk replacer plus ethoxyquin might influence the degradation and usage of feedstuffs. These might explain the increased starter intake in the MRE group.
The concentration of NEFA, the T-AOC, and the enzyme activity of CAT was positively correlated with Succiniclasticum, which is involved in the production of propionate (van Gylswyk, 1995). A lower abundance of Ruminococcaceae was detected in rats that were fed a high-fat diet (Zhao et al., 2017). The serum indicators of inflammation, such as TNF-α and IL-6, significantly increased. In this study, we also found that the antioxidative ability was negatively related to Ruminococcaceae, and the lower abundance of Ruminococcaceae further indicated a better health condition with ethoxyquin supplementation. Moreover, Alistipes, a potential opportunistic pathogen in diseases and highly relevant to dysbiosis and inflammation (Kong et al., 2019; Parker et al., 2020), was found to decrease and be negatively correlated with GSH-PX. These results also suggested that ethoxyquin improved the bacterial community.
## Conclusion
The results of this study suggest that milk replacers may not be sufficient to promote optimal growth performance in Holstein dairy calves during the early stages of life and that The addition of ethoxyquin could increase starter intake, thus narrowing the differences between the milk-fed and milk-replacer-fed calves. The results also suggested that milk replacer plus ethoxyquin enhanced the defensive ability and improved microbial composition to mitigate the negative effects of weaning.
## Data availability statement
The datasets presented in this study have been deposited in the NCBI repository, accession number PRJNA914621 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA914621).
## Ethics statement
The animal study was reviewed and approved by the Animal Care and Use Committee of Zhejiang A&F University, Zhejiang, China.
## Author contributions
XW and CW designed and supervised the study and revised the manuscript. XW, JZ, JY, and YW conducted the experiments. JZ, YZ, and JW performed the data analysis. XW drafted the manuscript. All authors read and approved the final version of the manuscript.
## Conflict of interest
YW was employed by Kemin (China) Technologies Co., Ltd. (Zhuhai, China). 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.
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|
---
title: Combination of Lactobacillus plantarum improves the effects of tacrolimus on
colitis in a mouse model
authors:
- Wei Lv
- Di Zhang
- Tian He
- Yingying Liu
- Limei Shao
- Zhongping Lv
- Xiaoping Pu
- Yufang Wang
- Ling Liu
journal: Frontiers in Cellular and Infection Microbiology
year: 2023
pmcid: PMC10040537
doi: 10.3389/fcimb.2023.1130820
license: CC BY 4.0
---
# Combination of Lactobacillus plantarum improves the effects of tacrolimus on colitis in a mouse model
## Abstract
The gut microbiome has been considered to play an important role in inflammatory bowel disease (IBD). Our previous study reported that tacrolimus-altered gut microbiota elicited immunoregulatory effects in both colonic mucosa and circulation, contributing to an increased allograft survival rate in mice. Here, we aimed to observe the changes in the tacrolimus-induced microbiome in a dextran sulfate sodium (DSS)-induced colitis mouse model and explore the possibility and efficacy of combination therapy with tacrolimus and the microbiome on colitis. Mice were divided into the control, DSS, tacrolimus monotherapy and tacrolimus plus *Lactobacillus plantarum* 550 (Lacto)-treated groups. The body weight, stool consistency, hematochezia and survival of mice were observed daily. Total RNA from colonic mucosa was extracted and subjected to transcriptome sequencing. Cecal contents were collected and the 16S rRNA sequencing was performed to characterize the gut microbiome and the ultrahigh- performance liquid chromatography-MS/MS (UHPLC-MS/MS) was used for targeted quantification of bile acids. The results confirmed that tacrolimus significantly ameliorated DSS-induced colitis in mice. Beneficial alterations of the gut microbiome characterized by a remarkable expansion of the genus Lactobacillus were induced by tacrolimus treatment. Oral supplementation with Lacto further improved the tacrolimus-mediated suppression of body weight loss in colitis, while the survival time of mice was further prolonged and the inflammation of colonic mucosa was obviously relieved. The immune and inflammation-related signaling pathways, including IFN-γ and IFN-α response, allograft rejection, IL2 STAT5 signaling and the inflammatory response pathways, were further downregulated in the tacrolimus plus Lacto cotreatment group. Cotreatment also improved the diversity of the gut microbiome and rescued the concentration of taurochenodeoxycholic acid (TCDCA) in colitis. The latter was positively correlated with the abundance of Lactobacillus but negatively related to the disease activity index score. Overall, our results indicated that *Lactobacillus plantarum* promoted the therapeutic effect of tacrolimus in experimental colitis, offering a promising strategy to combine tacrolimus and Lactobacillus in the treatment of colitis patients.
## Introduction
Inflammatory bowel disease (IBD), encompassing Crohn’s disease (CD) and ulcerative colitis (UC), is a group of chronic and relapsing inflammatory disorders that causes intestinal lesions and even extraintestinal manifestations (Wright et al., 2018). In the past decade, the incidence of IBD has significantly increased in newly industrialized countries or urbanized countries in Asia (Ng et al., 2017; Ng et al., 2019). This leads IBD to be a public health challenge worldwide. Considerable evidence suggests that the pathogenesis of IBD is characterized by an inappropriate and persistent mucosal immune response, caused by genetic and environmental factors (Caruso et al., 2020). The gut microbiome has been considered as a pivotal cause of the occurrence and development of IBD (Honda and Littman, 2012; Caruso et al., 2020). Dysbiosis in IBD manifests as a decrease in commensal bacterial diversity and alterations in the gut microbiota composition (Honda and Littman, 2012; Caruso et al., 2020). The finding that the genetically susceptible mice fail to develop colitis under germ-free conditions, and the effectiveness of antibiotics and probiotics in IBD therapy further support the important role of the gut microbiome. ( Podolsky, 2002; Caruso et al., 2020; Lee and Chang, 2021).
Tacrolimus (Tacro), a commonly used immunosuppressant in the transplantation area, is also recommended in the treatment of refractory IBD patients with impressive efficacy (Baumgart et al., 2006; Rodríguez-Lago et al., 2020). With the advantages of high oral bioavailability and the potent inhibitory effects on activated T-cells(Fellermann et al., 2002; Komaki et al., 2016), FK has been selectively utilized to induce remission in corticosteroid-refractory UC and refractory fistulizing perianal CD in recent years (Rodríguez-Lago et al., 2020; Gordon et al., 2022). However, due to the relatively narrow therapeutic window and dose-dependent side effects such as neurotoxicity, nephrotoxicity, metabolic disorders and opportunistic infection (Baumgart et al., 2006), it is recommended that tacrolimus be used as a bridging strategy or for short-term induction of remission in IBD (Rodríguez-Lago et al., 2020). Further studies are needed to shed light on the strategy that reduces the side effects and enhances the efficacy of tacrolimus in IBD.
Conventionally, tacrolimus exerts its immunosuppressive functions by binding to the FK506-binding protein to inhibit calcineurin phosphatase and block the transcription and secretion of cytokines, particularly interleukin (IL)-2, which leads to the suppression of T lymphocyte activation and proliferation (Thomson et al., 1995; Chow and Leong, 2007). In recent years, studies by our group and other groups have reported that tacrolimus alters the composition and bacterial taxa of the gut microbiota (Jiang et al., 2018; Zhang et al., 2018). We have demonstrated a significant expansion of Allobaculum, Bacteroides and Lactobacillus in tacrolimus-treated mice (Zhang et al., 2018). Innovatively, our study reported that the tacrolimus-induced microbiota elicited immunoregulatory effects in both colonic mucosa and circulation, causing a significantly increased proportion of CD4+CD25hiFoxP3+ regulatory T cells in peripheral blood mononuclear cells, mesenteric lymph nodes and colonic mucosa (Zhang et al., 2018). This suppressive function of tacrolimus–induced gut microbiota was further confirmed in a skin transplant mouse model. Furthermore, our study confirmed that tacrolimus-induced disorders of glucose metabolism were partially associated with the gut microbiota, and oral supplementation with butyrate might prevent and reverse tacrolimus-induced hyperglycemia in mice (Jiao et al., 2020). These data raise the possibility of combining immunosuppressive agents and the gut microbiome for the treatment of immune diseases. However, to our knowledge, the composition and function of the tacrolimus-induced microbiome in the colitis model and the underlying mechanism remain unclear.
In this study, we evaluated the role of tacrolimus and its impact on the gut microbiota, immune response and bile acid profiles in dextran sulfate sodium (DSS)-induced colitis mouse model. Furthermore, the combination of tacrolimus and the gut microbiome might be considered as a prospective strategy for the treatment of colitis patients in the clinic.
## Animal studies
The animal experiments were conducted in two different centers (West China Hospital and Chaoyang Hospital) under the same conditions to confirm the reliability of the results. Six-week-old male C57BL/6 mice were obtained from Beijing Vital River Laboratory Animal Technology Corporation Ltd. and the Experimental Animal Center of Sichuan University. All animals were housed in specific pathogen-free (SPF) conditions under constant temperature and humidity with a 12 h light-dark cycle. Standard diet and sterile water were supplied ad libitum during the initial 7-day adaptation period. All animal procedures were approved by the Animal Ethics Committees of Beijing Chao-Yang Hospital and the Animal Ethics Committee of West China Hospital of Sichuan University (Ref. No. 20211277A).
## Dextran sulfate sodium-induced colitis model and treatment
Mice were randomly divided into different groups of 6-10 animals each before the start of the experiment. Colitis was induced by adding $2.5\%$ (w/v) dextran sulfate sodium (DSS, M.W: 36000-50000, MP Biomedicals, Canada) to the drinking water for 7 days, then replaced with sterile water. Tacrolimus (Tacro, Astellas Ireland Corporation Ltd, Shenyang, China) at different dosages (0.1 mg/kg, 1 mg/kg or 10 mg/kg) was administered via oral gavage in sterile water once a day and maintained until the end of experiments. Lactobacillus plantarum 550 (Lacto) in powder form was isolated from pickle and provided as a gift from Technology Research Institute of Shuxi Condiments of Sichuan Cuisine Corporation Ltd (Chengdu, China), 1×108 CFU in 200 μL sterile water/mouse/day was given daily via oral gavage 3 days before DSS administration and maintained until the end of experiments. Mice in the control group were administered an equal volume of sterile water by gavage. Body weight and water consumption were recorded daily. The blood in the stool was detected by Pyramidon semiquantitative assay using a Fecal Occult Blood test kit (BA2020B, BaSO, Zhuhai, China) and recorded as “-”, “+”, “++”, “+++”, or “++++”. The disease activity index (DAI) was evaluated by scoring the body weight loss (%), stool consistency and blood in feces as described in the literature(Jang et al., 2019), with minor modifications (Supplementary Table 1). Mice were sacrificed at the end of the treatment, and the colon lengths were measured. Cecal contents were collected, snap frozen and kept at -80 °C for 16S rRNA sequencing and targeted bile acid metabolomics assays. Colonic mucosa was isolated and frozen until transcriptomics analysis. Sections of the proximal and distal colon were fixed in $4\%$ paraformaldehyde and stained with hematoxylin and eosin (H&E). Histological scores were calculated according to the criteria described in the literature (a score from 0 to 4 was given for each colon segment [proximal or distal] based on the severity of inflammation, and a combined score [proximal and distal] provided a total colonic histological score per mouse) (Liu et al., 2017).
## 16S rRNA amplicon sequencing
The total genomic DNA was extracted from cecal contents of mice using the TGuide S96 Magnetic Soil and Stool DNA Kit (TIANGEN Biotech Corporation Ltd, Beijing, China) according to the manufacturer’s instructions. The full-length 16S rRNA gene was amplified with universal primers (27F: 5’-AGRGTTTGATYNTGGCTCAG-3’; 1492R:5’-TASGGHTACCTTGTTASGACTT-3’) under the following conditions: 95 °C for 2 min; 25 cycles of 98 °C for 10 s, 55 °C for 30 s, and 72 °C for 90 s; and a final step at 72°C for 2 min. The purified PCR products were sequenced on the PacBio platform by Biomarker Technologies Corporation (Beijing, China). The circular consensus sequencing (CCS) reads were generated from the corrected original subreads by SMRT Link (version 8.0) following the setting parameters: minPasses ≥ 5 and minPredictedAccuracy ≥ 0.9. After barcode and primer identification followed by chimera removal, high-quality reads were obtained and clustered as operational taxonomic units (OTUs) with similarity over $97\%$ using USEARCH (version 10.0). Species annotation and taxonomic and diversity analyses were conducted with QIIME2 (version 2020.06). Chao1 and Shannon indexes were used to reflect alpha diversity, while nonmetric multidimensional scaling (NMDS) analysis was carried out to show the dissimilarity of beta diversity. The vegan package in R language was applied for ANOSIM analysis to measure the significant differences in beta diversity between groups. Linear discriminant analysis effect size (LEfSe) analysis with LDA score > 3 and statistical analysis of metagenomic profiles (STAMP) were performed to identify the significantly different bacteria between groups at the genus level.
## Bile acid targeted metabolomic analysis
The high-throughput targeted quantification of bile acids in mice cecal contents was performed at Biomarker Technologies Corporation (Beijing, China) by UHPLC-MS/MS. Aliquots (25 mg) from 35 samples were weighed and added 1000 μL of precooled extract solution containing $0.1\%$ formic acid and isotopically labeled internal standard mixture (acetonitrile-methanol-water, 2:2:1) was added per tube. After vortexing for 30 s, the mixture was homogenized at 35 Hz for 4 min, followed by sonication for 5 min in an ice-water bath. The latter 9-min circle was repeated three times, and then the tubes were incubated at -40°C for 1 h and centrifuged at 12000 rpm and 4°C for 15 min. The resulting supernatants were collected for UHPLC-MS/MS analysis.
An UHPLC System (Vanquish, ThermoFisher Scientific) equipped with a Waters ACQUITY UPLC BEH C18 column (150 *2.1 mm, 1.7 μm, Waters) was used for chromatographic separation. Mobile phase A included 1 mmol/L ammonium acetate and $0.1\%$ acetic acid in water, and the mobile phase B was acetonitrile. The column temperature was set at 50°C, while the autosampler temperature was maintained at 4°C. The injection volume was 1 μL. Mass spectrometry in parallel reaction monitoring (PRM) mode was carried out by a Q Exactive HFX mass spectrometer (Thermo Fisher Scientific). The ion source parameters were as follows: spray voltage = +3500/-3100 V, sheath gas (N2) flow rate = 40, aux gas (N2) flow rate = 15, sweep gas (N2) flow rate = 0, aux gas (N2) temperature = 350°C, capillary temperature = 320°C.
A total of 39 bile acids were identified in this study (detailed in Supplementary Table 2). The calibration standard solutions were diluted stepwise with a dilution factor of 2 before being subjected to UHPLC-PRM-MS analysis. The final concentration (cF, nmol/L) of the samples measured was obtained by multiplying the directly calculated concentration (cC, nmol/L) output from the system by the dilution factor. The targeted metabolite concentration (cM, nmol/kg) in the tissue samples was calculated according to the following formula (VF: final volume of sample; m: sample mass): CM[nmol·kg−1]=CF[nmol·L−1]·VF[μL]m[mg]
## Transcriptome analysis
Total RNA from mice colonic mucosa was extracted using the mirVana miRNA Isolation Kit (Ambion) following the manufacturer’s protocol. RNA integrity was assessed by an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Samples with an RNA integrity number (RIN) ≥ 7 were subjected to the subsequent analysis. The TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, San Diego, CA, USA) was used to construct the libraries. Transcriptome sequencing and analysis were conducted by OE Biotech Corporation Ltd. (Shanghai, China).
The libraries were sequenced on the Illumina HiSeq X Ten platform. Approximately 50 megabytes of raw reads for each sample were generated. Raw data were preprocessed using Trimmomatic. After removing the reads containing poly N and the low-quality reads, clean reads were obtained and mapped to the mouse genome (GRCm38.p6) using HISAT2. The fragments per kilobase of exon model per million mapped fragments (FPKM) value of each gene was calculated using Cufflinks, and the read counts of each gene were obtained by HTSeq-count. Differentially expressed genes (DEGs) were identified using the DESeq [2012] R package, followed by gene set enrichment analysis (GSEA). P value < 0.05 and fold change > 2 or fold change < 0.5 were set as the thresholds for significantly differential expression. To demonstrate the expression pattern of genes in different groups and samples, hierarchical cluster analysis of DEGs was performed. R software based on the hypergeometric distribution was employed for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Immune infiltration analysis was performed using Immune Cell Abundance Identifier (ImmuCellAI).
## Statistical analysis
Data are presented as the mean ± standard error of the mean (SEM). The GraphPad Prism (version 8.0; GraphPad Software, San Diego, CA, USA) was utilized for statistical analyses. Normality of distribution was confirmed using the Shapiro-Wilk test, and differences among groups were assessed using the Student’s t-test or two-way ANOVA when appropriate, with the Tukey’s post-hoc test. The log-rank test was used to compare survival rates. $P \leq 0.05$ was considered statistically significant.
## Tacrolimus ameliorated colitis in a DSS mouse model
Compared to the control mice, body weight loss, increased DAI scores and decreased survival rates were significantly observed in mice orally treated with $2.5\%$ DSS for 7 days (Figures 1A, B, E). The dosage of tacrolimus (10 mg/kg) was determined by our previous study, which is the highest tolerated dose that most closely resembled the serum concentration in humans (Zhang et al., 2018). No significant difference in body weight, DAI scores, survival rate or colon length was observed in the tacrolimus and control groups, supporting the safety of tacrolimus at this dosage. Body weight, DAI scores and survival rates in the DSS colitis model were significantly ($P \leq 0.0001$, $P \leq 0.0001$, $P \leq 0.01$, respectively) ameliorated by tacrolimus treatment (Figures 1A, B, E). Furthermore, colitis manifestations, including the shortened length, disruption of crypt-villus structure, goblet cell loss and immune cell infiltration, as determined by morphological changes and pathological scores were significantly ($P \leq 0.01$) ameliorated in the DSS + Tacro group compared to the DSS group (Figures 1C, D, F, G). All these results indicated that tacrolimus ameliorated DSS-induced colitis in both clinical manifestation and pathological changes.
**Figure 1:** *Tacrolimus alleviated DSS-induced colitis in mice. (A, B) Body weight changes and DAI scores of mice in different treatment groups. n=8 per group. (C, D) Gross view and length of colon. n=6 per group. (E) Survival rate of mice during the experiments. (F) Representative hematoxylin and eosin-stained images (100× and 400×) of colon sections. Scale bars, 200 μm (above) and 50 μm (below). (G) Histological scores of proximal and distal colons. n=6 per group. Data are presented as the mean ± SEM. **P < 0.01 and ****P < 0.0001. Significance reported for comparison of the DSS + Tacro and DSS groups. DAI, disease activity index; Con, control; DSS, dextran sulfate sodium; Tacro, 10 mg/kg tacrolimus.*
Lower dosages of tacrolimus (0.1 mg/kg and 1 mg/kg) were also used in the acute DSS colitis model in our study. Tacrolimus at these lower dosages attenuated the increased DAI scores and improved survival in colitis (Supplementary Figures 1B, E), whereas no significant differences in weight change and colon length were observed in the low-dose DSS + Tacro group compared to the DSS group (Supplementary Figures 1A, C, D). These data also supported the effectiveness of tacrolimus in the DSS mouse model. For the consistency and effectiveness of the study, tacrolimus was used at a dosage of 10 mg/kg in the following study.
## Tacrolimus induced beneficial alterations of the gut microbiome in DSS-induced colitis, characterized by a remarkable expansion of the genus Lactobacillus
To investigate the effect of tacrolimus on the diversity and composition of the gut microbiome in DSS-treated mice, 16S rRNA amplicon sequencing of DNA extracted from the cecal contents of mice was performed. The α-diversity of gut microbiota was not significantly different among the Control, Tacro, DSS and DSS + Tacro groups, manifested as the Chao1 index or Shannon index in Figure 2A.
**Figure 2:** *Tacrolimus triggered a significant increase in the genus Lactobacillus in colitis. (A) The microbial α-diversity in the cecal contents of mice. (B) β-diversity analysis based on the NMDS and ANOSIM methods. (C) Relative abundance of gut bacterial composition at the phylum level. (D) The LDA scores between the control and DSS-treated groups at the genus level. The threshold was set as 3. (E) The significantly different bacteria between the control and DSS-treated groups evaluated by STAMP. (F) Correlation analysis between Lactobacillus relative abundance and DAI score. A total of 28 samples from the Con, DSS and DSS + Tacro groups were included in this analysis. (G) The LDA scores of the DSS + Tacro group when compared with the DSS group at the genus level. (H) Significantly different genera between the DSS and DSS + Tacro groups evaluated by STAMP. (I)
Lactobacillus abundance in log 2 scale among different treatment groups. Data are presented as the mean ± SEM. *P < 0.05, ***P < 0.001, ns, no significance; Con, control; DSS, dextran sulfate sodium; Tacro, 10 mg/kg tacrolimus; NMDS, nonmetric multidimensional scaling; LDA, linear discriminant analysis; STAMP, statistical analysis of metagenomic profile; DAI, disease activity index.*
According to the β-diversity of the gut microbiota, NMDS analysis was used, and there was significant ($P \leq 0.001$) separation of microbial communities among the control, DSS and DSS + Tacro groups. The DSS group was located distant from the control group, while the position of the DSS + Tacro group was closer to the control group than to the DSS group (Figure 2B). Consistent with a previous study (Shin et al., 2015), DSS administration resulted in an obvious increase in the relative abundance of pylum_Proteobacteria and Deferribacteres and a decrease in the Firmicutes/Bacteroidetes ratio. All these changes were reversed by tacrolimus treatment (Figure 2C). LEfSe is a commonly used method to identify potential biomarkers with significant differences among groups. In our study, the abundances of the genera_Oscillospira, Bacteroides, Escherichia, Enterococcus, Turicibacter and Mucispirillum were significantly enriched, whereas the abundances of the genera_Lactobacillus, Allobaculum and *Clostridium were* significantly decreased in the DSS group compared to the normal control group (Figure 2D). Among the bacteria that differed significantly between the control and DSS groups at the genus level, Lactobacillus occupied the largest mean proportion, approximately $30\%$ (Figure 2E). Correlation analysis showed that the DAI scores of colitis were negatively correlated with the abundance of the genus_Lactobacillus, as shown in Figure 2F (R=-0.81; $P \leq 0.001$) and Supplementary Figure 2A (R=-0.76; $P \leq 0.001$).
In the DSS + Tacro group, LEfSe analysis found that the increased abundance of the genera_Oscillospira, Escherichia, Turicibacter, Mucispirillum, SMB53, Bilophila as well as the decreased abundance of the genera_Lactobacillus and Allobaculum in the colitis model were significantly reversed by tacrolimus treatment (Figure 2G). Tacrolimus remarkably ($P \leq 0.05$) restored the abundance of the genera_ Lactobacillus, Oscillospira, Turicibacter, etc. ( Figures 2H, I). Among these significant changes in the microbiota, the abundance of the genus_Lactobacillus showed the most significant changes, with the highest proportion (approximately $15\%$) in the DSS + Tacro group (Figure 2H). To validate our findings, we performed the same research in another center and confirmed similar trends, both showing a decrease in the genus_Lactobacillus in the DSS group and a restoration of its abundance when treated with tacrolimus (Supplementary Figures 2A, B). This strengthened our confidence in the findings, although the composition of gut microbiota is not similar in two centers.
## Lactobacillus plantarum 550 (Lacto) further ameliorated colitis combined with tacrolimus treatment
Lacto, a strain of the genus Lactobacillus plantarum, was recently isolated from pickle of Sichuan cuisine by our group and determined by 16S ribosomal DNA identification. Body weight changes in the DSS + 1Tacro (1 mg/kg) group and the DSS + Lacto group were not significantly different from those in the DSS group, whereas the combination of Lacto in the DSS + 1Tacro + Lacto group maintained the body weight at a higher level, even closer to that in the DSS + 10Tacro (10 mg/kg) group (Figure 3A). Moreover, Lacto further prevented body weight loss in DSS + 10Tacro mice (Figure 3A) compared to monotherapy with tacrolimus in the DSS model ($P \leq 0.05$). As determined by DAI, the combination of Lacto with either tacrolimus at doses of 1 mg/kg or 10 mg/kg significantly ($P \leq 0.01$ and $P \leq 0.0001$, respectively) decreased DAI scores compared to the DSS group (Figure 3B). Colon length, which is negatively related to the inflammation of colitis (Liu et al., 2017), was measured, and a significantly increased length was reported in the DSS + 10Tacro ($P \leq 0.01$) as well as the DSS + Tacro + Lacto groups ($P \leq 0.05$, $P \leq 0.01$, respectively), with the most increased length in the DSS + 10 Tacro + Lacto group (Figures 3C, D). Survival rates were significantly ($P \leq 0.01$) increased in the DSS + 10Tacro and DSS + 10Tacro + Lacto groups, with the latter group showing better survival (Figure 3E). Compared to the DSS group, cotreatment with tacrolimus and Lacto obviously ($P \leq 0.05$ and $P \leq 0.01$, respectively) relieved the inflammation of the colonic mucosa, as determined by the histological scores (Figures 3F, G). Overall, Lacto, combined with tacrolimus, further ameliorated colitis in a mouse model in clinical and histological manifestations.
**Figure 3:** *The combination of tacrolimus and Lactobacillus plantarum 550 achieved better remission in colitis. (A, B) Body weight changes and DAI scores of mice in different treatment groups. n=8 per group. (C, D) Gross view and length of colons. n=6 per group. (E) Survival rate of mice during the experiments. (F) Representative hematoxylin and eosin-stained images (100× and 400×) of colon sections. Scale bars, 200 μm (above) and 50 μm (below). (G) Histological scores of proximal and distal colons. n=6 per group. Data are presented as the mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001. The significance reported in (B), (D), (E), and (G) is for comparison all to the DSS group. DAI, disease activity index; Con, control; DSS, dextran sulfate sodium; 1Tacro, 1 mg/kg tacrolimus; 10Tacro, 10 mg/kg tacrolimus; Lacto, Lactobacillus plantarum 550.*
## The combination of tacrolimus and lacto significantly inhibited the proinflammatory signaling pathways
RNA sequencing data revealed that at the transcriptional level, the immune and inflammation-related signaling pathways, including the allograft rejection, IFN-γ response, IFN-α response, IL6 JAK STAT3 signaling, inflammatory response, TNFα signaling via NF-κB, complement and IL2 STAT5 signaling were significantly upregulated after DSS administration (Figure 4A). Most of these pathways, including the allograft rejection, TNFα signaling via NF-κB, IL6 JAK STAT3 signaling, inflammatory response and complement were significantly downregulated by tacrolimus (Figure 4B). When tacrolimus was combined with Lacto, the signaling pathways, including IFN-γ and IFN-α response, allograft rejection, IL2 STAT5 signaling and the inflammatory response, were further downregulated (Figure 4C), implying that the combined therapy of tacrolimus and Lacto further inhibited the inflammatory response in the colonic mucosa of colitis mice compared to the tacrolimus monotherapy group. The results of the gene set variation analysis are visualized in Figure 4D. The range of colors indicates the relative abundance of the signaling pathways mentioned above for each sample.
**Figure 4:** *Lactobacillus plantarum 550 supplementation modulated intestinal mucosa inflammation at the transcriptional level. (A–C) Gene set enrichment analysis. NES values greater than 2 were demonstrated. The results represent pathways modulated in the (A) DSS group versus Con group, (B) DSS + Tacro group versus DSS group and (C) DSS + Tacro + Lacto group versus DSS + Tacro group. (D) Results of gene set variation analysis for selected biological processes in all groups. (E) The profile of infiltrating immune cells in the Con, DSS, DSS + Tacro and DSS + Tacro + Lacto groups. Data are presented as the mean ± SEM. *P < 0.05. NES, normalized enrichment score; Con, control; DSS, dextran sulfate sodium; Tacro, 10 mg/kg tacrolimus; Lacto, Lactobacillus plantarum 550. n=3 per group.*
To demonstrate the profile of immune cells in the colonic mucosa, we analyzed the immune cell infiltration levels based on transcriptome sequencing data. As shown in Figure 4E, the proportion of monocytes and macrophages, especially the M1 subtypes, was elevated in the DSS group, while the M2 subtypes were slightly reduced with DSS intervention, in accordance with published reports overviewing the opposing roles of M1 and M2 macrophages in DSS-induced colitis (Lin et al., 2014). To a certain extent, tacrolimus suppressed the increase in the proportion of monocytes and macrophages, whereas coadministration of tacrolimus and Lacto significantly ($P \leq 0.05$) inhibited the expansion of these innate immune cells (Figure 4E) but not other immune cells, including dendritic cells (DCs), type 1 conventional dendritic cells (cDC1), type 2 conventional dendritic cells (cDC2), plasmacytoid dendritic cells (pDCs), B cells, memory B cells, B1 cells, follicular B cells, germinal center B cells, CD8+ cytotoxic T cells (Tc), naïve CD8+ T cells, Tγδ cells (Tgd), mast cells, M2 macrophages, NK cells, basophils, granulocytes and eosinophils (Supplementary Figure 3). Altogether, our results suggested that the downregulation of inflammatory signaling pathways may be one of the possible mechanisms accounting for the alleviation of colonic mucosal inflammation after Lacto supplementation.
## Combination of tacrolimus and Lacto improved the diversity of the gut microbiome and changed the bile acids profiles in colitis
Next, to explore whether the effect of Lacto supplementation on colitis remission had some relationship with alterations in the gut microbiome, similar to that previously observed in the tacrolimus-treated group (Zhang et al., 2018; Jiao et al., 2020), we compared the results of 16S rRNA sequencing in the presence and absence of Lacto. Increased α-diversity of the gut microbiome was observed in the combination treatment group compared to the tacrolimus-treated group, as reflected by the Chao1 and Shannon indexes (Figure 5A). The results from NMDS and ANOSIM analysis clearly indicated that the bacterial communities of these two groups differed from each other (Figure 5B). In contrast with tacrolimus treatment, the relative abundance of the pathogenic bacteria Proteobacteria was further reduced, while the abundance of Firmicutes and Bacteroidetes, the two major phyla composing the gut microbiota community in healthy humans, was better maintained by Lacto supplementation (Figure 5C). However, Lacto alone did not significantly affect the bacterial diversity or cause beneficial changes in the composition of gut microbiota in colitis (Figures 5D–F).
**Figure 5:** *Tacrolimus coadministered with Lactobacillus plantarum 550 changed the diversity of the gut microbiome and bile acid metabolism in colitis. (A, D) Gut microbial α-diversity determined by Chao1 and Shannon indexes. (B, E) β-diversity analysis based on the NMDS and ANOSIM methods. (C, F) Relative abundance of gut bacterial composition at the phylum level. (G, H) Correlation analysis between bile acid concentrations and Lactobacillus relative abundance. (I) Relative concentration of TCDCA on a log 2 scale. (J) Correlation analysis between DAI score and TCDCA concentration. (K) The normalized expression levels of bile acid receptors among all groups. Data are presented as the mean ± SEM. *P < 0.05, **P < 0.01, ns, no significance. NMDS, nonmetric multidimensional scaling; FDR, false discovery rate; DAI, disease activity index; Con, control; DSS, dextran sulfate sodium; Tacro, 10 mg/kg tacrolimus; Lacto, Lactobacillus plantarum 550; TCDCA, taurochenodeoxycholic acid; Tgr5, Takeda G protein-coupled receptor 5; Fxr, farnesoid X receptor; Vdr, vitamin D (1,25- dihydroxyvitamin D3) receptor; Rxra, retinoid X receptor alpha; Rxrg, retinoid X receptor gamma; Rxrb, retinoid X receptor beta.*
Bile acid metabolism is a biological process in which specific intestinal floras are actively involved (Wahlström et al., 2016). Recently, gut microbiome-derived bile acids have been reported to play a critical role in IBD patients and experimental colitis(Lavelle and Sokol, 2020; Sinha et al., 2020; Thomas et al., 2022). Considering the significant changes in diversity and composition after the combination of tacrolimus and Lacto, bile acids were also detected in our study through high-throughput sequencing. Among all the bile acids analyzed in the Con, DSS and DSS + Tacro groups, the cecal taurochenodeoxycholic acid (TCDCA) level was most strongly correlated with the relative abundance of the genus_Lactobacillus (Figure 5G). Furthermore, a significant positive correlation ($R = 0.82$; FDR<0.001) between the TCDCA concentration and the abundance of genus_Lactobacillus was observed in our experiments (Figure 5H).
The TCDCA levels that remarkably ($P \leq 0.01$) declined after DSS intervention were not restored by tacrolimus treatment. However, the combined treatment of tacrolimus plus Lacto drastically ($P \leq 0.05$) increased the TCDCA concentrations in comparison with tacrolimus treatment (Figure 5I). In addition, the cecal TCDCA concentrations of mice were negatively (R=-0.83; $P \leq 0.001$) correlated with the DAI score (Figure 5J), suggesting an association between this bile acid and colitis severity.
Bile acids can function as signaling molecules to activate their receptors, thereby affecting intestinal inflammation (Perino et al., 2021; Yang et al., 2021). As presented by our transcriptome analysis, DSS administration perturbed the expression of the bile acid-sensing receptors, including G-protein-coupled bile acid receptor 1 (Gpbar1, TGR5), farnesoid X receptor (FXR), vitamin D (1,25-dihydroxyvitamin D3) receptor (VDR), and retinoid X receptor (RXR), to various degrees, all of which were mitigated somewhat by tacrolimus treatment except TGR5, and this effect was amplified further by coadministration of Lacto (Figure 5K). Collectively, our findings demonstrated that the combination of tacrolimus and Lacto improved the diversity and composition of the gut microbiome as well as bile acid metabolism in colitis mice, which possibly contributed to the alleviation of intestinal inflammation. The detailed mechanisms remain to be further explored.
## Discussion
In this study, we confirmed that tacrolimus ameliorated DSS-induced colitis in a mouse model. Beneficial alterations of the gut microbiome characterized by a remarkable expansion of the genus Lactobacillus were observed after tacrolimus treatment. Oral supplementation with one of the species in this genus (*Lactobacillus plantarum* 550, Lacto) further ameliorated colitis. Compared to the monotherapy of tacrolimus, the combination of Lacto and tacrolimus further downregulated the proinflammatory signaling pathways while significantly inhibiting the proportion of monocytes and macrophages, especially the M1 subtypes in the colon. During this process, the diversity of the gut microbiome as well as bile acid metabolism was significantly changed, which may contribute to the remission of colitis.
Recently, tacrolimus has been recommended for the treatment of refractory IBD, showing efficacy in both human and animal colitis (Baumgart et al., 2006; Yoshino et al., 2010). In addition to the traditional calcineurin-dependent inhibitory effect on T cells, several studies have reported that the mechanism of tacrolimus-ameliorated colitis might be related to the suppression of activated macrophages (Yoshino et al., 2010; Cai et al., 2021), the restriction of dendritic cell migration and the subsequent differentiation of CD4+ T cells to Th1 and Th17 cells (Regmi et al., 2019), as well as protection against apoptosis-mediated intestinal epithelial injury (Satake et al., 2022). In accordance with previous research, we found a significant alleviation of DSS-induced colitis by tacrolimus treatment. Signaling pathways, including the IFN-γ response, TNFα signaling via NF-κB, IL6 JAK STAT3 signaling and inflammatory response pathways upregulated after DSS administration were significantly inhibited by tacrolimus.
The gut microbiome plays an important role in the regulation of the immune response (Shi et al., 2017). Except for the immunosuppressive effect of tacrolimus itself, previous studies by our group and other groups have reported that tacrolimus changes the diversity and composition of the gut microbiome, resulting in the regulation of graft rejection (Jiang et al., 2018; Zhang et al., 2018), glucose metabolism (Bhat et al., 2017; Jiao et al., 2020), infections (Tourret et al., 2017) and endothelial function (Toral et al., 2018). In this study, the changes in the tacrolimus-induced microbiome in a colitis model were first described, which was characterized by a significant increase in the abundance of Lactobacillus followed by Allobaculum at the genus level. This is consistent with our prior studies showing the abundant genera Lactobacillus and Allobaculum after tacrolimus treatment in normal mice (Zhang et al., 2018; Jiao et al., 2020). Allobaculum was favored as an indicator of defective immune responses (Dimitriu et al., 2013).
The genus Lactobacillus was commonly reported to be decreased in IBD and experimental colitis (Sartor, 2004; Wang et al., 2020), which was also confirmed in our present study. And restoration or supplementation of this probiotic was associated with the alleviation of intestinal inflammation (Oliva et al., 2012; Wang et al., 2020). Based on our previous research proposing that tacrolimus plus gut microbiota achieved an increased allograft survival rate (Zhang et al., 2018), this study confirmed that the combination of tacrolimus and Lactobacillus strains played a positive role in colitis. Since we observed that the colonic contents were inadequate for the detection of two-omics, especially in the condition of colitis, the cecal content was used following the protocol as previous studies (Wong et al., 2022). There are certain limitations to do this since the most representative alterations in the gut microbiome are in the area where inflammation occurs. However, the efficacy of combination therapy confirmed the decreased abundance of the genus_Lactobacillus in DSS-induced colitis model.
Efficacy of tacrolimus combined with Lactobacillus spp. has been demonstrated in lupus-prone mice (Kim et al., 2021), graft-versus-host disease (Beak et al., 2022), and adult-type atopic dermatitis (Moroi et al., 2011), which acted by modulating the balance of Treg cells and Th17 cells. Here, we chose Lacto, one strain recently isolated from pickle by our group in Sichuan cuisine and proven to generate short-chain fatty acids (butyric acid, acetic acid, propionic acid, etc.) in preliminary experiments as a supplement for tacrolimus treatment. In recent years, some strains from *Lactobacillus plantarum* were reported to attenuate colitis in mice by restoring the disturbed gut microbiota, affecting intestinal barrier functions and immunity-related gene expression(Sun et al., 2020; Wu et al., 2022). Our results showed better remission of colitis in the combination treatment group than in the tacrolimus monotherapy group, accompanied by further downregulation of inflammatory signaling pathways. As we showed in Figure 3A, the combination of tacrolimus and Lacto, at both lower dosage (1 mg/kg) and higher dosage (10 mg/kg) promoted the therapeutic effect of colitis in mice, compared to the monotherapy of tacrolimus. Especially, the efficacy of tacrolimus (1mg/kg) combined with Lacto was close to the tacrolimus (10mg/kg) monotherapy. All of these results have shown that the combination therapy may reduce the dosage of tacrolimus for the treatment of colitis, thereby reducing dose-related side effects. Furthermore, by analyzing transcriptome sequencing data, we first reported that tacrolimus combined with Lacto treatment decreased the proportion of both colonic monocytes and macrophages in colitis but not dendritic cells (DC), type 1 conventional dendritic cells (cDC1), type 2 conventional dendritic cells (cDC2), plasmacytoid dendritic cells (pDC), B cells, memory B cells, B1 cells, follicular B cells, germinal center B cells, CD8+ cytotoxic T cells (Tc), naïve CD8+ T cells, Tγδ cells (Tgd), mast cells, M2 macrophages, NK cells, basophils, granulocytes and eosinophils. Our data demonstrated that macrophages and the proinflammatory signaling pathway play a vital role in the amelioration of colitis induced by tacrolimus combined with Lacto.
Theoretically, an improved composition of the gut microbiome and bile acid metabolism might help explain the mechanism of amelioration after combination therapy. A recent study has shown that one strain from Lactobacillus casei ameliorated DSS-induced colitis in mice by increasing taurine-conjugated bile acids, and the activation of FXR by the increased TCDCA might exhibit anti-inflammatory effects (Wong et al., 2022). This possibly resulted from the activation of FXR shifting the polarization of macrophages toward the anti-inflammatory M2 phenotype, which promoted IL-10 secretion and inhibited IFN-γ production (Fiorucci et al., 2018). Published evidence also supports that colonic VDR signaling upregulated by 1,25−dihydroxyvitamin D (1,25(OH)2D3) or microbial metabolites (bile acids, butyrate, etc.) contributed to the restoration of macrophage subtype balance, thus ameliorating colitis (Zhu et al., 2019; Battistini et al., 2020). Moreover, the nuclear receptor RXR can form heterodimers with VDR or the peroxisome proliferator–activated receptor γ (PPAR γ) (Kiss et al., 2013), which is highly expressed in the colon and can be activated by butyrate (Desreumaux et al., 2001; Litvak et al., 2018). Either VDR/RXR or RXR/PPAR γ heterodimer activation was proved to protect against colonic inflammation (Desreumaux et al., 2001; Zhu et al., 2019). In our study, the observed increase in TCDCA concentration after combination treatment might not be attributed to a direct metabolic effect of the genus Lactobacillus on TCDCA, since this probiotic is mainly involved in the deconjugation, 7α−dehydroxylation and esterification of bile acids(Jia et al., 2018). Instead, this might result from complex factors such as regulation of enterohepatic circulation or bacterial interactions that deserve further research. Based on our results and the studies mentioned above, we speculated that the combination treatment of tacrolimus and Lacto induced beneficial alterations in the gut microbiome as well as the derived bile acid profile, which led to an upregulated expression of bile acid receptors (FXR, VDR, RXR) in colonic macrophages, thereby restoring the balance of the macrophage M1/M2 subtype and inhibiting the release of proinflammatory cytokine. As a result, the inflammatory signaling pathways were downregulated, and colonic inflammation was relieved. Additional experiments in vitro that correlating TCDCA, intestinal inflammation and *Lactobacillus plantarum* should be conducted to explore the mechanism underlying the efficacy of the combination therapy in colitis in the future.
## Conclusion
The narrow therapeutic window of tacrolimus requires a promising treatment strategy for IBD that reduces the side effects while enhancing its efficacy. In this study, we confirmed the efficacy of tacrolimus treatment and innovatively demonstrated alterations in the tacrolimus-induced gut microbiome in a colitis model. Lactobacillus plantarum promotes the therapeutic effect of tacrolimus in colitis, possibly resulting from alterations in the gut microbiome and bile acid profile, which lead to the maintenance of macrophage M1/M2 subtype balance. Our findings offer a prospective strategy to combine tacrolimus and Lactobacillus for the treatment of colitis patients.
## Data availability statement
The raw data of the 16S rRNA sequencing in this study is publicly available in the SRA database, accession number PRJNA940074.
## Ethics statement
The animal study was reviewed and approved by Animal Ethics Committees of Beijing Chao-Yang Hospital and the Animal Ethics Committee of West China Hospital of Sichuan University (Ref. No. 20211277A).
## Author contributions
WL and DZ contributed equally to this paper. WL performed the experiments and drafted the manuscript. DZ performed part of the experiment, analyzed the data, drew the figures and helped to revise the manuscript. TH, YL and LS provided ideas for writing and helped with the experiments and revision of the manuscript. ZL and XP isolated the *Lactobacillus plantarum* 550 and completed pre-experiments for strain performance testing. LL and YW designed and supervised this study. All authors contributed to the article and agree to be accountable for the content of the work.
## Conflict of interest
ZL and XP are employed by Technology Research Institute of Shuxi Condiments of Sichuan Cuisine Co. LTD.
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/fcimb.2023.1130820/full#supplementary-material
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|
---
title: Associations of dietary antioxidant micronutrients with the prevalence of obesity
in adults
authors:
- Yazhu Yang
- Haifeng Xu
- Yi Zhang
- Lin Chen
- Chengzi Tian
- Bihui Huang
- Youpeng Chen
- Lin Ma
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10040542
doi: 10.3389/fnut.2023.1098761
license: CC BY 4.0
---
# Associations of dietary antioxidant micronutrients with the prevalence of obesity in adults
## Abstract
### Background
Antioxidant micronutrients have a therapeutic potential for clinical treatment of obesity. NO research, however, has examined the connection between the complex level of dietary antioxidants and obesity.
### Materials and methods
We mainly aimed to investigate the relationship between a combination of antioxidants and obesity using the database of the national health and nutrition examination survey (NHANES). This cross-sectional study contains a survey of 41,021 people (≥18 years) in total ranging from 2005 to 2018. Multivariate logistic and weighted quantile sum (WQS) regression were performed to investigate the associations between these antioxidants, both individually and collectively, and the prevalence of obesity. The restricted cubic spline (RCS) regression was also utilized to analyze the linearity of these associations.
### Results
According to multivariate logistic models, we found that the levels of most antioxidants in the highest quartile were independently related to a lower prevalence of obesity, while a reverse result was observed in selenium (p for trend <0.05). The WQS index revealed that a total of the 11 antioxidants is negatively related to the prevalence of obesity and abdominal obesity (all $p \leq 0.001$), and iron/vitamin C have the greatest weight in the negative associations between antioxidant complex and obesity, as well as abdominal obesity. In addition, the RCS regression showed that retinol, vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, iron, and copper all had a non-linear association with obesity. Threshold effect analysis demonstrated that the inflection points of retinol, vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, iron, and cooper were 235.57, 374.81, 58.89, 891.44, 30.70, 43,410.00, 11,240.00, and 990.00 μg/day, respectively.
### Conclusion
Our study found that a high level of a complex of 11 dietary antioxidants is related to a lower prevalence of obesity and abdominal obesity, among this inverse associations iron and vitamin C have the greatest weight.
## 1. Introduction
Obesity [1], a chronic metabolic condition, is classified into two types: abdominal obesity and non-abdominal obesity. Abdominal obesity, which is resulted from an excess of buildup of fat caused by the imbalance between the body’s caloric intake and consumption, is the result of the interplay of genetics, environmental variables such as poor food, long-term inactive and insufficient activity, and other factors (2–5). According to a research based on NHANES population, the prevalence of obesity among U.S. adults was $43.4\%$ [6]. Obesity is considered a global pandemic, with health hazards affecting practically everyone, particularly abdominal obesity, that is more dangerous than non-abdominal obesity. Obesity [7] also raises the risk of cardiovascular illnesses including atherosclerosis, venous thrombosis, and hypertension, as well as diabetes, cancer, and even all-cause mortality. According to a WHO research in 2018 [8], at least 2.8 million individuals worldwide died of overweight or obesity each year. This is why controlling and managing obesity is critical.
The removal of reactive oxygen species (ROS) that harm an organism is a process known as anti-oxidation. To attain the objective of antioxidation, people can supplement with antioxidants (including vitamins such as vitamins A, C, and E, carotenoids such as α-carotene and β-carotene), or supplement with a suitable quantity of proteins and trace elements such as iron, zinc, selenium, copper, retinol, and so on (9–11). These elements (12–14) are also currently considered to be the nutritional micronutrients associated with obesity. Some studies (14–16) have revealed that obesity development rates are higher in places where micronutrient deficiencies are more frequent, while others have discovered that deficiencies in particular micronutrients may be related to increased aberrant fat deposition in the body. The effect of micronutrients on obesity may occur through changing leptin concentrations in the blood, which control food intake and energy expenditure, leading to changes in adipose tissue mass, and also by regulation of the inflammatory response.
However, considering that the impact of micronutrients is usually dependent on their interaction with one another, their overall effect on the body may differ from that of single antioxidant, and their contribution to the development of obesity may change. Therefore, in this study, the 11 antioxidant micronutrients mentioned above were studied as a whole to investigate their relationship with obesity, and then each antioxidant micronutrient was assessed individually to determine its proportional association with obesity, to examine the relationship between each antioxidant micronutrient and the prevalence of obesity.
## 2.1. Study and population
The study population was obtained from the National Health and Nutrition Examination Survey (NHANES) database, a major research program of the National Center for Health Statistics (NCHS) that analyzes the health and nutritional status of adults and children in the United States using a mix of interviews and physical examinations for disease preventive reasons. Here, we utilized data from the NHANES What We Eat in America (WWEIA), a research run by the US Departments of Agriculture (USDA) and Health and Human Services (DHHS), to acquire dietary recall data on vitamin intake which are available on the NHANES Dietary Data page1 in the study population, from eight consecutive cycles (2003–2018) in 2-year intervals.
Those with less than two valid 24-h food recalls were eliminated, as were participants under the age of 18 or pregnant with missing BMI or waist circumference data from 2003 to 2018. 80312 people were initially included in our analyses. Further, we excluded 10,713 participants with missing data on two valid 24-h dietary recalls and 28,578 people under the age of 18 or with missing data on BMI or being pregnant. Finally, a total of 41,021 and 39,947 participants were enrolled in our subsequent analyses for the association between the antioxidant micronutrients with obesity and abdominal obesity, respectively. The National Health Statistics Research Ethics Review Board authorized all procedures for this investigation, and signed informed permission was received from all participants; moreover, none of the authors of this study were involved in the collection or development of the NHANES database.
## 2.2. Dietary intake collection from two 24-h diet recalls
NHANES has performed two 24-h recalls to mobile examination center (MEC) participants: the first recall is administered in the MEC, and then a second recall is administered by phone 3–10 days later. According to the NHANES database, the dietary intake data are used to estimate the types and amounts of foods and beverages (including all types of water) consumed during the 24-h period before the interview (midnight to midnight), and to estimate intakes of energy, nutrients, and other food components from those foods and beverages.
Excluding nutrients derived through drugs, antacids or dietary supplements, we utilized the R software (Version 4.1.2) to extract the data about intake of 11 antioxidant-related micronutrients from two 24-h diet recalls, including vitamin C[DR1TVC and DR2TVC], iron[DR1TIRON and DR2TIRON], vitamin E as α-tocopherol[DR1TATOC and DR2TATOC], zinc[DR1TZINC and DR2TZINC], β-carotene[DR1TBCAR and DR2TBCAR], copper[DR1TCOPP and DR2TCOPP], α-carotene[DR1TACAR and DR2TACAR], vitamin A[DR1TVARA and DR2TVARA], retinol[DR1TRET and DR2TRET], β-cryptoxanthin[DR1TCRYP and DR2TCRYP], selenium[DR1TSELE and DR2TSELE].2 Finally, the average of two days intake, the actual dietary intake instead of the usual or habitual dietary intake, for each antioxidant was used to analyze due to the reason that using the Multiple Source Method (MSM) and the National Cancer Institute (NCI) method, the values are not expressively different when comparing the mean intake estimated using the two-day mean [17].
## 2.3. Obesity definition
Obesity and abdominal obesity were defined that the condition of BMI 30.0 kg/m2 are obese, while those who are abdominally obese must meet the condition of waist circumference > 102 cm for men or > 88 cm for women [18].
## 2.4. Covariates
As possible confounders such as socio-demographic traits, lifestyle, and behavioral patterns were identified, the following variables were incorporated into the model. We included factors such as age (years), male (male or female), education level (below, equal to, or above high school), race/ethnicity, and poverty status in the socio-demographics. We also included smoking status, alcohol drinking status, total calorie intake, and sedentary time as life behavioral factors. Moreover, for medical factors, we included HDL-C, total cholesterol, and eGFR, as well as diabetes and hypertension (including SBP and DBP).
## 2.5. Statistical analyses
Eleven antioxidant micronutrients were analyzed in connection to the obese and abdominally obese populations. The normality of continuous variables was tested using the Kolmogorov–smirnov statistic. Continuous variables were reported as mean (standard deviation [SD]) or medians (interquartile ranges [IQRs]) and compared using Student’s t-test (normal distribution) or the Mann–Whitney U test (non-normal distribution). After finding that the continuous variable representing antioxidant micronutrients was skewed, we log-transformed it to make it more normally distributed. Alternatively, absolute values (percentages) were used to describe categorical and dichotomous variables, with a X2 test used for comparison. All antioxidant micronutrients’ correlation coefficients were calculated using the Spearman’s correlation analysis, and the antioxidant micronutrient metabolites were sorted into quartiles with the bottom group serving as a reference.
## 2.5.1. Statistical model 1: Multivariate logistic regression model
As a preliminary step, odds ratios (ORs) and $95\%$ confidence intervals (CIs) were calculated using multivariate logistic regression models. By comparing the second, third, and fourth quartiles of antioxidant micronutrients with the first quartile, we were able to evaluate the correlation between the 11 micronutrients and the prevalence of adult obesity and abdominal obesity. Three models were also utilized, with constant adjustment for possible confounding variables: model 1, which was not adjusted for by any covariates; model 2, which was based on model 1, adjusted for age, gender, education level, race, and poverty; Model 3, which derived from model 2 with the inclusion of smoker, alcohol user, energy intake, sedentary time, total cholesterol, high-density lipoprotein cholesterol, dietary supplement use, glomerular filtration rate (eGFR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and diabetes as independent variables. We directly normalized total energy intake as a confounding factor in various regression analysis models to adjust the nutrients for the effect of total energy.
## 2.5.2. Statistical model 2: Weighted quantile sum (WQS) regression model
As a step, to further examine the beneficial correlations of 11 antioxidant micronutrient combinations with the prevalence of obesity and abdominal obesity, we used the WQS regression model, which is a weighted quartile sum technique paired with linear (continuous outcome) or logistic (binary outcome) regression. Additionally, a weighted linear index was computed by modifying the WQS regression model to capture the total body burden of all micronutrients, with individual weights revealing the relative importance of each element to the overall connection.
## 2.5.3. Statistical model 3: The restricted cubic spline (RCS) regression model
Finally, we utilized log2-transformed concentrations of each micronutrient as continuous variables and BMI or waist circumference as binomial outcome variables to conduct a threshold effects analysis of obesity prevalence. we investigated whether the shape of the relationship between log2-transformed micronutrient intake and the prevalence of obesity and abdominal obesity was non-linear using the restricted cubic spline (RCS) regression model and analysis of variance (ANOVA) plotted at three nodes (10th, 50th, 90th) for antioxidant micronutrients. A segmental linear connection between log2-transformed micronutrient and obesity and abdominal obesity was fitted using segmental regression if the analysis was non-linear, controlling for age, sex, education level, ethnicity, poverty, smoking, alcohol use, calorie intake, sedentary time, total cholesterol, HDL cholesterol, dietary supplement usage, eGFR, SBP, DBP, and diabetes mellitus. Threshold inflection points were recalculated using a recursive technique and tweaked as needed. We considered a value of p 0.05 to be statistically significant (two-sided).
## 3.1. Study participants characteristics
The characteristics of the study population are shown in Table 1, consisting of 20,394 ($49.70\%$) males and 20,627 ($50.30\%$) females with a mean age of 47.98 ± 18.93 years old. Participants with obesity were typically older (49.26 ± 17.36 vs. 47.24 ± 19.75), more men ($55.1\%$ vs. $47.5\%$), more high-school educated individuals ($24.7\%$ vs. $22.6\%$), more poverty ($22.4\%$ vs. $21.5\%$), higher BMI (35.99 ± 5.88 vs. 24.83 ± 3.18), higher level of total cholesterol (193.67 ± 41.20 vs. 190.98 ± 40.98), and more diabetes ($18.80\%$ vs. $8.1\%$). All the above differences between obesity and non-obesity groups were statistically significant ($p \leq 0.05$).
**Table 1**
| Variable | Total (n = 41,021) | Non-obesity (n = 25,934) | Obesity (n = 15,087) | p value |
| --- | --- | --- | --- | --- |
| Age, years* | 47.98 (18.93) | 47.24 (19.75) | 49.26 (17.36) | <0.001 |
| Male, %# | 20,394 (49.7%) | 13,626 (52.5%) | 6,768 (44.9%) | <0.001 |
| Education level, %# | | | | <0.001 |
| Below high school | 10,163 (24.8%) | 6,320 (24.4%) | 3,843 (25.5%) | |
| High school | 9,584 (23.4%) | 5,855 (22.6%) | 3,729 (24.7%) | |
| Above high school | 21,274 (51.9%) | 13,759 (53.1%) | 7,515 (49.8%) | |
| Race/ethnicity, %# | | | | <0.001 |
| Mexican American | 6,793 (16.6%) | 4,040 (15.6%) | 2,753 (18.2%) | |
| Other Hispanic | 3,599 (8.8%) | 2,262 (8.7%) | 1,337 (8.9%) | |
| Non-Hispanic White | 17,641 (43.0%) | 11,536 (44.5%) | 6,105 (40.5%) | |
| Non-Hispanic Black | 8,976 (21.9%) | 4,911 (18.9%) | 4,065 (26.9%) | |
| Other race | 4,012 (9.8%) | 3,185 (12.3%) | 827 (5.5%) | |
| Poverty, %# | 8,954 (21.8%) | 5,576 (21.5%) | 3,378 (22.4%) | 0.037 |
| Smoker, %# | 18,237 (44.5%) | 11,627 (44.8%) | 6,610 (43.8%) | 0.046 |
| Drinking, %# | 28,956 (70.6%) | 18,750 (72.3%) | 10,206 (67.6%) | |
| Body mass index, kg/m2* | 29.2 (6.9) | 24.83 (3.18) | 35.99 (5.88) | <0.001 |
| HDL-C, mmol/L* | 1.37 (0.40) | 1.44 (0.42) | 1.24 (0.34) | <0.001 |
| Total cholesterol, mg/dL* | 191.97 (41.08) | 190.98 (40.98) | 193.67 (41.20) | <0.001 |
| eGFR, ml/min/1.73 m2* | 94.60 (24.38) | 95.41 (24.17) | 93.20 (24.67) | <0.001 |
| Energy intake, kcal/day* | 2,054.05 (874.25) | 2,085.10 (894.69) | 2,000.68 (835.30) | <0.001 |
| Dietary supplement use, %# | 19,935 (48.6%) | 12,818 (49.4%) | 7,117 (47.2%%) | <0.001 |
| Sedentary time, hrs# | | | | <0.001 |
| <3 h | 6,691 (16.3%) | 4,516 (17.4%) | 2,175 (14.4%) | |
| 3–6 h | 19,318 (47.1%) | 12,485 (48.1%) | 6,833 (45.3%) | |
| >6 h | 15,012 (36.6%) | 8,933 (34.4%) | 6,079 (40.3%) | |
| SBP, mmHg* | 124.03 (18.83) | 122.51 (19.21) | 126.64 (17.85) | <0.001 |
| DBP, mmHg* | 69.78 (13.00) | 68.77 (12.72) | 71.53 (13.28) | <0.001 |
| Diabetes, %# | 4,926 (12.0%) | 2,092 (8.1%) | 2,834 (18.8%) | <0.001 |
## 3.2. Distribution of and correlation among antioxidant micronutrient levels
As a result, Supplementary Table S1 demonstrates the concentrations and distribution of the 11 antioxidant micronutrients, with vitamin C having the highest median intake (50th = 63.5 mg). Iron (50th = 13.1 mg), zinc (50th = 9.9 mg), vitamin E (50th = 6.9 mg),copper (50th = 1.1 mg), β-carotene (50th = 989.5ug), vitamin A (50th = 494.0ug), retinol (50th = 325.0ug), selenium (50th = 101.3ug) and α-carotene (50th = 73.5ug) then follow, with β-cryptoxanthin (50th = 41.0ug) having the lowest.
Furthermore, we analyzed the correlation among 11 antioxidant micronutrients. Most of the antioxidant micronutrients have a relatively high correlation with the other 10 vitamins, according to correlation analysis (Supplementary Figure S2). We found that the correlations between α-carotene and β-carotene, iron and zinc, and selenium and zinc have r value >0.7; copper and vitamin E, vitamin A and retinol, β-carotene and vitamin A, iron and vitamin A, copper and vitamin A, vitamin C and β-cryptoxanthin, selenium and iron, copper and iron, copper and zinc and copper and selenium have r value of 0.5–0.7 (all $p \leq 0.05$).
## 3.3. Associations of antioxidant micronutrients with obesity
The reference category was regarded as the lowest quartile among the 11 antioxidant micronutrients when they were separated into quartiles. Table 2 displays the findings from the logistic regression analysis in different models that were adjusted for the covariates to determine the prevalence rate of obesity linked to antioxidant micronutrients. After adjustment of the selected covariates, we found that vitamin A was significantly and negatively associated with obesity (p for trend <0.001). When comparing the fourth quartile with the reference quartile, vitamin A has a lower odd ratio (OR) (0.86, $95\%$CI: 0.80–0.92). Similar associations between α-carotene (OR = 0.90 [0.84–0.96], p for trend = 0.010), β-carotene (OR = 0.91 [0.85–0.97], p for trend <0.001), β-cryptoxanthin (OR = 0.93 [0.87–0.99], p for trend = 0.001), vitamin C (OR = 0.83 [0.78–0.88], p for trend <0.001), iron (OR = 0.78 [0.72–0.85], p for trend <0.001,), as well as copper (OR = 0.86 [0.79–0.94], p for trend <0.001) and obesity were also observed when comparing the fourth quartile with the reference quartile. Reversely, selenium was significantly and positively associated with obesity (p for trend <0.001), having a greater OR (1.38, $95\%$CI: 1.26–1.50) when the fourth quartile is contrasted with the reference quartile. However, no significant association was found between vitamin E, retinol, and obesity.
**Table 2**
| Micronutrients | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | p for trend |
| --- | --- | --- | --- | --- | --- |
| Micronutrients | OR | OR (95%CI) | OR (95%CI) | OR (95%CI) | p for trend |
| Vitamin E (mg/day) | Vitamin E (mg/day) | Vitamin E (mg/day) | Vitamin E (mg/day) | Vitamin E (mg/day) | Vitamin E (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.95 (0.90–1.01) | 0.90 (0.85–0.95) | 0.83 (0.78–0.88) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.99 (0.93–1.05) | 0.97 (0.91–1.02) | 0.93 (0.88–0.98) | 0.067 |
| Model 3 | 1.00 (Ref.) | 1.04 (0.98–1.11) | 1.03 (0.97–1.10) | 1.03 (0.96–1.10) | 0.672 |
| Retinol (μg/day) | Retinol (μg/day) | Retinol (μg/day) | Retinol (μg/day) | Retinol (μg/day) | Retinol (μg/day) |
| Model 1 | 1.00 (Ref.) | 0.98 (0.92–1.03) | 0.97 (0.92–1.03) | 0.87 (0.82–0.92) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.99 (0.93–1.04) | 0.99 (0.94–1.05) | 0.93 (0.88–0.99) | 0.055 |
| Model 3 | 1.00 (Ref.) | 1.01 (0.95–1.07) | 1.01 (0.95–1.07) | 0.95 (0.89–1.02) | 0.203 |
| Vitamin A (μg/day) | Vitamin A (μg/day) | Vitamin A (μg/day) | Vitamin A (μg/day) | Vitamin A (μg/day) | Vitamin A (μg/day) |
| Model 1 | 1.00 (Ref.) | 1.01 (0.95–1.06) | 0.93 (0.87–0.98) | 0.77 (0.72–0.81) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.03 (0.97–1.09) | 0.96 (0.91–1.02) | 0.82 (0.77–0.87) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.03 (0.97–1.10) | 1.00 (0.93–1.06) | 0.86 (0.80–0.92) | <0.001 |
| α-carotene (μg/day) | α-carotene (μg/day) | α-carotene (μg/day) | α-carotene (μg/day) | α-carotene (μg/day) | α-carotene (μg/day) |
| Model 1 | 1.00 (Ref.) | 0.97 (0.92–1.03) | 0.97 (0.91–1.02) | 0.83 (0.79–0.88) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.95 (0.90–1.01) | 0.94 (0.89–0.99) | 0.85 (0.80–0.90) | <0.001 |
| Model 3 | 1.00 (Ref.) | 0.97 (0.91–1.03) | 0.97 (0.91–1.03) | 0.90 (0.84–0.96) | 0.010 |
| β-carotene (μg/day) | β-carotene (μg/day) | β-carotene (μg/day) | β-carotene (μg/day) | β-carotene (μg/day) | β-carotene (μg/day) |
| Model 1 | 1.00 (Ref.) | 1.01 (0.96–1.07) | 0.93 (0.88–0.98) | 0.80 (0.76–0.85) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.03 (0.97–1.09) | 0.95 (0.90–1.01) | 0.83 (0.78–0.88) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.05 (0.98–1.11) | 1.01 (0.95–1.08) | 0.91 (0.85–0.97) | <0.001 |
| β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) |
| Model 1 | 1.00 (Ref.) | 1.04 (0.98–1.10) | 1.01 (0.96–1.07) | 0.89 (0.85–0.95) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.02 (0.96–1.08) | 0.99 (0.93–1.05) | 0.87 (0.82–0.92) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.06 (0.99–1.12) | 1.02 (0.96–1.09) | 0.93 (0.87–0.99) | 0.001 |
| Vitamin C (mg/day) | Vitamin C (mg/day) | Vitamin C (mg/day) | Vitamin C (mg/day) | Vitamin C (mg/day) | Vitamin C (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.99 (0.93–1.04) | 0.86 (0.81–0.91) | 0.77 (0.73–0.81) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.97 (0.92–1.03) | 0.84 (0.79–0.89) | 0.76 (0.72–0.81) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.00 (0.94–1.06) | 0.89 (0.83–0.95) | 0.83 (0.78–0.88) | <0.001 |
| Iron (mg/day) | Iron (mg/day) | Iron (mg/day) | Iron (mg/day) | Iron (mg/day) | Iron (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.90 (0.85–0.95) | 0.87 (0.83–0.92) | 0.73 (0.69–0.77) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.95 (0.90–1.01) | 0.97 (0.91–1.02) | 0.85 (0.80–0.90) | <0.001 |
| Model 3 | 1.00 (Ref.) | 0.92 (0.86–0.98) | 0.90 (0.84–0.97) | 0.78 (0.72–0.85) | <0.001 |
| Zinc (mg/day) | Zinc (mg/day) | Zinc (mg/day) | Zinc (mg/day) | Zinc (mg/day) | Zinc (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.94 (0.89–0.99) | 0.89 (0.84–0.94) | 0.84 (0.80–0.89) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.00 (0.95–1.06) | 1.01 (0.95–1.07) | 1.02 (0.96–1.09) | 0.917 |
| Model 3 | 1.00 (Ref.) | 1.05 (0.98–1.12) | 1.07 (0.99–1.15) | 1.10 (1.01–1.20) | 0.157 |
| Selenium (μg/day) | Selenium (μg/day) | Selenium (μg/day) | Selenium (μg/day) | Selenium (μg/day) | Selenium (μg/day) |
| Model 1 | 1.00 (Ref.) | 0.97 (0.92–1.03) | 1.02 (0.96–1.08) | 0.90 (0.85–0.95) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.04 (0.98–1.10) | 1.18 (1.11–1.25) | 1.14 (1.07–1.22) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.12 (1.05–1.19) | 1.33 (1.24–1.43) | 1.38 (1.26–1.50) | <0.001 |
| Copper (mg/day) | Copper (mg/day) | Copper (mg/day) | Copper (mg/day) | Copper (mg/day) | Copper (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.93 (0.88–0.99) | 0.86 (0.81–0.91) | 0.70 (0.66–0.74) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.98 (0.93–1.04) | 0.96 (0.90–1.01) | 0.83 (0.78–0.88) | <0.001 |
| Model 3 | 1.00 (Ref.) | 0.98 (0.92–1.05) | 0.98 (0.91–1.05) | 0.86 (0.79–0.94) | <0.001 |
According to the results from the restricted cubic spline regression, retinol (p for nonlinearity = 0.001), vitamin A (p for nonlinearity <0.001), α-carotene (p for nonlinearity = 0.001), β-carotene (p for nonlinearity <0.001), β-cryptoxanthin (p for nonlinearity <0.001), vitamin C (p for nonlinearity <0.001), iron (p for nonlinearity = 0.009) and cooper (p for nonlinearity = 0.003) all had a non-linear association with obesity (Figure 1). Threshold effect analysis demonstrated that the inflection points of retinol, vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, iron, and cooper were 235.57, 374.81, 58.89, 891.44, 30.70, 43,410.00, 11,240.00, and 990.00 μg/day, respectively (Supplementary Table S2).Take vitamin C and β-cryptoxanthin for example, when the daily intake of dietary vitamin C exceeded 43.41 mg, the risk of obesity decreased with the increase in daily intake ($p \leq 0.05$), but when the daily intake was less than 43.41 mg, the trend of increasing risk of obesity gradually stabilized and the results were not statistically significant ($p \leq 0.05$); while when the daily intake of dietary β-cryptoxanthin exceeded 30.70 ug, the risk of obesity decreased with the increase of daily intake ($p \leq 0.05$). Similarly, when the daily dietary intake of β-cryptoxanthin was more than 30.70 ug, the prevalence risk of obesity decreased with increasing daily intake ($p \leq 0.05$), and conversely, when its daily dietary intake was less than 30.70 ug, the prevalence risk of obesity increased with decreasing daily intake ($p \leq 0.05$).
**Figure 1:** *Restricted cubic spline (RCS) analysis with multivariate-adjusted associations between dietary antioxidant micronutrients (A: Vitamin E; B: Retinol; C: Vitamin A; D: α-Carotene; E: β-Carotene; F: β-Cryptoxanthin; G: Vitamin C; H: Iron; I: Zinc; J: Selenium; and K: Copper) and the prevalence of obesity in adults. Models are adjusted for age, sex, education level, race, poverty, smoker, alcohol user, energy intake, sedentary time, total cholesterol, high-density lipoprotein cholesterol, dietary supplement use, eGFR, SBP, DBP, and diabetes.*
## 3.4. Associations of antioxidant micronutrients with abdominal obesity
Similar results from logistic regression analysis were also observed between the 11 antioxidant micronutrients and abdominal obesity (Table 3). In the fully adjusted models, compared with the lowest quartile, the ORs of abdominal obesity in the highest quartile were 0.87 (0.81–0.93), 0.87 (0.81–0.93), 0.84 (0.78–0.90), 0.87 (0.81–0.93), 0.79 (0.74–0.85), 0.82 (0.75–0.89), 0.86 (0.79–0.94), and 1.16 (1.06–1.27) for vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, iron, copper, and selenium, respectively. Vitamin E, retinol, and zinc were not significantly associated with abdominal obesity, either.
**Table 3**
| Micronutrients | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | p for trend |
| --- | --- | --- | --- | --- | --- |
| Micronutrients | OR | OR (95%CI) | OR (95%CI) | OR (95%CI) | p for trend |
| Vitamin E (mg/day) | Vitamin E (mg/day) | Vitamin E (mg/day) | Vitamin E (mg/day) | Vitamin E (mg/day) | Vitamin E (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.92 (0.87–0.97) | 0.85 (0.80–0.90) | 0.69 (0.65–0.73) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.00 (0.94–1.06) | 1.01 (0.95–1.07) | 0.93 (0.88–0.99) | 0.038 |
| Model 3 | 1.00 (Ref.) | 1.05 (0.98–1.12) | 1.09 (1.02–1.17) | 1.06 (0.98–1.14) | 0.098 |
| Retinol (μg/day) | Retinol (μg/day) | Retinol (μg/day) | Retinol (μg/day) | Retinol (μg/day) | Retinol (μg/day) |
| Model 1 | 1.00 (Ref.) | 1.02 (0.97–1.08) | 1.06 (1.01–1.12) | 0.87 (0.82–0.92) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.98 (0.92–1.04) | 1.02 (0.96–1.08) | 0.96 (0.91–1.02) | 0.264 |
| Model 3 | 1.00 (Ref.) | 1.01 (0.95–1.08) | 1.05 (0.99–1.13) | 1.00 (0.94–1.08) | 0.355 |
| Vitamin A (μg/day) | Vitamin A (μg/day) | Vitamin A (μg/day) | Vitamin A (μg/day) | Vitamin A (μg/day) | Vitamin A (μg/day) |
| Model 1 | 1.00 (Ref.) | 1.10 (1.04–1.16) | 1.02 (0.96–1.07) | 0.83 (0.79–0.88) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.06 (0.99–1.13) | 0.96 (0.90–1.02) | 0.82 (0.77–0.87) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.08 (1.01–1.15) | 1.00 (0.94–1.08) | 0.87 (0.81–0.93) | <0.001 |
| α-carotene (μg/day) | α-carotene (μg/day) | α-carotene (μg/day) | α-carotene (μg/day) | α-carotene (μg/day) | α-carotene (μg/day) |
| Model 1 | 1.00 (Ref.) | 1.02 (0.96–1.08) | 1.14 (1.08–1.21) | 0.98 (0.93–1.03) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.94 (0.89–0.99) | 0.93 (0.87–0.99) | 0.81 (0.76–0.86) | <0.001 |
| Model 3 | 1.00 (Ref.) | 0.97 (0.90–1.03) | 0.97 (0.91–1.04) | 0.87 (0.81–0.93) | <0.001 |
| β-carotene (μg/day) | β-carotene (μg/day) | β-carotene (μg/day) | β-carotene (μg/day) | β-carotene (μg/day) | β-carotene (μg/day) |
| Model 1 | 1.00 (Ref.) | 1.00 (0.95–1.06) | 1.02 (0.96–1.08) | 0.90 (0.86–0.96) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.99 (0.93–1.05) | 0.91 (0.86–0.97) | 0.76 (0.72–0.81) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.00 (0.93–1.06) | 0.98 (0.91–1.04) | 0.84 (0.78–0.90) | <0.001 |
| β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) | β-cryptoxanthin (μg/day) |
| Model 1 | 1.00 (Ref.) | 1.04 (0.99–1.10) | 1.06 (1.00–1.12) | 0.93 (0.88–0.98) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.98 (0.92–1.04) | 0.94 (0.89–1.00) | 0.81 (0.76–0.86) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.02 (0.96–1.09) | 0.98 (0.92–1.05) | 0.87 (0.81–0.93) | <0.001 |
| Vitamin C (mg/day) | Vitamin C (mg/day) | Vitamin C (mg/day) | Vitamin C (mg/day) | Vitamin C (mg/day) | Vitamin C (mg/day) |
| Model 1 | 1.00 (Ref.) | 1.02 (0.97–1.08) | 0.92 (0.87–0.97) | 0.73 (0.69–0.78) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.94 (0.88–0.99) | 0.82 (0.77–0.87) | 0.72 (0.68–0.76) | <0.001 |
| Model 3 | 1.00 (Ref.) | 0.97 (0.91–1.04) | 0.87 (0.82–0.93) | 0.79 (0.74–0.85) | <0.001 |
| Iron (mg/day) | Iron (mg/day) | Iron (mg/day) | Iron (mg/day) | Iron (mg/day) | Iron (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.86 (0.82–0.91) | 0.75 (0.70–0.79) | 0.57 (0.54–0.60) | <0.001 |
| Model 2 | 1.00 (Ref.) | 0.98 (0.92–1.04) | 0.97 (0.91–1.03) | 0.86 (0.81–0.92) | <0.001 |
| Model 3 | 1.00 (Ref.) | 0.95 (0.88–1.02) | 0.92 (0.85–0.99) | 0.82 (0.75–0.89) | <0.001 |
| Zinc (mg/day) | Zinc (mg/day) | Zinc (mg/day) | Zinc (mg/day) | Zinc (mg/day) | Zinc (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.87 (0.82–0.92) | 0.73 (0.69–0.77) | 0.57 (0.54–0.61) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.00 (0.94–1.06) | 0.99 (0.93–1.05) | 0.99 (0.93–1.05) | 0.960 |
| Model 3 | 1.00 (Ref.) | 1.03 (0.96–1.10) | 1.03 (0.95–1.11) | 1.04 (0.96–1.14) | 0.157 |
| Selenium (μg/day) | Selenium (μg/day) | Selenium (μg/day) | Selenium (μg/day) | Selenium (μg/day) | Selenium (μg/day) |
| Model 1 | 1.00 (Ref.) | 0.85 (0.81–0.90) | 0.74 (0.70–0.78) | 0.53 (0.50–0.56) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.01 (0.95–1.07) | 1.08 (1.02–1.15) | 1.04 (0.98–1.11) | 0.062 |
| Model 3 | 1.00 (Ref.) | 1.05 (0.98–1.12) | 1.15 (1.07–1.25) | 1.16 (1.06–1.27) | 0.001 |
| Copper (mg/day) | Copper (mg/day) | Copper (mg/day) | Copper (mg/day) | Copper (mg/day) | Copper (mg/day) |
| Model 1 | 1.00 (Ref.) | 0.92 (0.87–0.98) | 0.77 (0.73–0.81) | 0.58 (0.54–0.61) | <0.001 |
| Model 2 | 1.00 (Ref.) | 1.00 (0.94–1.06) | 0.93 (0.88–0.99) | 0.82 (0.77–0.87) | <0.001 |
| Model 3 | 1.00 (Ref.) | 1.01 (0.94–1.08) | 0.95 (0.88–1.03) | 0.86 (0.79–0.94) | <0.001 |
Vitamin E (p for nonlinearity = 0.015), vitamin A (p for nonlinearity <0.001), α-carotene (p for nonlinearity <0.001), β-carotene (p for nonlinearity <0.001), β-cryptoxanthin (p for nonlinearity <0.001), vitamin C (p for nonlinearity <0.001), selenium (p for nonlinearity = 0.017) and cooper (p for nonlinearity = 0.020) all had a non-linear association with abdominal obesity (Figure 2). Threshold effect analysis demonstrated that the inflection points of vitamin E, vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, selenium, and cooper were 7,360.00, 385.34, 55.72, 820.30, 27.86, 42,813.00, 130.69, and 933.00 μg/day, respectively (Supplementary Table S3).
**Figure 2:** *Restricted cubic spline (RCS) analysis with multivariate-adjusted associations between dietary antioxidant micronutrients (A: Vitamin E; B: Retinol; C: Vitamin A; D: α-Carotene; E: β-Carotene; F: β-Cryptoxanthin; G: Vitamin C; H: Iron; I: Zinc; J: Selenium; and K: Copper) and the prevalence of abdominal obesity in adults. Models are adjusted for age, sex, education level, race, poverty, smoker, alcohol user, energy intake, sedentary time, total cholesterol, high-density lipoprotein cholesterol, dietary supplement use, eGFR, SBP, DBP, and diabetes.*
## 3.5. WQS regression analysis between the antioxidant micronutrients and obesity
Using the WQS regression analysis to explore the inverse associations of the combination of antioxidant micronutrients and obesity. WQS regression limits the direction of exposure-outcome relationships to the negative. The results showed that the combined index for the 11 antioxidant micronutrients was inversely correlated to obesity (adjusted OR = 0.88 [0.84–0.92], $p \leq 0.001$), abdominal obesity (adjusted OR = 0.87 [0.83–0.91], $p \leq 0.001$, Table 4). In the negative associations between antioxidant complex and obesity, we found that iron (weight = $40.20\%$) and vitamin C (weight = $37.60\%$) had the greatest weight (Figure 3). For the abdominal obesity, iron (weight = $41.60\%$) and vitamin C (weight = $41.60\%$) also had the highest weight among the 11 antioxidant micronutrients (Figure 3).
## 4. Discussion
After correcting for common confounders, our research of 11 dietary antioxidant micronutrients in a cross-sectional, nationally representative general US population revealed that a combination of 11 antioxidant micronutrients was protective against the prevalence of both obesity and abdominal obesity, i.e., a negative correlation existed between obesity and the combination of 11 antioxidant micronutrients. However, we observed that not all antioxidant micronutrients were reliably protective for those who were both overweight and had excess fat in their abdominal regions. In the WQS model, the weight of each micronutrient showed that practically all antioxidants, except selenium, were negatively related to obesity, notably iron and vitamin C. In our multiple logistic regression study, we found that almost all of the antioxidant micronutrients were independently correlated negatively with obesity, except vitamin E, retinol, and zinc, and that selenium was correlated favorably with obesity. Additionally, the RCS analysis showed that there was a nonlinear relationship between obesity and Retinol, vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, Iron, and Cooper, with inflection points at 235.57, 374.81, 58.89, 891.44, 30.70, 43,410.00, 11,240.00, and 990.00 g/day, respectively. While inflection points were found at 7,360.00, 385.34, 55.72, 820.30, 27.86, 42,813.00, 130.69, and 933.00 g/day for vitamin E, vitamin A, α-carotene, β-carotene, β-cryptoxanthin, vitamin C, selenium, and Cooper, respectively, in abdominal obesity.
Factors including as genetics, diet, good physical activity, environment, and socioeconomic status all have a role in the development of obesity which is strongly linked to oxidative stress [19]. Current evidence shows that oxidative stress may promote obesity by boosting preadipocyte proliferation, accelerating white adipose tissue (WAT) deposition, and modifying food intake and that obesity can produce systemic oxidative stress by numerous mechanisms, including superoxide generation by NADPH oxidase, oxidative phosphorylation, protein kinase C activation, polyol, and hexosamine pathways, further exacerbating obesity [20]. In the meanwhile, dietary antioxidants have gained popularity for their ability to neutralize free radicals in the body. As a result, growing studies are looking at the link between fat storage and antioxidant levels in obese people. Aeberli et al. found in Swedish children that dietary intake of antioxidant vitamins (vitamin E and vitamin C and β-carotene) was significantly associated with leptin levels ($p \leq 0.05$), suggesting that low concentrations of antioxidant vitamins may alter the genetic expression of leptin, leading to the development of leptin resistance and increasing the risk of obesity [21, 22]. Another cross-sectional investigation revealed that blood levels of vitamin C were substantially correlated with food intake [23]. Vitamin C scavenges free radicals and suppresses lipid peroxidation, which leads to increased vitamin C utilization and decreased vitamin C in the blood in obese people as a result of increased body fat and increased systemic oxidative stress [24]. Furthermore, research has revealed that vitamin deficits are linked to abdominal fat formation in obese persons. For example, one Indian research [25] discovered a negative relationship between vitamin C and total body fat. A case–control study in Thailand [26] found a negative association between BMI, waist, and hip circumference and serum concentrations of vitamin E and retinol, and another study [27] showed a significant association between vitamin A and insulin resistance in morbidly obese patients which may be due to the fact that high doses of vitamin A may inhibit the formation of mature adipocytes and also indirectly modifies insulin sensitivity by regulating the production of bioactive proteins secreted by adipocytes, including leptin and resistin. Retinol and carotenoids are precursors to vitamin A. According to certain studies, dietary beta-carotene and its blood levels are favorably associated [28]. Serum beta-carotene levels were also shown to be lower in obese persons, most likely because carotenoids are primarily distributed between serum and adipose tissue, and because adipose tissue is an essential storage tissue in humans, a greater proportion of carotenoid intake would be absorbed by adipose tissue in individuals with high adiposity than in those with low adiposity (29–31). This also implies that obese people need consume more carotenoids in order to satisfy their antioxidant requirements. Additionally, research has shown that carotenoids (32–34) which has been proven to improve insulin resistance, reduce the size of adipocytes and body fat tissue, lower pro-inflammatory indicators of obesity such as LDL-c and VLDL-c, and raise high-density lipoprotein cholesterol (HDL-c), among other benefits promote prevention. These previous studies are consistent with our partial results that vitamin A, vitamin C, iron, β-cryptoxanthin, and carotenoids are associated with obesity and are negatively correlated and are an independent protective factor.
Interestingly, we discovered that selenium is not an independent protective factor against obesity, but is positively related to it. Selenium is well-known for working as an antioxidant to influence the thyroid, immune system, and reproduction. Some studies have discovered a significant correlation between dietary selenium intake and obesity. Hawkes et al. study’s revealed that those who consumed more selenium gained weight, whereas those in the low-selenium group lost weight [35]. Liver cirrhosis and steatosis, which may be caused by obesity, were shown to be positively correlated with increased dietary selenium intake and blood selenium concentrations by Liu. et al. [ 36]. Furthermore, multiple studies have revealed a link between dietary selenium intake and blood selenium levels and an increased risk of type 2 diabetes in diverse groups (37–39). Meanwhile, *Excessive selenium* exposure has been seen with similar negative consequences in animal experiments, and one possible explanation is that selenoproteins activate the development of insulin resistance. New evidence suggest that selenium toxicity manifests as oxidative stress, blocked biofilm development, and enzyme function suppression when present in excessive amounts. The results match up with what we found.
Zinc’s possible association with obesity remains controversial. Zinc is a vital vitamin that aids in organism development, reproductive tissue repair, and cellular immunity. It has been linked to the degree of obesity in several studies. Zinc deficiency can stimulate fat deposition and raise body weight [37, 38, 40]. Similarly to this, the link between zinc status and obesity may be explained by the interaction between zinc metabolism and leptin, since zinc shortage reduces leptin levels in the blood in humans and rat adipocytes while zinc supplementation has the opposite effect [41, 42].Visceral adipose tissue accumulation was induced by long-term zinc supplementation in mice experiments without the involvement of adipogenesis or adipolysis [43]. Based on our findings, we found that zinc does not function as a standalone protective factor against obesity and has a modest protective impact against weight gain. It has been suggested that taking zinc supplements may weaken the immune system, lower HDL cholesterol levels, etc., which could lead to copper deficiency or an increase in copper deficiency [44], but researchers still need to figure out the exact mechanism.
Nevertheless, the antioxidant capacity of nutrients can be influenced by other members of the internal antioxidant network, which is another key factor to consider. For instance, vitamin C and vitamin E are known to work together to renew α-tocopherol in membranes and lipoproteins, which is crucial for protecting protein thiol groups from oxidation [45]. The change in carotenoids’ [33, 46] prooxidant and antioxidant behavior has also been found to depend on their interaction with vitamin C and vitamin E in biological membranes. Also zinc status can affect vitamin A metabolism, including its absorption, transport, and utilization [47]. Both the zinc-dependent conversion of retinol oxidation to the retina by the action of retinol dehydrogenase and the regulatory function of zinc in vitamin A transport mediated by protein synthesis are believed to be involved in this process.
Therefore, the biological effects of antioxidant micronutrients cannot be adequately explained by a single nutrient model. Recently, researchers in the area of nutritional health have begun to shift their attention from looking at the effects of individual nutrients on health to the effects of being exposed to combinations of nutrients. Here, the inverse association found between obesity and 11 different antioxidant combinations is evaluated using WQS regression, with iron and vitamin C accounting for the bulk of the impact.
Despite being the second most common metal on Earth, iron has poor bioavailability in humans because it often forms very insoluble oxides. Obesity is strongly linked to iron deficiency (48–50), and research [51] has found that one of the pathological mechanisms involved in obesity is iron deposition in the cytoplasm of adipocytes, which results in increased iron content in adipose tissue, possibly as a result of a fourfold increase in intracellular iron content in adipocytes and the expression of iron regulatory proteins. Some animal studies [52, 53] have revealed that a high iron diet may lead to elevated leptin levels in the body, whereas iron produces endocrine dysfunction in adipose tissue and decreases adipocyte lipocalin synthesis. This is consistent with our analysis that iron is independently associated with obesity and vice versa. In addition, we found from a threshold effect analysis that iron intake above 11.24 mg/day may contribute to reduced obesity prevalence. Vitamin C (VC, ascorbic acid) has been shown to prevent obesity by the followings [54]: (a) regulating lipid accumulation in adipocytes through direct action on differentiation mechanisms or by modulating motor behavior, (b) inhibiting lipolysis and thus reducing the export of fatty acids to the system, (c) inhibiting glucocorticoid production, and (d) directly interfering with adipose cell-macrophage interactions, (e) scavenging of reactive oxygen species, and (f) possible inhibition of the HIF-1a pathway. Our threshold impact study also revealed that consuming more than 43.41 milligrams of vitamin C per day may have contributed to lower obesity rates. Future dietary strategies for the prevention and treatment of obesity might be devised using these results as a reference dosage, but the precise intake for the population still has to be determined by experimental trials.
Our research has various benefits, not the least of which is that we are the first to explore the connection between 11 micronutrients and obesity in a single cohort. For one, to mitigate mistakes and prevent low correlation components from influencing the analysis, we accounted for the age, education, race/ethnicity, socioeconomic situation as well as other possible complicating variables like (BMI, smoking or not, total energy intake, etc.) which may influence the correlation between antioxidant micronutrients and obesity in the RSC model and the WQS regression model. Second, we have confirmed the accuracy of our findings by verifying them using two distinct models.
However, our study has several limitations. Firstly, we were restricted in our ability to draw causal inferences due to the cross-sectional design of the survey. Second, we may have introduced some unintentional bias into our study by limiting ourselves to focusing on the link between micronutrient intake and obesity and not also considering the role of micronutrient supplementation. At the same time, we did not investigate the relationship between circulating levels of antioxidant micronutrients and obesity, which might be a future research topic. Thirdly, our study’s analysis was limited to the NHANES cohort in the United States; the conclusions would have had greater weight if they had been corroborated by data from other nations or areas.
## 5. Conclusion
According to our research, the combination of 11 antioxidants is negatively associated with the prevalence of obesity, and iron/vitamin C have the greatest weight in the negative associations between antioxidant complex and obesity, as well as abdominal obesity. Future studies are required to analyze and determine the ideal intake amounts to lower obesity in at-risk individuals, as well as to better understand the interactions and complex of many micronutrients and their combined impact on obesity.
## 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
YY and HX designed research, drafted the manuscript, performed statistical analysis, extracted the data, and conducted analyses. YZ, LC, and CT took charge of software operation. LM, YC, and BH revised the manuscript. All authors reviewed, edited, and finalized 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.2023.1098761/full#supplementary-material
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|
---
title: Periodontitis may predict the use of prescription medicines later in life,
a database study
authors:
- Freja Frankenhaeuser
- Birgitta Söder
- Håkan Källmén
- Esa R. Korpi
- Jukka H. Meurman
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10040546
doi: 10.3389/fphar.2023.1146475
license: CC BY 4.0
---
# Periodontitis may predict the use of prescription medicines later in life, a database study
## Abstract
Medications used for the treatment of diseases also affect oral health. We investigated how having/not having periodontitis at baseline in 1985 was associated with purchases of medicines in the long term. The study paradigm is in the oral health-systemic health connections. We hypothesized that periodontitis links to purchases of medicines later in life. The study cohort consisted of 3,276 individuals from the greater Stockholm area, Sweden. Of them, 1,655 were clinically examined at baseline. Patients were followed-up for >35 years, using the national population and patient registers. The burden of systemic diseases and purchases of medicines were statistically analyzed comparing patients with ($$n = 285$$) and without ($$n = 1$$,370) periodontitis. The results showed that patients with periodontitis had purchased more of certain medications than non-periodontitis patients. Periodontitis patients purchased significantly more drugs used in diabetes ($$p \leq 0.035$$), calcium channel blockers ($$p \leq 0.016$$), drugs acting on the renin-angiotensin system ($$p \leq 0.024$$), and nervous system drugs ($$p \leq 0.001$$). Hence, patients with periodontitis indeed had purchased specific medications statistically significantly more than the periodontally healthy ones. This indicates that periodontitis, over time, might increase the risk for systemic diseases with the subsequent need for medication.
## 1 Introduction
Periodontitis or periodontal disease is a chronic inflammation of the gums and tooth supporting tissues, leading to attachment and bone loss, due to the immune response caused by accumulations of bacterial biofilm on the teeth. Numerous studies have verified the link between poor oral health and systemic health (Meurman and Bascones-Martinez, 2021). In particular, periodontal disease is associated with cardiovascular diseases and diabetes, but also with many other diseases (Humphrey et al., 2008; Pussinen et al., 2022). Recently, periodontitis was shown to associate even with the outcome of COVID-19 (Marouf et al., 2021; Gupta et al., 2022). For example, in the study of Orilisi and coworkers (Orilisi et al., 2021) it was shown that patients with oral health problems were referred to intensive care more often than those without. The pathomechanism here involved is the chronic oral infection that upregulates many cytokines and inflammatory mediators with subsequent systemic organ effects (Hansen and Holmstrup, 2022).
Little research is aimed at periodontitis and its effect on medication use (Wang et al., 2020). Anticholinergic and psychiatric medications are the most discussed in this context. Well-known oral side effects of drugs, in general, are hyposalivation, xerostomia, gingival overgrowth, hypersalivation, lichenoid reactions, and osteonecrosis of the jaws (Kaur et al., 2010; Miranda-Rius et al., 2015; Trackman and Kantarci, 2015; Glick et al., 2020; Yuan and Woo, 2020). Drugs for the treatment of hypertension and diabetes, are examples of medication that may cause hyposalivation with subsequent subjective xerostomia (Närhi et al., 1992). Low salivary flow presents a risk for dental diseases, in particular caries, but also periodontal health may worsen if the patient has dry mouth (Mizutani et al., 2015; Pająk-Łysek et al., 2021). However, it should be emphasized that the link between periodontitis and saliva secretion is not as straightforward as it is with caries (Rees, 1998).
In the present longitudinal cohort study, we investigated the association between baseline periodontal status and the purchase of prescription medicines later in life. We hypothesized that people with poor periodontal health would present with more systemic diseases and, consequently, would need medication more often than those who originally were periodontally healthy. The subjects of our study had been followed up for more than 35 years and the investigation was based on different national patient registers in Sweden.
## 2.1 Subjects of the cohort
Our database originates from the year 1985 and consists of 3,273 randomized subjects enrolled from the Stockholm metropolitan area, Sweden. All our participants were born from 1945–1954 on the 20th of each month. The basic cohort size was 105,798 persons (Söder et al., 1994). Since 1985 the subjects’ health parameters had been followed-up, now for over 35 years. In the present study, our sample consists of 1,655 patients, 824 men, and 831 women. Of these patients, 285 had had periodontitis at baseline in 1985 (Figure 1). In 1985, 1,676 patients were clinically examined at baseline. However, due to the fallout of 21 patients, our clinically examined and followed-up study group consists of 1,655 patients.
**FIGURE 1:** *Flow chart of cohort and registers available. In the present study, we mainly focused on the pharmacy register.*
## 2.2 The drug (pharmacy) register
The database used for examining the prescription of medication among patients is the Swedish National Pharmacology register. This register consists of the 1,655 subjects’ total procurement history of medications during the timespan of the years 2005–2017. The register contains altogether 469,789 purchases with 975 individual Anatomical Therapeutic Chemical (ATC) codes. For analysis, procurement of medication or the medication class was coded as one and no procurement as 0.
## 2.3 Periodontitis diagnosis
In the clinical oral examinations in 1985, the patients underwent oral examination where plaque index, gingival index (GI), and periodontal pocket probing (CAL) were registered. Periodontal pockets (PD) over 5 mm were recorded. A dichotomized variable was created for statistical analysis, where patients with periodontitis were coded one and periodontally healthy control subjects 0.
## 2.4 Socio-economic status
In the baseline data from 1985, patients were divided into higher or lower socio-economic classes based on income and level of education. Patients with a lower level of education and low or no income were coded as having lower socio-economic status and ones with income and a higher level of education were coded as higher. This was used as a covariate in our research. One dichotomized variable was created from the original baseline variable to indicate the patients’ economic status so that the subjects with high socioeconomic status were coded 0 and those with low 1, respectively.
## 2.5 Diagnoses before 1985
To control for diseases and subsequent systemic medicine use before 1985, the Swedish National Hospital register was used. Patients with at least one diagnosis given in hospital care were categorized in a dichotomized variable: a hospital diagnosis before 1985 was coded as one and no hospital diagnosis as 0, respectively. Hospitalization due to poisoning or pregnancy was disregarded.
## 2.6 Tobacco products
At baseline, the patient´s use of tobacco products was registered. A dichotomized tobacco products variable was created where patients who were smoking or using *Swedish snus* in 1985 were coded as one and patients not using tobacco products were coded as 0.
## 2.7 Gender
Research has shown a difference in oral health among men and women, where men often have worse oral health. To take this into account a dichotomized variable was created where women were coded as 0 and men as 1.
## 2.8 Statistical analyses
Descriptive statistics, Chi2, p-tests, and logistic regression analyses were conducted in SPSS 28.0 software. A single-sided hypothesis was used in this study, resulting in the use of one-tailed tests. Descriptive statistics as frequencies were conducted, differences between groups were tested by Mann-Whitney U-tests, and differences in the distribution of data were analyzed by Chi2. Logistic regression analyses of procurement of medicines with periodontitis as explaining variable were controlled for gender (men 1, women 0), tobacco products (yes 1, no 0), socio-economic (lower 1, higher 0), and if the subject had a diagnosis of systemic disease before 1985 (yes 1, no 0). Data reorganization and summation of the different registers were made in Visual Studio Code 2, Python 3.9.10 64-bit.
## 3 Results
The number of patients with periodontitis, tobacco usage, gender, and diagnoses at baseline are given in Table 1. Patients with periodontitis had not purchased more medications than the non-periodontitis subjects between the years 2005–2017. Fewer patients with periodontitis had acquired medications in general than periodontally healthy individuals ($89.5\%$ vs $93.4\%$). The medication categories most patients had purchased during the timespan was ATC category J, anti-infectives for systemic use ($$n = 1$$,379), as can be seen in Table 2. The second in frequency was medicines used for diseases of the nervous system ($$n = 1$$,197) and, third, respiratory medications ($$n = 1$$,157). Comparing the purchases by periodontitis patients with those of the periodontally healthy, periodontitis patients had purchased more drugs of the ATC category C, cardiovascular system, L, antineoplastic and immunomodulating agents, and P, antiparasitic products, insecticides and repellents (Table 2).
Looking at the pharmaceutical groups of drugs according to the ATC classification, there were medicines in five different groups that had been purchased more by the periodontally diseased patients. These were drugs used for diabetes ($$p \leq 0.035$$), calcium channel blockers ($$p \leq 0.016$$), drugs acting on the renin-angiotensin system ($$p \leq 0.032$$), lipid modifying agents ($$p \leq 0.024$$), and drugs used for other nervous system diseases ($$p \leq 0.001$$). Another fourteen different categories of medications used more by the periodontitis patients ($$p \leq 0.102$$–0.462) were cardiovascular drugs and beta-blocking agents, in particular, antineoplastic agents, drugs for endocrine diseases, immunosuppressants, anti-inflammatory, and antirheumatic drugs, topical products for joint and muscular pain, muscle relaxants, antigout preparations, analgesics, antiepileptics, antiparkinson drugs, psycholeptics (antipsychotics, anxiolytics, hypnotics, and sedatives) and antiprotozoals. The details age given in Table 3.
**TABLE 3**
| ATC classification | Total | Non-periodontitis | Non-periodontitis.1 | Periodontitis | Periodontitis.1 | p-value |
| --- | --- | --- | --- | --- | --- | --- |
| Alimentary tract and metabolism | Alimentary tract and metabolism | Alimentary tract and metabolism | Alimentary tract and metabolism | Alimentary tract and metabolism | Alimentary tract and metabolism | Alimentary tract and metabolism |
| Drugs used in diabetes | Drugs used in diabetes | Drugs used in diabetes | Drugs used in diabetes | Drugs used in diabetes | Drugs used in diabetes | Drugs used in diabetes |
| Have not purchased | 1499 | 1249 | (83.3%) | 250 | (16.7%) | |
| Have purchased | 156 | 121 | (77.6%) | 35 | (22.4%) | 0.035 |
| Cardiovascular system | Cardiovascular system | Cardiovascular system | Cardiovascular system | Cardiovascular system | Cardiovascular system | Cardiovascular system |
| Cardiac therapy | Cardiac therapy | Cardiac therapy | Cardiac therapy | Cardiac therapy | Cardiac therapy | Cardiac therapy |
| Have not purchased | 1444 | 1198 | (83.0%) | 246 | (17.0%) | |
| Have purchased | 211 | 172 | (81.5%) | 39 | (18.5%) | 0.300 |
| Beta blocking agents | Beta blocking agents | Beta blocking agents | Beta blocking agents | Beta blocking agents | Beta blocking agents | Beta blocking agents |
| Have not purchased | 1178 | 982 | (83.4%) | 196 | (16.6%) | |
| Have purchased | 477 | 388 | (81.3%) | 89 | (18.7%) | 0.162 |
| Calcium channel blockers | Calcium channel blockers | Calcium channel blockers | Calcium channel blockers | Calcium channel blockers | Calcium channel blockers | Calcium channel blockers |
| Have not purchased | 1271 | 1066 | (83.9%) | 205 | (16.1%) | |
| Have purchased | 384 | 304 | (79.2%) | 80 | (20.8%) | 0.016 |
| Agents acting on the renin–angiotensin system | Agents acting on the renin–angiotensin system | Agents acting on the renin–angiotensin system | Agents acting on the renin–angiotensin system | Agents acting on the renin–angiotensin system | Agents acting on the renin–angiotensin system | Agents acting on the renin–angiotensin system |
| Have not purchased | 1055 | 887 | (84.1%) | 168 | (15.9%) | |
| Have purchased | 600 | 483 | (80.5%) | 117 | (19.5%) | 0.032 |
| Lipid modifying agents | Lipid modifying agents | Lipid modifying agents | Lipid modifying agents | Lipid modifying agents | Lipid modifying agents | Lipid modifying agents |
| Have not purchased | 1139 | 957 | (84.0%) | 182 | (16.0%) | |
| Have purchased | 519 | 182 | (35.1%) | 103 | (19.8%) | 0.024 |
| Antineoplastic and immunomodulating agents | Antineoplastic and immunomodulating agents | Antineoplastic and immunomodulating agents | Antineoplastic and immunomodulating agents | Antineoplastic and immunomodulating agents | Antineoplastic and immunomodulating agents | Antineoplastic and immunomodulating agents |
| Antineoplastic agents | Antineoplastic agents | Antineoplastic agents | Antineoplastic agents | Antineoplastic agents | Antineoplastic agents | Antineoplastic agents |
| Have not purchased | 1601 | 1328 | (82.9%) | 273 | (17.1%) | |
| Have purchased | 54 | 42 | (77.8%) | 12 | (22.2%) | 0.161 |
| Endocrine therapy | Endocrine therapy | Endocrine therapy | Endocrine therapy | Endocrine therapy | Endocrine therapy | Endocrine therapy |
| Have not purchased | 1588 | 1315 | (82.8%) | 273 | (17.2%) | |
| Have purchased | 67 | 55 | (82.1%) | 12 | (17.9%) | 0.440 |
| Immunosuppressants | Immunosuppressants | Immunosuppressants | Immunosuppressants | Immunosuppressants | Immunosuppressants | Immunosuppressants |
| Have not purchased | 1601 | 1326 | (82.8%) | 275 | (17.2%) | |
| Have purchased | 54 | 44 | (81.5%) | 10 | (18.5%) | 0.399 |
| Musculo-skeletal system | Musculo-skeletal system | Musculo-skeletal system | Musculo-skeletal system | Musculo-skeletal system | Musculo-skeletal system | Musculo-skeletal system |
| Anti-inflammatory and antirheumatic products | Anti-inflammatory and antirheumatic products | Anti-inflammatory and antirheumatic products | Anti-inflammatory and antirheumatic products | Anti-inflammatory and antirheumatic products | Anti-inflammatory and antirheumatic products | Anti-inflammatory and antirheumatic products |
| Have not purchased | 561 | 467 | (83.2%) | 98 | (17.5%) | |
| Have purchased | 1094 | 903 | (82.5%) | 191 | (17.5%) | 0.360 |
| Topical products for joint and muscular pain | Topical products for joint and muscular pain | Topical products for joint and muscular pain | Topical products for joint and muscular pain | Topical products for joint and muscular pain | Topical products for joint and muscular pain | Topical products for joint and muscular pain |
| Have not purchased | 1484 | 1230 | (82.9%) | 254 | (17.1%) | |
| Have purchased | 171 | 140 | (81.9%) | 31 | (18.1%) | 0.370 |
| Muscle relaxants | Muscle relaxants | Muscle relaxants | Muscle relaxants | Muscle relaxants | Muscle relaxants | Muscle relaxants |
| Have not purchased | 1484 | 1231 | (83.0%) | 253 | (17.0%) | |
| Have purchased | 171 | 139 | (81.3%) | 32 | (18.7%) | 0.293 |
| Antigout preparations | Antigout preparations | Antigout preparations | Antigout preparations | Antigout preparations | Antigout preparations | Antigout preparations |
| Have not purchased | 1587 | 1316 | (82.9%) | 271 | (17.1%) | |
| Have purchased | 68 | 54 | (79.4%) | 14 | (20.6%) | 0.227 |
| Nervous system | Nervous system | Nervous system | Nervous system | Nervous system | Nervous system | Nervous system |
| Analgesics | Analgesics | Analgesics | Analgesics | Analgesics | Analgesics | Analgesics |
| Have not purchased | 643 | 538 | (83.7%) | 105 | (16.3%) | |
| Have purchased | 1012 | 832 | (82.2%) | 180 | (17.8%) | 0.222 |
| Antiepileptics | Antiepileptics | Antiepileptics | Antiepileptics | Antiepileptics | Antiepileptics | Antiepileptics |
| Have not purchased | 1526 | 1268 | (83.1%) | 258 | (16.9%) | |
| Have purchased | 129 | 102 | (79.1%) | 27 | (20.9%) | 0.123 |
| Anti-parkinson drugs | Anti-parkinson drugs | Anti-parkinson drugs | Anti-parkinson drugs | Anti-parkinson drugs | Anti-parkinson drugs | Anti-parkinson drugs |
| Have not purchased | 1607 | 1331 | (82.8%) | 276 | (17.2%) | |
| Have purchased | 48 | 39 | (81.3%) | 9 | (18.8%) | 0.388 |
| Psycholeptics | Psycholeptics | Psycholeptics | Psycholeptics | Psycholeptics | Psycholeptics | Psycholeptics |
| Have not purchased | 974 | 807 | (82.9%) | 167 | (17.1%) | |
| Have purchased | 681 | 563 | (82.7%) | 118 | (17.3%) | 0.462 |
| Other nervous system drugs | Other nervous system drugs | Other nervous system drugs | Other nervous system drugs | Other nervous system drugs | Other nervous system drugs | Other nervous system drugs |
| Have not purchased | 1548 | 1293 | (83.5%) | 255 | (16.5%) | |
| Have purchased | 107 | 77 | (72.0%) | 30 | (28.0%) | 0.001 |
| Antiparasitic products, insecticides and repellents | Antiparasitic products, insecticides and repellents | Antiparasitic products, insecticides and repellents | Antiparasitic products, insecticides and repellents | Antiparasitic products, insecticides and repellents | Antiparasitic products, insecticides and repellents | Antiparasitic products, insecticides and repellents |
| Antiprotozoals | Antiprotozoals | Antiprotozoals | Antiprotozoals | Antiprotozoals | Antiprotozoals | Antiprotozoals |
| Have not purchased | 1312 | 1094 | (83.4%) | 218 | (16.6%) | |
| Have purchased | 343 | 276 | (80.5%) | 67 | (19.5%) | 0.102 |
When looking at the ATC subclasses and specific drug preparations, differences were also detected in the purchase numbers of medicines between the periodontitis and non-periodontitis groups. In addition to the results given in Table 3, purchases of 18 specific drug preparations were significantly more common among periodontitis patients. They purchased more of the following preparations: insulin ($2.46\%$ vs. $0.95\%$, $$p \leq 0.017$$), calcium channel blocker felodipine ($8.42\%$ vs. $5.62\%$, $$p \leq 0.036$$), angiotensin-converting enzyme (ACE) inhibitor ramipril ($4.91\%$ vs. $2.99\%$, $$p \leq 0$$,05), HMG CoA reductase inhibitor simvastatin ($29.4\%$ vs. $22.6\%$, $$p \leq 0.007$$), opioid analgesics ketobemidone ($3.16\%$ vs. $1.09\%$, $$p \leq 0.004$$) and fentanyl ($1.75\%$ vs. $0.58\%$, $$p \leq 0.021$$), nicotine dependence drug varenicline ($5.26\%$–$2.04\%$, $$p \leq 0.001$$), and the nitroimidazole antibiotic metronidazole ($17.5\%$ vs. $12.4\%$, $$p \leq 0.01$$). Eleven preparations were excluded from the list because less than five subjects had purchased them.
