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--- title: 'Days Alive and Out of Hospital at 15 Days after Hip Replacement May Be Associated with Long-Term Mortality: Observational Cohort Study' authors: - Ah Ran Oh - Ji-Hye Kwon - Jungchan Park - Gayoung Jin - So Myung Kong - Sangmin Maria Lee journal: Diagnostics year: 2023 pmcid: PMC10047336 doi: 10.3390/diagnostics13061155 license: CC BY 4.0 --- # Days Alive and Out of Hospital at 15 Days after Hip Replacement May Be Associated with Long-Term Mortality: Observational Cohort Study ## Abstract We aimed to evaluate the association between days alive and out of hospital (DAOH) and mortality at 15 days after a hip replacement. From March 2010 to June 2020, we identified 5369 consecutive adult patients undergoing hip replacements and estimated DAOH at 15, 30, 60, and 90 days after surgery. After excluding 13 patients who died within 15 days after surgery, receiver operating characteristic (ROC) curves were then generated to evaluate predictabilities for each follow-up period. We compared the mortality risk according to the estimated thresholds of DAOH at 15 days after hip replacement. ROC analysis revealed areas under the curve of 0.862, 0.877, 0.906, and 0.922 for DAOH at 15, 30, 60, and 90 days after surgery, respectively. The estimated threshold of DAOH during the 15 postoperative days was 6.5. Patients were divided according to this threshold, and propensity score matching was conducted. In a propensity score-matched population with 864 patients in each group, the risk of mortality increased in patients with a lower DAOH 15 ($2.8\%$ vs. $8.1\%$; hazard ratio [HR] = 3.96; $95\%$ confidence interval [CI]: 2.24–6.99; $p \leq 0.001$ for one-year mortality, $5.2\%$ vs. $13.0\%$; HR = 3.82; $95\%$ CI: 2.33–6.28; $p \leq 0.001$ for three-year mortality, and $5.9\%$ vs. $15.6\%$; HR = 3.07; $95\%$ CI: 2.04–4.61; $p \leq 0.001$ for five-year mortality). In patients undergoing a hip replacement, DAOH at 15 days after surgery was shown to be associated with increased mortality. DAOH at 15 days may be used as a valid outcome measure for hip replacement. ## 1. Introduction Hip replacement surgery is a common and effective procedure for treating various hip conditions, including hip fractures and osteoarthritis [1]. With an increase in life expectancy and aging population, the demand for hip replacement surgery has continued to rise. While hip replacement surgery is generally considered safe, the mortality rate following the procedure remains relatively high, with some studies reporting rates as high as $0.5\%$ within the first year after surgery [2,3]. Therefore, it is essential to conduct studies on the outcomes of hip replacement surgery and to identify effective outcome measures that can accurately reflect long-term survival. This is a crucial area of research because the long-term survival of patients undergoing hip replacement surgery is closely related to their overall well-being and quality of life. In addition to mortality rates, postoperative complications can have a significant impact on a patient’s quality of life and long-term survival. Thus, it is essential to conduct studies on the outcomes of hip replacement surgery and to identify effective outcome measures that can accurately reflect long-term survival. Days Alive and Out of Hospital (DAOH) is a relatively new outcome measure that has gained popularity due to its simplicity and ease of calculation using readily available variables [4]. It was initially introduced to evaluate the outcomes of patients with chronic diseases but has since been validated in acute diseases [5] and various surgical procedures [4,6,7,8]. The measure calculates the time a patient is alive and out of the hospital, which reflects the overall well-being of the patient as well as the efficiency of the healthcare system. The use of DAOH is particularly relevant in hip replacement surgery because it provides an objective measure of the recovery period after the procedure. The Standardized Endpoints in Perioperative Medicine (StEP) initiative has recommended DAOH after surgery as a reliable outcome measure, emphasizing its potential to reflect patient-centered outcomes [9]. The strength of DAOH is that it can be determined by subtracting the total days of an initial or subsequent in-hospital stay from the total length of the period, providing an objective measure of the time a patient spends alive and out of the hospital. However, determining the optimal follow-up duration for DAOH in predicting long-term outcomes after surgery is a significant challenge. While a longer follow-up period would result in a stronger correlation with outcomes, a shorter follow-up period would enable DAOH to be obtained more quickly for more patients. The optimal follow-up period for DAOH after hip replacement surgery remains unclear, and this study aims to address this gap by analyzing the correlation between DAOH at different time points (15, 30, 60, and 90 days) and postoperative mortality. By identifying the optimal follow-up duration for DAOH in predicting long-term outcomes after hip replacement surgery, we hope to improve patient outcomes, reduce mortality rates, and optimize healthcare resource allocation following this common surgical procedure. ## 2. Materials and Methods Our study was a retrospective observational cohort study which was conducted from 17 January 2023 to 28 February 2023. This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. As the data were collected in de-identified form and posed minimal risk to the study patients, the need for institutional review board (IRB) approval was waived at our institution on 17 January 2023 (Samsung Medical Center, 81 Irwon-ro, Gangnam-gu, Seoul, Korea, 2023-01-060). The decision to waive IRB approval was made by the chairperson, Prof. S.W. Park, based on the criteria outlined in the institutional policies and procedures. Furthermore, we did not obtain written informed consent from individual patients, because the data used in the study were collected retrospectively from electronic medical records. To ensure patient privacy and confidentiality, all personal identifying information was removed from the dataset before analysis. The use of de-identified data minimized the potential risks to study participants, and we took appropriate measures to ensure data security and confidentiality throughout the study. We also followed the STROBE guidelines for reporting observational studies, which require a comprehensive and transparent reporting of study methods and results to improve the quality and reliability of scientific research. By adhering to these guidelines, we aim to promote scientific rigor and transparency when reporting the findings of our study, as well as facilitate the replication and extension of our research by other investigators. ## 2.1. Study Population and Data Collection We used the electronic medical records from the Samsung Medical Center, a tertiary referral hospital in Seoul, Korea, between March 2010 and June 2020. Our search criteria were limited to adult patients who underwent hip replacement surgery during this period. To ensure the privacy and confidentiality of the patients, the data were collected in a de-identified form using the “Clinical Data Warehouse Darwin-C” electronic archive system. This system enables the retrieval of data from electronic hospital records, including over 2.2 million surgeries, 1 billion laboratory results, 100 million disease codes, and 200 million prescriptions. Blood test results were automatically processed, which ensured the accuracy and consistency of the data collected. Mortality data were regularly checked and updated using the National Population Registry of the Korea National Statistical Office to ensure completeness. Medical records were reviewed by investigators who were not informed of patient mortality to prevent bias. In addition, we excluded patients who had died within 15 days after surgery from our analysis as their mortality could not be accurately attributed to the hip replacement surgery and might have introduced bias to our results. Overall, our study used a comprehensive and reliable database to identify eligible patients and collect data on important clinical outcomes. By using a large-scale electronic medical records system, we were able to collect data efficiently and accurately, which improved the validity and generalizability of our findings. ## 2.2. Study Outcomes and Definitions The primary endpoint of this study was to assess the relationship between one-year mortality and DAOH at 15, 30, 60, and 90 days after hip replacement surgery. Additionally, the relationship between DAOH and mortality during three- and five-year follow-up periods was also examined to investigate any potential long-term effects. DAOH was calculated using the same method as previously described [4]. Specifically, it was determined by subtracting the total duration of an initial or subsequent in-hospital stay from the defined time periods of 15, 30, 60, and 90 days. If a patient died within the defined period, DAOH was recorded as 0, reflecting that the patient was not alive and out of the hospital during that time. Thus, the DAOH value ranged from 0 to the defined time period, with a lower number indicating a poorer outcome. To assess the baseline health status of the patients, the Charlson Comorbidity Index was calculated using the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10) [10]. The Charlson Comorbidity Index is a widely used tool to assess comorbid conditions and predict the risk of mortality. It takes into account the presence of 19 different comorbid conditions, each of which is assigned a weighted score. The sum of the scores yields the total Charlson Comorbidity Index score, with a higher score indicating a greater number and severity of comorbidities. The Charlson Comorbidity Index was used in this study to adjust for potential confounding factors that could affect the relationship between DAOH and mortality. ## 2.3. Statistical Analysis In this study, categorical variables were presented as count and percentage, and continuous variables were represented as mean and standard deviation or median and interquartile range (IQR) based on the appropriate measure of central tendency. Comparison of categorical variables was performed using a chi-square test, and continuous variables were compared using a t-test or Mann–Whitney test. The receiver operating characteristic (ROC) curve analysis was conducted to evaluate the correlation between DAOH and mortality, and Youden’s Index was utilized to determine the optimal cut-off point. A comparison of ROC curves was made using the DeLong test [11]. After dividing patients into low and high groups based on the calculated cut-off points, the mortality rates were compared using a Cox regression analysis. The results were reported as hazard ratios (HRs) with $95\%$ confidence intervals (CIs). To evaluate the effect of demographic factors on the outcomes of interest, a subgroup analysis was conducted based on age (older or younger than 60 years), sex, type of anesthesia (general or regional), indication for surgery (emergency or elective), and total hip replacement. The demographic and baseline characteristics of each subgroup are presented as a forest plot. To further reduce bias and achieve balance between groups, propensity score matching was conducted using a 0.25 caliper. The matching process created a 1:1 matched population by pairing patients with similar preoperative characteristics to minimize the effect of confounding variables on the outcome of interest (mortality). The balance between groups was deemed successful if the absolute standardized difference (ASD) was less than $10\%$. All statistical analyses were performed using R version 4.2.0, and a p-value less than 0.05 was considered statistically significant. ## 3. Results This study included 5369 adult patients who underwent hip replacement surgery between March 2010 and June 2020 at Samsung Medical Center. Thirteen patients who died within 15 days after surgery were excluded, leaving a total of 5356 patients. The median age of the patients was 60 years old (interquartile range: 48–72). Among them, $86.4\%$ ($\frac{4628}{5356}$) underwent a total hip replacement. Receiver operating characteristic (ROC) curves were generated to assess the correlation between days alive and out of hospital (DAOH) and mortality during each follow-up duration (15, 30, 60, and 90 days). The area under the curve (AUC) for DAOH at 15, 30, 60, and 90 days were 0.862, 0.877, 0.906, and 0.922, respectively. The AUCs were significantly different between DAOH at 15 days and those of other follow-up durations. The optimal cut-off threshold value for DAOH at 30 days, as determined by the maximum Youden’s Index, was 6.5 days (Figure 1). The readmission rate within 15 days after surgery was $0.6\%$ ($\frac{30}{5356}$). The quartile values for DAOH at 15 postoperative days were 7, 9, and 10, respectively, and patients were divided accordingly. Table 1 presents the baseline characteristics and mortality of patients. Patients with a higher DAOH at 15 days postoperative tended to have a higher DAOH over longer follow-up periods and lower risk of mortality. The optimal cut-off value of DAOH at 15 postoperative days was estimated to be 6.5 days, and patients were classified at 7 days: 4334 ($80.9\%$) in the high and 1022 ($19.1\%$) in the low groups. Baseline characteristics are summarized in Table 2. The low group showed an increased risk of mortality ($0.7\%$ vs. $10.7\%$; HR = 14.90; $95\%$ CI: 10.05–22.10; $p \leq 0.001$ for the one-year follow-up; $1.5\%$ vs. $16.4\%$; HR = 10.71; $95\%$ CI: 8.07–14.22; $p \leq 0.001$ for the three-year follow-up; and $2.0\%$ vs. $19.5\%$; HR = 9.42; $95\%$ CI: 7.32–12.12; $p \leq 0.001$ for the five-year follow-up; Table 3). After propensity score matching, 864 study population pairs were generated, and an ASD less than $10\%$ suggested well-balanced covariates between the groups. The association between DAOH at 15 postoperative days and mortalities persisted in the propensity score-matched population ($2.8\%$ vs. $8.1\%$; HR = 3.96; $95\%$ CI: 2.24–6.99; $p \leq 0.001$ for one-year mortality; $5.2\%$ vs. $13.0\%$; HR = 3.82; $95\%$ CI: 2.33–6.28; $p \leq 0.001$ for three-year mortality; and $5.9\%$ vs. $15.6\%$; HR = 3.07; $95\%$ CI: 2.04–4.61; $p \leq 0.001$ for five-year mortality; Table 3). The results of the subgroup analysis showed that the association between DAOH and mortality was significant regardless of the demographic factors considered, including age (older or younger than 60 years), sex, type of anesthesia, indication for surgery, and total hip replacement (Table 4). ## 4. Discussion The results of our study indicate a strong correlation between DAOH and long-term mortality after hip replacement surgery. This relationship was also apparent for DAOH calculated 15 days after surgery. This information can be used to improve patient care by identifying patients who are at higher risk of mortality, and in taking steps to reduce that risk. Overall, these findings are significant because they provide a new way to assess patient outcomes following hip replacement surgery. By using DAOH as a measure of success, clinicians can better understand the long-term impact of the surgery and identify areas for improvement. An objective and standardized method for evaluating outcomes is of paramount importance in the field of medicine, particularly in clinical trials and quality improvement programs [9]. Traditional measures, such as hospital length of stay, have limitations because they do not fully encompass early mortality and fail to provide a comprehensive evaluation of patient outcomes. Therefore, the need for a reliable and objective measure of outcome that extends beyond the postoperative recovery period has been recognized. DAOH is a newer concept that has emerged as a potential solution to this issue [4,9]. It offers several advantages over traditional outcome measures because it does not necessitate the evaluation of individual events and has been demonstrated to be a reliable predictor of long-term mortality in a diverse patient population. In the context of surgical procedures, DAOH has particular importance as an outcome measure because it does not necessitate the evaluation of individual events. DAOH was initially introduced in 2017 [4] as a measure of perioperative outcomes and has since been confirmed through studies in diverse patient populations, including Swedish surgical patients [12], Canadian patients undergoing elective surgery [8], Danish patients undergoing hip and knee arthroplasties [13], and English patients undergoing emergency laparotomies [7]. As a result, it has been recognized as a comprehensive outcome measure that effectively reflects perioperative risks and complications. In addition, it can be easily calculated and incorporated into daily practice. Furthermore, an advantage of DAOH as a measure of outcomes is that it encompasses multiple cardiovascular events into a single, continuous metric which can be conveniently used in clinical trials. In summary, DAOH offers a reliable, comprehensive, and objective measure of patient outcomes, making it a valuable tool in clinical trials and quality improvement programs. Previous studies [7,8,13] and the StEP initiative [9] have recommended using DAOH at 30 days, which has become the most widely used approach. Our study added that DAOH at 15 days can also be used for patients undergoing hip replacement surgery. In fact, an important consideration when applying DAOH is determining the duration of the follow-up period. It is expected that the correlation with outcomes would improve as the follow-up period of DAOH increases. However, using a shorter follow-up period for DAOH can make it more accessible to a larger number of patients. A previous study warned that a mortality rate over $10\%$ can significantly impact DAOH [4] because in this scenario a DAOH value of 0 may indicate death rather than a longer hospital stay. Our results indicate that DAOH during a longer follow-up period had a significantly higher AUC value. However, even DAOH calculated 15 days after surgery had a decent predictive value for long-term follow-up. The importance of shortening the follow-up period required to evaluate postoperative outcomes has become increasingly significant in recent decades because there has been a systematic implementation of evidence-based perioperative care protocols such as fast-track or enhanced recovery pathways [14,15]. The introduction of these pathways has led to improved postoperative outcomes, including reduced hospital stay, lower medical costs, and improved complication rates [16]. In orthopedic replacement surgery, the implementation of rapid recovery systems has been shown to decrease hospital stays from 4–10 days to 1–3 days, with about $15\%$ of patients being eligible for outpatient surgery [17,18,19,20]. The shortened hospital stay may be closely related to our findings that DAOH can be used as an outcome measure 15 days after hip replacement surgery. There are several limitations to consider when interpreting our study results. Firstly, being a retrospective single-center study, our results may be biased by unidentifiable factors, despite statistical adjustments. Secondly, the long study period may have introduced changes in surgical techniques and postoperative care that could impact the results. Thirdly, we estimated the optimal cut-off point for DAOH at each follow-up period, but this could be highly influenced by the institution’s clinical protocol and therefore may not be generalizable to other patient populations. The results should only be considered as indicating a relationship between DAOH and the outcomes of hip replacement surgery. Further, well-designed studies with multi-center data are required to validate our findings. Despite these limitations, this study is the first to show a correlation between postoperative outcomes of hip replacement surgery and DAOH 15 days postoperative. Our findings may be useful for related investigation and quality improvement of hip replacement surgery. ## 5. Conclusions In conclusion, our study demonstrated a correlation between postoperative outcomes and DAOH 15 days after hip replacement surgery. Further studies are needed for DAOH at 15 days to be set as a useful outcome measure in patients undergoing hip replacement surgery. ## References 1. Karlson E.W., Mandl L.A., Aweh G.N., Sangha O., Liang M.H., Grodstein F.. Total hip replacement due to osteoarthritis: The importance of age, obesity, and other modifiable risk factors. Am. J. Med. (2003) 114 93-98. DOI: 10.1016/S0002-9343(02)01447-X 2. Hu F., Jiang C., Shen J., Tang P., Wang Y.. Preoperative predictors for mortality following hip fracture surgery: A systematic review and meta-analysis. Injury (2012) 43 676-685. DOI: 10.1016/j.injury.2011.05.017 3. Ouanezar H., Jalaguier T., Franck F., Pibarot V., Bothorel H., Saffarini M., Piton J.P.. Mid-term outcomes of titanium modular neck femoral stems in revision total hip arthroplasty. Ann. Transl. Med. (2019) 7 92. DOI: 10.21037/atm.2019.01.34 4. Myles P.S., Shulman M.A., Heritier S., Wallace S., McIlroy D.R., McCluskey S., Sillar I., Forbes A.. Validation of days at home as an outcome measure after surgery: A prospective cohort study in Australia. BMJ Open (2017) 7 e015828. DOI: 10.1136/bmjopen-2017-015828 5. Ariti C.A., Cleland J.G., Pocock S.J., Pfeffer M.A., Swedberg K., Granger C.B., McMurray J.J., Michelson E.L., Ostergren J., Yusuf S.. Days alive and out of hospital and the patient journey in patients with heart failure: Insights from the candesartan in heart failure: Assessment of reduction in mortality and morbidity (CHARM) program. Am. Heart J. (2011) 162 900-906. DOI: 10.1016/j.ahj.2011.08.003 6. Fanaroff A.C., Cyr D., Neely M.L., Bakal J., White H.D., Fox K.A.A., Armstrong P.W., Lopes R.D., Ohman E.M., Roe M.T.. Days Alive and Out of Hospital: Exploring a Patient-Centered, Pragmatic Outcome in a Clinical Trial of Patients with Acute Coronary Syndromes. Circ. Cardiovasc. Qual. Outcomes (2018) 11 e004755. DOI: 10.1161/CIRCOUTCOMES.118.004755 7. Spurling L.J., Moonesinghe S.R., Oliver C.M.. Validation of the days alive and out of hospital outcome measure after emergency laparotomy: A retrospective cohort study. Br. J. Anaesth. (2022) 128 449-456. DOI: 10.1016/j.bja.2021.12.006 8. Jerath A., Austin P.C., Wijeysundera D.N.. Days Alive and Out of Hospital: Validation of a Patient-centered Outcome for Perioperative Medicine. Anesthesiology (2019) 131 84-93. DOI: 10.1097/ALN.0000000000002701 9. Moonesinghe S.R., Jackson A.I.R., Boney O., Stevenson N., Chan M.T.V., Cook T.M., Lane-Fall M., Kalkman C., Neuman M.D., Nilsson U.. 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--- title: Astragalus Polysaccharide Promotes Doxorubicin-Induced Apoptosis by Reducing O-GlcNAcylation in Hepatocellular Carcinoma authors: - Mingzhe Li - Fangfang Duan - Zhiqiang Pan - Xiaomei Liu - Wenli Lu - Chao Liang - Zhaoqin Fang - Peike Peng - Dongwei Jia journal: Cells year: 2023 pmcid: PMC10047337 doi: 10.3390/cells12060866 license: CC BY 4.0 --- # Astragalus Polysaccharide Promotes Doxorubicin-Induced Apoptosis by Reducing O-GlcNAcylation in Hepatocellular Carcinoma ## Abstract The toxicity and side effects of chemotherapeutic drugs remain a crucial obstacle to the clinical treatment of hepatocellular carcinoma (HCC). Identifying combination therapy from Chinese herbs to enhance the sensitivity of tumors to chemotherapeutic drugs is of particular interest. Astragalus polysaccharide (APS), one of the natural active components in Astragalus membranaceus, has been reported to exhibit anti-tumor properties in diverse cancer cell lines. The aim of this study was to determine the effect of APS on Doxorubicin (Dox)-induced apoptosis in HCC and the underlying mechanism. The results showed that APS dose-dependently promoted Dox-induced apoptosis and enhanced endoplasmic reticulum (ER) stress. Additionally, APS decreased the mRNA level and protein stability of O-GlcNAc transferase (OGT), and increased the O-GlcNAcase (OGA) expression. Furthermore, OGT lentiviral transfection or PugNAc (OGA inhibitor) treatment reversed the ER stress and apoptosis induced by the combination of Dox and APS. A xenograft tumor mouse model confirmed that the combination of APS and Dox showed an advantage in inhibiting tumor growth in vivo. These findings suggested that APS promoted Dox-induced apoptosis in HCC cells through reducing the O-GlcNAcylation, which led to the exacerbation of ER stress and activation of apoptotic pathways. ## 1. Introduction Primary hepatocellular carcinoma (HCC) is the sixth-most common and the third-most lethal type of malignant tumors, with a rising incidence worldwide [1]. Generally, clinical therapeutic strategies for HCC include surgery, liver transplantation, microwave ablation, transarterial chemoembolization and systemic chemotherapy [2]. Currently, chemotherapy is still the main option for treatment of HCC [3]. However, chemoresistance has developed in HCC patients, which presents a major obstacle to the long-term efficacy of chemotherapeutic treatments [3]. Doxorubicin (Dox) is an anthracyline chemotherapeutic agent that is widely used to treat solid tumors such as liver cancer [4]. Although the use of Dox has been somewhat limited by its side effects, recent efforts have mainly conquered chemoresistance and enhanced the sensitivity of tumors to chemotherapeutic drugs via chemosensitizers. Studies show that many traditional Chinese medicines (TCM) can help improve sensitivity to chemotherapeutic drugs, enhancing anti-tumor effects [5]. Therefore, the development of chemosensitizers from Chinese medicine would serve as references for the clinical treatment of HCC. Astragalus membranaceus (A. membranaceus) has a long history of medicinal use in TCM. It is now commonly used in the clinic for its beneficial effects including regulation of the immune function, anti-aging and antiviral effects, radiation protection and anti-tumor effects [6,7,8]. APS is one of the most important natural active components in A. membranaceus, and possesses a variety of antitumor pharmacological effects, such as enhancing immunity, inhibiting proliferation, inducing apoptosis and inhibiting the transfer of tumor cells [8]. Additionally, it is reported to exert antitumor activity in solid tumors including breast cancer [9,10], lung cancer [11], gastric cancer [12] and hepatocellular carcinoma [6,13]. In combination therapy of HCC, APS enhanced the anti-cancer effects of Dox in H22 xenograft tumor mice, which might be mediated by regulating cytokine production as well as the gene and protein expression of MDR1 [13]. The mechanism of the adjuvant antitumor effect of APS has not been fully elucidated. O-GlcNAc modification of proteins is a unique posttranslational modification. Various nuclear and cytoplasmic proteins could be modified by O-GlcNAcylation on the free hydroxyl of select serine and threonine residues [14]. The modification cycle is mediated by the enzyme O-GlcNAc transferase (OGT), which could transfer N-acetylglucosamine to protein substrates, and the enzyme O-GlcNAcase (OGA), which remove this modification from proteins [15,16,17]. O-GlcNAcylation affects a wide variety functions of proteins, including transcription, subcellular localization, protein–protein interaction and protein stability [18]. Extensive research has shown that hyper-O-GlcNAcylation occurs in most malignant tumors, such as liver cancer, and it positively relates to oncogenesis and tumor progression [19]. In addition, reducing the level of O-GlcNAcylation can prevent cancer progression [20,21]. Studies have shown that the O-GlcNAc modification is associated with endoplasmic reticulum stress (ER stress) in many types of cancer [22,23,24]. OGT clearly induced the expression of ER stress responsive proteins GRP78 and IRE1α, which were down-regulated by OGT knockdown in NAFLD HCC cell lines [25]. Other studies have found that reducing O-GlcNAcylation led to the activation of the ER stress response in various cancer cells [22,24]. ER stress occurs when proteins cannot be folded correctly and accumulate in large amounts in the endoplasmic reticulum. If the ER stress continues, the activation of stress signals and irreversible dysfunction of the ER leads to cell apoptosis [26]. Three main pathways are involved in ER stress-induced cellular apoptosis, including the CHOP, caspase-12 and IRE1-ASK1-JNK pathways [27,28,29]. It has been reported that the CHOP pathway is pivotal in endoplasmic reticulum stress-induced apoptosis in neoplastic disease [30,31]. CHOP is involved in mitochondria-dependent apoptosis, in which the protein channels of the active Bcl-2 family permit apoptotic active substances (such as cytochrome C) to be released to cytoplasm [32]. Such events result in the activation of the downstream caspase family proteins, and ultimately lead to cell apoptosis. Herein, we demonstrated that APS enhanced Dox-induced apoptosis through decreasing the intracellular O-GlcNAcylation and inducing the ER stress response. The combination treatment of Dox and APS efficiently inhibited the growth of xenograft tumors in vivo. Our data may reveal the potential of APS as a chemotherapy sensitizer in the treatment of HCC. Moreover, the role of APS in reducing OGT expression and increasing OGA expression allows APS to play an extensive role in cancer therapy. ## 2.1. Cell Culture and Reagents for Cell Treatment Hep3B and L02 cells were obtained from the Cell Bank of CAS (Shanghai, China). Cells were grown in DMEM, plus $10\%$ FBS and $1\%$ Penicillin-Streptomycin solution (Gibco). APS powder (Macklin) was dissolved in DMSO, and Doxorubicin (Dox) was obtained from Sangon and dissolved in ddH2O. ## 2.2. Cell Viability Detection Cells were seeded at 5 × 103 each well and the viability was detected with the reagent of CCK-8 (Beyotime). Briefly, cells were cultured for 6 h, then treated with the indicated concentration of APS and/or Dox for 24 h. CCK-8 reagent was added into the wells and incubated for 30 min, then the absorbance of each well was measured at 450 nm. Each experiment was repeated three times with four duplicated wells in each group. ## 2.3. Flow Cytometry Cell apoptosis was analyzed using flow cytometry with an apoptosis detection kit (BD Company, Franklin Lakes, NJ, USA). Briefly, cells were stained with FITC and PI for 30 min, then resuspended with 1× binding solution. The samples were detected using a CytoFLEX flow cytometer (Beckman Coulter, Brea, CA, USA). ## 2.4. Western Blot Proteins extracted from cells and tumor tissues were subjected to quantification and electrophoresis, then transferred to PVDF membranes. The membranes were subjected to incubating with $5\%$ skimmed milk, specific primary antibodies and corresponding second antibodies. The primary antibodies were as follows: Rabbit anti-Cleaved Caspase-3 (#9664), -Bim (#2933), -Bax (#41162), -CHOP (#5554), -phospho-PERK (#3179), -phospho-eIF2α (#3398), -β-actin (#4967) antibodies; mouse anti-Bcl-2 (#15071) and -CTD110.6 (#9875) antibodies were purchased from CST. Rabbit antibodies of anti-OGA (ab124807), -P-gp (ab170904) and -GFPT1 (ab125069) were obtained from abcam. Antibody of OGT (11576-2-AP) was purchased from Proteintech. Mouse-anti-RL2 (MAI-072) antibody was purchased from Thermo Scientific. ## 2.5. Lentivirus Transfection Hep3B cells were transfected with lentivirus carrying ogt or vector control by HitransG A (Genechem Company, Shanghai, China) according to the manufacturer’s instructions. Briefly, cells were cultured in complete medium containing lentiviral particle stock and HitransG A for 16 h, then cultured in complete medium for another 56 h. The stably infected cells were obtained by screening with puromycin and infection efficiency was identified through Western blot analysis. ## 2.6. qRT-PCR Detection Total RNA was extracted and reverse-transcribed to cDNA with a reverse transcriptional kit (Takara). qPCR was performed using the kit of TB Green (Takara) and a Quant Studio 3 machine. The gene expression was normalized to GAPDH level and expressed as relative values. Designed primers were synthesized by Thermo Scientific. All primer sequences are shown in Table S1. ## 2.7. Immunofluorescence Hep3B cells were seeded in a confocal dish and received the indicated treatment for 24 h. Cells were fixed with $4\%$ paraformaldehyde, permeabilized with $0.2\%$ Triton X-100 for 5 min and blocked with $1\%$ BSA for 1 h. Then, the samples were incubated with primary antibodies for 18 h at 4 °C, followed by incubating with secondary antibodies at 37 °C in the dark. Cellular nuclei were counterstained with Hoechst 33342 (Invitrogen, Waltham, MA, USA). Images were captured through laser confocal microscopy (Leica, Deer Park, IL, USA). The primary antibodies including rabbit anti-Bax (#41162) and mouse anti-CHOP (#2895) were purchased from Cell Signaling Technology, and rabbit anti-Bip (ab21685) was purchased from abcam. ## 2.8. Animal Study Animal experiments were carried out in accordance with the approved guidelines of the research medical ethics committee of Shanghai University of Traditional Chinese Medicine, ethical approval reference number: PZSHUTCM220711026. Male BALB/c nude mice (4–6 weeks) purchased from Shanghai Slake were housed in SPF microbiological status. To establish the Hep3B xenograft tumors model, approximately 2 × 106 Hep3B cells were harvested and resuspended in 100 μL saline and injected subcutaneously into the flanks of each mouse at day 0. Seven days after the inoculation, the mice were randomly divided into four groups (five mice per group): control group, APS group, Dox group and APS + Dox group. A total of 50 mg/kg APS and/or 2 mg/kg Dox were administered via intraperitoneal injection every three days until day 28. Tumor size was measured every four days, and tumor volume was calculated with the formula: V = ab$\frac{2}{2}$, length (a) and width (b). ## 2.9. Immunohistochemistry The tumor tissues were embedded with paraffin and subjected to antigen retrieval in boiling citrate buffer. The slides were subjected to incubation with block solution for 5 min, primary antibodies for 18 h at 4 °C and HRP polymer for 10 min. Subsequently, the sections were treated with DAB reagent and nuclear staining with hematoxylin. The antibodies of Cleaved Caspase-3 (#9661) and CHOP (#2895) were obtained from CST, OGA (ab124807) was purchased from abcam and OGT (11576-2-AP) was purchased from Proteintech. ## 2.10. Statistical Analysis The results are given as means ± SEM. One-way ANOVA analysis is used for statistical analysis with SPSS 22.0 software. A value of $p \leq 0.05$ was regarded as statistically significant. ## 3.1. APS Enhances Dox-Induced Cell Death in Hepatocellular Carcinoma Hep3B Cells In this study, we first tried to determine whether APS could suppress cell viability of hepatocellular carcinoma Hep3B cells. Cells were treated with a series of concentrations of APS (0–100 mg/L). The CCK-8 assay showed that 0–50 mg/L APS had little effect on cell viability, and only 100 mg/L APS could slightly impair the viability of Hep3B cells (Figure 1A). Furthermore, we determined the viability of Hep3B cells in response to a range of Dox and APS concentrations. As shown in Figure 1B, the cell viability was only reduced to approximately $80\%$ at 1 μM of Dox. However, the cell survival rate was suppressed to about $60\%$ when treating with combination of 1 μM Dox and 10 mg/L APS. On the other hand, treatment with APS (0–50 mg/L) alone had little effect on cell survival, but 0–50 mg/L APS combined with 1 μM Dox dramatically decreased the cell survival dose-dependently compared to APS alone (Figure 1C). In addition, cell proliferation was also examined using the CCK-8 assay. As shown in Figure 1D, the combination of Dox and APS remarkably suppressed cell proliferation compared with the Dox group. Additionally, no significant difference was observed between the APS group and control group. To determine the role of APS on the cell viability of normal cells, we treated L02 cells with a range of concentrations of APS. The result of CCK-8 showed that L02 cell viability was not affected by 0–100 mg/L APS treatment (Figure 1E). Inconsistent with Hep3B cells, 10 mg/L APS in combination with Dox (0–1 µM) did not show synergistic inhibitory effects on L02 cell viability (Figure 1F). The combination treatment of 0–50 mg/L APS with 1 μM Dox decreased all cell viability to about $80\%$ in L02 cells. However, no concentration dependence was observed with the combination of 2, 10 and 50 mg/L APS with 1 μM Dox in terms of L02 cell viability (Figure 1G). Next, cell apoptosis was detected using flow cytometry, and we found that percentage of apoptosis cells was increased by Dox treatment, and the combination of Dox and APS dose-dependently promoted the apoptosis compared with Dox treatment alone (Figure 1H). These data implied that the combination treatment of Dox and APS down-regulated the cell viability of Hep3B, and APS dose-dependently enhanced Dox-induced apoptosis. ## 3.2. APS Induces ER Stress Response and Enhances the Dox-Induced Apoptosis in Hep3B Cells Studies have demonstrated that Dox induces the activation of the ER stress pathway in tumor cells, which is one of the most common mechanisms that leads to the reduction in chemotherapy sensitivity in HCC [33,34]. We first examined the effect of APS on the expression of ER stress signaling proteins in the absence or presence of Dox in Hep3B cells. As shown in Figure 2A, 50 mg/L APS slightly up-regulated the expression of p-PERK, p-eIF2α and CHOP. As expected, Dox treatment could induce the activation of the PERK pathway, and this effect was enhanced by the administration of APS in a dose-dependent manner, indicating that APS promotes ER stress signaling activation in Dox-treated Hep3B cells. The excessive and irreparable ER stress participates in the transition from survival mode to a death response, causing the activation of intrinsic apoptosis [35]. CHOP is known to promote mitochondria-mediated apoptosis by down-regulating the pro-survival protein Bcl-2 [36]. As shown in Figure 2B, treatment with 1 μM Dox decreased the level of Bcl-2 but failed to increase that of Cleaved Caspase-3, Bax and Bim. However, compared with Dox treatment alone, the combination of Dox and APS reduced the Bcl-2 level and up-regulated the level of Cleaved Caspase-3, Bax and Bim dose-dependently. Overall, these data implied that APS synergistically enhanced the activation of the ER stress response, and enhanced the Dox-induced ER stress-related apoptosis. ## 3.3. APS Down-Regulates O-GlcNAcylation through Decreasing OGT Level and Increasing OGA Level in Hep3B Cells O-GlcNAcylation is reported to faciliate the survival of various types of cancer cells by regulating ER stress [37,38]. To understand the role of APS in O-GlcNAcylation in hepatocellular carcinoma cells, Hep3B cells were treated with 50 mg/L APS. As shown in Figure 3A, APS significantly elevated the protein expression of OGA and down-regulated the levels of OGT, RL2 and CTD110.6 in Hep3B cells. Compared with Dox alone, the combined use of Dox and APS decreased intracellular O-GlcNAc modification dose-dependently. Meanwhile, we found that APS treatment dramatically down-regulated the OGT mRNA level, which was also dose-dependently decreased in the combination group (Figure 3B). Additionally, the OGA mRNA levels were clearly up-regulated in both the APS and combination groups (Figure 3C). In addition, we determined whether APS could modulate the protein stability of OGT. Cycloheximide (CHX) chase analysis revealed that the OGT protein level was not influenced by CHX treatment of 8 h, which might be due to the relatively long half-life of OGT (∼12 h) [39]. Meanwhile, we observed that APS treatment clearly impaired the protein stability of OGT (Figure 3D). These data suggested that APS down-regulated the expression and protein stability of OGT, up-regulated the expression of OGA and eventually diminished the O-GlcNAcylation level of Hep3B cells. In addition, the dysregulation of HBP enzymes were related to development of cancer. HBP enzyme-targeting strategies may be an effective method for cancer treatment [40]. Therefore, we examined the effect of APS on the expression of HBP enzymes. As shown in Figure S1A, APS had no effect on the transcript expression levels of almost all HBP enzymes, including GFPT1 (the first and rate-limiting enzyme of HBP) in Hep3B cells. Similarlly, the protein levels of GFPT1 were not affected by APS or the combined use of Dox and APS (Figure S1B). ## 3.4. APS Exacerbates ER Stress Response by Reducing O-GlcNAcylation in Hep3B Cells It has been reported that the inhibition of O-GlcNAcylation results in the enhancement of the ER stress response in tumor cells [22,24]. We next examined whether reducing O-GlcNAcylation by APS led to the activation of the ER stress response in Hep3B cells. Cells carrying lentivirius of ogt or control vector were treated with APS or in combination with Dox. Western blots showed that APS or combination treatment increased the expression of p-PERK and CHOP compared with the control group, but this effect was restored by OGT overexpression (Figure 4A). Next, an immunofluorescence assay was performed to confirm that O-GlcNAcylation is involved in ER stress regulation. As shown in Figure 4B,D, compared to the control group, the mean fluorescence intensity (MFI) and nuclear translocation of CHOP were elevated by Dox treatment, which were further strengthened in the combination group. As expected, these effects were significantly reversed by the overexpression of OGT. A similar effect was also observed in Bip staining (Figure 4C,E). Taken together, these results implied that enhancement of ER stress induced by APS alone or combination with Dox was mediated by low intracellular levels of O-GlcNAcylation. Increased levels of O-GlcNAcylation could reverse the ER stress induced by APS. ## 3.5. APS Promotes Dox-Induced Apoptosis by Decreasing Intracellular O-GlcNAc Levels Studies have noted that decreasing the level of O-GlcNAc using inhibitors or genetic knockout of OGT would promote apoptosis in cancer cells [23,24]. We then examined whether APS could enhance apoptosis by decreasing the O-GlcNAc level. As the data mentioned above confirm, the combination of Dox and APS exhibited higher expression of Cleaved Caspase-3, Bim, Bax and CHOP in comparison with Dox treatment alone. However, this effect was attenuated by treatment with PugNAc, an OGA inhibitor, which elevated the intracellular O-GlcNAcylation (Figure 5A). The rate of cell apoptosis was detected using flow cytometry, and we found that the increased apoptosis rate in the combination group was reversed by PugNAc treatment (Figure 5B). Similar to these results, we also used immunofluorescence staining to confirm that the elevated MFI of Bax in the combination group was significantly decreased upon treatment with PugNAc (Figure 5C). These results suggested that the enhancement of apoptosis through the combination of Dox and APS was correlated with the reduction in O-GlcNAcylation caused by APS in Hep3B cells. ## 3.6. APS Strengthens the Tumor Growth Inhibitory Effect of Dox in Hep3B Xenograft Tumor To further confirm the combination effect of APS and Dox on liver cancer growth in vivo, a subcutaneous xenograft tumor model was established with Hep3B cells. Administration of Dox effectively reduced the tumor size and weight compared to control group, and this effect was strengthened by combining with APS. No significant inhibiting effect of tumor growth was observed in the APS group (Figure 6A–C). These data indicated that APS synergistically inhibited tumor growth with Dox in vivo. To determine the expression of Cleaved Caspase-3, CHOP, OGT and OGA in the xenograft tumor in different groups, immunohistochemistry (IHC) analysis was performed. As shown in Figure 6D, in the Dox group, the expression levels of Cleaved Caspase-3 and CHOP were higher than that of the control group. Compared with Dox administration, the expression levels of Cleaved Caspase-3 and CHOP were further enhanced. As compared with the control group, the expression of OGT was decreased and OGA was increased upon APS treatment alone or combination treatment in cancerous tissue. Similar to the in vitro results, these data indicated that APS could potentiate Dox sensitivity, and promote cell apoptosis and the ER stress response in Hep3B xenograft tumors when combined with Dox, and these effects might correlate with the down-regulation of OGT or up-regulation of OGA in tumor tissue. ## 4. Discussion Astragalus polysaccharide (APS) is the main substance extracted from A. membranaceus, and it shows advantages in terms of anti-tumor effectiveness and low toxicity [41]. Studies show that APS exerts an anti-tumor role through inhibiting proliferation, inducing tumor cell apoptosis and regulating immune cell function [6,8,42]. Previous studies declared that APS was used for an adjuvant treatment to conventional chemotherapy to reduce treatment-associated adverse effects in patients [43], or to increase the tumor response to chemotherapies [13,44,45]. For instance, APS exerts a synergistic anti-tumor effect with adriamycin by enhancing the expression of cytokines or down-regulating the MDR1 mRNA level in gastric cancer or H22-bearing mice [13,44]. It is also confirmed in this study that APS decreased the elevated expression of MDR1 and P-glycoprotein induced by Dox in Hep3B cells (Figure S2). Nevertheless, the potential synergistical antitumor effect of APS on HCC and protein stability has not been fully elucidated. In the present study, we found that APS could enhance the apoptosis induced by Dox in hepatocellular carcinoma Hep3B cells in vivo and in vitro. More detailed studies revealed that APS exacerbated ER stress by down-regulating O-GlcNAcylation under Dox treatment, and finally promoted apoptosis in Hep3B cells. In cancer cells, glucose metabolism could be reprogrammed to obtain energy through glycolysis even under aerobic conditions and the activate the HBP pathway, which produces UDP-GlcNAc as the substrate for O-GlcNAc modification [46,47]. Aberrant elevated O-GlcNAcylation is related to the proliferation, progression and metastasis of cancer cells in various cancers including those of the breast, colon, pancreas, liver and lung [47,48,49]. Therefore, further research is needed to find potential therapeutic agents targeting hyper-O-GlcNAcylation [38]. Investigational OGT inhibitor is an ideal potential therapeutic option for cancers [25,50]. For example, OSMI-1, one of the OGT inhibitors, combined with Dox synergistically increased the apoptosis of HepG2 cells [23]. However, the off-target and toxic side effects of some OGT inhibitors prevent their application in vivo [51]. In this study, we found APS alone or in combination with Dox reduced the level of O-GlcNAcylation. Further study revealed that APS down-regulated the expression of OGT by decreasing the mRNA level and reducing the protein stability of OGT (Figure 3). A polysaccharide fraction from A. membranaceus has been confirmed to be safe through genotoxicity assays and an oral toxicity test with the NOAEL (no observed adverse effect level) of 5000 mg/kg/day for rats, which is a dose 30~40 times as high as the effective oral dose in humans [52]. Thus, APS may be used as a potential safe agent for reducing O-GlcNAc in cancer therapy in vivo. On the other hand, the biosynthesis of UDP-GlcNAc can be reduced by targeting the rate-determining enzyme of HBP, thereby starving OGT of its substrate. However, no significant inhibitory effect of APS was observed on these enzymes. We found that the reduction in O-GlcNAcylation by APS is mainly through regulating OGT and OGA. In ER stress, the activation of ER signaling recruits chaperones to facilitate proteins’ folding capacity and degrade misfolded proteins [45]. When the accumulation of misfolded proteins cannot be prevented, the activation of CHOP or eIF2α signals ultimately leads to cell apoptosis [53]. Transcription factor CHOP is involved in the regulation of genes that are responsible for cell apoptosis [54]. A variety of anticancer agents could induce the ER stress response, which strengthened or attenuated the anticancer effect depending on tumor type or tumor environment [55]. In our study, Dox treatment induced ER stress with the activation of PERK/p-eIF2α and increased the expression of CHOP in Hep3B cells. It was observed that APS increased the ER stress response, which was dramatically enhanced by the combination treatment of Dox and APS (Figure 2). Our finding is different from a previous study, which suggested that APS effectively suppressed UPR through inhibiting the PERK-eIF2α pathway in colon cancer cells [56]. Hence, further exploration of the effect of APS on ER stress in various cancer cells is needed. Recently, many reports have suggested that O-GlcNAcylation leads to the ER stress response [57]. In turn, various stresses (including ER stress and chemotherapy) increase the intracellular O-GlcNAcylation and directly affect the survival of cancer cells [58]. Studies have shown that a low level of O-GlcNAcylation leads to the ER stress response in both HCC and breast cancer cells [23,24]. Similarly, this study revealed that APS decreased the O-GlcNAc level and exacerbated the Dox-induced ER stress, accompanied by the up-regulation of p-PERK, p-e-IF2α, CHOP and Bip in Hep3B cells, which was dramatically recovered by OGT overexpression (Figure 2A and Figure 4). In fact, key molecules in the ER stress pathway such as eIF2α were reported to be modified by O-GlcNAc, which inhibits the phosphorylation of eIF2α and protects cells against ER stress-induced apoptosis [58]. Therefore, further studies are needed concerning whether APS is involved in regulating O-GlcNAc modification of critical molecules in ER stress. The O-GlcNAc modification of proteins is part of a pro-survival signaling program. Conversely, reducing O-GlcNAcylation levels sensitizes cells and tissues to injury [59,60,61]. Interestingly, our study showed that 50 mg/L APS reduced the O-GlcNAc level, but had no effect on cell viability and apoptosis (Figure 1). This phenomenon may be attributed to the insufficient activation of ER stress induced by APS, which was confirmed by the slight increased expression of PERK/eIF2α/CHOP upon APS treatment as mentioned previously. These findings are inconsistent with previous studies, which have suggested that APS promotes apoptosis in a number of cancer cells [38]. In this study, 1 μM Dox exhibited a significant activation of the ER stress response, but failed to increase Cleaved Caspase-3, Bax and Bim, resulting in insufficient apoptotic cell death. The combination of Dox and APS further aggravated the ER stress, and the apoptotic protein and apoptotic cell rate were also increased significantly. These data suggest that the ER stress induced by lowering O-GlcNAcylation through APS is a further attack on cells in the Dox-induced stress state. In addition, the Dox doses in this animal experiment were referenced to humans. In humans, the Dox treatment dosage ranges from 8 to 400 mg/m2 [62]. The risk of irreversible cytotoxicity increases sharply once the total administered dose exceeds 550 mg/m2 [63], which is equivalent to 9.5 times higher than the total dose in this mice experiment. Therefore, the in vivo dosage of Dox in this study was moderate and relatively safe, and may provide more information for clinical decisions. As mentioned above, APS had synergistic anti-tumor effects with chemotherapy drugs in some cancers. Though aberrant O-GlcNAcylation is associated with growth, proliferation and metastasis in these cancer cells and some chemotherapies increase the O-GlcNAc level, there is no study on APS’ anti-tumor effect through regulating O-GlcNAc. According to this study, the synergistic anti-tumor effect of APS via O-GlcNAc regulation could be further studied in other cancers in the future. ## 5. Conclusions In summary, the present study suggested that APS could enhance the sensitivity of hepatocellular carcinoma Hep3B cells to Dox and promote the efficiency of Dox in inhibiting xenograft tumor growth. 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--- title: 'Clinical Evolution of Preschool Picky Eater Children Receiving Oral Nutritional Supplementation during Six Months: A Prospective Controlled Clinical Trial' authors: - Carlos Alberto Nogueira-de-Almeida - Luiz Antonio Del Ciampo - Edson Zangiacomi Martinez - Andrea Aparecida Contini - Maria Eduarda Nogueira-de-Almeida - Ivan Savioli Ferraz - Matias Epifanio - Fabio da Veiga Ued journal: Children year: 2023 pmcid: PMC10047348 doi: 10.3390/children10030495 license: CC BY 4.0 --- # Clinical Evolution of Preschool Picky Eater Children Receiving Oral Nutritional Supplementation during Six Months: A Prospective Controlled Clinical Trial ## Abstract Objective: To determine if oral nutritional supplementation of picky eater children has a beneficial effect in addition to nutritional guidance on anthropometric parameters, nutrient intake, appetite, physical activity, and health complications. Methods: This is a randomized, single-blind, controlled clinical trial that included Brazilian picky eater children aged 24 to 60 months. The individuals were randomized into a control group (CG) ($$n = 17$$) and an intervention group (IG) ($$n = 18$$), and were followed up in seven meetings for 180 days (baseline plus one meeting every 30 days). The CG received nutritional guidance for food selectivity, while the IG received the same guidance plus oral nutritional supplementation. Anthropometric and nutrient intake assessments were carried out, and appetite, physical activity and health complications were investigated. Results: In the IG, the z-score of weight and height increased significantly over time ($p \leq 0.05$), while the body fat percentage (BFP) and BMI z-score remained unchanged. The percentage of inadequate intake of vitamins D, C and folate reduced in the IG over time compared to the CG ($p \leq 0.05$). In the IG, the score assigned by parents to the appetite scale increased over time ($p \leq 0.05$). There was no difference between the groups in the scores on the physical activity and global health scales, and in the number of health complications. Conclusions: Picky eater children that were supplemented increased their weight not by gaining fat, but due to an increase in stature, as shown by BMI z-score and BFP, that remained unchanged. Furthermore, they showed a decrease in inadequate micronutrient intake during the intervention. An improvement in appetite was also observed over time, attesting to the benefit of supplementation. ## 1. Introduction Feeding difficulties (picky eating) are characterized as situations in which the child has less food acceptance than expected, with the possibility of causing physical, emotional and family disturbances, along with repercussions with regard to growth and development, depending on the duration, intensity, time of diagnosis, and the action of health professionals and family. It is estimated that about $62\%$ of healthy infants and preschool children have some symptom of a feeding problem [1], which can either be a transitory situation or present throughout the growth period and persist into adulthood. The most common form of picky eating is selectivity [2], in which case the child is usually called a “picky eater”. When picky eating leads to lower calorie, macro and micronutrient intakes, growth can naturally be expected to be impaired. In the United Kingdom, Wright et al. [ 3] found lower weight and height among picky eaters, with $11\%$ of them below the fifth percentile of weight gain, a percentage three times higher than that found among children without picky eating. A previous study by the same group had already shown that many of the children diagnosed as having “failure to thrive”, at 15 months of age had previously presented some feeding difficulty [4]. Regarding micronutrients, picky eaters have variable outcomes [5]. Galloway et al. [ 6] found a low consumption of vitamins E and C, while Carruth et al. [ 7] observed a high prevalence of deficiency in calcium, zinc and vitamins D and E intakes. The treatment of picky eating includes nutritional guidance measures, preferably with an interdisciplinary approach [8], and, in some cases, oral supplementation. Clinical experiences on the effect of food supplementation on the growth of children with low appetite and selectivity, or whose dietary pattern was monotonous and with the inadequate intake of micronutrients, demonstrated an improvement in the general state of health [9]. The use of supplements with adequate energy content and balance in the composition of vitamins and minerals has shown results both in maintaining nutritional status and in improving the situation of specific vitamin deficiencies, allowing the professional to guarantee the nutritional security of the patient, that is, growth and adequate development, while implementing nutritional reeducation techniques [10,11]. In general, studies carried out with supplementation use two daily doses of 200 mL of isocaloric profile products (1 calorie per mL), equivalent to an offer of 400 calories daily, with an adequate balance of macronutrients and complete in micronutrients, in addition to fiber [10,12]. In Brazil, this type of supplement has been termed a “complete supplement” as defined by the Brazilian Association of Nutrology [13]. However, the treatment of picky eating still presents some questions not completely answered by science. Despite being a multicausal problem, there is a bottleneck in the picture when considering the negative impacts on food consumption, and health consequences are verified in the short-, medium-, and long-term [14]. Although studies can be found that justify the prescription of a complete supplement for all children with picky eating, these exist in small numbers to date, not allowing the execution of systematic reviews and meta-analyses, for example. Therefore, it is reasonable to ask whether it would not be possible to obtain adequate results through nutritional guidelines alone, without supplementation. Thus, the present study seeks to contribute to these questions, comparing two groups of picky eating children, randomly constituted, treated with or without the use of the supplement. The main objective was to determine whether the supplementation of picky eating children has a beneficial effect, evaluating the evolution of weight, height, body mass index (BMI), body composition, macro and micronutrient intake, appetite, physical activity, and the health complications of children supplemented compared to non-supplemented. ## 2.1. Study Design This is a randomized, single-blind, controlled clinical trial conducted from January 2021 to August 2022 in a private pediatric clinic in the city of Ribeirão Preto, Brazil. The study was approved by the Research Ethics Committee of the Federal University of São Carlos (UFSCar), process number 3.510.241. The clinical trial was registered in the Brazilian Registry of Clinical Trials, with Universal Trial Number (UTN): U1111-1223-7015 and register number RBR-6pxpvx. Written informed consent was obtained from the parents or guardians of all participants. ## 2.2. Eligibility of Participants Medical professionals and nutritionists from the city of Ribeirão Preto, Brazil, were invited to refer patients who presented complaints, as experienced by the family, of feeding difficulties. All individuals referred to the private clinic where the study was conducted, from January 2021 to August 2022, were assessed according to the inclusion criteria, configuring a convenience sample. The inclusion criteria were age between 24 and 60 months and a BMI z-score between −2 and +2. Referred patients were considered eligible for the study if they were considered picky eaters [2]. A total of 46 children were referred, and 43 were considered eligible. Exclusion criteria were the presence of cow’s milk protein allergy, lactose intolerance, impossibility of oral feeding, neoplasia, renal failure, liver failure or heart disease undergoing to be treated, genetic syndrome, anorexia nervosa, autism, attention deficit hyperactivity disorder, chronic diarrhea or inflammatory bowel diseases, and growth retardation and development related to chronic diseases. Among the 43 eligible children, two were excluded due to these criteria. After inclusion in the study, the participants ($$n = 41$$) were randomly allocated into two groups: [1] a control group and [2] an intervention group. Six participants expressed a desire to interrupt their participation in the study. Figure 1 summarizes the participants’ selection flowchart ($$n = 35$$), which included 17 individuals in the control group and 18 individuals in the intervention group. ## 2.3. Randomization and Masking All participants were randomised by a computer-generated list into one of the groups by simple randomisation at a ratio of 1:1. Allocation concealment was granted by Research Electronic Data Capture (REDCap). A pediatrician and a statistician were blinded to the study and were responsible for the randomization process, allocation concealment and outcome evaluation. A non-blinded nutritionist was responsible for the intervention. Participants were unblinded to the treatment proposed in the study. ## 2.4. Interventions and Control Groups The control group received nutritional guidance regarding feeding difficulties in seven meetings over 180 days (baseline plus one meeting every 30 days). General guidelines for healthy eating in childhood were addressed, mixed with guidelines for picky eaters [15]. Nutritional guidance was provided on [1] the food groups of the Brazilian food pyramid; [2] the amount of food to be ingested at each meal; [3] the methods of introducing low-acceptance foods, combined with well-accepted foods, without using blackmail, fights or rewards; [4] the selection of food in the supermarket; [5] culinary activities to be carried out at home; [6] playful activities that stimulate visual, olfactory and tactile contact with food; [7] games involving the feeding of children’s characters (princesses, superheroes, etc.); [ 8] time in front of electronic equipment; [9] sleep time; and [10] feeding at school. The intervention group received the same nutritional guidance from the control group in seven meetings over 180 days (baseline plus one meeting every 30 days). In addition, children received oral supplementation, with a product registered with the National Health Surveillance Agency (ANVISA) and available in the Brazilian market, with an energy density of 1 calorie per milliliter (mL). The supplement provided was a commercial formula (Milnutri Complete ®, Danone Nutricia São Paulo, Brazil), donated by the study sponsor. The dose of 200 mL was prescribed twice a day, daily, to all participants in this group, totaling 400 mL per day, which is equivalent to 400 calories per day, for 180 days. The preparation of the supplement was done by the parents at home. ## 2.5. Supplement Composition The detailed nutritional composition of the supplement offered to the intervention group is described in Table 1. ## 2.6. Study Outcomes and Dynamics of Intervention The primary outcome measures were anthropometry and body composition analysis (bioimpedanciometry). As secondary outcomes, we recorded nutrient intake over time. As a tertiary outcome, we recorded the score on the appetite scale, physical activity scale, global health scale, and health complications over time. The moments in which these outcomes were evaluated are described in Figure 2. There were no protocol changes after the start of the study. ## 2.6.1. Anthropometry Participants were weighed on a Welmy® brand (Santa Barbara do Oeste, Brazil) digital scale while only wearing underwear. Height was measured in a Seca-type wall estadiometer, with the patient standing barefoot, with their body straight and the nape touching the wall. Measurements were made according to standardized methodology [16]. After weight and height measurement, the body mass index (BMI) was calculated. Weight, height and BMI values were also obtained in z-score, according to World Health Organization (WHO) curves, in all consultations. Due to the physical growth characteristic of the pediatric age group, the WHO recommends that all reporting on weight, height and BMI be done using z scores, which is why this was our option in the present study [16]. ## 2.6.2. Bioimpedanciometry The percentage of fat was measured using the bioimpedanciometry equipment InBody®, model 270 (Rio de Janeiro, Brazil), which includes equations appropriate for the age group involved. Before the examination, the patients fasted for 4 h and, during the execution of the test, they were only wearing underwear. The body fat percentage (BFP) was obtained at moments T0, T3 and T6. ## 2.6.3. Assessment of Nutrient Intake One 24 h recall (R24h) was applied at moments T0, T3 and T6, and was obtained by the methodology of ‘multiple passages’ in three stages [17]. Estimated average requirement (EAR) and adequate intake (AI) of Dietary Reference Intake (DRI) were used to determine whether nutrient intake by the population was adequate [18]. Energy, carbohydrates, protein, lipids, minerals (iron, calcium, zinc and magnesium) and vitamins (vitamin A, D, C, B12 and folate) were determined. The diet data were double-checked during the transfer to Nutrilife Software (Nutrilife Nutrition Software, Maringá, Brazil), which was used to analyze food intake data. ## 2.6.4. Appetite Scale Since there are no validated appetite scales for pediatric patients in this age group in Brazil, appetite was evaluated using a qualitative scale, created by the authors for this study (not validated), in which the mother attributed a score from 1 to 10 for the following question: “Regarding your child appetite, write down the scale below the score that best corresponds to your perception, 1 meaning totally without appetite and 10 meaning a lot of appetite”. ## 2.6.5. Physical Activity Scale The aim of this analysis was to evaluate whether there was a change in the pattern of physical activity in a generic way, unrelated to sports activity, considering mobility, willingness to play, wakefulness, etc. Thus, it was evaluated through a qualitative scale, created by the authors for this study (not validated), in which the mother attributed a score from 1 to 10 for the following question: “Regarding your child disposition to play, move, walk, run, jump, write down the note that best corresponds to your perception, 1 meaning almost not moving and 10 moving a lot”. ## 2.6.6. Global Health Scale This was evaluated through a qualitative scale, created by the authors for this study (not validated), in which the mother attributed a score from 1 to 10 for the following question: “Regarding your general perception of your child’s health, write down the score below that best corresponds to your perception, 1 meaning not healthy and 10 meaning very healthy”. ## 2.6.7. Record of Health Complications Health complications during the intervention were collected using a form prepared by the authors in which parents wrote down the answers to the following questions: “During the last month, has your child had any health problems?”. When the answer was yes, the parents noted what problem(s) was/were observed. “ Have you noticed any complications that you related to the supplement offered by the study?”. When the answer was yes, the parents described the observed problem(s). Parents were asked to record these answers at home as they detected the problems, in order to avoid forgetting. Later, these data were discussed with the parents during the consultations, so that any doubts were resolved, and filling errors were corrected. The number of health complications that the participants had during all the consultations of the study was recorded in absolute numbers. ## 2.6.8. Check on the Use of the Supplement (Intervention Group) Parents in the intervention group received a form to fill in the total volume of supplement effectively consumed daily by their child. In follow-up meetings, parents were asked to return unused or partially used cans. The information in this form was compared with the total supplement returned to confirm its veracity. Any disagreements were discussed with the parents, and adjustments were made where necessary. ## 2.7. Statistical Analysis Baseline data between the groups were compared by Student’s t test, Fisher’s exact test, and the Wilcoxon test. The means of anthropometric data (expressed in z-score), energy and nutrient intake (expressed in kcal, g, mg and μg/day) and supplement intake (expressed in mL) were compared throughout treatment by linear models of mixed effects including time × group interaction terms, fitted using the “lme4” package of the R program. When the interaction terms are significant, we have evidence that the effect of time on the outcome variable is different for different groups. The mean number of health complications and scores on the appetite, physical activity and global health scales were compared by Poisson models including random effects, which were also fitted using the “mle4” package of the R program. The validity of the regression models was verified by residual analysis. The significance level used was $5\%$. Statistical analysis was performed using the R program, version 4.1.1. ## 3.1. Baseline Characteristics of Subjects Thirty-five individuals participated in the study, 17 in the control group and 18 in the intervention group. There was no difference in mean age, gender, or anthropometric parameters between groups at the baseline (T0) (Table 2). Only the score assigned by parents to the appetite scale differed significantly ($p \leq 0.01$). ## 3.2. Energy and Nutrient Intake There was no difference in energy and nutrient intake between the groups at baseline (T0). After starting supplementation (T3 and T6), the intervention group significantly increased the intake of iron, zinc and vitamins D, C, B12 and folate compared to the control group. The percentage of inadequate nutrient intake was similar among the groups at baseline (T0) and reduced in the intervention group at T3 and T6 for vitamins D, C and folate (Table 3). When evaluating the intake of nutrients in the intervention group (without comparing them to the control group), there was a significant increase ($p \leq 0.05$) in energy, carbohydrate, iron and vitamins C, D, and B12 intake between moments T0 × T6 (data not shown in tables). In the control group, this increase in energy and nutrient intake was not observed over time, but there was a significant reduction ($p \leq 0.05$) in iron and vitamin C intake between T0 × T6 (data not shown in tables). ## 3.3. Changes in Growth Indicators over Time The trajectories of BFP and weight for age, height for age, and BMI for age z-scores are shown in Figure 3. Baseline data showed no differences between groups. During the six visits (T1 to T6), all indicators remained statistically similar between the control and intervention groups. The participants of the control group showed no difference in the evolution of their z-score of weight, height, BMI and BFP (T0 × T6). In the intervention group, the z-score of weight and height increased significantly over time (T0 × T6) ($p \leq 0.05$), while the BFP and BMI z-score remained unchanged. The mixed linear model analysis results did not show significant time × group interaction effects when the z-score of weight ($$p \leq 0.422$$), height ($$p \leq 0.178$$), BMI ($$p \leq 0.648$$), and BFP ($$p \leq 0.496$$) were considered as dependent variables. Thus, we have no evidence that the effect of time on these response variables depends on the groups. ## 3.4. Health Complications and Scores on Appetite, Physical Activity, and Global Health Scales The trajectories of the number of health complications and scores on the appetite, physical activity and overall health scales are shown in Figure 4. Baseline data showed no differences between groups, except in the appetite scale score. Over time, there was no difference between groups regarding the score on the scales evaluated (appetite and physical activity) and the number of health complications. In the control group, the number of health complications increased significantly between T0 and T6. In the intervention group, the score assigned by the parents for the appetite scale increased significantly between T0 and T6. There was no sample loss over time in relation to the baseline. The statistical model did not show significant time × group interaction effects when the appetite scale ($$p \leq 0.052$$), the physical activity scale ($$p \leq 0.356$$), the global health scale ($$p \leq 0.289$$), and the number of health complications ($$p \leq 0.950$$) were considered as dependent variables. Therefore, there is no evidence that the impact of time on these outcome variables varies depending on the group. ## 3.5. Supplement Intake The adherence to supplement intake was $100\%$ of the children belonging to the intervention group. The average volume (mL) ingested monthly (T1 to T6) is shown in Figure 5A. The mean percentage ingested was above $80\%$ of the prescribed dose over time (Figure 5B), except in the last month (T6), which was $76.9\%$ of the prescribed dose. ## 4. Discussion Picky eating is a frequent condition that requires nutritional guidance and, often, supplementation. The present study aimed to compare two groups of children with picky eating who received clinical treatment over six months, one of which received oral nutritional supplementation. We observed that the supplemented children presented significant weight and height gain over time (T0 × T6) without raising their respective IMCs and BFP. This effect was not observed in the control group. Similar results were observed by Yackobovitch-Gavan et al. [ 9], who evaluated the effectiveness and safety of one year of nutritional supplementation on linear growth and weight gain in children between three and nine years of age. In this study, children that were supplemented and ingested at least $50\%$ of the supplement offered (“good consumers”) showed significant height gain without concomitant BMI elevation. In another study [19], similar results were found among picky eater children between 24 and 48 months of age and who were at risk of malnutrition, supplemented with two types of supplements for 90 days; the two supplemented groups showed significant gains in the percentiles of weight, weight/height, and BMI in relation to the group not supplemented [19]. Huynh et al. [ 12] conducted a study to observe the impact of nutritional supplementation and dietary counseling among picky eater children between three and four years of age presenting nutritional risk. After 48 weeks, the authors found improvement in the weight/height, weight/age and height/age indices and, similarly to our study, did not observe excessive weight gain or obesity. In the present study, despite the improvement in anthropometric indexes, there was no increase in BMI with the use of nutritional supplementation, which shows a proportional increase in the anthropometric parameters of these children. In a study conducted by Khanna et al. [ 19], an increase in BMI was observed after supplementation, but in this investigation the children initially had a lower weight percentile than that of height, which could help explain this finding. Thus, it is possible to observe that supplementation of eutrophic picky eater children, as we have shown, does not cause an undesirable increase in BMI. A fundamental aspect that the data of the present study showed refers to the fact that supplemented children presented an increase in their height z scores, which did not occur in the control group. This increase reflects the fact that picky eater children may have their growth slowed as a result of their feeding difficulties, as demonstrated by Jung et al. [ 20], Chau [21], Taylor et al. [ 22], and Viljakainen et al. [ 23]. In a 2018 publication, Ghosh et al. [ 24] reached results very similar to the present study, showing that supplementation was able to promote the catch-up in 90 days of intervention, with an evident difference between the supplemented and the control groups. Khanna et al. [ 19], studying picky eater children at nutritional risk, were able to promote catch-up in the supplemented group. Huynh et al. [ 12] also observed an increase in height z-scores, but there was no control group for comparison. To verify whether the weight gain observed among supplemented children is not actually the result of excessive fat deposition, it is essential that the evaluation contemplates the evolution of body composition, parallel to that of anthropometric measurements. Galloway et al. and Taylor et al., using dexacytometry, showed that picky eater children present lower BFP when compared to controls [22,25], but these researchers did not evaluate body composition changes after interventions. In our study, in the intervention group, there was no increase in BFP even with the occurrence of weight and height gain in this group. It is important to highlight that the assessment of BFP of supplemented patients reinforces the fact that supplementation allowed weight gain due to linear growth, ruling out that this growth was the result of weight gain due to the excessive accumulation of body fat. Regarding food intake, studies show that picky eater children may have deficient micronutrient intake [26,27,28]. In our study, we found a high prevalence of inadequate nutrient intake in both groups, such as vitamins D, C, iron, and folate, in the baseline data. Carruth et al. [ 7] interviewed 118 mothers of picky eater children between 24 and 36 months of age, finding a high prevalence of calcium, zinc and vitamin D and E deficiency, along with extremely low dietary variety. Kutbi showed that picky eaters consume less vegetables and fruits, less protein, and higher amounts of trans fats [29]. The main characteristic of our study was the evaluation of how this nutrient intake behaved after nutritional guidance among the control and intervention groups, and to compare it after supplementation at 3 and 6 months (T3 and T6). In the follow-up of these individuals, in the intervention group, the intake of iron and vitamins C, D and B12 was higher over time ($p \leq 0.05$) when compared to baseline, showing that supplementation is able to minimize some of the nutrient deficiencies frequently found in picky eater children. Some nutrients have a fundamental prominence in children’s growth, such as zinc and calcium, and their prolonged deficiencies can lead to anthropometric changes, as shown in the study published by Xue [28]. In our study, when we compared the intervention group at T0 and T6 after supplementation, a reduction of more than $50\%$ in the inadequacy of calcium, iron, zinc and vitamins A, C and B12 intake was observed. The deficiency of various micronutrients may be related to anorexia, often present in this group, and, fundamentally, to the low dietary variety classically observed in selectivity. Zinc is an essential nutrient in several and numerous physiological functions, including immune and antioxidant functions, growth, and reproduction. There is evidence to suggest that zinc deficiency, besides impairing growth, may be closely involved with anorexia, if not as an initial cause, then as an accelerating or exacerbating factor [30]. The energy intake between the two groups was similar in the three times evaluated; however, in the intervention group there was a significant increase between T0 and T6, probably linked to the consumption of the supplement, giving a greater contribution of energy, vitamins and minerals. The sum of higher nutrient intake may explain the improvement observed in the z-score parameters of weight and height in this group. Regarding the change in appetite, guidelines and/or nutritional interventions used in isolation to improve food refusal have shown conflicting results. In the present study, children in the control group received dietary guidance, but showed no improvement in the appetite scale score. On the other hand, children in the intervention group showed a significant increase in the score of the appetite scale, showing an improvement in food intake according to the parents’ perception. In the study by Khanna et al., [ 19] the researchers observed improvement in food acceptance in 63 individuals between zero and 21 years of age after a nutritional intervention program. Sharp et al. [ 31] also observed improved food acceptance in children between 13 and 72 months of age with chronic refusal of food and dependence on enteral feeding or oral supplementation in relation to the control group after a five-day nutritional intervention. In another study, Huynh et al. [ 12], using the “Visual Analogue Scales” scale, observed improved appetite among children between three and four years of age after dietary supplementation and dietary guidance. Naila et al. [ 32] observed improved appetite in children aged 12 to 18 months with severe malnutrition who received nutritional intervention for three months and psychosocial stimulation for six months. Using the “Early Childhood Appetite and Satiety Tool” scale to measure appetite in the children studied, the authors observed a significant improvement in the scale scores after the intervention; it is interesting to point out that the children in the control group (without malnutrition) who received dietary counseling but without nutritional supplementation and psychosocial support, also significantly improved their appetite scores after the observation period. In a study conducted in children between 17 and 32 months of age with severe malnutrition [33], there was an improvement in appetite after nutritional intervention performed for six weeks with a mixture of vitamins and minerals (“multivitamin + multimineral”); however, the same phenomenon was found in the placebo group. The correction of specific micronutrient deficiencies may contribute to increased appetite in children receiving nutritional interventions, while improved nutritional status and the consequent decrease in the frequency of infectious episodes may help explain increased appetite, even in children who have not received supplementation. However, it should be emphasized that different ways of evaluating appetite improvement after nutritional interventions can also sometimes contribute to divergent results. Several studies have shown the beneficial effects of nutritional interventions on physical activity and the quality of life of children and adolescents. Huynh et al. showed that, after nutritional supplementation and dietary counseling, children aged three to four years at nutritional risk showed increased physical activity, measured by the “Visual Analogue Scales”; the authors even considered this as a possible factor that may explain the increased appetite in picky-eater children receiving supplementation [12]. Verjans-Janssen et al. [ 34] studied the effect of nutritional interventions (without the use of supplements) and stimulation in children aged four to 12 years in the school environment and observed an increase in physical activity. Yu et al. [ 35] observed, among other benefits, decreased social anxiety (defined by a scale—Social Anxiety Scale for Children—SASC) after a program of nutritional intervention and stimulation of physical activity of eight months in Chinese children between eight and 11 years of age. In the present study, no differences were found between the level of physical activity and the general perception of health between the groups or within the groups throughout the study. An intervention that was more focused on nutrition (and not physical activity) may help explain these data. Moreover, the fact that we studied healthy children (even picky eaters) may have hindered parents’ perception of changes in the general health status of their children. In addition, the instrument used in our work regarding the general perception of children’s health by their parents may have been something “nonspecific”, as opposed to studies aiming to detect “punctual” changes, such as anxiety. Some studies on nutritional supplementation have shown varied results in reducing the incidence of disease. Fisberg et al. [ 10], in 2002, found a reduction in the number of days of disease among picky eater children that were supplemented. Alarcon et al. [ 36] observed a reduction in episodes of upper airway infection in the group of picky eater children between three and five years of age after nutritional supplementation and dietary counseling when compared to the group that received only dietary guidance, but the same effects were not found in relation to gastrointestinal symptoms. In the study by Huynh et al. [ 12], a reduction in the number of days in which children had acute diseases was observed throughout the study, especially regarding diarrheal episodes and respiratory infections. In the study of Dossa et al. [ 33], involving children between 17 and 32 months of age with severe malnutrition, there was a significant decrease in diarrhea episodes in the supplemented group after nutritional intervention; however, the same effect was found in children in the placebo group. Ghosh et al. [ 24] observed a reduction in respiratory infection episodes in Indian picky eater children between two and six years of age after nutritional intervention and dietary counseling when compared to those who received only food guidance. However, it is also known that the incidence of infectious episodes in these studies is often based on information collected retrospectively, which can lead to memory impairment, often compromising the analysis of the results obtained and generating controversial findings. In our study we did not observe differences in the number of health complications in the intervention group over time; however, in the control group there was a significant increase in such episodes. Due to the importance of micronutrients in the proper functioning of the immune system, combined with the fact that picky eater children are at risk of deficiency of these nutrients, this may help explain the decrease in infectious episodes after nutritional supplementation in the previously mentioned studies that enrolled picky eater children with compromised nutrition status. In our study, we studied eutrophic children, and the presence of illness is unlikely, but it is intrigant that non- supplemented children exhibit more disease episodes than supplemented. It is known that, for epidemiological reasons, many childhood diseases have annual periods of higher incidence, and it is possible to speculate that the present study has gone through some of these periods, and supplementation may have protected the intervention group, unlike the control group that presented several episodes of disease during the study period. In the present study, it was observed that $100\%$ of the children ingested the nutritional supplement, with consumption of more than $80\%$ of the recommended amount, except in the last month (T6), when consumption was $76.9\%$. Khanna et al. [ 19] obtained high adherence ($99\%$) to the consumption of two types of supplements offered for 90 days to two different groups of picky eater children between 24 and 48 months of age at risk of malnutrition; in this study, a good compliant to supplementation was considered as the intake of at least $75\%$ of the recommended volume. Ghosh et al. [ 24], studying the impact of nutritional intervention and dietary counseling performed in children between two and six years of age, observed high adherence to supplementation among the children studied ($98.4\%$); furthermore, in this study a good adherence to supplementation was considered as the intake of at least $75\%$ of the recommended volume. In our study, in addition to the notes made by parents regarding the amount of supplement consumed by children, there was a request for the return of the supplement not consumed or partially consumed, which allowed for the findings to be more reliable. Huynh et al. [ 12] obtained $100\%$ adherence to the use of the prescribed supplement, and $85\%$ of the children used both daily doses. The authors also mention that the use of oral supplements (instead of use via nasogastric tube) may have facilitated the ingestion by the children studied. Wright et al. [ 3] showed that 2.5-year-old picky eater children have a marked preference for liquids (especially dairy and similar), which are easier to accept, and this fact may help to explain the high adherence of children to oral supplementation among several studies. It is possible that, in our study, the sweet taste of the supplement helped with the acceptance. The present study has some limitations. Because of the COVID-19 pandemic, enrollment of participants was problematic, and the study involved a smaller sample size than expected. In addition, we used simple randomization in a small group. However, despite this problem, both groups were balanced at baseline values. Furthermore, despite the small sample size limiting the significance of the results, the statistical analysis provided satisfactorily narrow confidence intervals for the means of the variables of interest, as we can visualize in Figure 3 and Figure 4. In addition, our results are in line with earlier published trials about oral nutritional supplementation in picky eater children. As with all research of this nature, the impossibility of direct supervision of the use of nutritional supplements by children can produce potential biases to the results. The relatively short intervention may have limited the observation of more significant changes in some anthropometric parameters less sensitive to “acute” changes in diet. Finally, the use of unvalidated scales to measure some outcomes, such as the appetite of the children studied, may have influenced the results obtained; moreover, as in any study that uses questionnaires, the memory of the interviewees may impact the findings. ## 5. Conclusions Picky eater children that were supplemented did not gain excess fat. They increased their weight not by gaining fat, but due to an increase in stature, as shown by BMI z-score and BFP, that remained unchanged. Supplemented children had a higher intake of iron, zinc, folate and vitamins C, D, and B12 compared to controls. In addition, supplemented children showed improved appetite throughout the study. 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--- title: 'Effect of Remote Ischaemic Preconditioning on Perioperative Endothelial Dysfunction in Non-Cardiac Surgery: A Randomised Clinical Trial' authors: - Kirsten L. Wahlstrøm - Hannah F. Hansen - Madeline Kvist - Jakob Burcharth - Jens Lykkesfeldt - Ismail Gögenur - Sarah Ekeloef journal: Cells year: 2023 pmcid: PMC10047371 doi: 10.3390/cells12060911 license: CC BY 4.0 --- # Effect of Remote Ischaemic Preconditioning on Perioperative Endothelial Dysfunction in Non-Cardiac Surgery: A Randomised Clinical Trial ## Abstract Endothelial dysfunction result from inflammation and excessive production of reactive oxygen species as part of the surgical stress response. Remote ischemic preconditioning (RIPC) potentially exerts anti-oxidative and anti-inflammatory properties, which might stabilise the endothelial function after non-cardiac surgery. This was a single centre randomised clinical trial including 60 patients undergoing sub-acute laparoscopic cholecystectomy due to acute cholecystitis. Patients were randomised to RIPC or control. The RIPC procedure consisted of four cycles of five minutes of ischaemia and reperfusion of one upper extremity. Endothelial function was assessed as the reactive hyperaemia index (RHI) and circulating biomarkers of nitric oxide (NO) bioavailability (L-arginine, asymmetric dimethylarginine (ADMA), L-arginine/ADMA ratio, tetra- and dihydrobiopterin (BH4 and BH2), and total plasma biopterin) preoperative, 2–4 h after surgery and 24 h after surgery. RHI did not differ between the groups ($$p \leq 0.07$$). Neither did levels of circulating biomarkers of NO bioavailability change in response to RIPC. L-arginine and L-arginine/ADMA ratio was suppressed preoperatively and increased 24 h after surgery ($p \leq 0.001$). The BH4/BH2-ratio had a high preoperative level, decreased 2–4 h after surgery and remained low 24 h after surgery ($$p \leq 0.01$$). RIPC did not influence endothelial function or markers of NO bioavailability until 24 h after sub-acute laparoscopic cholecystectomy. In response to surgery, markers of NO bioavailability increased, and oxidative stress decreased. These findings support that a minimally invasive removal of the inflamed gallbladder countereffects reduced markers of NO bioavailability and increased oxidative stress caused by acute cholecystitis. ## 1. Introduction More than 200 million adults undergo major non-cardiac surgery every year and the numbers are increasing [1]. While surgery has the potential to improve life quality and expectancy, surgical procedures also lead to physiological alterations defined as ‘surgical stress’. The alterations affect all organ systems and comprise hypercoagulability, systemic inflammation, immunological disturbances, and endothelial dysfunction [2,3]. A healthy endothelium regulates vascular tone, blood fluidity, inflammatory processes, and controls coagulation, while endothelial dysfunction is associated with unfavourable vascular changes, such as loss of endothelium-dependent vasodilation and adoption of a pro-thrombotic endothelial cell phenotype, which are induced by pro-inflammatory processes [4]. Endothelial nitric oxide (NO) is a pluripotent and important signaling molecule acting relaxant in the vasculature. It is produced by the endothelial NO synthase (eNOS) from L-arginine in response to shear stress or autocoids, such as bradykinin and adenosine [5]. The development of endothelial dysfunction is mainly due to a reduced bioavailability of NO, which can be explained by a reduced synthesis and an inactivation of eNOS by oxidative stress [6,7]. There are different ways of assessing endothelial function. Asymmetric dimethyl arginine (ADMA) is an endogenous eNOS inhibitor competing with L-arginine for the binding site, and the NO production is often expressed as the L-arginine/ADMA ratio [6]. A functional method to evaluate the endothelial function is to measure the pulse amplitude in a finger both during reactive hyperaemia and at rest. A reactive hyperaemia index (RHI) is then calculated as the ratio of the two measurements [8]. The digital response to hyperaemia reflects, in part, the NO-dependent vasodilator function of the microcirculation [9,10]. Endothelial dysfunction and its severity are associated with cardiovascular morbidity. Several studies imply that both coronary and peripheral endothelial dysfunction predict disease progression and cardiovascular events [11,12,13,14,15]. More importantly, endothelial dysfunction correlates with myocardial injury and major adverse cardiac events after non-cardiac surgery [16]. Thus, impaired NO bioavailability might play an imperative role in patients developing myocardial injury after non-cardiac surgery [16,17,18,19], a serious complication associated with increased risk of postoperative mortality [20,21,22]. Identifying a method capable of diminishing endothelial dysfunction in response to surgical stress would therefore be invaluable. Remote ischemic preconditioning (RIPC) is a procedure with short episodes of ischaemia and reperfusion applied to a limb. This non-invasive procedure might possess systemic anti-inflammatory and anti-oxidative properties when introduced prior to non-cardiac surgery [23,24,25,26,27]. Furthermore, RIPC has been shown to reduce myocardial injury in non-cardiac surgery [28]. The mechanisms of RIPC are not fully understood despite decades of experimental and clinical research [29,30]. RIPC activates both a neuronal and a humoral response, as well all exerts systemic effects. In turn these responses activate intracellular signal transduction pathways such as the protein kinase C pathway and the reperfusion injury salvage kinase pathway. The preservation of mitochondrial functions and gene expression modulation are thought to be central mechanisms in the systemic effects of RIPC [31]. We hypothesised that RIPC stabilises the function of the endothelium during surgical stress of non-cardiac surgery. As such, our primary aim was to investigate the effect of RIPC on perioperative endothelial function, including markers of NO bioavailability and oxidative stress, in patients undergoing sub-acute laparoscopic cholecystectomy due to acute cholecystitis. Our secondary aim was to explore changes in endothelial function as a result of subacute laparoscopic cholecystectomies performed due to acute cholecystitis. ## 2.1. Trial Design and Setting The trial was a single centre, randomised clinical trial including patients from the Department of Surgery, Zealand University Hospital in Denmark. It was designed to test the effect of RIPC on postoperative endothelial function in patients diagnosed with acute cholecystitis undergoing subacute laparoscopic cholecystectomy. Patients were consecutively screened and included after providing oral and written informed consent between September 2019 and September 2021. The trial was approved by The Danish Data Protection Agency (no. REG-020-2019) and by the Regional Ethics Committee of Region Zealand Denmark (no. SJ-762). The trial was registered at ClinicalTrials.gov (no. NCT04156711) and reported according to the CONSORT Statement [32]. ## 2.2. Participants Inclusion criteria were adults (≥18 years) diagnosed with acute cholecystitis and scheduled for subacute laparoscopic cholecystectomy. Patients with symptoms for a maximum of seven days prior to surgery were eligible for inclusion if informed consent could be obtained. Surgeries were performed between 8 A.M. and 8 P.M. Patients fulfilling the following criteria were excluded: surgery within 30 days of inclusion, circumstances preventing RIPC on the upper extremity (e.g., fractures), simultaneous per operative endoscopic retrograde cholangiopancreatography (rendezvous ERCP), synchronous cholangitis, synchronous pancreatitis, or pregnancy. Demographic data on all patients, including co-morbidities and daily medications, were collected by research personnel. So were pre- and perioperative data, e.g., vitals, preoperative routine blood samples, perioperative data from the surgeon and anaesthesiologist, and data on the postoperative course. All patients were anaesthetised with propofol and remifentanil. The choice of analgesia was up to the anaesthesiologist in charge. ## 2.3. Randomisation and Blinding Patients were randomised into an intervention group or control group. A third party generated the allocation sequence at www.randomization.com (accessed on 8 August 2019) (allocation ratio of 1:1 in blocks of six). A sealed, opaque envelope containing the allocation group was opened by an investigator after patients had given their informed consent to participate. Patients were not blinded to the intervention, but the anaesthesiologist, surgeon and surgical staff were blinded. ## 2.4. Intervention All patients received standard care according to local guidelines during their hospital stay, regardless of study allocation (standard regimen of [1] antibiotics: intra venous infusion with metronidazole 500 mg every eight hours and piperacillin/tazobactam 4 g every six hours; [2] analgetic: acetaminophen 1 g every six hours and Ibuprofen 400 mg every eight hours, morphine tablets 10 mg pro necessitate; [3] isotonic saline in case of discomfort during fasting prior to surgery (six hours for food and two hours for thin liquids prior to surgery) and [4] glucose–insulin–potassium intravenous infusion during fasting, according to local guidelines, if patient was diagnosed with diabetes). Patients in the RIPC group underwent four cycles of five minutes of ischaemia and five minutes of reperfusion of one upper extremity. The intervention was performed with an electronic tourniquet device (Tourniquet 4500 ECL; VBM Medizintechnik, Sulz am Neckar, Germany) placed on one upper arm with a cuff inflation of 200 mm Hg as a minimum. If a patient had a systolic blood pressure >185 mmHg, tourniquet inflation to a minimum of 15 mmHg above the patient’s systolic blood pressure was required. RIPC was performed less than four hours prior to surgery (knife-to-skin). A biphasic pattern of RIPC-protection has been suggested with an ‘early phase’ activated instantly, lasting about 4 h and peaking within that timespan [33,34]. As such, RIPC was carried out prior to anaesthetic induction as an attempt to align the timing of the RIPC procedure with the peak protective effect. ## 2.5. Outcomes Our primary outcome was group-differences in perioperative changes in endothelial function, assessed as the reactive hyperaemia index (RHI), from baseline (preoperative assessment) to 2–4 h and 24 h (postoperative day 1, POD1) after surgery. Our secondary outcomes were group differences in perioperative changes in biomarkers of NO bioavailability: plasma L-arginine, plasma asymmetric dimethylarginine (ADMA), L-arginine/ADMA, plasma tetrahydrobiopterin (BH4), plasma dihydrobiopterin (BH2) and total plasma biopterin level. NO production was expressed as the ratio between L-arginine and ADMA [6]. The BH4/BH2-ratio was used as an indirect measure of the level of oxidative stress. In an oxidative stress-free environment BH2 levels are close to undetectable and all measurable biopterin are in the reduced form of BH4. However, under conditions of oxidative stress BH4 is highly susceptible to oxidation resulting in the formation of BH2. As such, BH4 decreases significantly and BH2 increases, correlating a lower BH4/BH2 ratio to more oxidative stress [35]. ## 2.6. Data Sources Non-invasive digital pulse amplitude tonometry (Endopat2000; Itamar Medical Ltd., Caesarea, Israel, Software Version 3.7.x) was used to assess endothelial function at patient inclusion (preoperative), 2–4 h after surgery and 24 h after surgery [36]. While the patient was in a supine position, a finger probe was placed on each index finger. The Endopat2000 assesses the digital pulse amplitude during five minutes of rest, during five minutes of blood flow occlusion (with a blood pressure cuff inflated to a supra-systolic pressure on the upper extremity), and finally during the subsequent five minutes of hyperaemia as the cuff is deflated [8]. A reactive hyperaemia index (RHI), a measure of endothelial function, is calculated automatically by the EndoPat2000 system. It is a ratio between the digital pulse amplitude during hyperaemia and at rest. The pulse amplitude is measured at both fingers in all three phases, allowing one finger to serve as a control, adjusting for systemic effects. Furthermore, as the RHI is analysed automatically any interobserver variability is diminished [8]. The proposed cut-off values for a normal RHI are ≥2.10, whereas <1.67 is considered abnormal and the range in between as borderline [37]. Whole blood was withdrawn into ethylenediamine tetra-acetic acid (EDTA) tubes. Blood was collected just prior to the Endopat assessment during patient inclusion (preoperative), 2–4 h after surgery and 24 h after surgery. The initial sampled blood was disposed. For analysis of biopterin, blood was collected in EDTA tubes (3 mL) with 75 μL freshly made 1,4-Dithioerythritol (DTE) solution (50 mg DTE in 10 mL Milli-Q water) added to minimise oxidation during analysis [38]. Samples were mixed gently before centrifugation (2000× g, 10 min, 4 °C) and plasma was aliquoted and stored immediately at −80 °C in Eppendorf tubes until analysis. Quantifying biopterin concentrations was determined using high-performance liquid chromatography (HPLC) with fluorescence detection employing iodine oxidation, as previously described [39]. For L-arginine and ADMA analysis, EDTA tubes (6 mL) containing whole blood were centrifuged at 2000× g, for 10 min, at 4 °C and plasma was aliquoted and stored immediately at −80 °C. HPLC with fluorescence detection was used for quantification [40]. ## 2.7. Statistical Analyses This was an explorative study and as such, no sample size calculation was performed. Patients undergoing RIPC, as described in the protocol, and having one preoperative blood sample and at least one postoperative blood sample withdrawn were included for analyses in this study. Categorical data are expressed as units (n, %) and compared between control patients and patients undergoing RIPC by Pearson’s Chi-squared test or Fisher’s Exact Test. Continuous data were visualised by histograms and quantile–quantile plots of residuals to control the data distribution and equality of variances. Parametric data were compared by Student’s t-test and presented as mean and standard deviation (SD), while non-parametric data were compared by the Mann–Whitney U test and presented as medians and ranges. Benjamini and Hochberg’s method to decrease the false discovery rate was applied to significant results as an adjustment for multiple testing. To analyse changes in outcomes over time with repeated measurements, we applied a constrained linear mixed model (using the ‘LMMstar’ package (version 0.7.6) in R [41]) including follow-up time (categorical) as a fixed effect. To account for the correlation in the repeated measurements and possible variance heterogeneity over time, we assumed an unstructured covariance pattern. We adjusted for duration of surgery and blood loss between groups, due to numerical differences in the variables between groups. This described method was applied for group differences according to the primary and secondary outcomes. The constrained linear mixed model for randomised studies uses one population mean at baseline, assuming that as all random samples are drawn from the same population, they must share the same true population mean. A linear mixed model was applied to a merged population of controls and patients undergoing RIPC when analysing the effect of surgery on RHI and concentrations of NO bioavailability markers (L-arginine, ADMA, L-arginine/ADMA, BH4, BH2, and total plasma biopterin). The population was merged to avoid reduction in sample size. This approach was accepted after primary analyses revealed neutral findings on the effect of RIPC. Furthermore, subgroup analysis on the effect of surgery in controls only showed no difference in significance-level of the results. Analyses were considered significant at a p-value of <0.05. We did a post-hoc power analysis applying the observed group difference and variance in the measurements of our primary outcome, RHI. Based on these estimates, our study had a power of 0.53, and to reach a power of 0.80, a sample size of 57 patients in each group would be necessary. All statistical analyses were performed using RStudio (RStudio Team [2019] [42]). ## 3.1. Patients Sixty patients undergoing surgery due to acute cholecystitis were included in this study during a 24-month period. The details on the patient flow are illustrated in Figure 1. A total of 210 patients admitted with acute cholecystitis met the inclusion criteria. However, 132 patients either declined to participate, relocated from other hospitals, or had surgery shortly after diagnosis, which was incompatible with inclusion and intervention. Hence, 78 patients were enrolled in the study and 18 were excluded immediately after surgery as perioperative endoscopic retrograde cholangiopancreatography was performed due to choledocholithiasis (an exclusion criteria). A such, 60 patients randomly allocated to either a RIPC group ($$n = 30$$) or a control group ($$n = 30$$) were analysed. The demographics of the study population, including distribution of comorbidities, daily medication, and ASA group, were comparable between the control group and intervention group. Details are shown in Table 1. All patients underwent general anaesthesia induced and maintained with propofol and remifentanil. Pre-medication and surgical characteristics did not differ between groups. Eleven patients had a surgical drain inserted perioperatively due to either gall bladder perforation or bleeding. After discharge, eight patients were readmitted, three of whom had complications related to their surgery (intraabdominal abscess, $$n = 2$$) or gallstone disease (cholangitis, $$n = 1$$). The remaining five patients had abdominal pain with spontaneous relief. RIPC was applied and completed within 4 h to skin incision, with a mean time from completed RIPC-procedure to skin incision of 2 h and 20 min (range 40–240 min). ## 3.2. The Effect of RIPC on Endothelial Function and Nitric Oxide Bioavailability All 60 patients had a preoperative measurement of RHI. All but one patient had at least one follow-up measurement within 24 h after surgery. The reason for missing measurements of RHI in one patient was postoperative nausea (four hours after surgery) and early discharge (<24 h after surgery). Patients in our study had a preoperative RHI value of 1.82 ($95\%$ CI 1.70–1.93). There were no differences in RHI between patients in the RIPC and control group over time (from preoperative to 24 h after surgery) ($$p \leq 0.07$$, Figure 2). Fifty-four patients had blood samples withdrawn for analyses at all three timepoints. One patient had blood samples withdrawn preoperatively and 2–4 h after surgery, whereas five patients had samples drawn preoperatively and 24 h after surgery. There were no differences in concentrations of L-arginine ($$p \leq 0.36$$), ADMA ($$p \leq 0.72$$), L-arginine/ADMA-ratio ($$p \leq 0.69$$), BH4 ($$p \leq 0.07$$), BH2 ($$p \leq 0.38$$), BH4/BH2-ratio ($$p \leq 0.11$$), or total biopterin concentration ($$p \leq 0.22$$) over time between patients undergoing RIPC or patients in the control group, Figure 2. ## 3.3. The Effect of Surgery on Endothelial Function and Nitric Oxide Bioavailability RHI did not change significantly in response to surgery ($$p \leq 0.83$$, Figure 3). However, both L-arginine and L-arginine/ADMA increased as an overall response to surgery ($p \leq 0.001$ and $$p \leq 0.01$$, respectively, Figure 3). L-arginine concentration preoperative was 44.8 (39.9–49.7) μmol/L and increased with +15.2 (7.55–22.8) μmol/L ($p \leq 0.001$). The preoperative L-arginine/ADMA ratio was 34.2 (28.1–40.4) and increased with +13.3 (2.83–23.7) 24 h after surgery ($$p \leq 0.01$$). The ratio had a numerical but non-significant decrease from the preoperative level till 2–4 h postoperatively −5.44 (−14.7–3.82). However, from the ratio at 2–4 h postoperatively until POD1, a significant increase was observed ($$p \leq 0.003$$). The overall effect of surgery on BH4/BH2 ratio was a reduction ($$p \leq 0.01$$). The preoperative BH4/BH2-ratio was 4.62 (1.71–7.52) and decreased significantly at 2–4 h after surgery with −2.28 (−4.35–−0.20), $$p \leq 0.04.$$ This decrease was also present and significant 24 h after surgery (−3.13 (−6.06–−0.19)), $$p \leq 0.03$$, Figure 3. There were no significant changes in relation to surgery in concentrations of ADMA, BH4, BH2, or total biopterin (Figure 3). ## 4. Discussion We did not demonstrate any effect of RIPC on endothelial function, assessed as a reactive hyperaemia index, 2–4 h or 24 h after laparoscopic cholecystectomy compared with the preoperative assessment. Neither did RIPC affect markers of NO bioavailability 2–4 h or 24 h after surgery compared with the preoperative measurements of these biomarkers. Surgery alone, however, did impact on plasma L-Arginine/ADMA ratio and BH4/BH2 ratio. The effect of RIPC on RHI has been investigated in one other trial with patients undergoing subacute hip fracture surgery, where endothelial function was assessed as a point-assessment of RHI on postoperative day one. The study found no significant effect of RIPC on postoperative RHI [43]. Apart from the RIPC procedure being initialised in the operating room just prior to surgery, the definition, duration, and number of cycles were identical to our RIPC intervention [43]. While no other studies, to our knowledge, have investigated the effect of RIPC on postoperative endothelial function in patients undergoing non-cardiac surgery, it has been investigated in patients with acute myocardial infarction prior to percutaneous coronary intervention with a significant effect lasting until one week after the intervention [44]. Studies in healthy volunteers [45,46] and in hypertensive patients [47] have also shown a beneficial effect of RIPC on endothelial dysfunction induced by ischaemia-reperfusion injury to a limb. However, the method of assessing endothelial function was different from ours, as either invasive flow-mediated dilation [45,47] or calculation of vascular conductance [46] was used. Furthermore, essential differences in the pathophysiology of endothelial dysfunction induced by ischaemia-reperfusion and that of a systemic surgical stress response must be expected. Moreover, our patients had several days of symptomatic cholecystitis prior to application of RIPC. As such, they were not only exposed to a surgical stress response but had potentially suffered from additional ischaemic gall bladder tissue in the days preceding RIPC and surgery. This could affect the beneficial effect of RIPC. A study design assessing the effect of RIPC on endothelial function in patients undergoing, e.g., elective laparoscopic cholecystectomy could clarify this question. Another way of investigating the endothelial condition is to measure the endothelial NO bioavailability, as reduced NO bioavailability is essential in the pathophysiology of endothelial dysfunction [48]. Endothelial NO is primarily produced by endothelial nitric oxide synthase (eNOS) from L-arginine. As ADMA is an endogenous eNOS inhibitor competing with L-arginine for the binding site the NO production is often expressed as the L-arginine/ADMA ratio. ADMA and NO bioavailability have been proven to correlate with a wide range of cardiovascular pathologies, e.g., myocardial infarctions, hemodynamic instabilities, acute heart failure and peripheral arterial disease [49,50]. Moreover, an essential cofactor for NO synthase is BH4. It modifies the enzyme’s activity in coexistence with its reduced form, BH2. They compete for the enzyme’s binding site, but when BH2 is bound to eNOS, it causes an uncoupling of the synthase in contrast to BH4. Per se, the BH4/BH2 (or total biopterin) ratio has been claimed to be a main parameter in defining the extent of eNOS uncoupling [48,49]. Uncoupling shifts the production of NO to superoxide and peroxynitrite, initiating a cascade of increased oxidative stress, and aggravating the reduced NO bioavailability [51,52]. To our knowledge, no other study has repeatedly investigated the effect of RIPC on circulating levels of L-arginine, ADMA or biopterin in patients undergoing non-cardiac surgery. However, one study reported significant protection of RIPC in contrast-induced acute kidney injury with simultaneously decreased levels of ADMA. Furthermore, RIPC has been demonstrated to reduce ADMA levels after induced ischaemia-reperfusion in healthy volunteers [53]. A beneficial effect of RIPC on other biomarkers of oxidative stress, such as malondialdehyde and superoxide dismutase, has been reported in both minor and major non-cardiac surgery [23,24,25,26]. We did not demonstrate an effect of surgery on RHI. There are several studies in elective minor and major non-cardiac surgery reporting a reduction of RHI in response to surgical stress [54,55,56]. An explanation for this difference might be that patients in our study suffered from acute inflammation for several days and had a decreased endothelial function already at preoperative measurement, as inflammation is known to affect endothelial function [57]. In accordance with the proposed cut-off values for RHI, the preoperative value in our study was in the range of ‘borderline’ between normal and abnormal values [37]. Another study exploring the endothelial function after major emergency abdominal surgery described RHI to be suppressed after surgery, but no preoperative value was measured, so the endothelial dysfunction could have been present prior to the acute surgical intervention, as in our study [16]. We found L-arginine and L-Arginine/ADMA ratio to be suppressed preoperatively but both increased significantly 24 h after surgery. These findings are consistent with previous published surgical studies [16,54,55]. Our results suggest that NO bioavailability is impaired because of the patients’ acute disease and although surgery causes a systemic stress response also capable of causing endothelial dysfunction [16,54,55], the minimally invasive laparoscopic removal of the gallbladder countereffects the systemic endothelial dysfunction caused by acute cholecystitis. This is in line with the clinical situation as patients with acute cholecystitis are often bedridden before surgery but ready for discharge within the same day as surgery is effectuated. Moreover, we found that the BH4/BH2-ratio changes significantly in the perioperative period. Compared to previous studies in non-cardiac surgery, it had a high preoperative level [16,54,55] and decreased significantly up to 24 h after surgery ($$p \leq 0.01$$). This supports our findings of increased NO biomarker availability after surgery. Our trial has strengths and limitations. It is one of the first studies to address the effect of RIPC on RHI in non-cardiac surgery and it is the first to examine the effect of RIPC on circulating levels of L-arginine, ADMA, and biopterin. Moreover, it was a randomised clinical trial and we excluded patients undergoing rendezvous ERCP or having synchronous cholangitis or pancreatitis to avoid introducing heterogeneity of the surgical or systemic stress response in our patient population. Attempts were made to include patients consecutively, but this was challenged due to a period of national COVID-19 lockdown and the logistic challenges of always having research staff available at the hospital. The intervention took 40 min and a maximum of 4 h from RIPC-procedure until surgery was accepted, but occasionally operating beds were re-prioritised an patients included in the study were postponed, resulting in exclusion due to our defined intervention-to-surgery time threshold. The trial was exploratory, and no sample size calculation was performed. It has been demonstrated that RHI measurements and data vary depending on the type of surgery and the population investigated [16,43,54,55]. Nonetheless, the lack of sample size calculation introduces the risk of type II errors. A post hoc power analysis based on our RHI measurements revealed a statistical power of 0.53 and a sample size of 57 patients in each group would have been necessary to reach a power of 0.8. Furthermore, it could be argued that a 24 h follow-up is too short. The RIPC procedure was applied in patients prior to anaesthesia. Therefore, patients might unknowingly have influenced our results. Though patients were instructed not to talk or move during the RIPC procedure, uneasiness causing movements, fear of discomfort or excitement might have increased blood pressure and attenuated the effect of RIPC. However, studies in awake volunteers have demonstrated effects of RIPC attenuating endothelial ischaemia-reperfusion injury [45,46], nonetheless it is a possible limitation to the study. Our patients underwent general anaesthesia with propofol and it is still debated to what extent propofol inhibits the effect of RIPC. An RCT demonstrated that RIPC abolished cardio-protection (measured by hs-troponin release) in patients undergoing cardiac surgery [58]. On the other hand, a study investigated the interference of anaesthesia on the cardio-protective effect of RIPC and demonstrated that both propofol and sevoflurane blocked the effect of RIPC [59]. Furthermore, in a recent non-cardiac multicentre trial of patients undergoing hip fracture surgery, a multivariable logistic regression showed no interaction between RIPC and type of anaesthesia [28]. In conclusion, RIPC did not influence endothelial function, markers of NO bioavailability, or the biopterin redox state up to 24 h after laparoscopic surgery for acute cholecystitis. In response to acute laparoscopic cholecystectomy, markers of NO bioavailability increased, and the stress-induced redox imbalance reflecting eNOS uncoupling decreased 24 h after surgery. This might reflect that operative removal of the diseased gallbladder counteracts the systemic endothelial dysfunction caused by acute cholecystitis, despite the introduction of minor surgical stress. Based on our findings, further trials investigating the effect of RIPC on postoperative endothelial function in non-cardiac surgery is needed. Preferably RCTs with both functional tests and biomarker outcomes reflecting endothelial function in patients undergoing elective non-cardiac surgery. An adequate sample size for both primary outcomes and for reasonable subgroup analyses, e.g., type of anaesthesia, is recommended. Moreover, measurements should be repeated for a longer period, e.g., up to 3–4 days, as the surgical stress response can last for several days. ## References 1. 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--- title: Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients authors: - Beiyi Shen - Wei Hou - Zhao Jiang - Haifang Li - Adam J. Singer - Mahsa Hoshmand-Kochi - Almas Abbasi - Samantha Glass - Henry C. Thode - Jeffrey Levsky - Michael Lipton - Tim Q. Duong journal: Diagnostics year: 2023 pmcid: PMC10047384 doi: 10.3390/diagnostics13061107 license: CC BY 4.0 --- # Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients ## Abstract Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors ($$n = 224$$) versus non-survivors ($$n = 28$$) in the general floor group, and (ii) survivors ($$n = 92$$) versus non-survivors ($$n = 56$$) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors ($p \leq 0.05$), and non-survivor CXR scores deteriorated at outcome ($p \leq 0.05$) whereas survivor CXR scores did not ($p \leq 0.05$). For IMV patients, survivor and non-survivor CXR scores were similar at intubation ($p \leq 0.05$), and both improved at outcome ($p \leq 0.05$), with survivor scores showing greater improvement ($p \leq 0.05$). Hospitalization and IMV duration were not different between groups ($p \leq 0.05$). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count ($p \leq 0.05$). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression. ## 1. Introduction Coronavirus disease 2019 (COVID-19) [1,2,3] has already infected 676 million people and killed more than 6.88 million worldwide (14 March 2023). The widespread outbreaks and recent spikes around the world, and the likelihood of recurrences have strained and will continue to strain healthcare resources. Radiological imaging of the lung is an essential tool in evaluating COVID-19 lung infection. In the early days of the pandemic, computed tomography (CT) [4] was used in China when reverse transcription polymerase chain reaction (RT-PCR) was less reliable and had a long turnaround time [5,6]. CT is, however, prone to cross-contamination and, thus, it is not widely used in the context of COVID-19 in the United States and elsewhere in the world, especially in the intensive care setting, due to the risk of cross-infection. By contrast, a portable chest X-ray (CXR) is convenient, readily available, can be brought to the patient’s bedside, and can be readily disinfected between uses [7,8,9,10,11,12,13,14,15,16]. Although a CXR has inferior diagnostic quality to CT, CXRs can be used to visualize characteristic ground-glass opacities and consolidation in the lungs associated with COVID-19 infection, helping with clinical diagnosis [17]. CXRs have become increasingly relevant in COVID-19 circumstance because a disproportionally large percentage of COVID-19 patients are put on invasive mechanical ventilators for a much longer duration compared with other similar lung infections [18]. Improved understanding of the temporal progression and disease severity of COVID-19 lung infection on CXRs has become more urgent. A few recent studies have related initial CXR scores of COVID-19 patients presenting to the emergency department (ED) to clinical outcomes, such as mortality, escalated care, length of hospitalization, and duration on a ventilator [19,20,21,22,23]. Results remain inconsistent and controversial, with some reporting good correlation of CXR scores with these clinical outcomes while others did not. To our knowledge, there has been no systematic evaluation of the temporal characteristics of lung CXR scores in COVID-19 patients and how these characteristics can be judiciously used to inform clinical decision-making. As such, the potential of CXRs in the COVID-19 pandemic has not yet been fully realized. This study sought to determine the prognostic values of longitudinal CXR scores in COVID-19 patients admitted only to the general floor (GF group) and patients treated with invasive mechanical ventilation (IMV group). For this, we analyzed CXR scores at different time points in those two groups using various statistical methods. We first demonstrated the demographics, comorbidities, vital signs, and laboratory values of those two groups, as these could be confounding factors. We then demonstrated our CXR scoring method. With those foundations, we then analyzed: (i) CXR scores of the general floor (GF) COVID-19 patients at the time of admission versus at outcome (discharged alive or dead) stratified by survivors and non-survivors; (ii) CXR scores of invasive mechanical ventilation (IMV) COVID-19 patients at the time of intubation versus at outcome stratified by survivors and non-survivors; (iii) CXR scores of IMV COVID-19 patients longitudinally for the first five days on IMV stratified by survivors and non-survivors; and (iv) the relationship between longitudinal CXR scores and clinical variables such as laboratory test results and vital signs. The results of these analyses were presented in the results section. We then discussed our results, mainly the temporal relationship between CXR scores and mortality as well as the relationship between CXR scores and clinical variables in the two groups in the discussion section. Limitations of the study and future directions were discussed as well. ## 2. Materials and Methods Patient selection and inclusion criteria: This retrospective study was approved by the University Institutional Review Board with an exemption of informed consent. Our study followed the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines for cross-sectional studies (http://www.equator-network.org/reporting-guidelines/strobe/, accessed on 14 February 2023). These hospital data have been used by others to address other clinical questions. There were 4542 persons under investigation (PUI) who presented to the ED at University Hospital between 8 March 2020 and 30 June 2020, of whom 1975 tested positive and were hospitalized. COVID-19 status was determined by RT-PCR. We further excluded patients with an incomplete history of comorbidities, no CXR within three days of their ED visit, and fewer than two CXRs. We should note that for regular floor patients, CXRs were typically acquired once, with repeats only as clinically indicated. For mechanically intubated patients, CXRs were usually performed essentially daily in our hospital (other hospital practices could differ), to check the position of the lines and tubes. The final sample sizes were: [1] survivors ($$n = 224$$) versus non-survivors ($$n = 28$$) for the GF group, and [2] survivors ($$n = 92$$) versus non-survivors ($$n = 56$$) for the IMV group. We should note that, essentially, all patients escalated to intensive care were placed on invasive mechanical ventilation in our cohort. The primary outcome was mortality. The following data were obtained: (i) duration of hospitalization; duration on IMV; duration post-IMV in the hospital; (ii) CXR scores and clinical variables for the GF group stratified by survivors and non-survivors at the time of ED admission and at outcome (discharged alive or dead); (iii) CXR scores and clinical variables for the IMV group stratified by survivors and non-survivors at the time of intubation and at outcome; and (iv) CXRs scores and clinical variables of five consecutive days on IMV stratified by survivors and non-survivors. CXR scores: A group of four board-certified chest radiologists of 10–20 years of experience and two radiology residents in training under attending supervision scored the CXRs for disease severity using the following criteria based on geographical extent and degree of opacity. The geographical extent score of 0–4 was assigned to each of the right and left lung fields depending on the extent of involvement with ground glass opacity or consolidation: 0 = no involvement; 1 = <$25\%$; 2 = 25–$50\%$; 3 = 51–$75\%$; 4 = >$75\%$ involvement. The right and left lung were scored separately and were added together. The degree of opacity score of 0–4 was assigned to each of the right and left lungs as: 0 = no opacity; 1 = ground glass opacity; 2 = mix of consolidation and ground glass opacity (less than $50\%$ consolidation); 3 = mix of consolidation and ground glass opacity (more than $50\%$ consolidation); 4 = complete white-out. The right and left lung were scored separately and added together. In short, the geographical extent score ranged from 0–8, and the opacity score ranged from 0–8. Each CXR was independently scored by two raters who were blinded to the clinical data and the average of the scores by the two raters was calculated as the final score. Clinical variables: Demographic information, chronic comorbidities, vital signs, and laboratory test results were collected. Demographic information included age, sex, ethnicity and race. Chronic comorbidities included smoking, diabetes, hypertension, asthma, chronic obstructive pulmonary disease, coronary artery disease, heart failure, cancer, immunosuppression and chronic kidney disease. Vital signs included heart rate (HR), respiratory rate (RR), pulse oxygen saturation (SpO2), systolic blood pressure (SBP), diastolic blood pressure (SBP) and temperature (temp). Laboratory test results included C-reactive protein [CRP], D-dimer (DD), ferritin, lactate dehydrogenase (LDH), lymphocytes (lymph), procalcitonin (procal), alanine aminotransferase (ALT), brain natriuretic peptide (BNP), creatinine (Cr), and troponin (TNT). These clinical variables were obtained for the GF group and IMV group stratified by survivors and non-survivors at the time of ED admission or initiation of IMV and at the time of outcome (discharged alive or dead). Statistical analysis: Statistical analysis was performed with IBM SPSS software (v26, Armonk, NY, USA). Group comparison of categorical variables in frequencies and percentages used chi-squared or Fisher exact tests. Group comparison of continuous variables in medians and interquartile ranges (IQR) used the Mann–Whitney U test. Intraclass correlation coefficient [24] was calculated to assess inter-reader agreement of the CXR scores. Unpaired t-tests were used to compare survivors and non-survivors and time points. Comparison across multiple time points used two-way repeated measures ANOVA with the inclusion of group, day and group * day interaction as the independent variables. p values for post-hoc t-tests were adjusted with the Bonferroni–Holm correction for multiple comparisons. A $p \leq 0.05$ was considered statistically significant unless otherwise specified. ## 3.1. Patient Characteristics Descriptive statistics of demographics and comorbidities of the GF group and the IMV group were compared between survivors and non-survivors as in Table 1. In the GF group, mortality was significantly associated with age, ethnicity, race, hypertension, COPD, coronary artery disease, heart failure, and chronic kidney disease ($p \leq 0.05$). In the IMV group, mortality was only significantly associated with age, smoking, hypertension, COPD, coronary artery disease, and heart failure ($p \leq 0.05$). Clinical variables which include vital signs and laboratory test results of the GF group at ED admission and at outcome by survivors and non-survivors were presented in Table 2A. At ED admission, essentially all these clinical variables (respiratory rate, SpO2, temperature, BNP, CRP, D-dimer, LDH, leukocyte, lymphocyte, procalcitonin) were significantly different between survivors and non-survivors ($p \leq 0.05$). At outcome, clinical variables (except lymphocytes, ($p \leq 0.05$)) were not significantly different between survivors and non-survivors ($p \leq 0.05$), which is due to the small sample size because these clinical variables were not generally obtained for GF patients prior to discharge. Similarly, the vital signs and laboratory test results of the IMV group at the time of intubation and at outcome by survivors and non-survivors are presented in Table 2B. At the time of intubation, none of the clinical variables (except D-dimer and leukocyte ($p \leq 0.05$)) was significantly different between survivors and non-survivors ($p \leq 0.05$). At outcome, essentially all these clinical variables (respiratory rate, SpO2, temperature, CRP, D-dimer, LDH, leukocytes, lymphocytes, procalcitonin) were significantly different between survivors and non-survivors ($p \leq 0.05$). ## 3.2. CXR Scores Examples of CXRs with a different geographic extent and different opacity scores are demonstrated in Figure 1. CXRs of COVID-19 positive patients showed hazy opacities and/or airspace consolidation, with a predominance of bilateral, peripheral, and lower lung zone distribution. Each geographic score and opacity score ranged from 0 to 8, with a higher score indicating worse disease severity. Each CXR was rated independently by two raters. The inter-reader agreement of CXR scores assessed by intraclass correlation coefficient was 0.93 ($95\%$ CI: 0.93–0.94) for the geographic score, and 0.88 ($95\%$ CI: 0.86–0.89) for the opacity score, indicating excellent inter-rater agreement. The correlation (Pearson’s correlation coefficient = 0.69) between the extent score and opacity score was moderate. CXR scores of the GF group obtained at ED admission and at outcome were stratified by non-survivors and survivors (Figure 2). Geographic and opacity scores behaved similarly, and they are discussed together, with geographic scores generally yielding bigger differences. Geographic and opacity scores were significantly higher (worse disease severity) in non-survivors compared to survivors at both time points ($p \leq 0.05$). Non-survivor scores significantly worsened at the second time point compared to the first time point ($p \leq 0.05$), but survivor scores did not ($p \leq 0.05$). CXR scores of the IMV group at intubation and at outcome were stratified by non-survivors and survivors (Figure 3). Scores were not significantly different between non-survivors and survivors at the time of intubation ($p \leq 0.05$) but were significantly different at outcome ($p \leq 0.05$). Both survivors and non-survivors showed significant improvement in scores ($p \leq 0.05$), but survivors showed a bigger improvement ($p \leq 0.05$). Comparing the GF and IMV patients, the CXR scores of the GF survivors were lower than those of the IMV survivors by 1 to 4 points on average out of a maximum of 8 points, whereas the CXR scores of the GF and IMV non-survivors were similar. CXR scores were plotted on five consecutive days on IMV (Figure 4). The scores were not significantly different between non-survivors and survivors on day 1 on IMV ($p \leq 0.05$) but diverged on subsequent days. Geographic scores were significantly different between groups at 2, 3, 4 and 5 days on IMV ($p \leq 0.05$). Opacity scores were significantly different between groups at 2 and 4 days on IMV ($p \leq 0.05$). ## 3.3. Histograms of Days in the Hospital and Duration on Ventilator The durations of hospitalization of the general floor group were stratified by survivors ($$n = 224$$) and non-survivors ($$n = 28$$) (Figure 5A). The number of days of hospitalization of the general floor group was not significantly different between non-survivors (median 4.5 days [IQR:2, 9.5]) and survivors (median 5 days [IQR = 3, 7], $p \leq 0.05$). The histograms of duration of hospitalization of the IMV group stratified by survivors ($$n = 92$$) and non-survivors ($$n = 56$$) (Figure 5B). The number of days of hospitalization was not significantly different between non-survivors (median 12.5 days [IQR:6.5, 22]) and survivors (median = 18.5 days [IQR = 13, 26], $p \leq 0.05$). IMV patients were in the hospital markedly longer than GF patients ($p \leq 0.01$ for both survivors and non-survivors). The duration on IMV and post IMV were stratified by survivors ($$n = 92$$) and non-survivors ($$n = 56$$) (Figure 6). The number of days on IMV was not significantly different between non-survivors (median 11 days [IQR:5, 19]) and survivors (median 10 days [IQR = 7, 19], $p \leq 0.05$). The number of days post-IMV was significantly different between non-survivors (median 0 days [IQR:0, 0]) and survivors (median 8 days [IQR:7, 14], $p \leq 0.05$). ## 3.4. Association of CXR with Laboratory Values and Outcomes The associations between CXR scores with clinical variables were estimated using correlation analysis (Table 3). Both geographic and opacity CXR scores were significantly correlated with LDH, RR, D-dimer, CRP, procalcitonin, ferritin, SpO2, and lymphocyte count. The geographic score was correlated with WBC, and opacity score was correlated with troponin and HR. No CXR scores were significantly associated with SBP, temperature and BNP. The clinical variables with the highest correlation included LDH, RR, D-dimer and CRP. The association between CXR scores and duration of hospitalization, IMV and post-IMV were estimated using a correlation stratified by survivals and non-survivors (Table 4). In the GF group, CXR scores were not correlated with the hospitalization duration ($p \leq 0.05$). Among the IMV non-survivors, CXR scores were correlated with hospitalization and IMV durations ($p \leq 0.05$), but not post-IMV duration. Among the IMV survivors, CXR scores were not correlated with hospitalization, IMV and post-IMV duration ($p \leq 0.05$) except for the geographic score with hospitalization. ## 4. Discussion This study characterized the relationship between longitudinal CXR scores and clinical outcomes (mortality, hospitalization duration, IMV duration, and clinical variables) in the general floor patients and mechanically ventilated patients. The major findings are: (i) GF non-survivor CXR scores were significantly worse at admission relative to GF survivor scores, and GF non-survivor CXR scores worsened at outcome whereas GF survivor CXR scores did not; (ii) IMV non-survivor and survivor CXR scores were similar at intubation, while both improved at outcome, but survivor scores showed larger improvement; (iii) hospitalization or IMV duration were not significantly different between non-survivors and survivors; (iv) CXR scores were significantly correlated with LDH, RR, D-dimer, CRP, procalcitonin, ferritin, SpO2, and lymphocyte count; and (v) IMV non-survivor CXR scores were correlated with IMV duration, but GF CXR scores were not correlated with hospitalization duration. ## 4.1. Temporal Progression of CXR Scores GF survivors showed significantly less severe lung involvement relative to GF non-survivors, IMV survivors, and IMV non-survivors at admission. These are not unexpected, but indicate correlation of severity of COVID-19 infection with likelihood of mortality, and CXRs could provide clinically useful information in a quantitative manner. Interestingly, GF non-survivors showed similar lung involvement severity compared to IMV patients at admission. This is not unexpected because most of the GF non-survivors were placed on comfort care; they would have been upgraded to IMV if they were full code. It is worth noting that the mortality outcome could thus depend on the patients’ will as well as the treatments they received. GF survivor lung involvement did not improve or worsen from admission to outcome, while GF non-survivor lung involvement worsened. It is surprising that GF survivor lung involvement did not improve. A possible reason is that GF survivor CXR scores were low to begin with and the CXR abnormality did not fully resolve at the time of discharge. It is also possible that GF survivors were hospitalized for shorter durations compared to GF non-survivors, and it may take time for lung abnormality to resolve. Nonetheless, these findings suggest that it is not necessary for CXR abnormalities to be completely resolved prior to hospital discharge. By contrast, IMV non-survivors and survivors showed similarly severe lung abnormality at IMV admission. The severity of lung infection in both IMV non-survivors and survivors was worse than that of GF survivors but was similar to that of GF non-survivors. Both IMV non-survivors and survivors showed improvement in CXR scores over five days on IMV and at discharge, with IMV survivors showing a larger improvement. Taken together, these observations suggest that an improvement in CXR scores is associated with IMV treatment. Other treatments under escalated care could also play a role in improving CXR scores. While there are multiple publications on non-longitudinal chest X-ray scores in COVID-19 patients [19,20,21,22,23], there have been no studies on longitudinal chest X-ray scores. We believe our comparisons of radiologist CXR scores at admission, at pre-IMV, and at IMV longitudinally between survivors and non-survivors are novel. Our results show that CXR scores at admission and at intubation differed from those at outcome, supporting the notion longitudinally that CXR scores could inform the clinical outcome and guide clinical care better than non-longitudinal chest X-ray scores. Longitudinal CXR scores offer important insights into disease progression in COVID-19 patients and facilitate disease management. This includes offering patients more supportive measures (such as oxygen supplementation and medications) and escalated care. In the context of ICU, CXR and CXR scores may be used to decide when to intubate, extubate, and reintubate as well as administer treatment regimens. ## 4.2. Geographic versus Opacity Scores The trends for geographic and opacity scores are simlar overall. However, the geographic scores appeared to show large differences, i.e., were likely more sensitive in lung disease severity. The geographic scores reflect the extent of lung involvement, whereas the opacity scores reflect the degree of opacity. These findings suggest that the extent of lung involvement is more informative than the degree of opacity the lung appears. These observations suggest that clinicians and radiologists could pay more attention to the extent of lung involvement instead of the degree of involvement when assessing CXRs of COVID-19 patients. Radiological scoring is widely used to stage lung disease severity, usually based on CT but not on CXRs because CT offers better sensitivity [25]. CT is not used in COVID-19 circumstances in most parts of the world because the equipment and suite are more difficult to disinfect and thus create concerns about cross-contamination of equipment, medical staff and patients. Quantitative CXR scoring is generally not a common practice in radiology. Radiologist reports of CXRs (including those of COVID-19) are qualitative. Our scoring system was adapted from those by Warren et al. [ 26] and Wong et al. [ 27]. In establishing our severity scoring system, a group of six chest radiologists worked together to reach consensus by evaluating two dozen images of portable CXRs of COVID-19 patients. In our scoring approach, the right and left lung were scored separately and added together. We additionally explored the sum and product of the geographic and opacity scores, and the results were similar. A few similar radiographic scoring systems have been used on COVID-19 CXRs in other studies [19,20,21,22,23], and most were based on a scale of 0–3. Each scoring system has its advantages and disadvantages. A simpler scoring system is likely to be easier to use and is efficient but may not have adequate dynamic range. A more sophisticated scoring system is likely to capture more information but may be more difficult to use and take a longer time to score. ## 4.3. CXRs Correlation with Other Clinical Variables For the GF group, essentially all the tabulated clinical variables were significantly different between survivors and non-survivors at ED admission, but none of the tabulated clinical variables was significantly different between survivors and non-survivors. This was because of the small sample size because these clinical variables were not generally obtained for GF patients prior to discharge as doing so was unnecessary. The differences were qualitatively similar to the differences at ED admission overall. For the IMV group, essentially none of the tabulated clinical variables was significantly different between survivors and non-survivors at the time of intubation but they were significantly different at outcome. These findings are not unexpected. Both survivors and non-survivors had similar CXR disease severity prior to mechanical ventilation treatment, but survivors showed improvement in CXR severity. These findings suggest that mechanical ventilation treatment improved CXR scores and patient overall outcomes. They also suggest that CXR scores are informative and could be used to monitor disease progression. The length of hospitalization or duration of IMV were not significantly different between survivors and non-survivors, but the number of days post-IMV was significantly different between non-survivors and survivors. This was because patients expired and were then removed from ventilators or patients were removed from ventilation due to a change in code status and then expired. Thus, non-survivors had a median of 0 days of post-IMV. CXR scores correlated with a few clinical variables, most notably with LDH, RR, D-dimer, and CRP, again indicating that CXRs are informative of COVID-19 disease severity. CXR scores also correlated with the duration of hospitalization and IMV in non-survivors in the IMV group. These findings suggest that CXR scores can be used to inform hospitalization and the need for IMV. The reason we did not observe a correlation in the GF cohort is likely because there was a larger percentage of patients who were not full code in the GF cohort than in the IMV cohort, which affected the duration of the hospitalization. ## 4.4. Limitations and Future Perspectives This study had several limitations. This was a retrospective study and thus could have residual confounding and unintentional data selection bias. Data were obtained from a single hospital, which may not generalize to other hospital settings. The decision to place patients on mechanical ventilators as well as mortality rates may depend on an individual hospital’s patient load, practice, available resources, and patients’ code status. Access to mechanical ventilators in this cohort was not a limiting factor in our hospital. Further studies using multiple institutional data and larger sample sizes as well as prospective studies are needed. Other therapies (except for mechanical ventilation) were not included and controlled for because the sample size was not adequately powered to do so. High mortality rates and severe CXR scores were associated with patients in palliative care. The therapeutic will of patients or family members could have influenced the outcomes and further studies are needed. We only correlated CXR scores with laboratory variables, hospitalization duration, IMV and post-IMV duration, but we did not correlate laboratory values with the severity of the COVID-19 disease, the length of the hospital stay, and mechanical ventilation as these have been reported extensively in the literature [28,29,30,31,32,33,34,35,36,37,38,39,40]. We did not compare different SARS-CoV-2 variants and thus this study cannot be generalized to different variants. There have been many studies demonstrating the value of utilizing machine/deep learning algorithms to detect SARS-CoV-2 infection using CXR scores [7,8,9,10,11,12,13,14,15,16,41]; it would be interesting to see whether CXR scores generated by machine/deep learning would produce similar results to those presented in this paper. ## 5. 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--- title: Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD authors: - Gagan Kalra - Hasan Cetin - Jon Whitney - Sari Yordi - Yavuz Cakir - Conor McConville - Victoria Whitmore - Michelle Bonnay - Jamie L. Reese - Sunil K. Srivastava - Justis P. Ehlers journal: Diagnostics year: 2023 pmcid: PMC10047385 doi: 10.3390/diagnostics13061178 license: CC BY 4.0 --- # Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD ## Abstract Background: The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of Ellipsoid Zone (EZ) At-Risk to study progression in nonexudative age-related macular degeneration (AMD). Methods: Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based EZ At-Risk percentage area measurement was calculated. Random forest-based feature ranking of EZ At-Risk was compared to previously validated quantitative OCT-based biomarkers. Results: The model achieved a detection accuracy of $99\%$ (sensitivity = $99\%$; specificity = $100\%$) for EZ At-Risk. Automatic EZ At-Risk measurement achieved an accuracy of $90\%$ (sensitivity = $90\%$; specificity = $84\%$) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean EZ At-Risk correlated with higher progression to GA at year 5 ($p \leq 0.001$). EZ At-Risk was a top ranked feature in the random forest assessment for GA prediction. Conclusions: This report describes a novel high performance DL-based model for the detection and measurement of EZ At-Risk. This biomarker showed promising results in predicting progression in nonexudative AMD patients. ## 1. Introduction Geographic atrophy (GA) is a late-stage finding in age-related macular degeneration (AMD) that results in atrophic changes in the outer retinal layers and retinal pigment epithelium (RPE). The progression to GA in AMD and resultant permanent vision loss currently affects approximately 8 million people aged 55 years or older in the United States [1]. Increased prevalence of GA in AMD is seen with increasing age and in those with European descent [2]. With improved life expectancy and an aging population, the prevalence of GA and AMD disease burden are likely to increase further in the future [3]. The progression from AMD to GA is irreversible but stochastic. This necessitates screening and monitoring in all patients with nonexudative AMD, which can be resource intensive. This motivates a search for predictive features of AMD and GA progression. Previously carried out analyses of the natural history of AMD have identified qualitative signs in early and intermediate AMD that show predictive potential in the progression of GA. These features include hyperreflective foci and drusen morphology [4,5,6,7]. Recently, the Classification of Atrophy Meetings (CAM) consensus included photoreceptor damage in the recent definitions of incomplete RPE and Outer Retinal Atrophy (iRORA). iRORA is an early indicator of disease progression and was defined as a cluster of optical coherence tomography (OCT)-based features. This cluster of features, as noted by CAM consensus definitions, are (i) the presence of hyper-transmission into the choroid, (ii) partial RPE attenuation or disruption, and (iii) evident photoreceptor loss [8]. The ellipsoid zone (EZ) is a hyperreflective band visualized in the outer retina on OCT that is constituted by the mitochondria of the outer part of the photoreceptor inner segments. The reflectance of EZ is a likely indicator of photoreceptor mitochondrial health [9]. Additionally, OCT-based measurements of EZ integrity and sub-RPE compartment metrics have high importance in predicting disease progression in nonexudative AMD and GA [4,10,11,12,13,14,15]. Specifically, EZ integrity loss adjacent to GA margins has shown an association with GA progression [12,14,16,17,18,19]. This has prompted research that investigates the spatiotemporal connection between GA expansion and regions of EZ loss [12,14,16,17,18,19]. Without advanced image analysis systems, evaluating large datasets necessitates time-consuming and expensive manual feature annotation on OCT and fundus autofluorescence images [20]. These annotations are subject to inconsistency due to human error, especially with large longitudinal datasets [20]. With several clinical trials investigating novel treatments for nonexudative AMD as well as the recent FDA approval of the first treatment for GA in AMD (i.e., pegcetacoplan), exploration of photoreceptor damage denoted by EZ loss as a biomarker for profiling progression is of great interest. Deep learning (DL)-based automated quantitative feature segmentation has addressed this challenge in clinical trials investigating a variety of pathologies and normative datasets [12,18,19,21,22,23,24]. Specifically, automated EZ segmentation using DL has shown great promise as an efficient and reproducible system for detecting and quantifying EZ integrity based on presence, distance from the RPE, or reflectivity [12,18,19]. However, there are no previously described DL systems that can automatically identify and measure areas of EZ specifically targeting at-risk areas for disease progression, such as GA development. This study describes a fully automated approach for the detection and pixel-wise segmentation of EZ regions at-risk of progressing to GA (EZ At-Risk) and/or degenerative changes using a DL-based method at the level of individual SD-OCT B-scans using a modified U-net architecture with more than 20 million parameters. Further, the measured percentage area of EZ At-Risk was tested as a potential biomarker for the prediction of GA progression in an independent dataset of nonexudative AMD patients. ## 2. Materials and Methods All patient data included in this institutional review board-approved study was de-identified before use in compliance with Health Insurance Portability and Accountability Act regulations (HIPAA) guidelines. This study complied with the tenets of the Declaration of Helsinki. Given the retrospective nature of this assessment and as it de-identified imaging data, the requirement for informed consent was waived by the institutional review board. ## 2.1. Imaging and Data Collection A total of 100,266 SD-OCT B-scans from 900 visits of 341 de-identified patients with nonexudative AMD with or without GA were utilized in model building and testing. The workflow and data-size at each stage of the process is summarized in Figure 1. These images were captured using one of two devices, a Heidelberg Spectralis HRA + OCT (Heidelberg Engineering, Heidelberg, Germany) or Cirrus HD-OCT (Zeiss, Oberkochen, Germany). The OCT images were acquired with a 6 × 6 mm macular volume cube raster protocol with 97 or 49 B-scans for the Spectralis device and 128 B-scans for the Cirrus device. This combined dataset from different manufacturers was used to simulate a real world scenario and prevent model brittleness due to lack of diversity in the dataset source. An independent dataset comprising 30,720 SD-OCT B-scans from 5-year follow-up of 120 de-identified patients (Figure 1) with nonexudative AMD with or without GA was included for the testing of EZ At-Risk as an independent biomarker in predictive analytics of GA progression. Sub-foveal GA (sfGA) in this dataset was defined as GA lesions that encroach on the foveal B-scan itself. Fovea-threatening involvement was defined as GA lesions that encroach on the central subfield (i.e., a fovea-centered circular region with a radius of 0.5 mm). Quantitative OCT-based biomarkers were defined as parameters that were measured on SD-OCT in a quantitative manner (such as a count of hyper reflective foci), in contrast to qualitative biomarkers (such as presence or absence of hyper reflective foci). Quantitative features from these images were obtained from a previously validated semi-automated DL-based approach [12,18,19,22]. These features included mean central subfield RPE–Bruchs membrane (BM) thickness (mean distance between the segmented RPE and BM layers on OCT B-scan), percentage area of partial EZ attenuation (percentage area of regions with distance between segmented EZ and RPE lines of <20 um on OCT B-scan), mean central subfield EZ–RPE thickness (mean distance between segmented EZ and RPE lines on OCT B-scan), percentage area of total EZ–RPE attenuation (percentage area of regions with distance between segmented EZ and RPE lines of 0um on OCT B-scan), and percentage area of RPE–BM attenuation (percentage area of regions with distance between segmented RPE and BM lines of 0um on OCT B-scan). ## 2.2. Ground Truth Retinal Layer Segmentation All OCT B-scans were first segmented using a previously validated DL-enabled automatic multi-layer segmentation platform for the following layers: EZ, Bruch’s membrane (BM), and RPE [21,22,25,26]. The segmented scans were rigorously corrected by trained, expert readers followed by an independent review by a senior expert image analyst to ensure high quality and consistency in the ground truth. Any discrepancies arising in the first two layers of review were reconciled with a retina specialist. This process comprised the previously validated triple-layer expert review for generation of ground truth [21,22,25,26]. GA on OCT was defined as a region’s outer retinal atrophy on the B-scan where there was the complete absence of EZ and RPE. This is denoted by the presence of hypertransmission and associated retinal atrophy, based on the CAM consensus definitions [8]. EZ At-Risk training masks were defined as regions of ellipsoid zone attenuation that excluded regions that have already progressed to GA. EZ At-Risk was defined as the occurrence of EZ_RPE thinning of ≤10 um excluding any areas of GA. ## 2.3. Ellipsoid Zone At-Risk Detection Model Binary segmentation masks for each retinal layer of interest (EZ, RPE, and BM) were exported for individual B-scans, and areas of EZ At-Risk were defined as regions of EZ attenuation (EZ-RPE thickness ≤ 10 um), excluding the areas of GA (as defined by confluence of EZ, RPE, and BM lines based on segmentation protocols). This allowed for the identification of well-defined retinal layer segmentation-based regions constituting the ground truth masks for DL model training, based on the assumption that these areas reflect pre-GA areas of outer retinal attenuation. These masks were dilated to a thickness of 10 pixels centered on the EZ segmentation line to achieve contextual mask enrichment from adjacent contiguous regions (Figure 2). ## 2.4. Training and Validation Data Eighty percent of all patients that were included in the study were randomly segregated into the training set (Figure 1). Visits from each patient with multiple visits were clustered together such that these data points only occurred in one dataset. This ensured the clear demarcation of datasets. The EZ At-Risk masks and corresponding original SD-OCT B-scans were used as training inputs for the DL model. Ten percent of all patients that were included in the study were randomly segregated into the validation set. This dataset was used to iteratively assess and improve performance during different epochs of model training. ## 2.5. Testing Data Ten percent of all patients were randomly segregated in a hold-out test set (Figure 1). This dataset was curated while ensuring that no visits from a patient in this dataset were used for model training. This previously unseen or hold-out dataset was then deployed for performance assessment of the fully trained model. Model performance was also compared between imaging obtained using the two different imaging device types. ## 2.6. Deep Learning Architecture A DL model was trained using a previously described UNet architecture with approximately 20 million parameters and 41 layers [27]. The architecture used images resized to 256 × 256 pixel as training inputs, kernel width of 5, early training stopping after 7 epochs without validation improvement, a batch size of 40, and samples per epoch of 200. The original OCT B-scans and DL outputs had matching scaling factors. This eliminated affection of the initial details of the original image. Additionally, the DL measurement was initially calculated with a very high precision (10,000th decimal place) to minimize errors in measurement due to resizing. A binary cross-entropy loss function and root mean squared optimizer was utilized in model training with a learning rate of 1 × 10−4. ## 2.7. Automatic Retraining After the initial round of training, a sample of 100 patches was randomly selected from the training set and profiled for their F-scores, such that the 30th percentile of F-scores was identified (Figure 1). The patches with F-scores lower than the 30th percentile were duplicated within the training set and a second round of training was conducted using the same model and parameters. This was used as a data augmentation method that allowed fine-tuning of the model on training examples with poor performance. ## 2.8. Statistical Analysis For the performance assessment of the DL model, the entire hold-out test set was evaluated for accuracy, specificity, and sensitivity for obtaining pixel-wise EZ At-Risk percentage area measurement. The receiver operator curve (ROC) was plotted for the DL model and the area-under-curve (AUC) was calculated to assess performance. Additionally, intraclass correlation (ICC) coefficient was calculated to compare the DL model output and ground truth measurements of the EZ At-Risk area. In an independent longitudinal dataset of 120 patients, the percentage of the EZ At-Risk area was assessed for correlation against growth in the GA area. Further, a random forest model with 10-fold cross validation was created in this independent dataset to predict progression to sub-foveal GA development using percentage area of EZ At-Risk, mean central subfield RPE–BM thickness, percentage area of partial EZ attenuation, mean central subfield EZ–RPE thickness, percentage area of total EZ–RPE attenuation, and percentage area of RPE–BM attenuation. Ranked feature importance was assessed for the EZ At-Risk area compared to other quantitative OCT-based biomarkers. The ROC was plotted for the random forest model and the AUC was calculated to assess performance. Pearson’s correlation was used to assess correlation of the EZ At-Risk area and growth in the GA area. A two-sample two-sided t-test was used to compare the mean EZ At-Risk area between eyes that showed growth in the GA area and those that did not show growth in the GA area. Statistical significance was assumed at $p \leq 0.05.$ The model training, statistical analysis, and data visualizations were performed using Python (v3.9.11) and R (v4.0.1, Bell Laboratories, Murray Hill, NJ, USA). Model training was carried out locally using workstations with an Intel Xeon processor (10 cores, 20 threads), dual NVIDIA RTX 2080-Ti graphics processing unit setup (12 gigabytes of VRAM each), and 128 gigabytes of system memory. ## 3.1. DL-Based Automated Detection and Measurement of Regions with EZ At-Risk Binary detection of the presence of EZ At-Risk at the level of the entire OCT volume using the fully automated DL model achieved an accuracy of $99\%$ with a sensitivity of $99\%$ and a specificity of $100\%$. This binary detection performance at the level of an individual OCT B-scan achieved an accuracy of $87\%$ with a sensitivity of $96\%$ and a specificity of $73\%$. Automated measurement of EZ At-Risk percentage area at the level of the individual OCT-B scan achieved an AUC of $97\%$, an accuracy of $90\%$, a sensitivity of $90\%$, and a specificity of $84\%$. The model output on the OCT B-scans can be visualized in Figure 3. The ROC visualizing the model performance across different thresholds is illustrated in Figure 4. In the subset analysis assessing model performance for the two device types, comparable performance was achieved with both devices (accuracy: $78\%$ vs. $83\%$) with slightly higher performance with the Spectralis device. The percentage area of regions with EZ At-Risk automatically detected using the DL-based model showed an ICC of 0.83 ($p \leq 0.001$) when compared with the ground truth annotation. These results are summarized in Table 1. ## 3.2. Correlation with Growth of GA Lesions In the independent assessment of the longitudinal follow-up of patients with nonexudative AMD, percentage area of automatically detected EZ At-Risk showed significant positive association with the growth of geographic atrophy at year 5 of follow-up ($p \leq 0.001$). The eyes that showed an increase in geographic atrophy lesion area showed a significantly higher ($p \leq 0.001$) mean percentage area of EZ At-Risk at the baseline ($7.3\%$) compared to eyes that did not show any increase in GA lesion area ($2.3\%$) at year 5 of follow-up ($$p \leq 0.001$$). Eyes that showed conversion to sub-foveal GA or threatening foveal involvement had higher EZ At-Risk at the baseline ($7.8\%$) compared to eyes that showed no conversion or fovea-threatening involvement ($1.8\%$) at 5 years of follow-up ($p \leq 0.001$). ## 3.3. Random Forest Prediction of Sub-Foveal GA The random forest generated with 10-fold cross-validation using only the higher-order OCT features achieved an AUC of 0.90 with a sensitivity of $80\%$ and a specificity of $90\%$ for prediction of conversion to sfGA or fovea-threatening involvement. The model features that showed the highest importance in achieving this classification were percentage area with total EZ attenuation, percentage area of EZ At-Risk, percentage area of partial EZ attenuation, baseline EZ–RPE central subfield thickness (in µm), baseline mean RPE–BM central subfield thickness (in µm), and percentage area of complete RPE atrophy. A representative case showcasing the utility of EZ At-Risk measured at the baseline in predicting sfGA or fovea-threatening involvement at year 5 is shown in Figure 5. The ROC curve for the random forest classifier is depicted in Figure 6. ## 4. Discussion This report demonstrates a DL-enabled high-performance automated model for the detection of regions of EZ At-Risk (accuracy = $99\%$, sensitivity = $99\%$, specificity = $100\%$) in patients with nonexudative AMD using OCT B-scans from different device types and pixel-accurate measurement of these regions (AUC = $97\%$, accuracy = $90\%$, sensitivity = $90\%$, specificity = $84\%$). Further, the utility of EZ At-Risk as an automatically measured GA biomarker was studied by assessing correlations between the percentage area of EZ At-Risk at baseline with growth in the GA area in an independent longitudinal cohort of nonexudative AMD patients over 5 years ($R = 0.48$, $p \leq 0.001$) (Figure 1). Finally, the ranked feature importance of this biomarker in predicting progression to sfGA and fovea-threatening involvement was shown to be one of the highest using a random forest classifier that comprised additional previously reported higher-order OCT features [12]. This report demonstrates the utility of DL-enabled fully automated approaches for quantifying EZ At-Risk regions in nonexudative AMD patients. This approach can detect EZ integrity loss, representing photoreceptor damage. Previous attempts to measure EZ integrity loss in other pathologies include an automated detection of EZ integrity loss related to trauma. This pipeline had an accuracy of $85\%$ with a sensitivity of $85\%$ and specificity of $85\%$ [28]. Another automatic DL-based quantification algorithm was used to detect EZ loss in 85 hydroxychloroquine retinopathy patients with an overall accuracy of $90\%$ [29]. Similarly, automated EZ integrity loss detection in mild diabetic retinopathy achieved an accuracy of $90\%$ in a small cohort of 13 patients [30]. A recent method utilized 40 OCT volumes from 40 patients with diabetic macular edema and retinal vein occlusion to develop an ensemble-based approach to achieve accurate segmentation and quantification of the photoreceptor layer [31]. This method was then utilized to automatically obtain photoreceptor thickness maps to assess GA progression in the FILLY clinical trial with 57 eyes over a 12-month follow-up [32]. Authors described utilization of the local progression rate in an attempt to holistically capture disease progression, as opposed to merely assessing global disease burden [32]. The current report, by comparison, describes a model that is trained on a much larger and diverse dataset comprising 341 patients with dry AMD for training and testing, followed by an independent longitudinal cohort of 120 eyes with a 5-year follow-up, which should significantly improve performance generalizability. Additionally, the current report achieves a highly accurate segmentation of EZ regions at-risk for progression to GA. This is in contrast to the previously described method that only achieves segmentation of the overall photoreceptor layer and requires additional thickness map analysis of potential regions at risk of progression [31,32]. Therefore, this novel approach described in the current report is higher-order in comparison to previously described methods. Even though a direct comparison is not possible between the methods due to the nature of differences between the segmentation outputs, the AUC of previously described ensemble approach was $96\%$ [31], which is lower in comparison to the currently described method (AUC = $97\%$). Additionally, the current method retained this high performance across different device types (i.e., Cirrus and Spectralis devices), whereas the previous reports are limited to only one device type [33,34]. The pixel-wise segmentation accuracy of EZ At-Risk regions of $90\%$ described in the current report is in line with the previous reports from other diseases [29,30,31]. Another important distinction from previously reported methods is the exclusion of regions with EZ loss in areas of pre-existing GA from the model training and analysis. This allowed for the measurement of EZ At-Risk—an improved biomarker targeting areas of de novo development of GA and potential expansion of existing GA lesions. Recent studies have used EZ integrity loss and sub-RPE quantification to assess GA expansion [4,10,11,12,13,14]. In an analysis of 30 eyes, 13 eyes were shown to have preceding outer retinal disruption and EZ loss on OCT imaging that progressed to GA [16]. In another report of 49 patients, authors described that sub-RPE drusenoid deposits and associated EZ loss were associated with the progression of GA [15]. In a previous report from 29 patients, authors described a predictive GA model that utilized an automated feature extraction pipeline. Even though that study’s results might not be generalizable due to small sample size, loss of EZ integrity was one of its top features for predicting GA growth [10]. In an analysis of 137 patients, authors described sub-RPE compartment changes and EZ integrity loss that allowed for the creation of an automated predictive model of sub-foveal GA progression [12]. A randomized clinical trial of risuteganib therapy in 39 subjects showed improvement in EZ integrity with associated improvement in visual acuity and slowed progression of GA [18]. Recently, independent randomized clinical trials of avacincaptad pegol and elamipretide have shown slower progression of GA in eyes that had reduction in EZ integrity loss [33,34]. The current report builds on these previously reported findings by utilizing a large training dataset for creation of a fully automated DL-model that can automatically measure areas of EZ At-Risk in areas where GA is not present. This measurement was used in an independent random forest analysis of 120 eyes based only on quantitative OCT features for prediction of progression to subfoveal GA and fovea-threatening involvement. In this analysis, the percentage of EZ At-Risk area was identified as one of the most significant features for predicting GA progression. Quantification of EZ At-Risk has clinical applications in predictive modeling and prognostication of nonexudative AMD and GA [4,10,11,12,13,14]. Additionally, automatically generated metrics could profile patients in large scale clinical trials. Patients with high levels of EZ At-Risk may be at higher risk for progression and selected for enrollment, resulting in higher yield, and reducing the number needed to treat. This study has some important limitations that should be acknowledged. The data and analysis came from a single, quaternary-level academic institution and the results derived from this study may not be generalizable to other populations or sites. Future work is currently underway that explores the applicability of this model in a large dataset from other clinical sites. In addition, this study did not thoroughly examine the variations in U-Net architecture to find the most optimal architecture for EZ At-Risk detection and segmentation as the purpose of this analysis was to establish novelty and proof of concept for EZ At-Risk itself. A more complex neural architecture may have improved model performance, and this is currently being tested for future applications of this model. Finally, the random forest feature ranked importance analysis was based on a relatively small sample size of 120 patients. Additionally, the absence of OCTs between follow-up intervals makes it challenging to determine the exact timing of sfGA conversion. Efforts are currently underway to validate these findings in a large external dataset that can allow for the standardization of the prediction time interval as well. However, this study utilized a large dataset for deep learning, using a previously validated semi-automated multi-layer retinal layer segmentation approach. The model performance could be generalized well across imaging devices tested. Further, percent area of EZ At-Risk area was tested as a biomarker for GA progression in an independent longitudinal dataset with 5 years of follow-up; it ranked highly in random forest-based feature importance in predicting sfGA and fovea-threatening involvement conversion. 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--- title: Age and Gender Differences in Anthropometric Characteristics and Motor Performance of 3 through 6 Young Kids Aged (Pilot Study) authors: - Almir Atiković - Ekrem Čolakhodžić - Edin Užičanin - Emilija Petković - Amra Nožinović Mujanović - Edin Mujanović - Jasmin Zahirović - Naida Mešković - Ana Lilić journal: Children year: 2023 pmcid: PMC10047391 doi: 10.3390/children10030590 license: CC BY 4.0 --- # Age and Gender Differences in Anthropometric Characteristics and Motor Performance of 3 through 6 Young Kids Aged (Pilot Study) ## Abstract Background: It is crucial to evaluate children’s motor coordination and strength to identify possible motor deficits on the right or left side of the body. However, whether a distinction exists in children aged 3–6 must be clarified. The goal of the current research was to investigate the differences in motor skills between preschool boys and girls, dominant and non-dominant hands or legs, in children of preschool age. [ 2] Methods: The present study was conducted on a sample of children (boys, $$n = 52$$; girls, $$n = 52$$; age range, 3–6 years). Three motor tests evaluated on both sides of the body served as the sample of factors used to measure athletic performance. Leg tapping (15 s), hand tapping (15 s), and a maximal hand grip strength (HGS) test kg. [ 3] Results: The study’s findings show no statistically significant variations in preschool boys’ and girls’ motor skills. Preschool girls had better results in the right leg tapping than preschool boys t [98] = 2.08; p ≤ 0.04. We found a significant difference between genders aged 3–4, 4–5, and 5–6 years. No correlation was found between the girls’ three variables and age. A small but significant positive correlation was found between dominant hand tapping and age r2 [52] = 0.21; p ≤ 0.01, dominant leg tapping and age r2 [52] = 0.20; p ≤ 0.01 and dominant HGS and age r2 [52] = 0.17; p ≤ 0.01. No noticeable differences were identified when comparing the dominant side with the non-dominant side in each group. The results show that most children prefer to use their right hand and right leg as their dominant sides. [ 4] Conclusion: The authors of this study focus on the functional (frequency of movements) and dynamic (differences in muscle strength between body sides) elements of asymmetry. Future studies should examine the influence of morphology on performance with the dominant or non-dominant body side. ## 1. Introduction Children’s physical exercise levels have changed considerably in recent decades [1,2]. For example, children are increasingly being transported to school by vehicle or bus rather than cycling or walking [3], indoor pursuits are replacing outdoor active play, and involvement in organized sports is decreasing [4,5]. Hypokinesis, or the absence of physical activity, has been addressed from various health-related perspectives and documented in numerous studies worldwide. Hypokinesis is frequently associated with excess weight and obesity and can be found in preschool-aged children. Over the last two decades, many studies have been conducted on preschool children’s physical activity levels and motor abilities [6,7]. Generally speaking, boys and girls are regularly guided into different gender-based physical activities from an early age. It is important to note that motor abilities and habits develop the most in the period from the third to the tenth year of life, and the fact is that they can be mainly influenced in the preschool age, that is, from the fourth to the seventh year of life [8]. It is impossible to compensate for the lack of motor tasks or their total absence during a child’s growth in later stages of development, growth, and maturation. Specifically, as a child matures and grows, the impact of different kinesiological stimulations on them decreases gradually (the so-called critical phases). From ages 4–6 years, a child’s motor and intellectual development could be slowed by a lack of motor experience and chances for participation in kinesiological activities, all forms of planned physical exercise [9]. It is believed that hand and leg preference and asymmetries in motor coordination and speed emerge at the same time. In typically developing five- to seven-year-old youngsters, Denckla [10] discovered right-handed finger repetition was quicker than left-handed finger repetition. While the proportion of kids who performed quicker with their right hand rose with age, the size of this right-hand advantage shrank as people aged. Hand preference indicates that the preferred hand is faster and more coordinated than the nonpreferred hand. The favored hand starts to outperform the nonpreferred hand in speed and coordination throughout the first five years of life. Hand grip strength (HGS) is a significant factor in growth, development, injury, exercise, rehabilitation, and regeneration [11]. Due to its importance as a marker for hand function, HGS has been assessed the most frequently. To establish baseline values to strive for when attempting to regain normal function and avoid early locomotor dysfunction in children, normative values of HGS must be established in a sample of healthy children [12]. Developing locomotor skills, the capacity to manage objects and nervous system maturity all improve in preschoolers. However, people’s basic motor abilities and physical fitness vary significantly [13,14]. Physical exercise is complex and involves several behavioral factors, including subjective elements (as in sports) and quantifiable elements (e.g., frequency, duration, and intensity). One of the most crucial movement skills is locomotion, which allows a child to manage an object in real-world circumstances, like throwing a ball. Therefore, a high level of movement proficiency could boost physical activity engagement [15]. There is a wealth of research on the connections between HGS and many essential medical indicators in various populations. Measurement of HGS is non-invasive, simple, and affordable. It may enable the investigation of acute changes in nutritional status and evaluation and prognosis of muscular strength in juvenile idiopathic arthritis, congenital myotonic dystrophy, and traumatic hand injuries [16,17,18,19]. Children’s height, weight, muscular mass, and bone density all impact HGS [20]. HGS is clinically essential for evaluating and comparing surgical techniques, tracking rehabilitation progress, documenting treatment reactions, and determining the degree of disability following injury. HGS is also used to evaluate the performance of athletes who rely on a proper level of grip strength to increase control and performance while minimizing potential injuries [21]. There is a wide range in HGS. To understand how grip strength changes with age, it is crucial to measure it during development. Without normative baseline data, we cannot distinguish between the impacts of growth, illness progression, and interventions, and surgical interventions considerably impact HGS [20]. Various dynamometers exist, including hydraulic, pneumatic, mechanical, and electronic devices [22]. These dynamometers’ mechanisms, performance, display mode, and energy supply differ. Among the most well-liked and extensively applied dynamometers is the TAKEI (Takei equipment) analog dynamometer grip A for infants (TKK5825 hand dynamometer). This study aimed to compare the motor skills of preschool boys and girls and establish how advanced their motor skills were. In addition, this study aimed to establish the dominant and non-dominant hand or leg in children of preschool age (3–6 years) and the percentage ratio. ## 2.1. Participants This study included $$n = 104$$ preschoolers between the ages of 3 and 6 years (mean age of boys: 4.35 years, standard deviation: 0.88 years, and mean age of girls: 4.49 years, standard deviation: 0.73 years). The sample included 52 girls ($50\%$) and 52 boys ($50\%$). Children were enrolled in private preschools in Tuzla, Bosnia and Herzegovina. If a child had upper limb orthopedic or neuromuscular treatment, they were excluded, musculoskeletal problems, or upper extremity activities daily, neurological conditions that affected their upper extremities, or visual, auditory, or vestibular deficits. ## 2.2.1. The Collected Data Before providing written consent, kids’ parents were informed of the testing regulations and standards. To reduce inter-observer bias, all data were collected by the same examiner who trained all children to perform the procedures. Each child’s age and gender were recorded. Body weight and height were measured with a precision of 0.05 kg and 0.1 cm using the standard height scale and a typical digital weighing scale. The variables used to assess physical fitness [8,23] included three motor tests measured on each bodily side: frequency of movement (foot tapping, number/15 s, and hand tapping, number/15 s); and maximal strength (grip strength, kg). Hand tapping is tapping the fingers alternately against tapping boards for 15 s; the number of accurate cycles (one cycle is two taps) during the 15 s is tallied. Leg tapping is striking with the leg against tapping boards in a counterclockwise motion for 15 s. Again, correct cycles (one cycle is two taps) are recorded. Grip strength: squeeze a Takei dynamometer with a hand in a rotationally neutral position as hard as feasible; the grip’s width is independently adjustable; the experiment is done. Before the test, the examiner demonstrated standardized positioning for holding the hand dynamometer bulb. All participants were instructed: “squeeze the bulb as hard as you can for the count of three seconds.” A 2–5 s rest period was provided between trials, allowing the examiner to record the maximal HGS. Three trials were performed with each hand to avoid fatigue, alternating between dominant and non-dominant hands by TAKEI (Takei equipment) analog dynamometer grip A for infants (TKK5825). ## 2.2.2. Data Analysis We calculated the measures of central tendency (M and SD) and the multiple Pearson correlation coefficient (r2) to identify the variables correlated and then performed an independent t-test; the means on the left and right sides should be compared, girls and boys, and the dominant and non-dominant side of the body. Next, calculate the value of Cohen’s d and the effect size correlation, rYl, using the t-test value for a between-subjects t-test and the degrees of freedom. Results with a p ≤ 0.05 were considered significant. So each analysis was done using SPSS 23.0 for Windows (IBM Corporation, New York, NY, USA). Cohen’s $d = 2$t/√(df)[1] rYl = √(t2/(t2 + df))[2] ## 2.3. Detection of Dominance Each kid was instructed to sit at a proper table in front of the examiner while facing the chair. The youngster was told to take a pencil from the table and mark the white paper with a circular or a line. The examiner noted that the hand that drew the shape was the dominant hand [24]. Three-foot preference tasks were systematically given to each subject: kick the ball [25,26,27]. ## 3. Results The characteristics of the study group are described briefly in Table 1 according to the two groups. Of the $$n = 104$$ participants, there were 52 ($50.0\%$) girls and 52 ($50.0\%$) boys. The mean age difference between boys and girls was not statistically significant (t [102] = −89; p ≤ 0.373). However, these data did not show any significant gender differences. Table 2 presents the results of descriptive details relating to the motor skills of young boys and girls in preschool (ages 3–6). By observing the values of the t-test presented in (Table 1), it can be assumed that the t-test for independent samples was used to identify the differences between the two participant groups. The findings revealed significant differences between preschool boys and girls in one out of eight tested variables. Preschool girls had better results in the right leg tapping than boys (t [98] = −2.08; p ≤ 0.04; $d = 0.42$; $r = 0.20$). Across all seven tests; there were no noticeable differences. No correlation was found between girls at all three variables and age. Results indicate no linear correlation. A Pearson correlation coefficient between boys’ age and dominant hand tapping, leg tapping, and hand grip strength tests was assessed with Pearson correlation coefficient, shown as a correlation in Figure 1, Figure 2 and Figure 3. A small but significant positive correlation was found between dominant hand tapping and age r2 [52] = 0.21; p ≤ 0.01), dominant leg tapping and age r2 [52] = 0.20; p ≤ 0.01 and dominant hand grip strength and age r2 [52] = 0.17; p ≤ 0.01. In each of the groups examined, there were no noticeable differences between the dominant and non-dominant sides Table 2. During the measurement, it is obvious that the dominant side of the body is more on the right arm or leg than the left. The results show us that the dominant compared to the non-dominant looks like this: Hands dominant: boys’ right hand $85.1\%$, girls’ right hand $96.1\%$, legs dominant: boys’ right leg $80.9\%$, girls’ right leg $88.2\%$. In general, the left side of the body is used less already in early preschool age. Age and motor skills are among the three age groups’ physical characteristics, which are presented in Table 3. values for hand tapping, leg tapping, and grip strength according to age. As (Table 4), show statistical significant difference was found for girls in the: HTL-left hand 3–$\frac{4}{4}$–5 y. (p ≤ 0.015); LTR-right leg 3–$\frac{4}{4}$–5 y. (p ≤ 0.003); LTR-right leg 4–$\frac{5}{5}$–6 y. (p ≤ 0.033); LTR-left leg 3–$\frac{4}{4}$–5 y. (p ≤ 0.005); LTR-left leg 4–$\frac{5}{5}$–6 y. (p ≤ 0.007); HGSR-right hand 3–$\frac{4}{4}$–5 y. (p ≤ 0.002); HGSL-left hand 3–$\frac{4}{4}$–5 y. (p ≤ 0.000). t-test results also showed differences between boys and girls in the: HTR-right hand 3–$\frac{4}{4}$–5 y. (p ≤ 0.038); HTR-right hand 4–$\frac{5}{5}$–6 y. (p ≤ 0.040); HTL-left hand 3–$\frac{4}{4}$–5 y. (p ≤ 0.004); HTL-left hand 4–$\frac{5}{5}$–6 y. (p ≤ 0.046); LTR-left leg 3–$\frac{4}{4}$–5 y. (p ≤ 0.007); HGSR-right hand 3–$\frac{4}{4}$–5 y. (p ≤ 0.046); HGSR-left hand 3–$\frac{4}{4}$–5 y. (p ≤ 0.005). ## 4. Discussion The hand-and-leg tapping HGS test is presently used because it is inexpensive and provides valuable information on muscle, nerve, bone, or joint disorders. However, it has also been linked to poor bone mineral density, poor cognition, and cardiovascular disease risk factors in kids and teenagers [28]. Boys were not significantly better at performing motor tests for predicting explosive and grip strength, while girls were not significantly superior in the frequency of simple movements. Our findings align with the findings provided by Bala et al. [ 6]. According to the study, all hand- and leg-tapping HGS scores significantly increased with age. Young children’s rapid increases in speed and grip power may be attributed to external variables like increased physical exercise with development or nutritional status, or they may result from variations in children’s growth rates concerning weight and height. Ploegmakers et al. [ 24] have confirmed these results. With advancing age, there is a linear trend toward better finger-tapping performance. The finger-tapping test for preschoolers did not significantly depend on the child’s gender. Exercise can help with speed and motor coordination [29]. One study included male and female pianists, and the authors found no significant effect of gender on finger-tapping speed [30]. This result might be explained by the training’s effect on balancing the differences between boys and girls. Performance, in terms of gender, among preschoolers likely depends on the nature of the task. For instance, studies on preschoolers’ visual-motor integration have consistently shown that females outperform boys [31]. It was discovered that the dominant hand’s grasp power was superior to that of the non-dominant hand. This might be brought on by the emergence of handedness between the ages of 3 and 6. As a result, the dominant hand is used for bodily tasks more often than the non-dominant hand. According to Souza et al. [ 32], the dominant hand’s grasp strength is $10\%$ higher than the non-dominant hand’s grip strength in both genders and at all ages. This was in line with the current research findings, which verified that toddlers between the ages of 3 and 6 have stronger dominant hands. Most research on how children acquire their motor abilities suggests that between the ages of 3 and 6 years, hand and leg preference begins to appear for several activities. By kindergarten, most typically developing kids regularly display a distinct hand preference, with about $90\%$ doing most things with their right hand or leg. The remaining $10\%$ of kids prefer using their left or right hands equally or exhibit delayed handedness. This suggests that age-related changes may persist throughout the school years [6,33,34]. Our research indicates identical results obtained through previous analyses. According to the findings, most children prefer to use their right hand and right leg as their dominant sides. Participants produced more significant hand- and leg-tapping HGS values using their dominant hand than their non-dominant hand, regardless of age or gender. Such findings are consistent with recent findings from a cross-sectional survey of more than 2000 children and teenagers. Furthermore, these improved outcomes for the dominant hand over the non-dominant hand are unrelated to any particular hand form [35]. The maturity of the corpus callosum or the varying growth rates of the cerebral hemispheres may be markers for the development of motor skills, precisely the developmental pattern of asymmetries in left- vs. right-sided performance. Performance differences between activities on one’s favored side and those on their nonpreferred side seem to follow a developmental trajectory that starts tiny (infancy), then (from preschool through adolescence) becomes more and more dominant in favor of one’s developing preferred side [36], and lastly, after the age of nine, the ability to perform repeated finger tapping, groups of finger movements, and repetitive foot tapping decrease and even “adjusts” between both the right and left sides of the body [37]. In contrast to children with neurodevelopmental disorders, this growth pattern appears differently in healthy males and healthy girls [38]. This study investigated the relationship between different levels of physical fitness and cognitive functions in boys and girls. Step counts, physical education classes, and gender were all linked to particular brain results. These results may be crucial for supporting children’s school education and successful health promotion. Physical education classes for separate sexes may help kids’ cognitive development more. In order to confirm these findings, randomized studies are required [14]. Some authors [39] believe that for this age, improving motor skills and other working conditions (like water) can have better effects on developing motor skills. The authors conclude that a water initiation program integrating educational methodologies based on motor games is more effective than a traditional program based on motor repetitions. ## 5. Conclusions In this article, the authors highlight the dynamical (differences in muscle strength between body sides) and functional (frequency of movements) aspects of asymmetry. Future studies should examine the influence of morphology on performance with the dominant or non-dominant body side. Early childhood education curriculum planners should be conscious of this and include fine motor training and activities on both the left and right sides of the body as a crucial component of the curriculum. The findings and data from this study have the potential to improve physical education programs that have a direct influence on children’s social and psychological health. This is due to the importance of hand holding in children’s play, handwriting, daily life, and sports activities. ## References 1. Andersen L.B., Harro M., Sardinha L.B., Froberg K., Ekelund U., Brage S., Anderssen S.A.. 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--- title: 'The Molecular Mechanism of the TEAD1 Gene and miR-410-5p Affect Embryonic Skeletal Muscle Development: A miRNA-Mediated ceRNA Network Analysis' authors: - Wenping Hu - Xinyue Wang - Yazhen Bi - Jingjing Bao - Mingyu Shang - Li Zhang journal: Cells year: 2023 pmcid: PMC10047409 doi: 10.3390/cells12060943 license: CC BY 4.0 --- # The Molecular Mechanism of the TEAD1 Gene and miR-410-5p Affect Embryonic Skeletal Muscle Development: A miRNA-Mediated ceRNA Network Analysis ## Abstract Muscle development is a complex biological process involving an intricate network of multiple factor interactions. Through the analysis of transcriptome data and molecular biology confirmation, this study aims to reveal the molecular mechanism underlying sheep embryonic skeletal muscle development. The RNA sequencing of embryos was conducted, and microRNA (miRNA)-mediated competitive endogenous RNA (ceRNA) networks were constructed. qRT-PCR, siRNA knockdown, CCK-8 assay, scratch assay, and dual luciferase assay were used to carry out gene function identification. Through the analysis of the ceRNA networks, three miRNAs (miR-493-3p, miR-3959-3p, and miR-410-5p) and three genes (TEAD1, ZBTB34, and POGLUT1) were identified. The qRT-PCR of the DE-miRNAs and genes in the muscle tissues of sheep showed that the expression levels of the TEAD1 gene and miR-410-5p were correlated with the growth rate. The knockdown of the TEAD1 gene by siRNA could significantly inhibit the proliferation of sheep primary embryonic myoblasts, and the expression levels of SLC1A5, FoxO3, MyoD, and Pax7 were significantly downregulated. The targeting relationship between miR-410-5p and the TEAD1 gene was validated by a dual luciferase assay, and miR-410-5p can significantly downregulate the expression of TEAD1 in sheep primary embryonic myoblasts. We proved the regulatory relationship between miR-410-5p and the TEAD1 gene, which was related to the proliferation of sheep embryonic myoblasts. The results provide a reference and molecular basis for understanding the molecular mechanism of embryonic muscle development. ## 1. Introduction Skeletal muscle development, also known as myogenesis, is a complex multistep process that requires very precise, space- and time-controlled regulation [1]. All trunk and limb skeletal muscles are formed by the dermomyotome and the dermomyotome-derived myotome, while head muscles are developed from the cranial mesoderm [2]. It starts with the specification of the myogenic lineage (i.e., mesodermal progenitors) differentiating into myoblasts [1]. In the early embryonic stage, muscle growth and development determine the number and cross-section of muscle fibers, and this biological process needs to go through four key stages. First is the myoblasts’ formation: the specific differentiation of pluripotent stem cells into myogenic progenitor cells is the basis, while the myogenic progenitor cells in the somites migrate to the limb and differentiate into myoblasts. Second is the formation of nascent myotubes: myoblasts gather to form nascent myotubes through primary fusion. Third is the formation of myotubes: myotubes are formed by the secondary fusion of additional mononucleated myoblasts with nascent myotubes under the control of specific factors. Fourth is the maturation of myofibers: with a series of complex processes, when the proliferation of myofibers reaches a certain number, proliferating stops, myofibers become thick, eventually forming mature myofibers [3]. Specific myogenic markers are expressed at different stages in cells of myogenic lineage during myogenesis, such as SIX homeobox $\frac{1}{4}$ (Six$\frac{1}{4}$), paired box 3 (Pax3), paired box 7 (Pax7), myogenic differentiation 1 (MyoD), myogenic factor 5 (Myf5), myogenic regulatory factor 4 (MRF4), myogenin (MyoG), myosin heavy chain (MyHC), and mouse muscle creatine kinase (MCK) [3,4]. The members of the MyoD family of myogenic regulatory factors (MRFs), MyoD, Myf5, MRF4, and MyoG, are master regulators of myogenic determination and differentiation. Transcription factor networks involving Pax3 are upstream of MyoD family proteins and induce myogenic specification of muscle progenitor cells [2]. Most sheep are monotocous, and the embryo size and pregnancy physiological structure are very similar to humans. Therefore, the pregnant sheep is a good model for the study of placental and fetal physiology [5,6,7,8]. Sheep are also a great model for research on embryonic skeletal muscle development. Muscle development during gestation has a potentially lasting effect on postnatal growth performance and muscle maintenance in full-grown animals. Moreover, the growth rate of prenatal skeletal muscle is significantly higher than that in the postpartum period. Previous studies have shown that sheep muscle development continues during the second half of gestation [9]. The histological characteristics of the ovine fetus have shown that myotube fusion, myofiber transformations, and modulation are the main factors that promote late-stage growth [10]. As such, the analysis of the temporal and spatial characteristics of fiber transformations and a comprehensive molecular understanding of the mechanisms that occur during the late stage are of utmost importance to interpreting the mechanism of muscle development. Understanding microRNAs (miRNAs) and their regulatory functions are key to the central dogma of genetics [11]. The study of competitive endogenous RNA (ceRNA) networks has led to the identification of new regulatory mechanisms for post-transcriptional regulation, which has facilitated the exploration of the regulation mechanism of genetic information [12,13]. The ceRNA network allows for different types of RNA transcripts (long noncoding RNA, circular RNAs, genes, and pseudogenes) to communicate with each other by competing with the binding of shared miRNA binding sites (MRE) [14]. In this study, we constructed a ceRNA network by integrating long noncoding RNA (lncRNA), miRNA, and mRNA data based on the whole transcriptome profile. Although specific functions of most miRNAs have yet to be elucidated, miRNA-mediated regulation is an indispensable part of the gene regulation mechanism [15,16]. Evidence has shown that miRNAs exert extensive regulatory functions on genes, which are closely related to the regulation of gene expression in various cell processes. In this study, small RNA sequencing data were combined with data on lncRNA and mRNA to construct an integrated ceRNA network. This network was subsequently used to identify key genes, miRNAs, lncRNAs, transcription factors, and signaling pathways. In addition, we verified the gene function of the De-miRNAs and target genes in sheep primary embryonic myoblasts to reveal the molecular mechanism of embryonic muscle development in sheep. ## 2.1. Sequencing and Quantitative Samples Nine pregnant Chinese Merino sheep were slaughtered humanely, three of which were 85 days pregnant, 105 days pregnant, and 135 days pregnant. The longissimus dorsi of the embryo on 85 days (D85), 105 days (D105), and 135 days (D135) was selected with three sample replicates for small-RNA sequencing (D85-1, D85-2, D85-3, D105-1, D105-2, D105-3, D135-1, D135-2, and D135-3). Quantitative real-time PCR (qRT-PCR) was used to validated the small-RNA sequencing data, and the samples were the original longissimus dorsi of Chinese Merino sheep embryos. Additionally, to examine the expression of key miRNAs and target genes, the longissimus dorsi from the key developmental stages of Hu sheep, including newborn; weaning; and 3-, 4-, 5-, and 6-month-old Hu sheep, as well as 4- and 6-month-old Gangba sheep, were selected and used for the qRT-PCR. ## 2.2. Small-RNA Sequencing Total RNA was extracted using TRIzol reagent (Invitrogen, Pleasanton, CA, USA), following the manufacturer’s protocol. Total RNA quantity and purity were analyzed using a Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA) with an RIN number > 7.0. Approximately 1 µg of total RNA was used to prepare a small RNA sequencing library using TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, CA, USA), according to the manufacturer’s instructions. Then, single-end sequencing (36 bp or 50 bp) was performed on an Illumina Hiseq 2500 at the LC-BIO (Hangzhou, China), according to the manufacturer’s protocol. The miRNAs’ raw data were uploaded to the Gene Expression Omnibus (GEO) database (GSE127287), which also contained lncRNA and mRNA data. ## 2.3. Differential Expression and Function Enrichment Analysis of miRNAs The raw reads were processed using the ACGT101-miR program (LC Sciences, Houston, TX, USA) to remove adapter dimers, low-quality sequences, short sequences (<18 nucleotides), N-containing sequences, and repetitive sequences. We compared the preprocessed sequences with the mRNA, RFam, and Repbase databases to obtain valid data and perform a length distribution statistical analysis. The differential expression of miRNAs based on normalized deep-sequencing counts was analyzed using edgeR software (p ≤ 0.05 and \|log2fold-change\| ≥ 1) [17,18]. In addition, functional enrichment analysis of the differentially expressed miRNAs (DE-miRNAs) identified Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed using the online resource DAVID (version 6.8) “https://david.ncifcrf.gov/ (accessed on 20 October 2018)” [19]. ## 2.4. Construction of the miRNA–mRNA Networks The biological role of miRNAs mainly involves negatively regulating the expression of downstream genes [20,21]. Therefore, the downstream target genes of differentially expressed miRNAs were predicted based on the MiRanda (v3.3a), TargetScan “https://www.targetscan.org/vert_80/ (accessed on 20 October 2018)”, and miRNet databases “https://www.mirnet.ca/ (accessed on 20 October 2018)” [22,23]. Finally, the target genes were predicted by the upregulated (downregulated) miRNAs, and the downregulated (upregulated) mRNAs were subjected to analysis to select reliable target genes and construct a miRNA–TG network [24]. ## 2.5. Construction of the miRNA–TG–Pathway Network The DE-miRNAs were uploaded to the BINGO plugin of Cytoscape (version 3.6.1). The gene annotation file of sheep “http://geneontology.org/ (accessed on 20 October 2018)” was downloaded and imported into BINGO (Cytoscape 3.9.1), with a P threshold for drawing [25]. Combined with the results of the miRNA–mRNA network, the same DE genes were extracted and uploaded to the plugin ClueGo and CluePedia (Cytoscape 3.9.1) to enrich the function [26,27]. The significance threshold was set to 0.05, and the minimum limit of gene clustering was 20. ## 2.6. Construction of the Integral lncRNA–miRNA–mRNA Interaction Networks In our previous study, Miranda software (v3.3a) was used to identify sites that contributed to the interaction of DE-miRNAs and differentially expressed long noncoding RNAs (DE-lncRNAs) [28]. The DE-miRNAs and their target genes were evaluated using the TargetScan database [29,30]. Python was used to build relationships between DE-lncRNA, DE-miRNAs, and DE-mRNAs. Then, a lncRNA–miRNA–mRNA ceRNA network was visualized using Cytoscape, followed by the extraction of modules representing densely connected nodes [31,32]. ## 2.7. qRT-PCR of miRNAs and Target Genes in Longissimus Dorsi For the small RNA sequencing data validation, U6 was used as the reference primer, and the expression levels of 5 miRNAs were validated in small-RNA sequencing tissues. Meanwhile, U6 and β-actin were used as the reference primer, and the expression of miR-410-5p, miR-493-3p, miR-3959-3p, TEAD1, ZBTB34, and POGLUT1 were analyzed by qRT-PCR in the longissimus dorsi of Hu sheep and Gangba sheep after birth. All primers were designed for the selected transcripts from the transcriptome database (Table S1). ## 2.7.1. RT-PCR of the miRNAs and Target Genes Using cDNA obtained from all of the longissimus dorsi of the Hu sheep and Gangba sheep after birth as templates, a PrimeScript RT reagent Kit with gDNA Eraser (Takara, Dalian, China) and synthesis of the miRNA first-strand cDNA (tail addition method) (Sangon, Beijing, China) were used to reverse transcribe the cDNA and miRNA, respectively, and which could be used for the subsequent experiments. ## 2.7.2. qRT-PCR of the miRNAs and Target Genes Using all of the cDNA as templates, qRT-PCR was conducted, and then the target genes and miRNAs were quantitated on a 7500 Real-time system (Applied Biosystems, Waltham, MA, USA), according to the instructions for the TB Green Premix Ex TaqTMII (Takara, Dalian, China). Each sample was run in triplicate. ## 2.7.3. Statistical Analysis The relative expression of the transcripts was calculated using the 2−ΔΔCt method. The data were analyzed with the statistical software SPSS 26.0, and the statistical data were reported as the mean ± standard deviation. GraphPad Prism 8 with the one-way ANOVA statistical method was used to detect differences in the expression between the different development stages tissues, and the Student’s t-test was used to compare the differences in the expression among the same tissues. a and b indicate a significant difference ($p \leq 0.05$); A and B indicate an extremely significant difference ($p \leq 0.01$). ## 2.8. Knockdown TEAD1 Gene Expression in Sheep Primary Embryonic Myoblasts According to the design principle of siRNA, three pairs of TEAD1 siRNAs and negative control siRNA were successfully designed and synthesized (Table S2). The sheep primary embryonic myoblasts, which were cultured, purified, and characterized from sheep embryos [33], were seeded on 12-well plates. On the second day, the medium was changed to Opti-DMEM (Gibco, Grand Island, NE, USA), three TEAD1 siRNAs and negative control siRNA (siNC) (30 ng/well) were transfected with Lipofectamine 3000 Agent (Invitrogen, Pleasanton, CA, USA), and the untreated group was used as the control. After 6 h, DMEM with $10\%$ FBS was used to replace the medium; 24 h later, the cells were washed twice with cold PBS, the total RNA was then extracted from cells with TRIZOL (Invitrogen, Pleasanton, CA, USA), and the cDNA samples were obtained according to the instructions for the PrimeScript RT reagent Kit with gDNA Eraser (Takara, RR047A, Dalian, China). Finally, qRT-PCR was used to measure the knockdown efficiency of three TEAD1 siRNAs. The siRNA with the highest efficiency was selected to construct the TEAD1 gene knockdown model (siTEAD1) in sheep primary embryonic myoblasts, and two methods were used to detect the proliferation of myoblasts after transfection. The first method used the cell count Kit-8 (Sangon, Beijing, China), which added $10\%$ CCK-8 solution to the cell culture medium, incubating at 37 °C for 2 h, and measuring the OD values of siTEAD1, siNC, and the control [34] with a 450 nm microplate reader (Tecan Infinite 200 Pro, Männedorf, Switzerland) at 24 h, 48 h, and 72 h after transfection. At the same time, tissue culture dishes (µ–Slides and µ–Dishes 35 mm, low, Ibidi) were used to carry out the scratch assay. When the cell fusion degree was $100\%$, the µ–Slides were carefully removed and replaced with Opti-MEM (Gibco, Grand Island, NE, USA) for culture. Photographic images at 0 h and 24 h were observed and collected using a microscope. Finally, the number of cell proliferation and healing areas in the different treatment groups were analyzed with ImageJ software [35]. The total RNA of the three transfection groups was used for the quantitative analysis of the growth and development of myoblasts and the expression of related genes. Using Pirmer5 to design the related gene primers (Table S1), the qRT-PCR method was the same as above. ## 2.9. Dual Luciferase Assay Online software tools (TargetScan and miRBase) were used to predict the targeting relationship between miR-410-5p and sheep TEAD1 gene (NCBI: XM-0151010093). The sequence approximately 300 bp upstream and downstream of the miR-410-5p binding site in the TEAD1 gene was cloned, and enzyme digestion sites were added at both ends. This sequence was linked to the polyclonal site of the psiCHECKTM-2 vector (Promega, Madison, WI, USA) through the double-enzyme digestion of XhoI and NotI, and the wild-type (psiCHECK2-TEAD1 3’UTR WT) and mutant-type (psiCHECK2-TEAD1 3’UTR MT) dual luciferase reporter vector of the TEAD1 gene were constructed by the InFusion method. The primers are shown in Table S1. The sheep primary embryonic myoblasts were seeded on 96-well plates (1 × 104 cells/well). The culture medium was changed to Opti-DMEM the next day, and TEAD1 dual luciferase reporter vectors (100 ng/well) and miR-410-5p mimics/inhibitor/NC (30 ng/well) were transfected with Lipofectamine 3000 Reagent (Invitrogen, Pleasanton, CA, USA). The test groups were as follows: psiCHECK2-TEAD1 3’UTR WT + miR-410-5p, psiCHECK2-TEAD1 3’UTR MT + miR-410-5p, psiCHECK2-TEAD1 3’UTR WT + miR-410-5p inhibitor, psiCHECK2-TEAD1 3’UTR MT + miR-410-5p inhibitor, psiCHECK2-TEAD1 3′UTR WT + NC, and psiCHECK2-TEAD1 3′UTR MT + NC. After 6 h, DMEM with $10\%$ FBS was used to replace the medium, and the untreated group was used as the control. After 24 h, the cells were washed twice with cold PBS, and then the cells were cultured according to the Dual-Luciferase® Reporter Assay System (Promega, Madison, WI, USA) manual, and the dual luciferase activity of each group was detected and calculated by the hluc fluorescence value/hRluc fluorescence value. ## 2.10. Validation of the Targeting Relationship between the DE-miRNAs and TEAD1 Gene The sheep primary embryonic myoblasts were seeded on 12-well plates and replaced with Opti-DMEM the next day. The MiR-493-3p mimics, miR-493-3p inhibitor, miR-3959-3p mimics, miR-3959-3p inhibitor, miR-410-5p mimics, and miR-410-5p inhibitor, as well as the negative control mimics NC and inhibitor NC, were transfected into sheep embryonic primary myoblasts (30 ng/well) using Lipofectamine 3000 Agent (Invitrogen, Pleasanton, CA, USA). After 6 h, the medium was replaced with DMEM containing $10\%$ FBS. After 24 h, the cells were washed twice with cold PBS, and then the cellular RNA was extracted with TRIZOL (Invitrogen, Pleasanton, CA, USA). The cDNA samples were obtained according to the instructions for the PrimeScript RT reagent Kit with gDNA Eraser (Takara, Dalian, China) kit. Finally, qRT-PCR was used to verify the efficiency of the overexpression and inhibition of three DE-miRNAs and the amount of the related gene expression. The primer design of the related genes are shown in Table S1. ## 3.1. Characterization of the miRNA Expression Profiles The average valid rate of the raw data obtained from each sequencing library was approximately $76.7\%$. The length distribution of the total number of valid data was calculated. Certain RNA sequences, including rRNA, tRNA, snRNA, and snoRNA, were searched against and filtered from the raw reads by aligning the sequences to the RFam and Repbase databases. The length distribution showed that $52.6\%$ of 22 nucleotides in length were clean reads; this indicates that most of the clean reads obtained were miRNA. All of the clean reads were aligned to the sheep genome and mammalian miRbase using the ACGT-miR101 program. A total of 4752 miRNAs were detected, 2275 of which have not previously been reported (Table S3). ## 3.2. Identification of the DE-miRNAs and Functional Analysis The DE genes were screened using a threshold of p ≤ 0.05. In the expression profile, a total of 505 miRNAs were differentially expressed (Table S4). The genes were classified into five categories according to the expression characteristics of the profiles: incremental type [107], decreasing type [151], high–low–high type [91], low–high–low type [45], and irregular type [111]. Then, a pairwise comparison of the three groups based on filter criteria was performed (D85 vs. D105, D105 vs. D135, and D85 vs. D135). As a result, 63 upregulated miRNAs and 43 downregulated miRNAs were found between D85 and D105, 106 upregulated miRNAs and 123 downregulated miRNAs between D105 and D135, and 172 upregulated miRNAs and 112 downregulated miRNAs between D85 and D135. Overlapping statistics were performed, and 16 DE-miRNAs were found to be overlapped in three groups (Figure 1). Detailed information on these 16 miRNAs was extracted from the corresponding miRNA profiles (Table S5). Among 16 DE-miRNAs, the sequence of chi-miR-450-5p_R + 2 and bta-miR-450 are the same, and the sequence of chi-miR-365-3p and bta-miR-365-3p are the same. After removing the duplicate miRNAs, 14 miRNAs were obtained for the subsequent network construction. To better understand the function of DE-miRNAs, the GO terms and KEGG pathways were analyzed. A total of 643 unique biological processes and 73 enriched pathways were identified (p ≤ 0.05), including signal transduction processes, such as MAPK, Wnt, regulation of actin cytoskeleton, Ras, focal adhesion, and the ErbB signaling pathway, based on the top 20 GO enrichment analysis results of the miRNAs (Figure S1A), including biological processes, cellular components, and molecular functions. These results suggest that spatiotemporal embryo development is related to intracellular protein breakdown and maintenance, energy and material metabolism, and signal transduction. Fourteen miRNAs were analyzed further to better understand their functions. Among the top 20 KEGG pathways (Figure S1B), the most highly enriched pathway was nucleotide excision repair. Furthermore, many pathways related to metabolism were also enriched, such as the Wnt signaling pathway, insulin signaling pathway, focal adhesion, and ErbB signaling pathway. ## 3.3. miRNA–mRNA Networks With 505 DE-miRNAs, over 100,000 target genes were screened in the database. Therefore, we focused on 14 important miRNAs selected for target prediction and screened a total of 1137 target genes. Then, by integrating the DE-mRNAs in the profile, 379 target genes were retained, and 944 miRNA–mRNA interaction pairs related to skeletal muscle fiber development were constructed (Figure S2). Each miRNA regulates multiple mRNAs and vice versa. A total of 253 target genes were obtained from seven downregulated miRNAs, and a total of 126 target genes were obtained from seven upregulated miRNAs. Among them, oar-miR-150 was found to regulate the most genes, regulating 100 genes. The average number of target genes regulated by the upregulated and downregulated miRNAs was over 35, except for bta-miR-450a. ## 3.4. miRNA–TG–Pathway Network Based on the miRNA–mRNA analysis, we performed an analysis of the miRNA–TG–pathway network to determine the biological processes and signaling pathways in which the miRNA target genes are mainly involved. From the BINGO results, the DE-genes were mainly concentrated in 198 biological processes (Table S6), which mainly cover five aspects (multicellular organismal processes, intracellular signal transduction, reproductive processes, animal organ development, and metabolic processes), of which the muscle tissue development, actin filament-based process, muscle structure development, and muscle cell differentiation processes attracted our attention (Figure S3A). From a diagram drawn using ClueGo and CluePedia (Figure S3B), five pathways were found to be enriched, namely, cytoskeleton organization, positive regulation of synaptic transmission, regulation of small-GTPase-mediated signal transduction, peptidyl-serine phosphorylation, and regulation of neuron differentiation. Over $47\%$ of genes were involved in the cytoskeleton tissue process. Finally, the DE genes involved in these five pathways were extracted, and miRNAs from these DE genes were used to construct a miRNA–TG–pathway regulatory network. In the miRNA–TG–pathway network (Figure S4), the enriched pathways are the insulin signaling pathway, Wnt signaling pathway, cell cycle, focal adhesion, adherens junction, ubiquitin-mediated proteolysis, ECM–receptor interaction, ErbB signaling pathway, tight junction, and p53 signaling pathway. Among them, the focal adhesion pathway was found to enrich the most differentially expressed target genes. Seven miRNAs enriched in this network were used for an in-depth analysis. ## 3.5. Integral lncRNA–miRNA–mRNA Interaction Networks By integrating the miRNA data and the previous lncRNA and mRNA data, we performed a ceRNAs analysis. The DE-lncRNAs were screened to predict their target miRNAs, and data including seven miRNAs selected from the miRNA–TG–pathway network were extracted for the construction of the lncRNA–miRNA–mRNA networks. Among them, 14 DE-lncRNAs were able to adsorb oar-miR-493-3p, bta-miR-450a, oar-miR-23b_R + 6, hsa-miR-410-5p, and oar-miR-3959-3p. However, chi-miR-365-3p and chi-miR-133a-5p were not able to bind to any DE-lncRNAs. Then, 1598 edges in the lncRNA–miRNA–mRNA networks were constructed based on the ceRNA mechanism (Figure 2A). By analyzing its topological characteristics, the top 25 nodes with the highest degree were identified (Figure 2B). Among them, oar-miR-493-3p had the highest frequency in the network, and was related to the genes (TEAD1, MAP1B, MCM9, BRI3BP, MPP7, DGKE, MAN1A2, and ZBTB34) and lncRNAs (MSTRG.4058, MSTRG.4324, MSTRG.2252, MSTRG.3879, and MSTRG.1470), which are considered to be of great significance in ceRNA networks. Furthermore, oar-miR-3959-3p and hsa-miR-410-5p also regulated multiple genes and lncRNAs simultaneously. As such, oar-miR-493-3p, oar-miR-3959-3p, and hsa-miR-410-5p were selected as important miRNAs. Similarly, according to the degree value of the lncRNAs in the network, MSTRG.3533, MSTRG.4324, and MSTRG.1470 were selected as important candidate lncRNAs. ## 3.6. Validation of the Small-RNA Sequencing Data The expression levels of several miRNAs were detected by quantitative real-time polymerase chain reaction (qRT-PCR) analysis to validate the accuracy of the small-RNA sequencing data. As shown in Figure 3, the results of the qRT-PCR were consistent with the sequencing data. ## 3.7. qRT-PCR of DE-miRNAs and Target Genes in Muscle Tissues of Hu Sheep and Gangba Sheep Three key DE-miRNAs (miR-410-5p, miR-493-3p, and miR-3959-3p) and three key target genes (TEAD1, ZBTB34, and POGLUT1) were quantitatively analyzed by qRT-PCR in the muscle samples of Hu sheep and Gangba sheep after birth. The results showed that the expression of miR-410-5p decreased gradually in the four developmental stages of Hu sheep, and the expression of miR-410-5p was significantly different at birth and at 6 months old ($p \leq 0.05$) (Figure 4A). The expression of miR-493-3p was the lowest at 4 months old, but the difference was not significant (Figure 4B). The expression of miR-3959-3p was not significantly different at the age of birth, weaning, and 4 and 6 months (Figure 4C). The expression of miR-410-5p, miR-493-3p and miR-3959-3p in the muscle tissues of Gangba sheep at the age of 4 and 6 months showed no significant difference separately (Figure 4A–C). There was no significant difference in the expression of these key DE-miRNAs in the muscle tissues at the same age of the Hu sheep and Gangba sheep. However, the expression of miR-410-5p in the Hu sheep was lower than that in the Gangba sheep of the same age (Figure 4A). TEAD1 is a common target gene of miR-410-5p and miR-493-3p. The expression of the TEAD1 gene had basically no change from birth to the age of 4 months, but it started to increase at the age of 5 months and reached the maximum at the age of 6 months; the expression of the TEAD1 gene of the Hu sheep at the age of 6 months was extremely significantly higher than that at birth, weaning, and 3 and 4 months ($p \leq 0.05$) (Figure 4D). The expressions of the TEAD1 gene in the 4-month-old and 6-month-old muscles of the Gangba sheep had no difference. A comparative analysis of the expression of the TEAD1 gene in the muscle of the two different sheep breeds showed that compared with the Gangba sheep, the expression of TEAD1 in the muscle of the 6-month-old Hu sheep was significantly increased ($p \leq 0.05$), but the difference was not significant at the age of 4 months. The expression of the POGLUT1 and ZBTB34 genes in the 6-month-old muscle of the Hu sheep also significantly increased ($p \leq 0.05$) (Figure 4E,F). ## 3.8. Knocked-Down TEAD1 Expression Inhibited the Proliferation of Sheep Primary Embryonic Myoblasts Three pairs of TEAD1 siRNAs were constructed and transfected into sheep primary embryonic myoblasts, and it was found that the knockdown efficiency of siRNA1 was better, which could reduce the expression of TEAD1 gene to approximately $40\%$ of the original, and had a lower miss efficiency (Figure 5A). Moreover, we found that the number of myoblasts in the TEAD1 knockdown group was significantly less than that in the siNC group and control group (Figure 5A). CCK8 was used to analyze the cell proliferation of the three groups: siTEAD1, siNC, and control. It was found that the cell proliferation of the siTEAD1 group was lower than that of the siNC and control groups at 24 h, 48 h, and 72 h after transfection (Figure 5B). At 24 h after transfection, there were significant differences between the control/siNC group and siTEAD1 group ($p \leq 0.05$). At 48 h after transfection, there were significant differences between the control/siNC group and siTEAD1 group ($p \leq 0.01$). At 72 h after transfection, the difference between the control group and siNC group was significant ($p \leq 0.05$), and the difference between the control group and siTEAD1 group was highly significant ($p \leq 0.01$). The results of the scratch assay showed that the amount of cell proliferation in the siTEAD1 group was significantly less than that in the control and siNC groups at 24 h after transfection, and the wound healing area of the control and siNC groups was also larger than that in the siTEAD1 group, indicating that the cell proliferation rate and wound healing degree in the siTEAD1 group were lower than those in the control and siNC groups (Figure 5C,D). A statistical analysis of the wound healing area of the three groups using ImageJ found that the cell proliferation areas in the control and siNC groups were both $0.14\%$ of the total scratch area. However, the siTEAD1 group was $0.08\%$, significantly lower than the control and siNC groups (Figure 5D). When the TEAD1 gene was knocked down, the expression levels of the MYF5, Pax7, MyoD, and MyoG genes, which are markers of myoblast proliferation and differentiation, were all downregulated, and the expression levels of Pax7 and MyoD were downregulated highly significantly ($p \leq 0.01$). In addition, the expression levels of the TEAD1 transcription target factor SLC1A5 and the downstream target gene FoxO3 were significantly downregulated ($p \leq 0.05$), while the expression of the downstream genes related to the growth and development of muscle cells, such as Mrpl21 and Ndufa6, had no significant changes in the sheep primary embryonic myoblasts (Figure 5E). ## 3.9. Validation of the Targeting Relationship between miR-410-5p and TEAD1 Gene by Dual Luciferase Assay The online software tools (TargetScan and miRBase) were used to predict the target relationship between miR-410-5p and the sheep TEAD1 gene. The prediction results showed that the 3’ UTR region (7843 bp–7889 bp) of the TEAD1 gene and miR-410-5p had an atypical binding site AAAGTGGT. Based on this binding site, TEAD1 wild-type and mutant-type dual luciferase vectors were constructed. Through dual luciferase activity detection, it was found that the psiCHECK2-TEAD1 3’UTR WT/MT + miR-410-5p inhibitor/NC transfection groups had no significant changes in luciferase activity. However, in the psiCHECK2-TEAD1 3’UTR WT + miR-410-5p mimics transfection group, a significant decrease in luciferase activity was detected. However, the luciferase activity of the psiCHECK2-TEAD1 3’UTR MT + miR-410-5p mimics transfection group remained unchanged (Figure 6), indicating that miR-410-5p could combine with the 3’UTR region of the TEAD1 gene, thereby reducing the expression of the TEAD1 gene. ## 3.10. Validation of the Targeting Relationship between DE-miRNAs and TEAD1 Gene The mimics and inhibitors of the DE-miRNAs (miR-410-5p, miR-493-3p, and miR-615-3p) were transfected into the sheep primary embryonic myoblasts. It was found that the cell proliferation rate of the group transfected with miRNA-410-5p mimics or miR-493-3p mimics was slower than that of the group transfected with miRNA-410-5p inhibitor or miR-493-3p inhibitor after 24 h, respectively (Figure 7A). The qRT-PCR showed that the expression level of the miRNAs in the groups that added three kinds of mimics was significantly higher than that in the inhibitor groups and control groups ($p \leq 0.01$) (Figure 7B). However, the expression of the TEAD1 gene was significantly decreased only in the miRNA-410-5p mimics group ($p \leq 0.01$). This shows that miR-410-5p and the TEAD1 gene did have a targeting relationship, which can downregulate the expression of the TEAD1 gene. Although miR-493-3p and the TEAD1 gene were also predicted to have a targeting relationship, the results showed that miR-493-3p could not downregulate the expression of the TEAD1 gene. In addition, compared with the miR-410-5p inhibitor group, the expression levels of Pax7 ($p \leq 0.01$), MyoD ($p \leq 0.05$), SLC1A5 ($p \leq 0.01$), and FoxO3 ($p \leq 0.01$) were also significantly decreased in the miR-410-5p mimics group, this result was consistent with the siRNA knockdown experiment. It can also be found that the expression levels of SLC1A5 ($p \leq 0.05$) and FoxO3 ($p \leq 0.05$) also significantly decreased in the miR-493-3p mimics group compared to the miR-493-3p inhibitor group; this suggests that although miR-493-3p cannot downregulate the expression of TEAD1, it may affect the expression of genes in this pathway and cell proliferation by regulating the expression of other genes (Figure 8). ## 4. Discussion From the data analysis results, in the D85 vs. D135 group, there were a large number of DE-miRNAs and DE-genes. The gene expression patterns of D85 and D135 were very different, as reported in previous studies [36]. Several genes directly or indirectly regulated muscle development and were continuously expressed at different stages. We speculated that the embryo maintained a small range of cell proliferation, differentiation, and migration during the late stage of pregnancy. From a previous WGCNA analysis between the lncRNAs and mRNAs by our group [28], the DE-lncRNAs and DE-mRNAs that targeted a binding relationship with miRNA were predicted through the database. In the present study, we predicted the target relationships between lncRNA–miRNA and mRNA–miRNA based on the DE-lncRNAs, DE-miRNAs, and DE-mRNAs, and we constructed a lncRNA–miRNA–mRNA network based on the whole transcriptome profile. Through the construction of multiple networks, the genes and miRNAs considered to be key factors were identified. MiRNAs (miR-493-3p, miR-3959-3p, and miR-410-5p) and lncRNAs (MSTRG.3533, MSTRG.4324, and MSTRG.1470) identified in the ceRNA network were enriched in the energy metabolism, muscle contraction, and oxidative phosphorylation pathways. All pathway analysis results indicated that the metabolic and oxidative phosphorylation pathways are significantly related to muscle development [37]. Heeley et al. showed that high levels of oxidative phosphorylation existed during the rapid development of mammalian skeletal muscle and the formation of myofibrils [38]. Therefore, we predicted that metabolism not only provides energy throughout embryonic skeletal muscle development but may also fine-tune embryonic muscle developmental patterns. The Wnt signaling pathway is a potential downstream target of myostatin, which can promote the growth and hypertrophy of postpartum skeletal muscle [39]. DE-miRNAs and DE-lncRNAs affect insulin by regulating target genes and ultimately participate in the regulation of skeletal muscle cells into adipocytes [40]. These noncoding RNAs (ncRNAs) play important regulatory roles in the induction of adipogenesis in skeletal muscle, adipose tissue, and myoblasts. In this study, through the analysis of ceRNA networks, three genes (TEAD1, ZBTB34, and POGLUT1) were identified. POGLUT1 is associated with muscle disorders. Muscle satellite cells are activated after muscle damage. Studies have shown that mutations in POGLUT1 inhibit the repair and regeneration of skeletal muscle injury. ZBTB34 is a new member of the BTB/POZ protein family, the function of which is not yet clear. It can be used as an important transcriptional regulatory protein in the cell growth process by participating in the regulation of the transcriptional activities of certain downstream genes [41]. TEADs (transcriptional enhanced associate domain transcription factors) are also called TEAD transcription factors, TEAD1 encodes a ubiquitous transcriptional enhancer factor that is a member of the TEADs family [42]. This protein directs the transactivation of a wide variety of genes and, in placental cells, also acts as a transcriptional repressor. Mutations in this gene cause Sveinsson’s chorioretinal atrophy. TEADs combine with their coactivators to form different complexes that will regulate gene expression. These genes are critical to embryonic development and organ formation (heart and muscle), and they participate in cell death and proliferation [43]. In the Hippo pathway, TEADs form a complex with transcriptional coactivators Yap (yes-associated protein, encoded by the gene Yap1), Taz (transcription coactivator with PDZ-binding motif, encoded by the Wwtr1 gene), and Vglls (vestigial-like proteins) [44]. Together Yap, Taz, and TEADs are the nexus of the Hippo signal transduction network. In addition, the Hippo kinase cascade, which consists of the kinases Mst1 (Stk4), Mst2 (Stk3), Lats1, and Lats2 are also at the center of the Hippo signal transduction network [45] (Figure 8), regulates cell fate and proliferation and apoptosis in organ development [46]. TEADs bind to CATTCC/GGAATG (MCAT or GTIIC motifs) to regulate gene expression, and they are frequently located close to the promoters of cardiac and skeletal muscle genes [44,47], as well as through binding enhancers [48,49]. The transcriptional coactivators Yap and Taz, which bind to and activate Teads, are phosphorylated by the Hippo pathway, which regulates the activity of Teads [44]. Yet, Tead1 binds muscle genes that are suppressed in rhabdomyosarcoma [50], which is consistent with the finding that Yap and Taz can also repress gene expression [51]. Taz and Yap have overlapping functions in promoting myoblast proliferation, but Taz then switches to enhance myogenic differentiation [45]. Watt et al. found that Yap is a regulator of C2C12 myogenesis, and the phosphorylation of Yap is necessary for C2C12 myoblasts to develop into myotubes [52]. Judson et al. identified Yap as a regulator of muscle satellite cell fate decisions. Yap expression rises during satellite cell activation and stays high until after the differentiation versus self-renewal decision is made. Yap’s constitutive expression maintains satellite cells, Pax7+ and MyoD+, and satellite cell-derived myoblasts, which promote proliferation but block differentiation. That Yap binds to TEADs was confirmed in myoblasts [53].Yap is also a critical regulator of skeletal muscle fiber size. Watt et al. found that protein synthesis and basal skeletal muscle mass are positively regulated by YAP. Mechanistically, they showed that Yap regulates muscle mass via interaction with TEADs [46]. Mechanically induced skeletal muscle growth is regulated by mTORC1, and Yap can regulate mTORC1. In skeletal muscle, mechanical overload promotes the upregulation of Yap expression, and the overexpression of Yap induces hypertrophy of skeletal muscle via an mTORC1-independent mechanism [54]. The mammalian target of rapamycin (mTOR) kinase signaling can be affected by hippo signaling. Yap regulates miR-29 production, which promotes the degradation of the phosphatase Pten. Pten blocks Akt, and Akt subsequently indirectly activates mTOR. The overexpression of Yap in the skin of mice or knockdown of Lats1 and Lats2 in cells increases mTOR activity [55]. Mst$\frac{1}{2}$ can interact with Akt1 to form complexes, and when Mst$\frac{1}{2}$ is knocked down, the activating phosphorylation of Akt1 and Akt substrates was reduced [56]. Moreover, Mst1 is phosphorylated by Akt, which inhibits Mst1 activity. Akt also blocks FoxO3 phosphorylation and nuclear translocation [57]. The molecular relationship between Hippo and mTOR signaling suggests that cell proliferation and protein synthesis could be coordinated through this crosstalk, since Yap and Taz stimulate cell proliferation and because mTOR signaling is necessary for protein synthesis during cell proliferation [44] (Figure 8). TEAD1 can specifically regulate the expression of cardiac troponin T, β-myosin heavy chain, smooth muscle α-actin, and skeletal muscle α-actin in mammalian and avian skeletal muscles [58,59]. In research on the growth and development of smooth muscle, the expression of TEAD1 was significantly regulated in smooth muscle cells and negatively related to the expression of smooth-muscle-specific genes. The TEAD1 gene can compete with cardiac myocytes to bind to the serum response factor (SRF), destroying the interaction between cardiac myocytes and SRF, thus weakening the expression of smooth-muscle-specific genes [60]. In addition, TEADs also play a crucial role in embryonic development and striated muscle gene expression, and the overexpression of TEAD1 leads to the accidental activation of GSK-3α/β and a decrease in nuclear β-catenin and NFATc1/c3 protein, and then regulates the expression of genes related to slow muscle [61]. At the same time, TEAD1 is critical to the normal development of the heart, and it is related to the expression of heart-specific genes and the hypertrophic response of primary cardiomyocytes to hormone and mechanical stimulation. The overexpression of the TEAD1 gene can induce the characteristics of cardiac remodeling related to cardiomyopathy and heart failure [62]. In addition, studies have shown that TEAD1 plays a vital role in myoblast growth, skeletal muscle development, muscle fiber hypertrophy, muscle regeneration, and myocardial development [63,64]. In this study, knocked-down TEAD1 expression by siRNA inhibited the proliferation of sheep primary embryonic myoblasts. The CCK8 experiments and scratch assay reached the same conclusion. Some studies have shown that the double knockout of the TEAD1 and TEAD2 genes leads to decreasing embryonic cell proliferation and increased apoptosis, and TEAD1, as the main coactivator of YAP, regulated cell proliferation and survival during mouse development [65]. Here, after the knockdown of the TEAD1 gene by siRNA or miR-410-5p, the expression levels of the SLC1A5 and FoxO3 genes were all significantly downregulated. Relevant studies found that, as the main downstream nuclear effector of Hippo signal transduction, SLC1A5 was a new TEAD1 transcription target [59]. TEAD1 regulates the functions of its downstream target genes Mrpl21, Ndufa6, and Ccne1 in myoblast growth, skeletal muscle development, muscle fiber hypertrophy, and muscle regeneration [63]. TEAD1 can regulate the expression of FoxO3a in skeletal muscle through calcineurin/MEF2/NFAT and IGF-1/PI3K/AKT, thus affecting the formation and transformation of skeletal muscle fiber types [66]. Therefore, during embryonic skeletal muscle development, TEAD1 may be involved in the regulation of muscle fiber differentiation, with similar results in some studies on poultry, pigs, and mice [67,68]. In this study, Gangba sheep are a local resource in Tibet that adapt to a high-altitude environment well. Gangba sheep grow slowly, and the weight at the age of 24 and 36 months are 25 and 35 kg, respectively. Hu sheep are a local breed in the Tai Lake basin of China, which grow rapidly in the early stage, especially at the age of 4 to 6 months. A ram (not wethers) at the age of 6 months can reach 45 kg or even more. The expression of the TEAD1 gene in 6-month-old Hu sheep was extremely significantly higher than at birth, weaning, and 4-month-old Hu sheep ($p \leq 0.01$), and the expression of the TEAD1 gene in the muscle of the two different sheep breeds showed that compared with Gangba sheep, the expression of TEAD1 in the muscle of 6-month-old Hu sheep significantly increased ($p \leq 0.05$). The expression of miR-410-5p was significantly lower at 6 months old than at birth in the Hu sheep ($p \leq 0.05$), and the expression of miR-410-5p in the Hu sheep was lower than that in the Gangba sheep of the age. From the prediction results, TEAD1 is the target gene of miR-410-5p. This shows that the expression level of the TEAD1 gene was positively correlated with the growth of sheep, while the expression of miR-410-5p was negative. Here, it was found that miR-410-5p and the TEAD1 gene did have a targeting relationship, which can downregulated the expression of the TEAD1 gene in sheep primary embryonic myoblasts. Xia et al. found that miR-410-5p promoted the development of diabetic cardiomyopathy, miR-410-5p was increased in the myocardial tissue of a diabetes mellitus rat model, cell apoptosis was reduced by the inhibition of miR-410-5p, and knockdown of miR-410-5p improved myocardial tissue structure and diabetes-induced cardiac function [69]. Rats on a high-fat diet exhibited LV dysfunction and heart fibrosis. In rats fed a normal diet, miR-410-5p overexpression caused cardiac fibrosis, whereas in rats fed a high-fat diet, miR-410-5p inhibition decreased cardiac fibrosis [70]. These results suggest that the high expression of miR-410-5p represses the growth of muscle cells. Our results also show that the expression level of miR-410-5p is relatively low, and the expression of TEAD1 is upregulated, whether in late pregnancy or in the period of rapid muscle development after birth. As discussed earlier, TEAD1 can promote the proliferation of myoblasts, while miR-410-5p can inhibit the proliferation of myoblasts. ## 5. Conclusions In conclusion, we constructed a co-expression network using whole transcriptome analysis. Three miRNAs (miR-493-3p, miR-3959-3p, and miR-410-5p), three lncRNAs (MSTRG.3533, MSTRG.4324, and MSTRG.1470), and three genes (TEAD1, ZBTB34, and POGLUT1) were identified as critical components in fibrogenesis in the fetus. Through the functional verification of the DE-miRNAs and target genes at the molecular and cellular levels, it was found that miR-410-5p and the TEAD1 gene had a significant targeted regulatory relationship. Downregulating the expression of the TEAD1 gene can significantly inhibit the proliferation of sheep embryonic myoblasts, downregulating the expression of the downstream genes of the TEAD1 gene and marker genes related to muscle development. The expression levels of the TEAD1 gene were significantly different in the sheep breeds with different growth rates. These results reveal that the TEAD1 gene and miR-410-5p could regulate the proliferation of myoblasts and are related with the growth and development of the embryonic muscle of sheep. The results presented here provide new insight into the molecular mechanisms of muscle development. ## References 1. Lehka L., Rędowicz M.J.. Mechanisms regulating myoblast fusion: A multilevel interplay. Semin. Cell Dev. Biol. (2020) 104 81-92. DOI: 10.1016/j.semcdb.2020.02.004 2. Endo T.. Molecular mechanisms of skeletal muscle development, regeneration, and osteogenic conversion. Bone (2015) 80 2-13. DOI: 10.1016/j.bone.2015.02.028 3. 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--- title: 'Physical Activity and Sedentary Behavior in High School Students: A Quasi Experimental Study via Smartphone during the COVID-19 Pandemic' authors: - Regina Márcia Ferreira Silva - Lauryane Fonseca Terra - Michele da Silva Valadão Fernandes - Priscilla Rayanne E. Silva Noll - Alexandre Aparecido de Almeida - Matias Noll journal: Children year: 2023 pmcid: PMC10047413 doi: 10.3390/children10030479 license: CC BY 4.0 --- # Physical Activity and Sedentary Behavior in High School Students: A Quasi Experimental Study via Smartphone during the COVID-19 Pandemic ## Abstract The objective of this study was to evaluate whether exposure to information about physical activity and its barriers can increase the level of physical activity and reduce the time exposed to sedentary behaviors in high school students involved in integrated professional and technological education during the coronavirus disease 2019 pandemic. This quasi experimental study was conducted with integrated education high school students, divided into two groups: Intervention Group (IG; $$n = 59$$) and Control Group (CG; $$n = 54$$). Physical activity and sedentary behavior were identified and measured using the International Physical Activity Questionnaire pre-and post-intervention for both groups. IG students received educational material thrice a week for four weeks. The focus of the material was the importance of physical activity and need to reduce the time exposed to sedentary behavior. The results revealed that IG students showed an average daily reduction of 47.14 min in time exposed to sedentary behaviors, while the CG students showed an increase of 31.37 min. Despite this, the intervention was not effective in improving physical activity levels in the IG and the mean reduction in the time exposed to sedentary behavior was not significant ($$p \leq 0.556$$). The intervention was ineffective in increasing the practice of physical activity and reducing the time exposed to sedentary behavior. ## 1. Introduction Physical activity is a concept defined as any movement that results in energy expenditure above resting levels [1]. Recently, it has been extended to people moving, operating, and acting in culturally specific spaces and contexts, influenced by diverse interests, emotions, and relationships [2]. Regular physical activity has been considered a important factor associated with the prevention of chronic non-communicable diseases [3,4,5]. Additionally, studies have recognized physical, psychological, and social benefits associated with regular participation in physical activities [6,7,8,9,10,11,12,13,14,15,16]. Despite this, the prevalence of physical inactivity is above $80\%$ among adolescents worldwide, when those who engage in (moderate to vigorous) physical activity for fewer than 60 min a day are considered physically inactive [17]. In Brazil, this percentage exceeds $83\%$ for this age group [18]. Physical inactivity is characterized by failure to meet current physical activity recommendations [19,20,21], while sedentary behavior is defined as any waking behavior characterized by an energy expenditure ≤1.5 metabolic equivalents (METs); for example, time spent sitting or lying down while awake [20]. The factors that make it difficult or prevent individuals from participating in physical activities are commonly called barriers [22]; therefore, it is possible for a person to be physically active (by complying with the recommendations) and simultaneously spend significant time engaging in sedentary behavior. Barriers can contribute to an increase in physical inactivity and sedentary behaviors. An example of sedentary behavior is the use of electronic devices and prolonged sitting, lying down, or reclining [20]. The barriers can be categorized into four dimensions: environmental; psychological, cognitive, and emotional; sociodemographic; and sociocultural [23,24,25,26,27,28]. In this sense, the coronavirus disease 2019, a disease caused by the SARS-CoV-2 virus that spread into a worldwide pandemic [29], can be considered a barrier to the practice of physical activity, as it directly affected two important public health concerns: physical inactivity and sedentary behavior [30]. The COVID-19 pandemic impacted society in an unprecedented way, characterized by the greatest interruption of the teaching–learning process in the history of world education [31,32]. Thus, educational institutions worldwide needed to adopt emergency remote teaching modalities [33,34]. To promote physical activity, actions are needed that encompass concepts related to physical inactivity, sedentary behavior, and barriers to physical activity. Our study used the ecological framework theory of health behavior, which states that behavioral change is a complex process that can be elicited through a multi-pronged approach targeting intrapersonal, interpersonal, organizational, and socio-community dimensions [35,36]. In this sense, considering that adolescents engage in significant cell phone use during their day [37,38], it can be expected that “positive” messages related to healthy lifestyle habits and physical activity will contribute to a change in sedentary behavior observed in these young people. Subsequently, a line of reasoning highlights the possibility of extrinsic motivation bringing the individual closer to the activity, suggesting an increase in autonomy and an internal incentive [39]. Accordingly, the World Health Organization suggests the use of smartphones to help reduce time spent in sedentary behaviors and increase the practice of physical activity [5]. Recent systematic reviews, including articles of moderate to high quality, identified that smartphone-based physical activity interventions, such as those delivered via application, were effective in increasing an individual’s amount of physical activity [40,41]. Another review noted the need for studies that developed physical activity interventions using mobile health for specific target groups [42]. Several intervention studies used smartphones to promote a healthier lifestyle [43,44,45,46]. In a recent study [44], five weeks of using a mobile application that sent notifications about nutrition and physical activity to Portuguese adolescents improved diet behavior in $28.6\%$ of participants. In addition, a $42.9\%$ increase in the level of physical activity was observed. In another study [43], an application was used to apply individual and collective challenges related to physical activity to Spanish adolescents for 10 weeks. The results showed that the application increased participants’ time spent on physical activity. Finally, an application used for four weeks with female adolescents in Singapore attenuated the decline in physical activity level among participants [45]. Evidence showed that smartphone-based interventions may be a promising strategy for increasing total physical activity time [47,48] and reducing time exposed to sedentary behaviors [49] in adolescents. Considering the above, an intervention that proposes increasing the level of physical activity and reducing exposure to sedentary behavior during the coronavirus pandemic could be an interesting strategy. Thus, this study aimed to assess whether exposure to information about physical activity and its barriers received via messaging applications can increase physical activity and reduce sedentary behaviors in high school students enrolled in professional and technological integrated education. ## 2.1. Study Design and Research Location This was a quasi-experimental study with a field trial design lasting four weeks. This study was conducted in September 2021 during the school year at a professional and technological education institution located in central Brazil. At that time, the population of Brazil had already been living with the COVID-19 pandemic for 18 months. The Midwest region of Brazil had the highest COVID-19 mortality rate in the country (301 deaths per 100,000 inhabitants), while the national rate was 249.9 deaths per 100,000 inhabitants [30,50]. Vaccine coverage at that time was just over $20\%$ of the adult population [30,50]. The region was engaged in social isolation, with mandatory use of face protection masks. This state had the seventh lowest rate of social isolation in the country; however, the institution had been practicing emergency remote teaching for over a year [30,50]. ## 2.2. Sample Participants were recruited among 207 students enrolled in integrated technical courses offered by a federal public institution of professional and technological education. Everyone received the invitation to participate by e-mail. The participants belonged to five groups encompassing the first and third years of technical courses in agriculture, information technology, and the environment. We determined the quantitative sample using a $5.0\%$ margin of error and a $95.0\%$ confidence level, resulting in a sample of 113 participants. We used block randomization [51], which is useful to resolve imbalances in the number of individuals. Each of the five groups corresponded to a randomization block; these blocks were randomized into two groups. Of a total of 113 high school students, 59 belonged to the Intervention Group (IG) and 54 to the Control Group (CG). ## 2.3. Inclusion and Exclusion Criteria For the inclusion criteria in the study, students should be properly enrolled in the institution. They should have a smartphone device with functionality and use of a text message application. Finally, they should have the cognitive ability to interpret and answer the questionnaires in the pre-and post-intervention periods. Students who responded inappropriately to questionnaires intended to collect information on these criteria were excluded. ## 2.4. Intervention The general focus of the interventions was to improve knowledge about the importance of physical activity and reduce the time exposed to sedentary behaviors. The content of the intervention was based on the strategy developed by the group ‘On Your Feet Britain (10 ways to sit less at work)’ [52] and an intervention carried out with university students [53]. We focused on activities described in the Physical Activity Guide for the Brazilian Population designed for children and young people from 6 to 17 years of age in the domains of free time, displacement, school, and household chores [54]. Currently, mobile technologies are essential to human life, as they bring convenience and practicality to the touch of the screen [55]. A special highlight is the smartphone, which offers various applications that support activities such as study, work, and leisure, among others [56]. One such application is WhatsApp, which has become a widely used communication tool for personal relationships and professional activities [57]. The intervention included sending eight illustrated and colored folders [58] over four weeks. The folders were sent thrice a week through the WhatsApp messaging application. The participants were asked to “reply” to confirm when they had received and read our messages. All folders are included in this study’s supplemental material. Folders were sent on Mondays and Wednesdays, and both folders were resent on Fridays (see Table 1). ## 2.5. Data Collection Data collection was performed using two questionnaires and a structured interview with open questions. The International Physical Activity Questionnaire (IPAQ), short version [59,60,61], was used to collect information on the level of physical activity and time exposed to sedentary behavior, and a 10-item questionnaire developed by the authors was used to collect information relevant to the research. The IPAQ was administered three days before and three days after the intervention to both participant groups. The questionnaire developed by the authors included 10 questions (satisfaction level with the project, language, content and terms; duration and number of questions; if they were encouraged to have a less sedentary week and encouraged to have a week with more physical activities; and score for the project from 0 to 10), and its function was to evaluate the intervention with open and closed answers. Therefore, its use occurred three days after the intervention only in the IG. The two questionnaires were conducted online via Google Forms. The interviews were conducted online with six participants selected from the IG. This amount is recommended for homogeneous samples [62]. Later, the interviews were transcribed and analyzed, and the results were grouped into thematic axes, categories, and indicators. All enrolled subjects voluntarily participated in this study (with parents’ consent and approval), following ethical principles. Our study was approved by the Ethics Committee of the Instituto Federal Goiano (No. 28163120.4.0000.0036). ## 2.6. Data Analysis The level of physical activity was identified through questions 1–3 of the IPAQ and were classified as low, moderate, and high. The physical activity level in the participants was classified as “low” (not meeting the criteria for the “moderate” or “high” categories), “moderate” (at least 20 min of vigorous physical activity three or more days/week; at least 30 min of moderate physical activity or walking five or more days/week; or any combination of walking, moderate, or vigorous physical activity reaching at least 600 metabolic equivalents of task (MET) minutes/week at least five days/week), and “high” (vigorous physical activity reaching at least 1500 MET minutes/week at least three days/week; or any combination of walking, moderate, or vigorous physical activity reaching at least 3000 MET minutes/week at least seven days/week) [34,59]. The time exposed to sedentary behavior (minutes/day) was identified through question 4 of the IPAQ. It was determined from the weighted average of the time sitting on a weekday and a weekend day according to the following equation: [(weekday sitting time ∗ 5 + weekend day sitting time ∗ 2)/7]. We used descriptive and inferential statistics. Average daily exposure to sedentary behaviors in IG and CG before and after the intervention was calculated by delta; absolute delta was calculated by subtracting the average time exposed to sedentary behavior from the post-moment by the pre-intervention moment. The comparison of the mean was carried out with the t-test (SPSS 26.0). To calculate the sample size, we used G-Power [63], with α = 0.05 (significance level) and β = 0.85 (power of the test) and found that the minimum sample of each group should be 31 participants. Content analysis was used to interpret qualitative data [64] from the interviews. All steps of this analysis were performed by two reviewers with experience in qualitative approaches. ## 3. Results The final sample consisted of 80 (26 female; 54 male) participants. After the four-week intervention period, 26 students had dropped out of the IG and three more dropped during the analysis stage. In Figure 1, based on the Consolidated Standards of Reporting Trials (CONSORT), we described the records of the intervention. Four participants dropped out of the CG. This is because they did not respond to the post-intervention questionnaire. The age of the CG was 15.9 ± 1.15 years, and that of the IG was 16.2 ± 0.94 years ($$p \leq 0.225$$), others characteristics are found in the (Table 2). The most frequent level of physical activity in the pre-and post-intervention of both groups was high (Table 3). In both groups, there was an improvement in the frequency of low and moderate levels. The high level remained at the same frequency before and after intervention in the IG and increased slightly in the CG. Regarding the time exposed to sedentary behavior, a reduction in the mean time of 47.14 min per day in the IG and an increase in the mean time of 31.37 min per day in the CG was observed. However, no significant differences were observed in pre-or post-intervention means in both groups (Table 4). Table 5 presents data on the perception of IG students in relation to participation in the intervention. We also verified whether the intervention contributed in any way to the increase in the practice of physical activity and the reduction in the time exposed to sedentary behaviors of the participants. Through the responses to the intervention evaluation questionnaire, we identified that the majority ($$n = 24$$; $80.0\%$) demonstrated satisfaction in having participated in the project, with the language used and the duration. Only $10\%$ ($$n = 3$$) of the participants did not make positive comments. The interview responses revealed a need for future similar interventions to provide more options, such as sending notifications and inclusion of challenges, photos, and videos: The sedentary behavior axis was divided into five categories: concept, the total number of daily hours, number of daily hours (leisure), contributing factors, and positive factors related to the reduction in time exposed to sedentary behavior. In Table 6, the physical inactivity axis includes the knowledge category for the percentage of physically inactive Brazilian adolescents. In the thematic axis of sedentary behavior, the participants declared that they spent between seven and fifteen hours daily exposed to sedentary behavior (Table 6). Regarding the factors that contribute to sedentary behavior, they mentioned mainly three personal factors (lack of information, lack of interest, and laziness) and three external factors (pandemic, technology, and modern life). Regarding the positive factors caused by the decrease in sedentary behavior, they mentioned the increase in the disposition and prevention of diseases. In the thematic axis of physical inactivity, regarding the knowledge of the percentage of Brazilian adolescents who are physically inactive, the participants reported between $70\%$ and $75\%$. ## 4. Discussion The present study evaluated whether exposure to information about physical activity and its barriers could improve the level of physical activity and reduce the time exposed to sedentary behaviors in high school students enrolled in integrated professional and technological education. The high number of physically inactive adolescents [18] and the large number of health problems that exposure to sedentary behaviors can cause in general [65] demonstrate the need to evaluate efficient ways to reduce the time exposed to sedentary behavior and increase the level of physical activity. After the intervention, a mean decrease of 47.14 (min/day) in exposure to sedentary behavior was observed in the IG. In contrast, in the CG, we verified an increase of 31.37 min per day in the average time in the sedentary behavior activities. While the changes in time exposed to sedentary behavior were not statistically significant between pre-and post-intervention, the findings still have clinical relevance since “every step counts”, no matter how small the increase in physical activity is [5]. Regarding the practice of physical activity, no significant differences were identified between groups, demonstrating that they were homogenous groups in relation to basal condition (before intervention). Two important factors could be contributed to this absence of significant differences. One was that, however motivational the messages sent to mobile applications were in increasing healthy behavior [66], adherence to social distancing and isolation strategies may have mitigated their effects. Another reason was the strong tendency to sedentary behavior during adolescence. Young adolescents who practice low levels of physical activity in early life tend to maintain low levels of physical activity practices in late adolescence and adult life [67]. As the COVID-19 pandemic was a totally unexpected situation, young people who did not practice adequate levels of physical activity before the establishment of measures to contain the transmission of the virus would not start the practice of physical activities during this period even if stimulated remotely, in this case, by application messages. The messaging application was chosen first because it is the platform most reportedly used by adolescents between 15 and 17 years old. Approximately $70\%$ of these young people use these technologies in their daily lives [37]. Another factor was the increase in the number of application downloads aimed at physical activity in the home during the pandemic [68,69]. A recent study observed the characteristics a smartphone application should contain to reduce sedentary behavior in adolescents. Among the characteristics mentioned, social relationships, messages, and updates were listed [70] as important. The mobile application used in the present study demonstrated good adherence by the sample analyzed. The language used was simple and visually appealing; approximately $80\%$ ($$n = 24$$) of the students reported being satisfied or very satisfied with the language, terms, and content used in the messages sent to them. These features are important as they arouse interest and facilitates use among the adolescent public. Although the format for disseminating the messages of the present study was well accepted or evaluated by the volunteers, more “relaxed” messages such as pictures or videos may receive more attention and greater dedication to the proposed changes in habits. Additionally, as the application was very popular among adolescents, there may have been a sharing of motivational messages between the experimental and control groups, confounding the interpretation of results. The duration of the intervention in this research is in line with a similar intervention [71]; both lasted four weeks. More than half of the students who participated in this research ($$n = 17$$; $56.6\%$) reported being satisfied or very satisfied with the duration of this intervention. An interventional study with university students for two, four, and six weeks showed that evidence, advice, reminders, and challenges sent by text messages have the potential to increase non-sedentary patterns [66,72]. Thus, similar interventions of longer duration may show significant effects. Currently, technologies are essential to human life, as they bring convenience and practicality to the touch of the screen [55]. A special highlight among the technologies is the smartphone, which offers various applications that allow activities such as study, work, and leisure, among others [56]. A widely used application is WhatsApp, which has become a widely used communication tool for personal relationships and professional activities [57]. In research concerning high school students [66,73], similar to this study, an average of two hours a day in sedentary leisure-time behaviors was identified. In accordance with other studies [74,75], the use of technologies was reported as a factor that contributed to exposure to sedentary behavior. Motivating physical activity practices with smartphone or cell phone applications are conflicting. Although these applications may be effective to improve physical activity [76], a meta-analysis review found a non-significant effect [77]. In this sense, we may assume that an uncontrolled confounder in the study may have influenced our results. For example, cell phone use to access messages may stimulate adolescents to continue using their phones, thus increasing the time involved in sedentary behaviors. To the best of our knowledge, this is the first study that investigated the use of mobile applications to influence increased sedentary behaviors in adolescents during an exceptional condition such as the COVID-19 pandemic. Furthermore, although our study sheds light on some important issues related to adolescent behavior (sedentary behavior and mobile use), it has some limitations. First, an important issue of the research design is that the sampling procedure did not ensure that the participants had same levels of PA experience before the lockdown period. However, in a real school context, it is not possible to evaluate classes of different intervention groups from the same school, to avoid communication bias. Second, we did not follow the post-intervention over time and the use of a self-reported questionnaire. Third, we cannot identify whether sedentary behavior continued to decrease or increase after the intervention. Fourth, during the pandemic, excessive use of the internet and social networking has been shown to contribute to an increase in depressive symptoms [34], which may have influenced the dropout of participants who possibly became “stressed” by so many online activities. We suggest that this be evaluated and considered in future studies. Finally, the lack of access to the data plan of some participants may have influenced the continuity of the intervention, which may have interfered with our results. Despite these limitations, given that knowledge surrounding adolescents’ physical activity during the lockdown and our study’s aim to shed light on the “unknown” period of the COVID-19 pandemic, we believe that the current study still made important advances in the literature targeting adolescents. Adolescents constitute an age group with high rates of sedentary behavior, and many interventions encounter problems in changing behaviors that hinder the increase of physical activity. ## 5. 