In total, 77 different individual preparations had been purchased more often by the periodontitis patients than the periodontally healthy. Among the 59 preparations that had not been significantly more purchased there were 17 different individual diabetes medications ($$p \leq 0.017$$–0.323), the channel blocker amlodipine ($22.5\%$ vs $18.8\%$, $$p \leq 0.076$$), ACE-inhibitor enalapril ($25.6\%$ vs $21.3\%$, $$p \leq 0.056$$), disulfiram ($1.4\%$ vs $0.7\%$, $$p \leq 0.097$$), analgesic morphine, and antispasmodics ($2.5\%$–$1.5\%$, $$p \leq 0.114$$).
Odds ratios for the different categories of medications mainly linked to periodontitis are given in Table 4 and Table 5. In the main categories and subtypes of medications purchased, positive odds ratios, with a confidence interval over one, were not detected. Regarding individual preparations, only simvastatin (ATC class C10AA01, OR = 1.4; CI = 1.04–1.86), ketobemidone (ATC class N02AB01, OR = 3.32; CI = 1.48–7.86) and metronidazole (ATC class P01AB01, OR = 1.46; CI = 1.03–2.08) showed positive odds ratios.
## 4 Discussion
To our knowledge, this is the first study investigating the procurement of medications and specific drug preparations in a 35-year perspective since the diagnosis of periodontitis, compared to the periodontally healthy subjects at baseline. The main finding was that periodontitis patients had purchased certain, but not at all medications, more frequently than we had expected. Hence, the patients did not tend to buy more medications in general, and differences were only seen between the periodontitis and periodontally healthy groups when analyzing the various ATC categories of medicines and the specific preparations within the categories. The results thus only partly confirmed our study hypothesis.
This area of research has been scarcely investigated earlier. Only one prior article was found on the use of systemic medications by periodontitis patients (Wang et al., 2020). Compared to that, our study showed fewer significant results. But it should be pointed out that the study by Wang and collaborators investigated matched subjects while our study was a longitudinal cohort study.
Nevertheless, results similar to those of the study of Wang et al. were found for insulin, oral hypoglycemics in general, ACE inhibitors, calcium channel blockers, lipid-lowering medications, and alpha-2 agonists. However, contrary to Wang et al., we could not establish a connection between periodontitis patients and the use of diuretics, anti-coagulants, bronchodilators, antidepressants, antipsychotic drugs, and anticonvulsants. This indeed can be explained by the differences in the study design and subjects.
As said, hundreds of different systemic medications affect the oral cavity. The main effect is hyposalivation with consequent xerostomia (Tanasiewicz et al., 2016). The salivation-altering medications identified in the present study were angiotensin II receptor blockers, analgesics, anti-infectives, anti-inflammatory medications, alpha-2 agonists, antigout medications, cardiovascular medications like calcium channel blockers, drugs used for diabetes, and those for nicotine dependence, immunosuppressants, and interferons and statins, respectively (Pająk-Łysek et al., 2021; Choo et al., 2022). Hyposalivation is a serious issue because it can increase the risk of diseases of the oral cavity. Poor oral health is also linked to a decrease in the patient’s quality of life (Barbe, 2018).
Xerostomia has been shown to affect $34\%$–$51\%$ of diabetic patients mainly through salivary dysfunction. The linkage between periodontitis and diabetes is well understood because poor glycemic control does worsen periodontal health (Rohani, 2019). Hence, it was no surprise that metformin and the 12 other diabetic medications here encountered were purchased more often by patients with periodontitis.
Merely three systemic individual preparations, namely simvastatin, ketobemidone, and metronidazole showed a positive odds ratio for long-term periodontitis in the present study. No research has been made on a link between opioids or painkillers and periodontitis diagnosis. Similar to our findings, a recent study, however, showed a connection between statins and periodontitis (Kwon et al., 2022). The use of antibiotics as a part of periodontal care varies substantially, necessitating clear guidelines (Feres et al., 2015). In Sweden, antibiotics are rarely used in standard periodontal care. Purchasing metronidazole more often, an antibiotic used mainly for Gram-positive bacteria and protozoa, would thereby indicate that either periodontal disease would increase the risk of infections or infections increase the risk of developing periodontitis. This, however, could not be verified based on the present material.
To account for the impact of long-term systemic medication before 1985 at the onset of this study, patients diagnosed with systemic diseases were identified by using the Swedish national registers for hospital treatment and open care. This was taken into account in the logistic regression analyses and having at least one diagnosis before 1985 was found to significantly increase the risk of purchasing the majority of ATC categories analyzed. Exceptions in this regard were drugs for diseases of the genito-urinary system, sex hormones, anti-infectives for systemic use, drugs for skin diseases, antiparasitic preparations, insecticides and repellents, and, finally, antineoplastic and immunomodulating agents.
A significant difference in the specific medication purchases was seen only in a few drug preparations in this study. This is not in line with current research on the connection between periodontitis and systemic diseases (Liccardo et al., 2019). When compared with patients without periodontitis, there was no significant difference in the purchase of cancer medications, and anti-rheumatic and neurological medication. This finding was unexpected as both different subtypes of cancers, rheumatic and neurological diseases such as Alzheimer’s and depression have been linked to periodontitis (Michaud et al., 2017; Nwizu et al., 2020; Kavarthapu and Gurumoorthy, 2021; Tuominen and Rautava, 2021; Zheng et al., 2021; Asher et al., 2022). Furthermore, the finding is not in line with our earlier research (Söder et al., 2015). However, the present results only represent the sample here used and do not necessarily give the full picture of the whole cohort.
Several covariables have been taken into account in this research. For instance, the purchasing of varenicline, a nicotine-dependence drug, is closely linked with tobacco use. Since a large percentage of the periodontitis patients were smokers, their purchasing varenicline could be expected. Smoking is a well-known risk factor affecting both systemic and periodontal health (Leite et al., 2018). Gender is another important factor as there are differences in oral health between men and women and likewise so when considering socioeconomic status (Boillot et al., 2011; Leng et al., 2015). Interestingly, our odds ratios showed only a few positive results regarding lower socio-economic status and gender, and only partly so concerning tobacco usage.
The strength of the present study is that it offers unique and substantial material with a long follow-up period. The multitude of preparations and a large number of purchases thus made the investigation reliable. This, as well as the relatively big sample size, allowed for conducting the detailed analysis. However, there is room for improvement when planning further studies. The size of the cohort could still be increased if possible. For example, melphalan, vinorelbine, pizotifen, betahistine, and riluzole were only used by one patient in the present material. Thus, there was no way to draw further conclusions in that respect. Furthermore, since the beginning of the study, the diagnostic criteria for periodontitis have been changed several times. Hence, if the most recent diagnostic criteria would have been used the material might look different. This is another weakness of the present study. Nevertheless, we have found a link between medication purchases and periodontitis, especially when looking at the specific preparations and subgroups of medications. Finally, this area has been sparsely studied before and, subsequently, studies with other cohorts are needed.
## 5 Conclusion
We conclude that patients with periodontitis had purchased only a few medication groups more than periodontally healthy subjects when looking at the main drug categories. This finding was contrary to our expectations. On the other hand, periodontitis patients had purchased more than 19 different subgroups of medications. These included diabetes drugs, calcium channel blockers, agents acting on therenin–angiotensin system, statins, and drugs for diseases of the nervous system. Of the specific preparations, only simvastatin, ketobemidone, and metronidazole had been purchased significantly more often by the periodontitis patients. Many of these drugs cause hyposalivation as their side effect which must be taken into account when counseling patients in general.
## 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 studies involving human participants were reviewed and approved by The Ethics Committee of the Karolinska University Hospital at Huddinge (Dnr $\frac{2007}{1669}$-31; $\frac{2012}{590}$-32; $\frac{2017}{2204}$-32). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
BS, JM, EK, and HK: conceptualization of overarching research goals and aims. FF and HK: data/evidence collection and formal analysis of the data. All authors participated in writing the manuscript and its critical review and revision.
## 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
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## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1146475/full#supplementary-material
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|
---
title: The association between retinal microvasculature derived from optical coherence
tomography angiography and systemic factors in type 2 diabetics
authors:
- Yi Li
- Kunfang Wu
- Zilin Chen
- Guihua Xu
- Dingding Wang
- Juanjuan Wang
- Gabriella Bulloch
- Grace Borchert
- Huiya Fan
journal: Frontiers in Medicine
year: 2023
pmcid: PMC10040575
doi: 10.3389/fmed.2023.1107064
license: CC BY 4.0
---
# The association between retinal microvasculature derived from optical coherence tomography angiography and systemic factors in type 2 diabetics
## Abstract
### Aims
To investigate the correlation between the retinal microvasculature using optical coherence tomography angiography (OCTA) and systemic factors in type 2 diabetes mellitus (T2DM) patients.
### Methods
This cross-sectional study obtained OCTA data from patients with T2DM administered at hospital and referred to ophthalmic services. Patient data about demographics, comorbid conditions, and blood biomarkers were extracted from electronic medical records. Data from OCTA scans obtained by CIRRUS HD-OCT Model 5,000 were obtained. Vessel density (VD) and perfusion density (PD) within the superficial capillary plexus, and foveal avascular zone (FAZ) area were automatically segmented. These parameters were tested for their correlations with systemic factors by univariate and multivariable linear regression analyses.
### Results
A total of 144 T2DM patients (236 eyes) were available for analysis, with mean age of 53.6 (SD = 10.34) and $56.9\%$ were male. Chronic kidney disease, cardiovascular disease, increased serum creatinine (Scr), red blood cell count (RBC), platelets (PLT), apolipoprotein B (APOB), and decreased urine albumin to creatinine ratio (UACR) were significantly associated with lower VD and PD (all $p \leq 0.013$). UACR and triglyceride (TRIG) were significantly correlated with FAZ area (all $p \leq 0.017$). In multivariate analyses, PLT, eGFR, and APOB were independent risk factors for retinal rarefaction, and UACR was a significant predictor of FAZ area.
### Conclusion
We found several systemic risk factors, such as PLT, renal function and lipid profiles were associated with PD, VD, and FAZ area among Chinese T2DM patients.
## Introduction
Type 2 diabetes mellitus (T2DM) is a worldwide epidemic that carries considerable morbidity, mortality, and financial burden from its deleterious complications and associations with other comorbid conditions. According to the latest International Diabetes Federation (IDF) diabetes atlas [1], an estimated 537 million people had diabetes in 2021, with this figure projected to reach 643 million by 2030.
Type 2 diabetes mellitus accounts for over $90\%$ of all diabetes worldwide [1, 2] and is characterized by chronic hyperglycemia and insulin resistance resulting from lifestyle and genetic factors. If uncontrolled, T2DM leads to vascular damage of the eyes, kidneys, and heart. [ 3] Increased vascular permeability, vascular cell apoptosis, and altered blood flow contribute to macrovascular (peripheral vascular disease and coronary heart disease) and microvascular (diabetic retinopathy and diabetic nephropathy) complications [4] which result in morbidity and eventually mortality if unmanaged. Therefore, early identification and risk stratification of T2DM patients who are at risk of vascular complications is an area of growing research for the control and prevention of poor outcomes.
The retina is a structure at the back of the eye that contains a rich network of microvasculature. Growing evidence suggests retinal imaging can detect microstructural changes to vascular networks, [5] and fundoscopy studies (6–8) report concordance between the retinal microvasculature and systemic risk factors such as hypertension, diabetes, and smoking. A recent study also discovered significant retinal microvascular alterations in diabetic patients with subclinical atherosclerosis. [ 9] These findings have led to the idea that the retina is the window to the cardiovascular system and its suggestion as a screening tool.
Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique that allows for three-dimensional visualization of retinal microvasculature networks with contrast for high-resolution imaging. Unlike fundoscopy, it can detect subtle microvascular abnormalities on retinal layers and choriocapillaris, which has led to its establishment for the early detection of diabetic retinopathy (DR). [ 10, 11] Additionally, OCTA can quantify the number of perfused vessels in the vascular bed (functional rarefaction) and perfused vessels in the tissue (structural rarefaction) [12], making it a useful tool for evaluating microvascular changes longitudinally in people with T2DM, dyslipidemia, and chronic kidney disease. ( 13–15) Despite the widespread use of OCTA for eye diseases, little is known about the impact of systemic risk factors on OCTA parameters in diabetic eyes. Therefore, this study investigated the association between OCTA-derived retinal microvasculature parameters and systemic factors to understand its impact on vascular function in a Chinese diabetic population.
## Study population
This cross-sectional study included T2DM patients who had admitted to and received ophthalmic consultation in Huizhou Central People’s Hospital from January 2021 to June 2022. This study was approved by the Institutional Review Board of Huizhou Central People’s Hospital (IRB approval number: kyl20210115) and followed the tenets of the Declaration of Helsinki. Written informed consent was obtained from all participants.
This study included patients with T2DM [2] aged >18 years old. Participants were excluded if they had: [1] severe media opacity (e.g., corneal disease, dense cataract, vitreous hemorrhage); [2] any ocular illness that may affect ocular circulation (e.g., glaucoma, retinal vascular occlusion, retinal detachment, exudative aged macular degeneration, pathologic myopia); [3] signal strength of OCTA scans <$\frac{5}{10}$, or OCTA scans with artifacts or segmentation errors; [4] a history of surgical treatments for eye diseases (except cataract) or laser treatment; [5] uncontrollable high blood pressure (HBP) (≥$\frac{180}{110}$ mmHg); [6] any severe systemic diseases (e.g., tumor, heart failure, and cerebral infarction);
## Obtaining data on systemic factors and blood biomarkers
Systemic factors were retrieved from patient electronic medical records (EMR) and included gender, age, time from diagnosis of T2DM, body mass index (BMI), blood pressure readings, smoking history, cardiovascular disease history, chronic kidney disease history, obesity, and blood biomarkers. These included systolic blood pressure (SBP), diastolic blood pressure (DBP), glucose, hemoglobin A1c (HbA1c), red blood cell count (RBC), hemoglobin (HGB), blood platelet (PLT), serum creatinine (Scr), estimated glomerular filtration rate (eGFR), urine albumin to creatinine ratio (UACR), total cholesterol (CHOL), triglyceride (TRIG), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), lipoprotein a (Lpa), apolipoprotein A (ApoA), apolipoprotein B (ApoB). All patients had their blood drawn at 8 AM after an overnight fast and before taking morning medications. Overnight first-void urine samples were also obtained. The eGFR was calculated based on the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [16]. Chronic kidney disease was defined as eGFR<60 ml/min/1.73m2. Body mass index was calculated as weight in kilograms divided by the square of height in meters. Obesity was defined as BMI ≥ 28 kg/m2.
## Ocular examinations and imaging
All patients underwent an ophthalmic examination, which included best-corrected visual acuity, intraocular pressure, silt lamp examination, fundus photographs, fluorescein fundus angiography (FFA), optical coherence tomography (OCT), and OCTA by a single trained technician. The presence of DR was confirmed based on FFA, and was categorized as NDR, mild non-proliferative DR (mild NPDR), moderate non-proliferative DR (moderate NPDR), severe non-proliferative DR (severe NPDR), and proliferative DR (PDR) according to the International Clinical Diabetic Retinopathy Severity Scales. [ 17] Patients underwent OCTA scanning using CIRRUS HD-OCT Model 5,000 (Carl Zeiss, Germany), which uses a super luminescent diode (SLD) with a central wavelength of 840 nm, and a scanning speed of 68,000 A-scans/s. The macular region was scanned using a 6 mm × 6 mm scan pattern, each consisting of 245 A-scan per B-scan. This was automatically divided into three fields: the foveal area (a central circle with a diameter of 1 mm), the parafoveal area (an annulus centered on the fovea with an inner ring with a diameter of 3 mm), and the perifoveal area (an annulus centered on the fovea with outer ring diameters of 6 mm; Figure 1).Vessel density (VD), perfusion density (PD), and foveal avascular zone (FAZ) parameters were quantitatively analyzed within the superficial capillary plexus (SCP), defined as the area extending from the inner limiting membrane to 110 μm above the retinal pigment epithelium. This was analyzed by built-in angiography software, which calculated the average VD and PD using a grid overlay according to standard ETDRS subfields. VD was defined as the total length of perfused vessels per unit area in the measurement region, and PD was defined as the total area of perfused retinal microvasculature per unit area on binarized vasculature images. FAZ was defined as a region within the foveal at the center of the retina devoid of retinal blood vessels. Area, perimeter, and circularity are FAZ parameters that we used for this study.
**Figure 1:** *Quantitative measurement of optical coherence tomography angiography (OCTA) 6 × 6-mm scans in a type 2 diabetes mellitus (T2DM) patient. (A) 6 × 6-mm en face image of the superficial capillary plexus (SCP). (B) B-scans with flow encoding show the slab segmentation (horizontal purple lines), which included the SCP. (C) Angioplex metrics, including vessel density, perfusion density and foveal avascular zone (FAZ) parameters. (D) OCT en face image of the superficial layer overlaid with the early treatment of diabetic retinopathy study grid (ETDRS).*
## Statistical analysis
All data analyses were performed using SPSS version 25.0 (IBM Corp, Armonk, NY, USA). Continuous data were represented as mean ± standard deviations (SD), categorical data were expressed as number (percentage, %). Univariate linear regression models were used to analyze potential associations between systemic risk factors and OCTA-derived metrics, with regression coefficients calculated to estimate the magnitude of microvascular change associated with predictor variables. Bonferroni correction for multiple comparison was performed to assess differences between FAZ parameters and VD, PD at each annulus. Multiple linear regression analyses were subsequently performed to determine independent risk factors of retinal microvascular dysfunction. Generalized estimating equations approach were used to adjust for correlations between paired eyes. A p-value of <0.013 ($\frac{0.05}{4}$) for VD, PD, and a p-value of <0.017 ($\frac{0.05}{3}$) for FAZ parameters were considered statistically significant for association.
## Results
A total of 191 patients underwent OCTA examinations. Thirty participants were excluded due to a history of reported ocular diseases, surgeries, or laser treatments, and 10 participants were excluded due to a history of severe systemic diseases or having type 1 diabetes. A further seven participants were excluded due to poor quality. 236 eyes of 144 T2DM patients were included for analysis, with a mean (SD) age of 53.61 (10.34) years, and $56.9\%$ males. Characteristics of participants are detailed in Table 1, and prevalence of hypertension ($33.3\%$), chronic kidney disease ($12.5\%$), smoking history ($22.2\%$), obesity ($7.6\%$), and cardiovascular disease ($6.3\%$) were noted amongst study subjects.
**Table 1**
| Characteristic | Subjects (n = 144) |
| --- | --- |
| Demographics | |
| Male, n (%) | 82 (56.9) |
| Age (y) | 53.61 ± 10.34 |
| DM duration (y) | 7.92 ± 5.48 |
| BMI (kg/m2) | 23.51 ± 3.39 |
| SBP (mmHg) | 128.36 ± 16.51 |
| DBP (mmHg) | 80.19 ± 10.04 |
| Comorbidities | |
| Hypertension, n (%) | 48 (33.3) |
| Chronic kidney disease, n (%) | 18 (12.5) |
| Cardiovascular disease, n (%) | 9 (6.3) |
| Smoking history, n (%) | 32 (22.2) |
| Obesity, n (%) | 11 (7.6) |
| DR, n (%) | 115 (79.9) |
| DR stage | |
| Mild NPDR, n (%) | 54 (20.1) |
| Moderate NPDR, n (%) | 16 (11.1) |
| Severe NPDR, n (%) | 22 (15.3) |
| PDR, n (%) | 23 (16.0) |
| Lab values | |
| HbA1c (%) | 9.69 ± 2.56 |
| Glucose (mmol/L) | 12.13 ± 5.69 |
| HGB (g/L) | 133.40 ± 19.30 |
| RBC (10^12/L) | 4.59 ± 0.77 |
| PLT (10^9/L) | 254.01 ± 73.72 |
| Scr (μmol/L) | 72 (IQR 61–88) |
| eGFR (mL/min/L.73m2) | 89.05 ± 24.84 |
| UACR (mg/g) | 19 (IQR 8–89) |
| CHOL (mmol/L) | 4.84 ± 1.23 |
| TRIG (mmol/L) | 2.34 ± 1.98 |
| HDL (mmol/L) | 1.00 ± 0.30 |
| LDL (mmol/L) | 3.05 ± 1.07 |
| Lpa (mg/L) | 0.29 ± 0.27 |
| APOA (g/L) | 1.19 ± 0.26 |
| APOB (g/L) | 1.12 ± 0.57 |
Table 2 describes the mean characteristics of vessel density and perfusion density within regions captured by OCTA. Mean SCP-VD in the parafoveal region was 14.97 ± 2.88 and 15.23 ± 2.31 mm−1 in the macular region. Mean SCP-PD in the parafoveal region was 0.36 ± 0.07 and 0.37 ± 0.06 in the macular region. The average FAZ area was 0.29 ± 0.12 mm2.
**Table 2**
| Optical coherence tomography angiography (OCTA) parameters | (n = 236 eyes) |
| --- | --- |
| Signal strength | 7.78 ± 1.28 |
| Vessel density (mm−1) | |
| Foveal | 5.76 ± 2.91 |
| Parafoveal | 14.97 ± 2.88 |
| Perifoveal | 15.67 ± 2.26 |
| Macular 6*6 mm | 15.23 ± 2.31 |
| Perfusion density | |
| Foveal | 0.13 ± 0.07 |
| Parafoveal | 0.36 ± 0.07 |
| Perifoveal | 0.39 ± 0.06 |
| Macular 6*6 mm | 0.37 ± 0.06 |
| FAZ parameters | |
| FAZ area (mm2) | 0.29 ± 0.12 |
| FAZ perimeter (mm) | 2.25 ± 0.55 |
| FAZ circularity | 0.68 ± 0.09 |
Table 3 demonstrates the association of various systemic factors with SCP-VD in anatomical regions captured by OCTA. Significant associations with signal strength, sex, cardiovascular disease, DR stage, CKD, RBC, PLT, Scr, UACR, and APOB for VD were apparent on univariate analysis (all $p \leq 0.05$). Following multivariable analysis, macular VD correlated positively with signal strength (β = 0.968, $p \leq 0.001$), eGFR (β = 0.601, $$p \leq 0.009$$), and APOB (β = 0.290, $p \leq 0.001$).Similarly, abnormal renal function was associated with reduced VD as measured by OCTA (Figure 2). Foveal VD was also significantly correlated with signal strength (β = 0.559, $$p \leq 0.001$$), diabetes mellitus (DM) duration (β = 0.576, $$p \leq 0.011$$), and PLT (β = 0.544, $$p \leq 0.003$$).
Table 4 demonstrates associations between systemic factors and SCP-PD. Univariate linear regression analysis showed that sex, signal strength, cardiovascular disease, DR stage, PLT, Scr, and APOB were associated with PD (all $p \leq 0.013$). Following adjustment for confounding factors, positive associations remained for signal strength (β = 0.027, $p \leq 0.001$), DM duration (β = 0.013, $$p \leq 0.012$$), PLT (β = 0.013, $$p \leq 0.002$$), eGFR (β = 0.017, $$p \leq 0.006$$), and APOB (β = 0.007, $$p \leq 0.002$$). The diagrams showing the correlations between OCTA parameters and systemic risk factors such as PLT, APOB and eGFR are shown in Figure 3.
Table 5 shows the association between systemic factors and FAZ parameters in OCTA 6 × 6-mm scans. Univariate linear regression analysis showed that DR stage, UACR, and TRIG were associated with FAZ parameters (all $p \leq 0.017$). In a multivariable-adjusted model, UACR was negatively associated with FAZ area (β = −0.029, $$p \leq 0.007$$) and FAZ perimeter (β = −0.159, $$p \leq 0.003$$), whilst age and chronic kidney disease positively impacted FAZ area (β = 0.030, $$p \leq 0.06$$; β = 0.128, $$p \leq 0.016$$, respectively), and FAZ perimeter (β = 0.117, $$p \leq 0.014$$; β = 0.688, $$p \leq 0.007$$, respectively).
**Table 5**
| Unnamed: 0 | Univariate | Univariate.1 | Univariate.2 | Univariate.3 | Univariate.4 | Univariate.5 | Multivariable-adjusted | Multivariable-adjusted.1 | Multivariable-adjusted.2 | Multivariable-adjusted.3 | Multivariable-adjusted.4 | Multivariable-adjusted.5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | FAZ area | P-value | FAZ perimeter | P-value | FAZ circularity | P-value | FAZ area | P-value | FAZ perimeter | P-value | FAZ circularity | P-value |
| Demographics | Demographics | Demographics | Demographics | Demographics | Demographics | Demographics | | | | | | |
| Sex | 0.022 | 0.245 | 0.053 | 0.525 | 0.016 | 0.186 | 0.017 | 0.358 | 0.037 | 0.666 | 0.008 | 0.520 |
| Age | 0.017 | 0.044 | 0.061 | 0.125 | 0.006 | 0.276 | 0.030 | 0.006 * | 0.117 | 0.014 * | −0.003 | 0.707 |
| DM duration | −0.001 | 0.932 | 0.000 | 0.996 | −0.005 | 0.360 | - | - | - | - | - | - |
| BMI | 0.002 | 0.483 | −0.006 | 0.612 | 0.001 | 0.738 | - | - | - | - | - | - |
| SBP | −0.009 | 0.342 | −0.041 | 0.336 | −0.006 | 0.340 | - | - | - | - | - | - |
| DBP | −0.012 | 0.180 | −0.058 | 0.107 | −0.008 | 0.230 | - | - | - | - | - | - |
| Signal strength | 0.003 | 0.722 | 0.009 | 0.804 | 0.006 | 0.232 | - | - | - | - | - | - |
| Comorbidities | Comorbidities | Comorbidities | Comorbidities | Comorbidities | Comorbidities | Comorbidities | | | | | | |
| Hypertension | −0.005 | 0.803 | 0.001 | 0.989 | −0.003 | 0.809 | 0.018 | 0.399 | 0.083 | 0.358 | 0.004 | 0.758 |
| Cardiovascular disease | −0.011 | 0.711 | −0.015 | 0.900 | 0.019 | 0.140 | - | - | - | - | - | - |
| Smoking | 0.000 | 0.999 | −0.002 | 0.982 | −0.002 | 0.884 | - | - | - | - | - | - |
| Chronic kidney disease | −0.007 | 0.854 | 0.054 | 0.767 | −0.045 | 0.017 | 0.128 | 0.016 * | 0.688 | 0.007 * | −0.072 | 0.018 |
| Obesity | 0.031 | 0.167 | −0.130 | 0.272 | 0.029 | 0.234 | - | - | - | - | - | - |
| DR stage | | | | | | | | | | | | |
| Mild NPDR | −0.052 | 0.010 * | −0.211 | 0.011 * | −0.017 | 0.215 | −0.063 | 0.009 * | −0.259 | 0.010 * | −0.016 | 0.282 |
| Moderate NPDR | 0.021 | 0.448 | 0.210 | 0.046 | −0.065 | 0.004 * | 0.028 | 0.343 | 0.249 | 0.034 | −0.070 | 0.005 * |
| Severe NPDR | −0.021 | 0.481 | −0.155 | 0.277 | −0.021 | 0.190 | 0.017 | 0.679 | −0.067 | 0.731 | −0.002 | 0.906 |
| PDR | −0.065 | 0.067 | −0.247 | 0.128 | −0.052 | 0.018 | −0.025 | 0.514 | −0.067 | 0.674 | −0.038 | 0.187 |
| Lab values | Lab values | Lab values | Lab values | Lab values | Lab values | Lab values | | | | | | |
| Glucose | −0.013 | 0.118 | −0.046 | 0.159 | −0.003 | 0.648 | - | - | - | - | - | - |
| HbA1c | −0.013 | 0.153 | −0.034 | 0.394 | −0.007 | 0.165 | - | - | - | - | - | - |
| HGB | −0.005 | 0.670 | −0.013 | 0.773 | −0.003 | 0.617 | - | - | - | - | - | - |
| RBC | −0.005 | 0.607 | −0.010 | 0.800 | 0.001 | 0.822 | - | - | - | - | - | - |
| PLT | −0.016 | 0.123 | −0.063 | 0.150 | −0.004 | 0.503 | - | - | - | - | - | - |
| Scr | −0.015 | 0.104 | −0.057 | 0.148 | −0.002 | 0.646 | - | - | - | - | - | - |
| eGFR | 0.009 | 0.371 | 0.032 | 0.460 | 0.001 | 0.916 | 0.027 | 0.059 | 0.113 | 0.069 | −0.012 | 0.270 |
| UACR | −0.024 | <0.001 * | −0.125 | 0.001 * | −0.001 | 0.897 | −0.029 | 0.007 * | −0.159 | 0.003 * | 0.005 | 0.375 |
| CHOL | −0.020 | 0.020 | −0.083 | 0.026 | 0.002 | 0.699 | −0.014 | 0.074 | −0.051 | 0.128 | 0.002 | 0.718 |
| TRIG | −0.017 | 0.007 * | −0.057 | 0.102 | −0.008 | 0.248 | −0.008 | 0.239 | −0.026 | 0.438 | −0.010 | 0.170 |
| HDL | 0.006 | 0.528 | 0.007 | 0.862 | 0.011 | 0.119 | - | - | - | - | - | - |
| LDL | −0.013 | 0.152 | −0.057 | 0.156 | −0.001 | 0.854 | - | - | - | - | - | - |
| Lpa | 0.007 | 0.447 | 0.029 | 0.585 | −0.004 | 0.668 | - | - | - | - | - | - |
| APOA | 0.004 | 0.720 | −0.008 | 0.862 | 0.007 | 0.351 | - | - | - | - | - | - |
| APOB | −0.007 | 0.216 | −0.033 | 0.124 | 0.008 | 0.026 | - | - | - | - | - | - |
## Discussion
In this study, the retinal microvasculature of a Chinese population with T2DM was examined for its correlation with systemic factors. Of note, several blood biomarkers and systemic influences were correlated with VD and PD regions of interest within OCTA scans after adjustment for confounding factors, including signal strength, DM duration, PLT, eGFR, and APOB. After multivariable analysis, age, chronic kidney disease, DR stage, UACR, and APOB correlated with FAZ parameters. Our results suggest the retinal microvasculature may be influenced by the presence of systemic factors.
## DM duration correlates with OCTA parameters
DM duration was independently associated with foveal VD and PD in the multivariable-adjusted model, indicating the long-term impact of abnormal blood glucose levels in the microvascular system. Our findings were in concordance with previous research by Czakó et al. [ 18], who found that DM duration was strongly associated with decreased retinal VD after interaction analysis with the effects of systemic risk factors, and by Qian et al. [ 11], who reported a negative correlation between DM duration and OCTA metrics such as SCP-VD and SCP-PD in 1118 DM patients. Furthermore, larger FAZ and lower retinal capillary densities in children and adolescents with diabetes were observed in a case–control study [19], and these changes are associated with DM duration and poor glycemic control.
Although DM duration was a significant risk factor for microvascular abnormalities, we found no correlations between OCTA parameters and HbA1c or blood glucose in univariate or multivariable models. In this study, we assessed T2DM patients with a relatively short period of diabetes ($71.5\%$, ≤10 years), and less than half of the patients ($43.3\%$) had poor glycemic control (HbA1c > $10\%$), which may not be representative of all disease durations, and the results should be interpreted with caution.
## Hypertension weakly correlates with OCTA parameters
Hypertension negatively impacted foveal VD and PD after controlling for confounding factors ($p \leq 0.05$), demonstrating some influence over vessel integrity. However, none of these correlations persist after Bonferroni correction. In spite of several observational studies [20, 21] not finding hypertension or blood pressure to be risk factors for microvascular complication in diabetics, multiple OCTA studies have demonstrated its impacts on retinal microvasculature, including Lee et al. [ 13] whom reported hypertension correlated with lower SCP-VD (β = −0.239, $$p \leq 0.039$$) in diabetic patients than hypertensive controls, and case-control studies by Sun et al. [ 22] and Donati et al. [ 23] demonstrating non-diabetic hypertensive eyes had decreased VD as well as increased FAZ after adjusting for sex, age, and ocular parameters. In addition, a longitudinal analysis of 4,758 T2DM patients with non- or mild DR demonstrated blood pressures conferred to risk of DR progression [24].
Hypertension is thought to contribute to accelerated microvascular impairment in individuals with T2DM. Chronic hyperglycemia results in global microvascular changes like thickening of the vascular basement membrane and increased endothelial permeability, and the presence of hypertension increases pressure along these membranes which accelerate the pathological change and weaken retinal capillary walls. Therefore, a deficit in perfusion density on OCTA should present as a red flag for underlying poor blood pressure control and could be a risk factor if investigated further. More studies with large-scale sample sizes and detailed blood pressure monitoring are required to clarify the impact of hypertension on retinal microvasculature and diabetes management.
## Chronic kidney disease and renal function correlate with OCTA parameters
Our results showed that eGFR was positively associated with VD and PD, which was in line with results from previous studies exploring correlations between renal function and retinal microvasculature. Yeung et al. [ 25] reported that patients with CKD (eGFR<60 mL/min/1.73m2) had lower parafoveal SCP-VD compared to those of control group ($p \leq 0.001$), with eGFR strongly related with SCP-VD in multivariate-adjusted models. Observational cross-sectional studies [26, 27] aimed at investigating the relationship between systemic risk factors and OCTA parameters in patients with systemic hypertension found a significant correlation between eGFR and retinal capillary density after adjusting for age, sex, and blood pressure, suggesting impaired renal function could be one of important risk factors in retinal microvascular alterations. Similarly, Zhuang et al. [ 28] demonstrated that decreased SCP-VD was independently correlated with lower eGFR among T2DM patients, while other investigators [29] found a significant relationship between lower SCP-VD, SCP-PD, and higher UACR in T2DM patients after controlling for systemic and ocular parameters.
In addition, our study showed that chronic kidney disease positively impacted FAZ area and perimeter, while UACR was negatively associated with FAZ area and perimeter after adjusting for multiple variables. Lee et al. [ 13] reported that lower eGFR was associated with greater FAZ size in diabetic patients, which suggested that abnormal renal function may have an impact on the foveal and adjacent small vessels. However, FAZ morphology can be variable even in healthy individuals [30, 31], this variation must be considered and posed as a challenge when assessing possible pathological FAZ alternations. A relatively low number of chronic kidney disease patients ($\frac{18}{144}$) in our study population may hinder the interpretation of these findings, larger longitudinal studies will be needed to examine the effects of renal function in OCTA-derived metrics.
## Aberrant lipid indices correlate with OCTA parameters
Our study suggested that APOB was positively correlated with parafoveal, perifoveal and macular VD and PD, after controlling for other variables. TRIG was negatively correlated with FAZ area, although this correlation did not persist in multivariable analysis.
Dyslipidemia is an established risk factor for microvascular complications. It is now recognized that elevated CHOL levels induced inflammatory reaction in the microvascular system, which occurs long before events in the large vessels. [ 32] A randomized placebo-controlled trial by Kaushik et al. [ 33] proved that cholesterol-reducing medications retards DR progression in diabetic patients with proper glycemic control and hypercholesterolemia. This observation corresponds well with a nested case–control study by Aryan et al. [ 34] whom indicated a positive association of serum CHOL levels with microvascular complications (OR = 1.1, CI:1.0–2.2, $$p \leq 0.004$$) on 444 T2DM cases and 439 controls, although this correlation disappeared after interaction analysis with demographic and systemic factors. A large-scale cohort study [35], on the other hand, found a significant correlation between elevated serum levels of TRIG, decreased HDL levels, and diabetes-related microvascular complications in 72,289 T2DM patients, implying that aberrant lipid indices may reflect retinal microangiopathy in diabetics.
While there is little evidence that LDL has a causal effect on the risk of microvascular disease, growing evidence [36, 37] has shown that compared to traditional lipid indices, ApoB provides incremental information on lipid metabolism and may play a significant role in the development of vascular disease. To date, only a few studies have looked into the relationship between ApoB and retinal vascular system in diabetics. Shi et al. [ 38] found that foveal SCP-VD measured from OCTA 3 × 3 mm scans were negatively correlated with serum ApoB levels in T2DM patients (β = −0.016, $p \leq 0.001$), however, this correlation was not significant after controlling for other risk factors.
## PLT correlates with OCTA parameters
Our study found that PLT was significantly associated with increased VD and PD in the foveal region after adjusting for other confounders. The influence of PLT on the microvascular system has so far remained uncertain. Considering the physical proximity of PLT to the vascular endothelium, a relationship between PLT and microvascular alterations is assumed. Yuan et al. [ 39] implicated that platelet hyperactivity in diabetic individuals may undermine tissue perfusion as well as contribute to microvascular occlusion. Data from 3,009 participants recruited for the Blue Mountains Eye Study (BMES) [40] revealed that higher PLT correlated with narrower arteriolar caliber and wider venular caliber, implying that elevated levels of PLT could have adverse effects on microvasculature. However, the mechanisms that underlie this association are unclear and research on this topic is sparse. Based on OCTA measurement, we speculate that PLT levels may be a marker for microvascular dysfunction in diabetic patients. More studies are required to corroborate this hypothesis.
## Limitation
There are several limitations of our present study. The first one is that the study was a single-center study with a relatively small sample size. Second, most participants in this study have mild or moderate diabetic retinopathy ($\frac{115}{140}$, $79.9\%$), while the effect of diabetic retinopathy has been taken into account in multivariable models, it may still have confounding effects on OCTA measurement due to the pathological change in DR itself. Thirdly, we did not account for ocular factors, such as axial length and refractive error in the analysis, as subjects with high myopia (axial length > 26 mm) were excluded. However, ocular magnification in OCTA images caused by varying axial lengths may interfere with accurate interpretation of OCTA measures. [ 10] Finally, VD and PD in the deep capillary plexus (DCP) could not be evaluated due to the limitations of built-in angiography software in the OCTA instrument, which may be more sensitive in detecting retinal microvascular changes in diabetic patients at an early stage.
In conclusion, this study provided evidence that systemic risk factors are associated with retinal microvasculature among T2DM patients in a Chinese population. Further longitudinal and large-scale studies are needed to corroborate our 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 the Institutional Review Board of Huizhou Central People’s Hospital. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
YL and KW drafted the manuscript and interpreted the results. YL, GX, and KW performed the data analysis. HF, ZC, GX, DW, and JW revised the manuscript for important intellectual content. GBu and GBo were involved in interpreting the results. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the Science and Technology Plan Project of Huizhou Science and Technology Bureau (grant number: 2021WC0106369).
## 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.
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|
---
title: The mechanism of action of paeoniae radix rubra–angelicae sinensis radix drug
pair in the treatment of rheumatoid arthritis through PI3K/AKT/NF-κB signaling pathway
authors:
- Jia Li
- Xiaofei Zhang
- Dongyan Guo
- Yajun Shi
- Shihao Zhang
- Ruiying Yang
- Jiangxue Cheng
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10040578
doi: 10.3389/fphar.2023.1113810
license: CC BY 4.0
---
# The mechanism of action of paeoniae radix rubra–angelicae sinensis radix drug pair in the treatment of rheumatoid arthritis through PI3K/AKT/NF-κB signaling pathway
## Abstract
Objective: To investigate the effects and mechanisms of Paeoniae radix rubra–*Angelicae sinensis* radix (P-A) drug pair in the treatment of rheumatoid arthritis (RA).
Methods: Mass spectrometry was employed to accurately characterize the main components of the P-A drug pair. Network pharmacology was used to analyze the main components and pathways of the P-A drug pair in the treatment of RA, and Discovery Studio software was used to molecularly dock the key proteins on the pathway with their corresponding compounds. The levels of serum TNF-a, IL-1β, and IL-6 were measured by enzyme linked immunosorbent assay (ELISA). The histopathology of the ankle joint was observed by hematoxylin-eosin (HE) staining, and the positive expression of p-PI3K, p-IKK, p-NF-κB, and p-AKT in the synovial tissue of the ankle joint was detected by immunohistochemical analysis. Finally, the expression of PI3K, IKK, and AKT and their phosphorylation levels were determined by western blot in each group of rats.
Results: Network pharmacology combined with molecular docking analysis revealed that the pharmacodynamic mechanism of the P-A drug pair for the treatment of RA may be related to the contents of caffeic acid, quercetin, paeoniflorin, and baicalein in the regulation of the expression of the PI3K/AKT/NF-κB signaling pathway and the targets of PIK3CA, PIK3R1, AKT1, HSP90AA1 and IKBKB in the pathway. Compared with the model group, the P-A drug pair significantly improved the pathological changes of the synovial tissue and reduced feet swelling in RA model rats. Moreover, it regulated the levels of TNF-α, IL-1β, and IL-6 in serum ($p \leq 0.05$). The results of the immunohistochemical analysis and western blot showed that the expression of PI3K, IKK, NF-κB, and AKT decreased after phosphorylation in the synovial tissue ($p \leq 0.05$).
Conclusion: The P-A drug pair exhibited an inhibitory effect on the hyperactivation of the PI3K/AKT/NF-κB signaling pathway in the synovial membrane of RA rats. The mechanism may be related to the downregulation of the phosphorylation levels PI3K, IKK, NF-κB, and AKT, which in turn decreased inflammatory cell infiltration and synovial membrane proliferation.
## 1 Introduction
RA is a chronic autoimmune disease that affecting joints, with a global prevalence of about $0.5\%$–$1\%$ (Zhang et al., 2019). It has become the world’s number one disabling disease due to its high risk of deformation and disability, which seriously affects patients’ quality of life (Fang et al., 2020; Roberts et al., 2020; McDonald et al., 2021). The main pathological features of RA are inflammatory changes in synovial tissues, cartilages, and bones, resulting in joint lesions triggered by synovitis, which can cause joint ankylosis and deformity. These pathologies severely impair the function of the joints in advanced stages and eventually lead to disability and death (Ngian, 2010; Alabarse et al., 2018; Scherer et al., 2020). The dampness heat obstruction type is the most common traditional Chinese medicine subtypes of RA and is it more severe. Currently, Western medicine mainly uses anti-inflammatory and anti-rheumatic drugs and biological immunosuppressants to treat RA. These drugs can improve pain symptoms, but they are expensive and cannot effectively control the progression of RA with a single use. In addition, the side effects include serious gastrointestinal reactions, cardiovascular injuries, and other toxic implications (Schnitzer, 2006; Mathews et al., 2016). In contrast, the natural active ingredients from traditional Chinese medicine have the advantages of low toxicity and few adverse effects, making them ideal candidates in the treatment of RA. They can not only effectively relieve the symptoms of RA but delay the progression of the disease to a certain extent. Thus, multi-target therapy via traditional Chinese medicine is a better choice (Boleto et al., 2019; Chen et al., 2019; Tasneem et al., 2019).
The P-A drug pair is commonly used in clinical practice to invigorate and tonify blood, and there are 342 blood invigorating and tonifying prescriptions that contain that drug pair in the Traditional Chinese Medicine Prescription Dictionary. Studies have shown that *Angelicae sinensis* radix has analgesic effects and it can inhibit various acute and chronic inflammatory reactions (Xu and Yuan, 2015). Angelicae sinensis radix-based medicine, Danggui Sini Decoction, can improve the symptoms and pathological progression of RA by affecting the intestinal microbiota and its metabolites (He et al., 2023). Huey-En Tzeng et al. showed that Paeoniae radix rubra has anti-inflammatory effects and it can effectively stimulate osteoclast differentiation in RAW264.7 cells and monocytes (Tzeng et al., 2018). Studies have shown the active components in Paeoniae radix rubra, such as paeoniflorin, can produce anti-inflammatory and immunomodulatory effects by restoring abnormal signal transduction in synovial cells (Zhang and Wei, 2020). In addition, quercetin can inhibit the release of pro-inflammatory mediators (Li et al., 2016; Tabrizi et al., 2020). However, the pharmacological basis and mechanism of action of the P-A drug pair for the treatment of RA remain unclear. In this study, we aimed to successfully replicate the early stage of heat poison and blood stasis syndrome in a rat model, and we have proved that P-A drug pair significantly improved the hemorheology, coagulation function, and inflammatory factor in rats, and the effect was better than that of Paeoniae radix rubra or *Angelicae sinensis* radix alone (Cheng et al., 2019a; Cheng et al., 2019b). According to previous studies, a collagen-induced arthritis (CIA) model was induced in rats stimulated by rheumatic fever (wind speed 6 m/s, relative humidity $90\%$, temperature 37°C for 15 d), and the dampness heat obstruction type CIA model rats were successfully prepared. The therapeutic effect of the P-A drug pair on the CIA model rats was preliminarily explored.
Ultra-performance liquid chromatography–quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS/MS) technique is a widely used qualitative method in the field of Chinese medicine, combining the efficient chromatographic separation capability of UPLC and the high sensitivity of Q-TOF high-resolution mass spectrometry. This technique can rapidly characterize the complex components of Chinese medicines (Zhang et al., 2020). Network pharmacology provides a new strategy to study the mechanism of action of traditional Chinese medicines by constructing a network to analyze the active component–target action pathways (Niu et al., 2021). In this study, we use the liquid chromatography-mass spectrometry technology, network pharmacological analysis, and in vitro experiments to study the mechanism of action of drugs in a more comprehensive manner. The key components in the P-A drug pair were identified by UPLC-Q-TOF-MS/MS, and in combination with the network pharmacology, the target and the mechanism of action of the P-A drug pair for the treatment of RA were predicted. Furthermore, the CIA rat model was established for preliminary verification. The serum levels of TNF-a, IL-1β, and IL-6 were determined by ELISA. The ankle joint histopathology was observed by HE staining. The positive expression of p-PI3K, p-IKK, p-NF-κB, and p-AKT in the synovial tissue of the ankle joint was detected by immunohistochemical, and the expression and phosphorylation levels of PI3K, IKK and AKT were determined by western blot. A flowchart of this study is shown in Figure 1.
**FIGURE 1:** *Detailed flowchart of the study.*
## 2.1 Materials
Paeoniae radix rubra and *Angelicae sinensis* radix were purchased from Shaanxi Sciendan Pharmaceutical Co., Ltd. and identified by Professor Cheng Huyin of Shaanxi University of Chinese Medicine. The rat TNF-α ELISA kit (MM-0180R1), rat IL-1β ELISA kit (MM-0047R1), and rat IL-6 ELISA kit (MM-0190R1) purchased from Jiangsu Enzyme Immunoassay Industrial Co.,Ltd. Bovine Type II collagen (L22S11C125306) was purchased from Shanghai Yuanye Bio-Technology Co.,Ltd., and full Frances adjuvant (J04GS149798) was purchased from Shaanxi Shuoyan Chemical Technology Co., Ltd. Antibodies against PI3K (ET1608-70), AKT (ET1609-47), p-AKT (ET1612-73), IKK (ET1611-15) and NF-κB (ET1604-27) were obtained from Hangzhou HuaAn Biotechnology Co.,Ltd., antibodies against p-PI3K (AF3241) was obtained from Affinity Biosciences Group Ltd., antibodies against p-IKK (bs-3232R) was obtained from Beijing Biosynthesis Biotechnology Co.,Ltd.
## 2.2.1 Preparation of the test solution
In the treatment of RA, the maximum dose of Paeoniae radix rubra was 60 g, and the maximum dose of *Angelicae sinensis* radix was 50 g. Combined with the total prescription dosage for the treatment of the dampness heat obstruction type RA, and the common ratio of *Angelicae sinensis* radix and Paeoniae radix rubra is 1:1, the dosage is 40 g of *Angelicae sinensis* radix and 40 g of Paeoniae radix rubra (Xing, 2022). According to the conversion coefficient table of 60 kg adult and animal drug dose, the daily dose of rats was calculated to be 7.2 g/kg. Angelicae sinensis radix and Paeoniae radix rubra dried herbs were ground into a coarse powder and mixed well, in the ratio of 1:1. The mixture was placed in a conical flask with a stopper, and 8 volumes of water was added. The mixture was extracted for 0.5 h and refluxed for 1.5 h. The distilled extract was filtered with four layers of gauze to obtain the P-A drug pair decoction. Five milliliters of the decoction was transferred to a 10-mL volumetric flask, and then the same volume of methanol was added into it. The mixture was passed through a 0.22-μm microporous filter membrane, and the filtrate obtained was used as the P-A drug pair decoction for testing.
## 2.2.2 Preparation of the control solution
The reference standards of chlorogenic acid, caffeic acid, ethyl gallate, 3-butyl-phthalide, benzoyloxypaeoniflorin, senkyunolide I, gallic acid, oxypaeoniflorin, vanillic acid, albiflorin, paeoniflorin, ferulic acid, 1,2,3,4,6-pentagalloylglucose, benzoylpaeoniflorin, paeonol, and ligustilide (the purities of all standards were above $98.0\%$) were supplied by Shanghai Yuanye Bio-Technology Co., Ltd. Appropriate amounts of the reference standards were mixed and dissolved in methanol in a 10-mL measuring flask, then filtered and set aside.
## 2.2.3 Chromatographic and mass spectrometric conditions
The determination was performed on Acquity UPLC®BEH C18 column (100 mm × 2.1 mm, 1.7 μm) with mobile phases consisting of $0.1\%$ formic acid acetonitrile (A) −$0.1\%$ formic acid aqueous solution (B) in gradient elution (0–1 min, 2 A; 1–5 min, $2\%$–$5\%$ A; 5–9 min, $5\%$–$9.5\%$ A; 9–12 min, $9.5\%$ A; 12–18 min, $9.5\%$–$15\%$ A; 18–25 min, $15\%$–$20\%$ A; 25–30 min, $20\%$–$50\%$ A; 30–37 min, $50\%$–$100\%$ A; 37–38 min, $100\%$ A; 38–40 min, $100\%$–$2\%$ A; 40–42 min, $2\%$ A). The column temperature was 30°C; the flow rate was 0.2 mL/min; and the injection volume was 2 μL.
A triple TOFTM 5600+ time-of-flight liquid mass spectrometer was used for quantitation. Electrospray ion source, positive and negative ion temperature were 600°C and 550°C, spray voltage was 5.5 kV and −4.5 kV respectively. Atomization gas was N2, scanning range was m/z100∼2000, cracking voltage was ±80V, collision energy was ±10 eV, and the total data collection time was 42 min.
## 2.3 Targets of the active compounds of the P-A drug pair and disease targets of rheumatoid arthritis
The targets of the drug were predicted by Swiss TargetPrediction database(http://swisstargetprediction.ch/) (Daina et al., 2019). With “rheumatoid arthritis” as the keyword from the GeneCards database (https://www.genecards.org/) (Stelzer et al., 2016) and DisGeNET database(https://www.disgenet.org/) (Piñero et al., 2021), the related targets of RA were obtained. In this study, we also screened and downloaded GSE206848 microarray data from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) (Barrett et al., 2013), In addition, we obtained samples of normal and RA synovial tissues, and used R software (p ≤ 0.05 and |logFC| ≥ 1) to determine differentially expressed genes and to generate volcano plots.
## 2.4 Network construction
The screened active compound targets and RA targets were mapped by Venny 2.1.0(https://bioinfogp.cnb.csic.es/tools/venny/index.html), and the intersection was obtained. The final target was the target of action of the P-A drug pair in RA. Using the STRING platform (https://string-db.org/) (Szklarczyk et al., 2021) with the confidence level set at 0.9, the target–protein interaction relationships for the action of the P-A drug pair in RA were obtained, saved in TSV format, and imported into Cytoscape 3.7.2 for topological analysis. Cytoscape software was used to construct the active compounds of the P-A drug pair and their target networks in RA. Topological analysis was also performed.
## 2.5 Analysis of gene ontology (GO) and kyoto encylopedia of genes and genomes (KEGG)
Based on the core targets screened in the above process, GO and KEGG signaling pathway analysis were performed on the intersecting targets using the cluster Profiler package (Yu et al., 2012) in R 4.0.2 software at $p \leq 0.05$, and the key signaling pathways for the treatment of RA with the P-A drug pair were explored.
## 2.6 Molecular docking
Molecular docking of key targets of PI3K/AKT/NF-κB pathway with their corresponding components was performed to predict their interactions. The SDF structure files of potential active compounds were downloaded from PubChem (https://pubchem.ncbi.nlm.nih.gov), and the PDB structure files of core targets were downloaded from PDB database (https://www.rcsb.org/) (Burley et al., 2017). The LibDock module in Discovery Studio 4.5 Client software was used for molecular docking, and the docking score value was obtained. The higher the score value, the more stable the ligand-receptor binding.
## 2.7.1 Experimental animals
SPF male SD rats, body weight 180–220 g, were purchased from Chengdu Dasuo Laboratory Animal Co., LTD., license number SCXK 2020–030. They were fed at humidity (50 ± 10) % and temperature (25 ± 2)°C and the animal experiment was approved by the Animal Experiment Ethics Committee of Shaanxi University of Chinese Medicine (Approval No. SUCMDL20210930001).
## 2.7.2 Modeling, grouping, and drug administration
Sixty SPF-grade male SD rats were randomly divided into six groups ($$n = 10$$): the normal control group, the model group, the Tripterygium glycoside tablet group (20 mg/kg) (Wang et al., 2012), and the low (3.6 g/kg), medium (7.2 g/kg), and high (14.4 g/kg) P-A drug pair dose groups, with 10 animals in each group. Except for the normal group, the remaining 50 only established the CIA model. Under the condition of ice bath, the 2 mg/mL bovine type II collagen solution was mixed with the same volume of Frauchian complete adjuvant by a homogenizer, and the emulsion was fully mixed and emulsified, and the bovine type II collagen emulsion was fully emulsified (emulsification standard: the emulsion was not dispersed when dropped into the water), and the concentration of collagen emulsion was prepared as 1 mg/mL each rat were subcutaneously injected with 0.1 mL of collagen emulsion at the caudal root and two hind paws on the first day for initial immunization. After 7 days, the rats in the experimental groups were repeatedly injected with the same dose of collagen emulsion at the same locations to enhance immunization. After 14 days, the rats showed severe swelling of the hind toes, and their ankle joint diameter increased by ≥ 2 mm. Thus, the CIA model rats were successfully established. At the beginning of 15 days of modeling, the rats were gavaged with the corresponding drugs in the low, medium, and high P-A dose groups, as well as Tripterygium glycoside tablet group once daily for 38 d. The vernier caliper was used to measure the thickness of the posterior plantar.
## 2.7.3 General observation of rats
The changes in the color of their body hair, mental and activity state, dietary habit, and joint swelling were observed daily.
## 2.7.4 Serum inflammatory factor level
One hour after the last administration, 5 mL of blood was taken from the abdominal aorta of rats. The blood sample stood for 30 min and then was centrifuged at 3,000 r/min for 15 min to separate the serum. The levels of TNF-α, IL-1β, and IL-6 in the serum were determined by using an ELISA kit, according to the manufacturer’s instructions.
## 2.7.5 Histopathology of rat ankle joint
The rats were executed by decervicalization, and their left hind feet were taken, fixed with $4\%$ paraformaldehyde, decalcified with $10\%$ EDTA solution, dehydrated by a series of ethanol gradient solutions, treated with xylene, embedded in paraffin, and sectioned. HE staining was used for routine staining. After dehydration, the sections were cleared with xylene and then sealed with neutral gum. The histopathological changes in the ankle joints of the rats were observed under a microscope.
## 2.7.6 Immunohistochemical
The left ankle joint tissue sections were dewaxed with xylene, and repaired antigen with sodium citrate buffer. After treating with $3\%$ hydrogen peroxide, the sections were rinsed, sealed with $10\%$ goat serum, and p-PI3K, p-IKK, p-NF-κB and p-AKT antibodies were added dropwise. The tissue sections were incubated overnight at 4°C. Biotin-labeled secondary antibodies were added for incubation, and the color developed by dropwise addition of diaminobenzidine chromogenic solution was observed. Hematoxylin was re-stained, dehydrated, cleared, sealed, and photographed. Quantitative analysis of the positive proteins was performed by using Image-*Pro plus* software.
## 2.7.7 Western blotting
Protein was extracted from the synovial tissue of the right ankle of the rats, and the bicinchoninic acid method was used for protein quantification. Briefly, 9 µL of the protein sample was separated by $10\%$ SDS-PAGE and closed with $5\%$ skim milk for 1 h. The primary antibody was added and incubated overnight, then the secondary antibody was added for incubation. An enhanced chemiluminescence kit was used for color development, and protein bands were photographed in a gel imager system. The relative protein expression (with β-actin as the internal reference protein) was analyzed.
## 2.8 Statistical methods
All data are expressed as the mean ± standard deviation, and Graph Pad Prism 9.0.0 software was used to process the data. One-way ANOVA and Tukey’s multiple comparisons tests were used to compare the data. $p \leq 0.05$ indicated that the difference was statistically significant.
## 3.1 UPLC-Q-TOF-MS/MS assay results
The detected data were imported into Peak View 2.2 software, and NIST 2017, NIST E&L_HR-MS/MS_1.0, TCM Library 1.0, TCM MS/MS Library were used as the matching library of P-A drug pair. Combined with the mass spectrum information of each compound, comparison with the reference standards, reference to the relevant literature data, and compounds in the P-A drug pair were identified and classified. A total of 41 compounds were identified, among which 19 components were found in Paeoniae radix rubra, 19 components identified in *Angelicae sinensis* radix, and there were three compounds common to both herbs. Among them, compounds 9, 12, 14, 15, 19, 20, 21, 22, 23, 26, 28, 29, 32, 33, 39, 40 were identified as gallic acid, chlorogenic acid, caffeic acid, vanillic acid, ethyl gallate, oxypaeoniflorin, paeoniflorin, albiflorin, ferulic Acid, 1,2,3,4, 6-pentagalloylglucose, paeonol, benzoyloxypaeoniflorin, senkyunolide I, benzoylpaeoniflorin, ligustilide, 3-butyl-phthalide, respectively, by comparison with reference standards. The identification results are summarized in Table 1, and the total ion flow diagram of the P-A drug pair is shown in Figure 2.
## 3.2 Component target acquisition and mapping analysis with RA targets
A total of 1,926 component targets were predicted by using Swiss Target Prediction database, and a total of 565 active component targets of the P-A drug pair were obtained after de-weighting. After combining the results of the GeneCards and the DisGeNET databases, a total of 5,792 RA-related targets were obtained after de-duplication. GSE206848 was filtered and normalized by using R software and related software packages to screen for differential genes, and a total of 2,805 differential genes were obtained for upregulated [1,975] and downregulated [830] RA. The volcano map of the differential genes is shown in Figure 3A. Using the Venny 2.1.0 online platform to obtain the common targets of the active ingredient targets of P-A drug pair and RA diseases, 57 intersection targets were finally obtained, as shown in Figure 3B. There were 37 upregulated genes (CFTR, ABCB1, EGFR, HSP90AA1, INSR, GRIA1, PDE4A, SLC28A2, EPHB1, TOP1, NOX4, CYP2C9, PTPN2, PIK3CA, SLC6A4, CDK2, PIK3R1, EPHA3, MCL1, CDK5R1, JAK1, NFE2L2, F3, PTPN11, ESR2, PRKDC, CA2, RORA, CNR1, MET, PTPN1, NAMPT, FLT1, EDNRA, EPHA1, CYP2C19, SERPINE1) and 20 downregulated genes (SCD, TYMS, SLC6A2, PTGER3, RBP4, EPHX1, ADORA3, AOC3, MME, MMP12, ACHE, HPSE, FUCA1, ACE, EPHB2, CTSB, CXCR2, CTSC, PTK2B, PRKCD).
**FIGURE 3:** *(A) Volcano plot of differential genes on GSE206848 chip (no differential genes are shown in black, the gene is upregulated and shown in red. The gene is downregulated and shown in green). (B) Venn diagram of the active ingredients in the P-A drug pair and RA target. (C) PPI network. (D) Network diagram of active ingredient–target pairs of the P-A drug pair (oval represents chemical compositions, and triangle represents targets).*
## 3.3 Network construction and analysis
The 57 RA targets of the P-A drug pair were imported into STRING database for analysis, and the confidence level was set to >0.9 to eliminate the isolated target proteins and obtain the protein interaction information. The network was imported into Cytoscape 3.7.2 software for visualization, and network topology analysis was conducted to obtain the PPI of the targets (57 nodes and 56 edges). As shown in Figure 3C, and according to the Degree value, the core targets of P-A drug pair for RA treatment were mainly PIK3R1, EGFR, PIK3CA, PTPN11, JAK1, PTPN1, MET, and HSP90AA1. The compound–target network was mapped by Cytoscape 3.7.2, as shown in Figure 3D, with 95 nodes (38 compounds and 57 targets) and 255 interacting edges. The Degree value was used to screen the main components of P-A drug pair in the treatment of RA. The topological analysis yielded the key compounds baicalein, naringenin, senkyunolide I, quercetin, isorhamnetin, albiflorin, paeoniflorin, 3-butyl-phthalide, and caffeic acid.
## 3.4 GO and KEGG enrichment analysis
GO and KEGG enrichment analysis of intersection targets was performed by using R 4.0.2 software. At the significance level of $p \leq 0.05$, 680 related biological processes (BPs), 44 related cell compositions (CCs), and 56 related molecular functions (MFs) were screened (Figures 4A–C). Through KEGG enrichment analysis, a total of 67 signaling pathways were selected according to $p \leq 0.05$, including PI3K-AKT, JAK-STAT, Rap1, and other signaling pathways, as shown in Figure 4D.
**FIGURE 4:** *(A-D) Results of GO-BP, GO-CC, GO-MF, and KEGG pathway enrichment analysis.*
## 3.5 Molecular docking prediction
The core targets PIK3R1, PIK3CA, AKT1, HSP90AA1 and IKBKB and their corresponding components on the PI3K/AKT/NF-κB pathway were predicted by molecular docking. The core targets were used as molecular dockingreceptors, and the active ingredients corresponding to the core targets were used as molecular docking ligands. LibDock was used for molecular docking. The docking patterns of some compounds and targets are shown in Figures 5A–I, and the molecular docking score heat map is shown in Figure 5J. According to the heat map, the core targets PIK3R1 and quercetin, PIK3CA and caffeic acid, AKT1 and quercetin, HSP90AA1 and paeoniflorin, and IKBKB and baicalein had the best intermolecular affinity.
**FIGURE 5:** *Molecular docking results. (A) PIK3CA and caffeic acid. (B) PIK3R1 and quercetin. (C–E) AKT1 target and benzoyloxypaeoniflorin, isorhamnetin, and quercetin. (F) IKBKB target and baicalein. (G) HSP90AA1 and paeoniflorin. (H) HSP90AA1 and oxypaeoniflorin. (I) Heat map of molecular docking score.*
## 3.6 P-A drug pair treatment alleviates the symptoms associated with RA rats
After the second immunization, the rats showed obvious arthritic features, poor mental status, decreased diet, body mass, decreased mobility, as well as obvious redness and deformation of the feet and limbs. The swelling of the feet of the rats in the Tripterygium glycoside tablet group and high-dose P-A drug pair group was significantly reduced (Figure 6).
**FIGURE 6:** *(A) Pathological phenotypic changes. (B) Left plantar swelling value. (C) Right plantar swelling value.*
## 3.7 Contents of TNF-α, IL-1β, and IL-6 in the serum
Compared with the normal control group, the levels of TNF-α, IL-1β, and IL-6 in the serum and ankle synovial tissue of rats in the model group increased ($p \leq 0.01$). Compared with the model group, the levels of TNF-α, IL-1β, and IL-6 in the serum of rats in the high-dose P-A drug pair group decreased ($p \leq 0.01$), as shown in Figure 7.
**FIGURE 7:** *Effects of the P-A drug pair on TNF-α, IL-1β, and IL-6 levels in the serum of RA rats. (A) Effect of the P-A drug pair on TNF-α contents. (B) Effect of the P-A drug pair on IL-1β contents. (C) Effect of the P-A drug pair on IL-6 contents. The data represent the mean ± SD. #p < 0.5, ##p < 0.01, ###p < 0.001, ####p < 0.0001 vs. control group; *p < 0.5, **p < 0.01, ***p < 0.001, ****p < 0.0001 vs. model group.*
## 3.8 Effect of P-A drug pair on histopathological changes
As shown in Figure 8, HE staining showed abnormal hyperplasia, disordered arrangement, and infiltration of a large number of inflammatory cells in the ankle synovium of rats in the model group, and the synovial layer was significantly thicker. The synovial membrane of the ankle joint of rats in the high-dose P-A drug pair group was notably recovered, and the inflammatory cell infiltration was reduced.
**FIGURE 8:** *Effect of the P-A drug pair on pathological changes in the synovium of the ankle joint in rats (HE, x 200). Black arrow represents synovial hyperplasia.*
## 3.9 Determination of p-PI3K, p-IKK, p-NF-κB, and p-AKT protein expression in the synovial tissue of the ankle joint of RA rats by immunohistochemical analysis
As shown in Figure 9, in the model group, p-PI3K, p-IKK, p-NF-κB, and p-AKT protein were expressed more than in the control group ($p \leq 0.0001$), indicating that there was inflammatory injury in the ankle synovial tissue of rats in the model group. The P-A drug pair in the high and medium dose groups, p-PI3K, p-IKK, p-NF-κB, and p-AKT protein expression in ankle tissue decreased ($p \leq 0.01$), indicating that the expression of inflammatory factors in RA rats could be inhibited by the P-A drug pair.
**FIGURE 9:** *(A) Distribution of p-PI3K, p-IKK, p-NF-κB, and p-AKT positive cells in the synovial tissue of the ankle joint in each group (immunohistochemical method, ×100). (B) Effect of the P-A drug pair on PI3K, IKK, and AKT protein expression and their phosphorylation levels. (C–F) Quantitative analysis of p-PI3K, p-IKK, p-AKT and p-NF-κB. (G) Quantitative analysis of p PI3K/PI3K, p-IKK/IKK and p-AKT/AKT. The data represent the mean ± SD. #p < 0.5, ##p < 0.01, ###p < 0.001, ####p < 0.0001 vs. control group; *p < 0.5, **p < 0.01, ***p < 0.001, ****p < 0.0001 vs. model group.*
## 3.10 Expression of PI3K, IKK, and AKT proteins and their phosphorylation levels in the synovial tissue of the ankle joint of RA rats by western blot analysis
As shown in Figure 9, the expression of p-PI3K/PI3K, p-IKK/IKK, and p-AKT/AKT in the model group rats after modeling was significantly increased, compared with the control group ($p \leq 0.001$). The expression of p-PI3K/PI3K, p-IKK/IKK, and p-AKT/AKT was inhibited to varying degrees by the P-A drug pair in the high and medium dose groups, with significant differences compared with the model group ($p \leq 0.01$).
## 3.11 Description of the mechanism
The core targets PIK3R1, PIK3CA, AKT1, HSP90AA1 and IKBKB and their corresponding components in the PI3K/AKT/NF-κB pathway were docked by molecular docking technology. PIK3R1 and quercetin, PIK3CA and caffeic acid, AKT1 and quercetin, HSP90AA1 and paeoniflorin, and IKBKB and baicalin showed the best intermolecular affinity. The rat model of CIA was established by injecting bovine type II collagen emulsion subcutaneously, at the root of the tail and in the paw of the two hind feet. After 38 days of continuous gavage with the P-A drug pair, serum TNF-a, IL-1β, IL-6 levels were measured in each group by ELISA. Ankle joint histopathology was observed by HE staining, and the p-PI3K, p-IKK, p-NF-κB, and p-AKT protein positive expression in the ankle synovial tissues of each group was detected by immunohistochemical analysis. PI3K, IKK, and AKT protein expression and their phosphorylation levels in the ankle synovial tissue of each group were determined by western blot. Combining the results of each experiment, we believe that the mechanism of action of the P-A drug pair in the treatment of RA may be related to the action of some key compounds, such as caffeic acid, quercetin, paeoniflorin, and baicalein, on key targets, including PIK3R1, PIK3CA, AKT1, HSP90AA1, and IKBKB, to downregulate the phosphorylation levels of PI3K, IKK, and AKT. This, in turn, inhibits the release of inflammatory mediators TNF-α, IL-1β, and IL-6 and suppresses the overactivation of the PI3K/AKT/NF-κB signaling pathway in the synovial membrane of RA rats (Figure 10).
**FIGURE 10:** *Mechanism display of P-A drug pair in the treatment of RA through PI3K/AKT/NF-κB signaling pathway.*
## 4 Discussion
RA is a chronic, progressive, and aggressive autoimmune disease with synovitis and extra-articular lesions as the main clinical manifestations (Zhang et al., 2013). Current treatment is mainly focused on reducing joint swelling and pain, controlling the development of arthritis, preventing and reducing joint destruction, and promoting the repair of damaged joints and bone (Wang, 2007). The clinical symptoms and pathological manifestations of the collagen-induced arthritis rat model are very similar to those of RA, making it ideal for studying RA. In the present study, after the establishment of the RA rat model by type II collagen induction, the rats in the model group showed lethargy, loss of appetite, obvious redness and swelling of the limbs, significantly lower body mass, and significantly higher feet swelling, compared with the normal control rats. In addition, hyperplasia of the ankle synovial tissue was observed, with obvious pathological changes. The expression levels of PI3K, IKK, and AKT protein phosphorylation in the model group increased, suggesting that the modeling of RA rats was successful. After the treatment with the P-A drug pair, the swelling of the feet was significantly reduced, the structure of the ankle synovial tissue tended to be normal, and the expression levels of PI3K, IKK, and AKT proteins in the ankle synovial tissue were reduced. These changes indicate that the P-A drug pair is effective in treating RA.
Excluding signaling pathways unrelated to the disease, the results of KEGG enrichment analysis showed that the drug treated RA by acting on PI3K-AKT, JAK-STAT, HIF-1, and other signaling pathways. The PI3K-Akt signaling pathway can regulate the release of inflammatory factors and the formation of enzymes related to proliferation, apoptosis, and inflammation to participate in the pathological process of RA. The PI3K-AKT signaling pathway was found to be widely present and aberrantly activated in RA synoviocytes (Harris et al., 2009). Inhibition of the expression of the PI3K-AKT signaling pathway or anti-apoptotic molecules can induce apoptosis in fibroblast-like synoviocytes, which is therapeutic for RA (Liu and Pope, 2003). PI3K/AKT phosphorylation can activate IL-1β to induce the expression of pro-inflammatory factor TNF-α, IL-6 expression, and the development of inflammation in rat joints (Jiang et al., 2020). In the physiological state, NFκB dimers are bound to their inhibitor IκB and are present in the cytoplasm in a non-activated form. NFκB is mainly localized in the nucleus of synoviocytes, and AKT phosphorylates activates IκB kinase (IKKα), leading to the degradation of IκB, an inhibitor of NFκB. This results in the release of the transcription factor NFκB from the cytoplasm for nuclear translocation and initiation of its target gene expression, thereby promoting cell survival (He et al., 2013). HSP90AA1 can block the nuclear factor κB pathway and reduce the level of inflammatory mediators (Guo et al., 2016). Studies have found that downregulation of the JAK1/STAT3 pathway can reduce proinflammatory cytokines, MMPs inhibition, and inflammatory cell apoptosis, thereby alleviating the clinical symptoms of RA (Wang et al., 2018). HIF-1 plays a role in inflammatory/innate immune responses (Sumbayev and Nicholas, 2010), and activation of the HIF-1 pathway causes downregulation of NF-κB, pro-inflammatory cytokines and plays a protective role in inflammation (Hirai et al., 2018). Therefore, the mechanism of P-A drug pair in the treatment of RA is related to the above inflammation-related pathways.
According to the above analysis results, in this study, the key proteins PIK3R1, PIK3CA, AKT1, HSP90AA1, and IKBKB and their corresponding components in the PI3K/AKT/NF-κB pathway were interlinked by molecular docking technology, and the results indicated that the binding stability between all the active compounds and the key targets was strong. In particular, quercetin, caffeic acid, paeoniflorin, and baicalin scored higher than the other compounds with the key targets. The results suggest that these four compounds are the active ingredients of the P-A drug pair for the treatment of RA. A previous study showed that quercetin reduced joint swelling and inflammation in mice with arthritis and alleviated joint damage (El-Said et al., 2022). By inhibiting the phosphorylation of IκB and IκB kinase, caffeic acid can inhibit the infiltration and secretion of inflammatory cells in joint synovium and play an anti-inflammatory and preventive role (Wang et al., 2017; Nouri et al., 2022). Baicalin can reduce the effect of IL-1β and TNF-α on RA fibroblast-like synovial cells (Humby et al., 2017). Paeoniflorin can reduce joint swelling and subcutaneous hematoma in CIA rats, decrease the mean arthritis index, and reduce the degree of bone destruction (Wu et al., 2014). These activities confirmed the effectiveness of these compounds in the treatment of RA. In this study, the key targets of PI3K/AKT/NF-κB pathway were verified in collagen-induced arthritis animal model. The results showed that P-A drug pair exerted their therapeutic effects on RA by down-regulating the phosphorylation levels of PI3K, IKK, NF-κB and AKT.
TNF-α, IL-1β, and IL-6 play an important role in the development and progression of RA. TNF-α induces endothelial cells to express adhesion molecules, promoting leukocyte adhesion and infiltration of the vascular endothelium. This leads to local inflammation, and it also directly stimulates collagenase synthesis in articular chondrocytes, promoting synovial inflammation (Abrahams et al., 2000; Vizcarra, 2003). An increase in IL-1β in the joint will exacerbate synovial inflammatory cell infiltration and promote the formation of vasospasm, ultimately leading to cartilage and bone destruction (Liu et al., 2020). The promotion of neutrophil migration and monocyte infiltration by IL-6 is an important mechanism in the pathogenesis of RA (Pandolfi et al., 2020). The results of the present study have shown that the expression levels of TNF-α, IL-1β, and IL-6 were significantly reduced in the serum of the high-dose P-A group, indicating that the anti-inflammatory effect of the P-A drug pair may be related to the inhibition of the production of pro-inflammatory factors.
In this study, UPLC-Q-TOF-MS/MS technology was used to comprehensively and accurately identify the active components in the P-A drug pair. Moreover, its mechanism of action in the treatment of RA was further explored by using network pharmacology and animal experiments. The results provide an important theoretical basis for the further development of P-A drug pair for the treatment of RA.
## 5 Conclusion
The P-A drug pair exhibited good therapeutic effects on RA rats. In this study, UPLC-Q-TOF-MS/MS was used to analyze and identify the key chemical components of the P-A drug pair, and network pharmacology to investigate the mechanism of action. The levels of serum TNF-a, IL-1β, and IL-6 were measured by enzyme linked immunosorbent assay (ELISA). The positive expression of p-PI3K, p-IKK, p-NF-κB, and p-AKT in the synovial tissue of the ankle joint was detected by immunohistochemical analysis. The expression of PI3K, IKK, and AKT and their phosphorylation levels were determined by western blot in each group of rats. It was speculated that components, such as caffeic acid, quercetin, paeoniflorin, and baicalein, might act on key targets, such as PIK3R1, PIK3CA, AKT1, HSP90AA1, and IKBKB, to downregulate the phosphorylation levels of PI3K, IKK, and AKT. Furthermore, the PI3K/AKT/NF-κB signaling pathway was regulated by inhibiting the release of inflammatory mediators TNF-α, IL-1β, and IL-6.
Through the comprehensive analysis of network pharmacology and pharmacodynamics, this study preliminarily interpreted the effect and mechanism of P-A drug pair in the treatment of RA, and provided reference for the study of the effect and mechanism of Chinese herbal medicine in the treatment of RA. However, this study has some limitations. For example, the docking results should be verified by experiments such as Isothermal Titration Calorimetry and Surface Plasmon Resonance, and the P-A drug pair and its potential active components, targets and signaling pathways for the treatment of RA have not been fully studied and verified by experimental pharmacology (in vitro or in vivo). In the following, we will carry out these studies, and we will also adopt cutting-edge methods and technologies based on experimental pharmacology, network pharmacology and multi-omics and their comprehensive analysis to investigate the specific effects and mechanisms of P-A drug pair on RA.
## 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 The Experimental Animal Ethics Committee of the Shaanxi University of Chinese Medicine.
## Author contributions
JL collected the data, performed the data analysis, and also wrote the first version of the manuscript. XZ, DG, and YS finalized the final manuscript. SZ and RY collected important background information. JC participated in the design of this study. 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/fphar.2023.1113810/full#supplementary-material
## Abbreviations
P-A, Paeoniae radix rubra–*Angelicae sinensis* radix; RA, rheumatoid arthritis; ELISA, enzyme-linked immunosorbent assay; HE, hematoxylin-eosin; CIA, collagen-induced arthritis; UPLC-Q-TOF-MS/MS, Ultra-performance liquid chromatography–quadrupole-time of flight-mass spectrometry; PPI, protein–protein interaction; GO, gene ontology; KEGG, kyoto encyclopedia of genes and genomes; BPs, biological processes; CCs, cell compositions; MFs, molecular functions.
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|
---
title: 'Pune GSH supplementation study: Analyzing longitudinal changes in type 2 diabetic
patients using linear mixed-effects models'
authors:
- Arjun Kolappurath Madathil
- Saroj Ghaskadbi
- Saurabh Kalamkar
- Pranay Goel
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10040593
doi: 10.3389/fphar.2023.1139673
license: CC BY 4.0
---
# Pune GSH supplementation study: Analyzing longitudinal changes in type 2 diabetic patients using linear mixed-effects models
## Abstract
Oral GSH supplementation along with antidiabetic treatment was shown to restore the body stores of GSH significantly and reduce oxidative DNA damage (8-OHdG) in Indian Type 2 diabetic (T2D) patients over 6 months in our recent clinical study. Post hoc analysis of the data also suggested that elder patients benefit from improved HbA1c and fasting insulin. We modeled longitudinal changes in diabetic individuals using a linear mixed-effects (LME) framework and obtained i) the distribution of individual trajectories with and without GSH supplementation and ii) the overall rates of changes in the different study arms. Serial changes in elder and younger diabetic individuals were also modeled independently to examine differences in their progression. The average linear trajectories obtained from the model explain how biochemical parameters in T2D patients progress over 6 months on GSH supplementation. Model estimates show improvements in erythrocytic GSH of 108 µM per month and a reduction in 8-OHdG at a rate of 18.5 ng/μg DNA per month in T2D patients. GSH replenishes faster in younger people than in the elder. 8-OHdG reduced more rapidly in the elder (24 ng/μg DNA per month) than in younger (12 ng/μg DNA per month) individuals. Interestingly, elder individuals show a substantial reduction in HbA1c ($0.1\%$ per month) and increased fasting insulin (0.6 µU/mL per month). Changes in GSH correlate strongly with changes in HbA1c, 8-OHdG, and fasting insulin in the elder cohort. The model estimates strongly suggest it improves the rate of replenishment in erythrocytic GSH stores and reduces oxidative DNA damage. Elder and younger T2D patients respond differently to GSH supplementation: It improves the rate of reduction in HbA1c and increases fasting insulin in elder patients. These model forecasts have clinical implications that aid in personalizing treatment targets for using oral GSH as adjuvant therapy in diabetes.
## Introduction
A large number of clinical and experimental studies have demonstrated the role of oxidative stress in developing type 2 diabetes (T2D) complications (Brownlee, 2005; Volpe et al., 2018; Burgos-Morón et al., 2019). However, the use of antioxidants as therapy isn’t recommended in healthcare practice due to the lack of evidence about their long-term safety and efficacy. Glutathione (GSH) is a major endogenous antioxidant in all cells and determines their redox status and is significantly low in T2D individuals (Townsend et al., 2003). Therefore, replenishing GSH should be a good strategy to improve systemic redox status. However, few clinical trials with GSH supplementation have been conducted in healthy and diabetic individuals. Most of these studies have concentrated on the effect of GSH supplementation on replenishing body stores of GSH; few have studied its impact on reducing oxidative stress, and even fewer on glycemic stress. Results of these trials (Allen and Bradley, 2011; Sekhar et al., 2011; Ritchie et al., 2015) have been difficult to interpret due to differences in the dose and duration of GSH supplementation and the site of outcome measurements, making the clinical recommendations difficult.
Our recent work (Kalamkar et al., 2022) has provided the most conclusive evidence regarding the effects of GSH supplementation in conjunction with antidiabetic treatment. The evidence from this clinical trial suggested that the long-term GSH supplementation offered protection from oxidative damage and improved HbA1c and fasting insulin, especially in elderly T2D patients. We, therefore, believe that GSH should be used as an adjunct therapy for T2D individuals. In our data, we observed significant differences in how individuals respond to GSH intervention. In addition to the factors such as age, diet, physical activity, dose, and length of GSH intervention, the basal amount of endogenous GSH is also responsible for this differential response among individuals. Therefore, we feel that the personalization of GSH supplementation based on endogenous GSH for T2D individuals could be an important addition to current clinical practices. To formulate effective personalized interventions of GSH with antidiabetic treatment, it is essential to understand the dynamics of longitudinal biochemical change and the variations between individual responses to GSH supplementation in detail. This would be largely useful in evaluating the progress of treatment and understanding the glucose control targets for diabetic individuals.
In this work, we have formulated longitudinal mixed-effects models (Laird and Ware, 1982; Brown and Prescott, 2006) to analyze the clinical data of diabetic individuals. Our mixed-effects (ME) models are hierarchical models, where the units of analysis are subject-level predictors (level two) with fixed and random effects. The framework of LME models also performs ‘shrinkage’ for estimating model parameters; that is, individual estimates obtained from LME models are shrunk towards a grand mean of the population level estimate compared to fitting separate linear models to each subject’s data (Bell et al., 2019). ME models have a long history of use in health and medicine since these models treat each patient not only as a member of a population but as an individual with unique characteristics (Gelman et al., 2012; Barr et al., 2013; Baldwin et al., 2014; Wang et al., 2019; Schober and Vetter, 2021). ME models thus allow estimating model parameters that describe between- and within-subject variability of individual responses. A two-level LME model provides reliable estimates in absolute, not just relative, physical units of the variables. This is beneficial for direct clinical use rather than the effect-size-based estimates of treatment effects obtained in our earlier work. We formulated two different LME models, namely, 1) with random intercepts and fixed slopes and 2) random intercepts and random slopes for each variable. These models were evaluated using best likelihood by Akaike’s Information Criteria (AIC) and non-singularity criteria and selected for optimal performance (Bates D. M. et al., 2015).
In our earlier study, we pointed out that the response in elder and younger cohorts was markedly different. We, therefore, analyzed these data separately with LME models.
## Clinical trial data
This study has been carried out using the data published in our work (Kalamkar et al., 2022), which was collected from the clinical trial entitled “Effect of glutathione supplementation on glucose homeostasis in diabetic patients” and registered with the Clinical Trials Registry -India (CTRI/$\frac{2018}{01}$/011257). The data set is freely available online (on the link: https://figshare.com/s/0803267e1d38c054cee6). The analysis of the clinical trial data was conducted with ethical approvals from the Institutional Ethical Committee (IEC) of Jehangir Hospital Development Center, Pune (JCDC ECN- ECR/352/Inst/NIH/2013), IEC of IISER Pune (IECHR/Admin/$\frac{2019}{001}$); and the Institutional Biosafety Committee (IBC) of SPPU (Bot/27A/15).
The dataset published in the trial comprised 250 known Indian diabetic individuals recruited between February 2016 and January 2018 who were already on anti-diabetic treatment. The clinical trial consisted of three groups: A control group comprising healthy, non-diabetic subjects and two groups of diabetic patients; in one of those, GSH supplementation (500 mg/day for 6 months) was carried out, namely, the DG group, and the other group without supplementation, the D group. The only difference between this D and DG group is the intervention, that is, supplementation with GSH. More importantly, D and DG are similar in nearly all respects, and covariate balance at the baseline has already been shown (Kalamkar et al., 2022).
## Measured variables and follow-up visits
Blood samples of each individual were collected at the time of recruitment and three and 6 months post-GSH supplementation. The dataset used in this study consists of the amounts of reduced (GSH) and oxidized (GSSG) glutathione, fasting and postprandial glucose (FPG and PPG), fasting and postprandial insulin (FPI and PPI), HbA1c, and 8-hydroxy-deoxy-guanosine (8-OHdG), a marker of oxidative DNA damage measured from all individuals.
## Statistical analysis
Descriptive statistics with the mean and standard deviation (SD) were used to describe different study groups in terms of metabolic outcomes at baseline and each subsequent follow-up. Biochemical parameters at different visits were compared using two-sample t-tests. The statistical significance of the comparisons was set at a p-value less than 0.05.
## Formulation of linear mixed-effect models
The formulation of linear mixed-effect (LME) models for each biochemical variable (GSH, GSSG, HbA1c, 8-OHdG, FPG, FPI, PPG, and PPI) assumed fixed and random effect parameters at different levels (Level 1: time, Level 2: individuals) in the study. The composite form of the model was written by combining the model equations from these different levels. This form of the model was further used to study the dependency of each effect at different levels and their nested structure in one another. The response variable Yij from subject i on the jth visit was modeled with subject-specific intercepts (bi0) and subject-specific slopes (bi1) against treatment time tij (where tij = 0, 3, 6 months for $j = 1$, 2, 3 visits respectively). An indicator variable Ti was assumed to take a value of 0 for the D group and one for the DG group (control and treatment with GSH supplementation, respectively). We denote the average intercept of diabetic individuals when all predictors are 0 by β0 (mean expected value of the response variable Y). β1 represents the average rate of change in Y during the treatment for the D group. β1+β2 represnts the average rate of change in the DG group. The difference in the rates of change between D and DG β2 represents the average treatment effect of GSH supplementation on Y.
We considered two candidate models of biochemical variables, namely, 1) random intercept and random slope (RIRS) model and 2) random intercept and fixed slope (RIFS) model for explaining the measured longitudinal data. We formulated RIRS models for the outcome variable Yij as Yij = bi0 + bi1 × tij + ϵij with subject-specific random slopes and intercepts bi0 and bi1 defined by bi0 = β0 + bi0 and bi1 = β1 + β2 × Ti + bi1 where bi0, and bi1 were assumed to be distributed as N (0; σ02) and N (0, σ12), with covariance σ01, respectively. In the RIRS model, fixed effects are β0, β1, β2 and random effects are bi0, bi1. The residual errors were assumed to be normally distributed with a variance of σe2. The composite form of the RIRS model for *Yij is* given by, Yij = β0 + bi0 + (β1 + β2 × Ti + bi1) × tij + ϵij.
RIFS models for outcome variable Yij were formulated with random intercepts and fixed slopes at subject level (level 2) defined by intercept, bi0 = β0 + bi0 and slope; bi1 = β1 + β2 × Ti. The random intercepts bi0 in the model were assumed to be distributed as bi0 ∼ N (0; σ02). The composite forms of the RIFS model for *Yij is* given by Yij = β0 + bi0 + (β1 + β2 × Ti) × tij + ϵij.
The design matrices for model equations and covariance matrices are described in further detail in Supplementary Sections S1.1, S1.2.
## Model parameters and fitting
The formulated models have been tested and fitted using the lme4 package in R (Bates D. et al., 2015); these calculations were confirmed using the fitlme package in Matlab and the mimosa package (Titz, 2020) for mixed effects models. Other packages, ggplot2, and tidyverse in R, were used for analysis and plots. RIFS and RIRS models were fitted for GSH, GSSG, 8-OHdG, HbA1c, FPG, FPI, PPG, and PPI. A suitable RIFS or RIRS model was selected for each response variable using the best AIC and non-singularity criteria (Bates D. M. et al., 2015).
RIFS models were fitted for five parameters, β0, β1, β2, σ0, σe and RIRS models were fitted with seven parameters, β0, β1, β2, σ0, σ1, σ01, σe. The fitted estimates for β and b, the vectors of fixed effect parameters, random effect parameters, respectively, are given by the Best Linear Unbiased Estimator (BLUE) of β^, and Best Linear Unbiased Predictor (BLUP) of b^, (Refer to Supplementary Section S1.3 for further details). The components of b^, bi0, and bi1, random effects represent person-specific intercepts (in both RIFS and RIRS) at the baseline and person-specific differences in the rate of change in the slopes (in RIRS only), respectively.
The statistical significance of the results of the LME estimates was determined as $p \leq 0.05.$ We have followed the uncorrected p-value to interpret the results through. To ensure completeness, we have performed corrections for multiple comparisons using the Bonferroni method. We applied these corrections for the estimates from LME models for each variable and across all results in both main and supplementary analyses. Those results, which continued to be statistically significant even after the corrections, were marked with a “#” in the corresponding tables. The reader should take this into consideration when evaluating the statistical findings.
## Analysis of elder and younger patients
The variation in response to GSH supplementation with age was studied as follows: *The data* was divided into 1) a subgroup of elder adults (EA) above 55 years and 2) the subgroup of younger adults (YA) below 55 years.
The model for EA is given by Yij = β0 + bi0 + (β1 + β2 × Ti) × tij + ϵij. The treatment variable Ti takes the value of 0 for EA in the D group and one for the EA in the DG group. The model was formulated similarly for YA as well.
## Analysing the age effects on outcomes
We studied the effects of the age of individuals on the outcome variables Y with different LME models by incorporating 1) continuous variable for the age of individuals at the recruitment and 2) categorical variable for elder and younger age groups. These model formulations are described in Supplementary Section S1.4.
The models considered in this analysis are the following:(i) Model 1: The original RIRS model in the study without age variables(ii) Model 2: RIRS model with a treatment-time interaction term, and three-way interaction term with age, treatment indicator, and time at the patient level (Level 2)(iii) Model 3: RIRS model with a three-way interaction term with age, treatment indicator, and time at the patient level (Level 2)(iv) Model 4: RIRS model with age groups as a categorical variable for pooling EA and YA at the patient level (Level 2) These models were fitted for all eight variables, and their performances were compared using AIC and BIC estimates after the likelihood ratio test.
## The structure of the data from the D and DG groups
A sample structure of the data from the clinical trial is given in Supplementary Table S1. This data format was prepared for analysis using the lme4 package. The dataset consisted of eight different measured variables of 201 individuals (100 in D, 101 in DG) who completed both the follow-up visits (3 and 6 months post-GSH supplementation). The Group IDs are encoded as 0 for D and one for DG.
## Estimating correlations between longitudinal changes in different variables
The correlation between individual-specific slopes of variables obtained from RIRS models was estimated using the Pearson correlation coefficient (Pearson, 1895). Correlation diagrams were obtained between all variables using the slopes for RIRS models fitted with 1) the whole data sets and 2) the unpooled data sets from elder individuals and younger individuals. The size of the circle in each cell of the correlation diagram represents the extent of correlation between compared variables. The blue color represents a positive correlation, and the brown represents a negative correlation.
## Making predictions for virtual individuals
The fitted model estimates were utilized to predict responses in virtual individuals with diabetes. We considered three new virtual individuals (V1, V2, and V3) and assumed arbitrary but reasonable baseline measurements of GSH, 8-OHdG, and HbA1c. We thus predicted trajectories in these subjects over 6 months. The scheme used for this purpose is described in Supplementary Section S1.5. The steps in this scheme perform the following:(i) The baseline values assumed for virtual subjects are shrunk towards the average intercept estimated by our LME model, and the individual specific random effects are obtained.(ii) Using the LME model estimates of the average intercept, random effect of the intercept, and the rate of changes in the slopes, we obtained the average linear trajectory for each virtual individual in the presence and absence of GSH supplementation.
## Observational summary of longitudinal changes in the D and DG groups
Group-wise statistics (mean and standard deviation) of the measured variables (GSH, GSSG, 8-OHdG, HbA1c, FPG, FPI, PPG, and PPI) for both D and DG in each of the three visits are described in Kalamkar et al. [ 2022]; these are summarized here for completeness in Table 1.
**TABLE 1**
| Variable | Mean (SD) in the D group | Mean (SD) in the D group.1 | Mean (SD) in the D group.2 | Mean (SD) in the DG group | Mean (SD) in the DG group.1 | Mean (SD) in the DG group.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Variable | Baseline visit | Second visit | Third visit | Baseline visit | Second visit | Third visit |
| GSH (μM) | 395 (225) | 428 (263) | 484 (255)∗∗∗ | 465 (352) | 1,129 (668)∗∗∗ | 1,021 (518)∗∗∗ |
| GSSG (μM) | 249 (150) | 236 (157) | 262 (137) | 163 (104) | 333 (214)∗∗∗ | 286 (204)∗∗∗ |
| 8-OHdG (ng/μg DNA) | 422 (124) | 404 (124) | 443 (110) | 471 (83) | 387 (112)∗∗∗ | 313 (135)∗∗∗ |
| HbA1c (%) | 8.4 (1.9) | 7.9 (1.7)∗∗ | 8.2 (1.8) | 8.5 (1.9) | 7.7 (1.5)∗∗∗ | 7.9 (1.5)∗∗∗ |
| FPG (mg/dL) | 160 (61) | 143 (47)∗ | 151 (58)∗ | 153 (59) | 141 (47) | 150 (59) |
| FPI (μU/mL) | 14.2 (10.4) | 12.7 (6.8) | 12.1 (7.7) | 12.6 (8.06) | 14.6 (13.8) | 13.9 (10.5)∗∗∗ |
| PPG (mg/dL) | 233.6 (84.1) | 216.9 (70.9) | 220.3 (83.6) | 221.9 (77) | 211 (80.4) | 218 (83.2) |
| PPI (μU/mL) | 43.4 (26.9) | 47.03 (33.3) | 40.5 (29.9) | 48.3 (47.7) | 49.5 (39.6) | 52.3 (43.8) |
GSH and GSSG were significantly increased, and 8-OHdG and HbA1c significantly decreased ($p \leq 0.001$) within 3 months in DG and continued to be so at 6 months as well. FPI of DG increased significantly within 6 months ($p \leq 0.001$). FPG, PPG, and PPI didn’t show significant changes. GSH in the third visit was also significantly increased in D, but not as much compared to the corresponding change in DG.
## LME estimates of the rates of change for the whole population
We fit RIRS and RIFS models for GSH, GSSG, 8-OHdG, HbA1c, FPG, PPG, FPI, and PPI (as described in Model parameters and fitting). These subject-wise trajectories obtained from RIRS models are shown in Figure 1. Individual trajectories are distributed around the group-wise average trajectory. Group-wise average intercepts are determined by β0; these are equal for both D and DG. The average slopes in D and DG are β1 and β1 + β2, respectively. This β2 denotes the difference between the average slopes in the two groups, that is, the treatment effect of GSH supplementation on outcomes. These estimates (β0; β1, and β2) are detailed in Table 2. Estimated random effects, that is, within-individual and between-individual variations, are described in Supplementary Tables S2, S3.
**FIGURE 1:** *Average treatment effects of GSH supplementation on biochemical changes estimated using LME Models. The fitted results of RIRS models for GSH, GSSG, 8-OHdG, HbA1c, FPG, FPI, PPG, and PPI (RIFS model fits are shown in Supplementary Figure S1) in D group and DG groups (figure panels marked with titles D and DG) are overlaid here with the longitudinal data from 201 individuals (100 D subjects in blue circles, 101 DG subjects in red circles) at different visits. Solid blue and red lines depict the fitted subject-specific mean trajectories in the D group and the DG group, respectively. The black dotted and solid lines represent the group-wise means for D and DG, respectively. Interquartile ranges of the data for D and DG groups are shown with vertical interval plots (25th-75th quartiles) at each visit. The average treatment effects of GSH supplementation (
β2
) are denoted on top of each panel corresponding to the DG group. The estimated
β2
was significant on the rate of changes in GSH (
β2
= 107.7 µM per month), GSSG (
β2
= 13.02 µM per month), 8-OHdG (
β2
= −18.5 ng/μg DNA per month), FPI (
β2
= 0.5 µU/mL per month) and PPI (
β2
= −4.1 µU/mL per month) levels. The significance levels of parameter estimate are given by ∗
$p \leq 0.05$, ∗∗
$p \leq 0.01$, and ∗∗∗
$p \leq 0.001.$ Abbreviations of the variables used here are HbA1c—glycated hemoglobin, GSH—reduced glutathione, GSSG—oxidized glutathione, PP glucose—postprandial glucose, PP insulin—postprandial insulin, and 8-OHdG—8-hydroxy-2-deoxy guanosine.* TABLE_PLACEHOLDER:TABLE 2 We find that β2 is significant for GSH, GSSG, and 8-OHdG (Table 2). Among the glycemic variables, β2 is significant only for FPI, and PPI but not for HbA1c, FPG, and PPG.
The mean erythrocytic GSH is estimated as 492 µM in individuals with diabetes. It increased slightly, at an average rate of 0.04 µM per month from the baseline during the study period in D. In DG, GSH increased at an average rate of 107.7 µM per month. Therefore GSH supplementation significantly improved GSH by about 22 percent (107.7 µM, $p \leq 0.001$) per month relative to baseline. Mean GSSG is estimated as 221 µM. In D and DG, GSSG increased at average rates of 4.7 and 17.7 µM per month, respectively, from the baseline (Figure 1). Thus GSSG rates are significantly improved ($p \leq 0.001$) by about six percent per month of the baseline (13.02 µM, $p \leq 0.001$). 8-OHdG is estimated to be 442 ng/μg DNA in diabetic individuals. It decreased in D and DG at average rates of 2.8 and 21.3 ng/μg DNA per month, respectively. Thus the effect of GSH supplementation significantly reduced 8-OHdG by four percent per month of the baseline (18.5 ng/μg DNA, $p \leq 0.001$).
HbA1c, FPG, and PPG changed at similar rates in D and DG (Figure 1), suggesting that the effect was negligible ($p \leq 0.05$). FPI and PPI are found to be affected significantly. Mean FPI is estimated as 13.4 µU/mL. FPI decreased at an average rate of 0.3 µU/mL per month in D. GSH supplementation significantly improved FPI at a rate of 0.2 µU/mL in DG. The average PPI is estimated as 48.8 µU/mL in individuals with diabetes. It decreased at average rates of 0.8 and 4.9 µU/mL per month in D and DG, respectively (Figure 1). GSH supplementation significantly enhanced FPI by four percent (0.5 µU/mL, $p \leq 0.001$) and reduced PPI rates by eight percent (4.1 µU/mL, $p \leq 0.001$) of the baseline per month.
Results obtained from RIFS models are shown in Supplementary Figure S1 and Supplementary Table S3. The parameter estimates of β2 from RIFS models are also found to be significant for GSH, GSSG, 8-OHdG, FPI, and PPI, leading to similar conclusions about the effects of GSH supplementation as in RIRS models.
We note that these results largely coincide with the results from previous work (Kalamkar et al., 2022). However, FPI and PPI, which were earlier reported not to be affected by GSH supplementation, are found to have a significant effect through the LME model-based analysis.
## Independent LME model estimates for ages above and below 55 years
Diabetes is an age-onset disease; an early diagnosis leads to an increased chance for complications to set in relatively early. We have earlier demonstrated that the effectiveness of GSH supplementation differed between the younger and elder populations using an age cutoff of 55 years, which was the median age of the study population (Kalamkar et al., 2022). We fit a separate LME for each of these two age groups. Model estimates obtained by fitting LME models independently for EA and YA are detailed in Supplementary Table S4.
GSH supplementation significantly affected GSH, 8-OHdG, HbA1c, FPI, and PPI in EA, and GSH, GSSG, 8-OHdG, and PPI in YA (β2 in Table 3, $p \leq 0.001$).
**TABLE 3**
| Subject ID | GSH (μM) | 8-OHdG (ng/μg DNA) | HbA1c (%) |
| --- | --- | --- | --- |
| V1 | 200 | 500 | 10 |
| V2 | 500 | 400 | 8 |
| V3 | 800 | 300 | 6 |
## GSH
Mean erythrocytic GSH in EA (488 µM) is estimated to be less than YA (497 µM). In YA of D, it decreased at an average rate of 6.9 µM per month, whereas in DG, GSH increased at an average rate of 104 µM per month (Supplementary Figure S2). In EA of D and DG, GSH increased at average rates of 6.5 and 111 µM per month, respectively (Figure 2). This clearly indicates that GSH supplementation resulted in a significant improvement in GSH by about 21 percent per month of their baseline in YA (111 μM, $p \leq 0.001$) and 22 percent per month in EA (105 μM, $p \leq 0.001$) with diabetes.
**FIGURE 2:** *Average treatment effects of GSH supplementation in elder diabetics. The fitted results of RIRS models for GSH, GSSG, 8-OHdG, HbA1c, FPG, FPI, PPG, and PPI variables (RIFS model fits are shown in Supplementary Figure S4) of elder adults (EA) are shown on different panels here with the longitudinal data (blue circles for D individuals and red circles for DG individuals) at different visits. The data from 107 elder adults (52 from D and 55 from DG) are overlaid with group-wise mean trajectories for D and DG groups represented by black dotted lines and solid lines, respectively. Interquartile data ranges for individuals (from D and DG) are shown with vertical interval plots (25th-75th quartiles) at each visit. The average treatment effects of GSH supplementation (
β2
) on the rate of changes (slope) denoted on top of corresponding panels which are significant on GSH (
β2
= 104 µM per month), 8-OHdG (
β2
= −23.7 ng/μg DNA per month), HbA1c (
β2
= −0.1% per month), FPI (
β2
= 0.6 µU/mL per month), and PPI (
β2
= −3.6 µU/mL per month) in elder adults. The significance of these parameter estimates and abbreviations of the variables are the same as in Figure 1.*
## GSSG
Interestingly, the effect on GSSG was significant in YA ($p \leq 0.01$) but not in EA. The mean GSSG in EA (231 µM) was estimated to be higher than YA (209 µM). When YA of D and DG were examined, GSSG increased at average rates of 1.9 and 18.4 µM per month, respectively (Supplementary Figure S2). It increased at average rates of 7.6 and 17.1 µM per month in EA of D and DG, respectively (Figure 2). This shows that GSH supplementation enhanced GSSG significantly per month by eight percent of the baseline (17.5 µM, $p \leq 0.001$) per month only in YA.
## 8-OHdG
The average 8-OHdG estimate is higher in EA (445 ng/μg DNA) than in YA (438 ng/μg DNA). In EA of both D and DG, 8-OHdG decreased at average rates of 3.3 and 27 ng/μg DNA per month during the study period (Figure 2). Similarly, it decreased at average rates of 2.1 and 14.16 ng/μg DNA per month in the YA of D and DG groups (Supplementary Figure S2). Thus, we find that GSH supplementation significantly reduced 8-OHdG from the baseline by 12.06 ng/μg DNA per month ($3\%$) in YA and 23.7 ng/μg DNA per month ($5\%$) in EA. These results suggest that oral GSH administration rapidly offers better protection from oxidative DNA damage in EA compared to YA.
## HbA1c
GSH supplementation was earlier reported to affect the HbA1c in the elder cohort significantly (Kalamkar et al., 2022). We examined LME estimates of both YA and EA to quantitate the effect on HbA1c. The average HbA1c is estimated at $8.3\%$ and $8.4\%$ in YA and EA, respectively. In EA of D, HbA1c decreased at an average rate of $0.02\%$ per month, while in DG, it decreased at an average rate of $0.12\%$ per month (Figure 2), suggesting that GSH supplementation improved HbA1c rates significantly by about $0.1\%$ per month in EA. Estimated HbA1c rates are not significantly different between YA of D and DG (Supplementary Figure S2).
## Fasting Insulin
Our earlier work (Kalamkar et al., 2022) found that oral GSH supplementation significantly changed FPI in elder patients. We quantitated the effect on FPI using LME model estimates (Supplementary Table S4). The average FPI is estimated to be 12.9 µU/mL in YA and 14 µU/mL in EA. In both EA and YA of D, FPI decreased at rates of 0.4 µU/mL and 0.1 µU/mL per month, respectively (Figure 2). The estimated rates were similar between the YA of the D and DG, indicating that the effect on FPI is negligible ($p \leq 0.05$). On the other hand, in EA of DG, FPI increased at a rate of 0.2 µU/mL per month, suggesting that GSH supplementation improved FPI rates significantly by 0.6 µU/mL per month. FPI increased by $4.3\%$ of the baseline per month in EA and negligibly in YA.
## Postprandial Insulin
Using LME models to fit the data, PPI was found to decrease in both YA and EA. The average PPI in YA and EA is estimated to be 46 and 51 µU/mL, respectively. In YA of D, PPI increased at a rate of 0.1 µU/mL per month, whereas in DG, it decreased at a rate of 4.7 µU/mL per month. PPI decreased at average rates of 1.6 µU/mL and 5.2 µU/mL per month in EA of D and DG, respectively.
## Fasting and Postprandial Glucose
The average FPG estimated in YA and EA are 156 and 150 mg/dL, respectively. In both YA and EA, the GSH supplementation effect wasn’t found to be significant. In both EAs of D and DG, FPG decreased at average rates of 1.7 and 0.9 mg/dL per month, respectively. Similarly, in YAs of D and DG, it decreased at average rates of 1.3 and 0.8 mg/dL per month, respectively. PPG estimated in YA and EA at the time of recruitment is 227 and 223 mg/dL, respectively. GSH supplementation decreased PPG by 2.5 mg/dL per month in EAs and increased PPG by 3.5 mg/dL per month in YA.
For exploratory purposes, we also analyzed the effects of the age using new candidate models as incorporated with age as a model variable (Model 2, Model 3, and Model four in Supplementary Section S1.4) for GSH, GSSG, 8-OHdG, HbA1c, FPG, FPI, PPG, and PPI. Results obtained by fitting with these models are shown in Supplementary Tables S5A–C. When we compared model fits from all four models using AIC and BIC estimates, our original RIRS model (Model 1) was found to be the better-fit model for all variables (Supplementary Table S5D).
## Changes in GSH correlate strongly with changes in HbA1c and 8-OHdG in EA
We estimated pairwise correlations between subject-specific slopes of GSH, GSSG, 8-OHdG, HbA1c, FPG, FPI, PPG, and PPI obtained from RIRS models. These correlation diagrams for the full population (pooled data) are shown in Figure 3A. Changes in GSH are found to be strongly correlated positively with GSSG (r > 0.6) and FPI (r > 0.9). Changes in GSH correlated negatively with 8-OHdG and PPI (r < −0.6). The other correlations are found to be relatively weaker.
**FIGURE 3:** *Correlation diagram between subject-specific changes (A) for the whole population and (B) for EAs. The correlation diagrams obtained between subject-specific random slopes from fitted RIRS models for different biochemical measures (GSH, GSSG, 8-OHdG, HbA1c, FPG, FPI, PPG, and PPI) are shown here. The strength and direction of correlation between subject-specific slopes are reflected in both color and size of the circular markers. The scales of Pearson’s correlation coefficient have been classified as low (r < 0.4), moderate (r < 0.6), strong (r > 0.6), or very strong (r > 0.8). Blue indicates a strong positive correlation, and red indicates a strong negative correlation. Abbreviations of the variables are the same as in Figure 1.*
Correlation plots for EAs alone are shown in Figure 3B. GSH slopes are strongly negatively correlated with 8-OHdG slopes (r = −0.71) and HbA1c slopes at moderate levels (r = −0.43). GSH slopes are strongly negatively correlated with PPI slopes (r = −0.74, Figure 3B); however, they are strongly positively correlated with FPI ($r = 0.75$).
In YAs (Supplementary Figure S3), GSH slopes are negatively correlated at moderate levels with 8-OHdG (r = −0.43) and PPI (r = −0.57) slopes. The correlation between GSH slopes and HbA1c slopes is negligibly small.
Taken together, the strengths of the correlations between the changes in GSH and outcome variables are evidently different between EAs and YAs.
We next use LME model estimates to help quantify the overall rates of changes that can be expected of individuals.
## Predicted trajectories for virtual diabetic individuals
Next, we describe the sample predictions obtained for three virtual individuals (V1, V2, and V3) using RIFS models. Baseline values assumed for these virtual individuals are given in Table 3.
The trajectories of GSH, 8-OHdG, and HbA1c obtained if they were with or without GSH supplementation are shown in Figure 4. RIFS models predicted the GSH of V1 close to 429 µM by the end of 6 months, whereas, on GSH supplementation, V1 ended up at 1,079 µM. Similar predictions were made for 8-OHdG and HbA1c for all these individuals (Figure 4).
**FIGURE 4:** *Model predictions for virtual individuals. Average trajectories of the concentration of GSH, 8-OHdG, and HbA1c predicted using RIFS models in virtual individuals (V1, V2, and V3) if they were to be followed up with GSH supplementation (red) and without GSH supplementation (blue) are shown for 6 months are depicted here. The baseline values assumed and the values predicted after 6 months are also marked for V1, V2, and V3. Abbreviations of the variables are the same as in Figure 1.*
This can also be modified to estimate 1) the average time required for a recruited individual to reach a particular level of a biochemical parameter given the baseline value and 2) the expected change in the level of a particular biochemical parameter with time.
Finding a patient’s potential trajectory has direct clinical and academic uses. This method, therefore, can be used on newly added subjects to predict different outcomes during 6 months, with or without GSH supplementation.
## Discussion
Our earlier study demonstrated population-level changes in GSH, GSSG, HbA1c, 8-OHdG, FPG, FPI, PPG, and PPI; these changes were further studied for younger and elder subgroups of the patients. The response in individual patients is, unsurprisingly, considerably varied; however, analyzing individual responses was beyond the scope of that study. In the present study, we are focused on explaining individual-level responses to GSH supplementation over the full study period of 6 months. We addressed this through a linear mixed-effects model framework. The major results of this study are to characterize the variability in the inter-individual biochemical response, in particular, determined by the age group of an individual. To the best of our knowledge, this is the first inter-individual analysis of the effects of GSH supplementation in patients with diabetes.
The response to GSH supplementation was analyzed in the earlier work (Kalamkar et al., 2022) by comparing 6-month changes in D and DG groups through population-level Cohen’s-d-based estimates. GSH supplementation was found to significantly affect GSH, GSSG, and 8-OHdG levels (at moderate levels of Cohen’s d > 0.6) and not for HbA1c, FPG, FPI, and PPG variables. The LME model framework helped analyze biochemical responses longitudinally and obtain more refined estimates that account for inter-individual and within-individual variations at two levels of hierarchy. We note that LME models describe linear trajectories over a 6-month duration. The estimates show that D and DG average trajectories lie between the 25th and 75th percentiles of the data at all visits; that is, these models are a good description of the data.
Model estimates were consistent with the effect size estimates in the earlier study (Kalamkar et al., 2022) for GSH, GSSG, 8-OHdG, HbA1c, FPG, FPI, and PPG variables but not for PPI. LME estimates determined that the GSH supplementation markedly enhanced the rate of replenishments in erythrocytic GSH stores by about $22\%$, GSSG stores by about $6\%$, and reduced oxidative DNA damage by about $4\%$ of the baseline month in diabetic patients. Importantly, these estimates are in the actual (not relative) physical units and are, therefore, directly interpretable for use in clinical applications.
We had identified an older subgroup separate from a younger diabetic population that benefits better from GSH supplementation through a post hoc subgroup analysis in our earlier study. That study wasn’t designed to evaluate this analysis explicitly, and as such, it was a weaker form of evidence. LME models provided a more formal way of comparing their differential responses; that is, two independent models described the responses in each of these two age classes. GSH supplementation improved the rates of 8-OHdG and HbA1c reduction in elder diabetic individuals more than in younger diabetic cohorts. LME models estimated the effect to be significant for FPI in elder patients, which supported our claims of a beneficial elder cohort. Model estimates for GSSG suggested a significant effect of GSH supplementation in younger patients (by 17 µM per month) but not in elder ones. In contrast to the earlier results, PPI model estimates were found to be significant in both elder and younger cohorts. Thus, our model-based analysis describes the extent to which diabetic patients above 55 can be expected to benefit from GSH supplementation.
LME model estimates further allow for examining the strength of the association between covariates. The results of the correlation analysis (in Figure 3; Supplementary Figure S3) show to what extent GSH intervention improves erythrocytic GSH stores and reduces DNA damage. Estimates from the elder and younger individuals also revealed that GSH changes were correlated strongly with changes in HbA1c and 8-OHdG in elder adults.
Finally, we have formulated a scheme (in Supplementary Section S1.5) that makes individual-specific predictions for newly recruited subjects with diabetes, given a baseline measurement by using the LME model estimates of the fixed-effects and random-effects parameters. In particular, this scheme can be utilized to make predictions of what changes might be expected in the biochemical levels. Alternatively, the average time required for a recruited patient to reach a particular range of biochemical parameters in diabetic subjects can be estimated. The fitted LME model estimates can be used to identify the extent of each subject’s response, whether they are in a better or worse condition than the average population response (Inzucchi et al., 2012; Kirkman et al., 2012). These schemes are of direct clinical and academic use to predict prospective trajectories, which can be a powerful addition to the clinician’s toolbox.
Strengths of this study include that it is based on the data available from diabetic individuals on a well-conducted, randomized control trial, which is one of the most extensive GSH supplementation studies so far. Using LME models, we evaluated the individual trajectories and associated variations within individuals and between individuals, which has not been done before in GSH intervention studies.
It is particularly important to keep in mind that our understanding of the results is based on the uncorrected p values. The practice of correcting for multiple comparisons has been a topic of debate among statisticians for several years now. Various opinions were found in the literature in opposition regarding the conditions under which a correction for multiple testing should be applied. We note that several highly cited reports over the years (Poole, 1991; Perneger, 1998; Cabin and Mitchell, 2000) recommend dismissing the usage of corrections with multiple comparisons. It was shown that when trying to reduce the rate of false positives (Type I error) for null associations, often leads to an increase in the rate of false negatives (Type II error) for those that are not null (Rothman, 1990). Also, these comparisons were often complained of being unnecessarily conservative, which makes this approach frequently fails to identify actual differences. However, for the interest of all readers, we have also incorporated significance levels after corrections for each comparison. Those readers who prefer statistically corrected results should follow the corresponding tables to determine which findings still retain significance and which did not after correction for multiple comparisons.
We had earlier identified the differential effects of GSH supplementation in elder and younger subgroups (Kalamkar et al., 2022). This study analyzed the longitudinal responses of GSH supplementation observed in these subgroups of diabetic individuals rigorously with a framework of the LME models. The subgroup of subjects above the median age of 55 is consistent with previous studies that show an increased risk of diabetes-related complications in individuals around this age. Several organizations have already developed guidelines specific to, or including, older adults on their annual Standards of Medical Care in Diabetes (American Diabetes Association, 2012). These reports also discuss the severity of diabetes complications in elders and the lack of high-level evidence on the effectiveness of different medications in diabetics (Leung et al., 2018). We think the onset of diabetes and complications should be addressed differently for elder and younger diabetic individuals, and treatments need to be planned separately from each other. The two independent LME models formulated for analyzing the longitudinal trajectories of elder and younger adults provided estimates of the treatment effect of GSH supplementation on each endpoint separately. This helps in identifying their extent of recovery and examining whether individuals are in a better or worse condition than the average profile in these subgroups on GSH supplementation for direct clinical use. We recommend planning large-scale clinical trials to examine these insights about GSH supplementation, especially in elder diabetic individuals. This could help in establishing novel benchmarks for caring for elder patients with diabetes. We have also analyzed different possible models to study the effect of the age of individuals on GSH supplementation. This will form the basis and motivate a number of future studies to examine many of the finer nuances of the effect of age on supplementation.
Some limitations of this study also need to be considered. Although antidiabetic treatments were not changed during the period of the study, patients did use different types of medication. We have not analyzed the combinatorial complexity of treatments further due to a lack of sufficient statistical power. It is possible that future work may uncover if GSH supplementation is particularly more effective with certain treatments than others. The results presented here can be the basis for future GSH intervention studies that advance precision diabetes research.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: 10.6084/m9.figshare.21786518.
## Ethics statement
The studies involving human participants were reviewed and approved by the Institutional Ethical Committee (IEC) of Jehangir Hospital Development Center, Pune (JCDC ECN- ECR/352/Inst/NIH/2013); Institutional Biosafety Committee (IBC) of SPPU (Bot/27A/15), Pune; and the Institutional Ethical Committee (IEC) of IISER, Pune (IECHR/Admin/$\frac{2019}{001}$). The clinical trial is registered with the Clinical Trials Registry-India (CTRI/$\frac{2018}{01}$/011257). Written informed consent to participate in this study was provided by the participants and apos; legal guardian/next of kin.
## Author contributions
AM and PG designed the study and performed the model computations. SG, PG, AM, and SK contributed to the analysis of the results and the editing of the manuscript. All authors have read and confirmed the published 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.2023.1139673/full#supplementary-material
## Abbreviations
T2D, Type 2 diabetes; HbA1c, glycated hemoglobin; GSH, reduced glutathione; GSSG, oxidized glutathione; PP glucose, postprandial glucose; PP insulin, postprandial insulin; 8-OHdG, 8-hydroxy-2-deoxy guanosine; LME, Linear Mixed-Effects; EA, Elder Adults.
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|
---
title: Inverse association of daily fermented soybean paste (“Jang”) intake with metabolic
syndrome risk, especially body fat and hypertension, in men of a large hospital-based
cohort
authors:
- Su-Ji Jeong
- Hee-Jong Yang
- Hee Gun Yang
- Myeong Seon Ryu
- Gwangsu Ha
- Do Yeon Jeong
- Sunmin Park
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10040601
doi: 10.3389/fnut.2023.1122945
license: CC BY 4.0
---
# Inverse association of daily fermented soybean paste (“Jang”) intake with metabolic syndrome risk, especially body fat and hypertension, in men of a large hospital-based cohort
## Abstract
### Introduction
Jang is a fermented soybean paste containing salt and is traditionally used as a substitute for salt to enhance the flavor of foods in Korea. It has been speculated that regular consumption of Jang may lower the risk of metabolic syndrome (MetS). We hypothesized that Jang intake was associated with the risk of MetS and its components after adjusting for potential confounders, including sodium intake. The hypothesis was investigated according to gender in a large city hospital-based cohort ($$n = 58$$,701) in Korea.
### Methods
Jang intake, calculated as the sum of the intakes of Chungkookjang, Doenjang, Doenjang soup, and Ssamjang (a mixture of Doenjang and Kochujang), was included in the semi-quantitative food frequency questionnaire (SQFFQ) administered to the cohort, and the daily Jang intake was estimated. The participants were categorized into low-Jang and high-Jang groups by 1.9 g daily Jang intake. MetS was defined according to 2005 revised United States National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria modified for Asians.
### Results
The participants in the low-Jang and high-Jang groups consumed an average of 0.63 g and 4.63 g Jang daily; their total sodium intake was about 1.91 and 2.58 g/day, respectively. The participants in the high-Jang group had higher energy, fiber, calcium, vitamin C, vitamin D, and potassium intake than those in the low-Jang group. After adjusting for covariates, the participants with the highest sodium intake (≥3.31 g/day) were positively associated with MetS risk in the quintiles of men and women. Among the MetS components, waist circumference, fat mass, and hypo-high-density lipoprotein (HDL)-cholesterolemia were positively associated with sodium intake in all participants and women. Unlike the association seen with sodium intake, Jang intake (≥1.9 g/day) was inversely associated with MetS components, including waist circumference, fat mass, serum glucose concentrations, and hypo-HDL-cholesterolemia in all participants and men, after adjusting for covariates including sodium intake.
### Discussion
Substituting salt for Jang in cooking may be recommended to prevent and alleviate MetS incidence, and its efficacy for MetS risk was better in men than women. The results can be applied to sodium intake in Asian countries where salt is used to promote flavor.
## Introduction
From the olden days, Asians have consumed grains, mainly rice, as a staple food and soybeans and vegetables as side dishes. Koreans have traditionally used oils made by squeezing sesame and perilla seeds, which have been added to the side dishes to provide flavor [1]. The salty taste was the primary flavor for foods, and salt also served as a preservative during the unavailability of the refrigerator [1]. However, Koreans have recently substituted salt with various fermented soybeans or Jangs containing salts to promote flavor [2]. Given the increasing evidence suggesting the role of excess sodium consumption as a risk factor for metabolic diseases, specifically hypertension, the health benefits of Jang intake have remained controversial [3, 4].
Soybeans were cultivated from 1,000–400 BC in Manchuria and from the Bronze Age in Korea [5]. Seventy percent of Korea’s land is mountainous, and it is not suitable to raise animals for food. Therefore, white rice is the staple diet, and soybeans are an excellent complementary source of specific amino acids. They are cooked with rice and included in various side dishes. Like grains, soybeans can be dried for preservation but need to be soaked for over 12 h and boiled for 2–3 h before cooking. Soaking soybeans in water was difficult for every meal during winter in the olden days; hence, they were fermented with or without salts, making them easier to cook [5]. The fermented soybeans include Doenjang, Chungkookjang, Kochujang, Kanjang, and SsamJang. Chungkookjang is the name given to soybeans fermented with rice straw but without salt at 42°C for two-three days, with salt added after fermentation for preservation [5]. After fermentation, chungkookjang was also added with salt. Doenjang, Kochujang, and Kanjang are made from Meju, a dried and fermented brick of soybean without salt, for 50–60 days under cool and dry conditions. Meju is mixed with salt and water, fermented for over 6 months, and then the water component is separated [5]. This water component is boiled and aged for over 6 months to form Kanjang, and the residues become Doenjang. Kochujang is produced by fermenting a mixture of Meju, glutinous rice powder, salts, and red pepper for over 6 months [6]. Ssamjang is made of mixing Doenjang and Kochujang. Jang, a fermented soybean paste containing salt, is high in sodium (Doenjang 4.43 g/100 g; Ssamjang 3.01 g/100 g; Kochujang 2.40 g/100 g), and it has been used for providing salty and umami taste to various Hansik dishes substituting for salt in Korea.
Hansik is a Korean-style meal containing cooked multigrain rice, black soybean, soup, fish, two vegetable dishes, and kimchi [1]. Jang is substituted for salt in all dishes, especially soup and vegetables, giving most dishes a better flavor [5]. Koreans have consumed more sodium, probably due to salt-based meals for a long time. Significant data suggest that high salt intake increases the risk of hypertension, cardiovascular diseases, kidney diseases, and even gastric cancer [7]. The World Health Organization (WHO) recommends the consumption of less than 2 g sodium intake (5 g salt) daily and the Dietary Approaches to Stop Hypertension (DASH) diet intervention for maintaining better metabolic health [7]. In the Health Examinees (HEXA) Study, a large-scale genomic cohort study, Korean adults in the highest quartile of sodium intake were at a higher risk of developing the metabolic syndrome (MetS) (OR = 1.11) than those in the lowest quartiles after adjusting for covariates [8]. Furthermore, potassium intake was found to be inversely associated with hypertension risk according to the data from the Korea National Health and Nutrition Examination Survey (KNHANES) 2007–2012 [9]. Sodium intake is higher in adults on a high-Hansik diet than those on a low-Hansik diet, yet a high-Hansik diet is inversely associated with MetS risk and diastolic blood pressure [10]. Gender differences are also seen when examining the effect of diets on MetS risk [11, 12]. Although a high Hansik intake has high sodium, it is suggested to improve hypertension and MetS risk. Therefore, the relationship between salt and the development of metabolic diseases may not be related to the amount of salt consumed per se but rather to what kind of foods containing salt is consumed.
Previous human and animal studies have shown that consuming Chungkookjang, Doenjang, Kanjang, and Kochujang lowers weight gain and improves insulin resistance, dyslipidemia, and hyperglycemia (6, 13–17). Jang is used as a substitute for table salt and could be one of the potential candidates to reduce MetS risk. However, no large epidemiologic study has been conducted to date on the effect of Jang intake on MetS. We hypothesized that Jang intake, including Chungkookjang, Doenjang, and Ssamjang, was associated with MetS risk after adjusting for potential confounders, including sodium intake. The hypothesis was investigated in subjects of both genders in a large city hospital-based cohort ($$n = 58$$,701) in Korea.
## Participants
A total of 58,701 volunteers (20,293 men and 38,408 women) were recruited from an urban hospital-based cohort involving multi-institutional hospitals in major cities in Korea. This cohort was part of the Korean Genome and Epidemiology Study (KoGES) conducted by the Korean National Research Institute of Health (NIH), the Korea Disease Control and Prevention Agency (KDCA), and the Ministry of Health and Welfare (MOHW) of Korea. The cohort was made aware of the public health issues associated with metabolic diseases [18]. The inclusion criteria for recruiting participants were age ≥40 and the presence of mild to moderate metabolic disease states at baseline [18]. The study was conducted after ethical approval from the Institutional Review Board of the National Institute of Health, Korea (KBP-2015-055) and Hoseo University, Korea (HR-034-01). The participants signed written informed consents.
## Anthropometric and biochemical measurements
On their visits to the hospital, the participants wore light clothes and removed their shoes to measure height, weight, waist, and hip circumference [19]. Body mass index (BMI) was determined by dividing the body weight (kg) by height in m squared (m2). The waist circumference was measured at the midpoint between the lower border of the rib cage and the iliac crest, at the level of the umbilicus, using a flexible inch tape. Body fat and skeletal muscle mass were estimated using a machine-learning prediction model generated in the Ansan/Ansung cohort. These parameters were measured using the Inbody 3.0 measurement device (Cheonan, Korea) based on the principle of bioelectrical impedance [20]. Skeletal muscle index (SMI) was calculated by dividing the appendicular skeletal muscle mass by height. A doctor measured blood pressure in the left arm in a sitting position with a sphygmomanometer.
After the subjects undertook an overnight fast, their blood was drawn using vacuum blood collection tubes with and without ethylenediaminetetraacetic acid (EDTA). Separated plasma and serum samples were used for biochemical analysis. Glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, creatinine concentrations, and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities were assayed from the fasting serum or plasma samples using a Hitachi 7,600 automatic analyzer (Hitachi, Tokyo, Japan). Glycated hemoglobin or hemoglobin A1c (HbA1c) was determined using an HbA1c Analyzer from EKF Diagnostics (Manchester, United Kingdom). In the Ansan/Ansung cohort, insulin resistance was also estimated using the prediction model generated by the homeostatic model assessment for insulin resistance (HOMA-IR) equation. The HOMA-IR is calculated by multiplying fasting serum glucose concentrations (mg/dL) by insulin (mU/L) concentrations and dividing the result by 405 [21]. The participants were classified into low-and high-insulin resistance with the cutoff of 2.32 HOMA-IR value [21]. The serum low-density lipoprotein (LDL) cholesterol concentrations were calculated using the Friedewald equation, and subjects with serum triglyceride concentrations ≥500 mg/dl were excluded. Serum high-sensitive C-reactive protein (hs-CRP) concentrations were measured with an enzyme-linked immunoassay (ELISA) kit (R&D Systems, Minneapolis, MN, United States). The estimated glomerular filtration rate (eGFR) was calculated using the modification of diet in renal disease (MDRD) formula: 175 X serum creatinine concentration−1.154 X age−0.203 X [0.742 if female].
Physical exercise was determined based on the questionnaires about exercise intensity and duration, and the intensity was categorized into light, moderate, and intensive. The light exercise included walking, office work, and dishwashing; moderate exercise included brisk walking, mowing the lawn, badminton, swimming, and tennis; and intensive exercise included climbing, running, football, basketball, and volleyball. Regular physical exercise was defined as daily exercise with a weekly aggregate of over 150 min of moderate exercise or over 100 min of intense exercise. The participants who did not belong to the regular or intense exercise groups were considered part of the low-exercise group. Alcohol consumption was assessed based on the type, amount, and frequency of alcohol intake during the previous 6 months. Daily alcohol intake was calculated by multiplying the drinking frequency by the average alcohol consumed on each occasion and dividing it by the period in days. Smoking status was stratified into current, past, or never based on >100 cigarettes smoked over the lifetime and smoking during the last 6 months before joining the study.
## MetS definition
MetS was defined based on 2005 revised National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP III) criteria for Asians. The cutoff of waist circumference was ≥90 cm for men and >85 cm for women, as established by the Korean Society for the Study of Obesity (KSSO), since they increase MetS risk in lower waist circumferences than Caucasians [22, 23]. The persons taking medication for hyperglycemia, dyslipidemia, and hypertension were assigned to the MetS group, although they had serum glucose concentration, lipid profiles, and blood pressure within a normal range.
## Daily food and nutrient intake
The KoGES committee established a semi-quantitative food frequency questionnaire (SQFFQ) for Koreans, including 106 foods commonly consumed in meals [24]. The SQFFQ was validated from the 3-day food records in four seasons [25]. The SQFFQ was filled by the participants based on food intake for the last 6 months and included the consumption frequency and amounts of the 106 food items with the selected serving sizes. The results of the food intake from the SQFFQ were converted into the intake of 23 nutrients, including sodium, using the computer-aided nutritional analysis program 3.0 developed by the Korean Nutrition Society [24]. The nutrient contents of the processed and cooked foods in SQFFQ were calculated based on the typical recipes. Sodium intake was calculated from the food intake determined from SQFFQ [8]. Lee et al. [ 8] calculated sodium intake using the SQFFQ, the same one used in the present study. The salt contents of the foods, such as Kimchi, Doenjang, and soup, were estimated using their regular recipes, although condiment contents, including salt, can be varied according to the individuals.
## Jang and sodium intake
Jang, a fermented soybean paste containing salt, was included as a food group in the SQFFQ, and it included Doenjang soup, Doenjang, Chungkookjang, and Ssamjang. The daily Jang intake was calculated from the frequencies and portion sizes of these four items as the other food items. Sodium intake from the Jang intake was calculated using the computer-aided nutritional analysis program 3.0. Due to different variations of Jang and sodium intake, Jang intake was categorized into tertiles or two groups (cutoff: 1.9 g/day; 25th percentile), while sodium intake was stratified into quintiles and two groups (cutoff: 1.5 g/day; 25th percentile).
## Dietary pattern analysis and dietary inflammatory index
The 106 food items were classified into 29 pre-combined food groups and used as independent variables in a principal component analysis (PCA) conducted to find the optimal factors representing dietary patterns in Korea. The optimal number of the classified factors was estimated based on eigenvalues of >1.5 in the PCA. In this cohort, the number of factors that met the criteria was four. The orthogonal rotation procedure (Varimax) was applied to generate the appropriate clusters, indicating dietary patterns [26]. Dietary factor-loading values of ≥0.40 were used to indicate significant contributions of food items by assigning names to the dietary patterns [26]. The primary food groups in the four different dietary patterns were fish, crabs, red meat, vegetables, kimchi, pickles, seaweed, and mushrooms for the Korean balanced diet (KBD); noodles, bread, fast foods, soups, and meat for the Western-style diet (WSD); beans, potatoes, green vegetables, seaweed, milk, nuts, and eggs for the plant-based diet (PBD); and rice in the rice-main diet (RMD).
The dietary inflammatory index (DII) was calculated based on the dietary inflammatory weights of foods and nutrients having anti-or proinflammatory properties (energy, 32 nutrients, four food products, four spices, and caffeine). The inflammatory weights of the foods and nutrients in the DII equation were adopted from a previous study [27]. However, the intake of the four spices, garlic, ginger, saffron, and turmeric, was removed from the DII original equation since they were not measured in the SQFFQ. The DII was calculated by multiplying the pro-(plus value) and anti-inflammatory weights (minus value) of the 38 dietary components by their daily intake and then dividing the sum of each item by 100.
## Stress status
Stress status was evaluated with 18 questions about physical and psychological stress at home and work, and each question was scored from zero (lowest stress) to three (highest stress). The overall stress status scores were estimated by a summation of the scores of the 18 questions, and higher stress status scores indicated that the participant’s stress status was high.
## Statistical analyses
Statistical analyses were carried out using the SAS version 9.3 software (SAS Institute, Cary, NC, United States). When the sample size was calculated using the G*Power program with effect size (0.05), significant level (α = 0.05), and power (β = 0.99), the sample size derived was 1,036. The sample size was satisfied for each gender. The descriptive statistics for the categorical variables (e.g., gender and lifestyle) were evaluated based on the frequency distributions of low-and high-Jang intake in each gender. The statistical differences in the frequency distributions were measured using the Chi-squared test. Adjusted means and standard errors of the low-Jang and high-Jang intake groups in each gender were calculated for continuous variables. The statistical differences between low-and high-Jang intake were determined using the analysis of covariance (ANCOVA) after adjusting for covariates, including age, residence area, education, income, energy intake, sodium intake, alcohol consumption, physical exercise, and smoking status. The adjusted odds ratio (ORs) and $95\%$ confidence intervals (CI) of Jang intake with MetS risk were measured by multiple regression analysis after covariate adjustment. p values <0.05 were considered to be statistically significant.
## Baseline characteristics of the participants
There was no difference in age between the low-and high-Jang intake groups. The participants, both men and women, with below high school education and income below 4,000 dollars per month, had a lower Jang intake than those with over high school education and income over 4,000 dollars (Table 1). Participants who exercised regularly consumed more Jang than those with a sedentary lifestyle. An evaluation of Jang intake based on the smoking status showed that male non-smokers had a lower Jang intake than former and current smokers, but women showed the opposite trend (Table 1). Alcohol intake was higher in men than women but did not vary with the Jang intake in both men and women. The stress status was linked to Jang intake, and both men and women participants with a high Jang intake had lower stress scores.
**Table 1**
| Unnamed: 0 | Men (n = 20,293) | Men (n = 20,293).1 | Women (n = 38,408) | Women (n = 38,408).1 |
| --- | --- | --- | --- | --- |
| | Low-Jang (n = 4,467) | High-Jang (n = 15,826) | Low-Jang (n = 8,646) | High-Jang (n = 29,762) |
| Age (years) | 57.0 ± 0.13a | 56.9 ± 0.10a | 52.5 ± 0.10b | 52.4 ± 0.07b*** |
| Education | Education | Education | Education | Education |
| ≤Middle school | 324 (12.3) | 1,429 (14.5) | 1,249 (19.1) | 5,489 (23.0) |
| High school | 2008 (76.4) | 7,426 (75.4) | 4,839 (73.9) | 17,032 (71.4) |
| ≥College | 296 (11.3) | 995 (10.1)⁑⁑ | 457 (6.98) | 1,325 (5.56)⁑⁑⁑ |
| Income | Income | Income | Income | Income |
| ≤$2000 | 327 (7.69) | 1,279 (8.49) | 955 (11.7) | 3,217 (11.5) |
| $2000–4,000 | 1,727 (40.6) | 6,480 (43.0) | 3,391 (41.7) | 12,594 (45.0) |
| >$4,000 | 2,201 (51.7) | 7,299 (48.5)⁑⁑ | 3,792 (46.6) | 12,178 (43.5)⁑⁑⁑ |
| Physical exercise (%) | 2,534 (57.2) | 9,418 (59.6)⁑⁑ | 4,356 (50.8) | 15,668 (52.8)⁑⁑ |
| Former smoking | 1834 (41.3) | 6,961 (44.1) | 116 (1.35) | 344 (1.16) |
| Smoking (%) | 1,212 (27.9) | 4,452 (28.2)⁑⁑⁑ | 195 (2.27) | 554 (1.87)⁑ |
| Alcohol (g/day) | 30.8 ± 1.16a | 30.7 ± 0.69a | 9.01 ± 0.86b | 9.02 ± 0.48b*** |
| Stress score (scores) | 14.0 ± 0.16c | 13.2 ± 0.09d | 16.1 ± 0.12a | 15.2 ± 0.07b***+++ |
## Food intake according to gender in the Jang intake groups
The men and women participants with a high Jang intake consumed 4.62 ± 0.03 and 4.64 ± 0.02 g Jang per day, respectively, while those with a low Jang intake consumed 0.60 ± 0.05 and 0.65 ± 0.04 g/day, respectively (Table 2). The multigrain rice intake was higher, but white rice intake was lower in male participants with a high Jang intake compared to those with a low Jang intake. Noodle and bread intakes were also higher in the low-Jang intake group than in the high-Jang intake group for both genders (Table 2). The intake of fruits and vegetables was higher in the high-Jang intake group than in the low-Jang intake group. Furthermore, the intake of kimchi, a fermented cabbage, was much higher in the high-Jang intake group than in the low-Jang intake group for both genders (Table 2). The intakes of seaweed, fish, beans, and nuts were higher in the high-Jang intake group than in the low-Jang intake group, but meat intake showed a trend opposite to that of seaweed. There were twice as many participants with high Jang intake in the KBD and PBD compared to those with low Jang intake in both genders. However, the trend was reversed in the participants having the RMD (Table 2).
**Table 2**
| Unnamed: 0 | Men (n = 20,293) | Men (n = 20,293).1 | Women (n = 38,408) | Women (n = 38,408).1 |
| --- | --- | --- | --- | --- |
| | Low-Jang (n = 4,467) | High-Jang (n = 15,826) | Low-Jang (n = 8,646) | High-Jang (n = 29,762) |
| Jang (g/day) | 0.92 ± 0.08b | 4.72 ± 0.05a | 0.87 ± 0.06b | 4.60 ± 0.04a+++ |
| Multigrain rice (g/day) | 526 ± 5.57b | 543 ± 3.32a | 467 ± 4.14c | 462 ± 2.39c***### |
| White rice (g/day) | 138 ± 4.97a | 117 ± 2.97b | 63.3 ± 3.70c | 65.1 ± 2.14c***++### |
| Noodles (g/day) | 65.4 ± 1.60b | 64.7 ± 0.96a | 46.9 ± 1.19c | 37.8 ± 0.69d***+++### |
| Bread (g/day) | 14.2 ± 0.52a | 12.9 ± 0.31b | 15.5 ± 0.38a | 13.0 ± 0.22b***++ |
| Fruits (g/day) | 194 ± 4.72c | 210 ± 2.82b | 243 ± 3.51a | 249 ± 2.03a***+++# |
| Vegetables (g/day) | 241 ± 3.64c | 289 ± 2.18a | 209 ± 2.71d | 255 ± 1.57b***+++### |
| Kimchi (g/day) | 135 ± 2.40b | 163 ± 1.43a | 108 ± 1.79c | 131 ± 1.03b***+++ |
| Seaweeds (g/day) | 1.58 ± 0.05c | 1.91 ± 0.03b | 1.80 ± 0.03b | 2.27 ± 0.02a***+++## |
| Fish (g/day) | 39.8 ± 0.85b | 43.8 ± 0.51a | 36.8 ± 0.63c | 39.0 ± 0.36b***+++ |
| Meats (g/day) | 45.5 ± 0.87a | 43.6 ± 0.52a | 32.1 ± 0.65b | 27.7 ± 0.37c***+++## |
| Beans1 (g/day) | 33.2 ± 1.19b | 45.0 ± 0.71a | 32.1 ± 0.88b | 43.9 ± 0.51a+++ |
| Nuts (g/day) | 1.31 ± 0.10c | 1.79 ± 0.06b | 2.01 ± 0.07b | 2.42 ± 0.04a***++ |
| KBD (%) | 982 (22.7) | 6,489 (41.0)⁑⁑⁑ | 1,540 (17.8) | 10,445 (35.1)⁑ |
| PBD (%) | 635 (14.2) | 3,646 (23.0)⁑⁑⁑ | 2,194 (25.4) | 11,572 (38.9)⁑⁑⁑ |
| WSD (%) | 2024 (45.3) | 8,179 (51.7) ⁑⁑⁑ | 2,794 (32.3) | 10,452 (35.1) ⁑⁑⁑ |
| RMD (%) | 1,657 (37.1) | 5,100 (32.2) ⁑⁑⁑ | 3,177 (36.7) | 9,387 (31.5)⁑⁑⁑ |
## Nutrient intake according to gender in the Jang intake groups
Energy intake did not meet the estimated energy requirement (EER) in men regardless of their Jang intake. In women, the EER was met only in the participants in the high-Jang intake group (Table 3). Carbohydrate intake was lower in the high-Jang intake group compared to the low-Jang intake group in both genders. However, the trends of protein and fat intakes were opposite to that of the carbohydrate intake in both genders. Fiber and calcium intakes were 1.3-fold higher in the high-Jang intake group than in the low-Jang intake group in both genders (Table 3). Along with sodium, potassium intake was higher in the high-Jang intake than in the low-Jang intake groups in both genders. However, the potassium-to-sodium intake ratio was lower in the high-Jang intake group compared to the low-Jang intake group in both genders. Dietary consumption of vitamin C did not meet the recommended intake levels (100 mg/day) in the low-Jang intake group but was met in the high-Jang intake group (Table 3). Similarly, vitamin D consumption also did not reach the recommended intake levels, although it was much higher in the high-Jang intake group than in the low-Jang intake group. DII was lower in the high-Jang intake group than in the low-Jang intake group for both genders. Moreover, the intakes of total polyphenols, flavonoids, and isoflavonoids were higher in the high-Jang intake group than in the low-Jang intake group for both genders (Table 3). These results indicated that the participants with a high Jang intake consumed a high-quality diet compared to those with a low Jang intake.
**Table 3**
| Unnamed: 0 | Men (n = 20,293) | Men (n = 20,293).1 | Women (n = 38,408) | Women (n = 38,408).1 |
| --- | --- | --- | --- | --- |
| | Low-Jang (n = 4,467) | High-Jang (n = 15,826) | Low-Jang (n = 8,646) | High-Jang (n = 29,762) |
| Energy (EEE%) | 81.7 ± 0.67d | 92.8 ± 0.41c | 90.1 ± 0.50b | 104 ± 0.29a***+++## |
| Carbohydrate (En%) | 73.3 ± 0.15a | 71.5 ± 0.09b | 72.8 ± 0.11a | 71.1 ± 0.07c**+++ |
| Fat (En%) | 12.7 ± 0.12d | 13.9 ± 0.07b | 13.2 ± 0.09c | 14.3 ± 0.05a***+++ |
| Protein (En%) | 12.5 ± 0.06d | 13.4 ± 0.03b | 12.9 ± 0.04c | 13.9 ± 0.02a***+++ |
| Fiber (g/day) | 12.2 ± 0.21c | 16.2 ± 0.13a | 11.5 ± 0.16d | 15.4 ± 0.09b***+++ |
| Calcium (mg/day) | 339 ± 5.82d | 453 ± 3.50b | 383 ± 4.35c | 509 ± 2.50a*+++ |
| Magnesium (mg/day) | 429 ± 2.25b | 448 ± 1.66a | 379 ± 1.67d | 392 ± 1.19c***+++ |
| Potassium (mg/day) | 1,812 ± 23.3c | 2,336 ± 14.1a | 1,905 ± 17.5c | 2,453 ± 10.0b***+++ |
| Sodium from all foods (mg/day) | 2,046 ± 20.6c | 2,752 ± 10.7a | 1,833 ± 14.9d | 2,497 ± 7.83b***+++ |
| Sodium from Jang (mg/day) | 25.9 ± 1.99b | 199 ± 1.29a | 28.0 ± 1.72b | 200 ± 0.86 a+++ |
| Ratio of K and Na | 1.13 ± 0.01b | 0.95 ± 0.006d | 1.27 ± 0.007a | 1.07 ± 0.004c***+++ |
| Vitamin C (mg/day) | 78.8 ± 1.53d | 106 ± 0.792b | 91.4 ± 1.14c | 122 ± 0.66a***+++### |
| Vitamin D (ug/day) | 4.84 ± 0.10c | 6.27 ± 0.08b | 6.02 ± 0.10b | 7.66 ± 0.06b+ |
| DII (scores) | −18.8 ± 1.83ab | −23.3 ± 1.19a | −14.7 ± 1.53a | −23.3 ± 0.96b+++ |
| Total polyphenol (mg/day) | 2,417 ± 24.7b | 2,758 ± 14.9a | 2,339 ± 18.5b | 2,634 ± 10.6c***+++ |
| Flavonoids (mg/day) | 26.3 ± 0.72d | 36.7 ± 0.44b | 35.5 ± 0.54c | 45.7 ± 0.31a***+++ |
| Isoflavonoids (mg/day) | 5.25 ± 0.17d | 8.86 ± 0.10b | 6.06 ± 0.12c | 9.80 ± 0.07a***+++ |
## Association between sodium intake and the risk of MetS components
Waist circumferences and fat mass were higher in the high-sodium group than in the low-sodium group in both genders. Interestingly, SMI was also higher in the high-sodium group than the low-sodium group (Table 4). There were no significant differences in serum glucose, LDL, HDL, AST, and ALT concentrations, HbA1c levels, insulin resistance by HOMA-IR, and systolic blood pressure (SBP) between the two groups. Serum triglycerides and diastolic blood pressure (DBP) were slightly higher in the high-sodium group than in the low-sodium group (Table 4). eGFR was also higher in the high-sodium group than in the low-sodium group, indicating that excess sodium from the diet can be removed through urine excretion.
**Table 4**
| Unnamed: 0 | Men (n = 20,293) | Women (n = 38,408) | Women (n = 38,408).1 | Women (n = 38,408).2 |
| --- | --- | --- | --- | --- |
| | Low-Jang (n = 4,467) | High-Jang (n = 15,826) | Low-Jang (n = 8,646) | High-Jang (n = 29,762) |
| BMI (mg/kg2) | 24.5 ± 0.05a | 24.6 ± 0.04a | 23.4 ± 0.04c | 23.5 ± 0.03b***++ |
| Waist circumferences (cm) | 85.6 ± 0.14a | 85.9 ± 0.11a | 77.1 ± 0.11c | 77.6 ± 0.08b***+++ |
| SMI (kg/m) | 7.19 ± 0.01b | 7.25 ± 0.01a | 6.04 ± 0.01d | 6.10 ± 0.01c***+++ |
| Fat mass (%) | 23.3 ± 0.07c | 23.4 ± 0.05c | 30.3 ± 0.05b | 30.5 ± 0.04a***++ |
| Serum glucose (mg/dL) | 99.9 ± 0.35a | 98.9 ± 0.28a | 93.3 ± 0.24b | 92.9 ± 0.22b*** |
| HbA1c (%) | 5.79 ± 0.02a | 5.77 ± 0.01a | 5.68 ± 0.01b | 5.67 ± 0.01b*** |
| Insulin resistance (N, %) | 667 (11.1) | 1,645 (11.5) | 727 (6.16) | 1,565 (5.88) |
| Serum total cholesterol (mg/dL) | 191 ± 0.68b | 191 ± 0.50b | 200 ± 0.51a | 200 ± 0.36a*** |
| Serum HDL (mg/dL) | 49.5 ± 0.25b | 49.3 ± 0.18b | 57.5 ± 0.18a | 57.1 ± 0.13a*** |
| Serum LDL (mg/dL) | 115 ± 0.63b | 115 ± 0.46b | 120 ± 0.47a | 120 ± 0.33a*** |
| Serum triglyceride (mg/dL) | 132 ± 1.55a | 136 ± 1.13a | 114 ± 1.16b | 115 ± 0.82b***+ |
| SBP (mmHg) | 127 ± 0.26a | 127 ± 0.21a | 120 ± 0.18b | 120 ± 0.17b*** |
| DBP (mmHg) | 78.5 ± 0.18a | 79.0 ± 0.13a | 73.8 ± 0.14b | 74.1 ± 0.10b***++ |
| Serum hs-CRP (mg/dL) | 0.17 ± 0.01a | 0.15 ± 0.01a | 0.12 ± 0.01b | 0.13 ± 0.01b***# |
| eGFR (ml/min/1.73 m2) | 81.7 ± 0.29d | 82.1 ± 0.22c | 87.5 ± 0.19b | 88.0 ± 0.16a***++ |
| Serum AST (U/L) | 25.0 ± 0.22a | 25.4 ± 0.16a | 22.9 ± 0.16b | 22.7 ± 0.12b***# |
| Serum ALT (U/L) | 25.2 ± 0.33a | 25.8 ± 0.24a | 20.2 ± 0.24b | 19.9 ± 0.17b*** |
The highest sodium intake (≥3.31 g/day) was positively associated with MetS risk by 1.3 times based on the lowest sodium intake (<1.33 g/day) in the quintile categories of all participants (Figure 1A). The association was not shown in gender difference (Figure 1A). However, sodium intake was not associated with MetS risk in all the participants when daily sodium intake was divided into two groups by 1.5 g/day. The cutoff was assigned conservatively, less than WHO recommended sodium intake (2 g sodium/day; 5 g NaCL/day) since the sodium intake measured from the SQFFQ could be underestimated. Sodium intake was associated with some components of MetS in all participants: A positive association with waist circumference and fat mass and an inverse association with serum HDL concentrations were observed with sodium intake. However, there was no association of sodium intake with serum glucose and triglyceride concentrations and blood pressure in all participants (Figure 1B). There was no association between sodium intake and the risk of MetS and its components in men. However, sodium intake was positively associated only with waist circumferences and fat mass only in women (Figure 1B).
**Figure 1:** *Association of sodium intake with metabolic syndrome (MetS) and its components according to gender. (A) Adjusted odds ratio and 95% confidence intervals of MetS with sodium intake by quintiles and (B) Adjusted odds ratio and 95% confidence intervals of MetS and its components with low-and high-sodium intake. (Cutoff: 1.5 g sodium intake/day) The cutoff point of each MetS component for logistic regression were as follows: MetS criteria based on the 2005 NCEP-ATP III criteria for Asians; <90 cm for men and 85 cm for women waist circumferences; <25% for men and 30% for women for fat mass; <110 mg/dL fasting serum glucose plus diabetic drug intake; <40 mg/dL for men and 50 mg/dL for women serum HDL cholesterol; <140 mmHg systolic blood pressure (SBP) or diastolic blood pressure (DBP) <90 mmHg plus hypertension medication.*
## Association between Jang intake and the risk of MetS components
BMI, waist circumference, SMI, fat mass, serum glucose concentrations, HbA1c levels, and insulin resistance were not significantly different between the low-and high-Jang intake groups in both genders (Table 5). In the lipid profiles, the serum triglyceride concentrations showed an interaction with gender types, but there was no significant difference between the low-and high-Jang groups in both genders (Table 5). There were no significant differences in the serum total cholesterol, HDL, and LDL concentrations in the low-and high-Jang intake groups. Serum hs-CRP concentration, an inflammation index, was lower in the high-Jang intake group than in the low-Jang intake group. SBP, DBP, eGFR, and serum AST and ALT concentrations were not significantly different between the low-and high-Jang intake groups, although they showed a gender difference (Table 5).
**Table 5**
| Unnamed: 0 | Men (n = 20,293) | Women (n = 38,408) | Unnamed: 3 | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | Low-Jang (n = 4,467) | High-Jang (n = 15,826) | Low-Jang (n = 8,646) | High-Jang (n = 29,762) |
| BMI (mg/kg2) | 24.7 ± 0.06a | 24.5 ± 0.04b | 23.5 ± 0.04c | 23.4 ± 0.03c***+ |
| Waist (cm) | 85.6 ± 0.17a | 85.3 ± 0.11a | 77.5 ± 0.13c | 77.8 ± 0.07b***# |
| SMI (kg/m) | 7.27 ± 0.02a | 7.27 ± 0.01a | 6.07 ± 0.01b | 6.06 ± 0.01b*** |
| Fat mass (%) | 23.4 ± 0.08b | 23.3 ± 0.05b | 30.4 ± 0.06a | 30.4 ± 0.03a*** |
| Serum glucose (mg/dL) | 99.5 ± 0.38a | 99.1 ± 0.28a | 93.4 ± 0.28b | 93.0 ± 0.20b*** |
| HbA1c (%) | 5.76 ± 0.02a | 5.78 ± 0.01a | 5.69 ± 0.01b | 5.67 ± 0.01b*** |
| Insulin resistance (N, %) | 466 (10.4) | 1846 (11.7)⁑ | 485 (5.61) | 1807 (6.07) |
| Serum total cholesterol (mg/dL) | 193 ± 0.80b | 192 ± 0.47b | 199 ± 0.59a | 200 ± 0.34a*** |
| Serum HDL (mg/dL) | 49.2 ± 0.29b | 49.7 ± 0.17b | 57.5 ± 0.21a | 57.0 ± 0.12a***# |
| Serum LDL (mg/dL) | 117 ± 0.73b | 116 ± 0.43b | 119 ± 0.55a | 120 ± 0.31a*** |
| Serum triglyceride (mg/dL) | 136 ± 1.83a | 133 ± 1.08a | 113 ± 1.36b | 116 ± 0.78b***## |
| SBP (mmHg) | 126 ± 0.32a | 126 ± 0.19a | 120 ± 0.20b | 121 ± 0.17b*** |
| DBP (mmHg) | 78.6 ± 0.21a | 78.4 ± 0.13a | 74.0 ± 0.16b | 74.3 ± 0.10b*** |
| Serum hs-CRP (mg/dL) | 0.17 ± 0.01a | 0.15 ± 0.01a | 0.13 ± 0.01b | 0.12 ± 0.004b**+ |
| GFR (ml/min/1.73 m2) | 81.9 ± 0.35b | 82.4 ± 0.21b | 87.4 ± 0.26a | 87.7 ± 0.15a*** |
| Serum AST (U/L) | 25.1 ± 0.26a | 25.3 ± 0.15a | 22.7 ± 0.19b | 22.8 ± 0.11b*** |
| Serum ALT (U/L) | 25.5 ± 0.39a | 25.7 ± 0.23a | 19.9 ± 0.29b | 20.0 ± 0.16b*** |
Jang, a fermented soybean containing salts, provides a salty and umami taste to food to enhance the flavors in Korean-style cooking. Overall, the Jang intake in tertiles was inversely associated with MetS risk and was inversely associated with specific components of MetS, such as waist circumference, fat mass, and serum glucose and hypo-HDL concentrations, after adjusting for age, energy intake, residence area, education, income, alcohol intake, smoking status, physical activity, and sodium intake. Figure 2A. The association between MetS and Jang intake was seen in gender differences. In the tertile classification of Jang intake, Jang intake was inversely associated with MetS only in men but not women (Figures 2B,C). In MetS components, Jang intake showed an inverse association with waist circumference, fat mass, and blood pressure in men (Figure 2B). However, in women, it was inversely associated with fat mass and serum glucose and hypo-HDL concentrations (Figure 2C).
**Figure 2:** *Association of Jang intake with metabolic syndrome (MetS) and its components according to gender (A) Adjusted odds ratio and 95% confidence intervals of MetS with Jang intake by tertiles in total participants (B) Adjusted odds ratio and 95% confidence intervals of MetS and its components with tertiles of Jang intake in men. Low intake <1.9 g Jang/day; Medium intake: 1.9 – <4.9 g/day; High intake: ≥4.9 g/day. (C) Adjusted odds ratio and 95% confidence intervals of MetS and its components with tertiles of Jang intake in women. Low intake <1.9g Jang/day; Medium intake: 1.9 – <4.9 g/day; High intake: ≥4.9 g/day, and (D) Adjusted odds ratio and 95% confidence intervals of MetS and its components with low-and high-Jang intake. (Cutoff: 1.9 g Jang intake/day) The cutoff points for logistic regression were as follows: MetS criteria; < 90 cm for men and 85 cm for women waist circumferences; <25% for men and 30% for women for fat mass; <110 mL/dL fasting serum glucose plus diabetic drug intake; <40 mg/dL for men and 50 mg/dL for women serum HDL cholesterol; <140 mmHg systolic blood pressure (SBP) or diastolic blood pressure (DBP) < 90 mmHg plus hypertension medication.*
When Jang intake was categorized into two groups, the high Jang intake was inversely associated with waist circumference, fat mass, and serum glucose and hypo-HDL concentrations, but not blood pressure, in all participants (Figure 2D). In men, Jang intake showed an inverse association with waist circumferences, fat mass, and blood pressure whereas, in women, Jang intake was inversely associated only with serum hypo-HDL concentrations (Figure 2D).
## Discussion
Hansik, a traditional Korean diet, is shown to be inversely associated with MetS risk [10]. This nutritional benefit of *Hansik is* partly related to Jang intake. Jang is made of fermenting soybeans and salt and is used to promote the flavor and taste of Korean dishes and provide a salty taste. The complementary amino acids provided by soybean are essential nutrients for Asians who consume grains, especially rice, as their staple food. Jang has been extensively used as a salt substitute in cooking many Korean dishes. Jang intake results in a high salt intake. Due to high salt content, it has been suspected that Jang intake may also be associated with metabolic diseases. However, previous animal research has shown that when Jang substitutes salt in the diet, it is beneficial in preventing hypertension and obesity and managing menopausal symptoms [28]. The present study demonstrated that high salt intake was positively associated with the risk of MetS and its components, namely waist circumference and fat mass, and inversely associated with serum HDL concentrations in both genders. However, interestingly, Jang intake was inversely associated with MetS and its components, especially waist circumference, body fat, hyperglycemia, and hypo-HDL cholesterolemia in all participants after adjusting for potential confounders, including sodium intake in this large city hospital-based cohort ($$n = 58$$,701). Their association was better in men than women. Jang intake was inversely associated with hypertension only in men and hypo-HDL cholesterolemia only in women. Therefore, MetS risk can be lowered when salt is replaced with Jang.
Excess salt intake is associated with an increased risk of hypertension, obesity, stroke, cardiovascular diseases, and stomach cancer [3, 4, 7]. The common etiology of the diseases is increased insulin resistance and immunity, but their mechanisms are controversial. A high sodium intake (5 g/day) increases insulin resistance after adjusting for age and caloric intake in a randomized clinical trial of 160 obese Korean participants compared to a low sodium intake (2 g/day) [29]. It may be related to several pathways: [1] *High sodium* increases serum leptin levels to decrease energy expenditure and increase energy intake, leading to hypertrophy of abdominal fat and making a vicious cycle [30], [2] *High sodium* elevates GLUT4 contents to increase glucose uptake in white adipose tissue, contributing to adipocyte hypertrophy [31], and [3] *High sodium* intake impairs glucose-stimulated insulin secretion by the attenuated β-cell mass increment to induce glucose intolerance [32]. Furthermore, a high salt intake also modulates immunity by stimulating inflammatory macrophage and T cells, not neutrophils, and altering intestinal microbiota composition [33]. It activates regulatory T cells (Tregs) and T helper (TH) 17 cells, which is possibly linked to promoting autoimmune diseases [34, 35].
Due to its high salt content, the health benefits of Jang have remained controversial. Since the olden days, Koreans have substituted salt with various types of Jang (fermented soybeans with salts) to enhance the flavor of foods. The present study has shown that higher sodium intake (≥3.31 g/day; 80th percentile) was positively associated with MetS risk in both genders. However, an equivalent sodium intake from Jang showed an inverse relationship with MetS risk. Isoflavonoids and peptides in Jang potentially protect against high sodium-induced insulin resistance and activation of Tregs and Th17 pathways [36]. Therefore, excess sodium intake potentially increases MetS risk, but substituting salt with Jang may not lead to an increased MetS risk.
However, the mechanism by which regular consumption of Jang lowers the risk of MetS remains unknown. One proposed mechanism is related to aldosterone’s role in sodium and water retention. Salt-dependent hypertension is associated with volume expansion, renal dysfunction, impaired renin-angiotensin-aldosterone pathway, and central stimulation of the sympathetic nervous system activity. An earlier study has shown that the intake of Kanjang instead of salt (equivalent sodium intake) leads to lower serum aldosterone concentrations, thereby alleviating menopausal symptoms in estrogen-deficient mice [37]. Kanjang intake also reduced visceral fat and serum glucose concentrations and elevated serum HDL concentrations in these mice [37]. Similar results were seen in the present study wherein Jang intake was inversely associated with fat mass and serum glucose concentrations and positively associated with serum HDL concentrations in adults aged over 40 when they consumed an equivalent amount of sodium through Jang.
Potassium intake is also known to counteract the effects of sodium on body water retention. Potassium intake (adequate intake = 4,700 mg/day) needs to increase in proportion to sodium intake (adequate intake = 2000 mg/day) [38]. However, in the present study, the dietary potassium-to-sodium ratio was lower in the high-Jang group (0.95 for men and 1.07 for women) than in the low-Jang group (1.13 for men and 1.27 for women) in both genders, and it was much lower than the recommended ratio (2.45). These results suggest that the inverse association of MetS risk with the high-Jang intake may not be related to the dietary potassium-and-sodium ratio. Dietary magnesium and calcium intakes are also associated with a lower MetS risk (39–41). In an animal study, sea salt intake decreased body fat in diet-induced obese animals compared to regular salt, which was associated with the high magnesium and sulfur content of sea salt [42]. In a meta-analysis of six cross-sectional studies, the dietary magnesium intake exhibited a weighted inverse association with MetS risk. The overall MetS risk was lowered by $17\%$ with every 100 mg/day increment of magnesium [43]. Jang is made of soybean and sea salt, and a high Jang intake may increase magnesium consumption. The magnesium consumption was higher in the high-Jang intake group than in the low-Jang intake group in both genders in the present study. Therefore, magnesium could have contributed to the reduction in MetS risk. Similarly, it is well-known that dietary calcium intake lowers MetS risk [39, 40]. Dietary calcium intake was inversely associated with the MetS risk in a meta-analysis involving 14,906 MetS patients. Increasing calcium intake by an additional 300 mg/day decreased MetS risk by $7\%$ [44]. In the present study, the adults in the high-Jang intake group had $33\%$ higher dietary calcium levels than those in the low-Jang intake group. Therefore, the intake of both calcium and magnesium could lower the MetS risk in the high-Jang intake group compared to the low-Jang intake group.
Jang is made by fermenting soybeans containing salts but also includes abundant flavonoids and isoflavonoids. The primary isoflavonoids in soybean are daidzein, genistein, S-equol, and glycitein. Daidzein is converted to S-equol in the gut by specific bacteria after consuming fermented soybeans. S-equol is a potent phytoestrogen that can alleviate menopausal symptoms and MetS [45, 46]. In human and animal studies [38, 39, 46, 47], isoflavonoid intake has been seen as beneficial for hypertension, dyslipidemia, and hyperglycemia [47, 48]. In the present study, the isoflavonoid intake was 60–$70\%$ higher in the high-Jang intake group compared to the low-Jang group. The isoflavonoid intake from Jang has potentially influenced the improvement in MetS risk. Along with isoflavonoids, soy proteins (40 g/day) and peptides decrease blood pressure in patients with hypertension [49]. Soybean peptides have an angiotensin-converting enzyme (ACE) and dipeptidyl-dipeptidases-IV inhibitory action, lowering hyperglycemia and blood pressure [50, 51]. Therefore, Jang consumption, which contains isoflavonoids and soy peptides, could lower the risk of MetS and its components.
Soybeans, traditionally consumed in Asian countries, are usually fermented with *Bacillus species* and function as a synbiotic – providing the advantages of both probiotics and prebiotics. Miso intake, the Japanese soybean paste containing salt, has been reported to attenuate sympathovagal imbalance toward a more parasympathetic nerve dominant state and brain sodium sensitivity in mice administered a high-sodium solution injection (0.28 M sodium) and salt-sensitive rats given $1.3\%$ salt solution [52, 53]. Chungkookjang, Doenjang, Kanjang, and Kochujang intakes have been shown to alleviate hyperglycemia, dyslipidemia, obesity, and hypertension in earlier human and animal studies (6, 13–17). The *Bacillus species* in Chungkookjang, Doenjang, Kanjang, and Kochujang have been extensively studied, and certain isolated *Bacillus species* have probiotic characteristics. Bacillus subtilis, Bacillus amyloliquefaciens, and *Bacillus velezensis* have fibrinolytic activity, ACE inhibitory activity. and antioxidant activity (54–56). Therefore, fermented soybeans could act as synbiotics, and their intake can lower the risk of MetS and its components, including hypertension, dyslipidemia, obesity, and hyperglycemia.
This large cohort study was novel in establishing the benefits of fermented Jang on the components of MetS, particularly in men. However, this study has some limitations. First, the results could not show a cause-and-effect relationship due to using data from studies with a cross-sectional design. Second, the quantum of Jang intake was calculated by aggregating the consumption of Doenjang, Doenjang soup, Chungkookjang, and Ssamjang (the combination of *Doenjang plus* Kochujang) based on self-reporting by participants. The quantity reported by the participants could be over or underreported. Kanjang intake was excluded from the total Jang intake, which could have resulted in some bias in the Jang intake measurements. However, Kanjang and table salt were used as condiments to cook foods, and their intake was included in the SQFFQ. However, their usage might be varied in the same food as the individuals, and the variation could not be reflected. Therefore, Doenjang, Doenjang soup, Chungkookjang, and Ssamjang in Korean diets can be representative foods for Jang intake.
In conclusion, Jang, fermented salty soybeans traditionally consumed as part of the Korean diet, was found to be inversely associated with the risk of MetS and its components, including waist circumference, body fat, and hypertension in men in this large hospital-based cohort study. Moreover, Jang intake was inversely linked only to hypo-HDL cholesterolemia in women. Therefore, not only a decrease in sodium intake but also substituting salt for Jang in cooking may prevent and alleviate MetS risk by improving gut microbiota composition and providing isoflavonoids. The results can be applied to sodium intake in Asian countries where salt is used to promote flavor. These results need to be validated through a large and well-controlled randomized clinical trial or a longitudinal study.
## 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 Institutional Review Board of the National Institute of Health, Korea (KBP-2015-055) and Hoseo University, Korea (HR-034-01). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
SP and DJ: conceptualization. H-JY, S-JJ, and MR: methodology. H-JY: resources. S-JJ and MR: data collection and analysis. SP: writing—original draft preparation. DJ and S-JJ: writing—review and editing. DJ, SP, and H-JY: supervision. All authors have read and agreed to the published version of the manuscript.
## Funding
This work was supported by “functional research of fermented soybean food (safety monitoring)” under the Ministry of Agriculture, Food and Rural Affairs and partly Korea Agro-Fisheries and Food trade corporation.
## 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.
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|
---
title: Neural mechanisms of expert persuasion on willingness to pay for sugar
authors:
- Ioannis Ntoumanis
- Alina Davydova
- Julia Sheronova
- Ksenia Panidi
- Vladimir Kosonogov
- Anna N. Shestakova
- Iiro P. Jääskeläinen
- Vasily Klucharev
journal: Frontiers in Behavioral Neuroscience
year: 2023
pmcid: PMC10040640
doi: 10.3389/fnbeh.2023.1147140
license: CC BY 4.0
---
# Neural mechanisms of expert persuasion on willingness to pay for sugar
## Abstract
Introduction: Sugar consumption is associated with many negative health consequences. It is, therefore, important to understand what can effectively influence individuals to consume less sugar. We recently showed that a healthy eating call by a health expert can significantly decrease the willingness to pay (WTP) for sugar-containing food. Here, we investigate which aspects of neural responses to the same healthy eating call can predict the efficacy of expert persuasion.
Methods: Forty-five healthy participants performed two blocks of a bidding task, in which they had to bid on sugar-containing, sugar-free and non-edible products, while their electroencephalography (EEG) was recorded. In between the two blocks, they listened to a healthy eating call by a nutritionist emphasizing the risks of sugar consumption.
Results: We found that after listening to the healthy eating call, participants significantly decreased their WTP for sugar-containing products. Moreover, a higher intersubject correlation of EEG (a measure of engagement) during listening to the healthy eating call resulted in a larger decrease in WTP for sugar-containing food. Whether or not a participant’s valuation of a product was highly influenced by the healthy eating call could also be predicted by spatiotemporal patterns of EEG responses to the healthy eating call, using a machine learning classification model. Finally, the healthy eating call increased the amplitude of the P300 component of the visual event-related potential in response to sugar-containing food.
Disussion: Overall, our results shed light on the neural basis of expert persuasion and demonstrate that EEG is a powerful tool to design and assess health-related advertisements before they are released to the public.
## 1. Introduction
The obesogenic environment in which consumers make food choices makes it difficult for them to maintain their healthy eating goals (de Ridder et al., 2017). Public health measures have failed to provide such support, since obesity rates are rising rapidly with far-reaching health consequences (Kelly et al., 2008; Dixon, 2010). Although sugar is a key cause of obesity (Yu et al., 2022), there is limited research exploring what can influence individuals to consume less sugar. We have recently demonstrated that a healthy eating call by an expert can significantly decrease the willingness to pay (WTP) for sugar-containing food (Ntoumanis et al., 2022). Here, we expand this line of research by investigating the neural correlates of this phenomenon, that is, which aspects of neural responses to the same healthy eating call can predict the efficacy of expert persuasion.
Despite the rapid growth of understanding of what can help consumers to make healthier food choices (Higgs, 2015; Leng et al., 2017; Cadario and Chandon, 2020), little is known about how nudge interventions can affect sugar consumption. Previous studies on this topic have mainly examined such eating nudges as health-related labels and visibility enhancements, with the results being inconsistent (Mai and Hoffmann, 2015; Bialkova et al., 2016; Romagny et al., 2017; Donnelly et al., 2018; Thiene et al., 2018; Drugova et al., 2020; Potthoff et al., 2020; Schubert et al., 2021). Critically, the types of nudges mentioned above are, in general, half as effective as healthy eating calls, i.e., written or oral injunctions aiming at changing unhealthy food choices (Cadario and Chandon, 2020). Indeed, healthy eating calls have successfully been used to reduce unhealthy food choices both in laboratory-based studies (Binder et al., 2020; Ha et al., 2020) and in field experiments (Mollen et al., 2013; van Kleef et al., 2015). However, only recently this type of intervention was applied for the first time against sugar consumption in laboratory settings (Ntoumanis et al., 2022) and its effectiveness was significant. In fact, the results suggested that a healthy eating call (first-person narrative) by a health expert decreased the WTP for sugar-containing food. Here, by using the same healthy eating call in a slightly different experimental design, we hypothesized that (H1) the healthy eating call would decrease individuals’ WTP for sugar-containing products.
Recently, electroencephalography (EEG) is used more and more to predict consumers’ preferences and choices (e.g., Hakim et al., 2021; Mashrur et al., 2022; Raiesdana and Mousakhani, 2022). EEG offers an opportunity to overcome the biases inherent in self-reports, such as dishonesty (Tourangeau and Smith, 1996), while at the same time, it allows to investigate the neural mechanisms underlying consumer behavior (Lin et al., 2018). An EEG-derived measure that is being increasingly used in this research area is the similarity of individuals’ neural activity, or intersubject correlation (ISC). As a marker of engagement and attention (Hasson et al., 2004; Dmochowski et al., 2012; Ki et al., 2016), ISC has successfully been used to predict population-wide music popularity (Leeuwis et al., 2021), movies’ box-office performance (Christoforou et al., 2017) and individual preferences for television ads (Dmochowski et al., 2014). Given these findings, ISC is considered to be a promising neurophysiological measure of advertising effectiveness in social contexts (Pozharliev et al., 2017), especially when the study designs include long-duration stimuli (Hakim and Levy, 2019). Here, we used EEG ISC to predict the efficacy of expert persuasion reflected in the change of the WTP following the intervention. In fact, we hypothesized that (H2) high ISC during listening to the healthy eating call would result in a large decrease in the WTP for sugar-containing products.
In addition to the similarity of neural responses to the healthy eating call, we also examined whether spatiotemporal patterns of EEG signals are predictive of expert persuasion, via a multivariate pattern analysis (MVPA). MVPA is typically used to decode the difference between conditions or groups of subjects, based on the observed spatiotemporal patterns of brain responses (Fahrenfort et al., 2018). Therefore, it allows quantification of experimental effects without a priori electrode selection. Previous EEG studies have used this methodology to successfully predict subsequent ratings of stimulus attributes (Bode et al., 2014; Turner et al., 2017), as well as decision-making related to the stimulus (Bode et al., 2012; Charles et al., 2014). Despite the increasing popularity of MVPA in EEG event-related studies (e.g., Turner et al., 2017) and fMRI studies using naturalistic stimuli (e.g., Saarimäki et al., 2016), it has not yet been applied to EEG studies using naturalistic stimuli. Given the multivariate nature of EEG (Peters et al., 1998), we consider this an important gap in the field.
Thus, the goals of conducting an MVPA were two-fold. First, we aimed to demonstrate a classification pipeline as a proof-of-concept for studying the EEG activity underlying the consumers’ acceptance of persuasion. Second, given that previous studies have successfully applied MVPA of EEG signals to predict decision-making related to non-naturalistic stimuli (e.g., Turner et al., 2017), we tested whether (H3) MVPA of EEG signals can also predict decision-making related to naturalistic stimuli. To reach these goals, we trained a machine learning classification model to predict the decrease in the WTP for sugar-containing products from patterns of EEG responses to the healthy eating call. Such a multivariate pattern analysis (MVPA) is usually performed in distinct time windows (Bode et al., 2014; King and Dehaene, 2014; Turner et al., 2017). If there is a statistically significant association between the variable of interest and the EEG patterns in a particular time window, then this time window is considered to contain the respective information of interest (Turner et al., 2017).
In order to better understand the neural mechanisms underlying expert persuasion, we also investigated how the healthy eating call affects particular event-related potentials (ERPs) elicited by viewing sugar-containing products that may prompt WTP decisions. ERP analysis has widely been used to discern the neural correlates of consumer behavior (for a review, see Lin et al., 2018). Of particular interest are long-lasting positive waves, such as the P300 component, which is known to reflect the allocation and maintenance of attentional resources (Polich, 2007) and was repeatedly recorded during product evaluation (Ryu et al., 2010; Wang and Han, 2014; Cai et al., 2021). Previous studies have disclosed that the P300 amplitude is increased, when seeing products that fit one’s preferences (Wang and Han, 2014), products that are recommended by others (Guo et al., 2016), products one craves (Svaldi et al., 2015; Biehl et al., 2020), and products that one is willing to buy (Jones et al., 2012; Lin et al., 2018). Since our healthy eating call aims to influence participants against sugar-containing products, we hypothesized that (H4) the P300 amplitude in response to sugar-containing food would decrease after listening to the healthy eating call. Moreover, earlier studies have speculated that P300 could predict WTP (Schaefer et al., 2016), while also being involved in social conformity (Guo et al., 2016). Therefore, we also hypothesized that (H5) the expected decrease in the WTP for sugar-containing food and the expected decrease in the P300 amplitude in response to sugar-containing food after the healthy eating call would be positively correlated with each other.
## 2.1. Participants
Forty-nine participants (29 females, aged 18–40 years, mean age = 22.50) were recruited via online advertisements. These participants were different from those who participated in our previous work (Ntoumanis et al., 2022). All of them reported that they were right-handed, healthy, had a normal or corrected-to-normal vision, had no history of psychiatric diagnoses or eating disorders, no neurological or metabolic illnesses, and were not taking any prescribed medication. Eating sweets, in general, was also an inclusion criterion, so that we filter out potential participants who might already dislike sugar altogether. The sample size was similar to or larger than previous studies exploring the relationship between WTP for products and EEG indices (Ramsøy et al., 2018; Liao et al., 2019). Four participants were excluded from all analyses for having excessively noisy EEG data. Excluding them from both the behavioral and the EEG analyses ensured that the results were based on the same consistent sample. The final sample consisted of 45 participants (27 females, aged 18–40 years, mean age = 22.51). All participants received a flat fee of 600 monetary units (MU) equivalent to ~21.95, with the correction for purchasing power parities (OECD, 2021). Additionally, they received a reward based on their decisions in the experimental task. The mechanism of how this reward was determined was explained to them in detail in the instructions prior to the experiment.
## 2.2. Stimuli
Ninety full-colored photographic pictures (200 dpi) of sweets and everyday products were used. The pictures represented products without packaging (Figure 1A) to avoid any confounding effect of the package (Motoki and Suzuki, 2020). All products existed in the market during the period of data collection. One-third of the products were labeled as “sugar-free”, another third were labeled as “sugar-containing” and the remaining were labeled as “non-edible” (the latter served as the control condition). The labels were not deceptive (e.g., the products labeled as “sugar-free” were indeed sugar-free) and were presented in different colors (blue, pink, and yellow), so that the participants could better distinguish the three conditions. The colors were randomized between participants. Since the meaning of the “sugar-free” label might not be clear to all participants, we pointed out that the “sugar-free” label indicates that the product does not contain sugar, as in Ntoumanis et al. [ 2022]. The pictures of the “sugar-containing” and the “sugar-free” products were the same as in Ntoumanis et al. [ 2022] and they have previously been pre-tested so that the perceived sweetness, tastefulness and healthfulness of “sugar-containing” products do not differ from that of “sugar-free” products (Ntoumanis et al., 2022).
**Figure 1:** *Trial structure and study design. (A) Sample trial of the bidding task with a product labeled as “sugar-containing”. In the beginning, the product was presented for 4 s (“early evaluation phase”). Next, a message was displayed at the top of the screen “How much are you ready to pay for this product?”. Participants had 10 s to indicate their willingness to pay (WTP) for this product. Last, a fixation cross was shown (2–6 s) and the next trial began. The trials of the other two conditions differed only in the label and in the presented product. (B) Experimental procedure. Participants first performed a block of the bidding task, then they listened to the healthy eating call and finally, they performed a second block of the bidding task. Here, the face of the doctor has been blurred due to copyright and ethical reasons.*
## 2.3. Bidding task
Figure 1A illustrates the procedure in the bidding task, which was similar to that in Ntoumanis et al. [ 2022]. At the beginning of each trial, a product was displayed for 4 s (“early evaluation stage”, Hutcherson et al., 2012). Afterward, participants had 10 s to indicate their WTP (“How much are you ready to pay for this product?”), in order to purchase this product at the end of the experiment (Plassmann et al., 2007; Hutcherson et al., 2012; Schmidt et al., 2018). The participants of the behavioral pilot study reported that 10 s was enough time for them to make a decision. The initial position of the marker on the WTP slider was randomized across trials (Martinez-Saito et al., 2019). The left and right keyboard keys allowed the participants to change the initial value of the slider to the value they wished, before pressing Enter key to confirm the bid. No response within the time limit resulted in a WTP of 0 MU, following previous studies (Hutcherson et al., 2012; Ntoumanis et al., 2022). The values of the slider ranged from 0 to 150 MU, with an increment of 10 MU, since this is the range of the actual prices of the products in the market. Each block contained the same amount of “sugar-free”, “sugar-containing” and “non-edible” products, with the order of the items being randomized across participants and blocks. Finally, a fixation cross was shown and the next trial began. The duration of the fixation cross was 2–6 s in order to prevent anticipation (Hutcherson et al., 2012; Schmidt et al., 2018).
At the beginning of the experiment, participants received 150 MU in cash as an endowment to use in the bidding task for purchasing products, since bidding decisions have been found to be sensitive to whether or not they are hypothetical (Lusk and Schroeder, 2006). The Becker-DeGroot-Marschak auction was employed in order to measure individual preferences and each participant’s exact WTP for every product (Becker et al., 1964; Plassmann et al., 2007). According to this auction, one trial was randomly selected at the end of the experiment. Let b denote the bid made by the participant in that trial. A random number n was also drawn from a known distribution (in our case, 0, 10,‥., 150 MU was chosen with equal probability). If b ≥ n, the participant received the product corresponding to that trial and paid a price equal to n. Otherwise, the participant did not receive the product but also did not pay anything (for a similar design, see Plassmann et al., 2007). The endowment was equal to the maximum WTP, so that participants do not have to worry about distributing their 150 MU over different products and they can treat each trial as if it were the only decision that counted (Plassmann et al., 2007; Ntoumanis et al., 2022).
## 2.4. Healthy eating call
The healthy eating call was the same as in Ntoumanis et al. [ 2022]: an audio first-person narrative by a nutritionist emphasizing the health risks of sugar consumption. It started with an introduction of the narrator, then 13 arguments were expressed sequentially, and finally, there were some closing remarks. The arguments against sugar consumption expressed in the narrative were retrieved from scientific sources (e.g., Lenoir et al., 2007). The narrative also contained clear evidence about the nutritionist’s positive view towards sugar-free products. The narrator was introduced to the participants as a nutritionist because communicators with high expertise have been found to be particularly persuasive (Deutsch and Gerard, 1955; Binder et al., 2020; Hang et al., 2020; Ntoumanis et al., 2022). The audio version of the healthy eating call was recorded by a professional male narrator in order to maximize participants’ engagement (duration = 7 min). The healthy eating call was written and presented to the participants in their native language. The English translation can be found here: https://osf.io/894mk/. While the narrative was being played, the static image of a doctor was displayed on the screen to maximize participants’ attention (Figure 1B). The narrative we used has been shown to not induce any of the basic emotions at a considerably high level (the average rating of each emotion was lower than 2.8 on a 5-point scale, see Ntoumanis et al., 2022).
## 2.5. Questionnaires
To explore the influence of consumer heterogeneity on the efficacy of expert persuasion, participants completed four questionnaires not earlier than 2 days prior to attending the experiment. The first questionnaire assessed demographic information, including gender, age, weight, height [for the calculation of the Body Mass Index (BMI)], and level of education (four levels: incomplete secondary education, secondary education, incomplete higher education, higher education). In addition, participants completed the Conformity scale (Mehrabian and Stefl, 1995; Keller, 2019; the internal consistency in the current study, α = 0.747), the Consumer susceptibility to interpersonal influence scale (Bearden et al., 1989, translated by us; the internal consistency in the current study, α = 0.701), as well as the Big 5 Personality traits questionnaire (Khromov, 2000; the internal consistency in the current study, α = 0.776). The latter was included because previous studies have revealed a relationship between personality traits and sugar consumption, as well as social conformity (Keller and Siegrist, 2015; Intiful et al., 2019; Parsad et al., 2019).
## 2.6. Procedure
Participants were told that the goal of the experiment was to study food preferences. They were asked to not eat anything for at least 3 h prior to the experiment (Hutcherson et al., 2012; Ntoumanis et al., 2022). This also limited the variability of their hunger level, which is a factor that has been shown to affect the amplitude of certain ERPs in response to food stimuli (Nijs et al., 2010b). Upon arrival at the laboratory, participants saw the real food products to be assured about the validity of the procedure. Then, they performed a practice session, where they had to bid on six of the products under the same conditions as in the subsequent experimental task. At the beginning of the experiment, participants performed a bidding task consisting of 90 trials (30 per condition). Next, they listened to the healthy eating call and afterward, they performed a second block of the bidding task. Figure 1B illustrates the experimental procedure. The stimulus presentation and response recording were controlled by PsychoPy (v2022.2.1; Peirce et al., 2019). On average, participants took approximately 1.5 h to complete the experiment, including the EEG setup.
## 2.7. Behavioral data analysis
The hypothesis that the healthy eating call would decrease the WTP for sugar-containing food, but would not change the WTP for sugar-free and non-edible products was specified prior to data collection, based on the results of Ntoumanis et al. [ 2022]. To test this hypothesis, a one-way, repeated-measures ANOVA taking Condition (three levels: sugar-containing, sugar-free, non-edible) as a within-subjects factor and the ΔWTP (i.e., the WTP for each product in the second block subtracted by the WTP for the same product in the first block) as a dependent variable was conducted. A significant interaction was further assessed by post hoc tests. Specifically, given the normal distribution of the data (as assessed by a Shapiro-Wilk test, $p \leq 0.05$), we conducted pairwise paired-samples t-tests, and p-values were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) correction (Benjamini and Hochberg, 1995). Adjusted p-values below 0.05 were considered statistically significant. Moreover, in order to investigate the efficacy of expert persuasion separately for each condition, we conducted one-sample t-tests to determine whether the delta of WTP for each condition was significantly different from 0 (two-tailed). The above analyses allowed us to test the hypothesis (H1).
To explore the relationship between consumer characteristics and the efficacy of expert persuasion, a series of correlation analyses was conducted. For this, we examined whether participants’ delta of WTP for each condition was significantly correlated with their questionnaire data using the Spearman’s correlation coefficient (due to the non-normal distribution of the questionnaire scores). This analysis was exploratory and no hypotheses were specified in advance.
## 2.8. EEG data recording and pre-processing
The EEG activity was recorded continuously with a BioSemi Active Two system at a sampling frequency of 500 Hz. Subjects were fitted with a standard, 64-electrode cap following the international 10–10 system, with linked mastoids as a reference electrode. To subsequently remove eye-movement artifacts, the vertical and horizontal electrooculogram (VEOG and HEOG) were also recorded with two auxiliary electrodes (one located ventrally to one eye and one located laterally to the other eye). In order to achieve a precise time alignment of all the stimuli presentations (including pictures and the healthy eating call), the stimulus presentation software sent triggers to a parallel port simultaneously with the presentation of stimuli. The timing of these triggers was set to be synchronized with screen refresh so that it captures the actual instead of the expected onsets. All offline signal processing and artifact correction was performed in MNE Python (v1.0.3; Gramfort et al., 2013).
For the ISC analysis, the data were preprocessed following the procedure described in Dmochowski et al. [ 2012] and Ntoumanis et al. [ 2023]. First, data were re-referenced to average reference (Shtyrov et al., 2013). Next, the segment of the EEG/EOG signal corresponding to the duration of the healthy eating call was extracted based on the triggers sent by the stimulus presentation software to a parallel port at the onset and offset of the stimulus. We further excluded the first 15 and the last 5 s of this segment, as it is recommended in Nastase et al. [ 2019], to avoid including in the analysis changes in the signal driven by the onset and offset of the stimulus. Data were downsampled at 250 Hz, high-pass filtered at 0.5 Hz, and notch-filtered at 50 Hz and 100 Hz, in order to remove drift and power line noise, respectively. Afterward, noisy channels were detected by visual inspection and the samples of these channels were interpolated based on the signals of the good sensors around them (Ki et al., 2016; on average 4.5 channels in one recording). Eye-movement artifacts were removed by Independent Component Analysis (ICA) using the infomax algorithm (Bell and Sejnowski, 1995). Samples exceeding 3 SDs of the mean of their respective channel were replaced with 0, and so were the samples 40 ms around such outliers (i.e., before and after; Cohen and Parra, 2016; Ntoumanis et al., 2023). For the ERP analysis, we preprocessed the data in the same way except that a different band-pass filter was applied: 0.1 Hz and 40 Hz cut-off frequencies, following previous ERP studies (Bredikhin et al., 2022). Also, the outliers in the ERP signals were detected based on the interquartile interval (IQR) instead of the SD, as in Rappaport et al. [ 2019]. In fact, epochs containing samples beyond the range [Q1−1.5×IQR, Q3+1.5×IQR], where Q1 and Q3 denote the 25th and the 75th percentiles, were rejected.
## 2.9. Intersubject correlation analysis
The ISC of EEG responses to the healthy eating call was estimated via a correlated components analysis (CorrCA; Dmochowski et al., 2012; Cohen and Parra, 2016). Briefly, this analysis finds components of the EEG data that are maximally correlated among subjects. Following previous studies, we estimated the ISC as the sum of the three most correlated components, in order to account for the overall neural synchronization regardless of the anatomical origin of each component (Cohen and Parra, 2016; Cohen et al., 2018; Ntoumanis et al., 2023). However, we also computed the forward model for each of the three strongest components to visualize the spatial distribution of the component activity (Cohen and Parra, 2016). The ISC was calculated in a leave-one-out approach, i.e., for each participant, there was one value denoting how correlated this participant’s brain activity was to the brain activity of all other participants during listening to the healthy eating call (Cohen and Parra, 2016; Ntoumanis et al., 2023). Taking into account the long duration of the narrative (7 min), we computed the ISC in sliding time windows of 10 s size and 8 s overlap (196 time windows in total). We selected this size based on a recent study which showed that the ISC can be most reliably measured on a time scale of 10 s (Madsen and Parra, 2022). The ISC analysis was performed in Matlab (release 2017b; MathWorks Inc, USA) using an adjusted version of the code shared by Cohen and Parra [2016]1. After calculating the leave-one-out ISC, we examined whether it was significantly correlated with the delta of WTP for each condition, using the Spearman’s correlation coefficient. This allowed us to test the hypothesis (H2).
## 2.10. Multivariate pattern analysis
An MVPA was conducted to investigate whether distributed patterns of EEG responses to the healthy eating call were predictive of the efficacy of expert persuasion (H3). Our analysis was similar to that conducted in Turner et al. [ 2017]. First, we converted the delta of WTP for sugar-containing products to a binary variable: “highly-influenced” if the delta of WTP for sugar-containing food was less than the median score (22 participants) and “not highly-influenced”, otherwise (23 participants). A machine learning logistic regression classification model was then trained to predict, based on distributed patterns of EEG activity evoked by the healthy eating call, whether or not a participant was highly influenced by the narrative. This was done repeatedly in time windows of 1 s length, taking into account the long duration of the healthy eating call (7 min originally, 6 min 50 s after the removal of onset/offset; see EEG data recording and pre-processing). Specifically, the features/input of this classifier were the mean EEG signal of each channel within the corresponding time window. To avoid overfitting, a 5-fold cross-validation was performed, and the classification accuracy for each time window was calculated as the average percentage of correct guesses across all the cross-validation runs in the corresponding time window (Saarimäki et al., 2022).
Statistical testing was performed by comparing the classification accuracy to an empirical chance distribution instead of the theoretical chance level (in our case, $50\%$), because the latter is considered too lenient (Combrisson and Jerbi, 2015). In order to obtain an empirical chance distribution, we repeated the analysis described above 1,000 times, but each time with the labels (“highly-influenced”, “not highly-influenced”) randomly shuffled before classification (Turner et al., 2017). To correct for multiple comparisons, we used a cluster-based correction. Specifically, we clustered the windows with a statistically significant classification accuracy on the basis of temporal adjacency and finally, we took the largest of these clusters (Maris and Oostenveld, 2007). This approach allowed us to explore sustained information in the EEG signals that were predictive of the delta of WTP for sugar-containing food.
As a control analysis, we examined whether the spatiotemporal patterns of EEG responses to the healthy eating call could also predict the delta of WTP for non-edible and sugar-free products. To that end, we first classified individuals based on the median scores and we repeated the same machine learning analysis. Then, we compared the proportion of significant windows (i.e., windows where the classification accuracy was statistically significant) between conditions, using a standard hypothesis test of proportions, as in Dmochowski et al. [ 2012]. We conducted this control analysis before correcting for multiple comparisons, because our cluster-based correction results by definition in only one significant cluster (Maris and Oostenveld, 2007), which makes further statistical testing difficult.
Finally, the feature weights were extracted for each time window with above chance classification accuracy and assigned to each channel. This provided us with a representation of the importance of each channel for the classification. Additionally, we repeated the same procedure outlined above with a different classifier (Support Vector Machine with linear kernel), but since it did not improve the classification results, it is not reported here. The MVPA analysis was performed in Python 3.10, using the Scikit learn package (Pedregosa et al., 2011).
## 2.11. ERP analysis
Based on the picture presentation at 0 ms (Figure 1A; “early evaluation phase”), grand average ERP epochs were selected from −100 to 1,000 ms. Baseline (from −S100 to 0 ms) correction was applied in each epoch. The P300 amplitude was calculated using the mean voltage of midline parietal electrodes (CPz, Pz, Oz) between 250 and 450 ms relative to the stimulus onset (Schubert et al., 2021). First, we examined whether the P300 amplitude in response to different product categories is modulated by the healthy eating call. To that end, we conducted a two-way repeated-measures ANOVA taking Condition (three levels: sugar-containing, non-edible, sugar-free) and Block (two levels: Block 1, Block 2) as within-subjects factors and the WTP as the dependent variable. This analysis allowed us to test our prespecified hypothesis (H4), but also to exploratorily investigate whether different product categories elicit different amplitudes of P300 in the first block (i.e., without any intervention). Next, we tested our prespecified hypothesis (H5) by calculating the Spearman’s correlation coefficient between the delta of WTP for sugar-containing products and the delta of P300 amplitude in response to sugar-containing products.
Moreover, we calculated the mean amplitude of one additional ERP component, the Late Positive Potential (LPP; Schubert et al., 2021), which was found to be elicited by viewing the products. This was calculated using the mean voltage of the same electrodes (CPz, Pz, Oz) between 450 and 750 ms relative to the stimulus onset, in line with previous studies (Schubert et al., 2021). Then, we conducted the same two-way repeated-measures ANOVA, as for the P300 amplitude, in order to examine whether the healthy eating call modulated the LPP in response to some products. This analysis was exploratory and no hypotheses were specified in advance.
## 3.1. Descriptive statistics
The average bid was 47.39 MU (SD = 23.83 MU). Overall, $83.65\%$ of all bids were higher than zero. One-sample Wilcoxon signed rank test showed that the median bid was significantly greater than zero ($W = 22$, 784, 625, $p \leq 0.0001$, effect size = 0.85), suggesting that most products were rewarding for the participants. The mean reaction time (RT) was 2.92 s (SD = 1.53), while participants failed to bid within the 5 s time limit only in $1.09\%$ of the trials. As data from the first block showed, participants did not bid differently for sugar-containing, sugar-free and non-edible products ($F = 0.033$, $$p \leq 0.97$$, generalized eta-squared = 0.001).
The BMI of the participants ranged from 16.96 to 32.45, with the mean BMI being equal to 22.10. Four participants scored lower than 18.5 (underweight), eight participants scored above 25 (overweight) and 33 scored in between (normal). The majority of the participants had a complete ($$n = 19$$) or incomplete ($$n = 18$$) higher education, while the remaining participants ($$n = 8$$) had a complete secondary education. Finally, the scores in the personality questionnaires are summarized in Supplementary Table 1.
## 3.2. The healthy eating call decreased WTP for sugar-containing food
First, we investigated the influence of the healthy eating call on the WTP for sugar-containing, sugar-free and non-edible products. A repeated-measures ANOVA revealed a statistically significant effect of Condition on the delta of WTP (F[2,88] = 6.876, $$p \leq 0.002$$, generalized eta-squared = 0.09). Subsequent paired t-tests showed that the delta of WTP for sugar-containing products was significantly lower than the delta of WTP for sugar-free (t[44] = −3.24, $$p \leq 0.002$$, Cohen’s $d = 0.48$) and non-edible products (t[44] = −2.19, $$p \leq 0.034$$, $d = 0.33$), while the delta of WTP for sugar-free food was not significantly different from the delta of WTP for non-edible products (t[44] = 1.85, $$p \leq 0.070$$, $d = 0.28$). This demonstrates that the healthy eating call influenced participants’ WTP decisions for sugar-containing, but not for sugar-free food products, relative to the control condition of non-edible products.
Apart from comparing the delta of WTP between conditions, we also examined whether this measure is significantly different from 0 for each condition separately. Independent one-sample t-tests showed that the delta of WTP for sugar-containing food products was significantly lower than 0 (t[44] = −3.73, $$p \leq 0.0005$$, $d = 0.56$), unlike the delta of WTP for sugar-free and non-edible products (t[44] = 0.82, $$p \leq 0.42$$, $d = 0.12$ and t[44] = −1.78, $$p \leq 0.08$$, $d = 0.27$, respectively). Figure 2 illustrates these findings, which support our hypothesis (H1) that the healthy eating call would decrease individuals’ WTP for sugar-containing products.
**Figure 2:** *Changes in participants’ WTP for sugar-containing, sugar-free and non-edible products after listening to the healthy eating call. A dashed horizontal line at 0 indicates no change in WTP. Dots represent individual subjects. Statistically significant differences are denoted with asterisks (*p < 0.05, **p < 0.01, ***p < 0.001). The “ns” denotes that the delta of WTP for neither the sugar-free nor the non-edible products was significantly different from 0.*
## 3.3. The efficacy of expert persuasion was not moderated by participant demographic characteristics
An exploratory correlation analysis was conducted to examine whether the efficacy of expert persuasion is significantly moderated by participant’s demographic characteristics and personality traits. Due to the non-normal distribution of most variables, a Spearman’s correlation coefficient was used. A negative correlation was found between the delta of WTP for sugar-containing food and scores in the Conformity scale (r = −0.30, $$p \leq 0.049$$). This indicates that the healthy eating call was particularly influential on participants who are, in general, prone to social influence. Also, we found a significantly negative correlation between the delta of WTP for non-edible products and scores in the Neuroticism scale of the Big 5 Personality traits questionnaire (r = −0.51, $$p \leq 0.0004$$). No other significant correlation was found between the delta of WTP and any other participant characteristic (Supplementary Table 2). Moreover, we compared the delta of WTP for each condition between males and females, with the results, however, not reaching statistical significance (two-samples t-tests; t[43] = 0.68, $$p \leq 0.51$$; t[43] = 1.68, $$p \leq 0.104$$; t[43] = 0.71, $$p \leq 0.48$$, for sugar-containing, non-edible and sugar-free, respectively).
## 3.4. ISC and efficacy of expert persuasion
First, we estimated the three most correlated components using EEG data corresponding to the whole duration of the narrative (Figure 3A). These correlated components were found to be similar to previous studies (e.g., Dmochowski et al., 2012; Cohen and Parra, 2016). Specifically, the first component revealed a strong positivity at occipital sites, probably because all participants were looking at the same picture while listening to the narrative (see Figure 2). The second component revealed a symmetric positivity at temporal sites, consistent with the auditory processing of the narrative.
**Figure 3:** *The results of the intersubject correlation (ISC) analysis. (A) Scalp projections of the three strongest correlated components. (B) The relationship between the ISC during listening to the healthy eating call and the delta of WTP for different product categories. For the category of sugar-containing products, the correlation was r = −0.29, with a one-tailed p-value = 0.027.*
Then, we calculated the leave-one-out ISC during listening to the healthy eating call. Considering the long duration of the healthy eating call, the ISC was calculated in sliding time windows and then it was averaged across all the time windows for each participant. Next, we calculated the Spearman’s correlation coefficient between these ISC values and the delta of WTP for each product category, separately. The results showed that the ISC during listening to the healthy eating call was negatively correlated with the delta of WTP for sugar-containing food (r = −0.29, $$p \leq 0.027$$; Figure 3B), supporting our hypothesis (H2). Notably, this significant p-value was obtained from a one-tailed hypothesis test (i.e., H0: r < 0), since our hypothesis (H2) was directional. In addition, the correlation between the ISC and the delta of WTP for the other two product categories was not found statistically significant (r = −0.03 and $$p \leq 0.87$$ for sugar-free, r = −0.10 and $$p \leq 0.50$$ for non-edible).
## 3.5. MVPA
A machine learning classification analysis was conducted to investigate whether spatiotemporal patterns of the EEG responses to the healthy eating call could predict the efficacy of expert persuasion (that is, the delta of WTP for sugar-containing food). To that end, we first labeled half of the participants as “highly-influenced” and the other half, “not highly-influenced” by the healthy eating call, based on the median delta of WTP for sugar-containing products. Then, we conducted the MVPA in 1-s time windows.
The classification accuracy was statistically significant in $22.25\%$ of the time windows (87 significant windows out of 400). After applying a cluster-based correction for multiple comparisons, there were two clusters whose classification accuracy remained statistically significant: one between the 165th and the 168th second of the healthy eating call and one between the 200th and the 203th. In other words, whether or not a participant was highly influenced by the healthy eating call could be best predicted by their EEG signal during these two periods. Specifically, in the first cluster (i.e., 165–168 s) the average classification accuracy was $65\%$, while the average accuracy of the empirical chance distribution was $49.5\%$. In the second cluster (i.e., 200–203 s) the average accuracy was $69\%$, while the average accuracy of the empirical chance distribution was $49.9\%$.
To better understand what was in the healthy eating call during these periods that evoked distinct neural responses between the participants who were highly influenced by it and those who were not, we extracted the corresponding text from the narrative. At the 160th second of the narrative, the following phrase was said: “Your skin will look younger. A study in the American Journal of Clinical Nutrition suggests that giving up sugar may result in your acne disappearing”, which finished at the 172th second. At the 198th second of the narrative, the following phrase was said: “Your blood pressure will also decrease, which means that your heart and your vessels will have less work to do”, which finished at the 204th second.
Finally, we extracted the feature weights of the machine learning algorithm for the two significant clusters of time windows. Although these maps cannot determine the sources of predictive information (Haufe et al., 2014), they provide a representation of the importance of each channel for the classification (Figure 4). This revealed that electrodes at the temporal sites were the main contributors to the significant classification accuracy that was observed between the 165 and 168 seconds of the healthy eating call. For the second significant cluster, it appears that the signal of frontal electrodes provided sufficient information to the classifier.
**Figure 4:** *Spatio-temporal decoding of the delta of WTP for sugar-containing food. A machine learning logistic regression was used to predict the delta of WTP for sugar-containing products (binary variable) based on distributed patterns of EEG responses to the healthy eating call. The blue line denotes the classification accuracy for each time window. A horizontal dashed line has been added at 50% as a reference, although statistical testing was performed based on permutations. The red bars indicate the clusters where the classification accuracy was statistically significant after a cluster-based correction for multiple comparisons. The feature weights of the classifiers in the significant cluster windows have been added at the top of the plot. These are the standardized activation patterns.*
As a control analysis, we repeated exactly the same MVPA for non-edible and sugar-free products. That is, we examined whether the EEG responses to the healthy eating call could also predict the delta of WTP for these two product categories. The classification accuracy was significant only in 27 time windows for non-edible products and in 16 time windows for sugar-free products. We employed a standard hypothesis test of proportions to test whether the observed ratios are drawn from disparate distributions, as in Dmochowski et al. [ 2012]. This showed that the proportion of significant windows was significantly higher in the sugar-containing condition than in the other two conditions (both p-values < 0.0001; Supplementary Figure 1).
## 3.6. The healthy eating call did not modulate the P300 and LPP amplitudes in response to any of the products
First, we conducted a series of ANOVAs to investigate whether the healthy eating call modulated the neural responses to sugar-containing, sugar-free and non-edible products, irrespective of WTP decisions. In these analyses, the dependent variable was the amplitude of the ERP component of interest (P300 or LPP) and the within-subjects factors were always the Condition (three levels: sugar-containing, sugar-free, non-edible) and the Block (two levels: Block 1, Block 2).
There was a significant main effect of Condition on the P300 amplitude [F[2,88] = 11.028, $p \leq 0.0001$, generalized eta-squared (ges) = 0.028] and a significant main effect of Block (F[1,44] = 9.59, $$p \leq 0.003$$, ges = 0.012). However, the interaction between Condition and Block was not statistically significant (F[2,88] = 0.34, $$p \leq 0.71$$, ges = 0.001). To better understand the significant main effects of Condition and Block, we conducted pairwise one-sample t-tests. These revealed that regardless of the Block, the P300 amplitude was weaker (less positive-going) for non-edible products compared to sugar-containing (t[89] = 3.60, $$p \leq 0.001$$, $d = 0.38$) and sugar-free products (t[89] = 4.96, $p \leq 0.0001$, $d = 0.52$), but there was no significant difference between the latter two conditions (t[89] = 1.33, $$p \leq 0.19$$, $d = 0.14$). Also, regardless of the Condition, the P300 amplitude was stronger (more positive-going) in the second block of the bidding task compared to the second (t[134] = 3.59, $p \leq 0.001$, $d = 0.32$). Finally, contrary to our hypothesis (H4), the P300 amplitude in response to sugar-containing food was significantly stronger (more positive-going) in the second block of the bidding task compared to the first (t[44] = 2.65, $$p \leq 0.011$$).
Similar results were found in terms of the LPP amplitude. Specifically, there was a significant main effect of Condition on the LPP amplitude (F[2,88] = 7.11, $$p \leq 0.001$$, generalized eta-squared (ges) = 0.021) and a significant main effect of Block (F[1,44] = 24.55, $p \leq 0.0001$, ges = 0.07). However, the interaction between Condition and Block was not statistically significant (F[2,88] = 0.04, $$p \leq 0.96$$, ges < 0.0001). To better understand the significant main effects of Condition and Block, we conducted pairwise one-sample t-tests. These revealed that regardless of the Block, the LPP amplitude was weaker (less positive-going) for non-edible products compared to sugar-containing ($t = 3.42$, $$p \leq 0.001$$) and sugar-free products ($t = 3.54$, $$p \leq 0.001$$), but there was no significant difference between the latter two conditions ($t = 0.27$, $$p \leq 0.79$$). Also, regardless of the Condition, the LPP amplitude was stronger (more positive-going) in the second block of the bidding task compared to the second ($t = 6.74$, $p \leq 0.0001$). Figure 5 illustrates the differences between the P300 and LPP amplitudes between conditions and blocks.
**Figure 5:** *P300 and LPP components shaded in gray in two consecutive time windows (earlier and later, correspondingly) in response to each product category, in the first (left) and the second (right) block of the bidding task. In both blocks, the mean P300 amplitude and the mean Late Positive Potential (LPP) amplitude were found to be significantly lower for non-edible products compared to sugar-containing and sugar-free products. The gray areas indicate the time window in which the P300 and the LPP were measured, based on previous literature.*
Finally, we tested our hypothesis (H5) by calculating the Spearman’s correlation coefficient between the delta of the P300 amplitude and the delta of WTP for each product category. However, no significant correlation was found: $r = 0.10$, $$p \leq 0.51$$ for sugar-containing, $r = 0.1$, $$p \leq 0.520$$ for non-edible, and r = −0.045, $$p \leq 0.77$$ for sugar-free products.