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--- title: End-to-End Automatic Classification of Retinal Vessel Based on Generative Adversarial Networks with Improved U-Net authors: - Jieni Zhang - Kun Yang - Zhufu Shen - Shengbo Sang - Zhongyun Yuan - Runfang Hao - Qi Zhang - Meiling Cai journal: Diagnostics year: 2023 pmcid: PMC10047448 doi: 10.3390/diagnostics13061148 license: CC BY 4.0 --- # End-to-End Automatic Classification of Retinal Vessel Based on Generative Adversarial Networks with Improved U-Net ## Abstract The retinal vessels in the human body are the only ones that can be observed directly by non-invasive imaging techniques. Retinal vessel morphology and structure are the important objects of concern for physicians in the early diagnosis and treatment of related diseases. The classification of retinal vessels has important guiding significance in the basic stage of diagnostic treatment. This paper proposes a novel method based on generative adversarial networks with improved U-Net, which can achieve synchronous automatic segmentation and classification of blood vessels by an end-to-end network. The proposed method avoids the dependency of the segmentation results in the multiple classification tasks. Moreover, the proposed method builds on an accurate classification of arteries and veins while also classifying arteriovenous crossings. The validity of the proposed method is evaluated on the RITE dataset: the accuracy of image comprehensive classification reaches $96.87\%$. The sensitivity and specificity of arteriovenous classification reach $91.78\%$ and $97.25\%$. The results verify the effectiveness of the proposed method and show the competitive classification performance. ## 1. Introduction According to the “World Vision Report” issued by WHO, at least 2.2 billion people in the world are visually impaired or blind, and nearly half of the visual impairments could be avoided through early prevention [1]. By 2020, the number of glaucoma patients reached 796 million worldwide, and age-related macular degeneration patients numbered 200 million people [2,3]. The number of patients with diabetic retinopathy is expected to reach 200 million by 2035 [4]. However, the noted irreversible blinding diseases can be prevented and treated in advance by an ophthalmologist’s examination of the ocular fundus [5]. In this relevant examination, the pattern and structure of retinal vessels are important clinical characterizations. In addition, retinal blood vessels in the human body are the only blood vessels that can be directly observed with non-invasive imaging technology. The morphology is also affected by various factors of cardiovascular disease, hypertension, arteriosclerosis, and other systemic diseases [6,7,8]. The changes in retinal vessels, including vascular caliber, branch morphology, and arteriolar-to-venular diameter ratio (AVR), can be used as the diagnostic basis for vascular related diseases. For example, the method of observing the aspect ratio of retinal arterioles and the asymmetry of venous branches is an early monitoring method for Alzheimer’s disease [9]. The risk value of coronary heart disease and other diseases is also associated with the ratio of arteriovenous diameters [10]. In addition, arterial stenosis in the cerebrovascular network is significantly correlated with retinal arteriolar diameter [11]. Therefore, it is of great significance to classify the retinal vessels accurately for the prevention and observation of many diseases. However, early retinal vessel segmentation and classification mainly rely on manual labeling by professional doctors [12], which requires a lot of time and effort. Moreover, due to the reliance on subjective criteria, the results of segmentation and classification can be different. Automatic segmentation and classification of retinal vessels can greatly reduce the workload of doctors and can also avoid the impact of different doctors’ subjective factors on the classification results. With the development of computer vision technology, there are many methodologies for retinal vessel segmentation, including image filtering technology [13,14], machine learning algorithm for feature extraction [15], and neural network research [16]. However, related work that is dedicated to the classification of arteriovenous vessels is significantly less than that on vascular segmentation [17]. U-shaped convolution network (U-Net) [18] is widely used in the field of fundus blood vessel segmentation because of its excellent effect in the field of medical image segmentation. Specifically, the U-Net network is superior to convolutional neural network (CNN) and the fully convolutional networks (FCN) networks in the field of retinal vessel segmentation [19]. At the same time, generative adversarial network (GAN) has also been widely used in the field of fundus images and has been proven to be beneficial for various tasks [20]. In recent years, the GAN network has made good progress in the field of medical image segmentation. Some proposed GAN also obtained high accuracy for retinal vessel segmentation [19]. In addition, the atrous spatial pyramid pooling (ASPP) module enables the network to expand the receptive field and capture multi-scale contextual information without increasing the complexity of the algorithm parameters. It also reduces the loss of detailed information, which makes the vascular feature information better preserved and can enrich the microvascular information. In the task of blood vessel segmentation and classification, the number of pixels in the background of the fundus image is far greater than the number of blood vessels. In order to make the network pay more attention to the generation of blood vessel pixels, the attention module is also designed to be added to the network structure. At the same time, residual connections are introduced into the downsampling process of the U-Net of a generator network to alleviate the problem of gradient disappearance and increase the sensitivity of the generator network to weight changes, which makes the generator improve the vascular classification effect. In addition, low contrast is a major obstacle to the retinal image in optical imaging [21]. Before network classification, the preprocessing operations need to be conducted to enhance the contrast of the data. To summarize, the contributions and novelty of the present study are highlighted as follows:A model based on GAN and improved U-Net is proposed for the automatic end-to-end classification of fundus arteriovenous vessels. The introduction of ASPP and attention modules can also improve the classification capability of the model. The classification results of the proposed model are highly competitive. A local contrast enhancement method was used to preprocess the input images. Through preprocessing, the problems of low overall brightness and poor contrast between blood vessels and background of the original fundus image data were solved. The effectiveness of this method was verified by ablation experiments. The proposed method allows simultaneous classification of vessel crossings in fundus images in addition to the classification of arterioles, which is innovative in the study of fundus vascular classification. The experimental materials used in this study are described in detail in Section 3. The design of the network model and the experimental process are described in Section 4. The results obtained are reported in Section 5. Section 6 summarizes this article. ## 2. Related Work Although extensive research has been carried out in the field of retinal vessel segmentation, little attention has been paid to the field of automatic classification of retinal vessels [22]. Based on the existing research, the methods of retinal artery and vein classification can be divided into two categories: traditional machine learning based methods and depth learning based methods. Manual features. In the research of vessel classification based on traditional machine learning methods, it is usually necessary to manually extract features and then classify arteries and veins. It is often accompanied by some post-processing steps. Sathananthavathi et al. [ 23] extracted features manually according to the morphological structure of retinal vessels; the BAT evolutionary algorithm and the random forest classifier were used for main feature determination and classification, respectively; and, finally, the post-processing was used at the bifurcation of retinal vessels. Srinidhi et al. [ 24] and Xu et al. [ 25] also used manual features combined with random forest classifier to classify arteries and veins. Vázquez et al. [ 26] used the optic disc to divide the retinal vessels into many segments, and then the vessel segments were classified by color information, and the final classification result of whole blood vessel was determined by voting of the connected blood vessel segments. Graph-based methods. Welikala et al. [ 27] avoided the use of hand-crafted features. The vascular network was first segmented from the retinal image. Bifurcations and crossover points were searched based on the retinal vascular skeleton, and vessel segments were segmented using the centerline. Finally, the vessel segments were fed into a convolutional neural network based on three convolutional and three fully connected layers to achieve the arteriovenous classification of retinal vessels. The classification rate with 47 features (the largest dimension tested) using OLPP in their own ORCADES dataset is only $90.56\%$, and the classification rate in the public dataset DRIVE is $86.7\%$. Zhao et al. [ 28] constructed the graph through image segmentation, skeletonization, and identification of significant nodes. They formalized the topology estimation and A/V classification into a pairwise clustering problem. The classification of blood vessels was effectively realized. Segmentation first and then classification. With the continuous development of deep learning, convolutional neural network has also been applied in the field of retinal vessel classification. Especially after U-Net [18] was proposed, it has performed well in the field of retinal vessel segmentation and classification, which leads to the realization of pixel-level segmentation and classification of fundus images. Li et al. [ 29] regarded arteriovenous classification as a three-classification task. First, the fundus image was preprocessed using the fuzzy removal technology, and then the image was classified by using the improved U-Net network. In order to improve the classification accuracy, the tracking algorithm was used as the post-processing method to further classify the blood vessels. Binh et al. [ 30] also regarded arteriovenous classification as a three-classification task, the improved U-Net model was used to classify retinal vessels, and the method of graph cutting was used for post-processing; the accuracy of their method is about $97.6\%$. End-to-end classification. Morano et al. [ 31] decomposed the joint task into three segmentation problems: arteries segmentation, veins segmentation, and vessel segmentation. Their classification network consisted of the straightforward application of an FCNN with a custom loss. The accuracy of classifying retinal vessels reached $95.45\%$. Galdran et al. [ 32] used CNN as a task classification network. The previously segmented vascular tree did not need to be included in their classification method. Fully automatic classification of retinal blood vessels was achieved. They also proposed a classification of uncertain blood vessels. For the benefit of retrieval, the investigation of comparison is concisely summarized in Table 1. ## 3.1. Dataset The Retinal Images vessel Tree Extraction (RITE) dataset [33] is used in this work, which is derived from the DRIVE dataset [34]. It has been widely used as evaluation criteria in research fields such as retinal vessel segmentation, vessel extraction, and vessel classification. As shown in Figure 1, the RITE dataset is composed of four parts: fundus image, mask image used to extract the region of interest, the vessel trees manually segmented, and the A/V reference standard. The A/V reference standard is generated by marking each vascular pixel. In Figure 1d, red represents artery (A), blue represents vein (V), green represents crossing parts of artery and vein (O), and white represents uncertain vessel (U), with a resolution of 565 × 584 pixels. ## 3.2. Preprocessing The collection process of image datasets inevitably has problems such as uneven lighting and noise. Similarly, the fundus image dataset is limited by imaging conditions such as low overall illumination and low contrast between blood vessels and background. These problems will have negative impacts on the further classification of fundus images. Therefore, this paper uses adaptive contrast enhancement (ACE) [35] to preprocess the original retinal fundus image. The ACE algorithm divides the image into high frequency and low frequency. The low frequency part is obtained by smoothing, blurring, and other low-pass filtering methods. The high frequency part is directly obtained by subtracting the low frequency part from the original picture. In ACE, the high frequency part is amplified and added to the original low frequency part to obtain the enhanced image. The color constancy and brightness constancy of the enhanced image are improved, and the image contrast is changed. The details of ACE are as follows: [1]y(i,j)=mx(i,j)+G(i,j)x(i,j)−mx(i,j), where x(i, j) represents the pixel value corresponding to the image coordinate (x, j) before preprocessing; mx(i, j) represents the low-frequency part, [x(i, j) −mx(i, j)] represents the high-frequency part, and G(i, j) represents the high-frequency amplification coefficient (contrast gain). The low-frequency part represents the local average value of the area with the window size of (2n + 1) × (2n + 1) centered on the image coordinate (x, j) pixel. The specific formula is as follows: [2]mx(x,j)=1(2n+1)2∑k=i−ni+n∑l=j−nj+nx(k,l) According to the relevant research on ACE [36], the high-frequency amplification coefficient G(i, j) is defined as a variational constant, which is inversely proportional to the local mean square error, as shown in Equation [3]. [ 3]G(x,j)=αδσx(x,j), where the value of δ is equal to the global mean square error of the image, which can reflect the dispersion of the image pixel value and the mean value. Constant α coefficient can linearly adjust the total amplification coefficient. The expression δx (i, j) represents the local mean square deviation, which can reflect the contrast change of the gray value of each pixel in the local area of an image. The expression of σ is shown in Equation [4]. [ 4]σx(x,j)=1(2n+1)2∑k=i−ni+n∑l=j−nj+nx(k,l)−mx(x,j)2 In addition, in order to avoid noise amplification or pixel value saturation caused by small local variance of the local part of image smoothing, the maximum value of G(i, j) is limited. The setting of the maximum of G(i, j) and the effects of all parameters is discussed in Section 5.5. The fundus images before and after preprocessing are shown in Figure 2. By comparing the original image, it can be seen that the contrast between the vascular pixels, especially the microvascular pixels and the background pixels, is significantly enhanced after preprocessing. ## 4.1. Method Architecture Overview The overall flow chart of the proposed method is shown in Figure 3. In the proposed method, ACE is used to preprocess the original retinal fundus image before performing network training and testing. The proposed model is designed using GAN. GAN is based on the idea of dynamic adversarial and can be divided into the generator and the discriminator parts. In this paper, the modified U-Net network is used as the generator and the convolutional network is used as the discriminator. The generator is used to generate the vessel classification prediction result from the input training fundus image data, which is labeled as Fake Image. The corresponding Real Image is the ground truth of vessel classification from trained fundus image data. The discriminator is used to discriminate the image source, and the discriminated results are marked as Real and Fake. Being marked as Real means the image comes from the real vascular classification data Real Image, and being marked as Fake means the image come from the vascular classification data Fake Image, which is generated by the generator. By repeatedly training the generator and discriminator, the Fake Image will be as close to the Real Image as possible. When the discriminator cannot distinguish between real and false numbers, the required vessel classification network training is completed. ## 4.2. Network Structure The design details of GAN are described in this section. The U-Net [18] network is used as the main design of the generator. The structure of the designed generator network is shown in Figure 4. The atrous spatial pyramid pooling (ASPP) [37] module is added to the downsampling process of the U-Net network. The attention module is introduced at the skip link of the U-Net network. To alleviate the problem of gradient disappearance, residual connections are introduced into the downsampling process of the U-Net network. The residual connections helps to increase the sensitivity of the generator network to weight changes, which makes the generator fully learn the distribution of retinal vascular pixels and improve the vascular classification effect. The main role of the discriminator is to provide a descent gradient for the generator. Complex discriminator gradients will cause the gradient of the generator to disappear, which will not achieve effective adversarial training [38]. In this paper, ordinary convolutional networks are used as a discriminator, with the structure shown in Figure 5. Figure 4: Improved U-Net network used as the generator (the ASPP Module is shown in Figure 6, and the Attention Module is shown in Figure 7). Figure 5: Discriminator network used. ## 4.2.1. ASPP Module The original U-Net network uses downsampling to expand the receptive field while reducing the resolution, but the pooling layer of traditional convolution in U-Net will lose the details of retinal images, which leads to problems such as incomplete microvessel segmentation and susceptibility to breakage. In this paper, the ASPP module is added, which utilizes dilated/atrous convolution with multiple expansion rates to stack into a pyramidal structure instead of normal convolution. The structure of the ASPP module is shown in Figure 6. The structure enables the network to expand the receptive field and capture multi-scale contextual information without increasing the complexity of the algorithm parameters. It also reduces the loss of detail information, which makes the vascular feature information better preserved and can enrich the microvascular information. Figure 6: The structure of the ASPP module. ## 4.2.2. Attention Module In the vascular classification task, the number of background pixels far exceeds the number of vascular pixels. The downsampled extracted feature maps are spliced directly with upsampling in the skip connection of traditional U-Net. Such a design produces a lot of redundant information and also leads to the deterioration of the extracted features. Inspired by Ashish Vaswani [39], the attention module is added to each skip to suppress excessive irrelevant information and make the model more concerned with the generation of vascular pixels. The details of the attention module are shown in Figure 7. In the figure, X is the feature map from downsampling and Y is the feature map from upsampling. After 1 × 1 convolution operation, X and Y are summed to highlight the features. After passing through ReLU and Sigmoid, the highlighted features are ranged between 0 to 1, which is the attention weight. It is assigned to the low-level feature after the attention weight is multiplied with X, and the attention allocation is achieved. Figure 7: The structure of the attention module. ## 5. Experiments and Results In this section, the experiments and results of the proposed method on the RITE dataset are detailed. Before going into the details of the implementation, the metrics to quantitatively evaluate the vascular classification results are described in Section 5.1. To ensure the validity of the proposed method, an ablation study is also described in Section 5.4. In addition, the effects of the parameters about the preprocessing are discussed in Section 5.5. ## 5.1. Evaluation Metrics Considering the multiple cases of actual and predicted values in fundus images, a confusion matrix applicable to vascular classification studies is established, shown in Table 2. Because the proportion of arteriovenous crossing (A-V crossing) pixels and uncertain vessel pixels in fundus images is small, they are classified together as an uncertain vessel category. According to the assessment methods widely used in the field of vascular classification [29,40,41,42,43], sensitivity (Sens), specificity (Spec), and accuracy (Acc) are used to assess the performance of the classification; the arteries are set as positive and the veins are set as negative. Sens, Spec and Acc are defined as follows:[5]Sens=TATA+FVa [6]Spec=TVTV+FAv [7]Acc=TA+TVTA+FVa+TV+FAv In order to quantitatively analyze the accuracy of uncertain vessels (including A-V crossing) classification, the index Acc_U is also introduced. To reflect a more comprehensive performance, the overall accuracy Acc_ all and the background segmentation accuracy Acc_B are added to evaluate the performance of the overall prediction of fundus images and the performance of the segmentation performance of non vascular pixels; Acc_U, Acc_All, and Acc005FB are defined as follows: [8]Acc_U=TUTU+FVu+FAu [9]Acc_All=TA+TV+TU+TB(TA+FAv+FAu+FAb+TV+FVa+FVu+FVb+TU+FUa+FUv+FUb+TB+FBa+FBv+FBu) [10]Acc_B=TBTB+FAb+FUv+FVb ## 5.2. Implementation Details Twenty color fundus images of RITE are selected randomly as the training set, and the remaining images are used as the test set. To increase the training data, a series of data enhancement operations such as horizontal flipping and rotating the images was performed. The network implementation is based on the Python language and the Pytorch framework, and the model is trained on a PC with a core i9-12900k CPU (3.8 GHz) and NVIDIA RTX3090-24GGPC (32 GB of RAM). During training, the batch_size was set to 1, the learning rate was 0.0008, and the optimizer was Adam. The experiment was trained for a total of 855 iterations and took about 7 h. ## 5.3. Classification Results The classification results of the proposed model on the RITE retinal images are presented in Figure 8. The validity of the proposed method is demonstrated by comparing the network prediction results of fundus images with the ground truth of vessel classification from the medical professional. In addition to the crude arteriovenous vessels, microvessels can also be more effectively and accurately classified. The details of the microvessel classification are magnified in Figure 8d. As described in Section 5.1, the results of the data quantified by the six evaluation metrics are shown in Table 3. In Table 4, the experimental result is compared with the results of related studies based on the three important evaluation statistics of Sens, Spec, and Acc. As can be seen from Table 4, the proposed method outperforms most models in all evaluation metrics and achieves the highest scores on Acc and Sens. It shows the competitive performance of the proposed method compared to state-of-the-art methods. The evaluation metric Sens can be used to indicate arterial classification performance. The value of Sens of the proposed classification method increased to $91.78\%$, which proves the advantages of the proposed method in arterial classification performance. Among the methods, Morano et al. [ 31] designed an FCNN classification network with custom losses. Their method has a greater classification performance for veins. The value of Spec was increased to $98.67\%$ in their method. However, the value of Sens only reached $78.07\%$. The balance between arterial classification and venous classification is the worst compared with other methods. The value of Sens also decreased by $13.71\%$ compared with the proposed method. In the whole fundus image, the pixel of A-V crossings is a small proportion. As such, the corresponding classification training samples that can be obtained are few. Therefore the accurate classification of the A-V crossings is a greater challenge. In most current studies of fundus image classification [31,32,43,44,45], the identification of the A-V crossings region is not considered. However, A-V crossings are an area of concern for physicians. For example, the retinal crossover sign is one of the common alterations of retinal vessels in the fundus of hypertensive eyes [46]. The phenomenon of compression at the A-V crossings regions is the retinal arteriovenous crossing sign. When the A-V crossings are compressed, they are called retinal arteriovenous crossings signs. Therefore, A-V crossings also need to be considered in the classification studies of retinal vessels in the fundus images. The classification results of the A-V crossings are shown in Figure 9. The results show that A-V crossings far from the optic disc regions would enable a more accurate classification. However, due to the dense and complex distribution of blood vessels near the optic disc, the classification in these regions is not ideal. ## 5.4. Ablation Study In order to verify the effectiveness of the proposed preprocessing and network improvement modules, six sets of ablation studies are conducted on the RITE dataset. The results of the ablation studies are shown in Table 5. The baseline model is the backbone network, where the original U-Net network is used as the generator network. Compared to baseline, adding residual connections RS improved Acc by $3.64\%$ and Sens by $7.23\%$. After the addition of the ASPP module (ASPPM), Acc was improved by $3.10\%$, and Sens/Spec were improved, respectively, by $15.74\%$/$3.02\%$. The addition of the attention module (AttM) increased Acc/Sens/Spec by $4.56\%$/$14.66\%$/$4.58\%$. Combining the residual connections, the ASPP module, results in an overall improvement for Acc/Sens/Spec/Acc_U by $4.91\%$/$16.59\%$/$5.54\%$/$7.78\%$, which verified the effectiveness in the improved network. Moreover, with the preprocessing method (Pre), the results of Acc, Sens, and Acc_U reach the optimum. In summary, the proposed method is verified to have potential in the vascular classification task. ## 5.5. Discussion In Equation [1], G(x, j) represents the high-frequency amplification coefficient (contrast gain). When it is a fixed constant, the high-frequency part is amplified in the same proportion. An overexposure phenomenon occurs in over-enhanced areas such as the edge of the fundus image and the center of the optic disc, as shown in Figure 10a. To avoid this phenomenon, the high-frequency amplification coefficient G(x, j) is defined as a variational constant in this work, which is inversely proportional to δx (i, j), as shown in Equation [3]; δx (i, j) represents the local mean square deviation, and its value is directly related to n. Figure 10b shows an example of a result where n is equal to 50; the overexposure phenomenon at the edge of the fundus image and the center of the optic disc is significantly improved compared with Figure 10a. Due to the addition of δx (i, j), the high frequency amplification factor G(x, j) becomes a variable of spatial adaptation. In places where the image changes violently, the high-frequency amplification coefficient decreases accordingly, which can avoid the Ringing effect. The function of α is to linearly adjust the value of G(x, j) to control the enhancement effect of the preprocessing. For example, the effect of α equal to 0.01 is shown in Figure 10c. In addition, since G(x, j) is inversely proportional to the local mean squared deviation, the local mean squared deviation may be small in areas where the image is smooth. If the local mean squared deviation is too small, and the G(x, j) will become large, which will lead to noise amplification or pixel saturation (i.e., pixel value exceeds 255). For example, this phenomenon can be observed easily, as shown in Figure 10d. When the noise is too large, the tiny vascular regions are greatly disturbed. Therefore, the maximum value of G(x, j) is limited to obtain better results. After comparing different preprocessing effects, this paper limits the maximum value of G(x, j) to 5. As a further detailed supplement, the parameter sensitivity analysis is shown in Figure 11. According to the analysis, the results show a slightly increasing trend when n is less than 6, and a significantly decreasing trend when n is greater than 6. When α is less than 0.9, it has a significant impact on the results, and when α is more than 0.9, it has a more subtle impact on the results. When the maximum setting is less than or equal to 5, for Acc_U has a slight improvement, and other results are slightly affected. When the maximum setting is greater than 5, all results basically show a downward trend. ## 5.6. Limitations In fundus images, the pixel and background of blood vessels are not balanced, which is also a common problem in medical images. In particular, the classification of A-V crossings are involved in this work. Its content and background are seriously unbalanced, and vascular crossing needs further attention by the network. At the same time, the classification accuracy is greatly affected by the preprocessing effect, but the data preprocessing needs to rely on manual adjustment. In the experimental process, we found that when the number of epochs increases to a higher level, the classification performance for arterial vein and vascular crossing rapidly decline. In future work, better preprocessing methods and solutions to imbalance problems can improve the accuracy of classification of vascular crossing and achieve more stable performance. ## 6. Conclusions In this paper, a method based on the combination of the GAN and U-Net networks is proposed for vessel classification of fundus images. This method preprocesses the input images by ACE to solve problems such as unclear data of original fundus images. In addition, the confusion matrix established in this study for vessel classification has reference value for comprehensive analysis and evaluation of prediction results. In the design of GAN, the original U-Net network is improved by introducing the residual connection, the null pyramid module, and the attention module. The proposed network structure not only achieves the end-to-end prediction for arterial and venous vessels, but also predicts the arterial–venous crosspoint region. Due to the complexity of the optic disc region and the small percentage of vascular cross-pixels, there is still much room for improvement in the accuracy of vascular cross-point recognition. Compared with the existing end-to-end methods, this study has improved the accuracy of vessel classification, which is important for retinal vessel classification to $96.86\%$. Experimental results show that the proposed method can effectively realize the automatic classification of arteriovenous and A-V crossings. This can be applied to the basic stage of screening and diagnosis of retinal vascular related diseases to improve the diagnostic efficiency of doctors, which has important significance for clinical practice. The proposed model design can also provide important reference for similar semantic segmentation tasks. In addition, the distribution of retinal vessels, arteriovenous intersection, and other characteristics have individual uniqueness, which have potential application value in biological recognition and other fields. In addition, some reliable future research can be continued based on the advantages and limitations of our research methods. For example, research directions to improve the accuracy of A-V crossings are needed. In addition, the fundus image enhancement method based on depth learning can be tried to solve the disadvantages of data optical imaging and improve the classification accuracy. Automated measurement of arteriovenous caliber ratios can be achieved based on arteriovenous classification, which has important implications for the diagnosis of diabetes. ## References 1. 1. World Health Organization World Report on VisionWorld Health OrganizationGeneva, Switzerland2019. World Report on Vision (2019.0) 2. Quigley H.A., Broman A.T.. 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--- title: 'Utility of monocyte distribution width in the differential diagnosis between simple and complicated diverticulitis: a retrospective cohort study' authors: - Chang-Yuan Chang - Tai-Yi Hsu - Guan-Yi He - Hong-Mo Shih - Shih-Hao Wu - Fen-Wei Huang - Pei-Chun Chen - Wen-Chen Tsai journal: BMC Gastroenterology year: 2023 pmcid: PMC10047462 doi: 10.1186/s12876-023-02736-0 license: CC BY 4.0 --- # Utility of monocyte distribution width in the differential diagnosis between simple and complicated diverticulitis: a retrospective cohort study ## Abstract ### Background Colonic diverticulitis is a leading cause of abdominal pain. The monocyte distribution width (MDW) is a novel inflammatory biomarker with prognostic significance for coronavirus disease and pancreatitis; however, no study has assessed its correlation with the severity of colonic diverticulitis. ### Methods This single-center retrospective cohort study included patients older than 18 years who presented to the emergency department between November 1, 2020, and May 31, 2021, and received a diagnosis of acute colonic diverticulitis after abdominal computed tomography. The characteristics and laboratory parameters of patients with simple versus complicated diverticulitis were compared. The significance of categorical data was assessed using the chi-square or Fisher’s exact test. The Mann–Whitney U test was used for continuous variables. Multivariable regression analysis was performed to identify predictors of complicated colonic diverticulitis. Receiver operator characteristic (ROC) curves were used to test the efficacy of inflammatory biomarkers in distinguishing simple from complicated cases. ### Results Of the 160 patients enrolled, 21 ($13.125\%$) had complicated diverticulitis. Although right-sided was more prevalent than left-sided colonic diverticulitis ($70\%$ versus $30\%$), complicated diverticulitis was more common in those with left-sided colonic diverticulitis ($61.905\%$, $$p \leq 0.001$$). Age, white blood cell (WBC) count, neutrophil count, C-reactive protein (CRP) level, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and MDW were significantly higher in the complicated diverticulitis group ($p \leq 0.05$). Logistic regression analysis indicated that the left-sided location and the MDW were significant and independent predictors of complicated diverticulitis. The area under the ROC curve (AUC) was as follows: MDW, 0.870 ($95\%$ confidence interval [CI], 0.784–0.956); CRP, 0.800 ($95\%$ CI, 0.707–0.892); NLR, 0.724 ($95\%$ CI, 0.616–0.832); PLR, 0.662 ($95\%$ CI, 0.525–0.798); and WBC, 0.679 ($95\%$ CI, 0.563–0.795). When the MDW cutoff was 20.38, the sensitivity and specificity were maximized to $90.5\%$ and $80.6\%$, respectively. ### Conclusions A large MDW was a significant and independent predictor of complicated diverticulitis. The optimal cutoff value for MDW is 20.38 as it exhibits maximum sensitivity and specificity for distinguishing between simple and complicated diverticulitis The MDW may aid in planning antibiotic therapy for patients with colonic diverticulitis in the emergency department. ## Background A diverticulum is a herniation through a weak site of the bowel wall that produces a small outpouching [1]. When the diverticular wall is eroded by increased intraluminal pressure or inspissated food particles, diverticulitis may occur [2]. Colonic diverticulitis is one of the most common causes of abdominal pain and lower gastrointestinal bleeding in the emergency department (ED). The prevalence of colonic diverticulitis is increasing not only in Western countries but also in Asian countries [3]. It is predicted that approximately $50\%$ of individuals aged 60 years or older have diverticulosis, whereas by the age of 80 years, this percentage is predicted to be approximately $70\%$ [4]. Of those who developed diverticulosis, $10\%$–$25\%$ experienced an acute episode of diverticulitis [5]. Colonic diverticulitis is usually diagnosed in the ED through intravenous (IV) contrast computed tomography, which is the modality of choice for the diagnosis and staging of colonic diverticulitis with a sensitivity of $94\%$ and a specificity of $99\%$ [6]. Simple acute diverticulitis is a self-limiting and mild disease. It is defined as localized inflammation without any abscess or perforation [7]. The clinical symptoms include lower abdominal pain, fever, constipation, and diarrhea. Outpatient treatment is required for patients who have simple non-septic diverticulitis, are immunocompetent, and can tolerate oral intake. However, approximately $15\%$ of diverticulitis cases have been reported to be complicated forms and were manifested with abscess, stricture, obstruction, fistulae to adjacent organs, or perforation [8–10]. As a consequence of bacterial translocation, fecal contamination, or phlegmon development, complicated diverticulitis may present with severe abdominal pain, bloating, dehydration, and signs of sepsis [11]. On physical examination, patients may exhibit peritonitis with rebound tenderness and guarding. Patients with complicated diverticulitis must receive treatment specific to their complications. The current therapeutic options for diverticulitis vary with disease severity, which can be determined based on clinical, radiological, and laboratory findings. When diffuse peritonitis is suspected given the findings of a physical examination, emergency surgery may be required even if imaging shows that the abscess is localized [12]. Therefore, early assessment of the severity of complicated diverticulitis and adequate resuscitation are important. The monocyte distribution width (MDW) is a novel hematological parameter assessed as part of the complete blood count (CBC) with the differential count. It helps in determining the size distribution of circulating monocytes, which are the first immune cells to respond to pathogenic organisms [13]. In a multicenter international European study, the MDW in combination with the white blood cell (WBC) count was suggested to be a novel screening test for the early detection of sepsis in the ED [14]. Comparison of diagnostic performance according to the Sepsis-3 criteria revealed that the MDW was not inferior to the C-reactive protein (CRP) or procalcitonin level in terms of area under the receiver operator characteristic (ROC) curve (AUC) values [15]. Few studies have focused on the efficacy of the MDW in diagnosing diseases other than sepsis. To our knowledge, the MDW has been used for the detection of the novel coronavirus disease (COVID-19) [16–18] and pancreatitis [19]. However, there is a lack of evidence on the efficacy of the MDW in early prediction of the severity of other diseases. In this retrospective cohort study, we aimed to investigate whether the MDW data preceding CT assessment is helpful in differentiating simple from complicated colonic diverticulitis in an ED. ## Study design and setting This retrospective cohort study was conducted at a university-affiliated medical center receiving approximately 150,000 ED visits annually. The study was approved by the Hospital Ethics Committee on Human Research. The study protocol was reviewed and qualified as exempt from the requirement to acquire informed consent. ## Patient selection Patients older than 18 years who presented to our ED between November 1, 2020, and May 31, 2021, and who received a diagnosis of acute colonic diverticulitis after abdominal CT was performed were included in this study. All the enrolled patients received the indicated blood examinations. Patients who had any other concomitant active inflammation or infection, were receiving any antibiotic course, had a final pathological diagnosis other than colonic diverticulitis, or had incomplete medical records or laboratory data were excluded from the study. Data were retrieved from the institutional electronic medical chart of the ED. ## Methods and measurements The collected variables included patient demographics and laboratory data. Blood tests were obtained at the same time inserting the IV line within an hour after the treating physicians’ examination of patients at the ED. Blood samples were acquired before antibiotic treatment and IV contrast CT scan. The patients’ age; sex; and comorbidities—such as diabetes mellitus, hypertension, ischemic heart disease, heart failure, liver cirrhosis, cholelithiasis, rheumatological disease, asthma, chronic obstructive pulmonary disease, chronic renal insufficiency, urolithiasis, cerebrovascular disease, and existing cancer—were recorded. Moreover, the body temperature upon triage and laboratory findings (including WBC, neutrophil, lymphocyte, and platelet counts; MDW; and hemoglobin, CRP, creatinine, alanine aminotransferase, blood sugar, sodium, and potassium levels) were recorded. All the patients’ admission diagnoses coded as diverticulitis were reviewed, then IV contrast CT images were confirmed by two ED physicians and revalidated with the radiologists’ formal final reports whether there was a perforation, abscess, or fistula formation. Based on the IV contrast CT findings, the patients were divided into simple colonic diverticulitis and complicated colonic diverticulitis groups. The radiographic features of simple diverticulitis on CT are enhancement of the colonic wall with segmental thickening and pericolic fat stranding, often disproportionately prominent compared to the amount of bowel wall thickening. As for the complicated types, accumulation with fluid or/with gas suggested abscess formation. And extravasation of gas and fluid into the pelvis and peritoneal cavity are the characteristics of diverticular perforation [20, 21]. The MDW was measured by the UniCel DxH 900 analyzer (Beckman Coulter, Brea, CA, USA) from K3EDTA vacutainer tubes within 2 h of collection, as recommended by the manufacturer. The analyzer uses volume, conductivity, and scatter properties of leukocytes technology to characterize and separate WBCs into 5 different groups (neutrophils, eosinophils, monocytes, lymphocytes, and basophils). The system furthermore calculates the means and standard deviations of these groups’ cell morphometric parameters [22]. The manufacturer of the hematology analyzer did not offer the unit for MDW, as previously reported [23, 24]. ## Statistical analysis Descriptive statistics were used to compare variables—baseline demographics, laboratory test results, and inflammatory biomarker measurements—between the two groups. Categorical variables are expressed as proportions, and continuous variables are expressed as medians with interquartile ranges (IQRs, quartile 1 through quartile 3). Univariate analysis was performed using the chi-square or Fisher’s exact test for categorical variables and the Mann–Whitney U test for continuous variables in order to identify predictors of complicated colonic diverticulitis. Variables with p-values < 0.10 in the univariate analysis were then subjected to backward stepwise logistic regression analysis. ROC curves were also used to assess the performance of inflammatory biomarkers— including the WBC count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), MDW, and CRP level—in order to distinguish simple from complicated colonic diverticulitis. Youden’s indices were calculated on ROC curves to find the best discriminatory cut-off values [25]. Test characteristics of MDW in terms of sensitivity, specificity, positive predictive value, and negative predictive value along with their $95\%$ CIs for the optimal cut-off value were also computed. A p-value ≤ 0.05 was considered statistically significant. All data were analyzed using SAS (version 9.1; SAS Institute, Cary, NC, USA). Post-hoc power of the study was estimated using GPower software (version 3.1.9.7; Heinrich Heine University Düsseldorf, Germany) with the α error probability set at 0.05. This study was approved by the Institutional Review Board of the Ethics Committee of China Medical University and Hospital (CMUH110-REC3-106). The requirement of informed consent from the patients was waived by the Ethics Committee of China Medical University and Hospital. ## Results From November 1, 2020, to May 31, 2021, 84,173 ED visits were recorded. A total of 176 visits were retrieved from the institutional electronic medical chart during the study period. Of the 16 patients who were excluded from the study, 2 had other concomitant infections, 1 was a repeat patient who had revisited the ED and was taking oral antibiotics for colonic diverticulitis, 6 had an initial ambiguous diagnosis of colonic diverticulitis on CT scans but a final pathological diagnosis of colon cancer and not diverticulitis, 4 had no CRP data, and 3 had no MDW data because their monocyte count was less than 100/μL. Thus, a total of 160 patients were enrolled in the study: 139 in the simple colonic diverticulitis group and 21 in the complicated colonic diverticulitis group (Fig. 1).Fig. 1The flowchart of the enrolled study patients Overall, the number of male patients was higher than that of female patients ($$n = 90$$/160, $56.25\%$). Most patients had right-sided colonic diverticulitis ($$n = 112$$, $70\%$): 14 in the cecum ($8.75\%$), 91 in the ascending colon ($56.875\%$), and 7 in the transverse colon ($4.375\%$). Seventeen patients ($10.63\%$) had other diverticula in different segments of the colon. Recurrent colonic diverticulitis was noted in 27 patients ($16.88\%$). Hypertension was the most prevalent comorbidity ($$n = 30$$, $18.75\%$). Comparison of the variables for the two groups revealed that the patients with complicated colonic diverticulitis were older, with a median age of 58 years (IQR: 41–72 years). Conversely, the patients with simple colonic diverticulitis had a median age of 44 years (IQR: 30–59 years; $$p \leq 0.008$$). Complicated colonic diverticulitis was more frequently detected in the left colon than in the right colon ($61.905\%$ versus $38.095\%$). By contrast, simple colonic diverticulitis was more prevalent in the right colon than in the left colon ($74.180\%$ versus $25.180\%$). The location of colonic diverticulitis significantly differed between the two groups ($$p \leq 0.001$$). However, no significant difference was noted in comorbidities between the two groups. Laboratory values—WBC ($$p \leq 0.008$$) and neutrophil ($$p \leq 0.003$$) counts, MDW ($p \leq 0.001$), and CRP level ($p \leq 0.001$)—were significantly higher in the complicated colonic diverticulitis group. By contrast, lymphocyte count ($$p \leq 0.001$$) and sodium level ($p \leq 0.001$) were significantly higher in the simple colonic diverticulitis group (Table 1).Table 1Distributions of baseline variables by colonic diverticulitis groupVariablesTotalSimple DiverticulitisComplicated Diverticulitisp-valuePatients, n (%)160 [100]139 (86.875)21 (13.125)Female sex, n (%)70 (43.75)62 (44.604)8 (38.095)0.642Age in years, median (IQR)45 (31.25–60)44 (30–59)58 (41–72)0.008Body mass index23.821 (20.701–26.892)23.875 (20.700–26.892)23.624 (20.695–26.388)0.950Body Temperature (℃), median (IQR)36.8 (36.5–37.375)36.8 (36.5–37.3)37.2 (36.45–37.5)0.273Recurrent diverticulitis, n (%)27 (16.875)21 (15.108)6 (28.571)0.129Multiple locations of diverticula, n (%)17 (10.625)15 (10.791)2 (9.524)1.000Location of diverticulitis, n (%)0.001* Right colon:112 [70]104 (74.820)8 (38.095) Cecum14 (8.750)14 (10.072)0 [0] Ascending colon91 (56.875)85 (61.151)6 (28.571) Transverse colon7 (4.375)5 (3.597)2 (9.524) Left colon:48 [30]35 (25.180)13 (61.905) Descending colon24 [15]19 (13.669)5 (23.810) Sigmoid colon24 [15]16 (11.511)8 (38.095)Comorbidities, n (%) Diabetes mellitus10 (6.25)9 (6.475)1 (4.762)1.000 Hypertension30 (18.75)25 (17.986)5 (23.810)0.551 Ischemic heart disease8 [5]7 (5.036)1 (4.762)1.000 Heart failure2 (1.25)2 (1.439)0 [0]1.000 Liver cirrhosis1 (0.625)1 (0.719)0 [0]1.000 Cholelithiasis10 (6.25)9 (6.475)1 (4.762)1.000 Rheumatologic disease2 (1.25)1 (0.719)1 (4.762)0.246 Asthma / COPD2 (1.25)1 (0.719)1 (4.762)0.246 Chronic renal insufficiency4 (2.5)3 (2.158)1 (4.762)0.434 Urolithiasis10 (6.25)7 (5.036)3 (14.286)0.127 Cerebrovascular disease1 (0.625)1 (0.719)0 [0]1.000 Existing cancer4 (2.5)4 (2.878)0 [0]1.000Laboratory Data, median (IQR) WBC × 10^9 /L11.15 (8.7–13.775)10.9 (8.6–13.6)13.3 (11.0–15.45)0.008* Neutrophils (%)76.75 (69.825–82)75.2 (68.8–81.4)80.1 (76.8–86.3)0.003* Lymphocytes (%)14.2 (9.7–20.05)14.9 (10.5–20.9)9.5 (5.9–14.25)0.001* MDW18.78 (17.44–20.6475)18.56 (17.21–19.77)22.66 (20.655–24.465)< 0.001* Platelets × 10^9 /L243 (196–286)244 (197–286)227 (188.5–310)0.893 NLR5.357 (3.478–8.363)5.040 (3.305–7.524)8.372 (5.431–15.057)0.001* PLR152.383 (121.295–207.622)148.457 (120.351–196.479)206.860 (141.476–366.163)0.017* CRP (mg/dL)3.835 (0.9675–9.06)3.46 (0.67–6.08)12.14 (5.235–15.42)< 0.001* Alanine aminotransferase (U/L)18.5 (12–26.75)18 (12–26)19 (12–34.5)0.750 Creatinine (mg/dL)0.8 (0.6525–0.9575)0.79 (0.66–0.95)0.86 (0.61–1.135)0.471 Sodium (mmol/L)139 (137–140)139 (138–140)137 (136–138)< 0.001* Potassium (mmol/L)3.7 (3.5–3.9)3.7 (3.6–3.9)3.7 (3.4–3.9)0.251 Glucose (mg/dL)104.5 (94–123)105 (94–123)103 (92–135)0.860IQR Interquartile range, COPD Chronic obstructive pulmonary disease, WBC White blood count, MDW Monocyte distribution width, NLR Neutrophil to lymphocyte ratio, PLR Platelet to lymphocyte ratio, CRP C-reactive protein, ALT Alanine aminotransferase* $P \leq 0.05$ Univariate and multivariable binary logistic regression analyses (Table 2) revealed that left-sided location and the MDW were the only two variables that were significant predictors of complicated colonic diverticulitis after adjusting for the other variables. The adjusted odds ratio (OR) of complicated colonic diverticulitis was 5.197 ($95\%$ confidence interval [CI], 1.651–16.359; $$p \leq 0.005$$) for left-sided location and 1.552 ($95\%$ CI, 1.290–1.867; $p \leq 0.001$) for the MDW.Table 2Univariate and multivariable binary logistic regression analyses showing independent predictors of complicated diverticulitisVariablesUnadjusted OR ($95\%$ CI)p-valueAdjusted OR ($95\%$ CI)p-valueAge1.037 (1.009–1.066)0.010– –Left colon4.829 (1.848–12.616)0.0015.197 (1.651–16.359)0.005WBC1.189 (1.051–1.346)0.006––CRP1.166 (1.083–1.256)< 0.001––MDW1.584 (1.304–1.923)< 0.0011.552 (1.290–1.867)< 0.001NLR1.063 (1.003–1.127)0.038––PLR1.005 (1.002–1.008)0.004––Sodium0.718 (0.594–0.867)0.001––Variables with $p \leq 0.10$ in Table 1 were selected into logistic regression. Neutrophils and lymphocytes were eliminated because of their multicollinearity with NLR and PLRThe value of the Hosmer–Lemeshow test for the multivariable logistic regression is 0.827 (> 0.05), which indicates that the model’s estimate fits the data at an acceptable levelWBC White blood count, CRP C-reactive protein, MDW Monocyte distribution width, NLR Neutrophil-to-lymphocyte ratio, PLR Platelet-to-lymphocyte ratio, OR Odds ratio, CI Confidence interval* $p \leq 0.05$ Further evaluation through ROC analysis was performed to determine the diagnostic value of the MDW for complicated colonic diverticulitis. The AUC values were as follows: MDW, 0.870 ($95\%$ CI, 0.784–0.956); WBC + MDW, 0.827 ($95\%$ CI, 0.725–0.928); CRP, 0.800 ($95\%$ CI, 0.707–0.892); NLR, 0.724 ($95\%$ CI, 0.616–0.832); WBC, 0.679 ($95\%$ CI, 0.563–0.795); and PLR, 0.662 ($95\%$ CI, 0.525–0.798) (Fig. 2 and Table 3). The largest AUC value was that for the MDW among all the inflammatory biomarkers for diagnosing patients with complicated colonic diverticulitis. When the MDW cutoff was 20.38, the sensitivity and specificity were maximized to $90.5\%$ and $80.6\%$, respectively, with a low positive predictive value of $41.3\%$ but a high negative predictive value of $98.3\%$ (Table 4). A post-hoc power analysis was performed through Gpower, which revealed that the sample size was adequate to achieve $100\%$ power (1–β) when the MDW cut-off value was 20.38. The power exceeded $80\%$ when the total sample size was greater than 20 (Fig. 3).Fig. 2Receiver Operator Characteristics (ROC) analysis of MDW and other inflammatory biomarkers (MDW = monocyte distribution width; CRP = C-reactive protein; NLR = neutrophil lymphocyte ratio; WBC = white blood cell; PLR = platelet to lymphocyte ratio.)Table 3Area under the curve (AUC) values of the inflammatory biomarkers for complicated diverticulitisInflammatory biomarkersAUCStandard errorp-value$95\%$ Confidence intervalMDW0.8700.045< 0.0010.784–0.956WBC + MDW0.8270.052< 0.0010.725–0.928CRP0.8000.048< 0.0010.707–0.892NLR0.7240.0590.0010.616–0.832WBC0.6790.0600.0080.563–0.795PLR0.6620.0710.0170.525–0.798MDW Monocyte distribution width, WBC White blood cell, CRP C-reactive protein, NLR Neutrophil lymphocyte ratio, PLR Platelet to lymphocyte ratioTable 4The test characteristics of monocyte distribution width for complicated diverticulitisCut-off valueSensitivity ($95\%$ CI)Specificity ($95\%$ CI)Positive predictive value ($95\%$ CI)Negative predictive value ($95\%$ CI)20.380.905 (0.696–0.988)0.806 (0.730–0.868)0.413 (0.328–0.504)0.983 (0.937–0.995)CI Confidence intervalFig. 3Sample size calculation with GPower for monocyte distribution width value of 20.38 ## Discussion From the results of the study, it was concluded that the increase in age, WBC count, neutrophil count, CRP level, NLR, PLR, and MDW were related to the severity of colonic diverticulitis. The gold standard diagnostic tool for acute diverticulitis is CT, in which complications can also be visualized. However, the schedule of CT at an ED may be delayed because of the high number of patients. The optimal use of CT for patients in whom complicated diverticulitis is suspected should be based on clinical and laboratory findings to minimize treatment costs and radiation hazards [26]. Therefore, recognizing the risk factors of complicated diverticulitis and providing the right treatment before CT imaging are crucial. Some findings of the present study are in accordance with previous findings. In particular, right-sided diverticulitis was more prevalent than left-sided diverticulitis ($70\%$ versus $30\%$); this finding is compatible with reports from other Asian countries [27–29]. However, a higher number of patients with complicated diverticulitis had left-sided diverticulitis than right-sided diverticulitis ($61.905\%$ versus $38.095\%$); this finding is similar to the findings of previous Japanese and Korean studies [3, 27]. We also found that patients with complicated diverticulitis were older than those with simple diverticulitis (median age, 58 versus 44 years); this finding is also compatible with previous findings. For instance, in a Japanese retrospective multicenter study involving 1,112 patients, although right-sided colonic diverticulitis was more prevalent among the study population ($70.1\%$), left-sided colonic diverticulitis was significantly more common among elderly patients ($61.0\%$) [30]. Right-sided diverticulitis differs from left-sided diverticulitis in many respects. While right-sided diverticulitis is usually congenital and solitary [31, 32], left-sided diverticulitis is usually associated with secondary causes, including dietary factors, constipation, increased colonic pressure, defecation habits, and an irritable bowel. Consequently, left-sided diverticulitis more commonly occurs in older patients [33]. In our study, the WBC and neutrophil count, MDW, and CRP levels were higher in the complicated colonic diverticulitis group ($p \leq 0.05$, Table 1); however, only the MDW was found to have a statistically significant association with complicated diverticulitis in the multivariable binary logistic regression analysis ($p \leq 0.001$, Table 2). The WBC count and CRP level are the most common indicators of the severity of intra-abdominal inflammation in the ED. A higher WBC count or CRP level usually indicates a higher level of inflammation. Several studies [26, 34–36] have attempted to calculate the optimal threshold for the WBC count and CRP level in distinguishing complicated diverticulitis from simple diverticulitis; however, so far, no consensus has been reached. In addition to the WBC count and CRP level, two easily accessible hemogram-derived parameters, namely the NLR and PLR, have been used to predict complicated diverticulitis [37–39]. One study reported that the NLR could predict the need for surgical intervention more accurately than the CRP level and WBC count [37]. Palacios Huatuco et al. recently found the NLR cutoff of 4.2 to be the best diagnostic approach, with a sensitivity of $80\%$ and specificity of $64\%$, for detecting complicated diverticulitis [38]. Mari et al. found that the PLR had lower diagnostic accuracy than the NLR (AUC values, $0.67\%$, and $0.75\%$, respectively) [39]. Circulating neutrophils and monocytes are the first response to pathogenic organisms. The MDW is a parameter that describes the size distribution of circulating monocytes. Several studies have reported that the MDW can be used for the early diagnosis of sepsis in the ED [13, 14, 40, 41]. Similarly, Şenlikci et al. found that the MDW can be used to differentiate mild pancreatitis from nonmild pancreatitis [19]. However, little known is about the efficacy of the MDW in detecting acute complicated diverticulitis. In our cohort, the MDW cutoff of 20.38 had a sensitivity of $90.5\%$ and a specificity of up to $80.6\%$. Moreover, it had the largest AUC value (0.870) for the diagnosis of acute complicated diverticulitis. The AUC value of the MDW for complicated diverticulitis was higher than those of other inflammatory biomarkers—CRP (0.800), NLR (0.724), WBC (0.679), and PLR (0.662; Table 3 and Fig. 2). The diagnostic accuracy of the MDW for complicated diverticulitis noted in our study was comparable with that of procalcitonin. In a previous study, the AUC of procalcitonin for complicated diverticulitis was 0.867, with a sensitivity of $81\%$ and specificity of $91\%$ [42]. However, procalcitonin is not routinely used as a biomarker in EDs. In Taiwan, the national health insurance reimbursement price for procalcitonin tests is 1,000 New Taiwan dollars (NT$) [43], which is approximately four times the price for CBC determination (NT$270, including differential WBC count and MDW) [44, 45]. Therefore, procalcitonin testing is preserved as an auxiliary test for patients with ambiguous diagnoses of sepsis or bacterial infection, which cannot be verified on the basis of the WBC count, NLR, or CRP level. In our study, the MDW was the only inflammatory biomarker that was found to be a significant predictor of complicated colonic diverticulitis after adjusting for other covariables in multivariable binary logistic regression analysis ($p \leq 0.001$, Table 2). In previous studies, the MDW was found to have some advantages over other biomarkers. In particular, the MDW can be easily measured from the CBC through a blood test in the ED. In addition, the results are obtained faster than those of a biochemistry panel. Use of the MDW has been reported to improve both the clinical and economic outcomes of patients with sepsis in the ED, with the estimated time to antibiotic administration being reduced from 3.98 h to 2.07 h and US$3,460 being saved per hospitalization (US$23,466 versus US$26,926) [46]. By using a combination of the MDW and advanced imaging (CT), ED physicians will be able to diagnose complicated diverticulitis more accurately and in a timely manner, to initiate antibiotic therapy, and to convince surgeons regarding early intervention. Recent guidelines have recommended avoiding the use of antibiotics for otherwise healthy patients with simple diverticulitis [47]. The high negative predictive value ($98.3\%$) of MDW could enhance physicians’ diagnosis and decision-making. Patients with colonic diverticulitis and normal MDW values are unlikely to be complicated. Hence, an early and accurate diagnosis of simple diverticulitis by using the MDW will help reduce the use of antibiotics. ## Limitations To our knowledge, this is the first study to evaluate the utility of the MDW for diagnosing colonic diverticulitis in the ED. However, our study has some limitations. First, the MDW cannot be measured when the peripheral blood sample for a patient has a monocyte count < 100/μL. In our study, three patients' MDW data were unavailable; these patients had simple diverticulitis. Second, because this was a retrospective study, medical records were not designed for research purposes and did not contain all parameters of interest to the investigators. For instance, the procalcitonin level was not measured for comparison with the MDW. Third, our classification of colonic diverticulitis was based on CT findings. CT has an accuracy of $98\%$ in diagnosing acute diverticulitis; thus, misdiagnosis may occur in $2\%$ of cases [48]. Nevertheless, abdominal CT imaging is still considered the gold standard for diagnosing acute diverticulitis and its complications [49]. Finally, this was a single-center study conducted in only one ED in East Asia; therefore, our finding that diverticulitis was more prevalent in the right colon may not be generalizable to all EDs and other populations. Further prospective studies with larger numbers of patients from multiple centers are needed to more accurately assess the role of the MDW in differentiating simple from complicated colonic diverticulitis. ## Conclusions Our study revealed that acute colonic diverticulitis was more prevalent in the right colon than in the left colon in Taiwanese patients. Patients with complicated diverticulitis were significantly older and predominantly had left-sided diverticulitis. In addition, a large MDW was found to be a significant and independent predictor of complicated diverticulitis preceding CT assessment in the ED. The optimal cutoff value for MDW is 20.38 as it exhibits maximum sensitivity and specificity for distinguishing between simple and complicated diverticulitis. The MDW may aid in initiating early antibiotic therapy for patients with complicated diverticulitis and in decreasing antibiotic use in patients with simple diverticulitis. ## References 1. Simpson J, Scholefield JH, Spiller RC. Pathogenesis of colonic diverticula. Br J Surg (2002.0) 89 546-554. DOI: 10.1046/j.1365-2168.2002.02076.x 2. Rege RV, Nahrwold DL. Diverticular disease. Curr Probl Surg (1989.0) 26 133-189. DOI: 10.1016/0011-3840(89)90031-2 3. Imaeda H, Hibi T. The Burden of diverticular disease and its complications: West versus East. 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--- title: Analysis of Self-Perceived Physical Fitness of Physical Education Students in Public Schools in Extremadura (Spain) authors: - Carmen Galán-Arroyo - David Manuel Mendoza-Muñoz - Jorge Pérez-Gómez - Claudio Hernández-Mosqueira - Jorge Rojo-Ramos journal: Children year: 2023 pmcid: PMC10047493 doi: 10.3390/children10030604 license: CC BY 4.0 --- # Analysis of Self-Perceived Physical Fitness of Physical Education Students in Public Schools in Extremadura (Spain) ## Abstract Adolescence is a stage of crucial physiological and psychological changes within the individual’s life cycle, where fitness work is important. With self-perception being crucial in relation to adolescent health and well-being, a positive perception of fitness is directly related to increased practice or higher level of physical activity (PA). Thus, the aims were: [1] to analyze, with the Visual Analogue Fitness Perception Scale for Adolescents (FP VAS A), the self-perceived physical fitness (PF) of high school students, [2] to investigate if there are differences according to sex and school location, and [3] to study the correlations between the items of the FP VAS A with age and body mass index (BMI). For this purpose, a cross-sectional study was designed with a total of 961 participants, $48.8\%$ boys and $51.2\%$ girls in secondary education, where $31.9\%$ studied in rural schools and $68.1\%$ in urban schools. The FP VAS A scale was used to assess self-reported PF. Regarding the results, there were statistically significant differences between sexes ($p \leq 0.001$), with boys showing higher scores than girls in all the items of the FP VAS A scale, with the exception of global flexibility. Inverse, mean and significant correlations were established between BMI and self-perceived overall PF (r = −0.202; $p \leq 0.001$), cardiorespiratory endurance (r = −0.226; $p \leq 0.001$) and movement speed (r = −0.268; $p \leq 0.001$). Between age and self-perceived cardiorespiratory endurance (r = −0.138; $p \leq 0.001$) an inverse, mean and significant correlation was also observed. In conclusion, boys showed a better self-perception of PF than girls for all physical abilities, with the exception of flexibility. School location was not shown to influence students’ self-perceived PF. In addition, most of the self-perceived PF abilities for overall fitness correlated inversely with BMI. ## 1. Introduction Physical fitness (PF) is considered as the capacity of the set of physical attributes available to the organism to perform different types of physical activity (PA) in a controlled and efficient manner, without generating excessive fatigue [1,2]. The work on PF is essential for its improvement or maintenance, since it is an indicator of great relevance for the development, growth and health of children and adolescents [3,4], and PF is even associated with cognitive functions and academic performances of students [5,6,7]. Therefore, childhood and adolescence, as stages where a large number of crucial physiological and psychological changes take place in the life cycle of the individual, are crucial in the work of PF since at these ages, healthy lifestyles and behaviors are established that will have an impact on the state of health and quality of life of the individual in later life [8,9]. The relationship between PF and health and the prevention of pathologies is very close, and is considered one of the most important markers of health [4,10]. An adequate level of PF during adolescence can improve cardiovascular function and may help protect these individuals from future cardiovascular disease in later life [11,12]. In this sense, working on PF from an early age is crucial, since these are the initial stages of atherosclerotic cardiovascular disease, where it usually manifests clinically and later appears in adulthood [13]. In addition, low levels of PF during adolescence in some physical capacities, such as cardiorespiratory fitness and strength, is associated with an increased risk of type 2 diabetes [14], myocardial infarction [15] and premature mortality [16] during adulthood. Conversely, high levels of fitness during these early stages are inversely related to total and abdominal obesity [17] and, specifically, strength, speed and aerobic capacity were shown to be significantly associated with adiposity and adiposity growth in childhood and puberty. Thus, improving fitness levels during these stages is paramount to counteracting future obesity [17,18]. In this sense, public health agencies have a great interest in assessing PF and investigating various methods to obtain an effective assessment in order to develop relevant interventions and to detect early the manifestation of some pathologies [4,19]. There are different means and methods to evaluate the PF of individuals, including laboratory tests together with the associated use of specific devices and instruments, which is the most objective way to obtain accurate measurements of the different parameters that make up PF [20]. However, these methods are oriented to an individual level, and are more expensive and require more time and qualified personnel to be performed correctly; therefore, in the educational field, this method would not be viable for all students in a physical education class to know and manage their PF values [21]. For this reason, other alternatives such as field tests have proven to be valid and reliable for these situations, consisting of cheap, minimal and easy to use materials, where several participants can perform the tests simultaneously, such as the test batteries to assess PF in young people and adults, including the European Physical Fitness Battery (EUROFIT) [22], Physical Activity and Health Battery for Adults (AFISAL) [23] and the Assessing Levels of Physical Activity Battery (ALPHA-Fitness) [24]. However, these assessments still require a large amount of time to obtain the results of all the individuals in a group and, in this sense, another alternative could be to use self-reported fitness assessments, which could solve this problem by having the whole class take the survey simultaneously, requiring only a few minutes to complete [25,26]. These instruments, such as the International Fitness Scale (IFIS) [26], the Visual Analogue Fitness Perception Scale for Adolescents (FP VAS A) [25] (the scale used in the present research) and the Delignières et al. [ 27] self-perception PF questionnaire, slightly modified by Jürimäe et al. [ 28], provide a subjective assessment of fitness for each of the component abilities, providing valuable information for adolescents to be aware of their deficiencies and thus be able to address them. Self-perception of PF is of great significance in adolescence in relation to the health and well-being of adolescents themselves, since, as has been proven in various studies, a positive perception of PF is directly related to a greater practice or higher level of PA [29,30]. This aspect can influence adolescents into adopting active and healthy lifestyle habits to improve the perception of their PF, as well as increase their self-esteem and confidence, helping them to face challenges and develop in other areas of their lives [30,31]. Currently, there are some studies that have compared in a secondary way the self-perception of PF between boys and girls, with boys obtaining a better self-perception of PF in all physical abilities except flexibility [30,32,33]. However, there are hardly any studies that compare the self-perception of PF and the location of the center (rural or urban), and there is controversy because there are previous studies where FP was measured from different tests and the rural population had a better FP [34,35], but recent articles show that in cities since there are now more extracurricular activities, they do more PA and might have better FP [36]. It could also be influenced by the socio-economic status of the participants. There is not much evidence regarding the relationship between age and body mass index (BMI) with self-perception of PF, and studies are limited to the level of PF and not to self-perception [37,38,39]. Therefore, in the present investigation, we intend to analyze, through the FP VAS A items, the self-reported PF of secondary school students and to investigate whether there are differences according to sex and center location. On the other hand, the correlations between the items of the FP VAS A with age and BMI will also be studied. ## 2.1. Participants The sample size was selected following the non-probability sampling method based on convenience sampling [40]. A total of 961 participants in secondary education were assessed. The inclusion criteria for participants were: (a) have informed parental consent; (b) is a student in the area of physical education in public schools in Extremadura at the secondary education level (from twelve to eighteen years of age). The study was conducted in accordance with the ethical provisions of the Declaration of Helsinki and the protocol was approved by the Bioethics Committee of the University of Extremadura (Registration Code $\frac{71}{2022}$). Table 1 shows the sociodemographic characterization of the sample. Of the total sample ($$n = 961$$), $48.8\%$ were boys and $51.2\%$ were girls, so the sample can be considered to be gender-balanced. Regarding school location, $31.9\%$ studied in rural schools and $68.1\%$ studied in urban schools. The schools located in towns with less than 20,000 inhabitants were considered as rural schools and those with more than 20,000 inhabitants as urban schools, following the criteria established by the Cáceres Provincial Council [41]. The mean age was 14.71 years (SD = 1.58) and the mean BMI (kg weight/height in meters2). ## 2.2. Procedure Based on the directory of public schools in Extremadura provided by the Ministry of Education and Employment of the Regional Government of Extremadura, contact details were selected for all those teaching at the Compulsory Secondary Education (CSE) (ages 12 to 16) and Baccalaureate (ages 16 to 18) levels. An e-mail was sent to all the selected centers addressed to the physical education teachers, informing them about the object of the study, a model of the instrument and parental informed consent forms. On the agreed day, a researcher went to the school and, after verifying that the parents or guardians of the participants who were in the physical education class had signed the informed consent form, proceeded to provide each student with a tablet with the URL link to the questionnaire. The questionnaire was elaborated with the digital application Google Forms and the researcher read aloud each item of the questionnaire to ensure that the participants had understood the questions. It was decided to use an e-questionnaire to more easily store all the responses in the same database, saving time and costs. The average time taken to complete the questionnaire was 10 min. All data were collected anonymously between September and December 2022. ## 2.3. Instruments Sociodemographic data: A questionnaire was prepared with six questions aimed at characterizing the sample based on sex, age, height, weight, grade and location of the center. Visual Analogue Fitness Perception Scale for Adolescents (FP VAS A): To assess self-reported PF in adolescents, the Visual Analogue Fitness Perception Scale for Adolescents was used [25]. The scale is composed of five items (general physical condition, cardiorespiratory fitness, muscular strength, speed–agility and flexibility). The instrument uses a Likert scale of 1–10 with 1: very poor level and 10: excellent level. The authors reported a reliability value of the instrument as a Cronbach’s alpha coefficient of 0.860. In our study, the reliability of the instrument was calculated from Cronbach’s alpha statistic and a value of 0.77 was obtained. This can be considered a satisfactory value according to Nunnally et al. [ 42]. ## 2.4. Statistical Analysis First, to determine the type of statistical tests to be used, the distribution of the data was explored to see if the assumption of normality was met using the Kolmogorov–Smirnov test. This assumption was not met, so it was decided to use nonparametric statistical tests. To analyze the differences between the scores for each of the variables studied, according to sex or type of center, the Mann–Whitney U test was used. A significance level of $p \leq 0.05$ was established. To determine the degree of relationship between each of the variables and age or BMI, the Spearman’s Rho test was used. For the interpretation of this statistic, we took into account the range established by Mondragón Barrera [43], who defined that coefficients between 0.01 and 0.10 indicate the existence of a low correlation, values between 0.11 and 0.50 imply a medium degree of correlation, values from 0.51 to 0.75 indicate a strong correlation, from 0.76 to 0.90 indicate a high correlation, and above 0.91 the correlation is perfect. To calculate the effect size of sex or center location for each of the variables, Hedges’ g was used. A value below 0.20 indicates no effect, values between 0.21 and 0.49 indicate a small effect, values between 0.50 and 0.79 indicate a moderate effect, and values above 0.80 indicate a strong effect [44]. Finally, Cronbach’s alpha was used to determine the reliability of the instrument. To interpret the values reported, the guidelines established by Nunnally et al. were chosen, which state that values below 0.70 would correspond to low reliability, values between 0.71 and 0.90 would correspond to satisfactory reliability and values above 0.91 would correspond to excellent reliability [42]. The data are presented as a number and percentage for sociodemographic variables and as mean (M) and standard deviation (SD) for scores obtained in each of the variables of the FP VAS A instrument. The software used for data analysis was the Statistical Package of Social Science, version 23 for MAC. ## 3. Results Table 2 shows the descriptive data (from the mean and standard deviation) and the differences for each of the items that make up the FP VAS A scale according to gender and center location. Boys obtained higher scores and, therefore, a better self-perception of PF than girls, in all items ($p \leq 0.001$) except in item 5 “My overall flexibility is” ($p \leq 0.001$), where girls showed higher scores and showed a better self-perception of flexibility. These differences were statistically significant in all items. Boys also obtained a higher overall score on the FP VAS A scale ($p \leq 0.001$) than girls, with these differences being statistically significant. With respect to the location of the center, no statistically significant differences were obtained in any item of the FP VAS A scale. However, rural center students showed slightly higher scores than urban center students on most of the items and on the overall FP VAS A scale score. Students from urban centers only obtained better scores on item 5 “My overall flexibility is”, with the scores of students from both types of centers coinciding on item 4 “My movement speed is”. To analyze the relationship between each of the items of the FP VAS A scale with age or BMI (Table 3), the Spearman’s Rho test was used. An inverse, low and significant correlation was obtained between the items 3 “My overall muscle strength is” ($p \leq 0.035$), 4 “My movement speed is” ($p \leq 0.009$) and age. Furthermore, for item 2 “My cardiorespiratory endurance” ($p \leq 0.001$) the correlation with age was inverse, medium and significant. BMI obtained an inverse, medium and significant correlation ($p \leq 0.001$) with overall physical fitness, with cardiorespiratory endurance capacity and with movement speed. For item 5 “My overall flexibility is” ($p \leq 0.033$), the correlation with BMI was inverse, low and significant. Globally, the FP VAS A scale had an inverse, low and significant correlation with age and inverse, medium and significant correlation with BMI. ## 4. Discussion One of the objectives of the present investigation was to analyze the differences in scores obtained in the self-perception of PF as a function of sex. The results obtained are supported by other studies [25,26,28,30,45,46,47], where boys have better self-perceived fitness for all physical abilities and for general fitness, with the exception of flexibility, where girls showed significantly higher self-perceived fitness scores than boys in our research. In other studies, the self-perception of flexibility was better in boys than in girls and, moreover, the differences obtained were not significant [25,30,47], which is clearly contrary to the results of our study. Jürimäe et al. ’s study analyzed how the self-perception of PF evolves throughout adolescence, and it was observed how boys from the ages of 14–15 years progressively decreased their self-perception of flexibility over the years, with girls showing a better self-perception of flexibility at the age of 16–17 years [28]. In relation to this, a large part of the participants were in the age range of 15 to 18 years, so this greater self-perception of flexibility by girls could be associated with the belief that girls are more flexible and boys are more rigid. However, no specific age was established where flexibility begins to decrease without training in boys, and this decrease depends on various factors, such as genetics, nutrition, lifestyle and level of PA [48,49]. Even so, it is common for flexibility to begin to decrease after puberty if a training routine is not maintained [50]. As observed in this research, boys have a more positive perception of their PF than girls, a fact that may be influenced by irregular PA practice or by a low level of PF of girls compared to boys, since it has been proven in previous research that the self-perception of PF has a direct correlation with the level of PA [30,51] and with the level of PF [25,26]. These differences in the self-perception of PF could be related to the physical self-concept, on how the adolescent perceives himself, with boys perceiving themselves better in the five physical competencies related to physical self-perception established by Fox et al.: PF, attractive body, sports competence, physical strength and self-confidence [52]. In this sense, in the present research, girls showed worse self-perception in strength ability, an ability typically associated with boys ahead of girls [53]. In the study by Crocker et al. a weak association was demonstrated for adolescent girls between perception of physical strength and physical self-esteem [54], and other studies claim that boys experience greater perception of physical strength because they perceive a stronger physical self-perception [33,55,56]. Another possible justification for this lower self-perception of PF by girls could be related to the appearance of the signs of puberty and its physiological modifications, appearing earlier in girls than in boys [57]. Girls may try to hide these changes in PA, considering them unattractive [33,58], negatively impacting their self-perception of PF. Another objective of the present research was to study whether there were differences in the self-perception of PF according to the students’ school location. This study shows that students from rural schools showed slightly higher self-perceived fitness scores in most physical abilities than students from urban schools; however, these differences were not significant. Therefore, we could not affirm that the location of the center influences self-perception of PF. There is not much literature that analyzes the physical perception of adolescents as a function of the location of the educational center. However, as mentioned above, the level of PA [30,51] and several studies have reported that young people in rural locations engage in a greater amount of PA than young people in urban locations [59,60], and this level of PA may influence their self-perceptions of PF. In this regard, several studies report that adolescents from rural populations report higher health-related quality of life, higher sleep quality and greater psychological well-being related to school environment and autonomy. These data may be related to the greater likelihood of urban adolescents to be influenced by physical factors such as pollution, less access to nature, high population density, and social factors such as fast-paced life and stress [61,62,63,64]. As for the correlations between self-perceived PF with BMI and age, the correlations between BMI and self-perceived PF were inverse, medium and significant for the items of overall physical fitness (r = −0.202), cardiorespiratory endurance (r = −0.226) and movement speed (r = −0.268). Therefore, according to these data, it could be affirmed that the lower the BMI, the better the self-perception of general PF. Other studies support these results to some extent, showing a strong inverse association between PF and overweight in adolescents [37,39], and a lower performance in PF in overweight and obese adolescents compared to those with a normal weight [37,65,66]. The same happens with the level of PA, where it has been observed in several studies that the higher the weight, the lower the level of PA in adolescents [30,67,68]. The lower level of PF and lower level of PA associated with overweight and obesity seems to influence self-esteem and perceptions related to body satisfaction, with overweight adolescents with a lower level of PA having more negative perceptions associated with physical perception and lower self-esteem [69,70,71]. The correlations between age and self-perceived PF were significant and inverse for the items referring to global muscular strength, movement speed and cardiorespiratory endurance; however, these correlations were low according to Mondragón Barrera [43] with the exception of cardiorespiratory endurance (r = −0.138), where the correlations were medium. ## 4.1. Practical Implications One of the main findings of the present research is that adolescent girls show a less positive self-perception of PF than adolescent boys. This lower self-perception of PF by girls could be related to the tendency of girls to decrease their PA level or directly quit sports practice [72] due to lack of time for practice [73], body image, physical–social anxiety and also due to fatigue/laziness [74]. Lack of motivation to exercise and the stress of educational tasks in subjects other than physical education make PA less of a priority on a daily basis [75]. Therefore, in the case of physical education, public administrations and education departments should emphasize continuous teacher training, using methodologies and designing learning situations that address the interests, motivations and expectations of students. It would be interesting to advocate for equal opportunities and ensuring that students feel competent in their learning and are interested in the practice of PA outside of school hours. In this research, it was also observed that a lower self-perception of PF by adolescents was related to a higher BMI for most of the self-reported physical abilities. This inverse association between PF and overweight in adolescents [34,36] with the level of PA has been observed in several studies where the higher the weight, the lower the level of PA in adolescents [30,64,65]. Therefore, the departments of education should advocate the fight against sedentary lifestyles and overweight among students, supporting educational plans that involve active methodologies, from the point of view of PA. These include the development of active classes in subjects other than physical education, active breaks between classes and use of other methods in which PA is included in the teaching–learning process of students. ## 4.2. Limitations This was a cross-sectional study; therefore, it was not possible to establish cause–effect relationships. In future research, it would be enriching to explore these results in greater depth in order to establish possible causal relationships. The participants in the present study are students from schools in the Spanish autonomous community of Extremadura, and the sociocultural variables of this community may have influenced the results obtained. Therefore, it would be interesting to develop this type of study in more regions of Spain and to be able to compare attitudes in other regions of Spain or extrapolate it to other countries in Europe and the world. In future research, studying self-perceived fitness relationships by cycle would be novel to analyze the evolution of self-perceived fitness over the years. ## 5. Conclusions It can be concluded that gender, age and BMI influenced self-perception of FP, but not the school environment. Therefore, in order to motivate students, especially girls, it would be interesting for educational administrations to promote initiatives to encourage PA through equal opportunities, improving their FP and enabling them to lead a healthier lifestyle, providing continuous training tools for teachers in order to make students competent, and implementing strategies such as the development of active classes in subjects other than physical education, active breaks between classes and the use of other methods in which PA is included in the teaching–learning process of students. 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--- title: Ultrasound Placental Remodeling Patterns and Pathology Characteristics in Patients with History of Mild SARS-CoV-2 Infection during Pregnancy authors: - Adelina Staicu - Camelia Albu - Georgiana Nemeti - Cosmina Ioana Bondor - Dan Boitor-Borza - Andreia Paraschiva Preda - Andreea Florian - Iulian Gabriel Goidescu - Diana Sachelaru - Nelida Bora - Roxana Constantin - Mihai Surcel - Florin Stamatian - Ioana Cristina Rotar - Gheorghe Cruciat - Daniel Muresan journal: Diagnostics year: 2023 pmcid: PMC10047494 doi: 10.3390/diagnostics13061200 license: CC BY 4.0 --- # Ultrasound Placental Remodeling Patterns and Pathology Characteristics in Patients with History of Mild SARS-CoV-2 Infection during Pregnancy ## Abstract Introduction: This research aims to describe a progressive pattern of ultrasound placental remodeling in patients with a history of SARS-CoV-2 infection during pregnancy. Materials and Methods: This was a longitudinal, cohort study which enrolled 23 pregnant women with a history of former mild SARS-CoV-2 infection during the current pregnancy. Four obstetricians analyzed placental ultrasound images from different gestational ages following COVID infection and identified the presence and degree of remodeling. We assessed the inter-rater agreement and the interclass correlation coefficients. Pathology workup included placental biometry, macroscopic and microscopic examination. Results: Serial ultrasound evaluation of the placental morphology revealed a progressive pattern of placental remodeling starting from 30–32 weeks of gestation towards term, occurring approximately 8–10 weeks after the SARS-CoV-2 infection. Placental changes—the “starry sky” appearance and the “white line” along the basal plate—were identified in all cases. Most placentas presented normal subchorionic perivillous fibrin depositions and focal stem villi perivillous fibrin deposits. Focal calcifications were described in only $13\%$ of the cases. Conclusions: We identified two ultrasound signs of placental remodeling as potential markers of placental viral shedding following mild SARS-CoV-2. The most likely pathology correspondence for the imaging aspect is perivillous and, respectively, massive subchorionic fibrin deposits identified in most cases. ## 1. Introduction The COVID-19 outbreak emerged in November 2019 and rapidly became the number one global public health issue [1]. Concerns arose regarding its impact on vulnerable populations, such as pregnant women, maternal–fetal effects, and long-term consequences [2,3]. Despite evidence that vertical transmission of SARS-CoV-2 is unlikely, it is essential to establish potential maternal–fetal short- and long-term consequences of SARS-CoV-2 infection, especially the potential neuro-cognitive and psychologic outcome of the offspring [4,5,6]. Pregnancy-associated severe acute respiratory syndrome coronavirus (SARS-CoV-2) infection seems to prime a generalized inflammatory response associated with hypercoagulability and micro-thrombosis in all maternal organs endorsed by the physiologic procoagulant state of pregnancy [3,7,8]. Since most pregnant women have asymptomatic or mild forms of disease according to the COVID-19 diagnostic criteria proposed by Wu et al., many go through the infection without being confirmed [9]. The succession of events leading to fetal attaint and even stillbirth in the context of COVID-19 infection in pregnancy is probably initiated by viral shedding to the placenta culminating with fetal infection [10,11,12]. Clinicians have been looking to find if placental changes, which may result in a fetal response, are translated by morphologic remodeling patterns that could be detected ultrasonographically [13,14]. This would allow the establishment of follow-up guidance to prevent obstetric complications. Prenatal ultrasound follow-up of placental remodeling has yielded little information concerning potential markers of viral effects [15,16]. The current study proposed the recognition of ultrasound placental remodeling patterns in patients with a history of mild SARS-CoV-2 infection during pregnancy with pathology confirmation as perivillous and, respectively, subchorionic fibrin deposits. ## 2.1. Study Design and Patient Selection This was a longitudinal, cohort study conducted in the 1st Clinic of Obstetrics and Gynecology, Cluj-Napoca, Cluj-Napoca County Emergency Hospital, Romania between March–December 2021. The reference group consisted of 67 patients with physiologic pregnancies presenting for prenatal surveillance and delivery in our institution during the above-mentioned time frame, with previous SARS-CoV-2 infection during the current pregnancy, but negative testing at birth. The episode of COVID had been confirmed by positive RT-PCR collected by nasopharyngeal swab during pregnancy and was contracted during the period of the delta wave, but viral typing was not available upon testing. The inclusion criteria were fulfilled by 23 patients. Exclusion criteria were patient refusal to enter the study, smoker status, patient comorbidities, history of/or current preeclampsia, gestational diabetes, intrauterine growth restriction, placental insufficiency, TORCH, viral infections other than SARS-CoV-2 and postdate pregnancy. Information regarding the onset, duration and severity of the SARS-CoV-2 infection, home isolation or hospital admission, and therapy requirement were collected. Maternal pregnancy history, first-, second- and third-trimester ultrasound reports, images, and fetal and neonatal outcome were retrieved from electronic patient records. ## 2.2. Image Analysis The ultrasound machine used for pregnancy examination was a Voluson E8 Expert with transabdominal GE/RAB2-5-D 3D/4D convex probe 1–4 MHz. Examinations were performed by obstetricians certified in Maternal and Fetal Medicine. Prenatal ultrasound monitoring of pregnancy was achieved according to international guidelines. Placental imaging studies were reviewed by four physicians certified in Maternal and Fetal Medicine. The evaluators viewed static images that they did not acquire personally. All experts, blinded to each other’s results, were asked to identify the presence and degree of the ultrasound remodeling placental pattern. An agreement index was calculated after each expert separately analyzed all the images. ## 2.3. Placental Pathology Placentas were sent for pathology examination at the Pathology department of IMOGEN Medical Research Institute within ECCHCN, regardless of the delivery route. Before the macroscopic examination, all placentas were immersed in $10\%$ formalin solution. Macroscopic examination and the examination of placental lesions were conducted according to the Amsterdam Placental Workshop Group Consensus Statement [17]. Microscopic examination of a minimum of 5 parenchymal sections and a section of membranes and umbilical cord following Hematoxylin-Eosin stain was performed. Microscopic maternal vascular malperfusion lesions and fetal vascular malperfusion lesions were assessed according to the Amsterdam Placental Workshop Group Consensus Statement [17]. Placental weight and fetoplacental ratio were evaluated [18]. Patterns of perivillous and subchorionic fibrin deposits were mapped and characterized. ## 2.4. Statistical Analysis Patient data was compiled into a database using Microsoft Excel®. SPSS 25.0 was used for the statistical analysis. Data were presented as arithmetic mean ± standard deviation for normally distributed or as median (25th–75th percentile) for those without, with absolute and relative frequencies if the data were qualitative. To appreciate correlations, the Pearson and Spearman coefficients were computed based on the linear or non-linear relationship between the data. The interpretation was made based on Colton rules [19]. To appreciate the inter-rater agreement, we computed the interclass correlation coefficient using the two-way mixed models, type absolute agreement for average measurement with $95\%$ confidence interval (CI) [20]. The alpha error considered was 0.05. ## 2.5. Ethics Statement The study protocol was approved by the Ethics Committee of the Cluj-Napoca County Emergency Hospital, Romania, number $\frac{2079}{30.03.2022.}$ Prior to being enrolled in the study, all patients provided their written informed consent elaborated according to the World Medical Association Declaration of Helsinki, including informed consent for neonatal information collection. The reporting of this study conforms to the STROBE statement [21]. ## 3. Results Twenty-three patients fulfilled the inclusion criteria. The mean maternal age at admission was 33.04 (SD ± 5.28 years (25–43 years). Clinical and obstetric patient data were depicted in Table 1. All study patients had a mild form of SARS-CoV-2 infection with mild symptomatology, including fever, cough, anosmia, ageusia, fatigue, myalgia and shortness of breath, and required only symptomatic treatment at home; none had been vaccinated for SARS-CoV-2. All patients had uneventful pregnancies, delivered at term between 38 to 41 weeks of gestation, eutrophic fetuses, with Apgar scores ≥ 8. There were no structural abnormalities in any of the pregnancies evaluated. Obstetric and neonatal outcomes were favorable in all cases, with no significant complications during the neonatal period and up to one year. ## 3.1. Ultrasound Findings Tailored Accord to the Timing of the SARS-CoV-2 Infection Fetal biometry and Doppler studies were normal, and no structural abnormalities were found in any of the evaluated pregnancies. Sudden onset idiopathic oligohydramnios was signaled at the third trimester morphology scan [30–36] weeks of gestation (WG) in four patients with a history of first-trimester SARS-CoV-2 infection and five patients with second-trimester infection. Dynamic evaluation of the placental morphology revealed what seems to be a particular pattern of placental remodeling starting from 30 WG towards term following SARS-CoV-2 infection during pregnancy. We noted the occurrence of dispersed hyperechoic foci, without posterior acoustic shadowing, scattered across the placenta, increasing in number and size with consecutive examinations, creating a “starry sky” appearance similar to the sonographic pattern described in acute hepatitis (Figure 1) [22]. Consequently, these foci conflate to form interlobular, chandelier-like, comma-shaped indentations. Later, lesions organize to form a consistent, chalky conglomerate along the entire basal plate, a “white line”, with bolded edges towards the chorionic plate forming white angles (Figure 2). Placental changes mimic the physiologic aging process but occur earlier in gestation; echo-dense foci are more widespread and organize in a short period to form the echoic white line. Various aspects of placental remodeling is rendered in Figure 3. Placental ultrasound features were more evident in patients with a history of the second-trimester SARS-CoV-2 infection, observed in 12 of 15 cases, emerging about 8–10 weeks following infection (Table 2). In the subgroup of patients with a history of SARS-CoV-2 infection during the third trimester, both cases with a history of infection onset at 28 WG exhibited these findings. A negative correlation with statistical significance was achieved between the timespan from the SARS-CoV-2 infection (in weeks) to the first mention of placental maturation and the maternal excess weight gain during pregnancy (r = −0.76, $$p \leq 0.004$$). A negative correlation with some statistical strength was found between the timespan from the SARS-CoV-2 infection (in WG) to the first mention of placental ultrasound changes and small placental weight (r = −0.47, $$p \leq 0.124$$) and fetal birth weight (r = −0.316, $$p \leq 0.318$$). Following the evaluation of placental imaging by the four maternal–fetal medicine investigators, the interclass correlation coefficient for average measurements from multiple evaluators with the absolute agreement was 0.68 $95\%$ CI [0.41–0.85], $p \leq 0.001$ for the “starry sky” placental aspect which represents a weak to good agreement. The average score was 1.17 ± 0.57. The inter-observer agreement for the “white line” aspect was 0.90 $95\%$ CI [0.81–0.95], $p \leq 0.001$, representing a good agreement. The average score was 0.54 ± 0.44. ## 3.2. Pathology Findings The pathology exam found 9 ($39.1\%$) small for gestational age (GA) and 2 ($8.7\%$) abnormally large for GA placentas. It was also found that $60\%$ of placentas from patients with second trimester viral infection were small for GA. Macroscopic examination revealed a peripheric white annular border and circummarginate membrane insertion (Figure 4f). Maternal vascular malperfusion lesions were noted in all cases, predominantly distal villous hypoplasia and accelerated villous maturation. Fetal vascular malperfusion lesions were less encountered. We rarely found focal calcifications ($13\%$), chorioangiosis ($8.7\%$), and inflammatory lesions ($8.7\%$). We noted a particular pattern of fibrin deposits around stem villi, represented by various amounts of continuous circumferential perivillous fibrin deposits. When few stem villi were involved the lesion was considered focal, and if most villi were affected, the lesion was considered frequent (Figure 4 and Table 3). ## 4. Discussion The present study signals potential ultrasound makers of SARS-CoV-2 infection during pregnancy—oligohydramnios, the “starry sky” placental appearance and the basal plate “white line”—which can emerge as risk factors for early placental maturation. This placental pattern is not an instrument to guide obstetric and perinatal management, but it can be an alarm tool mandating caution in pregnancy monitoring and peripartum care. The features appear randomly and seem to respect the same sequence of events. The earliest signs of advanced placental maturation were noted at 31–32 WG and were exhibited by $80\%$ of patients who experienced SARS-CoV-2 infection during the second trimester of pregnancy. There are approximately 8–10 weeks between the time of infection and the first mention of placental changes similar to the interval from infection to the identification of ultrasound evidence of attaint in the TORCH sequence entities [15]. Similar ultrasound diffuse spot-like echogenic foci involving the entire placenta were previously observed after a first trimester mild SARS-CoV-2 infection, confirmed to be calcifications by the pathology evaluation [13]. Postdate pregnancies are the cardinal exponents of placental calcifications, with placental aging initiating around 34–36 WG and becoming obvious after 40 WG, classically described as the Grannum classification stages [23]. Placental insufficiency may also exhibit progressive calcifications which do not resemble the pattern described in pregnancy 8–10 weeks following SARS-CoV-2 infection. Other factors associated to the occurrence of preterm placental calcifications have been outlined by clinicians, such as smoking, viral infections such as TORCH infections, Parvovirus B19 and Zika [24]. Ultrasound reports of pregnancies complicated by these infections delineate placentomegaly as the most prominent marker of viral shedding, with few mentions of placental calcifications and no description of a specific ultrasound pattern [24]. To avoid all possible bias, smokers, intercurrent viral infections other than COVID, as well as pregnancies complicated by placental insufficiency were excluded from the referential group. In our study, pathology examination found calcifications in only $13\%$ of the placentas. However, the “starry sky” aspect was described in half of cases, raising the possibility of another histological substrate which could explain imaging findings. The most likely correspondence would be the presence of perivillous fibrin deposits exhibited by $87\%$ of the cases. The lack of a definitive correlation between lesions can be justified by the small number of cases and by the preponderant focal aspect of the perivillous fibrin deposition in our group. Circummarginate insertion of placental membranes frequently encountered in our study is considered to have an insignificant clinical impact [25], being considered an incidental finding. However, its potential as a marker for SARS-CoV-2 infection during pregnancy should be explored in more extensive studies. As demonstrated by the good absolute agreement index between our evaluators, these elements are easy to identify during sonographic surveillance of pregnancies. It must be considered that the evaluators viewed static images that they did not acquire personally. In clinical practice, the entire placental volume is evaluated before establishing any diagnosis; therefore, identifying an element should be easier. Another potential ultrasound distress signal is the sudden third-trimester oligohydramnios encountered in some pregnancies following SARS-CoV-2, observed in two-thirds of patients with first-trimester infection and one-third of those with the second-trimester disease. Oligohydramnios and marked calcifications corresponding to Grannum grade 2–3 in focal zones associated with severe placental insufficiency were previous signaled after a mild, late-second-trimester, A wave SARS-CoV-2 infection [26]. The drop in amniotic fluid quantity, unaccompanied by fetal distress, may be secondary solely to placental damage as illustrated by the pathology report. Excess maternal weight gain during pregnancy seams to play a role in developing placental injury, especially if the infection occurred during the second trimester. This fact may be explained via the way SARS-CoV-2 enters the host cell using Angiotensin-converting enzyme 2 (ACE2) through S-proteins express on their surface [27]. ACE2 receptors are largely expressed in adipocytes in the white adipose tissue. Its link with diet options, especially high fat diets, further supports the claim that weight gain could lead to high concentrations of ACE2 in pregnant women [28]. This aspect may explain why the ultrasound and histopathological placental injury observed in our study were more critical in the patients that had the infection during the second trimester but had more weight gain during pregnancy than patients with infection during the first trimester but with a normal BMI at the end of pregnancy. Placental damage was not correlated with the clinical form of SARS-CoV-2 infection. Massive perivillous fibrin deposits were characteristic of placentas from paucisymptomatic COVID-19 pregnant women experiencing sudden intrauterine death [29,30]. Therefore, a sonographic marker signaling a possible major obstetric complication is a useful clinical tool to detect pregnancies requiring more detailed follow-up. To the best of our knowledge, the present study is the first cohort to report ultrasound placental remodeling patterns, oligohydramnios and pathology characteristics attributed to COVID-19 infection during otherwise normal pregnancy. None of the cases included in our analyses had been vaccinated since patient selection was made prior to January 2022; therefore, no bias factor which could influence viral impact upon placentas was involved. The small number of patients is explained by the strict inclusion criteria to potentially limit known factors responsible for early placental maturation or preterm calcifications and the limited period for selection. Another significant limitation was the selection of patients from a single department and the descriptive status of our study. To confirm our observation, a control group is much needed. However, the ideal witnesses would have to be selected prior to the SARS-CoV-2 pandemic since SARS infections in pregnancy are mostly asymptomatic, and many patients are not diagnosed or not declared. Also, most of the population is now vaccinated for SARS-CoV-2. ## 5. Conclusions COVID-19 is a widespread contagious disease whose effects on pregnancy are still under observation. 