## 4. Discussion
This study presents a first attempt to link, on the one hand, the behavioral effect of healthy eating by an expert on the WTP for sugar-containing food and, on the other hand, the neural responses to this healthy eating call with the use of EEG. At the behavioral level, the results illustrated a successful persuasion by the health expert. This behavioral effect was associated with two neurophysiological indices, namely, (i) the group-level ISC of EEG responses to the healthy eating call and (ii) the subject-level spatiotemporal patterns of EEG responses to the healthy eating call. Below, we further consider these findings in more detail.
The analysis of the behavioral data showed that the healthy eating call significantly decreased participants’ WTP for sugar-containing food. This result is consistent with our hypothesis (H1) and replicates the recent findings of Ntoumanis et al. [ 2022]. It also supports earlier research showing that healthy eating calls can, in general, be effective at reducing unhealthy eating (Mollen et al., 2013; van Kleef et al., 2015). In fact, our healthy eating call was a first-person narrative by an expert, which is a type of intervention that has been found to be effective in children populations, too (Binder et al., 2020). We speculate that an advantage of using first-person narratives, over such types of nudging as labeling, is their potential to change consumers’ underlying perception of unhealthy food instead of targeting individual decisions. Narratives can shape social norms, injunctive or descriptive, which can support their effectiveness (Robinson et al., 2013; Higgs, 2015; Higgs and Thomas, 2016).
In addition, the healthy eating call did not increase the WTP for sugar-free food (relative to the control condition of non-edible products). That the healthy eating call was more effective at reducing the WTP for sugar-containing food than at increasing the WTP for sugar-free food is supported by previous studies showing that nudge interventions are, in general, more efficient at reducing unhealthy eating than increasing healthy eating (Zlatevska et al., 2014; Cadario and Chandon, 2020), as well as by the concept of negativity bias (Baumeister et al., 2001).
It is worth mentioning that the current study included a control condition (non-edible products) and not a control group of subjects. However, our previous work showed that a control group of subjects, who listened to a control message unrelated to food, did not change their WTP for sugar-containing or sugar-free food (Ntoumanis et al., 2022).
Interestingly, we found that the delta of WTP for sugar-containing food was negatively correlated with the Conformity scale scores. The Conformity scale measures the reliance on others for decision-making, in a variety of social contexts (Mehrabian and Stefl, 1995). Thus, healthy eating calls may be particularly effective on consumers who are, in general, susceptible to social conformity. Our study is not the first to show that conformity moderates consumers’ decision-making. For instance, Martinelli and De Canio [2021] illustrate the moderating role of conformity in inducing non-vegan consumers to buy vegan food. Therefore, we encourage future studies to sample personality-related information from the participants, to account for the intersubject variability in consumer behavior (for a review, see Kassarjian, 1971).
Furthermore, we found a significant one-tailed correlation between the ISC during listening to the healthy eating call and the efficacy of expert persuasion. In fact, participants, whose neural responses to the expert’s narrative correlated more strongly with others, demonstrated superior compliance with the healthy eating call. Although this result has to be considered with caution due to the statistical insignificance of the two-tailed correlation ($$p \leq 0.054$$), it is consistent with our hypothesis (H2). ISC of EEG has previously been found to be positively correlated with subsequent memorization of audio narratives (Cohen and Parra, 2016). In our study, participants who exhibited high ISC during listening to the healthy eating call might have memorized more information contained in it, which might have increased their compliance with the expert. Overall, neural synchrony is a promising tool in neuroforecasting for movie and music popularity (Christoforou et al., 2017; Leeuwis et al., 2021), as well as for television ads (Dmochowski et al., 2014). Here, we show that neural synchrony may, in addition, be a promising tool in neuroforecasting of healthy eating advertisements.
Moreover, distributed patterns of brain activity during listening to the healthy eating call were found to contain predictive information about whether or not a participant was highly influenced by the healthy eating call. Although not all, several arguments in the narrative elicited divergent neural responses to participants who were highly influenced by them. Training a machine learning classifier with EEG data corresponding to those arguments significantly predicted whether or not a participant was highly influenced by the healthy eating call. Previous works have successfully predicted consumer decision-making based on EEG responses to non-naturalistic stimuli (Bode et al., 2014; Turner et al., 2017). Our proof-of-concept MVPA shows that it is also possible to predict consumer decision-making based on EEG responses to naturalistic stimuli, supporting our hypothesis (H3).
It is interesting to speculate what are the cognitive mechanisms that highly-influenced participants employed while listening to those arguments, which resulted in their neural activity being discernible. Information encoding is a possible such mechanism. That is, while listening to the health risks of sugar, highly-influenced participants might have encoded this information differently from not highly-influenced participants. Another interpretation could be that the information contained in the narrative activated some other related cognitive processes (e.g., knowledge retrieval) only in highly-influenced participants. Determining which of these two possible interpretations is true is a well-known challenge in MVPA (Weaverdyck et al., 2020). Despite the low spatial resolution of EEG, we can speculate which interpretation is the most likely, based on the scalp representation of the feature weights of the classifier (Figure 4). The electrodes that contributed the most in the classification process were located in temporal and frontal sites, supporting the information encoding interpretation. In fact, previous studies have reported that these brain areas show increased activity when one is feeling persuaded (Falk et al., 2010).
Furthermore, contrary to our hypothesis (H4), the healthy eating call did not decrease the P300 amplitude in response to sugar-containing food. In fact, the P300 elicited by sugar-containing products was even more positive going in the second vs. the first block of the bidding task. Our hypothesis was based on previous studies showing that the more we want a product, the stronger the P300 we exhibit when viewing it. However, the P300 can be elicited by stimuli of negative valence, as well (Conroy and Polich, 2007; Schienle et al., 2008). In our study, we speculate that pictures of sugar-containing food might induce higher levels of fear after the healthy eating call compared to the first session of the bidding task. This is supported by Schienle et al. [ 2008] who showed that fearful pictures elicit stringer P300 compared to neutral pictures. Consequently, our hypothesis (H5) was not supported, either.
Moreover, although not related to our main hypotheses, we found that both before and after the healthy eating call, non-edible products elicited weaker (less positive-going) P300 and LPP responses compared to edible products (sugar-containing or sugar-free). Earlier research using EEG to derive attention-related neural responses to food vs. non-food has revealed similar results (Nijs et al., 2008, 2009, Nijs et al., 2010a,b). This phenomenon has been explained on an evolutionary basis, that is, selective attention to food is an important characteristic of humans and animals (Nijs et al., 2010b).
Our study design has several advantages. First, the within-subjects design allowed us to measure the effect of the healthy eating call while minimizing the noise of intersubject variability. Second, participants made real choices—they were told that they would receive a product at the end of the experiment, which is considered to encourage sincere WTP ratings (Plassmann et al., 2007; Schubert et al., 2021). Third, the incorporation of EEG provided us with the opportunity to investigate potential neural signatures of the healthy eating call’s behavioral effect.
Also, our study has several limitations. For example, as in Ntoumanis et al. [ 2022], we used a sugar-containing label to ensure a clear discrimination between the conditions of our experiment. However, labels highlighting the content of sugar in products are not common in the country where the experiment was conducted. Thus, even if marketing companies, inspired by our healthy eating call, manage to successfully incorporate a similar narrative in advertisements to influence consumers against sugar, it would be more difficult for the consumers, than it was for our participants, to later spot and avoid sugar-containing food. In addition, unlike in the real market, the products were presented in our experiment without packaging. An attractive packaging, which is an important factor underlying food valuation (Motoki and Suzuki, 2020), may override the effects of a healthy eating call. Another limitation is the low spatial resolution of EEG, which does not allow for accurate localization of the effects. For instance, we found a relationship between the whole-brain ISC and the efficacy of expert persuasion, but using the ISC in specific brain regions might improve the significance of this result. To address this, future fMRI studies could conduct an intersubject representational similarity analysis (IS-RSA; Finn et al., 2020). Finally, we did not ask the participants to rate how impactful each argument presented in the healthy eating call is to them. This additional data would allow us to assess whether the MVPA classification accuracy co-varies with the importance of the arguments.
Taken together, our work contributes to our understanding of how healthy eating calls can reduce the WTP for sugar-containing food, which is important considering that sugar is the key cause of the growing obesity rates (Yu et al., 2022). By using EEG, we elucidated the neural mechanisms by which the brain responds to persuasive messages by experts. From a broader perspective, our results demonstrate that EEG is a powerful tool that can be used to predict the efficacy of health-related advertisements before they are released to the public.
## 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 Institutional Review Board, Higher School of Economics, Moscow, Russia. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
IN: conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing—original draft, writing—review and editing, visualization, and project administration. AD and JS: investigation. KP: conceptualization, writing—review and editing. VKo and AS: writing—review and editing. IJ: writing—review and editing, funding acquisition. VKl: conceptualization, writing—review and editing, funding acquisition. 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/fnbeh.2023.1147140/full#supplementary-material.
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|
---
title: Predictive importance of the visceral adiposity index and atherogenic index
of plasma of all-cause and cardiovascular disease mortality in middle-aged and elderly
Lithuanian population
authors:
- Abdonas Tamosiunas
- Dalia Luksiene
- Daina Kranciukaite-Butylkiniene
- Ricardas Radisauskas
- Diana Sopagiene
- Martin Bobak
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10040644
doi: 10.3389/fpubh.2023.1150563
license: CC BY 4.0
---
# Predictive importance of the visceral adiposity index and atherogenic index of plasma of all-cause and cardiovascular disease mortality in middle-aged and elderly Lithuanian population
## Abstract
### Background
Two indices: visceral adiposity index (VAI) and atherogenic index of plasma (AIP) during several recent years were implemented into epidemiological studies for predicting of cardiovascular diseases (CVD) and mortality risk. Our study aimed to evaluate the association of VAI and AIP with the risk of all-cause and CVD mortality among the Lithuanian urban population aged 45–72 years.
### Methods
In the baseline survey (2006–2008), 7,115 men and women 45–72 years of age were examined within the framework of the international study Health, Alcohol and Psychosocial Factors in Eastern Europe (HAPIEE). Six thousand six hundred and seventy-one participants (3,663 women and 3,008 men) were available for statistical analysis (after excluding 429 respondents with the missed information on study variables) and for them, VAI and AIP were calculated. The questionnaire evaluated lifestyle behaviors, including smoking and physical activity. All participants in the baseline survey were followed up for all-cause and CVD mortality events until December 31st, 2020. Multivariable Cox regression models were applied for statistical data analysis.
### Results
After accounting for several potential confounders, higher levels of VAI (compared 5th quintile to 1st quintile) were associated with significantly higher CVD mortality in men [Hazards ratio (HR) = 1.38] and all-cause mortality in women (HR = 1.54) after 10-year follow-up. CVD mortality significantly increased in men with 0 the highest AIP quintile compared with that for the lowest quintile (HR = 1.40). In women, all-cause mortality was significantly higher for the 4th quintile of AIP as compared with the 1st quintile (HR = 1.36).
### Conclusions
High-risk VAI levels were statistically significantly associated with all-cause mortality risk in men and women groups. The higher AIP level (5th quintile vs. 1st quintile—in men and 4th quintile vs. 1st quintile—in women) was significantly associated with increased mortality from CVD in the men group and increased all-cause mortality in the women group.
## 1. Background
Cardiovascular diseases (CVD) are the leading cause of death in most countries of Europe and other regions of the World [1, 2]. The mortality from diseases of the circulatory system in a population aged 0–64 years in Lithuania during the period 2001–2016 has been decreasing from 131 to 103 cases per 100,000 population [1]. Both increasing and decreasing trends of CVD morbidity and mortality indicators in the population are closely related to changes in the prevalence of biological and lifestyle risk factors such as arterial hypertension, smoking, hyperlipidaemia, physical inactivity, overweight and obesity, and other factors (3–6). Obesity is well-known and highly prevalent in most regions of Europe risk factor for CVD [7]. In Lithuania, the prevalence of obesity during the period from 1999 to 2009 increased by $4.2\%$ in men aged over 20 years (from 26.1 to $27.2\%$) and by $1.9\%$ in women (from 26.1 to $26.3\%$). In 2030, the obesity prevalence in Lithuania maybe $35.7\%$ in men and $36.0\%$ in women [7]. Body mass index (BMI) is the most widely used measure of obesity both in epidemiological studies of chronic non-communicable diseases and in clinical practice. However, BMI similar to waist circumference (WC), and another simple anthropometric measurement, cannot measure visceral and subcutaneous fat levels. It is demonstrated that visceral adipose and subcutaneous adipose tissues are related to the risk of CVD [8]. The level of visceral adipose tissue and subcutaneous abdominal fat could be very precisely evaluated using magnetic resonance imaging (MRI) or computed tomography (CT), but these methods are very expensive to be applied in epidemiological studies of CVD or even in the practice of family doctors [9]. Amato et al. a decade ago identified the visceral adiposity index (VAI) [10] which is calculated using a mathematical model and evaluates the level of visceral adipose tissue. The model uses both anthropometric (BMI and WC) and lipid parameters [triglyceride and high-density lipoprotein (HDL) cholesterol concentrations]. This index is also gender-specific: calculated separately for men and women using in the model different coefficients.
The atherogenic index of plasma (AIP) is another index quite recently been implemented in some epidemiologic studies of CVD (11–13). AIP is a logarithmic conversion of triglycerides into HDL cholesterol ratio, which as results of some epidemiological studies show is a stronger predictor of CVD risk as compared to individual lipid risk factors [total cholesterol, triglycerides, HDL cholesterol, and low-density lipoprotein (LDL) cholesterol] (14–16).
The association between individual lipid risk factors, overweight, and obesity same as some individual anthropometric measurements with risk of CVD incidence and mortality in the Lithuanian population was quite intensively studied, analyzed, and presented (17–19). But to the best of our knowledge, no study assessing the association between VAI and AIP with risk of all-cause and CVD mortality not only in the Lithuanian population but also in populations in other Baltic Sea countries: Latvia and Estonia. Therefore, our cohort study aimed to evaluate the association of VAI and AIP with the risk of all-cause and CVD mortality among the Lithuanian urban population aged 45–72 years.
## 2.1. Study sample
This prospective cohort study was performed as part of the international project Health, Alcohol and Psychosocial Factors in Eastern Europe (HAPIEE) [20]. The baseline survey was carried out (during 2006–2008) on Kaunas city (Lithuania) men and women aged 45–72 years. The study sample of 10,980 individuals, stratified by gender and age group, was randomly selected from the National population register. Seven thousand one hundred and fifteen individuals responded to the invitation to participate in the baseline survey (the response rate was $65\%$). All participants in the baseline survey follow-up for all-cause and CVD mortality events until December 31st, 2020. A total of 6,671 participants (3,663 women and 3,008 men) were available for statistical analysis after excluding 429 respondents with the missed information on study variables. Exclusion criteria: respondents for whom the nurses could not take a blood sample; respondents who refused to give blood for tests, and respondents who did not fill in the questionnaires correctly. The study was approved by the Kaunas Regional Biomedical Research Ethics Committee, Lithuania (11 January 2005; No. $\frac{05}{09}$) and by the Ethics Committee at University College London, UK. Written informed consent was obtained from all study participants.
## 2.2. Variables determined using a standard questionnaire
Sociodemographic factors (age and education), lifestyle factors (smoking habits and physical activity), angina pectoris, and the history of CVD [previous coronary heart disease (CHD) and stroke] were determined at the baseline survey using a standard questionnaire. The reliability and validity of the questionnaire were checked during a pilot study.
The education of the participants was categorized into 2 groups: [1] secondary, vocational, or lower education; [2] college and university education.
Smoking habits were categorized as never smoking, former smoking, and current regular smoking (regular smoking at least 1 cigarette per day).
The physical activity of the participants in their leisure time was assessed using 5 questions in the standard questionnaire. Physical activity was calculated by summarizing time spent per week during leisure time separately in autumn-winter and spring-summer seasons for activities such as walking, gardening, maintenance of the house, and other physical activities. The participants were divided into three equal groups (tertiles) according to their mean length of time spent per week on physical activities. The first tertile maximal cut-off was 10 h per week. This cut-off was used for determining of insufficient physical activity of study participants.
To assess the history of previous myocardial infarction of the participants, 2 questions from the standard questionnaire were asked: “*Has a* doctor ever told you that you have had a myocardial infarction?” and “*Has a* doctor ever told you that you have had a stroke?”. Angina pectoris was evaluated by G. Rose's questionnaire [21].
## 2.3. Anthropometric measurements
Height, weight, and WC were measured directly by trained nurses. Weight was measured, with participants minimally clothed without shoes, using medical scales. Weight values were recorded to the nearest 100 g. The height of participants (without shoes) was measured with an accuracy of one centimeter, using a stadiometer. WC was measured at the midpoint between the lower rib and the iliac crest over light clothing, using a tape meter. Measurements of the WC were recorded to the nearest 0.5 cm. BMI (kg/m2) was calculated as weight (kg) divided by the square of the height (m2).
## 2.4. Other clinical and laboratory measurements
Blood pressure (BP) was measured three times with an oscillometric device (Omron M5-1) after at least 5 min of rest in a seated position, and mean values of systolic BP and diastolic BP were taken.
A resting electrocardiogram (ECG) was recorded in the 12 standard leads, with calibration of 10 mm per 1 mV and a paper speed of 25 mm per s. ECG records were read by 2 independent experienced coders (trained cardiologists) using the 1982 edition of the Minnesota Code (MC) [22].
Blood samples were drawn for the measurement of total cholesterol, HDL cholesterol, LDL cholesterol, and triglyceride levels the morning after study participants fasted overnight. All these biochemical determinations were performed in the same laboratory (the WHO Regional Lipid Reference Center, Institute of Clinical and Experimental Medicine, Prague (Czech Republic) using standard laboratory methods. The concentration of glucose in capillary blood was determined by a Glucotrend glucometer [23].
VAI score was calculated according to the definition of Amato et al. [ 10] using the following sex-specific equations where triglycerides and HDL-cholesterol levels are expressed in mmol/L:
## 2.5. Definitions
Arterial hypertension was defined as systolic BP ≥ 140 mmHg and/or diastolic BP ≥ 90 mmHg, or usage of anti-hypertensive medication during last 2 weeks [24].
Increased level of total serum cholesterol was determined as total cholesterol concentration ≥5.0 mmol/L and increased fasting glucose level as glucose concentration in capillary blood ≥6.1 mmol/L [25, 26].
Insufficient physical activity was determined in the case when the mean time spent per week by study participants during leisure time for physical activities was lower than 10 h.
The participants were ranked from the lowest to the highest values of VAI and AIP and divided into five equal groups (quintiles) according to the levels of these variables (Table 1).
**Table 1**
| Unnamed: 0 | Men | Unnamed: 2 | Women | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | Min–max | Mean (SD) | Min–max | Mean (SD) |
| VAI quintiles | VAI quintiles | VAI quintiles | VAI quintiles | VAI quintiles |
| 1st quintile | 0.16 to 0.69 | 0.51 (0.12) | 0.27 to 0.85 | 0.65 (0.14) |
| 2nd quintile | 0.69 to 1.03 | 0.85 (0.10) | 0.85 to 1.22 | 1.03 (0.11) |
| 3rd quintile | 1.03 to 1.44 | 1.22 (0.12) | 1.22 to 1.71 | 1.44 (0.14) |
| 4th quintile | 1.44 to 2.21 | 1.78 (0.23) | 1.71 to 2.57 | 2.08 (0.24) |
| 5th quintile | 2.21 to 19.3 | 3.68 (1.94) | 2.57 to 24.0 | 4.13 (2.03) |
| AIP quintiles | AIP quintiles | AIP quintiles | AIP quintiles | AIP quintiles |
| 1st quintile | −0.87 to −0.27 | −0.40 (0.11) | −0.76 to −0.30 | −0.42 (0.10) |
| 2nd quintile | −0.27 to −0.10 | −0.18 (0.05) | −0.30 to −0.15 | −0.23 (0.04) |
| 3rd quintile | −0.10 to 0.04 | −0.03 (0.04) | −0.15 to 0.01 | −0.08 (0.04) |
| 4th quintile | 0.04 to 0.22 | 0.13 (0.05) | 0.01 to 0.16 | 0.07 (0.05) |
| 5th quintile | 0.22 to 1.15 | 0.40 (0.16) | 0.16 to 1.10 | 0.33 (0.15) |
Coronary heart disease (CHD) at baseline was determined by: [1] a documented history of myocardial infarction (MI) and/or ischemic changes on ECG coded by MC 1–1 or 1–2 [22]; [2] angina pectoris as defined by G. Rose's questionnaire (without MI and/or MC 1–1 or 1–2) [21]; [3] ECG findings coded by MC 1–3, 4–1, 4–2, 4–3, 5–1, 5–2, 5–3, 6–1, 6–2, 7–1, or 8–3 (without MI and/or MC 1–1, 1–2 and without angina pectoris). The previous stroke was determined according to a documented history of stroke.
CVD included CHD and/or stroke which were determined at the baseline survey.
## 2.6. Mortality outcome
We used data from the Kaunas mortality register based on death certificates with follow-up through December 31, 2020. Cause of death was categorized using the International Classification of Diseases, 10th Edition (ICD-10). All causes of death included ICD-10 codes A00-Z99. CVD-specific mortality was categorized using codes I00-I99.
## 2.7. Statistical analysis
All statistical analysis was performed using IBM SPSS Statistics (Version 27.0) (IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY, USA). We performed an analysis of study data separately for men and women. All descriptive characteristics [proportions, means, and standard deviations (SD)] were calculated and presented across the groups by vital status at the end of follow-up in two ways (alive and died from all causes; alive, died from CVD and died from other causes). Differences between groups were detected by independent sample t-test and ANOVA analysis with Bonferroni corrections for continuous variables. A Chi-squared test and Z-test with Bonferroni corrections were used for determining differences in categorical variables. P-values < 0.05 were considered statistically significant.
We fit Cox proportional hazards regression models to estimate the hazard ratio (HR) and $95\%$ confidence interval (CI) for quintiles of VAI and AIP with all-cause and CVD mortality. The participants who previously had CVD (CHD or/and stroke) were removed from the analysis of CVD mortality risk. Standardized multivariable Cox regression models were used to evaluate the effect size of VAI and AIP with three steps. Model 1 includes a single VAI and AIP quintile with 1st quintile as the reference group. In Model 2 age as, a continuous variable, is added. Model 3 was adjusted for all the variables in Model 2 plus education, physical activity, smoking status, and biological factors (arterial hypertension, total cholesterol, and fasting glucose) (all categorical). Risk of all-cause and CVD mortality was also assessed using the same 3 Cox regression models when VAI and AIP values in the model changed per 1 quintile.
## 3. Results
The mean duration and SD of the follow-up of the participants were 12.6 ± 2.79 years. During the follow-up, there were 1,444 all-cause deaths (882 men and 562 women) and 682 deaths from CVD [414 men and 268 women (232 and 150 deaths, respectively, among participants without CVD at baseline survey)] registered.
The characteristics of the respondents at the baseline survey, according to their survival status are presented in Tables 2, 3. Men and women who died from all-cause deaths and CVD deaths during the follow-up period were significantly older and less educated at the baseline survey than those alive at the end of the follow-up. During the initial study, the age-adjusted means of some biological factors, such as systolic and diastolic BP, triglycerides, the fasting glucose level had been higher, and HDL cholesterol level had been lower in men and women who died from all-cause deaths and CVD deaths during the follow-up period compared to those who were alive. Moreover, it was determined that the respondents who died from all-cause deaths and CVD deaths during the follow-up period had been more often diagnosed with diabetes mellitus, CVD, and arterial hypertension than those who were alive at the end of the follow-up. Men and women who died from all-cause deaths and CVD deaths during the follow-up period were lower physically active in their leisure time and their mean BMI and WC levels had been higher than those alive at the end of the follow-up. It's important, that mean levels of VAI, and AIP had been higher in men and women who died from all-cause deaths and CVD deaths during the follow-up period compared to those who were alive.
In Table 4, we present the multiple cox regression assessments of VAI in the prediction of risk of all-cause and cardiovascular mortality according to gender over 10 years. Based on the crude model (Model 1) assessments, the men with higher VAI levels (5th quintile) had a 1.26-fold increased risk of all-cause mortality and 1.78-fold increased mortality from CVD risk compared with men with lower VAI levels (1st quintile). An increase per quintile in the VAI significantly increased the risk of all-cause mortality (by $7\%$) and the risk of mortality from CVD (by $14\%$) in the men group. After additional adjustment for age (Model 2) the same risk of all-cause mortality and mortality from CVD remained statistically significant in the men group. However, after adjustment for age, education, physical activity, smoking status, and biological factors (Model 3) a significant relationship was determined for all-cause mortality risk per quintile of VAI (by $5\%$), and for risk of mortality from CVD (by $38\%$) than compared the men with higher VAI level (5th quintile) to men with lower VAI level (1st quintile).
**Table 4**
| Unnamed: 0 | All-cause deaths | All-cause deaths.1 | Unnamed: 3 | CVD deaths * | CVD deaths *.1 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| VAI level/cox models | HR | 95% CI | p | HR | 95% CI | p |
| Men | (n = 882) | | | (n = 232) | | |
| Model 1 | Model 1 | Model 1 | Model 1 | Model 1 | Model 1 | Model 1 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 0.87 | 0.70–1.08 | 0.201 | 1.04 | 0.74–1.47 | 0.803 |
| 3rd quintile | 1.07 | 0.86–1.32 | 0.552 | 1.47 | 1.07–2.01 | 0.018 |
| 4th quintile | 1.03 | 0.83–1.27 | 0.780 | 1.16 | 0.83–1.61 | 0.398 |
| 5th quintile | 1.26 | 1.03–1.55 | 0.024 | 1.78 | 1.31–2.42 | < 0.001 |
| Per quintile | 1.07 | 1.02–1.12 | 0.006 | 1.14 | 1.06–1.22 | < 0.001 |
| Model 2 | Model 2 | Model 2 | Model 2 | Model 2 | Model 2 | Model 2 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 0.85 | 0.68–1.06 | 0.141 | 1.02 | 0.73–1.44 | 0.903 |
| 3rd quintile | 0.99 | 0.81–1.23 | 0.990 | 1.37 | 0.995–1.88 | 0.054 |
| 4th quintile | 1.01 | 0.82–1.25 | 0.935 | 1.13 | 0.81–1.58 | 0.473 |
| 5th quintile | 1.31 | 1.07–1.60 | 0.010 | 1.85 | 1.36–2.52 | < 0.001 |
| Per quintile | 1.08 | 1.03–1.13 | 0.002 | 1.15 | 1.07–1.24 | < 0.001 |
| Model 3 | Model 3 | Model 3 | Model 3 | Model 3 | Model 3 | Model 3 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 0.91 | 0.73–1.14 | 0.411 | 1.05 | 0.75–1.48 | 0.788 |
| 3rd quintile | 0.99 | 0.81–1.23 | 0.982 | 1.28 | 0.93–1.76 | 0.134 |
| 4th quintile | 1.01 | 0.82–1.25 | 0.923 | 0.94 | 0.67–1.32 | 0.721 |
| 5th quintile | 1.21 | 0.98–1.49 | 0.080 | 1.38 | 1.00–1.90 | 0.047 |
| Per quintile | 1.05 | 1.00–1.10 | 0.046 | 1.06 | 0.99–1.14 | 0.116 |
| Women | (n = 562) | | | (n = 150) | | |
| Model 1 | Model 1 | Model 1 | Model 1 | Model 1 | Model 1 | Model 1 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 1.74 | 1.28–2.36 | < 0.001 | 1.93 | 1.22–3.06 | 0.005 |
| 3rd quintile | 1.75 | 1.29–2.37 | < 0.001 | 1.99 | 1.26–3.15 | 0.003 |
| 4th quintile | 2.13 | 1.59–2.87 | < 0.001 | 2.26 | 1.44–3.54 | < 0.001 |
| 5th quintile | 2.33 | 1.74–3.12 | < 0.001 | 2.75 | 1.78–4.25 | < 0.001 |
| Per quintile | 1.19 | 1.12–1.26 | < 0.001 | 1.22 | 1.12–1.33 | < 0.001 |
| Model 2 | Model 2 | Model 2 | Model 2 | Model 2 | Model 2 | Model 2 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 1.49 | 1.09–2.02 | 0.011 | 1.55 | 0.98–2.45 | 0.060 |
| 3rd quintile | 1.41 | 1.03–1.91 | 0.030 | 1.50 | 0.95–2.36 | 0.084 |
| 4th quintile | 1.60 | 1.19–2.15 | 0.002 | 1.55 | 0.99–2.43 | 0.056 |
| 5th quintile | 1.72 | 1.28–2.31 | < 0.001 | 1.87 | 1.21–2.89 | 0.005 |
| Per quintile | 1.11 | 1.05–1.18 | 0.001 | 1.12 | 1.02–1.22 | 0.014 |
| Model 3 | Model 3 | Model 3 | Model 3 | Model 3 | Model 3 | Model 3 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 1.48 | 1.08–2.01 | 0.014 | 1.51 | 0.95–2.40 | 0.081 |
| 3rd quintile | 1.35 | 0.99–1.84 | 0.057 | 1.28 | 0.81–2.04 | 0.291 |
| 4th quintile | 1.51 | 1.11–2.04 | 0.008 | 1.30 | 0.83–2.06 | 0.256 |
| 5th quintile | 1.54 | 1.13–2.08 | 0.006 | 1.50 | 0.96–2.36 | 0.078 |
| Per quintile | 1.08 | 1.01–1.15 | 0.020 | 1.06 | 0.962–1.16 | 0.254 |
In the women group, an increase per quintile in the VAI significantly increased the risk of all-cause mortality (by $19\%$) and the risk of mortality from CVD (by $22\%$) (Model 1). Also, by increasing the quintile of VAI (2nd, 3rd, 4th, 5th quintile) the risk of all-cause mortality risk and CVD mortality risk increased compared with the lowest VAI quintile (1st quintile). However, after adjustment for age, education, physical activity, smoking status, and biological factors (Model 3) a significant relationship was determined only for all-cause mortality risk per quintile of VAI (by $8\%$) in the women group. Such a significant relationship was not determined for the risk of mortality from CVD in the women group.
In Table 5, we present the multiple Cox regression assessments of AIP in the prediction of risk of all-cause and CVD mortality according to gender over 10 years. Based on the crude model (Model 1) an increase per quintile in the AIP significantly increased the risk of all-cause mortality (by $5\%$) and the risk of mortality from CVD (by $13\%$) in the men group. After additional adjustment for age (Model 2) the same risk of all-cause mortality and mortality from CVD remained statistically significant in the men group. However, after adjustment for age, education, physical activity, smoking status, and biological factors (Model 3) the men with higher AIP levels (5th quintile) had a 1.40-fold increased risk of mortality from CVD risk compared with men with lower AIP level (1st quintile).
**Table 5**
| Unnamed: 0 | All-cause deaths | All-cause deaths.1 | Unnamed: 3 | CVD deaths * | CVD deaths *.1 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| AIP level/cox models | HR | 95% CI | p | HR | 95% CI | p |
| Men | (n = 882) | | | (n = 232) | | |
| Model 1 | Model 1 | Model 1 | Model 1 | Model 1 | Model 1 | Model 1 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 0.85 | 0.68–1.05 | 0.127 | 0.94 | 0.67–1.31 | 0.703 |
| 3rd quintile | 0.98 | 0.79–1.21 | 0.830 | 1.36 | 0.99–1.85 | 0.055 |
| 4th quintile | 0.96 | 0.78–1.19 | 0.709 | 1.00 | 0.72–1.40 | 0.995 |
| 5th quintile | 1.19 | 0.97–1.46 | 0.092 | 1.72 | 1.27–2.32 | < 0.001 |
| Per quintile | 1.05 | 1.00–1.10 | 0.039 | 1.13 | 1.06–1.21 | < 0.001 |
| Model 2 | Model 2 | Model 2 | Model 2 | Model 2 | Model 2 | Model 2 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 0.82 | 0.66–1.01 | 0.064 | 0.90 | 0.64–1.26 | 0.539 |
| 3rd quintile | 0.94 | 0.77–1.16 | 0.581 | 1.30 | 0.96–1.78 | 0.094 |
| 4th quintile | 0.95 | 0.77–1.17 | 0.629 | 0.99 | 0.71–1.38 | 0.950 |
| 5th quintile | 1.24 | 1.02–1.52 | 0.035 | 1.81 | 1.34–2.44 | < 0.001 |
| Per quintile | 1.06 | 1.01–1.12 | 0.011 | 1.15 | 1.07–1.24 | < 0.001 |
| Model 3 | Model 3 | Model 3 | Model 3 | Model 3 | Model 3 | Model 3 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 0.88 | 0.71–1.09 | 0.248 | 0.95 | 0.68–1.34 | 0.782 |
| 3rd quintile | 0.95 | 0.77–1.18 | 0.655 | 1.25 | 0.91–1.71 | 0.171 |
| 4th quintile | 0.96 | 0.78–1.19 | 0.698 | 0.85 | 0.61–1.20 | 0.354 |
| 5th quintile | 1.16 | 0.95–1.43 | 0.152 | 1.40 | 1.03–1.91 | 0.033 |
| Per quintile | 1.04 | 0.99–1.09 | 0.102 | 1.07 | 0.99–1.15 | 0.079 |
| Women | (n = 562) | | | (n = 150) | | |
| Model 1 | Model 1 | Model 1 | Model 1 | Model 1 | Model 1 | Model 1 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 1.46 | 1.09–1.97 | 0.012 | 1.55 | 1.00–2.39 | 0.047 |
| 3rd quintile | 1.54 | 1.15–2.07 | 0.004 | 1.63 | 1.06–2.51 | 0.025 |
| 4th quintile | 1.88 | 1.42–2.50 | < 0.001 | 1.85 | 1.22–2.82 | 0.004 |
| 5th quintile | 1.92 | 1.45–2.55 | < 0.001 | 2.08 | 1.38–3.14 | < 0.001 |
| Per quintile | 1.16 | 1.09–1.23 | < 0.001 | 1.17 | 1.07–1.27 | < 0.001 |
| Model 2 | Model 2 | Model 2 | Model 2 | Model 2 | Model 2 | Model 2 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 1.23 | 0.92–1.66 | 0.168 | 1.23 | 0.80–1.89 | 0.355 |
| 3rd quintile | 1.31 | 0.98–1.76 | 0.070 | 1.33 | 0.87–2.05 | 0.189 |
| 4th quintile | 1.41 | 1.06–1.87 | 0.019 | 1.27 | 0.83–1.93 | 0.273 |
| 5th quintile | 1.46 | 1.10–1.93 | 0.009 | 1.47 | 0.97–2.21 | 0.069 |
| Per quintile | 1.09 | 1.02–1.15 | 0.007 | 1.08 | 0.99–1.18 | 0.094 |
| Model 3 | Model 3 | Model 3 | Model 3 | Model 3 | Model 3 | Model 3 |
| 1st quintile | 1 | | | 1 | | |
| 2nd quintile | 1.25 | 0.92–1.68 | 0.150 | 1.19 | 0.77–1.84 | 0.433 |
| 3rd quintile | 1.29 | 0.96–1.73 | 0.096 | 1.19 | 0.77–1.84 | 0.425 |
| 4th quintile | 1.36 | 1.01–1.81 | 0.040 | 1.08 | 0.71–1.67 | 0.714 |
| 5th quintile | 1.31 | 0.98–1.76 | 0.068 | 1.20 | 0.78–1.83 | 0.412 |
| Per quintile | 1.06 | 0.99–1.12 | 0.081 | 1.02 | 0.93–1.12 | 0.662 |
In the women group, an increase per quintile in the AIP significantly increased the risk of all-cause mortality (by $16\%$) and the risk of mortality from CVD (by $17\%$) (Model 1). Also, by increasing the quintile of AIP (2nd, 3rd, 4th, 5th quintile) the risk of all-cause mortality risk and CVD mortality risk increased compared with the lowest AIP quintile (1st quintile). However, after adjustment for age, education, physical activity, smoking status, and biological factors (Model 3) a significant association was not determined between AIP levels and all-cause mortality risk and the risk of mortality from CVD in the women group.
## 4. Discussion
In this study, we presented an independent association of two indices—VAI and AIP with risk of all-cause and CVD mortality in the middle-aged and elderly Lithuanian urban population.
We found that in Cox regression analyses, compared to the 1st VAI quintile, the 5th quintile was an independent positive predictor of all-cause and CVD mortality risk in males and females. This significant association remained after adjusting for several lifestyles and biological confounding factors for CVD mortality in men (HR = 1.38) and all-cause mortality in women (HR = 1.54). These data are consistent with previous studies in which researchers also found a positive association between increasing levels of VAI and risk of all-cause and CVD mortality [27, 28]. Other researchers demonstrated results that higher VAI was related to the risk of the incidence of CVD and cancer [29, 30].
VAI indirectly indicates visceral adipose tissue (VAT) deposits and functions in the human body [10]. This index could be used in epidemiological studies and even clinical practice instead of expensive diagnostic methods such as CT, MRI, or sonographic assessment [31, 32]. The anthropometric measurements such as WC, waist-to-hip ratio or abdominal sagittal diameter also could be used for the evaluation of regional intraabdominal fat deposition, but those measurements are characterized by low accuracy [33]. WC also could not differentiate VAT from subcutaneous adipose tissue in the abdomen which has a lower CVD risk level [29, 34]. A similar indicator recently proposed for the evaluation of VAT is anthropometrically predicted VAT [34, 35]. This indicator is also VAI sex-specific and the equations for calculation of the indicator include anthropometric measures (BMI, WC, and thigh circumference) and age. VAI is the more specific and precise index for evaluation of VAT as compared to anthropometrically predicted VAT because models for calculation of such includes not only anthropometric data (BMI and WC) but also lipid levels (HDL cholesterol and triglycerides) [10]. Several studies demonstrated that VAI was a better predictor of the incidence and mortality risk as compared to BMI and WC in the prognostic models alone [28, 36]. Whereas, some studies presented research data indicating that the impact of VAI was not significantly different for predicting CVD risk in women and type 2 diabetes in the non-diabetic population as compared with simply measured anthropometric biomarkers [30, 37]. In this study, we did not compare the impact of VAI and such anthropometric measurements as BMI, WC, waist-to-hip ratio, or waist-to-height ratio on the CVD mortality risk. Data from our previous study showed that among participants from three surveys the anthropometric measurements (BMI, WC, waist-to-hip ratio, and waist-to-height ratio) changing per 1 SD in multivariable-adjusted Cox's regression model significantly increased CVD mortality risk in men (HR varied from 1.40 to 1.49) but not in women [18].
The AIP is a logarithmically converted ratio of triglycerides to HDL cholesterol [11]. The results of our study showed that values of AIP were higher among males as compared to females. Such gender differences in AIP values were demonstrated in most similar studies [38, 39]. The results of ten observational studies performed in China, Turkey, and South Korea demonstrated that higher AIP values may be independently associated with the odds of coronary artery disease (CAD) [40]. The AIP could be also considered an independent predictor of CVD incidence and mortality risk [38, 41]. Results from a large-scale nationwide population cohort study carried on in the Republic of Korea showed that AIP HRs for CVD risk were higher as compared to HRs of triglycerides and HDL cholesterol when those variables were applied in the regression model alone [42]. Whereas, a study performed among 1,131 male patients with angiographically diagnosed CAD and without CAD found that the AIP predictive value for diagnosing CAD was not significantly different as compared with traditional blood lipids [43]. Our results have shown a positive association between increasing values of AIP and risk of CVD mortality after adjustment to many confounders in men but not women responders. In women responders, the risk of all-cause mortality was significantly higher by $36\%$ when the 4th quintile of AIP was compared with the 1st quintile. The association between AIP and CVD incidence and mortality is mainly explained by the correlation of the index with lipoprotein particle size: it is inversely related to LDL cholesterol particle diameter [44]. Results of some studies showed that small dense LDL cholesterol particles were related to higher risk of CVD [45, 46]. However, the methods for determining small dense LDL cholesterol particles are quite complicated and the cost-effectiveness of such methods is low, therefore those methods are not recommended for use in clinical practice and large-scale epidemiological studies of CVD [47].
VAI and API calculations are cheap methods and could be used not only in epidemiological studies but especially in clinical practice for cardiologists or endocrinologists instead of expensive diagnostic methods.
## 4.1. Strength and limitations of the study
The main strength of our study was: a large sample size, adjustment using many confounding variables, and a cohort study design. Our study also has some limitations. Despite many variables used in the adjustment procedure, it is still possible that some not measured confounding variables could interfere with a part of the associations between AIP or VAI and CVD or all-cause mortality risk. The findings of this study cannot be generalized to the Lithuanian countrywide population because our study was carried out only in an urban one-city a middle-aged and elderly population. Therefore, further epidemiological investigations are needed to be performed in different regions of the country. Finally, we used the baseline AIP and VAI levels for the assessment of the association of those indices with the risk of all-cause and CVD mortality. Any changes during the follow-up both of AIP, VAI, and confounding variables are missing. Despite the mentioned limitations, our study provided additional insight into the association between higher levels of these indices and the risk of mortality.
## 5. Conclusion
High-risk VAI levels were statistically significantly associated with all-cause mortality risk in men and women groups. The higher AIP level (5th quintile vs. 1st quintile—in men and 4th quintile vs. 1st quintile—in women) was significantly associated with increased mortality from CVD risk in the men group (HR = 1.40) and increased all-cause mortality risk in the women group (HR = 1.36). Thus, the calculation of VAI and AIP levels is simple and universally available, which makes study results easily applicable to clinical practice.
## 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 Kaunas Regional Biomedical Research Ethics Committee, Lithuania (January 11, 2005; No. $\frac{05}{09}$) and by the Ethics Committee at University College London, UK. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AT and DL conceived the idea, collected, analyzed the data, and co-wrote the manuscript. RR, DS, and DK-B contributed to writing the manuscript and the interpretation of data. MB contributed to the study concept and design and as well as supervised the research group. All authors contributed to the article and approved the final version of it.
## 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.
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|
---
title: 'Incidence, clinical features and risk factors of tacrolimus induced idiosyncratic
liver injury in renal transplant recipients: A nested case-control study'
authors:
- Binbin Lv
- Longshan Liu
- Xiaoman Liu
- Min Huang
- Xiao Chen
- Kejing Tang
- Changxi Wang
- Pan Chen
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10040645
doi: 10.3389/fphar.2023.1126765
license: CC BY 4.0
---
# Incidence, clinical features and risk factors of tacrolimus induced idiosyncratic liver injury in renal transplant recipients: A nested case-control study
## Abstract
Rare data reported tacrolimus-induced liver injury (tac-DILI) in real world. We performed a nested case-control analysis of 1,010 renal transplant recipients. Recipients with tac-DILI were randomly matched at a ratio of 1:4 by the year of admission to the remaining recipients without tac-DILI to explore risk factors. The incidence of tac-DILI was $8.9\%$ ($95\%$ CI = 7.2–$10.7\%$). The most common type was cholestatic pattern ($6.7\%$, $95\%$ CI = 5.2–$8.3\%$), followed by hepatocellular ($1.6\%$, $95\%$ CI = 0.8–$2.4\%$) and mixed patterns ($0.6\%$, $95\%$ CI = 0.1–$1.1\%$). $98.9\%$ of recipients with tac-DILI have mild severity. The latency period were 42.0 (range, 21.5–99.8 days), 14.0 (range, 9.0–80.3 days), 16.0 (range, 11.5–24.5 days), and 49.0 days (range, 28.0–105.6 days) for total, hepatocellular, mixed, and cholestatic patterns, respectively. Baseline ALP level (OR = 1.015, $95\%$ CI = 1.006–1.025, $$p \leq 0.002$$), age (OR = 0.971, $95\%$ CI = 0.949–0.994, $$p \leq 0.006$$), and body weight (OR = 0.960, $95\%$ CI = 0.940–0.982, $p \leq 0.001$) were independent risk factors. In conclusion, cholestatic pattern represents the most frequent type of tac-DILI. Young age, low body weight and abnormal baseline ALP level were risk factors.
## 1 Introduction
The prevalence of end-stage renal disease is increasing annually, and renal transplantation, rather than dialysis, is the optimal treatment option in terms of patient outcome. Standard maintenance immunosuppression regimens following renal transplantation commonly include calcineurin inhibitors (CNI), cyclosporine A or tacrolimus, mycophenolate mofetil (MMF), and corticosteroids (Bodell, 2015). Over the past few decades, tacrolimus has been the cornerstone of immunosuppressive therapy following transplants because it has proven to be effective in preventing acute rejection and maintaining graft function (Pirsch et al., 1997; Gonwa et al., 2003; Silva et al., 2007; Min et al., 2013). Moreover, tacrolimus is recommended as the first-line CNI in patients following renal transplantation by Kidney Disease: Improving Global Outcomes (KDIGO) clinical practice guidelines (Kasiske et al., 2010). However, the use of tacrolimus is associated with a variety of adverse reactions, such as nephrotoxicity, neurotoxicity, post-transplant diabetes mellitus, and hepatotoxicity, which may lead to poor prognosis in patients, with adverse symptoms ranging from milder ones such as headache and weight gain to more severe effects such as loss of renal function (Wijdicks, 2001; Nankivell et al., 2003; Rodriguez-Rodriguez et al., 2021).
To date, limited data have been published on tacrolimus-induced liver injury (tac-DILI), and the reference of tac-DILI is still limited to the drug label, for which the data originated from pre-marketing clinical trials. In the real-world, only few studies on tac-DILI have been reported. For example, one study reported that tacrolimus eventually resulted in cholestatic jaundice 60 days post-liver transplantation with increases in both alkaline phosphatase (ALP) and total bilirubin (TBIL) levels (Taniai et al., 2008). Another study showed that liver enzyme levels increased significantly 12 days post-renal transplantation, with aspartate aminotransferase (AST) elevated to 421 U/L, alanine aminotransferase (ALT) at 1242 U/L, and serum TBIL levels within the normal range (Mesar et al., 2013). Today, increasing accessibility to digital health data owing to the transition to electronic health records, together with the rising costs and known limitations of traditional clinical trials, real-world data offer enhanced research efficacy, which bridges the evidentiary gap between clinical research and practice (Corrigan-Curay et al., 2018).
Therefore, we aimed to investigate the clinical features of tac-DILI, including incidence, liver injury type, latency time, prognosis, and risk factors in renal transplant patients, in a real-world setting, which may serve as a reference for the diagnosis, prevention, and treatment of tac-DILI.
## 2.1 Study population and follow-up
This was a nested case-control study. Patients who had received renal transplantation between 1 January 2016, and 31 December 2021, were identified from the electronic medical records at the First Affiliated Hospital, Sun Yat-sen University. The electronic medical records contained detailed demographic, clinical, laboratory, imaging, and histologic (when available) data recorded both at presentation and at follow-up evaluation of the patients. The inclusion criteria were as follows: 1) age (no bar) and 2) receiving tacrolimus as a maintenance immunosuppressive agent for the first-time post-transplantation. The exclusion criteria were as follows: 1) secondary renal transplantation; 2) incomplete laboratory data (lack of data obtained within 7 days prior to tacrolimus medication or lack of follow-up liver function tests); 3) patients receiving tacrolimus therapy prior to admission without baseline data; 4) abnormal liver enzyme levels that reached the liver injury standards prior to medication or within 3 days post-surgery; 5) patients with malignancy, hepatic hemangioma, active hepatitis, or other underlying liver disease at admission. The study was approved by the Ethics Committee for Clinical Research and Animal Trials of the First Affiliated Hospital of Sun Yat-sen University (No. 2021199). This study was carried out in accordance with the requirements of the Declaration of Helsinki. No donor organs had been obtained from executed prisoners and that organs were procured after informed consent or authorization.
## 2.2 Case-control selection
Patients with tac-DILI were identified using the Roussel Uclaf causality assessment method (RUCAM). The date on which the liver met the criteria for injury was defined as the index date. We chose a risk-set sampling approach that patients with tac-DILI were randomly matched with the remaining patients without tac-DILI at a ratio of 1:4 by year of admission to identify risk factors. For each new tac-DILI case, a control patient was randomly extracted from the risk-set cohort in the same year. Random matching method adopted extraction from a random number table.
## 2.3 Diagnostic criteria for tac-DILI
According to the European association for the study of the liver (EASL) clinical practice guidelines (CPG) of DILI(EASL, 2019), patients meeting the following criteria were defined as suspected DILI cases: 1) ALT ≥5× upper limit of normal (ULN); 2) ALP ≥2× ULN, particularly with accompanying elevations in concentrations of gamma-glutamyl transferase (GGT) in the absence of known bone pathology driving the rise in ALP level; and 3) ALT ≥3× ULN and TBIL ≥2× ULN. For patients with abnormal liver enzyme levels prior to tacrolimus treatment, the ULN was replaced by the baseline values, and elevations were calculated proportionate to the modified baseline.
For patients who met the criteria for liver injury, we examined alternative causes of hepatitis in detail, including hepatitis A virus, hepatitis B virus, hepatitis C virus, Epstein-*Barr virus* or cytomegalovirus based on serological evidence and biliary stricture, hepatic artery thrombosis, portal/hepatic venous stenosis or thrombosis based on imaging evidence. Furthermore, we also assessed the possibility of concomitant agents that may contribute to DILI, including dose, the dates of the start and discontinuation of therapy, the date of onset of the first abnormal laboratory test result, the initial laboratory results at presentation and the liver histology results.
## 2.4 Causality assessment of tac-DILI
All patients with alternative causes of liver injury were excluded. The others were assessed using the RUCAM scale, and the evaluation criteria were as follows: 1) 1–2, unlikely; 2) 3–5, possible; 3) 6–8, probable; and 4) ≥ 8, highly probable. After the independent causality assessment, the suspected tac-DILI cases were reviewed carefully between two pharmacists and one clinician to ensure agreement (by consensus) on all assessments. Patients with a score of ≥3 were included in the DILI group.
## 2.5 Classification of tac-DILI patterns and severity
According to the EASL CPG of DILI, patients with DILI were categorized into three patterns based on the ratio R): 1) hepatocellular pattern: ALT alone is elevated ≥ 5-fold above the ULN or R ≥ 5; 2) cholestatic pattern: ALP alone is elevated ≥ 2-fold above the ULN or R ≤ 2; 3) mixed pattern: R > 2 to <5. The R value was calculated on the day when the peak liver enzyme value met the DILI standard (R = [ALT present/ALT baseline]/[ALP present/ALP baseline]).
The severity of DILI is classified into four grades: 1) grade 1 (mild): ALT ≥5 or ALP ≥2 and TBL <2 ULN; 2) grade 2 (moderate): ALT ≥5 or ALP ≥2 and TBL ≥2 ULN, or symptomatic hepatitis; 3) grade 3 (severe): ALT ≥5 or ALP ≥2 and TBL ≥2 ULN, or symptomatic hepatitis and one of the following criteria: a) INR ≥1.5, b) ascites and/or encephalopathy, disease duration <26 weeks, and absence of underlying cirrhosis, or c) other organ failure due to DILI; and 4) grade 4 (fatal/transplantation): death or liver transplantation due to DILI.
## 2.6 Outcomes of tac-DILI
We followed the prognosis of patients with tac-DILI, including recovery, improvement, no improvement, and aggravation. “ Recovery” was defined as a decrease in liver enzyme levels to the ULN or baseline value, ‘improvement’ was defined as a decrease in liver enzyme levels below the criteria of liver injury or baseline value, “no improvement” was defined as related liver enzyme levels not decreasing below the criteria of liver injury or baseline value, and “aggravation” was defined as liver enzyme levels beyond the peak value.
## 2.7 Statistical analysis
Continuous variables were summarized as medians (interquartile ranges, IQR). Categorical variables were presented as numbers and proportions. Comparisons of baseline demographic and clinical characteristics between cases and controls were performed using the χ 2 test for categorical variables and the independent t-test or Mann-Whitney U test for continuous variables. Variables with $p \leq 0.1$ in the univariate analysis were analyzed using the logistic regression model. A backward stepwise logistic regression model was used to analyze independent risk factors for tac-DILI. Associations were expressed as odds ratios (ORs) with $95\%$ confidence intervals (CIs). Statistical significance was set at $p \leq 0.05.$ *Data analysis* was performed using IBM SPSS statistics version 25 (SPSS Inc.) and GraphPad Prism 8 (GraphPad Software).
## 3.1 Study population
A total of 1,051 post-renal transplant recipients were enrolled in the study. Among these, 41 recipients were excluded based on the exclusion criteria. Of the 1,010 recipients enrolled, 99 met the liver injury criteria and were assessed using RUCAM (Figure 1).
**FIGURE 1:** *Study flow diagram.*
## 3.2 Clinical characteristics, severity, and outcome of patients with tac-DILI
The clinical characteristics of patients with tac-DILI are shown in Table 1 tac-DILI was confirmed in 90 patients, with an incidence of $8.9\%$ ($95\%$ CI = 7.2–$10.7\%$). A total of 59 patients with RUCAM scores of 3–5 was regarded as ‘possible’, and 31 patients with RUCAM scores of 6–8 were regarded as ‘probable’. The incidence of the different tac-DILI patterns is shown in Figure 2. These cases were evaluated and classified according to their R values at the time of liver injury. Among them, 16 presented with hepatocellular pattern, 6 with mixed pattern, and 68 with cholestatic pattern. A total of 89 tac-DILI cases showed mild and one case showed moderate severity.
Latency time indicates both the time from the initial dosing to the onset of liver enzyme abnormality and meeting the criteria of liver injury in patients with tac-DILI. The median latency time of liver enzyme abnormality was 15.0 (range, 9.0–18.0 days), 10.0 (range, 5.0–63.8 days), 15.0 (range, 7.5–23.0 days) and 15.5 days (range, 9.0–26.0 days) for total, hepatocellular, mixed and cholestatic patterns, respectively. The median latency time to meet the criteria of liver injury was 42.0 (range, 21.5–99.8 days), 14.0 (range, 9.0–80.3 days), 16.0 (range, 11.5–24.5 days) and 49.0 days (range, 28.0–105.6 days) for total, hepatocellular, mixed and cholestatic patterns, respectively (Table 1). Only three patients (with cholestatic pattern) switched to cyclosporine A due to tacrolimus intolerance. Dose reduction was performed in 79 tac-DILI cases, including 15 cases with hepatocellular pattern, three case with mixed pattern, and 61 cases with cholestatic pattern. Only eight patients continued tacrolimus treatment without dose adjustment following tac-DILI: one with hepatocellular pattern, one with mixed pattern, and six with cholestatic pattern. For co-medication during liver injury, 84 patients simultaneously received glucocorticoids and 88 received MMF.
The whole blood trough concentration (C0) of tacrolimus was measured in patients with tac-DILI on the day of liver injury, and the median value of C0 for the total population was 6.8 μg/L (range, 5.9–9.6 μg/L). However, when we compared the C0 values among the different patterns of liver injury, no significant differences were found. Furthermore, there was also no significant difference in C0 of tacrolimus between patients with and without tac-DILI (Table 2).
**TABLE 2**
| Characteristics | Unnamed: 1 | DILI group (n = 90) | Non-DILI group (n = 360) | p-Value |
| --- | --- | --- | --- | --- |
| Age, median (IQR), year | | 16.0 (9.0–37.3) | 39.0 (30.0–49.8) | 0.000 |
| | ≤18 years, n (%) | 48 (53.3%) | 28 (7.8%) | 0.000 |
| | >18 years, n (%) | 42 (46.7%) | 332 (92.2%) | |
| Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) |
| | Male | 46 (51.1%) | 233 (64.7%) | 0.017 |
| | Female | 44 (48.9%) | 127 (35.3%) | |
| Weight, median (IQR), kg | | 41.5 (21.6–55.2) | 58.5 (50.0–60.0) | 0.000 |
| BMI, median (IQR), kg/m2 | | 17.6 (14.3–20.9) | 21.5 (19.0–13.7) | 0.000 |
| | <18.5 kg/m2 | 50 (55.6%) | 34 (9.4%) | 0.000 |
| | 18.5–24 kg/m2 | 30 (33.3%) | 114 (31.7%) | |
| | >24 kg/m2 | 10 (11.1%) | 32 (8.9%) | |
| Ethnic groups, n (%) | | | | 0.542 |
| | Han | 87 (96.7%) | 352 (97.8%) | |
| | Other ethnic minorities | 3 (3.3%) | 8 (2.2%) | |
| Blood type, n (%) | | | | 0.373 |
| | A | 31 (34.4%) | 91 (25.3%) | |
| | AB | 8 (8.9%) | 36 (10.0%) | |
| | B | 22 (24.4%) | 105 (29.2%) | |
| | O | 29 (32.2%) | 128 (35.6%) | |
| Payment method, n (%) | | | | 0.405 |
| | Medical insurance | 78 (86.7%) | 323 (89.7%) | |
| | Self-paying | 12 (13.3%) | 37 (10.3%) | |
| Underlying disease, n (%) | Underlying disease, n (%) | Underlying disease, n (%) | Underlying disease, n (%) | Underlying disease, n (%) |
| | Diabetes mellitus | 4 (4.4%) | 33 (9.2%) | 0.145 |
| | Hypertension | 71 (78.9%) | 305 (84.7) | 0.182 |
| Biochemical parameters baseline values | Biochemical parameters baseline values | Biochemical parameters baseline values | Biochemical parameters baseline values | Biochemical parameters baseline values |
| | ALT [1–40 U/L], median (IQR), U/L | 20.0 (10.1–26.0) | 19.0 (12.0–26.0) | 0.681 |
| | AST [1–37 U/L], median (IQR), U/L | 24.0 (17.5–31.0) | 18.0 (13.0–23.0) | 0.000 |
| | ALP [0–110 U/L], median (IQR), U/L | 94.0 (80.0–105.3) | 71.0 (56.0–90.0) | 0.000 |
| | ALB [35–50 g/L], median (IQR), g/L | 42.0 (38.0–45.4) | 42.7 (38.4–47.0) | 0.111 |
| | TBIL [3.0–22.0 μmol/L], median (IQR), μmol/L | 9.0 (6.9–12.2) | 9.7 (8.0–11.8) | 0.116 |
| | CREA [53–115 μmol/L], median (IQR), μmol/L | 797.5 (590.0–990.3) | 861.5 (688.0–1,096.5) | 0.008 |
| | WBC [4.00-10.00 × 10^9/L], median (IQR), 10^9/L | 8.2 (6.3–9.8) | 7.5 (5.8–9.3) | 0.052 |
| | RBC [4.00-5.50 × 10^9/L], median (IQR), 10^9/L | 3.5 (3.0–4.2) | 3.6 (3.1–4.1) | 0.357 |
| | Hemoglobin [120–140 g/L], median (IQR), g/L | 102.0 (87.0–121.3) | 105.0 (91.0–120.0) | 0.451 |
| | Hematocrit [42.0-49.0], median (IQR), % | 30.2 (26.8–35.2) | 31.6 (27.4–35.0) | 0.305 |
| | Platelet [100–300 g/L], median (IQR), g/L | 208.0 (165.8–259.0) | 176.5 (143.0–222.0) | 0.001 |
| | INR [0.80-1.15] | 0.99 (0.94–1.1) | 0.99 (0.94–1.05) | 0.926 |
| C0 of tacrolimus at the first discharge, median (IQR), μg/L | | 7.6 (6.4–9.7) | 7.9 (6.6–9.6) | 0.545 |
Notably, 45 of the 90 patients with tac-DILI received hepatoprotective agents, with antioxidants being used most frequently, followed by anti-inflammatory agents, liver membrane protectors, cholagogues, and antidotes (Table 1). Regarding the outcomes of patients with tac-DILI, 34 recovered and 35 showed improvement, but 15 patients showed no improvement and six patients showed deterioration. In addition, patients with hepatocellular pattern showed the highest rate of recovery ($75.0\%$) and outcomes except recovery were found to be higher in patients with cholestatic pattern ($46\%$ improvement, $22.1\%$ no improvement, and $8.8\%$ deterioration). The median time to recovery was 65.0 (range, 16.8–397.8 days), 22.0 (range, 12.3–28.8 days), 8.0 (range, 4.5–70.0 days), and 390.5 days (range, 199.0–604.3 days) for total, hepatocellular, mixed and cholestatic pattern, respectively.
## 3.3 Changes in biochemical parameters associated with tacrolimus treatment
To further investigate whether other body functions were affected by tacrolimus treatment in patients with tac-DILI, various biochemical parameters were compared before and after tacrolimus treatment. As shown in Figure 3, the median values of ALT, AST, ALP, RBC, ALB, hemoglobin, and platelets increased significantly following tacrolimus treatment, whereas those of CREA and WBC decreased significantly.
**FIGURE 3:** *Changes in laboratory test values between baseline and at tac-DILI onset post-tacrolimus treatment in patients with tac-DILI (n = 90). (A) ALT; (B) AST; (C) ALP; (D) ALB; (E) TBIL; (F) CREA; (G) WBC; (H) RBC; (I) Hemoglobin; (J) Platelet; (K) INR. Horizontal bars represent the median value, boxes represent the interquartile range and whiskers indicate the minimum and maximum value. Wilcoxon’s test was used to compare laboratory test values between baseline and at tac-DILI onset post-tacrolimus treatment. *p<0.05. ALT: alanine aminotransferase; AST: aspartate aminotransferase; ALP: alkaline phosphatase; ALB: albumin; TBIL: total bilirubin; CREA: creatinine; WBC: white blood cell; RBC: red blood cell; INR: international normalized ratio.*
## 3.4 Risk factors for tac-DILI
90 patients with tac-DILI were matched with 360 non-tac-DILI patients; the characteristics of the tac-DILI and non-tac-DILI groups are presented in Table 2. The significant predictors of tac-DILI were subsequently introduced into the backward stepwise logistic regression model. Independent risk factors predicting tac-DILI were: ALP baseline (OR = 1.015, $95\%$ CI = 1.006–1.025, $$p \leq 0.002$$), age (OR = 0.971, $95\%$ CI = 0.949–0.994, $$p \leq 0.006$$), and weight (OR = 0.960, $95\%$ CI = 0.940–0.982, $p \leq 0.001$) (Figure 4).
**FIGURE 4:** *Independent risk factors for tacrolimus-induced liver injury.*
## 4 Discussion
Currently, data on tac-DILI are limited to reports from pre-marketing clinical trials and post marketing cases. To the best of our knowledge, this is the first study to describe the incidence, characteristics, prognosis, and risk factors of tac-DILI in renal transplant recipients. This study was supported by real-world data and included a relatively large sample size, which may provide a more reliable reference for the prevention and treatment of tac-DILI.
Administration of tacrolimus, cyclosporine A, and mTOR inhibitors may cause liver injury in patients following liver transplantation (Tischer and Fontana, 2014). A cohort study from the United States analyzed the incidence, clinical presentation, and outcomes of liver injury following liver transplantation; immunosuppressive agents, including azathioprine and tacrolimus, were regarded as inducers (Sembera et al., 2012). However, there is a lack of definite causality assessment conclusions for tac-DILI in this cohort study. Furthermore, liver transplantation itself is a risk factor for liver injury, which may be a confounding factor in identifying DILI (Zhenglu et al., 2007). Nevertheless, the diagnosis of DILI has always been difficult owing to the lack of diagnostic biomarkers and specific clinical features; therefore, physicians need to rely on the diagnosis by exclusion (EASL, 2019). Accordingly, the RUCAM scale was adopted in our study, which demonstrated the feasibility of identifying DILI (Teschke and Danan, 2016).
Triple immunosuppressive regimen including glucocorticoid, MMF and tacrolimus is used in the renal transplant recipients for the prevention of rejection. In our study, when the liver injury was suspected related to the immunosuppressant, tacrolimus dose was decreased or discontinued according to the concentration, and the recovery or alleviation of liver injury was observed, but the doses of glucocorticoid and MMF were maintained unchanged. Besides, more cases of liver injury were reported related to CNIs including tacrolimus in transplant recipients, as compared to low dose of glucocorticoid and MMF. Thus, we believed that tacrolimus may be a main factor causing the DILI. However, the co-administration of glucocorticoid and MMF may also potentially prompt the liver injury, and the result in this study may be better referenced for the transplant population.
In our study, the incidence of tac-DILI was $8.9\%$, which was in accordance with that described on the drug label ($1\%$–$10\%$, defined as common). Meanwhile, it is reported that tacrolimus therapy is associated with mild to moderate elevations in serum enzyme levels in $5\%$–$10\%$ of patients according to LiverTox (www.livertox.nih.gov) (LiverTox, 2012). However, no criteria for liver injury have been defined and no specific data support tac-DILI. The latency time of DILI varies from days to years (EASL, 2019). Even for the same drug, there was a difference in latency time among the different patterns of liver injury (Jiang et al., 2021). As observed in our study, the cholestatic pattern had the longest latency time, followed by the mixed and hepatocellular patterns. Furthermore, the latency time of abnormal liver enzymes in patients with DILI was significantly shorter than that of liver injury, which indicated that potentially idiosyncratic hepatotoxicity induced by tacrolimus possibly occurred prior to when the criteria for DILI were met. The reasons for this difference remain unclear but may be related to the mechanism of tacrolimus-induced hepatotoxicity, such as idiosyncratic metabolic or immunologic reactions (Ferjani et al., 2016; Hoofnagle and Bjornsson, 2019). As we could see in the results, there were only one case of moderate tac-DILI and 89 mild, and no moderate-severe, severe and fatal cases of tac-DILI, which was consistent with results shown on LiverTox. On the one hand, this may be attributed to the concomitant low elevated level of TBIL in patients with tac-DILI. On the other hand, this may be due to timely dose reduction and use of hepatoprotective agents.
Generally, the patterns may be related to prognosis. Although a study showed that there was no significant association between the type of liver injury and recovery time (Medina-Caliz et al., 2016), most studies suggest that cholestatic and mixed patterns of liver injury require a longer recovery time (Andrade et al., 2006; Bjornsson et al., 2007), which is consistent with the results of our study. Most patients with tac-DILI had a cholestatic pattern ($75.6\%$); however, their recovery rate ($26.5\%$) was significantly lower than that of patients with a hepatocellular ($75.0\%$) and mixed ($66.7\%$) patterns. Similarly, previous studies have also suggested that a prolonged disease course occurs more commonly in patients with cholestatic pattern (Chalasani et al., 2015). Most patients with tac-DILI have mild liver injury. On the one hand, this may be attributed to early detection and intervention, including withdrawal and dose reduction of tacrolimus and use of hepatoprotective agents. On the other hand, the liver injury caused by tacrolimus may be self-limiting (LiverTox, 2012).
Additionally, the present study is the first to report that tac-DILI tends to occur at a younger age. Drug use is recognized as a cause of pediatric liver disease, but little is known about DILI in children and adolescents (Ferrajolo et al., 2010; Ye et al., 2021). Age distribution analysis showed that $15.2\%$ and $53.3\%$ of non-tac-DILI and patients with tac-DILI, respectively, were ≤18 years of age. Owing to incomplete maturity of vital functions, children show significant differences in drug absorption, distribution, metabolism, and excretion compared to adults (Anderson, 2002), which may make children more susceptible to DILI. Low body weight is also an independent risk factor for tac-DILI. Physiological factors, such as body weight and organ volume, can affect drug clearance, which is associated with DILI. Moreover, allometric models that account for differences in body weight and age have been adopted to predict drug clearance (Mahmood, 2015; Shi et al., 2017).
The current study has some limitations. First, the study is susceptible to some bias because of the retrospective nature of the study and the results relied on the accuracy of the electronic medical records. Additionally, all the patients included in the study were from the post-renal transplantation population, which, while ensuring homogeneity and consistency in clinical practice, may reflect a specific case mix of post-renal transplantation instead of patients in other disease populations.
In conclusion, a relatively higher incidence of tac-DILI was found in renal transplant patients based on real-world data, and the most common liver injury type was of the cholestatic subtype. Young age, low body weight, and abnormal baseline ALP levels are independent risk factors for tac-DILI.
## 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 the Ethics Committee for Clinical Research and Animal Trials of the First Affiliated Hospital of Sun Yat-sen University (No. 2021199). Written informed consent from the participantsʼ 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
Conception and design: PC, CW Acquisition of data: BL, LL Analysis and interpretation of the data: BL, LL, XC, KT Drafting of the article: BL, PC Critical review: XL, MH.
## 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.
## Abbreviations
ALP, alkaline phosphatase; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CNI, calcineurin inhibitors; CPG, clinical practice guidelines; C0, blood trough concentration; DILI, drug-induced liver injury; EASL, European association for the study of the liver; GGT, gamma-glutamyl transferase; KDIGO, Kidney Disease: Improving Global Outcomes; MMF, mycophenolate mofetil; RUCAM, Roussel Uclaf causality assessment method; TBIL, total bilirubin; tac-DILI, tacrolimus-induced liver injury.
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|
---
title: 'Accompanying your children: Living without parents at different stages of
pre-adulthood and individual physical and mental health in adulthood'
authors:
- Yao Jiang
- Hanling Xiao
- Fan Yang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10040656
doi: 10.3389/fpubh.2023.992539
license: CC BY 4.0
---
# Accompanying your children: Living without parents at different stages of pre-adulthood and individual physical and mental health in adulthood
## Abstract
### Objectives
This study examined how living without parents at different stages of childhood and adolescence affects physical and mental health in adulthood.
### Methods
The data came from 3,464 survey respondents aged 18–36 in the 2018 China Labor-Force Dynamics Survey. Physical health was self-rated. Mental health was measured by the Center for Epidemiological Studies Depression scale. The ordered probit and ordinary least-squares regression analyses were employed to determine the associations between growing up without parents at different stages in pre-adulthood and individual physical and mental health in adulthood.
### Results
Individuals who did not live with their parents during their minor years were more likely to report worse physical and mental health in adulthood, compared to those who lived with their parents. This difference was heterogeneous among different age stages and genders.
### Conclusions
Absence of parents in the household has long-term impacts on the physical and mental health of children in adulthood, especially for females. The government should make feasible institutional arrangements to avoid the separation of minor children from their parents.
## Introduction
Numerous factors during childhood and adolescence have significant impact on an individual's health in adulthood [1, 2]. Current studies mainly focus on how the health of caregivers is impacted by caring for children and adolescents [3], or on the current health of children and adolescents (4–12). Research has not yet highlighted how parental absence at various stages of childhood and adolescence can affect physical and mental health in adulthood.
The situation of minors living without parents is generally due to parental divorce, death, or working away from the home (13–16). Based on the attachment theory [17], parental absence may harm children's physical and social psychological development and lead to negative outcomes eventually [18]. As an emotional bond of one person with another person, the behavior of attachment is a necessary psychological need that human beings are born with [19]. The early emotional bonds formed by children with their caregivers (mainly parents) have significant impacts on children's cognitive and socioemotional development throughout life (20–22). The attachment system serves two primary functions by providing instrumental and emotional support [23]. One is to protect individuals from potential threats or injuries, and the other is to regulate individual negative emotions following threatening or harmful events [23]. Children who maintained proximity to an attachment figure were more likely to receive care, comfort and protection [17]. If the attachment is lost or weakened such as parental absence, it may be detrimental to the physical and mental development of children and ultimately affect physical and mental health of children for a long time (17–23).
For the physical health, a study by Schwartz and McLanahan suggested that absence of the father during children's growing years can result in poor physical health outcomes to the children [13]. Whereas, the father, as the main provider of income for a family, determines the quality of child care and health care that children receive. The absence of the father may lead to poor care for the child, and may result in insufficient food and nutrition for the child, which may have negative impacts on the child's physical health [13].
For the mental health, the economic hardship of a single-parent household may cause depression and psychological distress in children [13]. Compared to non-bereaved children, children who lost a parent to death showed more serious mental illness [14]. In the first 2 years following the death of a parent, children experienced increased risk of psychiatric disturbance [15]. In a survey of children with multinational family backgrounds, it was found that compared with children living with both parents, children in households where the father was absent due to migrant work in Indonesia, Vietnam, and Thailand had greater odds of experiencing emotional disorders [16].
The absence of parents affects not only the physical and mental health of minors as they are growing up, but also their physical and mental health in adulthood (24–28). From adolescence through early adulthood, individuals from non-intact families are more likely to engage in adverse health-related behaviors including smoking, alcohol consumption, poor nutrition habits, and low physical activity, compared with those who grow up in intact families [24]. They also have worse self-reported health and more subjective health complaints. Temporary parental separation very soon after birth can have unfavorable effects on later psychological development, including vulnerability to addiction [25] and a certain degree of depression risk [26]. For instance, according to a 28-year cohort research which was consisted of 3,020 subjects in Finland, the $4\%$ of adult respondents who experienced temporary separation at birth had been treated in hospital due to a depressive episode, and the incidence was higher than that of respondents who did not experience temporary separation at birth [26]. Parental divorce can negatively affect the mental health of young adults [27]. However, experience of parental divorce in childhood may not be an indicator of adult psychiatric or somatic health issues [28]. Overall, although prior studies have examined the effects of absence of parents on current health of children or their health in adulthood, a consensus has not been reached.
As the main reason for separation of children from their parents in *China is* that the parents leave rural areas to go to work in cities, the left-behind children are the main component of the kids who did not spend childhood or adolescent with their parents [29]. As of 2020, the number of children left behind in rural China totaled 6.436 million [29]. While their parents work in cities, these so-called left-behind children may live with their grandparents, older brothers and sisters, other relatives, or alone. According to China Ministry of Civil Affairs, $96\%$ of these left-behind children aged under 16 years old are lived with their grandparents [30].
Children who are left behind have lower levels of physical and mental health than their peers [31]. The adverse effects of lack of parental care and attention tend to accumulate over time (32–34). Left-behind children are shorter than their peers due to insufficient intake of energy, protein, calcium, and other nutrients [32]. They may be at higher risk for stunted growth, unhealthy food preferences, lower physical activity, smoking, alcohol consumption, injuries, and incomplete vaccination [32]. In addition, they are more prone to negative emotions, social anxiety, and low self-esteem [33, 34]. However, on the positive side, children's health and experiences may benefit from the greater income earned by parents working abroad or in cities [35, 36].
On the whole, the parental absence harms more than it benefits the physical and mental health of minor children. Although some studies have shown that the effect of parental absence on children's health is lasting, its effect at different ages on children's physical and mental health in adulthood has not been widely explored. According to developmental psychology, minors have different developmental needs at different age stages [37, 38]. Therefore, children's needs on parental accompany in different age stages before adulthood may be heterogeneous (39–42). For example, at the age of 0–6, children may mainly need material care and emotional companionship from their parents [39]; for children aged 7–12, the development of living habits needs to be carried out under the supervision of their parents [40, 41]; when children are 13–15 years old, they generally enter a rebellious period, and parents need to help them deal with emotional problems in this stage [42]. Generally, compared with children whose parents are not absent, children whose parents are absent are less likely to be observed and satisfied with their development needs at different age stages before adulthood. This is more likely to have a negative and lasting impact on children's physical and mental health. Furthermore, as children growing older, they tend to be independent of their parents and the early attachment between children and parents may be gradually weakened [43]. Thus, the effect of parental accompany before adulthood on children's health outcomes in adulthood may present a decreasing trend. However, few studies have made attempts to empirically test the effect of parental absence at different age stages on children's health in adulthood.
In this paper, we divided period before individual adulthood into four stages based on Chinese situation-−0–6, 7–12, 13–15, and 16–18 years old. *In* general, 0–6 years old is a stage of preschool age in China, 7–12 years old is primary education stage, 13–15 years old is a stage of secondary school, and 16–18 years old is high school stage. This age division based on Chinese educational regime appropriately covers all stages of an individual before adulthood [44]. It also reflects the physical and mental development characteristics of individuals at different age stages before adulthood to a certain extent.
Moreover, gender is a vital social lens to promote the more careful and targeted child care [45]. Compared with boys, girls are more vulnerable to the inequality of being cared for before adulthood [46]. As mentioned above, the vast majority of left-behind children ($96\%$) live with their grandparents in China. These older grandparents are more influenced by the traditional patriarchal ideology than younger parents to a large extent, and they may take less care of female grandchildren than male grandchildren [47, 48]. Therefore, the absence of parents may have a greater negative impact on the physical and mental health of girls than boys. However, few studies have made attempts to test the heterogeneous influences of living without both parents during childhood and adolescence on individuals' physical and mental health in adulthood from the perspective of gender difference.
Hence, in this study, we used a nationally representative survey of 3,464 Chinese respondents with an average age of 28 years, and expanded the body of knowledge of this subject by focusing on the long-term effects of living without parents at different age stages before adulthood. We observed how living without parents during the age ranges 0–6, 7–12, 13–15, and 16–18 affected the physical and mental health of individuals in adulthood (Figure 1). In addition, we conducted a heterogeneity analysis based on gender. Our study should be of interest to researchers and public policy makers concerned with the welfare of children and adolescents.
**Figure 1:** *Study framework.*
## Data source
Our study used data from the 2018 China Labor-Force Dynamics Survey, a comprehensive study conducted by the Center for Social Science Survey at Sun Yat-sen University to collect information on Chinese education, work, migration, health, economic activities, and other interdisciplinary aspects [49]. The major objective of the survey was to provide basic public data for social science research in China. It was designed using a multistage cluster and stratified probability-proportional-to-size sampling strategy, and computer-assisted interviews were conducted in respondents' homes or by telephone. It collected data from 29 provincial administrative units across the country, so it is nationally representative. After cleaning the data by excluding the missing values, outliers, and other abnormal values, we obtained useful samples from 3,464 respondents. Because the data were collected by professionals at the university, its validity is assured.
## Participants
Among the analysis samples of this paper, males represented $45.4\%$ of the total participants. The age of the participants ranged from 18 to 36 years, with a mean value of 28.225 (SD = 5.206). The average years of school were 11.523 (SD = 3.913), between junior high school and senior high school. The logarithm of total annual income of the participants in 2017 had a mean value of 10.533 (SD = 0.878), with a minimum value of 5.704 and a maximum value of 14.914. A total of $70.2\%$ of the participants were married, $21.8\%$ had the habit of smoking, $16.9\%$ drank alcohol, and $36.4\%$ exercised regularly.
## Explained variables
Physical and mental health were the two explained variables in our analysis. Physical health status was self-rated (50–53). Respondents were asked “How would you evaluate your current health status?” and could rate their responses from 1 to 5 on a five-point Likert scale that included “very unhealthy,” “somewhat unhealthy,” “normal,” “somewhat healthy,” and “very healthy.” The Center for Epidemiological Studies Depression (CES-D) scale developed by Radloff was employed to assess the mental health of respondents [54, 55]. The CES-D is one of the most widely used scales for measuring depression and mental health [56, 57]. It has been verified as valid for the assessment of depression and mental health status in a Chinese context (58–62). The CES-D scale is scored from 20 to 80, and a higher score indicates a higher level of depression and worse mental health [54]. The Cronbach's alpha on CES-D scale is 0.949 in this study.
## Explanatory variables
The explanatory variables represented who the individual lived with at specific age ranges while growing up. Respondents were asked to recall, “Who did you live with when you were 0–6 / 7–12 / 13–15 / 16–18 years old?” *If a* child was living with both parents during a specific age range, the response was assigned a value of 0; other responses received a value of 1.
## Control variables
To adjust for potential confounding effects on associations between living without parents at different stages and individual physical and mental health in adulthood (63–66), we used several control factors in our regression model analyses. The variables used were gender (male = 1, female = 0); age in years; number of years of schooling; marital status (married = 1, unmarried = 0); logarithm of the total annual income of the respondent in 2017; smoking (habitual smoking = 1, otherwise = 0); drinking (habitual drinking = 1, otherwise = 0); and exercise (habitual exercise = 1, otherwise = 0). In addition, we controlled for regional effect based on the provinces where respondents were located.
## Analysis strategy
Descriptive statistics were computed to estimate the proportion of respondents living with and without parents at each age range, the current physical and mental health status, and the demographic characteristics of the respondents. In models for different age ranges of living without parents, multivariable analyses adjusted for the confounding effects of general factors affecting individual health in adulthood [67]. We used ordered probit regression models to analyze the effects of living without both parents at different stages on the physical health of individuals in adulthood. The main equations for this specification can be written as follows [68]: where i denotes an individual observation, physical healthi * represents the unobserved dependent variable, viz., individual's physical health, xi represents a vector of explanatory variable, β′ represents a set of parameters, and εi is a random error term which is followed normal distribution. *In* general, physical healthi * cannot be observed directly, but the categorical variable physical healthi can be observed. The relationship between physical healthi * and physical healthi can be represented as follows: where μ are the cut points, which are the unknown parameters to be estimated along with β′, and M are the possible outcomes for physical healthi. In this study, M ranges from “1” to “5.” To estimate the effects on individual mental health in adulthood, we used ordinary least-squares regression models. The specific ordinary least-squares model is as follows:
where mental healthi represents the explained variable of mental health which is measured by the CES-D scale, and xi means the explanatory variable. α0 denotes the intercept item, α1 and α2 are coefficients for explanatory variable and control variables, respectively. X means a set of control variables, and εi is the random error item.
Furthermore, there may be some observable systematic differences among individuals, and it is the respondents' family self-selection to live with parents or not. If we compared the two groups of respondents directly, the estimation results may be biased due to the self-selection of samples. Thus, to confirm the influences of living without parents at various stages of childhood on individual physical and mental health in adulthood, the propensity score matching (PSM) method was used to build a counterfactual framework. The main equations of PSM model can be written as follows [69]: Where ATTp and ATTm are the average effect of treatment on the treated. physical healthi and mental healthi are the explained variables, ASi denotes a binary treatment variable, specifically, taking a value of “1” for respondents who lived without both parents at 0–6 / 7–12 / 13–15 / 16–18 years old; otherwise, ASi = 0. p(Zi) represents the propensity scores estimated by PSM estimation, and Zi represents a set of covariates.
## Descriptive statistics
Table 1 reports the definitions of the variables employed in this study and the results of the descriptive analysis ($$n = 3$$,464). Of the explained variables, the average value for respondents' physical health was 3.995 (SD = 0.820) on the five-point Likert scale ranging from 1 to 5, which means the physical health of the respondents was generally between “normal” and “somewhat healthy.” The average value for mental health of respondents was 26.858 (SD = 8.217) on the CES-D scale ranging from 20 to 80.
**Table 1**
| Variable | Definition | Mean | SD | Min | Max |
| --- | --- | --- | --- | --- | --- |
| Explained variable | Explained variable | Explained variable | Explained variable | Explained variable | Explained variable |
| Physical health | 1 = very unhealthy; 2 = somewhat unhealthy; 3 = normal; 4 = somewhat health; 5 = very healthy | 3.995 | 0.820 | 1 | 5 |
| Mental health | Total score of the CES-D ranges from “20” to “80.” The higher CES-D score, the deeper depression, and the worse mental health | 26.858 | 8.217 | 20 | 80 |
| Explanatory variable | Explanatory variable | Explanatory variable | Explanatory variable | Explanatory variable | Explanatory variable |
| Living without parents (0–6 years) | 0 = living with parents; 1 = living without parents | 0.096 | 0.295 | 0 | 1 |
| Living without parents (7–12 years) | 0 = living with parents; 1 = living without parents | 0.104 | 0.305 | 0 | 1 |
| Living without parents (13–15 years) | 0 = living with parents; 1 = living without parents | 0.140 | 0.347 | 0 | 1 |
| Living without parents (16–18 years) | 0 = living with parents; 1 = living without parents | 0.219 | 0.413 | 0 | 1 |
| Control variable | Control variable | Control variable | Control variable | Control variable | Control variable |
| Gender | 1 = male; 0 = female | 0.454 | 0.498 | 0 | 1 |
| Age | Years old | 28.225 | 5.206 | 18 | 36 |
| Education | Years of schooling education of respondent | 11.523 | 3.913 | 0 | 23 |
| Marital status | 1 = married; 0 = unmarried | 0.702 | 0.457 | 0 | 1 |
| Logarithm of income | Logarithm of total annual income of respondent in 2017 | 10.533 | 0.878 | 5.704 | 14.914 |
| Smoking | 1 = have habit of smoking; 0 = else | 0.218 | 0.413 | 0 | 1 |
| Drinking | 1 = have habit of drinking; 0 = else | 0.169 | 0.375 | 0 | 1 |
| Exercise | 1 = have habit of exercise; 0 = else | 0.364 | 0.481 | 0 | 1 |
The explanatory variables, either living with parents (represented by a value of 0) or without parents (represented by a value of 1) before adulthood, were stratified into four different age stages. Of the total respondents, $9.6\%$ lived without their parents during the ages of 0–6, $10.4\%$ during the ages of 7–12, $14\%$ during the ages of 13–15, and $21.9\%$ during the ages of 16–18.
## Benchmark regression
Table 2 reports the ordered probit model and the ordinary least-squares model results. It can be observed from Table 2 that compared to individuals who lived with their parents, individuals who did not live with their parents during the ages of 0–6 had significantly worse physical health in adulthood (coefficient = −0.223, $p \leq 0.01$). A similar situation also occurred in the age ranges 7–12 (coefficient = −0.169, $p \leq 0.01$), and 13–15 (coefficient = −0.099, $p \leq 0.1$). However, there were no significant difference in the effects of living with and without parents at the ages of 16–18 on the physical health of individuals in adulthood.
**Table 2**
| Variables | Physical health (ordered probit) | Physical health (ordered probit).1 | Physical health (ordered probit).2 | Physical health (ordered probit).3 | Mental health (OLS) | Mental health (OLS).1 | Mental health (OLS).2 | Mental health (OLS).3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
| Living without parents (0–6 years) | −0.223*** | | | | 2.632*** | | | |
| | (0.064) | | | | (0.471) | | | |
| Living without parents (7–12 years) | | −0.169*** | | | | 2.178*** | | |
| | | (0.061) | | | | (0.454) | | |
| Living without parents (13–15 years) | | | −0.099* | | | | 1.355*** | |
| | | | (0.054) | | | | (0.402) | |
| Living without parents (16–18 years) | | | | −0.022 | | | | 0.696** |
| | | | | (0.046) | | | | (0.340) |
| Gender | 0.026 | 0.024 | 0.025 | 0.024 | −0.817** | −0.791** | −0.803** | −0.784** |
| | (0.047) | (0.047) | (0.047) | (0.047) | (0.345) | (0.345) | (0.346) | (0.346) |
| Age | −0.017*** | −0.016*** | −0.016*** | −0.015*** | 0.048 | 0.042 | 0.040 | 0.034 |
| | (0.004) | (0.004) | (0.004) | (0.004) | (0.032) | (0.032) | (0.032) | (0.032) |
| Education | 0.032*** | 0.031*** | 0.031*** | 0.031*** | −0.180*** | −0.175*** | −0.172*** | −0.173*** |
| | (0.006) | (0.006) | (0.006) | (0.006) | (0.042) | (0.042) | (0.042) | (0.042) |
| Marital status | 0.088* | 0.088* | 0.088* | 0.087* | −1.147*** | −1.150*** | −1.145*** | −1.141*** |
| | (0.050) | (0.050) | (0.050) | (0.050) | (0.369) | (0.369) | (0.370) | (0.370) |
| Logarithm of income | 0.046* | 0.045* | 0.045* | 0.045* | −0.216 | −0.207 | −0.212 | −0.203 |
| | (0.024) | (0.024) | (0.024) | (0.024) | (0.177) | (0.177) | (0.177) | (0.177) |
| Smoking | 0.072 | 0.073 | 0.073 | 0.071 | −0.512 | −0.521 | −0.527 | −0.517 |
| | (0.057) | (0.057) | (0.057) | (0.057) | (0.421) | (0.421) | (0.422) | (0.422) |
| Drinking | −0.009 | −0.005 | −0.005 | −0.005 | 1.222*** | 1.181*** | 1.172*** | 1.161*** |
| | (0.057) | (0.057) | (0.057) | (0.057) | (0.420) | (0.420) | (0.421) | (0.422) |
| Exercise | 0.084** | 0.085** | 0.086** | 0.086** | −0.032 | −0.046 | −0.062 | −0.069 |
| | (0.041) | (0.041) | (0.041) | (0.041) | (0.301) | (0.302) | (0.302) | (0.303) |
| Region | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| n | 3464 | 3464 | 3464 | 3464 | 3464 | 3464 | 3464 | 3464 |
| Pseudo R2 | 0.045 | 0.044 | 0.044 | 0.043 | | | | |
| R-squared | | | | | 0.050 | 0.048 | 0.045 | 0.043 |
Similarly, compared with individuals who lived with their parents, individuals who did not live with their parents during the ages of 0–6 had significantly worse mental health in adulthood (coefficient = 2.632, $p \leq 0.01$). A similar situation occurred during the ages of 7–12 (coefficient = 2.178, $p \leq 0.01$), 13–15 (coefficient = 1.355, $p \leq 0.01$), and 16–18 (coefficient = 0.696, $p \leq 0.05$).
## Sub-group regression by gender
Owing to the gender heterogeneity of our survey sample, we further explored the influence of living without parents at different age stages from the perspective of gender difference. Tables 3, 4 report the effects, separately for adult males and females.
Table 3 shows that the effect on adult physical health of living without parents during the age range 0–6 was statistically significant and negative for both males (coefficient = −0.161, $p \leq 0.1$) and females (coefficient = −0.285, $p \leq 0.01$). It suggests that compared with individuals who lived with their parents, both males and females who did not live with their parents during ages 0–6 had significantly worse physical health in adulthood. However, for the age range 7–12, living without parents had a significant and negative influence on physical health of females (coefficient = −0.217, $p \leq 0.01$) in adulthood, but not on that of males. Furthermore, for the age range 13–18, living without parents had no significant effect on the physical health of males nor females in adulthood. The results indicate that, in terms of long-term physical health outcomes, children need parents more in their early years than when they are older, and that females need the presence of parents for longer than males during their minor years.
In terms of mental health, columns 1, 3, and 5 of Table 4 show that, before the age of 15, living without parents had a significant negative effect on males' mental health in adulthood. Males who did not live with their parents before the age of 15 had greater stress and worse mental health in adulthood than those who lived with their parents before the age of 15. This effect became statistically insignificant in the age 16–18 range, as shown in column [7]. For females, it can be seen from columns 2, 4, 6, and 8 of Table 4 that, for all pre-adulthood age ranges (0–18 years), living without parents had a significant negative influence on mental health in adulthood. Compared with females who lived with their parents, females who did not live with their parents at all age stages of pre-adulthood had greater stress and worse mental health in adulthood.
From the influential coefficient, Tables 3, 4 show that, at each age stage, living without parents before adulthood had a greater negative effect on physical and mental health for females in adulthood than for males.
In terms of age distribution, the 0–6 age range was the only one showing a significant negative effect on the physical health of males in adulthood; after the age of 6, the effect was no longer significant. For females, living without their parents had a significant negative effect on their physical health in adulthood until the age of 12; after the age of 12, the effect became statistically insignificant. In terms of mental health, living without parents had a significant negative effect on males in adulthood until the age of 15; after the age of 15, this effect became statistically insignificant. However, during all pre-adulthood age ranges (0–18), living without parents had a significant negative effect on females' mental health in adulthood.
## Dealing with self-selection bias
Table 5 shows the effects of living without parents at the ages of 0–6, 7–12, 13–15, and 16–18 on individual physical and mental health in adulthood by adopting four types of matching methods: nearest-neighbor matching with caliper matching, radius matching, kernel matching, and local-linear regression matching.
**Table 5**
| Method | Method.1 | Nearest neighbor | Radius | Kernel | Local linear regression |
| --- | --- | --- | --- | --- | --- |
| 0–6 years | Physical health (ATT) | −0.215*** | −0.201*** | −0.192*** | −0.203*** |
| | | (−3.92) | (−4.04) | (−3.89) | (−3.19) |
| | Mental health (ATT) | 0.264*** | 0.276*** | 0.277*** | 0.278*** |
| | | (4.25) | (4.79) | (4.84) | (3.97) |
| 7–12 years | Physical health (ATT) | −0.145*** | −0.151*** | −0.142*** | −0.152*** |
| | | (−2.75) | (−3.15) | (−2.99) | (−2.46) |
| | Mental health (ATT) | 0.259*** | 0.234*** | 0.233*** | 0.234*** |
| | | (4.56) | (4.41) | (4.42) | (3.59) |
| 13–15 years | Physical health (ATT) | −0.132*** | −0.112*** | −0.110*** | −0.118** |
| | | (−2.89) | (−2.70) | (−2.67) | (−2.18) |
| | Mental health (ATT) | 0.174*** | 0.164*** | 0.163*** | 0.170*** |
| | | (3.56) | (3.59) | (3.61) | (3.06) |
| 16–18 years | Physical health (ATT) | −0.132*** | −0.112*** | −0.110*** | −0.118** |
| | | (−2.89) | (−2.70) | (−2.67) | (−2.18) |
| | Mental health (ATT) | 0.174*** | 0.164*** | 0.163*** | 0.170*** |
| | | (3.56) | (3.59) | (3.61) | (3.06) |
In the PSM analysis, the values of the average treatment effect on treatment (ATT) in the different matching methods were all significant. The results indicate that, after eliminating observable systematic differences, living without parents at the ages of 0–6, 7–12, 13–15, and 16–18 still had significant and negative effects on individual physical and mental health in adulthood. Thus, the PSM analysis showed that the results of this study are robust.
## Discussion
The Convention on the Rights of the Child of the United Nations Children's Fund pointed out that, the child shall have the right from birth to a name, the right to acquire a nationality and as far as possible, the right to know and be cared for by his or her parents [70]. Living with parents and being cared for by parents plays an important role in children's healthy growth and physical and mental health in their adulthood. In this study, we found that compared with children who lived with their parents, individuals who did not live with their parents during their minor years had poorer physical and mental health in adulthood. The results were heterogeneous in age stages and in gender.
Numerous previous studies have shown that the absence of parents has many negative effects on the physical and mental health of minor children (71–73). Children who are not raised by their parents are at higher risk of internet addiction, depression, anxiety, loneliness, suicidal ideation, drug abuse, wasting, stunting, and sickness (71–73). Further, existing studies have found that the effect of parental absence is far-reaching (24–28). Consistently, our empirical results in this study support the above conclusions. The results showed that in terms of physical and mental health, the effect of parental absence on their children is not only immediate, but also into adulthood. Therefore, our general conclusion is that the presence or absence of parents in the household as children grow has both current and long-term impacts on physical and mental health.
This study enriches the research on how adverse experiences in pre-adulthood have negative effects on individuals in adulthood [74]. Living with parents is beneficial, and arguably the most important support for children as they grow up [75, 76]. From this point of view, not living with parents while growing up can be regarded as an adverse experience of minors. Immediate negative impacts include malnutrition and autism (71–73), but long-term negative effects on physical and mental health in adulthood also are evident as found in this study.
Building on previous studies (39–42, 45, 46), we explored the effects of parental absence based on different age ranges and genders. We found heterogeneous results. Our data showed that as the age of a child increases, the negative effect of living without parents on physical and mental health in adulthood gradually decreases. For younger children, their self-care ability is weaker, they have more emotional needs from adults, and they need more companionship from parents, compared with older children, from the perspective of developmental psychology [77]. However, as children growing older, many of them try to become more autonomous from their parents [43]. During adolescence and near adulthood, peers, such as close friends or romantic partners, often replace parents and become their attachment figures [43]. This enlightens us that the younger the children, the more important it is for their parents to be living with them. This also indicates that parents' intervention in children's health should start from the early stage of their children's life.
We also found that the negative long-term impact of parental absence on physical and mental health is greater for girls than for boys. It implies that in the process of growing up, girls need parents' company more than boys. Therefore, it is particularly important for parents to accompany the growth of their female children. China has a historical tradition of prioritizing boys over girls, and compared to boys, girls may have poor access to parents' care, education, and health services [78]. If the parents are absent, the children may be taken care of by their grandparents, who have a more traditional idea of valuing boys over girls, and the girls are less well cared for. Therefore, the absence of parents has a greater impact on girls than boys. Thus, government and non-governmental organizations should formulate relevant policies and increase support for girls' parents to ensure that they are not separated from their minor children. In the long run, strengthening the publicity and education of gender equality, and giving incentive policies, are crucial for girls to get better care from their elders.
The findings of our study have strong practical significance for China. According to the Office of the Leading Group of the State Council for the Seventh National Population Census, as of 2020, a total of 6.436 million children in rural China were left behind when their parents moved from the country to cities to work [29]. These children were separated from their parents for most of the year and lived with other relatives, mainly grandparents, with some even living alone [79]. Our data analysis confirmed the results of previous studies, which showed that the absence of parents can have a negative effect on the physical and mental health of minors (71–73), and the negative effects can continue into adulthood (24–28). Therefore, it should be the direction of policy efforts to avoid the separation of parents and young children as much as possible.
The decision of parents to separate from their young children is undoubtedly difficult. It is not only an individual decision, but also related to the institutional nature of this issue. The government should make feasible arrangements to reduce the need to separate minor children from their parents. Non-governmental organizations and citizens can also play an active role in creating a social consensus that parents should stay with their minor children. A suboptimal strategy is to provide early intervention for children living without parents, in an attempt to prevent current and future physical and mental health issues.
Our study had some limitations that deserve mention. First, the data regarding who the respondents lived with before adulthood were obtained through the respondents' recall. Because human memory is known to be unreliable, the data may not completely accurately reflect the truth. If future research could track and monitor who minors live with until they reach adulthood, the conclusions could be more objective. Second, the mechanism influencing the long-term impacts of the absence of parents in childhood is still not clear. There is opportunity for future research in this direction. Third, the measurement of physical health in this study relied on one item of self-rated physical health, which may result in measurement bias. For this reason, future studies can use more objective measurements of individuals' physical health. Forth, previous studies have found that father and mother may play heterogeneous roles in children's psychological adjustment; however, restricted by the data availability, we cannot investigate the heterogeneous effects of living without father or mother on children's health in adulthood. Thus, the future study can consider testing the heterogeneous effects of living without father or mother.
## Conclusions
Employing data from a nationwide survey in China, this study analyzed how living without parents at different stages of childhood and adolescence affects an individual's physical and mental health in adulthood. Although the results were heterogeneous at different age stages and for different genders, our analysis showed that growing up without the presence of parents in the household can have a significant negative effect on the physical and mental health of individuals in adulthood. Therefore, the presence of a parent is important for children's health, and has a long-term effect. Future research exploring the mechanism of this effect will be key to furthering our understanding of the long-term effect of lack of parental companionship during childhood.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
YJ developed the method, wrote the results and discussion, and modified and edited the whole manuscript. HX wrote the literature review, theoretical analysis, results, and modified and edited the whole manuscript. FY proposed the idea of this paper, provided guidance in the theory, modified and edited the whole manuscript, and played the role of supervisor. All authors have read and agreed to the published 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.
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|
---
title: 'Project design and technology trade-offs for implementing a large-scale sexual
and reproductive health mHealth intervention: Lessons from Sierra Leone'
authors:
- Emeka Chukwu
- Sonia Gilroy
- Kim Eva Dickson
journal: Frontiers in Digital Health
year: 2023
pmcid: PMC10040671
doi: 10.3389/fdgth.2023.1060376
license: CC BY 4.0
---
# Project design and technology trade-offs for implementing a large-scale sexual and reproductive health mHealth intervention: Lessons from Sierra Leone
## Abstract
### Background
The Coronavirus 2019 (COVID-19) pandemic threatened decades of progress in sexual and reproductive health (SRH) and gender-based violence as attendance at health facilities plummeted and service uptake dwindled. Similarly, misinformation regarding COVID-19 was rife. The demographics in Sierra Leone are diverse in the education, economic, and rural/urban divide. Telecommunications coverage, phone ownership, and preference for information access medium also vary greatly in Sierra Leone.
### Aim
The aim of the intervention was to reach Sierra Leoneans at scale with information about SRH during the early stages of the COVID-19 pandemic. This paper presents the approach and insights from designing and implementing a large-scale mobile health (mHealth) messaging campaign.
### Method
Between April and July 2020, a cross-sectional multichannel SRH messaging campaign was designed and launched in Sierra Leone. Through a secondary analysis of project implementation documents and process evaluation of the messaging campaign report, the project design trade-offs and contextual factors for success were identified and documented.
### Result
A total of 1.16 million recorded calls were initiated and 35.46 million text messages (short message service, SMS) were sent to telecommunication subscribers through a two-phased campaign. In phase one, only $31\%$ of the 1,093,606 automated calls to 290,000 subscribers were picked up, dropping significantly at $95\%$ confidence level ($$p \leq 1$$) after each of the four weeks. In addition, the listening duration dropped by one-third when a message was repeated compared to the first 3 weeks. Lessons from phase one were used to design an SMS and radio campaign in the scale-up phase. Evidence from our analysis suggests that the successful scaling of mHealth interventions during a pandemic will benefit from formative research and depend on at least six factors, including the following: [1] the delivery channels’ selection strategy; [2] content development and scheduling; [3] the persona categorization of youths; [4] stakeholder collaboration strategies; [5] technology trade-offs; and [6] cost considerations.
### Discussion and Conclusion
The design and implementation of a large-scale messaging campaign is a complex endeavor that requires research, collaboration with other diverse stakeholders, and careful planning. Key success ingredients are the number of messages to be delivered, the format, cost considerations, and whether engagement is necessary. Lessons for similar low-and-middle-income countries are discussed.
## Background
Sexual and reproductive health (SRH) organizations continue to search for efficient and effective ways to reach young people in this age of mobile technologies (mHealth). Traditional health education strategies are becoming obsolete for the new generation of young people. There are 79 mobile phone subscribers per 100 individuals in Sierra Leone, representing 6.3 million Sierra Leoneans [1, 2]. However, phone ownership remains at 36 mobile phones per 100 individuals, meaning 2.87 million people [1]. While this teledensity may be lower than in many places around the world, it still presents the opportunity for a wider reach at a relatively lower overhead. Only $14.3\%$ of subscribers use the 100-MB internet group bracket per month, and only $7.1\%$ use 1 GB and above nternet data every month [1]. In addition, Facebook, one of the most dominant social media platforms in the country, has approximately 700,000 users [3]. WhatsApp is also popular in Sierra Leone among all age groups but predominantly among millennials. Policymakers and international audiences interested in national policy directions also follow updates through Twitter. Stakeholders in Sierra Leone have deployed many ongoing digital-enabled interventions, mostly facility-based applications [4, 5]. The search of mHealth and Sierra Leone in the PubMed database did not yield any client-facing mHealth intervention. This project is the first documented mHealth intervention, and also one of the leading interventions at this scale globally.
The Coronavirus 2019 (COVID-19) pandemic did not only put a strain on the already weak health system in Sierra Leone, but its fear also impacted service uptake and attendance at general health facilities. During the lockdown period that followed, there was a visible surge in cases of gender-based violence (GBV). Mobile technologies have been used to successfully deliver SRH messages to subscribers in Africa (6–9), Asia [10, 11], and the United States [12]. Some campaigns targeted recipients through social media channels in China [13], Turkey [14], and Hong Kong [15]. An interactive voice response (IVR) mobile content delivery channel has been used for maternal health (MH) [16], post-abortion care [11], and family planning (FP) [6]. IVR-delivered contents were mostly in the native local language (not in English). Short message service (SMS) interventions for family planning helped improve consumer knowledge by $14\%$ [8]. Social media has also been used in other regions for reproductive health-based demand generation, like China's peer-led safe sex Facebook group [13]. Other demand generation interventions include serious games to enhance sex education for young adolescents in Hong Kong [17]. Serious games use virtual reality-enabled games with engaging family planning information. A video-based mobile technology intervention has equally shown promise among adolescents in the United States [18]. In Kenya, the *Shujaaz multimedia* platform used various channels ranging from comic radio programs, Facebook campaigns, and SMS [19].
The United Nations Populations Fund (UNFPA), through the Saving Lives project, launched a multichannel intervention project between April and July 2020 to reach young people in Sierra Leone with critical SRH messages using mobile technology. The project involved the design and deployment of the interventions through multiple messaging channels. Multichannel here refers to the use of several media channels for spreading marketing and health promotion and education messages to consumers and service users, including via social media, print, mobile, television, etc. [ 20, 21]. The multiple channels adopted in this project were automated voice calls, SMS, radio jingles, and social media. The campaign followed earlier formative research studies with school counselors, community learning centers, and youth advisory panels [20]. The campaign helped to communicate the following: continuity of sexual reproductive health; how to seek help for gender-based-violence-related issues; and staying safe during the COVID-19 restrictions and countermeasures. We identified the initial target and priority population as Freetown residents. As of situation report 30, Freetown, the main urban center in Sierra Leone, had the highest number of COVID-19 infections (84 of the national 116) and cumulative deaths (all five deaths) [22]. The intervention was later scaled nationwide.
Sierra Leone has three major telecommunications service providers: Africell, Orange, and Q-cell. Our intervention worked with Africell because they responded positively to our call for partners and they had the widest audience reach in the country. We targeted all their approximately 3.9 million subscribers out of the national total of 6.9 million mobile subscribers [3].
The objective of the messaging campaign was to create awareness of the continuity of GBV and SRH services during COVID-19 restrictions. The aim of the present paper is to show the design choices, approach, and strategies for the pilot phase and for the nationwide scale-up phase of the intervention. The paper also discusses the messaging campaign's call pickup rates, duration of listening, overall effectiveness, and other lessons learned for future similar interventions. The lessons learned were also documented from designing and implementing the multi-intervention project in Sierra Leone using the framework from Allsop et al. [ 23].
## Materials and methodology
The approach for designing and the deployment of the multichannel intervention is first presented, followed by the evaluation. The intervention components were discussed, including content adaptation, transcription, recording, channel selection, message scheduling, and targeting. The internal document and report reviews and evaluation were conducted after the intervention to extract learnings for future interventions.
## Intervention setting
Sierra Leone covers a land area of 72,180 km2 with an estimated population of 8.4 million people [2, 24]. Based on the projections of the latest United Nations data, $43\%$ of the population resides in urban areas [2]. Freetown, the capital city, is the main urban district with a population of 802,639, split between the western urban and western rural areas. The country is made up of 14 health districts. Sierra *Leone is* one of the least developed countries in the world. Access to healthcare is limited by the inequitable allocation of skilled healthcare workers, poor service quality, geographical barriers, and high out-of-pocket expenses for health [24]. Moreover, health facilities are unevenly distributed, with referral hospitals concentrated in Freetown [25].
## Intervention formative research
In 2019, UNFPA conducted formative research to understand how young people use mobile phones and the best strategy for reaching young people using mobile technology with SRH messages. The report showed that phone ownership and phone type increased with education and income [20]. Young people, out of school, generally used basic phones and often could not read or write. Educated young people in secondary school used a mix of smartphones and basic mobile phones and were able to read and write. *Graduates* generally had smartphones and were comfortable using social media. User personas were developed for the six groups of participants, as in Figure 1. The personas show the demographic information, behavior/personality/lifestyle, their level of phone use expertise, current source of sexual reproductive health information, their preferred information source, and how the intervention can potentially help. The following paragraphs describe the multiple channels for spreading SRH messages. From the Leeds EPaCCS program evaluation hierarchy, it was determined that the published formative research addressed the pre-implementation usability and technical aspects [23].
**Figure 1:** *Six user personas developed as part of the UNFPA WiTok intervention approach. UNFPA, United Nations Populations Fund.*
## Intervention content adaptation, translation, and recording
The UNFPA Sierra Leone country office constituted an internal multidisciplinary task team to facilitate the development and adaptation of SRH and GBV content for delivery to Sierra Leoneans. The team included technical members specializing in maternal health, family planning, gender, communications, Monitoring and Evaluation (M&E), the audiovisual, and mHealth focal points. Furthermore, the team partnered with other UN agencies (UN Women and UNICEF) and government ministries (Ministries of Gender and Children's Affairs, and Health and Sanitation) to develop, review, and approve the messages. The team met regularly in person and later adopted WhatsApp group collaboration coupled with Zoom calls due to the COVID-19 measures. The team developed relevant messages through several brainstorming sessions around three thematic areas in line with the UNFPA core mandate, namely, GBV, FP, MH, and COVID-19. Select contents were agreed to and first developed into English master text transcripts through an iterative review process. The master transcripts were then interpreted and transcribed into the Krio language because *Krio is* widely spoken in Sierra Leone. We also back-translated to ensure the adequacy of the translation. The Krio messages were audio-recorded for radio and automated voice calls. The recorded messages had a 1-min restriction and were recorded in a jingle style. The English transcript master file was also used to adapt SMS format messages with the 160-character limit.
The number of unique messages developed by the thematic area is shown in Table 1.
**Table 1**
| Thematic area | Audio (Radio) | Audio (auto-calls) | Text (SMS) | Audiovisual (Social media) |
| --- | --- | --- | --- | --- |
| GBV | 6 | 1 | 6 | 6 |
| MH | 4 | 1 | 4 | 4 |
| FP | 2 | 1 | 2 | 2 |
| COVID-19 | 1 | 0 | 1 | 1 |
## Social media
The channels identified for the delivery of social media messages were WhatsApp groups and the UNFPA Sierra Leone Twitter account. Each message was shared every Monday, Wednesday, and Friday. On designated days, the first message was shared via UNFPA Twitter, and additional messages could be shared further thereafter. Ten team members provided a total of 39 WhatsApp groups, each with 200–250 members in Sierra Leone, where they recommended sharing the WhatsApp content. The messages were also shared through the UNFPA Facebook page on the same days. The content delivery strategy was to ensure alignment with other channels.
The maximum reach on UNFPA Sierra Leone social media handle was limited by the number of followers. The reach on WhatsApp could not be objectively determined beyond the group membership size. Our measure did not take into consideration potential additional shares and forwards of these messages.
## Radio
Based on advice from the communications analyst, four radio stations were needed to cover the entire Freetown area. Freetown was chosen because it is urban and with the highest number of COVID-19 infections in the country. Each of the four radio stations played a schedule of GBV, FP, MH, and COVID-19 messages in the mornings and evenings using the specified schedules.
For both phases of the campaign, the radio campaign reach has been estimated using the population data in Appendix 2 only. In the first phase of the campaign, the four [4] radio stations in Table 4 were used and their schedule is as shown. The radio stations used estimated audience as provided by the stations are in Appendix 2.
## Telecommunications
Messages were designed and delivered to telecommunications subscribers using different strategies throughout the life of the campaign. Initially, messages targeted UNICEF U-Reporters subscribers. Subsequently, Africell telecommunications subscribers were targeted based on their network data subscription with a pre-recorded automated voice call or SMS. Africell telecommunications is one of the three main subscribers in Sierra Leone. The messaging campaign was later scaled to a nationwide SMS campaign to all 3.9 million subscribers on the network.
## Technology decisions and trade-offs for telecommunication messages
This technical section details the technology decisions and trade-offs for IVR, voice calls, and SMS. The trade-offs were based on the cost, willingness of the telecommunications service providers, technical capacity, and security considerations. As part of our engagement, the National Telecommunication Authority of Sierra Leone (NATCOM) indicated that mobile network operators (MNOs) do not support hardware-based gateways. Further engagement with the MNOs showed that they support software-based gateway solutions only and that the leading software GSM gateway is an open source technology called Asterisk and its other licensed derivatives. Because of this, the research undertaken in March and April focused on Asterisk being open source and known in the industry (almost the only solution), and it was the natural option to investigate and consider it [26].
## UNICEF 2080 SMS short-code
The SMS channel has been used for SRH services with mixed success [27]. Findings from our research from 2019 show that current leading SMS options for interactivity are limited to RapidPro [28] and Textit [29]. They both have the same design, which includes an SMS flow designer and reporting interface. RapidPro is proprietary to UNICEF, and UNICEF has already configured the system in many of their UNICEF countries. The Textit application is licensed (proprietary) and available for anyone interested to acquire and use at a fee. They are both cloud-based services (i.e., hosted by a cloud service provider and not on UNFPA premises or Telecommunications premises). This takes away the need for hosting and server administration, as is the case for voice calls or IVR-based systems. The 67 FP messages developed in 2019 were designed into interactive flows and tested on the Textit SMS platform and uploaded on the U-Report SMS platform in November and December 2019.
The message flow for the 67 SRH messages was configured on the UNICEF RapidPro platform. The main advantage of the RapidPro flow system is that it enables message personalization based on recipient engagement with the platform. The original plan was to advertise the intervention short code and allow users to opt in for in-depth SRH messages, as seen in the proposed flyer in Figure 2. The lack of dedicated staff with the requisite capacity to focus on the project resulted in little platform monitoring and content obsolescence. As a result, UNICEF disabled the controls after 30 days (starting in December 2019).
**Figure 2:** *Proposed flyer for advertising the short code.*
## Africell 2422 SMS short-code
In addition, as part of a Memorandum of Understanding (MoU) between UNFPA and Africell, Africell delivered SMS content on behalf of UNFPA to registered subscribers through their own Africell SMS server platform. A decision was made to deliver the initial SMS messages using UNFPA as the ID instead of 2422, and this may change in the future. Content delivered on behalf of UNFPA by Africell was not interactive and were push-only-based systems. At the end of the campaign, Africell had delivered over 35.46 million FP, GBV, and MH messages nationwide using this approach. Africell emailed a monthly message delivery log with an aggregate bio-details distribution of recipients.
## Voice messages
Voice messages have been shown to be effective in increased service uptake [30, 31]. Technically, using Asterisk will require technical knowledge to manage the physical server hosting, its administration, and regular content updates. Server hosting can be either of three options: the internet cloud service providers (e.g., Google, AWS, or MS Azure); in the UNFPA office; or at the premises of the telecommunications service provider. The MoU with Africell ensured that Africell hosts and manages the message delivery with spare capacity on their server. A dedicated server managed by Africell would mean the added responsibility of server content updates, updating flow appearance, and assigning permissions to UNFPA.
The initial strategy was in three stages: first, to deliver recorded messages using the Africell existing system; second, to transition from an Africell temporary server to a UNFPA local server; and third, to evaluate the performance and throughput and choose the right server for the given scale desired. The first step of this process started as planned with Africell provisioning their server (spare capacity) with the UNFPA short code 2422 and delivering calls beginning 23 April 2020. However, the Africell system could not make the agreed 290,000 calls three times per week. The bottleneck meant that it took 1 week of daily calls at off-peak periods to complete the 290,000 calls (and). As a result, the efforts were discontinued after careful consideration of the technical trade-offs.
## Intervention partner collaborations
The WHO and its partners have identified collaboration as critical for the success of mHealth interventions, particularly in low- and middle-income countries [32]. The project was designed to reuse all existing systems from existing partners as much as possible. Stakeholder engagements were conducted with organizations that currently had an mHealth or SRH intervention or were planning one in the country. At the end of the conversation, two organizations were at the top of the collaboration and engagement list—UNICEF and the Directorate of Science, Technology and Innovation (DSTI) [33]. At UNFPA, a conceptual strategy for engaging and working with both stakeholders was outlined, as in Figure 3. The Directorate of Science, Technology, and Innovation has just launched a multi-sectoral USSD platform (*468#) and was testing it [34]. The engagement with UNFPA was to ensure integration and utilization for either messaging service enrollment or messaging service delivery to young people in Sierra Leone. Similarly, UNICEF has been operating the U-Report SMS platform for almost a decade in Sierra Leone. The aim of the engagement was to leverage the infrastructure, experience, and shared resources to bootstrap the project.
**Figure 3:** *Conceptual strategy for working with collaborating partners.*
## Collaboration with UNICEF
The RapidPro only supported SMS in Sierra Leone at the time of the project, though plans were ongoing to integrate other telecommunications channels such as USSD and Voice. The UNFPA Sierra Leone then opted to leverage the SMS infrastructure of RapidPro to deliver essential messages to young people.
In the spirit of One UN, UNFPA in 2019 signed an MoU with UNICEF to use the UNICEF's RapidPro platform to send up to one million interactive SRH SMS messages at no cost to the almost 200,000 registered U-Reporters. Conversely, UNFPA will advertise the 2080 short code to reach 20,000 young people through school counselors, community learning centers, youth advisory panels, social media, and other channels. The message flow was then configured in RapidPro after UNICEF Sierra Leone provided access. The high-level flow for on-demand SRH messages is shown in Figure 4.
**Figure 4:** *Workflow coded into RapidPro for automated and personalized SMS messaging. SMS, short message service.*
## Collaboration with DSTI
The initial aim was to use the DSTI multi-sectoral platform for the registration of users on the RapidPro platform. As the platform was under development, and the COVID pandemic had limited ability to conduct traditional awareness to drive enrollment and uptake, this option was not used. In addition, according to a recent tweet by the DSTI on 29 September 2022, a milestone 254,669 service usage was recorded on the *468# platform, mainly driven by use for West African Senior School Certificate Exams (WASSCE) results [34].
## Collaboration with Africell
The GSM Association (GSMA), in their eight-country mNutrition intervention in 2018, collaborated with local telecommunications providers in the project countries [35]. There are two main telecommunications service providers in Sierra Leone: Africell and Orange (formerly Airtel). Both have significant telecommunications infrastructure investment and user base. In March 2020, UNFPA reached out to Africell and Orange telecommunications requesting to collaborate on the WiTok mHealth campaign intervention.
Only Africell responded, and subsequently, based on several discussions, an MoU was executed between UNFPA and Africell. Based on the MoU, Africell made available an SMS, and IVR short code [2422], for text and voice content transmission (send and receive) on the Africell network. The MoU required the delivery of SMS to 355,000 subscribers (internet users) and recorded voice messages to 290,000 subscribers (non-Internet users) in the first instance. These numbers were arrived at in discussion with Africell and leveraging the outcome of our 2019 formative research. The 290,000 subscribers represent those covered by the Freetown cell coverage who have never registered for any internet bundle. Similarly, the 355,000 represent the subscribers in Freetown who have subscribed to an internet bundle once in the 90 days before the query.
Africell also offered to make their studio available for recording and provide UNFPA Asterisk server hosting for free. Africell agreed to deliver pre-recorded IVR-style messages to segments (by region or other metrics) or a percentage of their user base. Each voice message will be between 30 s and 1 min. Similarly, each SMS message will be 160 characters or less. UNFPA will pay an agreed lump sum every month for the invoicing service. Africell telecommunications will cover the monthly invoiced costs in excess of this amount. Under the agreement, three calls will be scheduled to 290,000 subscribers per week and three SMS messages to 355,000 subscribers per week. Subsequently, the MoU was extended to deliver three SMS messages per week to all 3.9 million Africell subscribers nationwide.
## Intervention content of messages deployed to young people
The UNFPA Sierra Leone country office constituted an internal multidisciplinary task team to facilitate the development and adaptation of SRH and GBV content for delivery to Sierra Leoneans. The project approach was different from the traditional interview approach [36]. In April 2020, an audiovisual consultant was engaged to support the interpretation and subsequent recording of translated messages from English transcripts to Krio audios for use as telecommunications voice messages and radio messages. A WiTok content team was inaugurated with technical members from MH, FP, and gender-based violence. In the task team were also the communications analyst, the M&E analyst, the audiovisual consultant, and the mHealth consultant. The group started meeting regularly and then extended to online sessions due to the COVID-19 crisis. The team used WhatsApp extensively for online collaborations in April and May 2020.
The content team developed English and Krio transcripts of GBV, FP, MH, and COVID-19 messages grouped for radio, telecommunication voice, and SMS (see Appendix 1). The radio messages were recorded in the Krio language, just like the telecommunications voice messages. The content audiovisual consultant working with a team of local experts recorded the messages through an iterative process. The recorded messages had a 1-min time limit and were recorded in a jingle style. The number of messages per health thematic area per delivery channel is illustrated in Figure 5. The voice messages for telecommunication calls were adapted with multimedia content for delivery via social media (i.e., Twitter, WhatsApp, and Facebook).
**Figure 5:** *Number of messages by health thematic technical area and delivery channel.*
## Intervention strategy and message delivery schedule
The strategy for content delivery was initially to send messages on GBV, FP, MH, and COVID-19 on alternate days synchronously across all channels in the first phase. However, a limitation in delivering all 290,000 calls a day on the Africell network forced a change in strategy. The strategy for the subsequent week was adjusted to delivering content per health thematic (GBV, MH, FP) area per week to mobile phone users weekly. The first health thematic area was GBV, followed by MH, followed by FP. The messages were targeted to clients based on their network data subscription status. It was untargeted based on lifestyle, gender, education, or economic status. Table 2 details the message schedule for the different health thematic areas and the content delivery channels [37]. The messages sent are in Appendix 1.
**Table 2**
| Week 1 message schedule (Trial week) (April 20–April 26) | Week 1 message schedule (Trial week) (April 20–April 26).1 | Week 1 message schedule (Trial week) (April 20–April 26).2 | Week 1 message schedule (Trial week) (April 20–April 26).3 | Week 1 message schedule (Trial week) (April 20–April 26).4 |
| --- | --- | --- | --- | --- |
| Day of week | Radio | Radio | Voice (IVR) | SMS |
| Monday | — | — | — | |
| Tuesday | MH, FP, MH, COVID (2 stations) | — | — | |
| Wednesday | MH, FP, MH, COVID | — | MH | |
| Thursday | MH, FP, MH, COVID | MH | — | |
| Friday | MH, FP, MH, COVID | MH | FP | |
| Saturday | MH, FP, MH, COVID | MH | — | |
| Sunday | — | MH | — | |
| Week 2 schedule for radio, voice calls, and SMS (27th April–3rd May) | Week 2 schedule for radio, voice calls, and SMS (27th April–3rd May) | Week 2 schedule for radio, voice calls, and SMS (27th April–3rd May) | Week 2 schedule for radio, voice calls, and SMS (27th April–3rd May) | Week 2 schedule for radio, voice calls, and SMS (27th April–3rd May) |
| Day of week | Radio | Voice (IVR) | SMS | Social media |
| Monday | GBV, FP, MH, COVID | GBV | GBV1 | GBV1 |
| Tuesday | GBV, FP, MH, COVID | GBV | — | |
| Wednesday | GBV, FP, MH, COVID | GBV | GBV5 | GBV5 |
| Thursday | GBV, FP, MH, COVID | GBV | — | |
| Friday | GBV, FP, MH, COVID | GBV | GBV4 | GBV4 |
| Saturday | GBV, FP, MH, COVID | GBV | — | |
| Sunday | — | GBV | — | |
| Week 3 schedule for radio, voice calls, and SMS (4th May–10th May) | Week 3 schedule for radio, voice calls, and SMS (4th May–10th May) | Week 3 schedule for radio, voice calls, and SMS (4th May–10th May) | Week 3 schedule for radio, voice calls, and SMS (4th May–10th May) | Week 3 schedule for radio, voice calls, and SMS (4th May–10th May) |
| Day of week | Radio | Voice (IVR) | SMS | Social media |
| Monday | GBV, FP, MH, COVID | MH | MH1 | MH1 |
| Tuesday | GBV, FP, MH, COVID | MH | — | — |
| Wednesday | GBV, FP, MH, COVID | MH | MH2 | MH2 |
| Thursday | GBV, FP, MH, COVID | MH | — | — |
| Friday | GBV, FP, MH, COVID | MH | MH3 | MH3 |
| Saturday | GBV, FP, MH, COVID | MH | — | — |
| Sunday | — | MH | — | — |
| Week 4 schedule for radio, voice calls, and SMS (11th May–17th May) | Week 4 schedule for radio, voice calls, and SMS (11th May–17th May) | Week 4 schedule for radio, voice calls, and SMS (11th May–17th May) | Week 4 schedule for radio, voice calls, and SMS (11th May–17th May) | Week 4 schedule for radio, voice calls, and SMS (11th May–17th May) |
| Day of week | Radio | Voice (IVR) | SMS | Social media |
| Monday | GBV, FP, MH, COVID | FP | FP1 | FP1 |
| Tuesday | GBV, FP, MH, COVID | FP | — | — |
| Wednesday | GBV, FP, MH, COVID | FP | FP2 | FP2 |
| Thursday | GBV, FP, MH, COVID | FP | — | — |
| Friday | GBV, FP, MH, COVID | FP | — | — |
| Saturday | GBV, FP, MH, COVID | FP | — | — |
| Sunday | — | FP | — | — |
| Week 5 schedule for radio, voice calls, and SMS (18th May–22nd May) | Week 5 schedule for radio, voice calls, and SMS (18th May–22nd May) | Week 5 schedule for radio, voice calls, and SMS (18th May–22nd May) | Week 5 schedule for radio, voice calls, and SMS (18th May–22nd May) | Week 5 schedule for radio, voice calls, and SMS (18th May–22nd May) |
| Day of week | Radio | Voice (IVR) | SMS | Social media |
| Monday | GBV, FP, MH, COVID | GBV | GBV2 | GBV2 |
| Tuesday | GBV, FP, MH, COVID | GBV | — | — |
| Wednesday | GBV, FP, MH, COVID | GBV | GBV3 | GBV3 |
| Thursday | GBV, FP, MH, COVID | GBV | — | — |
| Friday | GBV, FP, MH, COVID | GBV | GBV6 | GBV6 |
| Saturday | GBV, FP, MH, COVID | | — | — |
| Sunday | — | | — | — |
The schedule for radio continued with a mix of all messages daily except Sunday. The aim was that messaging would start the week of 21 April 2020. The proposed schedule is shown in Table 3.
**Table 3**
| Radio | Morning time | Evening time |
| --- | --- | --- |
| Radio Democracy | 7:00–7:15 am daily | After 7 pm daily |
| Africa Youth Voice | 10:00–11:00 am daily | 6:00–7:00 pm daily |
| SLBC | 9:30–10:00 am daily | 8:15–9:00 pm daily |
| Citizen Radio | 7:00–7:15 am daily | 7:00–7:15 pm daily |
## Document review and analysis
The second part of the methodology involved a combination of observational study and case study documentation detailing the methodology and approach leveraged in the deployment of a large-scale messaging intervention. A secondary analysis and process evaluation of project implementation documents and reports aim to understand the design trade-offs and contextual determinants of the successful scaling of a multichannel digital intervention during early stages of COVID-19 pandemic. The list of reviewed documents is shown in Table 4.
**Table 4**
| Data Source | Document |
| --- | --- |
| UNFPA | mHealth formative research report and article (20) |
| Africell telecoms | Monthly aggregate intervention message and call logs |
| UNFPA | Monthly mHealth consultant activity report |
| 116 GBV call center | Monthly news letter |
| UNFPA | Internal mHealth task team meeting notes |
According to the WHO classification of digital health intervention version 1, our messaging campaign targeted at potential or current end users of health services in Sierra Leone can be categorized as “intervention for clients” [38]. According to the classification, the caregivers of clients who receive service fall within these categories. The other three areas are interventions targeting healthcare professionals, health systems managers, and data services interventions. The intervention whose process is evaluated delivered targeted and untargeted health information to clients in Sierra Leone.
Multiple datasets, including quantitative data extracted from project implementation documents and reports of project evaluation, were repeatedly analyzed and triangulated to facilitate a better understanding of the determinants of the successful scaling of the interventions.
A framework approach was used for the data analysis while allowing for the emergence of new themes. A framework analysis involves the stages of familiarization with data, coding, indexing, charting, mapping, and interpretation [39]. Manual data analyses of project implementation documents and the reports of process evaluation were led by EC and SG. All three authors approved the analysis following reviews of extracts to facilitate immersion in the data to identify factors for scaling successful interventions and other contextual factors that shaped project results. Inductive coding was used to understand how the designed messaging interventions improved the health-seeking behavior of recipients [40] and increased the utilization of health services in urban and rural areas. Deductive coding was used identify contextual factors that influenced intervention scale-up.
## Measuring progress
Usually, messaging interventions are designed to achieve health information promotion and improve the health-seeking behavior of recipients [40]. These behaviors often manifest as increased utilization of health services and ultimately lead to improved health outcomes. In this intervention, the main measure was the reach, which was the number of individuals exposed to any of the channels during the messaging campaign. To measure the progress of the project and the reach of messages sent, UNFPA had one of two options to collect the implementation metrics: first, either self-generate the reporting metrics if there was a UNFPA-managed software system; or second, rely on the host organization to generate this information regularly (often monthly). This ability applied to both SMS and IVR/voice calls. Progress metrics were obtained by email from Africell for SMS and voice calls.
In all, a total of 1.16 million voice calls were made to 290,000 individuals in Freetown and 34.46 million SMS messages were sent to all 3.9 million Africell subscribers in Sierra Leone. The age distribution of national subscribers shows that $48\%$ did not have an age recorded, and $44\%$ were aged 25 years and above, while only $8\%$ were aged 18–24 years. Figure 6 shows the regional distribution of those who provided an age ($52\%$ of 3.9 million); the majority of registered young people aged 18–24 years were in the western region ($58\%$) category.
**Figure 6:** *Regional distribution of subscribers who are aged 18–24 years (n = 2.03 million).*
## Campaign reach
The distribution of reach for both campaign phases is presented in this section by channels.
For radio messages, the number of individuals reached is only determined by estimates from the radio stations themselves. This information will be obtained and made available appropriately. For social media, the primary reach can be obtained from the UNFPA Facebook and Twitter exposure metrics. In addition, the indirect reach through WhatsApp can be estimated by computing the number of members in groups; each message has been shared. WhatsApp was subjective in that this may not translate to actual views.
## SMS (telecommunications)
The distribution of these subscribers who received SMS in phase one by sex is shown in Figure 7A. In Freetown, $85\%$ of subscribers had provided age data at registration and were aged above 18 years. Only $28\%$ of internet subscribers were aged 18–24 years.
**Figure 7:** *Distribution of targeted subscribers by sex (pilot phase).*
## Automated calls (telecommunications)
Subscriber segments who have never subscribed to the internet to whom automated calls were initiated and their distribution by sex is shown in Figure 7B. A total of 1.09 million calls were initiated to this subscriber segment. The number of subscribers with age data above 18 years was $80\%$ of total subscribers, while information was not available for $20\%$. The percentage call pickup rate steadily reduced from $31\%$ to $16\%$ for the campaign's duration, as shown in Figure 8A. See Figure 8B for the distribution of picked-up calls presented by the listening duration. The number of subscribers who did not pick up the 1,093,606 calls was significant, representing $69\%$ in the first week and rising to $84\%$ in the fourth week. Equally, with a $95\%$ confidence level, the drop in the number of persons who picked up the calls from the first week to the fourth week was significant, with a $$p \leq 1$$ value. Similarly, those who completely listened to calls dropped significantly from $75\%$ to $30\%$ when the message content was repeated instead of when message content was not repeated ($$p \leq 1$$) (Figure 8B).
**Figure 8:** *Percentage of call pickups (A) and Percentage duration of listening (B).*
## Social media (Facebook, Twitter, and WhatsApp)
Social media content reach is measured by the number of unique message viewers, while engagement is the number of likes, shares, or comments on Facebook. On Twitter, engagement is the number of likes, retweets, and comments. See details of message distribution in Appendix 3. Each audiovisual message shared to Facebook (FB post), and Twitter (Twits) was also shared to the 39 WhatsApp groups. The corresponding message by thematic area was sent three times each week, on both Twitter and Facebook.
## Discussion, limitations, and lessons
This article highlights a multichannel campaign design and deployment approach while documenting lessons and reach.
## Telecommunications (SMS and automated calls)
The sex distribution of phone ownership registered to the telecommunications provider shows there were more male phone owners than female owners. This trend is the same for both internet subscribers and non-Internet subscribers (also called 2G subscribers). Due to the socioeconomic distribution of wealth in Sierra Leone, phone sharing is highly likely, and a different subscriber can use another individual's registered SIM card. We established from prior formative research that subscribers who belong to vulnerable groups often use basic phones that are not internet-enabled; these subscribers are also less likely to read and write [20]. The formative study also showed that those who cannot read English are less likely to read in any other local dialect (or language) (e.g., Krio). Thus, the nonliterate segment was determined as those without internet and targeted with pre-recorded automated calls in Krio.
We delivered 35.46 million SMS messages over the two phases of the campaign, clearly indicating that SMS is a viable approach for delivering messages. However, we are unable to ascertain how many of these messages were read and understood. A follow-up survey could elicit more information on campaigns like this. We believe this will have the highest impact as SMS messages are retained and can be forwarded and shared with others as needed, though SMS cannot be read by a nonliterate smartphone or internet-enabled phone users. However, it is also possible for nonliterate subscribers to get help from acquaintances to understand these messages.
## Radio jingles
Our strategy for radio jingles was to help deliver SRH content to mobile-disconnected communities. The actual reach via radio campaign remains subjective, as reported by the radio service providers. However, a follow-on survey in Malawi shows that radio is an effective channel for SRH information dissemination [41].
## Call pickup rates, duration of listening, and overall effectiveness
Registered young people aged 18–24 years represent $12\%$ and $13\%$ of those who picked up calls in the first 2 and last 2 weeks of the campaign, respectively. These numbers are low, considering that this group represents our primary target group for the dissemination of SRH information. However, there was a large percentage of subscribers without age information ($48\%$). Some of these could also be young people. Those that picked up the calls were divided into three groups: those that hung up before 10 s; those that hung up between 11 s and 24 s; and those that completed the message by listening for 25 s or more, which is a 75th percentile average, a measure of completion time of recorded messages.
The reasons for the different pickup and drop-out durations vary from lack of interest, being busy, to possible network challenges. As the call pickup rate was the highest (at $31\%$), and the cost per initiated call was about four times the cost of one SMS message, the automated call was not the most viable strategy for a cost-effective campaign. While message repetition for reinforced learning is a proven public health strategy for behavior change, this strategy is not best suited for automated call campaigns. This is demonstrated by the steady drop in the duration of complete listening, as shown in Figure 8B (week 1 – $80\%$, week 2 – $79\%$, week 3 – $75\%$, week 4 – $30\%$). In the fourth week, the week 1 FP message was repeated as in Figure 9. One strategy to determine the overall effectiveness of the campaign was to measure the service uptake. We used the rate of calls to the newly established 116 helpline to measure this service uptake or request. We acknowledge that other unrelated factors may have affected the uptake of the helpline. However, to our knowledge, no other campaign advertised the 116 helpline in the April campaign period.
**Figure 9:** *Phase one number of initiated calls and SMS messages per thematic area per week.*
## Limitations
Given that many subscribers ($48\%$) did not provide age information, it was difficult to deduce the complete number of young people (18–24 years) reached by the telecommunications campaign. The current figures are likely only a fraction of young people reached. In addition, since there was no way to determine whether an SMS gets delivered or has been read by the subscriber, impact measure would require more activities beyond the scope of this paper. In addition, the study did not evaluate the potential impact of service uptake at health facilities or the potential reduction to GBV violence as a result of the availability of the helpline or of receiving the messages.
This paper used the self-reported radio campaign coverage, which is the population of the respective district. This is subjective as not everyone has a radio, and not all age groups listen to the radio; hence, we believe the actual listenership is far lower than the population coverage quoted. For automated calls, we are not able to determine the exact reason for the given duration of listening. Though the intervention was a one-time intervention, which has now ceased, its aim was to ensure that Sierra Leoneans were aware of continuity of service during a pandemic.
## Conclusion
In this work, the experience of designing and implementing WiTok, a large-scale mobile SRH messaging campaign in Sierra Leone, is described. The design considerations and lessons from the deployment through multiple channels are discussed. These are, notably, the impact of a formative research to large-scale messaging solutions, intervention deployment, technology used, organization technology capacity, and cost considerations. In addition, there were trade-offs between two-way messaging and push-only messaging regarding reach.
A total of 1.09 million calls were initiated to 290,000 subscribers in Freetown, Sierra Leone. Similarly, 35.46 million SMS messages were sent out to 3.9 million subscribers nationwide in Sierra Leone. In both phases, SRH and GBV messages were aired at all 15 radio stations across the country. The maximum exposure (reach) on either UNFPA's Facebook or Twitter page was 4,910. Audiovisual messages were shared in at least 39 Sierra Leone WhatsApp groups.
There were twice as many male subscribers than female subscribers in Freetown, and this is nearly so nationally. The call pickup rate was $31\%$ in the first week and dropped to $16\%$ in the fourth week of the automated call campaign. Among those who picked up the call, those who listened for 24 s or more dropped from $75\%$ to $30\%$ when the message was repeated. The first campaign targeted 645,000 Africell subscribers in Freetown, in addition to those reached by four Freetown radio stations and social media. The national campaign (second campaign) targeted 3.9 million subscribers representing all active subscribers on the Africell network in Sierra Leone.
The difficulty in measuring reach was equally highlighted. During the intervention period, there was an increase in the national GBV calls received via the 116 call center helpline. While improvements may have been recorded, they may not be attributable to this multichannel messaging intervention campaign alone due to multiple projects implemented at the peak of the pandemic in Sierra Leone. Other implementing organizations had competing interventions to address similar project objectives.
## 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.
## Author contributions
KED is the approving authority for this study and reviewed drafts of this work. EC prepared the initial draft of this paper. SG supervised the intervention and provided technical review inputs to this 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.
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|
---
title: Hemoglobin level is negatively associated with sarcopenia and its components
in Chinese aged 60 and above
authors:
- Qiaoling Liu
- Jiuhong You
- Min Zhong
- Zhigang Wu
- Yunjie Geng
- Cheng Huang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10040688
doi: 10.3389/fpubh.2023.1081843
license: CC BY 4.0
---
# Hemoglobin level is negatively associated with sarcopenia and its components in Chinese aged 60 and above
## Abstract
### Introduction
Sarcopenia and low hemoglobin level are common in older adults. Few studies have evaluated the association between hemoglobin level and sarcopenia and with inconsistent findings. The multifaceted effects of sarcopenia on the human body and the high prevalence of anemia in the Chinese population make it necessary to explore the association between the two.
### Methods
Using the China Health and Retirement Longitudinal Study (CHARLS), we explored the association between hemoglobin with sarcopenia and its components in the Chinese population aged 60 and above. Multivariate logistic and Cox proportional hazards models were constructed to examine the association of hemoglobin level with sarcopenia and sarcopenia components in individuals aged 60 years or above. The subgroup analysis covered residence, body mass index level, drinking status, and smoking status were conducted. The possible difference of associations between sexes was also explored.
### Results
With a total of 3,055 people, the hemoglobin concentration in people without sarcopenia, possible sarcopenia, and sarcopenia are 14.34 ± 2.22, 14.64 ± 2.27, and 13.58 ± 2.02 g/dl, respectively. Cross-sectional analysis showed strong evidence that hemoglobin was negatively associated with sarcopenia [Odds Ratio (OR) = 0.95, $95\%$ Confidence Interval (CI): 0.90–0.99] and low height-adjusted appendicular skeletal muscle mass (OR = 0.91, $95\%$ CI: 0.86–0.97). On average, a per 1 g/dl higher hemoglobin level was associated with $5\%$ lower odds of sarcopenia (OR = 0.95, $95\%$ CI: 0.90–0.98). The cohort study of 1,022 people demonstrated a statistically significant negative association of hemoglobin level with low physical performance [Hazard Ratio (HR) = 0.92, $95\%$ CI: 0.85–0.99], merely with sarcopenia (HR = 0.92, $95\%$ CI: 0.84–1.00) and skeletal muscle mass (HR = 0.95, $95\%$ CI: 0.80–1.00). Sex-specific analysis suggested hemoglobin's association with sarcopenia, muscle mass, and physical performance in all sexes, with weaker magnitudes in females. Hemoglobin in urban residents and people with high body mass index (BMI) has a larger magnitude of the negative association with sarcopenia.
### Discussion
Hemoglobin level associates with sarcopenia, muscle mass, and physical performance in the Chinese population aged 60 and above, with sex-specific, residence-specific, and BMI-specific effects.
## Introduction
Sarcopenia is a syndrome of progressive loss of muscle mass, strength, and physiological function of the muscles as people age. It associates with mortality and a decline in physical function. The physical and functional decline associated with sarcopenia can have serious negative impacts on an individual's quality of life. People suffering from sarcopenia often experience reduced independence, which can lead to feelings of isolation and depression [1]. Additionally, they are more likely to suffer from chronic illnesses, such as type 2 diabetes mellitus and heart failure, which can further reduce their quality of life [2, 3].
The prevalence of sarcopenia in older Asian individuals ranges between 2.5 and $45.7\%$ [4]. In China, this number is between 8.9 and $38.8\%$ [5]. The seventh national population census in 2020 showed that the proportion of people aged 60 years and above in *China is* $18.7\%$, a total of 260 million people [6]. Risk factors related to sarcopenia, such as obesity and diabetes mellitus, have an upward trend [7, 8]. These findings suggested that China has a large vulnerable population, together with a high prevalence of sarcopenia. The lack of awareness of sarcopenia in clinical practitioners further casts shadows on healthy aging [9].
Anemia is a recognized risk factor for fatigue, mortality, and decreased functional capacity in elder individuals [10]. The Chinese population has a high prevalence of anemia with considerable geographic differences. In middle and eastern China, the all-age anemia prevalence is $13.4\%$, and this value is $34\%$ in western China [11, 12]. In the Chinese population aged over 60 and above, the age-adjusted prevalence of anemia varies from 8.5 to $35.4\%$ [13]. In Asian people, anemia is negatively associated with handgrip strength, muscle mass, and physical performance (14–16), all of which are components in sarcopenia diagnosis.
Hemoglobin is the well-established way that clinical practitioners establish the diagnosis of anemia. As anemia is associated with sarcopenia, it can be assumed that a low level of hemoglobin may associate with sarcopenia [17]. Insufficient hemoglobin can affect the oxygen delivery to skeletal muscle, and negatively impact muscle strength, as observed in people with chronic hypoxia [18]. Anemia is also positively associated with multiple inflammatory markers, which may affect muscle mass and physical performance in a negative way [18]. A few studies have proposed that hemoglobin was positively associated with muscle strength and physical function [17, 19]. Even so, these studies have mostly been with small-size samples [17], cross-sectional design [20, 21], or applied sarcopenia diagnosis criteria which is unsuitable in Asian populations [22].
Alerted by the insufficient clinical awareness of sarcopenia, the large Chinese population vulnerable to anemia and low hemoglobin, and the possible mechanism between hemoglobin and sarcopenia components, the association between hemoglobin and sarcopenia in Chinese population should be thoroughly explored. To the best of our knowledge, no large-scale studies of the Chinese population have elucidated the association of hemoglobin level with sarcopenia and its components using guidelines tailored for the Chinese. Therefore, we used a nationally representative, population-based survey [the China Health and Retirement Longitudinal Study (CHARLS)] to explore the aforementioned associations, with the aim of bridging the knowledge gap.
## Study population
The CHARLS is a national, population-based survey focusing on Chinese aged 45 years and above. A total of 450 representative communities from 28 provinces were selected using a multistep probability sampling strategy [23]. The first survey was started in 2011, and participants were followed every 2 years. A total of 17,705 respondents were interviewed in 2011, 18,605 respondents were interviewed in 2013, and 21,095 people were interviewed in 2015. Because biomarker and blood tests were only conducted in 2011 and 2015, data from these years were used. The inclusion criteria were [1] individuals aged 60 years or above in 2011; [2] available data regarding sarcopenia status; and [3] the possession of blood test data in 2011. People missing demographic or health information were excluded.
This study contained two sub-studies. [ 1] A cross-sectional analysis of the CHARLS 2011 population. Of the total of 17,705 participants, 14,650 people were excluded because of missing blood test data ($$n = 8$$,293), no sarcopenia relevant data ($$n = 2$$,341), no demographic or health information ($$n = 808$$), and ages below 60 years ($$n = 3$$,208). A total of 3,055 participants remained for the analysis. [ 2] In the cohort analysis, we further excluded 831 people who had either possible sarcopenia or sarcopenia in 2011 and removed 1,202 participants who had no sarcopenia data in 2015; thus, constructed a cohort of 1,022 people.
The Institutional Review Board at Peking University approved CHARLS (approval number: IRB00001052-11014 for biomarker collection; IRB00001052-11015 for main household survey including anthropometrics), and all of the participants were required to provide written informed consent before joining CHARLS.
## Assessment of sarcopenia and its components
The first expert consensus on sarcopenia in Chinese population [5] was published in 2021. The *Chinese consensus* highly considered the guideline issued by the Asian Working Group for Sarcopenia (AWGS) and recommended the use of its cutoff values for sarcopenia diagnosis in Chinese population [24]. Participants' sarcopenia status was assessed by three components: appendicular skeletal muscle mass (ASM), muscle strength, and physical performance.
The ASM was estimated using a validated equation derived from Chinese adults [25]. The equation has been applied in research which has similar study populations as that of our study [26, 27]. The equation is: Participants' height and weight were measured using a stadiometer and a digital floor scale, respectively, to the nearest 0.1 cm and 0.1 kg. The ASM derived from the abovementioned equation is consistent with the result from dual X-ray absorptiometry (DXA) [25]. In clinical practice, DXA requires specialized radiology equipment and experienced physicians to ensure testing accuracy. Bioelectrical impedance analysis (BIA) is a less expensive assessment technology and requires no specialists to perform. Both DXA and BIA are recommended for muscle mass evaluation, and their results are interchangeable [24, 28]. BIA criteria were used in this study to make findings applicable in a more generalized setting. Low muscle mass was defined as a height-adjusted muscle mass (ASM/Height2) <7.0 kg/m2 for males and below 5.7 kg/m2 for females [5].
Muscle strength was measured via handgrip strength, which was evaluated by asking participants to hold the dynamometer at a right angle (90°) and squeeze a YuejianTM WL-1000 dynamometer (Nantong Yuejian Physical Measurement Instrument Co., Ltd., Nantong, China) two times in each hand as hard as possible. The maximum reading was used for the sarcopenia diagnosis. The cutoff points for low muscle strength were 28 kg for males and 18 kg for females. CHARLS used a 5-time chair stand test to evaluate physical performance. The cutoff value of low physical performance was a test time ≥ 12 s for all sexes [29].
Possible sarcopenia was defined as either low muscle strength or low physical performance without low muscle mass. Sarcopenia was diagnosed when low muscle mass plus either low muscle strength or low physical performance was identified. Severe sarcopenia was defined as the co-existence of low muscle mass, low muscle strength, and low physical performance. As only 166 ($5.43\%$) participants had severe sarcopenia at baseline, severe sarcopenia was merged into sarcopenia to avoid sparse data bias [30]. Participants were categorized into no sarcopenia ($$n = 1$$,618), possible sarcopenia ($$n = 726$$), and sarcopenia ($$n = 711$$).
## Blood sample collection and analysis
The CHARLS project collaborated with the Chinese Center for Disease Control and Prevention (China CDC) to collect and process blood samples. Three tubes of blood were collected from each participant. One tube was immediately stored at 4°C and transported to the nearest CDC center or health center for complete blood count; the median time from collection to analysis was 97 min. The other two tubes were stored at −80°C for bioassay analysis at a national certified lab at Capital Medical University [31]. Cystatin C is a protein associated with muscle mass in some chronic disease patients [32]. In the CHARLS project, it was measured using a particle-enhanced turbimetric assay with a detection range of 0.5–8.0 mg/L.
## Covariates
CHARLS participants were interviewed using a computer-aided structured questionnaire. Demographic information, such as age, sex, socioeconomic level, and urban/rural residence, was collected. Socioeconomic level was collected from the participants' self-evaluation scale. Health status and functioning data, including smoking, drinking, body mass index (BMI), blood pressure, and diagnoses of hypertension/dyslipidemia/diabetes/kidney disease/heart failure/rheumatism, were collected. A participant was identified as a patient with the abovementioned diseases if the participant had been diagnosed by physicians or was on medication at the time of interview. Hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic pressure ≥ 90 mmHg or if the participant was on medications by the time of the interview. Diabetes was identified if the participant was on antidiabetic agents or had plasma glucose ≥ 200 mg/dl. BMI was categorized into underweight (below 18.5 kg/m2), normal weight (18.5–23.9 kg/m2), and overweight or obese (24 kg/m2 and above).
## Statistical analysis
Continuous data were presented as the mean with standard deviation (SD) or median with interquartile range (IQR). Categorical data are presented as n (%). The baseline data of all 6,263 participants were summarized and stratified by their sarcopenia status in the baseline year 2011. Comparisons of baseline characteristics among the groups were conducted using the Kruskal–Wallis test. Logistic regression was then performed to identify the association of hemoglobin with sarcopenia and with sarcopenia components (ASM, muscle strength, and physical performance).
All the abovementioned associations were then evaluated in a cohort analysis. As the sarcopenia test was conducted on the day of the interview, the follow-up period was defined as the interval between the interview day in 2011 and the interview day in 2015. Schoenfeld's residuals showed no violation of the proportional hazards assumption ($$P \leq 0.44$$). Cox proportional hazard models were used to calculate hazard ratios (HRs) with $95\%$ confidence intervals (CIs). The sex-specific association was then explored. Finally, analyses were conducted in the following subgroups: residence, BMI level, drinking, and smoking. All the analyses were performed using STATA 16.0/MP (StataCorp, USA). A two-sided P-value < 0.05 was considered to be statistically significant.
## Baseline statistics of the study population
The baseline statistics of the study population are presented in Table 1, as stratified by sarcopenia status in the baseline year. In the total of 3,055 participants, the prevalence of sarcopenia was $23.27\%$, and the prevalence of possible sarcopenia was $23.76\%$. Sarcopenia was more common in people of higher age, females, rural residents, unmarried people, less educated people, low socioeconomic level people, and people with low BMI/arthritis/rheumatism. People with higher cystatin C and lower hemoglobin were more commonly found to have sarcopenia (Table 1).
**Table 1**
| Characteristics | No sarcopenia | Possible sarcopenia | Sarcopenia |
| --- | --- | --- | --- |
| Number of people, n (row %) | 1,618 (52.96) | 726 (23.76) | 711 (23.27) |
| Age (year), mean (SD) | 66.75 (5.48) | 68.28 (6.10) | 71.84 (6.81) |
| Male, n (%) | 909 (56.18) | 401 (55.23) | 262 (36.85) |
| Urban residence, n (%) | 575 (35.54) | 270 (37.19) | 191 (26.86) |
| Married, n (%) | 1,360 (84.05) | 597 (82.23) | 504 (70.89) |
| Smoking, n (%) | 743 (45.92) | 335 (46.14) | 253 (35.58) |
| Drinking, n (%) | 583 (36.03) | 199 (27.41) | 177 (24.89) |
| Educational level, n (%) | Educational level, n (%) | Educational level, n (%) | Educational level, n (%) |
| No formal education | 482 (29.79) | 263 (36.23) | 398 (55.98) |
| Primary | 788 (48.70) | 349 (48.07) | 268 (37.69) |
| Secondary | 313 (19.34) | 109 (15.01) | 44 (6.19) |
| Tertiary and above | 35 (2.16) | 5 (0.69) | 1 (0.14) |
| Socioeconomic level, n (%) | Socioeconomic level, n (%) | Socioeconomic level, n (%) | Socioeconomic level, n (%) |
| Above average | 50 (3.09) | 25 (3.44) | 20 (2.81) |
| Average | 893 (55.19) | 386 (53.17) | 355 (49.93) |
| Relatively poor | 501 (30.96) | 219 (30.17) | 210 (29.54) |
| Poor | 174 (10.75) | 96 (13.22) | 126 (17.72) |
| BMI (kg/m2), mean (SD) | 22.98 (3.70) | 25.24 (3.40) | 19.90 (2.19) |
| Systolic blood pressure (mmHg), mean (SD) | 134.59 (22.11) | 138.06 (22.33) | 136.07 (25.07) |
| Diastolic blood pressure (mmHg), mean (SD) | 74.85 (11.45) | 76.27 (11.86) | 73.02 (12.23) |
| Comorbidities, n (%) | Comorbidities, n (%) | Comorbidities, n (%) | Comorbidities, n (%) |
| Hypertension | 467 (28.86) | 306 (42.15) | 181 (25.46) |
| Dyslipidemia | 164 (10.14) | 98 (13.50) | 46 (6.47) |
| Diabetes | 99 (6.12) | 68 (9.37) | 26 (3.66) |
| Kidney disease | 101 (6.24) | 67 (9.23) | 35 (4.92) |
| Heart disease | 224 (13.84) | 139 (19.15) | 116 (16.32) |
| Arthritis or rheumatism | 569 (35.17) | 301 (41.46) | 306 (43.04) |
| Cystatin C (mg/l), mean (SD) | 1.08 (0.27) | 1.11 (0.29) | 1.16 (0.34) |
| Hemoglobin (g/dl), mean (SD) | 14.34 (2.22) | 14.64 (2.27) | 13.58 (2.02) |
| Sarcopenia components, mean (SD) | Sarcopenia components, mean (SD) | Sarcopenia components, mean (SD) | Sarcopenia components, mean (SD) |
| Height-adjusted ASM (kg/m2) | 6.68 (1.13) | 7.10 (0.86) | 5.57 (0.91) |
| Handgrip strength (kg) | 33.02 (8.34) | 28.20 (9.07) | 23.34 (7.68) |
| Five-time chair stand time (s) | 9.16 (1.76) | 15.42 (6.20) | 14.97 (5.33) |
## Cross-sectional analysis of the association of hemoglobin with sarcopenia and its components
After adjustments for demographic factors, a very significant association ($P \leq 0.001$) was found between hemoglobin and sarcopenia. This association was not significantly changed ($$P \leq 0.04$$) with further adjustments for health-related factors. On average, a per 1 g/dl higher hemoglobin level was associated with $5\%$ lower odds of sarcopenia (OR = 0.95, $95\%$ CI: 0.90–0.98) (Table 2).
**Table 2**
| Unnamed: 0 | Model 1a | Model 1a.1 | Model 2b | Model 2b.1 | Model 3c | Model 3c.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | OR (95%CI) | P-value | OR (95%CI) | P-value | OR (95%CI) | P-value |
| Sarcopenia | Sarcopenia | Sarcopenia | Sarcopenia | Sarcopenia | Sarcopenia | Sarcopenia |
| Hemoglobin | 0.89 (0.85–0.93) | <0.001 | 0.94 (0.90–0.99) | 0.02 | 0.95 (0.90–0.99) | 0.04 |
| COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA |
| Low height-adjusted ASM | Low height-adjusted ASM | Low height-adjusted ASM | Low height-adjusted ASM | Low height-adjusted ASM | Low height-adjusted ASM | Low height-adjusted ASM |
| Hemoglobin | 0.85 (0.82–0.89) | <0.001 | 0.90 (0.86–0.95) | <0.001 | 0.91 (0.86–0.97) | 0.002 |
| Low muscle strength | Low muscle strength | Low muscle strength | Low muscle strength | Low muscle strength | Low muscle strength | Low muscle strength |
| Hemoglobin | 0.94 (0.90–0.99) | 0.02 | 0.96 (0.91–1.01) | 0.08 | 0.96 (0.92–1.01) | 0.16 |
| Low physical performance | Low physical performance | Low physical performance | Low physical performance | Low physical performance | Low physical performance | Low physical performance |
| Hemoglobin | 1.03 (0.99–1.07) | 0.11 | 1.02 (0.99–1.06) | 0.19 | 1.02 (0.99–1.06) | 0.22 |
Among sarcopenia components, a statistical association was found between hemoglobin and low height-adjusted ASM. On average, a per 1 g/dl elevated hemoglobin level was associated with $9\%$ lower odds of having low height-adjusted ASM (OR = 0.91, $95\%$ CI: 0.86–0.97). It should be noted that although no statistical significance ($$P \leq 0.16$$) was found in the association between hemoglobin and muscle strength, the upper $95\%$ CI is close to 1.00. No evidence ($$P \leq 0.22$$) between hemoglobin and low physical performance was found.
## Cohort analysis of the association of hemoglobin with sarcopenia and its components
A total of 1,022 people who had no sarcopenia in 2011 were followed up to 2015; 165 people ($13.41\%$) were diagnosed with sarcopenia, with an incidence rate of 336.61 per 10,000 person-years.
After adjusting for multiple covariates, consistent evidence was found between baseline hemoglobin and sarcopenia (HR = 0.92, $95\%$ CI: 0.84–1.00), low height-adjusted ASM (HR = 0.95, $95\%$ CI: 0.90–1.00), and low physical performance (HR = 0.92, $95\%$ CI: 0.85–0.99). No association between hemoglobin and low muscle strength was observed (Table 3).
**Table 3**
| Unnamed: 0 | Model 1a | Model 1a.1 | Model 2b | Model 2b.1 | Model 3c | Model 3c.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | HR (95%CI) | P-value | HR (95%CI) | P-value | HR (95%CI) | P-value |
| Sarcopenia | Sarcopenia | Sarcopenia | Sarcopenia | Sarcopenia | Sarcopenia | Sarcopenia |
| Hemoglobin | 0.87 (0.79–0.95) | 0.003 | 0.92 (0.84–1.01) | 0.08 | 0.92 (0.84–1.00) | 0.05 |
| COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA | COMPONENT OF SARCOPENIA |
| Low Height-adjusted ASM | Low Height-adjusted ASM | Low Height-adjusted ASM | Low Height-adjusted ASM | Low Height-adjusted ASM | Low Height-adjusted ASM | Low Height-adjusted ASM |
| Hemoglobin | 0.90 (0.85–0.95) | <0.001 | 0.94 (0.89–1.00) | 0.04 | 0.95 (0.90–1.00) | 0.05 |
| Low muscle strength | Low muscle strength | Low muscle strength | Low muscle strength | Low muscle strength | Low muscle strength | Low muscle strength |
| Hemoglobin | 0.94 (0.87–1.03) | 0.17 | 0.97 (0.89–1.06) | 0.52 | 0.98 (0.90–1.06) | 0.60 |
| Low physical performance | Low physical performance | Low physical performance | Low physical performance | Low physical performance | Low physical performance | Low physical performance |
| Hemoglobin | 0.92 (0.83–0.99) | 0.04 | 0.92 (0.85–0.99) | 0.03 | 0.92 (0.85–0.99) | 0.03 |
Due to the difference in the cutoff value of sarcopenia criteria between sexes, the sex-specific association between baseline hemoglobin and sarcopenia was explored. In males, hemoglobin was associated with sarcopenia, and a per 1 g/dl increase in hemoglobin was associated with a $10\%$ lower rate of sarcopenia (HR = 0.90, $95\%$ CI: 0.83–0.99). Hemoglobin in males was also statistically associated with low height-adjusted ASM (HR = 0.92, $95\%$ CI: 0.89–0.96) and with low physical performance (HR = 0.91, $95\%$ CI: 0.85–0.97). In females, hemoglobin was found to be marginally associated with sarcopenia (HR = 0.92, $95\%$ CI: 0.84–1.00) and with low height-adjusted ASM (HR = 0.96, $95\%$ CI: 0.93–1.00); strong evidence of association was also found in low physical performance (HR = 0.92, $95\%$ CI: 0.86–0.99). Hemoglobin was not evidently associated with low muscle strength in all sexes (Supplementary Table S1).
## Subgroup analysis for baseline hemoglobin and its association with sarcopenia
Figure 1 showed that the association between hemoglobin and sarcopenia was stronger in urban residents (HR = 0.89, $95\%$ CI: 0.80–0.98). Among people of different BMI levels, the hemoglobin-sarcopenia association was stronger in people with higher BMI levels. In the overweight/obese group, a per 1 g/dl higher hemoglobin was associated with a $20\%$ lower hazard rate of sarcopenia (HR = 0.80, $95\%$ CI: 0.72–0.89), with very strong evidence ($P \leq 0.001$). No evidence was found in underweight people. The hemoglobin-sarcopenia association was similar in people with various drinking and smoking statuses (Figure 1).
**Figure 1:** *Forest plot for multivariable Cox regression results to show the sex-specific association between hemoglobin and sarcopenia, stratified by residence, body mass index level, drinking status, and smoking status.*
## Discussion
In this study, the cross-sectional analysis demonstrated hemoglobin in Chinese individuals aged 60 and above was negatively associated with sarcopenia and low height-adjusted ASM. The cohort study showed that in the general population, hemoglobin was negatively associated with sarcopenia, low muscle mass, and low physical performance. In both sexes, hemoglobin was negatively associated with sarcopenia, low muscle mass, and physical performance, although the magnitude of association varies. No evidence of the association between hemoglobin and muscle strength was found. Subgroup analyses demonstrated that urban residents and people with higher BMI levels had a large magnitude of hemoglobin-sarcopenia association.
The overall prevalence of sarcopenia in the study population was $13.41\%$, with a higher prevalence in females, similar to other studies on Chinese population [33, 34]. Our findings were largely consistent with several previous studies [18, 21]. However, there were still some discrepancies in this field. A cross-sectional study on a Taiwanese population demonstrated that hemoglobin was only associated with physical performance and muscle strength, not with sarcopenia or muscle mass [17]. Erythropoietin receptors are expressed in human skeletal muscle [35]. Skeletal muscle mass is independently associated with body responsiveness to erythropoietin stimulating agents [36]. It is fairly possible that insufficient skeletal muscle led to decreased hemoglobin production, caused a reverse causality that could not be ruled out in a cross-sectional design and biased the result in that Taiwanese study.
There is a paucity of longitudinal research on hemoglobin and its association with sarcopenia, and there is even more scarce research on sex-specific associations. Along with aging, the decline in serum testosterone in males is associated with compromised hematopoiesis, further inhibiting hemoglobin production (37–39). The decline in hemoglobin in men usually starts in their 30s, whereas hemoglobin in women is slightly increased after menopause before starting to decline in their 60s [40]. In the study population, the hemoglobin level in the oldest quartile of males (mean age: 77.5 years) was on average $92.5\%$ of that in the youngest quartile (mean age: 61.5 years); in females, the mean hemoglobin in the oldest quartile (mean age: 77.4 years) was $96.4\%$ of that in the youngest quartile (mean age: 61 years). The above relatively smaller decline in hemoglobin in females as compared to males may explain the sex-specific differences in the magnitude of the association of hemoglobin with sarcopenia, ASM, and physical performance.
This study did not find evidence of the association of hemoglobin with muscle strength in either the cross-sectional or cohort analyses. This finding was consistent with a study on older individuals [41]. Yet, another study demonstrated hemoglobin was associated with handgrip strength in older individuals [42]. It is possible the higher average age of the participants (over 75 years) and the different sample selections of the above study may justify the conflicting results.
In the cohort study, we found a consistent association of hemoglobin with physical performance. The current research on the association between hemoglobin and physical performance remains controversial. Studies using Functional Independence Measure [43] demonstrated the association of hemoglobin with physical function and performance. A systematic review of fifteen randomized clinical trials and five observational studies reported a negative association between hemoglobin and fatigue but not with physical function [44]. We solely evaluated physical performance by the 5-time chair stand test without the gait speed test, and thus the evaluation may not fully reflect the participants' actual physical performance. The CHARLS gait speed test was set at a 2.5 m distance, which was much shorter than the recommended six meters [24]. The validity of the 2.5 m distance is unclear, although the 3-meter, 6-meter, and longer distance gait speed tests have been validated [45, 46]. As participants were more capable of completing a shorter distance, the 2.5 m test may likely overestimate the gait speed. Only $41.90\%$ of people had both chair stand test and gait speed test records. Statistically significant differences in height, weight, and handgrip strength were found between individuals with gait speed records and those without. Using people with available gait speed in this study will heavily limit the sample size and may bias the study result.
Urban residents and obese/overweight people had a larger magnitude of hemoglobin-sarcopenia association; the underlying mechanism accounting for the larger magnitude remains unclear. Urban residents are known to have higher hemoglobin levels than rural residents [47], and a higher BMI level has an inverse association with hemoglobin in older Chinese people [48]. It may be possible that there is a threshold, and only an over-threshold hemoglobin level is associated with sarcopenia. An in-depth analysis of urban/rural residents and people of different BMI groups is needed to further explore the abovementioned findings.
From a perspective of primary prevention and public health, increasing hemoglobin levels in people aged 60 and above can be better achieved through a multidisciplinary approach. Providing access to nutrient-dense foods that are rich in iron, vitamin B12, and folic acid. Older adults may have a reduced appetite or difficulty chewing, so it is important to provide meals that are easy to prepare, chew, and digest. In addition to dietary changes, regular exercise can help increase hemoglobin levels while strengthening muscle and improving physical performance. Low-impact aerobic activities such as walking, swimming, and biking may help increase circulation and promote healthy oxygen levels in the body. Moderate strength training can also help increase hemoglobin levels and build muscle strength to prevent sarcopenia [49]. Excessive alcohol intake (over 2 alcoholic drinks/day) should be restricted as they can lead to a decrease in hemoglobin levels [50]. It should consider the unique needs and challenges older adults face when designing interventions and involve healthcare providers, community organizations, and family caregivers in the process.
Despite all the efforts made in this study, there were several limitations. First, the core element of our research, the ASM, was calculated using a validated formula instead of BIA/DXA methods. Admittedly, using the formula may impair the generalization of our findings in non-Chinese population. However, this is unavoidable because our study was a secondary research using existing data. Considering the scale and representativeness of CHARLS, along with the consistence in our findings, we believe that this study makes its contribution to solving the urgent public health issue brought by sarcopenia. Readers are advised to be aware of the possible bias of ASM evaluation. Second, gait speed is an indicator of the overall health of the elderly [51]. Without adjusting gait speed, our findings may be biased toward the null. Third, confounding factors, such as inflammatory biomarkers and dietary patterns, were not adjusted due to data availability. All this can confound the results. Finally, selection bias, including volunteer bias, should be considered when interpreting our results.
To conclude, our study demonstrated that hemoglobin level is negatively associated with sarcopenia in the Chinese population aged 60 and above. Males, urban residents, and people with a high BMI have a larger magnitude of the negative association between hemoglobin level and sarcopenia. Along with the high prevalence of sarcopenia and an aging society, our findings may generate meaningful implications for preventing sarcopenia and promoting healthy aging in China.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://charls.pku.edu.cn/.
## Ethics statement
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The Institutional Review Board at Peking University approved CHARLS (approval number: IRB00001052-11014 for biomarker collection; IRB00001052-11015 for main household survey including anthropometrics), and all of the participants were required to provide written informed consent before joining CHARLS. Data were fully anonymized and it is impossible to re-identify any participants.
## Author contributions
QL and CH conceived the protocol. QL, JY, MZ, ZW, and YG contributed to the analysis and interpretation of data. QL and JY drafted the manuscript. QL critically revised the manuscript. All authors agree to be fully accountable for ensuring the integrity and accuracy of the work and 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
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## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1081843/full#supplementary-material
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|
---
title: Phosphate depletion in insulin-insensitive skeletal muscle drives AMPD activation
and sarcopenia in chronic kidney disease
authors:
- Ana Andres-Hernando
- Christina Cicerchi
- Gabriela E. Garcia
- David J. Orlicky
- Peter Stenvinkel
- Richard J. Johnson
- Miguel A. Lanaspa
journal: iScience
year: 2023
pmcid: PMC10040733
doi: 10.1016/j.isci.2023.106355
license: CC BY 4.0
---
# Phosphate depletion in insulin-insensitive skeletal muscle drives AMPD activation and sarcopenia in chronic kidney disease
## Summary
Sarcopenia is a common and devastating condition in patients with chronic kidney disease (CKD). Here, we provide evidence that the kidney-muscle crosstalk in sarcopenia is mediated by reduced insulin sensitivity and the activation of the muscle-specific isoform of AMP deaminase, AMPD1. By using a high protein-based CKD model of sarcopenia in mice and differentiated human myotubes, we show that urea reduces insulin-dependent glucose and phosphate uptake by the skeletal muscle, thus contributing to the hyperphosphatemia observed in CKD whereas depleting intramuscular phosphate needed to restore energy and inhibit AMPD1. Hyperactivated AMPD1, in turn, aggravates the low energy state in the muscle by removing free adenosine monophosphate (AMP) and producing proinflammatory factors and uric acid which contribute to the progression of kidney disease. Our data provide molecular and metabolic evidence supporting the use of strategies aimed to improve insulin sensitivity and to block AMPD1 to prevent sarcopenia in subjects with CKD.
## Graphical abstract
## Highlights
•*Uremic sarcopenia* is mediated by low muscle insulin sensitivity and phosphate uptake•Phosphate balance in plasma and muscle is dysregulated in CKD-mediated sarcopenia•Blockade of muscle AMP deamination improves renal dysfunction and muscle atrophy
## Abstract
Biological sciences; Physiology; Pathophysiology; Cell biology
## Introduction
Sarcopenia (reduced skeletal muscle mass and strength) is extremely common in patients with chronic kidney disease (CKD), particularly in those with accompanying risk factors such as obesity, heart failure, diabetes, and aging.1 The consequences of sarcopenia are severe because it can shorten lifespan, cause frailty, increase the risk for falls, compromise the ability for individuals to care for themselves, and increase the need for wheelchair use.2,3,4,5,6 Thus, identifying the cause and treatment of sarcopenia remains an important goal.
Although sarcopenia was once considered the natural consequence of low physical activity and reduction in muscle mass that accompanies aging, sarcopenia is now recognized as a condition associated with low grade systemic inflammation, protein-energy wasting, altered muscle composition with fat infiltration and a catabolic state.7,8,9,10,11,12,13 Other associations include apoptosis, autophagy, mitochondria loss, intracellular activation of angiotensin II, overexpression of myostatin, and metabolic acidosis.14,15,16 This has led to the idea that sarcopenia might be part of a disease process that may require specific treatments.17,18,19,20 Muscle has a higher energy demand than most other organs. To facilitate it, phosphocreatine is stored so it can provide phosphate for ATP synthesis rapidly in times of need.21 However, both ATP and phosphocreatine depend on sufficient intracellular phosphate to be present, and if intracellular phosphate is chronically low, then ATP synthesis is markedly impaired. Indeed, intracellular phosphate depletion results in weakness and loss of muscle strength, with rhabdomyolysis, and even sarcopenia. Thus, we hypothesize that intracellular phosphate could represent the “Achilles’ heel” in muscle bioenergetics and possibly a key risk factor for sarcopenia.
Intracellular phosphate depletion is often associated with low serum phosphate and systemic phosphate depletion associated with poor intake or excessive loss of phosphate.22,23 However, intracellular phosphate depletion can also occur with normal or even high serum phosphate levels, especially in the setting of insulin resistance or deficiency.24 *This is* because insulin has an important role in the uptake of phosphate into cells through Na-phosphate transporters.25,26 In diabetic ketoacidosis, for example, subjects can present with marked hyperphosphatemia despite intracellular phosphate levels being low.27 *In this* regard, CKD is consistently associated with insulin resistance.28 *Although serum* phosphate is often high, there have been no studies that have investigated intracellular phosphate levels in the individual with CKD. However, studies suggest that phosphate stores are likely borderline or low, not only because of the insulin resistant state but also in part because dialysis removes not only extracellular but also intracellular phosphate.29 Indeed, a study of stable hemodialysis patients (that lacked sarcopenia or protein energy wasting) found that both intracellular phosphate and ATP levels fell during dialysis.29 Intracellular phosphate depletion is also associated with the activation of AMP deaminase (AMPD). AMPD is an AMP-dependent enzyme that converts AMP into inosine monophosphate (IMP) thus lowering nucleotide pools. Three main isoforms of AMPD have been described to date including the muscle-specific isoform AMPD1. As part of the purine degradation pathway, the last product of AMP metabolism via AMPD in humans is uric acid.22,30 Even though uric acid has antioxidant properties, high uric acid levels have been associated with inflammation, oxidative stress, and mitochondrial dysfunction31,32,33,34,35,36, and correlate with the severity of kidney disease37,38,39 and sarcopenia.40 Consistently, urate lowering therapies like the use of xanthine oxidase inhibitors reduce sarcopenia in dialysis patients.41 We hypothesized that one of the main consequences in CKD is that the reduced insulin sensitivity dysregulates phosphate homeostasis leading to intracellular phosphate depletion and AMPD1 activation, shifting muscle fibers into a catabolic-prone state.
## CKD-dependent sarcopenia is associated with activation of AMPD1 in the skeletal muscle
A model of progressive CKD modified from Zhang et al.42 was developed in mice. To this end, a two-stage surgery followed by a high protein diet for 4 weeks was performed as detailed in the methods section and in Figure 1A. The effect of this model on kidney function was assessed by measuring plasma creatinine and blood urea nitrogen (BUN) as well as the urine albumin to creatinine ratio. As shown in Figures 1B–1D, the addition of a high protein diet markedly caused renal dysfunction in mice. In parallel with worse kidney function, mice on a high-protein diet also developed sarcopenia compared to mice undergoing sham operation (Figures 1E–1G). Sarcopenia was denoted by a shift to the left in the size of muscle fibers (smaller fiber, Figures 1E and 1F) and a significant elevation in serum CPK levels (Figure 1G).Figure 1CKD-dependent muscle atrophy is associated with nucleotide turnover and AMPD1 activation(A) Schematic of the mouse model employed to induce CKD-dependent muscle atrophy.(B–D) Blood creatinine, urea and urinary albumin excretion in wild type mice undergoing sham operation (black) or at different states of the model: polectomy (green), lateral nephrectomy and low protein diet (green) and lateral nephrectomy and high protein diet (red).(E–G) (E) Representative H&E image of gastrocnemius of mice undergoing sham or with CKD. White arrows denote inflammatory cells. Bar = 25 μm (F) Cross-sectional analysis of myofiber sizes in sham and CKD mice showing a shift to the left (smaller size) (150 myofibers/muscle measured) (G) Plasma CPK levels in mice from same groups as in B).(H) Intramuscular nucleotide pool (ATP, ADP, AMP and total nucleotides) in sham and CKD mice.(I) Schematic of AMP metabolism via AMPD1 after ATP metabolism.(J) Representative western blot for AMPD1 and myostatin in gastrocnemius of mice from the same groups as in B).(K and L) Intramuscular AMPD activity and uric acid levels in sham and CKD mice. Statistical analysis: B-D and G) One way ANOVA followed by Tukey’s multiple comparison tests. H,K-L) two-tail t-test. ∗$p \leq 0.05$ and ∗∗$p \leq 0.01$ versus sham. # $p \leq 0.05$ and ##$p \leq 0.01$ $$n = 5$$ mice per group.
Analysis of the intramuscular nucleotide pool in mice with sarcopenia revealed a dramatic reduction in all intramuscular adenine nucleotides ($40.9\%$ reduction in ATP + ADP + AMP levels compared with sham operation and high protein diet, $p \leq 0.01$,Figure 1H). Remarkably, the major reduction is observed in AMP levels with over a $60\%$ reduction between mice with sarcopenia and sham controls suggesting the presence of an active mechanism removing AMP from the muscle. In this regard, protein analysis from skeletal muscle extracts demonstrated the activation of AMPD1 (Figures 1I–1L) which participates in the deamination of AMP to inosine monophosphate as part of the purine degradation pathway, a catabolic route in which the final product in humans is uric acid (Figure 1I). Of interest, the activation of AMPD1 was associated with both a marked increase in its protein expression (Figure 1J) in parallel with increased myostatin, a well-described protein upregulated with muscle atrophy, and by a significant increase in overall activity (Figure 1K). As a result, intramuscular uric acid levels were significantly up-regulated in the muscle of sarcopenic mice (7.8-fold increase, $p \leq 0.01$).
## Reduced insulin-sensitivity in CKD drives intramuscular phosphate depletion in mice
Insulin resistance is a common finding in patients with CKD, particularly in those requiring dialysis43,44,45,46 including in non-diabetic patients.47,48 Even though circulating glucose levels in 14-h fasting mice undergoing CKD-dependent sarcopenia or sham operation did not differ (116.2 ± 14.7 mg/dL in mice with sarcopenia versus 107.7 ± 18.7 mg/dL in sham), insulin levels following an oral glucose exposure were significantly higher in wild type mice with sarcopenia (Figures 2A and 2B, black symbols). Of interest, this increase seems to be the consequence of reduced clearance of insulin as there was not a significant difference in insulin peak release following glucose exposure (40′ after glucose challenge) between sarcopenia and sham. Rather, the marked difference in insulin levels corresponded to later time points (60′-180′ after glucose challenge) suggesting that the reduced clearance could be secondary to lower sensitivity of tissues to insulin. Notably, unlike wild type mice, similar insulin sensitivity was detected in mice deficient for AMPD1 (AMPD1 KO) undergoing sham operation or with CKD (Figures 2A and 2B, red symbols). This would suggest that the loss of AMPD1 in the skeletal muscle improves insulin sensitivity in CKD. Consistently, and as shown in Figures 2C and 2D, AMPD1 KO mice with CKD demonstrated better insulin sensitivity than wild type animals with the same degree of CKD as denoted by greater reduction in blood glucose levels following an intraperitoneal insulin injection (0.5 U/kg). Reduced insulin sensitivity was associated with greater plasma levels of phosphorus in wild type mice with CKD (6.15 ± 1.04 mg/dL versus 3.35 ± 1.23 mg/dL in sham, $p \leq 0.01$) than in AMPD1 KO animals (4.17 ± 0.88 mg/dL in AMPD1 KO mice with CKD versus 2.96 ± 0.68 mg/dL in sham, $p \leq 0.01$), Figure 2E. Furthermore, high plasma phosphate in wild type but not in AMPD1 KO mice with CKD correlated with a significant reduction in intramuscular phosphate (1.40 ± 0.45 nmol/mg in mice with CKD versus 3.98 ± 0.74 nmol/mg in sham, $p \leq 0.01$, Figure 2F).Figure 2Reduced insulin sensitivity in CKD activates AMPD1 in the skeletal muscle(A and B) Plasma insulin levels over time (minutes) and area under the curve (AUC) in wild type (black) and AMPD1 KO (red) mice undergoing sham operation (open symbols) or after polectomy and lateral nephrectomy followed by 3 weeks of high protein diet (solid symbols) after an oral glucose (1.75 g/kg) tolerance test.(C and D) Plasma glucose levels and area under the curve (AUC) in wild type and AMPD1 KO mice undergoing CKD.(E) Plasma phosphate levels at sacrifice in the same mice as in A).(F) Intramuscular phosphate levels at sacrifice in the same mice as in A).(G) Representative western blot for AMPD1 and insulin dependent phosphate uptake in C2C12 myotubes control (scr, scramble) or silenced for AMPD1 (shAMPD1).(H) Representative western blot and densitometry for total and phosphorylated IRS1 and AKT in saline or insulin exposed control and AMPD1 deficient C2C12 myotubes.(I) AMPD activity in control and AMPD1 deficient C2C12 myotubes under 10 mM Pi (Pi10) or phosphate deprived (Pi0) conditions in the presence or absence of insulin.(J) Insulin-dependent phosphate uptake in control C2C12 myotubes in the presence of increasing amounts of urea. Statistical analysis: For B-D, F: One-way ANOVA followed by Tukey’s multiple comparison test. ∗∗$p \leq 0.01$ versus respective strain sham. # $p \leq 0.05$, ##$p \leq 0.01$ $$n = 5$$ mice per group. For G, I-J, One-way ANOVA followed by Tukey’s multiple comparison test. ∗∗$p \leq 0.01$ versus respective control. # $p \leq 0.05$, ##$p \leq 0.01$ Data graphed is the result of 5 independent replicates.
## Urea impairs insulin signaling and phosphate uptake driving AMPD1 activity in human myotubes
To better characterize the consequence of reduced insulin sensitivity in CKD and whether it is associated with AMPD1 activation in the skeletal muscle, we evaluated the response to insulin of murine C2C12 myotubes. We first assessed insulin signaling in control and AMPD1 knockout myotubes. Exposure of myotubes to insulin (25 μM) resulted in a significant increase in sodium-dependent phosphate transport as previously described in other cell types,25,26,49,50Figure 2G. Importantly, and as shown in Figure 2G, phosphate transport rates were up-regulated in AMPD1 deficient myotubes compared to control cells (2.54 ± 0.78 nmol/mg/15′ in control myotubes versus 3.84 ± 0.56 nmol/mg/15′ in AMPD1 deficient myotubes, $p \leq 0.05$, Figure 2G) in parallel with a substantially higher activation of insulin-dependent signaling targets including insulin-receptor 1 (IRS1) and AKT as denoted by analysis of their phosphorylated state by western blot (Figure 2H).
To test whether insulin-dependent intracellular phosphate levels could modify AMPD activity, we then analyzed AMPD activity in myotubes exposed to control medium containing 10 mM phosphate or to phosphate deprived medium in the presence or absence of insulin. Addition of insulin (25 μM) reduced AMPD activity in control cells (Figure 2I). AMPD activity was significantly higher in phosphate deprived cells compared to control (4.04 ± 0.86 μmol ammonia/min/mg in control myotubes versus 14.55 ± 4.53 μmol ammonia/min/mg in phosphate deprived myotubes, $p \leq 0.01$, Figure 2I). Of interest, insulin failed to lower AMPD activity in phosphate deprived cells thus indicating that the mechanism whereby insulin regulates AMPD activity is mediated by intracellular phosphate. Furthermore, and to determine how CKD reduced the sensitivity of the skeletal muscle to insulin, we analyzed insulin-dependent phosphate transport in the presence of urea which is commonly accumulated in the blood of patients with CKD. As shown in Figure 2J, urea significantly impaired insulin-dependent phosphate transport in a dose-dependent manner (from 0 to 100 mg/dL) indicating that the high blood urea nitrogen (BUN) present in patients with CKD may be responsible for the reduced insulin sensitivity in these subjects.
## AMPD1 activation in the skeletal muscle contributes to the progression of CKD in mice
To test the importance of muscle AMPD1 activation in CKD, we carried out the same approach depicted in Figure 1A in wild type control and AMPD1 KO mice. Of interest, at the end of the 4 weeks exposure to the high protein diet, AMPD1 KO mice demonstrated significantly better renal function than wild type counterparts as denoted by reduced plasma levels of creatinine (0.62 ± 0.14 mg/dL in wild type versus 0.38 ± 0.10 in AMPD1 KO, $p \leq 0.01$, Figure 3A), BUN (74.38 ± 20.11 mg/dL in wild type versus 46.83 ± 8.46 in AMPD1 KO, $p \leq 0.01$, Figure 3B) and urinary albumin excretion (76.13 ± 7.25 urinary albumin to creatinine ratio in wild type versus 49.13 ± 11.17 in AMPD1 KO, $p \leq 0.01$, Figure 3C). Consistent with improved renal function, kidney injury was reduced in AMPD1 deficient mice (Figures 3D–3G), particularly in the cortical and outer medullary S1 and S2 segments of the proximal tubule in which tubules from AMPD1 KO demonstrated a better integrity of the brush border area (Figure 3D) and reduced tubular dilatation (Figure 3E). Similarly, fibrosis, a hallmark of CKD progression, was substantially reduced in AMPD1 KO mice as denoted by less collagen deposition particularly in outer and inner medullary strips (Figure 3F) and significantly lower levels of hydroxyproline, a marker of severity in fibrotic tissues (270.1 ± 136.5 μg/g in wild type versus 90.1 ± 49.1 in AMPD1 KO, $p \leq 0.01$, Figure 3G). Importantly, the beneficial effects observed in AMPD1 KO mice undergoing CKD were not a consequence of lower intake of the high protein diet as no significant differences were observed in daily (2.62 ± 0.22 g/day versus 2.34 ± 0.16 in wild type mice with CKD) and 4-week cumulative food consumption between strains. Figure 3Deletion of AMPD1 ameliorates CKD in mice(A–C) Plasma creatinine, plasma urea and urinary albumin excretion in wild type (black) and AMPD1 KO (red) mice undergoing sham operation (open symbols) or after polectomy and lateral nephrectomy followed by 4 weeks of high protein diet (solid symbols): (D) Representative PAS kidney images from wild type or AMPD1 KO mice undergoing sham or with CKD. Bar = 50 μm (E) Tubular lumen area (dilatation) in the same mice as in A) (50–100 tubules/kidney measured) (F) *Representative picrosirius* red staining under brightfield and polarized light in renal outer and inner medulla of wild type or AMPD1 KO mice undergoing CKD. Bar = 50 μm (G) Renal hydroxyproline levels in the same mouse groups as in (A). Statistical analysis: One-way ANOVA followed by Tukey’s multiple comparison test. ∗∗$p \leq 0.01$ versus respective strain sham. ## $p \leq 0.01$ $$n = 8$$ mice per group.
## AMPD1 blockade protects against sarcopenia independently of the severity of the renal dysfunction
The data presented in Figure 3 would suggest that any potential benefit in blocking AMPD1 on sarcopenia could be the consequence of just improved renal function. Therefore, because the improvement in kidney function could potentially modify the risk for sarcopenia, we performed a subanalysis in which we grouped wild type and AMPD1 KO mice with similar creatinine (0.58 ± 0.12 mg/dL in wild type $$n = 7$$ mice versus 0.49 ± 0.07 in AMPD1 KO mice $$n = 4$$, P = not significant, Figure 4A) and BUN values (65.33 ± 11.38 mg/dL in wild type $$n = 7$$ mice versus 52.50 ± 7.68 in AMPD1 KO mice $$n = 4$$, P = not significant). During the 4 weeks on a high protein diet, weight loss in AMPD1 KO mice was not as marked as in wild type animals with similar kidney function (Figure 4B) with lower levels of creatine phosphokinase (CPK) in plasma (Figure 4C) indicative of improved energy balance and reduced muscle catabolism associated with the blockade of AMPD1. No significant differences in caloric intake were found between wild type and AMPD1 KO in the mice included in this sub-analysis. Consistently, AMPD1 KO mice with CKD demonstrated reduced muscle inflammation (Figure 4D) and preserved myofiber size (Figure 4E). As a result, the weights of gastrocnemius and tibialis anterior (TA) were significantly greater in AMPD1 KO mice compared to wild type animals with similar kidney function (Figure 4F) and muscle-derived casts in renal tubules were substantially reduced in AMPD1 KO mice (Figures 4G and 4H). Analysis of nucleotide pools revealed that the expected improvement in total nucleotide levels in AMPD1 KO mice (Figure 4I) was characterized by a much higher concentration of free AMP (2.41-fold increase in AMPD1 KO mice versus wild type, $p \leq 0.01$), ATP (0.32-fold increase in AMPD1 KO mice versus wild type, $p \leq 0.05$), and the AMP to ATP ratio (0.33 ± 0.11 AMP/ATP ratio in wild type versus 0.59 ± 0.11 AMP/ATP ratio in AMPD1 KO, $$p \leq 0.014$$). Of interest, the significant increase in AMP levels in the muscle of AMPD1 KO mice is paralleled with reduced clearance via AMPD as levels of intramuscular IMP and its downstream metabolites inosine, hypoxanthine and xanthine; are markedly low in AMPD1 KO mice (Figure 4J). Metabolic dysregulation secondary to reduced insulin sensitivity and negative energy balance in the skeletal muscle of mice with CKD leads to the utilization of protein as energy fuel and therefore to a switch favoring protein catabolism over synthesis. In this regard, glutamine is one of the most important amino acids primarily produced by the skeletal muscle whose supplementation has been shown to prevent loss of muscle mass.51,52,53,54 Intramuscular glutamine levels in AMPD1 KO mice with CKD are significantly higher than wild type animals (Figure 4K) in parallel with an up-regulated expression of glutamine synthetase (GluL), the enzyme that produces glutamine from glutamate and ammonia (Figures 4L–4N). Furthermore, we found that GLuL up-regulation in AMPD1 KO mice was associated with a reduction in intramuscular levels of the anti-myogenic factor, myostain, suggesting an inverse correlation between these two enzymes in the regulation of muscle mass. Figure 4Deletion of AMPD1 ameliorates muscle atrophy in CKD-matched mice(A) Plasma creatinine in wild type (black) and AMPD1 KO (red) mice undergoing sham operation (open symbols) or after polectomy and lateral nephrectomy followed by 4 weeks of high-protein diet (solid symbols). Creatinine values of CKD-matched mice chosen for further sub-analysis (6 wild type and 4 AMPD1 KO) are included in the blue square.(B) Body weight change during the length of the study in the same groups as in (A) (C) Plasma CPK levels in mice from same groups as in (A).(D) Representative H&E image of gastrocnemius of CKD-matched wild type and AMPD1 KO mice. White arrows denote inflammatory cells. Bar = 25 μm (E) Cross-sectional analysis of myofiber sizes in CKD-matched wild type and AMPD1 KO mice (150 myofibers/muscle measured) (F) Muscle weight of the same mice as in C).(G) Representative PAS staining of kidney medullas demonstrating greater tubular cast formation in wild type mice undergoing CKD compared to AMPD1 KO. Bar = 50 μm.(H) Quantitative analyses of cast positive tubules in CKD-matched wild type and AMPD1 KO mice (75–125 tubules/kidney measured) (I) Intramuscular nucleotide pool in the same mice as in (A).(J) Intramuscular AMPD-dependent downstream AMP-metabolites in the same mice as in (A).(K) Intramuscular glutamine levels in the same mice as in (A).(L–N) Representative western blot and densitometry analysis for myostatin, AMPD1 and glutamine synthetase (GluL) in gastrocnemius of mice from the same groups as in A). Statistical analysis: One-way ANOVA followed by Tukey’s multiple comparison test. ∗$p \leq 0.05$ and ∗∗$p \leq 0.01$ versus respective strain sham. # $p \leq 0.05$, ##$p \leq 0.01$ $$n = 4$$–8 mice per group.
## Discussion
Sarcopenia or muscle atrophy is a common consequence in CKD but the mechanism whereby declined renal function reduces muscle mass is still unclear. The data from our study supports a novel model of CKD-dependent muscle atrophy as depicted in Figure 5which is mediated by the activation of AMPD1 in the skeletal muscle and the removal of free AMP. We propose that the activation of AMPD1 as the result of low intramuscular phosphate is the consequence of a marked reduced sensitivity of myofibers to insulin. In this regard, insulin resistance is commonly found in patients with CKD55,56,57,58,59,60 in which the skeletal muscle represents the primary site of reduced insulin sensitivity. The proposed etiology of reduced insulin sensitivity in CKD is multifactorial and include physical inactivity, protein energy wasting, chronic inflammation,61 vitamin D deficiency,62 metabolic acidosis63, urea retention64, and renal anemia65,66 among others. In the present study, we found some of these factors taking place in mice undergoing CKD and sarcopenia including lower caloric intake, inflammation and high uremia. The effect of high urea on insulin resistance is mediated by a decrease in erythropoietin production by uremic toxins causing anemia. However, here we demonstrate that uremia decreases insulin sensitivity dose dependently in isolated myotubes suggestive of a local effect in the skeletal muscle independently of anemia. This is consistent with the findings of D’Apolito et al.67 suggesting that urea-dependent generation of reactive oxygen species and oxidative stress is the cause of muscular insulin resistance in mice with kidney dysfunction. This supports previous studies suggesting that improving insulin sensitivity could be beneficial in sarcopenia and the progression of CKD.68,69Figure 5Potential mechanism or kidney-muscle interplay in the progression of CKD and sarcopeniaIn CKD, reduced clearance elevates plasma levels of urea. In turn, increased uremia impairs insulin action on the skeletal muscle leading to a reduction of both insulin-dependent glucose and phosphate uptake causing metabolic dysregulation. Hyperphosphatemia is then the consequence of both reduced renal clearance and low uptake by the skeletal muscle. Metabolic dysregulation in the skeletal muscle is characterized by low phosphate, reduced glucose oxidation and protein synthesis and overall low ATP and phosphocreatine levels with AMPD1 activation. Hyperactive AMPD1, in turn, decreases the amount of AMP while promoting the formation of uric acid in the purine degradation pathway. As a result of a negative energy balance in the muscle, inflammation and protein turnover is manifest thus leading to muscle atrophy. In consequence, muscle-derived products from protein catabolism and AMPD1 activation are released to the circulation to further contribute to the progression of CKD.
Even though insulin sensitivity is traditionally associated with glucose transport and utilization, insulin plays an important role in promoting phosphate uptake as well.25,26 Insulin dependent phosphate uptake is sodium dependent and mediated by the slc20 family of transporters. Insulin-dependent phosphate uptake is particularly relevant in the skeletal muscle as phosphate is necessary for energy storage as ATP and phospho-creatine necessary for glucose phosphorylation and glycolysis. Therefore, a low intramuscular phosphate state would aggravate the low insulin sensitivity already present in CKD. Consistently, the loss of phosphate transport in slc20a1/Pit1 and slc20a2/Pit2 deficient mice causes myofiber dysfunction and atrophy.70 *It is* important to note that low intramuscular phosphate levels contrasts with circulating hyperphosphatemia commonly found in subjects with CKD. Therefore, the high phosphate in CKD would be the consequence of both decreased renal function and lower insulin-dependent phosphate uptake. Current strategies to regulate phosphate in CKD are limited to control its plasma levels. However, our data indicate that in CKD there is also a significant metabolic phosphate dysregulation intracellularly particularly in the skeletal muscle which needs to be repaired. Consistently, a recent NMR study of dialysis patients has confirmed that dialysis lower intracellular phosphate and ATP levels.29 This documents the insufficient energy backup in the muscle of dialysis patients bringing attention that simply removing extracellular phosphate may not fully address the problems in phosphate metabolism in CKD. Therefore, strategies aimed to reduce plasma phosphate like the use of phosphate binders or reducing intake should be better adjusted or given in combination with insulin sensitizing drugs particularly to those CKD subjects at greater risk of developing sarcopenia. In accordance, a recent meta-analysis of 16,800 type 2 diabetic patients71 reported that metformin, an insulin sensitizing drug, was associated with reduced risk of sarcopenia in patients with type 2 diabetes. It is also notable that exercise training programs, an established method to improve insulin resistance, improve both phosphate removal and reduce the risk of sarcopenia in dialysis patients.72,73 Phosphate regulates multiple molecular and signaling pathways. Of these, phosphate and phosphoinositides are known to inhibit AMPD activity in vivo and in vitro74 and low intracellular phosphate leads to AMPD activation.75 *In this* regard, we have previously shown that the activation of another isoform of AMPD, AMPD2, in insulin-insensitive and phosphate-deprived hepatocytes promoted endogenous glucose production from gluconeogenic factors in response to starvation.75 This would suggest that the effects of AMPD activation via low insulin sensitivity and low intracellular phosphate in CKD may affect multiple organs. Therefore, further studies aimed to analyze the systemic effects of AMPD activation in CKD are warranted.
Finally, one of the more relevant findings of our study is the spontaneous up-regulation and activation of glutamine synthetase (GluL) and the high levels of glutamine observed in the skeletal muscle of AMPD1 KO mice. Glutamine is one of the most abundant essential aminoacids and fuel source of several cell types (enterocytes and immune cells), a precursor of purine and pyrimidine synthesis and a negative regulator of protein catabolism. Even though it is still unclear whether glutamine supplementation prevents sarcopenia, beneficial effects of glutamine on muscle atrophy and muscle wasting have been reported.51,54,76 Importantly, our data support another potential benefit of glutamine synthesis in CKD besides the production of glutamine. Synthesis from glutamate requires ammonia as the nitrogen donor and therefore, the higher metabolic rate through this pathway in AMPD1 KO mice would help both detoxify ammonia and reduce urea production to ameliorate kidney dysfunction and improve insulin sensitivity. Thus, reduced ammonia and urea production via glutamine synthesis may therefore explain in part why AMPD1 deficient mice demonstrate improved renal function in our model compared to wild type counterparts.
In conclusion, we suggest a role for insulin resistance, intracellular phosphate depletion, and activation of AMP deaminase in the pathogenesis of sarcopenia in CKD. Measures that increase intracellular phosphate and improve insulin sensitivity and restore energy pools may represent a new approach for preventing and reversing sarcopenia.
## Limitations of the study
Besides AMPD1, AMPD3 is also expressed in the skeletal muscle. In this regard, some studies indicate that the expression of AMPD1 and AMPD3 genes may be coordinated in myocytes to effect production of an AMPD holoenzyme.77,78 Therefore, both isoforms may be required for proper AMPD activity. In this regard, Miller et al.79 demonstrated that the intramuscular overexpression of AMPD3 in mice and C2C12 myotubes depleted AMP levels and caused muscle atrophy and suggest the importance of AMPD3 in muscle waste and sarcopenia. However, it is important to note that our results indicate that the depletion of AMPD1 reduces IMP and its downstream metabolites in the muscle by >$80\%$ in both sham and CKD mice, indicating that AMPD1 is the main contributor of AMP deamination in the skeletal muscle. This is consistent with previous reports80 in which AMPD1 deficiency led to non-detectable IMP levels in the muscle of mice at baseline, following an exercise protocol or undergoing local ischemia Similarly, It would be helpful to know if the improvement in muscle mass was associated with an improvement in muscle strength. Ideally, it would great to knockout AMPD1 after kidney disease was induced to assure equivalent kidney disease in both groups. The studies were also performed in mice, which does not guarantee similar findings in humans.
## Key resources table
REAGENT or RESOURCESOURCEIDENTIFIERAntibodiesAnti-mouse MyostatinProteintech19142-1-AP; RRID:AB_10638615Anti-mouse AMPD1Proteintech19780-1-AP; RRID:AB_10644281Anti-mouse GLuLCell Signaling80636; RRID:AB_2799956Anti-mouse pIRS1Ser$\frac{636}{639}$Cell Signaling2388; RRID:AB_330339Anti-mouse IRS1Cell Signaling2382; RRID:AB_330333Anti-mouse pAKTSer473Cell Signaling4058; RRID:AB_331168Anti-mouse AKTCell Signaling9272; RRID:AB_32982Anti-mouse GAPDHCell Signaling5174; RRID:AB_10622025Goat Anti-Rabbit IgG HRP conjugatedCell Signaling7074; RRID:AB_2099233Goat Anti-Mouse IgG HRP conjugatedCell Signaling7076; RRID:AB_330924Chemicals, peptides and recombinant proteinsGlucoseSigmaG8270Insulin (human)Sigma91077CUreaSigmaU5378DMEM, high glucoseSigma11965DMEM, high glucose, no phosphatesSigma11971HematoxylineThermo Scientific22-110-639Eosin-YFisher ScientificSE23-500DMagnesium ChlorideSigmaM8266Sodium VanadateSigma590088Triton-XMilliporeMTX15681Tween 20MP BiomedicalsMP1TWEEN201Puromycin dihydrochlorideSanta Cruz Biotechnologiessc-108071Critical commercial assaysPhosphate Determination KitBiovisionK410Glutamate Determination KitBiovisionK629Glucose Determination KitBiovisionK606Creatinine Determination KitPointe ScientificC7548Uric Acid Determination KitBioassay SystemsDIUA-250Creatine Phosphokinase KitBiovisionK477Blood Urea Nitrogen Determination KitBioassay SystemsDIUR-100Albumin Determination KitEthos BiosciencesAlbuwell MAnti-Mouse Insulin ELISACrystal Chem90080Deposited dataOriginal Western blot and histology imagesMendeley Datahttps://data.mendeley.com/datasets/zjwhhpzgvm/1OligonucleotidesshRNA against human AMPD1Santa Cruz BiotechnologiesSc-141059-vOtherGlucometerAccu-CheckAccu-Check Guide MeterGlucose StripsAccu-CheckAccu-Check Guide Test StripsC2C12 cell lineATCCCRL-1772 RRID:CVCL_0188
## Lead contact
Further information and requests for resources and reagents should be directed and will be fulfilled by the Lead Contact, Miguel A. Lanaspa (Miguel.lanaspagarcia@cuanschutz.edu).
## Materials availability
Mouse lines generated in this study are available for any researcher upon reasonable request.
## Study approval
All animal experiments were conducted with adherence to the NIH Guide for the Care and Use of Laboratory Animals.81 All animal experiments and procedures were approved by the Animal Care and Use Committee of the Veterans Affairs Medical Center and the University of Colorado (Aurora, CO).
## Animals
CKD-induced sarcopenia was carried out as in42 with modifications. Male 10–12 weekold AMPD1 KO (B6;129_Ampd1tm1b(KOMP)Wtsi) or wild type littermates underwent subtotal nephrectomy in 2 stages. In the first stage, ∼$70\%$ of the right kidney was removed. Seven days later, the left kidney was removed, and mice fed a $12\%$ protein diet (Envigo, Indianapolis, IN, USA) to minimize mortality from uremia. One week later, mice were fed a $40\%$ protein diet for 4 weeks (TD_90018, Envigo, Indianapolis, IN, USA) to induce advanced CKD. Sham-treated control mice underwent both surgeries without damaging the kidneys and were fed the same diets. All mice were housed with 12-h light-dark cycles. Body weights and food consumption were assessed daily.
## Biochemical analysis
Blood was collected in Microtainer tubes (BD) from cardiac puncture of mice under isoflurane, and serum was obtained after centrifugation at 13,000 rpm for 2 minat room temperature. Serum parameters were determined biochemically with commercially available kits following the manufacturer’s instructions Phosphate: K410, Creatine phosphokinase (CPK): K777, Glutamate: K629 and Glucose K606 from Biovision, Milpitas, CA; Creatinine: C7548, Pointe Scientific, Canton, MI; *Blood urea* nitrogen (BUN): DIUR-100 and Uric Acid: DIUA-250 Bioassay Systems, Hayward, CA; Albumin: Albuwell M (Ethos Biosciences, Logan Township, NJ); Insulin: Ultra-sensitive mouse insulin ELISA kit, 90,080, Crystal Chem, Elk Grove Village, IL.
## Histopathology
Formalin-fixed paraffin-embedded kidney and muscle sections were stained with periodic acid-Schiff (PAS). Histological examination was performed across the entire cross section of the kidney and muscle from each mouse. The distribution of myofiber sizes was calculated on the basis of analysis of 750 myofibers per mouse (approximately 150 fibers/section). Tubular lumen area and cast formation were measured with an Aperio Scanscope. Kidneys were stained with Picro Sirius Red to assess for fibrosis as previously described.82 Images were captured on an Olympus BX51 microscope equipped with a 4-megapixel Macrofire digital camera (Optronics, Goleta, CA) using the PictureFrame Application 2.3 (Optronics). Composite images were assembled with the use of Adobe Photoshop. All images in each composite were handled identically.
## Nucleotide determination
Tissues were collected and snap frozen in liquid nitrogen before being homogenized in ice-cold in a buffer containing perchloric acid (0.5N) and EDTA (5 mM). Homogenates were then centrifuged, and supernatants neutralized with ice-cold 1 N KOH. Homogenates were separated UPLC and concentrations of total adenine nucleotides and degradation products quantified at 215 nm. Metabolite data was normalized to protein data obtained from resuspended pellets after neutralization.
## Oral glucose and insulin tolerance tests
For determination of glucose-dependent insulin levels, mice were fasted for 8 h. At baseline, blood was obtained from a tail snip and glucose measured using a One-touch Ultra 2 glucometer. The remaining blood was processed for plasma that was later used to determine the fasting insulin levels. The mice then received 1.75 g/kg body weight of a glucose solution (Sigma, G8769) in tap water by oral gavage. After the administration of glucose, dried blood and tissue were quickly removed from the tail wound and blood was collected again to prepare the plasma samples for measuring insulin levels. All of the plasma samples were frozen after collection and assayed later by ELISA (Mouse/Rat insulin kit, 90,080, Crystal Chem) according to the manufacturer’s protocol. For insulin tolerance test, mice were fasted for 4 h and blood collected for glucose determination as described above. Insulin (0.5 U/kg) was administered intraperitoneally with a 27G needle.
## Western blot analysis
Protein lysates were prepared from mouse tissue or C2C12 cells using lysis buffer containing $0.3\%$ Triton X-. Protein content was determined by the BCA protein assay (Pierce, Rockford, IL). Total protein (50 μg) was separated by SDS-PAGE [$10\%$ (w/v)] and transferred to PVDF membranes (Bio-Rad, Hercules, CA). Membranes were first blocked for 1 hat 25°C in $4\%$ (w/v) instant milk dissolved in $0.1\%$ Tris-buffered saline with Tween 20 TBS (TTBS) and incubated with the following primary rabbit-raised antibodies (1:1,000 dilution in TTBS): Myostatin (19142-1-AP, Proteintech; RRID:AB_10638615), AMPD1 (19780-1-AP; RRID:AB_10644281), GLUL (80,636, Cell Signaling; RRID:AB_2799956), pIRS1Ser$\frac{636}{639}$ (2388, Cell Signaling; RRID:AB_330339), IRS1 (2382, Cell Signaling; RRID:AB_330333), pAKTSer473 (4058 Cell Signaling; RRID:AB_331168), AKT (9272, Cell signaling; RRID:AB_32982) and GAPDH (5174, Cell Signaling; RRID:AB_10622025) and visualized using an anti-rabbit (no. 7074) horseradish peroxidase (HRP)-conjugated secondary antibody (1:2,000, Cell Signaling) using the HRP Immunstar detection kit (Bio-Rad). Chemiluminescence was recorded with an Image Station 440CF, and results were analyzed with the 1D Image software (Kodak Digital Science, Rochester, NY). Data for proteins of interest are expressed normalized to GAPDH expression.
## Studies in C2C12 myotubes
C2C12 cells were obtained from the AmericanType Cell Culture (ATCC, CRL-1772; RRID:CVCL_0188), grown and differentiated as per the ATCC recommendations. Stable deletion of AMPD1 was carried out with lentiviral particles containing shRNAs against murine AMPD1 (sc-141052-v, Santa Cruz Biotechnologies) followed by clonal selection with puromycin and AMPD1 expression analysis by western blot. Differentiated C2C12 myotubes were exposed to different compounds including insulin (91077C, Sigma), urea (U5378, Sigma) and complete 10 mM phosphate (11,965, Gibco) or phosphate free (11,971, Gibco) medium. Phosphate uptake assays were performed with [32P]orthophosphoric acid (120 μM) as radiotracer under constant sodium concentration (120 mM). Transport values were normalized by calculated (BCA, pierce) protein concentration.
## Statistical analysis
All numerical data are presented as the mean ± s.e.m. Independent replicates for each data point (n) are identified in figure legends. Data graphics and statistical analysis were performed using Prism 5 (GraphPad). Data without indications were analyzed by one-way ANOVA, Tukey post hoc test. A value of $P \leq 0.05$ was regarded as statistically significant. Animals were randomly allocated in each group using randomizer (www.randomizer.org). Power calculations for the number of animals assigned to each group were based on our previous publications and designed to observe a greater than $15\%$ difference in body weight difference between groups. *In* general, an n of 7-8 mice per group was used. No animals were excluded from the study and whenever possible experiments were done in a blond fashion. For example, for data analysis, except for western blot, single samples (plasma, homogenates,…) were first codified and decoded after determination. Similarly, histological records and scoring were done in a blind fashion.
## Data and code availability
•This study did not generate unique datasets or code.•This study did not generate new unique reagents, cell lines, or mouse lines.•Original western blot and histology images have been deposited at Mendeley and are publicly available as of the date of publication. The DOI is listed in the key resources table.
## Author contributions
R.J.J. and M.A.L. designed the research; A.A-H., D.J.O., P.S., R.J.J. and M.A.L. analyzed the data; A.A-H., C.C., G.E.G., and M.A.L. performed the research; A.A-H., P.S., R.J.J., and M.A.L. wrote the paper.
## Declaration of interests
Authors declare no competing interests.
## Inclusion and diversity
We support inclusive, diverse, and equitable conduct of research.