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--- title: Social Isolation Activates Dormant Mammary Tumors, and Modifies Inflammatory and Mitochondrial Metabolic Pathways in the Rat Mammary Gland authors: - Fabia de Oliveira Andrade - Lu Jin - Robert Clarke - Imani Wood - MaryAnn Dutton - Chezaray Anjorin - Grace Rubin - Audrey Gao - Surojeet Sengupta - Kevin FitzGerald - Leena Hilakivi-Clarke journal: Cells year: 2023 pmcid: PMC10047513 doi: 10.3390/cells12060961 license: CC BY 4.0 --- # Social Isolation Activates Dormant Mammary Tumors, and Modifies Inflammatory and Mitochondrial Metabolic Pathways in the Rat Mammary Gland ## Abstract Although multifactorial in origin, one of the most impactful consequences of social isolation is an increase in breast cancer mortality. How this happens is unknown, but many studies have shown that social isolation increases circulating inflammatory cytokines and impairs mitochondrial metabolism. Using a preclinical Sprague Dawley rat model of estrogen receptor-positive breast cancer, we investigated whether social isolation impairs the response to tamoxifen therapy and increases the risk of tumors emerging from dormancy, and thus their recurrence. We also studied which signaling pathways in the mammary glands may be affected by social isolation in tamoxifen treated rats, and whether an anti-inflammatory herbal mixture blocks the effects of social isolation. Social isolation increased the risk of dormant mammary tumor recurrence after tamoxifen therapy. The elevated recurrence risk was associated with changes in multiple signaling pathways including an upregulation of IL6/JAK/STAT3 signaling in the mammary glands and tumors and suppression of the mitochondrial oxidative phosphorylation (OXPHOS) pathway. In addition, social isolation increased the expression of receptor for advanced glycation end-products (RAGE), consistent with impaired insulin sensitivity and weight gain linked to social isolation. In socially isolated animals, the herbal product inhibited IL6/JAK/STAT3 signaling, upregulated OXPHOS signaling, suppressed the expression of RAGE ligands S100a8 and S100a9, and prevented the increase in recurrence of dormant mammary tumors. Increased breast cancer mortality among socially isolated survivors may be most effectively prevented by focusing on the period following the completion of hormone therapy using interventions that simultaneously target several different pathways including inflammatory and mitochondrial metabolism pathways. ## 1. Introduction Social isolation, characterized by perceived loneliness or a lack of social contact, is a powerful predictor of increased all-cause mortality [1]. Socially isolated individuals are more likely to develop ischemic heart disease, suffer from stroke, and die from these diseases than socially integrated individuals [2]. Social isolation also increases the risk of developing type 2 diabetes [3], dementia [4], worsens neurological disease symptoms [5], and cancer mortality [6,7]. The biological changes induced by social isolation that cause an increase in mortality remain unknown. Among the causes of social isolation are being elderly, poor, being discriminated due to race, ethnicity, religion, or gender identity, or having been diagnosed with a life-threatening disease such as cancer. Former U.S. Surgeon General Vivek Murthy published a book in 2020, entitled Together: The Healing Power of Connection in a Sometimes Lonely World to highlight how loneliness is a public health concern [8]. COVID-19 further brought an unprecedented level of social isolation to human societies worldwide [9]. Many studies have investigated the link between social isolation and breast cancer. In a pooled analysis of 9267 breast cancer patients, 16–$41\%$ were identified as feeling socially isolated when assessed 6 months to 2 years after their cancer diagnosis [10]. Moreover, socially isolated breast cancer survivors had a $43\%$ higher risk of recurrence and a $64\%$ higher risk of breast cancer-specific mortality than socially integrated survivors [10]. Many other studies have reported similar findings [11,12]. To reduce the risk of recurrence and breast cancer mortality among socially isolated patients, it is critical to determine the mechanisms of these interactions and to identify effective therapies to prevent recurrence. It is unlikely that social isolation causes a single gene change in mammary cancer that explains tumor recurrence. Rather, via the hypothalamic pituitary axis and autonomic nervous system, social isolation probably influences many biological systems that then alter the tumor microenvironment and the tumor itself. It has been suggested that successful cancer therapies include both tumor specific treatments and treatments that correct changes in host generated metabolites or dysfunctional neuroendocrine and pro-inflammatory and immune system, which all promote tumor growth [13]. In humans, the most frequently reported biological change linked to loneliness and social isolation is an increase in the circulating inflammatory markers [14,15]. In animal models, social isolation has been reported to impair metabolism [16] and mitochondrial oxidative phosphorylation (OXPHOS) [17,18]. If these changes explain the effects of social isolation on breast cancer recurrence, interventions that reverse them could reduce breast cancer mortality. Previously, we found that Jaeumganghwa-tang (JGT), a mixture of 12 herbs commonly and safely used in Asian countries for a wide range of ailments, reduced IL6 expression in mammary tumors and increased sensitivity to tamoxifen therapy in vitro and in an animal model [19]. Other studies have reported the ability of JGT to inhibit inflammatory cytokines in human mast cells in vitro [20] and in vivo in mice [21]. JGT also inhibited the growth of HT1080 human fibrosarcoma cells, human gastric carcinoma AGS, and human prostate carcinoma PC-3 cells in vitro [22]. An additional benefit of a herbal mixture is that it might be more potent and less toxic than single agents in reducing inflammation because the combinations potentiate the efficacy of individual herbs and counteract the harmful side effects of each other [23]. As a staple of traditional Asian medicine, JGT can be obtained from producers who follow strict quality control requirements and guarantee that all individual herbs are within the official specifications. Here, we investigated whether social isolation causes a resistance to tamoxifen therapy and/or causes responding tumors to re-emerge from dormancy. Single housing, which elicits anxiety and other fearful behaviors, is a well-established animal model of social isolation [24,25]. Different rodent models have been used to study the impact of social isolation on breast cancer risk. In these studies, social isolation increased mammary cancer risk in C3[1]/SV40 T-antigen (SV40Tag) mice [25], TgMMTVneu mice [26], and mammary carcinogen-treated mice [27,28]. In addition, aging rats housed singly developed more spontaneous mammary tumors than group-housed rats [24]. Furthermore, in the 4T1 syngeneic mouse mammary tumor model, social isolation significantly increased tumor growth [29] and cancer mortality [30]. However, no earlier studies have explored whether social isolation influences the effectiveness of hormone therapies against breast cancer. We found that after tamoxifen therapy was completed, social isolation induced the regrowth of dormant mammary tumors, increasing the risk of local mammary cancer recurrence. RNA sequencing data from mammary glands identified two key changes in socially isolated rats: enrichment of inflammatory pathways including IL6/JAK/STAT3 and the suppression of the OXPHOS pathway. JGT reversed these changes and maintained the dormancy of tamoxifen responsive mammary tumors in socially isolated rats. ## 2.1. Animals We used Sprague Dawley rats that are known to be responsive to tamoxifen therapy to investigate whether JGT modifies the effect of social isolation on tamoxifen response and local recurrence after tamoxifen therapy ended. Eighty Sprague Dawley rats were obtained from Envigo and arrived at 6 weeks of age at the Georgetown University Animal Facility located at the Department of Comparative Medicine. The rats were housed in groups of three per cage. All rats were fed a semi-purified AIN93G diet. The animals were housed in a temperature- and humidity-controlled room with a 12-h light–dark cycle. All animal procedures were approved by the Georgetown University Animal Care and Use Committee to ensure humane care. ## 2.2. Mammary Tumor Induction and Social Isolation ER+ mammary tumors were induced by the administration of 10 mg of 7,12-dimethylbenz[a]anthracene (DMBA, Sigma, St. Louis, MO, USA) diluted in 1 mL of peanut oil by gavage when the rats were 50-days of age. Tumor development was checked weekly and when the first tumor became palpable per animal, rats were divided into two groups: those kept group-housed (3 animals per cage, $$n = 40$$), or those housed singly (social isolation, $$n = 40$$). ## 2.3. Tamoxifen Therapy and Administration of Jaeumganghwa-Tang (JGT) When the first tumor per animal reached a diameter of ~11 mm, group-housed and socially isolated rats were divided into two additional groups. From this point forward, the experiment contained four groups: group-housed treated with tamoxifen ($$n = 19$$), group-housed treated with tamoxifen + JGT ($$n = 17$$), socially isolated treated with tamoxifen ($$n = 17$$), and socially isolated treated with tamoxifen + JGT ($$n = 18$$). Four group-housed and five socially isolated rats that never developed mammary tumors that reached 11 mm in diameter were not included in the study. Tamoxifen was added to the AIN93G diet at a concentration of 340 ppm tamoxifen citrate. JGT was administered via drinking water (500 mg/kg body weight). JGT was produced by Hanjung Pharmaceuticals (165-7 Sangseo-dong, Daedeok-gu, Daejeon, Korea) based on the formulation approved by the Korean Ministry of Food and Drug Safety (MFDS). This company manufactures JGT under the Good Manufacturing Practice (GMP) guidelines established by the MFDS. All individual herbs were within the specification of the Korean Pharmacocopia 11th edition, and the final quality control was established by the analysis of three index materials: berberine, glycyrrhizic acid, and paeoniflorin. In our study, JGT was in powder form and was used before the expiration date. The number was MJK701. The composition of the JGT is shown in Supplementary Table S1. ## 2.4. Monitoring Mammary Tumor Responses Response to tamoxifen treatment was divided into four categories: [1] complete response (CR, tumor disappearance); [2] partial response (PR, tumor stopped growing and/or shrank); [3] de novo resistance (DNR, tumor continued to grow regardless of tamoxifen treatment); and [4] acquired resistance (AR, tumor appeared after initiation of tamoxifen treatment and continued growing). Tumor response data were analyzed using the Chi test [2]. When a CR tumor remained nonpalpable for 9 weeks, tamoxifen was removed from the diet. Nine weeks of rat life corresponds to approximately 5 years of human life. Rats that received JGT with tamoxifen continued to receive JGT after the tamoxifen treatment ended. Regrowth of dormant mammary tumors, that is, local recurrence, was monitored for 9 weeks after tamoxifen administration. ## 2.5. Mammary Gland and Tumor Harvesting At the end of the tumor response monitoring period, all tumors and fourth mammary glands (if they were tumor-free) were harvested. Half of the samples per mammary gland and tumor were paraffin embedded for histopathological analysis, and the other half were processed for RNA and protein analysis and stored at −80 °C. ## 2.6. Tumor Pathologic Evaluation Formalin-fixed mammary tumors were embedded in paraffin and cut into 5 µm sections. Hematoxylin and eosin (H&E)-stained sections were then used for histopathological evaluation, which that was conducted by a veterinary pathologist, Dr. Galli, at Georgetown University. ## 2.7. RNA-Sequencing RNA from tumor-free mammary glands was extracted using the Qiagen RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The RNA concentration and purity were analyzed using a NanoDrop 1000 spectrophotometer (Thermo Scientific, Waltham, MA, USA). RNA-Seq was performed by Genomics and Epigenomics Shared Resource at Georgetown University Medical Center. Paired-end, dual-indexed libraries for RNA-Seq were constructed from 500 ng total RNA using the TruSeq Stranded Total RNA Kit (Illumina, San Diego, CA, USA) according to the manufacturer’s instructions. Briefly, coding RNA and multiple forms of non-coding RNAs were captured using bead-based cytoplasmic and mitochondrial rRNA depletion, cDNA synthesis, and PCR. The resulting sequencing libraries were assessed for quality using a BioAnalyzer 2100 with a DNA 1000 Kit (Agilent, Santa Clara, CA, USA) and quantified via fluorometry using the Qubit 4.0 (ThermoFisher). Libraries were sequenced on the NextSeq 550 system (Illumina) using the High Output Kit v2.5 (150 cycles) with a paired-end 75 bp read mode to an average depth of 50 M reads per sample. We used FastQC to check the raw data quality, lower quality reads, and trimmed Illumina adapter sequences. Gene expression levels were generated from Rsem [31] in combination with Bowtie2 and rat reference sequences (rn6). Differential expression analysis was performed using the DESeq2 package [32] in R, and FDR <0.1 as the cutoff point. A heat map was created for each set of filtered genes. Further functional analysis was performed using PANTHER v15.0 [33]. ## 2.8. RNA-Seq Data Analysis All raw data were passed through a FastQC quality check. Adapter trimming was first performed on raw data using cutadapt (v2.9). The reference genome was downloaded from Ensembl (Mus musculus release 99), and the reference genome index was built using Bowtie2 (v2.4.1). Paired-end trimmed read alignment and raw read count calculations were conducted using RSEM (v1.3.1). Statistical analyses were performed using the DESeq2 package (v1.26) in R software (v3.6). Genes with a p-value < 0.05 were considered as differentially expressed and used as the input for gene set enrichment analysis (GSEA) (v3.0, Broad Institute). ## 2.9. Knowledge-Guided Differential Dependency Network (KDDN) Analysis The network model was created using the KDDN app (v1.1.0) in Cytoscape (v3.6.0) with an automatic optimal parameter. PPI information was obtained from the search results produced by the STITCH database (v5.0). ## 2.10. Quantitative Real-Time Polymerase Chain Reaction RNA from mammary tumors was extracted as described for the mammary glands. Two micrograms of RNA from the mammary tumors and mammary glands was converted to cDNA using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Waltham, MA, USA) in a PTC-100 thermal cycler (Bio-Rad, Hercules, CA, USA). cDNA at 5 ng/µL mixed with BrightGreen 2X qPCR MasterMix-ROX (abm, Inc., Richmond, Canada), and gene-specific forward and reverse primers were used for real-time PCR. The PCR was carried out using the QuantStudio 12 K Flex Real-Time PCR System (Applied Biosystems). The relative standard curve method was used to calculate the expression levels of the gene targets normalized to the housekeeping gene Hprt1 in rat tissue. Primers used in qPCR analysis were designed using the IDT tool (Integrated DNA Technologies Coralville, IA, USA, primer sequence found in Supplementary Table S2). ## 2.11. Protein Extraction and Western Blotting Proteins were isolated from mammary glands, mammary tumors, and the brain using Pierce RIPA lysis buffer (Thermo Scientific), supplemented with Mini Complete Protease Inhibitor (Roche, Mannheim, Germany) and PhosSTOP phosphatase inhibitor (Roche). A BCA Protein Assay Kit (Thermo Scientific) was used to measure the protein concentration according to the manufacturer’s protocol. Protein extracts were separated on a 4–$12\%$ gradient denaturing polyacrylamide gel (SDS-PAGE) and transferred to a nitrocellulose membranes using the Invitrogen iBlot 7-min Blotting System. Unspecific reactions were blocked with $5\%$ non-fat dry milk diluted in Tris buffered saline + Tween 20 (TBST) for 1 h at room temperature. Membranes were then incubated overnight at 4 °C with specific primary antibodies (1:1000): receptor for advanced glycation end-products (RAGE) (37,647, Abcam, Cambridge, MA, USA) and pSTAT3 (9145, Cell Signaling Technology, Danvers, MA, USA). After washing with TBST, the membranes were incubated with the secondary antibody at room temperature for 1 h. Membranes were developed using SuperSignal chemiluminescent HRP substrate (Thermo Scientific) and the signals were captured using the Amersham imaging system (GE, Boston, MA, USA). After the development of pSTAT3 and RAGE, membranes were incubated with RestoreTM Western Blot Stripping Buffer (ThermoFisher Scientific) for 15 min, blocked with $5\%$ nonfat dry milk in TBST for 1 h, and incubated overnight with total STAT3 (1:1000, #9139, Cell Signaling) and β-actin (1:1000, #8457, Cell Signaling), respectively. Stripping was confirmed by developing membranes using an Amersham imaging system. Protein levels were determined by band intensity using Quantity One software (Bio-Rad), and pSTAT3 was normalized to total STAT3 and RAGE normalized to β-actin expression. ## 2.12. Lactate Dehydrogenase (LDH) Activity and Lactate Level Assays LDH activity and lactate levels were measured using a LDH Assay Kit (Abcam; ab102526) and L-lactate Assay Kit (Abcam; ab65331), respectively, according to the manufacturer’s instructions. The mammary gland was snap-frozen in liquid nitrogen and stored at −80 °C. The tissue was crushed in liquid nitrogen, and 100 mg and 30 mg were used for the LDH and lactate assays, respectively. The optical density of the samples was measured at 450 nm at the end of the reaction for the lactate assay and every 3 min for 90 min for LDH activity using a Synergy H1 microplate reader (Biotek, Winooski, VT, USA). Lactate levels and LDH activity were normalized to the protein levels measured using a BCA Protein Assay Kit (Thermo Scientific). ## 2.13. Statistical Analysis The Chi-square test was used to assess the tumor response to tamoxifen, tumor recurrence, and tumor histopathology. Differences in tumor burden were assessed using two-way repeated measures ANOVA followed by the Tukey post hoc test. The t-test was used to assess the differences in the gene and protein expression between the two groups. Differential gene and protein expression among tumors from different groups was assessed by three-way ANOVA or two-way ANOVA followed by the Holm–Sidak post hoc test. Differences between groups were considered statistically significant when the p values were ≤0.05. ## 3.1. Social Isolation Does Not Modify Tamoxifen Responsiveness during Therapy ER+ mammary tumors were induced in Sprague Dawley rats by administering DMBA via oral gavage. DMBA is a polycyclic aromatic hydrocarbon (PAH), and PAHs in the human environment are linked to an increased breast cancer risk [34]. When a rat developed a palpable mammary tumor, we divided animals into two groups: they were either socially isolated by single housing or allowed to remain group-housed (GH). Initiating social isolation (SI) at this time point creates a model that mimics patients who feel socially isolated because of the stress of being diagnosed with breast cancer. Furthermore, findings from a previous study suggested that SI promotes breast cancer growth only when implemented after tumors are already present [35]. The experimental design is illustrated in Figure 1. When the first mammary tumor reached a size of approximately 11 mm in diameter, the SI and GH rats were divided into two additional groups: those that were treated with tamoxifen and those that were treated with tamoxifen + JGT. Tamoxifen was administered at a concentration of 340 ppm, which is relevant for human tamoxifen exposure levels via the AIN93G diet, as previously described [36]. JGT at a dose of 500 mg/kg was administered via drinking water as described previously [19]. This dose corresponds to 81 mg/kg when converted to a human exposure equivalent [37] and is less than humans can be safely exposed to over extended time periods [38]. At least half of all DMBA tumors in Sprague Dawley rats respond to tamoxifen [36,39]. In the present study, $31\%$ ($$n = 18$$) of the 58 tumors that developed during the tumor monitoring period in GH rats exhibited a complete response, and $24\%$ ($$n = 14$$) a partial response. Social isolation non-significantly reduced the rate of complete responses to $21\%$ ($$n = 8$$ of a total of 39 tumors), but the rate of partial responses slightly increased to $28\%$ ($$n = 11$$; ns) (Figure 2A). The proportion of de novo resistant tumors (tumor never responded) was $16\%$ ($$n = 9$$) in the GH and $15\%$ ($$n = 6$$) in the SI group. Of all the tumors detected during tamoxifen therapy, $29\%$ ($$n = 17$$) of GH rats appeared after tamoxifen therapy started and $36\%$ ($$n = 14$$) in the SI group; these tumors were considered to represent acquired resistance. None of the differences were statistically significant. ## 3.2. JGT Increases Responsiveness to Tamoxifen As we have previously reported [19], giving rats JGT via drinking water significantly increased the rate of complete responses to tamoxifen in GH rats from $31\%$ to $52\%$ ($$n = 23$$ of a total of 44 tumors, $$p \leq 0.004$$) (Figure 2A). Similar results were observed in the SI rats, with the rate of complete responses increasing from $21\%$ to $36\%$ ($$n = 16$$ of a total of 45 tumors, $$p \leq 0.03$$). ## 3.3. Social Isolation Increases the Risk of the Regrowth of Dormant Mammary Tumors and Local Recurrence in Rats, and JGT Prevents this Increase Tamoxifen treatment was stopped 9 weeks after complete response, a timeframe representing a sustained response to this intervention. Social isolation significantly increased the risk that the responding tumors would emerge from dormancy and recur. Recurrence was defined as a tumor that regrew to at least 11 mm in diameter at the same location where the completely responding tumor was initially located. Figure 2B shows that among the GH animals, $45\%$ of responding tumors recurred, whereas the rate of recurrence increased to $75\%$ ($p \leq 0.001$) in the SI animals. The latency to tumor recurrence did not differ between the groups (Figure S1). As shown in Figure 2C, the tumor burden (sum of the area of all detected tumors per animal) after tamoxifen therapy was also significantly higher in SI rats than in GH rats. These findings indicate that the risk of regrowth of dormant mammary tumors is significantly greater in SI animals than in GH animals. In breast cancer patient populations, an increased rate of recurrence is associated with reduced overall survival, since recurring cancers are less responsive to therapies, and distant recurrence is generally fatal. JGT treatment continued after tamoxifen was removed in animals that had received a combination of tamoxifen and JGT. Continued JGT treatment prevented the increase in regrowth of dormant tumors and local recurrence in SI animals (Figure 2B). The percentage of recurrence in the SI rats decreased 3-fold from $75\%$ without JGT to $22\%$ with JGT ($p \leq 0.001$). JGT did not affect the incidence of local recurrence in GH rats (Figure 2B). ## 3.4. Tamoxifen and JGT Modify Tumor Histopathology Most DMBA-induced mammary tumors in Sprague Dawley rats are malignant adenocarcinomas [36,39], as was also observed in this study (Figure S2). Other malignant mammary tumors detected in tamoxifen-treated rats were squamous carcinomas, adenosquamous carcinomas, and lipid-rich mammary carcinomas (Figure S2). Tamoxifen can increase the ratio of benign to malignant DMBA tumors [39], likely reflecting, in part, its established cancer-preventive activities in humans [40]. JGT further increased the proportion of benign tumors in the SI rats from $26\%$ to $54\%$ ($p \leq 0.001$; Figure 2D). ## 3.5. Social Isolation and JGT Modify IL6/JAK/STAT3 and Oxidative Phosphorylation Signaling in Mammary Glands and Tumors We used RNA-Seq analysis to determine which signaling pathways were altered by social isolation and JGT treatment. We considered whether comparisons should be performed in mammary glands or tumors. If comparisons are made in tumors, they would have to occur between partially recurring or resistant tumors, and consequently, the data might be masked by tumor tamoxifen responsiveness rather than differences between GH and SI rats. In our earlier studies, both resistant and recurring tumors were associated with immunosuppression; therefore, differences between the control and experimental groups were seldom observed. Given the chemopreventive activities of tamoxifen, it is likely that relevant events also occur in normal, but carcinogen-exposed mammary glands. Hence, the fourth abdominal mammary gland was obtained from GH or SI rats for RNA-Seq. ## 3.5.1. Effects of Social Isolation Since we observed a difference in the proportion of rats with recurrent tumors after tamoxifen treatment, which was significantly higher in SI than GH rats, we used mammary glands obtained after tamoxifen therapy to determine which genes were significantly altered by social isolation. We identified 674 differentially expressed genes using the cutoff criteria of $p \leq 0.05$ and a fold-change ≥1.5 (Supplementary Table S3). Genes with an expression value of 0 in four or more samples were excluded. Gene Ontology (GO) analysis indicated that the main alterations in the mammary glands of GH and SI animals involved cell proliferation and cell metabolism (Figure S3A). We then performed gene set enrichment analysis (GSEA) to identify genes that may act together. The results from the GSEA analysis were used to identify the differentially expressed ‘Cancer Hallmark’ gene sets and KEGG pathways. The top Cancer Hallmark pathways enriched in the SI rats, compared with the group-housed rats, included IFNα, IFNγ, and inflammatory responses, and IL6/JAK/STAT3 and TNFα signaling via NFκB (Figure 3A and Figure 4A). The top Cancer Hallmark pathways suppressed in the SI were oxidative phosphorylation (OXPHOS), MYC targets VI and V2, E2F targets, and the G2M checkpoint (Figure 3A and Figure 4A). Many of these pathways are linked to mitochondrial metabolism and cell proliferation, reflecting the consistency between the results of GO and Cancer Hallmark pathway analyses. The results also indicated that the functions identified by GO analysis were disrupted rather than increased in the SI rats. Although MYC and E2F are often oncogenic, their inhibition also reflects mitochondrial dysfunction [41]. KEGG pathway analysis confirmed that SI upregulated the inflammatory pathways. Of the top 10 enriched pathways in the mammary glands of SI rats, six were inflammatory or immune cell signaling pathways (Figure 4B). The second top pathway that was inhibited in SI rats, compared with GH rats, in the KEGG analysis, was the OXPHOS pathway (Figure 4B). Other suppressed KEGG pathways in SI rats indicated ribosomal inactivation, impaired mismatch and nucleotide excision repair, DNA replication, and the tricarboxylic acid (TCA) cycle. ## 3.5.2. Effects of JGT on Socially Isolated Rats after Tamoxifen Treatment Since JGT prevented the increase in local recurrence in SI rats, we evaluated the differences in gene expression in the mammary glands of SI rats that either continued to receive JGT after tamoxifen treatment or that never received JGT. Using the same criteria as described above, 349 differentially expressed genes were identified (Supplementary Table S4). The top pathways identified in GO analysis were related to antibodies and immune responses (Figure S3B). Analysis using the ‘Cancer Hallmark’ gene set indicated that the top pathways suppressed by JGT in SI rats were IFNα and IFNγ, and inflammatory responses and IL6/JAK/STAT3 signaling (Figure 3D and Figure 4C), that is, the same pathways that were activated in SI rats compared with GH rats. In the KEGG pathway analysis, nine of the top 10 inhibited pathways in SI rats treated with JGT were the inflammatory and immune cell signaling pathways (Figure 4D). JGT caused an enrichment of OXPHOS in SI rats in both the Cancer Hallmark pathway analysis (Figure 3D and Figure 4C) and KEGG pathway analysis (Figure 4D). The TCA cycle pathway was also upregulated by JGT in the KEGG analysis of SI rats. ## 3.5.3. Genes Altered by Social Isolation and Reversed JGT in the IL6/JAK/STAT3 and OXPHOS Pathways We investigated the common genes that contributed to the change in the IL6/JAK/STAT3 and OXPHOS Cancer Hallmark pathways between the GH and SI rats and were reversed by JGT in the SI rats. ## IL6/JAK/STAT3 Pathway In the IL6/JAK/STAT3 pathway, 17 genes shown in Supplementary Table S6 were upregulated in SI rats and then reduced by JGT. The specific functions of the 17 genes are listed in Supplementary Table S6. Among these genes are CD14, CSF2, and CXCL10, which are linked to COVID-19 related cytokine storm (CD14 [42] and CXCL10 [43]) and acute respiratory syndrome (ARDS) (CSF2/GM-CSF [44]). ## OXPHOS Pathway Supplementary Table S7 shows the genes that contributed to the suppression of the OXPHOS Hallmark pathway in SI rats, and that JGT reversed. SI suppressed several genes linked to the TCA cycle and its activation as well as genes involved with mitochondrial electron transport chains. JGT upregulated the expression of the OXPHOS pathway genes in SI rats. Among the inhibited OXPHOS pathway genes in SI rats was MPC1, which transports pyruvate into the mitochondria, and the mitochondrial pyruvate dehydrogenase complex genes (PDHB and PDHX) that convert pyruvate to acetyl CoA (Supplementary Table S7). Because impaired OXPHOS may occur when less pyruvate is provided to the mitochondria and more is converted to lactate, we measured the lactate dehydrogenase (LDH, an enzyme that converts pyruvate to lactate) and lactate levels. However, as illustrated in Figure S4A,B, neither the LDH nor lactate levels were altered in the mammary glands of SI rats compared with GH rats. Since we used mammary glands rather than tumors in the RNA–Seq analysis and all animals had completed a long tamoxifen treatment, it is possible that different findings would have been obtained in non-tamoxifen treated mammary tumors, which were not available for this study. ## 3.5.4. Effects of JGT on the Tamoxifen-Treated Group-Housed Rats Finally, we determined whether JGT affects similar pathways in tamoxifen treated GH rats than in post-tamoxifen SI rats. Although JGT did not reduce the rate of local mammary tumor recurrences in GH rats, it improved their responsiveness to tamoxifen. Using the cutoff criteria noted above, we identified a total of 352 candidate genes that were significantly differentially expressed in the mammary glands of tamoxifen-treated GH rats compared with GH rats treated with tamoxifen + JGT (Supplementary Table S5). GO analysis implicated ‘opioid receptor binding’, ‘immune receptor activity’, and ‘cytokine binding’ as differentially activated GO molecular functions by JGT. ‘ Negative regulation of IL6 production’, ‘inflammatory response’, and ‘cytokine production’ were among the altered GO biological processes (Figure S3C). Although GO analysis results for JGT were different in post-tamoxifen SI rats and tamoxifen-treated GH rats, it was common in both analyses that the immune related functions were altered. In the Cancer Hallmark pathway analysis, IFNα, IFNγ, inflammatory responses, and IL6/JAK/STAT3 signaling were suppressed significantly by JGT in tamoxifen-treated GH rats (Figure 3G and Figure 4E). KEGG pathway analysis indicated that among the top 10 inhibited pathways in GH rats treated with JGT, six were cytokine or other immune cell signaling pathways (Figure 4F). Furthermore, both in the Cancer Hallmarks and KEGG pathways, JGT enriched the OXPHOS pathway (Figure 3G and Figure 4E,F). Other Cancer Hallmark enriched pathways in JGT-treated GH rats were Myc targets V1, mTORC1 signaling, adipogenesis, and cholesterol homeostasis. Each of these pathways is linked to mitochondrial function, and their upregulation by JGT may indicate that this herb mix improved the mitochondrial metabolism. Indeed, JGT had similar effects on many Cancer Hallmarks and KEGG pathways in tamoxifen-treated GH rats and post-tamoxifen SI rats. ## 3.5.5. Knowledge-Fused Differential Dependency Network (KDDN) Analysis To better understand the connections among differentially expressed genes in the Cancer Hallmark pathway analysis, we performed KDDN analysis to identify novel connections induced by SI or JGT in the IL6/JAK/STAT3 and OXPHOS pathways. KDDN discovers unique signaling connections (edges) between genes (nodes) that are present only in GH or SI rats [45]. Hub genes, represented as nodes with multiple edges, are particularly important. The unique edges in the IL6/JAK/STAT pathway that were present (green) or lost (red) in the KDDN analysis in SI rats when compared with GH rats or in SI rats treated with JGT are shown in Figure 3B,E, respectively. We observed that in the IL6/JAK/STAT3 pathway, connections from the Stat5a node to Ep300, Bcl2, and Socs1 were lost in the SI rats, but present in GH rats (Figure 3B). Ep300 functions as a histone acetyltransferase that regulates transcription via chromatin remodeling and can activate genes by suppressing histone deacetylase 1 (HDAC1) [46]. STAT5 binds to SOCS1 to provide feedback for the regulation of CD8+ T cells [47]. BCL2, in turn, can regulate immune cell survival by inhibiting apoptosis. The KDDN analysis indicates that these regulatory mechanisms were lost in rats with SI. JGT-treated SI rats regained the connection between Stat5a and Ep300 but lost the connection between Stat5a and Ptpn2 (Figure 3E). Since PTPN2 promotes FoxP3/Treg stability [48], the KDDN results suggest that JGT may inhibit immunosuppressive Foxp3 cells in the tumor microenvironment. Connections in the OXPHOS pathway (Figure 3C) included the hub gene Mrps30, which when downregulated, suppresses OXPHOS to promote breast cancer growth [49]. In the SI rats that exhibited increased risk of breast cancer recurrence, this gene was downregulated and had lost its connection to Ndufs3, Ndufa4 (both involved in mitochondrial membrane respiratory chain), and Vdac2 (pathway for metabolite diffusion across the mitochondrial outer membrane), and gained a connection to Suclg1 (TCA cycle) and Atp6v0c (enzyme transporter that acidifies intracellular compartments in eukaryotic cells). When SI rats were treated with JGT, Mrps30 gained a connection with Ndufc2 and lost a connection with Atp5fd (a member of the electron transfer complex V) (Figure 3F). Most novel connections in the KDDN analysis were detected in GH rats treated with tamoxifen + JGT compared with those treated with tamoxifen alone. Unique edges in the IL6/JAK/STAT and OXPHOS Hallmark pathways in JGT-treated GH rats are shown in Figure 3H,I. For example, CMPK2 is a mitochondrial nucleotide kinase that supplies deoxyribonucleotides for mitochondrial DNA (mtDNA) synthesis to activate the Nod-like receptor protein 3 (NLRP3) inflammasome complex. This complex upregulates IL1β to induce inflammation [50]. As shown in Figure 3H, the connection between the two hubs, CMPK2 and IL1β, was lost in the JGT-treated rats. In JGT-treated animals, IL1β was associated with CSFR3 (involved in granulocyte differentiation) and IL10RA (anti-inflammatory/immunosuppression). These findings indicate that JGT induced a new, anti-inflammatory connection to replace inflammatory gene connection in the mammary gland of tamoxifen-treated GH rats. KDDN analysis of the OXPHOS pathway indicates that gene interactions that promote the use of amino acids to generate ATP via the TCA cycle in GH rats (from Cox15 to Aldh6a1) are replaced by those that promote improved mitochondrial respiration (from Cox15 to Cox5a, Ndufc1, and Mrps30) in the JGT-treated GH rats (Figure 3I). In addition, JGT promoted the conversion of Acc2 to Glud1. Glud1 converts glutamate to α-ketoglutarate to activate the mitochondrial electron transport chain that generates the most energy to cells. In rats only treated with tamoxifen, Acc2 connects to Id2, which catalyzes the oxidative carboxylation of isocitrate to 2-oxoglutarate in the TCA cycle and to Htra2. Htra2 activates the cellular stress response. The new connections created by JGT treatment might explain how this herb mix upregulates the OXPHOS signaling pathway. ## 3.6.1. Inflammatory Genes While no single gene alone likely explains the complex effects of social isolation on breast cancer mortality, it is customary to verify the validity of key results from RNA-Seq and other omics analyses. We used RT-qPCR to validate the differential expression of the five genes most strongly linked to inflammation and tumor immune responses: chemokine Ccl7, cytokine Csf2/GM-CSF, cytokine receptors Il4r and Il18r1, and inhibitor of antitumor immunity through antigen presentation Lilrb3. The selected genes exhibited the minimum within-group variation in the RNA-Seq analysis. Consistent with the RNA-Seq data, Csf2, Ilr4, Il18r1, and Lilrb3 mRNAs were significantly upregulated in the mammary glands of SI rats compared with GH rats (Figure 5A–D). The modest increase in the expression of Ccl7 did not reach statistical significance (Figure 5E). We then determined whether any of the gene expression changes detected between the GH and SI rats after tamoxifen treatment were reversed in SI rats supplemented with JGT. Of the five genes that were significantly or non-significantly upregulated in the SI rats, compared with the GH rats, JGT significantly reduced the expression of Il18r1 and non-significantly reduced Lilrb3 expression in the SI rats (Figure 5C,D). ## 3.6.2. OXPHOS Genes We measured the expression of the eight OXPHOS pathway genes that were downregulated in the SI rats when compared with GH rats in the RNA-Seq analysis. Specifically, cytochrome c-1 (Cyc1) is a member of the mitochondrial electron transport chain complex III, fructose 1,6 bisphosphate-2 (Fbp2) catalyzes the conversion of fructose-1,6BP to fructose-6P in the glycolysis to feed TCA and ultimately OXPHOS, isocitrate dehydrogenase (NAD(+))3 is the non-catalytic subunit gamma (Idh3g) that converts isocitrate to α-ketoglutarate in the TCA cycle, malate dehydrogenase 1 (Mdh1) converts malate to oxaloacetate in the TCA cycle, and NADH:ubiquinone oxidoreductase subunit A9 (Ndufa9), NADH:ubiquinone oxidoreductase subunit AB1 (Ndufab1), and NADH:ubiquinone oxidoreductase subunit C1 (Ndufc1) are all part of complex I of the electron transport chain. Succinate dehydroxygenase complex iron sulfur subunit B (Sdhb) is a member of complex II of the electron transport chain. Fbp2, Idh3g, Ndufab1, and Ndufc1 were significantly lower in SI rats than in GH rats (Figure 6A–D). The expression of the other genes was reduced, but not significantly (Figure 6E–H). Supplementing SI rats with JGT significantly upregulated the expression of Cyc1, Idh3g, Ndfc1, Mdh1, and Sdhb, and tended to upregulate Ndufab1. The inhibition of Fbp2 and Ndufa9 in SI rats was not reversed by JGT (Figure 6A,G). Although we did not confirm significant differences in all the genes that were among the altered genes in the IL6/JAK/STAT3 and OXPHOS signaling pathways in RNA-Seq analysis, non-significant changes likely contribute to the overall function of the two signaling pathways. ## 3.6.3. Effect of JGT on Tamoxifen-Treated GH Rats As implied by the RNA-Seq data, we investigated whether in GH rats during tamoxifen treatment, JGT suppressed the chemokines Ccl12, Csf3r (receptor for CSF3), Mcemp1 (transmembrane protein possibly regulating mast cell differentiation and immune responses), or s1008a and s1009a (ligands for RAGE). Mcemp1, S100a8, and s100a9 were significantly downregulated by JGT (Figure 7A–C); the expression of Ccl12 or Csf3r was not altered (Figure 7D,E). These data suggest that RAGE signaling may be inhibited by JGT in tamoxifen-treated animals. ## 3.6.4. IL6 and STAT3 Expression in Mammary Tumors We also determined the changes in the IL6 and STAT3 levels in mammary tumors. Since there were insufficient numbers of tumors available for the analysis after tamoxifen therapy was completed, tumors were obtained from animals during tamoxifen treatment. In the histopathological analysis, some of the tumors were benign and some were malignant, as tamoxifen increases the proportion of benign DMBA tumors. Social isolation increased the Il6 mRNA levels (Figure 8A) and pSTAT3 protein levels (Figure 8B) in benign mammary tumors. No differences were observed in malignant tumors, but the Il6 levels were higher in malignant tumors than in benign tumors. JGT reduced the Il6 and pSTAT3 expression in mammary tumors; this reduction was observed in malignant tumors for Il6 and in benign tumors for pSTAT3. ## 3.7. Social Isolation Upregulates Receptors for RAGE in Mammary Glands and Tumors As RAGE ligands were suppressed by JGT, we determined whether RAGE expression was altered in SI rats. RAGE is an inflammatory receptor that activates NF-κB, resulting in the production of proinflammatory cytokines IL1 and IL6 [51]. RAGE mRNA and protein levels were significantly higher in SI rats than in GH rats in both the mammary glands and the brain (Figure 9A). In the malignant mammary tumors during tamoxifen treatment, Rage mRNA expression was higher in the SI rats than in the GH rats (Figure 9B). Paradoxically, benign tumors in the GH rats exhibited higher Rage mRNA levels than malignant tumors. JGT did not affect the RAGE levels (Figure 9B). We then determined whether the expression of the RAGE ligands S1008a or S1009a was altered in the benign or malignant mammary tumors between GH and SI animals during tamoxifen therapy (Figure 9C,D). However, JGT suppressed the expression of both S1008a and S1009a (Figure 9C,D), which is consistent with the data from the mammary glands (Figure 7B,C). ## 4. Discussion In the preclinical setting, we found that social isolation did not affect the responsiveness to tamoxifen treatment. However, after tamoxifen therapy ended, the risk of local mammary tumor recurrence increased by $60\%$ in SI rats compared in GH rats. Local recurrence in our study refers to a mammary tumor that responded to tamoxifen and consequently could no longer be palpated, but after tamoxifen treatment ended, the tumor reemerged and started to grow. In humans, this pattern of recurrence is often described as reflecting ‘dormancy’, which is a key feature of ER+ breast cancer and a major challenge in the eradication of this disease. If translatable to humans, the preclinical findings reported here suggest that the increased breast cancer mortality in socially isolated patients with ER+ disease [10] may mainly reflect recurrence after hormone therapy is completed. Thus, social isolation may be a major contributor to dormancy, and interventions to reduce its impact of social isolation may be most beneficial if focused on the period following hormone therapy. The mechanism(s) by which social isolation increases breast cancer mortality in humans remains unclear. Several studies have found tha t loneliness and/or social isolation induce a chronic inflammatory state, and the levels of circulating inflammatory markers are elevated in socially isolated individuals [14,15]. In contrast, socially integrated individuals exhibit reduced inflammatory markers [52]. RNA-Seq analysis of the mammary glands indicated that the top signaling pathways such as the IL6/JAK/STAT3 pathway, which is linked to increased inflammation, were upregulated in socially isolated animals. The IL6/STAT3 pathway is aberrantly hyperactivated in breast cancer [53], is linked to poor prognosis in patients [54], and may drive mammary tumorigenesis in SI rats. Among the inflammatory genes upregulated by social isolation in rats compared to the GH controls were Csf2 (also known as GM-CSF), Il18r1, Il4r, and Lilbr3. IL4R promotes breast cancer growth [55] and LILRB3 can block antitumor immune activation [56]. CSF2 is linked to the promotion of cancer stem cells via the activation of STAT3 and myeloid-derived suppressor cells [57]. IL18 signaling has been reported to be either pro-tumorigenic or suppressive in tumor development and progression [58]. OXPHOS is another pathway altered by social isolation. In earlier studies, social isolation has been reported to impair the respiratory chain complex, increase the formation of mitochondrial reactive oxygen species (ROS), and cause oxidative damage in various tissues [18], especially in the brain [17]. We found that genes in all four complexes of the respiratory chain, NADH dehydrogenase, succinate dehydrogenase, ubiquinol-cytochrome c oxidoreductase, and cytochrome oxidase were suppressed in SI rats. Furthermore, mitochondrial pyruvate uptake was likely inhibited because both the mitochondrial pyruvate transporter MPC1 and pyruvate dehydrogenase were downregulated in the mammary glands of the SI rats. SI also inhibited the signaling associated with glycolysis, which could reduce the substrates to promote OXPHOS. In addition, several key genes driving the TCA cycle were suppressed in socially isolated rats. While it remains controversial whether reduced OXPHOS increases or inhibits cancer [59], the suppression of OXPHOS is causally linked to diabetes, cardiovascular diseases, and obesity [60,61], which, in turn, are promoted by social isolation. Previous studies have identified a close association between OXPHOS and inflammation. Oxidative stress induces inflammation [62], which impairs mitochondrial function by suppressing the mitochondrial respiratory chain [63]. It is not clear whether social isolation in our study first induced the inflammation or dysregulation of OXPHOS or whether these activities occurred independently. Impaired OXPHOS is also linked to aging [64] and has been proposed to explain the inflammatory changes related to aging [65]. As aging is a risk factor for social isolation, it is possible that older individuals who are socially isolated develop more age-related physical problems and diseases than socially connected elderly people. In this study, we did not investigate whether changes in IL6/JAK/STAT3 and OXPHOS signaling explain the increased risk of mammary cancer recurrence in socially isolated rats. In the past, the cancer research field has been focused on identifying a single target that could explain, for example, why cancer progresses and develops resistance to treatments. It is now clear that this approach does not work, but instead, to control cancer, multiple targets need to be considered. Furthermore, cancer is affected by inputs from its environment that include immune, adipose, neuronal, and vascular cells. A recent paper highlights how cancer is affected by body wide influences [13]. Social isolation is an example of influences that are initiated far away from the tumor site, but nevertheless impact tumor growth. Thus, instead of targeting single genes that were differentially expressed in the mammary glands of SI rats, we used the anti-inflammatory herb mix JGT [20,21], which is one of the most widely used traditional herbal mixtures in East Asia. Jaeumganghwa-tang is the Korean name for the mixture, and JGT is called Zi-yin-jiang-huo-tang in China and Jin-koka-to in Japan. In our study, JGT reversed the enrichment of CD14, CSF2, and CXCL10 in the IL6/JAK/STAT3 Hallmark pathway in SI rats. These three genes are linked to the severity of COVID-19 infection [43,44,66]. Others have reported that social isolation impaired the antibody response to the COVID-19 vaccine [67]. Possible effectiveness of Asian herbal mixtures to reduce the symptoms of COVID-19 has been assessed in many studies, and one such mixture (NRICM101) is used in Taiwan for the clinical treatment of COVID-19 [68]. Our study suggests that the anti-inflammatory properties of JGT can prevent social isolation-induced recurrence of mammary tumors in rats. RAGE, a member of the immunoglobulin superfamily of cell surface molecules, activates NF-κB and increases the production of TGF-β, the pro-inflammatory cytokines IL1 and IL6 [51], and ROS [69]. RAGE is also causally linked to type 2 diabetes [70] and Alzheimer’s disease [71], and social isolation increases the risk of both diseases [3,4]. However, the effect of social isolation on RAGE has not been studied. We found an increase in the RAGE levels in SI rats in normal mammary glands, malignant mammary tumors, and in the brain compared with those in GH rats. However, social isolation did not affect the expression of RAGE ligands S100a8 and S100a9, both of which were inhibited by JGT. JGT did not suppress RAGE expression. These findings suggest that independent pathways drive how social isolation increases RAGE expression and how JGT reduces RAGE ligand expression. Nevertheless, the co-expression of both RAGE and NF-κB supports malignant progression, blocks apoptosis in malignant cells [72], and maintains sustained inflammation [73]. A causal link between RAGE, inflammation, and increased cancer risk, progression, and metastasis has been established [72]. In summary, our results suggest that social isolation may increase breast cancer mortality by allowing the regrowth of dormant mammary tumors and increasing the risk of recurrence after hormone therapy. The increase in recurrence in rats was prevented by the herbal mix JGT, which reversed the social isolation-induced increase in IL6/JAK/STAT3 signaling and the suppression of OXPHOS. How this inflammatory state or metabolic dysfunction occurs in socially isolated individuals remains to be elucidated. 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--- title: Serum Ferritin Levels in Severe Obstructive Sleep Apnea authors: - Christopher Seifen - Johannes Pordzik - Tilman Huppertz - Berit Hackenberg - Cornelia Schupp - Christoph Matthias - Perikles Simon - Haralampos Gouveris journal: Diagnostics year: 2023 pmcid: PMC10047524 doi: 10.3390/diagnostics13061154 license: CC BY 4.0 --- # Serum Ferritin Levels in Severe Obstructive Sleep Apnea ## Abstract Obstructive sleep apnea (OSA) has been associated with various acute and chronic inflammatory diseases, as has serum ferritin, an intracellular iron storage protein. Little is known about the relationship between severity of OSA and serum ferritin levels in otherwise healthy subjects. In this study, all polysomnographic recordings, serum levels of ferritin, C-reactive protein (CRP), and hemoglobin, as well as patient files from 90 consecutive, otherwise healthy individuals with suspected OSA who presented to a tertiary sleep medical center were retrospectively analyzed. For comparison, three groups were formed based on apnea–hypopnea index (AHI; none or mild OSA: <15/h vs. moderate OSA: 15–30/h vs. severe OSA: >30/h). Serum ferritin levels were significantly positively correlated with AHI ($r = 0.3240$, $$p \leq 0.0020$$). A clear trend of higher serum ferritin levels was found when patients with severe OSA were compared to those without or with mild OSA. Serum CRP and serum hemoglobin levels did not differ significantly among OSA severity groups. Age and body–mass index (BMI) tended to be higher with increasing OSA severity. The BMI was significant higher in patients with severe OSA compared to those without or with mild ($p \leq 0.001$). Therefore, serum ferritin levels may provide a biochemical surrogate marker for OSA severity. ## 1. Introduction Obstructive sleep apnea (OSA) is the most common type of sleep-disordered breathing with increasing prevalence [1]. Upper airway collapsibility during sleep is a major feature of OSA pathogenesis. Even when respiratory effort is still present, apneas or hypopneas caused by airway obstruction occur repeatedly [2]. Being overweight is the strongest risk factor for OSA [3]. Furthermore, OSA prevalence increases with growing age and is more frequently seen in males [4,5]. Previous studies have shown that OSA is related to risk of glaucoma [6], non-alcoholic hepatic steatosis [7], unfavorable oncologic outcomes following therapy for head and neck squamous cell carcinoma [8], coronary artery disease [9], stroke [10], arterial hypertension [11], and other adverse effects [12]; therefore, OSA is recognized as an important public health issue. In general patient care, serum ferritin is part of routinely ordered blood tests, and plays an important role in the evaluation of anemia. Ferritin is an intracellular storage protein that binds iron, and its intracellular concentration regulates the production of serum ferritin [13]. Serum ferritin levels have been linked to the presence or severity of different chronic inflammatory diseases in which iron dysregulation clearly plays a major role, e.g., arterial hypertension [14], type 2 diabetes [15], coronary artery disease [16], metabolic syndrome [17], and many others [18]. Moreover, serum ferritin is nonspecifically elevated in acute infectious and tumor diseases; thus, serum ferritin is widely recognized as an acute phase reactant and a surrogate marker of inflammation [13]. Apart from adult medicine, elevation of serum ferritin in uncontrolled inflammation has also been observed in pediatric practice, so its elevation is used as a biomarker in various clinical scenarios [19]. Pathologically elevated serum ferritin levels, as seen in hemochromatosis, can cause a variety of symptom constellations. In addition to joint complaints, common symptoms include abdominal pain, hair loss, fatigue, decreased libido or weight loss [20,21]. The majority of OSA patients suffer from very common comorbidities such as arterial hypertension, metabolic syndrome, type 2 diabetes, or coronary artery disease. Hence, a positive correlation between OSA severity and serum ferritin levels appears plausible; however, few studies have addressed this question, with conflicting results. Therefore, the purpose of the present research was to investigate any possible linkage of serum ferritin levels with the severity of OSA. We hypothesized that intermittent hypoxia, the hallmark pathophysiological effect of OSA at the peripheral tissue level, contributes to the increase in serum ferritin levels with increasing severity of OSA. To this end, only otherwise healthy subjects with a clinical suspicion of OSA have been included in the present study to minimize important confounding factors possibly altering the complex iron metabolism, such as very common concomitant inflammatory diseases and other pathological conditions such as anemia, neoplastic disease, or recent inflammatory conditions. ## 2.1. Study Participants and Data Collection We searched the databased of our sleep laboratory (which is part of a tertiary university medical center) from January 2020 to January 2022 for all patients whose clinical complaints were suggestive of underlying OSA and who underwent polysomnography (PSG) for the first time. All patients who participated in the study were screened for OSA due to history of snoring, apneas, or daytime sleepiness, or a combination of those; in other words, OSA screening was based on patient-reported sleep-related symptoms rather than routine health assessment or high-risk screening. A licensed technician ensured that each PSG was performed correctly overnight, and evaluation was performed by an ENT physician according to standard guidelines (American Academy of Sleep Medicine (AASM) [22]). For this study, the medical files of all patients were screened for baseline characteristics, e.g., age, body–mass index (BMI), and sex. Similarly, we looked for chronic diseases (e.g., type 2 diabetes, arterial hypertension, pulmonary disease, cardiovascular disease, or chronic mental health disorders), recent infections, malignancies, and daily or regular use of medications of all types. Only adult patients without any chronic diseases, current infections, history of malignancies, or daily or regular use of any type of medication were included in the present study. In other words, only patients who could be defined as healthy based on their records before the PSG exam were included in the study. In addition, we excluded individuals with central apnea, periodic breathing, or other types of sleep-related breathing disorders. We analyzed the PSG recording of each patient for selected parameters (in alphabetical order):− AHI-apnea–hypopnea index: apneas and hypopneas/h;− AI-apnea index: apneic events/h;− ARI-arousal index: arousal/h);− HI-hypopnea index: hypopnea events/h;− ODI-oxygen desaturation index: oxygen desaturation events (≥$4\%$)/h;− Percentage of N3 sleep (slow-wave sleep);− Percentage of REM sleep;− PLM-total number of periodic limb movements;− SI-snoring index: snoring events/h;− TST-total sleep time in minutes; and− t90-percentage of oxygen desaturation lower than $90\%$. Blood samples collected in the morning hours after the PSG exam (usually after 12 h of fasting) were analyzed for serum ferritin levels (ng/mL), serum C-reactive protein (CRP) levels (mg/L), and serum hemoglobin levels (g/dL). For further investigation three groups were formed based on OSA severity:[1]all male and female patients with AHI <15/h (“none/mild”);[2]all male and female patients with AHI 15–30/h (“moderate”);[3]all male and female patients with AHI >30/h (“severe”). ## 2.2. Statistical Analysis Statistical analysis was performed using SPSS Statistics, version 23 (IBM Corp., Armonk, NY, USA), and JMP (SAS Institute, Cary, NC, USA). Descriptive statistics were used for presenting OSA characteristics, anthropometry, and laboratory values. Variables were described using mean and standard deviation (SD), or median and interquartile range (IQR) where applicable. Since only “age” showed normal distribution, log-normalization was applied for those values (BMI, ferritin, CRP, and AHI) where Pearson correlation analysis, stepwise logistic regression analysis and subsequent multifactorial analysis of variance was applied. We considered the results significant when the p-value was <0.05 (), $p \leq 0.01$ (), and $p \leq 0.001$ (). Graphical illustration was performed using GraphPad Prism version 5.01 (GraphPad Software, Boston, MA, USA). Boxplots were used in the figures presenting median and IQR. For investigation of factors that contribute to AHI, we employed a two-step procedure. We first computed a stepwise feed-forward logistic regression analysis. To ensure stringent inclusion criteria, we fed the model only with those data that showed a significant correlation with AHI. Only age, and the log-normalized values for BMI, ferritin, and CRP reached a significance level < 0.05. For entering a single of these variables into the regression equation we set again the level of entry to 0.05 and only ferritin and age were able to enter the model. These factors were then used to compute a logistic regression analysis. ## 2.3. Ethical Statement Consent to use the data was given by all patients. All data were analyzed anonymously. The study methods complied with local research practices and data protection. The study was performed in accordance with the Declaration of Helsinki. ## 3.1. The Study Population A total of 432 patients underwent polysomnography for the first time during the period studied. A total of 342 patients had to be excluded because they were under 18 years of age, had chronic diseases, current infection, history of malignancies, daily/regular use of any type of medication, no OSA typical sleep-related breathing pattern, or double measurement at different time points. A total of 90 patients and their data sets were included for assessment in the present study. Group “none/mild” contained 30 patients (8 ($26.7\%$) female), the age was 19–66 years (42.11 ± 12.99 years) and BMI was 18–44 kg/m2 (25 (23–27.3) kg/m2). A total of 31 patients (7 ($22.6\%$) female) met inclusion criteria for group “moderate”. Age was 28–65 years (44.21 ± 11.48 years) and BMI was 22–42 kg/m2 (28 (26–30) kg/m2). Group “severe” contained 29 patients (2 ($6.9\%$) female), the age was 35–60 years (48.06 ± 6.92 years) and BMI was 22–40 kg/m2 (30 (26.5–32.5) kg/m2). All baseline characteristics of the study group are shown in Table 1. ## 3.2. Between Group Comparison of Polysomnographic Parameters In this initial analysis, we compared sleep parameters of the three study groups “none/mild”, “moderate”, and “severe”. All investigated respiratory parameters, as AHI, AI, and HI were significantly higher in group “moderate” compared to group “none/mild” ($p \leq 0.001$, $p \leq 0.01$, and $p \leq 0.001$, respectively), in group “severe” compared to group “moderate” ($p \leq 0.001$, $p \leq 0.001$, and $p \leq 0.05$, respectively), and in group “severe” compared to group “none/mild” ($p \leq 0.001$ for all parameters). Evaluation of pulse oximetry measurements showed that ODI and t90 were significantly elevated in group “moderate” compared to group “none/mild” ($p \leq 0.001$, and $p \leq 0.05$, respectively), in group “severe” compared to group “moderate” ($p \leq 0.001$, and $p \leq 0.01$, respectively), and in group “severe” compared to group “none/mild” ($p \leq 0.001$ for both parameters). PLM showed a tendency of higher values according to OSA severity, with significantly higher values in group “severe” compared to group “none/mild” ($p \leq 0.05$). Comparison between groups revealed no significant differences in TST. N3 sleep (%) was significantly lower in group “severe” compared to group “none/mild” ($p \leq 0.01$) and compared to group “moderate” ($p \leq 0.01$), while comparison between groups revealed no significant differences in REM sleep (%). The ARI was significantly elevated in group “severe” compared to group “none/mild” ($p \leq 0.001$) and compared to group “moderate” ($p \leq 0.01$). All above mentioned sleep parameters are shown in Table 2. ## 3.3. Ferritin, C-Reactive Protein, and Hemoglobin Serum Levels Analysis of the serum ferritin revealed a clear trend of elevated levels in group “severe” compared to group “none/mild”. In addition, a higher tendency of serum ferritin level was found in group “moderate” compared to group “none/mild”, but without statistical significance. Pearson correlation analysis identified a significant positive correlation between AHI and serum ferritin level ($r = 0.3240$, $$p \leq 0.0020$$), even after adjusting for the potentially confounder “age” ($r = 0.3639$, $$p \leq 0.0032$$). Median serum ferritin levels were 136 (92–194) ng/mL in men and 60 (35.5–131) ng/mL in women ($p \leq 0.01$). Contrarily, analysis of CRP or hemoglobin serum levels revealed no significant differences between patients with none/mild, moderate, or severe OSA. No patient suffered from acute or chronic inflammation and no patient had anemia. All above mentioned ferritin, CRP, and hemoglobin serum levels are shown in Table 3 and presented in Figure 1. The correlation analysis between AHI and serum ferritin level is presented in Figure 2. Although sex did not have a statistically significant association with AHI, we found significant correlations for age, BMI, ferritin and CRP as frequently described in the literature (Table 4). To assess which of these factors contributes to the variance in AHI, we conducted a stepwise feed-forward logistic regression analysis. These factors were then used to compute a logistic regression analysis. In the logistic regression analysis, only ferritin achieved a statistical power level of 0.80, and its influence on AHI was computed adjusted for age (statistical power level < 0.50). ## 4. Discussion In the present study, we demonstrated that serum ferritin levels tended to be higher with increasing severity of OSA in otherwise healthy subjects. A quite relevant difference was found when patients with severe OSA were compared to those without or with mild OSA. Accordingly, we demonstrated a significant positive correlation between AHI and serum ferritin levels. In contrast, serum CRP levels and serum hemoglobin levels did not differ significantly between groups with different OSA severity based on AHI. In our study cohort, age and BMI tended to be higher when OSA severity (as depicted by the AHI) increased, with quite significant differences for patients without or with mild OSA versus severe OSA. Our results are in accordance with a study by O’Brien et al. in 80 patients with OSA, where a significant association between higher serum ferritin levels and OSA was described [23]. The authors did adjustments for age, sex, and BMI, but not for possibly confounding chronic diseases, recent infection, malignancies, or medication, which could have affected their reported results. As a possible explanation the authors discussed the elevation of serum ferritin in the context of increased inflammation in patients with severe OSA. Other authors have previously advocated an enhanced production of red blood cells and increased hematocrit levels in patients with OSA [24]. However, in our study, after carefully controlling for the various relevant confounders, we provide evidence that serum CRP and hemoglobin levels, which are markers of systemic inflammation and anemia, respectively, were not affected by the severity of OSA. Regarding serum ferritin as an inflammatory marker, it could be argued that its elevation in patients with severe OSA in our cohort might be due to (or confounded by) the presumed stronger effect of obesity in this group than to OSA itself. However, this argument may be rather attenuated since comparable CRP levels in the different severity groups in our cohort were found. Another study by Ming et al. retrospectively compared 270 bariatric candidates and demonstrated that serum ferritin levels were significantly elevated in the moderate/severe OSA group compared to the no/mild OSA group [25]. Our results are contradictory to what was described by Thorarinsdottir et al. among 796 subjects with OSA that were part of the Icelandic Sleep Apnea Cohort [26]. In their study, OSA patients were compared to 637 randomly chosen Icelanders who participated in an epidemiological study. The authors proved significantly higher serum ferritin levels in OSA males and a trend in the same direction for OSA females. However, after adjusting for age, BMI, smoking history, hypertension, cardiovascular disease, and type 2 diabetes this difference in serum ferritin levels by OSA was no longer found. In addition, the authors found no significant associations between serum ferritin levels and OSA severity, both unadjusted and after adjusting for relevant confounders. Of note, Thorarinsdottir et al. found that serum ferritin levels were significantly higher in OSA males than OSA females [26], an observation that is also true for our cohort. Another study by Abakay et al. compared 44 patients with OSA to 46 controls and found no significant correlation between serum ferritin levels and OSA [27]. The number of studies on the influence of OSA on serum ferritin levels is quite limited. To our knowledge, our study is the first to provide evidence on serum ferritin levels in a large number of carefully selected, otherwise healthy participants who were first tested for OSA based on clinical sleep-related symptoms. Our study has the advantage that patients with any diseases that may have altered serum ferritin levels such as chronic diseases, malignancies, iron deficiency, hemochromatosis, or dysthyroidism were excluded from the study population. Moreover, measurement and evaluation of serum CRP and serum hemoglobin levels could also help to uncover undiagnosed or unreported inflammatory diseases or subclinical anemia to exclude such individuals from our study. All participants had serum CRP und serum hemoglobin levels within physiological range, and their levels did not differ significantly between groups. Of all the factors examined, only one correlation with ferritin was found in our data set, showing approximately statistically significant power. However, this correlation is roughly 0.33 and it is unlikely that higher numbers of cases would yield higher correlation coefficients. At best, the significance and statistical power would increase. It is striking that other factors that are typically associated with the AHI do not show any further significant correlation with the AHI if one adjusts for the ferritin measurements and their influence. For sleep medicine, the following novelty emerges, which should nevertheless be confirmed in its significance in larger collectives: Compared to CRP, ferritin is a more important laboratory marker for screening populations for the possible presence of a sleep disorder. Especially in healthy populations, sleep history questionnaires should be used to identify risk groups for sleep disorders, especially in the case of selective elevations of ferritin. Therefore, it should be discussed to include serum ferritin not only in the list of surrogate markers for chronic inflammatory diseases but also for OSA. These preliminary findings are quite relevant from a clinical viewpoint, because blood samples could be processed for ferritin even in an outpatient setting and therefore provide general medical practitioners, internal medicine, pulmonary medicine or other (e.g., ear, nose and throat) specialists with a handy surrogate biomarker to assist them in classification of OSA disease severity. Therefore, this could have further potential implications for the necessity and priority of OSA patient management on a personalized way. Additionally, these findings provide the background for further detailed study of the biochemical and molecular mechanisms involved in the interplay of nocturnal intermittent hypoxia and ferritin biochemical functional networks in humans. Of course, further investigations in larger OSA patient cohorts are needed to increase the validity of our preliminary data. Although a clear positive association between serum ferritin levels and OSA severity was demonstrated, a mechanistic explanation for the possible linkage between OSA and an altered iron metabolism remains unclear. A possible mechanistic explanation could be an increased oxidative stress level of the tissue periphery caused by severe OSA, resulting in an increased serum ferritin level. In this case, not only the pure number of apneic or hypopnea events, but rather the oxygen deficiency could be the causative factor. Previous studies suggest that PLMs are a matter of concern in patients with OSA [28], raising the question of whether elevated PLMs might contribute to elevated serum ferritin levels. However, the presence of PLMs has been associated with low serum ferritin levels [29]. Another explanation for the increased serum ferritin in severe OSA could be due to decreased N3 sleep. N3 sleep is considered the deepest stage of sleep, during which the body repairs and regrows tissues, strengthens the immune system, and builds bone and muscle [30]. The imbalance of sleep stages in severe OSA might contribute to the observed disturbance in iron metabolism. This consideration also applies to the increased ARI in severe OSA, as autonomic activation and sleep fragmentation are relevant consequences of arousals [31]. In contrast, a recent study described lower serum ferritin levels in children with autism spectrum disorders who suffered more frequently from sleep fragmentation [32]. Further studies should be designed to test the value of serum ferritin levels as a biochemical surrogate marker of OSA severity. In due course, prospective replication is needed to test the broader validity of the results of this exploratory analysis. Our study has certain limitations. First, our study was an observational study, not a randomized controlled trial, which may be considered a limitation. Second, no information was available on lifestyle habits of the participants such as alcohol consumption or cigarette smoking. These stimulants may have altered serum ferritin levels independently. Third, despite careful review of all records, we cannot guarantee that all chronic diseases, current infections, malignancies, or daily or regular use of medications of any kind were accurately reported by the patient at the time the medical record was obtained. Fourth, males constituted the majority of study subjects, especially when OSA severity increased. This sex bias may limit the population to which our results could be applied. Additionally, this sex bias may have altered serum ferritin levels independently. A similar trend has been previously found for CRP levels [33]. In addition, because of the retrospective design, it wasn’t possible to determine which of the included female study participants were premenopausal or postmenopausal. Finally, a remaining potential confounding factor is that serum ferritin levels in our cohort may have been influenced by BMI instead of OSA severity [34]. Future prospective studies with larger samples should focus on the molecular or cellular mechanisms involved. ## 5. Conclusions We provide evidence that severe OSA may be associated with elevated serum ferritin levels in otherwise healthy OSA subjects. Serum ferritin levels may be a valuable tool for clinicians not only to investigate anemia but also as a surrogate marker of OSA severity, especially in obese individuals. ## References 1. Franklin K.A., Lindberg E.. Obstructive Sleep Apnea Is a Common Disorder in the Population—A Review on the Epidemiology of Sleep Apnea*. J. Thorac. Dis.* (2015) 7 1311-1322. PMID: 26380759 2. 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--- title: Building an explanatory model for snakebite envenoming care in the Brazilian Amazon from the indigenous caregivers’ perspective authors: - Altair Seabra de Farias - Elizandra Freitas do Nascimento - Manoel Rodrigues Gomes Filho - Aurimar Carneiro Felix - Macio da Costa Arévalo - Asenate Aline Xavier Adrião - Fan Hui Wen - Fabíola Guimarães de Carvalho - Felipe Murta - Vinícius Azevedo Machado - Jacqueline Sachett - Wuelton M. Monteiro journal: PLOS Neglected Tropical Diseases year: 2023 pmcid: PMC10047533 doi: 10.1371/journal.pntd.0011172 license: CC BY 4.0 --- # Building an explanatory model for snakebite envenoming care in the Brazilian Amazon from the indigenous caregivers’ perspective ## Abstract ### Background In the Brazilian Amazon, snakebite envenomings (SBE) disproportionately affect indigenous peoples. Communication between indigenous and biomedical health sectors in regards to SBEs has never been explored in this region. This study aims to build an explanatory model (EM) of the indigenous healthcare domain for SBE patients from the perspective of the indigenous caregivers. ### Methodology/Principal findings This is a qualitative study involving in-depth interviews of eight indigenous caregivers who are representatives of the Tikuna, Kokama and Kambeba ethnic groups, in the Alto Solimões River, western Brazilian Amazon. Data analysis was carried out via deductive thematic analysis. A framework was built containing the explanations based on three explanatory model (EM) components: etiology, course of sickness, and treatment. To indigenous caregivers, snakes are enemies and present conscience and intention. Snakebites have a natural or a supernatural cause, the last being more difficult to prevent and treat. Use of ayahuasca tea is a strategy used by some caregivers to identify the underlying cause of the SBE. Severe or lethal SBEs are understood as having been triggered by sorcery. Treatment is characterized by four components: i) immediate self-care; ii) first care in the village, mostly including tobacco smoking, chants and prayers, combined with the intake of animal bile and emetic plants; iii) a stay in a hospital, to receive antivenom and other treatments; iv) care in the village after hospital discharge, which is a phase of re-establishment of well-being and reintroduction into social life, using tobacco smoking, massages and compresses to the affected limb, and teas of bitter plants. Dietary taboos and behavioral interdictions (avoiding contact with menstruating and pregnant women) prevent complications, relapses, and death, and must be performed up to three months after the snakebite. Caregivers are in favor of antivenom treatment in indigenous areas. ### Conclusions/Significance There is a potential for articulation between different healthcare sectors to improve the management of SBEs in the Amazon region, and the aim is to decentralize antivenom treatment so that it occurs in indigenous health centers with the active participation of the indigenous caregivers. ## Author summary The Brazilian Amazon is the region of the Americas that has the highest incidence of SBEs, and indigenous populations are disproportionately affected. In order for Brazil to achieve a reduction in mortality and disability from snakebite envenomations, it is vital to understand how indigenous and professional healthcare sectors interact in indigenous villages. The indigenous caregivers interviewed explained that the occurrence of a snakebite can be a natural event, usually due to the lack of attention of an indigenous person during their daily activities in the forest, or a supernatural event that was caused as a punishment or by witchcraft. The latter type of snakebites is more serious and it is difficult to intervene to promote healing. Indigenous caregivers recommend a series of rituals in order to protect an individual from snakebite or heal the already diseased body. In addition, a series of therapeutic resources derived from plants and animals is used for treatment and rehabilitation of the indigenous person. In this itinerary, there is no impediment on the part of the caregivers to include a stay in hospital in order to carry out the treatment with antivenom and other care deemed necessary by the doctors. This finding is crucial to improve the effectiveness of snakebite treatment in an integrated biomedical-indigenous model, thus reducing the contradictions and tensions present in the daily practices of both health teams and indigenous caregivers. The engagement with local caregivers to combine the indigenous health care model with a timely referral of SBE patients to a facility equipped with antivenom is a major determinant of success in the control of SBEs. ## Introduction Snakebite envenomings (SBEs) represent a medical emergency that mainly affects populations living in underdeveloped tropical countries [1]. The highest incidence of SBEs is reported in the Brazilian Amazon, with a disproportionate burden for indigenous populations [2]. In this region, the case reporting system shows a 7.5-fold higher incidence in indigenous villagers (333.5 SBE cases/100,000 inhabitants) compared to the non-indigenous population ($\frac{72.2}{100}$,000 inhabitants) [3]. In addition, case-fatality rate from SBEs was significantly higher among indigenous villagers ($1.4\%$) versus non-indigenous populations ($0.5\%$) [3]. Furthermore, the frequency of late medical care is significantly higher in indigenous villagers [3,4]. Antivenom is not provided in indigenous community health centers, and transport to urban areas is needed to complete the therapeutic itinerary [2,3,5]. In Brazil, health care for indigenous villagers is assigned to the Indigenous Healthcare Subsystem within the scope of the Unified Health System (Sistema Único de Saúde; SUS). The organizational model is based on Special Indigenous Health Districts (Distrito Sanitário Especial Indígena; DSEIs), which is a service organized to provide primary healthcare services to Brazil’s indigenous communities within their ethnocultural settings [6]. In this system, the gateway for treatment for SBE patient is a community health center, which is able to offer only basic first aid, such as cleaning the bite site and analgesics. Patients are then transferred to the DSEI central health bases where a subsequent transfer to the reference hospital is obtained for antivenom treatment, and, if necessary, the treatment of complications arising from the SBE, such as secondary bacterial infection, severe local tissue necrosis and acute kidney injury [3]. The establishment of an indigenous healthcare subsystem has not been sufficient to fully guarantee quality of care and good health indicators in indigenous populations [7]. As noted for SBEs, other health problems also have a disproportionate burden among indigenous villagers, such as parasitic and infectious diseases [8–10], anemia [11] and undernutrition [12,13], in a worrying setting of mixed morbidity with obesity, diabetes and hypertension [14–16], alcohol abuse [17] and mental disorders [18]. In addition to a scenario of an inadequate healthcare coverage and geographic barriers, which noticeably limits access to health services [19], there is a lack of effective communication between health agents guided by the hegemonic ideology of biomedicine, and indigenous caregivers in the villages. This may generate resistance to or low acceptability of the health services on the part of indigenous people, as well as prejudice against ancestral healing practices on the part of health professionals [20,21]. Understanding the interaction between indigenous and professional healthcare sectors in indigenous villages is a crucial factor if one is to improve the global effectiveness of indigenous health care. This study assumes that the elaboration of an explanatory model (EM) for the indigenous healthcare model offers an arsenal of information for reducing contradictions and tensions still present in the daily practices of health teams at the local level when seeking to prevent and treat SBEs. The engagement with traditional caregivers to combine the indigenous healthcare model with a timely referral of SBE patients to a facility equipped with antivenom also depends on this knowledge. In this study, we aimed to build an EM of the indigenous sector of healthcare for SBE patients from the perspective of indigenous caregivers, in the region of the Alto Solimões River, in the western Brazilian Amazon. ## Ethics statement This study involves collection of data from indigenous populations and consent was obtained from indigenous leaders from each village. After this consent was given, the study protocol was submitted to the Health Research Coordination at the National Council for Scientific and Technological Development (COSAU/CNPq) and to the National Indigenous Foundation (FUNAI). Subsequently, with the approvals from COSAU/CNPq and FUNAI, the protocol was submitted and approved by the National Research Ethics Commission (approval number 4,993,$\frac{083}{2020}$). All participants signed a consent form after reading of the study objectives and procedures. To ensure their understanding of the study, the researcher responsible for the interviews was always accompanied by a native speaker of the participant’s language. ## Study design We conducted explanatory descriptive study in the Brazilian Amazonia to understand the role of the folk healthcare sector in treating SBEs in indigenous villages, within a cultural process that is managed primarily by indigenous caregivers. In-depth interviews were performed in the indigenous village with caregivers from January to December 2021, in the indigenous village. The study was conducted according to the Consolidated Criteria for Reporting Qualitative Research (COREQ) guideline (S1 File). ## Setting The study was performed with indigenous caregivers living in the Special Indigenous Health Districts (SIHD) named Alto Solimões River, in the municipalities of Tabatinga, São Paulo de Olivença and Benjamin Constant, in the state of Amazonas, western Brazilian Amazonia. The SIHD is a healthcare administrative model that is responsible for providing primary health care to Brazil’s indigenous communities [3]. The SIHD Alto Solimões River serves a total of 70,519 indigenous inhabitants that live in 231 villages, in 13 health basic hubs for indigenous health with a complete multidisciplinary indigenous health team (doctors, nurses, dentists, psychologists, pharmacists, laboratory technicians, nursing assistants, oral health technicians, indigenous health agents, and boat operators). In this SIHD, the indigenous people belong to seven ethnic groups (Tikuna–the most numerous, Kokama, Kaixana, Kambeba, Kanamari, Witoto, and Maku-Yuhup). The SIHDs do not provide antivenom treatment for SBE patients, and only perform the first aid via wound cleaning and analgesics. When an SBE is confirmed, the patient is transferred to the nearest hospital in an urban area to receive the antivenom [3]. ## Research team and reflexivity One male researcher (ASF) interviewed the participants. He is of indigenous origin (Kambeba) and has a background in nursing, with a Master of Science degree in Public Health, and is a specialist and professor of Indigenous Health. The script of the interview was designed by the research team with the support of a licensed nurse who is a member of the Tikuna ethnic group from the community of Feijoal, in the SIHD Alto Solimões River, municipality of Benjamin Constant; his father was an indigenous village chief and his grandfather a shaman. The study team also included one physician and three nurses with extensive experience in SBE research, one educator who is a specialist in public health and qualitative research, one nurse who is a specialist in indigenous health with experience in the SIHD Alto Solimões River (has witnessed several SBEs in the study area), and one epidemiologist. ## Survey participants Indigenous caregivers over the age of 18 were invited to participate in the study. The selection of participants began with the discussions with the health managers and the multidisciplinary team of indigenous health workers of the municipalities, which together were defined as the caregivers working in the villages. In total, eight caregivers were included and interviewed in this research, these being five of the Tikuna, two of the Kokama and one of the Kambeba ethnic groups. Indigenous caregivers were selected from village actors with socially legitimate status, roles and power relationships with villagers who seek them out in case of health disorders, which were identified with the help of public authorities working in the indigenous district (Fig 1). The sample was of convenience, and all participants were indicated by the health professionals who work in the units of these villages. No refusals were observed. None of the participants had an established relationship with an author prior to study commencement. Fig 1: Location of the study area, in the Special Indigenous Health Districts named Alto Solimões River, in the state of Amazonas, western Brazilian Amazon.The location of the village of each caregiver is presented. The base used to create map is from the IBGE (Brazilian Institute of Geography and Statistics), which is freely accessible for creative use in shapefile format, in accordance with the Access to Information Law (12,527/2011) (https://geoftp.ibge.gov.br/cartas_e_mapas/bases_cartograficas_continuas/bc250/versao2019/). ## Data collection An experienced researcher conducted the in-depth interviews using a semi-structured script (Table 1) in a quiet, comfortable space in the participants’ homes. One silent observer from the research team was also present. Interviewer and observer introduced themselves to the participants at the beginning of the interview and gave a short personal background and explained their role in the study, as well the study objectives. The interviews lasted on average for an hour and were recorded via an audio recording device. The interviewer and silent observer took field notes. The interviews were transcribed, de-identified, and loaded into MAXQDA 20. Transcripts are available in S2 File. No interviewer-related biases were identified. Table 1 \| Question \| \| --- \| \| 1. Do snakebites occur in your village? \| \| 2. How often do they occur? \| \| 3. Have you ever seen snakebites occur? \| \| 4. Why do snakebites occur in your village? \| \| 5. Do snakebites have any meaning in your village? \| \| 6. When you find a snake what should be done? \| \| 7. When a snakebite occurs, what does the indigenous person feel? \| \| 8. What can happen to the indigenous person who was bitten by a snake? \| \| 9. What situations can aggravate snakebites? \| \| 10. What should be done immediately after snakebite? \| \| 11. What indigenous treatments are used for snakebites? \| \| 12. When the indigenous person is bitten, does he/she have to go to the hospital? To do what? \| \| 13. In which circumstances does the indigenous person need to go to the hospital? \| \| 14. How long after the snakebite does indigenous person normally go to the hospital? \| \| 15. What were the three most important snakebite cases you treated? \| \| 16. What does an indigenous person do to avoid being bitten by a snake? \| \| 17. How does an indigenous person protect the body so as not to be bitten? \| \| 18. If there were boots and chaps in the village would an indigenous person use them to protect themselves from snakes? \| \| 19. Is there any protective equipment used against snakebites that the indigenous person would like to use but they do not have in the village? \| \| 20. To end the interview, can you give a summary of what you can and cannot do regarding snakebites? \| Of the eight interviews, six were carried out in Portuguese, one was carried out with the help of an interpreter, and one was carried out with the support of the caregivers’s daughter and granddaughter, who helped the participant to remember details of the SBE he had experienced. ## Data analysis Interviews transcripts were analyzed using inductive content analysis. Data source triangulation was employed (transcripts, field notes) during the analysis. Two independent researchers (ASF and WM) created separate codebooks in MAXQDA 20 software, then discussed any discrepancies and established a common codebook for the analysis. The codebook developed was then applied to the interviews in an inductive content analysis [22]. Interview data was analyzed to understand indigenous caregivers’ explanations in an analytical framework with three themes: i) Etiology; ii) Course of sickness (with three subthemes: onset of symptoms, pathophysiology, and severity and prognosis); and iii) Treatment [23]. A deductive layer of coding was applied after the initial inductive analysis to identify subthemes in the etiology and treatment themes, which complemented the EM structure for SBE management from the indigenous healthcare domain perspective. Boundaries and points of interaction with popular (patients’ self-care and community support) and biomedical domains and the indigenous EM were presented. The analysis was concluded August 2022. ## Characteristics of the participants A total of eight indigenous caregivers were included, with seven men and one woman, and ages ranged from 47 to 82 years. The participants belonged to the Tikuna (five), Kokama (two) and Kambeba (one) ethnic groups. Regarding religious affiliations, six are of the Saint Order Cross and two are Catholic. Table 2 describes the roles by which caregivers recognize themselves in their villages. Table 2 \| Participant \| Gender \| Age \| Ethnic group \| Religion \| Social role \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| P1 \| Male \| 50 \| Tikuna \| Saint Cross Order1 \| P1 prefers to be recognized as a healer, not a pajé2. He explained that a healer does not know sorceries, so he’s not strong enough to fight evil, but he helps people, especially children. The healer knows how to use power because he takes away evil and protects himself. A healer knows medicine for all illnesses and pains, practices the good of healing all the time to help people. He emphasizes the use of dietary restrictions, the use of bitter tree bark and smoking using various substances as therapeutic resources. \| \| P2 \| Male \| 47 \| Tikuna \| Saint Cross Order \| P2 sees himself as healer and pajé. He has the gift of prayer to cure pain. He is a specialist in caring for indigenous children, especially in cases of vomiting and diarrhea. \| \| P3 \| Male \| 78 \| Kokama \| Saint Cross Order \| P3 sees himself as a healer whose specialty is the use of plants as therapeutic resources. He treats humans and domestic animals, such as dogs. \| \| P4 \| Male \| 64 \| Kambeba \| Catholic \| P4 recognizes himself as a healer. He resorts to using ayahuasca tea3 to expand his mind and his ability to diagnose and cure illnesses. He uses chants, massages, and smoking with hawk’s beak, feathers and talons as treatment resources. He has a special room to take care of sick people. \| \| P54 \| Male \| 55 \| Tikuna \| Saint Cross Order \| P5 recognizes himself as a pajé. He explained that he is seen in the village with fear and respect, for the power to dominate good and evil. He reported that he has difficulty passing on his knowledge to his children and grandchildren as they do not want to be associated with sorceries. \| \| P6 \| Male \| 82 \| Kokama \| Saint Cross Order \| P6 introduces himself as a shaman and has received healing power from God. His healing practices focus on the use of plants (ambé—Philodendron imbe, and cubiu—Solanum sessiliflorum, mainly). He works with three spirits. He worked as a rubber taper for 36 years and reported that he was bitten by snakes (jararacas) several times. \| \| P7 \| Male \| 52 \| Tikuna \| Catholic \| P7 recognizes himself as a pajé. In his childhood (5 years), the community recognized that he possessed healing powers. When he was 18 years old, he was taken from his village by a non-indigenous boss and brought to Manaus so that he could work in an Umbanda5 house teaching shamanic practices. He was rescued by public authorities five months after his arrival. He uses vegetable latex to treat several illnesses. \| \| P8 \| Female \| 52 \| Tikuna \| Saint Cross Order \| P8 recognizes herself as a shaman, prayer and healer. She began her activity at the age of 16, having been taught by her father and grandfather. She has the gift of divination and the ability to control situations of conflict. She uses and recommends the use of amulets for protection against physical and spiritual ills. She wears a bracelet made with jararaca skin as an amulet in her everyday life. \| ## Perpetrating snakes The indigenous caregivers are aware that SBEs are caused by the inoculation of a snake’s venom into the body of a human being. There is consensus that the Amazonian pit vipers (jararaca, in Portuguese; Bothrops atrox L.) are the main causative agents of ing in the region. This snake generally bites the feet and legs. The bushmaster (surucucu-pico-de-jaca, in Portuguese; Lachesis muta L.) causes a lower number of bites, but it is much feared due to its aggressiveness, more potent venom, and long length, which can reach up to 3 meters. As it is very long, bushmasters bite “only the buttocks and thigh of the individual” (Participant 3). This snake is very heavy and strong and, when its lunges, it knocks the victim to the ground. The third most-aggressive agent is the two-striped forest-pitviper (jararaca-verde or jararaca-papagaio, in Portuguese; Bothrops bilineatus Wied-Neuwied), a snake that lives in the tree branches and bites the face of an individual harvesting açaí or hunting arboreal animals like monkeys. Encounter with snakes during daily activities is an ordinary event for indigenous villagers, in particular for men, since thay have the social role of hunting, fishing and collecting fruits and materials for handcrafts and ornaments in the forest. To a lesser extent, women and children can also be affected when they help in the care of plantations. Due to this condition of continuous and intense exposure, all caregivers reported having already treated cases of snakebites. Participant 6 has been bitten before. Snakebites happen more frequently in the rainy season, as the river level rises and snakes are forced to migrate to the sandbanks and upland areas where the plantations and houses are located. According to Participant 1, snakes are most active from 6 pm to 3 am: “The snake also looks for food, right?” But there is an exception, according to the same pajé, for pregnant snakes. These are active and very aggressive at any time of the day. ## Natural versus supernatural causes of snakebites Participants believe that the snake is an animal that has a conscience and a will of its own. Analysis of the interviews shows that SBEs have both natural and supernatural causes. The snake is “an evil spirit” (Participant 8). Snakes are harmful, dangerous, treacherous, and vengeful towards humans; thus, an enemy to be fought. A fair reason for snakes to attack and bite a person is having been offended or disturbed in their natural space. An inattentive or careless person may step on a snake or get too close to it, and thus be attacked. It is very difficult to avoid this type of bite, “unless you do not leave your house” (Participant 5). SBEs can also have a supernatural cause, in two ways: According to the participants, the pajé is the only person able to discover whether snakebite has a supernatural etiology. For instance, a pajé can use tobacco and ayahuasca tea in rituals with protective chants that involve community members. This ritual aims at expanding consciousness and clarifies the underlying real cause of the snakebite (Fig 2). Fig 2: Etiology of snakebites according the caregivers’ perspective.A) The pajé is the only individual able to discover whether a snakebite has a natural or supernatural etiology, based on rituals including tobacco and ayahuasca. B) The natural cause of snakebites is related to attacks and bites of snakes disturbed in their natural space. An inattentive or careless person may step on a snake or get too close to it, and be attacked during their work in the forest. C) The supernatural cause of snakebites is mediated by a sorcerer’s spells. In this case, an indigenous person orders a sorcerer to arrange the death of his enemy or competitor via a snakebite envenoming. ## Preventing snakebites A snakebite is an unpredictable event; generally, no one is walking through the forest and thinks they are going to be bitten. In addition, the snake camouflages itself very well, it knows how to remain invisible to humans in the midst of the foliage. Therefore, SBEs could be prevented only if individuals are very attentive and careful during activities in the forest—not because they could see the snake and keep a distance from the potential aggressor, but to kill the snake before the attack. All the participants said that killing the snake before being attacked would be the best way to prevent SBEs. Indigenous villagers carry a machete with them for their activities in the forest, and this same tool is of great use for killing snakes. When one has to keep one’s mind occupied searching for food or other essential materials for survival, it is difficult to continuously keep your eyes on the ground. According to the participants, personal protective equipment such as boots and leggings are little used by indigenous people. According to them, they would accept using this type of protection if they received it. However, some quotes show that this equipment does not ensure total protection from envenoming, since "as the snake’s fangs are very long, worse than an injection needle, if it bites, you have had it” (Participant 3). In addition, “even if the snake’s teeth do not reach the individual’s skin, the venom will contaminate the boot, which will be unusable and must be discarded” (Participant 7). Some rituals were mentioned by the shamans are performed to protect individuals against snakebites. These rituals are performed in the village with the assistance of an indigenous caregiver or by the individual or family members. The rituals are briefly described in the Fig 3. Fig 3: Ritual performed by indigenous villagers of the Alto Solimões River area, western Brazilian Amazonia, for protection from snakebites.A) Prayers, chants and smoking. Prayers and chants were widely cited by the indigenous caregivers as viable forms of protection, and can be done by anyone before entering the forest. “During collective prayer rituals, snakes will reunite to know what is happening in the ritual, and understand that the people will be protected (Participant 3).” However, if the villagers use prayers and songs as a joke, they will be harmed. Rituals of protection may also include smoke from the burning of dried tobacco or garlic leaves, and bee and wasp hives. In this technique, with prayers, chants and smoking, people and environments are protected against all illnesses, including SBEs. The procedure must be repeated by the indigenous caregiver from time to time to renew the protection. B) Rituals of ‘acauã’, the snake-eater falcon. The laughing falcon, also called snake hawk (acauã or águia-cobreiro, in Portuguese Herpetotheres cachinnans L.), is known for its characteristic song (changing from a joyful to a sad sound) and for feeding on snakes. Participant 4 explained this ritual. At the new moon, one of these falcons is killed, and its feathers, beak and talons are removed, burned and used to make an infusion. A spoonful of this infusion is given to a newborn baby. This baby will grow up protected from snakebites as will acquire the look of a falcon. As falcons have excellent vision for capturing snakes in the wild during a flight, snakes will not be able to resist the falcon’s gaze, and will be chased away. The imitation of the falcon song by the indigenous people and chants about these snake predators are also used for protection, for yourself and for a loved one who is going to go into the forest. The falcons are recruited to take care of this person. C) Blessings of the legs using snake simulacrums. Some vines and bush stems have features reminiscent of snake skin. These simulacrums are used to beat the legs of individuals, especially children, who are going to start carrying out activities in the forest. This blessing promotes the protection of this child throughout life. The ritual "scares the snake away (Participant 6)". "The snake does not come close to him. When the jararaca looks at the person, it runs away (Participant 8)”. Participant 8 reports that she uses this form of protection for her children and husband, and recites the imperative "Don’t bite, snake, don’t bite, snake, don’t bite, snake!", while performing the ritual. D) Amulets made from snake parts. Participant 4 explains that wearing a necklace with a bushmaster fang brings protection from evil and from the attack of snakes. The same pajé also quotes another amulet, a stone or pearl that is extracted from the inside of the boa constrictor’s head. This stone has the power to hypnotize animals, such as birds and other prey, that would be attracted to the bushmaster’s mouth. A human who carries this stone would have the same power as the serpent, and not recognized as a different one, i.e., a potential prey. Additionally, "the amulet will increase the visual force of its carrier, saying ‘you are mine’ to the prey via thought (Participant 4)”. She explains that the bracelet works as a shield, and the pajé recognizes an evil person who comes close to her. In the case of SBEs caused by the work of sorcerers, only a pajé can identify the cause and reverse the situation. Participant 1 reports a case in which the target of the spell was his uncle, who was also a sorcerer. Sorcerers, being people that are feared and hated by their enemies, are frequent targets of sorcery. On a visit he paid to his uncle’s house, he almost stepped on a small jararaca, but was not attacked. He was not attacked as the snake was not assigned to him, and was determined to find its target, his uncle. Upon seeing the urgent situation, he used a machete to kill the snake. He can only kill this snake used in sorcery because he was a good person. A person who was not good could also become a victim of the snake, which would continue to search for its target. ## Onset of symptoms The snake is not always seen after the victim is bitten, so pain is the hallmark of snakebite envenoming. A numbness of the leg may occur, and the victim is no longer able to suspend this limb. The pain rises from the bite site and spreads throughout the body. Actually, pain in the affected limb is not even the main complaint immediately after the bite. Severe headache combined with dizziness is cited as a sign that appears quickly after the bite, as a result of the disorder triggered by the envenoming. The ill person may become lethargic, starts to moan and scream in pain, begin vomiting, can’t walk or see, and may pass out abruptly: “It looks like the person is dying (Participant 1)”. “ Usually they fall to the ground”, said Participant 2. Bleeding from the fang marks and from other orifices and spitting and vomiting blood are signs that appear quickly as well. Regarding the bleeding, Participant 6 said the following about a child he saw: ## Pathophysiology From the indigenous caregivers’ perspective, SBE pathogenesis is a process that starts with the injection of the snake’s venom, which is responsible for the onset of the disorder. If the spread of the venom through the body is not promptly stopped, by a tourniquet for example, there is a progression of the illness, and systemic symptoms such as headache, dizziness, and bleeding will appear, with the possibility of death. The manifestations of the disease are associated with the rotting of the limb, which can progress to the bones and blood, until the person dies. If the venom penetrates the body too quickly, as in cases of sorcery, or if the patient takes too long to be treated, allowing time for the venom to spread, there is nothing else to do, and death will be certain. Envenoming caused by sorcery are more severe because the venom will be strengthened by the harmful elements invoked by the sorcerer, such as “tobacco smoking spiritually manipulated”, explained Participant 1. ## Severity and prognosis After the onset of symptoms, the main determining factor for the prognosis of the case is the underlying cause of SBEs. Except in cases in which the indigenous caregiver manages to intervene early in cases of sorcery, snakebite victims die instantly, or very quickly after being bitten, without time to be treated. In parallel with the treatment, a series of dietary and behavioral interdictions are employed to prevent complications to the patients in the three months following snakebite (Fig 4). The indigenous caregivers understand that disobedience to these rules can aggravate the case, with the return of pain, swelling and bleeding, as the limb starts to rot again. In other words, relapses may result from the non-observance of these rules. Fishes are the main source of protein among indigenous villagers, and the exclusion of many fish species from the diet is what characterizes the major dietary changes to be implemented. Fishes with a long and fusiform body (aruanã or sulamba—Osteoglossum bicirrhosum Vand; traíra—Hoplias spp.), sharp teeth (piranha–Serrasalmus rhombeus Linnaeus), or with stingers (mandi–Pimelodus spp., surubim—Pseudoplatystoma corruscans Spix & Agassiz, bodó—family Loricariidae, pirarara—Phractocephalus hemioliopterus Bloch & Schneider, piramutaba—Brachyplatystoma vaillantii Valenciennes), cannot be eaten. The prohibition can also be due to other characteristics of the fish, such as aggressive habits, feeding behavior, smell, or other anatomical characteristics. Other fish species prohibited for SBE patients are pacu (family Myleinae), piau (Leporinus friderici Bloch), pirapitinga (Piaractus brachypomus G. Cuvier), pirarucu (Arapaima gigas Schinz), and branquinha (Psectrogaster spp.). Participant 2 explains the following: 1) Piranha, with its sharp teeth, “eats the flesh from the inside, inflaming the flesh inside the wound. Then, the leg swells”; 2) Catfishes with their stingers “look like they’re puncturing the flesh, then it starts to inflame as well”; 3) Sulambas, as well as the snakes, are descendants of the Cobra Grande (Big Cobra), a mythological creature that is an enemy of humankind. Fishes that can be eaten without restriction are sardines (Triportheus spp.) and curimatã (Prochilodus spp.). Pork and tapir meat, very salty or fatty dishes, are also prohibited. The meat of venison and wild birds in general are allowed. Fig 4: Some dietary and behavioral interdictions used to prevent complications arising from snakebites.A) Fish are the main source of protein among indigenous villagers, and the exclusion of many fish species from the diet is what characterizes the major dietary changes. Consumption of these forbidden foods by a snakebite victim, causes rotting of the affected limb, increases pain and swelling, and restarts bleeding, which can lead to limb paralysis, amputation, and even death. Fishes with a long and fusiform body (traíra—Hoplias spp.), sharp teeth (piranha–Serrasalmus rhombeus Linnaeus), or those with stingers (surubim—Pseudoplatystoma corruscans Spix & Agassiz), cannot be eaten. Tapir meat is also prohibited. Sardines (Triportheus spp.) and curimatã (Prochilodus spp.) can be eaten without any restrictions. The meat of wild birds in general are allowed. B) Contact with pregnant women is totally contraindicated for snakebites patients. Thus, it is common for indigenous people bitten by snakes to move away from the community to avoid contact with women in general, and even avoid eating a meal prepared by a pregnant woman. Contact with a woman who is menstruating or who has recently had sex with her husband, and pregnant women is totally contraindicated for snakebites patients up to three months after the bite. The rule extends to contact with the husband who had intercourse with his wife and to the husband of the pregnant woman. This includes being taken care of by her, getting close to her, and listening to her voice, looking at her or being looked at by her, or eating a meal prepared by a woman in any of these situations. Depending on the indigenous caregiver, this rule is valid for the entire family of the patient. The outcome for patients who fail to comply with this rule is the worsening of the case, and culminates in death: “A man was bitten by a snake, he was already well, 30 days after the bite, but suddenly a woman (referring to a pregnant woman), he didn’t even look at her, he only heard her voice when she passed by. Not five minutes later, the man started screaming. This worried us a lot. As always, at the time, we worked very hard, and we had all the preparations, we had the preparations: feather, everything from the hawk, we went to arrange things and drink this vine (referring to ayahuasca tea) for the ritual, but he died (Participant 4).” “When the woman is pregnant, don’t go near her. People die! You’ve got to stay away. If it’s five meters away… If the woman gets close to him, blood starts coming out of his teeth, mouth, sometimes nose, then it’s worse” (Participant 5). Participant 7 explains about the danger of a pregnant woman who has contact with a SBE patient. In this case, the ‘little animals’ (he says that these little animals are the snake’s offspring) that are in the envenomed patient pass to the pregnant woman, and she starts to have intense pain. The indigenous caregiver will need to intervene in these cases, and the ritual to reverse the disease in this pregnant woman lasts a whole day. Lastly, snakebite patients should not have a sexual intercourse during the three months of treatment, due to the risk of the same complications as mentioned above. ## Treatment SBEs generally occur in the forest, during fishing, hunting and forestry, and affected individuals require an immediate transfer to the village. In the village, the therapeutic itinerary may be planned depending on the patient’s severity and the perception of the different actors, and transport to the hospital is started whenever possible. In the hospital, the patient will receive medical care, especially antivenom treatment, and then return to the village to continue the treatment with the indigenous caregiver (Fig 5). Fig 5: Therapeutic itinerary of an indigenous snakebite patient from the perspective of the indigenous caregivers from the moment of the bite to rehabilitation.Snakebite envenomings occur mostly in the forest, during fishing, hunting and forestry. At this time, self-care practices include the use of tissues of the dead snake that caused the injury. Bitten individuals require immediate transfer to the village, and rituals and plant-derived medicines and bile are part of the therapeutic arsenal. In the community, the therapeutic itinerary may be planned depending on the patient’s severity and the perception of the different actors in the village. In some cases, there may be a refusal to go to the hospital, or there are no means of transport available (1). However, transport of the patient to the hospital is done almost whenever possible (2). In the hospital, the patient will receive medical care, especially antivenom treatment, and then return to the village to continue the treatment with the indigenous caregiver in the rehabilitation phase. Dietary taboos, and sexual and social interdictions are noticed throughout this itinerary. ## Immediate self-care after SBEs After an SBE, some therapeutic strategies are available for self-care such as the use of tissues from the perpetrating snake itself, which is killed whenever possible. A piece of snake meat can be placed over the fang marks to “suck out the venom and minimize pain” so that the patient can be transported to the village. If the venom that the snake carries “doesn’t hurt for her, it doesn’t hurt for us”, explained the shaman Participant 8. Still using the dead snake’s body, its skin can be tightly tied to the affected limb, making a tourniquet, with the aim of preventing “the venom venom from spreading up into the body”. Alternatively, bitten individuals can use a cloth or piece of vine collected locally to make a tourniquet. The ingestion of the dead snake’s tissues and fluids (meat, heart, liver, tail tip, and bile) is also a treatment strategy that can be used by the bitten individual. To go out hunting or fishing “you can also take with you painkillers such as metamizole and paracetamol and the bile of animals” (Participant 1). Participant 1 recommends: “Kill that snake. Then you take a little bit of his meat, a little bit of his meat, then you put it where the fang is (referring to the bite marks), you know? Then, this meat sucks… its venom. Sucked, sucked, sucked, sucked like this. Put a little on. Then, with a little, it stopped, right, the pain”. “Take off the skin. Tie it where, where is the pain like this, its skin. Take it off and tie it so it doesn’t spread up (the venom) into your body, like this. Tie it tight. Tie it.” "That solves it." “And if, if you can stand it, to stand it, to stand it (when arriving in the village), take a little more and eat the meat”. "That’s to stop the pain." After the primary care, the individual should make an effort to get to the village as quickly as possible. At this moment, his companions are recruited to help him make the journey. Participant 3 emphasizes that “it is important that hunters always walk in twos because, if something happens to the partner, there is the other one to help, to carry him”. Participant 5 confirms this by saying that “if the guy gets sick when he is out there in the woods… in the igapó… the guy has to shout for others, for other people to go look for him…” ## Care in the village Severe SBEs can be transported straight to the hospital, without care by the indigenous caregiver. However, if there is time, the indigenous caregiver can employ several treatment strategies, including rituals and medicines. As SBEs can have natural or supernatural causes, it is necessary that the treatment is comprehensive to contemplate these two possibilities. To decide on the best therapeutic strategy for a specific patient, the indigenous caregivers can use the ayahuasca tea with chants involving the villagers. Clarifying the underlying cause of SBE, such as the orders of sorcerers to kill an individual by snakebite, is key for therapeutic prescription. “Then we went to get the plants (referring to ayahuasca). There, women and men singing all the rituals of nature… You won’t hear any kind of negative things because nature will rebuke evil, because we fight against evil. That is our goal.” ( Participant 3) An important therapeutic resource used by the pajés is the use of tobacco (Nicotiana tabacum L.) by the smoking technique, in which the indigenous caregiver exposes the patient to the smoke generated by the controlled burning of the dried leaves of the plant. Smoking aims to “to suck out the snake venom” and remove disturbing fluids from the patient, their cohabitants, and places, and to attract beneficial energy from the natural elements, reversing them for the good of the patient. In this ritual, tobacco may be combined with other substances, such as the hive of the jandaíra bee (Melipona interrupta Lat.), breu-branco resin or oil (Protium heptaphyllum March.) and cachaça (a distilled spirit made from fermented sugarcane juice). Smoking combined with prayers is part of the healing repertoire that will continue until hospital discharge and patient’s reintroduction into the community, as the SBE can be the result of the evil eye, envy and spiritual aggressions. A Kokama caregiver (Participant 3) explains the carrying out of a ritual to treat snakebite patients, though one that is no longer used. In this ritual, the victim and other villagers paint themselves with natural paint made with korimã (jenipapo, in Portuguese; fruits of Genipa americana L.). Community members carve pieces of wood into the shape of snakes. The pajé will then announce that the next day the patient will be able to walk. On the patient’s face, a figure is drawn, in the shape of an S, representing a snake. In a kind of theatrical staging, tree branches are used to beat wooden snakes. The pajé emphasizes that at that time “the snakes will be afraid”. Everyone participates singing and walking through the woods on the banks of a river. Everyone watches the victim bathe in the river: the venom from the patient’s body is transferred to the water. Venom will thus be diluted in the river and carried away by the current. At the end of the ritual, the victim comes out of the water happy and greets the people who watched him: “God will have watched over the sick person”. In this phase, the study participants quoted as medicines to treat snakebites the i) ingestion of a few drops of bile from the surucucu (bushmasters, Lachesis muta L., or adult Amazonian lancehead, Bothrops atrox Wagl.) or lowland paca (Cuniculus paca L.); ii) ingestion of a preparation based on the wall of anthills, crumbled and mixed with the sap of young leaves of the açaí palm (Euterpe oleracea Mart.) and capeba leaves (also called pariparoba, Piper umbellatum L.); iii) ingestion of infusions of copaiba (Copaifera langsdorffii Desf.) and ginger (Zingiber officinale R.). Some of these oral preparations have emetic properties, and, according to the pajés, they act by expelling the venom from the patient’s body. ## The role of the hospital Nowadays, some SBE patients, especially the most severe ones, are sent straight to the hospital. Some villages have community health centers, with indigenous health agents who will provide first aid to patients, administering painkillers, and arranging transport to the hospital in the city. The referral of the patient without the assistance of the indigenous caregiver is understood in different ways: some who live in areas closer to the cities, in greater contact with non-indigenous society, no longer seem to worry about it, while others resent the fact of this structural aspect of the culture being lost. However, none of the caregivers are opposed to patients seeking medical treatment in the hospitals. Participant 4 explains that the patient must be referred to the hospital “for his safety, that many times, he can have other problems, some organic problems, like that, in the body. So, he can have a problem and in that problem, he can have low immunity, he can get worse and not resist death.” Moreover, antivenom is cited as the treatment developed by doctors against snake’s venom present in the patient’s body. ## Care when returning to the village Hospital treatment is not understood as the final stage of treatment for snakebites. A re-establishment of a sense of well-being by the reintroduction of the individual into social life is planned upon the patients return to the village This phase of "radical cure" of the envenoming usually lasts 3 months, and aims at the organic, social and psychological rehabilitation of the individual. The indigenous caregiver seeks resources in rituals, the same as described above (tobacco smoking and ayahuasca) and new classes of medicines. At this time, the application of massages and compresses to the affected limb has a prominent place in the patient’s recovery. Procedures include massage with animal fats (guariba monkey, jaguar, alligator, stingray, anaconda), topical use of copaiba oil, and bandages with latex extracted from the sandbox tree (assacu, in Portuguese; Hura crepitans L.), guariuba (guariúba or capinuri, in Portuguese; Clarisia racemosa, Ruiz & Pav.), and milktree (pau-de-colher, in Portuguese; Tabernaemontana echinata Vell.). Medicines for oral or topical use made from bitter plants are also indicated at this stage. The sap of the leaves of cubiu (Solanum sessiflorum Dunal) and capeba (Piper umbellatum L.) were the most widely recommended. The sap obtained from the leaves Kalanchoe pinnata Lam. ( língua de pirarucu or corama, in Portuguese), Justicia acuminatissima (Miq.) Bremek (sara-tudo, in Portuguese), Inga spp. ( inga tree), and Echinodorus grandiflorus Cham. & Schltdl. ( chapéu de couro, in Portuguese) were also quoted. Infusions prepared with the bitter bark of the mango tree (Mangifera indica L.), yellow mombin tree (Spondias mombin L.), and orange tree (Citrus sinensis L.) are also on this list. Participant 2 explains the mechanism of action of bitter plants as follows: “It’s her sap, because it’s strong, it’s a pungent taste, it’s bitter… That’s it, with that it heals, it cuts the effect of the snake’s venom. After the person takes the snake antivenom, then it (bitter plant) helps combined with the antivenom” "It heals the bite site." “With that, it soothes the pain.” “Soothes the pain. That’s why we use these plants up there.” Some medicines require more details regarding the technique involved in their preparation, as participant 3 explains about the cubiu leaves: “Take three cubiu leaves. Then, you knead them well knead, extract the juice, strain it well, and put it in a liter of water. Give the patient about three glasses. It doesn’t matter that it has already been four days, five days from bite. So, he/she will drink the juice. We sweeten it with very sweet sugar and then you put oil in it, food oil or electric oil, well-seasoned with oil. Then, it’s ready. Then you will give it to the patient who was bitten by a snake. If he’s about to die, but if God allows… But if you drink that… Even if you’ve already taken snake antivenom…” The same participant mentioned some cases in which he successfully used the cubiu medicine. In one of them, a young man was bitten on the leg by a snake, and was hospitalized for five days in the city. According to the caregiver’s report, the doctor released the man “even in a severe condition because there was nothing else to do in the hospital”. Then, the man returned to the village, silent and quiet as a result of the SBE: the boy’s father said he did not know “if he was alive or dead” when he sought help from the indigenous caregiver. Although the “leg was already rotting and purple”, after starting treatment with the medicine, he felt fine, the pain stopped, the swelling subsided and he stopped coughing up blood. According to the pajé’s prediction, after three days, the young man would walk again. And so it happened: on the third day the boy started walking slowly and went out to urinate in the backyard of the house. “ Only through God was it possible for him to get well”, as the indigenous caregiver said. ## Indigenous caregivers and their role The way in which the participants see themselves in relation to their healing practices and recall the constitution of knowledge and skills linked to them, represented two relevant topics in this study for the understanding of the uses of indigenous medicine in the treatment of SBEs in the villages where they circulate. As for the first topic, participants recognize themselves as healers, pajés, prayers, or practitioners of natural medicine. In their quotes, the definitions of these terms present a polysemic condition, which results from variations in attributions, knowledge and skills corresponding to healing and protection practices, even within the same ethnic group. In addition, positive and negative connotations were attributed to them, depending on the value of the attributions, the talent to master healing and protection practices and the good or bad handling of supernatural forces. Considering the Tikuna ethnicity, according to Participant 1, the attributions of a healer and a pajé are more relevant to the extent to which they can cure and protect people and combat witchcraft. A prayer, on the other hand, although he can practice healing and protection, has less strength to fight sorcery because he is not able to discover whether the SBE has a supernatural etiology. Participant 8 associates attributions of healing, shamanism and prayer. In her narrative, this action, as well as the volume of demands for her care, is perceived by villagers as a demonstration of her talent to master healing and protection practices. Regarding the management of supernatural forces, Participant 1 presented the figure of the sorcerer in opposition to healers, pajés, prayers, and practitioners of natural medicine. He reported that a sorcerer knows how to harm someone by wielding supernatural forces, such as spirits, and that he can cause an SBE by using tobacco to govern a snake’s behavior and potentiate its venom. Finally, it is worth noting that Participant 5 stated that in his village the pajé is seen with fear, distrust, and prejudice, similar to the figure of the sorcerer. Regarding the second topic, it is worth noting that this plurality of identities is reiterated in the narrative of the indigenous caregivers about the way in which they constituted themselves as healing agents throughout their lives and in the multiplicity of their knowledge and practices. Considering the Tikuna caregivers (Participants 1, 2 and 8), and the Kokama caregiver (Participant 3), it was observed that healing abilities were perceived by themselves and family members already in childhood. Knowledge in this regard, therefore, was transmitted gradually by older family members, following the logic of a dispersed learning that is subject to everyday events. Participant 8, however, indicated the increase in a learning agenda, since in her early youth she would have spent two years accompanying her maternal grandfather, also a caregiver, in order to learn the practices. From the circumscription of these milestones in the course of the lives of the indigenous caregivers (the shared perception of healing skills and the time taken to acquire knowledge for the practices), it can be inferred that the caregivers are also political agents in their villages. In other words, authority as healing agents was produced in the narratives and interpersonal relationships. According to Participant 2, in his first performance as a caregiver, he was sought out by a mother with a sick child. He asked her three tim“s: “Do you believe me?” And he added: "If you believe me, I’ll make your daughter well". As for the multiplicity of knowledge and skills that constitute healing practices, the variety of elements used in medicines and rituals, differences in food taboos and prohibitions, and cultural hybridity are notable. In his rituals, Participant 1 makes medicines from the bitter barks of trees and fruits, blows tobacco cigarette smoke to expel the snake’s venom and clean the wound, and repeats prayers. In the treatment of the sick person, he indicates the prolonged adoption of food taboos and sexual prohibitions, as well as the application of drops of bile directly on the wound to relieve the pain. He uses smoking to also promote protection of the indigenous person. Participant 2’s healing ritual also involves prayers and smoking. In the treatment, the tongue of the pirarucu and bitter plants are used to make medicines. He indicates food that should be avoided, but does not recommend prohibitions of any kind. Although he uses prayers, blowing smoke to protect the body from illness, he reported that there are no supernatural ways to protect yourself from snakebites. The most effective way would be to avoid going out at certain times and paying attention to where you step. Participant 8, like the two aforementioned participants, uses copaiba leaves and ginger tea in her rituals, as well as Catholic prayers and blowing on the wound with tobacco to relieve the pain. She determines food taboos and prohibitions related to pregnant women. Participant 3 indicates that the best protection against snakebite is to kill the snake. In her rituals, he uses the cubiu leaf, prayers and chants of her ethnicity. In addition, a role-play in which all villagers participate can also be added to the healing ritual. Finally, it is worth mentioning cultural hybridity. In the narratives of all the participants, it was noticed that the healing practices related to indigenous medicine are influenced by previous contact of the interviewees with non-indigenous people. And, although practices related to Catholicism were narrated more often, others were mentioned, such as divination through prayers and the use of ayahuasca tea, which was defined by Participant 1 as a drink that reveals unknown things. ## SBEs among indigenous villagers, a breakdown of the physical and social bodies The few ethnographies available report an ambiguous relationship between indigenous people and snakes. Among the Tikuna, the great snake (Yewae) is the possessor of the fish tree (Ngewane), an enchanted tree that exists since the beginning of the world. When the weather is rainy and windy, the leaves of this tree fall and small eggs, similar to frog spawn, begin to appear on its trunk. The eggs will metamorphose into caterpillars. The caterpillars grow and descend to the trees’ roots because of the lightning and thunders. The rain increases and the caterpillars come out transformed into various types of large and small fishes. The fish spread through the waters and take over water streams, lakes, igapós, and rivers, serving to feed people [24]. Similar myths are apparently shared by different peoples of the lowlands of the Amazon. The Baniwas, in the Negro River region, for instance, contend that fish and snakes are descendants of the Big Snake: fish and snakes are both enemies of humanity (walimanai), but people also depend on them for food. The creator god (Niãpirikoli), fights against snakes, killing them or expelling them from the lakes to provide space for humans. While defeating snakes facilitates human access to fishing sites, it simultaneously reduces the abundance of fish [25]. This constitutive association between fish, an essential food, and snakes, an enemy, marks the constant pursuit for balance between the different natual beings. The disrespect to this order affects humans in a harmful way, and they can be threatened by other humans, such as enemies directly or through sorcerers, or by non-humans, such as snakes, and extra-humans, such as the spiritual entities of the forest. The causes of SBEs vary in the explanations of the pajés, and even within the same ethnic group, and can be interpreted as an accidental encounter with an aggressor agent, or as a consequence of breaking with traditions, which makes the individual more susceptible to the snake’s attack. In the words of some caregivers, this increased risk of being bitten is explained as a form of punishment. Finally, the snake’s attack can be an effect of sorcery that is directed at an individual for various reasons, such as when their prestige increses in the village, if they accumulate properties, and in case of disagreements. SBEs as a punishment for inappropriate behavior or caused by supernatural causes, such as spells, has also been reported by traditional caregivers in Ghana [26]. In addition, the belief that SBEs caused by a curse or witchcraft are more lethal than natural SBEs has also been observed in traditional caregivers in Ghana and Eswatini [27,28]. Indigenous villagers’ experiences of SBEs are not just a disturbance of their body physiology. The transition from a healthy state to an illness involves a biological change, but also changes the social status and identity of the patient and the community. More than just an unlucky encounter with an aggressive venomous agent, SBEs are part of the indigenous imagination, and require a series of collective obligations from villagers, from childhood and throughout life, for prevention and treatment. Thus, the rotting of the body that is caused by an SBE, which can generate disabilities and death, is the outcome of the disrespect of traditions that organize the social and political life, and include social and environmental interdicts, and dietary taboos. The fulfillment of the aforementioned precepts keeps the individual in harmony with the social and political organization and enables the indigenous person to return faster to their role in the village. All prevention and treatment strategies are based on maintaining or restoring the balance of the sick person, because the process of healing the body to return to collective experiences is a goal that depends on the commitment of many people. In the EM presented here, the SBE is an ‘illness’, not a ‘disease’, from the perspective of the pajés and indigenous villagers. According to Kleinman [23], disease is a health problem from the biomedical perspective, and is reconfigured only as a change in the biological structure and functioning. This actually occurs in the pajés’ comprehension, and local effects and systemic manifestations resulting from unclottable blood are understood as the outcome of the venom spreading through the body. However, the way of interpreting SBEs by the indigenous caregivers evokes a broader experience of symptoms and suffering; to the sick person and the members of family and villagers, the ‘illness’ is a breakdown of social ‘body’. During the illness, the sick person is temporarily exempt from performing ‘normal’ social roles (such as hunting or fishing) but is expected to see being sick as undesirable and so they are under the obligation to try and get well as quickly as possible, cooperating with the advice of the indigenous caregiver in order to get better by complying with the social, environmental, and dietary interdicts. ## Body construction, prevention and treatment of SBEs For indigenous societies, the body is built in relation to the environment and to other villagers. The body is the synthesis of natural elements and an equalizer of substances, in a constant search for balance, in a structure that can only be understood within the cosmology of these peoples. As the boundary of the body is neither clear nor constant, several disturbances by natural and supernatural aggressors transform this body, leading to illness. Thus, the prevention of an SBE should be interpreted as keeping indigenous individuals within the limits of the human condition, in a continued effort, which requires great caution in performing daily activities, food and sexual interdictions, and participation in rituals. Body transmutation, continuously invoking or permanently incorporating components from nature’s entities, explains the mechanism by which preventive practices against SBEs work. In this sense, if it is possible to incorporate components of the body of a snake predator, such as a falcon, into the human body, protection will occur through the ability of these individuals to have more accurate vision in order to circulate in the forest, and to scare away snakes, which will see the indigenous person as a potential risk. Another strategy is to incorporate characteristics of the snake itself by using amulets made from snake parts, or by participating in rituals performed with snake simulacrums. Apparently, the physical characteristics and malice of a snake merged into the human body will benefit the individual and protect him in his activities. In the case of either deaths or worsening, persistence or appearance of new symptoms, the ultimate cause of the SBE will be reviewed by the caregiver in an attempt to relate the patient’s particular illness to his physical and social environment. Sorcery can be identified in this way, but undoing the spell may not always be possible. Sorcerers use various forms of supernatural techniques to bring misfortune to others, for which the specific invisible cause is not always discovered by the pajés in order to intervene on behalf of the victim, to eliminate the cause by counter attack. At this level, the cause of the disease is divorced from the snake’s position in the ecosystem, from a western scientific perspective, and prevention will focus more on cosmological or social causes. However, preparing the body of these individuals to make them less vulnerable becomes a complex challenge without accurate knowledge of the underlying cause of the threat, which is the exclusive knowledge of the sorcerer. Rituals with blessings and chants, and with tobacco, act at the level of recording the underlying cause of the SBE, and not producing a barrier against the snake itself, and are used as rites for the fabrication and transmutations of the person. The use of tobacco deserves to be highlighted due to its common use in protection and healing rituals among the indigenous peoples of the lowlands of South America. Lévi-Strauss points out that tobacco is a food in the spirits’ conception, and thus participates in the constitution of the body, providing the ability to communicate between man and the supernatural order (more intense in the shamans) and also functions as an ontological converter between humans and extra-humans [29,30]. As this author has already pointed out in this study, it was found that other substances share these same properties with tobacco (garlic leaves, bee and wasp hives, resins), ‘sucking out’, in the language of the caregiver, the venom inoculated by the snake and rebalancing the sick person’s body. ## (Symbolical) Efficacy of the SBE treatment prescribed by the indigenous caregivers The biomedical healthcare systems focus on the individual in an instrumental disinterested universe; while, in the indigenous system, the individual is part of a collective structure in an intentioned cosmology [31,32]. If the healing is cosmic in the indigenous tradition, in the case of biomedicine, it is chemical [32]. This way of understanding the world reflects on the clinical reality of indigenous societies, and on the way of measuring treatment efficacy. The healing process conducted by the caregivers is imbricated in a structure of trusting relationships between the caregivers, the patient and the community. Thus, it is interesting the way in which the participants of this study emphasize the effectiveness of their practices, and stress their positive results in detailed narratives. In this sense, the ritual’s efficacy has three complementary components: the sorcerer’s belief in the effectiveness of his techniques; the patient’s belief in the sorcerer’s power; and the faith and expectations of the community [33]. In addition, the efficacy in healing is largely attributed to the performative aspects of ritual, since the caregivers enact the healing process by calling upon a number of esthetic resources (their chants and prayers), to create a heightened and engaged experience of the participants [31,32]. The attributes for the purpose of healing are generally invoked by the pajés from sensitive characteristics of several elements of nature, such as plants and animals. As we observed in this study, the results of the use of a plant or parts of animals are not always expected from its ingestion or topical use, but from its addition to tobacco for smoking or even in rituals. Rituals with chants and blessings include plants and animals that have an attribute considered by the indigenous people as appropriate for the purpose of healing: smell, shape, viscosity, bitterness, sourness, color, and texture [31,34]. The incantations are recited over an intermediate object whose essential function is to provide the incantation with a material support and serve as a vehicle for the therapeutic power, and transfer it to the patient [35]. In this study, bitterness, a crucial characteristic for treatment of SBEs by using plants and bile, is also required in models of efficacy of other ethnic groups, such as those of the Matsigenka [31]. Thus, many bitter plants are used as enchantment vehicles intended to heal wounds of different etiologies, such as SBEs. Although the symbolic properties are of greater interest to indigenous people, these plants commonly produce tannins as secondary metabolites, whose astringent and healing properties could have an effect on the injury caused by snake venom. Moreover, the efficacy of the SBE treatment due to an inhibitory activity on snake toxins cannot be completely ruled out, since many plant-derived molecules have had this ability demonstrated in vitro [36]. However, according to our findings, explaining the efficacy of the SBE treatment prescribed by caregivers in the study area in terms of the particular chemical composition of the plants used as vehicles for incantation does not do justice to the indigenous conception of therapeutic efficacy. It should also be noted that the therapeutic failure, represented by the aggravation of the case, recurrence of symptoms, and even death, is apparently not a reason to question the validity of a treatment method, nor the healing power of a shaman, as already mentioned for the Desana of the Negro River [37,38]. Some SBE cases are caused by serious violations or antipathies (sorcerer-mediated SBEs), or are aggravated by someone not respecting behavioral prohibitions and dietary rules, which set in motion attacks in an intentioned universe characterized by visible and invisible beings. Contamination by female blood or other fluids is also perceived as a break in the balance between psychic instances or humors and qualities of the body, and weakens patients in recovery, which can make them disabled or even lead to death [25,35]. Furthermore, in many indigenous societies, the tendency is to explain any death that interrupts a person’s normal life cycle, before old age, as caused by sorcery [35]. ## Boundaries between indigenous and biomedical domains Within the context of the restructuring of indigenous health policies over the last 20 years in Brazil, and the consequent creation of the Special Indigenous Health Districts from 1999 onwards, the inclusion of indigenous health agents, which are selected among the villagers themselves, appears as a central element of this model [39]. However, contact between different health systems often leads to some kind of conflict or competition [40]. Regarding SBEs, the use of folk medicine and traditional self-care practices are often recorded around the world as the cause of late medical assistance and poor prognosis [41–44]. Our results do not support the simplistic thought that the belief in the efficacy of traditional practices delays the decision to seek the health service in indigenous villagers, as indigenous caregivers are not against transporting patients to the hospital; on the contrary, they recommend referral in these cases. Thus, indigenous caregivers can serve as a point of contact between indigenous and western medicines. The practices carried out by the shamans for the rehabilitation of the SBE patient after hospital discharge are seen as complementary and necessary by the participants. This combination of practices for rehabilitation of SBE patients was also observed in Eswatini, whose main reasons that led patients to resort to traditional caregivers for rehabilitation were the supposedly unsatisfactory treatment in the hospital [28]. The exclusive use of traditional practices may therefore be related to difficulties of access, rather than socio-cultural variables generally considered as "cultural barriers". In fact, this study revealed that indigenous medicine is adaptable to the introduction of biomedical practices and technologies, such as antivenom treatment, postponing the stage of social and body rehabilitation of indigenous people until after hospital discharge. Transfer to hospital actually may have a greater effect on the therapeutic itinerary prescribed by the caregivers than on the biomedical system, in addition to removing the indigenous patient from their community, temporarily undoing affective ties and with the cultural reality. The introduction of indigenous healing practices in the hospital environment could partially resolve this ambiguity. However, conflicting attempts between pajés trying to exercise their healing practices in hospitals and health professionals have been recorded in the Brazilian Amazon, with the need for a judicial resolution to ensure the treatment requested by the indigenous patients [45]. The high burden of SBEs among indigenous populations, combined with the structuring of a health system that provides care coverage to many villages, has raised the possibility of decentralizing antivenom treatment to indigenous districts [3]. This would increase access by indigenous groups to proper healthcare, respecting the nexus of these individuals with their territorial, social, and clinical reality. Recently, a care package guideline was validated with the participation of health professionals working in the Amazon region, an essential step for training health professionals that work in indigenous healthcare units [46]. Thus, antivenom treatment would be inserted as a life-saving tool in a world of diverse social, natural, and supernatural representations, in combination with indigenous medical practices. ## Limitations Regarding the criterion of theoretical saturation of qualitative data, this study probably did not reach complete saturation due to the sample size. This is a sample of an indigenous population that is difficult to interview due to the need to preserve the identity of the caregivers. During the interviews, the researchers realized that certain rituals were not described in full, and some participants declared that they kept some elements of the ritual secret in order to preserve their culture. The efficacy of the indigenous medicine is an interesting and complex question. In this work, as much as possible, we avoided classifying the procedures indicated by indigenous caregivers as effective, non-effective or deleterious or judging them in any other way. We believe that it is not up to researchers, at the risk of a colonialist attitude, to make judgments about ancient practices, especially those of a mythical-religious nature. Except for proven deleterious procedures, such as the use of tourniquets, the other practices still lack methodological resources to confirm or refute any benefit to the patient. In addition, we tried our best to respect the role that the caregivers play in their village, including the way they see themselves and the way they prefer to be addressed. ## Concluding remarks The understanding of SBEs as a sickness in Amazonian indigenous groups of the Alto Solimões River involves the continuous building of the body via proper ritual and behavioral acts, combined with the interaction with other humans and non-humans, including the supernatural ones. In this social and clinical reality, shamans and other indigenous caregivers have the ability to invoke the healing properties of plants, animals, minerals and supernatural entities through the performance of rituals of recognized symbolic efficacy in the villages. Diagnosis and treatment of SBEs are not aimed solely at curing the individual problem, but at converting a biological disorder into a social disorder that is highly mobilizing and that needs to be repaired. It is therefore a matter of analyzing the collective process at stake, which aims to modify or regulate political, economic, or social relations that unite or oppose individuals. The EM presented in this study, although a simplification of a complex clinical reality, in which the SBE represents just one of so many etiological representations of the disorders that affect humans, can serve as a basis for cross-cultural comparisons in the future, as well as for the planning of health actions that truly integrate indigenous and biomedical practices. Our results indicate that the actions of indigenous caregivers do not constitute barriers to the decentralization of antivenom treatment to indigenous health units, in pluralist though combined indigenous-biomedical therapeutic itineraries. ## References 1. Gutiérrez JM, Calvete JJ, Habib AG, Harrison RA, Williams DJ, Warrell DA. Snakebite envenoming.. Nat Rev Dis Prim (2017.0) 3 17063. DOI: 10.1038/nrdp.2017.63 2. Fan HW, Monteiro WM. 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--- title: Multimodal CRISPR perturbations of GWAS loci associated with coronary artery disease in vascular endothelial cells authors: - Florian Wünnemann - Thierry Fotsing Tadjo - Mélissa Beaudoin - Simon Lalonde - Ken Sin Lo - Benjamin P. Kleinstiver - Guillaume Lettre journal: PLOS Genetics year: 2023 pmcid: PMC10047545 doi: 10.1371/journal.pgen.1010680 license: CC BY 4.0 --- # Multimodal CRISPR perturbations of GWAS loci associated with coronary artery disease in vascular endothelial cells ## Abstract Genome-wide association studies have identified >250 genetic variants associated with coronary artery disease (CAD), but the causal variants, genes and molecular mechanisms remain unknown at most loci. We performed pooled CRISPR screens to test the impact of sequences at or near CAD-associated genetic variants on vascular endothelial cell functions. Using CRISPR knockout, inhibition and activation, we targeted 1998 variants at 83 CAD loci to assess their effect on three adhesion proteins (E-selectin, ICAM1, VCAM1) and three key endothelial functions (nitric oxide and reactive oxygen species production, calcium signalling). At a false discovery rate ≤$10\%$, we identified significant CRISPR perturbations near 42 variants located within 26 CAD loci. We used base editing to validate a putative causal variant in the promoter of the FES gene. Although a few of the loci include genes previously characterized in endothelial cells (e.g. AIDA, ARHGEF26, ADAMTS7), most are implicated in endothelial dysfunction for the first time. Detailed characterization of one of these new loci implicated the RNA helicase DHX38 in vascular endothelial cell senescence. While promising, our results also highlighted several limitations in using CRISPR perturbations to functionally dissect GWAS loci, including an unknown false negative rate and potential off-target effects. ## Author summary Genome-wide association study (GWAS) is a method designed to identify genetic variants that increase the risk to develop common human diseases such as heart attacks or schizophrenia. While powerful, this method has one major limitation: it cannot unambiguously pinpoint the genes responsible for the diseases. This important step requires investigators to experimentally test genes located near the variants identified by GWAS for functions related to the diseases of interest. Here, we used a technique called CRISPR to test if genes near genetic variants implicated by GWAS in heart attacks modulate the functions of vascular endothelial cells. Endothelial cells form the inner layer of blood vessels, and play a critical role in the development of the pathology (atherosclerosis) that leads to heart attacks. In total, we found 26 regions in the human genome that include heart attacks-associated variants and that influence the functions of endothelial cells. Focusing specifically on one of our findings, we showed that the gene DHX38 regulates a process called senescence, which in turn modulates how endothelial cells respond to stimuli that promote heart attacks. ## Introduction Coronary artery disease (CAD) remains the main cause of mortality in the world despite widely available drugs (e.g. statins) and the known benefits of simple prevention strategies (e.g. exercise). Part of the complexity to prevent and treat CAD resides in our incomplete understanding of atherosclerosis, the pathophysiological process largely responsible for CAD initiation and progression. Atherosclerosis is triggered by many environmental risk factors and other intrinsic stimuli, and results in the dysregulation of vascular wall homeostasis due to the accumulation of cholesterol-rich lipoproteins and a maladaptive inflammatory state [1,2]. Human genetics provides a framework to dissect the biological pathways and cellular networks implicated in atherosclerosis. Genome-wide association studies (GWAS) have already identified >250 loci associated with CAD [3–5]. However, the functional characterization of genes that modulate CAD risk at GWAS loci is labor-intensive. It is further complicated by the fact that most CAD variants are non-coding and are in linkage disequilibrium (LD) with a multitude of other DNA sequence variants. Half of the CAD GWAS loci do not associate with traditional risk factors. We and others have hypothesized that some of the CAD variants, which are enriched in open chromatin regions found in human vascular endothelial cells, directly modulate endothelial cell functions [6,7]. The functional characterization of two CAD genes in endothelial cells, PLPP3 [6] and AIDA [7], has further supported this hypothesis. Vascular endothelial cells have critical roles in atherosclerosis [8,9]. Upon activation, they express adhesion molecules necessary for monocyte rolling and attachment (e.g. E-selectin, ICAM1, VCAM1) and weakening of their cell-cell junctions can facilitate monocyte transmigration into the intima. Furthermore, dysfunctional endothelial cells adopt an atheroprone behaviour with changes in calcium (Ca2+) signalling [10], decreased bioavailability of the vasodilator nitric oxide (NO) and increased production of reactive oxygen species (ROS). Dysfunctional endothelial cells can also undergo senescence, which is a stress response that results in stable cell cycle arrest [11]. It can be induced by different stimuli (e.g. from ROS) and senescent cells accumulate in aging tissues to impair normal functions. Senescent cells present with cell-to-cell phenotypic heterogeneity, including transcriptomic variability, that depends on the stress inducers and cell types [12,13]. Senescence has been divided between primary and secondary senescence [14]. Primary senescence occurs in cells in direct response to the stress and these cells can induce secondary senescence in the surrounding cells through paracrine signalling mediated by the secretion of inflammatory cytokines, growth factors and proteases, altogether termed the senescence-associated secretory phenotype (SASP) [15]. Senescent endothelial cells are characterized by a pro-inflammatory and atheroprone phenotype that involves increased production of adhesion molecules (e.g. E-selectin, ICAM1, VCAM1) and ROS, impaired Ca2+ signaling and reduced NO bioavailability [16]. To date, endothelial cell senescence has not been implicated as a potential pathological mechanism for CAD based on GWAS discoveries. The development of pooled CRISPR-based screens now allows perturbation experiments to test sequences at or near most sentinel and LD proxy variants associated with CAD for a role in human vascular endothelial cells [17]. Moreover, by using inhibition (KRAB) or activation (VP64) domains tethered to an inactivated Cas9 (dCas9), it is possible to mimic loss- or gain-of-function effects that might elude perturbations due to classic Cas9 insertion-deletions (indels) [18–20]. Here, we carried out pooled CRISPR screens for six endothelial phenotypes relevant to atherosclerosis (presentation of adhesion proteins at the cell membrane (E-selectin, ICAM1 and VCAM1), production of NO and ROS, and intracellular Ca2+ concentration) using three different Cas9 perturbation modalities (double-strand break induction (Cas9), inhibition (dCas9-KRAB or CRISPRi) and activation (dCas9-VP64 or CRISPRa)). Through these experiments, we aimed to identify CAD-associated variants that modulate endothelial functions. More generally, we also evaluated whether pooled CRISPR screens are a robust and comprehensive methodology to characterize GWAS loci, especially when the likely causal variants are non-coding. Our results suggest that the method is useful to prioritize variants and genes, but requires additional experimental validation to rule out false positive and negative findings. ## FACS-based pooled CRISPR screens for endothelial functions The design of our sgRNA library is summarized in Fig 1A. To target genomic regions associated with CAD, we collected 92 GWAS sentinel variants at 89 CAD-associated loci [21–24] and retrieved their proxy variants in strong LD (r2 >0.8 in populations of European ancestry). Using this strategy, we derived a set of 2,893 variants (92 GWAS sentinel and 2,801 LD proxy variants) (Fig 1A and S1 Table). For each of these variants, we designed a maximum of five high-quality sgRNAs (Fig 1B). The mean distance between sgRNA potential cut-sites and the targeted variants was 22-bp based on available PAM sites (Fig 1C). Using FORECAST [25], we estimated that 10±$16\%$ of the CRISPR/Cas9-mediated deletion alleles would disrupt the targeted variants (S1 Fig). After quality-control steps, we generated a list of 7,393 sgRNA that targeted sequences at or near 1,998 variants at 83 CAD loci (S2 Table). On average at each CAD locus, our sgRNA library covered 76±$22\%$ of the targeted variants (Fig 1D and S3 Table). Of the 83 tested loci, we could capture $100\%$ of the targeted variants at 20 CAD loci and ≥$80\%$ of variants at 38 loci (S3 Table). The majority of the targeted variants are in intronic ($70.8\%$) or intergenic ($10.2\%$) sequences, $7.7\%$ of the variants overlap with ATAC-seq peaks identified in immortalized human aortic endothelial cells (teloHAEC), and $4.1\%$ are located in predicted enhancers identified in human primary endothelial cells and connected to target genes by the EpiMap Project (Fig 1E and S4 Table) [7,26]. Even if most targeted variants are non-coding, we decided to include Cas9 as a perturbation modality in our screens because we reasoned that indels within or near regulatory sequences (e.g. transcription factor motif) could impact cellular phenotypes [27–29]. Fig 1: Pooled CRISPR screens to identify CAD variants and genes that modulate vascular endothelial functions.(A) From 92 loci associated with coronary artery disease (CAD) risk by genome-wide association studies (GWAS), we identified 2893 sentinel and linkage disequilibrium proxy variants for testing. For each of these variants, we attempted to design a maximum of five high-quality guide RNAs (sgRNAs) within a 100-bp window. In the design of the library, we also included sgRNAs that target genes essential for cell viability, as well as sgRNAs that target the coding sequence and promoter of genes that control endothelial cell functions (known genes, positive controls). (B) Number of sgRNAs per targeted variant that passed stringent quality-control filters. In total, we designed 7393 sgRNAs against 1998 CAD-associated variants (mean and median number of sgRNA per variant are 3.7 and 5, respectively). (C) Distribution of the absolute distance of the sgRNA cut-site relative to the targeted variant in base pairs (the vertical dashed line indicates mean sgRNA distance). (D) Fraction of variants at each locus that are successfully targeted by our pooled CRISPR screens. Each row represents one of the CAD loci that we tested. In green is the fraction of variants—including sentinel and LD proxies—for which we designed high-quality sgRNAs and obtained results for the endothelial function phenotypes. On average, 76% of variants at any given CAD locus are captured in the screens (vertical dashed line). (E) Most severe annotation for the 1998 CAD variants targeted by the lentiviral sgRNA libraries using ENSEMBL’s Variant Effect Predictor (VEP) module. (F) As a control step, we sequenced the plasmid library to ensure even representation of sgRNAs in the pool. Then, we produced four independent batches of lentiviruses which we used to infect teloHAEC cells that stably express Cas9, dCas9-KRAB (CRISPRi) or dCas9-VP64 (CRISPRa). Following antibiotic selection and TNFα treatment (for Cas9 and CRISPRi), we stained teloHAEC for cell surface markers (E-selectin, ICAM-1, VCAM-1) or intracellular signaling molecules (reactive oxygen species (ROS), nitric oxide (NO), calcium (Ca2+)). By flow cytometry, we sorted cells from the bottom and top 10 percentiles of the marker distributions, and sequenced sgRNAs found in each fraction. We utilized lentiviruses to deliver our pooled CRISPR libraries to teloHAEC that stably express one of three Cas9 variants (Cas9, CRISPRi, CRISPRa) (Fig 1F). We treated Cas9 and CRISPRi (but not CRISPRa) infected cells with TNFα in order to find genes that can block (Cas9, CRISPRi) or induce (CRISPRa) a pro-inflammatory response. We labelled cells with fluorescent antibodies against E-selectin, VCAM1, or ICAM1, or with fluorescent dyes for signalling molecules (ROS, NO, Ca2+), and sorted cell populations by flow cytometry (FACS) to collect the bottom and top $10\%$ cells based on fluorescence intensity (Figs 1F and S2–S4). We amplified and sequenced the sgRNAs from the FACS cell fractions to identify sgRNAs that have a significant effect on endothelial functions. For each screen, we performed and combined results from at least four independent biological replicates (sgRNA-level correlation analyses between replicates are in S5 Table). Quality-control analyses of sorted cell fractions showed a good representation of sgRNA diversity (mean Gini index = 0.076±0.01) and a good read coverage per sgRNA (mean number of aligned reads per sgRNA = 1995±2981) (S5 Fig). Analysis of the $10\%$ most variable sgRNAs across all assays revealed clustering of samples along the Cas9 modalities (Fig 2A). Fig 2: Quality-controls of the pooled CRISPR screens for vascular endothelial cell phenotypes.(A) Two-dimensional uniform manifold approximation and projection (UMAP) representation of 148 fluorescence-activated cell sorting (FACS) samples based on the normalized read counts of the top 10% most variable sgRNAs across all samples. (B) Density distributions of effect sizes (Beta, x-axis) across all Cas9 variants for essential genes and the rest of the sgRNA library. Positive betas indicate that sgRNA are enriched in the cell fractions when compared to the input library, while negative betas indicate a depletion of sgRNA across all samples. We observed a depletion of sgRNA targeting essential genes with all three Cas9 variants. (C-E) Rank of all control sgRNAs and targeted CAD variants in the (C) Cas9, (D) CRISPRi and (E) CRISPRa screens for three adhesion proteins: E-selectin (left), VCAM1 (middle) and ICAM1 (right). For each panel, the y-axis corresponds to the effect sizes (Beta, comparing top vs bottom FACS 10% fractions). For the Cas9 and CRISPRi experiments, we found an enrichment of sgRNAs targeting the coding and promoter sequences of genes encoding adhesion proteins in the bottom 10% cell fractions (negative Betas). In contrast, sgRNAs targeting the promoter of these genes were enriched in the top 10% cell fractions in the CRISPRa experiments. In green and blue, we highlight sgRNAs targeting coding exons and promoters, respectively. The number in front of the name of each control sgRNA indicates its rank in the corresponding analysis. ## Effects of CRISPR knockout, inhibition and activation in teloHAEC To assess Cas9 efficiency in our experiments, we included in the library 330 sgRNAs against the coding sequence of genes essential for cell viability. For Cas9 and CRISPRi, we found a strong depletion of sgRNAs targeting essential genes among the sequenced FACS cell fractions (Kolmogorov-Smirnov (KS) test $P \leq 2.2$x10-16 and $$P \leq 1.6$$x10-13, respectively) (Fig 2B). We also noted a minor but significant shift toward depletion in the sgRNA count distribution of essential genes for the CRISPRa experiments (KS test $$P \leq 3.7$$x10-6), potentially due to steric hindrance effects by the dead Cas9 moiety near the transcriptional start site of these genes or the toxic impact of gene over-expression (Fig 2B). As an additional quality-control step in our experiment, we designed sgRNAs against the coding and promoter sequences of SELE, ICAM1 and VCAM1, which encode the three adhesion proteins measured in our FACS assays (Fig 1F). We observed significant depletion of sgRNAs targeting coding exons and promoter regions of these genes in the top vs. bottom $10\%$ FACS fractions with Cas9 or CRISPRi (Figs 2C and 2D and S6). In the CRISPRa experiments, the same sgRNAs were enriched in FACS fractions with high E-selectin, ICAM1 or VCAM1 levels (Figs 2E and S6). The three other endothelial phenotypes measured in our experiments—NO and ROS production, and Ca2+ signalling—are physiological readouts that are not the product of a single gene. In the absence of confirmed positive control genes that we could target to validate our system, we carefully calibrated the flow cytometry assays for these readouts using appropriate agonists/inducers. Our screens are sufficiently sensitive to detect sgRNAs targeting CAD loci that have strong effects on these hallmarks of endothelial dysfunction. It is also important to mention that CRISPR perturbation screens will miss variants or genes that cause small phenotypic effects, for instance because of gene redundancy or cellular compensation, or because of an impact on cell proliferation. Our pooled CRISPR screens compared sgRNA frequencies between the bottom and top $10\%$ fractions for each cellular readout. Using MAGeCK’s maximum likelihood estimation method that combines results for sgRNA that target the same variant (Methods), we identified 51 significant variant-endothelial phenotype results (false discovery rate (FDR) ≤$10\%$) involving 42 different variants located within 26 CAD loci (Fig 3A and S4 Table). The majority of these 42 variants is located in non-coding regions: 30 variants are in introns, 5 variants are in intergenic regions, four variants are in exons and three variants are in promoters (S4 Table). The 42 variants were also not enriched in ATACseq peaks identified in teloHAEC ($9.5\%$ ($\frac{4}{42}$) vs. $7.7\%$ for all targeted variants in the screens; binomial $$P \leq 0.56$$). We found significant results for almost all combinations of Cas9 modality and FACS phenotypes, and most of these results were specific to a single combination (Fig 3A). For 15 CAD loci where we could target all LD proxies with sgRNAs (Fig 1D and S3 Table), we detected no significant signals in our CRISPR assays, suggesting that genes within these genomic regions modulate CAD risk through different functions or cell types, or that our functional assays were not sensitive enough to capture their effects. When compared with genomic loci with no significant results, CAD loci with at least one significant variant in our CRISPR screens were not better captured by designed sgRNAs (median coverage $73\%$ vs. $78\%$ of LD proxies, Wilcoxon’s test $$P \leq 0.70$$) but had significantly more LD proxies (median 41 vs. 9 variants, Wilcoxon’s test $$P \leq 1.1$$x10-4). Fig 3: Discovery and validation of CRISPR perturbations that induce atheroprone vascular endothelial cell phenotypes.(A) Heatmap of CAD-associated variants that are significant (false discovery rate (FDR) ≤10%) for at least one of six endothelial phenotypes tested in the teloHAEC pooled CRISPR screens. Each row corresponds to a combination of Cas9 variant and cellular readout, and each column corresponds to a CAD variant. For each variant, we added the name of a nearby gene to simplify locus identification, although we do not imply that these genes are causal. Dendrograms of rows and columns represent hierarchical clustering based on euclidean distance. The FDR is capped at 0.1%. (B) Validation by flow cytometry of six hits from the pooled CRISPR screens. For each validation, we used the top sgRNA from the pooled CRISPR screens to target the variant/locus with the corresponding Cas9 variant. We compared the distribution of the fluorescence intensity of the cellular markers (x-axis) between the sgRNA identified in the screens and a safe harbor negative control sgRNA. We assessed statistical significance using the Kolmogorov-Smirnov (KS) test, all validations shown are significant (KS P-value <2.2x10-16). Validations were performed in at least three independent experiments for each sgRNA (S6 Table). For E-selectin and ICAM1, the fluorochrome is PE; for ROS, the fluorochrome is FITC. Several of the CAD loci identified by GWAS have been implicated in blood lipid metabolism (e.g. LDLR, APOE, PCSK9). Because genetic variation within these loci are likely to influence risk through an effect on lipid levels, we did not anticipate to identify them in our endothelial cell functions CRISPR screens. Of the variants that mapped to 10 lipid loci included in our screens, all were negative across the different endothelial phenotypes tested except rs118039278 located in an intron of LPA (CRISPRa for ICAM1, FDR<0.001, Fig 3A). Although LPA is not expressed in teloHAEC, CRISPRa could induce its ectopic expression and the encoded Lp(a) lipoprotein has been shown to induce endothelial dysfunction [30]. ## Validation using single sgRNA experiments To validate our results, we selected eight CRISPR perturbations at seven CAD loci and performed individual sgRNA infection and FACS experiments (S6 Table). For this validation step, we prioritized variants that were significant for >1 cellular phenotypes and that had strong effect sizes in the CRISPR screens. For each experiment, we compared the distribution of the FACS-based cellular phenotype between control sgRNAs and the best sgRNA targeting each selected CAD variant (Fig 3B). Across three independent biological replicates, we could validate six of the eight selected CRISPR perturbations (one-tailed t-test $P \leq 0.05$, S6 Table). One of the replicated sgRNA implicated an outstanding candidate CAD gene: for MAT2A, targeting Cas9 to the synonymous rs1078004 variant increased ROS production in TNFα-treated teloHAEC (Fig 3B). MAT2A encodes a methionine adenosyltransferase that is responsible for the biosynthesis of S-adenosylmethionine, a precursor of the potent antioxidant glutathione [31]. ## Base editing validates a putative functional CAD variant near FES A CRISPRa/E-selectin perturbation that we replicated implicated rs12906125, a variant at the FURIN/FES locus previously prioritized as potentially causal for CAD by transcriptomic and epigenomic profiling in human endothelial cells [32]. The corresponding Cas9 knockout and CRISPRi results for E-selectin were non-significant, suggesting that gene activation is required to reveal an endothelial phenotype as this locus (S4 Table). rs12906125 is in strong LD with the CAD sentinel variant rs2521501 (r2 = 0.91), is located in the FES promoter and overlaps an ATAC-seq peak as well as a H3K27ac-defined enhancer that physically interacts with the FURIN promoter (Fig 4A) [7]. The same SNP is an eQTL for FES in human primary aortic endothelial cells [32] and arterial tissues from GTEx. In the CRISPRa experiment using RNA-seq, we found a significant up-regulation of both FES (log2(fold-change (FC)) = 3.75, adjusted $$P \leq 8.5$$x10-173, rankcis = 1, ranktrans = 4) and FURIN (log2FC = 0.78, adjusted $$P \leq 1.5$$x10-10, rankcis = 2, ranktrans = 197) (Fig 4B). FURIN, which encodes a proprotein convertase, represents a strong candidate CAD causal gene at this locus: its specific knockdown in human endothelial cells reduces atheroprone characteristics such as monocyte-endothelial adhesion and transmigration [34]. In contrast, FES, which encodes a tyrosine protein kinase that can control cell growth, differentiation and adhesion, has not been implicated in vascular endothelial cell biology. To determine which of the two is the more likely causal CAD gene, we changed the genotype at rs12906125 in teloHAEC using adenosine base editing. teloHAEC are heterozygous at rs12906125 (A/G) and we could generate clones with the G/G genotype (because of the sequence context, we could not get A/A teloHAEC cells with cytosine base editors directing C>T edits on the other strand). In unstimulated cells, genotype at rs12906125 had no impact on the expression of FURIN and FES (Fig 4C). However, because rs12906125 maps in the middle of an ATAC-seq peak and a binding motif for NFκB/p65 [32], we reasoned that the genotypic effect could be revealed by an inflammatory stimulus. Indeed, TNFα treatment reduced the expression of FURIN in teloHAEC with the A/G and G/G genotypes, whereas its effect on FES expression was genotype-dependent (Fig 4C). When we compared E-selectin expression at the mRNA and protein levels between A/G and G/G clones, the difference was not significant, potentially because we could not generate A/A clones and the effect of the A/G-to-G/G base edit on FES expression was modest in comparison to the CRISPRa impact (S7 Fig). While we cannot exclude FURIN as an excellent candidate causal CAD gene, our base editing experiment suggests that there might be >1 causal genes at this locus and that FES should be tested in future experiments aimed at determining its precise biological function(s) in atherosclerosis. Fig 4: Characterization of a CAD-associated regulatory variant located within an enhancer at the FURIN/FES locus.(A) CRISPRa perturbations highlighted rs12906125 as a potential regulatory variant for FURIN and FES. The variant overlaps an ATAC-seq peak in the promoter of FES and a H3K27ac-defined enhancer that physically interacts with the FURIN promoter through chromosomal loops predicted by the ABC model applied to teloHAEC Hi-C data [7,33]. (B) Within a 2.5-Mb window, FES and FURIN are the top two differentially expressed genes when targeting rs12906125 by CRISPRa in teloHAEC. The inset plot shows the induction of both FES and FURIN expression with sgRNA_06939 when compared to the control safe harbor sgRNA. (C) teloHAEC are heterozygous (A/G) at rs12906125. We used base editing to change the genotype at rs12906125 to G/G. There was no significant difference in expression for FES and FURIN when comparing unstimulated teloHAEC with the A/G and G/G genotypes. However, upon activation with TNFα, we found that the reduction in FURIN levels was independent from the rs12906125 genotype whereas for FES, the reduction was genotype-dependent. Numbers above the bars are Student’s t-test P-values. We tested at least six clones of each genotype. ## Loss of DHX38 function induces vascular endothelial cell senescence Two of the validated sgRNAs target exonic variants (rs2074626, rs2240243) in DHX38 (Fig 5A). This GWAS CAD signal is located near an association signal for LDL-cholesterol (LDL-C), but a co-localization analysis suggests that the two signals are likely distinct (Fig 5A, coloc posterior probabilities H3:$80.9\%$ and H4:$19.1\%$). Nonetheless, we cannot rule out the possibility that the DHX38 variants may also contribute partially to CAD through an effect on LDL-C. DHX38 encodes an RNA helicase involved in splicing, and mediate Cas9 nuclease effects on E-selectin (Fig 3A and 3B) and VCAM1 (as validated by subsequent analyses, S6 Table). We confirmed the DHX38-related E-selectin result using Cas9 ribonucleoprotein complexes (S8 Fig). In RNA-seq experiments with a sgRNA targeting DHX38 (seven days post-infection, TNFα treatment), DHX38 was not down-regulated and we found few reads mapping to DHX38 with Cas9-mediated indels (<$2\%$). However, we noted a strong gene expression signature suggesting an effect on cell proliferation with the modulation of genes involved in the p53, G2/M checkpoint and E2F target genes pathways (Fig 5B and S7 Table). To reconcile these observations, we hypothesized that endothelial cells with DHX38 detrimental indels undergo senescence-mediated cell cycle arrest, have a growth disadvantage and induce a response in surrounding cells without DHX38 indels through the SASP in a TNFα-stimulated environment. Fig 5: Disruption of DHX38 induces vascular endothelial cell senescence.(A) Perturbations with the Cas9 nuclease highlighted two synonymous variants (rs2074626, rs2240243) in the DHX38 gene for several endothelial phenotypes. DHX38 is located downstream of the HP and HPR genes, which have previously been associated with LDL-C levels. However, the CAD and LDL-C GWAS signals are distinct based on co-localization analyses (posterior probability for two independent association signals (H3) = 80.9%). (B) Gene-set enrichment analysis results for differentially expressed genes identified by RNA-seq in teloHAEC between a sgRNA targeting a DHX38 coding exon and a safe harbor negative control sgRNA. Only pathways with a Benjamini-Hochberg-corrected P-value <0.05 and normalized enrichment scores (NES) <-1 or >1 are shown. (C) Experimental design for the characterization of DHX38 using the fluorescent marker CRIMSON in place of an antibiotic resistance gene. We did all experiments in teloHAEC that stably express Cas9. We monitored the impact of a DHX38 sgRNA on cell proliferation, indel induction, gene expression and senescence-associated β-galactosidase (SA-βGal) activity. (D) Comparison of endothelial cell proliferation between teloHAEC with a DHX38 sgRNA or a safe harbor negative control sgRNA. The differences in the number of CRIMSON+ cells were not significant two or four days post-infection. However, there were 27% less CRIMSON+ cells with DHX38 sgRNA relative to the safe harbor control at seven days post-infection (Student’s t-test P-value = 7.3x10-8). Results are mean ± standard deviation for 6 replicates for safe harbor and three replicates for two DHX38 targeting sgRNA. (E) Quantification of DHX38 indels by tracking of indel by decomposition (TIDE) analysis. As expected, we found no indels in the CRIMSON- cells (S8 Table). However, in CRIMSON+ cells that received a DHX38 sgRNA, we found indels with an average frequency of 15%, 42% and 40% at day 2, 4 and 7, respectively. Results are mean ± standard deviation for 6 replicates for safe harbor and three replicates for two DHX38 targeting sgRNA. (F) Expression levels of DHX38 and CDKN1A in CRIMSON- and CRIMSON+ teloHAEC that have received a sgRNA that targets DHX38 or a safe harbor region (negative control). There were no significant differences in DHX38 expression levels at day 2. However, at day 4 and 7, DHX38 was significantly down-regulated and CDKN1A was significantly up-regulated in CRIMSON+ cells that received the DHX38 sgRNA. N.S., not significant. We provide Student’s t-test P-values when P<0.05. Bars are mean normalized expression and error bars represent one standard deviation. (G) Quantification of senescent teloHAEC by flow cytometry using senescence-associated β-galactosidase (SA-βGal) staining. At day 4 and 7 post-infection, there were significantly more senescent cells in the CRIMSON+ DHX38 sgRNA experiment than in the CRIMSON- cells or in the CRIMSON+ cells that received the safe harbor sgRNA. We used the DNA damaging agent etoposide as a positive control to induce senescence. N.S., not significant. We provide Student’s t-test P-values when P<0.05. Results are mean percentage SA-βGal+ teloHAEC and error bars represent one standard deviation. To test this hypothesis, we replaced the antibiotic resistance marker by a fluorescence protein (CRIMSON) in the sgRNA vector in order to sort and characterize at different timepoints teloHAEC stably expressing Cas9 that have or not received a DHX38 sgRNA (Fig 5C). While the fraction of CRIMSON+ cells is similar for safe harbor and DHX38 sgRNAs two- and four-days post-infection, it is significantly lower after seven days (Fig 5D). Although we did not capture many DHX38 indels in the RNA-seq experiment, we could detect a high frequency of indels (15–$40\%$) in CRIMSON+ cells already two days post-infection (Fig 5E and S8 Table). Importantly, we also measured a down-regulation of DHX38 expression levels in CRIMSON+ cells (Fig 5F). In CRIMSON+ cells with DHX38 sgRNA, we measured an up-regulation of CDKN1A (encoding the CDK2 inhibitor p21WAF1/Cip1) and detected a higher number of cells with β-galactosidase activity when compared to CRIMSON- cells or CRIMSON+ cells with a safe harbor sgRNA (Fig 5F and 5G). These characteristics are hallmarks of cell senescence. We validated the effect of these sgRNAs on DHX38 and CDKN1A expression levels using Cas9 ribonucleoprotein complexes in primary human coronary artery endothelial cells (S9 Fig). Activation of the senescence program is specific to DHX38 and not a general response to DNA damage induced by this particular sgRNA as four different sgRNAs targeting two different DHX38 exons impaired endothelial functions in the CRISPR screens. ## Two validated CRISPRa perturbations do not yield candidate CAD genes Beside the FURIN/FES locus described above, we replicated two other CRISPRa perturbations that targeted intronic variants in CNNM2 (rs78260931) and ZNF664 (rs12311848) (Fig 3A and 3B). We used RNA-seq experiments to identify genes up-regulated near these variants that could mediate the CRISPRa effects on endothelial functions. While it has been reported that CRISPRa can lead to non-specific transcriptional effects such as the up-regulation of IL6 [35], we used safe harbor sgRNAs to control for such effects, IL6 was not differentially expressed in our experiments, and additional controls suggested a certain specificity of our CRISPRa results (S10 Fig). Analysis of the RNA-seq data for the CRISPRa experiment at the CNNM2-rs78260931 locus revealed no evidence of differential expression for nearby genes (in cis, the closest differentially expressed gene was NFKB2 located 568 kb away (log2FC = 0.33, adjusted $$P \leq 0.008$$)). We also manually inspected the sequence reads that mapped to the CNNM2 region but did not find un-annotated genes that were differentially expressed (Fig 3A). Thus, based on our results, we cannot prioritize a candidate causal gene at this CAD locus. While this CRISPR signal could be a false positive finding due to an off-target effect, it is worth noting that the result is specific to this region and not a sgRNA-specific artifact because three of the four sgRNAs that we targeted at rs78260931 gave consistent results in the CRISPRa-ICAM1 screen (S4 Table). Targeting CRISPRa at rs12311848 did not increase the expression of ZNF664 but the expression of CCDC92, a gene located 29 kb upstream (log2FC = 0.74, adjusted $$P \leq 9.2$$x10-5, rankcis = 3, ranktrans = 696, Fig 6B). The sentinel CAD variant identified by GWAS at this locus is rs11057401, a missense variant in CCDC92. We targeted four sgRNAs at rs11057401 but did not detect significant effects in the Cas9 nuclease nor CRISPRi screens. This result suggests that CRISPRa gain-of-function experiments are necessary to detect the impact of this locus on endothelial dysfunction. To support this hypothesis, we ectopically over-express the main CCDC92 isoform in teloHAEC. While we measured a strong induction in CCDC92 levels, we could not detect a significant change in the expression of ICAM1, the corresponding endothelial phenotype identified in the CRISPRa screen (S11 Fig). Therefore, either CCDC92 is not the causal CAD gene at this locus, ectopic over-expression of the main CCDC92 isoform is not sufficient to mimic the CRISPRa effect, or the screen result is spurious. Fig 6: Validated CRISPRa effect at the CNNM2 and CCDC92/ZNF664 loci do not nominate candidate causal CAD genes.(A) Locus view for the CAD locus with nearby gene CNNM2. We provide the position of the sentinel CAD variant (rs11191416) and the putative functional variant identified in the pooled CRISPR screen (rs78260931). The LD proxies and sgRNAs tested are also shown. ATAC-seq and RNA-seq data in resting teloHAEC are from ref. [7]. (B) Locus view for the CAD locus with nearby genes ZNF664 and CCDC92. We provide the position of the sentinel CAD variant (rs11057401) and the functional variant identified in the pooled CRISPR screen (rs12311848). The LD proxies and sgRNAs tested are also shown. ATAC-seq and RNA-seq data in resting teloHAEC are from ref. [7]. (C) Uniform manifold approximation projection (UMAP) for 11,756 cells from human right coronary arteries analyzed by single-cell RNA-sequencing [36]. We color-coded cells based on the level of expression of candidate causal CAD genes identified and characterized in this study. We used the expression of the endothelial cell marker gene CDH5 (encoding VE-Cadherin) to identify endothelial cells (circle in top left panel). All five candidate genes are expressed in human vascular endothelial cells from coronary arteries. Finally, to determine if some of the candidate genes identified in this study could exert an effect in vascular endothelial cells in vivo, we re-analyzed single-cell RNA sequencing data from human coronary arteries [36]. We used the endothelial marker gene CDH5 to identify a cluster of endothelial cells and confirmed that these cells express DHX38, MAT2A, CCDC92, FES and FURIN, prompting future efforts to dissect the role of these genes in this cell type in atherosclerosis (Fig 6C). ## Discussion As for most complex human diseases, many GWAS loci associated with CAD do not include obvious candidate causal genes nor implicate known pathophysiological mechanisms. To elucidate their mechanisms and gain insights into atherosclerosis, we carried out multiple CRISPR screens to test if CAD variants impact vascular endothelial functions. By combining six different endothelial cell readouts and three Cas9 modalities, we identified sequences at or near 42 variants at 26 CAD loci (Fig 3A). This list is depleted of variants that modulate CAD risk through an effect on lipid metabolism and enriched for loci of unknown functions (S3 Table). We found sequences near ARHGEF26, ADAMTS7, and GUCY1A3, genes previously implicated in leukocyte transendothelial migration [23], endothelial cell angiogenesis [37], and NO signaling [38], respectively. We also retrieved rs17163363, an intronic variant in MIA3 that controls the expression of AIDA in endothelial cells [7]. To document the false positive rate of our results, we tested eight sgRNAs prioritized in our screens and could validate six of them using the same FACS-based endothelial function readouts. There were also variants and genes that we expected to find but did not recover. For instance, we did not identify rs17114036, a likely functional variant that controls the expression of PLPP3 in endothelial cells, although this negative result may arise because the underlying enhancer requires hemodynamic stress to be active [6]. Furthermore, our screens did not yield variants at CAD loci that include PECAM1 (adhesion protein CD31) and NOS3 (endothelial NO synthase), two genes with important roles in endothelial cells. As for PLPP3, it might be that we did not activate endothelial cells with the right stimulus to detect the functional impact of these variants/genes in our assays. It is also possible that some loci will require the precise engineering of alleles (e.g. using base or prime editing) to detect a cellular phenotype, or that the phenotypic effect of a variant at the cellular level is too low to distinguish a true signal from the experimental noise inherent to any large-scale omics approach. One lesson learned from our experiments is that the false negative rate of such pooled CRISPR screens is likely not negligeable, implying that variants or genes should not be ruled out simply based on a non-significant CRISPR perturbation result. We designed our sgRNA library using a variant-focused approach. However, it is likely that some of the findings from our CRISPR screens result from loss- or gain-of-function effects on endothelial genes independently of the causal variants. For instance, we identified and validated sgRNAs near synonymous variants in DHX38 and MAT2A using the Cas9 nuclease. While synonymous variants can have phenotypic consequences, it is more likely that these variants are in LD with the causal variants but were captured in our screens because they targeted loss-of-function indels to the DHX38 and MAT2A coding sequences. Similarly, ectopic activation or inhibition of gene expression by CRISPRa and CRISPRi can highlight candidate endothelial genes even if the sgRNAs do not directly overlap causal variants. The main finding of our CRISPR experiments is the identification of DHX38 as a strong candidate causal gene for CAD. Through Cas9-mediated deletions, we found that loss of DHX38 functions in endothelial cells impairs cell cycle progression, induces the expression of the cell cycle inhibitor CDKN1A, and increases β-galactosidase activity, all hallmarks of cellular senescence. DHX38, also known as PRP16, encodes an RNA helicase implicated in splicing with previously described functions in tumorigenesis [39] and retina degeneration [40]. Interestingly, deregulation of RNA splicing through aging has been proposed as one mechanism leading to aging-related chronic diseases through an effect on cellular senescence [41]. While our results suggest that DHX38 influences CAD risk by modulating endothelial dysfunction and senescence, it is possible that part of the CAD association signal at the locus is also due to nearby variants in weak linkage disequilibrium that associate with LDL-C (Fig 5A). Some of the genes prioritized in our CRISPR perturbation screens (e.g. FES, DHX38) likely play essential cellular functions in endothelial cells. This could raise concerns about the specificity of some of our results and their relevance for CAD in vivo. But there is an alternative scenario in which the disruption of essential genes within GWAS CAD loci, either by genetic variants or CRISPR perturbations, impairs cellular phenotypes (as measured by FACS) and leads to endothelial dysfunction, a known pathological mechanism for CAD. A recent large-scale trans-ancestry meta-analysis provides additional support to this model. Beside confirming that CAD GWAS loci are enriched for regulatory sequences identified in endothelial cells, the study also found an enrichment of genes with essential functions, such as genes involved in cell cycle progression, division and replication (including CDKN1A, the senescence marker used in Fig 5F)[5]. Therefore, maybe some of the variants and genes found in our CRISPR screens modestly modulate cellular functions, leading to endothelial dysfunction, atherosclerosis and CAD. Ultimately, the phenotypic characterization of hypomorphic alleles of these essential genes in mouse models (using endothelium-specific targeting techniques) may be needed to address this important question. We acknowledge several additional limitations of our approach: [1] the six selected cellular readouts may not completely capture how genetic variation associated with CAD influences endothelial functions, [2] Cas9 nuclease can introduce large untargeted truncations, which may disrupt >1 genes [42], [3] Cas9-mediated indels could miss the targeted variants, [4] CRISPRi can silence transcriptional activity over a long distance that can cover many genes, thus complicating the interpretation of the findings [43], and [5] CRISPRa is considered to be mostly effective when targeted to sequences that are proximal to the targeted genes [44]. In particular for CRISPRa, we presented two examples of validated perturbations targeting non-coding variants (ZNF664/CCDC92 and CNNM2, Figs 3B, 6A and 6B) for which we could not assign causal genes based on transcriptomic analyses. These results cast doubts in using CRISPRa to characterize distal regulatory sequences, especially if it is not supported by orthogonal results. Along the same line, we noted weak correlations of our screen results between CRISPR modalities (Fig 3A). While it is possible that certain genomic regions are more amenable to a specific type of perturbation (e.g. coding sequences with Cas9 nuclease), it also possible that some of the CRISPR results seen with a single modality are false positive hits due to the method rather than the screened cellular phenotypes. This needs to be considered carefully when selecting hits for further downstream functional characterization. Endothelial cell senescence is both a physiological and pathological process [45]. In health, it signals the system for vascular endothelium repair. Senescence also increases with age and in response to traditional CAD risk factors. When it overcomes the regeneration capacity of the system or upon stress, senescence causes endothelial dysfunction and can lead to vascular diseases. Senescent cells accumulate at the sites of atherosclerosis in human blood vessels [46,47] and their selective elimination using transgenic strategies or drugs (senolytics) delays atherogenesis progression in mice [48]. Our data suggest that CAD-associated DHX38 variants–and potentially variants at other loci awaiting functional characterization–affect key endothelial functions, potentially by inducing premature senescence. This observation links a large body of literature that has implicated senescence in atherosclerosis with variants and genes that modulates endothelial functions. As clinical trials to test the efficacy of senolytics on vascular diseases are now in discussion [49], it will be important to explore whether specific CAD variants or polygenic scores are predictive of their clinical response. ## Design of the sgRNA library We retrieved 92 sentinel genetic variants associated with coronary artery disease (CAD) at genome-wide significant levels (P-value ≤5x10-8) from four GWAS meta-analyses available at the time of the design of this experiment [21–24]. For the design of the sgRNA library, we included all sentinel variants as well as variants in strong LD (r2 >0.8 in the 1000 Genomes Project European-ancestry populations). Because the four large CAD GWAS available when we designed our sgRNA library included mostly individuals from European-ancestry populations, we limited our search for LD proxies to this group. For each variant—sentinel and LD proxy—we identified all possible sgRNA in a 100-bp window centered on the variant itself. Our primary objective in designing this library was to identify high-quality sgRNAs that map as close as possible to the targeted SNPs, independently of genomic annotations. We prioritized sgRNA with the highest predicted quality using the CRISPR OffTarget Tool (version 2.0.3) [50] with a Targeting_guide_score ≥ 20 and the “matches with 0 mismatches” = 1 and “matches with 1 mismatch” = 0 settings. We discarded sgRNA that overlapped heterozygous variants, indels and/or multi-allelic variants in the teloHAEC genome (build hg19). In total, we excluded 895 variants from our screen due to difficulties in designing high quality sgRNAs in their vicinity ($86.5\%$ did not pass our sgRNA score threshold, $2.4\%$ overlapped with a heterozygous variant in teloHAEC, and $11.2\%$ both did not pass the quality threshold and overlapped a variant). We selected sgRNA targeting essential genes from a previously published study [51]. For potential positive control genes (SELE, SELP, ICAM1, VCAM1, PECAM1, NOS3, VWF, SOD2, SOD3, GPX3, CAT, ITPR1, ITPR2, ITPR3, ATP2A2, ATP2A3, PLN, CAV1, and TRPV4), we selected sgRNA from the Human GeCKOv2 CRISPR knockout pooled library [52]. We also selected sgRNA that targeted the promoter (300-bp window before the transcriptional start site) of positive control genes for the CRISPRa (dCas9-VP64) experiments. For all selected loci (variants, coding sequences, gene promoters), we retained the five top scoring sgRNA for the library design. Finally, we added two sgRNA for the SELE locus (SELE_g1, SELE_g2) that we frequently use to validate TNFɑ stimulation. This resulted in a final library of 8051 sgRNA (S2 Table). The sgRNA were synthesized in duplicates by Agilent Technologies (Cat-#: G7555B) to accommodate the specific requirements of the Cas9/dCas9-KRAB and dCas9-VP64 (specific MS2 tracrRNA) experiments. We amplified each specific pool of oligonucleotides as previously described [19], with the following small modifications: we performed two PCR using NebNext High fidelity Master mix (Cat-#: M0541L). The first PCR was used to amplify each pool separately using 2.5ng of pooled oligonucleotides and 500nM of each primer (for the Cas9/dCas9-KRAB library, we used U6_subpool_fwd and Guide_CM_barcode1_rev; for the dCas9-VP64 library, we used U6_subpool_fwd and Guide_MS2_Barcode2_rev). Cycling conditions for PCR1 were 98°C for 30 sec, then 15 cycles of 98°C for 10 sec; 55°C for 10 sec; 72°C for 15 sec and a final step of 72°C for 2 min and a 10°C hold. We performed the second PCR to add homologous sequences, using the U6_screen_fwd and Tracr_rev oligonucleotides for the Cas9/dCas9-KRAB library, and the U6_screen_fwd and Tracr_MS2_rev oligonucleotides for the dCas9-VP64 library, in both cases using ⅕ of PCR1 as template. Cycling conditions for PCR2 were 98°C for 30 sec, then 10 cycles of 98°C for 10 sec; 55°C for 10 sec; 72°C for 15 sec and a final cycle of 72°C for 2 min and 10°C hold. See table S9 Table for primer details. After gel extraction and PCR purification, we performed Gibson assembly in both respective vectors (pHKO9-Neo and lentisgRNA(MS2)-zeo backbone addgene 61427). For pHKO9-Neo, we replaced the Crimson fluorescent gene in the pHKO9-Crimson-CM vector (gift from Dan Bauer’s lab) by a neomycin resistance (NeoR) sequence. Briefly, we amplified the NeoR gene from our pCas9-Neo vector [53] using BsiWI-Neo-Fwd and MluI-Neo_rev primer (S9 Table). After digestion by BsiWI and MluI, we cloned the segment in pHKO9-Crimson_CM, which had been digested with BsiWI and MluI. We amplified each library using ten independent maxi-preparations (Macherey-Nagel cat# 740424). To control the quality of both libraries, we sequenced them on an Illumina HiSeq4000 instrument and calculated the Gini index, which summarizes read distribution across sgRNA in a given pool. For a good-quality sgRNA library, the expected Gini index is ≤0.2, and we obtained Gini indexes of 0.050 and 0.052 for the Cas9/dCas9-KRAB and dCas9-VP64 library, respectively. ## Engineering of teloHAEC cell lines to stably express Cas9 variants or base editors TeloHAEC are immortalized human aortic endothelial cells obtained by over-expressing telomerase (ATCC CRL-4052). These cells have a normal female karyotype (46;XX) and exhibit many of the properties and functions of human vascular endothelial cells [7]. We previously showed that the teloHAEC transcriptional and epigenomic responses to TNFα treatment is highly correlated with the responses of primary human coronary artery endothelial cells to the same stimulation [7]. We cultivated teloHAEC in Vascular Cell Basal Medium supplemented with Vascular Endothelial Cell Growth kit-VEGF (ATCC through Cedarlane PSC-100-030 and PSC-100-041). We generated our teloHAEC cells models expressing either Cas9, dCas9-KRAB or dCas9-VP64 + MPHv2 using Addgene vectors #52962, #46911, #61425 and #89308, and base editors using plenti-U6-gRNAentry-EFS-ABE8e-(D10A)SpRY-P2A-Blast, called telo-HAEC ABE8e-SpRY. We carried out lentiviral infection as previously described [53]. ## Pooled CRISPR screen experiments We produced four batches of lentiviruses for each sgRNA library pool (Cas9/dCas9-KRAB, dCas9-VP64). We infected each teloHAEC cell line (Cas9, dCas9-KRAB, dCas9-VP64) at a multiplicity of infection of 0.3 using each batch of viruses separately. Following viral infection, we selected cells using zeocin (teloHAEC-dCas9-VP64) or G418 (teloHAEC-Cas9/-dCas9-KRAB) for five (teloHAEC-dCas9-VP64) or seven days (teloHAEC-Cas9/-dCas9-KRAB) in vascular cell basal medium (ATCC PCS-100-030) to remove any cells that did not incorporate a vector. After selection, we stimulated cells expressing Cas9 or dCas9-KRAB using TNFɑ (10ng/μl) for four hours to induce a pro-inflammatory response; we did not stimulate cells expressing dCas9-VP64, reasoning that the VP64 transcriptional domain should activate gene expression. Following TNFɑ stimulation, we immunostained cells (around 50M cells) with antibodies linked to phycoerythrin for adhesion molecules (E-selectin (BD BIOSCIENCES Cat-#: 551145), VCAM-1 (Cat-#: 12-1069-42), ICAM-1 (Cat-#: 12-0549-42)) or we incubated with fluorescent dye-based reagents for endothelial signaling markers: (nitric oxide (NO) (DAF-FM Diacetate, Cat-#: D23844)), reactive oxygen species (ROS) (CM-H2DCFDA, Cat-#: C6827), calcium signaling (Fura Red, Cat-#: F3021)). We calibrated the FACS assays with positive control treatments to make sure that we could robustly detect changes in the measured phenotypes. Antibodies and fluorescent dye-based reagents were titrated to use optimal concentrations. We also quantified how teloHAEC were responding to ionomycin for calcium signaling, to sodium nitroprusside for NO and to TNFα for reactive oxygen species. For adhesion molecules, we utilized sgRNA targeting coding exons and promoter regions of SELE, ICAM1 and VCAM1 as positive controls. Unless otherwise stated, we purchased all antibodies and dyes from ThermoFisher Scientific. Subsequently, we sorted stained cells by flow cytometry on a BD FACSARIA FUSION flow cytometer to collect the top and bottom $10\%$ of fluorescently labeled cells. For each experiment, we infected 20-50M cells at a multiplicity of infection (MOI) of 0.3 (740–1860 cells/sgRNA) and analyzed a similar number of cells (20-50M) for FACS, resulting in approximately 500 to 1200 cells/sgRNA. FACS traces were generated with FlowJo (BD Biosciences). We extracted genomic DNA from both top and bottom $10\%$ cell fractions separately (around 5M cells in each fraction) using the QIAGEN DNeasy Blood and Tissue kit (Cat No. 69504) according to manufacturer’s instructions. ## Amplification and sequencing of pooled CRISPR experiments We amplified sgRNA sequences from genomic DNA via PCR, followed by a cleanup step using the QIAGEN QIAquick PCR purification kit (Cat-#: 28104) according to the manufacturer’s instructions. We used the primer sequences and PCR settings as previously described in ref. [ 19]. We created sequencing libraries using Illumina TruSeq adapters according to the manufacturer’s protocols. We sequenced the libraries on an Illumina Hiseq4000 instrument at the McGill University and Genome Quebec Innovation Centre (MUGQIC). Generally 6 samples were multiplexed per sequencing lane for a target read coverage of ~500 reads per sgRNA per sample (S5B Fig). ## Computational analysis of pooled CRISPR screen data We processed raw sequencing data from the BCL to the FASTQ format using bcl2fastq at MUGQIC. Raw FASTQ reads were quality-controlled using FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and MultiQC [54]. We performed downstream analysis of sgRNA sequencing data using MAGECK (v.0.5.9) [55]. We quantified sgRNA sequences using MAGECK count against the list of sgRNA sequences in the library (S2 Table), allowing for no mismatches in the sgRNA sequence. We then tested the difference in sgRNA counts between the bottom and top $10\%$ flow cytometry fractions for each readout using MAGECK maximum likelihood estimation (mle) method with median normalization [56]. Predicted functional impact of variants and overlap with genomic features was computed using VEP [57], biomart and Goldmine in R [58–60]. ## UMAP representation of sample-level count data We normalized raw sgRNA counts using variance stabilizing transformation (vst) in DESeq2 [61]. To account for baseline differences between plasmid preparations, we further normalized samples to their respective vector library by dividing the vst normalized sgRNA count by the vst normalized count of the Cas9/dCas9-KRAB or dCas9-VP64 library, respectively. We calculated principal components using the top $10\%$ most variable sgRNA (805 sgRNA) across all cell sorted samples based on normalized counts. Next, we used the loadings from the first three principal components in Uniform Manifold Approximation and Projection (UMAP) [62] to create a two-dimensional embedding of the normalized sgRNA count data. Each dot in the UMAP plot represents one sequenced sample (top or bottom $10\%$ of stained cells). ## Analysis of sgRNAs targeting essential genes To test for potential effects of sgRNA on endothelial cell death and proliferation, we compared sgRNA counts of all samples across the same cellular model (Cas9, dCas9-KRAB, dCas9-VP64) against the respective baseline vector library sgRNA count using MAGECK mle. We used sgRNAs targeting essential genes in the teloHAEC Cas9 cellular model as positive controls. ## Single sgRNA validation We individually cloned each sgRNA for validation as previously described [63]. We produced lentiviruses, infected cells, performed antibiotic selection, and stained cells as for the pooled CRISPR screen. We analyzed cells using flow cytometry (BD FACSCelesta (BD Biosciences, San Jose, CA, USA) equipped with a 20 mW blue laser (488 nm), a 40 mW red laser (640 nm), and a 50 mW violet laser (405 nm). For each experiment, we measured the mean fluorescent intensity (MFI) obtained for sgRNA of interest and compared it with the MFI for control sgRNA (safe-harbor and/or scrambled sgRNA). Safe-harbor sgRNA sequences were based on Pellenz et al. [ 64]. We performed each experiment at least three times. For statistical analyses, we used Student’s t-test and determined that a sgRNA had a significant effect on the measured phenotype when a one-tailed P-value ≤0.05. For DHX38 Crimson experiments, we individually cloned each sgRNA (2 different guides were used for DHX38 (sg10966 and sg11664) and 2 for Safe-Harbor, respectively) in pHKO9-Crimson-CM vector (gift from Dan Bauer’s lab). We produced lentiviruses, infected cells and performed flow cytometry on a BD FACSARIA FUSION flow cytometer at day 2, day 4 and day 7 post-infection. We analyzed the percentage of Crimson positive cells and we sorted Crimson positive and negative cells to extract RNA in each fraction. We extracted total RNA using RNeasy Plus Mini Kit (Qiagen cat #: 74136). We measured RNA integrity and concentration using Agilent RNA 6000 Nano II assays (Agilent Technologies) on an Agilent 2100 Bioanalyzer and Take3 on Cytation V (Biotek). We reverse transcribed 750ng of total RNA using random primers and 1 U of the MultiScribe Reverse Transcriptase (Applied Biosystems) in a 20 μL reaction volume at 100 mM dNTPS and 20 U of RNase inhibitor with these three steps: 10 min at 25°C, 120 min at 37°C and 5 min at 85°C. We followed the MIQE guidelines to assess quality and reproducibility of our qPCR results [65]. We performed qPCR in triplicates for all samples using: 1.25 μL of cDNA ($\frac{1}{50}$ dilution), 5 μL of Platinum SYBR Green qPCR SuperMix-UDG (Life Technologies) and 3.75 μL of primer pair mix at 0.8 μM on a CFX384 from Biorad. We used the following thermal profile: 10 min at 95°C, and 40 cycles of 30 s at 95°C, 30 s at 55°C and 45 s at 72°C. We carried out melting curve analyses after the amplification process to ensure the specificity of the amplified products. We also simultaneously performed qPCR reactions with no template controls for each gene to test the absence of non-specific products. Cq values were determined with the CFX Manager 3.1 (Bio-Rad) software and expression levels were normalized on the expression levels of the house-keeping genes TATA-box binding protein (TBP), hypoxanthine-guanine phosphoribosyltransferase (HPRT), and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) using the ΔΔCt method. The primer sequences are in S9 Table. ## Single-guide RNP validation in human primary endothelial cells We nucleofected Human Coronary Artery Endothelial Cells (HCAEC) (Lonza through Cedarlane CC-2585) using Nucleofector 4D and P5 Primary cell 4D Nucleofector X kit S (Lonza, V4XP-5032) according to the supplier’s recommendations. First, we warmed-up 2 ml of media without antibiotics (same as for teloHAEC) per condition in a 6-wells plate format. We annealed crRNA and TracrRNA (from IDT, Alt-R CRISPR-Cas9 crRNA XT and Alt-R CRISPR-Cas9 tracrRNA) in nuclease duplex buffer at 30uM, 5 min at 95°C then cool down to room temperature to obtain gRNA. We incubated 6ul of the gRNA (2 different guides were used for DHX38 (sg10966 and sg11664) and 2 for Safe-Harbor, respectively) with 1ul of Cas9 (IDT, Alt-R S.p. Cas9 Nuclease V3) diluted ⅓ and 18ul of P5 solution for 10 minutes at RT. We added 300K cells resuspended in 5ul of supplemented P5 buffer and mixed with the 25ul of RNP complex. We used protocol EH-100 for nucleofection. Afterward, we added 70ul of media without antibiotics per condition and transferred 100ul of each condition per well. We changed the media 24 hrs later and passed them on day 4 to extract gDNA/RNA and cultivated them for another 3 days. We also extracted gDNA/RNA on day 7. gDNA and RNA/cDNA and qPCR are performed as described above. ## PCR for determination of CRISPR-Cas9-induced indels or base editing efficiency We isolated gDNA using QuickExtract DNA Extraction Solution (Epicentre, QE0905) from 1x105 cells. We used 100 or 200 ng of gDNA as a template for PCR reaction with the corresponding primers (see S9 Table). gDNA from parental teloHAEC cells was used as control. Obtained PCR products were analysed by electrophoresis on a $1\%$ agarose gel prior to Sanger sequencing. We used TIDE (tracking of indels deconvolution) software for analysis [66]. ## Base editing of rs12906125 We infected our teloHAEC ABE8e-SpRY population with a guide in rs12906125 locus, CGGGACGGTCGGGCCGGTCC, cloned in pHKO9-Neo-CM as previously described [63]. teloHAEC are endogenously heterozygous at rs12906125 (A/G) and the treatment with ABE8e-SpRY should edit the genotype to G/G. Because of multiple Cs near the SNP, it was not possible to engineer clear edits towards the A/A genotype using cytosine base editors. After 3 weeks of proliferation, we derived clones by limiting dilution and extracted gDNA for PCR. We performed PCR as already described in the previous section with primers sequences (see S9 Table) and we used EditR software for analysis [67]. 45 clones out of 60 were analyzed by EditR and 15 out of 45 were edited (perfect edit, with the A/G genotype at rs12906125 edited to the G/G genotype, ~$30\%$ editing efficiency). We extracted also RNA to perform FES and FURIN mRNA expression by qPCR. RNA extraction, cDNA and qPCR we generated as described in the previous section. We compared expression with and without TNFα (10ng/μl) treatment for four hours. ## Assays cell senescence Using the same experimental design (DHX38-Crimson), we performed beta-galactosidase staining using the CellEvent Senescence Green Flow Cytometry Assay Kit from Invitrogen on day 2, day 4 and day 7 following the manufacturer’s protocol. Briefly, we trypsinized and we fixed the cells with $2\%$ paraformaldehyde solution for 10 minutes at room temperature, washed them in $1\%$BSA/PBS and incubated for 1h30 in $\frac{1}{500}$ working solution. After incubation, we washed the cells with $1\%$BSA/PBS and analyzed them by flow cytometry. We measured the β-galactosidase fluorescence signal in positive and negative Crimson cells independently. As positive control, non-infected cells were treated with 20 μM of Etoposide (Sigma, E1383-25) for 2, 4 and 7 days. ## Transcriptome data analysis For RNA-seq analysis, we extracted RNA using RNeasy plus mini kit from Qiagen (cat #: 74136). RNA-seq experiments were carried out by the Centre d’Expertise et de Services Genome Quebec using rRNA-depleted TruSeq stranded (HMR) libraries (Illumina) on an Illumina Hiseq 4000 instrument (paired-ends, 100-bp reads) and by The Center for applied Genomics (Toronto) using rRNA-depletion library prep on an Illumina NovaSeq-SP flow cell. We quality-controlled raw fastq files with FastQC and multiQC [67]. We used kallisto (v. 0.46.0) to quantify transcript abundances [68] against ENSEMBL reference transcripts (release 94) followed by tximport to calculate gene-level counts [69]. We utilized regularized log-transformation (rlog) in DESeq2 [61] as input for principal component analysis (PCA). DESeq2 [69] was further used to identify differentially-expressed genes between teloHAEC cell models (Cas9, dCas9-VP64) infected by lentiviruses with safe-harbor sgRNA or sgRNA identified in the pooled CRISPR screens. We excluded genes expressed with less than 10 reads across all samples from the analysis. We performed shrinkage for effect size estimates using apeglm using the lfcShrink method [70]. Genes differentially expressed with a Benjamini-Hochberg adjusted p-value ≤ 0.05 were considered significant (S10 Table). Gene set enrichment analysis was performed using the R package fgsea using 100,000 permutations against the Hallmark gene sets from msigdbr (https://igordot.github.io/msigdbr/) [71,72]. We quantified short indels in the RNA-seq data of DHX38 (sgRNA_10966) and MAT2A (sgRNA_02249) using the tools transIndel and Genesis-Indel, which are specifically designed to identify indels in the unmapped read fraction of samples [73,74]. ## Overexpression of open reading frames (ORF) in teloHAEC We obtained pEntry vectors containing the CCDC92 ORF from John D. Rioux’s lab. First, we cloned the gateway cassette from pLVX-EF1α-attR1-ccdB-attR2-IRES-puro-emGFP digested with XbaI (NEB, cat no R0145S) and ligated with Quick ligase from NEB (cat no M2200) in pLVX-EF1α-IRES-mCherry (refer as Empty-mCherry thereafter) also digested by XbaI and dephosphorylated using Fast AP (ThermoFisher cat no FEREF0654). Briefly, we used 50ng of digested vector and a ratio of 3:1 of insert. We performed ligation for 15 min at room temperature and then we transformed 2μL of ligation in One shot ccdB Survival 2 T1 Competent cells (Life Technologies, cat no A10460) and plated on LB agar ampicillin plates. Both vectors were gifts from John D. Rioux’s lab. We individually cloned each ORF using Gateway LR Clonase II Enzyme mix protocol (Life Technologies, cat no 11791020) in the new pLVX-EF1α-attR1-ccdB-attR2-IRES-mCherry vector. Briefly, we used 300ng of each pEntry/pDONR ORF vector with 300 ng of pLVX-EF1α-attR1-ccdB-attR2-IRES-mCherry with 4μL of 5X LR buffer and TE pH8.0 to 16 μL, then we added 4 μL of LR enzyme and incubated for 1 hr at 25°C. After this incubation, we added 2 μL of proteinase K and incubated for another 10 min at 37°C. We transformed 1 μL of each reaction in One Shot Stbl3 Chemically Competent E. coli (Life Technologies, cat no C7373-03) and plated on LB agar ampicillin plates. All vectors and ORF sequences have been validated by Sanger sequencing. ## Analysis of scRNA-seq data from human coronary arteries Single-cell gene expression matrix from human right atherosclerotic coronary arteries (three male and one female donors), was downloaded from NCBI GEO (GSE131780, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131780). The data was re-analyzed using the Seurat package in R with a standard single-cell clustering pipeline. Gene expression data was normalized using the SCTransform function from Seurat (v.3.2.3), regressing out the percentage of mitochondrial gene expression. Principal components analysis was performed, followed by dimensional reduction with Uniform Manifold Approximation and Projection (UMAP) using the first 20 principal components as input. Gene expression was visualized on the first two UMAP dimensions using the kernel density function (plot_density) from the Nebulosa package (v.0.99.92)[75] for endothelial cell marker and candidate genes. ## Statistics and data analysis Unless noted otherwise, we performed all data and statistical analyses in R (v.3.6.0) using Rstudio. We ran our analyses on a high performance computing cluster (Beluga) from Calcul Quebec/Compute Canada. For MAGECK variant-level analyses, permutation-based FDR of ≤$10\%$ were considered significant. For RNA-seq analysis, genes with a Benjamini-Hochberg adjusted P-value in DESeq2 ≤0.05 were considered significant [75]. 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--- title: Volumetric imaging of human mesenchymal stem cells (hMSCs) for non-destructive quantification of 3D cell culture growth authors: - Oscar R. Benavides - Holly C. Gibbs - Berkley P. White - Roland Kaunas - Carl A. Gregory - Alex J. Walsh - Kristen C. Maitland journal: PLOS ONE year: 2023 pmcid: PMC10047548 doi: 10.1371/journal.pone.0282298 license: CC BY 4.0 --- # Volumetric imaging of human mesenchymal stem cells (hMSCs) for non-destructive quantification of 3D cell culture growth ## Abstract The adoption of cell-based therapies into the clinic will require tremendous large-scale expansion to satisfy future demand, and bioreactor-microcarrier cultures are best suited to meet this challenge. The use of spherical microcarriers, however, precludes in-process visualization and monitoring of cell number, morphology, and culture health. The development of novel expansion methods also motivates the advancement of analytical methods used to characterize these microcarrier cultures. A robust optical imaging and image-analysis assay to non-destructively quantify cell number and cell volume was developed. This method preserves 3D cell morphology and does not require membrane lysing, cellular detachment, or exogenous labeling. Complex cellular networks formed in microcarrier aggregates were imaged and analyzed in toto. Direct cell enumeration of large aggregates was performed in toto for the first time. This assay was successfully applied to monitor cellular growth of mesenchymal stem cells attached to spherical hydrogel microcarriers over time. Elastic scattering and fluorescence lightsheet microscopy were used to quantify cell volume and cell number at varying spatial scales. The presented study motivates the development of on-line optical imaging and image analysis systems for robust, automated, and non-destructive monitoring of bioreactor-microcarrier cell cultures. ## Introduction In 2002, the Food and Drug Administration (FDA) announced a new science-based initiative to modernize quality management of pharmaceutical manufacturing and product quality by developing and implementing technologies that measure, control, and/or predict quality and performance of a process or product [1]. Utilizing Quality by Design (QbD) principles, traditional pharmaceutical and cell-based therapy manufacturers have recently been encouraged to develop and utilize Process Analytical Technologies (PATs) that perform real time or near real time monitoring of key variables throughout the manufacturing process to ensure quality of the final pharmaceutical product [2, 3]. In cell-based therapy manufacturing, PATs are designed to increase understanding of the cell culture process and facilitate the monitoring and control of critical process parameters (CPPs) that directly influence the quality and safety of the final cell product [4]. Ideal PATs operate in on- or in-line configurations, permitting automated and (near-) real time non-destructive measurements and analysis. The identification of CPPs for stem cell cultures and the development and deployment of PATs will improve the ability to study novel cell lines and expansion methods and enhance the capability to monitor, control, and ultimately predict the final product quality [2, 5]. Cell-based therapies, or cytotherapies, have the potential to address an unmet need for therapies that cure or treat chronic diseases such as cancer, osteoporosis, diabetes, and stroke [6–9]. Entry into the clinic will require billions of cells per indication per year, and one critical challenge in upstream cytotherapy manufacturing is the efficient large-scale expansion of stem cells to maximize yield while maintaining safety and therapeutic efficacy [10]. Even with the latest generation of multi-stacked cell factories, two-dimensional (2D) monolayer cultures have limited surface area for expansion, are labor- and reagent-intensive, and require serial passaging, which renders them sub-optimal for large-scale cellular expansion [11]. Three-dimensional (3D) cell cultures better mimic the in vivo stem cell niche than standard monolayer cultures while exploiting the 3rd spatial dimension for cellular expansion [12–14]. Bioreactor-microcarrier suspension cultures are the most promising 3D cell culture method as bioreactors can be scaled up to volumes of 1,000 liters, and microspheres greatly increase the available surface area to volume ratio, reduce labor and reagent use, and can be functionalized for specific needs [10], [15–18]. Furthermore, several groups have shown that 3D microcarrier human mesenchymal stromal cell (hMSC) cultures can provide a greater cell yield than 2D monolayer cultures without compromising viability, identity, or differentiation potential [19–23]. Recently, our group used spherical biodegradable gelatin methacryloyl (gelMA) microcarriers and bioreactor suspension cultures to demonstrate scalable expansion, rapid harvest, and non-destructive 3D in toto sub-micron visualization of induced pluripotent stem cell-derived hMSCs (ih-MSCs) via reflectance confocal microscopy (RCM) without the need for detachment from the microcarrier surface [24]. In regenerative medicine and biopharmaceutical manufacturing involving expansion and harvest of living cells, cell number is the most fundamental cell culture process parameter that requires quantification. Cell enumeration is needed to evaluate viability and proliferation, and in functional assays where activity is normalized to cell number such as engraftment [25, 26]. While there is no single established cell enumeration method for microcarrier cultures, essentially all off-line methods are destructive as they require either detachment of cells from the microcarrier surface and/or membrane lysing and exogenous labeling [27]. Two of the most common off-line cell enumeration and viability assays, trypan blue dye exclusion and live/dead fluorescence using Calcein AM and propidium iodide (PI), both of which are based on membrane integrity, are destructive to the samples and remove potentially valuable morphological and spatial distribution information [28–34]. Additionally, cell number can be measured on-line or in-line via a number of optical techniques, such as optical density measurements, in situ microscopy, micro-flow imaging, imaging and flow cytometry, and IR and fluorescence spectroscopy, and non-optical methods based on dielectric spectroscopy, acoustic measurements, or the chemical analysis of off-gas, media, protein, or DNA content in a sample [35–53]. Non-visualization cell enumeration methods are incapable of providing insight on cell morphology or spatial distribution, which in traditional monolayer cultures are readily monitored using in-process brightfield or phase-contrast microscopy; these CPPs are informative and potentially predictive features of cellular fate, proliferation, and functional potential in monolayer cultures [4, 5, 30–34, 54, 55]. The evaluation of microcarrier surface confluency and spatial distribution of cells can provide insight into the culture microenvironment and better enable automated, objective real-time release of intermediate upstream cell cultures once a certain confluency threshold is reached [30, 56]. An ideal biomass monitoring PAT for bioreactor-microcarrier anchorage-dependent cell culture performs measurements on-line or in-line and in toto, leaving cells attached to microcarriers and cell-microcarrier aggregates undisturbed so as to preserve cell morphology and 3D spatial distribution information. Several imaging, microscopy, and visualization methods for cell enumeration in microcarrier-based cultures have been investigated, but an industry standard has yet to be determined. Automated image analysis could be incorporated into image- or visualization-based assays for more rapid and robust quantification [57–59]. Trypan blue dye exclusion and live/dead fluorescence labeling both require exogenous contrast agents, so they cannot be incorporated into on- or in-line assays. Off-line fluorescence-based direct cell enumeration assays are, however, used to correlate experimental on-line cell enumeration or biomass sensors [35, 60]. These assays have been based on total fluorescence intensity as opposed to 3D spatial volume which considers 3D cell morphology. Volumetric fluorescence microscopy can be used to characterize cell density, distribution, and morphology, but requires destructive exogenous fluorescent markers [61, 62]. We previously demonstrated RCM, based on back-scattered elastic photons, could be employed to achieve label-free, sub-micron in toto visualization of hMSCs attached to spherical microcarriers [24]. This optical method allows for cell enumeration, but raster scanning a 3D volume of ~1503 μm3 is too slow for on-, in-, or even off-line measurements. The rapid and photo-efficient light sheet microscopy (LSM) technique presents a more viable method for non-destructive monitoring of microcarrier cultures [63, 64]. Fortunately, elastic scattering light sheet microscopy (esLSM), also known as light sheet tomography (LST), can be used for in toto imaging of hMSCs attached to microcarriers and quantification of cell number while preserving cell morphology. Contrast is generated from elastically scattered photons as opposed to more traditional light sheet fluorescence microscopy (LSFM) that utilizes fluorescence for imaging [65–67]. Here, we report a proof-of-concept study on volumetric optical imaging and semi-automated image analysis for off-line fluorescence and on-line elastic scattering quantification of cell number and volume of hMSCs cultured on spherical hydrogel microcarriers in toto, without the need for cellular detachment. The off-line fluorescence assay utilizes LSFM and CellTracker Green cytoplasmic and DRAQ-5 nuclear labeling for cell volume quantitation and direct cell enumeration of single microcarriers and large aggregates. This fluorescence assay is the first imaging-based assay to use volumetric data to more accurately characterize the 3D microcarrier cell culture and the first to directly enumerate cells within large aggregates in toto. The on-line assay utilizes esLSM and image analysis (ELIAS) to quantify cell volume of single microcarriers and aggregates non-destructively. Cell number from the fluorescence assay was correlated to cell volume from the ELIAS assay. The ELIAS assay has the capability to be adopted as an on-line PAT for robust non-destructive monitoring of bioreactor-microcarrier cell culture growth which would improve process and quality control for cytotherapy manufacturing. ## Induced pluripotent stem cell-derived hMSC (ih-MSC) culture Passage 4 ih-MSCs were first expanded in low-density monolayer cell culture in complete culture medium (CCM) (α-Minimum Essential Medium, $10\%$ fetal bovine serum, 2 mM L-glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin) to obtain the required cell numbers. The ih-MSCs [68] were cultured in a rotating wall vessel (RWV) bioreactor (RCCS-8DQ bioreactor (Synthecon, Houston, TX) fitted with 10 mL high aspect ratio vessels [69] on custom-fabricated 120 ± 6.2 μm diameter gelMA microcarriers [24]. For this purpose, approximately 110,000 gelMA microcarriers with a combined growth area of 50 cm2 and 5x104 cells (1000 cells/cm2) were incubated in 10 mL of CCM in the RWV bioreactor at 24 revolutions per minute. Half of the media was replaced with fresh CCM every 2 days. Specimens were recovered and fixed at passages 4 and 7 on days 3 and 7, for a total of four samples. ## Sample preparation At day 3 and day 7 of RWV bioreactor culture, CCM was removed and microcarrier-expanded cells were suspended in 1 mM concentration of CellTracker Green (CTG) for 45 minutes. The CTG target is distributed uniformly in the cell cytoplasm, and was used here to visualize 3D cell morphology and quantify cell volume. Cells were fixed with $4\%$ paraformaldehyde (PFA) and stored in phosphate buffered saline (PBS) at a concentration of 3 mg particles/mL PBS for long-term storage. Fixed microcarrier-cell samples were incubated with a 5 μM DRAQ-5 DNA and 6.5 μM DiI plasma membrane staining buffer at 37º C for 30 minutes with agitation, then rinsed with PBS. The far-red fluorescent DRAQ-5 stain was used for cell nuclei visualization and direct cell enumeration. The orange fluorescent DiI label was used to illustrate a simpler staining method for visualization of the plasma membrane only. The microcarriers were embedded in $1\%$ agarose in a custom-designed and 3D-printed sample chamber (S1 Fig) [70]. A 300 μL aliquot was loaded into each sample chamber at a concentration of 6 mg particles/mL agarose. The chamber enables dual-sided lightsheet illumination, trans-illumination for widefield imaging, and >180° sample rotation for multi-view acquisition and optimized sample positioning. ## Off-line fluorescence-based cell enumeration and cell volume quantification The Zeiss Z1 Lightsheet microscope, with a 20X 1.0 NA (water) detection objective lens and 10X 0.2 NA illumination objective lenses, was used for in toto imaging of fixed ih-MSCs attached to spherical microcarriers. The 488 nm (power $5\%$) and 638 nm (power $9\%$) lasers were used to excite the CTG and DRAQ-5 fluorophores, respectively. The voxel size was 0.2 x 0.2 x 0.45 μm3 to satisfy Nyquist sampling requirements. The emission filters used were 505–545 nm and 660+ nm for CTG and DRAQ-5, respectively. Dual objective illumination with pivot scanning and online max fusion was used to improve illumination of microcarrier aggregates and reduce acquisition time. The camera integration time was set to 20 ms per frame, or 50 FPS, and the illumination power was adjusted to use the full dynamic range of the detector. Imaris image analysis software was used to view and analyze the 3D volumes. For direct cell enumeration, the ‘Spot’ function was used on the DRAQ-5 volumes with an object size filter of 10 μm for automatic detection of cell nuclei, followed by a manual high-pass intensity threshold to further segment out cell debris and ultimately enumerate only cell nuclei (S2 Fig). For quantification of cell volume, the ‘Surface’ function was used on the CTG volumes with a manual high-pass intensity threshold and size filter to remove cell debris from quantification (S3 Fig). No preprocessing of the data was required for cell segmentation as the gelMA microcarriers produce little background signal [24]. For enumerating microcarriers, the ‘Surface’ function was used on the CTG volume with a low-pass intensity-based threshold to create a solid object. Then, the ‘Spot’ function with a 90 μm size filter was used to automatically enumerate individual spherical microcarriers. ## On-line elastic scattering-based cell volume quantification The agarose-embedded and mounted microcarrier-cell samples were used to evaluate the feasibility of the ELIAS method for label-free, non-destructive, in toto imaging and characterization of 3D microcarrier cell culture growth. For elastic scattering imaging on the Z1 Lightsheet microscope, the 638 nm laser (power $0.1\%$) was used to illuminate the sample. The laser blocking filter and emission filters were removed from the optical path. The camera acquisition time was minimized to 10 ms per frame, and the laser power was adjusted until there were no saturated pixels when viewing a cell. Dual-sided illumination, pivot scanning, and online max fusion were turned on. The Imaris ‘Surface’ function was used to segment the cells and microcarriers from each other and the agarose. For cell volume quantification, a high-pass intensity threshold and a high-pass size filter were used (S4 Fig). For microcarrier enumeration, a low-pass intensity threshold and 90 μm size filter were used (S5 Fig). ## 3D visualization via lightsheet microscopy To demonstrate the ability to use volumetric fluorescence and label-free elastic scattering microscopy for direct and non-destructive quantitative monitoring of cell culture growth, ih-MSCs attached to gelMA microcarriers were imaged at four timepoints using lightsheet microscopy (Fig 1). The gelMA microcarriers, which have a refractive index (n) of 1.35 [24], permit 3D visualization of the entire microcarrier surface and core, enabling direct cell enumeration in toto via DRAQ-5-labeled nuclei and quantification of cell volume using LSFM (S1 and S2 Videos) and esLSM (S3 and S4 Videos) while preserving the integrity of the cell morphology. Fig 1: LSM and hydrogel microcarriers enable direct cell enumeration (DRAQ-5) and volume (CTG) quantification in toto.Representative 2D LSM max. intensity projections of ih-MSCs attached to gelMA microcarriers at passage 4 day 3 (P4 D3), passage 4 day 7 (P4 D7), passage 7 day 3 (P7 D3), and passage 7 day 7 (P7 D7) using a-d) CTG fluorescence and e-h) elastic scattering contrast. i-l) DRAQ-5 labeled nuclei were used to estimate the m) normalized frequency of counted ih-MSCs attached to single gelMA microcarriers at P4 D3 (n = 30), P4 D7 (n = 20), P7 D3 (n = 23), and P7 D7 (n = 26). A zero-truncated Poisson distribution is fit over the sampled data (red line). Scale bar = 25 μm. The CTG volumes reveal the individual cell morphologies, and there appeared to be an increase in total cell volume from day 3 to day 7 in both passages (Fig 1a–1d). The label-free elastic scattering shows similar cell morphology and cell growth over time as the fluorescence data (Fig 1e–lh). This suggests both contrast methods can be used to view cells and quantify cell volume. The DRAQ-5-labeled nuclei data show an increase in cell density from day 3 to day 7 within each passage (Fig 1i–1l). A maximum of 19 cells per microcarrier was observed at Passage 4 Day 7 using the DRAQ-5 data (Fig 1j). The DRAQ-5 volumes were used to monitor the distribution of cells/microcarrier on single microcarriers over time (Fig 1m). Aggregates were excluded from the histograms to avoid weighting against single microcarriers. Similarly, non-populated microcarriers were not studied. A zero-truncated Poisson distribution was fit to the histograms to account for excluding empty microcarriers from acquisition and analysis [71]. These data suggest a decrease in cells/microcarrier or cell density at passage 7 compared to passage 4 overall. The elastic scattering signal seems to originate from the cytoplasm, and the nucleus tends to appear as a cavity that exhibits little to no elastic scattering signal (Fig 1h). This cytoplasm-dominant elastic scattering phenomena has been previously reported [72], and here we similarly show that nuclear-bound fluorescent markers and elastic scattering microscopy provide complimentary information on different cell regions. ## Optical sectioning and hydrogel microcarriers In LSM, a stack of thin (~2 μm) planes is sequentially illuminated within the microcarrier sample, allowing more precise localization of interesting higher-resolution biological phenomena, such as a cell infiltrating the center of a gelMA microcarrier (Fig 2). The infiltration is clearly discernible in a 3D rendering of the dataset using both fluorescence and elastic scattering contrast (S5 and S6 Videos). In the 2D max. intensity projection of the microcarrier 3D volume from the LSM, it is not possible to discern the cell process burrowing into the core of the microcarrier (Fig 2a). This appears to be a large, binucleated cell wrapping around ~$\frac{1}{3}$ of the microcarrier (Fig 2b). A max. intensity projection of the middle $\frac{1}{3}$ volume of the microcarrier allows for visualization of the cell process extending into the core of the microcarrier (Fig 2c). There is, interestingly, a clear delineation of the nuclear envelope and microcarrier infiltration outlined by DiI staining (Fig 2d) [73]. Assuredly, the elastic scattering mode is also able to visualize this cell infiltration, illustrating that complex cell-microcarrier interactions and cell morphologies can be visualized and characterized with esLSM (Fig 2e). Fig 2: The optical sectioning capabilities of LSM permits visualization into the hydrogel microcarrier.a) 2D max. intensity and orthogonal projection of P7 D3 microcarrier with a single CTG-expressing cell. 2D max. intensity projections using the middle 1/3 of the volume to view the interior of the microcarrier using b) DRAQ-5, c) CTG, d) DiI plasma membrane stain, and e) elastic scattering contrast. Owing to the optical sectioning capabilities of LSM and refractive index matching of the microcarriers, biological features such as cell infiltration into the microcarrier can be visualized (white arrow). Scale bar = 25 μm. ## In toto imaging of large aggregates Owing to the superior optical properties of the hydrogel microcarrier and optical sectioning ability of LSM, large cell-microcarrier aggregates that form later in culture with increased cell growth can reach volumes > 4 mm3 and still be imaged in toto. This permits semi-automatic cell enumeration, microcarrier enumeration, and cell volume quantification of large microcarrier aggregates using both off-line fluorescence and the ELIAS methods (Fig 3). The CTG data shows a complex network of cellular connections throughout the aggregate (Fig 3a). The DiI plasma membrane stain, which does not require a live incubation period for conversion into a fluorescent marker, similarly reveals a large cellular network (Fig 3b). There were 5,673 individual cell nuclei enumerated using the DRAQ-5 data (Fig 3c). Using the elastic scattering data, which provides slight contrast for the gelMA material, 1,754 microcarriers were detected, for an average of 3.32 cells per microcarrier (Fig 3d). The elastic scattering modality also reveals the cells throughout the entire microcarrier aggregate (Fig 3d). Small scatterers in the agarose and cell debris on the microcarrier surfaces can be segmented out with intensity- and size-based filters as cells scatter at higher intensity values and are larger than the debris (Fig 3e). The higher-resolution, merged projection of the DRAQ-5 and CTG data illustrates the density of cells within aggregates (Fig 3f). The elastic scattering and DRAQ-5 zoomed-in merged projection reveals similar cell morphologies as the CTG data even for this large aggregate (Fig 3g). A 1 mm sweep in depth through the aggregate further exemplifies that elastic scattering can visualize both microcarriers and the complex network of cellular connections that create an aggregate in culture (S7 Video). Fig 3: Large aggregates can be imaged and analyzed in toto using fluorescence and label-free LSM.2D maximum fluorescence intensity Z-projections of ih-MSCs labeled with a) CTG, b) DiI, and c) DRAQ-5. (d) Elastic scattering also allows for visualization of cells within large aggregates. e) Small scatterers in agarose and cell debris can be segmented out based on intensity and/or size using Imaris. f) CTG + DRAQ-5 merge and g) elastic scattering + DRAQ-5 merge at higher resolution. Scale bar = 400 μm. ## Cell enumeration and cell volume quantification An off-line fluorescence-based assay for direct cell enumeration and cell volume quantification of cell expansion on microcarriers was developed. This method is based on the volume of cellular fluorescence as opposed to total fluorescence intensity. The DRAQ-5 data show that the average cells/microcarrier increased from day 3 to day 7 during both passages, but at a greater rate during passage 4 than passage 7 (Fig 4a). However, there was a lower average cells/microcarrier for both timepoints in passage 7 compared to passage 4. Additionally, larger aggregates were seen in passage 4 than passage 7 at day 7, and the average cells/microcarrier of aggregates > 50 microcarriers was 5.60 and 4.20 for passage 4 day 7 and passage 7 day 7, respectively. The average single cell volumes quantified by CellTracker Green fluorescence and elastic scattering showed similar overall trends (Fig 4b); there was little change in the average cell volume throughout passage 4, but passage 7 cells were larger in volume overall and actually decreased in volume from day 3 to day 7. This study of microcarrier cell growth shows that CellTracker Green and elastic scattering data allow quantification of cell volume. Both fluorescence and elastic scattering modalities showed a linear correlation between total cell volume and nuclear fluorescence-validated cell number at both passages throughout both timepoints (Fig 4c–4f). The modalities showed almost equivalent goodness-of-fit values; 0.98 at passage 4 and 0.93 at passage 7. Fig 4: Optical imaging and semi-automated image analysis enable rapid characterization of microcarrier-expanded cell growth in toto.a) The overall average CPM at each sampled timepoint for all microcarriers sampled including aggregates from the DRAQ-5 data. b) The average single cell volume quantified by the fluorescence-based and the elastic scattering-based methods. Data are presented as mean with standard deviation (error bars). The DRAQ-5 labeled cell number versus the total cell volume quantified from the CellTracker Green fluorescence for all c) passage 4 and d) passage 7 samples. The DRAQ-5 labeled cell number versus the total cell volume quantified by the ELIAS method for all e) passage 4 and f) passage 7 samples. The linear trends and R2 values are shown. ## Discussion Visualization-based monitoring of cytotherapeutic cells during expansion has provided an evidence-based, cost-effective, and minimally-invasive means to assess culture health in real time; however, standard widefield microscopy methods used to evaluate monolayer cultures do not readily translate to 3D microcarrier-based cultures. Our work here is aimed at addressing this need for high-throughput evaluation of cells grown on spherical microcarriers by using fast and photo-gentle lightsheet microscopy combined with image analysis for robust and (semi-) automated analysis. There are two key innovations of this work. First, is the development of an off-line, volumetric, fluorescence-based assay using LSFM and image analysis for direct cell enumeration and cell volume quantification of ihMSC-microcarrier samples that range over an order of magnitude in size: from 100 μm early in culture to > 1 mm later in culture. Second, is the development and demonstration of a non-destructive assay for monitoring of cellular growth via the utilization of esLSM and image analysis that could be further developed into an on-line PAT using microfluidics connected to the bioreactor culture for sampling. In this work, the fluorescence-based assay is used as ground truth data to correlate the elastic scattering-based assay data, but as validated, esLSM can be used for label-free monitoring of microcarrier samples independently of fluorescence-based assays. These experiments demonstrate, for the first time, direct cell enumeration and cell volume quantification of large microcarrier aggregates, and that non-destructive elastic scattering contrast can be used to monitor microcarrier-bioreactor cell culture growth in toto, without the need for cellular detachment, membrane lysing, or exogenous labeling (S6 Fig). The minimal refractive index mismatch between the hydrogel microcarriers and surrounding agarose medium permits high-resolution visualization of cell morphology even throughout large cell-microcarrier aggregates. Similarly, the refractive index differences between the gelMA microcarrier, surrounding agarose, and cell matter cause all 3 classes of objects to scatter at varying intensities and therefore, they can be segmented from each other. Only a handful of studies have focused on the development of optical imaging systems and methods for studying microcarrier-based cell cultures. Jakob et al. used confocal microscopy and LSFM to image MDCK-II cells on Cytodex 3 microcarriers, but only acquired half microcarrier stacks and required sample rotation to visualize cells along the entire microcarrier surface [63]. The optical projection tomography methodology used in Jakob et al. increases the acquisition time and amount of data needed to accurately reconstruct the 3D cell-microcarrier sample compared to more conventional z-stacks for 3D data reconstruction. Duchi et al. demonstrated that optical sectioning via LSFM enables imaging of small cell-microcarrier clumps, but did not image or analyze clumps or aggregates of more than 5 microcarriers as their study focused on single cell motility and distribution on individual microcarriers [74]. In situ microscopy and micro-flow imaging enable direct enumeration of cells attached to microcarriers, but both methods are widefield techniques limited to visualizing the proximal half of the microcarrier and neither has the spatial resolution nor contrast for single cell visualization [43], [48]. Odeleye et al., using a custom in situ microscope, was only able to image and analyze the proximal microcarrier surface and struggled to enumerate cells accurately once aggregation began [75]. Microflow imaging was used to broadly characterize microcarrier confluency of single microcarriers and small clumps, but the authors did not investigate the ability to analyze large aggregates [76]. Similarly, the map projection analysis method used by Baradez and Marshall to characterize cell growth on individual microcarriers from confocal microscopy data is likely unfeasible for large cell-microcarrier aggregates with true 3D structure [77]. Imaging cytometry presents an attractive solution for on-line visualization and quantification of cells attached to microcarriers, but few studies have used imaging cytometry characterized cells attached to spherical microcarriers or large cell aggregates [46, 47, 78]. The linear relationship between cell number and cell volume quantified by non-destructive in toto ELIAS provides confirmation that this method could be used to image and characterize samples in an aqueous environment. An ELIAS PAT for on-line monitoring of microcarrier cell cultures would further incorporate microfluidic chips and hydraulic flow to sample microcarriers from the bioreactor to the lightsheet for rapid analysis and back to the bioreactor culture [79, 80]. Furthermore, single objective lightsheet systems or oblique plane microscopy could better enable on-line imaging of microfluidic samples as there is only a single sample-facing objective and more space for sample mounting and translation below or above the objective [81, 82]. Additional motivation is presented with development of microfluidic chips composed of a polymer with a refractive-index matched to water which is compatible with the presented ELIAS method [83]. These systems and methods are more complex than traditional sampling and imaging methods, but would enable non-destructive, robust, and automated analysis of microcarrier-based and other suspension cultures. Once a cell culture process for cytotherapy manufacturing is standardized and the trend line between off-line fluorescence-validated cell enumeration, on-line ELIAS-quantified, and cytotherapy product quality is confirmed, deviations from the trend line could indicate issues with the culture health and quality. As few as 10–20 populated microcarriers may be needed to quantify the average cells per microcarrier throughout a culture [43]. The in situ and in toto study of large cell aggregates has, up to this point, been minimal due to the limited imaging depth of conventional microscopes and the opacity of large cell-microcarrier aggregates [75, 77, 84, 85]. Additionally, large aggregates are notoriously difficult to manipulate for study and can cause sampling errors [43, 86]. In toto or in situ volumetric imaging of these structures would enable analysis of cell morphology, density, and spatial distribution of cell viability throughout an aggregate [30]. Fortunately, the optical sectioning and the decoupled illumination and detection arms of LSM enable imaging deep (>>1 mm) into aggregates. Moreover, the combination of water-dipping objective lens with a long working distance (2 mm), objective lens axial correction collar to fine tune the refractive index mismatch, and hydrogel microcarriers allows for sub-micron resolution volumetric optical imaging of cells attached to spherical microcarriers in toto. Because all the pixels in a frame are acquired in parallel, acquisition time can still be relatively short (~1–2 minutes/4D dataset) even for large aggregates >1 mm in depth. The stripe artifacts, which arise from scattering and/or absorption of the illumination beam by small objects such as air bubbles, debris, or un-melted agarose particles, in LSM images can be removed via a number of hardware and image-processing methods, such as digital scanned lightsheet microscopy (DSLM) with pivot scanning or median digital filtering [87]. Future image analysis pipelines could include corrections for uneven illumination, striping artifacts, or photobleaching [88]. Photobleaching, a chemical alteration of fluorophores that render them unable to fluoresce, is not a concern in esLSM as it does not involve an energy transition; however, extra care has to be taken in esLSM to utilize the full dynamic range of the detector without saturation, even with very low incident laser power. Standard widefield microscopes, routinely used to visualize and qualitatively evaluate cell culture health, integrate photons from in- and out-of-focus planes, making them suitable for thin samples. Dimensionality reduction, by using a single 2D image for volumetric readouts of 3D cell cultures, leads to cell enumeration and segmentation errors that increase with cell growth and cell-microcarrier aggregates [58, 60, 86]. Cell volume could not be quantified from a single 2D projection of the microcarrier. Volumetric microscopy does enable single cell morphological measurements, such as sphericity or nuclear-to-cytoplasmic volume ratio, for low confluency microcarriers where cells do not overlap (S8 Video). More accurate 3D segmentation methods are needed for single cell segmentation and characterization at moderate or high confluency levels where cells overlap. High-throughput LSM enabling single cell morphological monitoring and profiling could be performed with a more robust cell segmentation method for 3D microcarrier cell cultures [46, 89]. In this study, there was a decrease in cell proliferation and an increase in cell size in passage 7 compared to passage 4, which may be due to replicative senescence [90, 91]. Additional image analysis and more frequent culture sampling could be undertaken to see if time-dependent senescence can be visualized and characterized for microcarrier cultures, as has been investigated for monolayer cultures [32, 33, 92]. Although high-throughput single cell morphological cytometry was not performed here, the presented fluorescence- and elastic-scattering cell growth characterization methods preserve all the spatial (3D cell morphology, distribution) information that is lost to cell lysing and detachment (S9 Video). The ability to characterize single cell morphology will better facilitate real-time decision making regarding the health, quality, or futility of a cell culture process. ## Conclusion We present optical imaging and image analysis methods for direct cell enumeration and cell volume quantification for microcarrier-expanded cells using in toto LSFM and esLSM. Both academic researchers and industry cytotherapy manufacturers would greatly benefit from the ability to monitor and quantify cell culture growth using a non-destructive imaging-based PAT. To the best of our knowledge, this is the first time direct cell enumeration of microcarrier culture aggregates has been reported in the literature. We used LSM to characterize the entire microcarrier surface, whereas other imaging-based microcarrier growth monitoring methods require either cell membrane lysing or cell-microcarrier detachment, or can only study the proximal half of the microcarrier. 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--- title: The Skin-Whitening and Antioxidant Effects of Protocatechuic Acid (PCA) Derivatives in Melanoma and Fibroblast Cell Lines authors: - Jaehoon Cho - Hyeonbi Jung - Dong Young Kang - Nipin Sp - Wooshik Shin - Junhak Lee - Byung Gyu Park - Yoon A Kang - Kyoung-Jin Jang - Se Won Bae journal: Current Issues in Molecular Biology year: 2023 pmcid: PMC10047566 doi: 10.3390/cimb45030138 license: CC BY 4.0 --- # The Skin-Whitening and Antioxidant Effects of Protocatechuic Acid (PCA) Derivatives in Melanoma and Fibroblast Cell Lines ## Abstract The skin is the most voluminous organ of the human body and is exposed to the outer environment. Such exposed skin suffers from the effects of various intrinsic and extrinsic aging factors. Skin aging is characterized by features such as wrinkling, loss of elasticity, and skin pigmentation. Skin pigmentation occurs in skin aging and is caused by hyper-melanogenesis and oxidative stress. Protocatechuic acid (PCA) is a natural secondary metabolite from a plant-based source widely used as a cosmetic ingredient. We chemically designed and synthesized PCA derivatives conjugated with alkyl esters to develop effective chemicals that have skin-whitening and antioxidant effects and enhance the pharmacological activities of PCA. We identified that melanin biosynthesis in B16 melanoma cells treated with alpha-melanocyte-stimulating hormone (α-MSH) is decreased by PCA derivatives. We also found that PCA derivatives effectively have antioxidant effects in HS68 fibroblast cells. In this study, we suggest that our PCA derivatives are potent ingredients for developing cosmetics with skin-whitening and antioxidant effects. ## 1. Introduction The skin is the most voluminous layer of the human body, playing the critical role of being a protective barrier against environmental influences and maintaining homeostasis [1,2]. In addition, the skin plays a vital cosmetic role in the human body and protects against water loss of the body and environmental harm [3]. Many factors could contribute to aging skin, including extrinsic factors, such as sun exposure, air pollution, and smoking, and intrinsic factors, such as genetics. However, the aging of body organs is initiated at birth, and the skin is no exception. Skin aging is distinguished by pigmentation, sunspots, uneven skin color, loss of elasticity, wrinkling, and rough-textured appearance [4]. Therefore, skin care is quickly becoming important in terms of skin health and beauty. Melanin is an essential factor in determining the skin, hair, and eye color of the body, playing a critical role in photoprotection through the absorption of solar ultraviolet radiation (UVR) [5,6]. In normal conditions, skin pigmentation by melanin synthesized in melanosomes plays the role of a photoprotective effect against UVR-induced DNA damage. In particular, α-melanocyte-stimulating hormone (α-MSH) induced by UVR exposure plays a leading role in protecting against DNA damage while mediating the induction of pigmentation in keratinocytes and melanocytes of skin cells [7,8,9]. However, hyperpigmentation by excessive production of melanin is a cause of dermatological problems, such as freckles, senile lentigo (age spots), melasma, melanoma, and post-inflammatory melanoderma [10,11,12]. The effect of skin whitening, known as skin lightening, is related to regulating the production of excessive melanin by performing melanogenesis-inhibitory activity and free radical scavenging capacity [13]. Therefore, various skin-whitening cosmetic agents are needed for dermatological treatment, skin beauty, and prevention. Oxidative stress is a phenomenon caused by an imbalance between the production and accumulation of reactive oxygen species (ROS) in cells and tissues and the ability of a biological system to detoxify these reactive products [14,15,16]. In addition, oxidative stress plays a fundamental role in the pathogenesis of chronic diseases, such as neurodegenerative diseases, cancers, or infection by the human immunodeficiency virus (HIV), related to increased ROS production [17,18,19]. As already stated, skin aging progresses when one is born and is accelerated by oxidative stresses caused by intrinsic and extrinsic skin aging factors, such as hormonal changes, inflammation, lifestyle, smoking, and UVR [20]. Excessive oxidative stress in the skin is caused by inflammation, pigmentation, acne, blackhead, and melanoma [21]. Therefore, since oxidative stress is one of the significant causes of skin aging or skin disorders, it is crucial to properly neutralize it for dermal health, care, and beauty. Plants are known as one of the sources used in the food, pharmaceutical, and cosmetic industries. Many products, such as supplements, nutricosmetics, and cosmetics are traditionally based on botanical ingredients [22]. Products of plant materials, including extracts, are used for the purpose of skin care and treatment of skin diseases, and they also contribute to the protection and restoration of skin barrier homeostasis [23]. Although the need for natural ingredients to make cosmetic products has increased, the effect of natural cosmetics, such as organic cosmetics, acts slower than conventional products. Some natural products could trigger allergic reactions. In addition, natural cosmetics may lack the exact substances that are needed for a particular type of skin. Thus, it is very important to use enough active ingredients with a specific efficacy in skin care. Recently, the main research trend has been the delivery of cosmetic substances for reforming cutaneous and subcutaneous layers of the skin in a healthy condition. In addition, there are many efforts to effectively improve the delivery of a specific natural compound by modifying the chemical structure [24,25]. The modified compounds generally undergo not only an efficacy evaluation but also a toxicity evaluation. Protocatechuic acid (PCA), which is chemically known as 3,4-hydroxybenzoic acid, is one of the leading secondary metabolites of phenolic acid found in plant-based materials, such as vegetables and fruits. It is well known that PCA has antioxidative, anti-inflammatory, and osteoporotic activities that prevent aging-related diseases [26,27,28]. A study demonstrated that PCA suppresses LPS-induced inflammatory stress in BV2 microglia by regulating SIRT1/NF-κB pathway [29]. PCA showed an anti-inflammatory effect in LPS-stimulated BV2 microglia through the NF-κB and MAPK signaling pathway [30]. In addition, PCA has biological activities, such as anti-wrinkle and anti-skin-aging properties, through its antioxidative effects in vitro and in vivo [31]. Therefore, PCA is often used as a beauty ingredient in cosmetics with antioxidant and senescence-inhibiting activities [32]. Although it is becoming clear that PCA may be useful as a beauty intervention in the improvement of various human skin troubles, the potential of PCA derivatives in anti-melanogenesis and skin whitening as cosmetic ingredients has never been reported. One study revealed that PCA, modified for improving drug delivery, effectively has an anti-inflammatory effect for the therapy of osteoarthritis [33]. In this study, we chemically synthesized various PCA derivatives (e.g., PCA-C3, C4, C5, C6, C7, and C12) and conjugated them with alkyl esters. We purchased PCA-C0, C1, and C2 for comparative experiments. The PCA derivatives effectively inhibited the production of melanin induced by treatment with α-MSH in B16 melanoma cells. They also have been shown to have antioxidant activity similar to the effect of ascorbic acid, being a powerful antioxidant in HS68 fibroblast cells. We suggest that PCA derivatives are potent ingredients for skin-whitening and skin-care cosmetics. ## 2.1. Synthesis of PCA Derivatives PCA-C0 (TCI C0055), C1 (TCI M1943), and C2 (D0571) were purchased from Tokyo Chemical Industry. Cellular tyrosinase activity was determined using a previously described method with modification. A series of alkyl esters of PCAs (e.g., PCA-C3, C4, C5, C6, and C7, and PCA-C12) were obtained by acid-catalyzed one-step esterification reaction utilizing dicyclohexylcarbodiimide (DCC) as an activating reagent, using a previously described method [34] and following a general procedure. Therefore, a solution of 3,4-dihydroxybenzoic acid (3.0 mmol) and alcohol (3.0 mmol) in THF (30 mL) cooled at 0°C was added to a solution of DCC (4.0 mmol). After the reaction mixture was stirred at room temperature, the solvent was partially evaporated under reduced pressure, and the residue was extracted with ethyl acetate three times. The combined organic layer was washed with brine, dried over sodium sulfate, and evaporated under reduced pressure. The resulting mixture was purified using fresh silica gel chromatography. The structures of the synthesized alkyl esters were characterized by 1H NMR. 1H NMR spectra were measured for acetone-d6 solutions at 25 °C using a JEOL JNM-ECX300 spectrometer, and the chemical shifts are reported in ppm. PCA-C3: PCA-C3 was obtained in $34\%$ yield as white power. 1H NMR (300 MHz, acetone-d6): δ 7.51 (d, $J = 2.0$ Hz, 1H), 7.47 (dd, $J = 8.0$, 2.0 Hz, 1H), 6.93 (d, $J = 8.0$ Hz, 1H), 4.32 (t, $J = 7.2$ Hz, 2H), 1.74 (sex, $J = 7.2$ Hz, 2H), 1.01 (t, $J = 7.2$ Hz, 3H). PCA-C4: PCA-C4 was obtained in $40\%$ yield as white power. 1H NMR (300 MHz, acetone- d6): δ 7.52 (d, $J = 2.8$ Hz, 1H), 7.45 (dd, $J = 8.0$, 2.8 Hz, 1H), 6.89 (d, $J = 8.0$ Hz, 1H), 4.23 (t, $J = 7.2$ Hz, 2H), 1.70 (quin, $J = 7.2$ Hz, 2H), 1.45 (sex, $J = 7.2$ Hz, 2H), 0.97 (t, $J = 7.2$ Hz, 3H). PCA-C5: PCA-C5 was obtained in $46\%$ yield as white power. 1H NMR (300 MHz, acetone- d6): δ 7.52 (d, $J = 2.0$ Hz, 1H), 7.46 (dd, $J = 8.5$, 2.0 Hz, 1H), 6.88 (d, $J = 8.5$ Hz, 1H), 4.24 (t, $J = 7.0$ Hz, 2H), 1.72 (quin, $J = 7.0$ Hz, 2H), 1.47 (m, 4H), 0.93 (t, $J = 7.0$ Hz, 3H). PCA-C6: PCA-C6 was obtained in $48\%$ yield as white power. 1H NMR (300 MHz, acetone- d6): δ 7.53 (d, $J = 2.0$ Hz, 1H), 7.45 (dd, $J = 8.0$, 2.0 Hz, 1H), 6.89 (d, $J = 8.0$ Hz, 1H), 4.23 (t, $J = 6.8$ Hz, 2H), 1.73 (quin, $J = 6.8$ Hz, 2H), 1.46 (m, 2H), 1.38 (m. 4H) 0.92 (t, $J = 6.8$ Hz, 3H). PCA-C7: PCA-C7 was obtained at $37\%$ yield as white power. 1H NMR (300 MHz, acetone- d6): δ 7.51 (d, $J = 2.0$ Hz, 1H), 7.48 (dd, $J = 8.4$, 2.0 Hz, 1H), 6.85 (d, $J = 8.4$ Hz, 1H), 4.24 (t, $J = 6.8$ Hz, 2H), 1.72 (quin, $J = 6.8$ Hz, 2H), 1.35 (m, 8H), 0.87 (t, $J = 6.8$ Hz, 3H). PCA-C12: PCA-C12 was obtained in $58\%$ yield as white power. 1H NMR (300 MHz, acetone- d6): δ 7.48 (d, $J = 2.0$ Hz, 1H), 7.43 (dd, $J = 8.0$, 2.0 Hz, 1H), 6.81 (d, $J = 8.0$ Hz, 1H), 4.23 (t, $J = 6.8$ Hz, 2H), 1.73 (quin, $J = 6.8$ Hz, 2H), 1.31 (m, 18H), 0.85 (t, $J = 6.8$ Hz, 3H). ## 2.2. Cell Culture and Cell Viability Assay B16 F1 melanoma (No. 80007; Korean Cell Line Bank, Seoul, Korea) and HS68 fibroblast (CRL-1635; American Type Culture Collection, Manassas, VA, USA) cell lines were maintained in Dulbecco’s Modified Eagle Medium (Gibco; 22400-089) plus $10\%$ FBS and $1\%$ penicillin at 37 ℃ in $5\%$ CO2. Cell viability was assayed by measuring the blue formazan that was metabolized from 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) by mitochondrial dehydrogenase. The cells were resuspended in the medium one day before treatment at a density of 3 × 103 cells per well in 96-well culture plates. The B16 and HS68 cells were incubated with or without various PCA derivative concentrations (e.g., C0, 1, 2, 3, 4, 5, 6, 7, and 12) for 24 h. MTT (5 mg/mL) was added to each well and incubated for 3 h at 37 °C. The formazan product was dissolved by adding DMSO, and absorbance was measured at 570 nm using an ultra-multifunctional microplate reader (TECAN, Durham, NC). All measurements were performed in triplicate and repeated at least three times. ## 2.3. Melanin Quantification Assay B16 melanoma cells were seeded at 2 × 104 cells/well and cultured in a 48-well plate for 24 h. The cell culture medium was changed, and the cells were treated with/without 200 nM of α-melanocyte-stimulating hormone (α-MSH) (SIGMA; M4135) and the indicated concentrations of protocatechuic acid (PCA) derivatives and further incubated at 37 °C for 72 h. After that, the supernatant of the culture medium was harvested, and the cells were gently washed three times with phosphate-buffered saline and then detached with trypsin/EDTA. The melanin contents of the supernatant and cells were lysed by incubation in 200 μL of 1 N NaOH at 80 °C for 2 h. The intracellular and extracellular melanin contents were transferred to a 96-well plate, and absorbance was measured at 405 nm. All measurements were performed in triplicate. ## 2.4. DPPH Antioxidant Assay HS68 fibroblast cells were seeded at 3 × 103 cells per well and cultured in 96-well culture plates for 24 h. Cells were treated with/without various indicated concentrations of PCA derivatives and further incubated at 37 °C for 48 h. The antioxidant effect of PCA derivatives was measured using a DPPH Antioxidant Assay Kit (Biovision; K2078) according to the manufacturer’s instructions. Absorbance was measured at 517 nm in endpoint mode at RT, protected from light. All measurements were conducted in triplicate. ## 2.5. Statistical Analyses All experiments were conducted at least three times. The results are expressed as the mean ± standard error of the mean. Statistical analyses were performed using a one-way analysis of variance (ANOVA) with Tukey’s post hoc test using the SAS 9.3 software program (SAS Institute, Inc., Cary, NC, USA). A p-value < 0.05 () was considered to indicate a statistically significant difference (#indicates the significance level). ## 3.1. Chemistry As mentioned above, it is well known that PCA plays a role in various pharmacological activities, mainly attributable to its antioxidant and anti-inflammatory properties. It was demonstrated in one study that increased PCA hydrophobicity could improve anti-inflammatory activity and anti-mutagenicity [35]. Thus, we chemically synthesized PCA derivatives conjugated with different lengths of alkyl esters to change the hydrophobicity. For the synthesis of PCA-C3–C7 and C12, we used an acid-catalyzed one-step esterification reaction with dicyclohexylcarbodiimide (DCC) as an activating reagent (Figure 1A). Then, a series of PCA derivatives (PCA-C3–C7 and C12) was obtained for this study (Figure 1B and Figures S1–S6). ## 3.2. Inhibitory Effect on α-MSH-Induced Melanogenesis Exhibited by PCA Derivatives in B16 Melanoma Cells Before studying the anti-melanogenic effects of PCA derivatives, we hypothesized that the increased lengths of alkyl esters on PCA derivatives might increase cell apoptosis in B16 melanoma cells. So, we investigated their cytotoxicity by analyzing the cell proliferation of B16 melanoma cells in the presence of increasing concentrations of different PCA derivatives for 24 h (Figure 2A). These results imply that an increased hydrophobicity on PCA derivatives affects cell viability. Therefore, it was also indicated by the results of our cytotoxicity assay that the cells exhibit higher levels of cell death as the length of alkyl esters increases. In the PCA-C0–C6, cell viability was not affected by a concentration of less than 1 µg/mL, and PCA-C7 and C12 did not affect cell viability in concentrations lower than 1.5 µg/mL and 0.25 µg/mL. Next, we evaluated the inhibitory effects on α-MSH-induced melanin synthesis in B16 melanoma cells. We demonstrated that PCA derivatives show different inhibitory effects according to the length of the alkyl esters (Figure 2B). To evaluate the inhibitory effects of PCA derivatives, we performed a melanin quantification assay to investigate the extracellular or intracellular melanin content in the absence or presence of PCA derivatives in α-MSH-induced cells. It is well known that kojic acid inhibits melanin synthesis by inhibiting the tyrosinase enzyme [36]. So, we used kojic acid as a positive control in this study. It was shown in our results that PCA derivatives contribute to downregulating the extracellular and intracellular melanin contents compared to PCA-C0, which is not conjugated with alkyl esters in B16 melanoma cells (Figure 2B and Table S1). In particular, the treatment with 1 µg/mL of PCA-C5 and C6 significantly decreased the extracellular and intracellular melanin contents more than one-half-fold compared to only α-MSH-induced control. However, 1 µg/mL of PCA-C5 and C6 was the concentration without cytotoxicity to cells. It is indicated by these results that an increase in hydrophobicity of PCA derivatives due to conjugating alkyl esters increased the inhibitory effect of α-MSH-induced melanin synthesis. In addition, these data might mean that increased hydrophobicity improves the delivery of PCA-C5 and C6 into the cells. It has been reported in some studies that long-chain alkyl esters are frequently used to increase the biological activities of drugs [37,38]. ## 3.3. Antioxidative Effect of PCA Derivatives on HS68 Fibroblast Cells We first investigated the cell proliferation of HS68 fibroblast cells in an increasing concentration of PCA derivatives to assess the cytotoxicity and evaluate the antioxidative ability of PCA derivatives (Figure 3A). Although PCA-C0, which is not conjugated with alkyl esters, did not influence cell viability, despite the treatment with a high concentration, treatment with PCA derivatives decreased cell viability with an increase in their concentrations. Notably, PCA-C7 and C12 had high cytotoxicity in HS68 fibroblast cells, which means increased hydrophobicity due to conjugating alkyl ester chains enhances cell death. We treated HS68 fibroblast cells with 5, 10, or 20 µg/mL of PCA derivatives for 24 h to determine antioxidant effects. We evaluated the free radical scavenging ability of PCA derivatives using a DPPH radical scavenging assay. Although PCA-C0 resulted in a decrease in free radical activity, PCA derivatives, especially PCA-C5 and C6, effectively decreased free radicals, showing an antioxidant effect (Figure 3B). In particular, PCA-C6 had the most effective antioxidant activity compared to ascorbic acid as a positive control. These results indicate that the conjugation of alkyl esters to PCA increased the hydrophobicity of PCA and that its characteristic enhanced an increase in the antioxidant activity in HS68 fibroblast cells. We suggest that PCA derivatives could contribute to anti-melanogenesis and antioxidant effects (Figure 4). ## 4. Discussion PCA is a natural phenolic compound well known for its antioxidative, anti-inflammatory, and anti-osteoporotic activities [26,27,28]. For example, PCA could protect against cell damage, apoptosis, and renal ischemia–reperfusion injury by reducing oxidative stress and tissue damage in rat cardiac muscle [39,40]. In addition, it was described in some studies that excessive inflammation, which can induce diseases such as arthritis, type 2 diabetes, and different types of cancer, was reduced by PCA [41,42]. It was clarified in one study that PCA compounds have biological activities, such as antioxidant activity, an anti-wrinkle effect, and collagen synthesis, as cosmetic ingredients [31]. However, the use of PCA as an anti-skin-aging ingredient is not suitable due to the high dose required. In this study, we chemically synthesized PCA derivatives containing alkyl esters of different lengths to lower their effective concentrations. We showed that our PCA derivatives have a cosmeceutical effect via skin-whitening and antioxidant activities at low concentrations. Melanogenesis is the process of the production of melanin pigment, which is produced in melanosomes by melanocytes. The process contains a series of enzymatic and chemical reactions in melanosomes, producing two types of melanin: eumelanin and pheomelanin [5,26,36,43]. α-MSH is a primary melanocyte-stimulating hormone that stimulates melanogenesis triggered by UVR exposure. α-MSH binds to the melanocortin 1 receptor (MC1R), which induces the activation of MCIR downstream, the cAMP/CREB signaling pathway for melanin production in melanocytes [26,44,45]. Various skin-whitening compounds extracted from natural products exert their effects by regulating the production of melanin through many mechanisms, including inhibiting the core signaling pathway and suppressing melanogenic gene expression [36]. One study demonstrated that [6]-shogaol, which is the major shogaol in the ginger rhizome, inhibits α-MSH-induced melanogenesis through the acceleration of ERK- and PI3K/AKT-mediated MITF degradation [45]. In addition, a PCA extracted from pears suppressed the conversion of ATP to cAMP downstream of MC1R, which resulted in the inhibition of melanogenesis by inhibiting melanogenic gene expression and melanin synthesis in B16F10 cells. We chemically synthesized PCA derivatives containing alkyl esters of various lengths to improve PCA delivery. These PCA derivatives, especially PCA-C5 and C6, effectively inhibited melanin synthesis at low concentrations as the lengths of alkyl esters increased (Figure 1 and Figure 2). However, PCA-C7 and PCA-C12 had cytotoxicity, although melanin synthesis had an inhibitory effect. ROS, often referred to as free radicals, such as superoxide anion radical (O2−•) and hydroxyl radical (•OH), are harmful to the skin [46]. ROS accumulated in the body induce oxidative stress in the skin. Eventually, ROS cause various skin disorders, such as acne, blackhead, and melanoma [21]. The* generation of oxidative stress in the skin is usually caused by UV/visible light/infrared radiation, pollution, skin microbiota, and stress. It was revealed in some studies that single compounds extracted from natural products had antioxidant effects on the skin. A natural compound extracted from Centella asiatica (L.) had antioxidant effects by elevating the transcription of antioxidant enzymes, such as CAT, GPx1, SOD1, and SOD2, and inhibiting the expression of an MMP9 transcript in human foreskin fibroblasts [47]. In addition, it was demonstrated in another study that natural compounds extracted from Artemisia iwayomogi (Dowijigi) decrease UV-mediated oxidative stress [48]. As mentioned above, it is well known that PCA has an antioxidant effect on the body. One group reported that PCA plays a role in oxidative scavenging stress from UVA irradiation damage in human dermal fibroblasts [31]. In our results, we indicated that PCA derivatives, especially PCA-C6, conjugated with alkyl esters of various lengths decrease oxidative stress more effectively than PCA-C0 in the HS68 fibroblast cells (Figure 3). It was indicated in our results that PCA derivatives chemically synthesized with alkyl esters have the potential to scavenge free radicals as the alkyl esters are longer. Many natural-derived substances have been used for the purpose of skin beauty or treatment [49]. Although synthetic or semi-synthetic substances have a lower safety than natural sources with non-toxicity and fewer side effects [50,51], they are used for cosmetic formulations of various purposes. We know that PCA derivatives presented in this study need to undergo the additional investigations for efficacy and safety through in vivo testing. However, we carefully suggest that alkyl ester-conjugated PCA derivatives can be applied as anti-skin-aging ingredients of cosmeceuticals at low concentrations. ## 5. Conclusions Protocatechuic acid (PCA), a type of natural phenolic acid, has been known to have not only an antioxidant effect but also various biological properties, such as antibacterial, anticancer, antiviral, and anti-inflammatory effects in vitro and in vivo. In this study, we investigated the anti-melanogenesis or skin-whitening and antioxidant effects of synthesized PCA derivatives. We chemically synthesized PCA derivatives conjugated with alkyl esters of different lengths. We performed a cell cytotoxicity assay for deciding effective concentrations. Then, we demonstrated that PCA derivatives, especially PCA-C5 and C6, more effectively suppress the melanin biosynthesis in the B16 melanoma cells and inhibit the free radical activity in the HS68 fibroblast cells at low concentrations than only PCA. It can be concluded that our PCA derivatives are potentially effective anti-skin-aging ingredients for cosmeceuticals. 