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|
---
title: Deletion of NADPH oxidase 2 attenuates cisplatin-induced acute kidney injury
through reducing ROS-induced proximal tubular cell injury and inflammation
authors:
- Ho-Ching Chen
- Hsin-Yu Hou
- Junne-Ming Sung
- Chi-Chang Shieh
journal: Frontiers in Medicine
year: 2023
pmcid: PMC10040743
doi: 10.3389/fmed.2023.1097671
license: CC BY 4.0
---
# Deletion of NADPH oxidase 2 attenuates cisplatin-induced acute kidney injury through reducing ROS-induced proximal tubular cell injury and inflammation
## Abstract
### Backgrounds
Cisplatin is a commonly used chemotherapeutic agent in cancer treatment. However, its high nephrotoxicity limits its therapeutic application and efficacy. Cisplatin induces nephrotoxicity mainly through oxidative stress and inflammation. Reactive oxygen species (ROS) in the kidneys mainly arise from nicotinamide adenine dinucleotide phosphate (NADPH) oxidases 2 (NOX2), which is highly upregulated during ischemia-reperfusion injury and diabetes mellitus. However, its role in cisplatin-induced acute kidney injury (AKI) remains unknown.
### Methods
A 8-10-week-old NOX2 gene-knockout and wild-type mice were injected with 25 mg/kg cisplatin intraperitoneally for experiments.
### Results
We investigated the role of NOX2 in cisplatin-induced AKI and found that NOX2-mediated ROS production is a key inflammatory mediator of proximal tubular cell injury in cisplatin-induced AKI. NOX2 gene-knockout alleviated cisplatin-induced renal function decline, tubular injury score, kidney injury molecule-1(Kim-1) expression, and interleukin (IL)-6 and IL-1α levels with a reduction of ROS production. Moreover, in cisplatin-induced AKI, intercellular adhesion molecule 1 (ICAM-1) and the chemoattractant CXC ligand 1 (CXCL1) were highly expressed in association with neutrophil infiltration, which were all attenuated by deletion of NOX2.
### Conclusion
These data indicate that NOX2 aggravates cisplatin nephrotoxicity by promoting ROS-mediated tissue injury and neutrophil infiltration. Thus, appropriate targeting of NOX2/ROS pathway may minimize the risk of cisplatin-induced kidney injury in patients receiving cancer therapy.
## Introduction
Cisplatin is a common chemotherapy agent used to treat several kinds of cancers. However, dose-dependent renal toxicity has been observed in clinical use and results in renal dysfunction and acute tubular necrosis [1]. Approximately $\frac{1}{3}$ of patients developed acute nephrotoxicity after a single dose of cisplatin [2]. Furthermore, repeated low-dose cisplatin administration may cause the acute kidney injury (AKI) to progress to chronic kidney disease with renal fibrosis [3]. This potentially lethal toxicity limits the use of this agent in cancer patients. Therefore, it is essential to understand the pathogenesis of cisplatin-associated nephrotoxicity in order to develop preventive strategies.
The uptake of cisplatin in the kidneys is through the organic cation transporters [4]. Overexpression of organic cation transporter 2 in human proximal tubular cells enhances cisplatin uptake and increases the risk of toxicity [5]. Cisplatin uptake into renal tubular cells causes proximal tubular cell toxicity, mitochondrial dysfunction, DNA damage, and decreased antioxidant glutathione levels, leading to the accumulation of reactive oxygen species (ROS) [6, 7]. ROS are important in maintaining normal kidney gluconeogenesis, electrolyte transport, glucose transport, and hemodynamics [8]. However, excessive ROS may cause cellular damage in a self-enhancing loop, causing oxidative injuries, autophagy, necrosis, apoptosis, and inflammation (9–11). Furthermore, persistent oxidative stress contributes to kidney fibrosis and chronic kidney disease in repeated low-dose cisplatin exposure in cancer patients [12, 13]. Several antioxidant compounds, including N-acetylcysteine, dimethyl sulfoxide, and heme oxygenase-1, have been reported to protect against cisplatin-induced AKI in in vivo models, by suppressing ROS accumulation in the tissue (14–16).
Nicotinamide adenine dinucleotide phosphate oxidases (NOXs) contribute significantly to intracellular ROS generation in different animal models of kidney injury [17, 18]. However, the role of NOXs in cisplatin nephrotoxicity is still unclear. There are seven isoforms of the NOX family (NOX1–5, Duox$\frac{1}{2}$). Different NOX isoforms are regionally distributed along the nephron segments [19, 20]. The classic NOX2 is found primarily in phagocytic cells but is also expressed in renal proximal tubular cells. NOX2 upregulation was found in different animal kidney injury models including Akita and streptozotocin-treated diabetic mice, hypertension, and ischemia–reperfusion AKI (21–24). These data suggested that NOX2 plays an important role in renal oxidative stress and renal injury in certain kidney diseases. However, the role of NOX2 mediated ROS generation in cisplatin-induced AKI is not fully elucidated. In this study, we hypothesize that cisplatin nephrotoxicity is induced through NOX2-mediated mechanisms. Here we used a mouse model to show the role of NOX2 and potentially NOX2-produced ROS in proximal tubular cell injury and renal inflammation after cisplatin treatment.
## Mice
Mice deficient in Cybb, the gene encoding the gp91phox subunit of NOX2, (B6.129S-Cybbtm1Din/J, No. 002365) were purchased from Jackson Laboratory. C57BL/6 mice purchased from National Laboratory Animal Center were used as WT mice. All mice have been routinely backcrossed to C57BL/6 background, undergone genome-wide genotyping to confirm the genetic background, and housed in the animal facility of the Laboratory Animal Center at National Cheng Kung University. All mice were housed at the National Cheng Kung University Laboratory Animal Center and maintained under a temperature-controlled, 12 h light/dark cycle and specific pathogen-free conditions. We injected 25 mg/kg of cisplatin (CDDP) intraperitoneally into 8–10-week-old male mice, which were euthanized at 0 and 3 days after injection. Four to five mice were included in each group, and each experiment was repeated 2–3 times. All experimentation protocols were approved by the Institutional Animal Care and Use Committee (IACUC), at National Cheng Kung University.
## Western blotting
Mouse renal cortices were lysed using 1× phosphate-buffered saline containing a protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, United States). Protein concentrations were determined using a bicinchoninic acid protein assay kit (Thermo Fisher Scientific, Waltham, MA, United States). Denatured proteins were separated through sodium dodecyl sulfate-polyacrylamide gel electrophoresis and then transferred to polyvinylidene fluoride membranes, blocked with $5\%$ non-fat milk in Tris-buffered saline/Tween buffer at 4°C overnight, and incubated with the following primary antibodies: rabbit anti-mouse NOX1 (GeneTex, Irvine, CA, United States), mouse anti-mouse NOX2 (gp91phox) (BD Transduction Laboratories, Franklin Lakes, NJ, USA), rabbit anti-mouse NOX4 (GeneTex), and rabbit anti-mouse GAPDH (GeneTex) at 1:500, 1:8000, 1:2000, and 1:10,000 dilution, respectively. The membranes were washed with Tris-buffered saline/Tween buffer and incubated with the appropriate horseradish peroxidase-conjugated secondary antibodies. Relative intensities of the proteins band were quantified using ImageJ version 1.49 (U.S. National Institutes of Health, Bethesda, MD, United States).
## Immunofluorescence staining
Mouse kidneys were fixed, embedded in paraffin, and cut into 3-μM-thick sections with a microtome RM2235 and incubated with blocking solution using Novolink polymer detection system kit (Leica Biosystems, Wetzlar, Germany). The cortical sections of the kidney were deparaffined and rehydrated before incubation with rabbit anti-mouse aquaporin 1 (1:250; Abcam, Cambridge, United Kingdom) and mouse anti-mouse gp91phox (1:125; BD Transduction Laboratories) antibodies. 488-conjugated goat anti-rabbit immunoglobulin G and DyLight 594-conjugated goat anti-mouse albumin were used as secondary antibodies at 1:200 dilution, respectively. Cell nuclei were counterstained with 4′,6-diamidino-2-phenylindole. Images were captured under a fluorescence microscope (Carl Zeiss, Jena, Germany) equipped with AxioVision version 4.9.1 (Carl Zeiss, Jena, Germany).
## Glutathione fluorometric assay
The tissue levels of glutathione and glutathione disulfide were measured with a glutathione fluorometric assay kit (BioVision, San Francisco, CA, United States), per the manufacturer’s instructions. Fresh renal cortices were lysed with a glutathione assay buffer, and perchloric acid (6 N) was added to achieve a uniform emulsion; this was followed by centrifugation at 13,000× g for 2 min. In perchloric acid–preserved samples, we added potassium hydroxide (6 N), a glutathione quencher, and an OPA probe. Samples were measured on a fluorescence plate reader.
## Blood urea nitrogen and creatinine measurement
The mouse blood was allowed to stand for 1 h at room temperature and was then centrifuged at 100× g for 30 min; the supernatant was collected for further analysis. Blood urea nitrogen (BUN) and creatinine levels were measured using a FUJI DRI-CHEM slide (FUJIFILM, Tokyo, Japan) and FUJI DRI-CHEM Analyzer (FUJIFILM).
## Periodic acid–schiff staining
Sections were deparaffined, dehydrated, and stained using a periodic acid–schiff (PAS) staining kit (Abcam, Cambridge, United Kingdom), as described by the manufacturer. Tubular injury was scored by a blinded observer who examined cortical area of kidney tissue (× 200 magnification) of PAS-stained sections. Twenty images in each group were captured with a light microscope (Carl Zeiss). Histopathological changes were evaluated by the percentage of damaged renal tubules, as indicated by tubular cell necrosis, tubular dilatation, loss of the brush border, and cast formation. Tubular injury was quantified based on a 5-point scoring system, with 0, 1, 2, 3, 4, and 5 point(s) corresponding to 0, <10, 11–$25\%$, 26–$50\%$, 51–$75\%$, and ≥ $76\%$ tissue damage, respectively.
## Immunohistochemical staining
The cortical area of kidney tissue was cut into 3-μM-thick sections, as described earlier, which were blocked before incubation of goat anti-mouse anti-Kim1 (R&D Systems, Minneapolis, MN) or mouse anti-mouse anti-ICAM1 (Abcam, Cambridge, United Kingdom) at 1:800 and 1:2000 dilution, respectively. Immunohistochemical staining was performed using a Novolink polymer detection system (Leica Biosystems, Wetzlar, Germany), as described by the manufacturer. The sections were counterstained with hematoxylin. Images were captured with a light microscope (Carl Zeiss) equipped with AxioVision version 4.9.1 at 400× magnification. The respective signal area and density were measured using ImageJ (version 1.49; U.S. National Institutes of Health, Bethesda, MD, United States).
## Luminex assay
The supernatants of the renal cortex were assayed for the following proteins, cytokines, and chemokines: Kim-1, vascular endothelial growth factor (VEGF), soluble intercellular adhesion molecule (sICAM), sP-selectin, interleukin (IL)-4, IL-5, IL-6, IL-10, IL-17A, monocyte chemoattractant protein 1 (MCP-1), GM-CSF, IL-1α, MIP-2 (CXC ligand 2 [CXCL2]), and KC (CXC ligand 1 [CXCL1]). These assays were performed in 96-well plates, and compounds were detected with a MILLIPLEX MAP kit (Merck, Darmstadt, Germany), as described by the manufacturer.
## Flow cytometry
The kidneys were cut into small pieces, digested in a complete medium containing collagenase D (1,400 μg/mL) and DNase I (100 μg/mL) for 45 min at 37°C, and then passed through a 70-μM strainer. For leukocyte purification, the samples were subjected to Percoll gradient centrifugation. Single-cell suspensions were blocked with Fc block (anti-CD$\frac{16}{32}$, catalog no. 553142; BioLegend, San Diego, CA, United States) and stained with fluorescently conjugated antibodies against CD45 (30-F11), CD11b (M$\frac{1}{70}$), CD11c (HL3), Ly6G (1A8), and F$\frac{4}{80}$ (BM8). All antibodies were obtained from BioLegend and BD Transduction Laboratories (Franklin Lakes, NJ, United States). A single-stained control for each fluorochrome was made for compensation for each experiment. Labeled cells were detected by flow cytometry with FACS Calibur (BD Biosciences) or FACS Canto II instruments (BD Biosciences) and analyzed with the FlowJo software program (FlowJo, LLC, Ashland, OR, United States).
## Cell culture
Human kidney tubular epithelial cells (HK2) (BCRC number: 60097) derived from normal kidney transfected with human papilloma virus 16 were obtained from Bioresource Collection and Research Center, Taiwan, and cultured in DMEM/F12 (Gibco) containing $10\%$ FBS. The cell culture was kept at 37°C and $5\%$ CO2 condition.
## Treatment of HK2
HK2 (1 × 106 cells/well for 6-well plates) were plated overnight in a complete medium. Cells were treated with or without 5 μM diphenyleneiodonium chloride (DPI), a potent irreversible ROS inhibitor, at 37°C for 30 min then incubated with 2 μM cisplatin (CDDP) for 24 h. The levels of ROS were measured using a fluorescent dye 2′, 7′-dichlorofluorescein diacetate (DCFDA) cellular ROS assay kit (Abcam). Cells were harvested and stained with DCFDA at 37°C for 30 min then analyzed with flow cytometry.
## Statistical analysis
All quantitative data are expressed using the mean ± standard error of the mean values. Data were evaluated with a Student’s t test between two groups or a one-way or two-way analysis of variance when comparing more than three groups. SPSS (version 23; IBM Corporation, Armonk, NY, United States) was used for all statistical analyzes, and $p \leq 0.05$ was statistically significant. The statistically differences between groups are indicated with *$p \leq 0.05$, **$p \leq 0.01$, ns: no significance.
## Increased expression of NOX2 in cisplatin-induced AKI
Three days after cisplatin injection, the NOX2 protein level significantly increased in cisplatin-induced AKI (Figures 1A,C), whereas the NOX1 and NOX4 protein levels did not increase (Figures 1B,D). We verified the location of NOX2 expression in the renal tubular cells after cisplatin induction with immunofluorescence staining (Figure 1E). In the normal condition, no NOX2 expression was observed in the wild-type (WT) normal saline (NS) group. NOX2-knockout (KO) mice did not exhibit NOX2 expression in either the cisplatin or NS group. ROS play important role in cisplatin kidney toxicity; thus, we performed a glutathione fluorometric assay to determine the amount of ROS production. We observed that oxidative stress was significantly increased in the WT cisplatin-treated group compared to the NOX2-KO group ($58\%$ increase), indicating that cisplatin-related ROS generation in kidney tissues may be largely through NOX2 (Figure 2). We also confirmed that the NOX inhibitor DPI was significantly reduced ROS production in cisplatin-treated human kidney tubular epithelial cells injury in vitro (Supplementary Figure S1). *Both* genetic and pharmacological inhibition of NOX2-derived ROS in cisplatin-induced AKI could be a potential therapeutic target.
**Figure 1:** *Increased NOX2 expression in cisplatin-induced AKI. Three days after cisplatin injection, western blot analysis was used to examine the main NOX family expression in cisplatin-induced AKI. (A) Western blots of NOX1, NOX2, and NOX4. (B–D) Quantitative analysis of NOX2 indicated overexpression compared to WT mice. However, NOX1 and NOX4 decreased after cisplatin injury. (E) Immunofluorescence staining suggests NOX2 expression in the epithelial cells along the renal proximal tubule in cisplatin-induced AKI. NOX2-KO mice did not show NOX2 expression in the normal saline and cisplatin groups. Data represent the mean ± standard error of four mice per group. Scale bar = 50 μM. WT-NS, wild type-normal saline; WT-CDDP, wild type-cisplatin; AQP1: aquaporin 1.* **Figure 2:** *NOX2-KO mice markedly attenuate the cisplatin-induced ROS increase in WT mice. The glutathione disulfide/glutathione ratio, an indicator of oxidative stress, was increased 3 days after cisplatin injection in the WT group. In contrast, ROS were a significantly blunt in the NOX2-KO CDDP group compared with the WT-CDDP group. Data represent the mean ± standard error of five mice per group. The experiment was repeated twice with similar results.*
## NOX2 deficiency protected against cisplatin-induced AKI in vivo
We observed that the knockout of NOX2 attenuated cisplatin-induced renal damage with a significantly decrease of serum creatinine level ($30\%$ decrease) (Figure 3A) and a lower level of BUN (Figure 3B) 3 days after cisplatin injection. PAS staining demonstrated that kidneys in the WT-CDDP group had a more severe tubular injury with cast formation, tubular dilation, and necrosis. In contrast, a deficiency of NOX2 attenuated the degree of tubular injury score (Figures 3C,D). In addition, kidney injury molecule-1 (Kim-1), an early AKI biomarker, is a sensitive indicator of proximal tubular injury. Immunohistochemistry and quantitative data demonstrated that Kim-1 was significantly lower in the NOX2-deficient CDDP group (Figures 4A,B) This was further confirmed through a Luminex assay, which revealed that the disruption of NOX2 significantly reduced the protein level of Kim-1 ($78\%$ decrease) after cisplatin injection (Figure 4C). These results further confirmed that NOX2 KO decreases the severity of cisplatin-induced proximal tubular cell injury and AKI.
**Figure 3:** *Mice with NOX2 deletion had milder cisplatin-induced AKI. (A) Serum creatinine levels indicated that NOX2-KO suppressed the cisplatin-mediated decrease in renal function. (B) BUN levels had a lower trend in NOX2 KO-CDDP mice compared with WT CDDP mice. (C,D) PAS staining of renal tubules revealed cisplatin-induced cast formation, tubular dilatation (black arrow), and tubular epithelial cell injury in WT CDDP mice. Quantitative analysis of PAS staining indicated NOX2 deletion suppressed the tubular injury score 3 days after cisplatin injection. Data represent the mean ± standard error of five mice per group. Scale bar = 100 μM.* **Figure 4:** *Mice with NOX2 deletion had decreased Kim-1 levels in cisplatin-induced AKI. (A,B) Kim-1, a biomarker of renal tubular injury, was overexpressed and accumulated in the WT CDDP group’s peritubular area (black arrow). Immunohistochemical analysis indicated that the area of Kim-1 was greater after cisplatin and reduced in the NOX2 KO mice. (C) NOX2 deletion reduced Kim-1 protein levels in cisplatin-induced AKI, as indicated by the Luminex assay. Data represent the mean ± standard error of five mice per group. Scale bar = 100 μM. The experiment was repeated twice with similar results.*
## NOX2 deletion attenuated cisplatin-induced pro-inflammatory cytokines expression
Cisplatin induces the expression of various cytokines in renal tissue, which was associated with further renal damage [25]. We observed that the pro-inflammatory factors IL-6 and IL-1α were significantly reduced (by 64 and $47\%$, respectively) in the NOX2-KO CDDP group compared to the WT CDDP group (Figures 5A,B). IL-6 and IL-1α are important mediators of the acute-phase response and are released in the event of sepsis, ischemic injury, or toxicity. IL-6 is secreted by macrophages, and IL-1α is produced mainly by activated neutrophils as well as by macrophages, epithelial cells, and endothelial cells. However, MCP-1 exhibits a chemotactic activity for monocytes, which did not exhibit an obvious change in either the WT CDDP or NOX2-KO CDDP group (Figure 5C). We did not find that Th1, Th2, or Th17 cytokines exhibited any difference in the NOX2-KO CDDP group (Figures 5D–G). IL-10 production by dendritic cells was found to have a protective effect on cisplatin-induced AKI [26]. Our data also indicated a mild elevation of IL-10 in the NOX2-KO CDDP group compared to the WT CDDP group (Figure 5H). Thus, these data indicate that NOX2 deletion prevented the cisplatin-induced change in cytokines.
**Figure 5:** *Cytokine expression with Luminex assay in cisplatin-induced AKI in the WT and NOX2-KO groups. (A,B) IL-6 and IL-1α proinflammatory factors were significantly reduced in the NOX2 KO-CDDP group compared to the WT-CDDP group. (C–G) MCP-1 and T-helper lymphocyte cytokines such as IFN-γ, IL-4, IL-5, and IL-17A did not show significant changes between the WT-CDDP and NOX2 KO-CDDP groups. (H) IL-10 was significantly reduced in the WT-CDDP group compared with the WT-NS group. IL-10 was mildly recovered in the NOX2 KO-CDDP group compared with the WT-CDDP group. Data represent the mean ± standard error of five mice per group. The experiment was repeated twice with similar results.*
## Reduction of neutrophil infiltration in attenuated cisplatin-induced AKI in NOX2 deficient mice
Inflammatory cells of the immune system, such as neutrophils, macrophages, and dendritic cells, infiltrate the kidney tissue and affect the development of cisplatin-induced AKI. We further assessed the composition of immune cells in the kidneys to elucidate the underlying mechanism by which NOX2 deficiency alleviates AKI. The gating strategy included Ly6G+, CD11b+ neutrophils, F$\frac{4}{80}$+, CD11C− macrophages; and F$\frac{4}{80}$−, CD11c+ dendritic cells (Figure 6A). Flow cytometry revealed that the neutrophil count in the kidneys and the percentage of total CD45+ cells were significantly increased on days 3 after cisplatin injection in the WT group. However, NOX2 deficiency reduced neutrophil infiltration (by $38\%$) in the kidneys (Figures 6B,D,E). No differences were found in cell numbers or percentages of kidney macrophages and dendritic cells in cisplatin-treated WT mice and NOX2-KO mice (Figures 6C,F–I). Taken together, these data suggest that NOX2 deletion reduces neutrophil infiltration and attenuates cisplatin-induced AKI, consistent with the decreased levels of pro-inflammatory cytokines IL-6 and IL-1α (Figures 5A,B). Since neutrophils may constitute an important innate immune response in cisplatin-induced nephrotoxicity [27], our data suggest NOX2-induced neutrophil infiltration and inflammation may play a role in cisplatin-induced AKI.
**Figure 6:** *Mice with NOX2 deletion had reduced neutrophil infiltration in cisplatin-induced AKI. (A) Whole kidneys were homogenized into a single-cell suspension for flow cytometric analysis of immune cells. Hierarchical gating was performed to identify Ly6G+, CD11b+ neutrophils; F4/80+, CD11C− macrophages; and F4/80−, CD11c+ dendritic cells. (B,D,E) Neutrophil infiltration was increased on day 3 after cisplatin injection in both WT and NOX2 KO groups. However, the neutrophil percentage of total CD45+ cells was significantly reduced in the NOX2-KO CDDP group on day 3 compared to that of the WT CDDP group. (C,F–I) The percentages of macrophage and dendritic cells among total CD45+ cells were significantly reduced in the WT CDDP group on day 3 compared to the WT on day 0. However, the numbers and percentages of total CD45+ macrophage and dendritic cells were not significantly different between the WT CDDP and NOX2-KO CDDP groups on day 3. Data represent the mean ± standard error of 3–5 mice per group.*
## Significantly decreased ICAM-1 and CXCL1 expression in cisplatin-induced AKI in NOX2-KO mice
To study the mechanism of reduced neutrophil infiltration in the kidneys observed in NOX2-KO mice treated with cisplatin, we analyzed the neutrophil recruitment cascade. Chemoattractants, such as CXCL1 and CXCL2 are key players in neutrophil activation. Activated neutrophils interact with endothelial cells, which slows them down. This is mediated by binding molecules, such as ICAM-1, P-selectin, and E-selectin. We observed that sICAM and CXCL1 were greatly reduced (by 32 and $51\%$, respectively) in the NOX2-KO CDDP group (Figures 7A,D). Immunohistochemical staining also revealed the area of ICAM-1 labeling was significantly increased in WT-CDDP group but not in NOX2-KO CDDP mice (Figures 7G,H). However, compared to WT CDDP mice, no significant differences in P-selectin and CXCL2 were noted in NOX2-KO CDDP mice (Figures 7B,E). We also assessed VEGF, which regulates vessel growth after AKI. VEGF decreased markedly after cisplatin in both WT and NOX2-KO mice. ( Figure 7C). GM-CSF was previously demonstrated to be expressed in tubular cells after renal injury to promote macrophage alternative activation [28]. However, GM-CSF expression did not differ among the four groups (Figure 7F). These results demonstrate that NOX2-KO mice exhibited significantly decreased ICAM-1 and CXCL1 expression consistent with lower neutrophil infiltration in AKI.
**Figure 7:** *Cisplatin-induced increase in ICAM-1 and CXCL1 was reduced in NOX2-KO mice. (A,G,H) sICAM expression with Luminex assay was decreased in the NOX2-KO CDDP group compared to the WT CDDP group on day 3, and the area of ICAM-1 labeling (black arrow) through immunohistochemical staining was increased in WT-CDDP but not in NOX2 KO-CDDP mice. (B,C,F) sP-selectin, VEGF, and GM-CSF levels with Luminex assay were not significantly different between the WT CDDP and NOX2-KO CDDP groups on day 3. (D,E) The level of chemokine CXCL1 was significantly lower in the NOX2-KO CDDP group on day 3. However, another chemokine, CXCL2, showed no change between the WT CDDP and NOX2-KO groups. Data represent the mean ± standard error of five mice per group. Scale bar = 100 μM. The experiment was repeated twice with similar results.*
## Discussion
In this study, we demonstrated the hitherto unknown importance of NOX2-mediated ROS production in cisplatin-induced AKI. ROS play an important role in cell signaling, oxygen sensing, apoptosis, gene expression, and cell defense (29–33). The major source of renal ROS generation in cisplatin-induced AKI is NADPH oxidases [34]. NOX2-mediated ROS play an important pathogenetic role in different kidney diseases. Angiotensin-converting enzyme-2 attenuates diabetic kidney injury in the Akita mouse model in association with decreased NOX2 activity [20]. It has been shown that NOX2 modulates ischemia–reperfusion injury in delayed graft function and that its absence is associated with reduced inflammation and fibrosis [35]. Our findings also demonstrated that the deletion of NOX2 was associated with reduced ROS level and proinflammatory cytokines in cisplatin-induced AKI. Moreover, the lowered endothelial adhesion molecule ICAM-1 and chemokine CXCL1 may contribute to the reduction in neutrophil infiltration in NOX2 deficient mice in cisplatin-induced renal damage.
The pathogenesis of cisplatin-induced AKI is complex and involves both the innate and adaptive immune systems [25, 36]. Cisplatin is cleared from the body by the kidney mainly by glomerular filtration. The renal proximal tubular cells are exposed to cisplatin toxicity through tubular secretion and uptake. The renal tubules may get injured by several mechanisms including apoptosis, DNA damage, direct toxicity, and inflammation [11]. Although the secretion of IL-1, IL-6, and IL-18 in cisplatin-induced kidney injury has been well documented, the inhibition of these cytokines does not protect against cisplatin-induced AKI [25]. Our results revealed that NOX2 KO prevents the increase in pro-inflammatory cytokines including IL-6 and IL-1α induced by cisplatin (Figures 5A,B). The levels of other cytokines, namely, interferon-γ, IL-4, IL-5, and IL-17A, did not significantly change in our study (Figures 5D–G). IL-10, an anti-inflammatory cytokine, was significantly reduced in WT cisplatin-induced AKI in our study. NOX2-KO mice with cisplatin injury exhibited a trend of IL-10 recovery (in terms of level) compared to WT mice (Figure 5H). Put together, these data indicate that NOX2 in renal epithelial cells increased ROS generation to promote pro-inflammatory cytokines and reduce anti-inflammatory cytokines.
After the initial tubular injury, there is an immediate inflammatory response, resulting in increased renal vascular endothelium permeability and the release of pro-inflammatory cytokines and chemokines. Numerous inflammatory cells, including neutrophils, macrophages, dendritic cells, and lymphocytes, infiltrate the kidneys and are important in the progression of cisplatin-induced AKI (25, 37–40). Therefore, we investigated the distribution of immune cells after cisplatin-induced AKI. Notably, NOX2 deletion reduced the cisplatin-induced increase in neutrophil infiltration (Figure 6E; Supplementary Figure S2). Activated neutrophils are important first responders in the innate immune system. They may cause damage in renal tubular cells by establishing an inflammatory cascade in the tissue. There are some controversial studies on the pathogenetic role of neutrophils in cisplatin-induced AKI. Depletion of neutrophils using the anti–GR-1 antibody or anti-Ly6G antibody did not affect renal cisplatin-induced injury [25, 41]. However, a recent study revealed that blocking neutrophil infiltration by inhibiting the leukotriene B4 axis has protective effects against cisplatin-induced AKI [27]. Our study suggested that the inhibition of NOX2-mediated ROS generation may attenuate kidney neutrophil infiltration and protects against cisplatin-induced AKI.
Neutrophil transmigration requires complex steps with a consequence of adhesive interaction with endothelial cells. Neutrophil recruitment depends on E-selectin, P-selectin, and ICAM-1 in the peritubular capillary [42]. Increased vascular permeability and renal cytokines expression promote neutrophil transmigration. It has been shown that using anti-ICAM-1 antibody could significantly reduce neutrophil infiltration [43]. Several research showed that IL-6 plays an important role in local inflammation and augmentation of ICAM-1 in vascular endothelial cells amplifying neutrophil recruitment (44–46). Chemokines CXCL1 and CXCL2 act sequentially to guide neutrophil crawling and transmigration during inflammation [47]. Mice deficient in the CXCL1 receptor had a less renal tubular injury in cisplatin-induced AKI [48]. We found that ICAM-1 and CXCL1 expression significantly decreased in cisplatin-induced AKI in NOX2-KO mice (Figures 7D,H), implicating the essential role of neutrophil recruitment in this ROS-dependent regulation.
Our study is the first to show that NOX2 was highly expressed in cisplatin-induced AKI. We hence used NOX2-KO mice to elucidate the importance of NOX2-derived ROS in mediating cisplatin-induced kidney injury. NOX2-KO mice exhibited attenuated renal damage and improvements in renal function and tubular injury as well as ROS reduction (Figures 2, 3). Moreover, the early AKI biomarker Kim-1 expressed by proximal tubular epithelial cells after injury was also significantly reduced in NOX2-KO mice (Figure 4). Our data indicate that NOX2 is pathogenic in cisplatin-induced AKI because the upregulation of NOX2 in renal tubular epithelial cells results in excessive ROS production, increases renal injury, and enhances neutrophil infiltration through chemokine CXCL1 and vascular endothelial ICAM-1 overexpression. Of the NOX isoforms, NOX4 is also upregulated in the various renal pathological process [49, 50]. A recent study indicated that NOX4 aggravates cisplatin-induced AKI via programmed cell death and inflammation [51]. The western blotting data in that study, like our results (Figures 1A,C), also showed NOX2 upregulation in cisplatin-treated HK2 cells. According to previous studies in different disease models, NOX isoform expression was also different. NOX4 expression was not upregulated in response to cisplatin treatment in our experiments may be due to different mouse strains and cisplatin dosages used in the experiments. Excessive ROS from NOX2 maybe have another pathway to inhibit the expression of NOX1 and NOX4.
Our study has some limitations. The cell types that generate nephrotoxic NOX2-derived ROS were not identified in the present study. We found NOX inhibitor indeed reduced the production of intracellular ROS in cisplatin-treated human kidney tubular epithelial cells (Supplementary Figure S1). NOX2-derived ROS are critical in regulating the function and gene expression of neutrophils (31–33). NOX2-deficient neutrophils have higher pro-inflammatory activities; this explains the more severe arthritis seen in the absence of NOX2 in mice [29, 30]. However, cisplatin-induced AKI may have a different tissue microenvironment as compared with that of arthritis-inflamed joints. Further studies about the possible role of NOX2 and NOX2-derived ROS from the renal infiltrated neutrophil in cisplatin-induced AKI are needed.
In summary, ROS derived from NOX2 in renal tubular epithelial cells play a critical role in the pathophysiology of cisplatin-induced AKI (Figure 8); specifically, these ROS enhance proximal tubular cell injury and severity of renal function decline in cisplatin-induced AKI. Neutrophil infiltration, which may presumably be through NOX2-derived ROS, were associated with the generation of the chemokine CXCL1 and the vascular endothelial adhesion molecule ICAM-1 by pro-inflammatory cytokines stimulation such as IL-6 and IL-1α. NOX2 KO in vivo significantly attenuated cisplatin-induced AKI and inflammation, indicating that precise control NOX2/ ROS pathway may be a novel therapeutic strategy against cisplatin-induced AKI.
**Figure 8:** *Schematic summarizing the mechanism of NOX2-induced ROS in the renal proximal tubular cells. NOX2 contributes to cisplatin-induced AKI and involves an increase Kim-1 and proinflammatory cytokines, IL6 and IL-1α which also increase neutrophil infiltration with higher endothelial adhesion molecule ICAM-1 and chemokine CXCL1. The box in the figure is NOX2 which has multiple membrane-bound subunits of NADPH oxidase including gp91phox, p22phox, p67phox, p47phox, and Rac.*
## 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 Institutional Animal Care and Use Committee (IACUC), at National Cheng Kung University.
## Author contributions
H-CC and H-YH performed the experiments, analyzed the data, and designed the study. H-CC wrote the manuscript. H-CC, H-YH, J-MS, and C-CS analyzed the data and contributed to the discussion. C-CS and J-MS reviewed and edited the manuscript.
## Funding
This work was supported by the Ministry of Science and Technology, Taiwan (MOST107-2314-B-650-009) and by grants from EDAH (EDAHP106031 and EDAHP108029).
## 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.2023.1097671/full#supplementary-material
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|
---
title: A low-protein maternal diet during gestation affects the expression of key
pancreatic β-cell genes and the methylation status of the regulatory region of the
MafA gene in the offspring of Wistar rats
authors:
- Tonantzin C. Sosa-Larios
- Ana L. Ortega-Márquez
- Jesús R. Rodríguez-Aguilera
- Edgar R. Vázquez-Martínez
- Aaron Domínguez-López
- Sumiko Morimoto
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10040775
doi: 10.3389/fvets.2023.1138564
license: CC BY 4.0
---
# A low-protein maternal diet during gestation affects the expression of key pancreatic β-cell genes and the methylation status of the regulatory region of the MafA gene in the offspring of Wistar rats
## Abstract
Maternal nutrition during gestation has important effects on gene expression-mediated metabolic programming in offspring. To evaluate the effect of a protein-restricted maternal diet during gestation, pancreatic islets from male progeny of Wistar rats were studied at postnatal days (PND) 36 (juveniles) and 90 (young adults). The expression of key genes involved in β-cell function and the DNA methylation pattern of the regulatory regions of two such genes, Pdx1 (pancreatic and duodenal homeobox 1) and MafA (musculoaponeurotic fibrosarcoma oncogene family, protein A), were investigated. Gene expression analysis in the pancreatic islets of restricted offspring showed significant differences compared with the control group at PND 36 ($P \leq 0.05$). The insulin 1 and 2 (Ins1 and Ins2), Glut2 (glucose transporter 2), Pdx1, MafA, and Atf2 (activating transcription factor 2), genes were upregulated, while glucokinase (Gck) and NeuroD1 (neuronal differentiation 1) were downregulated. Additionally, we studied whether the gene expression differences in Pdx1 and MafA between control and restricted offspring were associated with differential DNA methylation status in their regulatory regions. A decrease in the DNA methylation levels was found in the 5' flanking region between nucleotides −8118 to −7750 of the MafA regulatory region in restricted offspring compared with control pancreatic islets. In conclusion, low protein availability during gestation causes the upregulation of MafA gene expression in pancreatic β-cells in the male juvenile offspring at least in part through DNA hypomethylation. This process may contribute to developmental dysregulation of β-cell function and influence the long-term health of the offspring.
## 1. Introduction
The developmental origins of health and disease (DOHaD) concept states that challenges during critical developmental time windows (conception through pregnancy and lactation) alter fetal development with persistent, life-long effects on offspring phenotypes and metabolism [1]. Indeed, the environment faced during development can permanently change not only the body's structure and function but also its responses to environmental influences encountered in later life [2, 3].
The period from conception to birth is a time of cellular replication and differentiation, the functional maturation of organs and body systems, and rapid growth. These processes are very sensitive to alterations in nutrient availability, including maternal undernutrition [4, 5].
Studies in humans and animal models have demonstrated that low maternal dietary protein intake during gestation can cause offspring to be susceptible to different disorders in adulthood including diabetes mellitus, cardiovascular diseases and obesity [6]. One of the main organs affected by reduced protein availability in utero is the endocrine pancreas, which undergoes several structural and functional adaptations to maintain glucose homeostasis [7], increasing offspring susceptibility to the development of type 2 diabetes (T2D) [6, 8].
Using rodents as animal models, our research group and other groups have previously reported the effects of a maternal low-protein diet (LP) on altered carbohydrate metabolism in offspring [9] and some adverse changes, such as impairments in pancreatic β-cell development and in the responses of peripheral tissues to insulin [10], low glucose-stimulated insulin secretion [11] at different ages (juvenile, adult, old) [12], impaired glucose tolerance [13] and alterations in the proportion of pancreatic islets and islet size distribution relative to those of offspring from mothers fed a control diet [14].
Based on the evidence that malnutrition alters the expression of the fetal genome [15, 16], we chose to study some key genes related to metabolism and β-cell function in pancreatic islets from offspring exposed to low protein availability in utero. Our approach was to select specific genes including Ins$\frac{1}{2}$ (insulin 1 and 2), Glut2 (Glucose transporter 2, which encodes the primary glucose transporter and sensor involved in sensing glucose in rodent β-cells) and Gck (which encodes glucokinase, a protein that initiates the metabolism of glucose after entering β-cells and constitutes the rate limiting step of this process) [17]. Additionally, the expression of genes encoding specific transcription factors, such as Pdx1 (pancreatic and duodenal homeobox 1), MafA (musculoaponeurotic fibrosarcoma oncogene family, protein A), Atf2 (activating transcription factor 2) and NeuroD1 (neuronal differentiation 1), that are important elements in the expression and regulation of crucial genes for the function and preservation of β-cells, was also studied (18–20).
Considering that epigenetic changes may provide a link that translates environmental exposures into pathological mechanisms, we also studied the methylation status of the regulatory regions of the genes encoding two major pancreatic β-cell transcription factors, Pdx1 and MafA.
## 2.1. Animal care and management
All animal procedures were approved (BRE-783) by the Animal Experimental Ethics Committee of Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán” in accordance with the Official Mexican Guidelines for the Care and Use of Laboratory Animals (NOM-062-ZOO-1999). Virgin female albino Wistar rats (15–17 weeks old, weighing 220–260 g) were housed under controlled temperature (22–23°C) and humidity (30–$50\%$) conditions with 12-h light/dark cycles and ad libitum access to water and food (Purina Laboratory 5001 rodent chow, Purina Mexico). Females were mated overnight with proven male breeders on postnatal day (PND) 120. A microscopic examination of the vaginal smear was performed to confirm pregnancy after matting. Pregnant rats were transferred to individual cages and allocated randomly to 1 of 2 groups ($$n = 15$$ each): the control group (C) was fed a chow diet (with $20\%$ casein), and the second group of rats was fed an isocaloric low-protein (LP) diet ($10\%$ casein). The composition of the diets is shown in Table 1. Births occurred 21 days after conception, which was designated PND 0. To ensure homogeneity of the evaluated offspring, all litters were adjusted to 10 pups per dam at PND 2. After delivery, mothers were fed a control diet, and the offspring at weaning were also fed a control diet until the end of the study. The weight of the offspring was recorded at birth, at weaning and at PND 36 and 90. Juvenile (PND 36) and young adult male (PND 90) offspring were studied. We only used male rats for the study in order to avoid the effect of variation in the concentrations of sex steroid hormones produced during the female estrous cycle, which in our experience modify not only glucose metabolism but also insulin gene expression.
**Table 1**
| Ingredient | Control diet (%) | Low protein diet (%) |
| --- | --- | --- |
| Casein | 20.0 | 10.0 |
| Cystine | 0.3 | 0.15 |
| Choline | 0.165 | 0.165 |
| Vitamin mix | 1.0 | 1.0 |
| Mineral mix | 5.0 | 5.0 |
| Cellulose | 5.0 | 5.0 |
| Corn oil | 5.0 | 5.0 |
| Carbohydrates | | |
| Corn starch | 31.76 | 37.34 |
| Dextrose | 31.76 | 37.34 |
## 2.2. Glucose tolerance test at 36 and 90 days postnatal life
Eight to 10 rats (different litters) per group (C and LP at 36 and 90 days of postnatal life) were fasted for 6 h before an intraperitoneal (IP) glucose tolerance test in which 1 g/kg body weight of D-glucose (G7021, Sigma Aldrich, Mexico) was administered i.p. at 09:00 h. Blood was taken by the retro-orbital bleeding technique [21] at 0, 30, 60, 90, and 120 min. Blood was collected into polyethylene tubes and allowed to clot at 4°C. The blood samples were centrifuged at 1,500 g for 15 min at 4°C. Serum samples were kept at −20°C until assayed.
## 2.3. Biochemical analysis
Fasting serum concentrations of glucose, cholesterol and triglycerides were determined enzymatically with the automated SYNCHRON CX 5 Delta system (Beckman Coulter Co., Fullerton, CA, USA), comprising a MULTI™ SYNCHRON CX calibrator and GLUH LUH 2x300 (B24985), CHOL 2x300 [467825] and TG 2x300 [445850] kits (Synchron Systems, Beckman Coulter Co.). Insulin concentrations were measured by solid-phase radioimmunoassay (RIA) (RI-13K, Millipore, MA, USA). The inter- and intra-assay coefficients of variation (CVs) were 4 and $6\%$, respectively. Number of rats per group, 8–10.
## 2.4. Pancreatic islet isolation
Pancreatic islets were obtained using the collagenase digestion procedure of Lacy and Kostianovsky [22] with some modifications. Briefly, each rat was euthanized by decapitation [23]; then, the abdomen was opened, and the pancreatic duct was cannulated. The pancreas was then distended with cold Hanks' balanced salt solution (HBSS) plus 10 mg/pancreas of collagenase V (C9263, Merck, Mexico). The excised pancreas was then cut into approximately 1 mm pieces and incubated at 37 ± 1.0°C at 120 rpm for 15 min, and digestion was terminated by adding cold HBSS (24020117, Gibco BRL, Gaithersburg, MD, USA). Pancreatic islets were hand-picked individually under a stereoscopic microscope. Two hundred to 300 islets were collected in tubes, and 1 ml of QIAzol Lysis Reagent (79306, Qiagen, CA, USA) was added. Then, all tubes were stored at −70°C until processing.
## 2.5. Gene expression and quantitative PCR
RNA was extracted from pancreatic islets using the RNeasy lipid tissue kit (74804, Qiagen, CA, USA). The quality and integrity of RNA were analyzed by spectrophotometry on a BioDrop instrument (BioDrop Inc., Cambs, UK) and agarose gel electrophoresis, respectively. Only samples with OD $\frac{260}{280}$ ratios between 1.8 and 2.1 were used for reverse transcription. Complementary DNA (cDNA) was synthesized using the Transcriptor First-Strand cDNA Synthesis Kit (04379012001, Roche Life Science, CA, USA) according to the manufacturer's specifications. To analyze the differential expression of the genes of interest, cDNA samples were subjected to qPCR using TaqMan probes and a Roche Light Cycler 2.0. The qPCR cycling conditions were 95°C for 10 s, 60°C for 30 s, and 72°C for 40 s (40 cycles). The oligonucleotides were designed at www.oligo.net, and their sequences are shown in Table 2. *The* gene expression level was normalized to β-actin (Actb) as a constitutive control gene, and the relative gene expression was determined using the 2−(ΔΔCT) method [24]. Three independent experiments were conducted in duplicate, ~1 μg of RNA was obtained from 100 islets (see Supplementary Table S1 for a detailed description of the number of rats used).
**Table 2**
| Gene | Forward | Reverse | Amplicon size (nt) |
| --- | --- | --- | --- |
| Ins 1 | CAACATGGCCCTGTGGAT | CTTGGGCTCCCAGAGGAC | 64 |
| Ins 2 | CGAAGTGGAGGACCCACA | TGCTGGTGCAGCACTGAT | 128 |
| Glut2 | GCCTTCGGAGTGTCTTGG | GGCAGGGACTCCAGTCAG | 68 |
| Gck | CTGGATGACAGAGCCAGGAT | CTGGAACTCTGCCAGGATCT | 69 |
| Pdx1 | GGAGGTGTTGTGCCCTCA | CTAAGGCCGGAAGGCAGT | 65 |
| MafA | GACTTGCACAAGGGTCAAAGA | CCGGGTTCAAAGGTGAGTTA | 75 |
| Atf2 | GAGTCTCGTCCACAGTCCTTG | AGTTGTGTGAGCTGGAGACG | 75 |
| NeuroD1 | GCAGAAGGCAAGGTGTCC | TTTGGTCATGTTTCCACTTCC | 89 |
| Actb | AAGGCCAACCGTGAAAAGAT | ACCAGAGGCATACAGGGACA | 77 |
## 2.6. Evaluation of global genomic DNA methylation
Genomic DNA was isolated from pancreatic islets with the QIAamp DNA isolation kit (51104, Qiagen, CA, USA). The quality and integrity of the DNA were assessed by spectrophotometry on a BioDrop spectrophotometer (BioDrop Inc, Cambs, UK) and agarose gel electrophoresis. Only samples with OD $\frac{260}{280}$ ratios between 1.8 and 2.1 were used. Global DNA methylation analysis was performed on 100 ng of genomic DNA using a commercially available Global DNA Modification Kit (Imprint Methylated DNA Quantification Kit; MDQ1, Sigma, MO, USA) to detect the relative levels of methylated DNA based on the ELISA principle following the manufacturer's instructions (see Supplementary Table S1 for a detailed description of the number of rats used).
## 2.7. Sodium bisulfite DNA conversion and sequencing
Since it was observed that the expression of two transcription factors essential for islet β-cell function, Pdx1 and MafA, was modified as a result of a protein-restricted maternal diet, we sought to study the DNA methylation status of their promoters. For this purpose, 2 μg of genomic DNA from pancreatic islets was analyzed with an EZ DNA Methylation-Gold Kit (D5005, ZYMO Research, CA, USA) and capillary sequencing (GENEWIZ, NJ, USA). The proximal 5′ flanking region of the *Pdx1* gene, which encompasses nucleotides −275 to +1 relative to the transcription start site, was considered [25]. Regarding MafA, the 5′ flanking region between nucleotides −8118 and −7750 relative to the transcription start site [26] was studied. The primers were designed using the MethPrimer program [27]. The sequences were as follows: *Pdx1* gene proximal promoter, forward 5′-AGGATAGGAGAGATTAGTTTGTTGA-3′, reverse 5′-CTACAAACCAAACCTTAAAACACT-3′; MafA gene promoter, forward 5′-TGGGGTTTGGTAAATGTTTTTATT-3′; reverse 5′-CCCTCCAACAAACACTTCAATATACT-3′. DNA fragments of interest were PCR-amplified, and the corresponding DNA fragments were cloned into pGEM-T Easy (Promega, WI, USA). At least 10 independent clones were selected and sequenced using T7 sequence primers (see Supplementary Table S1 for a detailed description of the number of rats used).
## 2.8. Statistical analysis
Data are presented as the mean ± standard error of the mean (SEM). All statistical analyses were performed using SigmaStat 3.5 software (Systat, CA, USA) for Windows. Data with a normal distribution were compared by Student's t-test, while the Mann–Whitney test was employed for non-normally distributed data. Significance was assigned at $P \leq 0.05.$
## 3.1. Caloric intake and weight gain in mothers
Caloric intake was significantly higher ($p \leq 0.05$) in the mothers fed the protein-restricted diet during gestation compared to control mothers ($$n = 15$$ each); however, the net weight gain during pregnancy was similar regardless of the diet consumed (Figure 1).
**Figure 1:** *(A) Caloric intake (Kcal) and (B) Weight gain (g) in mothers fed control (C) and restricted (R) diet during pregnancy. Data are expressed as the mean ± SEM from 15 rats/group. *P < 0.05 compared with control.*
## 3.2. Somatometric indicators
Birth weight was slightly but significantly lower ($p \leq 0.05$) in the pups of mothers fed a protein-restricted diet during gestation than in those from mothers fed a control diet. At weaning, the same tendency was observed; that is, the weight of the restricted animals was significantly lower than that of the control. At PND 36 and 90, there was no difference in the weight of the offspring of mothers fed different diets (Figure 2).
**Figure 2:** *Body weight of offspring from mothers fed a control diet and protein-restricted diet at birth (A), weaning, PND21 (B), PND36 (C), and PND 110 (D). Data are expressed as the mean ± SEM from 8 to 10 rats/group. *P < 0.05 compared with control.*
## 3.3. Glucose tolerance
Different maternal diets during gestation produced differential glucose tolerance in the offspring at PND 36 and 90. In pups from mothers fed a protein-restricted diet during gestation, the glucose tolerance was lower than that in control pups, at both investigated ages. The area under the curve showed the same trend (Figure 3).
**Figure 3:** *Glucose tolerance in the offspring of mothers fed a control diet (filled bars) or a protein-restricted diet (empty bars). (A) Glucose tolerance in offspring at PND 36. (B) Corresponding area under the curve. (C) Glucose tolerance in offspring at PND 90. (D) Corresponding area under the curve. Data are expressed as the mean ± SEM from 8 to 10 rats/group. *P < 0.05 compared restricted (R) vs. control (C).*
## 3.4. Biochemical parameters
The fasting glucose concentration in the offspring of mothers fed a protein-restricted diet was not modified by the effect of the maternal diet at PND 36 or 90, and the values were similar between the control and protein-restricted diets at both ages (Table 3).
**Table 3**
| Unnamed: 0 | PND 36 | PND 36.1 | PND 90 | PND 90.1 |
| --- | --- | --- | --- | --- |
| | C | R | C | R |
| Glucose (mmol/L) | 5.99 ± 0.42 | 5.90 ± 0.46 | 6.63 ± 0.35* | 6.63 ± 0.69 |
| Insulin (pmol/L) | 24.15 ± 7.5 | 25.05 ± 7.5 | 169.05 ± 31.5* | 169.35 ± 30.0* |
| Triglycerides (mmol/L) | 0.83 ± 0.16 | 0.83 ± 0.07 | 1.02 ± 0.14 | 0.95 ± 0.08* |
| Cholesterol (mmol/L) | 1.33 ± 0.12 | 1.60 ± 0.16† | 0.87 ± 0.19* | 0.98 ± 0.10* |
The glucose concentration in the offspring at PND 90 was significantly higher than the value at PND 36 but within normoglycemic values. The insulin concentration was similar between the control and restricted groups at both time points but was higher at PND 90 than at PND 36. The triglyceride concentration was not different between the control and restricted groups at either of the two ages; however, the concentration was higher in restricted male offspring at PND 90 than at PND 36. At PND 36, cholesterol was higher in restricted offspring than in the control but remained in the normal range. At PND 90, the cholesterol concentrations were similar regardless of the diet of the mother but were lower than those at PND 36.
## 3.5. Gene expression
To investigate the effect of a protein-restricted maternal diet on the expression of key pancreatic genes, we studied a series of targets related to insulin production and regulation in pancreatic islets from male offspring. *The* genes encoding insulin (Ins1 and Ins2), two glucose sensors (Glut2 and Gck) and a battery of transcription factors that regulate insulin gene transcription and constitute vital elements in the preservation and function of mature β-cells (Pdx1, MafA, Atf2, and NeuroD1) were studied. We observed that the expression of the studied genes was only different in protein-restricted compared with control offspring at PND 36. Figure 4 shows the expression normalized to the constitutive gene β-actin. *The* gene expression of Ins1 and Ins2, the glucose sensor Glut2, and the transcription factors Pdx1, MafA, and Atf2 in juvenile offspring was increased by a restricted maternal diet, whereas decreased expression was observed for Gck and NeuroD1. At PND 90, there were no statistically significant differences between the control and restricted groups (data not shown).
**Figure 4:** *Gene expression relative to the constitutive gene β-actin. The expression of different genes in PND 36 male offspring from control and restricted mothers is shown. (A) Insulin 1, (B) Insulin 2, (C) Glucose transporter 2 (Glut2), (D) Glucokinase (Gck), (E) Pancreatic and duodenal homeobox 1 (Pdx1), (F) Musculoaponeurotic fibrosarcoma oncogene family A (MafA), (G) Activating transcription factor 2 (Atf2), (H)
NeuroD1. Data are expressed as the mean ± SEM of three independent experiments conducted in duplicate. *P < 0.05 compared with control.*
## 3.6. Gene interaction analysis
The Ingenuity Pathway Analysis (IPA) engine (version 70750971) was used for a central analysis and construction of the signaling pathway networks of insulin secretion and type 2 diabetes mellitus. These two pathways were chosen as indicators of the major long-term metabolic disturbances in the offspring due to low protein availability in utero. The examined genes (Pdx1, MafA, Ins1, Ins2, Glut2, Gck, NeuroD1, and Atf2) were selected as multifunctional signals that appear to orchestrate pancreatic β-cell deterioration in this condition (Figure 5).
**Figure 5:** *Regulatory networks related to the insulin secretion signaling pathway and type 2 diabetes mellitus signaling, as predicted by QIAGEN's Ingenuity Pathway Analysis considering the studied genes: Ins, Insulin; Slc2a2, Solute carrier family 2 member 2 (also known as Glut2); Gck, Glucokinase; Pdx1, Pancreatic and duodenal homeobox 1; MafA, Musculoaponeurotic fibrosarcoma oncogene family A; Atf2, Activating transcription factor 2; NeuroD1, Neuronal differentiation 1. Molecules in the pathway showed in red had increased gene expression, whereas those in green had decreased gene expression. Solid lines indicate direct interactions between factors. Yellow lines indicate the participants in the canonical route of type 2 diabetes mellitus signaling, and blue lines indicate the insulin secretion signaling pathway.*
## 3.7. Global DNA methylation
Considering that intrauterine nutrition affects the epigenome of the offspring, we evaluated global DNA methylation in pancreatic islets from male rats at PND 36 and 90 from mothers fed a control or protein-restricted diet during gestation. We found a significant reduction in global DNA methylation in the restricted group compared with the control at PND 36 (Figure 6). In adults (PND 90), there were no differences in global methylation between control and restricted offspring.
**Figure 6:** *Global DNA methylation of pancreatic islets from male rats at PND 36 (A) and PND 90 (B) born to mothers fed a control or protein-restricted diet during gestation. Data are expressed as the mean ± SEM from five rats/group. *P < 0.05 compared with control.*
## 3.8. DNA methylation of the MafA and Pdx1 gene regulatory region
Since we observed an increase in the gene expression of most of the transcription factors studied in juvenile offspring, we chose two of the most important ones for pancreatic β-cell function to determine whether this overexpression was due to differential DNA methylation in their regulatory regions. In addition to the fact that global DNA methylation was decreased in the juvenile offspring of mothers fed a low-protein diet compared to a control diet, we analyzed the DNA methylation status of the 5' regulatory region (between nucleotides −8118 and −7750 relative to the transcription start site) of MafA and the proximal promoter of Pdx1. We found that a protein-restricted maternal diet induces a decrease in the content of DNA methylation of several CpGs within the 5' regulatory region in the MafA gene in pancreatic islets compared to the control (Figure 6); the differences in methylation compared to that of controls were statistically significant. No cytosine methylation was found in the proximal promoter region of Pdx1 (Figure 7).
**Figure 7:** *Schematic representation of the DNA methylation pattern of the MafA and Pdx1 genes in islets from control and restricted offspring. (A) Maps of the regulatory region and proximal promoter of MafA and Pdx1, respectively. Thick bars crossing the main line represent CpG dinucleotides. (B) DNA methylation status in the studied regions of MafA and Pdx1. Filled circles correspond to methylated CpG sites, empty circles correspond to unmethylated sites, columns represent each CpG site, and rows show all individual clones analyzed. For the scheme, two independent biological samples were analyzed.*
## 4. Discussion
In the current study, we observed the effect of a protein-restricted maternal diet during gestation on male offspring in rats. Our results show that low protein availability in utero is associated with changes in the gene expression of some key factors in the functioning of pancreatic β-cells of the offspring without a significant variation in blood glucose and insulin levels, as previously reported [12].
It is well known that stressful conditions in utero impair the functions of vital organs, including the pancreatic islets, leading to decreased function later in life [2], while phenotypic modifications caused by environmental stress conditions in offspring occur to adapt to adverse circumstances encountered in utero [28]. In this regard, it is important to emphasize that pancreatic β-cells possess remarkable adaptive plasticity in order to produce insulin according to metabolic requirements [17]. However, to maintain homeostasis and compensate for the need for insulin to maintain glucose levels, β-cells may become depleted of their insulin pool reserves, leading to diabetes [29].
Our previous studies with the same animal model used in the present study showed that despite minimal differences in circulating glucose and insulin, a maternal low-protein diet during pregnancy impairs the insulin secretory response to glucose in the offspring [12]. In this model, pancreatic islets from juvenile offspring (PND 36) stimulated with low (5 mM) and high (11 mM) glucose showed insulin hypersecretion compared to controls; however, they did not show a difference in glucose-stimulated insulin secretion between low-and high-glucose conditions, as is exhibited by a normal glucose sensing mechanism [29]. Long-term hypersecretion of insulin could deplete β-cell reserves and consequent insulin-deficient states, which in turn lead to major metabolic disturbances such as diabetes [17, 29]. Our finding of insulin gene (Ins1 and Ins2) upregulation and *Gck* gene downregulation may be at least partially related to those findings and to our previous finding of an impaired insulin secretory response to glucose in the pancreatic islets of the offspring of mothers fed a protein-restricted diet during gestation [12].
With all of the above as background, we studied the effect of low protein availability in utero on the expression of several key transcription factors that participate in the regulation of glucose metabolism. To our knowledge, we have demonstrated here for the first time that feeding pregnant rats a protein-restricted diet during pregnancy produces increased expression of Ins1, Ins2, Glut2, Pdx1, MafA, and Atf2 and decreased gene expression of NeuroD1 and Gck in their male juvenile offspring. Previous results by Chamson-Reig et al. [ 30] reported no change in *Pdx1* gene expression by effect of maternal low protein (LP) diet in male offspring. The apparent discordance of our data with these is likely related to the LP model ($8\%$ protein content), the temporal window of protein restriction (week 1 and 2 of gestation) and the study of the entire pancreas, whereas we used $10\%$ protein content throughout gestation and pancreatic islets of young male offspring were studied.
To enable a meaningful interpretation of the gene expression data obtained here, we used Ingenuity Pathway Analysis (IPA) software, which analyzes gene expression patterns in the context of a database based on the scientific literature [31] that shows mechanistic networks for biological functions and diseases (Figure 4). The analysis showed shared molecular pathways of genes; for example, the coexpression and interaction [18] of Atf2, MafA, Pdx1, NeuroD1 [18, 32], and Gck [33] have been reported in the β-pancreatic cell. Additionally, an interdependence between these genes has been found for Pdx1, which regulates not only insulin genes but also the MafA and *Glut2* genes and Pdx1 itself [13, 18, 34]. Additionally, the interaction of genes that are expressed to regulate β-cell function, insulin gene transcription [35], insulin secretion (MafA, SLC2A2, and NeuroD1) [36], pancreas development, perinatal α- and β-cell proliferation [37] and β-cell survival [35] was observed. The studied genes are involved in regulatory networks related to the insulin secretion signaling pathway [36] and T2D signaling, two of the main pathways in the offspring that are affected by low protein availability during gestation [12, 38]. Our results are in agreement with those obtained by Aguayo-Mazzucato et al. [ 33], who reported that overexpression of Pdx1 increases MafA mRNA levels. Moreover, Arantes et al. found increased expression of Pdx1 mRNA in PND 28 offspring of mothers fed a low protein diet during pregnancy compared with controls [39], and Rodriguez-Trejo et al. found a similar result in offspring at PND 7 [40].
It has been proposed that the link between in utero nutritional challenges and altered gene expression is acquired epigenetic alterations that increase disease susceptibility later in life [5, 15, 41, 42]. Epigenetic modifications are related to mechanisms that modulate chromatin structure and accessibility to transcription factors, including DNA methylation, histone modifications, and non-coding RNAs. The most extensively studied mechanism is the control of gene expression by the methylation of cytosine nucleotides in the promoter regions of genes. Hypomethylation of the cytosine bases in CpG islands located in a DNA promoter sequence allows increased gene expression [5]. Additionally, changes in methylation patterns (hypo- and hypermethylation) are observed during periods of inadequate nutrition [6, 43].
In the present investigation, we found that the expression of the studied genes was increased by the effect of a protein-restricted maternal diet and that global DNA methylation of pancreatic islets was decreased. Given this fact, we used a candidate gene approach and studied the methylation pattern of MafA and Pdx1, two of the most important genes in the preservation and regulation of pancreatic β-cell function [44]. We observed a pattern of decreased methylation of the CpGs in the 5′ flanking region of the MafA gene (between −8118 and −7750), and the percentage of methylation was lower in the pancreatic islets of juvenile offspring of mothers fed a protein-restricted diet during gestation compared with controls. These data suggest that a protein-restricted maternal diet produces an upregulation of MafA gene expression at least in part through DNA hypomethylation. It is well established that this region is critical for MafA gene transcription in the pancreas through coordinated actions with other conserved promoter regions [45]. Furthermore, mutation [46] or loss of MafA has been shown to contribute to T2D progression [47].
Our results confirm and support the findings reported by other authors who demonstrate that prenatal nutrition induces differential changes in the promoter methylation of specific genes, such as the PPARα [48], glucocorticoid receptor (GR) and PPARγ genes [49], in the liver of juvenile offspring of mothers fed a protein-restricted diet during pregnancy. In both cases, it was shown that these epigenetic changes result in increased expression of these genes.
The second candidate gene that we selected to study its methylation status was Pdx1, but we were unable to find any change in the methylation pattern in its proximal promoter region. In contrast, in a model of intrauterine growth retardation (IUGR), Park et al. reported not only DNA methylation but also histone acetylation and histone methylation as a cascade of epigenetic events leading to silencing of Pdx1 and consequently decreased Pdx1 expression, impaired insulin secretion and the onset of diabetes in adult rats [25]. The contrast observed between both studies could be explained by the difference in the stress models of the offspring in utero, on the one hand, ours is a model of protein restriction in the maternal diet and on the other hand, the study of Park et al., is a model of ligation of the uterine arteries to cause IUGR in which not only protein but all nutrient supply is restricted due to altered placental blood flow, which most likely affects epigenetic regulatory factors more drastically.
Additionally, changes in DNA methylation have been reported in pathological states of the human pancreas, such as diabetes mellitus. Volkmar et al. analyzed the methylome of freshly isolated islets in patients with type 2 diabetes and healthy subjects and found differential hypomethylation at $96\%$ of sites (266 of 276 CpGs) in type 2 diabetes (T2D) [16]. Moreover, Dayeh et al. identified 1,649 individual CpG sites and 853 genes that exhibit differential DNA methylation in pancreatic islets from T2D patients compared with non-diabetic donors, and $97\%$ of the CpG sites showed decreased DNA methylation and increased gene expression [50]. In both reports, the genes studied were linked to β-cell functionality, cell death and adaptation to metabolic stress.
*In* general, the link between differential DNA methylation and gene activity may be quite complex; therefore, it is still difficult to conclude unequivocally whether altered DNA methylation in vivo has direct effects on gene expression. However, our results add to the evidence of epigenetic changes under adverse conditions in utero, such as low protein availability, on the physiology of offspring, which could translate into disease in adulthood. It remains to be determined whether the epigenetic changes found here will translate into functional effects that impact pancreatic β-cell function. Our findings open new opportunities to identify molecules and mechanisms participating in the developmental programming of pancreatic β-cells, which will help in developing strategies and/or interventions to prevent T2D risk.
## 5. Conclusions
The present study showed that low protein availability during gestation programs the expression of some master genes of beta-cell function in male juvenile offspring, up-regulating them and as a global effect, decreasing the percentage of total DNA methylation. The down-regulation of MafA gene expression, was carried out at least in part, by decreased methylation of CpGs in the 5' flanking region (between −8118 and −7750). This process may contribute to developmental-dysregulation of β-cell function and influence the long-term health of the offspring.
## Data availability statement
The data presented in the study are deposited in the FigShare repository, accession number: https://doi.org/10.6084/m9.figshare.22137659.v1.
## Ethics statement
The animal study was reviewed and approved by Animal Experimental Ethics Committee of Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán”.
## Author contributions
SM designed and conducted the study, analyzed and interpreted data, wrote, reviewed, and edited the manuscript. TCS-L designed the functional experiments and performed lab work, analyzed and interpreted data, wrote, reviewed, and edited the manuscript. ALO-M designed the functional experiments and performed lab work, analyzed and interpreted data, reviewed, and edited the manuscript. AD-L, ERV-M, and JRR-A performed lab work, reviewed, and edited the manuscript. SM, TCS-L, and ALO-M are guarantors of this work and as such had full access to all of the data in the study and take responsibility for the integrity of the data. All authors read 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/fvets.2023.1138564/full#supplementary-material
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|
---
title: Ginaton reduces M1-polarized macrophages in hypertensive cardiac remodeling
via NF-κB signaling
authors:
- Jie Wang
- Enze Cai
- Xiangbo An
- Junjie Wang
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10040779
doi: 10.3389/fphar.2023.1104871
license: CC BY 4.0
---
# Ginaton reduces M1-polarized macrophages in hypertensive cardiac remodeling via NF-κB signaling
## Abstract
Introduction: Macrophages play a critical role in cardiac remodeling, and dysregulated macrophage polarization between the proinflammatory M1 and anti-inflammatory M2 phenotypes promotes excessive inflammation and cardiac damage. Ginaton is a natural extract extracted from Ginkgo biloba. Because of its anti-inflammatory properties, it has long been used to treat a variety of diseases. However, the role of Ginaton in modulating the diverse macrophage functional phenotypes brought on by Ang II-induced hypertension and cardiac remodeling is unknown.
Methods: In the present study, we fed C57BL/6J mice in the age of eight weeks with Ginaton (300 mg/kg/day) or PBS control, and then injected Ang II (1000 ng/kg/min) or saline for 14 days to investigate the specific efficacy of Ginaton. Systolic blood pressure was recorded, cardiac function was detected by echocardiography, and pathological changes in cardiac tissue were assessed by histological staining. Different functional phenotypes of the macrophages were assessed by immunostaining. The mRNA expression of genes was assessed by qPCR analysis. Protein levels were detected by immunoblotting.
Results: Our results showed that Ang II infusion significantly enhanced the activation and infiltration of macrophages with hypertension, cardiac insufficiency, myocardial hypertrophy, fibrosis and M1 phenotype macrophages compared with the saline group. Instead, Ginaton attenuated these effects. In addition, in vitro experiments showed that Ginaton inhibited Ang II-induced activation, adhesion and migration of M1 phenotype macrophages.
Conclusion: Our study showed that Ginaton treatment inhibits Ang II-induced M1 phenotype macrophage activation, macrophage adhesion, and mitigation, as well as the inflammatory response leading to impaired and dysfunctional hypertension and cardiac remodeling. Gianton may be a powerful treatment for heart disease.
## Introduction
Cardiovascular disease is a major public health concern worldwide (Benjamin et al., 2019). Hypertension is the leading cause of cardiovascular disease, affecting approximately 1 billion people globally (Mouton et al., 2020). The prevalence of hypertension is increasing by more than $10\%$ every year (Houston et al., 2011). Recent studies have shown that chronic high blood pressure can lead to cardiac hypertrophy, inflammation, fibrosis, and, ultimately, heart failure (Peet et al., 2020). Therefore, therapeutic strategies aimed at alleviating hypertension and preventing myocardial hypertrophy, inflammation, and fibrosis may help prevent hypertension-induced cardiac insufficiency or heart failure (Berthiaume et al., 2012; Dodd et al., 2012; Cavalera et al., 2014). During the development of hypertension, chronic pressure overload is closely associated with myocardial hypertrophy and inflammation, increasing the risk of heart failure and sudden death (DeBerge et al., 2019). Angiotensin II (Ang II) is a bioactive octapeptide derived from the renin–angiotensin–aldosterone system. Elevated levels of Ang II and its receptor type 1 angiotensinogen (AT1) promote cardiac remodeling and play an important role in the occurrence and development of hypertension (Yin et al., 2022). In addition to the known hemodynamic effects, Ang II also mediates cardiac remodeling through multiple signaling pathways, including protein kinase B/mammalian target of rapamycin (AKT/mTOR), mitogen-activated protein kinase (MAPK), nuclear factor-κB subunit (NF-κB), and transforming growth factor-β/mothers against decapentaplegic homolog (TGF-β/Smad) (Jin et al., 2015; Samak et al., 2016; Trial, et al., 2017; Mouton et al., 2018). Ang II stimulates hypertrophic remodeling by promoting proinflammatory cell infiltration into cardiac tissue. Recent studies have shown that Ang II stimulation can increase the adhesion of macrophages/monocytes to endothelial cells and subsequently migrate to cardiac tissue through endothelial cells, generate proinflammatory cytokines, activate multiple signaling pathways, and lead to cardiac remodeling (Peet et al., 2020). However, the roles of different functional phenotypes of macrophages in Ang II-induced hypertensive myocardial remodeling are unclear.
Macrophage monocytes are innate immune system pleiotropic cells that are critical for the initial inflammatory response and subsequent wound healing following injury in many tissues, including the heart (Labonte et al., 2014; Liu et al., 2014). Macrophages also play a central role in inflammation and host defense (Sica et al., 2012). Macrophages are monocyte-derived cells derived from proinflammatory (M1) and repair (M2) macrophages (Sica et al., 2007; Biswas et al., 2010). M1 phenotypic macrophages express many proinflammatory mediators, including tumor necrosis factor α (TNF-α), IL-1, IL-6, reactive nitrogen, and oxygen intermediates with potent microbicidal and tumoricidal activities, while M2 phenotypic expression molecules include resistin-like α (also known as FIZZ1), arginase 1 (Arg1), chitinase 3-like 3 (also known as Ym 1), IL-10, and Mrc1 (also known as CD206) (Gordon et al., 2010). These molecules are thought to be involved in parasite infection, tissue remodeling, and tumor progression (Mantovani et al., 2002). Recent studies have reported increased levels of proinflammatory cytokines, such as IL-6, secreted by M1 phenotypic macrophages in patients with unstable angina and myocardial infarction; moreover, high levels predict adverse outcomes (Hanna et al., 2020). In vitro studies have found that M1 phenotype macrophages can also induce smooth muscle cell proliferation and release vasoactive molecules, including NO, endothelin, and eicosanoids, which are important consequences of lipoprotein oxidation and cytotoxicity (Khallou-Laschet et al., 2010; Tsimikas et al., 2011; Ndisang et al., 2014). Early arteriosclerotic plaques are infiltrated by M2 phenotype macrophages; however, with plaque progression, M1 phenotype macrophages gradually increase and dominate (Khallou-Laschet et al., 2010). The present study investigated the role of different phenotypes of macrophages in Ang II-induced hypertensive myocardial remodeling to provide new opportunities for the treatment of cardiovascular diseases.
Ginaton is a natural product extracted from *Ginkgo biloba* that has been used to treat cardiovascular and cerebrovascular diseases for decades (Li et al., 2020). Ginaton has well-known anti-inflammatory, antioxidant, and antiapoptotic effects (Gevrek et al., 2018). Previous studies demonstrated that Ginaton can downregulate the TGF-β signaling pathway and effectively improve renal interstitial fibrosis and the inflammatory response (Liang et al., 2021). However, the role of Ginaton in mediating Ang II-induced cardiac remodeling remains unclear.
Herein, we investigated the role of macrophages of different phenotypes in Ang II-induced myocardial remodeling in hypertension and the potential protective effect of Ginaton on Ang II-induced cardiac remodeling, providing a new therapeutic target for cardiovascular diseases.
## Animal study
In this study, 8-week-old C57BL/6 mice purchased from Jackson Laboratory (Sacramento, CA) were treated as wild-type (WT). We treated the mice with Ang II (1,000 ng/kg/min) or saline for 14 days. The hypertensive myocardial remodeling models and controls were established using osmotic micropumps (Alzert Model 1002, DURECT, Cupertino, CA). Ginaton injection was purchased from Chi Sheng Chemical Cooperation (Taiwan, China). To investigate the potential protective effects of Ginaton, we administered Ginaton (300 mg/kg/day) to the mice intragastrically the day before surgery, at a dose according to that reported previously (Sun et al., 2015). Phosphate-buffered saline (PBS) treatment was used as a control. All animal experiments were carried out in accordance with the National Institutes of Health (NIH) Guidelines for the Care and Use of Laboratory Animals, which were approved by the Dalian Medical University Animal Committee.
## Measurement of systolic blood pressure and cardiac function
We used a tail-cuff device (BP-98, Softron, Japan) to record systolic blood pressure (SBP) and heart rate in all mice. We placed the mice on a retainer, completely exposing the tail, and then placed the tail-cuff device to measure their SBP and heart rate. Cardiac function was measured in each mouse using a 30 MHz probe (Vevo 1100 system, VisualSonics, Toronto, Canada). By measuring the echocardiography of each mouse, we recorded the end-diastolic and end-systolic left ventricular anterior wall (LVAW) thickness, left ventricular posterior wall (LVPW) thickness, ventricular inner diameter (ID), LV ejection fraction (EF%), and LV fractional shortening (FS%).
## Histopathology and immunofluorescence
Heart tissue was fixed in $4\%$ POM for >24 h. Part of the heart tissue was paraffin-embedded, while the other part was OCT-embedded. Masson’s trichrome and hematoxylin and eosin (H&E) staining were performed on paraffin sections (4 μm). The OCT sections (8 μm) were treated with wheat germ agglutinin (WGA). Cardiac sections were stained with anti-α-SMA (ab124964, Abcam) by immunohistochemistry at 4°C overnight. The next day, after washing with PBS, the sections were incubated with the secondary antibody and DAB substrate. The color reaction was stopped with ddH2O, and the sections were incubated with hematoxylin. Frozen sections or cells were fixed with $4\%$ POM for 15 min at room temperature and then incubated with anti-CD68 (ab283654, Abcam), anti-CD206 (ab300621, Abcam), or anti-iNOS (ab283655, Abcam) at 4°C overnight. The next day, after washing with PBS, the sections were incubated with fluorescently labeled antibodies at room temperature for 30 min and then with DAPI at room temperature for 3 min. Pictures of the sections were taken at ×100/×200 magnification on a fluorescence microscope (Olympus, BX53, Japan).
## RNA isolation and quantitative real-time PCR
Total RNA was extracted from fresh heart tissue or cells. cDNA (1 μg) was obtained using PrimeScript RT reagent (Takara, Japan). Quantitative real-time PCR was performed with an SYBR Green Premix Pro Taq HS qPCR Kit (AG11701). All mRNA levels were normalized to GAPDH by ΔΔCt analysis.
## Western blot analysis
Fresh heart tissue was added to RIPA lysis buffer to obtain total protein. Protein (25 μg) was subjected to SDS-PAGE and transferred to polyvinylidene fluoride membranes. The membranes were blocked with skim milk for 30 min at room temperature and then incubated with primary antibodies at 4°C overnight. The next day, after washing with TBST, the membranes were incubated with conjugated secondary antibodies (1:3000, CST) at room temperature for 1 h, and the bands were visualized by chemiluminescence. The main antibodies used in this study were: anti-TGF-β (ab215715, Abcam), anti-p-Smad2 (ab280888, Abcam), anti-Smad2 (ab33875, Abcam), anti-p-AKT (ab38449, Abcam), anti-AKT (orb11276, Biorbyt), anti-p-ERK$\frac{1}{2}$ (ab214036, Abcam), anti-ERK$\frac{1}{2}$ (ab184699, Abcam), anti-p-IKKα (ab138426, Abcam), anti-IKKα (ab32041, Abcam), anti-p-P65 (ab76302, Abcam), anti-P65 (ab16502, Abcam), and anti-GAPDH (ab8245, Abcam).
## Cell adhesion and migration analysis
Macrophages were obtained from the tibias and femurs of WT mice and cultured in 1640 medium (Meilunbio, MA0215). Human umbilical vein endothelial cells (HUVECs) were cultured in ECM. Proclones from macrophages to adherent HUVECs were distributed as previously described (Yin et al., 2022). We pretreated HUVECs with Ginaton (100 μg/ml) or PBS for 4 h and then with Ang II (100 nM) or saline for 24 h. Macrophages were labeled with PKH-26 fluorescent dye and added to HUVECs at a ratio of 10:1. After incubation for 1 h, the non-adherent cells were washed with PBS, while the adherent cells were photographed by microscopy.
HUVECs were cultured and treated as described in the adhesion experiments. Macrophages (5 × 104) were added to the upper chamber of a 24-well Transwell plate (8 μm pore, Coning), and HUVEC-conditioned media were added to the lower chamber of the plate. After 24 h of incubation, the migrated cells were fixed, stained with DAPI, and observed by microscopy.
## Statistics
Data are presented as means ± SD. Statistical analysis was performed using GraphPad Prism software. For statistical comparison, a one-way ANOVA was used, followed by Dunnett’s multiple comparison tests with the control group. Student’s unpaired t-tests were used to compare the two groups. $p \leq 0.05$ was considered statistically significant.
## Ginaton treatment alleviates hypertension and cardiac dysfunction caused by Ang II
As shown in Figure 1A, WT mice were treated with Ginaton (300 mg/kg) and Ang II (1,000 ng/kg/min) for 14 days. PBS was used as the control. After 14 days of Ang II infusion, the SBP in the Ginaton-treated group was significantly lower than that in the PBS-treated group. However, after Ang II infusion, heart rates were similar in the Ginaton and PBS treatment groups (Figure 1B). Echocardiography showed enhanced cardiac function 14 days after Ang II infusion, with increased EF% and FS% in the PBS-treated group, while treatment with Ginaton effectively mitigated this response (Figures 1C, D, Supplementary Table S1).
**FIGURE 1:** *Treatment with Ginaton relieves Ang II-induced hypertension and cardiac dysfunction. (A) Ginaton (300 mg/kg/day) or PBS (internal control) was administered intragastrically to 8-week-old C57BL/6J mice. Mice were then infused with Ang II (1,000 ng/kg/min) or saline for 14 days. The average systolic blood pressures were recorded before and after Ang II infusion (n = 4). (B) Heart rates in each group (n = 8). (C) M-mode echocardiographs to assess left ventricular function (n = 8). (D) Ejection fractions (EF%) and fractional shortenings (FS%) (n = 8). The results are expressed as means ± SD; n, number of animals per group.*
## Ginaton treatment mitigates the Ang II-induced cardiac hypertrophic response
Next, we determined the role of Ginaton in Ang II-induced cardiac hypertrophy. After 14 days of Ang II infusion, PBS-treated mice exhibited the characteristics of myocardial hypertrophy, including increased left ventricular thickness, heart weight/body weight (HW/BW), heart weight/tibial length (HW/TL), and increased cardiomyocyte cross-sectional area; however, these effects were mitigated by Ginaton (Figures 2A, B). Ginaton also reduced the expression of hypertrophy markers (ANF and BNP) after Ang II infusion (Figure 2C). The primer sequences are listed in Supplementary Table S2. As shown in Figure 2D, Ginaton downregulated the Ang II-induced activation of AKT and ERK$\frac{1}{2}$ expression. Therefore, Ginaton attenuated the Ang II-induced cardiac hypertrophy response.
**FIGURE 2:** *Ginaton administration rescues Ang II-induced cardiac hypertrophy. Ginaton (300 mg/kg/day) or PBS (internal control) was administered intragastrically to 8-week-old C57BL/6 J mice. Mice were then infused with Ang II (1,000 ng/kg/min) or saline for 14 days. (A) H&E staining of heart tissues in each group (left, n = 4, scale bar = 0.5 mm) and the ratios of heart weight to body weight (HW/BW) and HW to tibial length (HW/TL) (right, n = 8). (B) Heart sections stained with wheat germ agglutinin (WGA) (left, n = 6, scale bar = 20 μm) and quantification of myocyte cross-sectional areas (right, n = 6). (C) qPCR analyses of atrial natriuretic factor (ANF) and brain natriuretic peptide (BNP) mRNA levels (n = 6). (D) Western blot analyses of the expression of phosphorylated (p)-AKT, AKT, p-ERK1/2, ERK1/2, and GAPDH proteins in heart sections (left, n = 4) and the relative protein levels of each (right, n = 4). The results are expressed as means ± SD; n, number of animals per group.*
## Ginaton treatment reduces Ang II-induced cardiac fibrosis
Masson’s trichrome staining to investigate the role of Ginaton in Ang II-induced cardiac fibrosis showed that Ginaton treatment significantly reduced collagen deposition in the Ang II-infused heart compared to the PBS group (Figure 3A). Similarly, the number of α-SMA-positive myofibroblasts and the expression levels of α-SMA, collagen I, and collagen III mRNA were downregulated in Ginaton-treated mice compared to the PBS group (Figures 3B, C). As shown in Figure 3D, Ang II infusion induced increased TGF-β and Smad2 protein expression, while Ginaton treatment alleviated this response. These results suggested that Ginaton participates in Ang II-induced myocardial fibrosis through the TGF-β-Smad2 pathway.
**FIGURE 3:** *Ginaton reduces fibrosis and collagen deposition in Ang II-infused mice. Ginaton (300 mg/kg/day) or PBS (internal control) was administered intragastrically to 8-week-old C57BL/6J mice. Mice were then infused with Ang II (1,000 ng/kg/min) or saline for 14 days. (A) Masson’s trichrome staining of cardiac sections (left, n = 6, scale bar = 50 μm) and quantification of the areas of collagen deposition (right, n = 6). (B) Immunohistochemical staining of heart tissues with antibody against α-smooth muscle actin (α-SMA) (left, n = 6, scale bar = 20 μm); quantification of α-SMA+ areas (right, n = 6). (C) qPCR analyses of α-SMA, collagen I, and collagen III mRNA levels (n = 6). (D) Western blot analyses of the expression of TGF-β, p-Smad2, Smad2, and GAPDH proteins in heart sections (left, n = 4) and the relative protein levels of each (right, n = 4). The results are expressed as means ± SD; n, number of animals per group.*
## Ang II infusion induces M1 but not M2 phenotype macrophage activation, while Ginaton treatment downregulates the Ang II-induced inflammatory response
To investigate the expression of different macrophage phenotypes in Ang II-infused hearts and the mechanism by which Ginaton improved Ang II-induced cardiac remodeling, we co-stained with the CD68 macrophage biomarker and the iNOS M1 phenotype macrophage biomarker. Immunofluorescent staining revealed that Ang II infusion induced high expression of iNOS+ M1 phenotype macrophages and that Ginaton reduced this Ang II-induced increase (Figure 4A). Furthermore, co-staining for the CD68 macrophage biomarker and the CD206 M2 phenotype macrophage biomarker showed no significant difference between Ang II infusion and Ginaton treatment on CD206+ M2 phenotype macrophages (Figure 4B). Similarly, Ang II infusion increased the mRNA levels of molecules associated with M1 phenotypic expression, including IL-1β, IL-6, TNF-α, and MCP-1, while Ginaton rescued this Ang II-induced inflammatory reaction. The levels of M2 phenotypic expression molecules, including Arg1, Ym1, and IL-10 mRNA levels, did not differ between the Ang II infusion and Ginaton treatment groups (Figures 4C, D). Moreover, IKKα and p65 phosphorylation were reduced in the Ginaton-treated group compared to the PBS-treated group (Figure 4E). These results suggested that Ginaton relieved Ang II-induced cardiac inflammation via the accumulation of M1 phenotype macrophages in the heart.
**FIGURE 4:** *Ginaton decreases Ang II-induced M1 phenotype macrophage activation and inflammation through the NF-κB signaling pathway. Ginaton (300 mg/kg/day) or PBS (internal control) was administered intragastrically to 8-week-old C57BL/6J mice. Mice were then infused with Ang II (1,000 ng/kg/min) or saline for 14 days. (A) Immunofluorescence staining in cardiac sections with CD68 and iNOS antibodies (left, n = 6, scale bar = 50 μm) and quantification of CD68+ and iNOS+ macrophages in heart sections (right, n = 6). (B) Immunofluorescence staining of cardiac sections with CD68 and CD206 antibodies (left, n = 6, scale bar = 50 μm) and quantification of CD68+ and CD206+ macrophages in heart sections (right, n = 6). (C) qPCR analyses of IL-1β, IL-6, TNF-α, and MCP-1 mRNA levels (n = 6). (D) qPCR analyses of Arg1, Ym1, and IL-10 mRNA levels (n = 6). (E) Western blot analyses of p-IKKα, IKKα, p-P65, P65, and GAPDH expression in heart sections (left, n = 4) and the relative protein levels of each (right, n = 4).*
## Ginaton treatment reduces Ang II-induced M1 macrophage activation in vitro
To confirm the effect of Ginaton on the activation of M1 phenotype macrophages induced by Ang II, we pretreated macrophages with Ginaton (100 μg/ml) or PBS for 4 h and with Ang II (100 nM) or saline for 24 h. We then co-stained the cells with the macrophage biomarker CD68 and the M1 phenotype macrophage biomarker iNOS. Immunofluorescence staining showed that Ang II treatment induced high expression of iNOS+ M1 phenotype macrophages and that Ginaton reduced the increase in iNOS+ M1 phenotype macrophages induced by Ang II in vitro (Figure 5A). Moreover, Ginaton reduced Ang II-induced mRNA expression of the M1 phenotype molecules IL-1β, IL-6, TNF-α, and MCP-1 (Figure 5B). Neither immunofluorescence staining nor qPCR results showed differences in M2 phenotype macrophages between the Ginaton and Ang II treatment groups (Figures 5C, D).
**FIGURE 5:** *Application of Ginaton rescues the expression of M1 phenotype macrophages. Macrophages were treated with Ginaton (100 μg/ml) or PBS for 4 h and then with Ang II (100 ng/ml) or saline for 24 h. (A) Immunofluorescence staining in macrophages with CD68 and iNOS antibodies (left, n = 3, scale bar = 50 μm) and quantification of iNOS+ intensity (left, n = 3). (B) qPCR analyses of IL-1β, IL-6, TNF-α, and MCP-1 mRNA levels (n = 6). (C) Immunofluorescence staining of macrophages with CD68 and CD206 antibodies (left, n = 3, scale bar = 50 μm) and quantification of CD206+ intensity (left, n = 3). (D) qPCR analyses of Arg1, Ym1, and IL-10 mRNA levels (n = 6). The results are expressed as means ± SD; n, number of animals per group.*
## Ginaton treatment inhibits macrophage adhesion and migration in vitro
Coculture experiments of macrophages with HUVECs and Transwell assays showed that Ang II treatment increased macrophage adhesion and migration to HUVECs, an effect that was inhibited by Ginaton (Figures 6A, B). The results showed that Ginaton could reduce the adhesion and migration of macrophages to HUVECs.
**FIGURE 6:** *Ginaton reduces the increase of Ang II-induce macrophage adhesion and mitigation. (A) Human umbilical vein endothelial cells (HUVECs) were pretreated with Ginaton (100 μg/ml) or PBS for 4 h, and then with saline or Ang II (100 nM) for 24 h. PKH-26 staining of macrophages (left, n = 6, scale bar = 100 μm) and analysis of adherent cells (right, n = 3). (B) Macrophages (5 × 104) were added to the upper chamber on Transwell plate, and HUVEC-conditioned medium was added to the lower chamber of the plate. DAPI staining of macrophages (left, n = 6, scale bar = 100 μm) and analysis of mitigrated cells (right, n = 3). The results are expressed as mean ± SD; n, number of animals per group.*
## Discussion
The results of the present study demonstrated the protective effect of Ginaton on Ang II-induced cardiac remodeling in mice. After 14 days of Ang II infusion, the mice showed significantly elevated SBP, upregulated expression of M1 phenotype macrophages, monocyte/macrophage adherence to vascular endothelial cells, and subsequent migration to the heart tissue across the endothelium, producing proinflammatory cytokines, whose activation led to multiple signaling pathways of hypertensive cardiac remodeling. In contrast, Ginaton alleviated Ang II-induced hypertension, the inflammatory response, and monocyte/macrophage adhesion and migration, which might provide a new perspective for the treatment of hypertension (Figure 7).
**FIGURE 7:** *Operational representation of the method through which Ginaton controls cardiac remodeling caused by Ang II. By encouraging M1 phenotype macrophages to adhere to the endothelium and infiltrate the heart, Ang II infusion increases NF-κB activation, which in turn causes cardiomyocyte hypertrophy, fibrosis, and subsequent cardiac remodeling. Ginaton administration successfully reverses these effects.*
Hypertension is a major risk factor for heart failure and is characterized by chronic low-grade inflammation, whichleads to poor cardiac remodeling (Mikolajczyk et al., 2021). Although macrophages play a critical role in cardiac remodeling, dysregulated macrophage polarization between the proinflammatory M1 and anti-inflammatory M2 phenotypes promotes excessive inflammation and cardiac damage (Gullestad et al., 2012; Varol et al., 2015). M1 phenotype macrophages express many proinflammatory mediators, including TNF-α, IL-1, IL-6, and iNOS, while M2 phenotype macrophages express molecules including Arg1, IL-10, Ym1, and CD206 (Lavine et al., 2014; Kologrivova et al., 2021). Many key transcription factors are involved in macrophage polarization, such as signal transducers and activators of transcription (STATs), interferon regulatory factors (IRFs), nuclear factors (NF-κB), activator protein (AP) 1, and peroxisome proliferator-activated receptor (PPAR)-γ (Ohmori et al., 1997; Bouhlel et al., 2007; Odegaard et al., 2007; Satoh et al., 2010; Krausgruber et al., 2011; Lawrence et al., 2011; Oeckinghaus et al., 2011; Schonthaler et al., 2011). NF-κB is a family of transcription factors involved in many biological processes, including the immune response, inflammation, cell growth, survival, and development. NF-κB protein is usually expressed by a series of inhibitors, including κBα immobilized in the cytoplasm. The typical NF-κB activation pathway depends on IκBs, particularly IκBα-inducible degradation (Mitchell et al., 2016). The degradation of IκBα is mediated through its phosphorylation by IκB kinase α (IKKα) (Behar et al., 2013). The present study focused on macrophage phenotypes and their impacts on the outcome of hypertensive cardiac remodeling. We found that following infusion with Ang II, macrophages tended to switch to an M1 phenotype and expressed higher levels of iNOS and more pro-inflammatory cytokines such as IL-1β, IL-6, TNF-α, and MCP-1. Ang II infusion also increased p-IKKα and P-p65 expression. In further investigations, we examined the expression of CD206+ M2 phenotype macrophages and proinflammatory cytokines such as Arg1, Ym1, and IL-10. We found that Ang II infusion did not significantly increase the expression of M2 phenotype macrophages. These results suggested that Ang II infusion activated the proinflammatory response in the heart and that the activation of macrophage adhesion and migration was a major cause of cardiac remodeling.
Ginaton, a natural product extracted from *Ginkgo biloba* leaves, has been widely used worldwide and has therapeutic effects on a variety of diseases (Tian et al., 2017). Recent studies have found that treatment with Ginaton alleviated renal interstitial fibrosis through the TGF-β pathway and that Ginaton also reduced TNF-α, IL-1β, and 5-hydroxytryptamine levels in the hippocampus of mice with heart failure (Zhang et al., 2019). In the present study, after treatment with Ginaton, Ang II-induced hypertrophy and fibrosis were both alleviated. Moreover, we detected the expression of macrophages with different phenotypes in Ang II-infused hearts. The results showed markedly decreased levels of M1 phenotype macrophages and proinflammatory cytokines after Ginaton treatment, with no obvious change in M2 phenotype macrophages or proinflammatory cytokines. Ginaton also reduced the Ang II-induced activation of macrophage adhesion and migration. These results indicate that Ginaton may offer an approach for the treatment of hypertensive cardiac remodeling.
In conclusion, our results showed that Ginaton treatment inhibited Ang II-induced M1 phenotype macrophage activation, adhesion and mitigation, and the inflammatory response, leading to impaired and dysfunctional hypertension and cardiac remodeling. Therefore, Ginaton may be a powerful treatment for heart 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 authors.
## Ethics statement
The animal experiments were performed in accordance with the National Institutes of Health (NIH) Guidelines for the Care and Use of Laboratory Animals, and the study protocols were approved by the Animal Committee of Dalian Medical University.
## Author contributions
JW and EC performed the histological staining and the in vitro studies. JW performed molecular biology experiments. XA and JW wrote, revised, and gave their approval for the final version of the manuscript to be published. The final manuscript was read and approved by all authors.
## 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.2023.1104871/full#supplementary-material
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|
---
title: 'Mental and physical conditions associated with physical inactivity among Farhangian
University students during virtual classes: A cross-sectional study'
authors:
- Morteza Homayounnia Firouzjah
- Morteza Pourazar
- Saeed Nazari Kakvandi
journal: Frontiers in Psychology
year: 2023
pmcid: PMC10040784
doi: 10.3389/fpsyg.2023.1094683
license: CC BY 4.0
---
# Mental and physical conditions associated with physical inactivity among Farhangian University students during virtual classes: A cross-sectional study
## Abstract
### Background
The level of mobility and general health has decreased among students in virtual classes during COVID-19 pandemic. The present cross-sectional study aims to investigate the mental and physical conditions related to inactivity among the students of Farhangian University during the virtual classes.
### Methods
This is a cross-sectional study. 475 students (214 females and 261 males) were selected as the statistical sample of the study based on Morgan’s Table from Farhangian University, Iran. The statistical population includes students studying at Farhangian University of Mazandaran province that using Convenience Sampling the sample size based on Morgan’s Table, 475 students consisting of 214 females and 261 males were randomly selected as the statistical sample of the study. The research instruments of this study include International Physical Activity Questionnaire, Saehan Caliper (SH5020), Coopersmith Self-Esteem Scale, Beck Depression Questionnaire, and Nordic Skeletal and Muscular Disorders Questionnaire. For data analysis, independent sample t-test was employed to compare two groups. All analyses were conducted using spss24 software.
### Results
With respect to students’ skeletal-muscular disorders, findings proved that both genders suffered physical conditions during virtual classes. The research findings showed that the average weekly activity level among women is 634 Met/min with a standard deviation of ±281, and the average weekly activity level among men is 472 Met/min with a standard deviation of ±231. Fat percentage by gender, men’s average fat percentage is $47.21\%$ (S. D ± 4.74) and women’s average fat percentage is $31.55\%$ (S. D ± 4.37). Also, the self-esteem scores of male and female students were obtained 29.72 and 29.43, respectively, and the difference between the two was considered significant ($p \leq 0.05$). On the other hand, $67\%$ (No. 25) of female students and $32\%$ (No. 12) of male students suffered from high depression. Also, based on students’ skeletal-muscular disorders, findings of our study showed that both genders suffered physical conditions during virtual classes.
### Conclusion
This study suggests increasing the level of physical activity to reduce body fat mass, increase mental health and reduce skeletal disorders, which can be properly accomplished through university planning and prioritizing the health of male and female students.
## Introduction
Industrial development, upsetting situation of COVID-19, holding of virtual classes, and life mechanization have created major effects on individuals’ lifestyles and have brought sedentary lifestyles to societies (Nayak et al., 2022). This inactivity in students, as the active class of future society, can cause structural and strategic problems for any country (Yang et al., 2022). Therefore, inactivity should be considered as a serious major problem for the future of the country (Montero-Simó et al., 2022). The role of comprehensive education is in providing education for all humanistic dimensions, which includes not only the intellectual aspect of the individual, but also all psychological and physical aspects (Yuan et al., 2022). This type of comprehensive and inclusive education is beneficial for educational environments such as universities and colleges (Demchenko et al., 2021). Today, in order to better adapt to the surrounding environment, individuals in society need a balance of physical readiness and body composition (Mazza et al., 2020), and if they do not have favorable conditions in terms of physical condition and body composition, they usually become aloof, pessimistic and isolated; in other words, they will not have proper mental balance (Izquierdo, 2005).
Although the measures taken to combat the epidemic have contributed to many people staying at home around the world, this has led people to a sedentary life at home (Chen et al., 2020; Demirci, 2020).
The measures taken to combat the corona disease forced many people around the world to stay at home which led to a sedentary life and weighted gain (obesity; Chen et al., 2020). Obesity is a chronic condition that occurs as a result of intervention in individuals’ genetics or their environment, which is definitely affected by society, culture, psychological, metabolic, biochemical and genetic conditions (Kim et al., 2021). Research findings showed that male students who were living in the city were more likely to be obese ($39.4\%$) than male students in the suburbs ($35.5\%$). Likewise, female students in the city were more exposed to obesity ($20.6\%$) compared to females in the suburbs ($19.1\%$). Findings have shown that male students living in the city are more likely to be obese ($39.4\%$) than male students who live in the suburbs ($35.5\%$). Likewise, $20.6\%$ of female students living in the city were exposed to obesity compared to those female ones ($19.1\%$) living in the suburbs. The increasing prevalence of obesity and overweight among children and adults in the United States of *America is* a warning for doctors and public health officials (Halpern et al., 2021). In different countries, obesity is probably caused by a decrease in physical activity and an inappropriate way of lifestyle. High levels of health and hygienic indices and physical capabilities reflect the health and potentialities of a society (Wang et al., 2021). It has been well proven that the level of physical activity begins to decrease during adulthood and with age resulting to weight gain, which is associated with weight gain. The evidence that proves the relationship between physical activity in childhood and adolescence and inactivity in individuals in the form of longitudinal studies is rare or does not exist at all. A longitudinal study of 5,700 men and women found a link between childhood activity and obesity in adulthood. It is possible that those who do not engage in sports activities and then become overweight are genetically predisposed to this factor because both physical activity and body size are affected by genetic factors (Chaput et al., 2020). In the last few years, inactivity has become widespread in such a way that since the 1990s, it has been proposed as one of the main factors of death due to cardiovascular diseases (Ralapanawa and Sivakanesan, 2021). Those individuals who have done intense physical activity enjoy better health conditions than those with moderate and light activities (Rosenberger et al., 2019). Studies on the level of inactivity and public health showed that the increase in inactivity is associated with the increase in obesity, as well as decrease in physical activity with a decrease in general health (McCoy and Morgan, 2020). Research related to the level of mobility in different stratum of society has progressed such that it has been shown that the effect of the education level on food intake, obesity and other health risk factors (mobility level) has changed over time (Su et al., 2022). In addition to the fact that regular physical activity leads to an increase in physical readiness, it has been shown that there is a significant relationship between high physical readiness and high self-esteem (Reddy et al., 2021). Some researchers showed that yoga practice and physical exercises strongly influence individuals’ personality, their coping skills and cognitive performance (Pascoe et al., 2021; Farhang et al., 2022; Sinha and Kumari, 2022). Compared to the other groups, Yoga practitioners had higher sattva Guna (balance feature) and preferentially, employed brain regions associated with self-regulation and inhibitory control. Also, other researchers stated that physical activity is essential for children’ current and future health, though most of them do not do 60 min of moderate to intense physical activity daily (Ito et al., 2021; Tandon et al., 2021).
As mentioned, the sedentary life caused by the Corona period leads to obesity, but in addition to the physical effects, it may also affect some psychological factors such as self-esteem (Wang et al., 2020). Studies have indicated that exercising affects self-esteem, and a sense of competence and control. Programs related to physical activity are among the most common ways to increase self-esteem (Mazereel et al., 2021). In addition, compared to complex and heavy activities, simple activities such as aerobic sports have the greatest effect on self-esteem (Ryan, 2008). It has been proved that low self-esteem is related to depression, low mental health and less progress in education (Sánchez-SanSegundo et al., 2022). The impact of physical activity (PA) in reducing the symptoms of depression in children and adolescents has been reported by Dale et al. [ 2019] through the analysis of 26 articles (Dale et al., 2019). Also, positive effects of PA on the prevention of depression has been reported in older adults (Smaradottir et al., 2020). Despite this, students are prone to depression and examining the role of physical activity on their level of depression is of great importance. a growing body of evidence to suggest that, regular exercise can significantly reduce the risk of depression, anxiety and is considered useful in the prevention of about 25 diseases (Wu et al., 2020). In this regard, evidence from adult studies shows that physical activity is inversely related to depressive symptoms (Kandola et al., 2019). These findings showed Exercise as an intervention for anxiety and depression has been demonstrated in both of the animal studies and human clinical trials.
Beyond the physical conditions of people, COVID-19 is associated with significant mental pressure, which strongly affects mental health (Brooks et al., 2020; Torales et al., 2020). One of the psychological factors that can be related to *Corona is* self-esteem. Self-esteem is related to dissatisfaction with body image in obese ones who follow weight loss diets (Chang and Kim, 2022). *In* general, it has been well proven that a decrease in individuals’ self-esteem is related to a decrease in their general health level (Hajek and König, 2019). The relationship between self-esteem and obesity has not been well proven yet (Fields et al., 2021). Although challenges related to self-esteem have significant results on one’s health, due to incomplete results, it is difficult to argue that low self-esteem is a consequence rather than a cause (Bleidorn and Schwaba, 2018). The decrease in muscle volume and general excessive thinness caused by lack of movement endangers health of the body’s skeleton (Jestratijevic et al., 2022). As a result of excessive muscle wasting, the trunk will not be able to perform its functions in maintaining the body and preserving its natural alignment, hence, resulting in bad standing, bad sitting situations, and overall wrong movement habits (Wijngaarde et al., 2020). This makes the spine and chest unable to grow normally and remain in a normal state. In addition to the ugliness and deformity of the body, the unnatural curvature of these organs, causes the blood flow and breathing to not be performed properly and naturally (Hast and Garrison, 2000).
The most important consequences of lack of movement are illness and reduction of muscle volume and strength (Bonnet et al., 2019). The above items are related to each other and there is a direct relationship between the two factors of muscle cross section and the amount of power that a muscle is able to generate. On the other hand, maintaining the skeletal balance of the body is the responsibility of the muscles, especially the amount of strength and power of each muscle. Those individuals who do not have enough physical preparation (readiness), get tired sooner during performance of physical activities. Muscle fatigue in the body will naturally reduce physical ability and reduce the power level that the muscle can represent during working situations (Blocquiaux et al., 2020). In other words, working with tired muscles is the same as working with weak muscles, and the adverse effects that occur as a result of muscle weakness in a person are also like those of working in extreme fatigue conditions. Fatigue and posture may be the cause and effect of each other. In this way, the presence of fatigue caused by other factors such as physical activities in the body can be influential in disrupting the balance of an appropriate posture. On the other hand, lack of having a proper body posture is a reason for causing more fatigue; the more the body size is out of proportion and balance, the more energy is required to keep it straight, because the muscles related to the way the body is positioned to maintain balance have less mechanical merit. On the other hand, they should be involved in activities, which, in turn, will cause body fatigue (Gaudiino and Di Stefano, 2021). If the child’s daily activity is less than normal, the weight of other parts of his/her body will gradually decrease and the weight and volume of the subcutaneous fat tissue will increase, which eventually leads to the child’s obesity. Due to the fact that subcutaneous fat does not accumulate in all parts equally, and in some parts such as around the abdomen, hips, and in general, the middle part of the body is more than the organs (arms and legs), the child’s body position becomes abnormal (Lemaitre et al., 2021).
According to the reports of Center for Disease Control and Prevention in 2018, $29\%$ of school students did not pay attention to physical education classes (Brustio et al., 2018). Meanwhile, another study report stated that students with daily physical activity demonstrate higher academic performance (Páez-Maldonado et al., 2020). Physical activity is a behavior that has many proven health benefits, and it is noteworthy that it is one of the most effective ways to prevent chronic diseases such as coronary heart disease and diabetes (Speelman et al., 2011).
This study aims to determine the frequency and conditions (mental and physical) associated with inactivity in students of Farhangian University during the virtual classes. The main objective will be to measure the level of physical activity and effects of inactivity on mental and physical factors among students studying at Farhangian University of Mazandaran province.