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--- title: Body Adiposity Partially Mediates the Association between FTO rs9939609 and Lower Adiponectin Levels in Chilean Children authors: - Carolina Ochoa-Rosales - Lorena Mardones - Marcelo Villagrán - Claudio Aguayo - Miquel Martorell - Carlos Celis-Morales - Natalia Ulloa journal: Children year: 2023 pmcid: PMC10047575 doi: 10.3390/children10030426 license: CC BY 4.0 --- # Body Adiposity Partially Mediates the Association between FTO rs9939609 and Lower Adiponectin Levels in Chilean Children ## Abstract Children carrying the minor allele ‘A’ at the fat mass and obesity-associated protein (FTO) gene have higher obesity prevalence. We examined the link between FTO rs9939609 polymorphism and plasma adiponectin and the mediating role of body adiposity, in a cross-sectional study comprising 323 children aged 6–11 years. Adiponectin and FTO genotypes were assessed using a commercial kit and a real-time polymerase chain reaction with high-resolution melting analysis, respectively. Body adiposity included body mass index z-score, body fat percentage and waist-to-hip ratio. To investigate adiponectin (outcome) associations with FTO and adiposity, linear regressions were implemented in additive models and across genotype categories, adjusting for sex, age and Tanner’s stage. Using mediation analysis, we determined the proportion of the association adiponectin-FTO mediated by body adiposity. Lower adiponectin concentrations were associated with one additional risk allele (βadditive = −0.075 log-μg/mL [−0.124; −0.025]), a homozygous risk genotype (βAA/TT = −0.150 [−0.253; −0.048]) and a higher body mass index z-score (β = −0.130 [−0.176; −0.085]). Similar results were obtained for body fat percentage and waist-to-hip ratio. Body adiposity may mediate up to $29.8\%$ of the FTO-adiponectin association. In conclusion, FTO rs9939609-related differences in body adiposity may partially explain lower adiponectin concentrations. Further studies need to disentangle the biological pathways independent from body adiposity. ## 1. Introduction In the last decades, overweight and obesity prevalence has increased worldwide, affecting adults, children and adolescents in developed and developing countries [1]. In Chile, childhood obesity has steadily risen since the late 1980’s [2,3]. By 2020, prevalence of childhood overweight and obesity reached up to $64\%$, across different school grades [4]. At all ages the life course of obesity is associated with various detrimental health effects, such as metabolic syndrome (MetS) and type 2 diabetes (T2D). It is estimated that around $60\%$ of those suffering from obesity in their early life will show at least one metabolic alteration related to non-communicable chronic diseases (NCDs) in their adulthood, such as hypertension, dyslipidemia, insulin resistance or metabolic syndrome [5,6,7]. Obesity, a condition of excessive body fat, has been linked to lower concentrations of plasma adiponectin, a type of adipokine secreted by the adipose tissue [8]. Adiponectin has received special attention due to its pleiotropic role and beneficial effects on tissues such as skeletal muscle, liver, heart, and kidney tissues [9]. Several studies have provided evidence of adiponectin’s anti-inflammatory, anti-atherogenic [10,11] and insulin-sensitizing effects [12,13,14]. Moreover, lower plasma adiponectin has been observed among obese and T2D individuals [15,16], which has drawn more attention as a promising therapeutic target against T2D and cardio-metabolic traits. Although there are several behavioral risk factors that contribute to obesity, genetic load plays a relevant role in the etiology of obesity. Since 2007, more than 1100 independent loci from almost 60 genome-wide association studies (GWAS) have been identified in association with several obesity traits [17]. Among them, the strongest susceptibility gene identified is the Fat Mass and Obesity (FTO)-associated gene [18]. The relationship between obesity and the minor allele ‘A’ of the FTO single-nucleotide polymorphism (SNP) rs9939609 is well established [18,19] among adults and children from diverse ethnic backgrounds, including Chileans [20,21,22,23,24,25]. For example, a study in Caucasian adults found that carriers of the A-allele had higher odds of overweight and obesity by $19\%$ ($95\%$ CI 1.06; 1.20) and $27\%$ (1.20; 1.34), respectively [23]. Moreover, the FTO rs9939609 risk variant has been suggested to predispose carriers to obesity-related diseases, such as T2D, and this association may be explained by body adiposity [23,26,27]. The link between the FTO rs9939609 risk variant and plasma levels of adiponectin is less clear. One study reported that adults carrying the rs9939609 SNP risk variant had no differences in adiponectin circulating levels [28], while others showed that A allele carriers had significantly lower adiponectin concentration [21,29], which was attenuated after BMI adjustment, suggesting that such association might occur through changes in body adiposity [29]. The evidence from studies in populations of children is limited. Therefore, we sought to research the association of plasma adiponectin concentration with the FTO rs9939609 genotypes (AA, TA, and AA) and measurements of general and central adiposity in Chilean children. In addition, we investigated the potential role of adiposity markers as mediators in the association between FTO rs9939609 and adiponectin. ## 2.1. Study Design and Population This cross-sectional study included a sample of children living in the Biobío Region of Chile. Participants were 6 to 11 years old and free of any chronic disease. Participants with incomplete data on anthropometric and adiposity measures, FTO genotype and circulating adiponectin levels were excluded from the study. Out of the 361 recruited individuals, 37 participants with missing data on body fat percentage (BF%) and one individual with adiponectin blood concentration beyond five standard deviations were removed from the analyses. The final analytical sample size was $$n = 323$.$ ## 2.2. Plasma Determinations A sample of 4 mL of fasting peripheral blood was collected. Plasma adiponectin was determined using a commercial ELISA kit (Linco Research, St. Charles, MO, USA) and a multi-reader (Synergy 2, Biotek, Winooski, VT, USA). ## 2.3. Anthropometric Measurements Data on weight (kg), height (cm), body mass index (BMI, kg/m2), waist–height index (WHtR) and BF% were collected using an anthropometrics manual [30]. Briefly, weight was assessed on light clothing and without shoes on a Tanita scale (TANITA TBF-300, TANITA, Tokyo, Japan; 1 g accuracy). Height was measured using wall-mounted stadiometers (Seca, model 208, 0.1 cm precision). The waist circumference was measured at the midpoint between the last rib and the upper border of the iliac crest with a non-elastic flexible tape (Seca, model 201, accuracy 0.1 cm). Body composition (body lean mass, body fat mass and BF%) was determined using a bioelectrical impedance analysis (TANITA TBF-300, Tokyo, Japan). The pubertal stage was established by a pediatrician according to the Tanner criteria [31]. BMI z-score was computed following the WHO definitions. Body weight was divided by height in meters squared and then normalized based on age and sex. Nutritional status was classified, as normal weight (z-score BMI > −2SD and <+1SD); overweight (z-score BMI > +1SD or <+2SD); obesity (z-score BMI > +2SD), as proposed by WHO [32]. ## 2.4. Identification of Allelic Variants of FTO rs9939609 Polymorphism We used the Mini Kit QIAamp DNA Blood (Qiagen GmbH, Hilden, Germany) to extract genomic DNA from leukocytes following the manufacturer’s instructions. The DNA amplification by real-time polymerase chain reaction (PCR) amplifications and the high-resolution melting analysis (HRM) were performed with the thermocycler Rotor-Gene 6500 (Corbett Research, Sydney, Australia). Briefly, 100 ng/µL of genomic DNA were incubated at 95 degrees Celsius (C) for 10 min with 3.0 mM magnesium, 12.5 µL SensiMix HRM (Quantace) reagent, 1 µL EvaGreen dye and 600 nM primers. It was followed by 40 cycles at 95 C for 15 s, 59 C for 10 s and 72 C for 10 s following a standardized protocol [33]. We based our primer selection on the work by López-Bermejo and colleagues. We used forward primer: 5′-AACTG GCTCTTGAATGAAATAGGATTCAGA-3′ and reverse primer: 5′-GTGATGCACTTGGATAGTCTCTGTTACTCT-3′ [34]. A graphic representation of PCR product sequence of 182 base pairs can be found in Supplementary Figure S1. Wild type (TT), heterozygous (TA) and homozygous for the mutation (AA) genotypes were identified using the HRM method, which allows variations in single nucleic acids to be identified by detecting small differences in the DNA melting temperature [35]. Our HRM analysis investigated melting temperatures from 70 to 85 C, with increments of 0.1 C in each PCR cycle [33]. PCR melting curves were obtained using the Rotor-Gene 6500-incorporated software and compared with the melting curves from samples with known sequence (controls), using $95\%$ confidence interval. The melting curves of TT, TA and AA FTO genotypes are shown in Supplementary Figure S2. During HRM technique standardization, 20 random samples underwent sequencing (Faculty of Biological Sciences, Pontificia Universidad Católica de Chile) in order to confirm genotypes and be used as controls. As an example, Supplementary Figure S3 displays the sequencing data of one control sample. We carried out a $3\%$ agarose gel electrophoresis in order to confirm the presence of a single PCR product. All samples were analysed in duplicates, with a $98\%$ genotyping success rate. ## 2.5. Ethics We adhered to the Declaration of Helsinki [1964], the Convention of the Council of Europe regarding human rights and biomedicine [1997], and the Universal Declaration on the human genome and human rights (UNESCO, 1997). Moreover, we met the requirements of the Chilean legislation in the field of biomedical research, data privacy and bioethics, according to Decree No. 114 of 2010, Law No. 20,120, and Decree update on 14 January 2013. In addition, this study protocol was approved by the Bioethics Committee of the Vice-Rectory of Research of Universidad de Concepción with number 352-2019 and date January 2019. ## 2.6. Statistical Analyses The study population characteristics were presented as mean and standard deviation (SD), or as median and interquartile range (IQR) for continuous variables with normal or non-normal distribution, respectively. Absolute and relative frequency was used to describe categorical variables. Linear regressions were used to investigate associations between circulating concentrations of adiponectin (dependent variable) and various measurements of general and central adiposity: BMI z-score, BF % and waist-to-height ratio (WHtR), as exposures (independent variables). To account for potential confounders, three statistical models were used. Model 1 was unadjusted, Model 2 was adjusted for sex, age and Tanner’s stage, and Model 3 added an adjustment for the FTO SNP rs9939609 genotype. The FTO SNP rs9939609 genotype was coded according to an additive model where the FTO genotype was coded as 0 = TT (homozygous for the wild type allele); 1 = TA (heterozygous for risk allele); 2 = AA (homozygous for the risk allele). The Chi-square test was used to estimate the Hardy–Weinberg equilibrium of the FTO alleles. Blood adiponectin concentrations were transformed to their natural logarithm to approximate normal distribution. The results were expressed in beta estimates and $95\%$ lower and upper confidence intervals (β, $95\%$ CI), for the variation in log-transformed adiponectin concentrations in µg/mL, per one unit increase in adiposity measures. Next, we studied the relationship between adiponectin concentrations and the variants of the FTO SNP rs9939609 genotype in linear associations using statistical Models 1 and 2. For the independent variable, we followed two approaches: [1] as a numerical variable according to the additive model (0 = TT; 1 = AT; and 2 = AA); and [2] as genotype categories, comparing carriers of the AT or AA genotype with the TT genotype, respectively, thus using the wild type as the reference group. Further, a third model was performed with additional adjustment for a marker of general adiposity, BMI z-score. The results were expressed in beta estimates and $95\%$ lower and upper confidence intervals (β, $95\%$ CI), for the variation in log-transformed adiponectin, per one additional risk allele (approach 1), or among carriers of AT or AA, compared with TT carriers, respectively (approach 2). Finally, we used model 3 to interrogate the role of body adiposity as a potential mediator in the association between adiponectin concentrations and the FTO SNP rs9939609 genotype, using mediation analysis from the mediation package in R [36]. This analysis dissects the total effect of the exposure (presence of one additional FTO rs9939609 risk allele) on the outcome (adiponectin concentration) into the direct and indirect effects. The direct effect is the part of the effect that goes directly or through mediators other than those currently studied. The indirect effect represents the portion of the effect that goes via (is mediated by) the variable under study (adiposity markers); then, the proportion mediated is quantified and expressed in percentage mediated. Further, this analysis works under the sequential ignorability assumption, which assumes no unmeasured confounding. Note that, despite that the notation in the mediation analysis uses the word ‘effect’, their results must not be interpreted as causal, given the observational and cross-sectional design of this study. Quasi-Bayesian confidence intervals were constructed for the estimated effects with 5000 simulations. Results are expressed as proportion mediated in percentage and $95\%$ Cis. A schematic representation of the mediation analysis concept is displayed in Figure 1. All analyses were performed using R statistical software v.4.0.1 (R Foundation, Vienna, Austria). ## 3.1. Sample Description The study population’s characteristics are shown in Table 1. Briefly, participants were on average 8.8 (SD ± 2.2) years old, were more often at pre-pubertal stage ($77.4\%$) and female ($50.8\%$), and they lived in the Biobío Region, Chile. ## 3.2. Associations between Circulating Adiponectin and FTO rs9939609 Table 2 shows the inverse association between the presence of one additional risk allele at SNP rs9939609 and adiponectin concentration (βadditive= −0.075 [−0.124; −0.025], $$p \leq 0.003$$) controlling for age, sex and Tanner’s stage. For the FTO genotype, only the homozygous for the risk allele (AA) showed an association with adiponectin concentrations (βAA vs. TT = −0.150 [−0.253; −0.048], $$p \leq 0.004$$). This association remained after adjusting the analysis by BMI (β = −0.104 [−0.101; −0.004], $$p \leq 0.041$$) (Table 2 and Figure 2). ## 3.3. Link between Circulating Adiponectin and Body Adiposity Using Model 2, we found that natural log-transformed adiponectin levels in blood were inversely associated with various measures of overall (BMI z-score and total BF %) and central obesity (WHtR), independent of sex, age and Tanner’s stage. A one-unit increase in BMI z-score and one-percent increase in total body fat were associated with lower adiponectin concentration (β = −0.130 log μg/mL [$95\%$ CI −0.176; −0.085], $$p \leq 4.63$$ × 10−8 and β= −0.012 [−0.016; −0.007], $$p \leq 2.38$$ × 10−7, respectively) (Table 2 and Figure 3). Similarly, the increase in one unit of WHtR was related to lower adiponectin (β = −1.386 [−1.959; −0.814], $$p \leq 2.91$$ × 10−6) (Table 2). After adjusting for the FTO SNP rs9939609, the associations remained (Table 2). ## 3.4. Body Adiposity as Partial Mediator of the Adiponectin–FTO rs9939609 Association Further, we investigated the potential role of general and central adiposity markers as mediators in the FTO genotype–adiponectin associations. In the mediation analysis, we observed that higher adiposity measures of BMI, WHtR and BF%, respectively, mediate a proportion from $23.9\%$ (6.8; 68.0, Pmediation = 0.006, for WHtR) to $29.8\%$ (10.4; 79.0, Pmediation = 0.003, for Z-score BMI) of the total effect of one additional FTO rs9939609 risk allele on plasma adiponectin levels, called the indirect effect (Table 3). ## 4. Discussion In this population of Chilean children, we found associations between lower plasma adiponectin and the presence of one additional A allele at the FTO rs9939609 polymorphism, and across FTO genotypes. Moreover, inverse associations between adiponectin and measures of general and central adiposity markers (BMI Z-score, WHtR and BF%) were observed. Furthermore, we quantified the proportion of the adiponectin–FTO association explained by body adiposity differences, finding that up to $30\%$ of the association is mediated by FTO-related levels of BMI Z-score, WHtR or BF%. Adiponectin is an endocrine factor secreted by adipocytes [37] and displays beneficial effects on various tissues, such those related to glucose uptake and fatty acid metabolism [38,39]. Additionally, lower adiponectin concentrations are related to intermediate risk factors for T2D, such as higher blood glucose, insulin, and triglycerides [33,40]. Experimental studies show that plasmatic adiponectin and other hormones regulating food intake and satiety regulation, such as leptin and ghrelin, are associated with FTO polymorphisms [40]. Several observational studies in populations of diverse ethnic backgrounds (Caucasian, Mexican, Turkish, Indian and Chinese) have investigated the relationship between FTO rs9939609 and plasma adiponectin, reporting significant associations [20,21,22,23,24,25]. In agreement, our study in a pediatric Chilean population found a significant relationship between lower concentrations of adiponectin and the presence of one additional A allele at rs9939609, in multivariate regressions adjusted for age, sex and Tanner’s stage. Similar results were found among AA carriers, as compared to wild-type (TT) carriers, while no significant results were found when comparing TA with TT carriers. Nevertheless, our results contradict studies carried out in Tunisian [41] and Iranian [28] adults and in Romanian children [42], which failed to find significant differences in adiponectin levels across FTO rs9939609 genotypes. Moreover, a randomized controlled trial on a two-year calorie restriction revealed that the presence of the A allele did not influence adiponectin levels in response to the intervention, although they found significant associations at baseline [43] similar to other studies [44,45]. Mechanisms underlying the potential relationship FTO-adiponectin have been elucidated to a limited extent. On the one hand, lower plasma adiponectin concentration has been observed among obese or higher BMI adults [46,47] and among obese children [48,49], in line with our results. On the other hand, adiposity excess and metabolic syndrome is well known to be prevalent among carriers of the A allele at rs9939609 [18,19]. Moreover, we previously reported significant associations of rs9939609 with higher body adiposity [20], obesity [33] and MetS prevalence in Chilean children, finding the strongest effect among AA carriers [50]. The current study provides additional evidence on the role of body adiposity in the observed associations using two approaches. Firstly, additional adjustment of the association adiponectin-FTO for BMI z-score resulted in an attenuated beta estimate size, although the associations remained significant. This observation is in line with previous studies [21,22,23,24,25,26,27,28,29] which performed multivariate analysis and found attenuation of the association after accounting for body adiposity measures. Further, we implemented a more advanced method, mediation analyses, in order to compute FTO’s indirect effect on adiponectin levels, defined as the effect that goes through the mediator (body adiposity). We found that, of the total effect of FTO on adiponectin concentrations, from $23.9\%$ (WHtR) to $29.8\%$ (BMI z-score) is mediated by FTO-related differences in general or central body adiposity, with the largest effect attributed to the BMI z-score. Nevertheless, there is still a major part of the association that is not explained by FTO-related adiposity, but by other biological pathways beyond the scope of this study. Given the possible role of adiponectin as a promising therapeutic target against highly prevalent metabolic diseases such as T2D [51], future studies aiming to unravel mechanisms related to FTO rs9939609 regulating plasma adiponectin concentrations different from body adiposity are warranted. Based on the available evidence, part of the unexplained effect might be determined by the gene-environment interactions. Some studies suggest that FTO polymorphism’s effect may be modulated by lifestyle factors [52], such as diet quality and physical exercise [21,53]. For example, macronutrient–gene interactions might affect obesity phenotypes, potentially by regulation of FTO and IRX3 gene expression [54]. Recent research shows that adherence to a Mediterranean dietary pattern, higher intake of unsaturated fatty acids, whole grains, polyphenols and probiotics improves the inflammatory biomarker profile, including the elevation of plasma adiponectin [55,56]. Moreover, it is well known that physical exercise triggers a number of signaling pathways stimulating the production of the bioactive molecules, adiponectin among them, that exert beneficial health effects [57]. A study reported that among individuals with the lowest physical activity levels, those carrying the A allele at rs9939609 had lower plasma adiponectin concentrations as compared to carriers of the TT genotype [21]. More studies are needed to dissect the extent to which environmental factors may contribute to attenuating the genetic predisposition to adverse metabolic health outcomes conferred by FTO risk genotypes. Our study has some strengths. We used a well-characterized children population, including details on their pubertal stage as a biochemical marker of obesity. In addition, we used a formal mediation analysis test, an advanced statistical method to compute the proportion of the effect. Among the weaknesses of our study, we count a study population of a restricted size, although we were able to detect significant associations. Further, we used classic anthropometric and bio-impedance measures of body fat, while more accurate methods for body composition are preferable, such as dual-energy X-ray absorptiometry and computed tomography [58]. Moreover, we had limited data in lifestyle factors and other genetic polymorphisms regulating adiponectin production, such as ADIPOQ or genetic ancestry. About the latter, a previous work from our research group performed the ethnic characterization of the same study sample based on Amerindian haplogroups assessed in mitochondrial DNA [33]. The authors observed that $85\%$ had Amerindian lineages, and that among normal-weight and obese children, the proportion of non-Amerindian individuals did not differ per group. Thus, we do not expect that genetic ancestry affects our results. Further, the cross-sectional design of our study restrains the mediation analysis interpretation, given that the studied mediators were collected at the same time point as blood collection for adiponectin measurement. In addition, it contains some strong assumptions, such as the ignorability assumption, wherein no potential unmeasured confounding is present [36]. Although we addressed the relevant confounders, unmeasured confounding should not be ruled out. For this reason, the results from our observational study must not be interpreted as causal; however, they do provide evidence to inspire future well-powered studies and the use of causal inference methods. Finally, these results are not generalizable, and replication of the findings to other populations merits further research. ## 5. Conclusions To the extent of our knowledge, this is the first study to formally suggest that the association between adiponectin and the FTO risk variant might be partly mediated by changes in body adiposity induced by FTO rs9939609 in a Chilean children population. Further research is needed to unravel the biological pathways linking SNP rs9939609 and adiponectin independently from body adiposity. More studies using accurate methods to assess body fat and a causal design are needed confirm these findings. 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--- title: Determinants of dropout and compliance of children participating in a multidisciplinary intervention programme for overweight and obesity in socially deprived areas authors: - Hevy Hassan - Selinde Snoeck Henkemans - Jolande van Teeffelen - Kees Kornelisse - Patrick J E Bindels - Bart W Koes - Marienke van Middelkoop journal: Family Practice year: 2022 pmcid: PMC10047623 doi: 10.1093/fampra/cmac100 license: CC BY 4.0 --- # Determinants of dropout and compliance of children participating in a multidisciplinary intervention programme for overweight and obesity in socially deprived areas ## Abstract ### Background Children with overweight and obesity in socially deprived areas (SDAs) are less likely to complete and be compliant to a weight-loss programme. ### Objectives To identify factors associated with dropout and compliance of a multidisciplinary weight-loss programme in SDA. ### Methods This prospective longitudinal cohort study included children (6–12 years) with overweight and obesity in a 12-week multidisciplinary intervention living in SDA in Rotterdam, the Netherlands. Potential predictive variables for dropout and compliance included were age, sex, the weight of the child and parents, quality of life, and referral status (self-registration or referral). A Cox proportional hazards model was performed to study the association between dropout and its potential predictive variables, whereas logistic regression analyses were used for the potential predictors for compliance. ### Results A total of 121 children started the intervention programme. Forty-one ($33.9\%$) children dropped out and 68 ($56.2\%$) were compliant with the intervention. The risk of dropping out of the intervention was significantly lower for a child with overweight parents than for those with parents with normal weight (adjusted hazard ratio [HR] 0.22 [$95\%$ confidence interval, CI 0.063–0.75]), and for those with parents with obesity (adjusted HR 0.18 [$95\%$ CI 0.060–0.52]). No other potential predictive variables were associated with dropout or compliance. ### Conclusion Children from SDA participating in a weight-loss programme have a relatively high dropout and a low compliance rate. Parental weight seems to be an important predictor for dropout of children from SDA, where children with normal weight or obese parents have the highest risk of dropout compared with children of overweight parents. ## Introduction Various multidisciplinary weight-loss intervention programmes have been initiated aiming to decrease the prevalence of paediatric obesity.1 However, intervention programmes for obese children have often only resulted in small and short-term changes regarding weight status.2 The limited effectiveness of these intervention programmes is often thought to be the result of low compliance3–6 and high dropout rates.7 Compliance is the overall attendance of a child to an intervention programme, while dropout is defined as children who prematurely disengaged. Compliant children have been found to attain a significantly larger reduction in body mass index (BMI)4,8 and waist circumference6 than their noncompliant peers. Dropout and noncompliance both increase healthcare costs due to inefficient use of resources.9–11 In addition, dropout discourages families to re-enter a weight-loss programme in the future.9,10 Given these interactions and consequences, there is a high need to reduce dropout rates and to optimize compliance in intervention programmes targeting children with overweight and obesity. Particularly low attendance rates of intervention programmes are reported in children with overweight and obesity in socially deprived areas (SDAs).5 While, the prevalence of childhood obesity is relatively high in deprived areas.12,13,14 Targeted interventions directed at these lower socioeconomic groups are therefore needed. More insight into factors associated with compliance and dropout may help to develop and improve these interventions. Previous research showed multiple predictors of dropout and compliance in multidisciplinary interventions for children who are overweight or obese. Reported predictors for dropout are older age,4,16,17 ethnicity,17,18 a higher baseline BMI,18 and overweight or obese parents13 or siblings.16 Furthermore, children whose parents have low motivation10 are more at risk of dropping out. Similarly, predictors of noncompliance, such as low family income and ethnicity12 have been identified. While multiple predictors of dropout and noncompliance for multidisciplinary interventions regarding weight loss in children who are overweight or obese have already been established, research specifically focussing on SDAs is lacking. Therefore, the aim of this study was to identify factors associated with dropout and compliance in a multidisciplinary weight-loss intervention programme for children with overweight or obesity in SDAs in Rotterdam, The Netherlands. ## Study design and participants For the present study, data from a prospective longitudinal cohort conducted among children who were registered and started the Kids4Fit intervention programme was used. Kids4Fit is a 12-week multidisciplinary intervention programme for overweight and obese children living in SDAs in Rotterdam, The Netherlands.6 The programme was carried out at 4 locations in deprived areas, where often children of ethnic minorities and low social economic status live. More details are reported elsewhere.6 Children aged 6–12 years who were overweight or obese and registered to the Kids4Fit programme between October 2012 and August 2014 were included in the study. Exclusion criteria for participation were underlying medical pathologies, comorbidities, and inability to function in a group. All parents, and children aged 12 years, provided written informed consent before taking the first measurements. Children were referred to the intervention by healthcare professionals, e.g. general practitioners, dietitians, paediatricians, youth healthcare workers, or self-subscribed. Children who signed up were placed on a waiting list. As soon as in 1 location a group of 8–12 children could be formed, children on the waiting list were able to start the programme. Data collection was performed at 4 time-points: when children signed up (baseline), at the start of the programme (T1), at the end of the 12-week intervention (T2), and 52 weeks after the start of Kids4Fit (T3). For the current study purpose, we used preintervention data (measured at T1) for the analyses and therefore only included children who actually started with the intervention (Fig. 1). Fig. 1.: Flow chart of participants who signed up for the Kids4Fit intervention programme (2012–2014). ## Intervention Each child and its parents received information on the intervention during an individual intake session with the treatment providers before start of the programme. The Kids4Fit intervention consisted of child group sessions during a 12-week intervention period: eighteen sessions with a physiotherapist, 4 sessions with a dietician, and 4 sessions with a child psychologist. The sessions with the child psychologist were also assumed to be attended by the parents. ## Measurements The questionnaire for parents requested information on potential predictors, parental education (high [at least bachelor level] and low [up to secondary school level]), ethnicity (both parents born in the Netherlands, at least 1 parent born abroad), referral (signed up to Kids4Fit on own initiative or referred to Kids4Fit by healthcare provider), previous attempts made to reduce the child’s overweight under supervision (yes, no), parent is willing to change own lifestyle (yes, no), and weight status of the mother (normal weight [BMI ≤24.9], overweight [BMI 25.0–29.9], (morbid) obesity [BMI ≥30.0]). If the weight status of the mother was not available, anthropometric measurements of the father were used instead. The height and weight of the participating children were measured at all 4 time-points. The height was measured to the nearest 0.1 cm (SECA 217 freestanding mobile stadiometer) and weight to the nearest 0.1 kg (SECA 716 weighing scale). BMI-z scores were calculated with World Health Organization (WHO) reference data from height and weight measures.19 Furthermore, information was gathered on physical activity level of the child (inactive [<3 days ≥60 min of exercise] or active [exercise ≥60 min each day]),20 eating breakfast (7 days per week). Self- and proxy-report (in children below the age of 8 years) Health-Related Quality of Life (HRQoL) questionnaires were filled out20,21 to evaluate HRQoL in children. The global HRQoL score is given by the sum of all 23 items informing on the physical (8 items), emotional (5 items), social (5 items), and school functioning (5 items) of the child. Higher scoring children (scale range 0–100) have a better HRQoL. For the current study, the total HRQoL was used. Treatment providers registered attendance during the 12-week intervention. The start of the intervention is the first consult with the physiotherapist, i.e. the first attendance date. Based on the first attendance date, “time-to-event” was calculated, measured from the start of the intervention to the event, i.e. dropout. Time-to-event was transformed into weeks. Children who prematurely disengaged from the intervention programme were considered dropouts, i.e. children who stopped before the end of the 12-week intervention programme. For the secondary outcome, children with a ≥$75\%$ overall attendance rate were regarded compliant. ## Statistics Data analysis was performed using SPSS (version 25.0). Statistical significance was set at $P \leq .05.$ Descriptive statistics were used to present preintervention characteristics of children who started the intervention. Cox regression was performed for the outcome dropout and logistic regression for the outcome compliance. For both models, univariate and multivariate analyses were performed using the variables age, sex, BMI-z, HRQoL, parental weight status, and subscription to the intervention (signed up on own initiative or referred). Missing values at T1 were imputed with baseline data for HRQoL scores (8 values imputed), attending sports club (6 values imputed), eating breakfast (7 values imputed), and whether parents were willing to change their lifestyle (8 values imputed). For parental BMI missing data were also imputed using data from baseline, and if not available data from T2 or T3 was used (17 values imputed). To generate survival curves for graphic representation, BMI-z scores were categorized into overweight and obesity using the WHO Growth Reference for school-age children.22 Children with a BMI-z score >1 were classified as overweight and a BMI-z score >2 corresponded with obesity. ## Results Two hundred and seventeen children registered for the intervention between October 2012 and August 2014. Of the registered children, 63 were excluded (did not want to participate or did not met the inclusion criteria) and 44 lost to follow-up during the period they were on the waiting list (Fig. 1). A total of 121 children started the intervention and were therefore included in the present study (Fig. 1). Characteristics of the study population are shown in Table 1. The mean (SD) age of children was 8.9 (1.8 years) and $40.5\%$ were boys. In most families ($79.3\%$) at least 1 parent was born outside the Netherlands. Table 1. \| Unnamed: 0 \| Total (n = 121) \| Dropout of intervention (n = 41) \| Completed intervention (n = 80) \| \| --- \| --- \| --- \| --- \| \| Age in years [mean (SD)] \| 8.9 (1.8) \| 8.7 (1.8) \| 9.0 (1.8) \| \| Sex (boy) \| 49 (40.5) \| 15 (36.6) \| 34 (42.5) \| \| BMI-z child [mean (SD)] \| 2.7 (0.7) \| 2.7 (0.6) \| 2.7 (0.8) \| \| Overweight or obese siblings \| 27 (22.3) \| 7 (17.5) \| 20 (26.3) \| \| Unknown \| 5 (4.1) \| \| \| \| Attends sports club \| 34 (28.1) \| 10 (24.4) \| 24 (30.4) \| \| Physical activity \| Physical activity \| Physical activity \| Physical activity \| \| Active (exercise ≥60 min each day) \| 26 (21.5) \| 9 (22.0) \| 17 (21.5) \| \| Inactive (<3 days ≥60 min of exercise) \| 24 (19.8) \| 5 (12.2) \| 19 (24.1) \| \| Eating breakfast 7 days per week \| 89 (73.6) \| \| \| \| Unknown \| 1 (0.8) \| 31 (75.6) \| 58 (73.4) \| \| HRQoL—global score [median (IQR)] \| 80.4 (17.4) \| 82.6 (14.7) \| 79.3 (20.7) \| \| Physical score [median (IQR)] \| 81.3 (25.0) \| 84.4 (17.2) \| 81.3 (28.1) \| \| Psychosocial score [median (IQR)] \| 80.0 (19.6) \| 80.0 (19.2) \| 80.0 (21.7) \| \| Ethnicity \| Ethnicity \| Ethnicity \| Ethnicity \| \| Both parents born in the Netherlands \| 14 (11.6) \| 5 (13.9) \| 9 (12.2) \| \| At least 1 parent born outside the Netherlands \| 96 (79.3) \| 31 (86.1) \| 65 (87.8) \| \| Parental education \| Parental education \| Parental education \| Parental education \| \| High (at least bachelor level) \| 16 (13.2) \| 4 (10.0) \| 12 (15.4) \| \| Low (up to secondary level) \| 102 (84.3) \| 36 (90.0) \| 66 (84.6) \| \| BMI parent (mothera, or if not available fatherb) [mean (SD)] \| 30.8 (6.2) \| \| \| \| Signed up on their own or referred \| Signed up on their own or referred \| Signed up on their own or referred \| Signed up on their own or referred \| \| Parent signed up to Kids4Fit on their own initiative \| 28 (23.1) \| 6 (15.4) \| 22 (27.8) \| \| Referred to Kids4Fit by healthcare provider \| 90 (74.4) \| 33 (84.6) \| 57 (72.2) \| \| Previous attempts have been made to reduce the child’s overweight under supervision \| 68 (56.2) \| 24 (60.0) \| 44 (57.9) \| \| Parent is willing to change own lifestyle \| 110 (90.9) \| 36 (87.8) \| 74 (93.7) \| During the 12-week intervention period, 41 ($33.9\%$) children dropped out of the intervention programme, and 68 ($56.2\%$) were compliant with the intervention. Compliance was highest with child psychologist sessions ($63.6\%$) and lowest for physiotherapist sessions ($52.1\%$). Of the parents, 61 ($50.4\%$) attended ≥$75\%$ of child psychologist sessions. Figure 2 shows the overall Cox survival curve for dropout of children during the intervention. The univariate and multivariate Cox proportional hazards models showed an association between parental BMI and the child’s hazard of dropping out (Table 2), in which parents with overweight was the reference group for both parents with normal weight, and obesity. The hazard of dropping out of the intervention was significantly lower in a child with overweight parents than for those with parents with normal weight (adjusted hazard ratio [HR] 0.22 [$95\%$ confidence interval, CI 0.063–0.75]) and for those with parents with (morbid) obesity (adjusted HR 0.18 [$95\%$ CI 0.060–0.52]). In addition, none of the other factors were associated with dropout of the child. No significant association was found between compliance and any of the possible predictive factors (Table 3). Table 3. \| Unnamed: 0 \| Univariate analysisOR (95% CI) \| Multivariate analysisOR (95% CI) \| \| --- \| --- \| --- \| \| Age \| 1.03 (0.84–1.26) \| 0.94 (0.72–1.23) \| \| Sex (boy) \| 1.08 (0.52–2.24) \| 1.56 (0.60–4.08) \| \| BMI-z \| 0.76 (0.45–1.29) \| 0.48 (0.21–1.09) \| \| HRQoL total score \| 1.01 (0.98–1.04) \| 1.02 (0.98–1.05) \| \| BMI parent categorized \| BMI parent categorized \| BMI parent categorized \| \| Normal weight \| 2.06 (0.61–6.96) \| 2.21 (0.62–7.92) \| \| (Morbid) obese \| 1.92 (0.59–6.21) \| 2.07 (0.60–7.11) \| \| Signed up on their own or referred (signed up on own) \| 0.66 (0.28–1.60) \| 0.48 (0.17–1.36) \| In all analyses, the BMI-z of the child was not significantly associated with dropout nor with compliance. A graphical representation of the cumulative dropout for BMI-z of the child and parental BMI are shown in Fig. 3. Fig. 3.: Cox survival curves of dropout of children from the intervention, with BMI-z scores categorized into overweight and obese (a), parental BMI categorized into normal weight, overweight and (morbid) obese (b) (2012–2014). ## Discussion This study aimed to identify variables associated with dropout and compliance of a multidisciplinary weight-loss intervention programme in SDAs. In our analyses, the only significant association found was between parental BMI and the hazard of dropping out of the child. In our study, $33.9\%$ of children dropped out, which is slightly lower than the median of $37\%$ found in a review on paediatric obesity management by Dhaliwal et al.23 This is noteworthy because children in deprived areas, as compared with nondeprived areas, are usually less likely to complete a weight-loss intervention programme.5,7,11 However, there is still substantial room for improvement. Previous research showed that children who have parents who are more involved, adhere better to the intervention and lose significantly more weight.24 In our study, parents were expected to attend psychologist sessions, and the compliance for this part of the intervention was low. Therefore, we believe that professionals should try to improve parental involvement in all parts of the interventions, e.g. motivate the parents to participate when they are expected, and to involve the parents more in other parts of the intervention. Previous literature shows that a higher parental BMI leads to a higher dropout of the child during a weight-loss intervention.12 In the present study, parental BMI was categorized (normal weight, overweight, obesity), as there was a nonlinear correlation between the parental weight status and the dropout of the child. A statistically significant lower hazard of dropout for children with overweight parents was seen compared with children with obese parents and parents with normal weight, suggesting that children whose parents are overweight have the lowest risk of dropout from the intervention. We can only speculate about the possible explanation of our findings. One could speculate that normal weight parents may not see the urgency of losing weight and may worry less about weight. Though it is also possible that parents with overweight may not recognize the urgency to lose weight. The higher risk of dropout found in the group of children with obese parents may be explained by the notion that these parents may see the urgency, however, the necessary lifestyle adjustments might be more difficult for them. Since parental weight appears to play a role in the dropout of the child during an intervention programme, a customized programme that fits the parents’ weight category may reduce the dropout of the child. A possible adjustment might be to offer information adapted to the weight status of the parents, and as mentioned before to motivate parents to (actively) participate during the child’s intervention. However, further qualitative research should therefore aim to get insight into reasons of dropout within families with different weight status. Pott et al. showed that children who have parents with low motivation were more at risk of dropping out.16 Surprisingly, we found no association between the motivation of parents and dropping out of the child in our study. This may be due to the threshold for motivation as families had to show high motivation at the start of the Kids4Fit programme, parents who signed up on their own initiative might have been even more intrinsically motivated than their referred peers, since they must have been actively looking for ways to reduce the weight of their child. Absence of significance might be also due to the relatively small sample size, resulting in a large CI. No associations were found for the outcome compliance. However, previous research showed that children with higher baseline BMI were less likely to be compliant than those with lower BMI.24 This is a reason for concern, since participating and completing a weight-loss intervention is even more crucial for children with a higher BMI. Previous qualitative studies18 have pointed out that the complexity of daily life, e.g. lack of financial resources and busy daily schedule, is an important barrier for compliance and dropout of interventions.24,25 In this study, these factors were unfortunately not available and therefore could not be taken into consideration, and it therefore remains unknown if these factors play a role in this specific study population. ## Strength and limitations A strength of our study is that we evaluated determinants of compliance and dropout of an existing weight-loss intervention that runs specifically in SDAs. Research in SDAs is still limited and often challenging because people who have a low socioeconomic status can be more difficult to reach.26 Due to the relatively small sample size, our study had limited power to detect differences between the variables and our outcomes (e.g. dropout and compliance), even though associations might have been present. For example, only 28 parents signed up on their own initiative compared with 90 families who were referred by a healthcare professional. Due to the small sample size, the rule of 1 in 10 could not be applied,27 since the chosen variables were considered to be too important to exclude. In addition, information on weight and height of both mothers and fathers was asked, but most times only that of the mother was reported back. Therefore, we used mother’s BMI in most of the cases, and only if not available that of the father. ## Conclusion Children from SDAs participating in a weight-loss programme have a relatively high dropout and noncompliance rate. Parental BMI seems to be an important predictor for dropout of children from deprived areas, where children with normal weight or obese parents have the highest risk of dropout compared with children of overweight parents. To improve compliance and reduce dropout of the children, the importance of the intervention should be discussed with the parents at baseline, and during the intervention parents should be more involved in all parts of the programme. If necessary, the intervention may be adjusted to the parental weight status. However, future (qualitative) research is needed regarding the reasons of dropout within families with different weight status. ## Funding Fonds Achterstandswijken Rotterdam. ## Ethical approval Medical Ethics Review Committee (METC-2012-479) of the Erasmus MC in Rotterdam, the Netherlands. ## Conflict of interest None declared. ## Data availability The data are not publicly available due to privacy or ethical restrictions. ## References 1. Sahoo K, Sahoo B, Choudhury AK, Sofi NY, Kumar R, Bhadoria AS.. Childhood obesity: causes and consequences. J Family Med Prim Care (2015) 4 187-192. PMID: 25949965 2. 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A multidisciplinary intervention programme for overweight and obese children in deprived areas. Fam Pract (2017) 34 702-707. PMID: 28985299 7. Lenders CM, Manders AJ, Perdomo JE, Ireland KA, Barlow SE.. Addressing pediatric obesity in ambulatory care: where are we and where are we going?. Curr Obes Rep (2016) 5 214-240. PMID: 27048522 8. Law C, Cole T, Cummins S, Fagg J, Morris S, Roberts H.. A pragmatic evaluation of a family-based intervention for childhood overweight and obesity.. Public Health Res (2014) 2 2050-4381 9. Skelton JA, Irby MB, Beech BM, Rhodes SD.. Attrition and family participation in obesity treatment programmes: clinicians’ perceptions. Acad Pediatr (2012) 12 420-428. PMID: 22795203 10. Braet C, Jeannin R, Mels S, Moens E, Van Winckel M.. Ending prematurely a weight loss programme: the impact of child and family characteristics. Clin Psychol Psychother (2010) 17 406-417. PMID: 19946946 11. Halvorson EE, Skelton JA.. Appointment attendance in a pediatric weight management clinic. Clin Pediatr (Phila) (2012) 51 888-891. PMID: 21622690 12. Ligthart KAM, Buitendijk L, Koes BW, van Middelkoop M.. The association between ethnicity, socioeconomic status and compliance to pediatric weight-management interventions—a systematic review. Obes Res Clin Pract (2017) 11 1-51 13. Jelalian E, Hart CN, Mehlenbeck RS, Lloyd-Richardson EE, Kaplan JD, Flynn-O’Brien KT, Wing RR.. Predictors of attrition and weight loss in an adolescent weight control programme. Obesity (Silver Spring) (2008) 16 1318-1323. PMID: 18356834 14. Li W, Buszkiewicz JH, Leibowitz RB, Gapinski MA, Nasuti LJ, Land TG. Declining trends and widening disparities in overweight and obesity prevalence among Massachusetts public school districts, 2009–2014. Am J Public Health (2015) 105 e76-82. PMID: 26270317 15. Cameron AJ, Spence AC, Laws R, Hesketh KD, Lioret S, Campbell KJ.. A review of the relationship between socioeconomic position and the early-life predictors of obesity. Curr Obes Rep (2015) 4 350-362. PMID: 26627493 16. Pott W, Albayrak O, Hebebrand J, Pauli-Pott U.. Treating childhood obesity: family background variables and the child’s success in a weight-control intervention. Int J Eat Disord (2009) 42 284-289. PMID: 19189408 17. Zeller M, Kirk S, Claytor R, Khoury P, Grieme J, Santangelo M, Daniels S.. Predictors of attrition from a pediatric weight management programme. J Pediatr (2004) 144 466-470. PMID: 15069394 18. Niet J, Timman R, Jongejan M, Passchier J, Akker E.. Predictors of participant dropout at various stages of a pediatric lifestyle programme. Pediatrics (2011) 127 e164-e170. PMID: 21149433 19. Cole TJ, Flegal KM, Nicholls D, Jackson AA.. Body mass index cut offs to define thinness in children and adolescents: international survey. BMJ (2007) 335 194. PMID: 17591624 20. Kemper HO, Stiggelbout M.. Consensus over de Nederlandse norm voor gezond bewegen.. Tijdschrift voor Gezondheidswetenschappen (2000) 78 180-183 21. Varni JW, Seid M, Kurtin PS.. PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Med Care (2001) 39 800-812. PMID: 11468499 22. de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J.. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ (2007) 85 660-667. PMID: 18026621 23. Dhaliwal J, Nosworthy NM, Holt NL, Zwaigenbaum L, Avis JL, Rasquinha A, Ball GDC.. Attrition and the management of pediatric obesity: an integrative review. Child Obes (2014) 10 461-473. PMID: 25496035 24. Heinberg LJKE, Berger NA, Lawhun SA, Cuttler L, Seabrook RC., Preedy FR. Handbook of anthropometry: physical measures of human form in health and disease (2010) 457-465 25. Grootens-Wiegers P, van den Eynde E, Halberstadt J, Seidell JC, Dedding C.. The ‘Stages Towards Completion Model’: what helps and hinders children with overweight or obesity and their parents to be guided towards, adhere to and complete a group lifestyle intervention. Int J Qual Stud Health Well-being (2020) 15 1735093. PMID: 32148191 26. Freimuth VS, Mettger W.. Is there a hard-to-reach audience?. Public Health Rep (1990) 105 232-238. PMID: 2113680 27. van Smeden M, de Groot JA, Moons KG, Collins GS, Altman DG, Eijkemans MJ, Reitsma JB.. No rationale for 1 variable per 10 events criterion for binary logistic regression analysis. BMC Med Res Methodol (2016) 16 163. PMID: 27881078
--- title: 'Alcoholic Liver Disease-Related Hepatocellular Carcinoma: Characteristics and Comparison to General Slovak Hepatocellular Cancer Population' authors: - Dominik Šafčák - Sylvia Dražilová - Jakub Gazda - Igor Andrašina - Svetlana Adamcová-Selčanová - Radovan Barila - Michal Mego - Marek Rác - Ľubomír Skladaný - Miroslav Žigrai - Martin Janičko - Peter Jarčuška journal: Current Oncology year: 2023 pmcid: PMC10047624 doi: 10.3390/curroncol30030271 license: CC BY 4.0 --- # Alcoholic Liver Disease-Related Hepatocellular Carcinoma: Characteristics and Comparison to General Slovak Hepatocellular Cancer Population ## Abstract Hepatocellular carcinoma (HCC) has multiple molecular classes that are associated with distinct etiologies and, besides particular molecular characteristics, that also differ in clinical aspects. We aim to characterize the clinical aspects of alcoholic liver disease-related HCC by a retrospective observational study that included all consequent patients diagnosed with MRI or histologically verified HCC in participating centers from 2010 to 2016. A total of 429 patients were included in the analysis, of which 412 patients ($96\%$) had cirrhosis at the time of diagnosis. The most common etiologies were alcoholic liver disease (ALD) ($48.3\%$), chronic hepatitis C ($14.9\%$), NAFLD ($12.6\%$), and chronic hepatitis B ($10\%$). Patients with ALD-related HCC were more commonly males, more commonly had cirrhosis that was in more advanced stages, and had poorer performance status. Despite these results, no differences were observed in the overall (median 8.1 vs. 8.5 months) and progression-free survival (median 4.9 vs. 5.7 months). ALD-HCC patients within BCLC stage 0–A less frequently received potentially curative treatment as compared to the control HCC patients ($62.2\%$ vs. $87.5\%$, $$p \leq 0.017$$); and in patients with ALD-HCC liver function (MELD score) seemed to have a stronger influence on the prognosis compared to the control group HCC. Systemic inflammatory indexes were strongly associated with survival in the whole cohort. In conclusion, alcoholic liver disease is the most common cause of hepatocellular carcinoma in Slovakia, accounting for almost $50\%$ of cases; and patients with ALD-related HCC more commonly had cirrhosis that was in more advanced stages and had poorer performance status, although no difference in survival between ALD-related and other etiology-related HCC was observed. ## 1. Introduction Alcohol-related liver disease (ALD) is one the most prevalent liver diseases worldwide. The latest WHO Global status report on alcohol and health estimates approximately three million alcohol-contributed deaths in 2016, which represents $5.3\%$ of all deaths worldwide. Alcohol was generally responsible for $7.2\%$ of premature deaths worldwide. Chronic alcohol abuse over years results in alcoholic fatty liver in most patients. Approximately one third of these subjects will progress into alcoholic steatohepatitis, and up to $20\%$ will develop alcohol cirrhosis, which may progress to hepatocellular carcinoma for 0.5–$2.6\%$ annually [1]. Liver cancer is the sixth most frequently diagnosed cancer and the third most common cause of cancer-related deaths worldwide in 2020. Among all histological subtypes, hepatocellular carcinoma (HCC) represents $75\%$–$85\%$ of all diagnosed cases. Almost $90\%$ of cases develop in the context of underlying chronic liver disease [2]. Genomic studies that evaluated the whole HCC genome using high-throughput methods have identified at least two distinctive mutation patterns called molecular classes. Proliferative class is characterized by activation of RAS, mTOR and insulin-like growth factor. Non-proliferative class displays mutations in beta-catenin gene (CTNNB1) [3] and is more closely associated with chronic inflammation. These classes are usually associated with different etiologies, although the clinical differences have not been clearly attributed to different molecular patterns. Alcoholic liver disease-related HCC is usually associated with the non-proliferative class of HCC [2]. Therefore, one of the hypotheses was that ALD-related HCC would be more commonly diagnosed in cirrhotic patients, associated with the degree of liver dysfunction, and that patients with ALD-related HCC (ALD-HCC) would have better outcomes than patients with HCC due to other etiologies. Chronic alcohol intake also influences the immune system of the tumor microenvironment. Increased gut permeability enhances the translocation of immunomodulatory microbiota-derived pathogen-associated molecular patterns (PAMPs), which suppress hepatic immune responses. Presence of neutrophils in liver parenchyma is typical for alcoholic hepatitis, and alcoholic steatohepatitis is associated with increased accumulation of MSDCs and suppression of T-cell recruitment. Changes in the immune system response are also presented in the peripheral blood. Many studies show the negative impact of increased inflammatory indexes such as neutrophil to lymphocyte ratio (NLR) [4], platelet to lymphocyte ratio (PLR) [5], and systemic immune inflammation index (SII) [6] on the overall survival of patients with HCC. The aims of this study are (a) to describe the differences between patients with ALD-related HCC and patients with HCC in chronic liver disease (CLD) caused by other etiologies, and (b) to describe the influence of systemic inflammation on the survival of this cohort of patients. ## 2. Methods We performed a multicenter retrospective study of patients diagnosed and treated for HCC at eight centers in Slovakia during the period from 2010 to 2016. All relevant patients were screened for eligibility according to the inclusion criteria. ## 2.1. Patient Selection The inclusion criterium was the diagnosis of HCC consistent with EASL-EORTC guidelines [7] (HCC confirmed by either histopathological examination or magnetic resonance imaging). The exclusion criteria were as follows: (a) etiology not available, (b) cryptogenic etiology, and (c) combined or rare etiologies. Patients were managed according to local standards valid at the time of diagnosis. ## 2.2. Data Collection All data were collected retrospectively from the patients’ charts. Case report forms were completed by D.S. with the on-call assistance of P.J. Collected data included baseline blood test results at the time of diagnosis (hematology, biochemistry and hemocoagulation). If any condition that might have influenced baseline values was present (e.g., acute bacterial infection), the laboratory results from a later time were used. Data about the underlying liver disease, tumor characteristics, liver function, performance status, comorbidities, treatment, and outcomes were collected. Liver cirrhosis was diagnosed either by histopathological examination of the resected/explanted liver parenchyma or by a combination of clinical imaging (ultrasonography, CT, magnetic resonance imaging and laboratory findings). The Child–Pugh score was used to estimate cirrhosis severity. The performance status was evaluated using the Eastern Cooperative Oncology Group (ECOG) Scale. The CT scans for thorax, abdomen and pelvis minor were used to identify extrahepatic spread and for final staging. Barcelona Clinic Liver Cancer (BCLC) staging system was used to assess prognosis. We also calculated the neutrophil to lymphocyte ratio (NLR), the platelet to lymphocyte ratio (PLR), the serum aspartate aminotransferase to platelet ratio index (APRI). ## 2.3. Inflammatory Indexes Were Calculated as: (a) Systemic inflammation index (SII): SII = P × N/L, where P, N and L are the peripheral blood platelet, neutrophil and lymphocyte counts per liter, respectively. The optimum cut-off point for SII for a favorable prognosis was determined to be ≥330 × 109 cells/L for adverse prognosis [6]. (b) Neutrophil–lymphocyte ratio (NLR): N/L where N and L are the peripheral neutrophil and lymphocyte counts per liter, respectively, and the cut-off used was >4 for adverse prognosis [4]. (c) Thrombocyte lymphocyte ratio (TLR): T/L where T and L are the peripheral blood platelet and lymphocyte counts per liter, respectively, and the cut-off used was >150 for adverse prognosis [5]. Alcohol consumption thresholds of 20 g per day in women and 30 g per day in men, confirmed in medical records, were used to determine the high risk of alcoholic liver disease. The treatment response was evaluated using CT scans or MR imaging according to the modified Response Evaluation Criteria in Solid Tumor. Finally, CRF also included the date of death extracted either from the patients’ medical records or from the database of Slovak Health Care Surveillance Authority. The study protocol is in accordance with the 1964 Helsinki declaration, its later amendments, and the principles of good clinical practice. The study protocol was approved by The Ethics Committee of East Slovakia Oncological Institute, on 27 May 2021 (approval code, EK/$\frac{2}{05}$/2021). The committee waived the need for the specific patients’ informed consent due to the retrospective nature of the data collection (the data already existed and were not a result of a research activity) and the usage of anonymous data only. ## 2.4. Statistical Analysis Patients were classified according to liver disease etiology to the ALD-related HCC group and other etiologies (composite control group). Patients were censored per analysis based on the availability of data (retrospective study). Survival is reported in months as median (min–max). Variables are reported as absolute and relative counts (categorical) or mean ± standard error of mean (interval variables). Inflammatory indexes and AFP are reported as median (interquartile range) because of skewed distribution. Inflammatory indices are calculated as referenced here and were used after natural logarithm transformation due to extremely skewed distribution. Ln transformation was also necessary for AFP levels. Baseline comparison of categorical variables—chi-squared, continuous variables T-test or Mann–Whitney test, respecting the tests’ assumptions. Survival curves were constructed using the Kaplan–Meier procedure with log-rank comparison of factors. Adjusted survival hazard ratios produced by Cox regression. ## 3. Results Overall, 483 HCC patients were screened: 54 patients were excluded based on missing data about etiology of chronic liver disease, and 429 patients were included in the analysis. A total of 412 patients ($96\%$) had cirrhosis at the time of diagnosis. The most common etiology (Table 1) was alcoholic liver disease alone (207 pts; $48.3\%$), or in combination with chronic hepatitis B or C (9 pts; $2.1\%$), followed by chronic hepatitis C (64 pts; $14.9\%$), NAFLD (54 pts; $12.6\%$), chronic hepatitis B (43 pts; $10\%$), with one hepatitis B and C coinfected patient, and cryptogenic etiology (40pts; $9.3\%$). Eleven patients ($2.6\%$) had liver disease due to various minor etiologies. Statistical significance for differences in counts $p \leq 0.0001$ (chi-squared). Median follow up was 252 days (1–4725 days). Figure 1 shows STROBE flowchart of the data collection and analysis. Baseline comparison (Table 2) showed that patients with ALD-related HCC were more commonly males, more commonly had cirrhosis that was in more advanced stages, and had poorer performance status. Despite these results, no differences were observed in overall and progression-free survival. First administered treatment in the respective groups is summarized in Table 3. Treatment data was available for the whole cohort, however, in the statistical analysis we omitted patients who received second line chemotherapy (four patients; $0.8\%$) or a combination of TACE and sorafenib (one patient; $0.2\%$) as the first treatment option. There was no difference in the proportion of patients receiving potentially curative or palliative treatments between ALD-related HCC and control group (palliative treatment being the more common modality at $83.6\%$ in ALD-related HCC vs $80.1\%$ in the control group; $$p \leq 0.326$$) in the whole cohort. If analyzed by BCLC stages, ALD-HCC patients within BCLC stage 0–A less frequently received potentially curative treatment as compared to the control HCC patients ($62.2\%$ vs. $87.5\%$) Furthermore, $37.8\%$ of patients with ALD-related HCC were offered only palliative treatment even in the BCLC 0-A stage compared to only $12.5\%$ of patients from the control group HCC ($$p \leq 0.017$$). Treatment response assessment was available in 479 patients. No significant difference in first line therapy treatment response was observed between ALD-related HCC and the control group, $$p \leq 0.122$$ (Table 4). No difference was observed between the ALD-related HCC group and the control group in unadjusted overall survival and unadjusted progression-free survival. Figure 2 shows Kaplan–Meier survival curves for both groups along with confidence intervals. There is significant overlap of confidence intervals during the complete follow-up period. Moreover, after adjustment for age, sex, first line treatment and BCLC class, there was no difference in the hazard ratios between ALD-related HCC and the control group (HR 1.057, $95\%$ CI 0.866–1.291; $$p \leq 0.584$$). The analysis of individual predictors of survival (Table 5) shows that significant predictors are the same for both ALD-related HCC and control group patients, except for age, which is not significantly related to survival in the control group HCC. However, the univariate HRs suggest that survival in ALD-related HCC is more dependent on liver function than in non-ALD-HCC. The presence of diabetes mellitus did not have an association with mortality. Use of statins was infrequent, thus not included in the analysis. To assess the relative weight of the survival predictors in both ALD-related HCC and control group HCC we input the predictors into a multivariate model that contained parameters reflecting liver function (MELD), performance status, and tumor parameters (max size and number of lesions). In this comparison (Table 6), in the case of ALD-related HCC MELD score seems to have a higher relative impact on survival than in control group HCC (adjusted MELD OR 1.65 vs 1.305 albeit with overlapping confidence intervals). Systemic inflammatory index, neutrophil–lymphocyte ratio and thrombocyte–lymphocyte ratio are relatively new indexes that are associated with cancer survival. In this cohort there was no difference in all three indexes between ALD-related HCC and control group HCC. Furthermore, all three indexes were significantly associated with survival. Therefore, we evaluated their association with survival for the whole cohort. Figure 3 shows that patients with SII of more than 330 × 109 cells/L have significantly better survival; the same was observed for NLR—Figure 4 (cut-off for favorable prognosis was ≤4) and also TLR—Figure 5 (cut-off for favorable prognosis was ≤150). ## 4. Discussion The risk factors for HCC display significant geographical variability. Data from the Global HCC BRIDGE study show that the most common HCC risk factors are chronic hepatitic C in the US and chronic hepatitis B in China and South-East Asian countries. However, this study completely lacked data from Central and Eastern Europe [8]. The only other sources on HCC risk factors are expert estimates based on opinions and conjectures between alcohol consumption and cancer incidence rates. Particularly in Slovakia, no empirical data were available until now. These estimates show that alcoholic liver disease is one of the most serious risk factors for HCC. According to GLOBOCAN estimates, 245,000 cases of liver cancer out of a total of 854,000 cases worldwide were due to this disease in 2015. The percentage of ALD in newly diagnosed HCC worldwide ranges from $6\%$ in Iran to 50–$60\%$ in Eastern European countries [9]. The annual incidence of HCC in patients with cirrhosis due to alcoholic liver disease ranges from 2.1–$5.6\%$ [9,10,11,12]. This risk can be further increased by other factors such as obesity [13] or smoking [14]. In this study we tried to partially fill in the missing data on the HCC risk factors in Slovakia. Since the data were collected from centers throughout Slovakia, we consider these results as representative for our country. As there are many commonalities between the countries in Eastern Europe, particularly regarding alcohol consumption [15] and chronic viral hepatitis prevalence [16], these results could also be indicative of the HCC risk in the surrounding countries. It is clearly visible that, in this geographic area, alcoholic liver disease is the dominant risk factor of HCC with a wide margin, including accounting for confidence intervals. These results show that ALD was the major risk factor for HCC for $48.3\%$ of patients. This value is higher compared to the estimates published by Baecker et al. using the GLOBOCAN 2012 data, who calculated the population attributable fraction risk of HCC for ALD at $36\%$ for Central Europe [17], and is almost the same as the estimate reported by the Global Burden of Disease Liver Cancer Collaboration which estimated the alcoholic liver disease contribution of liver cancer deaths to be $46\%$ in central Europe [18]. Patients in our cohort who had ALD-related HCC were more likely to have cirrhosis at the time of diagnosis compared with controls. The same results were reported in a retrospective analysis by Hester et al. [ 19], in which cirrhosis at the time of HCC diagnosis was observed more frequently in patients with ALD-related HCC ($94.8\%$) compared to HBV ($85.1\%$) and NASH-related HCC ($83.7\%$). The prevalence of cirrhosis in HCC patients with HCV background was comparable to ALD ($93.3\%$) [19]. Almost identical results were reported by a prospective study of 103 hepato-gastroenterology centers in France ($93.9\%$ of patients with ALD-related HCC vs. $73.2\%$ for non-alcoholic etiology, $p \leq 0.001$) [20]. Alcoholic liver disease is usually included in the non-proliferative molecular class of HCC. Therefore, we expected to observe at least some clinical differences at the time of diagnosis. Indeed, as is shown in Table 2, patients with ALD-related HCC had on average marginally smaller maximal tumor size, which was borderline insignificant, and on the other hand these patients had poorer liver function as shown by worse Child–Pugh and MELD scores. A similar study by deLemos et al. from five clinical centers in the US on a much larger sample size ($$n = 5327$$) did not find a difference in the maximal tumor size; however, patients with ALD-HCC were less frequently diagnosed within the Milan criteria, at least numerically, although the difference approached significance [21]. Despite expectations, patients did not differ significantly in AFP levels, although numerical difference in medians was quite substantial. This was probably due to extreme variability of AFP levels in both ALD-HCC and control HCC groups. The previously referenced study by deLemos indeed found that AFP levels were lower in ALD-HCC despite identical maximal tumor size [21]. Poorer liver function is also commonly associated with ALD-HCC in the literature. Schutte et al. [ 2012] reported worse function based on Child–Pugh score when comparing patients with HCC based on ALD vs. viral etiology of HCC [22]. Similar results, yet with even greater differences in proportions of patients within each Child–Pugh stage among ALD-HCC, viral HCC and NAFLD-related HCC, were also reported by Hester et al. [ 19]. Another study that corroborates this finding was published by Bucci et al. [ 2015]. Both Child–Pugh (6.7 vs. 6.3, $p \leq 0.001$) and MELD scores (11.7 vs. 10.4, $p \leq 0.001$) were significantly worse in the ALD group at the time of HCC diagnosis [23]. Poorer metabolic function at the time of HCC diagnosis was also described by Constentin et al. [ 2018] in the prospective CHANGH cohort study. Again, a significantly lower proportion of Child–Pugh A and a higher proportion of Child–Pugh C patients was observed in the ALD-HCC group compared to non-ALD ($39.3\%$ in ALD vs. $66.0\%$ in non-ALD for Child–Pugh A and $21.2\%$ vs. $10.3\%$ for Child–Pugh C [20]. Only a study by Trevisani et al. [ 2007] did not find a difference in metabolic liver function at the time of HCC diagnosis. In this study, the proportions of patients within the Child–Pugh A stage were comparable in all subgroups ($67.8\%$ of patients with ALD-HCC vs. $69.7\%$ with HCV-related HCC vs. $61.4\%$ with HBV-related HCC vs. $65.2\%$ with multiple causes of chronic liver disease, $$p \leq 0.787$$) [24]. Besides liver function, patients with ALD-HCC also had poorer overall functional status measured by ECOG scale. Only about $74\%$ of patients with ALD-HCC were in the ECOG stage 0 or 1 compared to almost $86\%$ of patients with HCC due to other etiologies. This has also been confirmed in the literature, however, the data are not uniform. Costetin et al. also reported a lower proportion of ALD-HCC patients within ECOG stage 0 or 1, compared to non-ALD-HCC patients. However, it is important to note that in our cohort patients had better performance status overall compared to in the French cohort [20]. In the other study, Bucci et al. did not find any difference in performance status; however, his control group included only HCV patients with HCC and not a mixed control group as in our study and the study by Costentin et al. As HCV patients are commonly substance abusers this may explain their performance status comparable to ALD patients [23]. Interestingly, we have also found differences in the first line treatment of HCC among different etiologies of CLD. Patients with ALD-HCC, even in the BCLC 0–A stage, significantly more commonly received only supportive treatment compared to the control group. We did not observe differences in administered treatment in other BCLC stages. Similar results have been reported from the ITA.LI.CA database by Bucci et al., where as much as $30.4\%$ of ALD-HCC patients received palliative treatment irrespective of their BCLC stage, compared to only $19.8\%$ of patients with HCV-HCC. Furthermore, in the French cohort, authors reported that significantly fewer patients with ALD-HCC received potentially curative treatment compared to the control group ($16.3\%$ vs. $27.1\%$) [20], and this was confirmed in yet another study by deLemos et al. [ 21]. The aforementioned authors commonly cite reasons such as delayed diagnosis due to lower rates of HCC screening due to poorer access to healthcare, decreased compliance or even prejudice against alcohol consumers; however, the data exploring the causes are lacking. In addition, curative treatment strategies can provide slightly different long-term outcomes. In our study, all subjects underwent radiofrequency ablation; however, some meta-analysis favors microwave ablation in long-term follow-up [25]. Moreover, there are many additional cofactors that can influence hepatocellular cancer development and survival. Published meta-analyses suggest that regular statin [26], aspirin [27] and metformin [28] can reduce the risk of hepatocellular cancer development. Some of these can even improve the overall survival of patients with diagnosed cancer [29]. Because of the small number of regular users included in this study, this parameter was not analyzed. Diabetes mellitus is another well-accepted risk factor for hepatocellular cancer development [2], and some studies also confirmed its negative influence on recurrence-free survival [30] and overall survival [31]; however, in our study the univariant analysis did not confirm these outcomes in both subgroups. Despite differences in performance status and treatment, no significant difference in overall survival (8.1 months in patients with HCC based on ALD vs. 8.5 months in non-ALD patients, $$p \leq 0.315$$) or DFS/RFS (4.9 months vs. 5.7 months, $$p \leq 0.528$$) was observed in our study. Not all previously published data on ALD-HCC survival are consistent. In the ITA.LI.CA cohort, authors reported lower overall survival in ALD-HCC patients (32.4 months) than in HCV patients (40.6 months; $$P \leq 0.002$$). Notably, the reported overall survival is extremely high compared to both our data and to data from the literature. More intriguing is the fact that patients with ALD-HCC had identical survival to the HCV-HCC patients when only patients from regular HCC surveillance program were analyzed [23]. Decreased overall survival in ALD-HCC patients was also reported in the French cohort (9.7 months in non-alcoholic group vs 5.7 months in patients with ALD ($$P \leq 0.0002$$); however, this difference disappeared when patients were separated into BCLC stages [20]. The largest cohort of HCC patients from the US multicenter study also found that ALD-HCC patients had lower overall survival compared to non-ALD-HCC patients (1.07 vs. 1.41 years, $P \leq 0.001$); however, this analysis was not adjusted for differences in treatment or BCLC stage [21]. We suppose that better molecular class and tumor characteristics are offset by poorer liver function and functional status, thus the survival is worse than, or at best not different from patients with HCC due to other etiologies. The hallmark of alcoholic liver damage is the inflammation, which also plays a significant role in cancerogenesis. Approximately $90\%$ of all HCCs arise from persistent chronic inflammatory process due to viral infection, NASH, or regular alcohol consumption [32]. Furthermore, alcohol consumption leads to increased gut permeability and increased PAMP (pathogen-associated molecular pattern) levels, which suppress the immune response by increasing the number of tumor-associated macrophages with M2 phenotype and MSDC cells that suppress the CD8+ cytotoxic anti-tumor immune response. In addition, the marked deposition of neutrophils in the tumor tissue of alcoholic steatohepatitis facilitates the escape of tumor cells from the immune response. Additionally, a significant overproduction of IL 1 and IL 17 in patients with HCC has been described [33]. Changes in the tumor microenvironment are also reflected in the peripheral blood. All three inflammatory indices used herein have shown that they significantly influence the course of hepatocellular cancer. Our study supports the influence of systemic inflammation as well as direct (geno)toxicity on the prognosis of HCC patients, and also serves as further validation of the respective cut-offs of these indices. The main topics for discussion in the field are the optimal cut-off and the inclusion of these indices in prognostic models such as BCLC to guide personalized therapy in HCC patients, which is a goal of future studies in the field. ## 5. Conclusions The present study is the first study from Slovakia to collect real data on the risk factors for hepatocellular carcinoma. Alcoholic liver disease is the most common cause of hepatocellular carcinoma in Slovakia, accounting for almost $50\%$ of cases. Patients with ALD-related HCC more commonly had cirrhosis that was in more advanced stages and had poorer performance status. Patients with ALD-related HCC were more frequently offered palliative treatment only, even in the BCLC 0–A stage. 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--- title: 'Sedentary Behaviour and Physical Activity Levels during Second Period of Lockdown in Chilean’s Schoolchildren: How Bad Is It?' authors: - Ricardo Martínez-Flores - Ignacio Castillo Cañete - Vicente Pérez Marholz - Valentina Marín Trincado - Carolina Fernández Guzmán - Rodrigo Fuentes Figueroa - Gabriela Carrasco Mieres - Maximiliano González Rodríguez - Fernando Rodriguez-Rodriguez journal: Children year: 2023 pmcid: PMC10047652 doi: 10.3390/children10030481 license: CC BY 4.0 --- # Sedentary Behaviour and Physical Activity Levels during Second Period of Lockdown in Chilean’s Schoolchildren: How Bad Is It? ## Abstract Objective. The objective of this study was to compare the levels of sedentary behaviour and physical activity in relation to sociodemographic variables of Chilean schoolchildren before and during the COVID-19 pandemic. Methods. This retrospective study considered a non-random sample of 83 boys and 232 girls, and their respective parents, who attended public schools ($$n = 119$$) and private schools ($$n = 196$$) in Chile. A self-report instrument was applied that included sociodemographic variables, sedentary behaviour (SB), and physical activity (PA) in the second period of the pandemic in 2021. Results. The main results show that pre-pandemic SB had significant differences when compared between sexes, except for television time. During the pandemic, there was no significant difference in television time or telephone time. There were no significant differences by sex before and during the pandemic. When comparing the SB scores, video game time in boys decreased ($p \leq 0.001$), as did video game time in girls ($p \leq 0.001$), and computer time in boys ($p \leq 0.001$) and girls ($p \leq 0.001$). Telephone time increased in boys ($p \leq 0.001$) and girls ($p \leq 0.001$), as did television time ($p \leq 0.001$). Likewise, PA increased in boys (Δ + 9.51min) and girls (Δ + 3.54 min) during the pandemic ($p \leq 0.001$). Conclusions. Both PA and SB underwent changes according to sex before and during the second period of the COVID-19 pandemic in Chilean schoolchildren. ## 1. Introduction The global population has been severely affected by the COVID-19 pandemic: more than 4.6 million people have been affected by the disease, including children and young people [1]. As a preventive measure, millions of people worldwide have practised social distancing, isolation, and quarantine. However, compliance with these measures has brought with it health problems related to decreased physical activity (PA) and increased sedentary behaviour (SB), as well as a psychological impact associated with the pandemic’s state of uncertainty [2]. In Chile, multiple dimensions of daily life have also been affected, such as the development of social and educational relationships [3], due to mobility restrictions and confinement, causing children and youth to spend a lot of time at home [4]. Current literature points out that the lifestyle caused by COVID-19 has led to an increased prevalence of SB [5,6], which is defined as behaviours that do not exceed a basal energy expenditure of 1.5 METs—metabolic equivalent of task—[7], such as lying down or sitting in front of a screen, among other behaviours [8]. Additionally, being physically inactive corresponds to not complying with international PA recommendations [9]. For 15 years, the global pattern of physical activity did not present significant modifications, highlighting Latin America as having a higher prevalence of physical inactivity [10]. Regarding PA during the pandemic, recent studies have found a decrease in PA in both children and adolescents [11], and the older the child, the lower the level of PA [12]. Furthermore, there was a decrease in PA during the pandemic in children aged 1 to 5 years old in Chile [13]. Additionally, data collected on sedentary lifestyles show that Chilean schoolchildren spend more than half of their free time sitting as a consequence of confinement, intensifying the risk of acquiring different diseases in the future [14]. In addition, the amount of screen time spent by Chilean children has been shown to be higher than it was before the pandemic [15]. Sociodemographic factors could also be associated with levels of PA [16]. For example, boys have higher levels of PA than girls [17,18] mainly for social reasons. Additionally, Gutiérrez et al. [ 19] point out that socioeconomic status can be associated with differences in PA levels, where people with high socioeconomic income achieve higher PA levels. Likewise, family characteristics are influential for the active behaviours of children and adolescents, with family members acting as promoters, companions, and facilitators of active behaviours [20]. In this sense, parental behaviours related to physical activity and eating habits are of utmost importance for the same behaviours in schoolchildren [21]. These differences in levels of PA due to the low socioeconomic status of children and their families can impact children’s physical and mental well-being and lead to larger social and educational gaps [22]. Likewise, previous evidence also suggests that PA can be an answer to narrowing these gaps [23,24,25]. However, the influence of the level of PA and SB, according to sociodemographic factors related to family environment, has not been studied in schoolchildren during the period of lockdown during the pandemic. Consequently, the objective of this study has been to determine the impact of the second period of confinement due to the COVID-19 pandemic on sedentary behaviours and the level of physical activity in Chilean school boys and girls, with the intention of recognizing the real magnitude of the issue. This is to deliver information that allows the authorities to take improvement measures and changes aimed at schoolchildren and their families. ## 2.1. Study Design and Participants This study has a retrospective design and employs a non-random sample of voluntary participants. A total of 315 schoolchildren (83 boys and 232 girls), and their respective parents, from public ($37.8\%$) and private ($62.2\%$) schools participated. The families were divided into those with only one child and those with more than one child. The availability of a car was also considered a good predictor of socioeconomic level, and those families with no car were separated from those with only one car or more than one car. Finally, we also separated the sample by socioeconomic status: low, medium, and high. The data were collected between 1 August and 10 September 2021, while most of the schools were still closed. The inclusion criteria were schoolchildren and adolescents between 8 and 14 years of age and their parents or guardians who had voluntarily agreed to participate in the study. The exclusion criteria corresponded to students or parents with visible physical or cognitive difficulties (medically confirmed), which could affect their health by participating in the study. In addition, schoolchildren < 8 years and >14 years and parents or guardians < 18 years were excluded. This study only analysed the results obtained from schoolchildren and does not include information from parents. ## 2.2. Instruments A questionnaire was a self-reported online survey undertaken in Spanish from July to September 2020 using the SurveyMonkey platform (San Mateo, CA, USA), and was composed of four items that included sociodemographic characteristics (Supplementary S1). The first section covered sociodemographic characteristics such as sex (male, female), age (in years), residence (urban, rural), number of children at home, and school type (private, public). To establish the socioeconomic level, the level of family wealth was used, through The Family Affluence Scale (FAS) questionnaire, which estimates socioeconomic level according to the number of vehicles, place of residence, type of room, family vacations in the last 12 months, and computers available at home [22]. To determine the PA of schoolchildren, the Youth Activity Profile (YAP) questionnaire was used, which assesses the level of PA in and out of school. This instrument, validated to assess moderate–vigorous PA (MVPA), provides estimates of MVPA and SB that approximated values from an objective monitor [26,27]. This self-report questionnaire was designed to discover the PA reached in the last 7 days and was designed for use in children and adolescents from 8 to 17 years old (grades 4–12). It contained 3 items on activities and sports performed during the last week. The items were: [1] activity at school, [2] activity out of school, and [3] sedentary behaviours. The first item includes PA during breaks, classes, and lunchtime. The second item is PA outside school, including PA before and after school and on the weekends. The last item contains questions about SB, such as time video games, computer time, television time, and telephone time, excluding time spent studying. The items were rated on a Likert-type scale from 1 to 5 (1 = low PA; 5 = high PA). A translation of the original questionnaire into the Spanish language was conducted; two independent Spanish researchers with English knowledge translated the original YAP into Spanish. Then, differences were adjusted to reach a consensus. Second, the Spanish version was back-translated to English by two other independent researchers. Finally, a different researcher fluent in English compared the original YAP with the new version translated into English. The new version was called YAP-S (Youth Activity Profile in Spanish). A pilot administration was conducted of the YAP-S in a small group of children and adolescents ($$n = 20$$) and additional refinements were made based on the feedback [27]. The YAP score was transformed using Fairclough’s equations [27] to minutes per day of MVPA. To establish whether the subjects were physically active, time > 60 min MVPA/day was considered, and those who did not comply were considered physically inactive (<60 min/day). To determine active travel, the PACO: pedal and walk to school questionnaire, a self-report of travel mode, distance, and travel time to and from school was used. A study of reliability was performed through the kappa coefficient, weighted kappa, and intraclass correlation coefficient (ICC), and its respective confidence interval (CI) for questions about active commuting in children and adolescents in Chile showed high reliability [28,29]. The instrument used to determine self-perceived physical fitness in schoolchildren was the International Fitness Scale (IFIS) questionnaire, which corresponds to a validated test for the assessment of general physical fitness [23], and has 5 items corresponding to the components of general physical fitness, cardiorespiratory fitness, muscular strength, speed/agility, and flexibility. The response section gives options by a Likert-type scale from 1 to 5 (1 = low PA; 5 = high PA) in each question before and during the pandemic. This test was chosen because the restrictions of the second period of the pandemic made it impossible to evaluate fitness objectively or in a more practical context. However, this section from the questionnaire was not used for the analysis in this present report. ## 2.3. Procedures School principals were invited from the cities of Viña del Mar and Concón (Valparaíso region) and Talcahuano and Concepción (Biobío region) in Chile. Of the schools that agreed to participate, the parents were informed of the objectives and characteristics of the study. The instrument in the Biobío region was applied in an online format because they were in quarantine. In the Valparaíso region, it was applied in paper format because the schools were in hybrid and face-to-face classes. In the online format, the student’s parents or caregivers received the questionnaire through a link via email and WhatsApp (Mountain View, CA, USA). The online questionnaire was conducted through the SurveyMonkey platform (San Mateo, CA, USA). In paper format, the questionnaire was administered to the children in person in the classroom with the help of the physical education teacher. Parents or caregivers were sent the questionnaire home in a sealed envelope and had a maximum of 7 days to return it with their respective answers. For both versions, the questionnaire was applied in August and September 2021, and its application lasted approximately 14 min. ## 2.4. Ethical Aspects Prior to completing the questionnaire, the parents or guardians responsible for the schoolchildren received informed consent, where they were notified of the characteristics of the questionnaire and the objectives of the study. After reading and accepting the consent, the children agreed to participate in the research in a document specifying the characteristics of the study and its objective. This research was approved by the ethical committee of the Pontificia Universidad Católica de Valparaíso (BIOEPUCV-H 363-2020). ## 2.5. Statistical Analysis Statistical analysis was performed using SPSS version 25. Continuous values, such as the level of physical activity and sedentary behaviour, are presented as mean and standard deviation (M ± SD). Categorical variables are presented as frequencies (%). To compare the sociodemographic factors, the Chi-square test was used. For the comparison of means of continuous variables, Student’s T-test was performed. GraphPad Prism (GraphPad, San Diego, CA, USA) was used for graph design. The level of statistical significance was set at $p \leq 0.05$, with $95\%$ confidence. ## 3. Results A total of 315 schoolchildren ($26\%$ boys and $74\%$ girls) and their respective parents belong to the public ($37.8\%$) and private ($62.2\%$) schools. Of the total families, some had only one child ($25.4\%$, of which $32.5\%$ were boys and $22.4\%$ were girls) while others had more than one child ($74.6\%$, of which $67.5\%$ were boys and $77.6\%$ were girls). The availability of a car was also considered, and we separated those families that did not have a car ($24.1\%$) and those that had more than one child ($74.6\%$). Finally, we also separated the families by socioeconomic level: low ($39.8\%$), medium ($28.7\%$), and high ($31.5\%$). Table 1 shows the sociodemographic characteristics of the schoolchildren in the study. Within these, significant differences were found in the variables of socioeconomic level, such as age (higher in girls than boys) and type of school, where being enrolled in private school had a higher prevalence among girls. Additionally, car availability was higher in girls than boys, and, in consequence, socioeconomic status was lower in boys than girls. However, the number of children by sex was not significant. Table 2 shows the SB before and during the pandemic. In all SB variables, there were statistical differences by sex. The time spent playing video games was higher in boys than in girls before ($p \leq 0.001$) and during the pandemic ($p \leq 0.001$). Similar results were found in computer time, which was higher in boys than in girls both before ($p \leq 0.001$) and during ($p \leq 0.001$) the pandemic. Finally, the same thing happened in telephone and television time, which was higher in boys than in girls in both periods. Although this table is not shown, some SSB tended to decrease during the pandemic. These results will be presented more clearly in Table 3. According to the results, PA compliance remained low (does not meet recommendations) both before and during the pandemic ($74.3\%$ and $74.2\%$, respectively). Figure 1 shows the comparison between before and during the pandemic of the SB variables. As shown in the figure, all sedentary behaviours, except for time spent playing video games in boys ($$p \leq 0.618$$), were significant. These results prove that both before and during the pandemic, the time spent in sedentary activities exceeded > 1 h per day of use. In another way, Figure 2 shows that the PA was low in both sexes, before and during the pandemic. Table 3 shows the comparison between before and during the pandemic of SB and PA. All comparisons of both SB and PA gave significant differences in both sexes. The telephone time and television time scores increased during the pandemic in boys and girls. However, video game time and computer time scores decreased during the pandemic in both boys and girls. The same was true for min/day of physical activity, which increased significantly during the pandemic in boys (44.3 to 53.9 min/day) and girls (50.9 to 53.2 min/day). ## 4. Discussion The aim of this study was to compare the levels of SB and PA in relation to sex, as a sociodemographic variable of Chilean schoolchildren before and during the COVID-19 pandemic. PA, like physical condition, has been associated with several positive health outcomes [24,25]. PA has been associated with less premature death and is considered an effective primary and secondary preventive strategy for at least 25 chronic medical conditions [24]. Moreover, sedentary time has been associated with multiple detrimental health outcomes, including all-cause mortality, cardiovascular disease, type 2 diabetes, and metabolic syndrome, and could be an independent risk factor for PA [30]. These factors have a series of implications for public health, where many times the focus is more on PA than on sedentary time and behaviours. The main results show that SB before the pandemic had significant differences when compared by sex, except for time spent on television. During the pandemic, there was no significant difference in television time and telephone time. There were no significant differences by sex before and during the pandemic. All values were significant when comparing the SB and PA scores before and during the pandemic. In this study, it was found that television time increased significantly between before and during the pandemic. However, when comparing the results by sex, the difference was not significant. Previous research found that children in the early stages of the pandemic increased their screen time on both television and video games by more than 3 h per day [31]. This may have been due to limited mobility and little activity during confinement. In this regard, Ranjbar et al. [ 32] note that one of the favourite activities during the pandemic while schools were kept closed was watching television ($13.8\%$). Another cause that can be associated with the increase in television time is parental mental health problems, where it was found that the greater the parental anxiety, the more time children spent in sedentary behaviours such as watching television [33]. In addition, this study showed that computer time for more than one hour per day decreased significantly in both boys and girls compared to before and during the pandemic. When comparing the results by sex, the difference is also significant. Previous research found data showing an increase in internet addiction by $36.7\%$ [34] during the pandemic. This could explain the decrease in computer time due to increased time spent on mobile devices and television. On the other hand, other research associated computer time with a worse classroom climate [14]. Regarding the time spent playing video games before and during the pandemic in children, although there were changes, these were not significant. We can contrast this information with that provided by Kim and Lee [35], where internet gamers were classified into four profiles: casual, moderate, potential risk, and addicted. Of these profiles, the only ones that had a significant increase were those with a profile addicted to video games. Taking this information into account, we can assume that the time spent playing video games did not have a significant change. This could occur because, in Chile, there are still no cases of video game addiction, possibly because this trend has not become as massive as it is in South Korea or China, where these cases are beginning to be more and more frequent [36]. In another sense, the time of telephone use in boys remained the same before and during the pandemic, but in girls, it increased significantly, by more than one hour. This is consistent with the findings of Cívico et al. [ 37], where the results show that boys had more problematic use than girls before COVID-19 confinement, according to the perceptions of their families. There are records indicating that the amount of time that children and adolescents occupy the telephone is around 2 h per day, which, upon reaching adolescence, increases according to age, reaching an average of 4 h per day and/or 30 h per week [38,39]. However, the time of telephone use in this study has been considered as recreational use; therefore, the increase in its use to perform school activities could be a factor to consider in this increase. Regarding compliance with PA recommendations, a slight increase was observed during the pandemic in boys, but this was only maintained in girls. Comparing the average minutes of PA per day, both girls and boys increased the time. According to the study conducted by Rossi et al. [ 12], PA increased as a result of factors that allowed children and adolescents to be more physically active, among the most important of which is being male, since it would benefit the increase in minutes of PA. Other important factors were complying with a daily routine, performing PA outdoors, and having more free time by not attending school in person, among others. However, the mean time spent by both groups is below compliance with the PA recommendations (60 min of moderate–vigorous physical activity). A possible explanation for this could be that, according to previous studies, there is a worldwide prevalence of physical inactivity. According to the scientific study ANIBES [40], $55.4\%$ of children and adolescents do not comply with the physical activity recommendations proposed by the World Health Organization (>60 min MVPA/day). Based on the results obtained, future research could collect data to determine which sociodemographic factor most affects the prevalence of compliance with PA recommendations. In addition, the study could be replicated in other regions of the country to compare the results and obtain a representative sample of the situation at the national level. In addition, the situation could be compared with developed countries and the causes of the possible results could be investigated. Having all these data will allow the design of action plans to intervene in the population and seek to correct the problems in complying with PA recommendations. ## 5. Strengths and Limitations The main limitation has been the collection of data from an online questionnaire, with the subjectivity that the use of these instruments implies. However, it was not possible to incorporate more objective methods, due to the situation of intermittent confinement and the restrictions decreed during the pandemic. As a strength, the incorporation of an adequate sample, from various regions of the country that could be extrapolated to other realities, given the similarity of the pandemic situation, stands out. In addition, a complete variety of sedentary behaviours was incorporated, which allows for a more complete vision of the situation. ## 6. Conclusions Sedentary behaviour such as time on the telephone and television increased during the pandemic in the whole sample of Chilean schoolchildren, while time on video games and computers decreased. The comparison with Student’s T-test between sexes in SB, such as television time, telephone time, and computer time, was statistically significant ($p \leq 0.001$). From this research, it was possible to determine that the mean PA increased in boys (Δ + 9.51 min) and girls (Δ + 3.54 min) during the pandemic ($p \leq 0.001$), since people’s interest in participating in sports increased during the pandemic due to the need to move after months of confinement. Despite the increase in PA time, schoolchildren failed to meet the PA recommendations established by the World Health Organization (>60 min MVPA/day). As for the practical implications of this work, strategies should be implemented at school to promote and increase PA practice in this current period, which could be called “post-pandemic”. In addition, effective government proposals to increase PA are needed to satisfy the already existing interest in the population. Further research is needed to determine whether other sociodemographic factors may have influenced the PA outcomes of Chilean schoolchildren and compliance with the recommendations. Second, the future research should investigate the medium and long-term effects of the pandemic on PA and SB. Finally, many lessons were learned from the pandemic in various areas, including PA and SB. The system was not prepared to deal with the health situation with a certain normality, and caused harmful effects on people’s PA. In the future, the use of technology must be in favour, and physical practice in open spaces cannot be prohibited. Rather, reforms should be made in order to provide the multiple and powerful social, mental, and health benefits of PA. ## References 1. Coronavirus (COVID-19) Dashboard [Internet] 2. Bravo Cucci S., Kosakowski H., Núñez Cortés R., Sánchez Huamash C., Ascarruz Asencios J.. La actividad física en el contexto de aislamiento social por COVID-19. GICOS Rev. 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--- title: Correction of Local Brain Temperature after Severe Brain Injury Using Hypothermia and Medical Microwave Radiometry (MWR) as Companion Diagnostics authors: - Oleg A. Shevelev - Marina V. Petrova - Elias M. Mengistu - Mikhail Y. Yuriev - Inna Z. Kostenkova - Sergey G. Vesnin - Michael M. Kanarskii - Maria A. Zhdanova - Igor Goryanin journal: Diagnostics year: 2023 pmcid: PMC10047658 doi: 10.3390/diagnostics13061159 license: CC BY 4.0 --- # Correction of Local Brain Temperature after Severe Brain Injury Using Hypothermia and Medical Microwave Radiometry (MWR) as Companion Diagnostics ## Abstract The temperature of the brain can reflect the activity of its different regions, allowing us to evaluate the connections between them. A study involving 111 patients in a vegetative state or minimally conscious state used microwave radiometry to measure their cortical temperature. The patients were divided into a main group receiving a 10-day selective craniocerebral hypothermia (SCCH) procedure, and a control group receiving basic therapy and rehabilitation. The main group showed a significant improvement in consciousness level as measured by CRS-R assessment on day 14 compared to the control group. Temperature heterogeneity increased in patients who received SCCH, while remaining stable in the control group. The use of microwave radiometry to assess rehabilitation effectiveness and the inclusion of SCCH in rehabilitation programs appears to be a promising approach. ## 1. Introduction After emerging from a coma, patients who have experienced severe brain damage often pass into a state of chronic disorder of consciousness (DOC) for an indefinite term, including a vegetative state (VS) and minimally conscious state (MCS) [1,2,3]. The increase in the number of these patients induces significant social and economic problems, complicated by a lack of detailed understanding regarding diagnosis, prediction of outcomes, and determination of the principles of therapy and rehabilitation. Neurological examination is of primary importance when assessing the condition of patients in VS and MCS. Thus, the use of the Coma Recovery Scale-Revised (CRS-R) enables assessment of the current state of the auditory, visual, motor, speech, and communicative functions and the level of wakefulness of patients, which allows practitioners to identify and register the signs of consciousness and to distinguish VS from MCS [4,5]. The main therapeutic strategy in patients with DOC mainly consists of maintaining vital organ functions, preventing infectious complications, and optimizing nutritional support [6]. The priorities in choosing rehabilitation methods are determined not by the proven effectiveness of a particular strategy, but by their availability in a particular healthcare center, and are often based on assumptions about their possible positive impact on processes that improve brain function [7]. There is no sufficiently deep understanding of the processes of the revival of consciousness after emerging from a coma. Technological development of an objective instrumental assessment of the rehabilitation dynamics and new approaches to methods aimed to increase the level of consciousness is an urgent problem in the previously described extremely severe patients [8,9]. In this study, we focused on the horizons of implementing a new objective diagnostic methodology that would allow us to understand one of the assumed key pathophysiological mechanisms of impaired consciousness, as well as to define the cause-and-effect linkage within this process and the possibility of subsequent correction of the above-described vicious circle. The adaptation of new strategies could be a valuable supplement to the diagnostic work-up for researchers and clinicians who have to deal with patients after severe brain injury, and enhance the effectiveness of the ongoing rehabilitation program, which in turn will decrease mortality among patients with DOC. One of the promising research methods for the brain’s functional state is noninvasive passive microwave radiometry (MWR). The basic principle of this method is based on registering the power of tissue’s electromagnetic radiation (EMR) in a microwave range of 1–7 GHz [10]. The EMR power is proportional to the intensity of the metabolism, which allows actual values of the tissue temperature to be obtained at a depth of 4–7 cm from the skin surface. Thus, it becomes possible to measure the temperature of the cerebral cortex. The accuracy of the method is ±0.2 °C, which is demonstrated in comparison with the results of a thermal sensor implanted directly into the brain [11]. In the standard version of the procedure, an antenna is installed on the surface of the scalp for locating the EMR (Figure 1), and the temperature in 18 standard projections of the cortex of the left and right hemispheres (nine symmetrical areas on the right and left) is sequentially measured (Figure 2). This method was previously used by our team in a study of temperature heterogeneity in healthy and damaged brains [12]. Thus, it was demonstrated that in healthy individuals ($$n = 120$$) the average temperature of the left and right hemispheres varied in the range of 36.74 ± 0.37 °C–36.64 ± 0.32 °C, and the highest possible temperature difference registered between relatively “cold” and “heated” areas of the cerebral cortex did not exceed 2 °C (ΔT), averaging 1.4 ± 0.25 °C. In the acute period of the formation of ischemic cerebral infarction, regardless of the area of localization of the lesion, the average temperature of the right and left hemispheres in patients increased up to 37.94 ± 0.28 °C–38.0 ± 0.45 °C. At the same time, cerebral hyperthermia develops at a normal basal temperature in $32\%$ of patients, meaning its occurrence is hidden. The values of ΔT in patients with extensive areas of ischemic lesion can exceed 4 °C, due to the formation of hyperthermia foci reaching 40–41 °C, and averaging 2.4 ± 0.26 °C. Investigation of cerebral cortex temperature disruption patterns in patients in VS and in MCS, which developed due to various causes, showed that the average temperature of the cerebral cortex of both hemispheres is in the range of 36.98 ± 0.18 °C ($$n = 69$$), while ΔT equals 1.2 ± 0.26 °C. It is important to note that the measurements were carried between 12.00–18.00 h, which corresponds to the period of daily acrophase of brain temperature [13]. A correlation analysis demonstrated that healthy participants were characterized by the presence of positive medium strength bonds between the temperature values of nine symmetrical regions of the left and right hemispheres. The correlation coefficients (K, Spearman rank correlation coefficient) ranged from 0.504 to 0.747. On the first day of the ischemic cerebral infarction, K varied from −0.370 to 0.848, demonstrating the development of sufficient temperature heterogeneity of the cerebral cortex. Strong positive correlations between temperature values in the symmetrical regions of the left and right hemispheres were typical for patients with VS and MCS. The K-coefficients ranged from 0.937 to 0.971, reflecting the similarity of the temperature distribution in the cerebral cortex and a decrease in temperature heterogeneity. These results demonstrated that moderate temperature heterogeneity of the cerebral cortex is typical for healthy individuals, while heterogeneity is significantly increased in patients in the acute period of ischemic incidents, compared to patients with DOC, in which it is reduced. The brain temperature is an integral marker of the functional activity of its regions, which enables assessment of the nature of the connections between them, with the assumption that temperature heterogeneity reflects functional heterogeneity. This approach allows us to consider indicators of the level of brain temperature heterogeneity from the standpoint of the theory of functional systems [14,15,16]. High temperature heterogeneity of the brain in patients with schizophrenia is typical for the pharmaco-resistant forms of the disease, and remission is accompanied by a reduction of temperature heterogeneity [17]. The detection of such dynamics allows us to assume, that, by using methods of reducing high or increasing low functional (temperature) heterogeneity, it is possible to achieve a positive therapeutic effect. ## 2. Correction of Brain Temperature Balance Disruption in Patients with Severe Brain Injuries Neurogenic fever and cerebral hyperthermia are not only the markers of acutely developing brain lesions, but also an important link in pathogenesis that worsens the course and outcomes of the disease, which can be stopped by using temperature-lowering technologies [18]. The high neuroprotective capabilities of hypothermia associated with metabolic suppression and the genetic cell response to reduced temperatures make hypothermia attractive for clinical use in the acute period of brain injuries. Current therapeutic hypothermia technologies mostly use general cooling of the patient, decreasing the body temperature to 32–33 °C, which is accompanied by various side effects and complications [19]. Meanwhile, selective hypothermia of the cerebral cortex, achieved by selective craniocerebral cooling (selective craniocerebral hypothermia—SCCH; Figure 3 and Figure 4), allows for a decrease in the temperature of the brain surface at the depth of the local tissues, which is necessary for the suppression of metabolism and expression of cytoprotective genes. By using SCCH, only the surface temperature of the brain is lowered, insignificantly affecting the temperature of the basal structures and body temperature [20]. The use of SCCH in the development of neuroprotection mechanisms has been demonstrated earlier in experimental and in clinical trials, including during the acute period of ischemic stroke [21]. The SCCH methodology involves a cryoapplicator helmet that utilizes channels to circulate coolant and remove surface heat from the scalp. This maintains the scalp temperature at between 3–7 °C, due to the close contact between the helmet and the head surface. The device contains control feedback sensors, including one on the inner surface of the helmet and two supplementary sensors for measuring tympanic and axillary temperature, which helps to maintain the set temperature. After the cooling procedure is complete, the helmet is removed, and the patient undergoes a rapid spontaneous rewarming of the cerebral cortex. This technique has several advantages, including its ease of use and the possibility of inducing cerebral hypothermia in awake patients, comatose patients, and even healthy individuals. Additionally, local cooling helps to reduce the cold load on the body, which helps to prevent complications that are often associated with general therapeutic hypothermia, such as shivering, muscle tremors, and the need for sedation. The therapeutic effects of SCCH on metabolism and gene expression and wider systems biology in the brain cells have been discussed and new directions for patient rehabilitation have been proposed [22,23]. The utility of SCCH in patients with DOC, after severe cerebral injuries, does not seem obvious. The main destructive events have already happened, and the use of hypothermia, especially along with general cooling technologies, bears risks for patients. At the same time, a characteristic feature of the brain temperature balance in this category of patients is a significant decrease in temperature heterogeneity of the cerebral cortex, as well brain circadian rhythm disruption [24]. We have suggested that selective hypothermia of the cerebral cortex, achieved by using SCCH, can be used to increase temperature heterogeneity, providing modification of the cooling procedure. In the acute period of ischemia, the SCCH procedure has the objectives of lowering the temperature of the cerebral cortex by 4–6 °C and correcting temperature heterogeneity. It was noted that, after a 16–24-h SCCH session, smooth spontaneous rewarming should be used, which strengthens the therapeutic effect. The warming is applied only to the cerebral cortex since the basal temperature does not decrease below the level of very mild hypothermia. The smooth rewarming at the end of the session involves a gradual (with two- or three-hour intervals) increase of the brain temperature from 3–5 °C to 10–12 °C, then to 15–17 °C. This procedure takes about 5–6 h. The blood flow in the cooled tissues decreases during the SCCH procedure, while smooth rewarming prevents rapid resumption of blood flow. In turn, rapid spontaneous rewarming (for example, when removing the helmet from the patient’s head), leads to a rapid restoration of the blood flow, leading to a reperfusion effect [25]. Presumably, short procedures with rapid spontaneous warming of the cerebral cortex might induce fluctuations in vascular reactions and might affect the level of temperature heterogeneity of the cerebral cortex. The duration of the procedure should allow reduction of the temperature of the cortex by 1.5–2 °C. It is believed that, under these conditions, neuroprotective processes might be initiated. Since the use of SCCH was found to be safe and did not create any complications or side effects in critical patients, we developed a method of craniocerebral cooling for patients with DOC. The methodology includes a course (10–12 sessions) of short SCCH procedures (120 min), during which the temperature of the scalp is decreased to 3–7 °C and the cerebral cortex by at least 1.5–2 °C. The procedure ends with a quick spontaneous rewarming. This study aims to define the features of the brain temperature balance of patients in VS and MCS, developed after severe cerebral injuries (ischemic and hemorrhagic stroke, and cerebral lesions), as well as to assess the effects of SCCH on cerebral cortex temperature heterogeneity and processes of restoration of consciousness. ## 3. Materials and Methods A total of 111 patients with DOC were included in the study. Inclusion criteria were as follows: patients with DOC, developed after severe focal brain damage (including consequences of ischemic and hemorrhagic strokes or severe traumatic brain injury), not earlier than 30 days after cerebral injuries. Exclusion criteria were as follows: anoxic brain damage (due to cardiac arrest or asphyxia) with extensive diffuse damage of the cerebral cortex, sepsis, cardiac arrhythmias, initial hypothermia (body temperature below 35.5 °C), or terminal stages of the disease. Patients were randomized into two groups. The main group ($$n = 60$$ patients) included two subgroups. The first subgroup of the main group (O1) included patients in VS ($$n = 39$$): women (W)—15 (mean age 36.7 ± 4.4), men (M)—24 (mean age 43.3 ± 3.4). The second subgroup of the main group (O2) included patients in MCS-minus ($$n = 21$$): W—7 (mean age 44.6 ± 7.7), M—14 (mean age 47.5 ± 3.2). The comparison group ($$n = 51$$ patients) also included two subgroups. The first subgroup of the control group (C1) included patients in VS ($$n = 32$$): W—20 (mean age 46.9 ± 3.2), M—12 (mean age 44.1 ± 4.1); the second (C2) included patients in MCS-minus ($$n = 19$$): W—10 (mean age 56.1 ± 3.5), M—9 (mean age 49.2 ± 3.0). In all subgroups, the results were registered on the first and the fourteenth day. The mortality rate was compared on the thirtieth day of follow-up. Patients in the main group underwent 10 sessions of SCCH lasting 120 min, during fourteen days of follow-up. Patients in the control group did not undergo SCCH. In both groups, all patients received standard neurotropic therapy and underwent standard rehabilitation procedures. A device for craniocerebral hypothermia, ATG-01, (Russia Federation) was used (Figure 3). The surface of the craniocerebral region was cooled using a cryoapplicator helmet that maintained the scalp temperature at 3–7 °C (Figure 4). The cooling procedure was completed by removing the helmet, after which the patients underwent rapid spontaneous warming of the cerebral cortex. The MWR of the cerebral cortex was performed using an RTM-01-RES device (Figure 5). The internal temperature was registered in nine areas of the cerebral cortex projections of the left and right hemispheres (Figure 6), before the first procedure and by the end of the tenth procedure. The temperature values in the projections of the frontal cortex and the axillary cavity were registered before the session, every 30 min until the end of the procedure, and 30 min after termination. The procedure was carried out under standard conditions for a time period of 12–18 h in the intensive care unit, at a 25–27 °C room temperature and 75–$80\%$ humidity. The level of consciousness was assessed according to the Coma Recovery Scale-Revised (CRS-R, 2004) with an assessment of the severity of functions in the following scores: hearing ability, visual function, mobility and speech functions, communication, and the level of wakefulness. The analysis included CRS-R scale data obtained in the main group before the first procedure and on the fourteenth day after the tenth SCCH procedure. In the control group, the data (according to the CRS-R scale) were evaluated on the first day and the fourteenth day. Statistical analysis was performed using the SPSS Statistics 21.0 application package. The differences were considered significant at p ≤ 0.05. ## 4. Results It was found that the average temperature before the first SCHH procedure in the projection area of the frontal lobes of the left (LH) and right hemispheres (RH) in patients of both groups in VS and MCS-minus did not statistically differ (36.4 ± 0.11 °C and 36.4 ± 0.09 °C, respectively). Correlation analysis revealed the presence of strong positive correlations between symmetrical areas of the cerebral cortex ($r = 0.86$–0.92), which indicated a low level of temperature heterogeneity of the cerebral cortex. The axial temperature was 36.4 ± 0.09 °C. After 30 min of SCCH, the temperature of the LH and the RH started to reduce, by the ninetieth minute it reached 33.9 ± 0.38 °C and 33.5 ± 0.53 °C, respectively, and by the end of the procedure it dropped by 2.4–3.1 °C. After removing the cooling helmet from the patient’s head, the temperature in the LH and RH returned to the initial values within an hour. During the entire cooling procedure, the axial temperature did not change. Analysis of the functions on the first day of the study in patients subgroup O1 of the main group (VS, $$n = 39$$), according to the CRS-R scale, revealed that the total assessment of the level of consciousness equalled 4.5 ± 0.33, and in patients of the control group in subgroup C1 (VS, $$n = 32$$) it was 4.3 ± 0.37. On the fourteenth day of the study, after the tenth SCCH procedure in the O1 subgroup, the score reached 8.7 ± 0.91 ($p \leq 0.001$) and, in the O1 subgroup, it was 6.8 ± 0.49 ($p \leq 0.001$). In the O1 subgroup, hearing ability, visual function and speech functions, communication and the level of wakefulness increased very significantly ($$p \leq 0.00083$$). The motor function increased slightly less ($p \leq 0.005$). In control subgroup C1, only hearing ability and visual function increased very significantly ($p \leq 0.001$), motor, speech and communicative functions increased less significantly ($p \leq 0.005$), and the level of wakefulness remained the same. The averaged data indicate that patients in VS who received the SCCH procedure reached the MCS-minus level, whereas, in the C1 subgroup, the changes were fewer. While reflecting the general trend, the average values do not reflect the heterogeneity of the results. Thus, in the O1 subgroup, the best results (CRS-R > 16 points) were obtained in six patients ($15.4\%$); in three patients, CRS-R values reached 16–19 points (MCS-plus), and in three more patients they reached 20–21 points, indicating an approximation to full consciousness. In the control group, C1, the best results (CRC-R > 11–13 points) were achieved in five patients ($15.6\%$), and these corresponded to the MCS-minus level. According to the CRS-R scale, on the first day, the O2 subgroup patients ($$n = 21$$) scored a total of 11.3 ± 1.00 points, and the C2 subgroup patients ($$n = 19$$) scored 9.1 ± 0.57 points. On the fourteenth day of the study, after the course of SCCH in the O2 subgroup, the values increased up to 18.2 ± 0.70 points ($p \leq 0.001$), speech function increased in patients in the C2 subgroup ($p \leq 0.05$), and the total score increased, although not significantly, to 10.1 ± 0.86 ($p \leq 0.1$). In the O2 patients who underwent a course of SCCH, the best results (CRS-R > 16, MCS-plus) were obtained in eight patients ($38.1\%$), and in five patients in this group, CRS-R values reached 20–23 points, indicating a significant improvement of consciousness. In the control C2 group, in four patients ($21\%$), a level of 12–16 points was reached on the fourteenth day, which corresponded to the MCS-plus level. Changes in functions according to the CRS-R scale in patients in the main groups and control groups are presented in Table 1 and Table 2. On the fourteenth day, the correlation analysis revealed an increase in the temperature heterogeneity of the cerebral cortex in patients in VS and MCS, in comparison with the data obtained before the course of hypothermia. There was a wide variety in correlation coefficients ($r = 0.36$–0.87), which indicated an increase in the level of temperature heterogeneity. The correlation coefficients did not change significantly in the control group of patients ($r = 0.83$–0.86). The analysis of the mortality rate carried out after 30 days revealed that six patients of the O1 subgroup ($15.4\%$) died. In the O2 subgroup, all of the patients were alive. In the control group, seven patients ($21.9\%$) died in subgroup C1, while four patients ($21.1\%$) died in subgroup C2. Six patients ($10\%$) died in the main groups, and 11 patients ($21.6\%$) died in the control groups. The leading causes of death in both groups were sepsis, thromboembolic complications, and multiple organ failure. There were no complications or side effects specific to hypothermia reported. ## 5. Discussion In this pilot study, we tested the hypothesis that the level of temperature heterogeneity of the cerebral cortex may reflect the nature of the disruption of the functional connections between the cortical regions. Undoubtedly one of the limitations of our study is the moderate sample size; however, considering the high mortality rate in such patients, it is challenging to recruit a larger number of individuals and this could take a long time. Despite this, we are continuing to collect data but, at this intermediate stage, our main aim of the study was to show the possibility of using completely new and safe methods to facilitate diagnosis and increase the rehabilitation potential in such vulnerable patients. The obtained results demonstrate the potential benefit of implementing MWR and SCCH as effective auxiliary techniques in the treatment and rehabilitation of patients with DOC. The decrease of heterogeneity in the acute period of ischemic stroke [26] and its increase in patients with DOC in this study were accompanied by an improvement in clinical status and consciousness level, to a certain extent confirming this hypothesis. The mechanisms of positive changes in patients under the influence of hypothermic effects on the brain are associated with well-known effects [27]. They include the following metabolic conditioned reactions that develop with a decrease in cerebral temperature: restriction of oxygen and substrate consumption, inhibition of excitotoxicity reactions and receptor-mediated interactions of signaling molecules, restriction of the development of oedema and inflammatory response, apoptosis, and proliferation [28]. In addition, a small range of temperature variation (1–3 °C) becomes sufficient for the expression of genes encoding a wide range of different stress-protective proteins, including cold shock proteins (CSPs) and heat shock proteins (HSPs) [29,30]. An increase in temperature reduces CSPs production, and warming provokes an increase in HSPs production, even at low temperatures. CSPs and HSPs are generally referred to as stress proteins with a high potential for neuroprotection [31,32]. The representatives of CSPs, RBM3 (RNA binding motif 3) and cold-inducible RNA binding protein (CIRBP), provide a reduction of the negative effect of various damaging factors on neurons, with a decrease in temperature of even 1 °C [33,34], and reduce the amount of damage to neurons caused by hypoxia, increase neuronal tolerance to damage, inhibit apoptosis, and contribute to the restoration of the microtubular system of neurons after damage [35,36]. The temperature increase at the cessation of hypothermia provokes an intensive production of HSP70 and HSP90, which are characterized by the most significant cytoprotective effects [37]. During the period of warming from 33 °C to 37 °C, the expression of CHPs is inhibited, while the expression of HSP70 escalates as the temperature increases [38]. It is assumed that warming after cooling activates cellular respiration and the formation of free radicals that provide signals for the expression of the genes involved in the development of stress-protective reactions. In this study, we demonstrated that even small temperature fluctuations are a significant signal for the expression of the HSPs. This method of induction of hypothermia, allowing fluctuations in the body temperature of animals within the range of 34–35 °C, facilitated an almost four-fold reduction of the brain lesions caused by occlusion of the middle cerebral artery [39]. The authors associate this powerful neuroprotective response with cooling, accompanied by the activation of the HSPs. The protective role of HSP protein in hypothermia in terms of cerebral ischemia has also been demonstrated in other experimental models [40]. Stress proteins that contribute to the development of neuroprotection and activate the processes of neuroregeneration and neuroplasticity include hibernation proteins. These proteins protect animals during torpor and when emerging from it. Torpor development in spontaneously hibernating animals is associated with the fibroblast growth factors. For example, FGF21 is a key inducer of hibernation and has a direct neuroprotective effect [41], reducing the amount of neuronal death induced by glutamate. FGF21 enhances the neuroprotective effect of the CSPs RBM3 protein, contained in the cortical neurons of rats, and is expressed even during very mild hypothermia (35 °C) [42]. FGF21 promotes remyelination [43], increases the integrity of the BBB (blood–brain barrier), and reduces brain oedema, damage volume and neurological deficit after experimental TBI [44]. This reduces the inflammatory response and the size of brain infarction in an ischemic stroke model [45]. It also improves indicators of biochemical markers of brain damage after hypoxic–ischemic injury in adult rats [46]. UCP proteins (uncoupling proteins) play an important role in warming animals when they emerge from torpor; one of these, rUCP1 (thermogenin) [47], stimulates synaptogenesis and neurogenesis, and demonstrates antioxidant properties. Under the influence of UCP, the expression of the brain neurotrophic factor (BDNF) [48] increases. Another representative of the natural hibernation process, irisin, helps to reduce the volume of brain lesions in rats after occlusion of the medial cerebral artery and inhibits apoptosis reactions [49]. The use of irisin in experimental models stabilized the BBB, stimulated the accumulation of BDNF, and reduced the volume of myocardial and lung damage after total ischemia [50,51]. Lowering the brain temperature to 33 °C reduced the volume of damage during occlusion of the middle cerebral artery in rats and was accompanied by an increase in ubiquitin (ubiquitous) synthesis and protein ubiquitination, which may be one of the important mechanisms of neuroprotection [52,53]. It is important that the effects of the gene expression and the accumulation of stress proteins persist for more than one day. The course of daily SCCH sessions, providing a decrease in the temperature of the brain surface by 2.5–3 °C, could cause the accumulation of stress proteins which, hypothetically, could have a positive effect on the processes of improving consciousness. To a certain extent, these assumptions are confirmed by the clinical results obtained in this study. ## 6. Conclusions The results of this study emphasize the high value of the use of MWR as companion diagnostics to measure the temperature balance of the cerebral cortex, and demonstrated positive effects of the use of SCCH in patients in VS and MCS (Figure 7). It is important to conduct further in-depth studies, with larger sample sizes, and more precise patient selection according to their age, gender, and aetiology of cerebral injuries, and search for biomarkers that are involved in the processes of improving consciousness in patients with DOC. Although our study is limited by a moderate sample size, it is essential to note that mortality rates for this patient category are high, and collecting data from a larger sample might be time-consuming. Our main priority in conducting this study was to demonstrate the potential use of innovative and safe methods as diagnostic tools and devices to correct pathophysiological processes in comatose patients. 