## Research methodology
The method applied in this study is survey type and cross-sectional research design. The statistical population includes all male and female students studying at Farhangian University of Mazandaran province. Students were selected randomly and clustered in such a way that initially the campuses of Mazandaran province were divided based on the city and their population, and students of the cities were randomly selected. The number of samples were selected based on the population; hence, more samples would be allocated to campuses with larger populations; In the following, based on the statistics received from Farhangian University of Mazandaran province, according to the sample size based on Morgan’s Table, 475 students consisting of 214 females and 261 males were randomly selected as the statistical sample of the research. Students with intellectual disabilities, epilepsy, vestibular problem, and hearing or visual impairment were excluded from the study. Ethics approval was obtained from the appropriate institutional ethics review board in Farhangian University. The ethnicity of students was controlled; they were all Iranian. Participants were not told about the purpose of the study. They signed informed consent form and authorized their participation in the study. They were also informed that the data gathered in this study would be kept completely private.
## The amount of physical activity
After obtaining informed consent, the amount of physical activity was calculated using International Physical Activity Questionnaire (Craig et al., 2003). In this questionnaire, the physical activities performed by the individuals during the last week were asked, and activities performed for more than 10 min were recorded, which included job activities, moving manner, doing household chores, and leisure activities. This questionnaire inquires the amount of intense and moderate physical activity and walking during the last week. According to the scoring protocol of IPAQ questionnaire, the amount of physical activity of a person can be extracted and reported in two ways:
## The total amount of physical activity of the individual during the last week in terms of MET-minutes/week
MET (Tan et al., 2021) is a unit used to estimate the energy consumption due to physical activity. The value of one MET is approximately equal to the amount of energy consumption of a person at rest. All physical activities can be classified as multiples of the amount of energy consumption in a resting state. In this questionnaire, 3.3 METs for walking, 4 METs are considered for moderate physical activity, and 8 METs for intense physical activity. To calculate the total amount of physical activity in a week, the amount of walking (MET × minutes × day) should be added together with the amount of moderate physical activity (MET × minutes × day) and the amount of intense physical activity (MET × minutes × day) in last week.
## Classification of individuals’ physical activity in three levels: Low, medium and high
High physical activity means that an individual has intense physical activity at least 3 days a week and a total of at least 1,500 MET-minutes, or that s/he does any combination of intense, moderate, walking activities for seven or more days, with a total of at least 3,000 MET—minutes per week. Moderate physical activity means that an individual has at least 20 min of intense physical activity 3 days a week or more, or that 5 days or more a week has at least 30 min of intense, moderate activity or walking. Low physical activity means that an individual does not report any activity or the reported physical activities do not meet the criteria of high or moderate physical activity (Fogelholm et al., 2006). In the present study, after conducting preliminary studies on the necessity of conducting research on two categories of low and moderate activity intensity, the high physical activity category of the intended samples were disregarded; therefore, the category of high activity level will be excluded from the study, and the current study will be based on the level of activity and percentage of body fat, as well as the level of self-esteem of two classes with a low and moderate level of physical activity.
To determine the validity of the questionnaire, the content validity method was used. Its reliability was measured by the test–retest method, and the correlation coefficient was obtained 0.62 for the awareness and attitude section and 0.74 for the performance section (Moghaddam et al., 2012).
## Fat percentage
Also, the participants’ fat percentage was measured with a Saehan (SH5020) fat meter (caliper) made in England in three points of the body (men: chest, thigh, abdomen) and (women: triceps, upper arm, thigh; Onsori and Galedari, 2015). In order to increase the reliability of the subcutaneous fat measurement process, each part of the body was measured three times with a specific time interval, and all measurements were performed on the right side of the body (Soltani et al., 2018). To determine the fat percentage of the individuals, the measured values were put into Jackson Pollock’s fat measurement formula and the fat percentage was calculated. The standing height of the participants was measured using Height meter model 216 (Seca). For this purpose, the subjects stood such that the rear part of their shoulders touched the height measuring device; They kept their hands next to their body and close to their feet. The weight of each person was measured using a (Seca) model scale.
## Coopersmith self-esteem questionnaire—short form
Ryden (1978; Morrison et al., 1973) is a 58-item self-report, pencil-paper questionnaire, 8 of which are lie-detectors, and the other 50 items are divided into four subscales of general self-esteem, social self-esteem, family self-esteem, and educational self-esteem. The purpose of this questionnaire is to evaluate students’ self-esteem. This test has different forms. The original test was primarily designed for 8–15-year-old (form A, or school form), but a later revision was designed for subjects over 16 (form C, or adult form). Some items were rewritten to adapt the original form for adults’ use (form C; for instance, children were replaced with individuals, and school with work). There is also a short form of the test (form B, Coopersmith and Brout, 1961) which consists of 25 items and is extracted from the 50-item scale. Coopersmith designed this form as an alternative form for when time is limited. The reliability coefficient of this test is also reported as 0.77. Coopersmith’s self-esteem scale was also standardized in Iran (Narimani and Mousazadeh, 2010).
## Depression scale
Beck depression questionnaire was first developed by Beck et al. [ 1961] and Jackson-Koku [2016]. Beck [1979] made a major revision to cover a wider range of symptoms and provide more consistency with the diagnostic criteria for depressive disorders in Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). Beck’s depression questionnaire is a type of self-assessment test and can be completed in 5 to 10 min. The test consists of a total of 21 items related to different symptoms, in which the participants must answer on a four-point scale from zero to three. These articles cover areas such as sadness, pessimism, feelings of incapability and failure, guilt, sleep disturbances, loss of appetite, self-loathing, etc. Accordingly, 2 items are devoted to emotion, 11 items to cognition, 2 items to overt behaviors, 5 items to physical signs and 1 item to interpersonal semiotics. Thus, this scale determines different degrees of depression from mild to very severe, and its scores range from a minimum of 0 to a maximum of 63. Cronbach’s alpha coefficient of this questionnaire was reported as 0.84 (Farshchi et al., 2018).
## Musculoskeletal disorders
In order to examine skeletal-muscular ailments by the doctor, Nordic questionnaire was employed, which is a standardized questionnaire for examining disorders and disease associated with working and daily affairs (López-Aragón et al., 2017). The reliability of this scale has been reported as 0.73 using Cronbach’s alpha (Namnik et al., 2016).
For data analysis, multivariate analysis of variance (MANCOVA) were conducted on dependent variables with activity group as an independent variable and gender as a covariate. Independent pair sample t-test statistical method was applied to compare two groups. Statistical level of significance for all analyses was set at $p \leq 0.05$, and effect sizes were calculated as partial ƞ2 (ƞ2p). All analyses were carried out using spss24 software.
## Results
According to the research findings (Demographic characteristics of participants) and the ratio of male and female students in Farhangian University of Mazandaran province, $59\%$ of the participants of this study were male and $41\%$ were female students. The findings of this study showed that $73\%$ of the (No. 348) students had low physical activity and $27\%$ (No. 127) had sufficient physical activity.
Also, among $73\%$ of (No. 348) sedentary students, $40\%$ were women (No. 138) and $60\%$ were men (No. 210). On the other hand, among $27\%$ of students (No. 127) with sufficient mobility, $85\%$ were men (No. 109) and $15\%$ were women (No. 18). Table 1 shows the mean and standard deviation of depression, self-esteem and fat percentage based on gender and activity level.
**Table 1**
| Unnamed: 0 | Sex | Activity | Mean | Number | Standard deviation (SD) | T | sig |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Fat percentage | Male students | Active | 20.19 | 71 | 4.62 | | |
| Fat percentage | Male students | Inactive | 21.54 | 190 | 4.43 | 1.22 | 0.004 |
| Fat percentage | Male students | Total | 21.47 | 261 | 4.74 | | |
| Fat percentage | Female students | Active | 30.15 | 52 | 4.43 | 1.45 | 0.002 |
| Fat percentage | Female students | Inactive | 31.64 | 162 | 4.18 | | |
| Fat percentage | Female students | Total | 31.55 | 214 | 4.37 | | |
| Self-esteem | Male students | Active | 30.82 | 192 | 5.47 | 2.37 | 0.001 |
| Self-esteem | Male students | Inactive | 29.45 | 69 | 5.16 | | |
| Self-esteem | Male students | Total | 29.72 | 261 | 4.37 | | |
| Self-esteem | Female students | Active | 29.46 | 164 | 6.46 | 2.97 | 0.004 |
| Self-esteem | Female students | Inactive | 28.48 | 50 | 6.37 | | |
| Self-esteem | Female students | Total | 29.43 | 214 | 6.52 | | |
| Depression | Male students | Active | 20.64 | 192 | 6.31 | 1.86 | 0.004 |
| Depression | Male students | Inactive | 27.39 | 69 | 6.29 | | |
| Depression | Male students | Total | 25.71 | 261 | 6.43 | | |
| Depression | Female students | Active | 20.52 | 164 | 5.67 | 2.17 | 0.003 |
| Depression | Female students | Inactive | 29.61 | 50 | 6.44 | | |
| Depression | Female students | Total | 25.65 | 214 | 6.3 | | |
Also, the research findings from examination of pervasiveness of obesity of the students showed that the average fat percentage of the subjects was 25.19 (S. D ± 7.44), which according to gender, the average fat percentage of men was obtained $47.21\%$ (S. D ± 4.74) and the average fat percentage of women was reported $31.55\%$ (S.D. ± 4.37). This difference between men and women is considered significant with $p \leq 0.05.$ Also, the average level of physical activity of all students is 578 Met/min (S.D. ± 284), and the present study showed that the average weekly activity level among women is 634 Met/min with a standard deviation of ±281 and the average weekly activity level among men is 472 Met/min with a standard deviation of ±231 (Table 1; Figure 1).
**Figure 1:** *Mean body fat percentage in two active and inactive groups based on gender.*
Also, regarding students’ level of self-esteem, findings proved that $74\%$ of all male and female students (No. 356) had high self-esteem and $26\%$ of them (No. 119) had low self-esteem. Among these $74\%$, the share of female students is $46\%$ (No. 164) and share of male ones is $54\%$ (No. 192). On the other hand, among those who had low self-esteem, female students accounted for $42\%$ (No. 50) and male ones accounted for $58\%$ (No. 69). However, no significant difference was found between the level of activity and self-esteem.
Based on this, the total self-esteem of the participants was 28.6 (SD ± 5.7). The average score of the students with high self-esteem is 30 and the average score of the students with low self-esteem is 21. Also, the self-esteem scores of male and female students were obtained 29.72 and 29.43, respectively. The difference between the two was considered significant ($p \leq 0.05$; Figure 2).
**Figure 2:** *Mean self-esteem scores in two active and inactive groups based on gender.*
Also, regarding the level of depression among students, the findings presented that $82\%$ of all male and female students (No. 438) had low depression and $18\%$ of them (No. 37) had high depression. Among these $82\%$, share of the female students is $45\%$ (No. 203) and share of male ones is $55\%$ (No. 236). On the other hand, female students accounted for $67\%$ (No. 25) and male ones accounted for $32\%$ (No. 12) of high depression. However, no significant difference was found between the level of activity and depression (Figure 3; Table 2).
**Figure 3:** *Mean depression scores in two active and inactive groups based on gender.* TABLE_PLACEHOLDER:Table 2 As illustrated in Table 3, the analysis of MANCOVA with gender as covariate showed that, a significant difference between individuals in two levels of activity (active, inactive) in the variables of self-esteem [F[1, 472] = 17.14, $$p \leq 0.001$$, ƞp2 = 0.035], body fat percentage [F[1, 472] = 37.107, $$p \leq 0.001$$, ƞp2 = 0.073] and depression [F[1, 472] = 316.43, $$p \leq 0.001$$, ƞp2 = 0.401], respectively. The results of independent pair samples t-test showed that the active group of active people had better average scores in all three variables of self-esteem, fat percentage and depression, respectively, compared to inactive people, all (Ps < 0.05).
**Table 3**
| Musculoskeletal disorder | Disorder score of active male students | Disorder score of inactive male students | T | Sig | Disorder score of active female students | Disorder score of inactive female students | T.1 | Sig.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Musculoskeletal disorder | Mean ± SD | Mean ± SD | | | Mean ± SD | Mean ± SD | | |
| All disorders | 1.92 ± 2.35 | 3.92 ± 6.35 | 2.24 | 0.003 | 1.62 ± 3.35 | 4.92 ± 6.35 | 3.24 | 0.043 |
| Total disorders in the last 12 months | 1.35 ± 2.48 | 4.74 ± 5.19 | 1.98 | 0.034 | 1.28 ± 2.97 | 6.41 ± 3.93 | 2.62 | 0.036 |
| Total disorders in the last 7 months | 1.56 ± 2.82 | 5.42 ± 2.32 | 2.63 | 0.042 | 1.67 ± 2.28 | 7.46 ± 3.14 | 3.57 | 0.049 |
| Total disorders leading to prevention of physical activity | 2.12 ± 1.47 | 5.15 ± 2.27 | 2.86 | 0.038 | 1.37 ± 3.83 | 7.37 ± 2.46 | 2.64 | 0.027 |
## Discussion
The present study aimed to investigate the mental and physical conditions related to inactivity among the students of Farhangian University during the virtual classes due to COVID-19 pandemic. Determining the prevalence and pattern of mental disorders and musculoskeletal pain is the first step in the prevention, diagnosis and treatment of such disorders. This is despite the fact that a targeted and acceptable documented study in this field has not been conducted during COVID-19 era, when students had to use virtual space for classrooms instead of physically attending the class. Therefore, through this research results, appropriate solutions and detailed plans can be taken to alleviate the mental and physical conditions of students.
Results of this study suggests that the prevalence of inactivity among students is high, and about $73\%$ of all individuals did not participate in any of the intense and moderate activities, while in other countries such as Saudi Arabia, the prevalence of physical inactivity includes more than $43\%$ of society (Al-Hazzaa, 2007). In United States, the prevalence of overweight is $36\%$ and obesity is $21\%$ (Davis and Gergen, 1994; Gordon-Larsen, 2001). This amount is reported as $18\%$ (Ramos de Marins, 2001) in Ireland and $33\%$ (McCarthy et al., 2002) in Brazil. Also, the present research results showed that female students had less physical activity ($73\%$) than male ones ($51\%$). Male students demonstrated more physical activity at all levels than female ones. In relation to the number of female students to male ones, the current research results indicated that the activity level of female students was much lower than that of male students, which could be due to the fact that during the Corona period, social restrictions and the closure of sports halls were more for girls. On the other hand, lack of physical activity facilities for both male and female students had a significant impact on their lack of exercise.
Furthermore, researchers have stated that highly-educated individuals have a low level of activity, while other researchers (Lewis, 2005) showed that the amount of physical activity decreases in those with low education. Moreover, based on a study (Hajian-Tilaki and Heidari, 2007), no significant relationship was found between physical activities and education levels, which is consistent with the study of other researchers (Trockel et al., 2000; Wilsgaard et al., 2005). Meanwhile, the average level of physical activity in this study showed that there is a significant difference between the level of physical activity of men and women, i.e., men, participated in this study, have a higher level of physical activity than women. Meanwhile, the average level of physical activity in this study showed that there is a significant difference between the level of physical activity of men and women, i.e., men, participated in this study, have a higher level of physical activity than women. Moreover, during the last three decades, a significant increase in obesity among children and adults has been observed (Carter et al., 2011). This issue has spread to the point that by the increase in individuals’ education level, their body fat percentage increases, too (Marmot, 2003). At the same time, other researchers have reported results contrary to this finding (Morrill et al., 1991) that of the present study, educated ones had a not very high amount of fat mass. The prevalence of obesity in *Venezuela is* $74\%$ for men and $56\%$ for women (Campos et al., 2003), which is consistent with results of this study regarding the difference between women and men’ level of physical activities. However, this rate in *Palestine is* $48\%$ for men and $65\%$ for women (Campos et al., 2003), which proves contradictory results with our findings. According to all the findings, the present study showed that the difference in fat mass between sedentary and sufficiently active participants was significant. This means that sedentary ones had a higher fat mass. Another research has shown that the low participation of individuals in educational programs is related to the decrease of their self-esteem, which have a positive correlation (Suss et al., 1996).
This means that by reducing study hours, students’ self-esteem decreases, too; this is in line with results of the present study because the students of Farhangian University had high self-esteem. So, it can be concluded that individuals’ level of self-esteem probably increases by the increase in their education level. Moreover, no significant relationship was observed between the level of activity and high/low self-esteem, which means that both low-activity and sufficient activity groups showed high self-esteem scores; *In this* regard, other research findings found a significant relationship between self-esteem and obesity (French et al., 1995), which is consistent with the findings of our study; in contrast to these results, another study proved that obese women had lower self-esteem (Pesa et al., 2000). This could be due to the fact that with by the weight increase, the amount of mobility would reduce, and the individual will have fewer social connections and less participation in daily activities, which can possibly reduce his/her self-esteem. In the case of the present study, it can be said that due to the high educational level among the participants and their status in high social and cultural levels, low mobility could not impact their self-esteem. According to the research results (Scherrer and Preckel, 2019), it is stated that self-esteem does not change significantly with changes in the amount of fat mass, in line with the findings of the present study, because there was no statistically significant difference between sedentary and physically active students in their body fat mass. While in an opposite claim (Davis and Gergen, 1994; Guinn et al., 1997; Anderson et al., 2006; Wang et al., 2009), they showed that there is an inverse relationship between individuals’ body weight and self-esteem, i.e., with a decrease in body weight, the amount of self-esteem increases, and with an increase in body weight, self-esteem decreases. On the other hand, other scientists (Childress et al., 1993) claimed that the level of self-esteem among overweight children was significantly lower than their normal counterparts, which is not consistent with the findings of this study, because a significant difference was not observed between the amount of fat mass as well as the activity level with the level of self-esteem, perhaps the reason for this difference can be attributed to the age differences between children and adults.
With respect to the level of depression of Farhangian University students, the findings showed that the level of depression of female students during the COVID-19 pandemic and virtual classes was higher than that of male ones, but this was relatively small, and it can be expected with the increase of psychological and counseling interventions, the case would be reduced. On the other hand, no significant difference was found between the level of activity and depression.
Also, in relation to the degree of the musculoskeletal disorders of the students, findings suggested that among active male students, the highest frequency of pain, discomfort and numbness in last 12 months were related to the wrists and hands (No.17), and in the last 7 days related to back (No. 16), and the most skeletal pain that caused them to stop physical activity in the last 12 months was pain in the knees (No. 21). Also, in sedentary male students, the highest prevalence of pain, discomfort and numbness in the last 12 months was related to the back (No. 42) and in the last 7 days, it was related to the thigh (No. 64), and the most skeletal pain in the last 12 months which made them quit physical activity was related to back pain (No. 74). On the other hand, in female students, the highest prevalence of pain, discomfort and numbness in the past 12 months was related to neck (No. 15) and during the last 7 days, it was related to wrists and hands (No. 22), and the most skeletal pain that caused them to leave physical activity in the last 12 months was related to shoulder pain (No. 31). Also, in sedentary female students, the highest frequency of pain, discomfort and numbness in the last 12 months was related to the back (No. 72), and in the last 7 days, it was related to the shoulders (No. 103), and the most skeletal pain in the last 12 months, which made them quit physical activity was related to back pain (No. 108).
*In* general, findings of this study are similar to the global statistics of the COVID-19 pandemic and its impact on individuals’ physical and mental factors. It seems that the conditions related to the corona epidemic, as a result, presence of the students in-person, can have negative effects on students. Therefore, it can be concluded that even students and their level of understanding of benefits of physical activity cannot prevent mental and physical problems for them. It is expected that students who use modern scientific resources and are relatively aware of the dangers of obesity and inactivity are not exposed to such injuries, however, in practice, during virtual classes, students of Farhangian University were not sufficiently active; as a result of which physical and mental conditions are observed among them. Also, results related to the degree of psychological factors (self-esteem and depression) showed that in terms of mental health, both female and male students were not in a satisfactory condition due to lack of training and other related factors. It can be concluded that during COVID-19 era, due to health restrictions and government policies regarding quarantine and pandemic, and on the other hand, holding virtual classes and sitting next to communication devices for class, students did not have enough time for exercise. Actually, they did not enjoy physical activity because physical activity was not a priority for students of Farhangian University during the COVID-19.
Therefore, this study suggests increasing the level of physical activity to reduce body fat mass, enhance mental health, and reduce skeletal disorders, which can be properly accomplished through organized university planning and prioritizing health of the male and female students. However, findings of the present study were only limited to the students of Farhangian University in Iran. It is suggested to investigate the effects of inactivity associated with Corona on the physical and mental factors in a wider student’s community of and even students from different nations in future studies.
## 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 Farhangian University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MH contributed to the conceptualization, data curation, investigation, methodology, project administration, resources, supervision, validation, visualization, and writing (reviewing and editing) of the study. MP and SNK contributed to the conceptualization, data curation, investigation, methodology, and writing (reviewing and editing) of the study. 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.
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---
title: ALDH2 attenuates myocardial pyroptosis through breaking down Mitochondrion-NLRP3
inflammasome pathway in septic shock
authors:
- Ying Zhang
- Ying Lv
- Qingju Zhang
- Xingfang Wang
- Qi Han
- Yan Liang
- Simeng He
- Qiuhuan Yuan
- Jiaqi Zheng
- Changchang Xu
- Xiangxin Zhang
- Zichen Wang
- Huaxiang Yu
- Li Xue
- Jiali Wang
- Feng Xu
- Jiaojiao Pang
- Yuguo Chen
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10040788
doi: 10.3389/fphar.2023.1125866
license: CC BY 4.0
---
# ALDH2 attenuates myocardial pyroptosis through breaking down Mitochondrion-NLRP3 inflammasome pathway in septic shock
## Abstract
Cell survival or death is critical for cardiac function. Myocardial pyroptosis, as a newly recognized programmed cell death, remains poorly understood in sepsis. In this study, we evaluated the effect of aldehyde dehydrogenase (ALDH2) on myocardial pyroptosis and revealed the underlying mechanisms in sepsis. We established a septic shock mice model by intraperitoneal injection of Lipopolysaccharide (LPS, 15 mg/kg) 12 h before sacrifice. It was found that aldehyde dehydrogenase significantly inhibited NOD-like receptor protein 3 (NLRP3) inflammasome activation and Caspase-1/GSDMD-dependent pyroptosis, which remarkably improved survival rate and septic shock-induced cardiac dysfunction, relative to the control group. While aldehyde dehydrogenase knockout or knockdown significantly aggravated these phenomena. Intriguingly, we found that aldehyde dehydrogenase inhibited LPS-induced deacetylation of Hydroxyacyl-CoA dehydrogenase trifunctional multienzyme complex α subunit (HADHA) by suppressing the translocation of Histone deacetylase 3 (HDAC3) from nuclei to mitochondria. Acetylated HADHA is essential for mitochondrial fatty acid β-oxidation, and its interruption can result in accumulation of toxic lipids, induce mROS and cause mtDNA and ox-mtDNA release. Our results confirmed the role of Histone deacetylase 3 and HADHA in NOD-like receptor protein 3 inflammasome activation. Hdac3 knockdown remarkedly suppressed NOD-like receptor protein 3 inflammasome and pyroptosis, but Hadha knockdown eliminated the effect. aldehyde dehydrogenase inhibited the translocation of Histone deacetylase 3, protected ac-HADHA from deacetylation, and significantly reduced the accumulation of toxic aldehyde, and inhibited mROS and ox-mtDNA, thereby avoided NOD-like receptor protein 3 inflammasome activation and pyroptosis. This study provided a novel mechanism of myocardial pyroptosis through mitochondrial Histone deacetylase 3/HADHA- NOD-like receptor protein 3 inflammasome pathway and demonstrated a significant role of aldehyde dehydrogenase as a therapeutic target for myocardial pyroptosis in sepsis.
## Introduction
Sepsis is a life-threatening condition characterized by organ dysfunction due to unregulated host immune response against infection (Singer et al., 2016), and is widely recognized as the ultimate cause of death from many diseases. Nearly 50 million cases of sepsis were reported worldwide in the year of 2017 (Kempker and Martin, 2020; Rudd et al., 2020), and this number was considered significantly underestimated. More than $60\%$ of patients with severe sepsis or septic shock were reported to have cardiac dysfunction (Pulido et al., 2012), the presence of which is related to mortality as high as $70\%$–$90\%$ (Parrillo et al., 1990; Merx and Weber, 2007; Beesley et al., 2018; Hollenberg and Singer, 2021). Therefore, exploring novel therapeutic targets for septic shock-induced cardiac dysfunction is a key research imperative.
Programmed death of cardiomyocytes is one of the critical mechanisms of impaired cardiac function (Del Re et al., 2019). In the last two decades, several types of programmed cell death were newly recognized and interpreted, such as necroptosis, ferroptosis and pyroptosis. Of all the types of cell death, pyroptosis is most closely related to infection and inflammation. It is also referred as Gasdermin-dependent inflammatory programmed cell death, and characterized by rapid rupture of cell membrane and the release of inflammatory cytokines and cellular contents (Bergsbaken et al., 2009; Li et al., 2019). Thus, pyroptosis can be triggered by inflammation and further greatly amplifies the inflammation response, which may play key role in septic shock-induced cardiac dysfunction. NOD-like receptor protein 3 (NLRP3) inflammasome/Caspase-1/Gasdermin D (GSDMD) pathway is the well-known canonical regulatory pathway of pyroptosis. NRLP3 inflammasome is a multi-protein complex composed of intracellular receptor NLRP3, adaptor protein apoptosis-associated speck-like protein (ASC) and precursor pro-Caspase-1 (Swanson et al., 2019; Xue et al., 2019). The activation of this complex ultimately cleaves pro-Caspase-1 to its active form (He et al., 2016; Jo et al., 2016), which mediates the maturation and secretion of IL-1β and IL-18, and the cleavage of GSDMD, a key executor of pyroptosis (Kovacs and Miao, 2017; Shi et al., 2017; Kang et al., 2018). The existence of pyroptosis in cardiomyocytes induced by sepsis was proved by several studies (Li et al., 2021; Xiong et al., 2022), however its regulatory mechanism remains unclear and needs further research.
Mitochondrial homeostasis is essential for myocardial survival. Mitochondrial fatty acid β-oxidation is the major pathway for fatty acid degradation and energy supply of cardiomyocytes, and its disturbance leads to energy shortage and mitochondrial instability (Houten et al., 2016; Panov et al., 2022). It has been reported that mitochondrial instability is closely associated with NLRP3 inflammasome activation (Zhou et al., 2011; Wang et al., 2020). However, whether fatty acid β-oxidation disorder is associated with NLRP3 inflammasome activation and cardiomyocyte pyroptosis remains unknown. Hydroxyacyl-CoA dehydrogenase trifunctional multienzyme complex α subunit (HADHA), known as the α-subunit of mitochondrial trifunctional enzyme complex, is a key protein for fatty acid β-oxidation (Liu et al., 2020), the enzymatic activity of which can be regulated by acetylation at Lysine 303 (Chi et al., 2020). Loss of HADHA activity or mutations in its encoding gene can lead to cardiomyopathy and other disorders (Miklas et al., 2019).
Mitochondrial aldehyde dehydrogenase (ALDH2) is well-known for its role in maintaining mitochondrial homeostasis through aldehyde scavenging and antioxidant effects (Chen et al., 2014). Aldh2 rs671 genetic mutation is an independent risk factor for cardiovascular diseases (Pang et al., 2019; Bartoli-Leonard et al., 2020), and about $30\%$–$50\%$ of East Asian individuals carry this mutation, causing a $90\%$ loss of its enzyme activity (Gross et al., 2015). However, as a protein mainly located in mitochondria, it is still unclear whether ALDH2 affects fatty acid β-oxidation and plays a role in myocardial pyroptosis.
This study will demonstrate the role of ALDH2 as a therapeutic target for myocardial pyroptosis, and elucidate novel mechanisms focusing on the mitochondrion-NLRP3 inflammasome interaction.
## Animals and treatment
Six to 8 weeks old C57BL/6J mice, Aldh2 knockout mice (Aldh2 −/−) were used. Only male mice were chosen for experiment to avoid sex hormone interference. Mice were housed in an appropriate environment (23.0°C ± 2.0°C, $45\%$–$50\%$ humidity) with a $\frac{12}{12}$-light/dark cycle, and they had access to food and water ad libitum until experimentation. They were randomly divided into different groups. Model was established by administrating 15 mg/kg of Lipopolysaccharide (LPS, Sigma) intraperitoneally (i.p.) 12 h before sacrifice. Alda-1 (20 mg/kg i.p., Sigma-Aldrich) or necrosulfonamide (NSA, 20 mg/kg i.p., Abcam) was administered half an hour before LPS injection. An equal amount of pathogen-free normal saline (NS) was used as control.
## Cell culture and treatment
The rat cardiomyocyte cell line (H9C2) was cultured in a high-sugar medium (DMEM, Gibco) containing $1\%$ penicillin/streptomycin and $10\%$ fetal bovine serum (FBS, Gibco), and were exposed to $95\%$ O2 and $5\%$ CO2 at 37°C. After starving with serum-free medium overnight and replacing with complete medium, H9C2 cells were stimulated by LPS (2 μg/mL, 24 h) and ATP (40 μmol/L, 45 min) with or without Alda-1 (20 μmol/L, 30 min before LPS challenge). And H9C2 cells were stimulated by 4-HNE (40 μmol/L, 12 h) with or without Alda-1 (20 μmol/L, 30 min before stimulation).
## Echocardiography
Septic mice were anesthetized (inhaled $2\%$ isoflurane) for echocardiography, which was performed by a Vevo770 imaging system. Two-dimensional and M-mode images of the heart were collected. Cardiac function was measured in at least five repetitive cardiac cycles. The subsequent procedure was to calculate the left ventricular ejection fraction (LVEF%), fraction shortening (FS%), and heart rate (HR).
## Cytokine measurement
Mouse cardiac IL-1β, IL-6 levels were measured using R&D ELISA kits, TNF-α was measured via eBioscience ELISA kit. The levels of mice IL-18 were measured using RayBiotech ELISA kit. The levels of mice LDH were measured with a biochemical analyzer (Mindray, Shenzhen, China).
## ALDH2 activity
The mitochondria were isolated from myocardial tissue using the issue mitochondria isolation kit (Beyotime) according to the manufacturer’s instructions. Mitochondrial protein concentration was detected. Then samples were incubated with sodium pyrophosphate, NAD+, and propionaldehyde for 10 min. NAD+ was reduced to NADH, which was used to determine ALDH2 activity. Production of NADH was determined by spectrophotometric absorbance at 340 nm. ALDH2 activity was expressed as nmol NADH/min per mg protein (Wang et al., 2011).
## Murine sepsis score
Murine sepsis score was used to evaluate the severity of sepsis (Shrum et al., 2014; Sulzbacher et al., 2020). The score includes seven aspects, namely, appearance, level of consciousness, activity, response to stimulus, eyes, respiration rate, and respiration quality; each part is divided into 0–4 levels, total score ranges from 0 to 28.
## Morphological imaging of pyroptosis
To observe the morphology of cardiomyocytes, the cells were seeded in confocal dish, which were stimulated by LPS (2 μg/mL, 24 h) and ATP (40 μmol/L, 6 h) with or without Alda-1 (20 μmol/L, 30 min before LPS challenge). PI (1 μg/mL, 10 min) was added to the culture medium to evaluate cell membrane integrity. Static bright-field and fluorescent images of pyroptotic cells were captured using a confocal microscope (Leica, Wetzlar, Germany). All imaging data are representative of at least three randomly selected fields. The images were processed using ImageJ software. Results were expressed as the number of PI-positive cells/total cells × $100\%$.
## Dihydroethidium fluorescence staining
Dihydroethidium fluorescence staining was used to assess the production of ROS. The samples were incubated with dihydroethidium solution (5 μmol/L; Beyotime Biotechnology) in a light-protected humidified chamber at 37°C for 30 min. The images were observed under a fluorescence microscope (Olympus, BX43, Tokyo, Japan).
## Transferase-mediated dUTP Nick-End labeling (TUNEL) staining
An ApopTag® In Situ Apoptosis Detection Kits (Millipore) was used for TUNEL staining. The sections were observed and photographed under a fluorescence microscope (Olympus, BX43, Tokyo, Japan). Results were expressed as the number of TUNEL-positive cells/total cells × $100\%$.
## Immunohistochemical staining
Heart tissue paraffin sections were dewaxed in xylene and dehydrated in a graded series of ethanol. Following this, the sections were incubated with $3\%$ H2O2 and blocked with $5\%$ BSA. For immunohistochemical analyses, the sections were probed with primary antibody against caspase-1 (1:100, CST) and then incubated with DAB. Finally, the sections were observed and photographed under microscope.
## Immunofluorescence staining and NLRP3/ASC speck formation
In short, the samples were infiltrated with $0.1\%$ Triton X-100 and blocked with $5\%$ goat serum, then incubated with primary antibody at 4°C overnight. The secondary antibody was incubated for 2 h, then incubated with DAPI to label the nucleus. The NLRP3/ASC speck formation is a sign of NLRP3 inflammasome activation by double staining for NLRP3 (ABclonal 1:200) and ASC (Abcam 1:200). The images were observed under fluorescence microscope or confocal microscope.
## Nuclear, cytoplasmic and mitochondrial protein extraction
Cytoplasmic and nuclear proteins were extracted using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo) according to the manufacturer’s instructions. In brief, H9C2 cells were harvested with trypsin-EDTA and lysed in CER. After centrifuging at 15,000 rpm for 5 min, the supernatant was collected as a cytosolic fraction. The remaining pellet was suspended in NER, and the supernatant was collected as nuclear fraction.
Mitochondria were isolated by cell mitochondrial isolation kit (Beyotime). In brief, after lysing and centrifuging at first time, the cell-debris pellet in the collection tube was used to extract nuclear protein, the supernatant was transferred to a new microcentrifuge tube and centrifuged at 16,000 g for 10 min, the remaining pellet was suspended with lysis buffer as mitochondrial protein. Nuclear protein was extracted using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo).
## Flow cytometry
After treatment, H9C2 cells were stained at RT for 60 min with FAM-FLICA working solution (ImmunoChemistry Technologies) and mixed every time 15 min, followed by the addition PI staining for 5 min at RT. After being washed three times with wash buffer, cells were trypsinized to suspend. The FAM-FLICA/PI double-positive ratio was analyzed with Cytoflex cytometry and related analyzer software (Beckman, California, United States).
## Small-interfering RNA transfection
H9C2 cells were plated in 6-well plates and transfected with 100 nM siRNA using Lipofectamine RNAiMAX (Thermo Fisher Scientific) according to the manufacturer’s instructions. The siRNA sequences for rat Aldh2 (5′-GUGGAUGAGACUCAGUUUATT-3′), rat Hdac3 (5′-GGGAAUGUGUUUGAAUAUGUTT-3′) and rat Hadha (5′-GUGUAGAACUGCUGAAACUTT-3′); and negative control were synthesized by Genepharma (Shanghai, China).
## Mitochondrial respiration function
Mitochondrial respiratory function was detected with the Seahorse XF Cell Mito Stress Test Kit (Agilent Technologies, Santa Clara, California) using Seahorse XFe96 analyzer. Briefly, 1*106 cells were seeded in a cell culture microplate. After the treatment, cells were equilibrated with XF assay media. Sensor cartridge was hydrated in Calibrant at 37°C in a non-CO2 incubator overnight. Then, the mitochondrial respiratory function was analyzed by XFe96 analyzer with compounds.
## Transfection of mtDNA
Mitochondria were isolated by tissue or cell mitochondrial isolation kit (Beyotime), according to the manufacturer’s instructions. mtDNA was isolated from the mitochondrial pellet with a DNeasy blood and tissue kit (Qiagen). The mtDNA was incubated with 100 mM hydrogen peroxide for 50 min at 37°C to generate oxidized mtDNA. H9C2 cells were transfected isolated mtDNA or oxidized mtDNA (2 mg/mL, 6 h) via Attractene (Qiagen) according to the manufacturer’s instructions.
## Measurement of oxidative mtDNA
Mitochondria DNA was extracted from H9C2 cells by Allprep tissue/cell RNA-DNA extraction kit (Aidlab Biotech) according to the manufacturer’s instructions. The oxidative mtDNA was measured with 8-OHdG quantification kit (Cell Biolabs) which quantified the levels of 8-OHdG (the marker of oxidized DNA), as the manufacturer’s instructions.
## Western blot and co-immunoprecipitation
Protein samples were separated by $8\%$–$12\%$ SDS-PAGE and transferred to nitrocellulose membranes (Millipore). After blocking with $5\%$ milk in TBST for 1h, the membranes were incubated overnight at 4°C with primary antibodies. Subsequently, membranes were washed and incubated with secondary antibodies (1:10,000) and detected using the chemiluminescence method. The intensity of the bands was quantified by ImageJ software. Antibodies included anti-NLRP3 antibody (1:1,000, CST), anti-GSDMD antibody (1:1,000, Abcam), anti-N-GSDMD antibody (1:1,000, Abcam), anti-Capspase-1 p20 antibody (1:1,000, AdipoGen), anti-4-HNE antibody (1:1,000, Abcam), anti-HDAC3 antibody (1:1,000, Proteintech), anti-HADHA antibody (1:1,000, Abcam), anti-Ace-lys antibody (1:1,000, Abcam), anti-H3 antibody (1:1,000, Proteintech), anti-COX4 antibody (1:1,000, Abcam), anti-PCNA antibody (1:1,000, Abcam). Anti-GAPDH (1:5,000, Invitrogen) was used as an internal control.
For acetylation-immunoprecipitation, cells were lysed with immunoprecipitation buffer [supplemented with TSA (10 mM)] and sonicated. The samples were immunoprecipitated with protein A/G beads (Sigma) overnight at 4°C, washed three times in lysis buffer, resolved by loading buffer, and analyzed by Western blotting.
## Reverse transcription quantitative polymerase chain reaction
The total RNA was extracted from cardiac tissue with TRIzol (Invitrogen), according to the manufacturer’s instructions. Then, 2 μg of total RNA was reverse transcribed into cDNA. The 7500 quantitative polymerase chain reaction machine (Thermo Fisher Scientific) was used for real-time polymerase chain reaction analysis. Actin was used as an internal control.
## Statistical analysis
The continuous data were presented as mean ± SEM. Group comparisons were performed by one-way analysis of variances (ANOVA) with Tukey’s post hoc test or Student’s t-test. Survival was presented by Kaplan-Meier curves, and the log-rank test was used for comparing survival rate between groups. $p \leq 0.05$ was considered statistically significant (2-tailed).
## Aldh2 knockout aggravates septic shock-induced cardiac dysfunction and mortality
The mice model of septic shock was established by intraperitoneal injection of LPS as shown in Figure 1A. Compared with the control group, LPS treatment induced a very high murine sepsis score, while Aldh2 knockout (Aldh2 −/− genotype) and Alda-1 treatment further aggravated and reduced the score relative to the LPS group, respectively (Figure 1B). To further clarify the beneficial effect of ALDH2 activation, we also studied the role of its activator Alda-1 on the short-term (12 h) survival rate of septic mice. In the septic shock group, the 12-hour survival rate was $55.5\%$, Aldh2 knockout exacerbated the survival rate to $33.3\%$, while Alda-1 treatment improved the survival rate to $80\%$ (Figure 1C). Results of cardiac echocardiography showed that, compared with the control group, LPS significantly reduced the left ventricular ejection fraction (LVEF%) and fraction shortening (FS%), which is consistent with the results of our previous studies (Pang et al., 2019). Although Aldh2 −/− genotype per se did not affect cardiac function, Aldh2 knockout exacerbated LPS-induced cardiac dysfunction, while Alda-1 significantly ameliorated it (Figures 1D–G). LPS was also found to significantly reduce the activity of ALDH2 in cardiac tissue of wild-type mice (Figure 1H).
**FIGURE 1:** *Effect of ALDH2 on LPS-induced cardiac dysfunction and mortality. (A) Experimental modeling. Mice were treated with or without LPS (15 mg/kg, i.p. for 12 h). Alda-1 (20 mg/kg, i.p.) was given 30min before LPS injection. (B) Murine Sepsis Score, n = 7 per group. (C) Survival rate was monitored up to 12 h. A Kaplan-Meier plot was used to show the survival rate of mice from each group, n = 12 per group. (D–G) Representative echocardiographic images from different groups and the quantitative analysis of echocardiography, n = 11 per group. (H) The quantitative analysis of ALDH2 enzymatic activity, n = 4 per group, Mean ± SEM. ****p < 0.0001; ***p < 0.001; **p < 0.01; *p < 0.05; ns = not significant.*
## ALDH2 attenuates myocardial pyroptosis through NLRP3 inflammasome/caspase-1/GSDMD pathway in septic shock
To evaluate the effect of ALDH2 on cardiac injury and the potential mechanisms, we performed staining to assess myocardial morphology and cell death. Compared with the control group, LPS challenge increased inflammatory cell infiltration and broken myocardial fibers as revealed via hematoxylin-eosin (HE) staining (Figure 2A), and overtly elevated the numbers of dead cardiomyocytes as showed by TUNEL staining (Figures 2B, C), which were further aggravated and effectively alleviated in Aldh2 knockout (Aldh2 −/−) mice and mice pre-treated with Alda-1, respectively. The level of plasma LDH, a marker of organ damage, also indicated the protective effect of ALDH2 in sepsis (Figure 2D). As for the inflammatory cytokines IL-1β, IL-6, and TNF-α, the results showed that the transcription and expression levels of these cytokines were markedly elevated by LPS stimulation (except the expression of TNF-α, $$p \leq 0.052$$). Alda-1 pretreatment did significantly reduce the transcription and expression levels of these cytokines (Figures 2E, F). The plasma IL-18 level was also consistent with these results (Figure 2I). Collectively, the above results suggest that ALDH2 attenuates inflammation and cardiomyocyte death in septic shock-induced cardiac dysfunction.
**FIGURE 2:** *ALDH2 attenuates LPS-induced myocardial pyroptosis and inflammation in septic shock. (A) Representative HE staining images of cardiac tissue, red arrows indicate inflammatory infiltration, scale bar: 100 μm. (B,C) Representative TUNEL staining images of cardiac tissue and the quantitative analysis, scale bar: 50 μm. (D) The quantitative analysis of LDH release levels, n = 4 per group. (E,F) The quantitative analysis of RT-qPCR and ELISA results including IL-1β, IL-6, and TNF-α. N = 3–5 per group. (G,H) Representative morphological changes of pyroptosis in bright field and PI staining and the quantitative analysis of the PI positive H9C2 cells, red arrows indicate bubbling of pyroptotic cells, scale bar: 25 μm. (I) The quantitative analysis of IL-18 release level, n = 4 per group. Mean ± SEM, ****p < 0.0001, ns = not significant.*
To further validate the occurrence of pyroptosis, we performed immunofluorescence and observed the typical morphological changes of pyroptosis. In response to LPS and ATP stimulation, H9C2 cells firstly underwent deformation, and then the cell membrane gradually expanded, developed bubble-like herniations, and finally the cell membrane ruptured. Alda-1 was found to alleviate these changes (Figures 2G, H). To further clarify the role of pyroptosis in septic shock-induced cardiac dysfunction, necrosulfonamide (NSA), a well-recognized chemical inhibitor of pyroptosis (Rathkey et al., 2018), was administered 30 min prior to LPS treatment to C57/BL6 male mice, and it significantly improved LPS-induced cardiac dysfunction as shown in echocardiography (Supplementary Figure S1). To further validate the role of ALDH2 in pyroptosis, we evaluated the protein expression of N-GSDMD, a key marker that mediates membrane perforation in pyroptosis. Compared to wild-type mice, LPS challenge triggered even a higher level of N-GSDMD and N-GSDMD/GSDMD ratio in Aldh2 −/− mice (Figures 3A, B). On the contrary, pretreatment with Alda-1 significantly reduced the LPS-induced upregulated levels of N-GSDMD and the N-GSDMD/GSDMD ratio (Figures 3C, D). These findings illustrated that ALDH2 protects against septic shock-induced cardiac dysfunction through inhibiting cardiomyocyte pyroptosis.
**FIGURE 3:** *ALDH2 inhibits myocardial pyroptosis through NLRP3/Caspase-1/GSDMD signaling pathway. (A-F) Representative immunoblots and quantification of N-GSDMD, GSDMD, NLRP3 and caspase-1 p20 protein in LPS-stimulated Aldh2
−/− and WT mice (GAPDH; loading control), n = 3-4 per group. (G) Representative immunofluorescence tissue images showing caspase-1 (green) and DAPI (blue), scale bar: 50 μm. (H) Representative immunohistochemical images showing caspase-1 (brown), scale bar: 50 μm. (I–L) H9C2 cells were stimulated by LPS plus ATP with or without pre-treated Alda-1. (I) Representative immunofluorescence images showing caspase-1 (green) and DAPI (blue), scale bar: 100 μm. (J,K) Representative flow cytometry graphs showing positive-caspase-1/PI double staining and the quantitative analysis of the double positive, n = 3 per group. (L) Representative immunofluorescence images showing NLRP3/ASC speck, scale bar: 25 μm. Mean ± SEM. ****p < 0.0001; ***p < 0.001; **p < 0.01; *p < 0.05; ns = not significant.*
NLRP3 inflammasome/Caspase-1 signaling pathway is the canonical pathway of pyroptosis (Jo et al., 2016; Gaul et al., 2021). We investigated the protein levels of NLRP3 and the activated Caspase-1 form Caspase-1 p20, a sign of NLRP3 inflammasome activation. Compared to wild-type mice, LPS challenge triggered even a higher level of NLRP3 and Caspase-1 p20 in Aldh2 −/− mice (Figures 3A, B). On the contrary, pretreatment with Alda-1 significantly reduced LPS-induced upregulated levels of NLRP3 and Caspase-1 p20 (Figures 3E, F). Both immunohistochemistry staining and immunofluorescence staining showed increased expression of Caspase-1 in LPS-challenged cardiac tissue, which was further enhanced in Aldh2 −/− mice, while Alda-1 pretreatment significantly reversed it (Figures 3G, H). The results of flow cytometry and Caspase-1 immunofluorescence staining were consistent in H9C2 cells, which showed increased the number of double positive ratio and increased expression of Caspase-1 in LPS and ATP group compared with the control group, while the changes were attenuated by Alda-1 pretreatment (Figures 3I–K). We also examined NLRP3/ASC speck formation, another sign of NLRP3 inflammasome activation (Qiao et al., 2021). We observed an increase in the number of cells with NLRP3/ASC specks induced by ATP in LPS-primed cells compared with Alda-1 pretreatment cells (Figure 3L). These results demonstrated that activated ALDH2 attenuated NLRP3 inflammasome activation. Collectively, these findings demonstrated that ALDH2 may protect against septic shock-induced cardiac dysfunction through inhibiting cardiomyocyte pyroptosis mediated by NLRP3 inflammasome/Caspase-1/N-GSDMD pathway.
## ALDH2 inhibited the activation of NLRP3 inflammasome through reducing mtDNA and ox-mtDNA
To explore the potential role of ALDH2 in regulating the mitochondrion-NLRP3 inflammasome pathway, we performed in vitro studies focusing on the mitochondrial DNA release. Compared with the control group, LPS in addition to ATP significantly increased the cellular level of mtDNA as well as ox-mtDNA, while ALDH2 activation remarkably reduced LPS-induced mtDNA and ox-mtDNA release (Figures 4A, B). To further verify the roles of mtDNA and ox-mtDNA in NLRP3 inflammasome activation, mtDNA or ox-mtDNA was transfected to the H9C2 cells in vitro. Western blot and fluorescence staining results showed that both mtDNA and ox-mtDNA transfection directly elevated the expression of NLRP3 and NLRP3/ASC speck formation (Figures 4C–E). Collectively, these data suggest that mitochondrial damage plays an important role in mediating NLRP3 inflammasome activation, which may be through the release of mtDNA and ox-mtDNA.
**FIGURE 4:** *MtDNA and ox-mtDNA promotes NLRP3 expression and NLRP3 inflammasome activation. (A,B) The levels of total mtDNA or 8-OHdG (ox-mtDNA) in LPS plus ATP-stimulated or control H9C2 cells pre-incubated with or without Alda-1, n = 3-5 per group. (C,D) Representative immunoblots and the relative quantification analysis of NLRP3 with or without mtDNA or ox-mtDNA transfection, n = 4 per group. (E) Representative immunofluorescence images showing NLRP3/ASC speck, scale bar: 25 μm. Mean ± SEM. ****p < 0.0001; ***p < 0.001; *p < 0.05; ns = not significant.*
Results of dihydroethidium (DHE) staining showed that LPS triggered excessive oxidative stress in murine cardiac tissue, an effect that was significantly reduced by ALDH2 (Figure 5A). Toxic aldehyde 4-hydroxynonenal (4-HNE) as a product of oxidative stress interacting with lipids, is a marker of oxidative stress. LPS challenge significantly increased the accumulation of protein adducts of 4-HNE, which was significantly reduced by ALDH2 activation (Figures 5B, C). Consistent with those observations, mitochondrial respiratory function reflected by the reduced oxygen consumption rate (OCR) was altered by LPS and ATP treatment, and was aggravated by Aldh2 knockdown in H9C2 cells (Figures 5D–G), while it was reversed by Alda-1 pretreatment (Figures 5H–K). In addition, we found that ROS scavenger NAC significantly improved mitochondrial damage caused by LPS and ATP treatment (Supplementary Figure S2A). To verify the effect of ALDH2 on mtDNA/ox-mtDNA release, H9C2 cells were stimulated by 4-HNE with or without Alda-1. Compared with the control group, 4-HNE significantly increased mtDNA and ox-mtDNA release, while ALDH2 reduced it, suggesting that sepsis triggered mtDNA and ox-mtDNA release through oxidative stress (Figures 5L, M).
**FIGURE 5:** *ALDH2 suppresses myocardial toxic aldehyde accumulation, oxidative stress, and improves mitochondrial respiratory function. (A) Representative dihydroethidium (DHE) staining images in cardiac tissue, scale bar: 200 μm. (B,C) Representative immunoblots and quantification of 4-HNE-protein adducts in cardiac tissue, the LPS stimulated mice were pre-treated with or without Alda-1, n = 4 per group. (D–K) Mitochondrial respiration measurements of OCR in H9C2 cells treated with Aldh2 siRNA or negative control (NC) or Alda-1, quantification of basal respiration, ATP production, and maximal respiration. (L,M) The levels of total mtDNA or ox-mtDNA in 4-HNE-stimulated or control H9C2 cells pre-incubated with or without Alda-1, n = 4 per group. Mean ± SEM. ****p < 0.0001; ***p < 0.001; **p < 0.01; *p < 0.05; ns = not significant.*
## ALDH2 elevated acetylation level of HADHA through suppressing the translocation of HDAC3 from nuclei to mitochondria
To further elaborate the mechanism by which ALDH2 regulates mitochondrial homeostasis, we focused on mitochondrial membrane proteins. It was found that although HADHA protein levels were unchanged, sepsis significantly reduced HADHA acetylation levels compared with controls, which was reversed by ALDH2 (Figure 6A). To further determine the role of HADHA in mitochondrial respiratory dysfunction, we silenced Hadha in H9C2 cells with siRNAs and found that Hadha knockdown impaired ALDH2-induced improvement of OCR (Figures 6B–E). Moreover, Hadha knockdown effectively impaired ALDH2-induced improvement of pyroptosis after LPS and ATP stimulation (Figures 6F, G). These results indicate that deacetylation of HADHA is responsible for mitochondrial damage and cardiomyocytes pyroptosis.
**FIGURE 6:** *HADHA deacetylation is responsible for mitochondrial damage and cardiomyocytes pyroptosis, and ALDH2 suppresses HADHA deacetylation. (A) Representative immunoblots of HADHA acetylation levels in LPS plus ATP-stimulated or control H9C2 cells with or without pre-treated Alda-1, n = 3 per group. (B–E) Mitochondrial respiration measurements of OCR in Hadha silenced or negative control H9C2 cells with or without LPS plus ATP stimulation, quantification of basal respiration, ATP production, and maximal respiration. (F,G) Representative flow cytometry graphs showing positive-caspase-1/PI double staining in Hadha silenced or negative control H9C2 cells with or without LPS plus ATP stimulation and quantitative analysis of the double positive, n = 4 per group. Mean ± SEM. ****p < 0.0001; ***p < 0.001; *p < 0.05; ns = not significant.*
Histone deacetylase 3 (HDAC3) is an important acetylase enzyme (Chen et al., 2012; Nguyen et al., 2020). LPS and ATP treatment with or without Alda-1 did not affect the total protein level of HDAC3 (Figure 7A). Interestingly, we found that Hdac3 knockdown effectively increased HADHA acetylation level (Figure 7B). So we evaluated whether the location of HADC3 altered. Interestingly, after LPS plus ATP stimulation, HDAC3 obviously translocated from nuclei to mitochondria compared to the control group, and ALDH2 inhibited the translocation of HDAC3 as evidenced by WB and fluorescence colocalization staining (Figures 7C–G). Hadha knockdown and Hdac3 knockdown obviously increased and decreased the number of cells with NLRP3/ASC speck triggered by ATP in LPS-primed cells, respectively. When Hadha and Hdac3 were knocked down at the same time, Hadha knockdown effectively impaired the beneficial effect of Hdac3 knockdown for inhibiting NLRP3 inflammasome activation after LPS and ATP stimulation (Figure 7H). Hdac3 knockdown also effectively inhibited the protein level of Caspase-1 p20 (Figures 7I, J). To verify whether ALDH2 affects the acetylation level of HADHA by regulating HDAC3, we interfered with Hdac3 and Aldh2 at the same time. It was found that Aldh2 knockdown significantly aggravated the decreased acetylation level of HADHA induced by LPS and ATP, however the adverse consequences of which was eliminated by Hdac3 knockdown (Figure 7B). It was validated that ALDH2 affected HADHA acetylation via regulating HDAC3.
**FIGURE 7:** *ALDH2 suppresses the translocation of HDAC3 from nucleus to mitochondria. (A) Total levels of HDAC3 and the quantitative analysis, n = 3 per group. (B) Representative immunoblots of HADHA acetylation levels in Hdac3 silenced or Aldh2 silenced or negative control H9C2 cells with or without LPS plus ATP stimulation, n = 3 per group. (C) Representative confocal images showing HDAC3 (green), Mito Tracker (red) and DAPI (blue), scale bar: 10 μm. (D,E) Total levels of HDAC3, and the cytoplasmic and nuclear levels of HDAC3 proteins, and the quantitative analysis. Histone 3 and GAPDH were used as loading control of nuclear and cytoplasmic fractions, respectively, n = 3-4 per group. (F,G) The level of HDAC in mitochondrial and nuclei and the quantitative analysis. PCNA and COX4 were used as loading control of nuclear and mitochondrial fractions, respectively, n = 5 per group. (H) Representative immunofluorescence images showing NLRP3/ASC speck in Hdac3 silenced or Hadha silenced or negative control H9C2 cells with or without LPS plus ATP stimulation, scale bar: 25 μm. (I,J) Representative immunoblots and the quantification of Caspase-1 p20 protein in H9C2 cells (GAPDH; loading control), n = 6, Mean ± SEM. ****p < 0.0001; ***p < 0.001; *p < 0.05; ns = not significant.*
Collectively, these findings demonstrated that ALDH2 may protect against myocardial pyroptosis through mitochondrial HDAC3/HADHA-NLRP3 inflammasome pathway in septic shock (Figure 8).
**FIGURE 8:** *A diagram showing ALDH2 as a therapeutic target to protect against septic shock-induced myocardial pyroptosis.*
## Discussion
This study illustrated that septic shock might trigger NLRP3/Caspase-1/GSDMD-dependent myocardial pyroptosis due to activating the mitochondrion-NLRP3 inflammasome pathway via the release of mtDNA and ox-mtDNA. LPS promoted the translocation of HDAC3 from the nucleus to the mitochondria and thereby increased the deacetylation of the mitochondrial fatty acid β-oxidation enzyme HADHA, disturbing mitochondrial homeostasis and leading to overwhelming mitochondrial oxidative stress, which was supposed to be responsible for the increase of mtDNA and ox-mtDNA fragments. We also found that Aldh2 knockout and ALDH2 activation significantly aggravated and reduced myocardial pyroptosis, respectively. Alda-1, an agonist of ALDH2, remarkably reduced pyroptosis and rescued septic shock-induced cardiac dysfunction, possibly through clearing toxic aldehydes and inhibiting the translocation of HDAC3 to protecting ace-HADHA, decreasing NLRP3 inflammasome activation, and maintaining mitochondrial homeostasis. This study clarified novel mechanisms regulating myocardial pyroptosis and elucidated the underlying mechanisms of ALDH2 as a therapeutic target in myocardial pyroptosis.
In this study, we established a mouse mode of sepsis by administering LPS intraperitoneally with a relatively high dose of LPS (15 mg/kg) and successfully activated pyroptosis, indicating that myocardial pyroptosis may be less likely to be activated than apoptosis (Jiang et al., 2021). Based on the murine sepsis score and echocardiography findings, this model simulated septic shock. Cardiac dysfunction depends largely on cell death and inflammation (Mann, 2015; Zhang et al., 2017; Adamo et al., 2020). Pyroptosis is characterized by excessive inflammation and amplification of cellular injury (Bergsbaken et al., 2009), which can not only cause the death of cardiomyocytes, but also triggers a vicious circle, aggravating the inflammatory milieus and myocardial damage. Consistent with other studies, we found that inhibition of pyroptosis significantly improved the survival rate and cardiac function in sepsis. Caspase-1-dependent signaling is considered as the canonical pathway of pyroptosis (Swanson et al., 2019; Xue et al., 2019), and Caspase-11 mediates a non-canonical pathway of pyroptosis (Man and Kanneganti, 2015). Caspase family members such as Caspase-$\frac{3}{6}$/8 have recently been found to participate in pyroptosis (Fritsch et al., 2019; Zheng et al., 2020). Our study confirmed that septic shock induced myocardial pyroptosis through NLRP3 inflammasome/Caspase-1/GSDMD pathway, and inhibition of pyroptosis was found to alleviate septic shock-induced cardiac dysfunction.
The interaction between mitochondria and inflammasome, as well as its effects on diseases, have always been the focus of research (Zhong et al., 2016; Huang et al., 2020). Previous study showed that newly-synthesized ox-mtDNA led to NLRP3 inflammasome activation in macrophages (Zhong et al., 2018), while this study suggested that both oxidized- and non-oxidized mitochondrial DNA released from damaged mitochondria enhanced the expression and activation of NLRP3 in cardiomyocytes, which might be critical factors in mitochondria-NLRP3 inflammasome pathway. Oxidative stress is known to promote the formation of damaged mtDNA fragments and ox-mtDNA. We found that LPS promoted the deacetylation of HADHA, whereas HADHA could not properly catalyze the fatty acid β-oxidation process, resulting in insufficient energy supply as well as oxidative stress and impaired mitochondrial respiratory function due to the accumulation of long-chain saturated fatty acid acylcarnitine and triglyceride. When the *Hadha* gene was interfered by siRNA, similar phenomenon was observed.
The present study demonstrated for the first time the protective effect of ALDH2 on myocardial pyroptosis induced by sepsis. In this study, we also explored whether ALDH2 protected mitochondria through pathways other than aldehyde clearance and found that ALDH2 significantly rescued mitochondrial inner membrane protein HADHA from deacetylation. When the *Hadha* gene was interfered with, the beneficial effect of Alda-1 no longer exists. An increasing body of evidence has shown that acetylation of mitochondrial proteins is important for the regulation of mitochondrial function (Dittenhafer-Reed et al., 2015; Carrico et al., 2018; Deng et al., 2021). HDAC3 is widely known as an epigenetic regulator which inhibits the dissociation of DNA from histone octamer and compacts and curls the chromatin to block gene transcription. HDAC3 can shuttle between nucleus and cytoplasm and play different functions according to its location (Jiang et al., 2022). It was reported that ALDH2 could translocate to the nucleus and bind with HDAC3 to inhibit the expression of the lysosomal proton pump protein ATP6V0E2, resulting in impaired lysosomal function (Zhong et al., 2019). In this study, we found that ALDH2 remarkably inhibited the cytoplasmic shuttle of HDAC3 induced by sepsis, and it might be related to the binding of the two proteins. This study is the first to demonstrate that ALDH2 may inhibit sepsis-induced translocation of HDAC3 from nucleus to mitochondria.
Besides NLRP3, a variety of inflammasome-forming NLRs have been identified (including NLRP$\frac{1}{2}$/$\frac{6}{7}$/12, NLRB and NLRC$\frac{4}{5}$), which typically contain an evolutionarily conserved tripartite structure, consisting of a N-terminal effector domain, a central nucleotide-binding domain (NBD/NACHT) and a C-terminal autoregulatory LLR domain (Broz and Dixit, 2016; Olsen et al., 2022). However, the assembly of each is determined by unique pattern recognition receptors (PRRs) in response to pathogen-associated molecular patterns (PAMPs) or endogenous danger signals in the cytosol of the host cell (Rathinam and Fitzgerald, 2016; Olsen et al., 2022). A review published in 2021 has summarized the various activators of different inflammasomes, as shown in the figure below (Carriere et al., 2021). In our present study, we focused on the effect of ALDH2 on NLRP3 inflammasome through HDAC3/HADHA/ROS/mtDNA axis. It can be figured out that in addition to NLRP3, AMI2 inflammasome can be triggered by cytosolic DNA and may be a potential target of ALDH2. Moreover, further literature search showed that ROS production could activate NLRP1 (Xu et al., 2019; Fenini et al., 2020) and inhibits NLRP6 (Li et al., 2022). Therefore, NLRP$\frac{1}{6}$ and AIM2 are all potential downstream targets of ALDH2 in addition to NLRP3. However, whether other inflammasome-forming NLRs are possible targets of ALDH2 still need further investigation.
The dose of LPS used in our previous study was 4 mg/kg for 6 h to simulate cardiac dysfunction induced by mild sepsis (Pang et al., 2019). In this study, severe sepsis, septic shock is discussed. Mechanistically, in a severe state, cardiac function declines rapidly, ER stress and autophagy can no longer self-compensate and maintain balance, thereby led to cardiomyocyte death and impaired cardiac function. Interventions that directly target cell death during the severe shock phase may be the focus of rescue. Therefore, inflammatory programmed cell death, pyroptosis, is the key process of myocardial injury in septic shock, which is the focus of our research.
However, some limitations of our study should be considered. First, whether ALDH2 plays a role in the non-canonical pathway of pyroptosis needs further study. Second, the procedure of the release of mtDNA or ox-mtDNA from mitochondria into cytoplasm has not been investigated because of technical challenges. Third, the mechanism by which ALDH2 regulates HDAC3 has not been fully studied in this study, which will be illuminated in further research.
In conclusion, this study suggests that ALDH2 may protect against septic shock-induced myocardial pyroptosis by inhibiting the mitochondrion-inflammasome pathway through clearing aldehydes and blocking the translocation of nuclear HDAC3 into mitochondria and the consequent HADHA deacetylation. This study elucidates novel mechanisms of myocardial pyroptosis and provides a new therapeutic target for septic shock-induced cardiac dysfunction.
## 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 the Institutional Animal Care and Use Committee of Qilu Hospital of Shandong University.
## Author contributions
YC and JP conceived and designed experiments. YZ performed most of the experiments with the help of YLV, QZ, SH, XW, QH, CX, XZ, HY. YZ, YLI, and ZW performed data analysis. YC, JP, QY, JZ, LX, JW, and FX assisted in revising the draft. All authors have 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/fphar.2023.1125866/full#supplementary-material
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title: Metabolism-related long non-coding RNA in the stomach cancer associated with
11 AMMLs predictive nomograms for OS in STAD
authors:
- Wenjian Jin
- Kongbo Ou
- Yuanyuan Li
- Wensong Liu
- Min Zhao
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10040790
doi: 10.3389/fgene.2023.1127132
license: CC BY 4.0
---
# Metabolism-related long non-coding RNA in the stomach cancer associated with 11 AMMLs predictive nomograms for OS in STAD
## Abstract
Background: The metabolic processes involving amino acids are intimately linked to the onset and progression of cancer. Long non-coding RNAs (LncRNAs) perform an indispensable function in the modulation of metabolic processes as well as the advancement of tumors. Non-etheless, research into the role that amino acid metabolism-related LncRNAs (AMMLs) might play in predicting the prognosis of stomach adenocarcinoma (STAD) has not been done. Therefore, This study sought to design a model for AMMLs to predict STAD-related prognosis and elucidate their immune properties and molecular mechanisms.
Methods: The STAD RNA-seq data in the TCGA-STAD dataset were randomized into the training and validation groups in a 1:1 ratio, and models were constructed and validated respectively. In the molecular signature database, This study screened for genes involved in amino acid metabolism. AMMLs were obtained by Pearson’s correlation analysis, and predictive risk characteristics were established using least absolute shrinkage and selection operator (LASSO) regression, univariate Cox analysis, and multivariate Cox analysis. Subsequently, the immune and molecular profiles of high- and low-risk patients and the benefit of the drug were examined.
Results: Eleven AMMLs (LINC01697, LINC00460, LINC00592, MIR548XHG, LINC02728, RBAKDN, LINCOG, LINC00449, LINC01819, and UBE2R2-AS1) were used to develop a prognostic model. Moreover, high-risk individuals had worse overall survival (OS) than low-risk patients in the validation and comprehensive groups. A high-risk score was associated with cancer metastasis as well as angiogenic pathways and high infiltration of tumor-associated fibroblasts, Treg cells, and M2 macrophages; suppressed immune responses; and a more aggressive phenotype.
Conclusion: This study identified a risk signal associated with 11 AMMLs and established predictive nomograms for OS in STAD. These findings will help us personalize treatment for gastric cancer patients.
## Introduction
Cancer rates have been rising at an alarming rate in recent years, particularly regarding gastrointestinal cancer, which is linked to a high rate of morbidity and mortality. As indicated by the “2020 Global Cancer Report” published by the World Health Organization, gastric cancer ranks fifth and fourth in incidence and mortality, respectively (Sung et al., 2021). Although events of both new cases and deaths from gastric cancer are falling worldwide, more than one million people suffer from this disease annually (Thrift and El-Serag, 2020).
Thus, it is urgent to continuously improve the diagnosis and prognosis evaluation system of gastric cancer. Many variables, such as the natural environment, lifestyle, infection, genetics, etc., may contribute to the onset and progression of tumors (Yang et al., 2021a).
The part played by metabolism in tumor onset and progression is another factor that has been progressively uncovered. Research has shown that metabolites, including tumor metabolites as clinical illness indicators, may alter DNA and protein modification via chemical modification and metabolite-macromolecular interactions, and that this is important for the regulation of DNA, RNA, and protein activities (Park et al., 2020). Abnormal changes in energy metabolism are an important sign of malignant tumors. Tumor cells can plunder energy and substrates for anabolism through metabolic reprogramming, thereby promoting their survival and rapid proliferation (Tarrado-Castellarnau et al., 2016). Glucose and fatty acid metabolic abnormalities are involved in carcinogenesis, metastasis, treatment resistance, and cancer stem cell survival (Park et al., 2020). Huang et al. also found that abnormal iron metabolism was significantly related to lymphohematopoietic tumors, which set off a research upsurge on iron metabolism-related targets, hoping to obtain more strategies for the treatment of lymphohematopoietic tumors. Amino acids, one of the three major nutrients, were also found to be intimately linked to the onset and progression of tumors. Ren et al. reported that amino acid metabolism is associated with colorectal cancer (Ren et al., 2022). Zhao et al. found that amino acid metabolism is linked to the prognosis of liver cancer and the immune landscape (Zhao et al., 2021). Nevertheless, the link between amino acid metabolism and gastric cancer, the second most common cancer of the digestive system, is yet to be thoroughly investigated.
Long non-coding RNA (LncRNA) is a type of RNA not involved in coding that is over 200 nt in length, and numerous studies on LncRNA have emerged in the past decade (Bridges et al., 2021). LncRNAs can regulate cell proliferation, differentiation, signal transduction, and inflammatory responses in the human body through different pathways, and participate in the development of various diseases including cancer (Chen et al., 2019), diabetes (Feng et al., 2017), and cardiovascular disease (Jin et al., 2021). Moreover, LncRNA is implicated in many different metabolic pathways and may affect posttranslational modifications of key metabolic enzymes in a direct or indirect manner (Bridges et al., 2021).
The study by Dai et al. found that LncRNAs related to amino acid metabolism were linked to the prognosis of breast cancer, (Dai et al., 2022) suggesting a novel approach to the therapy of this disease. However, there is currently insufficient data from studies to conclude that LncRNAs involved in amino acid metabolism are linked to the outcomes (prognosis) of patients suffering from gastric cancer. This research aimed to discover new gastric cancer therapeutic targets. Although the lack of tissue-specific expression patterns and sequence conservation makes LncRNA research more difficult, it also makes it more valuable.
The surrounding environment in which tumor cells live constitutes the tumor microenvironment (TME). TME factors, including immune cell infiltration, perform a crucial function in tumor onset and progression. Therefore, immunity is closely related to tumors. Not only that, but immune cell infiltration also determines the prognosis of patients with malignant tumors (Ge et al., 2019). Immune checkpoint inhibitors (ICPIs) are used in gastrointestinal tumors as a new form of immunotherapy (Jin et al., 2020). However, due to various reasons such as individual differences and tumor drug resistance, the role of ICPIs is still limited. Immune checkpoints are critical. Studies have shown that LncRNAs also play an important role in regulating immune responses, such as T cell development, differentiation and activation, as well as the production of inflammatory mediators (Heward and Lindsay, 2014). Amino acid metabolism can also affect the tumor immune microenvironment (TIME). A research found that cysteine can promote tumor cell proliferation, enhance their invasiveness, and inhibit T cell activity (Levring et al., 2012). Additionally, some studies have found that leucine (Hayashi et al., 2013), serine (Ma et al., 2017), and other amino acids can promote T cell activation and proliferation. Therefore, an in-depth study of the relationship between amino acid metabolism-related LncRNAs (AMMLs) and the prognosis and immunity of gastric cancer might offer fresh perspectives for the immunotherapy of this disease.
## Data acquisition and processing
The flow chart for the analysis of this study is shown in Supplementary Figure S1. This study retrieved and collected STAD-related expression and clinical data from the TCGA database. 374 amino acid metabolism-related genes were obtained from REACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES, which was contained in the Molecular Signatures Database v7.5.1 [GSEA | MSigDB (gsea-msigdb.org)]. In addition, the corresponding expression data of the 374 genes were obtained in the TCGA transcription data. AMMLs were screened based on Pearson’s correlation analysis with a filter condition of $p \leq 0.001$ and |correlation coefficient|>0.5. Then, utilizing the “limma” R package, based on FDR<0.05 and |logFC|>1, differential analysis was performed between gastric cancer samples and normal samples to obtain differentially expressed LncRNAs.
## Risk establishment and verification of signatures
Based on AMMLs differentially expressed in gastric cancer samples and tumor samples. First, This study conducted univariate Cox regression analysis to identify 24 AMMLs linked to overall survival (OS) in STAD ($p \leq 0.05$). These 24 AMMLs were then subjected to LASSO analysis. A total of 11 characteristic AMMLs and their correlation coefficients were obtained. These 11 AMMLs were used to determine the patient’s risk score. The calculation formula is shown below: Risk Score = S (Expi ∗ Coefi). After that, patients were categorized into high- and low-risk groups (categories) as per their median score. Kaplan-Meier (K-M) analysis, log-rank test, and time-dependent receiver operating characteristic (ROC) curve analysis were conducted with the “survival,” “survivaler” and “survivalROC” R packages to judge the OS of different risk groups, and the accuracy of the prognostic model. Furthermore, This study employed univariate and multivariate Cox regression analyses to verify if AMMLs-related risk scores independently functioned as prognostic indicators for STAD. To confirm the efficacy of this prognostic model, This study computed a risk score by applying similar regression coefficients, formulas, and genes for both the validation and the combined cohorts. Additionally, This study explored the robustness of the model in an integrated group classified based on different clinical traits (age, sex, grade, etc.).
## Co-expression network
To determine the link between AMMLs and mRNA, This study constructed an mRNA-LncRNA co-expression network model using Cytoscape_v3.9.1, a network visualization software.
## Nomogram
The findings of the multivariate analysis were utilized in the development of nomograms for anticipating one-, three-, and five-year survival rates. The “rms” R program was adopted to construct and illustrate the findings. Values of discriminant performance and prediction nomograms were determined by Harrell’s C-index and calibration curve.
## Gene set enrichment analysis (GSEA)
First, the “limma” R program was implemented to detectdifferentially expressed genes (DEGs) between high- and low-risk categories, and the screening conditions were $p \leq 0.05$ and |LogFC|>1. After that, the “clusterProfiler” R program was employed to conduct gene enrichment analysis based on Gene Ontology (GO). An FDR value of <0.05 was required for the pathway and function enrichment analysis to be deemed significant. Next, GSEA software (version 4.2.3) was employed to evaluate the “c2. cp.kegg.v7.5. symbols.gmt” gene set in low- and high-risk categories to determine which genes were enriched. This study screened enrichment results with a nominal p-value <$5\%$ and FDR <$25\%$.
## Immune-related features
This study started by using the ESTIMATE technique to derive each patient’s immune and stromal scores. This study then explored the differences (variations) in immune, stromal, and ESTIMATE scores in the TIME of STAD patients across low- and high-risk categories. This study next used a single-sample GSEA (ssGSEA) algorithm to compare the immune functions of patients across low- and high-risk categories and elucidate the correlation between the risk score and the TIME in STAD patients. Subsequently, This study explored the level of immune cell infiltration in high- and low-risk groups through different algorithms such as CIBERSORT, EPIC, QUANTISEQ, and XCELL. Since immune checkpoint inhibitors are widely used in tumor therapy, This study compared the levels of various widely used immunosuppressors and immune checkpoints across high- and low-risk categories. These immune checkpoints and immunosuppressive factors were obtained from previously published articles.
## Analysis of drug sensitivity
To evaluate targeted drugs for different risk groups and sensitivity to chemotherapeutics, This study predicted the maximal inhibitory concentration (IC50) with the help of the “pRRophetic” R package.
This study evaluated treatment response premised on IC50 of each sample using the pRRophetic algorithm to compare the sensitivity of prospective medications typical of AMMLs between high- and low-risk categories. Twelve targeted drugs, including A.770041, ABT.263, AG.014699, AICAR, AMG.706, AP.24534, AS601245, ATRA, AUY922, Axitinib, AZ628, and AZD.0530, were shown to be more effective in high-risk categories. This may provide insights into new treatment options for STAD patients (Figure 12A).
**FIGURE 12:** *Drug candidates targeting AMMLs. IC50s of (A). (A)770041 (B). ABT.263 (C). AG014699 (D). AICAR (E). AMG.706 (F). AP.24534 (G). AS601245 (H). ATRA (I). AUY922 (J). Axitinib (K). AZ628 and (L). AZD.0530 between the low- and high-risk patients. AMMLs, LncRNAs related to amino acid metabolism, *p < 0.05; **p < 0.01; ***p < 0.001.*
## Statistical analysis
This study used R soft 4.1.2 software to analyze all the data. Pearson correlation analysis was conducted to study the co-expression of amino acid metabolism genes and LncRNA. The prognostic factors were determined by LASSO regression analysis. This study used univariate and multivariate COX regression analyses to ascertain if the risk score independently acted as a predictive marker for STAD. An evaluation of the risk model’s specificity and sensitivity was executed using the area under the ROC curve (AUC). Categorical variables were subjected to a comparison with the chi-square test and the Fisher’s exact test. To compare data of factors between risk groups, a Student’s t-test was employed.
## Data sources and basic clinical information
In total, 407 mRNA expression profiles were acquired from TCGA, comprising 375 tumors and 32 normal tissue samples. The clinical data of STAD samples, including age, gender, grade, TNM stage, etc. 374 genes involved in the metabolism of amino acids were obtained from REACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES, in the Molecular Signatures Database. The list is presented in Supplementary Table S1.
## Screening for AMML differentially expressed in normal and gastric cancer samples and associated with prognosis
First, the expression data of 374 amino acid metabolism-related genes were extracted from the TCGA database. Then, relevant LncRNAs were screened based on Pearson correlation analysis, and the screening conditions were |correlation coefficient (r)|>0.4 and FDR<0.05. In total, 1724 AMMLs were obtained (Supplementary Table S2). Subsequently, 327 AMMLs with differential expression were identified by performing differential analysis between normal and tumor tissues utilizing the “Limma” R package (Supplementary Table S3). Differential AMMLs are shown on a volcano plot in Figure 1. Then, 24 AMMLs ($p \leq 0.05$) linked to OS were obtained as per the univariate COX regression analysis of 327 AMMLs. The forest map shown in Figure 2A displays the HR values and confidence intervals (CI) for the 24 AMMLs, whereas the heat map in Figure 2B shows the specific variations in expression.
**FIGURE 1:** *Volcano plot of 327 differentially expressed AAMRLs in STAD. AAMRLs, Amino acid metabolism-related LncRNAs; STAD, Stomach adenocarcinoma.* **FIGURE 2:** *Screening with AMMLs. (A) Forest plot of AMMLs linked to STAD OS. (B) Heatmap of differential expression of AMMLs linked to STAD OS. AMMLs, Amino acid metabolism-related LncRNAs; STAD, Stomach adenocarcinoma; *p < 0.05; **p < 0.01; ***p < 0.001.*
## Development and verification of a predictive model based on AMMLs
A total of 371 samples were obtained after merging expression data with survival data. Patients were randomized into test and training sets on a 1:1 ratio. The sample size for the training set was 187, whereas the value for the testing set was 184. Then, based on the 24 AMMLs obtained in the training cohort, the LASSO regression algorithm was employed to correct overfitting and underfitting in the training group. Finally, 11 stable AAMLs were obtained as the best LncRNAs for prognostic models, as shown in Figures 3A, B. The specific 11 AMMLs and the corresponding correlation coefficients are depicted in Figure 3C. The risk score was calculated as follows: Riskscore = LINC01697 × 0.553770368482515 + LINC00460 × 0.00341458368213084 + LINC00592 × 0.29999979722043 + MIR548XHG × 0.0967424091144317+ LNCOG× −0.0246619595544397 + LINC02728 × 0.351554070674873 + RBAKDN ×0.317036345024823 + LINC01094× 0.192056510214801 + LINC00449 × −0.122953284509337 + LINC01819 × −0.00910281613561449 + UBE2R2-AS1 × −0.136924432797998
**FIGURE 3:** *LASSO Regression for STAD Patient Risk Models Based on AMMLs. (A) 10-fold cross-validation of variable selection in LASSO models. (B) The distribution of LASSO coefficients for 11 AMMLs. (C) 11 characteristic AMMLs and their correlation coefficients. LASSO, Least absolute shrinkage and selection operator; STAD, Stomach adenocarcinoma; AMMLs, Amino acid metabolism-related LncRNAs.*
## Get its risk score
Patients in the low- and high-risk categories were divided premised on the medium risk score. Figure 4A shows the expression of 11 AAMLs in STAD patients, with LINC01697, LINC00460, LINC00592, MIR548XHG, LNCOG, LINC02728, RBAKDN, and LINC01094 being expressed at a high level in the high-risk group, whereas LINC00449, LINC01819, and UBE2R2-AS1 were expressed at a low level, of course, This needs to be verified in experiments. This study observed that patients with higher risk scores for STAD had a lower likelihood of survival, as depicted in Figure 4B. K-M survival analysis found that high-risk patients ($p \leq 0.001$) had a considerably shortened OS duration, as depicted in Figure 4C. Similar patterns of expression, risk, and survival were observed between the test and composite groups and the training set (Figures 4D, E, G, H). Furthermore, high-risk individuals had substantially shorter OS duration in both the test ($$p \leq 0.001$$) and combined groups ($p \leq 0.001$). These all validate the accuracy of this prognostic model. This study further explored the precision of prognostic features through the ROC curve. The predicted AUC values for 1-, 3-, and 5- years in the test group were 0.723, 0.638, and 0.667, correspondingly, and the predictive power of the prognostic model was significantly better than that of age (0.544), sex (0.532), and grade (0.560). and staging (0.579), as shown in Supplementary Figures 2A, D. Also, this finding was also consistent in the test set and the combined set (Supplementary Figures 2B, C, E, F).
**FIGURE 4:** *The prognostic significance of 11 AMMLs models in testing, training, and whole cohorts. (A,D,G). Survival curves of two groups of patients in training, test, and the whole group. (B,E,H). Model presentation of AMMLs with survival status and time based on testing, training, and the full set of risk scores. (C,F,I). Expression of 11 AMMLs in training, test and comprehensive cohorts in high and low-risk categories. AMMLs, LncRNAs related to amino acid metabolism.*
This study classified patients according to their clinical characteristics to further examine the link between risk scores and patient prognosis. Patients in the high-risk category were shown to have a dismal prognosis across all demographics, including different ages (>65 and ≤65 years), genders (female and male), grades (G1-2 and G3), and stages (I-II and III-IV). The low-risk category also exhibited improved survival status in contrast with that of the high-risk category across different N stages (N0, and N1-N3). In M and T stages, M0 and T3-4 showed the same performance as above, and the OS of the low-risk category was elevated. Details are shown in Supplementary Figures 3A–G, I–L, N Conversely, the difference in survival across the two groups in terms of M1 and T1-2 was insignificant; this could be because of the small sample sizes in these groups, as depicted in Supplementary Figures 3H, M. As per these findings, the prognostic model is highly accurate and stable.
This study next conducted univariate and multivariate analyses to investigate if the risk model had any impacts on the prognostic factors of patient survival. Univariate analysis results illustrated that age ($$p \leq 0.004$$), stage ($p \leq 0.001$), and Risk score ($p \leq 0.001$) can affect the prognosis of STAD patients. The multivariate analysis illustrated that age ($p \leq 0.004$), stage ($p \leq 0.001$), and Risk score ($p \leq 0.001$) independently acted as prognostic indicators for patients (Figure 5A). Furthermore, as depicted in Figure 5B, tumor grade and stage differed between high- and low-risk patients.
**FIGURE 5:** *The risk score independently functions as a prognostic marker in STAD patients. (A). Univariate and multivariate overall survival prognostic analysis of clinical parameters and risk scores. (B). Heatmap of prognostic features and clinicopathological findings in STAD AMMLs. *p < 0.05; **p < 0.01; ***p < 0.001.*
## Co-expression network of amino acid metabolism-associated mRNAs and LncRNAs
This study created and visualized an mRNA-lncRNA co-expression network in Cytoscape to additionally observe the links between genes involved in amino acid metabolism and 11 AAMLs (version 3.9.1, http://www.cytoscape.org/), as depicted in Figure 6.
**FIGURE 6:** *Co-expression network of 11 AMMLs and genes involved in the metabolism of amino acids. AMMLs, Amino acid metabolism-related LncRNAs.*
## Creation of predictive nomograms
This study designed the nomogram incorporating patients’ features and demonstrated its accuracy using Harrell’s concordance index (C-index) and a calibration curve, which showed consistency between the nomogram’s predicted and actual survival over 1, 3, and 5 years (Figure 7).
**FIGURE 7:** *Nomogram for anticipating OS in GC patients. (A) Nomogram of prognostic models and clinicopathological factors founded on 11 AMMLs. (B) Performance of the nomogram model determined by the calibration curve. The ideal nomogram is shown as the diagonal dotted grey line. *p < 0.05, **p < 0.01; ***p < 0.001. OS, overall survival; GC, gastric cancer; AMMLs, Amino acid metabolism-related LncRNAs.*
## Enrichment analysis
The “limma” R program was first used to screen for DEGs between high and low-risk categories. The screening conditions were $p \leq 0.05$ and |LogFC|>1, and 284 DEGs were screened. The “clusterProfiler” program in R was subsequently employed to conduct a GO enrichment study. Figure 8A demonstrates that the majority of these genes undergo enrichment in immune-related functions. This study utilized GSEA to contrast high- and low-risk STAD patients for the enrichment of biologically functioning pathways to better understand the differences between the two groups. p-values <$5\%$ and FDRs <$25\%$ were considered meaningful enrichments. A considerable enrichment in the high-risk patients was found in tumor proliferation, angiogenesis, and tumor resistance-related pathways, such as VEGF SIGNALING PATHWAY, MAPK SIGNALING PATHWAY, and JAK-STAT SIGNALING PATHWAY which may lead to faster tumor progression. Moreover, immune-related pathways such as those involving T cell receptors, B cell receptors, the generation of IGA in the gut, and natural killer cells, were all considerably enriched (Figures 8B, C). Furthermore, there were no substantially enriched pathways discovered in the low-risk population. This difference between the two categories could be the factor that led to the reduced survival rate of the high-risk patients.
**FIGURE 8:** *Enrichment analysis. (A) GO analysis shows the enrichment of most immune-associated biologic processes. (B,C) GSEA analysis shows enriched pathways in high-risk groups. GSEA, gene set enrichment analysis; LncRNAs, long non-coding RNAs; GO, Gene Ontology; MF, molecular function; CC, cellular component; BP, biological process; KEGG, Kyoto Encyclopedia of Genes and Genomes.*
## Immune correlation analysis
Given that the high-risk category was shown to have an enrichment of immune-related pathways, This study investigated how prognostic models and immunity are linked with each other. This study first used the ESTIMATE method to assess the matrix and immune scores of the patient’s immune milieu. Results showed that high-risk individuals’ matrix, immune, and comprehensive scores were all substantially elevated and different from those of the low-risk individuals, implying that the immune microenvironment of the two groups was significantly different (Figures 9A–C). This study next used ssGSEA to determine whether there were any remarkable differences in immune functions between the two groups and discovered that 12 immune processes, including Types I and II IFN Response, and APC co-inhibition, were substantially upregulated in the high-risk patients (Figure 9D). This study also used algorithms like MCPCOUNTER, CIBERSORT, EPIC, QUANTISEQ, and XCELL to investigate immune cell infiltration across distinct risk categories. Figure 10 shows that the patient’s risk scores were positively linked to fibroblasts, B cells, M1and M2 macrophages, and Tregs cells, but inversely linked to uncharacterized cells. Variations in immunocyte infiltration between high- and low-risk categories could be a key factor in the diverse outcomes observed between them. Due to the widespread use of immune checkpoints in tumors, This study investigated whether high-risk patients might gain more benefit from immune checkpoint inhibitors by comparing their levels of checkpoint expression with those of low-risk patients. The increased expression of immune checkpoints in the high-risk category compared to the low-risk population implies that immune checkpoint inhibitors may be very effective in high-risk patients. ( Figure 11A). Since elevated levels of Tregs and macrophage infiltration in the high-risk population secrete some immunosuppressive factors, we explored to verify the expression levels of these immune factors in the high-risk group versus the low-risk group. It was found that the expression levels of cytokines (IL4, IL10, IL13, TGFB1, TGFB2, TGFB3, etc.) were higher in the high-risk group, which further suggests that elevated levels of Tregs and macrophage infiltration in the high-risk population play a suppressive role in the immune microenvironment. ( Figure 11B). These data suggest that the high-risk patients exhibit indolence in tumor immunity, which might account for their poor prognosis.
**FIGURE 9:** *ESTIMATE calculates the purity of STAD. (A) Estimate Score (B). Immune Score (C). Stromal Score. (D) Variations in immune function across high and low-risk categories calculated by ssGSEA algorithm. STAD, Stomach adenocarcinoma; ssGSEA, single-sample gene set enrichment analysis. *p < 0.05; **p < 0.01; ***p < 0.001.* **FIGURE 10:** *The relationship between immune cell infiltration and risk score in the tumor immune microenvironment of STAD patients calculated by different algorithms. (A) EPIC (B). QUANTISEQ (C). MCPCOUNTER (D). CIBERSORT (E). XCELL. *p < 0.05; **p < 0.01; ***p < 0.001.* **FIGURE 11:** *Variations in the expression of immune-associated genes between low- and high-risk groups. (A) Expression of immune checkpoints in high- and low-risk patients with gastric cancer. (B) Expression of immunosuppressive cytokines across the two groups. *p < 0.05; **p < 0.01; ***p < 0.001.*
## Discussion
Under normal physiological conditions, the body maintains a dynamic balance of metabolism, but this metabolic state will be altered to some degree in a disease state. Therefore, detecting the metabolic level of the corresponding substance can help diagnose and judge the disease. Amino acids, as one of the main nutrients in the human body, have recently been the subject of substantial research in the field of oncology. Studies have found that amino acids may promote tumor progression. For example, when extracellular cysteine is reduced or lacks endogenous transsulfuration activity, cysteine production can support tumor cell proliferation in vivo (Zhu et al., 2019). It is well known that tumor cells grow rapidly in the early stage of tumors, but tumor angiogenesis is insufficient, and tumor cells obtain more energy through glycolysis, this phenomenon is called the “WarburgEffect” in this study. However, in the case of glucose starvation, tumor cells may obtain energy through autophagy. Tumor cell-related autophagy often yields amino acids and other metabolites, and new research suggests that non-essential amino acids may replace glucose as tumor cells’ functional substances (Uneyama et al., 2017). Amino acids have also been extensively studied in gastric cancer. Wang et al. disclosed that apoptosis was promoted in gastric cancer cells by the deprivation of glucose, whereas amino acids directly counteracted this effect (Wang et al., 2014). According to certain studies, the plasma amino acid level may distinguish between gastric ulcers and gastric cancer. Among them, the content of glutamine, histidine, arginine, and tryptophan in the plasma of gastric ulcer patients was elevated as opposed to that of tumor patients, while the content of ornithine was the opposite (Jing et al., 2018).
Liu et al. [ 2018] found that free amino acids in gastric juice can help the diagnosis of early gastric cancer. Abnormal metabolism of tumor cells can cause alterations in the TME, which in turn influences immune cell infiltration and promotes tumor immune escape. Numerous recent studies have shown that aberrant amino acid metabolism in patients with tumors might alter the TIME. Also, studies have found that a variety of amino acids or their transporters can promote T cell activation and proliferation, including leucine (Hayashi et al., 2013), methionine (Sinclair et al., 2019), serine (Ma et al., 2017), alanine (Ron-Harel et al., 2019), and so on. Research has also discovered that arginine deficiency in tumors not only leads to the anti-tumor response of T cells but also induces the generation of myeloid-derived suppressor cells (MDSCs) (Fletcher et al., 2015). In addition, T lymphocytes differentiate into Tregs but not into helper T cells in the presence of glutamine deficiency (Klysz et al., 2015). All of these things point to the significance of amino acid metabolism in the TIME. At present, amino acid metabolism-related factors are also effective targets for the treatment of tumors. For example, glutaminase inhibitor CB-893 (Johnson et al., 2018), glutamine metabolism inhibitor JHU083 (Leone et al., 2019), and arginase 1 inhibitor INCB001158 (Steggerda et al., 2017) can not only inhibit tumor progression but also increase the immune system in TME cell infiltration. However, there is no report on the value of AMMLs in assessing immune infiltration and clinical outcomes in gastric cancer. LncRNAs related to amino acid metabolism remodel the TME, and whether this change affects the prognosis and immunotherapy response of STAD patients is of great significance for us to explore.
So far, this is the first research project that uses AMMLs to establish a prognostic model in STAD. Pearson correlation and univariate Cox regression analyses can effectively detect cellular senescence LncRNAs linked to disease prognosis predicated on the RNA-seq data set downloaded from TCGA and genes related to amino acid metabolism. The LASSO regression technique was used to develop a prediction model that included 11 AMMLs. In this research, 371 samples were randomized into the training and test sets, of which the training set and the test set contained 187 and 184 samples, correspondingly. The prognostic model was proven to be reliable across all three study groups (training, test, and whole groups).
The 11 AMMLs used to construct prognostic models, included 7 risk genes notably, LINC01697, LINC00460, LINC00592, MIR548XHG, LINC02728, RBAKDN, and LINC01094, and 4 protective genes, namely LINCOG, LINC00449, LINC01819, and UBE2R2-AS1. Furthermore, This study found that some of these 11 LncRNAs have been previously reported in tumors. For example, LINC01697 has a diagnostic and prognostic function in lung adenocarcinoma and oral squamous cell carcinoma (Liu et al., 2019; Li et al., 2020). LINC00460 has been reported in various tumors such as head and neck squamous cell carcinoma (Yang et al., 2021b), cervical cancer (Lin et al., 2020), bladder cancer (Li et al., 2021), colorectal cancer (Ruan et al., 2021), and is implicated in tumor proliferation, migration, mesenchymal transition, drug resistance, and increased tumor progression (Meng et al., 2020; Cheng et al., 2021). Wang and Yang et al. also found that LINC00460 could promote the progression of gastric cancer (Wang et al., 2018; Yang et al., 2020). Xu et al. reported that the lncRNA UBE2R2-AS1 targeted the miR-877-3p/TLR4 axis, thereby promoting apoptosis in glioma cells (Xu et al., 2021). In colorectal cancer, upregulated long intergenic non-protein-coding RNA 1094 (LINC01094) is associated with a dismal prognosis and altered cellular function (Zhang et al., 2022). The RBAKDN gene has been linked to the prognosis of patients with cervical cancer. ( Ye et al., 2021). Zhang et al. [ 2020] discovered that lung adenocarcinoma recurrence was linked to LINC01819. Previous studies have found that LINC00592 (Cheng et al., 2019) LINC02728 (Lai et al., 2021) LINC00449 (Zhang et al., 2021) are associated with gastric cancer prognosis. Non-etheless, reports of MIR548XHG are rare, only Shan et al. [ 2022] have found significant increase in MIR548XHG expression in endometriosis. Therefore, more research is needed in this area.
Pathways associated with B cell receptors, T cell receptors, and natural killer cell-mediated cytotoxicity were shown to be predominantly enriched in the high-risk category as per the GSEA analysis. Neither the impact of LncRNAs involved in amino acid metabolism nor their links to the immune milieu in gastric cancer has been studied. As a first step, the tumor purity of STAD patients in the high and low-risk categories was determined utilizing the ESTIMATE method. High-risk patients had greater values for all three scores (immune, stromal, and estimate scores) compared to those at lower risk. In addition, 12 out of 13 immune functions were different between the two groups, with the high-risk patients showing considerably higher immune function. The infiltration levels of immune cells in the STAD TME were subsequently investigated utilizing algorithms like CIBERSORT, EPIC, MCPCOUNTER, QUANTISEQ, and XCELL, and it was discovered that cancer-associated fibroblasts (CAFs), M2 macrophages, and Tregs cells were positively linked to risk scores. Previous studies have found that CAFs are associated with tumor size, tumor invasion depth, and metastasis (Quail and Joyce, 2013). CAFs typically secrete CXCL12, TGF-β, LOXL2, HGF, and IL-22 to promote tumor progression. This is consistent with the results of this study showing increased cytokine secretion of CAFs in high-risk patients. Tumors may manifest as cells that affect tumor growth, invasion, and metastasis by secreting these cytokines in gastric cancer. This negatively impacts the prognosis of gastric cancer. There are now two recognized types of macrophages, M1 and M2. It is widely accepted that M1 macrophages perform a fundamental function in inflammatory and immune response activation, whereas M2 macrophages are implicated in oncogenesis. Tumor-associated macrophages (TAM) also promote tumor angiogenesis and invasion by producing inflammatory factors, chemokines, and growth factors. For example, Wu et al. found that TAM-derived CXCL8 promotes tumor invasion and induces angiogenesis by stimulating tumor cells to secrete MMP-9 and VEGF (Wu et al., 2020). Wang et al. reported that tumor-associated fibroblasts promote immune escape by secreting IL-10 (Wang et al., 2015). In addition, This study discovered that M2 macrophages infiltrated the TME to a greater extent in high-risk patients and that cytokines such as IL-10, CXCL8, and CCL5 were expressed at a high level in the high-risk category as well, as determined by differential analysis. This further supports the accuracy of the findings in this study.
Chemotherapy and targeted therapy are widely used in gastric cancer. Immune checkpoint analysis shows that high-risk groups may have a higher sensitivity to immune checkpoint inhibitors. Twelve targeted pharmaceuticals, including AICAR, Axitinib, and ATRA, were shown to have increased sensitivity among high-risk patients. This study could offer a new approach to treating gastric cancer. The findings of this pharmacological screening, however, will need to be confirmed in larger clinical studies.
There are still some shortcomings in this study. First of all, data samples are mainly from TCGA data sets, which are limited and single. We will further investigate this in multi-center or multi-data sets. Second, the study had no underlying experimental validation. Therefore, basic experiments on LncRNAs associated with amino acid metabolism in gastric cancer will be further carried out in the future, mainly focusing on relevant mechanisms and signaling pathways.
## Conclusion
In summary, this study explored the prognostic and molecular immunological features of amino acid metabolism-related LncRNAs in gastric cancer. It was found that this prognostic model can offer an insightful perspective on the prognosis of patients with STAD, and provide innovative ideas for gastric cancer therapy in the future.
## Data availability statement
The original contributions presented in the study are publicly available. This data can be found here: Supplementary Material.
## Author contributions
Conception and design of this study: WJ and KO; acquisition of data and analysis and interpretation of data: YL; statistical analysis and drafting of the manuscript: WL and MZ; access to funding: MZ. All authors read and approved the final manuscript.
## Conflict of interest
The authors state that the conduct of this study did not involve any business or financial relationships that could be interpreted as potential conflicts 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.2023.1127132/full#supplementary-material
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|
---
title: 'The path between socioeconomic inequality and cognitive function: A mediation
analysis based on the HAALSI cohort in rural South Africa'
authors:
- Sianga Mutola
- F. Xavier Gómez-Olivé
- Nawi Ng
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10040802
doi: 10.3389/fpubh.2023.1011439
license: CC BY 4.0
---
# The path between socioeconomic inequality and cognitive function: A mediation analysis based on the HAALSI cohort in rural South Africa
## Abstract
### Background
Socioeconomic position (SEP) strongly predicts late-life cognitive health, yet the pathways between SEP and cognitive function remain unclear. This study assessed whether and to what extent the association between SEP and cognitive function in the adult population in rural South *Africa is* mediated by some health conditions, behavioral factors, and social capital factors.
### Methods
In this cross-sectional study, we used data from the 2014–15 “Health and Aging Africa: A Longitudinal Study of an INDEPTH Community in South Africa” (HAALSI) cohort, including 5,059 adults aged 40+ years from the Agincourt sub-district in Mpumalanga Province, South Africa. SEP, the independent variable, was measured based on ownership of household goods. Cognitive function, the dependent variable, was assessed using questions related to time orientation and immediate and delayed word recall. We used the multiple-mediation analysis on 4125 individuals with complete values on all variables to assess the mediating roles of health conditions (hypertension, diabetes, obesity, and disability), behavioral factors (leisure physical activity, alcohol consumption, and tobacco smoking), and social capital factors (community's willingness to help, trust, sense of safety, and social network contact) in the association between SEP and cognitive function.
### Results
Compared to adults in the poorest wealth quintile, those in the richest wealth quintile had better cognition (β = 0.903, $p \leq 0.001$). The mediation analysis revealed that health conditions mediated $20.7\%$ of the total effect of SEP on cognitive function. In comparison, $3.3\%$ was mediated by behavioral factors and only $0.7\%$ by social capital factors. In the multiple-mediator model, $17.9\%$ of the effect of SEP on cognitive function was jointly mediated by health conditions, behavioral factors, and social capital factors.
### Conclusion
Low socioeconomic position is a significant factor associated with poor cognitive function among adults aged 40 years and above in South Africa. Health conditions mainly mediate the effects between SEP and cognitive function. Therefore, actions to prevent and control chronic health conditions can serve as the entry point for intervention to prevent poor cognitive function among people with low socioeconomic status.
## Introduction
Socioeconomic position (SEP) strongly predicts late-life cognitive health [1, 2]. Yet, the mechanistic pathways of the effects of SEP on cognitive function remain unclear, especially among populations in low- and middle-income countries (LMICs). As life expectancy worldwide has been rising since 1950, with a significant increase in the proportion of older adults, there is a need for more research to understand the effect of SEP on cognitive function among the older population [3]. The United Nations (UN) estimated that there were 703 million persons aged 65 years or over in the global population in 2019 and that this number would double to 1.5 billion in 2050. Furthermore, the growth of the older adult population has been projected to be higher in low- and middle-income countries (LMICs). For instance, the percentage of the population aged 65 years or over almost doubled from $6\%$ in 1990 to $11\%$ in 2019 in Eastern and South-Eastern Asia and from $5\%$ in 1990 to $9\%$ in 2019 in Latin America and the Caribbean [4]. In South Africa, the fertility rate has dropped from 2.7 in 2001 to 2.3 in 2013, while life expectancy has steadily risen from 53 years in 2001 to 60 years in 2014, thereby increasing the population of older adults. The number of elderly persons in South *Africa is* expected to reach 7 million by 2030, compared to 4 million in 2011. Yet, socioeconomic disparities have remained high in certain areas since the Apartheid regime (5–7).
A large volume of research evidence from high-income contexts shows a strong association between SEP and several health outcomes, including cognitive health among older adults (8–17). From these studies, some factors that influence cognition among older populations have also been documented, such as; biological processes, genetic inheritance, psychological factors, social interaction, childhood deprivation, and SEP (18–20). In Southern Africa, there is scarce research investigating the association between SEP and cognitive function. Some of these studies have shown that low SEP, low education level attained, unemployment, living with HIV/AIDS, childhood deprivation, cardiometabolic diseases, and low social capital are associated with poor cognitive function among older populations (21–25). However, the said studies did not assess the multiple intermediary factors which may mediate the association between SEP and cognitive function. Therefore, there is a need for more research that utilizes models which include multiple factors to fully understand the association between SEP and cognitive function and the possible intermediary factors in the association. This knowledge is essential because the effect of a predictive factor on an outcome is likely to be transmitted by intermediary factors known as mediators [26].
Our study assessed whether and to what extent the association between SEP and cognitive function in the adult population aged 40 years and above in rural South *Africa is* mediated by health conditions, behavioral factors, and social capital factors. Furthermore, we used the social determinants of health (SDH) framework to explain how inequalities in upstream structural determinants (SEP) influence the more downstream intermediary factors (health conditions, behavioral, and social capital factors) and result in inequalities in health and well-being (cognitive function in this study) [27]. The SDH framework also includes a broad range of social conditions in which people live and work, as well as the distribution of money, power, and access to resources related to education and health care, job opportunities, and social contacts [28].
We hypothesized that the SEP association with cognitive function is mediated by health conditions, behavioral and social capital factors among the rural adult population in South Africa and tested the following hypotheses:
## Study design and study site
This study used the baseline data (2014–15) of the “Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa” (HAALSI) cohort, which is a representative population-based cohort in the Agincourt sub-district in Mpumalanga Province, in rural North-Eastern South Africa. This paper utilized data from the baseline sample of a random selection of 5,059 adults aged 40 years and above recruited from the 2013 round of the Agincourt Health and Demographic Surveillance System (HDSS) [29]. The Medical Research Council (MRC)/Wits Rural Public Health and Health Transitions Research Unit has run the Agincourt HDSS since 1992 [30]. The setting is predominantly experiencing high levels of socioeconomic inequalities due to the Apartheid regime [31]. Despite minor improvements in socioeconomic conditions in the *Agincourt area* post-Apartheid, there are still high unemployment levels and disparities in essential services, such as the lack of access to electricity supply, piped water and tarred road coverage [30].
## Data collection
The study sampling and recruitment methods have been described elsewhere [29]. In brief, HALSI recruited men and women aged 40 years or above on July 1, 2014, who had lived in the study area for at least 12 months before the 2013 Agincourt HDSS census round. Though HAALSI focuses on older adults, the study recruited adults aged 40 years and over, considering the low life expectancy at birth in SSA, mainly due to HIV/AIDS epidemic [32]. The fieldworkers collected data through face-to-face interviews at home using a Computer Assisted Personal Interview (CAPI) system. The interviews covered demographic, education, employment, possession of durable goods and other items like livestock, physical and cognitive functioning, social networks, self-reported health history and non-communicable disease outcomes. The interviews were conducted using xiTsonga, the local language. HAALSI translated all interview materials from English and back-translated them to ensure the instrument's validity and reliability.
## Ethics
The current study used the anonymous HAALSI data available in the public domain and accessible after formal application at https://haalsi.org/data. HAALSI received ethical approvals from the University of the Witwatersrand Human Research Ethics Committee (ref. M141159), the Harvard T.H. Chan School of Public Health, Office of Human Research Administration (ref. C13–1608–02), and the Mpumalanga Provincial Research and Ethics Committee. Once identified, potential participants were informed about the study and asked to provide informed consent in xiTsonga, the local language, or in English. Participants unable to read had a witness and used an inked fingerprint as a signature. The participants' autonomy and privacy were strictly ensured, considering the personal nature of the data they gave. They were also allowed to disengage from the study if they could not continue [29].
## Independent variable: Socioeconomic position
In our study, we utilized models that included multiple possible intermediary factors in the association of SEP and cognitive function among older adults in post-Apartheid South Africa. In the HAALSI, the household interview included: consumption and expenditures, labor income; business income; government transfers; remittances; housing characteristics, ownership of durable goods, land, livestock and financial assets, and food security. Individual participants were asked about their work status, working hours, income, unemployment, disability income and pensions. The Agincourt HDSS created a wealth index from principal components analysis of household characteristics and ownership of household items, vehicles and livestock [16, 29, 33]. The wealth index score was estimated using the random-effects probit model, and the scores were subsequently categorized as the wealth quintiles. Quintile 1 represented the poorest fifth of the population, whereas quintile 5 represented the wealthiest fifth [29]. In our study, the wealth quintiles represent SEP.
## Dependent variable: Cognitive function
HAALSI assessed cognitive function using five validated brief cognitive tests, including items on orientation, immediate and delayed recall, and numeracy, which were adopted from the US. Health Retirement Study (HRS) [21, 29, 34]. The adopted HRS tool was harmonized with the Oxford Cognitive Screen (OCS-Plus), a domain-specific cognitive assessment designed for low-literacy settings, especially in LMICS. The tool was then translated and back-translated before piloting it on about half the HAALSI cohort (a sample of 1,402 men and women aged 40–79) to test its validity. The intra-class correlation between similar basic orientation measures in OCS-Plus and HAALSI assessments was 0.79. Therefore, the tool was found to be appropriate for the older population of Agincourt with high levels of illiteracy, where the HAALSI sample was taken from [35]. After the pilot study, the following were the five tests used to assess cognition: [1] time orientation: respondents were asked to report the current year, month, and day and the name of the current South African president (one point for each correct answer; four points total); [2] immediate word recall: respondents were asked to recall as many words as possible from 10 words read aloud by the interviewer (one point for each word correctly recalled; 10 points total); [3] delayed word recall: respondents were asked to list the words recalled about 1 minute after an interceding question was asked (one point for each word correctly recalled; 10 points total); [4] counting: respondents were asked to count sequentially from 1 to 20 (one point); and [5] numerical patterns: respondents who successfully counted to 20 were asked to complete the numeric sequence starting from the number two, four, six, and so on (one point). The overall cognitive score ranged from 0 to 26 [21, 23, 24]. However, we did not include the last two numeracy tests (counting and numerical patterns) in the present analysis as they are more likely to reflect the individual level of schooling rather than the aging-related cognitive impairment [21]. We used the sum of the responses to the first three tests (ranging from 0 to 24) as the continuous cognitive function score. See Supplementary Table A1 for details of questions and their response categories used to define cognitive function in this study.
## Mediators between SEP and cognitive function
The potential mediator variables included in this study fell under three categories: health conditions, behavioral factors, and social capital factors.
The health conditions included obesity, diabetes, hypertension, and disability. Our study did not include HIV/AIDS in the analysis because we did not have access to the HAALSI data on HIV serostatus, HIV viral load, and the presence of antiretroviral therapy (ART) for HIV-positive patients that required a more extended application. We assessed obesity based on measured height and weight using body mass index (BMI; kg/m2) categories which were defined as per standard definitions as follows; obese ≥30 BMI, overweight BMI 25 to <30, normal BMI 18.5 to < 25, and underweight BMI < 18.5 [23]. Hypertension was defined as systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg or if the respondents were currently on antihypertensive medication at the interview. Diabetes was defined as blood glucose ≥ 7 mmol/l (126 mg/dL) in the fasting group (defined as >8 h) or blood glucose ≥11.1 mmol/l (200 mg/dL) in non–fasting samples or self-reported current diabetes treatment at the time of the interview. Individuals with missing fasting information were considered not fasting [23]. The respondents were asked to report difficulty or inability to do the following five activities of daily living (ADL): bathing, eating, getting in/out of bed, toileting, and walking across a room. For analysis, a dichotomous variable was generated as a proxy for disability, taking one if the respondent reported difficulty on one or more ADLs (1+ ADL) and 0 otherwise [25].
Behavioral variables included leisure physical activity, history of alcohol intake, and history of tobacco smoking. Leisure physical activity was assessed by asking the respondents if they engaged in vigorous or moderate-intensity physical activities lasting more than 10 min in their spare time. We assessed alcohol consumption by asking whether the respondent was currently consuming an alcoholic drink such as beer, wine, spirits, fermented cider, or traditional alcohol such as thothotho. We also assessed tobacco smoking by asking whether or not the respondent was currently smoking tobacco products such as cigarettes, cigars, or pipes. In this case, we can assume that the wealth index as a measure of SEP is more stable over time and could precede the hypothesized mediators. Additionally, evidence has shown that SEP is one of the many factors influencing a person's alcohol and tobacco use. Despite the ubiquitousness of alcohol and tobacco, their use increases with high SEP because it is considered as a prestigious lifestyle behavior [36].
The social capital variables included the assessment of the community's willingness to help, trust in other members of the community, feeling of safety, and social network contact. Participants were asked if most people in the village were willing to help their neighbors using a Likert scale with categories of strongly agree, agree, disagree, and strongly disagree. We assessed the level of trust in other community members by asking the respondents whether most people in the village could be trusted using the same Likert scale. The responses to the Likert scale were dichotomised to “yes” if the respondents indicated that they strongly agreed or agreed and “no” if they disagreed or strongly disagreed. Community safety was assessed by asking: “*In* general, how safe is your village?”. The responses (1 = extremely safe, 2 = safe, 3 = not safe, and 4 = extremely unsafe) were dichotomised (3 and 4 recoded to 0 representing “Not safe” and 1 and 2 recoded to 1 representing “Safe”). The participants' social network contact was assessed by asking participants to describe how often they typically interacted with up to seven individuals in person over the past 6 months (1 = every day or almost every day, 2 = a few times per week, 3 = once per week, 4 = a few times per month, 5 = once per month, 6 = a few times in the past 6 months, and 7 = not at all). *We* generated a new categorical variable, social network contact per week, where 0 = less than once (a few times per month, once per month, a few times in the past 6 months, and not at all), 1 = once or twice per week, 2 = three or more times per week [29].
Figure 1 depicts the hypothesized pathways between SEP (wealth quintiles) and cognitive function, demonstrating the possible indirect effect mediated by health conditions and behavioral and social capital mediators.
**Figure 1:** *Directed acyclic graph showing the hypothesized mediators in the association between socioeconomic position and cognitive function.*
## Potential confounders
The potential confounders included in the analysis included age group (40–49; 50–59; 60–69; 70–79; ≥ 80 years old), sex (male or female), marital status (married or unmarried), employment status (employed or unemployed), education (no education or some education), and self-rated childhood health (good or bad). Education level and employment status, which may be highly correlated in other settings, were both included as confounders because the types of jobs in Agincourt are not education-driven or academic-dependent. Agincourt is an underdeveloped rural South Africa where mainly manual and vocational-skills-based employment is available [31]. Therefore, this study considered education level and employment status as independent factors.
## Statistical analysis
We assessed for collinearity between the variables and checked for the percentage of missing values for each variable included in our study. After finding high collinearity between literacy and education, we dropped literacy and retained education. In this study, we only included observations with no missing data in all variables (complete case analysis), representing $81.5\%$ of the sample (4,125 individuals out of the total 5,059 individuals in HAALSI). Supplementary Table A2 reports the number and percentages of individuals with complete values and those with missing values on each variable. The missing values ranged from $0.1\%$ (marital status) to $8.1\%$ (diabetes). A total of 4,125 individuals had complete values on all the variables, representing $81.5\%$ completeness. Next, we analyzed descriptive statistics for the socio-demographic characteristics of the participants according to wealth quintiles. We conducted multivariable linear regressions by including groups of mediators and controlling for possible confounders, one at a time and all at once in the full model, in assessing the association between SEP and cognitive function with a statistical significance set at $p \leq 0.05.$
The next step involved mediation analysis using the multiple–mediator model [37]. The multiple-mediator model provided a more accurate assessment of mediation effects than the single-mediator model, which permits only one mediator at a time [26]. We used the command “sem” in Stata 16.1 to conduct mediation analyses for each of the hypothesized mechanistic pathways of the association between SEP and cognitive function, as depicted in the directed acyclic graph in Figure 1. The mediation analysis procedures involved five steps, with four models analyzed (health conditions model, behavioral model, social capital model, and the full model), as discussed below.
The health conditions model was run, specifying a direct path from SEP to cognitive function while controlling for confounders. The indirect path between SEP and cognitive function, mediated by health conditions while controlling for confounders, was the sum of the products of coefficients for the path from SEP to each of the health conditions (hypertension, diabetes, obesity, and disability) and coefficients for the path from each of the health conditions to cognitive function. The total effect (TE) of SEP on cognitive function was the sum of the natural direct effect (NDE) of SEP on cognitive function and the natural indirect effect (NIE) of SEP on cognitive function. The proportion of the effect of SEP on cognitive function mediated by each set of mediators is the quotient of NIE divided by TE multiplied by 100 (NIE/TE*100). The analysis repeated this process for the remaining two mediators (behavioral and social capital mediators).
All the mediators were included in the full model, controlling for confounders. Additionally, we conducted a non-parametric bootstrapping procedure with 500 replications which repeated sampling from the data set and estimated the indirect effect in each resampled data set. By repeating this process 500 times, an empirical approximation of the sampling distribution of the mediator effect in the association between SEP and cognitive function was built and used to construct the $95\%$ confidence intervals (CI) for the indirect effect coefficients. Bootstrapping was achieved by adding the option vce [bootstrap, reps[500]] to the sem Stata command for each model specified above, following Preacher and Hayes [38] 's multiple-mediator procedures.
## Characteristics of the participants
The characteristics of the participants in this study, stratified by household wealth quintiles, are presented in Table 1. More people aged 80+ years belonged to the poorest wealth quintile than the richest wealth quintile (13.2 vs. $6.4\%$). Participants in the poorest wealth quintile were more likely to not work ($88.1\%$) than those in the richest wealth quintile ($76.7\%$). A higher proportion in the poorest wealth quintile ($66.1\%$) had no formal education than participants in the richest wealth quintile ($23.1\%$). More participants from the wealthiest wealth quintile reported being hypertensive ($63.1\%$), diabetic ($14.1\%$), and obese ($41.3\%$) than participants from the poorest wealth quintile ($53.0\%$, $6.7\%$, and $19.2\%$, respectively). Regarding social network contact per week, more participants in the poorest wealth quintile ($11.5\%$) reported having no contact per week than those in the richest wealth quintile ($8.8\%$).
**Table 1**
| Unnamed: 0 | Wealth quintiles | Wealth quintiles.1 | Wealth quintiles.2 | Wealth quintiles.3 | Wealth quintiles.4 |
| --- | --- | --- | --- | --- | --- |
| | 1 (poorest) | 2 | 3 | 4 | 5 (richest) |
| | (N = 1,046) | (N = 1,001) | (N = 991) | (N = 1,007) | (N = 1,014) |
| | N (%) | N (%) | N (%) | N (%) | N (%) |
| Gender | Gender | Gender | Gender | Gender | Gender |
| Female | 544 (52.0%) | 546 (54.5%) | 541 (54.6%) | 550 (54.6%) | 533 (52.6%) |
| Male | 502 (48.0%) | 455 (45.5%) | 450 (45.4%) | 457 (45.4%) | 481 (47.4%) |
| Age group | Age group | Age group | Age group | Age group | Age group |
| 40–49 | 181 (17.3%) | 188 (18.8%) | 185 (18.7%) | 170 (16.9%) | 194 (19.1%) |
| 50–59 | 298 (28.5%) | 265 (26.5%) | 256 (25.8%) | 285 (28.3%) | 306 (30.2%) |
| 60–69 | 249 (23.8%) | 229 (22.9%) | 251 (25.3%) | 285 (28.3%) | 290 (28.6%) |
| 70–79 | 180 (17.2%) | 187 (18.7%) | 187 (18.9%) | 165 (16.4%) | 159 (15.7%) |
| 80+ | 138 (13.2%) | 132 (13.2%) | 112 (11.3%) | 102 (10.1%) | 65 (6.4%) |
| Marital status | Marital status | Marital status | Marital status | Marital status | Marital status |
| Not married | 639 (61.1%) | 566 (56.5%) | 484 (48.8%) | 448 (44.6%) | 343 (33.8%) |
| Married | 406 (38.9%) | 435 (43.5%) | 507 (51.2%) | 556 (55.4%) | 671 (66.2%) |
| Employment status | Employment status | Employment status | Employment status | Employment status | Employment status |
| Unemployed | 920 (88.1%) | 866 (86.9%) | 839 (84.7%) | 840 (83.7%) | 775 (76.7%) |
| Employed | 124 (11.9%) | 130 (13.1%) | 151 (15.3%) | 164 (16.3%) | 236 (23.3%) |
| Education level | Education level | Education level | Education level | Education level | Education level |
| No education | 690 (66.1%) | 554 (55.6%) | 445 (45.0%) | 383 (38.3%) | 234 (23.1%) |
| Some education | 354 (33.9%) | 442 (44.4%) | 544 (55.0%) | 618 (61.7%) | 778 (76.9%) |
| Childhood health | Childhood health | Childhood health | Childhood health | Childhood health | Childhood health |
| Bad | 144 (13.8%) | 118 (11.8%) | 115 (11.6%) | 116 (11.5%) | 128 (12.6%) |
| Good | 902 (86.2%) | 883 (88.2%) | 875 (88.4%) | 889 (88.5%) | 885 (87.4%) |
| History of hypertension | History of hypertension | History of hypertension | History of hypertension | History of hypertension | History of hypertension |
| No | 478 (47.0%) | 428 (43.6%) | 426 (44.2%) | 355 (36.1%) | 365 (36.9%) |
| Yes | 539 (53.0%) | 554 (56.4%) | 538 (55.8%) | 628 (63.9%) | 625 (63.1%) |
| History of diabetes | History of diabetes | History of diabetes | History of diabetes | History of diabetes | History of diabetes |
| No | 898 (93.3%) | 838 (90.9%) | 809 (88.8%) | 806 (87.7%) | 802 (85.9%) |
| Yes | 64 (6.7%) | 84 (9.1%) | 102 (11.2%) | 113 (12.3%) | 132 (14.1%) |
| Obesity | Obesity | Obesity | Obesity | Obesity | Obesity |
| Underweight | 87 (9.1%) | 71 (7.7%) | 46 (5.0%) | 24 (2.6%) | 30 (3.1%) |
| Normal | 433 (45.1%) | 384 (41.6%) | 346 (37.9%) | 320 (34.3%) | 236 (24.6%) |
| Overweight | 256 (26.7%) | 253 (27.4%) | 246 (26.9%) | 277 (29.7%) | 296 (30.9%) |
| Obese | 184 (19.2%) | 216 (23.4%) | 275 (30.1%) | 313 (33.5%) | 396 (41.3%) |
| Disability | Disability | Disability | Disability | Disability | Disability |
| No disability | 938 (89.8%) | 886 (88.6%) | 886 (89.4%) | 928 (92.2%) | 952 (94.1%) |
| With disability | 107 (10.2%) | 114 (11.4%) | 105 (10.6%) | 78 (7.8%) | 60 (5.9%) |
| Activity | Activity | Activity | Activity | Activity | Activity |
| Inactive | 313 (30.0%) | 315 (31.5%) | 322 (32.6%) | 356 (35.4%) | 322 (31.8%) |
| Active | 731 (70.0%) | 685 (68.5%) | 667 (67.4%) | 650 (64.6%) | 692 (68.2%) |
| Currently consume alcohol | Currently consume alcohol | Currently consume alcohol | Currently consume alcohol | Currently consume alcohol | Currently consume alcohol |
| No | 695 (66.6%) | 741 (74.1%) | 775 (78.2%) | 811 (80.5%) | 865 (85.3%) |
| Yes | 349 (33.4%) | 259 (25.9%) | 216 (21.8%) | 196 (19.5%) | 149 (14.7%) |
| Currently smoke tobacco | Currently smoke tobacco | Currently smoke tobacco | Currently smoke tobacco | Currently smoke tobacco | Currently smoke tobacco |
| No | 906 (86.8%) | 899 (89.9%) | 899 (90.9%) | 926 (92.0%) | 964 (95.1%) |
| Yes | 138 (13.2%) | 101 (10.1%) | 90 (9.1%) | 81 (8.0%) | 50 (4.9%) |
| Social network contact per week | Social network contact per week | Social network contact per week | Social network contact per week | Social network contact per week | Social network contact per week |
| No contact | 111 (11.5%) | 95 (10.1%) | 108 (11.5%) | 108 (11.2%) | 87 (8.8%) |
| One or two | 606 (62.7%) | 582 (61.9%) | 544 (57.9%) | 578 (59.8%) | 598 (60.7%) |
| Three + | 250 (25.9%) | 263 (28.0%) | 287 (30.6%) | 281 (29.1%) | 300 (30.5%) |
| People willing to help | People willing to help | People willing to help | People willing to help | People willing to help | People willing to help |
| Unwilling to help | 36 (3.6%) | 41 (4.2%) | 28 (2.9%) | 35 (3.6%) | 21 (2.1%) |
| Willing to help | 975 (96.4%) | 935 (95.8%) | 940 (97.1%) | 949 (96.4%) | 977 (97.9%) |
| Trust in community | Trust in community | Trust in community | Trust in community | Trust in community | Trust in community |
| No trust | 63 (6.2%) | 74 (7.6%) | 45 (4.7%) | 64 (6.5%) | 50 (5.0%) |
| Have trust | 949 (93.8%) | 901 (92.4%) | 922 (95.3%) | 919 (93.5%) | 948 (95.0%) |
| Community safety | Community safety | Community safety | Community safety | Community safety | Community safety |
| Did not feel safe | 34 (3.4%) | 52 (5.3%) | 51 (5.3%) | 43 (4.4%) | 34 (3.4%) |
| Felt safe | 977 (96.6%) | 923 (94.7%) | 917 (94.7%) | 942 (95.6%) | 964 (96.6%) |
## Associations between socioeconomic position and cognitive function, adjusting for health conditions, behavioral and social capital factors, and potential confounders
Table 2 displays the five models, with more details in Supplementary Table A3; the basic model, health conditions model, behavioral model, social capital model, and the full model. A positive coefficient for a given category of participants indicates a higher cognitive score than that of the reference group of participants.
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Basic model β (95% CI) | Health conditions model β (95% CI) | Behavioral model β (95% CI) | Social capital model β (95% CI) | Full model β (95% CI) |
| --- | --- | --- | --- | --- | --- | --- |
| Wealth quintiles | Wealth quintiles | Wealth quintiles | Wealth quintiles | Wealth quintiles | Wealth quintiles | Wealth quintiles |
| | 1 (poorest) | Ref. | Ref. | Ref. | Ref. | Ref. |
| | 2 | 0.226 (−0.104, 0.556) | 0.129 (−0.217, 0.475) | 0.210 (−0.114, 0.533) | 0.206 (−0.129, 0.541) | 0.0934 (−0.253, 0.440) |
| | 3 | 0.479 (0.145, 0.812) | 0.364 (0.0135, 0.715) | 0.480 (0.152, 0.809) | 0.411 (0.0724, 0.750) | 0.331 (−0.0202, 0.683) |
| | 4 | 0.596 (0.260, 0.933) | 0.323 (−0.0316, 0.678) | 0.633 (0.302, 0.965) | 0.553 (0.213, 0.893) | 0.359 (0.00450, 0.714) |
| | 5 (richest) | 1.271 (0.924, 1.617) | 1.043 (0.678, 1.409) | 1.216 (0.874, 1.559) | 1.262 (0.912, 1.612) | 1.034 (0.668, 1.399) |
| Health conditions | Health conditions | Health conditions | Health conditions | Health conditions | Health conditions | Health conditions |
| Hypertension | Hypertension | Hypertension | Hypertension | Hypertension | Hypertension | Hypertension |
| | No | | Ref. | | | Ref. |
| | Yes | | 0.0978 (−0.135, 0.331) | | | 0.105 (−0.127, 0.337) |
| Diabetes | Diabetes | Diabetes | Diabetes | Diabetes | Diabetes | Diabetes |
| | No | | Ref. | | | Ref. |
| | Yes | | −0.368 (−0.731, −0.00399) | | | −0.408 (−0.769, −0.0464) |
| Obesity | Obesity | Obesity | Obesity | Obesity | Obesity | Obesity |
| | Normal | | Ref. | | | Ref. |
| | Underweight | | −0.766 (−1.276, −0.255) | | | −0.605 (−1.116, −0.0934) |
| | Overweight | | 0.521 (0.241, 0.800) | | | 0.461 (0.180, 0.742) |
| | Obese | | 0.836 (0.543, 1.129) | | | 0.789 (0.495, 1.084) |
| Disability | Disability | Disability | Disability | Disability | Disability | Disability |
| | No disability | | Ref. | | | Ref. |
| | With disability | | −1.061 (−1.522, −0.601) | | | −0.749 (−1.207, −0.292) |
| Behavioral factors | Behavioral factors | Behavioral factors | Behavioral factors | Behavioral factors | Behavioral factors | Behavioral factors |
| Leisure activity | Leisure activity | Leisure activity | Leisure activity | Leisure activity | Leisure activity | Leisure activity |
| | Inactive | | | Ref. | | Ref. |
| | Active | | | 1.513 (1.290, 1.736) | | 1.378 (1.135, 1.621) |
| Consume alcohol | Consume alcohol | Consume alcohol | Consume alcohol | Consume alcohol | Consume alcohol | Consume alcohol |
| | No | | | Ref. | | Ref. |
| | Yes | | | −0.622 (−0.898, −0.346) | | −0.508 (−0.801, −0.215) |
| Smoke tobacco | Smoke tobacco | Smoke tobacco | Smoke tobacco | Smoke tobacco | Smoke tobacco | Smoke tobacco |
| | No | | | Ref. | | Ref. |
| | Yes | | | −0.579 (−0.983, −0.176) | | −0.336 (−0.773, 0.102) |
| Social capital factors | Social capital factors | Social capital factors | Social capital factors | Social capital factors | Social capital factors | Social capital factors |
| People willing to help | People willing to help | People willing to help | People willing to help | People willing to help | People willing to help | People willing to help |
| | Unwilling | | | | Ref. | Ref. |
| | Willing | | | | 0.0223 (−0.605, 0.650) | −0.00169 (−0.666, 0.662) |
| Trust in community | Trust in community | Trust in community | Trust in community | Trust in community | Trust in community | Trust in community |
| | No trust | | | | Ref. | Ref. |
| | Have trust | | | | 0.872 (0.387, 1.358) | 0.679 (0.165, 1.192) |
| Community safety | Community safety | Community safety | Community safety | Community safety | Community safety | Community safety |
| | Felt safe | | | | Ref. | Ref. |
| | Did not feel safe | | | | −0.836 (−1.373, −0.298) | −0.977 (−1.554, −0.400) |
| Social contact per week | Social contact per week | Social contact per week | Social contact per week | Social contact per week | Social contact per week | Social contact per week |
| | No contact | | | | Ref. | Ref. |
| | Once or twice | | | | 0.0390 (−0.319, 0.397) | 0.172 (−0.199, 0.543) |
| | Three + | | | | 0.370 (−0.0211, 0.761) | 0.460 (0.0549, 0.864) |
| Constant | Constant | 9.390 (8.920, 9.860) | 9.282 (8.764, 9.800) | 8.481 (7.988, 8.975) | 9.306 (8.406, 10.21) | 8.501 (7.514, 9.489) |
| Observations | Observations | 4800 | 4240 | 4793 | 4664 | 4125 |
In the basic model, we observed a gradient of wealth effects on cognitive function. Participants in households in the richest wealth quintile had 1.271 units of cognitive function score higher than their counterparts in the poorest ($95\%$ CI: 0.924, 1.617). Furthermore, the older participants had lower cognitive function units than their younger counterparts. Similarly, the results showed a gradient of effects of education on cognitive function, where participants with some education had higher cognitive function than those without formal education.
When we included health condition variables in the model, the effect sizes observed across the different wealth quintiles became smaller. The participants belonging to households in the wealthiest quintile had 1.043 points of cognitive function score higher than those in the poorest wealth quintile ($95\%$ CI: 0.678, 1.409). Participants with hypertension had higher cognitive function scores at β = 0.0978 ($95\%$ CI: −0.135, 0.331) compared to those with no hypertension, while those with diabetes had lower cognitive function scores (β = −0.368, $95\%$ CI: −0.731, −0.00399) than those with diabetes. We also observed a lower cognitive function score among underweight participants (β = −0.766, $95\%$ CI: −1.276, −0.255) than those with normal weight. The overweight and obese participants showed higher cognitive function scores [β = 0.521 ($95\%$ CI: 0.241, 0.800) and β = 0.836 ($95\%$ CI: 0.543, 1.129) respectively], than those with normal body weight. The cognitive function score for participants who reported having a disability was −1.061 ($95\%$ CI: −1.522, −0.601), units lower than those who did not have a disability.
The participants in the wealthiest wealth quintile were observed to have a higher cognitive function score of 1.216 ($95\%$ CI: 0.874, 1.559) than those in the poorest wealth quintile when we included the behavioral variables. The participants who were active in the leisure activity had a higher cognitive function score of 1.513 ($95\%$ CI: 1.290, 1.736) than those who were inactive. Those who were currently consuming alcohol had a lower cognitive function score of −0.622 ($95\%$ CI: −0.898, −0.346) than those who were not currently consuming alcohol. We also observed that the participants who were currently smoking tobacco had lower cognitive function than those who were not smoking tobacco (β = −0.579, $95\%$ CI: −0.983, −0.176).
The effect sizes observed across the different wealth quintiles in a different model became more prominent when the analysis included the social capital variables. The cognitive function score for participants in the richest wealth quintile was 1.262 units higher than those in the poorest ($95\%$ CI: 0.912, 1.612). We also observed a gradient in the effects of social network contact per week on cognitive function, with those with more frequent contact showing a higher cognitive function score than their counterparts with less or no social network contact. The participants who trusted the community had a higher cognitive score (β = 0.872, $95\%$ CI: 0.387, 1.358) than those who distrusted their community.
In the full model with all the mediators, the cognitive function score for the participants in the richest wealth quintile was reduced to 1.034 ($95\%$ CI: 0.668, 1.399) units higher than those in the poorest wealth quintile.
## Assessment of health conditions, behavioral and social capital as mediators in the association between socioeconomic position and cognitive function
Table 3 displays the results of the mediation analysis, reporting the total effects of SEP on cognitive function (TE), as well as the natural direct effects (NDE) and the natural indirect effects (NIE) through the mediators. The results showed a coefficient for the NIE of SEP on cognitive function mediated by the health conditions of 0.06 ($95\%$ CI: 0.04, 0.08), behavioral factors of 0.01 ($95\%$ CI: −0.01, 0.03), and social capital factors of 0.0017 ($95\%$ CI: −0.005, 0.009). These coefficients corresponded to the proportion of the effect of SEP on cognitive function mediated by health conditions of $20.7\%$, behavioral of $3.3\%$, and social capital factors of $0.7\%$. In the full model, the three groups of mediators jointly mediated $17.9\%$ of the effect of SEP on cognitive function.
**Table 3**
| Unnamed: 0 | Health conditions model β (95% CI) | Behavioral model β (95% CI) | Social capital model β (95% CI) | Full model β (95% CI) |
| --- | --- | --- | --- | --- |
| Total effect | 0.29 (0.20, 0.37) | 0.30 (0.22, 0.37) | 0.298 (0.22, 0.38) | 0.28 (0.20, 0.37) |
| Natural direct effect | 0.23 (0.15, 0.31) | 0.29 (0.21, 0.37) | 0.296 (0.21, 0.37) | 0.23 (0.15, 0.32) |
| Natural indirect effect | 0.06 (0.04, 0.08) | 0.01 (−0.01, −0.03) | 0.0017 (−0.005, 0.009) | 0.05 (0.02, 0.08) |
| Proportion not mediated | 79.3% | 96.7% | 99.3% | 82.1% |
| Proportion mediated | 20.7% | 3.3% | 0.7% | 17.9% |
## Discussion
The Social Determinants of Health framework guided the hypothesis generation, selection of variables, and understanding of the mechanistic pathways by which the association between SEP and cognitive function is mediated [27]. The study found an association between SEP and cognitive function after controlling for socio-demographic factors, including sex, age, education, employment, marital status, and childhood health, and these findings are similar to the findings by Yang et al. [ 39]. Additionally, our study findings introduce a novel understanding of how the effects of SEP on cognitive function could potentially be mediated. This is important because there is a limited number of studies that have investigated the mechanistic pathways of the effect of SEP on cognitive health, particularly in South Africa where there is a unique socioeconomic context resulting from the Apartheid regime. The findings suggest that an individual's SEP impacts their health status, behavior, and social capital, and therefore, their cognitive health. However, the study results showed that a significant proportion ($82\%$) of the effects of SEP on cognitive function were not mediated by health conditions, behavioral, and social capital mediators and that only $17.9\%$ was mediated. The mediation analysis further showed that health conditions contributed the most to explaining the SEP–cognitive function association, with a mediation effect of $20.7\%$, followed by behavioral factors of $3.3\%$ and social capital factors of $0.7\%$ when we included the three groups of the mediators independently in the models. The two latter-mediated effects of SEP on cognitive function by behavioral factors [0.01 ($95\%$ CI: −0.01, 0.03)] and social capital factors [0.0017 ($95\%$ CI: −0.005, 0.009)] were found to be not statistically significant.
Our study found that having diabetes and having a disability resulted in poorer cognitive health. This study's finding is consistent with evidence from Taiwan, where health-related factors were associated with cognitive impairment in older adults [40]. However, our study findings showed that hypertension and obesity were positively correlated with cognitive function, consistent with findings in a study among individuals aged 55–85 in Newcastle and Australia [41]; as well as in a study in Malawi where poorer cognitive health was associated with lower BMI in the subsistence agriculture context of Malawi's Longitudinal Study of Families and Health (MLSFH), where calorie deficiency is a public health concern [25]. In the Malawi study [25], obesity was perceived as a positive outcome and a sign of belonging to higher SEP and having access to an adequate food supply. Additionally, it has been suggested that poor rural populations in Agincourt, involved in agricultural manual work for their livelihoods, have less likelihood of developing sedentary-related obesity [33]. However, a systematic literature review of 17 scientific articles revealed impairments in cognition among obese adults [42]. Therefore, the high cognitive function among obese participants observed in our study should be understood in the context of existing inequalities in access to wealth and food among post-apartheid rural South Africans. Similarly, we may use this reasoning to understand the higher cognitive function found among hypertensive participants compared to the non-hypertensive participants in this study. All in all, as posited by Hafeman and Schwartz [43], there is a possibility of an interaction between SEP and health status, such that the overall effects of poor health status on cognitive function may be more pronounced in participants with lower SEP compared to participants with higher SEP.
Regarding behavioral factors, this study showed current tobacco smoking and non-engagement in moderate to vigorous physical activity were associated with poorer cognitive function. The adverse impact of tobacco smoking on cognitive health has been widely studied, and the available evidence shows that tobacco alone may contribute to adverse mental and physical health outcomes [44]. The effects of moderate to vigorous physical activities on cognition among adults aged 40 years and above have also been documented in other regions and are consistent with our findings [45, 46].
The study results also showed poor cognition among participants currently consuming alcohol, consistent with other research findings, including poor cognitive health, crime, road traffic crashes, high Human Immuno-deficiency Virus (HIV) transmission, and substance abuse (47–49). It is important to note that our analysis did not use other measurements of alcohol consumption, such as the number of units of alcohol and frequency, but only used the “current” questions for tobacco use and alcohol consumption because mediation analysis assumes a time-sequence between exposure, mediator, and outcome variables. Based on our findings, we suggest that public health interventions aimed at preventing cognitive health decline among adults should include tackling alcohol consumption, as also suggested by other researchers [47].
The social capital components analyzed in our study can explain health inequalities observed because they may influence health-related social norms and the diffusion of health-related information [50]. This study has confirmed findings from previous research showing that persons with low social support have a higher risk of poor cognitive health [51, 52]. Those with higher social network contact enjoyed higher cognitive health [24].
## Strengths and limitations
The strengths of this study include the utilization of comprehensive theoretical and methodological approaches that involve the inclusion of multiple mediators in the mediation analysis, thereby broadening the explanatory spectrum of the SEP—cognitive function association among adults aged 40 years and above. Another significant strength of our study is that it is based on a representative sample from a rural South African district, a world region rarely studied. Additionally, the in-person interviews (home visits) made it possible to include individuals with low literacy and education levels who are generally underrepresented in aging and dementia-related research.
Based on Judd and Kenny [53], we held the following assumptions to assess the mediating effects of the health conditions, behavioral factors, and social capital in the association between SEP and cognitive function: [1] SEP has a direct effect on cognitive function, and [2] SEP has an indirect effect on cognitive function through (a) health status, (b) some behavioral factors, and (c) social capital (see Figure 1). However, caution needs to be exercised when conducting mediation analysis using cross-sectional data (53–55). Mediation analysis using cross-sectional data as performed in several studies (56–58) can be justified if the temporality between the independent, mediator and dependent variables could be assumed, i.e., the hypothesized independent variable predates the mediator, which in turn predates the outcome variable [55]. In the HAASI cohort baseline data, SEP was measured by the housing characteristics and ownership of durable goods accumulated over a long time and hence assumed to predate the mediators and cognition status measured in older age [29].
We acknowledge some limitations of this study. Although the study used a comprehensive framework incorporating three pathways and adjusted for several mediator-outcome confounders, there are other potential mediating factors and unmeasured confounders not available in the HAALSI dataset that could bias the estimates reported in this study. Other possible mediators mediating the association between SEP and cognitive function include HIV serostatus, diet quality, working conditions, depression, and life satisfaction. Based on previous studies, the prevalence of HIV/AIDS is relatively high, even among older adults in Agincourt, South Africa [59, 60]. Therefore, we recommend further research, including investigating the role of HIV-associated neurocognitive disorder (HAND) [61] in explaining part of the associations and mediation between SEP and cognitive function, in addition to other aging-related neurodegenerative diseases.
Additionally, our study could not infer causal conclusions due to the cross-sectional study design adopted. Finally, the proportion of missing values was relatively high ($18.5\%$), excluding some participants from the analysis. However, the sample size was large enough to support the conclusions. Our sensitivity analysis compared the results of complete case analysis vs. multiply-imputed analysis using three different models: (i) complete case analysis with bootstrapping (the results presented in our paper); (ii) complete case analysis without bootstrapping; and (iii) multiple imputation analysis without bootstrapping. The sem command in Stata software cannot run multiply-imputed data with bootstrapping. The three analyses did not yield different results that invalidated our conclusions; therefore, we decided to keep the analysis based on individuals with complete information on all variables in the analysis.
## Conclusion and implications
The study shows that low SEP is a significant factor associated with poor cognitive function among adults aged 40 years and above in this rural South African setting. However, only $17.9\%$ of the total effects of SEP on cognitive function appeared to be mediated by the three sets of mediators, mainly by health conditions ($20.7\%$). Most importantly, the study findings imply that improving SEP may substantially prevent cognitive decline in later—life. Reducing the inequalities in access to wealth and improving overall physical health among the older population, as a high-risk group, can be effective interventions to improve their well-being and quality of life, precisely their cognitive function.
## 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 below: https://haalsi.org/data.
## Author contributions
SM, FXG-O, and NN conceptualized the research idea. SM and NN analyzed the data. SM prepared the first draft of the paper. FXG-O and NN provided critical inputs on the manuscript. All authors have provided substantial and critical inputs and approved the final draft 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/fpubh.2023.1011439/full#supplementary-material
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|
---
title: Reduced cardiovagal baroreflex sensitivity is associated with postural orthostatic
tachycardia syndrome (POTS) and pain chronification in patients with headache
authors:
- Bridget R. Mueller
- Carly Ray
- Alyha Benitez
- Jessica Robinson-Papp
journal: Frontiers in Human Neuroscience
year: 2023
pmcid: PMC10040804
doi: 10.3389/fnhum.2023.1068410
license: CC BY 4.0
---
# Reduced cardiovagal baroreflex sensitivity is associated with postural orthostatic tachycardia syndrome (POTS) and pain chronification in patients with headache
## Abstract
### Background
Non-cephalgic symptoms including orthostatic intolerance, fatigue, and cognitive impairment, are common in patients with chronic headache disorders and may result from alterations in the autonomic nervous system. However, little is known about the function of autonomic reflexes, which regulate cardiovascular homeostasis and cerebral perfusion in patients with headache.
### Methods
Autonomic function testing data from patients with headache collected between January 2018 and April 2022 was retrospectively analyzed. Through review of EMR we determined headache pain chronicity and patient self-report of orthostatic intolerance, fatigue, and cognitive impairment. Composite Autonomic Severity Score (CASS), CASS subscale scores, and cardiovagal and adrenergic baroreflex sensitivities were used to quantify autonomic reflex dysfunction. Descriptive analyses (Mann-Whitney-U or χ2, as appropriate) determined associations between autonomic reflex dysfunction and POTS as well as chronic headache. Binomial logistic regression adjusted for age and sex. Spearman’s rank correlation determined the association between the total CASS score and the number of painless symptoms reported by each participant.
### Results
We identified 34 patients meeting inclusion criteria, of whom there were 16 ($47.0\%$) with orthostatic intolerance, 17 ($50.0\%$) with fatigue, 11 ($32.4\%$) with cognitive complaints, and 11 ($32.4\%$) with Postural Orthostatic Tachycardia Syndrome (POTS). The majority of participants had migraine ($$n = 24$$, $70.6\%$), were female ($$n = 23$$, $67.6\%$) and had a chronic (>15 headache days in a month) headache disorder ($$n = 26$$, $76.5\%$). Reduced cardiovagal baroreflex sensitivity (BRS-V) independently predicted chronic headache [aOR: 18.59 (1.16, 297.05), $$p \leq 0.039$$] and POTS [aOR: 5.78 (1.0, 32.5), $$p \leq 0.047$$]. The total CASS was correlated with the total number of non-painful features in the expected direction ($r = 0.46$, $$p \leq 0.007$$).
### Conclusion
Abnormal autonomic reflexes may play an important role in pain chronification and the development of POTS in patients with headache.
## 1. Introduction
Postural Orthostatic Tachycardia Syndrome (POTS), a form of orthostatic intolerance, is common in patients with primary headache (Khurana and Eisenberg, 2011; Cook and Sandroni, 2018). In the transition from an acute episodic pain disorder to a chronic pain disorder, patients fail to return to their neurological baseline. Orthostatic intolerance and other non-painful features including fatigue, mood alterations, and impaired cognitive function, frequently become unremitting and cause significant disability (Spierings and van Hoof, 1997; Minen et al., 2016; Aurora and Brin, 2017; Karsan and Goadsby, 2021). Although there is evidence that these symptoms are more prevalent in chronic compared to episodic headache, Peres et al. [ 2002]; Zwart et al. [ 2003]; Zucca et al. [ 2020] the neurobiology underlying their association to pain chronification is not well understood.
In patients with neurodegenerative and neuroinflammatory disorders, there is evidence that several non-painful features are associated with abnormal autonomic reflexes. Autonomic reflexes rely on caudal central nervous system (CNS) neurocircuitry and peripheral components of the autonomic nervous system (ANS) to maintain cardiovascular homeostasis and cerebral perfusion [30]. In a study examining ANS function in patients with Parkinson’s disease, a negative correlation between fatigue severity and heart rate response to deep breathing (HRDB), a commonly measured autonomic reflex, was reported (Olivola et al., 2018). In addition, baroreflex dysfunction was demonstrated in patients with both Multiple Sclerosis (MS) (Adamec et al., 2013; Habek et al., 2017) and Alzheimer’s disease (AD) (Meel-van den Abeelen et al., 2013; Femminella et al., 2014). The baroreflex regulates the cardiovascular response to orthostasis and other perturbations in homeostasis (Ogoh et al., 2010; Purkayastha et al., 2018; Ogoh and Tarumi, 2019). The neuroanatomy of the baroreflex involves vagal afferents which synapse in the brainstem, vagal efferents which exit the brainstem to modulate heart rate, and adrenergic sympathetic efferents which exit the spinal cord to regulate the vasomotor reactivity of peripheral blood vessels (Kaufmann et al., 2020). In MS, baroreflex dysfunction has been posited to underly the high prevalence of syncope and Postural Orthostatic Tachycardia Syndrome (POTS) (Adamec et al., 2013; Habek et al., 2017). In AD, impaired blood pressure recovery following an orthostatic challenge predicted a faster rate of cognitive decline (de Heus et al., 2020). These findings indicate deficits in autonomic reflexes may lead to orthostatic intolerance, fatigue, and cognitive impairment.
In contrast to patients with neurodegenerative and inflammatory neurologic conditions, quantification of autonomic dysfunction in patients with headache has focused on examination of vasomotor reactivity (Mamontov et al., 2016), plasma catecholamine levels (Mosek et al., 1999), and resting heart rate variability, a measure that correlates to prefrontal cortical and limbic network activity. Only a few studies have utilized more comprehensive autonomic testing that probes autonomic reflexes. One small study compared patients with low frequency episodic migraine and healthy controls and showed that an autonomic reflex, the Valsalva ratio, a measure of cardiovagal activity, did not differ between the two groups (Cortelli et al, 1991). However, a larger study including both episodic and chronic headache disorders, demonstrated patients with headache had reduced HRDB compared to healthy controls (Martin et al., 1992). There have been no studies examining the relationship between autonomic reflexes and the presence of orthostatic intolerance, fatigue, and cognitive impairment in patients with headache.
We hypothesize that abnormal autonomic reflexes play a role in the development of non-painful, features and in the chronification of pain, explaining their significant overlap. Our rationale is as follows. First, there is evidence in humans and animals that the autonomic reflex critical to a normal cardiovascular response to standing, the baroreflex, is also involved in modulating pain sensitivity. Reduced baroreflex sensitivity (BRS) is associated not only with orthostatic intolerance, but with increased sensitivity to pain and is present in patients with chronic pain disorders including fibromyalgia and lower back pain (Steptoe and Sawada, 1989; Nosaka, 1996; Shoemaker and Goswami, 2015). The neural pathway underlying the relationship between the baroreflex and pain processing involves the nucleus tractus solitarus (NTS), a brain region with anti-nociceptive activity and the major vagal afferent relay station. Second, there is evidence that reduced BRS is associated with abnormal cerebral perfusion (Laosiripisan et al., 2015). Abnormal patterns of cerebral perfusion have been demonstrated in chronic migraine, Petolicchio et al. [ 2016]; Bogdanov et al. [ 2019] chronic non-migraine headache (Gilkey et al., 1997; Karacay Ozkalayci et al., 2018), chronic fatigue syndrome (Staud et al., 2018; Li X. et al., 2021) and impaired cognition (Li R. et al., 2021; Coffin et al., 2022; Wang et al., 2022). Finally, a recent, small study examining skin biopsies of patients with migraine reported nearly $50\%$ had evidence of small fiber neuropathy (Stillman et al., 2021). While the mechanism of small fiber neuropathy in patients with headache is unclear, sympathetic denervation impairs the vasomotor response to orthostasis and is present in neuropathic POTS (Stillman et al., 2021). These findings raise interesting questions regarding the role of the peripheral ANS in the development of chronic pain, as well as the non-painful features which commonly accompany it including orthostatic intolerance, fatigue, and impaired cognition.
We conducted a retrospective chart review of consecutive patients with headache who presented for autonomic function testing (AFTs) at a single center. We included both migraine and non-migraine headache syndromes, as we anticipated that the caudal CNS and peripheral neural circuitry underlying autonomic reflexes would be agnostic to the specific headache diagnosis. Through review of electronic medical records (EMR) we determined headache pain chronicity and patient self-report of non-painful features including orthostatic intolerance, fatigue, and cognitive impairment. Our overarching hypothesis was that autonomic reflex abnormalities underlie both chronification of headache pain and the development of non-painful features, accounting for their frequent co-occurrence. Specifically, we sought to investigate whether autonomic reflex dysfunction, as quantified by the validated Composite Autonomic Severity Score (CASS) and BRS would be: [1] more common in patients with chronic as opposed to episodic headache, and [2] correlated with the presence of orthostatic intolerance and other non-painful features.
## 2.1. Study design and sample
We performed a retrospective chart review of patients with headache who received a full battery of autonomic function testing (AFT) at the Mount Sinai Autonomic Laboratory between January 2018 and April 2022. Due to the COVID-19 pandemic, the laboratory was not testing patients from March 2020 through June 2020. Further, the Valsalva Maneuver, an aerosolizing procedure, was not performed during July 2020-April 2021 and November 2021-February 2022, when COVID-19 transmission rates were high and non-essential aerosolizing procedures were prohibited by our institution. The sample size was determined by the number of patients meeting eligibility requirements during the study period.
Using a consecutive approach, we retrospectively chart-checked all patients who received autonomic testing during the stated time periods, using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD10) codes, review of clinical documentation, and referral notes to identify patients with headache. Eligibility criteria included a diagnosis of headache for more than 6 months prior to autonomic testing, clinical documentation of headache characteristics, and an age between 15 and 75 years. Exclusion criteria included COVID-19 infection within the preceding 6 months of autonomic testing, a medical condition associated with dysfunction of the ANS (e.g., diabetes, Parkinson’s disease), and unclear documentation of headache frequency and semiology. In addition, patients taking medications that impacted cardiovascular function (e.g., beta-blockers or angiotensin-receptor II blockers) or had a significant anti-cholinergic burden (e.g., tricyclic antidepressant at doses higher than 20 mg per day) were excluded. The Mount Sinai Hospital Institutional Review Board approved the use of patient data for this retrospective study and waived the requirement for informed consent.
## 2.2. Data collection and characterization of study sample
Clinical documentation and autonomic function testing referrals in our institution’s electronic health record system (Epic, Verona, WI) were retrospectively reviewed to obtain patient’s self-reported sex, age at time of autonomic testing, average number of headache days per month, and presence of fatigue, cognitive complaints, and orthostatic intolerance.
Headache diagnoses were obtained through ICD10 codes used for visit billing and confirmed or clarified through review of clinical documentation and correspondence with referring headache physician or neurologist. Headache syndromes were classified based on the International Classification of Headache Disorders, Third Edition (ICHD-3). Both migraine and non-migraine headache disorders were eligible. Patients were characterized as having a chronic headache disorder if they reported an average of 15 or more headache days per month during the three months preceding autonomic testing. If patients reported an average headache frequency < 15 days per month during the three months prior to testing, they were characterized as having an episodic headache disorder. Patients were classified as having fatigue if a provider documented in notes or billing codes the presence of “lethargy,” “fatigue,” or “excessive sleepiness.” A patient was characterized as having cognitive impairment if a provider documented in notes or billing, impaired “attention,” “focus,” “memory,” or “brain fog.” A patient was characterized as having orthostatic intolerance if a provider documented in notes or billing codes the presence of orthostatic “dizziness” or “lightheadedness.” In addition, elements of the autonomic testing were used to confirm orthostatic intolerance according to standard criteria for Postural Orthostatic Tachycardia Syndrome (POTS) which are a symptomatic sustained increase in heart rate ≥ 30 beats per minute for adults, and ≥40 beats per minute for patients 15–19 years of age during tilt upward compared to supine baseline in the absence of orthostatic hypotension. The upright portion of the tilt table testing is 10 min. The number of concurrent non-painful, features (fatigue, orthostatic intolerance, and cognitive impairment) was also summed for each patient (from 0 to 3).
## 2.3. Clinical autonomic nervous system assessment
A standard battery of autonomic function tests (AFTs), as previously described, Low [1992]; Low and Opfer-Gehrking [1999] including quantitative sudomotor axon reflex testing (QSART), heart rate response to deep breathing (HRDB), Valsalva maneuver (VM), and tilt table testing was performed for all patients at our center. Testing equipment and data analysis software are supplied by WR Medical Electronics Co., (Maplewood, MN). The QSART was performed by placing a capsule containing acetyl choline (ACh) on the skin in four standardized locations (forearm, proximal leg, distal leg, and foot). The capsule was attached to an automated system which delivers a small continuous electrical stimulus to the capsule causing iontophoresis of ACh into the skin, which triggers a reflexive sweat response collected by the capsule. The total sweat volume was measured and compared to standardized values. Second, a non-invasive continuous beat-to-beat blood pressure monitoring device was attached to the participant’s finger (Nexfin system1) and a 3-lead surface electrocardiogram attached to the chest. Continuous blood pressure (BP) and heart rate (HR) were recorded during the VM (forced exhalation to a pressure of 40 mmHg for 15 s) and during a standardized paced deep breathing exercise (HRDB). Finally, the participant rested in the supine position for 5 min before being secured to the tilt table and tilted upright 75 degrees. Continuous HR and BP and orthostatic symptoms were recorded for 10 min while the patient was upright.
## 2.4. Description of autonomic nervous system measures
Continuous HR and BP were recorded, and the raw data stored during the VM, HRDB and tilt table testing. Using visual inspection of graphically represented HR and BP data combined with functionality in the WR Testworks software, two markers of reflexive HRV are calculated. The Valsalva ratio (VR) is calculated by dividing the highest HR during the VM to the lowest HR immediately following the maneuver. During the deep breathing task, the HR rises and falls in rhythm with the breath. For HRDB, the average change in HR from peak inspiration to expiration is calculated for the five consecutive cycles of breath which yield the largest result.
The Composite Autonomic Severity Score (CASS) is an age- and sex-adjusted summary score which provides an overall measure of autonomic reflexive function and sudomotor, vagal, and adrenergic sub-scores [9]. The sudomotor sub-score uses data from the QSART, the vagal sub-score is based on the VR and HRDB, and the adrenergic sub-score is based on BP changes during VM and tilt table testing. The authors of the CASS suggest that a CASS < 2 is normal, and that a CASS of 2–3 represents mild autonomic neuropathy (Low et al., 2003). We chose a more stringent threshold of a CASS ≥ 3.
Baroreflex sensitivity (BRS) was calculated as previously described (Palamarchuk et al., 2014). Briefly, the Valsalva maneuver data is visually inspected by a trained, blinded technician. BRS-V is a measure of the compensatory cardiac response to a decrease in BP evoked during the forced expiration against a closed glottis and is calculated by dividing the change in RR interval during phase 2E of the VM by the change in systolic blood pressure. It is expressed as milliseconds/mmHg. BRS-A, reflective of primarily beta-1 adrenergic activity is expressed in mmHg/S and it is calculated by dividing the change in systolic blood pressure during phase 3 by the time required for SBP to recover following the release of VM Normal values of BRS-V and BRS-A have been established, as well as suggested groupings of abnormal values into five (BRS-V) and three (BRS-A) ascending categories (Low, 1993).
## 2.5. Statistical analysis
Descriptive statistics were performed for demographic and clinical variables. We used the Mann-Whitney U test to compare continuous variables and the Chi-square analysis to compare categorical variables, respectively, between chronic and episodic headache groups as well as migraine and non-migraine headache, without correction for multiple comparisons. While CASS normative scoring is adjusted for age and sex, BRS is not. Therefore, multivariate logistic regression adjusting for age and sex was performed as appropriate for analyses involving BRS. Finally, Spearman’s rank correlation was performed to determine the association between the total CASS score and the number of non-painful features. All analyses were two-tailed and conducted at the alpha = 0.05 level using SPSS version 24.
## 3.1. Participants
Our sample ($$n = 34$$) had an average age of 26 years and $67.6\%$ were female (Table 1). The majority of participants had migraine ($70.6\%$) and satisfied criteria for a chronic headache disorder ($76.5\%$). Supplementary Table 1 lists the N of each headache disorder. The self-reported average monthly headache frequency ranged from 1 to 30 days. For participants with episodic headache, the median was 3 headache days per month, while for those with chronic headache, the median was 30 headache days per month. Most participants were relatively recently diagnosed, with a median disease duration of 5 years (IQR, 1.0, 12.0) Four patients were excluded due to insufficient documentation of headache frequency and characteristics. Two patients were excluded due to taking amitriptyline at a dose greater than 20 mg per day. There was no information regarding disease duration for four participants.
**TABLE 1**
| Unnamed: 0 | Overall N = 34 | Episodic N = 8 | Chronic N = 26 | p-value |
| --- | --- | --- | --- | --- |
| Sex (Female) | 23 (67.6) | 3 (37.5) | 20 (76.9) | 0.079 |
| Age at time of testing [median (1q, 3q)] | 26.0 [18.0, 37.5] | 40.5 [28, 53] | 26.0 [21, 39] | 0.039 |
| Duration of headache syndrome (years) [median (1q, 3q)] | 5.0 [1.0, 12.0] | 9.5 [5.0, 13.0] | 5.0 [2.1, 15.0] | 0.038 |
| Headache days per month [median (1q, 3q)] | 30.0 [13.0, 30.0] | 3.0 [1.4, 7.0] | 30.0 [25.0, 30.0] | <0.001 |
| Daily headache | 19 (55.8) | 0 (0.0) | 19 (73.1) | <0.001 |
| Report of a non-painful feature | 19 (55.8) | 2 (25) | 17 (65.4) | 0.044 |
| Fatigue | 17 (50.0) | 1 (12.5) | 16 (94.1) | 0.039 |
| Orthostatic intolerance | 14 (41.4) | 1 (35) | 13 (61.5) | 0.611 |
| Postural orthostatic tachycardia syndrome (POTS) | 11 (32.3) | 1 (12.5) | 10 (38.5) | 0.176 |
| Cognitive complaint | 11 (32.3) | 2 (25) | 9 (34.6) | 0.170 |
| Number of painless symptoms (median, 1q, 3q) | 1.0 [0.0,2.0] | 0.0 [0.0,0.75] | 1.0 [0.0,3.0] | 0.031 |
| Taking headache prevention medication | 20 (58.8) | 3 (37.5) | 18 (69.2) | 0.124 |
The majority of participants had at least one of the non-painful features ($55.8\%$, Table 1) with approximately half reporting fatigue ($50.0\%$), while nearly one-third reported cognitive complaints ($32.3\%$) and/or orthostatic intolerance ($32.3\%$). The majority ($58.8\%$) of participants reported taking a daily medication to prevent headache at the time of autonomic testing. See Supplementary Table 2 for list of medication classes.
## 3.2. Description of autonomic measures
Abnormal CASS scores were prevalent with 15 ($44.1\%$) participants meeting criteria for autonomic dysfunction (CASS ≥ 3). Abnormalities were seen in all three autonomic sub-scores (cardiovagal, adrenergic, and sudomotor), but were most common in the sudomotor score. Ninety-three percent of participants meeting criteria for autonomic dysfunction had sudomotor deficits, with all levels of severity represented (score of 1–3). Adrenergic deficits were seen in $66.7\%$ of participants with autonomic dysfunction, but nearly all were at the lowest severity level (1 out of 4) with only three participants scoring a 2 out of 4. Cardiovagal abnormalities were present in $46.7\%$ of participants with autonomic dysfunction, again nearly all at the lowest severity level (score 1 out of 3). Eighty percent of patients with autonomic dysfunction had deficits in two sub-scores and $20\%$ had deficits in all three. An abnormal BRS-V was present in $47.1\%$ of patients, but the majority ($82.2\%$) of deficits were mild or moderate (score 1–3 out of 5). Reduced BRS-A was present in $88.2\%$ of patients, and again, the majority of deficits were mild and moderate (score of 1–2) with only three participants scoring a 3 out of 3. Autonomic indices did not differ between migraine and non-migraine headache disorders ($p \leq 0.05$ for all comparisons. See Supplementary Table 3).
## 3.3. Clinical characteristics and autonomic reflexes in participants with episodic versus chronic headache
Duration of headache syndrome in years did not differ between patients with chronic and episodic headache. However, compared to patients with episodic headache, those with chronic headache were younger when headaches began. Non-painful features were more common in patients with chronic headache compared to episodic headache (Table 1).
Participants with chronic headache had a significantly higher total CASS compared to patients with episodic headache and a greater proportion of participants with chronic headaches met criteria for autonomic dysfunction ($58.3\%$) compared to those with episodic headache ($12.5\%$) (Figure 1 and Table 2). In patients with chronic headache, deficits in adrenergic and sudomotor function were most common. Reduced BRS-V was also more common in those with chronic headache ($57.7\%$) compared to episodic ($12.5\%$). This association remained significant in multivariate regression [aOR: 18.6 (1.2, 297.0), $$p \leq 0.039$$]. The proportion of participants with abnormal BRS-A scores did not differ between those with chronic and episodic headache disorders.
**FIGURE 1:** *(A–D) Frequency of autonomic dysfunction (CASS ≥ 3), and reduced cardiovagal baroreflex sensitivity (BRS-V) in patients with episodic and chronic headache and with and without POTS. * = main effect, $p \leq 0.05.$* TABLE_PLACEHOLDER:TABLE 2
## 3.4. Association of autonomic reflexes with non-painful features (orthostatic intolerance, fatigue, and cognitive impairment)
The total CASS was correlated with the total number of non-painful features in the expected direction ($r = 0.46$, $$p \leq 0.007$$). A reduced BRS-V was also significantly associated with the report of at least one non-painful feature ($$p \leq 0.002$$). Reduced BRS-V was demonstrated in $63\%$ ($\frac{12}{19}$) of participants reporting at least one non-painful feature and $82\%$ ($\frac{9}{11}$) of participants reporting at least two. There was no correlation between BRS-A and non-painful features.
With regard to orthostatic intolerance specifically, neither CASS scores nor BRS-A categorization differed between participants who did or did not meet criteria for POTS ($p \leq 0.05$). However, a greater proportion of participants with POTS had an abnormally reduced BRS-V compared to those without POTS (72.7 versus $34.8\%$ $$p \leq 0.038$$). In multivariate regression, BRS-V maintained its significant association with POTS [adjusted odds ratio, aOR: 5.78 (1.0, 32.5), $$p \leq 0.047$$].
## 4. Discussion
In this study examining autonomic function in patients with headache, we found that autonomic dysfunction was common. Consistent with our hypothesis, the prevalence and severity of autonomic reflex dysfunction was greater in patients with chronic headache but did not differ between migraine and non-migraine headache disorders. In addition, we demonstrated that the cardiovagal component of baroreflex sensitivity (BRS-V) was associated with both POTS and chronic headache. Finally, we showed increasing severity of autonomic reflex dysfunction correlated with the number of non-painful features (orthostatic intolerance, cognitive impairment, fatigue) over the entire sample. Together, these novel findings suggest that dysfunction of autonomic reflexes should be investigated further as a potential mechanism underlying pain chronification and the development of non-painful features in people with chronic pain conditions.
Our results demonstrate that autonomic dysfunction in patients with chronic headache and non-painful features is widespread and involves abnormal baroreflex, adrenergic, and sudomotor function. As we hypothesized, function of autonomic reflexes did not differ between patients with migraine and non-migraine headache syndromes. Deficits in adrenergic function and the sudomotor reflex, which relies on post-ganglionic sympathetic cholinergic efferents, were most prominent and suggest significant pathology in the sympathetic nervous system (SNS). The SNS is a physiologically diverse and complex system with an extensive network of efferent projections to all organ systems. Future studies should examine the function of post-ganglionic sympathetic efferents to other organ systems in patients with headache.
Our findings demonstrate that adrenergic baroreflex sensitivity (BRS-A) was frequently diminished in all our patients but did not distinguish those with episodic versus chronic headache. In contrast, a greater proportion of patients with chronic headache had reduced BRS-V compared to episodic headache. Several possible mechanisms could explain the relationship between BRS-V and chronic headache. Reduced baroreceptor vagal afferent input to the nucleus tractus solitarus (NTS), a brain region with anti-nociceptive activity (Oley et al., 1982; Blomqvist et al., 2000; Pickering et al., 2003) may lead to hyperalgesia and vulnerability to chronic pain. In addition, deficits in BRS-V are associated with reduced cerebral perfusion and hypoperfusion is a powerful promoter of neuroinflammation and gliosis, two pathologies strongly linked to numerous headache disorders (Laosiripisan et al., 2015). Finally, there is evidence in animals and humans that that nociception suppresses the baroreflex through both top-down (Nosaka, 1996) and bottom-up processes (Pickering et al., 2003) to optimize fight and flight activities. The amygdala and hypothalamus are higher order brain regions involved in the suppression of baroreflex during pain (Nosaka, 1996). There is also evidence in animals that noxious stimulation leads to an increase in substance P in the NTS and a suppression of BRS-V, but not BRS-A (Pickering et al., 2003). Therefore, a feed-forward loop may exist (Figure 2.) An initial acute episode of nociception that suppresses BRS, may reduce cerebral perfusion and/or result in decreased activity in anti-nociceptive brain regions, increasing vulnerability to future painful episodes, which further decreases BRS (Figure 2). Interestingly, while the duration of headache illness did not differ between patients with episodic and chronic illness, patients with chronic headache were significantly younger at the time of headache onset compared to patients with episodic headache. Therefore, it is possible that developmental windows exist when the neural circuitry of the baroreflex is particularly vulnerable to long-term suppression by painful experiences.
**FIGURE 2:** *Positive feed-forward loop illustrating potential connections between pain, cardiovagal baroreflex sensitivity (BRS-V), and non-painful features. NTS, nucleus tractus solitarus.*
We also found reduced BRS-V was present in a greater proportion of patients who reported a non-painful feature, and more broadly, that the severity of autonomic dysfunction, as measured by the total CASS score correlated to the number of painless symptoms in patients with headache. *Participants* generally reported more than one non-painful symptom. Thus, autonomic phenotyping of individual non-painful features was not feasible. However, BRS-V was an independent predictor of POTS, a disorder that is characterized not only by orthostatic intolerance, but fatigue and cognitive impairment (Cook and Sandroni, 2018). Cardiovascular exercise, an important treatment for POTS has been shown to increase baroreflex sensitivity.
We propose three potential mechanisms connecting abnormal reflexes to fatigue and cognitive impairment to be tested in future studies. The first is reduced or dysregulated cerebral perfusion. Abnormal baroreflex function and sympathetic denervation of the vasculature may both impair the regulation of cerebral perfusion (Asil et al., 2007; Liau et al., 2008; Nasr et al., 2011), which has been demonstrated in chronic fatigue syndrome, Staud et al. [ 2018]; Li X. et al. [ 2021] and in patients with cognitive impairment (Li R. et al., 2021; Coffin et al., 2022; Wang et al., 2022). Second, reduced vagal afferent activity from baroreceptors has been associated with changes in the sleep/wake cycles (Tsai et al., 2021; Karemaker, 2022) day-time sleepiness (Peckerman et al., 2003) and reduced performance in cognitive tasks (Reyes del Paso et al., 2004; Paso et al., 2012; Herman and Tsakiris, 2021). There is evidence that this occurs through bottom-up processing, as vagal afferent input to the NTS may alter higher cortical networks (Kerfoot et al., 2008; Garcia et al., 2017). Finally, inflammation may link abnormal autonomic reflexes to fatigue and cognitive impairment, as well as headache chronification. Deficits in both adrenergic and sudomotor CASS sub-scores suggest the presence of post-ganglionic sympathetic denervation. The loss of sympathetic efferents is associated with decreased anti-inflammatory action at local β-adrenergic receptors and increased activity at systemic, pro-inflammatory α-adrenergic receptors. Sympathetic denervation is specifically associated with higher levels of pro-inflammatory tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) (Schneemilch and Bank, 2001). Elevations in proinflammatory cytokines, including IL-6, have been demonstrated in patients with orthostatic intolerance (Okamoto et al., 2015), fatigue (Montoya et al., 2017) and cognitive impairment (Flores-Aguilar et al., 2021). Sensory sensitization, a process closely linked to headache chronification (Scheuren et al., 2020) and POTS, Bigal et al. [ 2008]; Cortez et al. [ 2021] may also result from increased levels of pro-inflammatory cytokines (Kawasaki et al., 2008).
Our study has several limitations. First, the presence of non-painful features was determined by patient self-report to a clinical provider, not a standardized questionnaire. Therefore, severity, frequency, and duration of fatigue, orthostatic intolerance, and cognitive impairment could not be assessed. Second, patients are typically referred for autonomic testing by a physician to evaluate a symptom associated with ANS dysfunction. Therefore, the prevalence of autonomic symptoms and POTS in our sample may not be generalizable and larger prospective studies are needed. Relatedly, our data suggest that patients with episodic headaches are less likely to present with the non-painful symptoms of headache associated with ANS dysfunction that may prompt a referral for autonomic testing. Therefore, our study sample did not contain a sufficient number of patients with episodic headache to permit correction for multiple comparisons and the significance of the univariate analyses and regression analyses should be interpreted with caution. Future prospective studies are planned to address this limitation. Finally, additional confirmatory markers of autonomic dysfunction such as intraepidermal nerve fiber density on skin biopsy or catecholamine plasma levels in response to standing, were not obtained. In an acknowledgment of this limitation, we chose a higher CASS threshold to identify autonomic dysfunction.
In conclusion, our findings demonstrate that reduced cardiovagal baroreflex sensitivity was associated with the presence of non-painful features including orthostatic intolerance, fatigue, and cognitive impairment, as well as pain chronification in patients with headache. In addition, we found deficits in autonomic reflexes that suggested the presence of widespread pathology of the sympathetic nervous system and highlight the importance of the peripheral nervous system in leading to both orthostatic disorders and chronic pain. Future studies should probe potential mechanisms linking autonomic reflexes to the development of non-painful features and pain chronification by measuring cerebral perfusion and markers of inflammation in patients with chronic pain.
## Data availability statement
The original contributions presented in this 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 Icahn School of Medicine at Mount Sinai Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
BM drafted the manuscript and analyzed the data. CR performed the chart review. AB computed autonomic measurements including the Composite Autonomic Severity Score (CASS). JR-P contributed to the design and edited 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/fnhum.2023.1068410/full#supplementary-material
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|
---
title: 'Health-related quality of life and associated factors among people living
with HIV/AIDS in Sichuan, China: A cross-sectional study'
authors:
- Hua Zhong
- Fuling Wei
- Yuqing Song
- Hong Chen
- Zhao Ni
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10040810
doi: 10.3389/fpubh.2023.1133657
license: CC BY 4.0
---
# Health-related quality of life and associated factors among people living with HIV/AIDS in Sichuan, China: A cross-sectional study
## Abstract
### Purpose
This study aimed to explore health-related quality of life (HRQoL) and its associated factors among people living with HIV/AIDS (PLWH) in Sichuan, China.
### Methods
A total of 401 PLWH were recruited from the city of Panzhihua between August 2018 and January 2019. Demographic characteristics and disease-related data were collected by self-administered questionnaires and medical system records. Health-related quality of life (HRQoL) was measured by the medical outcome study HIV health survey (MOS-HIV), which measured ten subdimensions and two summarized dimensions, the physical health summary score (PHS) and the mental health summary score (MHS). Logistic regression models were used to explore the variables independently associated with quality of life.
### Results
The PHS and MHS measured by MOS-HIV were 53.66 ± 6.80 and 51.31 ± 7.66, respectively. Younger age, higher educational level, no methadone use, higher CD4 lymphocyte counts, less symptom counts and heathy BMI significantly were associated with higher HRQOL in the univariate χ2-test analysis. Education level was found to have a significant influence on patients' quality of life, both in physical health ($$P \leq 0.022$$) and mental health ($$P \leq 0.002$$) dimensions. Younger age ($$P \leq 0.032$$), higher CD4 lymphocyte counts ($$P \leq 0.007$$), less symptom counts ($P \leq 0.001$) and health BMI level ($P \leq 0.001$) were positively related to the PHS of quality of life in the multivariable logistic regression model.
### Conclusion
The HRQoL of PLWH in Sinchuan Province was relatively low. Age, educational level, methadone use, CD4 lymphocyte counts, symptom counts and BMI were positively related to quality of life. This study indicates that health caregivers should pay more attention to comorbidity issues and mental health in PLWH, especially for those with lower education levels, unhealthy body mass index, more symptomatic presentation and older age.
## Introduction
The human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) pandemic is a serious global challenge [1]. According to the Joint United Nations Program on HIV/AIDS (UNAIDS), there were 37.7 (30.2–45.1) million people living with HIV/AIDS (PLWH), and 36.3 (27.2–47.8) million people died from AIDS-related illnesses worldwide by the end of 2021 [1]. In China, there are 1.147 million people living with HIV/AIDS [2].
The government of China has implemented the “Four Free and One Care” policy since 2003 in response to the HIV/AIDS epidemic [3]. It is well known that one of the “Four Free” was to provide free highly active antiretroviral therapy (HAART) for all PLWH who meet the criteria for antiviral treatment. They can obtain free antiviral medication and health counseling every 3 months at the outpatient department of the infectious disease hospital or a general hospital with an infectious disease department. Thanks to the development of clinical treatment techniques and HAART, AIDS has transformed from a fatal infectious disease to a manageable chronic illness [4]. The use of HAART in PLWH has substantially decreased the morbidity and lethality caused by HIV/AIDS symptoms [5], boosted their immune system [6], and improved their health-related quality of life (HRQoL) [7]. A new “beyond viral suppression” model suggested adding a “fourth 95” to the UNAIDS' 95–95–95 target to ensure that $95\%$ of PLWH with suppression have good HRQoL (8–10).
HRQoL is a multidimensional concept that includes dimensions such as physical health, psychological health, social functioning, and perception of general health [11]. It emphasizes the importance of an overall subjective feeling of wellbeing pertaining to aspects of morale, happiness and satisfaction. Changes in HRQoL, including functional status and personal perceptions, may last throughout the rest of the PLWH's life [12].
Many previous studies around the world have proven that social problems such as cultural beliefs [13], sociodemographic characteristics [14, 15], socioeconomic characteristics (15–17), presence of comorbidities [18], stage of the disease [19], psychological [20], and clinical factors [18] can affect HRQoL among PLWH. Furthermore, alcohol drinking [18], depression [21], and spiritual belief in their disease and medication can also affect the mental and physical aspects of HRQoL [20, 22]. Although the HRQoL of PLWH has significantly improved after treatment with HAART [9], drug-related side effects [15], poor adherence to HAART [18], and irregular medical follow-up have impaired HRQoL [15].
Many studies (20, 23–25) have explored the determinants of HRQoL in PLWH in China and found that older age, single, unemployment, low education, living in rural areas, and low CD4+ cell count are associated with low HRQoL among PLWH in China. Sichuan Province has one of the highest incidences of HIV/AIDS in China [26]. Since the first AIDS case was reported in Sichuan in 1991, the province has reported more than 110,000 surviving HIV infections/patients as of 2017 (ranking first in China), with a $0.13\%$ survival rate (ranking fourth in China). The number of new cases in the province continues to grow, ranking first in China for five consecutive years [27]. However, there was a scarcity of studies reporting the HRQoL of PLWH. In the literature, several instruments, including the Medical Outcomes Study 36-item short-form health survey [28], 12-item short-form health survey [29], Medical Outcomes Study HIV Health Survey (MOS-HIV) and World Health Organization quality of life (WHOQOL)-BREF [30], have been used to evaluate the HRQoL of PLWH, but MOS-HIV is the most widely used measurement to assess HRQoL for PLWH with well-established psychometric properties [31].
Knowing the determinants of HRQoL would enable PLWH, their families, healthcare providers and policy makers to develop relevant and holistic interventions to improve the general wellbeing and overall HRQoL of PLWH [32]. In particular, HRQoL in PLWH is critical for monitoring the impact of drug therapy on disease progression [33]. Thus, the aim of this study was to investigate HRQoL status and associated factors in PLWH by using the MOS-HIV.
## Study design and participants
This was a cross-sectional study conducted from August 2018 to January 2019 in Panzhihua city, Sichuan Province, China. HRQoL was used as the main indicator, and the sample content was designed to be 328 with reference to the relevant literature and the mean survey formula. The required sample size was finally determined to be 410 cases because $20\%$ of invalid questionnaires were considered. Regarding the sites for conducting the sampling, 2 districts or counties in Panzhihua were randomly selected to conduct the survey. Finally, Renhe District and Miyi County were chosen. The researcher inquired about the number of PLWH in different districts or counties of Panzhihua City in that year to calculate the relative proportion to determine the sample size assigned to the two sites. And the convenient sampling sites were selected as the Fourth People's Hospital of Renhe District and the People's Hospital of Miyi County in Panzhihua City, respectively, both of which were designated as PLWH getting free medications, testing and consulting. Participants were recruited through convenience sampling. The inclusion criteria of participants were [1] HIV seropositivity, [2] age older than 17 years, [3] willing to fill out the study questionnaire. People with mental illness or cognitive impairment were excluded.
## Ethical considerations
The study was approved by the West China Hospital Medical Ethics Committee (study ID#: 20170430). The objectives and procedures of the study were verbally explained to participants. Before the study, participants signed written informed consent.
## Data collection
All data were collected through paper questionnaires. First, the investigators received training about the administration of the questionnaires. Second, all participants were informed of the purpose, content and potential risks of the study before the investigation. Participants independently completed the anonymous questionnaires. If the participants had difficulty understanding or reading the questionnaire, the investigators explained it in detail and recorded the answer if inquired. After the questionnaire was completed by participants recruited by the local CDC, the investigators immediately completed the questionnaire in person to verify whether the respondent had answered all the questions. If the patient refused to answer and the incomplete part of the questionnaire was >$20\%$, the questionnaire was discarded. Each participant was paid ¥50 (equivalent to $7) as compensation for transportation.
## Sociodemographic characteristics
Sociodemographic characteristics included sex, age, ethnicity, educational level, marital status, religion, sexual orientation, religious beliefs, occupation, alcohol consumption, residence, smoking habits, drinking habits, BMI and per capita monthly household income. In this study, age was divided into three groups: young adults aged 18–45 years, middle-aged adults aged 45–60 years and elderly adults aged 65 years and above. Participants were classified into 4 categories according to the weight determination criteria for Chinese adults published by the National Health and Wellness Commission Participations, which were underweight (<18.5), healthy weight (18.5–24.9), overweight (25–27.9), and obese (≥28).
## Disease-related characteristics
We chose methadone use, infection route, disclosure status, CD4 cell count, symptom counts, duration since HIV diagnosis, duration of treatment, viral load and the 30 most common symptoms for now according to the literature review and clinical experience. Years since HIV diagnosis, duration of treatment, CD4 cell count and viral load from database and clinic records. Disclosure status, infection route, and symptoms were obtained through patient self-reporting. If there were missing data in the database or clinic records, the investigator asked the patient for relevant information.
## Health-related quality of life
HRQoL was assessed by the Medical Outcomes Study HIV Survey (MOS-HIV), which is a brief, comprehensive measure of health-related quality of life used extensively in HIV/AIDS [33]. It consists of 35 items and measures 10 dimensions, including general health, physical function, role function, social function, cognitive function, pain, mental health, energy/fatigue, health distress, and quality of life. Of these 10 domains, 8 were multi-item and 2 (Social Function and Quality of Life) consisted of a single item. Ten subdimension scores and two summary dimensions (physical health summary score, PHS and mental health summary score, MHS) were obtained by adding the raw item scores of the respective scales and then transforming them into a 0–100 scale, with higher scores indicating better functioning and wellbeing. The simplified Chinese version of the MOS-HIV questionnaire has been reported to have good validity and reliability [34, 35]. The Cronbach's α of the PHS and MHS scales was 0.87 and 0.89, respectively. The Cronbach's α for individual dimensions was >0.70 [34].
## Statistical analyses
Data were inputted by two researchers through EpiData 3.1, and all statistical analyses were performed using SPSS 24.0. A P-value of <0.05 was considered statistically significant. Descriptive statistics analysis was performed by mean ± standard deviation (SD) and median, interquartile range (IQR), frequency, and constituent ratio, as appropriate. We divided PHS and MHS scores into dichotomous variables by 50, in which scores above or below 50 can be considered better or worse HRQoL [36]. Univariate analysis χ2-test for categorical data and Spearman's correlation for continuous data were performed to examine the quality of life of PLWH with different demographic characteristics and disease-related characteristics. Intragroup comparisons for multiple categorical variables were also performed by Fisher's exact test. Binary logistic regression analysis was conducted to explore factors associated with HRQoL in PLWH. Because there is no universal consensus in choosing predictor variables in a multivariable logistic regression analysis, we included all the variables with P ≤ 0.2 in the univariate analysis and all clinically significant variables regardless of their P-values, based on the literature evidence. The Hosmer–Lemeshow goodness-of-fit statistic with a P-value > 0.05 was considered a well-fitting regression model, and the percentage of the variability predicted by the model was explained by the Nagelkerke R2.
## Sociodemographic and disease-related characteristics of participants
Finally, 410 participants were enrolled in the study. However, A total of 9 questionnaires with missing values >$20\%$ were excluded, and the final number of valid questionnaires collected was 401. Among the participants, $64.7\%$ ($$n = 258$$) were male and $35.2\%$ ($$n = 141$$) were female. Their mean (SD) age was 50.02 (15.06) years. Most participants were of Han ethnicity ($$n = 340$$, $85.4\%$), married ($$n = 227$$, $58.2\%$), and employed ($$n = 304$$, $76.9\%$). Nearly half of the participants were living in rural areas ($$n = 209$$, $53.3\%$). The sociodemographic characteristics of the participants and the HRQoL in different groups are presented in Table 1.
**Table 1**
| Variables | Total (N = 401) | PHS | PHS.1 | PHS.2 | PHS.3 | MHS | MHS.1 | MHS.2 | MHS.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Bad (<50) | Good (≥50) | c2/ Spearman's ρ | p | Bad (<50) | Good (≥50) | c2/ Spearman's ρ | p |
| Sex | | | | 1.87 | 0.171 | | | 0.09 | 0.760 |
| male | 258 | 59 | 199 | | | 93 | 165 | | |
| Female | 141 | 41 | 100 | | | 53 | 88 | | |
| Age groups | | | | 9.896 | 0.019 | | | 1.32 | 0.720 |
| 18–45 | 168 | 31 | 137 | | | 60 | 108 | | |
| 45–60 | 117 | 29 | 88 | | | 46 | 71 | | |
| ≥60 | 115 | 40 | 75 | | | 39 | 76 | | |
| Age, mean (SD)a | 50.02 (15.06) | 48.35 (14.75) | 55.03 (14.96) | −0.19 | <0.001 | 50.21 (14.09) | 49.69 (15.34) | 0.02 | 0.741 |
| Marital status | | | | 4.64 | 0.200 | | | 2.95 | 0.399 |
| Unmarried | 56 | 11 | 45 | | | 26 | 30 | | |
| Cohabitation or married | 227 | 52 | 175 | | | 80 | 147 | | |
| Separation or divorce | 67 | 23 | 44 | | | 23 | 44 | | |
| Bereaved spouse | 40 | 11 | 29 | | | 13 | 27 | | |
| Ethnicity | | | | 1.45 | 0.485 | | | | |
| Han | 340 | 85 | 255 | | | | | | |
| Yi | 55 | 16 | 39 | | | | | | |
| Others | 3 | 0 | 3 | | | | | | |
| Education level | | | | 12.22 | 0.016 | | | 18.81 | 0.001 |
| Illiterate | 42 | 17 | 25 | | | 14 | 28 | | |
| Primary school | 146 | 42 | 104 | | | 57 | 89 | | |
| Senior high | 130 | 30 | 100 | | | 38 | 92 | | |
| Junior high or technical secondary school | 49 | 8 | 41 | | | 15 | 34 | | |
| Junior college or Bachelor's degree | 30 | 3 | 27 | | | 21 | 9 | | |
| Sexual orientation | | | | 7.18 | 0.067 | | | 5.05 | 0.168 |
| Homosexualit | 22 | 7 | 15 | | | 8 | 14 | | |
| Heterosexuality y | 339 | 84 | 255 | | | 119 | 220 | | |
| Bisexual | 10 | 0 | 10 | | | 6 | 4 | | |
| Asexuality | 10 | 5 | 5 | | | 6 | 4 | | |
| Religious beliefs | | | | 0.26 | 0.610 | | | 0.01 | 0.915 |
| Yes | 46 | 13 | 33 | | | 17 | 29 | | |
| No | 343 | 85 | 258 | | | 124 | 219 | | |
| Occupation | | | | 5.86 | 0.053 | | | 1.5 | 0.471 |
| Employed | 304 | 67 | 237 | | | 107 | 197 | | |
| Unemployed | 48 | 15 | 33 | | | 19 | 24 | | |
| Retirement | 43 | 16 | 27 | | | 19 | 29 | | |
| Residence | | | | 2.1 | 0.147 | | | 1.22 | 0.270 |
| Cities and towns | 183 | 40 | 143 | | | 72 | 111 | | |
| Rural | 209 | 59 | 150 | | | 71 | 138 | | |
| Per capita monthly income/yuan | | | | 3.46 | 0.630 | | | 10.64 | 0.059 |
| 0–499 | 88 | 25 | 63 | | | 26 | 62 | | |
| 500–999 | 82 | 23 | 59 | | | 22 | 60 | | |
| 1,000–1,499 | 64 | 16 | 48 | | | 27 | 37 | | |
| 1,500–1,999 | 32 | 7 | 25 | | | 12 | 20 | | |
| 2,000–2,499 | 39 | 11 | 28 | | | 18 | 21 | | |
| ≥2,500 | 83 | 15 | 68 | | | 38 | 45 | | |
| Smoking habits | | | | 0.04 | 0.979 | | | 0.46 | 0.793 |
| Never Smoking | 249 | 62 | 187 | | | 94 | 155 | | |
| Smoking | 124 | 32 | 92 | | | 43 | 81 | | |
| Quitted smoking | 27 | 7 | 10 | | | 9 | 18 | | |
| Drinking habits | | | | 1.83 | 0.177 | | | 0.51 | 0.477 |
| Yes | 91 | 18 | 73 | | | 36 | 55 | | |
| NO | 310 | 83 | 227 | | | 110 | 200 | | |
| BMI | | | | 20.81 | <0.001 | | | 0.80 | 0.849 |
| Underweight (<18.5) | 49 | 19 | 30 | | | 16 | 33 | | |
| healthy weight (18.5–24.9) | 235 | 68 | 167 | | | 86 | 149 | | |
| Overweight (25–27.9) | 83 | 6 | 77 | | | 30 | 53 | | |
| Obese (≥28) | 23 | 7 | 16 | | | 10 | 13 | | |
## HIV-related variables
The mean duration after being diagnosed with HIV was 3.7 (SD = 3.3) years, ranging from 1 month to 30 years. All participants received ART with an average duration of 2.8 (SD = 2.4) years. Most of the participants' ($$n = 284$$, $72.6\%$) immune systems were weakened by HIV, while more than half of the patients had viral suppression ($$n = 249$$, $75.2\%$) and disclosed the HIV-infected condition ($$n = 287$$, $74.1\%$). The most common form of infection was sexual contact ($$n = 184$$, $47.1\%$). Most participants reported that they had more than one symptom, and only $14.5\%$ ($$n = 58$$) of people indicated that they were not bothered by HIV-related symptoms (Table 2).
**Table 2**
| HIV-related variables | Total (N = 401) | PHS | PHS.1 | PHS.2 | PHS.3 | MHS | MHS.1 | MHS.2 | MHS.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Good (≥50) | Bad (<50) | χ2/ Spearman's ρ | p -value | Good (≥50) | Bad (<50) | χ2/ Spearman's ρ | p -value |
| Methadone | | | | 10.83 | 0.003 | | | 0.06 | 1.000 |
| Yes | 10 | 7 | 3 | | | 4 | 6 | | |
| No | 388 | 94 | 295 | | | 141 | 248 | | |
| Infection route | | | | 3.85 | 0.279 | | | 4.01 | 0.261 |
| Sexual contact | 184 | 42 | 142 | | | 59 | 125 | | |
| Mother-to-child transmission | 1 | 10 | 15 | | | 0 | 1 | | |
| Blood transmission | 25 | 0 | 1 | | | 11 | 14 | | |
| Unknown | 180 | 47 | 133 | | | 73 | 107 | | |
| Disclosure | | | | 0.39 | 0.535 | | | 2.88 | 0.90 |
| Yes | 287 | 75 | 212 | | | 44 | 56 | | |
| No | 100 | 23 | 77 | | | 99 | 188 | | |
| CD4 cell count(cells/mm3) | | | | 15.60 | <0.001 | | | 0.46 | 0.794 |
| <200 | 6616.8 | 26 | 40 | | | 26 | 40 | | |
| 200–500 | 21855.7 | 56 | 162 | | | 77 | 141 | | |
| >500 | 10727.3 | 14 | 93 | | | 37 | 70 | | |
| Viral load (copies/ml) | | | | 9.99 | 0.013 | | | 8.42 | 0.029 |
| <50 | 249 | 188 | 61 | | | 160 | 89 | | |
| 50–9,999 | 65 | 55 | 10 | | | 45 | 20 | | |
| 10,000–99,999 | 13 | 6 | 7 | | | 4 | 9 | | |
| >100,000 | 4 | 2 | 2 | | | 4 | 0 | | |
| Symptoms countsa | | | | −0.28 | <0.001 | | | −0.09 | 0.056 |
| Mean ± SD | 4.64 ± 5.94 | | | | | | | | |
| Min–max | 0–30 | | | | | | | | |
| Duration since HIV diagnose (years)a | | | | 0.01 | | 0.815 | | 0.03 | 0.544 |
| Mean ± SD | 3.71 ± 3.26 | | | | | | | | |
| Min–max | 0–23 | | | | | | | | |
| Duration received ART (years)a | | | | 0.02 | 0.020 | | | −0.02 | 0.760 |
| Mean ± SD | 2.81 ± 2.43 | | | | | | | | |
| Min–max | 0–12 | | | | | | | | |
| PHS | | | | | | | | 2.99 | 0.084 |
| Bad | | | | | | 44 | 57 | | |
| Good | | | | | | 102 | 198 | | |
| MHS | | | | 2.99 | 0.084 | | | | |
| Bad | | 44 | 102 | | | | | | |
| Good | | 57 | 198 | | | | | | |
## Symptoms
The majority of participants suffered fewer than 10 symptoms (Table 2). The top 5 symptoms reported by patients were insomnia ($32.90\%$), fatigue ($29.00\%$), forgetfulness ($28.10\%$), joint pain ($24.60\%$) and dry mouth ($24.40\%$). Most participants' symptoms were light (Table 3). The least reported symptom was mouth ulcers. We found that anxiety and depression were not the most frequent symptoms among PLWH. In this study, the number of patients who reported having symptoms of anxiety and depression was 68 ($16.6\%$) and 46 ($11.3\%$), respectively.
**Table 3**
| Symptom | N | % |
| --- | --- | --- |
| Insomnia | 135 | 0.34 |
| Fatigue | 119 | 0.3 |
| Forgetfulness | 115 | 0.29 |
| Joint pain | 101 | 0.25 |
| Dry-mouth | 100 | 0.25 |
| Muscle pain | 91 | 0.23 |
| Headache | 88 | 0.22 |
| Rash | 83 | 0.21 |
| Weakness | 81 | 0.2 |
| Thirst | 77 | 0.19 |
| Shortness of breath after activity | 76 | 0.19 |
| Inattentiveness | 76 | 0.19 |
| Anxiety | 68 | 0.17 |
| Decrease in appetite | 65 | 0.16 |
| Daytime sweating | 59 | 0.15 |
| Night sweating | 52 | 0.13 |
| dilute stool | 48 | 0.12 |
| Depression | 46 | 0.11 |
| Mouth ulcers | 46 | 0.11 |
| Nauseating | 45 | 0.11 |
| Diarrhea | 44 | 0.11 |
| Fever | 40 | 0.1 |
| Abdominal pain | 39 | 0.1 |
| Bloating | 38 | 0.09 |
| Fear | 32 | 0.08 |
| Vomit | 32 | 0.08 |
| Inability to adapt | 31 | 0.08 |
| Respite | 28 | 0.07 |
| Chills | 26 | 0.06 |
| Shortness of breath | 25 | 0.06 |
## HRQoL of participants
The Cronbach's α coefficient for the MOS-HIV in this study was 0.659. According to the questionnaire, the HRQoL of the participants in terms of PHS, MHS and 10-dimensional scores are presented in Table 4. The mean physical health summary (PHS) and mental health summary (MHS) scores were 53.66 (6.81) and 51.31 (7.66), respectively, and there were 300 ($74.8\%$) participants with good PHS and 255 ($63.6\%$) participants with good MHS based on the standardized mean score of 50 [36]. Of the 10 dimensions, the highest mean was found in the role functioning dimension (87.39 ± 29.37), while the lowest mean was found in the general health perceptions subscale (61.51 ± 21.37). There was no significant correlation between PHS and MHS (Spearman's correlation = 0.20; $$P \leq 0.017$$) and no significant correlation between PHS and MHS rank material, which was divided by 50 to be considered a good or poor health situation (Pearson chi-square = 2.99, $$P \leq 0.084$$).
**Table 4**
| Unnamed: 0 | Median (IQR) | Mean (SD) |
| --- | --- | --- |
| Summary scores | Summary scores | Summary scores |
| Physical health summary score | 55.1 (49.9–58.7) | 53.6 (6.8) |
| Mental health summary score | 53.0 (46.9–57.0) | 51.3 (7.6) |
| Dimension scores | Dimension scores | Dimension scores |
| General health perception | 60.0 (45.0–75.0) | 61.5 (21.3) |
| Physical functioning | 91.7 (83.3–100.0) | 87.2 (16.9) |
| Role functioning | 100.0 (100.0–100.0) | 87.3 (29.3) |
| Social functioning | 100.0 (60.0–100.0) | 82.9 (23.8) |
| Cognitive functioning | 80.0 (70.0–92.5) | 79.7 (16.0) |
| Pain | 88.8 (77.8–100.0) | 84.1 (17.5) |
| Mental health | 76.0 (60.0–88.0) | 74.5 (16.9) |
| Energy/fatigue | 65.0 (60.0–75.0) | 64.8 (11.7) |
| Health distress | 95.0 (75.0–100.0) | 84.2 (20.4) |
| Quality of life | 75.0 (50.0–75.0) | 65.8 (16.1) |
## Factors associated with HRQoL
In the univariate analysis (Tables 1, 2), the PHS was significantly higher in participants who were younger ($$p \leq 0.036$$), had better education ($$p \leq 0.005$$), had fewer symptoms of HIV ($p \leq 0.001$), had a CD4 level >500cells/mm3 ($$p \leq 0.004$$), had a viral load <10,000 copies/ml, did not use methadone ($$p \leq 0.001$$) and had a BMI between 18.5 and 27.9 ($p \leq 0.001$). Significantly higher MHS scores were reported by people with higher education levels ($$p \leq 0.001$$) and fewer symptoms. However, there was no correlation between disclosure, sex, sexual orientation and HRQoL score.
The association of the predictor variables with the PHS and MHS categories were explored using multivariate analysis and multivariable logistic regression analyses and are presented in Table 5.
**Table 5**
| Outcomes | Variables | OR | 95%CI | 95%CI.1 | P-value |
| --- | --- | --- | --- | --- | --- |
| | | | Upper limits | Lower limits | |
| PHS | Symptoms counts | 0.9 | 0.86 | 0.94 | 0.0 |
| | BMI | 0.001 | | | |
| | Underweight vs. healthy weight (reference) | 1.09 | 0.37 | 3.25 | 0.871 |
| | Overweight vs. healthy weight (reference) | 0.55 | 0.16 | 1.88 | 0.34 |
| | Obese vs. healthy weight (reference) | 5.62 | 1.44 | 21.96 | 0.013 |
| | CD4 counts | 0.037 | | | |
| | 200–500 vs. < 200 (reference) | 0.01 | 0.33 | 0.14 | 0.769 |
| | >500 vs. < 200 (reference) | 0.08 | 0.53 | 0.26 | 1.088 |
| | Methadone | 0.07 | 0.01 | 0.45 | 0.005 |
| | Age | 0.97 | 0.95 | 0.99 | 0.012 |
| | Education level | 0.022 | | | |
| | Primary school vs. illiterate (reference) | 0.09 | 0.02 | 0.49 | 0.005 |
| | Senior high vs. illiterate (reference) | 0.18 | 0.04 | 0.89 | 0.036 |
| | Junior high or technical secondary school vs. literate (reference) | 0.33 | 0.07 | 1.56 | 0.161 |
| | Junior college or Bachelor's degree vs. illiterate (reference) | 0.3 | 0.05 | 1.76 | 0.184 |
| MHS | Symptoms counts | 0.96 | 0.92 | 1.00 | 0.043 |
| | Education level | 0.002 | | | |
| | Primary school vs. illiterate (reference) | 5.21 | 1.75 | 15.53 | 0.003 |
| | Senior high vs. illiterate (reference) | 3.64 | 1.48 | 8.98 | 0.005 |
| | Junior high or technical secondary school vs. illiterate (reference) | 6.64 | 2.62 | 16.85 | 0.0 |
| | Junior college or Bachelor's degree vs. illiterate (reference) | 5.53 | 1.90 | 16.11 | 0.002 |
Only educational level and symptom counts were both significant in PHS and MHS in the univariable analysis and multivariable logistic regression analyses. However, there was no significant correlation between PHS and MHS by Spearman's correlation or χ2-test. Youth population, higher education, health BMI level, CD4 level ≥500 cells/mm3, fewer symptoms, not using methadone and shorter time receiving ART were associated with good PHS in the univariable analysis, whereas only education level and symptom counts were associated with MHS. Some variables significantly associated with PHS and MHS in the univariate analyses were not significant in the multivariable models (Table 4). The Hosmer–Lemeshow goodness-offit test p-values were 0.628 and 1 for the PHS and MHS models constructed, respectively, suggestive of well-fitting models. It is assumed that $33.2\%$ of the variability in the PHS (Nagelkerke R2 = 0.33) and $7\%$ of the variability in the MHS (Nagelkerke R2 = 0.07) is explained by these models.
## Discussion
The results of the study showed that the total physical health score (PHS) and mental health score (MHS) of HIV/AIDS in Sichuan Province were 53.6 ± 6.8 and 51.3 ± 7.7, which were higher than those in previous studies surveyed in other areas of China (37–40). The reason may be that some time has passed since the previous studies were conducted. The impact of this illness on their quality of life is substantially lower than a few years ago because of the significant improvement in mortality and morbidity in PLWH with the implementation of ART [21]. With the implementation of the “Four Free and One Care” policy, PLWH do not need to pay for medications, testing and counseling, can even obtain relief money from the government. Antiviral treatment and the four-free care policy help PLWH in poor areas of China receive better treatment, which helps to control disease and improve their quality of life.
The dimensions that scored higher were role function, physical function and health distress, which is similar to other studies [21, 41, 42]. This is probably because HIV/AIDS can be controlled like any other chronic disease with the implementation of ART [41]. The rate of severe illness decreased, and patients did not suffer significant impairment in physical or social functioning. The participants reported lower scores in the quality of life, energy/fatigue and general health perception dimensions, consistent with the results of previous studies [21, 41, 43]. The lowest mean score was 61.52 for general health perception. The highest score in the energy/fatigue dimension is 95, and only in this dimension did no patient report a full score, 100. This result indicated that HIV-infected peoples' quality of life was still impaired and that each participant's energy was affected to varying degrees. Fatigue is the second most common symptom, with a $29\%$ reporting rate. The causes of fatigue are complex. The disease itself, comorbidities, drug effects and even psychological factors may cause fatigue [41, 44, 45]. We suggest that fatigue management and self-perception of health promotion are areas that need further attention for researchers.
The study revealed that education level was significantly associated with both the PHS and MHS of PLWH. The vast majority ($80.1\%$) of participants in our study had only received a junior high school education or less. A further $10.5\%$ were illiterate. Consistent with the findings of previous studies [42, 46, 47], educational level was a protective factor of HRQoL. Moreover, having no formal education is a barrier to accessing health services and increasing PLWH vulnerability [48]. This result was also confirmed in this study, even if PLWH with primary education had a much lower risk of low quality of life than illiterate PLWH. The impact of education level on PLWH is multifaceted. It plays an important role in the spread and prevalence of HIV [49] and is more likely to lead to risky sexual behavior [50]. Well-educated PLWH were more likely to show good knowledge, positive attitudes toward HIV/AIDS [48] and good social support [47] and cognitive level [42, 46], which leads to better self-management and health outcomes. Although education level is difficult to change, we recommend that more health information and education be provided through brochures, bulletin boards, and public online platforms. More tailored patient education should be conducted during the patient's hospitalization and at follow-up visits.
Symptoms of HIV have also been reported to be associated with HRQoL in many previous studies [22, 32, 51, 52], and more serious complications can lead to poor quality of life. In our study, we found that the number of symptoms was a risk factor for HRQoL, both in PHS and MHS. The most common symptoms reported by the participants in our study were sleep disturbances, fatigue, forgetfulness, joint pain and dry mouth. Sleep disturbances, fatigue, and forgetfulness have received extensive attention in previous studies [35, 53, 54]. There are many factors that influence the symptoms, including the impact of AIDS on the immune system [53, 54], the adverse effects of medication [35, 55, 56] and psychosocial factors [45, 55, 57]. Many studies have revealed that joint pain (58–62) and xerostomia are also common symptoms in PLWH [63, 64]. In a cross-sectional survey of 195 PLWH conducted in Brazil, $40\%$ of patients reported dry mouth symptoms [63]. In a cross-sectional survey of 312 PLWH conducted in Italy, $34\%$ reported joint pain [65]. They have all been shown to have a negative impact on the QOL of PLWH [63, 65, 66]. Possible causes are joint pain and salivary gland hypofunction due to ART [59, 64, 67]. It has also been suggested that joint pain may be related to inflammatory responses mediated by inflammatory factors, but the exact mechanisms need to be further elucidated [68]. This suggests that we need to include a full range of physical, psychological, and social care in future studies to reduce the impact of symptoms on the quality of life of PLWH and implement proven effective interventions in health services. Meanwhile, PLWH's joint pain and dry mouth need more attention by researchers and medical services team members.
Age was an independent risk factor that was significantly associated with the physical health of PLWH, which was similar to other studies [69, 70] but different from George's research [21]. It may be that older PLWH experience a greater burden of age-related comorbidities, poorer social determinants of health and even ageism [71]. Murzin's team [71] showed that ageism transcended multiple interactions and environments from dating to healthcare and community services to society because of social determinants, health provider issues and structural challenges. Therefore, we suggest that the government should pay more attention to older people, especially in the advent of an aging society.
This study showed a significant relationship between HRQoL PHS and CD4 lymphocyte counts. As CD4 lymphocyte counts increased, physical HRQoL improved. The findings were consistent with the results of other studies [31, 72, 73]. A few studies have reported that lower CD4 counts may affect physical health (74–76). CD4+ cell count is an indicator of clinical progression and can reflect the impact of therapeutic efforts. Because of the “Four Frees and One Care” Policy, all HIV/AIDS patients in China can receive free ART in China. However, a relatively low CD4+ cell count of participants was also found in our study. This suggests that strategies to enhance medication adherence may be needed.
BMI is considered to be an important factor associated with patients' quality of life. In our study, both malnutrition and obesity may have contributed to the impairment of physical health. This is consistent with previous studies [77, 78], and some studies showed that obesity or malnutrition affects adult PLWH muscle strength [79] and the risk of frailty [80, 81] and comorbidities in PLWH followed up for 12 years [78]. Even, BMI can be a predictor tool for death risk in Ethiopian adults living with HIV on ART [82]. However, it is noteworthy that the PHS scores of the overweight group were the highest, even exceeding the healthy BMI level. Obesity may be due to a lack of physical activity and poor lifestyle [21], while underweight may be associated with disease consumption [77]. We need more research to reveal whether the burden of being overweight will or will not affect patients' psychosocial functioning and how it could happen.
In addition, only $75.2\%$ of participants in this study achieved viral suppression, which is much lower than the $92.4\%$ in Beijing [78] but close to the figure for Liangshan, Sichuan [79]. This may be attributed to the relatively short duration of HIV diagnosis and antiretroviral treatment in most of our participates. In addition, the comparison of data from two districts in China also illustrates the large differences in treatment effectiveness between regions. In particular, there is the largest base of HIV-infected patients in Sichuan Province. To achieve the “95 95 95” targets, there is a greater need for more targeted programs in less economically developed regions to explore critical influencing factors and efficient management interventions. Rationalize the allocation of limited resources to the whole process of HIV/AIDS management.
Although some studies have shown that women with HIV/AIDS have a higher quality of life than men [80, 81], we did not find any differences in quality of life between male and female HIV/AIDS patients. The difference might be due to the study population, sampling method, and measurement tools used to assess HRQoL among studies. A cross-sectional study of sex differences in quality of life among PLWH found that total physical health scores and mental health scores among PLWH were not statistically significant by sex [82].
Disclosure of HIV infection in this study was $71.6\%$, higher than other studies [21, 83]. The majority of participants chose to disclose to their spouses ($58.9\%$) and children ($46.7\%$), and a small proportion disclosed the disease to parents ($19.6\%$) and friends ($7.4\%$). This may be one of the reasons why the MHS score in this study was higher than that in other studies.
One thing that should not be overlooked in our study is that PLWH explain only $7\%$ of the PHS, and only two factors are included in the final multivariate regression. The reason may be that the sample was a general adult PLWH population with higher psychological scores than those reported in other literature [84, 85], and fewer people reported anxiety ($17\%$) and depression ($11\%$) among the symptom questionnaires, which indicates a relatively high level of mental health in this study sample. Second, nearly half of the PLWH in this sample had received only primary education and below, and their cognitive level of mental health might not be sufficient [86]. Last, previous studies have shown that disclosure, sexual orientation and other factors have a significant impact on the mental health of PLWH [87], the level of disclosure in this sample population is higher than that in previous studies [21, 83], and the sexual orientation is mostly heterosexual. It would be easier to tap into special populations such as men who have sex with men (MSM), sexual service providers or adolescent groups to identify the relevant influencing factors.
There were limitations in our study that should be acknowledged. First, it is a population with a relatively recent history of antiretroviral treatment but with many symptoms that seem more related to treatments used in the past. However, the type of treatment and regimens used are not available. We were unable to analyze whether the patient-reported symptom presentation was related. Second, the study was conducted in a single city in Sichuan. Future studies need to be conducted in other Sichuan provinces and even nationwide to verify whether there are differences in PLWH quality of life across geographic areas. Third, we could not determine the exact causality between HRQoL and the related factors due to the cross-sectional design. Therefore, there is a need for longitudinal research and multicenter studies to confirm our findings and explore their causality.
## Conclusion
In conclusion, the HRQoL of PLWH in Panzhihua improved compared to previous studies. Alleviating symptoms of HIV and preventing comorbidities, especially insomnia and fatigue, are important for patient life treatment. We recommend that further research should be conducted to explore factors affecting quality of life so that we can develop more comprehensive interventions to prevent HIV/AIDS. Moreover, more care and support should be given to patients, especially for those with lower educational attainment, unhealthy body mass index, more symptomatic presentation and older age.
## 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 Sichuan University Medical Ethics Committee. 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
HZ: writing manuscripts and data processing. FW: data entry and writing manuscript. YS, ZN, and HC: draft touch-ups and guidance on writing. 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
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