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--- title: Enhancement of Ultrasound B-Mode Image Quality Using Nonlinear Filtered-Multiply-and-Sum Compounding for Improved Carotid Artery Segmentation authors: - Asraf Mohamed Moubark - Luzhen Nie - Mohd Hairi Mohd Zaman - Mohammad Tariqul Islam - Mohd Asyraf Zulkifley - Mohd Hafiz Baharuddin - Zainab Alomari - Steven Freear journal: Diagnostics year: 2023 pmcid: PMC10047674 doi: 10.3390/diagnostics13061161 license: CC BY 4.0 --- # Enhancement of Ultrasound B-Mode Image Quality Using Nonlinear Filtered-Multiply-and-Sum Compounding for Improved Carotid Artery Segmentation ## Abstract In ultrasound B-mode imaging, the axial resolution (AR) is commonly determined by the duration or bandwidth of an excitation signal. A shorter-duration pulse will produce better resolution compared to a longer one but with compromised penetration depth. Instead of relying on the pulse duration or bandwidth to improve the AR, an alternative method termed filtered multiply and sum (FMAS) has been introduced in our previous work. For spatial-compounding, FMAS uses the autocorrelation technique as used in filtered-delay multiply and sum (FDMAS), instead of conventional averaging. FMAS enables a higher frame rate and less computational complexity than conventional plane-wave compound imaging beamformed with delay and sum (DAS) and FDMAS. Moreover, it can provide an improved contrast ratio and AR. In previous work, no explanation was given on how FMAS was able to improve the AR. Thus, in this work, we discuss in detail the theory behind the proposed FMAS algorithm and how it is able to improve the spatial resolution mainly in the axial direction. Simulations, experimental phantom measurements and in vivo studies were conducted to benchmark the performance of the proposed method. We also demonstrate how the suggested new algorithm may be used in a practical biomedical imaging application. The balloon snake active contour segmentation technique was applied to the ultrasound B-mode image of a common carotid artery produced with FMAS. The suggested method is capable of reducing the number of iterations for the snake to settle on the region-of-interest contour, accelerating the segmentation process. ## 1. Introduction Improving the axial resolution (AR) will bring many benefits for numerous applications in ultrasound imaging, such as localizing single microbubbles for super-resolution, segmenting the cross-sectional area of a carotid artery for stenosis assessment and measuring the intima-media thickness of a common carotid artery for diagnosing cardiovascular diseases [1,2,3,4,5,6]. Numerous methods have been proposed to improve the AR in ultrasound B-mode imaging. They include the use of wideband excitation signals and the employment of pre/post signal processing techniques [7,8,9,10]. Short excitation pulses, such as a broadband square pulse signal, are able to produce a narrow main lobe along the axial direction. However, the side lobe produced by the excitation signal is also relatively very high due to spectral leakage from the diffraction effect [7,11]. When the side-lobe energy of a strong scatterer exceeds the main lobe energy of an adjacent weak scatterer, the weak scatterer becomes easily obscured or distorted. Therefore, developing a powerful side-lobe reduction method is crucial [12]. The high side lobe is the main contributor to a low image contrast ratio (CR) [13]. Moreover, the penetration depth with the square pulse is considerably shallow because the acoustic attenuation in the tissue increases with the imaging depth and the excitation signal frequency. Increasing the square pulse energy level may not be the best solution to improve the imaging penetration depth. This is due to the limitation set by the Food and Drug Administration (FDA) that requires the acoustic pressure to be within the safety limit [7,14]. This is done to ensure that the acoustic intensities will not cause thermal and mechanical damage to the imaged tissue. Thus, to improve the penetration depth and the AR, researchers have turned to chirp-pulse-coded excitation signals. The long pulse duration of the chirp signal combines two important features which are high energy with a low mechanical index and a broadband bandwidth [7]. A chirp signal is able to produce good AR and penetrate deeper into the imaging medium. Employing chirps or coded excitation signals requires a matched or mismatched filter during the receive beamforming stage to compress the echoes [7]. Pulse compression is a method used to restore the AR when using a chirp excitation signal. One of the main drawbacks of this method is the trade-off between the AR and CR of the B-mode image. A matched filter can produce a better AR with a low CR, while a mismatched filter is able to reduce the side lobe at the cost of broadening the main axial lobe. Sacrificing one metric to improve another is not the best solution for ultrasound B-mode imaging. In [15], the authors highlighted the importance of improving the AR for enhanced endoscopic ultrasound imaging. However, their compression pulse weighting method (CPWM) applied to the chirp excitation signal was not able to improve the AR. The CPWM approach produced a 0.13 mm main lobe at −20 dB, while the sine wave and matching filter both achieved 0.12 mm. A new beamforming technology named filtered-delay multiply and sum (FDMAS) was introduced in 2014 by Giulia Matrone et al. [ 16]. It could provide a better contrast than conventional delay and sum (DAS) and took less time to compute than minimum variance. However, the improvement in AR was very minimal when compared to DAS. This could be seen in multiple publications [16,17,18]. Qualitatively, no significant changes could be seen from the lateral and axial beam profiles at the full width at half-maximum (FWHM) and −20 dB. However, the numerical results presented by Giulia Matrone et al. showed that FDMAS was able to improve the AR by approximately $10\%$ compared to DAS. The two most common compounding techniques used in plane-wave imaging with DAS and FDMAS beamformers are coherent and noncoherent averaging [19,20]. Compounding is used to reduce clutter noise after beamforming for each plane-wave transmission. This is because the side lobe of the target occurs at different spatial locations, and they are loosely correlated for different plane-wave transmissions. This phenomenon happens because each plane wave during transmission is assigned a unique time delay. Meanwhile, the primary lobe of the target position remains constant and is significantly correlated with steered plane waves [21]. However, with conventional compounding technique, the noise reduction inside the anechoic region is minimal. Thus, a high number of plane waves with large steering angles are required to produce a high-contrast B-mode image with a low frame rate [22]. The challenge of acquiring a good-quality B-mode image of a moving object is undeniable. Thus, using a limited number of compounding angles to achieve high frame rates is mandatory. This may not be achievable with the conventional compounding technique. In our previous work [23], we proposed a nonlinear compounding technique known as filtered multiply and sum (FMAS). However, we just gave a brief introduction of the concept and did not explain in detail the potential applications and main theory behind the improvement of AR. Several researchers [24,25,26,27,28] also implemented and compared our proposed method, but the results they obtained and presented had several flaws. First, the dynamic range used to present all the B-mode images in their work was 70 dB, which was very high compared to the conventional range of 40 to 60 dB. Displaying the B-mode image with a high dynamic range (≥60 dB) will include all clutter noise that is present in the B-mode image processed with DAS thus making it qualitatively look like a very low quality image. If the same B-mode image is presented with a dynamic range of 40 to 60 dB, it may appear better in terms of a high-contrast image, though no changes in spatial resolution can be achieved. Second, the improvements in spatial resolution and noise reduction achieved by other researchers were still comparably low with what we present in this work. This can be due to the improper selection of the band-pass filter (BPF) after the autocorrelation process. The selection of the BPF will determine whether only the frequencies of interest are extracted, or noise is also included. Image enhancement is of great importance in biomedical image segmentation. Image segmentation is a crucial step in establishing a solid foundation for clinical diagnostics, such as 3D anatomical reconstruction and image analysis and visualization [29,30,31]. Active contour is a popular segmentation approach for separating desired regions from a variety of biomedical images [32,33,34]. Thus, in this study, balloon-snake active-contour (BSAC) segmentation is performed on the common carotid artery B-mode image to prove that the AR enhancement with the proposed FMAS method can reduce the number of iterations and computational time during the segmenting process. Less clutter noise and a good image resolution are two main criteria for a better segmentation [22]. In this work, we discuss in detail the theory behind the FMAS algorithm and how it is able to improve the spatial resolution mainly in the axial direction with as few as three plane waves. The low complexity of the proposed method also means it has a high potential to be implemented for real-time imaging. We also demonstrate how FMAS may be used in a practical biomedical image segmentation application. ## 2.1. Coherent Plane-Wave Imaging (PWI) To efficiently improve the frame rate, unfocused plane waves have been proposed to insonify the whole region of interest (ROI), where the ultrasound image is reconstructed by revisiting the same set of received radio frequency (RF) channel data when beamforming each image pixel. A number of plane-wave transmissions are usually used, and both the resolution and contrast of the final image can be improved by compounding the low-quality images beamformed for individual plane-wave transmissions. Details about ultrasound beamforming and plane-wave imaging can be found elsewhere, and readers are referred to the following publications [19,35]. It requires a total of N steered plane waves (n) to attain a comparable quality as a focused image at z mm depth [20], [1]N=LaλF=La2λz where F is the F-number calculated as z/La, La is the aperture’s length, λ is the signal wavelength, and θn, the steering angle, is given by:[2]θn=arcsinnλLa≈nλLa where n is defined as [3]n=−(N−1)/2,...,(N−1)/2 The primary focus of this research was to investigate how the compounding approach, FMAS, affected imaging results when employing different numbers of compounding angles with the DAS and FDMAS beamformers, rather than to determine the optimum number of compounding angles. Based on their experimental settings, numerous studies have proposed a specific number of compounding angles as having the best image quality [19,20,21]. Image quality does not improve beyond a certain number of compounding angles, according to several studies [19,21], but instead degrades due to poor noise suppression near the main lobe. These limitations were taken into consideration when presenting the selected number of compounding angles, N, and the steering angle increment, Δθ∘ in Table 1. The sector angles, θmax∘,θmin∘ and ±12∘ were set for all compounding conditions. ## 2.2. DAS and FDMAS Beamforming Beamforming is one of the most important steps in ultrasound B-mode imaging used to reconstruct the received echo from the imaged medium. The initial steps of DAS and FDMAS are identical. The RF signal that each element, i, receives in this case is the signal, si(t). To calculate the required focusing delay, τi, to temporarily align the signals received by each element, the following equation is used:[4]τi(x,Z)=(Zcosθn+xsinθn+La2sinθnc+Z2+(xi−x2)c where x is the lateral location of the beamformed pixel with a step size of λ/3, and Z is the vector of axial pixel locations given by [5]Z=z1z2⋮zdepth c is the sound speed, zdepth is the maximum imaging depth, and xi is the distance between the ith element and the centre of the transducer. The aligned RF signal, vi(x,Z), is the RF signal with the focusing delay compensated, si(x,Z), and it can be expressed by the following equation:[6]vi(x,Z)=si(t−τi(x,Z)) The aligned signals in DMAS, as opposed to DAS, undergo a procedure similar to autocorrelation, which is denoted by the equation:[7]rDMAS(x,Z)=∑$i = 1$E−1∑m=i+1Esgnvi(x,Z)vm(x,Z)×(\|vi(x,Z)vm(x,Z)\| where m=i+1 is the aligned RF signal at the mth element and E is the total number of elements on the imaging probe. The second-harmonic and direct-current (DC) components are created by multiplying two RF signals with the same frequency. In order to extract the second-harmonic from rDMAS(x,Z), a BPF is used. As a result, rFDMAS(x,Z) is produced. To create a vertical imaging line, l, at a specified lateral location x, a number of time delays are determined for each depth Z. ## 2.3. Filtered-Multiply-and-Sum (FMAS) Compounding Following DAS beamforming for individual steered plane waves, the FMAS compounding procedure is performed. The beamformed RF frames are autocorrelated to generate the multiply-and-sum (MAS) frames, as opposed to the traditional compounding process, which involves adding and averaging all the steered plane waves after beamforming. The following is the MAS equation:[8]ComMAS=∑n=N−1∑k=n+1NsgnVn(t)Vk(t)×\|Vn(t)Vk(t)\| where V(t) denotes a collection of aligned RF signals vi(t) ($i = 1$ to 128) for each directed plane wave. The method is comparable to how the autocorrelation function works. The proposed method is faster than FDMAS because the number of multiplications, B, involved in the autocorrelation for FMAS is equal to the number of compounding angles of N as indicated by [9]B=N2−N2 To generate a filtered-multiply-and-sum (FMAS) compounded signal, the RF signals obtained from MAS must be band-pass-filtered. The explanation is the same as in FDMAS, where two separate frequency spectra (DC and second-harmonic) are formed by multiplying two RF signals with the same frequency [16] (the 6th figure in Section 3 exhibits the related frequency components). The RF signals are then filtered, Hilbert transformed for envelope detection and log-compressed to generate the B-mode image. ## 2.4. Active Contour Segmentation The user-defined approximation of an object’s border or contour serves as the starting point for active-contour-based segmentation techniques [36,37,38]. The initial contour then evolves, establishing the actual object boundaries. The aim of active contour generation is to evolve continuously over a predefined number of iterations in order to minimise the overall external energy (image gradient) and internal energy (contour form) [39,40]. The total snake energy is calculated so that it is always at its minimum at the end of each iteration. By resolving the following Euler–Lagrangian equation, it is possible to minimise the total snake energy using the calculus of variation [39]:[10]δδSα(s)Vs(s)+δ2δS2β(s)Vs(s)−∇EextV(s)=0 where the points on the contour, V(s), are represented by s∈[0,1]. The first derivative, Vs(s), gives a measure of the contour control’s elasticity (stretching) strength via α(s), whereas the second derivative, Vss(s), offers a measure of the stiffness (bending) strength of the contour control via β(s). The external energy of an image is represented by Eext(V(s)). ## 2.5. Simulation and Experimental Setup To validate the FMAS compounding method, various measurements were done on point targets (wire) with a diameter of 120 μm in deionized and degassed water, on a tissue-mimicking phantom (040GSE, CIRS, Norfolk, VA, USA) and in vivo. Anechoic segments (10 mm to 50 mm in depth) and point targets (10 mm to 60 mm in depth) of the tissue-mimicking materials were scanned. A right common carotid artery cross section of a healthy volunteer was used to collect in vivo data. A linear array transducer of 128 elements (L3-$\frac{8}{40}$EP, Prosonics Co. Ltd., Gyeongju-si, Republic of Korea) with a 4.79 MHz centre frequency and a $57\%$ bandwidth at −6 dB was used to collect all the data. The multipurpose imaging system, ultrasound array research platform II, (UARP II, University of Leeds, Leeds, UK), stimulated the transducer with a two-cycle sinusoidal wave signal with a centre frequency of 5 MHz [41,42,43]. The signals received were sampled at a frequency of 80 MHz. Table 2 contains all of the simulation and experimental parameters. ## 2.6. Performance Evaluation The quality of the final B-mode images obtained by the DAS, FDMAS and DAS-FMAS approaches can be evaluated using two key metrics: spatial resolution and CR. The function developed in [44] was used to measure the primary lobes of the point target, a nylon wire with a diameter of 120 μm placed at a depth of 30 mm in deionized and degassed water at −6 dB and −20 dB in order to calculate the AR and lateral resolution (LR). A cyst’s detectability was expressed using the CR by comparing values between the cyst’s ROI and its background. The contrast-to-noise ratio (CNR) in ultrasound images was used to quantify the cyst’s contrast and detectability. In order to measure the CNR and CR, the B-mode image of a 3.0 mm diameter cyst with anechoic fluid located at a depth of 15 mm was generated using the 040GSE phantom by creating two identically sized sections. The first area was established inside the cyst, and the second area was established at the same depth outside the cyst. This requirement ensures that the attenuation with depth would not have an effect on the measurements. The following are the CR and CNR equations: [22,45], [11]CR(dB)=20log10μcystμback [12]CNR(dB)=20log10\|μcyst−μback\|(σcyst2+σback2 where the means of the image intensities inside and outside the cyst are μcyst and μback, respectively, and their variances are σback2 and σcyst2. All simulation and experiment findings were reported as a mean value with one standard deviation, determined from 10 measurement runs. During the repeated measurements, the transducer was not moved along the elevation axis. The Dice coefficient and mean intersection over union (MIoU) metrics were used to assess the performance of the BSAC segmentation for a tissue-mimicking phantom. The Dice coefficient is the evaluation index most often used in segmentation. The higher the Dice coefficient, the greater the similarity between two samples. The MIoU determines how closely the two sets of elements overlap. The overlap area between the estimated segmentation and the ground truth is compared to the combined area of the two. The Dice and MIoU equations are as follows [46,47]:[13]Dice(A,B)=2\|A∩B\|\|A\|+\|B\| [14]MIoU(A,B)=\|A∩B\|\|A∪B\| where A represents the estimated segmentation and B represents the ground truth. ## 3.1. Simulation Results It was expected that with spatial compounding, the LR would be improved. This was due to the side lobe cancellation in the lateral direction. With both DAS and FDMAS, the LR increased with the number of compounding angles, but there were only small changes in the AR. This was because the AR was mainly determined by the bandwidth of the excitation signal regardless of the beamforming techniques or the number of compounding angles. However, with the proposed FMAS compounding technique, the AR was improved significantly when compared to that with DAS and FDMAS. This was explained by the concept that the geometry and appearance of the objects in PWI were influenced by the beam concentration or directivity. Steered plane waves had distinct intensity distributions for each angle. The beam pattern and its intensity distribution were changed in accordance with the angle’s increase or decrease. This phenomenon was mostly visible on the lateral side lobes and the axial lobes in the axial direction, where it appeared at different locations depending on the steering angles. In order to analyse this phenomenon in detail, Field II simulations were performed to obtain the emitted pressure fields for different steering angles at a 30 mm depth. The setup for the simulation is given in Table 2. The emitted pressure fields simulated for steering angles −12∘, 0∘ and +12∘ are shown in Figure 1. The normalized pressure fields at $x = 0$ mm as highlighted by the dashed line in Figure 1b are shown in Figure 2 for the three steering angles. The variations between pressure fields steered at ±12∘ and 0∘ in the axial direction were clearly visible. There was a phase shift of 0.02 mm between peak pressure points between those steering angles. While the shift was invisible between plane waves steered at −12∘ and +12∘ since both steered pressure waves appeared at the same spatial location. The simulation to measure the phase shift between the pressure points was repeated on the point target located at 30 mm depth. Figure 3 shows the received RF signals for a wire target with plane waves steered at ±12∘ and 0∘. The 0.02 mm shift between RF peaks was also found in the axial direction for the point target between steering angles of +12∘ and 0∘. This is shown in Figure 4a. However, this shift was 0.06 mm for the experimental point target between steering angles of +12∘ and 0∘. This is shown in Figure 4b. Even though the variation was too small to be considered in conventional compounding techniques, which apply averaging between the steered plane waves, this was not the case when a procedure similar to autocorrelation was used in the proposed technique. For example, when averaging three points as shown in Figure 4a, it produced an amplitude value of 0.87, while applying a procedure similar to autocorrelation as given by Equation [8] for the same three points produced an amplitude value of 1.39. Even a 0.02 mm variation between the aligned RF signals produced a significant difference in the main lobe values in the axial direction when FMAS compounding was applied. Further implications of the phase shift in the RF signals in the axial direction could be seen from the experimental result. Due to many other factors such as phase aberration, the variation between the RF signals further increased up to 0.06 mm. There, as shown in Figure 4b, the steering effect caused the RF signals appearing at $x = 30.77$ mm to have the amplitude values of 1 and −0.4522 for steering angles ±12∘ and 0∘, respectively. The B-mode images obtained from Field II were processed for seven point targets located from a 10 mm to a 60 mm depth, and they are shown in Figure 5a–c. The corresponding beam patterns along the lateral direction at a $z = 50$ mm depth and the axial direction at $x = 0$ mm for DAS, FDMAS and DAS-FMAS are shown in Figure 5d, and Figure 5e, respectively. The proposed new compounding technique, FMAS, was able to eliminate the grating lobes that appeared at the 10 mm depth in both lateral directions (−10 mm and 10 mm) when beamformed with DAS as shown in Figure 5a. The new compounding method also reduced axial and side lobe in both axial and lateral directions. This is shown in Figure 5d,e. Up to 7 dB of peak side lobe (PSL) along the lateral direction was reduced with the new compounding technique, although FDMAS and FMAS used the same mathematical theorem, a process similar to autocorrelation. The beam pattern produced along the axial direction with FMAS was almost the same as that using FDMAS. The signal intensity level with FMAS was lower than that with DAS except at the depth of elevation focus. This was because all signals had been normalized to their maximum value. The explanation for this phenomenon was the same as what happened with FDMAS. When RF signals with almost identical frequency components from two steering angles were multiplied, DC and second-harmonic components were produced. The second-harmonic component, as shown in Figure 6 with a lower amplitude level, was used to form all images in DAS-FMAS. Thus, the signal had a lower intensity level. Low-signal intensities at a deeper location could be amplified by applying time gain compensation (TGC). Axial lobes (ALs) that occurred when plane waves were steered were visible below the point targets located at the depths of 10 mm and 20 mm, as shown in Figure 5a. Both FDMAS and FMAS were able to reduce these ALs. The spatial distributions of ALs for different steering angles were diverse, thus, when a process similar to autocorrelation took place, the decorrelation between the ALs was higher, making more ALs attenuated. The ALs mainly occurred at around the −45 dB level with DAS and were attenuated to below −60 dB with FDMAS and DAS-FMAS, as shown in Figure 5e. The CR for the simulated cyst located at the 30 mm depth was significantly improved with DAS-FMAS with just three steering angles (−12∘, 0∘ and +12∘). Although FDMAS was able to improve the CR, clutter noise inside the anechoic region was still visible and not fully eliminated. The border definition for all cysts was improved with FDMAS, but more improvement was obtained with DAS-FMAS due to the further reduction of clutter noise. This can be seen clearly in Figure 7. The attenuation of clutter noise inside the cyst region was because lateral side lobes leaking into the anechoic region were significantly reduced, thus making the edge steeper and improving the border definition. To analyse in detail the effect of the proposed technique on a point target, B-mode images and beam profiles along the axial and lateral directions were plotted for a point target at a depth of 30 mm, as shown in Figure 8. It can be seen in Figure 8c that the side lobes along the lateral direction were nearly fully suppressed for an imaging dynamic range of 50 dB. However, this was not the case for DAS. With three compounding angles, the noise cancellation did not take place effectively. The ALs were still visible at approximately 31 mm of depth with DAS, as shown in Figure 8a. Although FDMAS was able to tackle the noise problem along the axial and lateral directions, the PSL produced along the lateral direction was higher than that with DAS-FMAS, which can be seen in Figure 8d. The PSL along the lateral direction at a depth of 30 mm for DAS was −31.9 dB, while for FDMAS it was −38.7 dB. For DAS-FMAS, the PSL along the lateral direction was reduced to −68 dB. The proposed technique, DAS-FMAS, produced narrower main lobes along the axial direction compared to DAS and FDMAS. This can be seen in Figure 8e. Complete AR measurements on the wire target at the 30 mm depth for DAS, FDMAS and DAS-FMAS ($$n = 1$$ to $$n = 25$$) are presented in the 13th figure (a,b) in Section 3.2. ## 3.2. Experimental Results The experimental results on seven wire targets are presented in Figure 9. Thirteen steered plane waves, as given by Table 1, were used. A high number of compounding angles were able to eliminate the grating lobes in DAS. DAS-FMAS demonstrated a remarkable improvement since the side lobes along the axial and lateral axes were significantly reduced. A wire target at a depth of 30 mm was selected in order to evaluate the spatial resolution of the DAS-FMAS method and compare it with conventional methods. Figure 10 shows B-mode images for $$n = 3$$ to $$n = 25$$ compounding angles for the wire target that were formed with DAS, FDMAS, and DAS-FMAS. The corresponding axial and lateral beam profiles for the wire target are given in Figure 11 and Figure 12, respectively. The AR results for DAS, FDMAS and DAS-FMAS at the FWHM, −6 dB level were progressively more consistent with a compounding angle increase from $$n = 3$$ to $$n = 25$.$ The AR improved significantly with DAS-FMAS compared to DAS and FDMAS. When $$n = 3$$, the AR with DAS-FMAS was $43\%$ and $12.5\%$ better than that with DAS and FDMAS, respectively. When using 25 compounding angles, the AR improved by $44\%$ and $47\%$, respectively, when compared to DAS and FDMAS. The ARs for DAS and FDMAS did not show any significant differences from $$n = 5$$ to $$n = 25$$ except for $$n = 3$.$ All results for the AR at −6 dB for different numbers of compounding angles are shown in Figure 13a. It was unexpected to see any improvement in AR for DAS and FDMAS through spatial compounding since its effect was in the lateral direction. This can be seen from the beam profiles along the axial direction, as shown in Figure 11 for the wire target at the 30 mm depth for all investigated techniques. AR results at the −20 dB level for DAS, FDMAS and DAS-FMAS showed almost the same pattern as those at −6 dB. At $$n = 3$$, the AR with DAS-FMAS was improved by $32\%$ from DAS and $31\%$ from FDMAS. While with 25 compounding angles, the AR was improved by $26\%$ from DAS and $28.5\%$ from FDMAS. The complete results for AR at −20 dB with all compounding angles are shown in Figure 13b. With DAS-FMAS, the PSL in the axial direction was attenuated by 33 dB and 48 dB more than with DAS and FDMAS, respectively, for $$n = 3$.$ With 25 compounding angles, DAS-FMAS was able to reduce the PSL by 28 dB and 25 dB, respectively, more than DAS and FDMAS. All results for PSLs in the axial direction for different numbers of angles are shown in Figure 13c. Figure 13d shows the LR results for DAS, FDMAS and DAS-FMAS at −6 dB. For all investigated techniques, a high LR was attained with fewer compounding angles, and DAS-FMAS yielded the best outcomes. In comparison to DAS and FDMAS, the LR for DAS-FMAS was enhanced by $36\%$ and $19\%$ for $$n = 3$.$ DAS-FMAS outperformed DAS and FDMAS by $37\%$ and $20\%$ when the number of compounding angles reached $$n = 25$.$ The LR for all approaches remained constant after $$n = 13$.$ Figure 13e shows the LR results for DAS, FDMAS and DAS-FMAS at −20 dB. The LRs at −20 dB with $$n = 3$$ for DAS, FDMAS and DAS-FMAS were 1.4 mm, 0.93 mm and 0.57 mm, respectively. DAS-FMAS outperformed DAS and FDMAS by $59\%$ and $38\%$, respectively. With $$n = 25$$, the LR with DAS-FMAS showed improvements of $38\%$ and $20\%$ compared to DAS and FDMAS, respectively. Beyond $$n = 5$$, there were no changes in the LR at −20 dB for all techniques investigated. The lateral PSL results for DAS, FDMAS and DAS-FMAS are presented in Figure 13f. As the steered plane waves increased from $$n = 3$$ to $$n = 25$$, the PSL results showed an improvement for all techniques investigated. DAS-FMAS produced the best outcomes in comparison to DAS and FMAS. At $$n = 3$$, DAS-FMAS reduced the PSL by 14.7 dB and 10.3 dB more than DAS and FDMAS, respectively. When compared to DAS and FDMAS, the PSL with DAS-FMAS was 11.1 dB and 23 dB lower for $$n = 25$.$ The experimental results on cysts with diameters of 1.3 and 3.0 mm at depths of 15 and 45 mm with 13 compounding angles as given in Table 2 are shown in Figure 14a–c. FDMAS and DAS-FMAS improved the CRs for all cysts in circles i, ii, iii and iv as compared to DAS. When compared to DAS and FDMAS, DAS-FMAS reduced more clutter noise. This can be seen on the B-mode image of the 1.3 mm diameter cyst (marked as circle iii) which was barely visible with DAS and FDMAS but the contrast was improved with DAS-FMAS. The lateral beam profiles at 15 mm and 45 mm are shown in Figure 14d and Figure 14e, respectively. A cyst with a 3 mm diameter located at a 15 mm depth as marked by circle ii in Figure 14 was chosen to measure the images’ CR and CNR. The B-mode images for the cysts with DAS, FDMAS and DAS-FMAS are shown in Figure 15a, Figure 15b and Figure 15c, respectively. All images are displayed with a 50 dB dynamic range. In general, FDMAS and DAS-FMAS performed better than DAS, where more clutter noise was reduced inside the anechoic region. The beam profile along the lateral direction at the 15 mm depth is given in Figure 16. When the number of steered plane waves increased from $$n = 3$$ to $$n = 25$$, the clutter noise level of 3.0 mm diameter cysts kept decreasing. Figure a shows the CR results for the 3.0 mm diameter cyst at the 15 mm depth. As the number of compounding angles increased, the CRs for all techniques continued to increase. With $$n = 3$$, DAS-FMAS outperformed DAS and FDMAS in CR by 14.1 dB and 7.29 dB, respectively. The CR for DAS-FMAS was −49.8 dB with $$n = 25$$, which was higher than that for DAS (−26.1 dB) and FDMAS (−27.9 dB). Figure 16 illustrates that DAS-FMAS could eliminate clutter noise within anechoic regions by attenuating it to a level lower than −60 dB. The CNRs for the 3.0 mm diameter cyst at a 15 mm depth are given in Figure 17b. As opposed to all other performance indices, the CNR for DAS-FMAS was the lowest compared to those using DAS and FDMAS. The CNR did not show significant variations for DAS-FMAS from $$n = 3$$, 2.9 dB to $$n = 25$$, 2.8 dB. The CNR for FDMAS kept decreasing for the same compounding range of $$n = 3$$ to $$n = 25$$, while for DAS the CNR kept increasing. The reduction of clutter noise outside the cyst reduced the CNR for DAS-FMAS. The destructive speckle regions in DAS were filled with clutter noise. Once the clutter noise was reduced, the destructive region became more visible as the dark spot. This can be seen from the beam profile shown in Figure 16. Outside the cyst regions, the speckle variation was higher with FDMAS and DAS-FMAS. One of the ways to solve the low CNR problem was by using despeckling, which reduced the speckle fluctuation. The clutter noise reduction at a 45 mm depth for all techniques was less than that at a 15 mm depth. This was mainly due to the low SNR at deeper locations. DAS-FMAS still performed better than the other two techniques, even at the deeper location. The level of clutter noise inside the 3.0 mm diameter cyst continued to decrease as the number of steered plane waves increased from $$n = 3$$ to $$n = 25$.$ ## 3.3. Effects of Different Beamforming Techniques on Segmentation The sizes of the cyst areas of the CIRS phantom, as highlighted by green dashed lines in Figure 15, were measured and compared to their nominal values. The region size was calculated by counting the pixels within the cyst and multiplying them by their axial and lateral pixel sizes. The B-mode image pixel size along the axial direction was calculated to be 9.625 μm. With a diameter of 3.0 mm, the nominal cyst area was 7.07 mm2. The measured cyst sizes approximated their nominal values when the number of steered plane waves increased from $$n = 3$$ to $$n = 25$$ for all techniques investigated, as shown in Table 3. However, DAS-FDMAS was able to achieve almost the exact nominal value with a low number of compounding angles, $$n = 5$$, compared to the other two techniques. This showed the new compounding technique improved the BSAC-based segmentation process. However, the cyst size measured with DAS and $$n = 25$$ still differed from the nominal value by 0.22 mm2. For cysts with a diameter of 3.0 mm, the time for the snake to reach the segmented cyst border, represented by the contour shown in green in Figure 15, was computed by setting the number of iterations to 100. Depending on how noisy the object is, a snake’s total convergence time from the object’s centre to the intended border will vary. If the snake is unable to reach its minimal energy due to high clutter noise, it may take longer. Table 3 shows the time it took for the snake to converge to the 3.0 mm cyst boundaries with DAS, FDMAS and DAS-FMAS. In comparison to FDMAS and DAS-FMAS, the snake convergence time with DAS was much longer for all numbers of compounding angles. All of the investigated methods demonstrated that the time it took for the snake to converge decreased as the number of steered plane waves increased. It also demonstrated that reducing clutter noise within the cyst region allowed the snake to converge on the cyst boundary faster, and DAS-FMAS helped provide a more accurate segmentation and estimation of the cyst size when compared with FDMAS. Table 4 shows the results for the Dice coefficient and MIoU with DAS, FDMAS and DAS-FMAS. In general, the results of all investigated methods for Dice and MIoU indices improved as the number of compounds increased from $$n = 3$$ to $$n = 25$.$ Both FDMAS and DAS-FMAS outperformed DAS. Starting from $$n = 5$$, the high Dice coefficient and MIoU values obtained with the DAS-FMAS compounding technique indicated that the predicted segmentation area was approaching the ground truth. The main reason for this achievement was the reduction of clutter noise, which allowed the contour to evolve to actual cyst borders. ## 3.4. In Vivo Images The in vivo B-mode images obtained from DAS, FDMAS and DAS-FMAS are presented in Figure 18. All the images are shown with a 50 dB dynamic range. The suppression of clutter and noise when using FDMAS and DAS-FMAS was noticeable on the B-mode images starting with $$n = 3$.$ When the number of steered plane waves increased to $$n = 25$$, DAS-FMAS suppressed noise and clutter in the common carotid artery (CCA) and the nearby anechoic region. The side lobes in the CCA were still visible with FDMAS. In contrast, the vast majority of the imaging region using DAS was still dominated by clutter noise. Clutter noise reduction and spatial and contrast resolution enhancement in the CCA region facilitated a better segmentation. The arrows in $$n = 9$$ images with DAS and FDMAS indicate the side lobe leaking into the CCA anechoic regions. When the carotid border was being segmented, such leaks became an obstacle to the formation of contours. The segmentation procedure was complicated by the existence of clutter noise artefacts in the ultrasound B-mode image, which prevented BSAC from converging on the desired border. Thus, employing DAS-FMAS to reduce side lobes and clutter noise in the CCA anechoic region enhanced the segmentation process overall. Furthermore, the clutter noise region (highlighted by arrows on Figure 18) impairing the segmentation process with DAS and FDMAS could be falsely identified as a plaque. The corresponding inaccurate diagnosis could lead to improper treatment. A good segmentation output is also necessary for the three-dimensional reconstruction of the CCA from two-dimensional transversal ultrasound B-mode imaging, and it is anticipated that the DAS-FMAS image-based segmentation could also benefit this application. ## 4. Conclusions In this paper, we addressed in depth a new compounding technique that we recently developed, which was based on the autocorrelation technique. Recently, many researchers have adopted our method for their research, but none of them have addressed the theory underlying the enhancement of the ultrasound image’s AR, which was the primary contribution of this technique. The autocorrelation process and the phase misalignment produced by angular steered plane waves were the two key factors contributing to the capability of the proposed method to increase the quality of the B-mode image. 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--- title: The Insulin Receptor Substrate 2 Mediates the Action of Insulin on HeLa Cell Migration via the PI3K/Akt Signaling Pathway authors: - Anabel Martínez Báez - Ivone Castro Romero - Lilia Chihu Amparan - Jose Ramos Castañeda - Guadalupe Ayala journal: Current Issues in Molecular Biology year: 2023 pmcid: PMC10047682 doi: 10.3390/cimb45030148 license: CC BY 4.0 --- # The Insulin Receptor Substrate 2 Mediates the Action of Insulin on HeLa Cell Migration via the PI3K/Akt Signaling Pathway ## Abstract Insulin signaling plays an important role in the development and progression of cancer since it is involved in proliferation and migration processes. It has been shown that the A isoform of the insulin receptor (IR-A) is often overexpressed, and its stimulation induces changes in the expression of the insulin receptor substrates (IRS-1 and IRS-2), which are expressed differently in the different types of cancer. We study the participation of the insulin substrates IRS-1 and IRS-2 in the insulin signaling pathway in response to insulin and their involvement in the proliferation and migration of the cervical cancer cell line. Our results showed that under basal conditions, the IR-A isoform was predominantly expressed. Stimulation of HeLa cells with 50 nM insulin led to the phosphorylation of IR-A, showing a statistically significant increase at 30 min (p ≤ 0.05). Stimulation of HeLa cells with insulin induces PI3K and AKT phosphorylation through the activation of IRS2, but not IRS1. While PI3K reached the highest level at 30 min after treatment (p ≤ 0.05), AKT had the highest levels from 15 min (p ≤ 0.05) and remained constant for 6 h. ERK1 and ERK2 expression was also observed, but only ERK2 was phosphorylated in a time-dependent manner, reaching a maximum peak 5 min after insulin stimulation. Although no effect on cell proliferation was observed, insulin stimulation of HeLa cells markedly promoted cell migration. ## 1. Introduction Insulin plays an important role in the development and progression of cancer because it is involved in the processes of cell growth and proliferation due to its stimulatory effects on DNA synthesis in various tissues [1]. Insulin activates a tyrosine kinase receptor, the insulin receptor (IR), which undergoes autophosphorylation and phosphorylates endogenous substrates. Two different isoforms of the IR are generated by alternative splicing, IR-A and IR-B, which differ by the absence (IR-A) or presence (IR-B) of a 12-amino acid insert encoded by exon 11. IR-B is mainly expressed in the major insulin target tissues, whereas IR-A is predominantly expressed in the embryo and fetal tissues, central nervous system (CNS), hematopoietic cells, and several types of cancer cells [2]. When the IR is stimulated, the first proteins activated are adapter proteins, known as insulin receptor substrates (IRS). IRS-1 and IRS-2 are widely expressed in humans and are, therefore, the most studied proteins in the family [3]. There is a relationship between IRS-1 and 2 and various types of cancer, such as breast [4,5,6,7,8], lung [9], prostate [10], hepatocarcinoma [11,12,13], neuroblastoma [14,15], head and neck [16], colorectal [17,18], esophageal squamous cell carcinoma [19], non-small cell lung cancer [20], and glioblastoma multiforme [21]. It has been observed that the expression and function of the IRS may vary in the different types of cancer. For example, IRS-1 has been associated with proliferation, growth, and anti-apoptosis, whereas IRS-2 has been linked to metastasis, motility, and invasion [22,23,24,25,26]. IRS proteins participate in canonical pathways, the phosphorylation of which is induced by the insulin receptor. The IR activates two main signaling pathways: the insulin receptor substrate/phosphatidyl inositol 3-kinase pathway (IRS/PI3-K) and the Ras/mitogen-activated protein kinase (MAPK) pathway. Both pathways regulate most of the effects of insulin, those associated with the regulation of energy metabolism, gene expression, and mitogenic effects [27]. The relationship between the expression levels of IRS-1 and IRS-2 and the activation of insulin signaling pathways has been poorly studied in cervical cancer cells. Although it has already been shown that SiHa cells (HPV16 +) express both IR-A and IR-B, only the activation of IR-A was related to the activation of Akt and ERK$\frac{1}{2.}$ Akt and ERK$\frac{1}{2}$ participate in the phosphatidylinositol 3-kinase (PI3K) and MAPK pathways, respectively [28]. However, the roles of adaptor proteins IRS1 and IRS2 have not been investigated in this type of cancer. The objective of this study was to investigate which isoform of the insulin receptor is expressed in the HeLa cervical cancer cell line and to analyze the role of IRS-1 and IRS-2 in the signaling pathway of the insulin receptor and in regulating the proliferation and migration of HeLa cells (HPV+). ## 2.1. Chemicals and Reagents Recombinant human insulin was obtained from Sigma (St. Louis, MO, USA). MTS reagent was from PROMEGA (Wisconsin, WI, USA). TRIzol reagent, DNase I, EDTA, oligo dT, RNAsin, RT reaction buffer, DTT, dNTP’s, reverse transcriptase, Taq DNA polymerase, and MgCl2 were purchased from Invitrogen (Waltham, MA, USA). Protease and phosphatase inhibitor cocktail were obtained from Sigma. The 2D Quant commercial kit was from GE Healthcare Life Sciences (Chicago, IL, USA). SuperSignal™ West Femto Maximum Sensitivity Substrate was from Thermo Scientific (Waltham, MA, USA). ## 2.2. Cell Isolation and Culture The human cervical cancer HeLa cell line was purchased from ATCC (Rockville, MD, USA), and human mammary epithelial MCF7 and human breast adenocarcinoma MDA-MB-231 cell lines were donated by Dra. Elizabeth Langley (National Cancer Institute, Mexico City, Mexico). Dulbecco’s modified Eagle’s medium (DMEM) and DMEM/F12 culture media were purchased from GIBCO. Heat-inactivated fetal bovine serum (FBS) and penicillin-streptomycin were obtained from GIBCO BRL (Carlsbad, CA, USA). HeLa cells were cultured and maintained in DMEM, and MCF7 and MDA-MB-231 cell lines were cultured and maintained in DMEM/F12 supplemented with $10\%$ FBS and antibiotics (penicillin/streptomycin 100 µg/mL) at 37 °C in a humidified atmosphere of $95\%$ air and $5\%$ CO2. Cell viability was determined by Trypan blue dye exclusion method. The cell lines were seeded under sterile conditions at different densities. All cell lines were serum starved for 12 h prior to each experiment; cells were treated with 50 nM insulin (recombinant human insulin was purchased from Sigma, St. Louis, MO, USA) for indicated times. ## 2.3. Cell Proliferation Assays by MTS The MTS assay was used to assess cell proliferation and cell viability. HeLa cells (5 × 103 cells/well) were seeded in 96-well flat-bottomed tissue culture plates in three replicates, and incubated and supplemented with DMEM (low concentration of glucose 1 g/L) for 24 h. Next, the cells were washed once with 1X phosphate buffered saline (PBS) (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4). The 96-well tissue culture dishes were serum starved for 2 h. Cell proliferation was stimulated with 10, 50, or 100 nM insulin, and cell viability and proliferation were evaluated at 24, 48, and 72 h post-treatment. After the stimulation with insulin at different concentrations for the specified time, the medium was replaced with 2 mL of DMEM fresh medium supplemented with 0.25 mg/mL MTS reagent [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt] per well, and cells were incubated for 4 h at 37 °C. Then, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide formazan crystals were quantified at 595 nm using an absorbance microplate reader (iMark Microplate Reader, Bio-Rad, Hercules, CA, USA). All experiments were performed in three independent experiments in triplicate. ## 2.4. RT-PCR The mRNA levels of IR, IRS1, IRS2, and GAPDH were detected using RT-PCR. Total RNA was isolated from the cells using TRIzol reagent (Invitrogen, Waltham, MA, USA). The total RNA (1.5 μg) was used for cDNA synthesis. Briefly, RNA was incubated with 1μL DNase for 15 min at room temperature, and the reaction was stopped by adding 1 μL EDTA (25 mM). The tubes were boiled at 65 °C for 10 min. Next, 1 μL of oligo dT (thymidine) (0.5 μg/μL) was added to each sample, incubated at 70 °C for 10 min, and placed on ice. For reverse transcription, a reaction mixture solution (0.5 μL of RNAsin (40 U/μL), 4.0 μL of RT reaction buffer (5X), 2.0 μL of DTT (0.1 M), 1.0 μL of dNTP’s (10 mM), and 0.5 μL of reverse transcriptase (200 U/μL) was added to each tube and incubated at 37 °C (1 h) and 70 °C (15 min). Finally, tubes were placed on ice or stored at −20 °C until use. PCR was performed using the Taq PCR Master Mix kit (Invitrogen). PCR profiles for each primer pair were initially standardized over a series of cycles to ensure that all experimental reactions were performed within the linear range. The oligonucleotide primer sequences are listed in Table 1. The PCR products were analyzed by electrophoresis on $1.5\%$ agarose gels. ## 2.5. Immunoprecipitation and Western Blot For protein analysis, cells were washed once with cold phosphate-buffered saline (PBS), lysed with RIPA buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, $0.5\%$ sodium deoxycholate, $0.1\%$ SDS, $1\%$ Nonidet) plus a protease and phosphatase inhibitor cocktail, and then boiled for 5 min at 95–100 °C. For protein quantification, we used a 2D Quant commercial kit (GE Healthcare Life Sciences, Chicago, IL, USA). From whole cell lysates, 40 μg of protein per lane was separated by SDS-PAGE and assayed by immunoblotting using specific antibodies for proteins of the IR signaling pathway, whereas 1.5–2 mg of protein was used for immunoprecipitation (IP) of IRS-2. Detailed information on the primary and secondary antibodies are given in Table 2. Proteins were detected by chemiluminescence using the commercial kit SuperSignal™ West Femto Maximum Sensitivity Substrate from Thermo Scientific (Waltham, MA, USA) using a C-Digit Blot Scanner (LI-COR Biosciences). β-Actin was used as a control to normalize the values of proteins of interest obtained by densitometry. Densitometric analysis was performed using ImageJ 1.47 software (National Institutes of Health, Bethesda, MD, USA). ## 2.6. Cell Migration Assays HeLa (5 × 105/well) cells were seeded into cell-adherent 6-well plates and incubated for 24 h to form a monolayer confluence. Monolayers were washed twice with 1X PBS and incubated for 24 h in serum-free DMEM to establish the quiescence of cells, and then plates were incubated for 2 h with mitomycin C (12.5 μg) to eliminate the proliferative effect. For wound-healing assay, monolayers were vertically scratched using a p200 pipette tip after cells reached a confluency of 90–$95\%$, and later washed to eliminate detached cells. A control photographic image was taken using a Nikon Eclipse TS 100 (40×) with a camera attachment. Subsequently, fresh culture serum-free medium containing different insulin concentrations (10 nM, 50 nM, and 100 nM) was added to each well, and the plates were incubated for 24 h. A second photographic image was taken for each condition. The rate of cell migration was measured as the percentage of wound area occupied by cells compared with the initial wound area using TScratch Software [30]. ## 2.7. Statistical Analysis The differences between treatment groups were analyzed using ANOVA, and statistical significance was determined using Tukey’s HSD test. In all cases, statistical significance was set at $p \leq 0.05.$ SPSS software (IBM, Armonk, NY, USA) was used for the statistical analysis. ## 3.1. Insulin Receptor (IR) Isoforms Are Differentially Expressed in HeLa Cells Figure 1 shows that the HeLa cells predominantly express IR-A (600 bp), with only a slight expression of IR-B (636 bp), whereas in the MCF-7 cell lines, similar amounts of both isoforms were expressed. Amplification of IRS-1 (763 bp) and IRS-2 (116 bp) fragments was observed at nearly equal levels in the two cell lines. ## 3.2. Effect of Insulin Treatment on Cell Proliferation Cell proliferation is one of the main deregulated processes in cancer. We studied HeLa cell proliferation in response to insulin treatment. We used an MTS assay, a colorimetric method used to quantify viable cells based on the reduction of MTS to formazan by NAD-dependent dehydrogenase enzymes in metabolically active cells. Formazan was quantified by measuring the absorbance at a wavelength of 490 nm. As shown in Figure 2, we observed the proliferation of HeLa cells in response to different doses of insulin (10, 50, and 100 nM) and at different stimulation times (24, 48, and 72 h). The control group consisted of the unstimulated cells. There was a slight tendency for proliferation to increase with the 50 and 100 nM doses after 24 h of stimulation; however, the differences were not statistically significant. ## 3.3. Insulin Activates IR and IRS-2 but Not IRS-1 in HeLa Cells We evaluated the ability of insulin to activate the insulin receptor and the IRS-$\frac{1}{2}$ substrates. As shown in Figure 3A, the expression of the non-phosphorylated β subunit of the insulin receptor did not change during the different incubation periods. The phosphorylated form showed a significant time-dependent increase under stimulation with 50 nM insulin (p ≤ 0.05), reaching a maximum peak at 30 min. By analyzing the signaling pathways downstream of the IR, we found that insulin (50 nM) was able to stimulate IRS-2 tyrosine phosphorylation in the HeLa cells at different times (Figure 3B). After 15 min of stimulation, phosphorylated IRS-2 increased with respect to the control, reaching a peak at 30 min (p ≤ 0.05). Interestingly, we did not observe IRS-1 phosphorylation in response to insulin treatment; however, the total protein levels did not change in the HeLa cells. There was phosphorylation of IRS-1 in the MCF7 cells (positive control) but no phosphorylation of IRS-1 in the MDA-MB-231 cells (negative control) (Figure 3C). These data suggest that, in this cell context, only the IRS2 pathway is activated, and IRS1 is not activated in response to insulin treatment. ## 3.4. PI3K/Akt1 Pathway Is Up-Regulated by Insulin in HeLa Cells Next, we analyzed the signaling pathways downstream of IRSs. Two signaling pathways may be activated in response to insulin, the PI3K and MAPK cascades. The activation of the PI3K pathway was measured by PI3K and Akt1 phosphorylation, and the MAPK pathway was measured by Erk$\frac{1}{2}$ phosphorylation. Figure 4 shows the phosphorylation of PI3K and Akt1 after stimulation of the HeLa cells with 50 nM insulin. After insulin stimulation, the total protein content did not increase over time. The phosphorylated form of PI3K increased over time and was higher 30 min after insulin stimulation (p ≤ 0.05) (Figure 4A). In Figure 4B, we show that insulin treatment increased the expression of the total AKT protein over time, with AKT reaching its highest expression 30 min after stimulation. However, phosphorylated p-Akt1 predominated at 15 min (p ≤ 0.05) and remained constant until 6 h after insulin stimulation. ## 3.5. MAPK Signaling Pathway Is Not Activated by Insulin Treatment in HeLa Cells IRS activates the MAPK signaling cascade MAPK. To explore whether the mitogenic pathway is also activated in the HeLa cells in response to insulin we measured Erk$\frac{1}{2}$ phosphorylation. The data showed that the total ERK$\frac{1}{2}$ proteins and their phosphorylation did not increase over time (Figure 4C). These data suggest that insulin was not able to activate the MAPK signaling pathway in our cellular model. ## 3.6. Insulin Induces Migration of HeLa Cells We investigated the effect of insulin on cell migration using a wound-healing assay at different insulin doses. Figure 5 shows that 50 and 100 nM insulin significantly increased HeLa cell migration compared with the group of cells that did not receive insulin treatment at 48 h. This effect was different in the case of the HaCaT cells (non-transformed cells), where the percentage of the open area was lower in all insulin doses compared with the control group without treatment. ## 4. Discussion Several studies have suggested that the insulin signaling pathway plays an important role in the development and progression of cancer, as it is involved in cell growth and proliferation processes due to its capacity to stimulate DNA synthesis in various tissues [1]. Several epidemiological studies and experimental models of insulin resistance and hyperinsulinemia have shown a correlation between insulin levels and cancer development. In cancer patients affected by insulin resistance, the increase in circulating levels of insulin is combined with the frequent overexpression of the insulin receptor in cancer cells, resulting in the abnormal stimulation of non-metabolic effects of the IR, such as cell survival, proliferation, and migration [31]. Alterations in insulin signal transduction increase the risk of cancer development. Additionally, different groups have suggested that IRS1 and IRS2 are involved in cell growth, proliferation, migration, and metastasis [3]. Many studies have focused on the increased expression level or activity of IRSs in different human cancers, including breast, lung, and colorectal cancer, and have correlated these with poor prognosis, potentially defining IRSs as oncogenic proteins [32]. Notably, there is very little information in the literature related to the role of the insulin signaling pathway in the carcinogenesis of cervical cancer. It has been reported that progesterone upregulates IRS-2 expression, altering the levels of IRS-1 and IRS-2 in HeLa cells expressing progesterone receptors [33]; however, very little is known about the role of the insulin signaling pathway in cell proliferation and migration in cervical cancer. Additionally, it has been shown previously that SiHa cells (HPV16 +) express both IR-A and IR-B [28]. This suggests that the insulin signaling cascade is involved in the growth and proliferation of cervical cancer cells. In this study, we investigated the activation of the insulin signaling pathway associated with insulin treatment in the HeLa cell line. Initially, we characterized the HeLa cell line based on the expression of the IR and two substrates, IRS-I and IRS-2. As shown in Figure 1, the HeLa cells predominantly expressed IR-A (600 bp) under basal culture conditions. In contrast, the MCF7 cells (positive control) expressed both IR-A (600 bp) and IR-B (630 bp). Similarly, Serrano et al. [ 28] showed that C33-A cells only express IR-A, whereas the SiHa cervical cell line expresses both isoforms. IR-A is predominantly expressed in fetal tissues; this isoform is less expressed in differentiated tissues from adults, such as the liver, muscle, and adipose tissue, classic targets of the metabolic effects of insulin, where IR-B expression predominates. However, IR-A continues to be expressed in some adult tissues, which are not the typical targets of insulin. For example, IR-A is often overexpressed in breast cancer [34], thyroid cancer, colon cells [35], and hepatocellular carcinoma [36]. The IR-A was more potent than the IR-B in mediating cell migration, invasion, and in vivo tumor growth in triple-negative breast cancer [37]. Although the precise biological roles of the two IR isoforms are unknown, it has been suggested that cancer cells preferentially express isoform A because they dedifferentiate and recover a ‘fetal-like’ phenotype [3,36]. We focused on studying the effect of different concentrations of human recombinant insulin (10, 50, and 100 nM) using an MTS assay to assess cell proliferation at 24, 48, and 72 h. Our results hinted at a minimal increase in the proliferation of HeLa cells treated with different insulin doses, but statistical analysis of the data showed no significant difference with respect to the control, suggesting that insulin does not affect the proliferation of HeLa cells. These results are similar to those reported by Serrano et al. [ 28] in the SiHa and C33-A cervical cancer cell lines, as they did not observe any effect on proliferation upon stimulation with IGF-I, IGF-II, or insulin in these cell lines. However, in thyroid cancer, insulin at supra-physiological concentrations promotes thyroid cell proliferation [38]. In addition, a previous study showed that astrocyte cell numbers increased in a dose-dependent manner upon insulin treatment [39]. This could indicate that the proliferative effect of insulin is tissue-specific and dependent on the insulin concentration. In addition, IRS1 is related to cell proliferation in cancer, and we did not observe the phosphorylation of IRS1 in this study. Next, we found that insulin stimulated IR autophosphorylation, consistent with the presence of receptors in HeLa cells. Downstream of the insulin receptors, we observed the expression of IRS-1 and IRS-2. Surprisingly, only the phosphorylation of IRS-2 increased; in contrast, we did not observe the activation of IRS1. These findings suggest that in this cell line, IRS-2 is predominantly active. IRS-2 is generally related to processes such as metastasis, migration, and cell invasion in different types of cancer, while IRS-1 is related to proliferation. IRS2 is expressed at high levels in breast carcinoma cells of the basal-like/triple-negative breast cancer (TNBC) subtypes, and it regulates tumor cell migration, invasion, and glycolytic metabolism. The different functions of IRS1 and IRS2 in breast cancer are further evidenced by the fact that mouse mammary tumors lacking IRS2 have a significantly diminished ability to metastasize to the lungs, whereas tumors lacking IRS1 but expressing elevated IRS2 have enhanced metastatic potential [4,24,40,41]. In contrast, a recent study provided evidence that IRS1, rather than IRS2, is a dominant regulator of pancreatic alpha-cell function [42]. In breast cancer, IRS1 overexpression also promotes the growth and proliferation of BT 20 cells and induces the formation of larger tumors in vivo [43]. In lung cancer, tumors with low IRS-1 and high IRS-2 expression were associated with poor outcomes in adenocarcinoma and squamous cell carcinoma, indicating a potential role for IRS-2 in the aggressive behavior of non-small cell lung cancer [25]. These findings indicate that IRS1 and IRS2 play different roles depending on the cellular context; IRS2 is primarily responsible for cell motility and metastasis, whereas IRS1 is mainly important for cell proliferation [3]. IRS1- and IRS2-induced signaling is highly modulated during many cancer processes, such as cell motility, metastasis, and cell proliferation. Therefore, we focused on studying the molecular mechanisms involved in controlling the migration of HeLa cells after insulin treatment. In our model, we observed increased PI3K and Akt phosphorylation; however, we did not observe significant phosphorylation of ERK$\frac{1}{2.}$ These data suggest that the PI3K pathway is activated mainly in response to insulin. Other studies have shown that in transgenic mice that do not express IRS-1, there is an increased function of IRS-2 and very high PI3K/Akt/mTor activity [44]. In addition, Hippo signaling interacts with AKT signaling by regulating IRS2 expression to prevent liver cancer progression [12]. However, in SiHa cells, a cell line transformed with HPV genotype 16, both the PI3K and MAPK pathways are activated in response to insulin and IGF-1 [28]. Carcinogenesis is complex. Normal cells undergo multiple genetic mutations before transformation to the complete neoplastic phenotype of growth, invasion, and metastasis. We investigated the effect of insulin on cell migration. Tumor cells are known to have accelerated metabolic rates and high glucose demand in a nutrient-poor environment [45,46]. The combination of these factors may result in a metabolic dependence on a continuous energy and nutrient supply for cells within the tumor mass [47]. We used a relatively low concentration of glucose (1000 mg/mL; 5.55 mM) in our experiments; according to Ishida et al. [ 48], a low glucose concentration increased the total migration length of HeLa cells and that HeLa cells under a glucose concentration gradient exhibit random motion rather than chemotaxis. However, the differences in migration of the HeLa cells they used are observed at a concentration of 0.7 mM; therefore, although we cannot rule out an effect on migration by the concentration of glucose used in our experiments, we consider that this does not essentially change the interpretation of the observations. As expected, our results showed a statistically significant increase in HeLa cell migration upon stimulation with insulin. This correlates with several reports on neuroblastoma and breast cancer, where the overexpression of IRS-2 promoted cell motility, invasion, and metastasis [40,44]. In addition, insulin promotes the migration of neural cells [49], thyroid cells [38], vascular smooth muscle cells [50], and advanced prostate cancer (PCa) [51]. Actually, the actions of insulin in PCa cells may be suppressed by inhibiting the downstream signaling molecules PI3K and ERK$\frac{1}{2}$ [51]. Interestingly, a recent study revealed that the ability of IRS2 to promote invasion is dependent upon upstream insulin-like growth factor 1 receptor (IGF-1R)/IR activation and the recruitment and activation of PI3K, which are functions shared with IRS1. In addition, a 174-amino-acid region in the IRS2 C-terminal tail, which is not conserved in IRS1, is required for IRS2-mediated invasion [52]. ## 5. 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--- title: Correlation between Lower Esophageal Sphincter Metrics on High-Resolution Manometry and the Clinical Presentation of Patients with Newly Diagnosed Achalasia authors: - Daniel L. Cohen - Eyal Avivi - Anton Bermont - Fahmi Shibli - Narges Azzam - Basem Hijazi - Fadi Abu Baker - Vered Richter - Haim Shirin - Amir Mari journal: Diagnostics year: 2023 pmcid: PMC10047697 doi: 10.3390/diagnostics13061136 license: CC BY 4.0 --- # Correlation between Lower Esophageal Sphincter Metrics on High-Resolution Manometry and the Clinical Presentation of Patients with Newly Diagnosed Achalasia ## Abstract Background: Achalasia is characterized by aperistalsis with poor relaxation of the lower esophageal sphincter (LES). We aimed to systematically assess whether LES metrics on high-resolution manometry (HRM) correlate with the symptomatic or endoscopic presentation of patients with achalasia. Methods: A retrospective study was performed at two tertiary medical centers. All cases of newly diagnosed, untreated achalasia were reviewed for demographics, symptoms, and endoscopic findings. These were correlated with HRM metrics, including LES basal pressure (LESP), integrated relaxation pressure (IRP), percent LES relaxation, and esophagogastric junction (EGJ) morphology. Results: 108 achalasia patients were included; 56 ($51.9\%$) were men, with a mean overall age of 55.6 ± 17.9 years old. Achalasia subtypes included $23.1\%$ with Type I, $65.7\%$ Type II, and $11.1\%$ Type III. Mean LESP was 40.9 ± 13.7 mmHg, IRP 26.8 ± 11.5 mmHg, with $36\%$ ± $20\%$ LES relaxation. On univariate analyses, a higher IRP was associated with age < 50 ($$p \leq 0.028$$), female sex ($$p \leq 0.030$$), Arab ethnicity ($p \leq 0.001$), weight loss ($$p \leq 0.016$$), a tortuous esophagus ($$p \leq 0.036$$), and resistance at the EGJ ($$p \leq 0.033$$). However, on multivariate regression analyses, only ethnicity remained significantly associated with IRP. No unique variables were associated with either LESP or percent LES relaxation. Achalasia subtype and Eckardt score were not associated with any LES metrics. Non-Type 1 EGJ morphology was associated with a lower LESP. Conclusions: LES metrics on HRM do not appear to correlate with the clinical or endoscopic presentation of patients with untreated achalasia. ## 1. Introduction Achalasia is a rare neurological disorder characterized by the absence of esophageal peristalsis and the inability of the lower esophageal sphincter (LES) to properly relax [1]. It typically presents with symptoms of dysphagia, chest pain, vomiting, and weight loss [1,2,3]. Achalasia is a chronic lifetime condition that profoundly disturbs patients’ quality of life and work productivity [1]. High-resolution manometry (HRM) is the gold standard for evaluating esophageal motility and LES function [1]. HRM studies are performed and interpreted according to the Chicago classification, currently in its fourth version (CCv4.0) [4]. Per the Chicago classification, achalasia is defined as an abnormal median integrated relaxation pressure (IRP) value with $100\%$ failed peristalsis. IRP is a measure of the deglutitive relaxation of the LES. It is calculated as the mean of the maximal deglutitive relaxation in the 4-second window beginning at UES relaxation. Achalasia is further divided into three subtypes: Type I (with absent contractility), Type II (with panesophageal pressurization), and Type III (with spasm) [4]. While manometric measurements of the LES, specifically IRP, are required to diagnose achalasia, few studies have evaluated the correlation between LES metrics and clinical presentations. One study from the era of conventional line-tracing manometry showed that higher LES relaxation pressures were associated with higher total symptom scores and, specifically, regurgitation [5]. However, a more recent study showed that there was no correlation between IRP or basal LES pressure and symptoms when using the overall Ekhardt score or its dysphagia component [6]. Additionally, no studies have evaluated the relationship between LES metrics and the typical endoscopic findings in achalasia. Further, while IRP is the metric used for the diagnosis of achalasia, no other LES metrics are incorporated into the diagnostic criteria of the Chicago classification [4]. Hence, other evaluations of the LES, including LES basal pressure, percent LES relaxation, and esophagogastric junction (EGJ) morphology, have been rarely described in respect of achalasia [7]. Therefore, we aim to evaluate if there are correlations between LES metrics on HRM and the clinical presentation of newly diagnosed achalasia patients, including both symptomatology and endoscopic findings. We hypothesize that higher LES pressures may suggest a more significantly closed EGJ and, thus, more significant symptoms. ## 2.1. Study Design A retrospective study was performed at two tertiary medical centers. HRM studies were reviewed for all patients newly diagnosed with achalasia from 2018 to 2022. Demographic data, including age, gender, ethnicity, body mass index (BMI), and comorbidities were recorded. Presenting symptoms, including dysphagia, chest pain, and weight loss, were taken from the patients’ charts. Due to linguistic difficulties in distinguishing between certain terms, as well as the setup of the electronic medical records, it was frequently impossible to differentiate between regurgitation, vomiting, heartburn, and reflux symptoms. Thus, these symptoms were combined into a single variable and considered positive if any one of these symptoms was present. Eckardt scores were used when available [8]. Subjects under the age of 18 were excluded. If a subject underwent multiple studies during the study period, only the first (index) study was included. Patients with previously diagnosed or treated achalasia were excluded. All subjects required a complete HRM study for inclusion. Any incomplete HRM study was excluded, for example, any study with less than eight evaluable swallows, failure to traverse the LES, or any technical difficulties. ## 2.2. HRM Protocol and Interpretation At each center, HRM was performed using the same system (ManoScanAR, Medtronic, Minneapolis, MN, USA). HRM studies were performed according to the standard protocol, as per the Chicago classification [4], and the results were recorded. The following parameters were evaluated: number of swallows completed, IRP, LES basal pressure, EGJ morphology, and the subtype of achalasia-I, -II, or -III. As few patients had Type 2 or Type 3 EGJ morphology, these subjects were combined together for statistical analysis. Additionally, the percentage of LES relaxation was calculated using the LES basal pressure as the baseline and the IRP as the post-swallow relaxation pressure. All HRM studies were reviewed by an expert in esophageal motility (D.L.C. or A.M.) to confirm the diagnosis of achalasia and its subtype. Additionally, the HRM metrics used for this study were obtained from this re-analysis and not simply taken from the original report. ## 2.3. Endoscopic Findings at Gastroscopy All patients’ endoscopy reports were reviewed by senior gastroenterologists for suggestive findings of achalasia, including: the presence of esophageal residue, a dilated esophagus, a tortuous esophagus, and resistance at the EGJ [9]. Residue was defined as the presence of either residual liquid or solid material in the esophagus. A dilated esophagus was defined solely based on the endoscopist’s description and did not include any radiographic studies. If any of these endoscopic findings were not reported in the procedure report, then it was recorded as negative. If endoscopy procedure images were available, these were evaluated secondarily to confirm what was written in the endoscopy report text. ## 2.4. Statistical Analysis Categorical variables are presented as frequency and percentage. All continuous variables were normally distributed and are presented as mean and standard deviation. The Pearson chi-square test and Fisher’s exact test were used to compare the categorical variables. The Mann–Whitney test or Student t-test was performed to compare continuous variables. Multiple linear regression models (enter and stepwise methods) were performed to assess the effects of the independent variables on IRP. All statistical tests were two-sided, with a p-value of <0.05 considered significant. Analyses was performed using IBM SPSS Statistics v.28 software. ## 3.1. Details of the Achalasia Cohort The cohort consisted of 108 newly diagnosed achalasia patients. Details of the cohort—including demographic, medical, symptomatic, endoscopic, and manometric data—can be found in Table 1. The mean age was 55.6 ± 17.9 years old, with $51.9\%$ being male. Dysphagia was present in $93.5\%$, while chest pain, regurgitation, and weight loss were less common. For the 51 patients with an Eckardt score available, the mean score was 6.1 ± 2.3. Nearly half of the cohort were reported to have a dilated esophagus or resistance at the EGJ. The majority of cases were Type II achalasia, and over $90\%$ had a Type 1 EGJ morphology. The mean LESP was 40.9 ± 13.7 mmHg, with an IRP of 26.8 ± 11.5 mmHg. ## 3.2. Relationship between LES Metrics and Clinical Presentation Statistical analyses were performed to evaluate for any correlations between the LES metrics on HRM—LESP, IRP, and percent relaxation—and the clinical presentation or endoscopic findings (Table 2). In terms of demographics, higher IRP values were found in those younger than 50 years old compared to those above 50 (29.6 ± 11.4 vs. 25.2 ± 11.3, $$p \leq 0.028$$), women compared to men (29.0 ± 12.9 vs. 24.8 ± 9.7, $$p \leq 0.030$$), and in Arabs compared to Jews (32.4 ± 13.4 vs. 23.1 ± 8.3, $p \leq 0.001$). Differences were also found in clinical presentations. Patients with weight loss were found to have higher LESP (43.5 ± 15.3 vs. 38.8 ± 11.8, $$p \leq 0.037$$) and IRP values (29.5 ± 12.3 vs. 24.7 ± 10.4, $$p \leq 0.016$$) compared to those without. No differences were noted in LES metrics in the other symptoms, symptom duration, or Eckardt score (for those for whom an Eckardt score was available). Endoscopically, both a tortuous esophagus (32.1 ± 8.9 vs. 26.0 ± 11.6, $$p \leq 0.036$$) and resistance at the EGJ (29.0 ± 12.9 vs. 24.9 ± 9.7, $$p \leq 0.033$$) were associated with higher IRP values. The other endoscopic findings were not. Additionally, there were no significant differences in LES metrics among the achalasia subtypes. In total, six demographic and clinical variables were associated with IRP (Table 2). There were no variables associated with LESP or percent LES relaxation that did not also include IRP. ## 3.3. Multivariate Regression Analyses A multivariate regression analysis was performed, including the three clinical variables (weight loss, a tortuous esophagus, and resistance at the EGJ) found to be significantly associated with an elevated IRP. In this model, controlling for age, sex, and ethnicity, all three of the clinical variables ceased to be significantly influential on IRP (Table 3). Only Arab ethnicity remained significant. An additional regression analysis using the stepwise method also only found ethnicity to be significant (B = −9.283, $p \leq 0.001$, CI −13.453, −5.113). ## 3.4. Effect of EGJ Morphology on LES Metrics Finally, the LES metrics were compared between patients with Type 1 EGJ morphology and those with either Type 2 or 3 (Table 4). These analyses found that LESP was higher in those with Type 1 morphology than the other types (41.9 ± 13.7 vs. 30.9 ± 9.5, $$p \leq 0.010$$), while IRP and percent relaxation were not affected by EGJ morphology. ## 4. Discussion This is the first study to systematically evaluate the correlation between LES metrics on HRM and the clinical presentation of patients with achalasia. We found that although several clinical and endoscopic variables were associated with a higher IRP on univariate analysis, none of these remained significant after controlling for demographic variables. Patients presenting with achalasia often suffer from dysphagia ($90\%$), heartburn ($70\%$), regurgitation or vomiting ($45\%$), chest pain ($25\%$), and weight loss ($10\%$) [1]. The treatments for achalasia, which have been shown to improve symptoms, aim to open up the LES and, thus, allow food to more easily pass into the stomach. Based on this, it appears that it is the closed, unrelaxed LES that is the main cause of symptoms. Therefore, we hypothesized that HRM metrics that suggest a more tightly closed LES, such as elevated LESP or IRP, may be associated with more severe symptoms. Since we found that no symptom or endoscopic variable was associated with LESP or percent LES relaxation alone but only in association with IRP, we focused our evaluations solely on IRP. While a higher IRP was associated with weight loss, a tortuous esophagus, and resistance at the EGJ on univariate analyses, as may be expected from poorer relaxation of the LES, these relationships did not remain significant when controlling for demographic variables. Thus, we failed to show any significant correlation between LES metrics and clinical presentation, including the Eckardt score. The reason why increasing IRP values do not seem to cause more symptoms or more severe symptoms is unclear. It seems to support the idea that there is a threshold value at which symptoms occur when it is crossed (for example, IRP > 15 mmHg, as per the Chicago classification [4]), but that the absolute IRP value is less important. This also appears to be the case for post-treatment achalasia patients, where studies have shown that if the IRP is brought down below a certain threshold (for example, IRP < 10 mmHg after pneumatic dilation), patients clinically do well [10,11]. Our findings are similar to what has been reported in the literature. For example, Mikaeli et al. did not find a correlation between LESP and chest pain in achalasia patients [12]. More recently, Jain et al. evaluated the correlation between IRP and both the total Eckardt score and the individual dysphagia component in achalasia patients [6]. They found that while there was a univariate correlation between IRP and both of these scores, both became non-significant in multivariable logistic regression analyses. In addition to IRP, they evaluated the correlation between the distensibility index (DI) measured by functional luminal imaging probe (FLIP) and symptoms, finding that DI alone correlated with dysphagia score. Thus, they concluded that DI appears to be a better predictor of symptoms in achalasia than HRM metrics. Demographics are known to play a role in IRP. Similar to our findings, male gender and older age have been previously reported to be associated with lower IRP values in achalasia patients [13,14]. Surprisingly, our study found ethnicity to be even more significantly associated with IRP values. The reason for this is unclear, but it may be related to genetic variations in immunogenic HLA haplotypes that have been reported in achalasia populations [15,16]. Future studies evaluating the role of ethnicity and genetics in achalasia patients are certainly warranted [17]. We also found that IRP did not predict the subtype of achalasia, although Type III achalasia trended towards a higher IRP. Other studies have also evaluated the relationship between IRP and achalasia subtypes with conflicting results. Blais et al. found no differences in IRP between the subtypes of achalasia and concluded that IRP cannot be used to differentiate between the three subtypes of achalasia [18]. However, other studies have shown higher IRP measurements in Types II and III compared to Type I achalasia [19,20]. Associations between EGJ morphology and HRM metrics have previously been assessed, mainly in patients with gastroesophageal reflux disease (GERD). The presence of Type 2 or 3 EGJ morphology, which manometrically signifies the presence of a hiatal hernia, has been independently associated with pathologic reflux [21,22,23]. Further, Type 3 morphology has been associated with reduced LESP [21]. In our study, we found that the presence of Type 2 or 3 EGJ morphology in achalasia patients was associated with a lower LESP than Type 1 morphology, but EGJ morphology did not impact IRP values. Our study has some limitations. As this was a retrospective study, data on the clinical presentation of patients and endoscopic findings were collected via chart review. As such, details of their symptoms and endoscopic findings may not have been thoroughly reported in their chart and, therefore, may be lacking. Additionally, while data on the presence of symptoms was collected for all subjects, an Eckardt score was only available for about half the subjects. Finally, data on complementary studies such as barium esophogograms were not available, nor were there data on how they were treated for achalasia or their clinical course. In conclusion, our findings help better understand the function of the LES in achalasia patients, although LES metrics on HRM do not appear to correlate with the clinical or endoscopic presentation of patients with newly diagnosed achalasia. ## References 1. Pandolfino J.E., Gawron A.J.. Achalasia: A systematic review. JAMA (2015) 313 1841-1852. DOI: 10.1001/jama.2015.2996 2. Jeon H.H., Kim J.H., Youn Y.H., Park H., Conklin J.L.. Clinical Characteristics of Patients with Untreated Achalasia. J. Neurogastroenterol. Motil. (2017) 23 378-384. DOI: 10.5056/jnm16177 3. Fisichella P.M., Raz D., Palazzo F., Niponmick I., Patti M.G.. Clinical, radiological, and manometric profile in 145 patients with untreated achalasia. World J. Surg. (2008) 32 1974-1979. DOI: 10.1007/s00268-008-9656-z 4. Yadlapati R., Kahrilas P.J., Fox M.R., Bredenoord A.J., Prakash Gyawali C., Roman S., Babaei A., Mittal R.K., Rommel N., Savarino E.. 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--- title: Determination of Complex Formation between Drosophila Nrf2 and GATA4 Factors at Selective Chromatin Loci Demonstrates Transcription Coactivation authors: - Emma Neidviecky - Huai Deng journal: Cells year: 2023 pmcid: PMC10047698 doi: 10.3390/cells12060938 license: CC BY 4.0 --- # Determination of Complex Formation between Drosophila Nrf2 and GATA4 Factors at Selective Chromatin Loci Demonstrates Transcription Coactivation ## Abstract Nrf2 is the dominant cellular stress response factor that protects cells through transcriptional responses to xenobiotic and oxidative stimuli. Nrf2 malfunction is highly correlated with many human diseases, but the underlying molecular mechanisms remain to be fully uncovered. GATA4 is a conserved GATA family transcription factor that is essential for cardiac and dorsal epidermal development. Here, we describe a novel interaction between Drosophila Nrf2 and GATA4 proteins, i.e., cap‘n’collar C (CncC) and Pannier (Pnr), respectively. Using the bimolecular fluorescence complementation (BiFC) assay—a unique imaging tool for probing protein complexes in living cells—we detected CncC–Pnr complexes in the nuclei of Drosophila embryonic and salivary gland cells. Visualization of CncC–Pnr BiFC signals on the polytene chromosome revealed that CncC and Pnr tend to form complexes in euchromatic regions, with a preference for loci that are not highly occupied by CncC or Pnr alone. Most genes within these loci are activated by the CncC–Pnr BiFC, but not by individually expressed CncC or Pnr fusion proteins, indicating a novel mechanism whereby CncC and Pnr interact at specific genomic loci and coactivate genes at these loci. Finally, CncC-induced early lethality can be rescued by Pnr depletion, suggesting that CncC and Pnr function in the same genetic pathway during the early development of Drosophila. Taken together, these results elucidate a novel crosstalk between the Nrf2 xenobiotic/oxidative response factor and GATA factors in the transcriptional regulation of development. This study also demonstrates that the polytene chromosome BiFC assay is a valuable tool for mapping genes that are targeted by specific transcription factor complexes. ## 1. Introduction Nrf2 (NF-E2-related factor 2) is a transcription factor of the cap’n’collar (cnc) subfamily that plays an essential role in cell protection by mediating transcriptional responses to environmental toxins (xenobiotics) and oxidative stimuli [1,2]. Nrf2 can bind to antioxidant response elements (AREs) and activate a series of antioxidant and detoxifying genes [3,4,5]. Under basal conditions, Nrf2 is inhibited by Keap1 (Kelch-like ECH-associated protein 1)—an E3 adaptor protein that binds to Nrf2 in the cytoplasm and mediates its proteasomal degradation. Xenobiotic and oxidative compounds disrupt the Keap1–Nrf2 interaction, releasing Nrf2 to enter the nucleus and activate target genes [1,6]. Mutations that misregulate Nrf2 are associated with many diseases, including cancer [7], respiratory diseases [8], neurodegeneration [9], and cardiovascular diseases [10]. However, the complete range of roles that Nrf2 plays in both pathology and physiology remains to be fully identified. Recent studies in several model systems have demonstrated that Nrf2 can directly target and regulate developmental genes [11,12]. For example, murine Nrf2 can control adipogenesis through activating adipogenetic genes [13,14,15], promote cell proliferation via the transcriptional activation of glucose metabolic enzymes [16], and promote neuronal stem cell differentiation by activating genes that inhibit self-renewal [15]. CncC—the Drosophila homolog of Nrf2—regulates metamorphosis through the activation of ecdysone biosynthetic and response genes in specific tissues [17]. During neuronal remodeling, CncC regulates dendrite pruning by activating gene expression of proteasomal subunits [18]. The molecular mechanisms and interacting partners that mediate the role of Nrf2 in the transcriptional regulation of development remain to be fully explored. GATA family proteins are conserved transcription factors that are essential for the regulation of a broad range of early developmental programs [19]. Among the six major mammalian GATA factors, GATA4 regulates gene expression in cardiac progenitor cells during heart development [20]. Mutations in GATA4 are associated with congenital heart defects [21,22]. GATA4 regulates heart development through interaction with multiple cofactors, including NKX2-5 [22]. Molecular interactions between NKX2-5 and GATA4 have been identified in several studies [23,24]. It was found that the NKX2-5–GATA4 interaction facilitates the chromatin binding and transcriptional activities of GATA4 in COS-1 cells [25]. The Drosophila pannier (pnr) gene encodes the zinc finger transcription factor Pannier (Pnr), which is the homolog of human GATA4 [26,27,28]. Pnr is involved in multiple developmental processes during embryonic and imaginal development [29,30,31]. Mutation and shRNA depletion of Pnr alter Drosophila heart development [26,32]. Pnr binds to and activates cardiac developmental genes, including Hand, Mef2, and mid, in cooperation with the Drosophila NKX2-5 homolog Tinman (Tin) [33,34,35,36,37]. However, there are many genes that are regulated by Pnr beyond the genes coactivated with Tin, indicating that other factors cooperate with Pnr in transcriptional regulation. Several lines of evidence suggest that GATA factors can regulate xenobiotic response genes. For example, GATA4 can directly bind to and activate the microsomal epoxide hydrolase gene EPHX1 in HepG2 cells [38]. In Drosophila, GATA-binding sites were identified at the promoters of Cyp6g1 [39]. Transcriptional assays of Drosophila embryos found that Pnr is required for the activation of some P450 xenobiotic response genes, including Cyp4p1, Cyp6a2, and Cyp6v1 [30]. Interestingly, upregulation of GATA4 was observed along with cardiac injury in rats treated with PM2.5 [40]. Nevertheless, the full-range molecular functions of GATA factors in the transcriptional regulation of xenobiotic/oxidative responses remain to be elucidated. In this study, we identified a novel interaction between CncC and Pnr using the bimolecular fluorescence complementation (BiFC) assay—a unique imaging tool for probing protein complexes in living cells. We visualized CncC–Pnr BiFC complexes in the nuclei of Drosophila embryonic and salivary gland cells. Mapping the binding of the CncC–Pnr BiFC complex on the polytene chromosome revealed that the CncC–Pnr complex tends to bind to euchromatic interband regions, with a preference for loci that are not highly occupied by CncC or Pnr alone. Furthermore, genes within these loci were demonstrated to be coactivated by CncC and Pnr. These results suggest that CncC and Pnr interact with one another at specific genomic loci and coactivate transcription, revealing a new mechanism whereby CncC and Pnr co-regulate development and/or xenobiotic response. ## 2.1. CncC and Pnr Form Nuclear Complexes in Embryonic Mesodermal Cells To determine whether CncC and Pnr interact in living cells, we applied the bimolecular fluorescence complementation (BiFC) assay [41] to visualize potential CncC–Pnr complexes in living tissues at different developmental stages of Drosophila. CncC and Pnr BiFC fusions (i.e., proteins fused to the N- and C-terminal fragments of YFP) were ubiquitously expressed using the UAS-GAL4 expression system with the tub-GAL4 driver. BiFC fluorescence was detected in the nuclei of cells in embryos (Figure 1A), indicating that CncC and Pnr form complexes in the nucleus. We were unable to check the formation of CncC–Pnr BiFC complexes at later developmental stages in this experiment because the global overexpression of CncC, driven by the tub-GAL4 driver, is lethal at the early first-instar larval (L1) stage [42]. ## 2.2. CncC and Pnr Form Protein Complexes on Chromatin To examine the detailed subcellular localization of the CncC–Pnr complex, we expressed CncC and Pnr BiFC fusions in polyploid salivary gland cells using the Sgs3-GAL4 driver. In most cells, CncC–Pnr BiFC signals were detected exclusively on the polytene chromosome and enriched in euchromatic interband regions (Figure 1B). In a small portion of cells (<$10\%$), accumulation of BiFC signals was also detected in the nucleoplasm, likely due to the overexpression of BiFC fusion proteins (Figure S1A). YFP–CncC localized to both the nucleoplasm and polytene chromosome, whereas YFP–Pnr bound only to the polytene chromosome (Figure 1B and Figure 2A) [17]. The expression levels of BiFC fusions and YFP fusions were comparable at both the transcriptional and protein levels (Figure 1C and Figure 3A). Co-expression of YC–dKeap1 and YN–Pnr fusion proteins did not produce detectable fluorescence in salivary gland cells (Figure 1B), although both dKeap1 and Pnr bind to many interband regions on the polytene chromosome (Figure 2A) [17]. Therefore, the BiFC assay revealed specific molecular interactions between CncC and Pnr. The cnc gene also codes for CncB—a chromatin-binding protein that is required for the normal embryonic development of Drosophila [43]. Compared with CncC, CncB shares the same DNA-binding domain but lacks the N-terminal dKeap1-interacting domains (Figure 1B). YFP–CncB fusion proteins bind to many interbands on the polytene chromosome [44], but co-expression of YC–CncB and YN–Pnr did not produce detectable fluorescence in salivary gland cells (Figure 1B). This suggests that Pnr interacts with the N-terminal domain of CncC. Selective BiFC complexes were formed by CncC and Pnr, but not by CncB or dKeap1 in combination with Pnr, indicating that CncC and Pnr form specific complexes on chromatin. ## 2.3. CncC and Pnr Form BiFC Complexes at Specific Genomic Loci The polytene chromosome BiFC assay, for the first time, allowed a direct visualization of protein complexes at specific genomic loci [44,45]. To investigate the chromatin-binding specificity of the CncC–Pnr complex, we compared the genomic loci that were primarily occupied by CncC–Pnr BiFC complexes with those bound by CncC and Pnr separately on polytene chromosome spreads (Figure 2 and Figure S2). Genome-wide distributions of YFP–CncC and YFP–Pnr on polytene chromosomes were visualized by anti-GFP immunostaining. BiFC complexes could not be detected by immunostaining, since the anti-GFP antibody recognizes both YN and YC fragments; thus, the BiFC complex and separate YN and YC fusion proteins cannot be distinguished. Therefore, intrinsic CncC–Pnr BiFC fluorescence was visualized on polytene chromosome spreads that were prepared using an acid-free squash protocol, which preserved the live YFP fluorescence [46]. CncC–Pnr BiFC complexes and the separately expressed YFP–CncC and YFP–Pnr proteins all bind to a large proportion of interband regions (Figure 2A). Both BiFC complexes and YFP fusion proteins selectively occupy certain loci at significantly higher levels compared to their binding levels at adjacent chromatin regions. The loci that are strongly occupied by CncC–Pnr complexes differ from those occupied by YFP–CncC or YFP–Pnr that are expressed separately (Figure 2B). For example, the fluorescence intensity of CncC–Pnr BiFC was significantly higher at the 89E and 33B loci compared to adjacent loci (for example, 91A and 34A), while no predominant YFP–CncC or YFP–Pnr signals were detected at 89E and 33B (Figure 2B and Figure S2A,B). On the other hand, loci that are highly occupied by CncC—such as the 74EF and 75B ecdysone-response puffs [17]—were not predominantly bound by CncC–Pnr BiFC or YFP–Pnr (Figure S2C). Similarly, no predominant signals of CncC–Pnr BiFC or YFP–CncC were detected at some Pnr-favoring loci, such as 91A and 34A (Figure 2B). Taken together, these results suggest that YFP–CncC and YFP–Pnr bind to many chromatin loci independently of one another, and that CncC–Pnr BiFC complexes form at or bind to some loci that are not primarily occupied by CncC or Pnr. This result also verifies the polytene chromosome BiFC assay as a valuable tool to probe the binding of protein complexes at specific genomic loci. Interestingly, both live imaging and immunostaining revealed strong YFP–Pnr signals at the polytene chromosome chromocenter—the centromeric region of the Drosophila genome (Figure 1B and Figure 2A). It was reported that a cell-cycle-dependent GATA factor, Ams2, binds to and controls the centromere in fission yeast through the regulation of histone H3 variant CENP-A [47], but the role of GATA factors at centromeres is largely unknown. It will be interesting to explore the potential function of Pnr/GATA in centromere architecture and pericentric heterochromatin in the future. ## 2.4. Regulation of Transcription by the CncC–Pnr Complex To investigate whether CncC and Pnr regulated transcription in a way that was associated with their interactions at specific genomic loci, we examined gene transcription levels at the 89E and 33B loci in salivary glands that overexpressed CncC–Pnr BiFC, and we compared them with the levels of same transcripts regulated by overexpressed YFP–CncC, YFP–Pnr, or YFP (wild-type control) (Figure 3). Among the 21 genes at the 89E and 33B loci that were examined, three (dKeap1, Or33b, Or33c) were activated by CncC overexpression and two (Pxd and Tom70) were activated by Pnr overexpression (Figure 3B). None of these genes were significantly activated by both ectopic CncC and Pnr. Interestingly, seven other genes (Glut3, Actn3, Dad, Ns1, Ada1-2, Ada1-1, Wdr81) were specifically activated by the CncC–Pnr BiFC (by 3–15-fold) but not activated by the individually expressed CncC or Pnr (Figure 3B). dKeap1 transcription was activated by CncC (by around 9-fold) and was slightly activated by Pnr, whereas it was dramatically activated by the CncC–Pnr BiFC (by 30-fold) (Figure 3B). Therefore, the CncC–Pnr complex can specifically target and activate certain genes. The number of genes regulated by the CncC–Pnr complex remains unclear. The CncC–Pnr BiFC complex moderately occupied many chromatic loci. Thus, CncC and Pnr may interact with one another and co-regulate many genes. To determine whether the CncC–Pnr interaction is required for the activation of all CncC or Pnr target genes, we examined the effects of CncC overexpression, Pnr overexpression, and CncC–Pnr BiFC for 19 genes that were identified as common targets of CncC and Pnr based on genome-wide transcriptional analyses of cncC or pnr mutations [48,49,50]. Some of these genes (i.e., Ctr1A, Cyt-b5, Atx2, bbc, ifc) were activated by CncC, and some (i.e., Cpr, Ctr1A, fra) were activated by Pnr, but only one (Egfr) was specifically activated by CncC–Pnr BiFC (Figure 3C). This result suggests that many genes, although regulated by both CncC and Pnr, are activated through mechanisms independent of the CncC–Pnr interaction. It has been previously demonstrated that CncC and Pnr can regulate transcription in cooperation with other cofactors, such as dKeap1 and Tin, respectively [35,44]. Among the genes that were targeted and activated by CncC–Pnr BiFC complexes at 89E and 33B, none of them were revealed as CncC- or Pnr-regulatory genes in previous genome-wide transcription analyses [30,48]. Given that both CncC and Pnr are critical for embryo development [17,32], we next investigated whether these genes were regulated by endogenous CncC and Pnr in embryos (Figure 3D). We compared the transcription levels of these genes in homozygous cncC or pnr null mutants and in relevant heterozygous embryos (cnc−/+ or pnr−/+). Two independent null alleles for cncC (cncK6 and cncK22) and pnr (pnrVX6 and pnr1) were employed. The levels of Glut3, Actn3, and Wdr81 transcripts reduced in both cncC and pnr mutants. Ns1 and Ada1-1 were significantly downregulated in at least one allele of cncC and pnr mutants. Dad, dKeap1, and Ada1-2 were downregulated in cncC mutants (Figure 3D). Thus, most of the genes that were identified as CncC–Pnr BiFC targets in this study are likely real target genes of endogenous CncC and/or Pnr in embryos. Mapping BiFC complexes on polytene chromosomes has previously allowed us to reveal novel genes that are coactivated by dKeap1 and CncC [44]. This study provides another successful example supporting the polytene chromosome BiFC assay as a valuable tool for the identification of genes that are specifically targeted by a protein complex. No classic Nrf2/CncC-binding element ARE was identified in genes targeted by the CncC–Pnr BiFC complex, indicating that CncC probably binds to these genes through the interaction with Pnr. Consistent with this hypothesis, the Cnc isoform lacking the N-terminal domain lost the interaction with Pnr (Figure 1B). Both genome-wide ChIP-Seq assays in murine cells and polytene chromosome staining in Drosophila revealed that Nrf2/CncC binds to many developmental genes in an ARE-independent manner [3,17]. The mechanisms that mediate the specific binding of Nrf2/CncC to these genes are not well understood. This study hints at a possible model where the binding of Nrf2 family proteins to some developmental genes is mediated by interaction with GATA factors (Figure 4B). In support of this model, GATA factors have been previously demonstrated as pioneer factors that can open local chromatin and facilitate the recruitment of other transcription factors [19]. The complete molecular mechanism whereby CncC/Nrf2 and Pnr/GATA4 factors interact and co-regulate transcription remains to be explored in Drosophila and mammalian model systems. ## 2.5. Genetic Interaction between CncC and Pnr Most of the CncC–Pnr co-target genes identified in this study are developmental genes, indicating that CncC and Pnr may co-regulate development. Therefore, we tested the genetic interaction between CncC and Pnr during Drosophila development. CncC overexpression driven by tub-GAL4 caused severe embryonic lethality, with less than $10\%$ of animals surviving to the early first-instar larval (L1) stage (Figure 4A). Depletion of Pnr by RNAi reduced the viability at third-instar larval (L3), pupal, and adult stages. Combinatory expression of pnr-RNAi significantly rescued the survival ratio of CncC-overexpressing animals to ~$57\%$ at the L1 stage, ~$27\%$ at the L3 stage, and ~$16\%$ at the pupal stage (Figure 4A). Depletion of Pnr by RNAi had no significant effects on the transcript or protein levels of CncC (Figure 3D and Figure S3). These results suggests that CncC and Pnr function in the same pathway in the regulation of development, likely mediated by the developmental genes that are controlled by the CncC–Pnr complex. It remains to be explored how the CncC–Pnr interaction regulates developmental genes and programs. As an oxidative and xenobiotic response factor, Nrf2 is globally expressed in all cell types [51]. Previous studies also showed that the cncC transcript isoform was expressed in all cells during embryogenesis, and CncC proteins were detected in many tissues [17,44,52,53]. Although the basal expression levels of CncC/Nrf2 are very low in unstressed conditions, CncC/Nrf2 can directly target and regulate developmental genes [13,14,15,16,17]. Pnr regulates developmental genes and processes in multiple tissues, such as embryonic mesoderm and imaginal discs [29,30,31]. Combined with their genetic interaction detected in this study, we hypothesize that CncC and Pnr form complexes and regulate developmental genes in some cell types. Our current study provides evidence in support of further functional studies of the CncC–Pnr complex in specific tissues during Drosophila development. Taken together, our study revealed novel molecular and genetic interactions between the CncC xenobiotic response factor and the Pnr transcription factor in Drosophila. CncC and Pnr interact at specific genomic loci where they mediate the activation of target genes, suggesting a model whereby the CncC–Pnr complex regulates development through binding and activating specific developmental genes (Figure 4B). It was revealed that Nrf2/CncC can regulate developmental genes in a redox-independent manner, but the underlying molecular mechanisms remain to be explored. This study identified GATA4 as a novel cofactor of Nrf2 in the regulation of development. Given the role of both Nrf2 and GATA4 in cardiac development, our study also hints at potential crosstalk between classic oxidative/xenobiotic response signaling and the transcription-regulatory network controlling cardiac development. It is well established that Nrf2 can protect the cardiovascular system through the regulation of redox hemostasis in blood vessels, blood cells, and the myocardium [54]. The pathogenic roles of Nrf2 in cardiovascular diseases, including atherosclerosis and heart failure, have also been revealed by several studies [10,54]. Despite the roles of Nrf2 in cardiovascular specific cell protection, the function of Nrf2 in cardiovascular development remains unclear. The discovery of the Nrf2–GATA4 interaction suggests the presence of a novel molecular regulatory network that underlies cardiac development, providing more insight into the roles of Nrf2 and GATA factors in development and diseases. ## 3.1. Drosophila Stocks Plasmids encoding CncC and Pnr fused to YFP were constructed using the pUAST vector and microinjected in the w1118 background. UAS-YC-CncC, UAS-YC-CncB, and UAS-YC-dKeap1 strains were obtained from Osamu Shimmi. UAS-YN-Pnr was provided by Rolf Bodmer. cncK6 and cncK22 were obtained from Dirk Bohamn. Sgs3-GAL4, tub-GAL4, pnrVX6, pnr1, pnr-RNAi, and UAS-YFP were obtained from the Bloomington Stock Center. All stocks were maintained at 25 °C according to the standard protocol. Null mutations of cncC (cncK6, cncK22) and pnr (pnrVX6, pnr1) are embryonically lethal and, thus, were maintained in combination with the TM6, Tb, Sb, Hu, e, Dfd-YFP balancer. To isolate null mutants, embryos were collected on apple-juice plates and aged for 20–22 h at 25 °C, and then sorted based on the Dfd-YFP marker under a Leica MZ10 F fluorescence stereomicroscope. In the genetic interaction assay, tub-GAL4/TM6 females were crossed with UAS-YFP-CncC, UAS-pnr-RNAi, or UAS-YFP-CncC;UAS-pnr-RNAi males. F1 flies without tub-GAL4 were used as controls and identified based on Dfd-YFP (in the first instar stage) and Tb (in pupal and adult stages) markers on the TM6 balancer. ## 3.2. Live Imaging of BiFC Fluorescence in Salivary Gland Cells To express YFP or BiFC fusion proteins in salivary glands, UAS transgenic lines or double-transgenic lines were crossed with Sgs3-GAL4. Salivary glands were isolated from F1 early wandering third-instar larvae and stained with 10 µg/mL Hoechst 33258 in PBS. After brief washing, they were mounted in PBS and immediately imaged using a Nikon Eclipse 80 fluorescence microscope with a SPOT Insight 4 MP color digital camera. The signals were pseudo-colored and merged in the RGB color space. ## 3.3. Imaging of BiFC Fluorescence on Polytene Chromosomes Double-transgenic lines expressing BiFC fusion proteins were crossed with Sgs3-GAL4. F1 larvae were maintained at 21 °C to enhance the polyploidy of salivary gland cells. Salivary glands were isolated from early wandering third-instar larvae. Polytene chromosome spreads were prepared using an acid-free squash technique to avoid quenching of the YFP and BiFC fluorescence [46]. One pair of dissected salivary glands was incubated in freshly prepared $2\%$ paraformaldehyde in Brower’s Fixation Buffer (0.15 M PIPES, 3 mM MgSO4, 1.5 mM EGTA, $1.5\%$ NP40, pH 6.9) for 3 min, in PBST (PBS + $0.2\%$ Triton X-100) for 3 min, and in $50\%$ glycerol for 5 min. The salivary glands were then squashed in 10 μL of $50\%$ glycerol and stained with 0.2 µg/mL Hoechst 33258 in PBS before mounting in $80\%$ glycerol with 10 mM Tris, pH 9.0. Images were acquired on a Nikon AX-R confocal microscope. BiFC signals were visualized using 504 nm excitation and 542 nm emission wavelengths. ## 3.4. Immunostaining Polytene chromosome spreads were prepared from the salivary glands of early-wandering third-instar larvae using conventional squash and immunostaining protocols [46]. The antibodies used for immunostaining were anti-GFP (1:200, Fitzgerald Industries, Acton, MA) and Alexa Fluor 488 conjugated goat anti-rabbit secondary antibody (1:2000, Invitrogen, Waltham, MA, USA). The specificities of the antibodies had been tested and verified (Figure S2D). The stained samples were mounted in VectaShield (Vector Laboratories, Newark, CA, USA) and imaged using a Nikon Eclipse 80 fluorescence microscope with a SPOT Insight 4 MP color digital camera. ## 3.5. Western Blotting Ten pairs of salivary glands were homogenized in 50 μL of ice-cold IP buffer (20 mM Tris-HCl pH 8.0, $0.2\%$ Triton X-100, 150 mM NaCl, 5 mM EDTA, 2 mM NaVO3, 1 mM PMSF, and 1.5 μg/mL aprotinin). Proteins were separated using a $10\%$ Tris-glycine gel and transferred to nitrocellulose membranes (Bio-Rad, Hercules, CA, USA). Membranes were blocked with $5\%$ milk in TBST (TBS + $0.1\%$ Tween-20) and then probed with primary antibodies against GFP (1:1000, Fitzgerald Industries, Acton, MA, USA), CncC [17], and tubulin (12G10, 1:500, Developmental Studies Hybridoma Bank, Iowa City, IA, USA), followed by 1:3000 HRP-conjugated goat anti-rabbit or goat anti-mouse secondary antibodies (Bio-Rad, Hercules, CA, USA). The membranes were developed using ECL reagents (GE Healthcare, Chicago, IL, USA) and then exposed to X-ray film (AGFA, Mortsel, Belgium). ## 3.6. Transcript Quantitation by RT-qPCR mRNAs from 10 pairs of salivary glands dissected from early-wandering third-instar larvae in PBS, prepared using DEPC water, were extracted using the RNeasy kit (Qiagen, Germantown, MD, USA). Isolated mRNA was treated with RQ1 RNase-Free DNase (Promega, Madison, WI, USA) and reverse-transcribed using the iScriptTM cDNA Synthesis kit (Bio-Rad, Hercules, CA, USA). 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--- title: A Cross-Sectional Study on the Prevalence and Risk Stratification of Chronic Kidney Disease in Cardiological Patients in São Paulo, Brazil authors: - Farid Samaan - Bruna Bronhara Damiani - Gianna Mastroianni Kirsztajn - Ricardo Sesso journal: Diagnostics year: 2023 pmcid: PMC10047703 doi: 10.3390/diagnostics13061146 license: CC BY 4.0 --- # A Cross-Sectional Study on the Prevalence and Risk Stratification of Chronic Kidney Disease in Cardiological Patients in São Paulo, Brazil ## Abstract Chronic kidney disease (CKD) provides a worse prognosis for patients with heart disease. In Latin America, studies that analyzed the prevalence and risk stratification of CKD in this population are scarce. We aimed to evaluate CKD prevalence and risk categories in patients of a public referral cardiology hospital in São Paulo, Brazil. This was a cross-sectional study based on a laboratory database. Outpatient serum creatinine and proteinuria results performed between 1 January 2021 and 31 December 2021 were analyzed. CKD was defined by estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 and proteinuria, by the albumin/creatinine ratio in a spot urine sample (UACR) >30 mg/g. A total of 36,651 adults were identified with serum creatinine levels (median age 72.4 [IQR, 51.0–73.6] years, $51\%$ male). Among them, $51.9\%$ had UACR dosage ($71.5\%$ with UACR < 30 mg/g, $22.6\%$, between 30–300 mg/g, and $5.9\%$ with UACR > 300 mg/g). The prevalence of CKD was $30.9\%$ ($15.3\%$ stage 3a, $10.2\%$ stage 3b, $3.6\%$ stage 4, and $1.7\%$ stage 5), and the distribution of patients in the risk categories of the disease was: $52.0\%$ with low-risk, $23.5\%$, moderate risk, $13.0\%$, high risk, and $11.2\%$, very high. In an outpatient setting, the prevalence of CKD in cardiological patients was almost three times ($31\%$) that of the general population; about half of the individuals evaluated ($48\%$) were not screened for an important risk marker (proteinuria), and approximately a quarter of these patients ($24\%$) were in the high or very high CKD risk categories. ## 1. Introduction Chronic kidney disease (CKD) is a public worldwide health problem affecting 10–$14\%$ of the adult population, including $12\%$ of people with hypertension, $15\%$ with diabetes, and $30\%$ of the elderly [1]. The World Health Organization defined this condition as the world’s most neglected non-communicable disease [2]. As an oligosymptomatic disease in its early stages, CKD is globally underdiagnosed [3]. Moreover, most countries do not have national systems for surveillance and assistance in all stages of CKD [2,3]. Brazilian and international guidelines recommend conducting serum creatinine and urinary protein testing, at least once a year, for patients at risk of CKD [4,5]. These two tests are simple, inexpensive, and widely available in the Brazilian Unified Health System and in most countries. Nevertheless, studies have shown that less than $6\%$ of patients with CKD are diagnosed in the early stages of the disease [3]. In addition, only $25\%$ of CKD-risk patients undergo adequate screening in primary health care, and $40\%$ of cases are lately referred to a nephrologist [6,7]. Among potentially dangerous comorbidities for CKD, heart diseases stand out due to their high prevalence in the general population [8]. CKD has a high impact on the prognosis of patients with coronary artery disease, heart valve diseases, and chronic cardiomyopathies [9,10,11]. In addition, proteinuria is an independent risk factor for hospitalization due to heart failure and death, even in patients with preserved glomerular filtration rate [12]. Thus, considering that the detection of silent CKD through laboratory records is feasible and can produce important information to scale prevention programs [13,14,15], this study aimed to evaluate the CKD prevalence and risk categories in patients of a public referral cardiology hospital. ## 2.1. Study Design and Reference Population This retrospective cross-sectional study is based on records of laboratory tests performed between 1 January 2021 and 31 December 2021 in a public hospital in São Paulo city, Brazil. This hospital is one of three tertiary cardiology referral services for a metropolitan region with 20 million inhabitants, performing alone annually about 200,000 outpatient consultations, 25,000 emergency consultations, 8000 hemodynamic procedures, 2000 cardiac surgeries, and 5000 intra-hospital hemodialysis sessions [16]. Only laboratory results performed on adults at the outpatient level were analyzed, excluding test results from patients under 18 years old and those performed on hospitalized patients. This study was performed following the Declaration of Helsinki and was approved by the institutional research ethics committee under the certificate of presentation of ethical assessment number 58300122.9.0000.5462. ## 2.2. Data Source, Variables, and Definitions Participants’ demographic data, such as gender and date of birth, and serum creatinine and urinary protein results, were obtained from the laboratory records. From the serum creatinine level, age, and gender, the estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation [17]. The diagnosis of CKD was defined by an eGFR < 60 mL/min/1.73 m2 and classified in stages 3a (45–59 mL/min/1.73 m2), 3b (30–44 mL/min/1.73 m2), 4 (15–29 mL/min/1.73 m2), or 5 (<15 mL/min/1.73 m2) following international guidelines [5]. The lowest value of creatinine level was used for CKD classification in patients with two or more dosages of creatinine in one year to avoid overestimating the prevalence of CKD. The ethnicity variable was not assessed in the CKD-EPI equation due to the unavailability of race data, the high degree of miscegenation in the Brazilian population, and previous study reporting that the adjustment of race data does not add accuracy for estimating renal function in this population [18]. The proteinuria was evaluated by the albumin/creatinine ratio in a spot urine sample (UACR) since this is a routine test contained in the institutional protocol for the admission of adults and having the best sensitivity and specificity for the screening of CKD [5]. Proteinuria was defined by UACR > 30 mg/g and categorized into the levels mild or absent (<30 mg/g), moderate (30–300 mg/g), or severe (>300 mg/g) following current guidelines [4,5]. According to the CKD risk map, which considers eGFR ranges and the albuminuria categories, participants were classified as having low, moderate, high, or very high CKD risk [5]. Patients at low CKD risk show eGFR > 60 mL/min/1.73 m2 and UACR < 30 mg/g. Moderate risk is determined by conditions with eGFR > 60 mL/min/1.73 m2 and UACR between 30 and 300 mg/g, or eGFR between 45 and 59 mL/min/1.73 m2 and UACR < 30 mg/g. The high CKD risk category includes patients having any of the following combinations: eGFR > 60 mL/min/1.73 m2 and UACR > 300 mg/g, eGFR 45–59 mL/min/1.73 m2 and UACR 30–300 mg/g, or eGFR 30–44 mL/min/1.73 m2 and UACR < 30 mg/g. Values of eGFR 45–59 mL/min/1.73 m2 and UACR > 300 mg/g, eGFR 30–44 mL/min/1.73 m2 and UACR > 30 mg/g, or eGFR < 30 mL/min/1.73 m2 regardless of UACR determine patients at a very high CKD risk. Finally, patients were grouped according to the main medical subspecialty of the institution’s outpatient clinic where they were treated. Thus, the group classes were coronary heart disease, valvular heart disease, arrhythmias, cardiomyopathies, hypertension, dyslipidemias, and others (vascular surgery, cardiogeriatrics, congenital heart disease, sports medicine, and heart transplantation, among others). The historical series of the most prevalent outpatient consultations were considered for this classification. Hence, patients treated at the coronary heart disease and angioplasty outpatient clinics were grouped into the coronary heart disease category. The patients treated at the electrophysiology and pacemaker outpatient clinics were grouped into the arrhythmias category. Conventionally, patients treated by diverse medical subspecialties in two or more outpatient clinics were categorized by the most frequent specialty of treatment or the first medical visit recorded during the analyzed period. The dates and frequencies of the patients’ medical appointments were obtained from the hospital’s administrative database. ## 2.3. Statistical Analysis Categorical variables were expressed in frequency and, according to the Kolmogorov–Smirnov normality test result, continuous variables were expressed as mean (standard deviation) or median (interquartile range). Intergroup frequency comparisons were performed using the Chi-square test, while Student’s t-test and Mann–Whitney U-test were performed on quantitative variables with normal and non-normal distribution, respectively. Test results were considered statistically significant when the p-value was <0.05. A logistic regression model was applied to evaluate the effect of gender, age, and primary outpatient clinic on variables used in a binary way: eGFR > or <60 mL/min/1.73 m2; UACR < or >30 mg/g; and CKD risk categories, high and very high or the others. The evaluated effects were represented by the odds ratio, with $95\%$ confidence intervals. The independent variables were evaluated separately in each univariate model and then together in a multiple regression model. All the statistical analyses were conducted using SPSS software version 18.0 (SPSS Inc., Chicago, IL, USA). ## 3. Results A total of 57,288 ambulatory dosages of serum creatinine were performed in the studied period. After excluding repeated dosages in the same patient ($$n = 20$$,486) and dosages in patients under 18 years old ($$n = 151$$), 36,651 adults were evaluated for CKD prevalence. Among them, 19,031 also had UACR dosages and therefore were evaluated for risk category of CKD (Figure 1). The patients’ median age was 72.4 years (interquartile range, 51.0–73.6), and $51.3\%$ were males. The most frequent outpatient clinic source of the patients was coronary artery disease ($31.6\%$), followed by valvular heart disease ($14.2\%$), arrhythmias ($10.1\%$), cardiomyopathies ($9.4\%$), hypertension ($9.1\%$) and dyslipidemia ($5.8\%$). Patients classified as belonging to ‘other’ outpatient clinics classification were $19.6\%$ of the total. CKD was detected in $30.9\%$ of the participants, of which $15.3\%$ were in stage 3a, $10.2\%$ in stage 3b, $3.6\%$ in stage 4, and $1.7\%$ in stage 5. Dosages of UACR were conducted in $51.9\%$ of patients, of which $71.5\%$ had mild or absent albuminuria, $22.6\%$ moderate, and $5.9\%$ had severe proteinuria. Thus, according to the CKD risk categories, in $52.0\%$ of the patients, the risk was low, $23.5\%$ moderate, $13.0\%$ high, and $11.2\%$ very high (Table 1). The UACR dosage was significantly more frequent in females ($53.2\%$) than in males ($50.7\%$) ($p \leq 0.001$). Regarding the participants’ age group, the frequency of UACR dosage was higher the older their age group ($55.5\%$ in >75 years, $53.0\%$ in 60–74 years, $50.5\%$ in 45–59 years, $36.8\%$ in 30–44 years, and $24.7\%$ in 18–29 years ‘group) ($p \leq 0.001$). The percentage of UACR requests was higher in the hypertension outpatient clinic ($81.3\%$), followed by dyslipidemia ($76.4\%$), cardiomyopathies ($61.9\%$), coronary artery disease ($49.4\%$), arrhythmias ($46.8\%$) and valvular heart disease ($38.4\%$) groups ($p \leq 0.001$) (Table 2). Reduced eGFR (eGFR < 60 mL/min/1.73 m2) was more frequent in females ($31.7\%$) than in males ($30.1\%$) and in the elderly age groups ($56.8\%$ in >75 years, $30.7\%$ in 60–74 years, $25.5\%$ in 45–59 years, $8.4\%$ in 30–44 years, and $2.5\%$ in 18–29 years’ group). Cardiomyopathies was the outpatient clinic category with the highest percentage of patients showing reduced eGFR ($32.4\%$), followed by arrhythmias ($31.4\%$), hypertension ($31.2\%$), coronary artery disease ($29.6\%$), dyslipidemia ($29.2\%$), and valvular heart disease ($27.3\%$) clinics (Table 3 and Table S1). Albuminuria (UACR > 30 mg/g) was more frequent in men ($30.1\%$) than in women ($26.9\%$) and in the elderly age groups ($36.9\%$ in >75 years, $28.5\%$ in 60–74 years, $26.0\%$ in 45–59 years, $22.4\%$ in 30–44 years, and $23.3\%$ in 18–29 years’ group). The outpatient clinic category with the highest percentage of patients with albuminuria was dyslipidemia ($32.7\%$), followed by valvular heart disease ($31.9\%$), coronary heart disease ($28.7\%$), hypertension ($28.0\%$), arrhythmias (24.1), and cardiomyopathies ($22.8\%$) (Table 3 and Table S2). Similar percentages of high or very high risk of CKD were observed between men ($24.1\%$) and women ($24.2\%$). High or very high-risk categories were more frequent in the elderly age groups ($41.9\%$ in >75 years, $23.7\%$ in 60–74 years, $20.2\%$ in 45–59 years, $7.8\%$ in 30–44 years, and $3.4\%$ in 18–29 years’ group). The outpatient clinic with the highest percentage of patients showing high or very high CKD risk was arrhythmias ($24.5\%$), followed by cardiomyopathies ($24.1\%$), dyslipidemia ($23.4\%$), valvular heart disease ($23.3\%$), hypertension ($22.5\%$), and coronary heart disease ($21.7\%$) (Table 3 and Table S3). The distribution of patients on the CKD risk map is graphically shown in Figure 2. Each cell contains the absolute number of participants and the corresponding percentage in parentheses. eGFR, estimated glomerular filtration rate; UACR, urinary albumin-creatinine ratio; CKD, chronic kidney disease; CAD, coronary artery disease; VHD, valvular heart disease. In multiple regression analysis, by testing the variables gender, age group, and outpatient clinic category, the factors independently related to reduced eGFR (<60 mL/min/1.73 m2) were female gender, the most advanced age groups, and the outpatient clinics for coronary artery disease, hypertension, arrhythmias, and cardiomyopathies, compared with valvular heart disease (Table S1). Factors associated with UACR > 30 mg/g were male gender, age > 75 years old, and outpatient clinics for hypertension, coronary artery disease, valvular heart disease, and dyslipidemia categories, compared with the cardiomyopathies category (Table S2). The factors associated with the highest risk categories of CKD (high or very high) were the most advanced age groups and outpatient clinics for cardiomyopathies, and arrhythmias categories, compared with the coronary heart disease category (Table S3). ## 4. Discussion The present study showed that adults of a referral cardiology hospital had a $31\%$ prevalence of CKD based on their eGFR and a $28\%$ prevalence of proteinuria. In $24\%$ of patients tested for proteinuria, the CKD risk category was high or very high. Testing for proteinuria, an important prognostic factor, was not performed in about half of the patients. Patients with CKD have a higher prevalence of cardiovascular diseases than the general population due to older age and a higher prevalence of hypertension and diabetes mellitus. In addition, non-traditional risk factors for cardiovascular diseases may be present, such as bone mineral disease/vascular calcification, inflammation, oxidative stress, hyperuricemia, and hypervolemia [8,9]. Nevertheless, few studies have been specifically designed to determine the prevalence of CKD in people with heart diseases and their main subtypes, such as coronary heart disease, cardiomyopathies, valvular heart disease, and arrhythmias. Even considering that the definition of CKD includes people with proteinuria regardless of reduced eGFR, the evaluation of CKD prevalence performed in this study was based only on eGFR of all participants who had a creatinine dosage [5]. It has been recognized worldwide that there is a failure in the screening of CKD by measuring proteinuria [19,20]. Therefore, patients tested for proteinuria are more likely to be sicker and have a higher prevalence of CKD than those not tested. In fact, in our study, participants who had the UACR dosage were older and predominantly female compared to those who did not. Older age and female gender are known risk factors for CKD due to the lower number and nephron mass [21]. Nevertheless, the prevalence of CKD in this study was even higher than the average prevalence reported in previous studies on patients with heart diseases ($31\%$ versus $26\%$ [range, 8–$38\%$]), which could be explained by the higher age (72 vs. 61 [range, 42–63] years) and lower frequency of male patients ($51\%$ vs. $54\%$ [range, 37–$76\%$]) in the present study than in the previous reports (Table S4) [3,10,22,23,24,25,26,27,28,29,30,31]. On the other hand, the prevalence of proteinuria in our study was lower than the average values observed in previous reports ($28\%$ vs. $34\%$ [range, 8–$62\%$]) [3,22,23,24,27,30]. This result could be explained by the lower frequency of male patients in our study than in previous ones since men are known to have a higher chance of presenting proteinuria than women. Gender differences in the prevalence of proteinuria may be related to prescribing patterns, differences in responses and adherence to therapies, as well as hormonal factors [32]. However, as expected, the main factor associated with the prevalence of proteinuria in previous studies was diabetes mellitus [3,10,22,31]. Unfortunately, this association could not be evaluated in our study. The proteinuria testing rate in our entire cohort was $52\%$, higher than in previous studies on patients at risk for CKD, such as those with hypertension or diabetes mellitus (3–$40\%$) [19,20,33,34]. This difference could be a consequence of conducting this research in a specialized teaching hospital, where adherence to institutional protocols and guidelines may be higher than in other health care institutions. Even so, as proteinuria is an important risk factor for worsening cardiovascular outcomes, the absence of this screening in about $50\%$ of the participants should be viewed with concern [12,35]. It is well established that late diagnosis of CKD is associated with increased morbidity, mortality, and resource utilization [36]. In fact, both reduced glomerular filtration rate and proteinuria are recognized as worse prognostic factors in the guidelines of scientific societies of cardiology [37,38,39]. The UACR request was higher in the hypertension outpatient clinic and less in the valvular heart disease one. Assuming that all patients with cardiovascular disease are at risk for CKD, this difference could reflect that physicians in the former clinic are more aware of the importance of CKD risk stratification than in the latter one. Our study indicated, indirectly and with some limitations, that the patients with the highest prevalence of proteinuria and reduced eGFR came from the valvular heart disease and the cardiomyopathies outpatient clinics, respectively. Studies designed to determine the prevalence of CKD considering the subtypes of heart diseases are scarce (Table S4). Thus, it was not possible to contrast these results with others; and they need to be confirmed in further studies specifically designed for this purpose. The limitations of this study must be acknowledged. First, the interpretation of CKD prevalence estimates must be performed cautiously, considering the data source used (laboratory results) and the retrospective study design. Second, we could not evaluate other characteristics of the participants regarding CKD risk and severity. Hence, comorbidities (e.g., diabetes, hypertension, dyslipidemia, and obesity) and laboratory identification of certain conditions (e.g., anemia and disorders of calcium, phosphorus, and parathyroid) could not be addressed. In addition, many patients are usually seen once a year as part of the typical dynamic of a public, tertiary, and referral hospital. Therefore, we could not apply the three-month interval of creatinine dosages to define CKD [5]. Finally, the adopted methodology does not incorporate a precise definition of the participants’ baseline cardiological diagnosis. Thereby, the diagnostic inferences should be considered with caution. Moreover, the main diagnosis could not be confirmed in patients with more than one cardiovascular condition. ## 5. Conclusions In an outpatient setting, the prevalence of CKD in cardiological patients was almost three times that of the general population. 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Inpatients with coronary heart disease have a high prevalence of chronic kidney disease based on estimated glomerular filtration rate (eGFR) in China. Heart Vessel. (2007.0) 22 223-228. DOI: 10.1007/s00380-006-0964-7 28. Glynn L.G., Reddan D., Newell J., Hinde J., Buckley B., Murphy A.. Chronic kidney disease and mortality and morbidity among patients with established cardiovascular disease: A West of Ireland community-based cohort study. Nephrol. Dial. Transplant. (2007.0) 22 2586-2594. DOI: 10.1093/ndt/gfm222 29. Gomez P., Ruilope L.M., Barrios V., Navarro J., Prieto M.A., Gonzalez O., Guerrero L., Zamorano M.A.S., Filozof C.. Prevalence of Renal Insufficiency in Individuals with Hypertension and Obesity/Overweight: The FATH Study. J. Am. Soc. Nephrol. (2006.0) 17 S194-S200. DOI: 10.1681/ASN.2006080914 30. Kramer H., Luke A., Bidani A., Cao G., Cooper R., McGee D.. Obesity and Prevalent and Incident CKD: The Hypertension Detection and Follow-Up Program. Am. J. Kidney Dis. (2005.0) 46 587-594. DOI: 10.1053/j.ajkd.2005.06.007 31. Reddan D.N., Szczech L.A., Tuttle R.H., Shaw L.K., Jones R.H., Schwab S.J., Smith M.S., Califf R.M., Mark D.B., Owen W.F.. Chronic Kidney Disease, Mortality, and Treatment Strategies among Patients with Clinically Significant Coronary Artery Disease. J. Am. Soc. Nephrol. (2003.0) 14 2373-2380. DOI: 10.1097/01.ASN.0000083900.92829.F5 32. Luyckx V.A., Brenner B.M.. Low birth weight, nephron number, and kidney disease. Kidney Int Suppl. (2005.0) 68 S68-S77. DOI: 10.1111/j.1523-1755.2005.09712.x 33. Samaan F., Fernandes D.E., Kirsztajn G.M., Sesso R.D.C.C., Malik A.M.. Quality indicators for primary health care in chronic kidney disease in the public service of a city in the State of São Paulo, Brazil. Cad. Saúde Pública (2022.0) 38 e00090821. DOI: 10.1590/0102-311x00090821 34. Samaan F., Gutierrez M., Kirsztajn G.M., Sesso R.C.. 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--- title: 'Proceedings from the 2021 SAEM Consensus Conference: Research Priorities for Interventions to Address Social Risks and Needs Identified in Emergency Department Patients' authors: - Liliya Kraynov - Aaron Quarles - Andrew Kerrigan - Katherine Dickerson Mayes - Sally Mahmoud-Werthmann - Callan E. Fockele - Herbert C. Duber - Kelly M. Doran - Michelle P. Lin - Richelle J. Cooper - Nancy Ewen Wang journal: Western Journal of Emergency Medicine year: 2023 pmcid: PMC10047718 doi: 10.5811/westjem.2022.11.57293 license: CC BY 4.0 --- # Proceedings from the 2021 SAEM Consensus Conference: Research Priorities for Interventions to Address Social Risks and Needs Identified in Emergency Department Patients ## Abstract ### Introduction Emergency departments (ED) function as a health and social safety net, regularly taking care of patients with high social risk and need. Few studies have examined ED-based interventions for social risk and need. ### Methods Focusing on ED-based interventions, we identified initial research gaps and priorities in the ED using a literature review, topic expert feedback, and consensus-building. Research gaps and priorities were further refined based on moderated, scripted discussions and survey feedback during the 2021 SAEM Consensus Conference. Using these methods, we derived six priorities based on three identified gaps in ED-based social risks and needs interventions: 1) assessment of ED-based interventions; 2) intervention implementation in the ED environment; and 3) intercommunication between patients, EDs, and medical and social systems. ### Results Using these methods, we derived six priorities based on three identified gaps in ED-based social risks and needs interventions: 1) assessment of ED-based interventions, 2) intervention implementation in the ED environment, and 3) intercommunication between patients, EDs, and medical and social systems. Assessing intervention effectiveness through patient-centered outcome and risk reduction measures should be high priorities in the future. Also noted was the need to study methods of integrating interventions into the ED environment and to increase collaboration between EDs and their larger health systems, community partners, social services, and local government. ### Conclusion The identified research gaps and priorities offer guidance for future work to establish effective interventions and build relationships with community health and social systems to address social risks and needs, thereby improving the health of our patients. ## BACKGROUND Although the concept of social medicine has existed for nearly two centuries, the contemporary medical community has only more recently acknowledged the interconnectedness of socioeconomic status and health. Often credited as the founder of social medicine, physician Rudolf Virchow in 1848 helped establish the newspaper Medical Reform and brought attention to the social origins of illness.1,2 More recently, multiple medical organizations, including the American College of Physicians,3 the American Academy of Pediatrics,4 and the American Academy of Family Physicians,5 have advocated addressing social risks and needs in clinical settings to improve health outcomes. Patients with unmet social risks and needs, such as food insecurity or unstable housing, have a higher prevalence of depression, diabetes, and hypertension, among other health issues.6 Children with unmet social risks and needs have a higher prevalence of disease, such as asthma,7,8 and have worse control of conditions such as type 1 diabetes.9 These children are also more likely to experience obesity, diabetes, and cardiovascular disorders in adulthood.10 Those with multiple social risks and needs experience a cumulative effect on their health.11–13 Emergency departments (ED) function as a health and social safety net,14,15 regularly taking care of patients with high social risks and needs.16 Nearly one in four ED patients is food insecure, and one in five reports choosing between food and medication.17 Patients seen in the ED experience a high prevalence of financial insecurity,18 unreliable transportation,19 unemployment,20,21 and housing instability.21,22 Visits to the ED present unique opportunities to intercede and address the social risks and needs of patients. Most of the emergency medicine (EM) literature on social determinants of health focuses on identifying and screening for social risks and needs.16 Few studies have examined ED interventions to address social risks and needs. In this article, we describe the research gaps and priorities for interventions addressing social risks and needs identified as part of the 2021 Society for Academic Emergency Medicine (SAEM) Consensus Conference – From Bedside to Policy: Advancing Social Emergency Medicine and Population Health through Research, Collaboration, and Education. ## METHODS The leadership team of the 2021 SAEM Consensus Conference session on social risks and needs screening identified three topics for review: 1) instruments used for social risks and needs screening in the ED; 2) implementation of social risks and needs screening in the ED; and 3) interventions for patients with social risks and needs in the ED.23 In this paper we address the third topic, presenting gaps in current knowledge and research priorities focused on interventions for patients with identified social risks and needs. For consistency across these three topics, we have adopted the definitions for social determinants of health as per Alderwick et al: social risk, defined as social conditions associated with poor health; and social need, defined as these social conditions with which patients would like assistance in addressing.24 ## Literature Review We conducted a literature review building upon a previously published systematic review on ED patients’ social risks and needs.16 With the assistance of a health sciences librarian, we used a PubMed search strategy that identified 2,085 articles across the three objectives (Appendix A). A review of titles and abstracts resulted in 151 potentially relevant articles across the continuum from screening through interventions. We complemented the PubMed search with a review of the Social Interventions Research and Evaluation Network (SIREN) Evidence and Resource Library, which compiles research on medical and social care integration.25 Based on titles and abstracts, authors HD and CF identified an additional 22 potentially relevant articles. Of the 173 total manuscripts identified, 18 applied to our topic—interventions for identified social risks and needs—after review of the full article. We excluded articles if they had not been conducted in the ED or an urgent care within a hospital. Articles with interventions conducted across a hospital or health system, even if they did not focus primarily on ED patients, were included if the intervention was also incorporated into the ED. We then supplemented our article searches by checking the references within these 18 publications for additional pertinent articles to our topic; we identified four additional articles. In total, 22 articles were included in our review (Figure 1).26–47 ## Initial Derivation of Research Gaps and Priorities For each included study, we extracted data pertaining to study objective, design, outcomes, results, limitations, and noted study quality and risk of bias issues. This data was summarized in an analysis matrix (Microsoft Excel for Mac, version 16.52 (Microsoft Corporation, Redmond, WA). Our group thematically analyzed data from the analysis matrix; we then identified research gaps and drafted preliminary research priorities. We shared the draft research priorities with external expert reviewers from the Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation,48 Health Leads,49 and SIREN,50 incorporating their feedback into a document outlining preliminary research gaps and priorities (Appendix B). ## Consensus-building and Derivation of Final Research Gaps and Priorities The SAEM Consensus Conference was convened in two sessions virtually over Zoom (Zoom Video Communications, Inc, San Jose, CA) on April 13 and 27, 2021 (Figure 2). Preliminary research gaps and priorities (Appendix B) were presented to participants of the Consensus Conference during the moderated first session on April 13. Conference participants included academic EM faculty and residents, community emergency physicians, and medical students. Then, scripted moderated discussions followed based on the previously identified gaps. Participants were allowed time to give verbal feedback. After the presentation session, registered conference participants provided feedback using an electronic survey (Table 1). A free-text option was included in the survey. The survey questions were developed and distributed by the Consensus Conference leadership for each objective subgroup. Survey feedback was incorporated into a revised list of research priorities, and the revised list was presented in small groups during session two of the SAEM 21 Consensus Conference on April 27. Participants were then sent a second survey asking them to rank what they believed were the top three research priorities for social risks and needs interventions in the ED. Priorities were scored and then ranked, using the following formula: Priorities were ranked as high, medium, or low based on the top one-third, middle one-third, and lowest one-third of votes, respectively (Table 2). ## FINDINGS and DISCUSSION Overall, our workgroup identified 22 studies evaluating social risks and needs interventions among ED patients.26–47 Initial group discussions identified an abundance of gaps and unanswered questions. We elected to group these gaps into generalized, broad categories rather than focus on granular issues that would not address the breadth of our objective. Of the 22 studies, one was a systematic review,42 five were randomized control trials (RCT) or secondary analyses of an RCT,29,33–35,43 while the rest were observational studies. Study size ranged from 19 to 34,225 with most studies including several hundred participants. We identified two studies performed at a non-academic community hospital; the remaining 20 studies were conducted at academic centers.41,45 Eight studies explicitly mentioned including non-English speaking patients; of these studies, Spanish was the predominant non-English language.30,33–35,39,43,44,46 Nine studies did not explicitly state whether they included non-English speakers.26–28,32,36,40,41,45,47 Only one study included a rural site.32 ## Gap 1: Assessing Intervention Effectiveness Our literature review revealed a variety of outcome measures used to evaluate intervention performance. Twelve studies relied on the number of referrals placed to community resources,26–29,36–42,47 six reported community resource utilization,26,29,35,39,44,47 six reported healthcare utilization,27,39,43–46 and only one analyzed cost savings.44 Four studies described patient satisfaction with the intervention,26,28,39,41 and six presented self-reported health improvements as outcomes.26,32,34,37,38,42 Our group discussions noted a lack of patient-centered outcomes in past studies. Expert comments, discussions during the Consensus Conference, and survey results agreed that identifying appropriate patient-centered outcomes, such as hunger-free days, improvement in housing, and symptom reduction should be a high research priority in the future. We noted a literature gap in evaluating intervention cost and cost savings for patients and healthcare systems. One of our expert reviewers agreed that this should be an area of future exploration. Another expert reviewer noted that cost savings would be challenging to measure (eg, secondary to cost-shifting), and research surrounding cost may prematurely divert attention from examining the efficacy of the interventions. As cost is generally not a patient-centered outcome and is borne by the healthcare system or insurers, and because our goal is to improve the health and quality of life for patients, our workgroup chose to prioritize questions related to intervention effectiveness, rather than cost. The initial research priorities included a question regarding the hypothesized time horizon for evaluating the impact of interventions, given concern that time frames for seeing impact from interventions addressing social needs might be longer than examined in most traditional medical studies. This question was presented during the first session on April 13, ranked low in the first survey, and did not receive any votes in the final survey. We ultimately did not include this question separately in the final research priorities, but a consideration of timeframe is inherent in the questions evaluating intervention effectiveness. We identified only four comparative effectiveness studies of social need interventions.33–35,43 Three separate questions were initially presented during the Consensus Conference addressing the comparative effectiveness of interventions. All three ranked highly in the first survey. Based on discussions during the conference, we combined these into question 2 below, which also rated as high priority in the final survey. The following research priorities were developed to address the assessment of interventions: ## Gap 2: Integration of Interventions into the ED Environment Our literature review revealed that while some studies have examined interventions in practice and comment on implementation, no study has sought to evaluate implementation rigorously. While implementation strategies will vary based on location, studies examining the operationalization of interventions can guide the uptake and maintenance of interventions in other EDs. Many questions regarding logistical barriers and catalysts to implementation remain. For instance, who should deliver the intervention (eg, physician, nurse, social worker, case manager, patient navigator)? Our literature review found that social workers, case managers, and resource navigators tended to be responsible for implementing ED-based social needs interventions.26,27,30,33–35,37,38,40–46 No study directly compared the uptake of an intervention based on whether members of the clinical team (eg, physicians, nurses) or ancillary staff (eg, social workers, case mangers) delivered the intervention. Expert reviewers emphasized the need to assess which staff should be involved and how interventions should be structured. Participants also emphasized staffing limitations as a barrier to uptake and the need for support staff to be included in future research designs and methods. Studies examining the timing of the intervention during the ED visit (eg, waiting room, in the exam room, post-ED visit), the burden of intervention documentation, how the intervention affects length of stay, and whether the intervention increases task burden will be essential for the uptake of and adherence to the intervention. After incorporating all feedback, the final research priorities are as follows, with the first ranking medium priority and the second ranking high priority: ## Gap 3: Engagement with Medical and Social Systems The final research gap, engagement with medical and social systems, arose during conference discussions on the use of technology in interventions. The initial gap and associated research questions proposed by our workgroup focused on different technology used in interventions (Appendix B). Our literature review found that most interventions relied on phone calls, made either by patients or non-clinical staff, to link patients with resources.26,27,35,37,38,41,43–46 Four studies reported interventions integrated into the EHR in some manner.27,40,44,45 Two studies examined the benefit of using texting for linkage to community resources.28,46 However, expert reviewers were more interested in whether interventions linked patients with resources, as well as EDs with larger health and social systems, rather than the technology used for linkage. For example, they felt it was more important to know that an intervention establishes communication between the ED and the organization providing services to patients rather than whether they used phone calls, faxing, a phone app, EHR referrals, or another form of technology. Like the expert reviewers, participants in the conference discussion highlighted the need for good communication between patients and medical or social resources, and between the ED and other community resources (eg, food banks, shelters), the larger health system (eg, primary clinics, pediatric clinics), emergency medical services (EMS), and local government. Again, the emphasis was more on facilitating communication between stakeholders, rather than the technology itself. One participant commented that while EDs present an opportunity to address social needs, EDs do not exist in a silo; interventions will not succeed without buy-in from and communication with the larger health and social systems. 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Am J Emerg Med (2003.0) 21 454-7. PMID: 14574650 48. 48 Office of the Assistant Secretary for Planning and Evaluation (ASPE). U.S. Department of Health and Human Services Available at: https://aspe.hhs.gov/ Accessed November 22, 2022 49. 49 Health Leads Available at: https://healthleadsusa.org/ Accessed November 22,2022. Health Leads 50. 50 Social Interventions Research & Evaluation Network (SIREN) University of San Francisco CA Available at: https://sirenetwork.ucsf.edu/ Accessed November 22, 2022. Social Interventions Research & Evaluation Network (SIREN)
--- title: Patient-Centered Outcomes of an Emergency Department Social and Medical Resource Intervention authors: - Rohit Gupta - Anthony Wang - Daniel Wang - Daniela Ortiz - Karen Kurian - Thiago Halmer - Michael S. Jaung journal: Western Journal of Emergency Medicine year: 2022 pmcid: PMC10047735 doi: 10.5811/westjem.2022.10.57096 license: CC BY 4.0 --- # Patient-Centered Outcomes of an Emergency Department Social and Medical Resource Intervention ## Abstract ### Introduction Few studies have examined the impact of emergency department (ED) social interventions on patient outcomes and revisits, especially in underserved populations. Our objective in this study was to characterize a volunteer initiative that provided community medical and social resources at ED discharge and its effect on ED revisit rates and adherence to follow-up appointments at a large, county hospital ED. ### Methods We performed a cross-sectional analysis of ED patients who received medical and social resources and an educational intervention at discharge between September 2017–June 2018. Demographic information, the number of ED return visits, and outpatient follow-up appointment adherence within 30 and 90 days of ED discharge were obtained from electronic health records. We obtained data regarding patient utilization of resources via telephone follow-up communication. We used logistic regression analyses to evaluate associations between patient characteristics, reported resource utilization, and revisit outcomes. ### Results Most patients ($55.3\%$ of 494 participants) identified as Latino/Hispanic, and $49.4\%$ received healthcare assistance through a local governmental program. A majority of patients ($83.6\%$) received at least one medical or social resource, with most requesting more than one. Patients provided with a medical or social resource were associated with a higher 90-day follow-up appointment adherence (odds ratio [OR] 2.56; $95\%$ confidence interval [CI] 1.05–6.25, and OR 4.75; $95\%$ CI 1.49–15.20], respectively), and the provision of both resources was associated with lower odds of ED revisit within 30 days (OR 0.50; $95\%$ CI 0.27–0.95). Males and those enrolled in the healthcare assistance program had higher odds of ED revisits, while Hispanic/Latino and Spanish-speaking patients had lower odds of revisits. ### Conclusion An ED discharge intervention providing medical and social resources may be associated with improved follow-up adherence and reduced ED revisit rates in underserved populations. ## INTRODUCTION In the last two decades, the growth in the number of annual emergency department (ED) visits in the United States has outpaced the number expected by population growth by nearly two-fold.1,2 There has been a concomitant increase in the proportion of safety-net EDs serving high volumes of patients who are underinsured or enrolled in Medicaid.3,4 These trends are in part due to health inequities ingrained by social structures and economic systems, known as social determinants of health (SDoH).5 Both race/ethnicity and socioeconomic status have been strongly associated with disparities in attendance at safety-net hospitals as well as morbidity and mortality.5–10 Repeated ED utilization is also linked to higher mortality rates, especially in elderly patients.11 Patients with frequent ED revisits have limited connections to community resources and reduced comprehension of discharge instructions.12 Decreasing ED revisits may help alleviate high ED volumes, which are associated with increased in-hospital mortality, longer times to treatment initiation, and a higher likelihood of leaving against medical advice.13–15 There is a growing body of literature on the effectiveness of linking patients to primary care services from the ED and addressing SDoH to decrease hospital crowding.16,17 The ED is uniquely positioned to serve as a critical site to facilitate addressing social needs and promoting these linkages.18–20 For example, the Health Leads model and Highland Health Advocates both use help desks to connect patients to community-based resources from the ED; however, there remains a lack of evidence regarding how these approaches impact ED utilization outcomes.21, 22 Further, there is limited literature describing the utilization of social worker services, case management, and implementation of community interventions from an ED setting.23–25 Housing status, food insecurity, employment status, insurance status, education status, ability to pay for utilities, and availability of transportation are SDoH domains that can be targeted for intervention by multidisciplinary teams.26–28 While there are promising results from studies using vertical approaches that address one single SDoH domain, there are limited studies that have investigated the impact of programs that target multiple SDoHs.29,30 In this study we sought to assess a volunteer initiative that provided community medical and social resources at ED discharge and its effect on ED revisit rates and adherence to follow-up appointments at a large, county hospital ED. ## Study Design and Setting We conducted a retrospective, cross-sectional study of ED patients at a large, county hospital (89,000 annual ED visits) in Houston, TX, who received a volunteer patient discharge intervention between September 1, 20171–June 1, 2018. This service was provided by a student-led organization of roughly 60 undergraduate volunteers from a nearby university. Texas did not expand Medicaid coverage under the Affordable Care Act, and most patients in this health system are underinsured or use a county financial assistance program (FAP) for medical services within the hospital system.31,32 This study received institutional review board approval. ## Intervention Volunteers underwent biannual eight-hour trainings covering intervention procedures, resources provided to patients, and simulations of common patient encounters (Supplemental File 1). Spanish language competency of volunteers was assessed by native speakers. Teams of 3–4 volunteers with one supervising “shift leader” rotated from 1 pm-9 pm Monday to Saturday through a lower acuity treatment area for patients with an Emergency Severity Index of 3 or higher. The inclusion criterion was any patient marked for discharge in the care area displayed on the care area electronic board. Volunteers reviewed the patient with a nurse to confirm discharge status and to obtain the after-visit summary. Patients to be discharged to a skilled nursing facility, in-patient rehabilitation, or correctional facility were not approached. Low-acuity treatment areas were targeted as they had individual patient rooms with space for the volunteer teams to deliver the intervention and had a higher proportion of patients discharged compared to high-acuity areas. Patients who agreed to participate were asked questions from a standardized questionnaire to gather demographic information. Interventions were conducted in English or Spanish depending on patient preference. Patients were then provided a standardized educational intervention that involved reviewing their medication list and follow-up appointments and emphasizing the importance of medication and appointment adherence. Finally, patients were offered information on a variety of local and federal social and medical resources given in their preferred language. Resources were provided based on patients’ interest in receiving each resource. Medical resources included information on prescription discount cards, lists of pharmacies, primary care clinics, or low-cost dental clinics. Social resources included information on programs such as FAPs for rent, supplemental nutrition programs, and subsidized transportation programs. Each intervention lasted 5–15 minutes. Patients were called one week after discharge by volunteers and asked questions from the standardized questionnaire regarding medication adherence, adherence at follow-up appointments, and utilization of resources that they received in the ED. Two additional attempts were made to reach patients who did not answer the first call at 30 minutes and again at one week after. ## Data Collection Patient responses during the intervention and follow-up calls were recorded using standardized forms. Additional patient information including demographics, ED chief complaint, and outcome variables was obtained from electronic health records (EHR) and recorded in a standardized tool. We used the patients’ listed ZIP codes as a proxy for socioeconomic status,33 and median household income data was obtained from the 2013–2017 American Community Survey.34 Data was de-identified and stored in a secure database. ## Outcomes The primary outcome was the frequency of ED revisits to any Harris County-funded hospital, with a secondary outcome of adherence to follow-up clinic appointments. Revisits and appointment adherence were evaluated within 30 and 90 days after initial ED discharge, as prior studies have used these times as endpoints, and more than 30 days may be required to enroll or experience impact from new services.35–37 The 90-day outcomes were inclusive of ED revisits and appointment attendance within the initial 30 days. ## Analysis Patients who were less than 18 years of age or pregnant at the time of the intervention were excluded from data analysis. We also excluded patients with missing identifying information on the standardized forms. Patient characteristics and outcomes were analyzed using descriptive and inferential statistics. We used binomial logistic regression to assess the relationship among independent variables (patient demographics, type of resources provided at ED discharge, and reported resource utilization at follow-up call) and dependent variables (follow-up appointment adherence and ED revisits), using SPSS Statistics for Windows, version 26 (IBM Corp., Armonk, NY). We performed a residuals analysis to identify outliers with standardized residuals greater than 2.5 standard deviations, which were removed from the final analysis. ## Characteristics of Study Subjects A total of 614 patients received the intervention during the study period (Figure). Patients below 18 years of age [104], pregnant at the time of discharge [7], or with missing medical record numbers or ED visit dates [9] were excluded. We included a final 494 patient encounters in the data analysis. The median patient age was 43 years (Table 1). Most patients were female ($55.3\%$), and the majority identified as Latino/Hispanic ($55.3\%$). Primary Spanish speakers made up over one third ($35.2\%$) of all patients. The most frequent chief complaints were abdominal pain ($19.6\%$), generalized pain ($8.5\%$), and headache ($6.1\%$). About half of the patients ($49.4\%$) were enrolled in the county healthcare FAP. We found that $33.4\%$ of patients were uninsured, and only $13.6\%$ had insurance coverage. These characteristics overall reflected the general ED population at this hospital.31 ## Main Results A total of 413 patients ($83.6\%$) requested at least one resource at discharge, with 329 (66.6) requesting more than one resource. The most requested medical and social resources were dental care information and information on food and insurance assistance, respectively (Table 2). From 494 ED encounters included in this study, volunteers contacted 158 patients ($32\%$)in a follow-up call one week after discharge. Compared to patients who were not successfully contacted, this patient population did not significantly differ in gender ($$P \leq 0.29$$), race/ethnicity ($$P \leq 0.18$$), language ($$P \leq 0.89$$), or insurance status ($$P \leq 0.12$$). Of the contacted patients, 81 ($51.3\%$) reported using a resource received from the intervention. Of all patients, 76 ($15.4\%$) returned to the ED at least once within 30 days of discharge, and 114 ($23.1\%$) returned within 90 days. Components of our intervention were associated with improved outcomes of decreased odds of ED revisits and improved attendance of follow-up appointments (Table 3). Patients who requested both medical and social resources from the intervention was associated with lower odds (odds ratio [OR] 0.50, $95\%$ confidence interval [CI] 0.27–0.95) of an ED revisit at 30 days compared to those requested no resources. Those who reported using a resource received from the intervention (OR 0.46, $95\%$ CI 0.24–0.92) had lower odds of revisiting at 90 days. There were higher odds of outpatient follow-up appointment adherence for patients who received a social resource at discharge (OR 4.75, $95\%$ CI 1.49–15.20), and those who received a medical resource (OR 2.56, $95\%$ CI 1.05–6.25). We observed a difference in the odds of ED revisits and attendance of follow-up appointments associated with some patient characteristics. Increased odds of an ED revisit within 30 days of discharge were seen in males (OR 1.76, $95\%$ CI 1.07–2.88) and patients enrolled in the county FAP (OR 2.11, $95\%$ CI 1.15–3.87). Males also had higher odds (OR 1.91, $95\%$ CI 1.25–2.91) of revisiting at 90 days. Patients in the 3rd–5th quintile median household income had lower odds of attendance to follow-up appointments within 30 days of ED discharge (OR 0.38, $95\%$ CI 0.16–0.90). In contrast, primarily Spanish speakers had lower odds of an ED revisit (OR 0.53, $95\%$ CI 0.33–0.85) and higher odds of attending at least one follow-up appointment at 30 and 90 days. Hispanic/Latino patients had lower odds of revisiting the ED within 90 days compared to Black patients (OR 0.52, $95\%$ CI 0.33–0.83) as well as higher odds of follow-up attendance at 30 and 90 days. Patients enrolled in a county FAP also had higher odds of follow-up attendance compared to uninsured patients. ## DISCUSSION Our findings indicate that ED discharge interventions focused on patient needs and providing social and medical resources may assist in promoting appropriate patient access to the healthcare system after ED discharge. The most requested resources were information on local dental, primary care, and pharmacy services, as well as food and health insurance resources. Similar needs were identified in surveys of ED patients who made early or frequent returns to the ED after their initial ED discharge.38,39 These patients reported difficulty scheduling a primary care appointment, attending outpatient appointments due to lack of insurance, and finding transportation to attend follow-up appointments.38,39 In our study, patients who requested both social and medical resources had lower rates of adherence to follow-up compared to those who requested only one category of resources, possibly indicating that patients with multiple needs had more barriers to appointment adherence. Furthermore, patients reported the discharge process of their initial ED visit was rushed, unprepared, and left them confused.38 Our volunteer-led service was designed to address these factors more comprehensively during ED discharge. Despite identified patient needs, interventions dedicated to providing SDoH resources are sparse. Wassmer et al described using a peer counseling program that provided education on medical and social needs in the ED.40 Patients who had visited the ED four or more times in the previous year were counseled during their ED visit and in subsequent visits, with a decrease in ED utilization over two years extending past the follow-up period of the study. A population-based approach to ED social interventions may improve the effectiveness of addressing SDoH by identifying risk factors for ED revisits and developing interventions to target specific population needs. This study found that male gender, Black race, and use of the county FAP were associated with increased odds of in-system ED revisits. Other studies have reported mixed results on the association between these factors and ED usage. One study found an association between male gender and higher ED revisit rates in older adults.11 However, others demonstrated no such association or an inverse association,41–44 which likely demonstrates that the impact of gender may be influenced by other risk factors. Multiple studies have demonstrated higher ED revisit rates among Blacks compared to other ethnic groups; however, this may be due to differences in average income, enrollment in Medicare and Medicaid, implicit bias against this group within medical systems, and lack of access to primary care physicians.39,44,45 The impact of using a healthcare FAP for addressing healthcare costs has not been well characterized. Similar to the findings in this study, Wassmer et al found that patients receiving financial assistance from a county program in California had higher utilization of the ED,40 which was speculated to be due to younger, lower income patients on financial assistance than those enrolled in public insurance programs. Interestingly, although the use of a county FAP was associated with increased odds of ED revisit, this was also associated with increased odds of follow-up appointment attendance at 90 days post-discharge. Possibly, the cost of appointments is ameliorated by the assistance program, and for similar reasons these patients receiving financial assistance may be less deterred from revisiting the ED. Our study differed from preceding literature on the impact of English proficiency. Ngai et al demonstrated that patients with limited English proficiency have a higher likelihood of an unplanned ED visit within 72 hours of ED discharge compared to English speakers, even after adjusting for potential confounders.46 The opposite trend was observed in this study, with lower odds of a return to the ED within 90 days in primary Spanish speakers. The reason for this is likely multifactorial. Previous studies suggest that less acculturated Hispanic adults, measured by citizenship status and length of stay in the US, use fewer healthcare resources overall than more acculturated counterparts, and those who are undocumented may fear discovery and deportation, avoiding ED use for non-urgent reasons.47,48 Finally, having a higher median income was significant for lower odds of follow-up appointment adherence, but not a significant risk factor for ED revisits. Previously, lower socioeconomic status has been established as a risk factor for increased ED utilization, but its impact on appointment adherence has been debated.3,49 Dedicated personnel in the ED setting are likely needed to effectively attend to patients’ overlapping medical and social gaps. Many healthcare organizations employ ED social workers, case managers, and patient navigators who address the impact of SDoH through patient counseling, referrals to community services, and patient discharge planning.50 The advantage provided by this personnel is supported by multiple systematic reviews demonstrating that their work reduces ED revisits.24,51 However, a social worker-based intervention may not be feasible at all hospitals, which may be understaffed in high-volume, safety-net facilities treating patients with complex medical and social problems.27 Our study explored the possibility of using trained volunteers to perform an educational intervention. The Health Leads models similarly used volunteer patient advocates to connect patients with social resources.21 Recruiting volunteers for our intervention allowed for more patients to be educated on available resources. Such a model may be scalable to other hospital settings, as implementation required minimal training of volunteers and an upfront investment of time to collect information about county and federal resources. In our experience, this investment was associated with a reduction of ED revisits similar to that seen in complex care coordination systems, suggesting that dedicated volunteers may serve as an adequate patient navigator proxy. Further studies are warranted to examine the impact volunteers and such ancillary staff has on patient outcomes. ## LIMITATIONS As this study used a retrospectively reviewed cross-section of patients’ phone interviews and EHRs, causation cannot be inferred between the intervention and revisits or follow-up adherence. This was a single-site study at a county ED assessing patients at low-acuity units; therefore, our findings may not be generalizable to other ED settings. We were unable to collect data on a control cohort of patients who did not receive this intervention due to resource-limitations, and we did not calculate the proportion of participants of all ED patients triaged to these acuity areas during the study period. Most patients in this study were either uninsured or used a county FAP covering care for in-system healthcare services only, and there was no method to track out-of-system healthcare encounters after discharge. We used convenience sampling to select patients during times when volunteers were present in the ED. Patients discharged during late evening or morning hours were not included, which may have skewed the characteristics of the population studied. ZIP code data was used as a proxy for socioeconomic status and may not have been representative of each patient’s income. Recall bias may be introduced via patient self-reporting of usage of medical and social resources during the follow-up call. Non-response bias may have been introduced as only one follow-up call was made, and further follow-up calls were constrained by available resources, but we did not observe a significant difference between patients who were and were not reached. ## CONCLUSION The outcomes from this intervention suggest that there is an opportunity to improve patient engagement with the healthcare system by providing resources that address social determinants of health. This suggests that a standardized in-person approach may reduce ED revisits and improve outpatient follow-up. Future investigation is needed to examine the best methods for implementation, comparing in-person and non-individualized interventions, and cost effectiveness of programs to address SDoH in the ED that meet patients’ social needs and promote healthcare accessibility. ## References 1. 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--- title: Crocin Attenuates NLRP3 Inflammasome Activation by Inhibiting Mitochondrial Reactive Oxygen Species and Ameliorates Monosodium Urate-Induced Mouse Peritonitis authors: - Ruth Sangare - Iskander Madhi - Ji-Hee Kim - YoungHee Kim journal: Current Issues in Molecular Biology year: 2023 pmcid: PMC10047758 doi: 10.3390/cimb45030134 license: CC BY 4.0 --- # Crocin Attenuates NLRP3 Inflammasome Activation by Inhibiting Mitochondrial Reactive Oxygen Species and Ameliorates Monosodium Urate-Induced Mouse Peritonitis ## Abstract Crocin is a hydrophilic carotenoid pigment found in the stigma of Crocus sativus or the fruit of Gardenia jasminoides. In this study, we investigated the effects of Crocin on the activation of the nucleotide-binding oligomerization domain, leucine-rich repeat, and pyrin domain containing 3 (NLRP3) inflammasome in J774A.1 murine macrophage cells and monosodium urate (MSU)-induced peritonitis. Crocin significantly inhibited Nigericin-, adenosine triphosphate (ATP)-, MSU-induced interleukin (IL)-1β secretion, and caspase-1 cleavage without affecting pro-IL-1β and pro-caspase-1. Crocin also suppressed gasdermin-D cleavage and lactate dehydrogenase release and enhanced cell viability, indicating that Crocin reduces pyroptosis. Similar effects were observed in primary mouse macrophages. However, Crocin did not affect poly(dA:dT)-induced absent in melanoma 2 (AIM2) and muramyl dipeptide-induced NLRP1 inflammasomes. Crocin decreased Nigericin-induced oligimerization and the speck formation of apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC). Crocin also dramatically alleviated the ATP-induced production of mitochondrial reactive oxygen species (mtROS). Finally, Crocin ameliorated the MSU-induced production of IL-1β and IL-18 and the recruitment of neutrophils during peritoneal inflammation. These results suggest that Crocin suppresses NLRP3 inflammasome activation by blocking mtROS production and ameliorates MSU-induced mouse peritonitis. Thus, Crocin may have therapeutic potential in various NLRP3 inflammasome-related inflammatory diseases. ## 1. Introduction Inflammasomes are multiprotein complexes that regulate caspase-1 activation in innate immune cells and induce inflammation against pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs). Several types of inflammasomes have been identified, including the nucleotide-binding oligomerization domain, leucine-rich repeat, and pyrin domain containing 1 (NLRP1), NLRP3, NLR Family caspase activation and recruitment domain (CARD) containing 4 (NLRC4), and absent in melanoma 2 (AIM2) [1]. Among these, NLRP3 inflammasome was widely investigated and demonstrated a close relation to various inflammatory diseases. The NLRP3 inflammasome is formed by the sensor molecule NLRP3, the adaptor protein apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), and the effector pro-caspase-1. The NLRP3 protein has a pyrin domain (PYD), and the ASC protein consists of PYD and CARD domains. Upon activation, NLRP3 interacts with ASC via PYD, and multiple ASCs combine into a single macromolecular focus, termed an ASC speck. Finally, pro-caspase-1 is recruited to the complex through its CARD domain by ASC to form the NLRP3/ASC/pro-caspase-1 complex [1]. The assembly of the NLRP3 inflammasome leads to self-cleavage and the activation of caspase-1, as well as the subsequent proteolysis of pro-interleukin (IL)-1β and pro-IL-18 into mature and functional IL-1β and IL-18, respectively. Activated caspase-1 also cleaves gasdermin-D (GSDMD), which then forms a membrane pore that facilitates the release of cytosolic contents, such as IL-1β and lactate dehydrogenase (LDH) and induces inflammatory cell death in the process of pyroptosis [2]. NLRP3 inflammasome activation is believed to be mediated by a two-step process: priming (signal 1) and activation (signal 2) [3]. Priming with the Toll-like receptor 4 (TLR4) ligand lipopolysaccharide (LPS) drives the activation of transcription factors, including nuclear factor-kappa B (NF-κB), and upregulates the inflammasome components NLRP3, caspase-1, and pro-IL-1β. Signal 2 (activation) is provided by various PAMPs or DAMPs, such as extracellular adenosine triphosphate (ATP), a bacterial toxin (e.g., Nigericin), and monosodium urate crystals that activate multiple upstream signaling events. These include K+ efflux, Ca2+ signaling, lysosomal disruption, mitochondrial reactive oxygen species (mtROS) production, and mitochondrial dysfunction [4]. Considering the diversity of activators and the complexity of signaling pathways of the NLRP3 inflammasome, it is not surprising that the NLRP3 inflammasome has been shown to be involved in multiple diseases, such as inflammatory bowel diseases, gout, atherosclerosis, obesity, type 2 diabetes, multiple sclerosis, Alzheimer’s disease, Parkinson’s disease, and cancers [5]. Thus, the NLRP3 inflammasome has been suggested as a potential drug target for neurological, metabolic, and inflammatory diseases [6]. Crocin (crocetin digentiobiose ester) is a hydrophilic carotenoid pigment found in the stigma of Crocus sativus (commonly known as Saffron) or the fruit of Gardenia jasminoides [7,8]. These plants are primarily used in many kinds of cuisines as flavoring and coloring agents as well as in traditional medicines for the treatment of edema, headache, fever, jaundice, and hypertension [9,10]. A number of studies have demonstrated that Crocin has a wide range of activities, including antioxidant [11,12], anti-inflammatory [13,14], anti-cancer [15], anti-atherosclerotic [16,17], anti-depressant [18], cardioprotective [19], and hepatoprotective effects [20]. Several studies have shown that Crocin inhibits NF-κB activity and suppresses proinflammatory mediators and cytokines [14,21,22]. However, the effects of Crocin on NLRP3 inflammasome activation (signal 2) have not been investigated. In this study, we demonstrate that Crocin suppresses the signal 2 process of the NLRP3 inflammasome by inhibiting mtROS production and alleviates monosodium urate (MSU)-induced peritonitis. ## 2.1. Materials Crocin, LPS (phenol extracted from Salmonella enteritidis), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), ATP, uric acid, and other reagents were purchased from Sigma-Aldrich (St. Louis, MO, USA). LDH Detection Kit was purchased from DoGenBio (Seoul, Republic of Korea). Nigericin, poly(dA:dT), and muramyl dipeptide (MDP) were purchased from InvivoGen (San Diego, CA, USA). Antibodies against caspase-1, ASC, and NLRP3 were purchased from Adipogen (San Diego, CA, USA). Antibody against β-actin was purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). The antibody against IL-1β was purchased from R&D Systems (Minneapolis, MN, USA). Lipofectamine™ 3000 Transfection Reagent, MitoSOX™ Red, MitoTracker™ Green FM, MitoTracker™ Deep Red FM, Dulbecco’s modified Eagles’ medium (DMEM), fetal bovine serum (FBS) and antibodies against CD11b-APC, and Ly-6G/Ly-6C-PerCP-Cyanine5.5, goat anti-rabbit IgG (H+L) secondary antibody, HRP, and the goat anti-mouse IgG (H+L) secondary antibody, HRP were purchased from Thermo Fisher Scientific (Waltham, MA, USA). ## 2.2. Preparation of MSU Crystals MSU crystals were prepared as described previously [23]. Briefly, Uric acid (0.2 g) was dissolved and heated in 40 mL of 0.01 M NaOH, followed by their adjustment to pH 7.2 at 70 °C. The solution was filtered and cooled overnight in a cold room. The crystals that formed were washed and dried. The shape and size of the crystals were examined by a microscope. The MSU crystals were suspended in PBS at a concentration of 20 mg/mL. ## 2.3. Isolation of Mouse Peritoneal Macrophages Male C57BL/6 mice, purchased from DBL Co., Ltd. (Eumseong, Republic of Korea), were used between 8 and 12 weeks of age (body weight 25–30 g). All animal studies were conducted in accordance with the principles and procedures of the Pusan National University Institutional Animal Care and Use Committee. Thioglycollate (TG) broth (DIFCO, Detroit, MI, USA)-elicited macrophages were harvested 3 days after the intraperitoneal injection of TG (2.5 mL) into mice and were isolated as previously reported [24]. After 4 h of incubation on cell culture plates, adherent macrophages were immediately used for experimentation. ## 2.4. Cell Culture and Treatment The J774A.1 murine macrophage cell line was obtained from the Korean Cell Line Bank (Seoul, Republic of Korea). Mouse peritoneal macrophages or J774A.1 cell (5 × 105) was cultured in DMEM supplemented with $10\%$ heat-inactivated FBS at 37 °C in a humidified atmosphere of $5\%$ CO2 and $95\%$ air. For NLRP3 inflammasome activation, the cells were primed with 0.1 μg/mL LPS for 3 h and washed twice with PBS. After adding DMEM, cells were pretreated with 125, 250, or 500 μM of Crocin for 3 h and then stimulated with Nigericin (4 μM) for 1 h, ATP (5 mM) for 1 h, or MSU (300 μg/mL) for 6 h. MDP, an NLRP1 inflammasome trigger, or poly(dA:dT), an AIM2 trigger, were transfected with Lipofectamine™ 3000 Transfection Reagent according to the manufacturer’s instructions for 6 h. ## 2.5. Measurement of IL-1β and IL-18 Concentrations After the treatment of J774A.1 cell and mouse peritoneal macrophages as described in Section 2.4, the culture supernatant was collected, and IL-1β levels were quantified by ELISA (R&D Systems) according to the manufacturer’s instructions. IL-1β and IL-18 levels in peritoneal exudate after peritonitis induction was quantified by ELISA (R&D Systems) according to the manufacturer’s instructions. ## 2.6. Protein Precipitation of Cell Culture Supernatants for IL-1β and Caspase-1 Proteins in J774A.1 cell and peritoneal macrophage culture supernatants were precipitated using ice-cold methanol/chloroform as described previously [25] with some modifications. The supernatant was harvested by centrifugation at 400× g for 5 min and precipitated using an equal volume of methanol and 0.25 volumes of chloroform. Samples were vortexed for 10 s and incubated for 5 min on ice. After centrifugation at 1700× g for 10 min at 4 °C, the upper phase was removed. The pellet was washed with ice-cold methanol, followed by centrifugation at 1700× g for 10 min at 4 °C. The pellet was dried for 30 min, resuspended in a Laemmli sample buffer, and stored at −20 °C. ## 2.7. Western Blot Analysis The cytosolic extracts were harvested in an ice-cold lysis buffer ($1\%$ Triton X-100 and $1\%$ deoxycholate in PBS). Protein content in cytosolic extracts was determined using Bradford reagent (Bio-Rad; Hercules, CA, USA). Proteins in each sample were resolved using $10\%$ SDS-polyacrylamide gel electrophoresis (SDS-PAGE), transferred to a polyvinylidene difluoride (PVDF) membrane, and incubated with the appropriate antibodies. Proteins were visualized using an enhanced chemiluminescence detection system (Amersham Biosciences, Piscataway, NJ, USA) with horseradish peroxidase-conjugated secondary antibodies. An anti-actin antibody was used as a loading control for the cytosolic protein. ## 2.8. LDH Release Assay The total and secreted amounts of LDH was determined using an LDH cytotoxicity kit according to the manufacturer’s instruction. Cell supernatants of J774A.1 and mouse peritoneal macrophages were measured at 450 nm (reference wavelength 600–650 nm) using a microplate reader (Bio-Rad). ## 2.9. Cell Viability Assay The cell viability was assessed using a microculture colorimetric assay based on MTT. After the treatment of J774A.1 cell and mouse peritoneal macrophage as described in Section 2.4, MTT was added to each well at a final concentration of 50 μg/mL. After incubation for 3 h at 37 °C in $5\%$ CO2, the supernatant was removed, and the formazan crystals that were produced in viable cells were solubilized with dimethylsulfoxide (DMSO). The absorbance of each well was measured at 570 nm using a microplate reader (Bio-Rad). ## 2.10. ASC Oligomerization Assay ASC oligomerization was determined using disuccinimidyl suberate (DSS) as described previously [26]. Briefly, after the treatment of J774A.1 cell with Crocin and Nigericin as described in Section 2.4, the cells were washed with ice-cold PBS and lysed in an ice-cold buffer (20 mM HEPES-KOH, pH 7.5, 150 mM KCL, $1\%$ NP-40, protease inhibitor cocktail, and 1 mM sodium orthovanadate). The lysate was centrifuged at 5000× g for 10 min at 4 °C. The pellets were washed twice in ice-cold PBS, resuspended in 500 μL PBS, and sonicated. The resuspended pellets were treated with 2 mM DSS at 25 °C for 30 min with rotation. Crosslinked pellets were collected by centrifugation at 1700× g for 15 min at 4 °C and resuspended in a Laemmli sample buffer for Western blotting. ## 2.11. Immunofluorescence Microscopy J774A.1 cells were cultured directly on glass coverslips in 24-well plates. After the treatment of J774A.1 cell with Crocin and Nigericin as described in Section 2.4, the cells were fixed with methanol for 10 min at 4 °C and blocked with $5\%$ bovine serum albumin in PBS for 1 h at room temperature (RT). The cells were then stained with an anti-ASC antibody (1:500) for 16 h at 4 °C and secondary fluorescein isothiocyanate-conjugated IgG antibody (1:5000) for 2 h at RT. Nuclei were stained with 1 μg/mL of 4,6-diamidino-2-phenylindole (DAPI) and then analyzed by fluorescence microscopy with an Axioplan 2 microscope (Zeiss, Jena, Germany). The number of ASC specks was counted manually. ## 2.12. Measurement of mtROS After treatment of J774A.1 cell with Crocin and ATP as described in Section 2.4, cells were stained with a MitoSOX™ Red mitochondrial superoxide indicator according to the manufacturer’s instructions. Cell fluorescence was monitored by flow cytometry (BD Accuri C6 flow cytometer, BD Biosciences, San Jose, CA, USA). Data were analyzed using the FlowJo software. ## 2.13. MSU-Induced Murine Peritonitis Male C57BL/6 mice (8–12 weeks old) were intraperitoneally injected with Crocin (125 or 250 mg/kg body weight) or PBS. After 2 h, peritonitis was induced by the intraperitoneal administration of 2 mg of MSU crystals dissolved in 0.5 mL PBS. The control mice were intraperitoneally injected with the same volume of PBS. The mice were euthanized 6 h later. Their peritoneal cavities were washed with 5 mL PBS, and the total number of peritoneal exudate cells (PECs) was counted using a hematocytometer. Lavage fluids were analyzed for IL-1β and IL-18 levels by ELISA. PECs were subjected to staining and flow cytometric analysis. Neutrophil numbers in the PECs were determined by multiplying the total cell numbers by the percentage of (Ly-6G/Ly-6C)+/CD11b+ cells. ## 2.14. Statistical Analysis All results are expressed as the mean ± SEM. Each experiment was performed in duplicate and repeated at least three times. Statistical analyses were performed using the GraphPad Prism 7 software. All data represented a normal distribution and were statistically analyzed using a one-way analysis of variance (ANOVA), followed by Tukey’s multiple comparisons tests. A value of $p \leq 0.05$ was considered statistically significant. ## 3.1. Crocin Suppresses NLRP3 Inflammasome Activation in Nigericin, ATP, or MSU-Stimulated Macrophages We first examined whether Crocin affected IL-1β secretion in response to NLRP3 inflammasome triggers. In the preliminary experiment, we determined the dose of Nigericin, ATP, and MSU in LPS-primed J774A.1 cells by examining the level of cleaved caspase-1 p20 (Supplementary Figure S1). The pretreatment of LPS-primed J774A.1 cells with Crocin significantly inhibited Nigericin-, ATP-, or MSU crystal-induced IL-1β secretion in a dose-dependent manner (Figure 1A). MCC950, a specific inhibitor of NLRP3 inflammasome, was used as a positive control. To further define whether the decreased IL-1β secretion resulted from the inhibitory actions of Crocin on caspase-1 activation and IL-1β maturation, we examined the levels of the active forms of caspase-1 and IL-1β in the cell culture supernatant using Western blotting. As shown in Figure 1B, Crocin markedly reduced the cleaved (active) caspase-1 p20 and active IL-1β p17 levels. These results suggest that Crocin suppresses NLRP3 inflammasome-induced caspase-1 activation and IL-1β maturation. To confirm inhibitory effects of Crocin are not due to its cytotoxicity, we assessed MTT assay in response to Crocin. As shown in Supplementary Figure S2, Crocin with a concentration of up to 500 μM showed no cytotoxicity. ## 3.2. Crocin Inhibits NLRP3 Inflammasome-Induced GSDMD Cleavage and Pyroptosis Since the NLRP3 inflammasome triggers GSDMD cleavage and pyroptosis, we examined the effects of Crocin on the GSDMD cleavage using Western blotting. Nigericin, ATP, and MSU crystal increased the cleaved form of GSDMD, while Crocin pretreatment decreased the cleaved forms of GSDMD (Figure 2A). LDH release as a result of inflammasome-induced pyroptosis was also decreased by Crocin pretreatment in a dose-dependent manner (Figure 2B). Consistent with this result, Crocin ameliorated the decreased cell viability in the MTT assay (Figure 2C). These results indicate that Crocin inhibits NLRP3 inflammasome-triggered GSDMD cleavage and pyroptosis. ## 3.3. Crocin Inhibits NLRP3 Inflammasome in Mouse Peritoneal Macrophages To confirm that Crocin suppresses NLRP3 inflammasome not only in the cell line, we isolated mouse peritoneal macrophages and investigated the effects of Crocin on the activation of the NLRP3 inflammasome. Crocin inhibited Nigericin-induced IL-1β secretion (Figure 3A) and the levels of cleaved caspase-1 p20, active IL-1β p17, and cleaved form of GSDMD (Figure 3B). In addition, Nigericin-induced LDH release and cell viability were ameliorated by Crocin treatment (Figure 3C,D). These results suggest that Crocin suppresses the NLRP3 inflammasome, not only in the cell line but also in primary macrophages. ## 3.4. Crocin Does Not Affect NLRP1 or AIM2 Inflammasome To check whether Crocin inhibits other inflammasomes, such as NLRP1 or AIM2, treatments were performed using MDP (an NLRP1 inflammasome trigger) and poly(dA:dT) (an AIM2 inflammasome trigger). As shown in Figure 4A, the levels of cleaved p20 caspase-1 and active p17 IL-1β in response to MDP or poly(dA:dT) were unchanged by Crocin pretreatment. Moreover, IL-1β secretion and LDH release were not significantly affected by Crocin pretreatment (Figure 4A,C). These results suggest that Crocin does not affect the NLRP1 or AIM2 inflammasomes. ## 3.5. Crocin Suppresses ASC Oligomerization and ASC Speck Formation During NLRP3 inflammasome activation, the formation of high molecular weight ASC oligomers is a critical step for subsequent caspase-1 activation. We found that the stimulation of LPS-primed J774A.1 cells with Nigericin promoted the formation of ASC dimers and ASC oligomers in the cell pellets, while pretreatment with Crocin markedly suppressed ASC oligomerization (Figure 5A). Since Crocin did not decrease the total ASC levels in cell lysates, the suppressive effect of Crocin was not due to reduced ASC levels. We also examined the effect of Crocin on ASC speck formation using immunofluorescence microscopy. The speck represents an ASC concentration, which is identified by the ASC antibody. Following Nigericin treatment, ASC multimers were observed in speck-like forms in the cytosol and out of the cell. It can also be observed that the nucleus is smaller following Nigericin treatment, which is consistent with previous reports [27]. Pretreatment with Crocin remarkably reduced the formation of Nigericin-induced ASC specks (Figure 5B,C). These results suggest that Crocin suppresses NLRP3 inflammasome activation by inhibiting the ASC multimer formation. ## 3.6. Crocin Suppresses mtROS Production To investigate the effect of Crocin on upstream signaling, we examined mtROS levels in LPS-primed J774A.1 cells using a MitoSOX red mitochondrial superoxide indicator. While the level of mitochondrial superoxide increased by the NLRP3 inflammasome trigger ATP, ATP-induced mitochondrial superoxide levels were dramatically diminished by Crocin pretreatment (Figure 6A,B). These results suggest that Crocin inhibits mtROS production during NLRP3 inflammasome activation. ## 3.7. Crocin Inhibits IL-1β and IL-18 Production and Recruitment of Neutrophils in MSU-Induced Peritonitis The MSU-induced peritonitis model was used to investigate the inhibitory effects of Crocin on NLRP3-mediated inflammation. The intraperitoneal injection of MSU leads to NLRP3 inflammasome-dependent peritonitis, which is characterized by IL-1β and IL-18 production and the neutrophil influx into the peritoneal cavity [28]. Mice were pre-treated with Crocin for 2 h, followed by an intraperitoneal injection of MSU. As expected, Crocin pretreatment considerably decreased MSU-mediated IL-1β and IL-18 production (Figure 7A,B), as well as the total cell number of peritoneal exudates (Figure 7C) and neutrophil recruitment (Figure 7D,E) in mice. These results indicate that Crocin alleviates MSU-mediated peritonitis by inhibiting the NLRP3 inflammasome in vivo. ## 4. Discussion In this study, we investigated the inhibitory effect of Crocin on NLRP3 inflammasomes in mouse macrophages and a mouse peritonitis model. Crocin pretreatment significantly inhibited the secretion of IL-1β, the cleavage of caspase-1 and IL-1β, and the cleavage of GSDMD and pyroptosis in response to the NLRP3 inflammasome triggers: Nigericin, ATP, and MSU. Crocin could inhibit the NLRP3 inflammasome by reducing the expression of NLRP3 inflammasome components, such as NLRP3 and pro-caspase-1 (at the priming step), as we found in a previous study that Crocin suppresses NF-κB activity [14]. Thus, to minimize the effects on the priming step, we incubated the cells with LPS for 3 h and washed out the remaining LPS. The cells were then treated with Crocin and stimulated with NLRP3 inflammasome triggers. As we expected, pro-caspase-1, pro-IL-1β, and NLRP3 levels were not altered by the NLRP3 inflammasome triggers or by Crocin. Therefore, our experimental system could exclude the effects on the priming step, and our results suggest that Crocin suppresses the NLRP3 inflammasome activation step (signal 2). Similar results were observed in mouse primary macrophages in response to Nigericin treatment. In contrast, Crocin did not inhibit the activation of NLRP1 or AIM2. Thus, our findings suggest that Crocin acts as an NLRP3 inflammasome-specific inhibitor. Similar to the in vitro effects, Crocin attenuated the secretion of IL-1β and IL-18 and neutrophil recruitment in an MSU-induced mouse peritonitis model. Abnormal uric acid metabolism results in MSU crystal deposition in the joint and periarticular tissues, which leads to gout: a common form of inflammatory arthritis [28]. The inflammatory response to MSU activates resident macrophages to produce IL-1β and induces neutrophil recruitment [28]. The intraperitoneal injection of MSU crystals in mice has been used as an animal model of gout [28,29,30]. The observation that Crocin alleviated the MSU-induced inflammatory response supports the suggestion that Crocin can ameliorate the symptoms of gout. The finding that Crocin inhibited MSU-induced inflammation indicates that Crocin might relieve NLRP3 inflammasome-related diseases. Zhang et al. [ 31] recently reported that Crocin alleviates NLRP3 inflammasomes in diabetic kidneys. Our results are consistent with theirs, although they showed that Crocin suppressed the production of IL-1β and IL-18 by inhibiting the expression of the NLRP3 inflammasome. ASC oligomerization is considered an essential event for NLRP3 inflammasome activation. Upon activation, NLRP3 inflammasome proteins oligomerize to form scaffolds to aggregate ASC in filaments, resulting in large ASC specks [1]. Many ASC specks appear to be released during pyroptosis [32]. Extracellular and intracellular ASC specks remain active [33]. Thus, extracellular ASC specks can directly activate caspase-1 and can be engulfed by the surrounding cells, resulting in danger signals that activate and trigger an inflammatory cascade and participate in antigen presentation [32,34,35]. Consistent with these reports, we observed ASC specks in the extracellular space (Figure 5B) and cytosol (Figure 5A,B) in response to Nigericin. Extracellular ASC specks have been related to many diseases, including rheumatoid arthritis (RA), systemic lupus erythematosus, gout, psoriasis, allergic rhinitis, chronic obstructive pulmonary disease, Alzheimer’s disease (AD), Parkinson’s disease (PD), cancers, and viral infections [32]. We found that ASC oligomerization was decreased by Crocin pretreatment. Thus, Crocin could have therapeutic potential for the aforementioned diseases, although the remedial effects of Crocin on these diseases have not yet been proven. Supporting this logic, Crocin reportedly has mitigating effects on RA, AD, and PD [36,37,38,39]. Signal 2 of NLRP3 inflammasome activation is regulated by various signaling pathways, and there is currently no universal model for its activation [3]. NLRP3 seems to act as a sensor of disrupted homoeostasis, including a perturbed mitochondrial function [40]. Mitochondrial dysfunction induces mtROS generation, which exacerbates mitochondrial damage. Mitochondrial dysfunction or mtROS can trigger NLRP3 activation [41]. In the present study, Crocin attenuated mtROS production induced by an NLRP3 inflammasome trigger. These results suggest that Crocin attenuates the NLRP3 inflammasome by reducing mtROS. This suggestion is supported by reports demonstrating that ROS and oxidized mtDNA activate the NLRP3 inflammasome [41,42,43] and that many chemicals suppress the NLRP3 inflammasome by reducing ROS production [6,44,45,46]. Consistent with our findings, some studies have reported that Crocin alleviates hydrogen peroxide-induced ROS production in the nerve cells [21], dexamethasone-induced ROS production in osteoblasts [47], and methylglyoxal-induced mitochondrial superoxide production in osteoclasts [48]. Recently, it was reported that several mitochondrial components, including cardiolipin and mitochondrial DNA (mtDNA), translocated to the cytosol during mitochondrial damage and triggered NLRP3 activation [41,49,50]. Furthermore, mitochondria-associated endoplasmic reticulum membranes (MAM) facilitated NLRP3 inflammasome assembly [51]. Further studies are needed to define the role of Crocin in signaling pathways, including mtDNA, cardiolipin, and MAM. In summary, Crocin suppressed Nigericin-, ATP-, and MSU-induced IL-1β secretion, caspase-1 cleavage, GSDMD cleavage, and pyroptosis in mouse macrophages. Crocin also attenuated ASC oligomerization and mtROS production. In vivo, Crocin treatment alleviated MSU-induced peritonitis. These collective results suggest that Crocin attenuates the NLRP3 inflammasome by inhibiting mtROS production. Therefore, Crocin might have therapeutic potential for anti-inflammatory drugs to treat NLRP3 inflammasome-related diseases. 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--- title: NGAL as Biomarker of Clinical and Subclinical Damage of Kidney Function after Coronary Angiography authors: - Iliyana Petrova - Alexander Alexandrov - Georgi Vladimirov - Hristo Mateev - Ivaylo Bogov - Iva Paskaleva - Nina Gotcheva journal: Diagnostics year: 2023 pmcid: PMC10047760 doi: 10.3390/diagnostics13061180 license: CC BY 4.0 --- # NGAL as Biomarker of Clinical and Subclinical Damage of Kidney Function after Coronary Angiography ## Abstract Contrast-induced acute kidney injury (CI-AKI) is a serious complication after angiographic examinations in cardiology. Diagnosis may be delayed based on standard serum creatinine, and subclinical forms of kidney damage may not be detected at all. In our study, we investigate the clinical use in these directions of a “damage”-type biomarker—neutrophil gelatinase-associated lipocalin (NGAL). Among patients with a high-risk profile undergoing scheduled coronary angiography and/or angioplasty, plasma NGAL was determined at baseline and at 4th and 24th h after contrast administration. In the CI-AKI group, NGAL increased significantly at the 4th hour (Me 109.3 (IQR 92.1–148.7) ng/mL versus 97.6 (IQR 69.4–127.0) ng/mL, $$p \leq 0.006$$) and at the 24th hour (Me 131.0 (IQR 81.1–240.8) ng/mL, $$p \leq 0.008$$). In patients with subclinical CI-AKI, NGAL also increased significantly at the 4th hour (Me 94.0 (IQR 75.5–148.2) ng/mL, $$p \leq 0.002$$) and reached levels close to those in patients with CI-AKI. Unlike the new biomarker, however, serum creatinine did not change significantly in this group. The diagnostic power of NGAL is extremely good—AUC 0.847 ($95\%$ CI: 0.677–1.000; $$p \leq 0.001$$) in CI-AKI and AUC 0.731 ($95\%$ CI: 0.539–0.924; $$p \leq 0.024$$) in subclinical CI-AKI. NGAL may be a reliable biomarker for the early diagnosis of clinical and subclinical forms of renal injury after contrast angiographic studies. ## 1. Introduction With the increase in cardiovascular diseases worldwide, the relevance of contrast radiographic examinations as coronary or periphery angiographies is constantly growing as they provide valuable diagnostic and therapeutic management opportunities. One of the complications after the administration of a contrast agent is the early deterioration of kidney function. The term “contrast-induced nephropathy (CIN)” was introduced in 1999 by the European Society of Urogenital Radiology (ESUR) and was defined as the elevation of serum creatinine (SCr), with more than 0.5 mg/dL (44.2 µmol/L) or over $25\%$ observed up to three days after the intravascular administration of contrast media in the absence of another etiologic cause [1]. Later, KDIGO introduced the AKIN criteria and the term “contrast-induced acute kidney injury (CI-AKI)”, emphasizing that it should not be differentiated from other forms of acute kidney injury (AKI). There was accepted a definition of CI-AKI as increased serum creatinine > 0.3 mg/dL (26.5 µmol/L) or its relative elevation by 1.5–1.9 times (≥$50\%$) as compared to its baseline values [2]. Despite the tendency to unify these definitions in the present literature [3,4], the term “contrast-induced nephropathy” or the corresponding reference limit of SCr still prevails in previously published studies regarding angiographic studies in cardiology. The frequency of CI-AKI after coronary intervention varies between $11.3\%$ and $14.5\%$ [5], but it can increase in special situations (such as acute myocardial infarction (MI) and chronic kidney disease (CKD)) to $36.9\%$ [6]. A relatively high frequency of CI-AKI is also reported after transcatheter aortic valve implantation (TAVI)—$22\%$—which may be important for the long-term prognosis of the intervention [7,8]. In patients with peripheral vascular disease, the frequency of CI-AKI is $11\%$, but it can reach $40.84\%$ in cases with acute ischemia of the lower extremities due to severe comorbidity [9]. Although CI-AKI can sometimes be a transient condition, with the normalization of renal function within 7–10 days, in a number of patients, its occurrence can have unpredictable and serious consequences. According to data from the American National Cardiovascular Data Registry (NCDR), in patients with AKI after percutaneous coronary intervention (PCI), the in-hospital rates of MI, bleeding, and death were $3.8\%$, $6.4\%$, and $9.6\%$, respectively, compared with $2.1\%$, $1.4\%$, and $0.5\%$, respectively, in patients with no AKI reported. The rates of these events were the highest in the patients with AKI demanding dialysis [6]. The long-term consequences are also not to be underestimated, leading to an increased incidence of major adverse cardiac and cerebrovascular events among patients with CI-AKI within eight years of follow-up ($54\%$ vs. $15\%$ vs. without CI-AKI) [10]. Additionally, a number of studies indicate that it may also have a long-term effect on kidney function [10,11]. According to the Alberta Registry [11], the proportion of patients with a sustained loss of kidney function three months after coronary angiography was $5.9\%$ of patients without AKI, $28.2\%$ of patients with mild AKI, and $59.1\%$ among patients with moderate or severe AKI. According to other authors, the progressive deterioration of renal function within five years may be accelerated and more severe in patients with CI-AKI [10]. Assuming these consequences and the lack of specific therapy for CI-AKI, the main efforts of clinicians are focused on various preventive measures and timely diagnostic strategies. The elevation of the standard biomarker of kidney function serum creatinine, influenced by various external and internal factors, can be registered only when a new steady-state condition is reached, which can take different lengths of time after the impact of the damaging factor. Additionally, SCr lacks the kinetic properties necessary for real-time measurements of kidney dysfunction, the sensitivity to diagnose tubular injury before excretory failure develops, and the specificity to distinguish tubular injury from other causes for the elevated SCr level [12]. These considerations turn it into a delayed and insensitive marker of renal dysfunction. On the other hand, in the last 10 years, the existence of a number of molecules such as KIM-1 (Kidney Injury Molecule-1), NGAL (Neutrophil Gelatinase-Associated Lipocalin), L-FABP (Liver Fatty Acid Binding Protein), and others, of which production increases rapidly after acute ischemic/toxic damage to the renal tubules, has been demonstrated [13,14,15,16,17]. Accumulating evidence for the application of new biomarkers and their ability to identify multiple additional processes in renal structures has led to the introduction of a new conceptual framework for AKI and their classification as “damage biomarkers” [18,19]. In comparison to standard “functional biomarker” serum creatinine, their main advantage is the distinction of subclinical forms of kidney injury and the earlier diagnosis of acute kidney damage. In the field of interventional cardiology, where patients are usually discharged the day after contrast angiography, the pointed limitations of SCr may lead to the delayed or miss-diagnosis of CI-AKI. On the other hand, a number of clinical studies focused on NGAL [17,20] and KIM-1 [20,21], as well as some newer molecules such as Midkine [22] and micro-RNAs [23], have demonstrated early elevation within four to six hours after contrast administration. Elevation of cystatin C >$10\%$ at the 24th hour after exposure can predict an increase in serum creatinine >0.3 mg/dL [24] and reliably distinguish patients with CI-AKI [25]. L-FABP [26] and IL-18 [27,28] also were reported to increase before the 12th hour after angiography, but some authors have mentioned controversial results of limited specificity [20,29,30]. NGAL, as one of the more reliable early biomarkers, has been widely studied not only in terms of contrast kidney damage but also in other conditions such as critically ill patients in intensive care units or AKI after aortocoronary bypass surgery, with demonstrated good diagnostic power in individual studies [31,32,33] and some meta-analyses [34,35,36]. An important role in congenital kidney disease is demonstrated by the meta-analysis focused on urinary biomarkers in congenital hydronephrosis secondary to pelvic–ureteric junction obstruction. There are reported significantly higher levels of NGAL in this specific population [37]. The valuable role of new biomarker” is complemented by another advantage—the diagnosis of subclinical forms of kidney damage. Substitution of SCr in the definition of “CIN” with the same change in cystatin C (>$25\%$ increase from baseline levels) leads to the diagnosis of twice as many cases with contrast-induced renal damage—$16.52\%$ versus $37.19\%$ [38]. Additionally, some authors reported that the “NGAL (+)/serum creatinine (−)” result in critically ill patients is an independent predictor of adverse outcomes regardless of the presence or absence of functional impairment [39]. Focusing on invasive cardiology, there is still no consistent and clear definition of subclinical CI-AKI, and no reliable single structural biomarker has been identified yet. Additionally, there is no available specific description of the dynamic changes after contrast administration. Active comparison of biomarkers between patients developing CI-AKI and subclinical forms of kidney damage is not yet properly addressed. In that context, our aim is to investigate the diagnostic power of an established structural biomarker—NGAL for the early diagnosis of CI-AKI and the subclinical form of CI-AKI—among patients undergoing a scheduled coronary angiography. ## 2.1. Study Populations The investigation of the diagnostic significance of NGAL was conducted in a sample including a total of 45 patients. Inclusion criteria were the presence of a high cardiovascular risk profile (defined by arterial hypertension, diabetes mellitus type 2 (DM), metabolic syndrome, and/or dyslipidemia), coronary artery disease (defined by a history of myocardial infarction, previous percutaneous coronary intervention, or aortocoronary bypass (ACB) revascularization), and preserved kidney function (GFR ≥ 60 mL/min/1.73 m2, calculated according to the MDRD (Modification of Diet in Renal Disease) formula). In the study, we selected only patients with stable angina and stable clinical conditions undergoing elective coronary angiography with/without percutaneous angioplasty. The main exclusion criteria were different forms of acute coronary syndrome (acute myocardial infarction with and without ST–elevation (STEMI, NSTEMI)), hemodynamic instability, or cardiogenic shock; the need for emergency cardiac or other surgical intervention; advanced and decompensated chronic congestive heart failure (NYHA functional class IV); established left ventricular (LV) ejection fraction (EF) ≤$35\%$ by echocardiographic measurement; history of AKI in the last month; moderate CKD (30–60 mL/min/1.73 m2) and end-stage CKD with renal-replacement therapy; the presence of liver dysfunction; neoplasm; clinical and laboratory data on acute inflammatory disease. All patients underwent an invasive angiographic examination using the same low-osmolar contrast agent—Iomeprol. For the prevention of contrast-induced nephropathy, active hydration with normal saline solution was carried out in a dose according to body weight and current clinical condition, as assessed by the attending physician. All participants provided written informed consent to participate before coronary angiography and blood sampling. The study protocol was reviewed and approved by the appropriate institutional review board (National Heart Hospital Ethics Committee; Protocol No. $\frac{11}{04.12.2013}$). ## 2.2. Laboratory Measures According to the study design, blood samples for serum creatinine and plasma NGAL were obtained from all selected patients as follows: on the day before the angiographic examination (designated as baseline), at the 4th hour, and at the 24th hour after the patient’s return from the catheterization laboratory (defined as samples at the corresponding hour after the contrast administration). The laboratory measurement of serum creatinine was made according to the Jaffe method, and NGAL was determined with a turbidimetric method and a reagent kit NGAL TestTM from BioPorto Diagnostics A/S (Denmark, Copenhagen), applied to an Olympus 400 biochemical analyzer. ## 2.3. Study Endpoints and Definitions The main objective of the study is the development of contrast-induced acute kidney injury after angiographic examination, defined as an absolute (≥44 μmol/L) or a relative (≥$25\%$) increase of serum creatinine from baseline or a $25\%$ decrease in glomerular filtration rate (calculated by the MDRD formula) up to 48 h after contrast administration. The patients who met these criteria were classified as the group with CI-AKI. The subclinical form of CI-AKI was accepted in patients without specific dynamics of serum creatinine but a documented increase of plasma NGAL ≥$25\%$ from baseline values. The rest of the patients with no CI-AKI or a subclinical form of CI-AKI served as the control group. ## 2.4. Statistical Methods (Statistical Analysis) We used SPSS (Statistical Package for the Social Sciences) version 16.0. for data processing in the study. The continuous variables are presented as average values ±standard deviation (SD) or the median (Me) and interquartile range (IQR), where this is necessary. For the estimation of dependencies between the descriptive categorical variables, we applied the Pearson chi-square test. When more than $20\%$ of the cells in the table on conjugation had expected frequencies lower than 5 and/or if there was an expected frequency less than 1 in any cell, we applied Fisher‘s exact test. The one-sample Kolmogorov–Smirnov test and the Shapiro–Wilk test were used for the estimation of the form of frequency distribution relative to the form of normal distribution. For the comparison of rank data between more than two independent groups, when the shape of the frequency distribution differs from normal, the nonparametric Kruskal–Wallis test was applied. The comparison of repeated measurements on the continuous variables of serum creatinine, GFR, and NGAL was performed using the nonparametric Wilcoxon signed ranks test. To investigate the equality between the average values of more than two groups, the test for one-factor dispersion analysis (analysis of variance—ANOVA) was applied. Immediately after it, in order to establish significance and conduct multiple comparisons, Tukey post hoc tests were applied. When verifying a form with a normal distribution of the studied variable in the individual groups, with a view to conducting a comparative pairwise analysis, t-tests for two independent groups (independent-samples t-test) or t-tests for two dependent groups (paired-samples t-test) were applied. The presence of a frequency distribution of the studied indicator with a shape different from the normal one necessitated the application of the nonparametric Mann–Whitney test when comparing two independent groups. The diagnostic ability of the studied parameters (creatinine, GFR, and NGAL) was assessed by applying ROC analysis (receiver operating characteristic) and calculating the area under the curve (AUC). A value of AUC of 0.5 matches the lottery, whereas the value of 1.0 is relevant to a perfect biomarker. The used critical level of significance is α = 0.05, and the corresponding zero hypothesis is true when the p-value is smaller than α. ## 3. Results Among the 45 patients in the study, CI-AKI was diagnosed in 12 ($26.7\%$) patients (CI-AKI group). The subclinical CI-AKI group included 15 ($33.3\%$) patients, and the control group was formed by 18 ($40\%$) patients. The risk profile of the total sample was defined by arterial hypertension ($$n = 45$$/$100\%$), dyslipidemia ($$n = 44$$/$97.8\%$), diabetes mellitus type 2 ($$n = 41$$/$91.1\%$), overweight (BMI 25–29.9 kg/m2 at $$n = 17$$/$37.8\%$), and obesity (BMI ≥ 30 kg/m2 in $$n = 21$$/$46.7\%$). The distribution between the individual groups ais presented in Table 1, with no significant differences reported between them. The main indication for the angiographic examination in the studied cohort of patients was stable angina pectoris ($$n = 39$$/$86.6\%$), and some of them have a history of myocardial infarction ($$n = 17$$/$37.8\%$). Previous percutaneous coronary intervention (PCI) ($$n = 15$$/$33.3\%$) or operative coronary revascularization ($$n = 7$$/$15.6\%$) and heart failure II–III by NYHA class ($$n = 13$$/$28.9\%$) also contributed to the cardiac profile of the sample. Diagnostic angiography was performed in $48.89\%$ of all selected patients, and in $51.11\%$, it was necessary to switch to one-stage PCI. As evident from Table 1, in patients with CI-AKI, there was a trend for the predominance of multivessel coronary disease and a significantly higher frequency of previous aortocoronary bypass revascularization. The latter had an essential role in determining the need for the application of a larger amount of contrast media for the angiographic visualization of anatomy among such patients. The evaluation of renal function was carried out by the simultaneous measurement of serum creatinine and plasma NGAL in a series of blood samples obtained at baseline before and at the 4th and 24th hour after the coronary angiography. For the diagnosis of CI-AKI, creatinine was monitored until the 48th hour after the contrast administration. Detailed information on the values of these indicators, presented as the median and interquartile range (IQR) in the different patient groups, is shown in Table 2. In the control group of patients, serum creatinine and corresponding GFR remained unchanged after the contrast administration compared to baseline levels (Table 2). A similar trend was registered with measurement of the new biomarker NGAL—values reported at baseline (Me 80.3 (66.9–86.2) ng/mL), at the 4th hour (Me 76.6 (65.3–87.6) ng/mL) and at the 24th hour (Me 78.0 (66.4–88.5) ng/mL) were extremely close ($$p \leq 0.943$$; $$p \leq 0.653$$ respectively). In patients with the development of CI-AKI, dynamic changes were found in all the investigated biomarkers (Figure 1). Serum creatinine increased from baseline levels as the median (93.5 (88.3–105.9) µmol/L) in this group 24 h after the contrast angiography (124.5 (108.5–136.3) µmol/L; $$p \leq 0.002$$) and remained high until the 48th hour (106.0 (91.8–119.5) µmol/L; $$p \leq 0.007$$). The change in GFR follows the same trend, but in the opposite direction—compared to the initial levels (69.0 (66.0–78.2) mL/min), a significant decrease was reported at the 24th hour (50.5 (47.7–54.5) mL/min, $$p \leq 0.002$$). The laboratory measurements of plasma NGAL in this group of patients showed that the median (IQR) initial value was 97.6 (69.4–127.0) ng/mL, but it already increased very quickly at the 4th hour after the contrast administration to 109.3 (92.1–148.7) ng/mL ($$p \leq 0.006$$). A significant increase in the biomarker continued at the 24th hour, where the reported levels were even higher and reached an average of 131.0 (81.1–240.8) ng/mL ($$p \leq 0.008$$) (Figure 1C). In the group with subclinical CI-AKI, the standard marker of renal function, serum creatinine, maintained its levels and was almost unchanged at the 24th hour (Me 79.0 (71–96) μmol/L, $$p \leq 0.292$$) and the 48th hour (Me 76.5 (69.5–89.5) μmol/L, $$p \leq 0.889$$) compared to the baseline values (Figure 2A). GFR calculated for the corresponding time intervals also did not show a significant change (Table 2). However, the changes in plasma NGAL among this group of patients are interesting. Compared to the baseline values presented as the median (IQR 80.0 (44.4–94.1) ng/mL), already on the 4th hour after the end of the angiographic examination, a strong increase in the biomarker was registered, with a level of 94.0 (75.5–148.2) ng/mL ($$p \leq 0.002$$). At the 24th hour after the examination, increased values compared to the baseline (100.7 (55–132.2) ng/mL, $$p \leq 0.001$$) remained significantly higher (Figure 2C). Moreover, we conducted an additional analysis focused on the comparison of each indicator in the same time interval in the different groups. Standard markers of renal function (serum creatinine and GFR) in patients who developed CI-AKI were significantly higher compared to the control group of patients (Figure 3A). In the same comparison, the baseline levels of NGAL did not differ significantly between the two groups, but with the onset of acute kidney injury and the increase of NGAL, the levels reached at the 4th and 24th hours after the contrast administration were significantly higher compared to the controls ($p \leq 0.005$) (Figure 3B). The comparative analysis of the group with subclinical CI-AKI was carried out against the other two groups—the control group and the group with CI-AKI. The comparison with the control group demonstrated no significant difference between all reported values of serum creatinine and GFR (Figure 3A). The increase in NGAL, however, which was observed in the group with subclinical CI-AKI, was significantly higher at the 4th hour compared to controls ($$p \leq 0.024$$) (Figure 3B). The comparison of the groups with CI-AKI and subclinical CI-AKI shows that while all the values of serum creatinine and GFR were significantly different between the two groups ($p \leq 0.05$), the initial levels of NGAL and the subsequently monitored dynamic levels were extremely close, and no statistically significant difference was found between them. The simultaneous general presentation of these trends for the three groups can be found in Figure 3. ROC analysis provides a clear insight into the diagnostic value of the new biomarker. In patients with CI-AKI, the plasma NGAL at the 4th hour after the contrast administration demonstrated AUC 0.847 ($95\%$ CI: 0.677–1.000; $$p \leq 0.001$$), sensitivity $83.33\%$, and specificity $83.33\%$ at a cut-off value of 90.20 ng/mL. The diagnostic power remained significant, considering the changes at the 24th hour after the angiographic examination—AUC 0.806 ($95\%$ CI: 0.617–0.994; $$p \leq 0.005$$) with a sensitivity of $75\%$, a specificity of $77.78\%$, and a cut-off value of NGAL 88.30 ng/mL (Figure 4). In patients with a subclinical form of CI-AKI, the good diagnostic prediction of NGAL was preserved at the 4th hour after the angiographic examination—AUC 0.731 ($95\%$ CI: 0.539–0.924; $$p \leq 0.024$$), with sensitivity $73.33\%$ and specificity $72.22\%$ at a cut-off value of 85.65 ng/mL (Figure 5). ## 4. Discussion In our study, the follow-up of renal function with the standard biomarker serum creatinine and the new biomarker NGAL showed that in the patients of the CI-AKI group, all indicators changed significantly after the administration of a contrast media. Creatinine peak values were reached at the 24th hour, and the reported glomerular filtration rate in this hour range was correspondingly the lowest. Plasma NGAL demonstrated a significant increase at 4th and 24th hour from baseline, but the statistical analysis indicated that the majority of the biomarker increase occurred in the first few hours after contrast administration. Contrast-induced acute kidney injury (or contrast-induced nephropathy, as used in the past) continues to be a serious problem after diagnostic and therapeutic contrast-enhanced angiography, ranking third as a cause of hospital-acquired acute renal failure [40]. As a result of the implementation of various preventive strategies in the last decade, a tendency to reduce the incidence of CI-AKI has been reported [41,42]. On the other hand, a number of authors have emphasized that intra-arterial (ia) administration of contrast media through a catheter during angiography, with or without percutaneous coronary intervention, is associated with a higher incidence of post-contrast AKI than intravenous (iv) administration [43,44]. While some literature data have reported rates of CI-AKI after coronary interventions in the general population between $11.3\%$ and $14.5\%$ [5], other authors emphasize that the combination of more risk factors leads to a significant increase in its frequency—from $26.6\%$ up to $36.9\%$ [6]. In our study, patients with a high risk profile (arterial hypertension, dyslipidemia, DM type 2 in >$90\%$ of patients) and a high ischemic burden with clinically manifested or proven coronary heart disease (stable angina pectoris > $85\%$, history of MI, previous PCI or ACB revascularization > $48\%$ of all patients) were enrolled. The frequency of CI-AKI was found to be $26.7\%$, which is close to the $24.1\%$ reported in the literature in a cohort of patients with type 2 diabetes mellitus [45]. NGAL is a small protein that is normally expressed in healthy individuals in very small amounts in various types of cells in the body [46]. Immediately after the occurrence of acute kidney injury, the production of NGAL is increased in the distal parts of the nephron, the thick ascending part of the Henley’s loop, the distal tubules, and the collecting ducts [47,48], which results in increased urinary and plasma NGAL levels due to increased secretion from the apical and basolateral surface of the nephron epithelium. Experimental studies have shown that plasma NGAL increases as a result of the “backflow” of increased synthesis molecules to the systemic circulation [49]. Some authors emphasize that AKI leads to a dramatic increase in RNA expression for NGAL in distant organs [49] such as the liver and lungs. The overexpressed protein, released into the circulation, forms a distant systemic pool that is the source of the plasma levels of NGAL. In the hope that “renal troponin” may have been discovered, a number of authors have focused on it in various clinical situations—contrast-induced nephropathy, critically ill patients in intensive care units, patients after cardiac surgery, and others. Special attention is paid to cohorts of patients with chronic kidney disease, where some authors have reported specific biomarker kinetics and higher baseline NGAL concentrations [50,51,52]. In this direction, several studies in the literature have reported the clinical use of NGAL among patients with coronary heart disease and baseline-preserved renal function who underwent elective angiography [17,31,32,33,53,54]. As Bachorzewska-Gajewska [32] demonstrated in one of the first studies, which a number of other authors [31,33,55] subsequently confirmed, NGAL significantly increases in the first hours after contrast administration. In the study by Shaker et al. [ 33], it was demonstrated that compared to the baseline values of plasma NGAL of 52.5 ± 13.8 ng/mL, at the 4th hour after angiographic examination, an increase to 88.5 ± 16.4 ng/mL was reported ($p \leq 0.001$); at the 24th hour, the values were 63.6 ± 10.5 ng/mL ($p \leq 0.001$). The regression analysis proved a positive significant correlation between the levels of the new biomarker and serum creatinine in each of the studied time intervals. According to Padhy et al. [ 31], serum NGAL is a biomarker with a “narrow diagnostic window” in which peak values can be reached within 4 h after contrast angiography examination and remain significantly higher for up to 24 h but, by 48 h, can be completely normalized. Liao et al. [ 17] reported in their study of 240 patients that the diagnostic power of serum NGAL was extremely good—at six hours after contrast examination, the area under the curve (AUC) was 0.81 ($$p \leq 0.03$$), with a sensitivity of $97.64\%$ and a specificity of $67.78\%$ at a cut-off point of 96.35 ng/mL; at the 24th hour, the AUC was 0.89 ($p \leq 0.01$), and sensitivity $96.63\%$ and specificity $68.72\%$ were at the established reference level of 97.57 ng/mL. In summary, NGAL is emerging as an early biomarker for contrast-induced AKI, with a significant increase within 24 h after the procedure [17,27,32,33,54], significantly rising before the rising of the standard serum creatinine, which has significantly different values that are registered only on the second day [32,54]. The results of our study fully confirm dynamic changes in NGAL and prove its good diagnostic value as an early marker for the onset of CI-AKI, considering the data from the ROC analysis (at 4th hour—AUC 0.847, cut-off point of plasma NGAL 90.20 ng/mL, sensitivity $83.33\%$, specificity of $83.33\%$). These results fully correlate with those cited in the literature (AUC 0.81–1.00 at 4–6 h [18,42] and AUC 0.89 at 24th hour [42]) and presented in several meta-analyses focused on CI-AKI [34,35,36]. It should be noted, however, that the increase in serum creatinine within the first 24 h after angiography is somewhat different from the trends described in some studies [32,50,55]. This may be due to design differences across studies. In many study designs, it is assumed that the laboratory samples were obtained at different times, with creatinine being reported only at baseline and at the end of the observed period (within 48–72 h after the procedure) [32,50,55]. In contrast, we examined both biomarkers from blood samples obtained simultaneously at the same time interval, allowing a full parallel comparison of their dynamic changes. Furthermore, our selected cohort of patients with predominant diabetes mellitus type 2 ($91.1\%$) was completely different from most studies [17,27,31,32,54,55,56,57], where the incidence of DM was between $14\%$ and $34\%$. From this perspective, the only study reporting the role of NGAL exclusively in diabetic patients is presented by Ashalatha et al. [ 45], and its results showed that serum creatinine and NGAL increased significantly as early as the 4th hour, and a difference (when compared to the “non-CIN” group) was observed at the 24th hour, but only for creatinine. The authors concluded that patients with diabetes mellitus and preserved renal function are more likely to suffer from subclinical renal impairment, considering them much more susceptible to contrast damage, leading to the early elevation of biomarkers. They assumed that the dynamic changes of indicators may be different in diabetic patients and in individuals without DM [45]. This direction of scientific reasoning once again emphasizes the importance of a comprehensive time-wise study of kidney dysfunction in a completely different aspect—the existence, diagnosis, and prognosis of subclinical forms of damage. As already mentioned, the modern understanding of AKI emphasizes a diagnostic approach based on the simultaneous measurement of functional and structural biomarkers [18,19]. Based on this concept, patients undergoing invasive procedures with contrast administration should also be screened for the development of renal injury or impaired function by the evaluation of both types of biomarkers for AKI [58]. Although some authors propose the introduction of terms such as “CI-AKI with structural damage” or “CI-AKI with kidney dysfunction”, this distinction remains only theoretically grounded [58]. In the literature, there is no clear definition of subclinical CI-AKI or a clearly defined frequency of this group of events. Initially, some authors [45] only describe that among the group “without CIN”, there are individuals with an increase in NGAL similar to that reported in CIN. On the other hand, other studies [51,59] suggest a limit, such as an increase of >$25\%$ of the biomarker, or assume that for the diagnosis of subclinical AKI, the biomarker must increase by two times compared to its baseline levels [52,60]. In the studies of Breglia et al. [ 61] and Rozenfield et al. [ 62], an absolute value is introduced as a reference limit, above which the biomarker is reported as positive. While some authors have reported an incidence of subclinical AKI of $11.1\%$ [52], others have reported a “biomarker (+)/creatinine (−)” cohort with an incidence of $32.6\%$ [59] to $44\%$ [62]. A meta-analysis [39] of 10 prospective studies among critically ill patients found that based on the “NGAL (+)/serum creatinine (−)” result, up to $40\%$ more cases of AKI could be identified, which would have been missed if the standard creatinine-based AKI criteria are used. Our results are complementary to the sources reported in the literature regarding the frequency of this form of renal damage. In our study, we accepted subclinical CI-AKI to be defined as an increase in plasma NGAL of >$25\%$, which fully corresponds to the change in the biomarker registered in the group with the clinically manifested form of CI-AKI. In relation to the entire studied sample, this result was found in $33.3\%$ of patients, which may exceed some of the cited sources [52] but covers the wide range reported by others [62]. As already noted, the selection of a cohort of patients with diabetes mellitus may have altered the sample profile, delivering a higher risk of subclinical renal impairment [45]. Monitoring of NGAL in such cases may help to detect AKI before the change in serum creatinine. In the study by Alharazy et al. [ 51], among 100 patients undergoing elective coronary angiography ± PCI, the diagnostic role of serum NGAL and cystatin C in the detection of “CIN” was investigated. The authors reported that this event (defined as a >$25\%$ rise in serum creatinine by 48 h) occurred in $11\%$ of patients ($$n = 11$$/100), and both biomarkers had good diagnostic performance at 24 h after the contrast angiography. It is interesting to note that a limit of $25\%$ increase in serum NGAL was registered in $$n = 7$$/11 patients with “CIN” and in $$n = 12$$/87 patients in the “non-CIN” group. An elevation of cystatin C of >$25\%$ was found in only four of the “CIN” patients and among one of the remaining cohorts. The authors hypothesized that, in addition to diagnosing contrast-induced renal injury, the new biomarkers are likely to capture cases with subclinical AKI. The latter is only described as a finding, but no further analysis has been conducted in this direction. On the other hand, an assumption is made that such patients are likely to be missed in the standard diagnostics of CIN. We have managed to advance further in our study by the inclusion of a group with subclinical CI-AKI, monitoring dynamic changes in biomarkers at all time intervals in parallel with the changes observed in a group with clinically manifested CI-AKI. It can be seen from the obtained results in the patients with subclinical CI-AKI that serum creatinine (respectively, GFR) had no significant changes compared to its initial levels through the entire observation period (48 h after the end of angiography). In terms of dynamics, however, plasma NGAL increased significantly as early as the 4th hour after the contrast administration and maintained its high levels until the 24th hour. The diagnostic power of the new biomarker for early detection of renal damage is relatively good, as shown by the ROC analysis, with AUC 0.731 ($$p \leq 0.024$$), sensitivity $73.33\%$, and specificity $72.22\%$ at a cut-off value of 85.65 ng/mL. The comparative analysis against the other two groups clearly shows the positioning of this cohort of patients in the whole sample. Compared to the control group, patients with subclinical CI-AKI were significantly distinguished only by the values of plasma NGAL, which increased early after the end of contrast angiography (at the 4th hour). Compared to the group with CI-AKI, a significant difference was found only in the dynamics of serum creatinine (respectively, GFR). Based on these data, we can conclude that the group with subclinical CI-AKI occupies an intermediate place in the clinical continuum between patients without renal function impairment and patients with clinically manifested contrast-induced nephropathy. In other words, if the classic creatinine-based definition of AKI is solely applied in clinical practice, the only patients who will differ from the entire study cohort are those with CI-AKI. Patients in the control group and those with an “NGAL+” result, having close values (p = NS) of serum creatinine levels that do not change over time after the contrast study, will be misidentified as individuals without impaired renal function. The first study designed to specifically investigate subclinical AKI was reported by the Akrawinthawong group et al. [ 52] and was conducted among 63 patients, with GFR < 90 mL/min/1.73 m2, undergoing routine angiographic examination. CI-AKI was defined according to the AKIN criteria (>0.3 mg/dL or >$50\%$ increase in serum creatinine up to 48 h), while “subclinical AKI” was defined as an increase in serum NGAL ≥ 2 times compared to baseline levels, without a concurrent change in creatinine. According to the results, CI-AKI was diagnosed among $$n = 8$$/63 ($12.7\%$) of the examined patients, while in seven others ($$n = 7$$/63; $11.1\%$), subclinical AKI was identified. A general examination of all patients with AKI (defined by AKIN and subclinical form) showed that $23.8\%$ had certain renal dysfunction after the administration of contrast media, and in all of them, the peak values of NGAL were significantly higher than at baseline levels. The study confirms the fact that the application of one of the new structural biomarkers captures almost twice as many cases of AKI. However, the authors emphasize the need for more research in this direction. Our study differs from the aforementioned one by Akrawinthawong et al. [ 52] in terms of the selected patient cohort, including patients with different levels of renal function (GFR is in a wide range, from 15 to 90 mL/min), which is also reflected in the different baseline levels of NGAL cited by the authors (360.29 ± 227.94 ng/mL at GFR 15–30 mL/min versus 114.02 ± 57.42 ng/mL at GFR 60–90 mL/min). Considering that the existence of chronic kidney disease affects the level of NGAL [50,63] as well as our preliminary data in this direction [64], we turned our attention to a more strictly selected sample, according to renal function, examining separately the characteristics of NGAL among patients with GFR values above 60 mL/min. Breglia et al. [ 61] also reported the occurrence of CI-AKI and subclinical CI-AKI after intra-arterial administration of contrast material in patients with GFR > 60 mL/min, but they applied a combination of structural biomarkers urinary NGAL, IGFBP7 (insulin-like growth factor-binding protein 7), and TIMP-2 (tissue inhibitor of metalloproteinase-2), measured 4–8 h post-procedure. According to the study design, CI-AKI was defined according to the KDIGO criteria (creatinine >0.3 mg/dL or >$50\%$ compared to baseline levels), and subclinical CI-AKI is defined as a positive result for the relevant biomarker (TIMP-2 и IGFBP7 > 0.3 (ng/mL)$\frac{2}{1000}$; NGAL > 90 μg/L). Out of a total of 100 selected patients with performed neurological invasive procedures, none of them was found to have CI-AKI according to the KDIGO criteria. At the same time, the studied biomarkers were positive in 13 patients (the NGAL (+) result was found in 5 patients, and TIMP-2 and IGFBP7 (+) results in 10 patients; in 3 patients, all biomarkers were positive). The critical message from this study is that while in a low-risk patient population (assessed as such according to the Mehran score), no cases of CI-AKI defined by creatinine were observed; subclinical CI-AKI, defined as the elevation of biomarkers of the lesion, was found in >$10\%$ of patients with contrast diagnostic/therapeutic procedures. The importance of subclinical forms of AKI is noted by a number of authors. Haase‘s meta-analysis demonstrated that the “NGAL (+)/serum creatinine (−)” patient cohort had an increased risk of prolonged ICU stay, initiation of renal-replacement therapy, and increased mortality [39]. According to other authors, patients with positive new biomarkers (urinary KIM-1 and urinary NGAL) compared to patients with negative results have an increased risk of renal-replacement therapy and in-hospital mortality [65,66]. A study of STEMI patients undergoing primary angioplasty reported that $44\%$ of patients had an “NGAL(+)/creatinine(−)” result (defined as NGAL values ≥100 ng/mL), and $14\%$ of patients had an “NGAL (+)/creatinine (+)” result. Compared to the “NGAL (−)/creatinine (−)” subgroup, NGAL positivity alone resulted in a significantly increased risk of adverse in-hospital events ($46\%$ vs. $64\%$, respectively; OR 2.1; $95\%$ CI 1.1–4.5; $$p \leq 0.05$$) [62] Other authors [67] demonstrated that even the baseline measurement of higher NGAL values could be associated with an increased frequency of major adverse events and overall mortality among patients from the third quartile (143.9 to 567.9 ng/mL) compared to a group from the first quartile (25.4 to 83.7 ng/mL). Recent studies in the field of subclinical CI-AKI demonstrate, in addition to a higher frequency of these events when applying the NGAL-based definition ($18\%$), the persistence of subclinical damage for more than one month among half of the patients [68]. In summary, our presented data contribute to the current scientific literature on the diagnosis of subclinical forms of acute kidney injury after contrast angiography, demonstrating the characteristics of renal biomarkers in a relatively homogeneous cohort of patients in terms of high-risk profiles and baseline renal function. A number of the conducted studies included mixed cohorts of patients and focused primarily on the application of NGAL as a diagnostic marker for a clinically apparent form of CI-AKI. An advantage of our study is the investigation with an equal focus on clinical and subclinical forms of AKI, applying the current ADQI recommendations and two different types of renal biomarkers. ## Limitations of the Study The relatively small sample size should be noted. Nonetheless, both in the field of contrast nephropathy and in a number of other emerging fields related to imaging methods, relatively small cohorts with less than 100 patients have been reported [33,52]. The relatively narrow follow-up interval of the patients, up to 48 h after the contrast examination, did not allow us to establish whether some of the patients with subclinical forms of kidney damage did not demonstrate a delayed increase in serum creatinine. On the other hand, the definition we apply for CI-AKI is in accordance with the one adopted in the literature [1,4]. Considering that NGAL is described in a number of sources as a marker with a “narrow diagnostic window” [31] in the first hours after exposure to the damaging agent, we have fully justified the biomarker follow-up in our study to be as early as possible after the contrast administration procedure. ## 5. Conclusions Contrast-induced acute kidney injury is a potentially serious complication after angiographic studies, with long-term consequences on renal function and overall survival. The lack of specific treatment, the unconvincing success of a number of preventive measures, and the complex involvement of multiple pathophysiological mechanisms determine the continued relevance of this pathology. The acceptance of CI-AKI as a transient benign disorder of renal function and the disadvantages of serum creatinine as a diagnostic marker can only lead to the underestimation of the problem. 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--- title: Cannabinoid Receptor 1 Agonist ACEA and Cannabinoid Receptor 2 Agonist GW833972A Attenuates Cell-Mediated Immunity by Different Biological Mechanisms authors: - Nuchjira Takheaw - Kanyaruck Jindaphun - Supansa Pata - Witida Laopajon - Watchara Kasinrerk journal: Cells year: 2023 pmcid: PMC10047765 doi: 10.3390/cells12060848 license: CC BY 4.0 --- # Cannabinoid Receptor 1 Agonist ACEA and Cannabinoid Receptor 2 Agonist GW833972A Attenuates Cell-Mediated Immunity by Different Biological Mechanisms ## Abstract Cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2) are components in the endocannabinoid system that play significant roles in regulating immune responses. There are many agonists for the cannabinoid receptors; however, their effects on T cell regulation have not been elucidated. In the present study, we determined the effects of the CB1 selective agonist ACEA and the CB2 selective agonist GW833972A on T cell responses. It was found that both agonists impaired anti-CD3 monoclonal antibody induced T cell proliferation. However, ACEA and GW833972A agonists down-regulated the expression of activation markers on CD4+ and CD8+ T cells and co-stimulatory molecules on B cells and monocytes in different manners. Moreover, only GW833972A suppressed the cytotoxic activities of CD8+ T cells without interfering in the cytotoxic activities of CD4+ T cells and NK cells. In addition, the CB2 agonist, but not CB1 agonist, caused the reduction of Th1 cytokine production. Our results demonstrated that the CB1 agonist ACEA and CB2 agonist GW833972A attenuated cell-mediated immunity in different mechanisms. These agonists may be able to be used as therapeutic agents for inducing T cell hypofunction in inflammatory and autoimmune diseases. ## 1. Introduction The endocannabinoid system is composed of endocannabinoids, cannabinoid receptors, and the enzymes responsible for the synthesis and degradation of the endocannabinoids [1,2]. Two primary endocannabinoid receptors, cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2), have been identified. Both receptors constitute a class of cell membrane receptors in the G protein-coupled receptor family [1,2]. Endogenous ligands for the cannabinoid receptors are biologically active fatty acids, including N-arachidonoylethanolamine (anandamide (AEA)) and 2-arachidonoyl glycerol (2AG) [3,4]. Although the endocannabinoid system is broadly expressed in almost all organs throughout the body, the expression levels of cannabinoid receptors are highest in the nervous and immune systems [5]. The endocannabinoid system has been demonstrated to play important roles in various biological and pathological conditions, including the regulation of cognitive and immune responses [6,7,8]. In the immune system, immune cells highly express cannabinoid receptors and also produce endocannabinoids. The effect of endocannabinoids in innate and adaptive immune responses have been demonstrated [8,9,10,11,12]. Upon reacting to their ligands, CB1 and CB2 receptors are activated and cell signaling occurs, resulting in the regulation of T cell functions [10,11,13,14,15]. However, CB1 and CB2 activation by various types of agonists showed different outcomes on the regulation of CD4+ and CD8+ T cell responses. The cannabis plant contains numerous compounds, including cannabinoids [16]. These phyto-cannabinoids were demonstrated to activate the cannabinoid receptors and affect various human biological activity, including the immune system [17,18]. Accordingly, phyto-cannabinoids and cannabis-based treatments have attracted attention as pharmacological agents for the treatment of several disorders. However, various adverse effects of using these substances have been observed [19,20]. This evidence precludes the widespread use of phyto-cannabinoids and cannabis in the clinic. Substantial efforts have thus been focused on developing synthetic cannabinoid receptor selective agonists, which specifically target either CB1 or CB2, for use as therapeutic agents with less adverse effects. Currently, several CB1 and CB2 selective agonists have been developed [21,22,23]. The synthetic cannabinoid receptor agonists are different in chemical structure and function. Their structural characteristics permit them to interact with CB1 or CB2 expressed on the cell surface [24]. Some of these synthetic agonists were demonstrated to modulate the immune function and have therapeutic potential for several disorders [21,25,26]. New cannabinoid selective agonists are currently the focus of academic and commercial attempts. However, the effect of many cannabinoid receptor agonists on T cell regulation have not been elucidated. In this study, therefore, we focused on studying the effects of the CB1 selective agonist ACEA [27] and CB2 selective agonist GW833972A [28] on the regulation of CD4+ and CD8+ T cell responses. The effects of ACEA and GW833972A on T cell responses have not been reported. Hence, the present study provides the poly-pharmacological properties and therapeutic potential of ACEA and GW833972A. ## 2.1. Antibodies Anti-CD3ε mAb (clone OKT3) (Ortho Pharmaceuticals, Raritan, NJ, USA) and anti-CD28 mAb (clone L293) (BD Bioscience, San Jose, CA, USA) were used. PE-conjugated anti-CD25, anti-PD-1, anti-LAMP-1, isotype-matched control mAbs, FITC-conjugated anti-CD4 mAb, APC-conjugated anti-CD8 mAb, and PEcy7-conjugated anti-CD19 mAb were purchased from BD Bioscience. PEcy7-conjugated anti-CD3 mAb, PE-conjugated anti-CD14 mAb, and PE-conjugated anti-IL-2, anti-IFN-γ, and anti-TNF-α mAbs (BioLegend, San Diego, CA, USA) were used. PE-conjugated anti-CD69 mAb and FITC-conjugated anti-CD86, CD80, HLA-DR and HLA-ABC mAbs (ImmunoTools, Friesoythe, Germany) were used. FITC-conjugated isotype-match control mAb were produced in our laboratory. ## 2.2. Reagents Arachidonyl-2′-chloroethylamide hydrate (ACEA; CB1 agonist), Rimonabant hydrochloride (SR141716A; CB1 antagonist/inverse agonist), GW833972A (CB2 agonist), SR144528 (CB2 antagonist/inverse agonist), and dimethyl sulfoxide (DMSO) (Sigma-Aldrich, St. Louis, MO, USA) were used. These cannabinoid receptor agonists and antagonists were dissolved in DMSO at a concentration of 20 mM and stored at −20 °C until used. Saponin (Amresco, Solon, OH, USA) was used. Carboxyfluorescein succinimidyl ester (CFSE), brefeldin A, and monensin were obtained from Sigma-Aldrich. RPMI 1640 medium and fetal bovine serum (FBS) (Gibco, Grand Island, NY, USA) were used. Ficoll-Hypaque solution (IsoPrep)(Robbins Scientific Corporation, Sunnyvale, CA, USA) was used. Finally, 7-AAD solution was purchased from BioLegend. ## 2.3. Cells P815 cell line (gift from Prof. Dr. Seiji Okada, Kumamoto University, Kumamoto, Japan) and K562 cell line (acquired from ATCC) were maintained in RPMI-1640 medium supplemented with $10\%$ heat-inactivated fetal bovine serum (FBS), 40 μg/mL gentamycin, and 2.5 μg/mL amphotericin B ($10\%$FBS-RPMI 1640) at 37 °C in a humidified $5\%$ CO2 atmosphere. Peripheral blood mononuclear cells (PBMCs) were isolated from heparinized whole blood or buffy coat of healthy individuals obtained from The Thai Red Cross Society, Chiang Mai, Thailand, using standard Ficoll-Hypaque density gradient centrifugation. ## 2.4. Cell Proliferation Assay CFSE dilution technique was used for cell proliferation assay. PBMCs at 1 × 107 cells/mL were labeled with CFSE at 1 μM final concentration. The labeled cells were washed and re-suspended in $10\%$ FBS-RPMI 1640. For investigation the effect of CB1 and CB2 agonists on T cell proliferation, CFSE labeled PBMCs at 1 × 105 cells (at 100 µL final volume) were stimulated with immobilized anti-CD3 mAb clone OKT3 (12.5 ng/mL) or kept unstimulated in the presence of various concentrations of ACEA (CB1 agonist), GW833972A (CB2 agonist), or relevant concentrations of DMSO at 37 °C in $5\%$ CO2 incubator for 5 days. Cells were, then, harvested for measuring the CFSE reduction by a flow cytometer (BD AccuriTM C6, BD Biosciences) and analyzed by FlowJo software. For testing the effect of CB1 and CB2 antagonists on CB1 and CB2 agonists, CFSE labeled PBMCs at 1 × 105 cells (at 100 µL final volume) were cultured with immobilized anti-CD3 mAb clone OKT3 (12.5 ng/mL) or medium in the presence of SR141716A (CB1 antagonist) or SR144528 (CB2 antagonist) for 15 min at room temperature. Then, ACEA, GW833972A, or relevant concentrations of DMSO were added and cultured for 5 days in $5\%$ CO2 incubator. T cell proliferation was determined by a flow cytometer (BD AccuriTM C6). ## 2.5. Assay for T Cell Activation Markers PBMCs at 1 × 106 cells (at 1000 µL final volume) were cultured with or without immobilized anti-CD3 mAb in the presence of CB1 or CB2 agonists or relevant concentrations of DMSO. After incubation, the cells were stained for CD69 and CD25 markers at day 1 and PD-1 marker at day 3. Briefly, the Fc receptors on the harvested cells were blocked with PBS containing $10\%$ human serum (blood group AB). Cells were stained with PE-anti-CD69, -CD25, -PD-1 mAb, or isotype-matched control mAb. Then, cells were fixed and permeabilized with $4\%$ paraformaldehyde and $0.1\%$ saponin in PBS containing $5\%$ FBS-$0.1\%$ NaN3. The FITC-anti-CD4 mAb, PEcy7-anti-CD3 mAb, and APC-anti-CD8 mAb were added into the cells and incubated for 30 min. The stained cells were determined by a flow cytometer (BD AccuriTM C6). Gating strategy was shown in Supplementary Figure S1. ## 2.6. Assay for Co-Stimulatory Molecules on Monocytes and B Cells PBMCs at 1 × 106 cells (at 1000 µL final volume) were cultured with or without immobilized anti-CD3 mAb in the presence of CB1 agonist, CB2 agonist, or DMSO in a $5\%$ CO2 incubator for 18 h. The Fc receptors on the harvested cells were blocked with PBS containing $10\%$ human serum (blood group AB) and stained with PE-anti-CD14 mAb, PEcy7-anti-CD19 mAb, and each FITC-mAbs (anti-HLA-ABC, -HLA-DR, -CD80, or -CD86 mAb) or FITC-isotype-match control mAb. The expression levels of co-stimulatory molecules on CD14+ monocytes and CD19+ B cells were determined by a flow cytometer (BD AccuriTM C6). Gating strategy was shown in Supplementary Figure S2. ## 2.7. T Cell Cytotoxic Activity Assay The P815 cell line at 1 × 107 cells/mL was labeled with 2 µM final concentration of CFSE. The CFSE labeled P815 (4 × 104 cells) were pre-incubated with anti-CD3 mAb or kept in culture medium at 37 °C for 30 min. The CFSE labeled P815 cells (target cells) were co-cultured with PBMCs (effector cells) at effector cells:target cells (E:T) ratios of 2.5:1, 5:1, 10:1, or without effector cells. The CB1 agonist, CB2 agonist, or DMSO were added. The co-cultured cells (at 100 μL final volume) were incubated in a $5\%$ CO2 incubator for 24 h. Cells were harvested, washed, and suspended in 7-AAD solution. The percentage of dead target cells (CFSE+ 7-AAD+) were determined by a flow cytometer (BD AccuriTM C6). Gating strategy and representative flow cytometric data were exhibited in Supplementary Figure S3. For determination of T cell degranulation, PBMCs were co-cultured with anti-CD3 mAb, pre-incubated P815 cells, or P815 cells at E:T ratios of 10:1. The co-cultured cells (at 100 μL final volume) were incubated with CB1 agonist, CB2 agonist, or DMSO in $10\%$ FBS-RPMI 1640 containing 1 µM monensin and PE-anti-LAMP-1 mAb or PE-isotype-match control mAb. Cells were cultured in a $5\%$ CO2 incubator for 24 h. The cells were harvested, washed, fixed and permeabilized. The intracellular staining was performed by adding FITC-anti-CD4 mAb, PEcy7-anti-CD3 mAb, and APC-anti-CD8 mAb. The percentage of LAMP-1 positive cells in CD8+ T cells and CD4+ T cells was measured by a flow cytometer (BD AccuriTM C6). Gating strategy was shown in Supplementary Figure S4. For analysis of IFN-γ producing CD8+ T cells, the anti-CD3 mAb pre-incubated P815 cells or P815 cells were co-cultured with PBMCs at E:T ratios of 10:1. The cells (at 100 μL final volume) were cultured in the presence of CB1 agonist, CB2 agonist, or DMSO in $10\%$ FBS-RPMI 1640. The cells were kept in a $5\%$ CO2 incubator for 24 h. During incubation at 19 h, protein transport inhibitors (1 μg/mL brefeldin A and 1 μM monensin) were added. After 24 h incubation, cells were intracellularly stained with PEcy7-anti-CD3 mAb, APC-anti-CD8 mAb, and PE-anti-IFN-γ mAb or PE-isotype-match control mAb. The stained cells were measured by a flow cytometer (BD AccuriTM C6). Gating strategy for analysis of IFN-γ positive cells was demonstrated in Supplementary Figure S4. ## 2.8. NK Cell Cytotoxic Activity Assay CFSE labeled K562 cells (target cells) at 5 × 104 cells were co-cultured with PBMCs (effector cells) at E:T ratios of 20:1, 40:1, or without effector cells. The CB1 agonist, CB2 agonist, or relevant concentrations of DMSO were added into the cell culture (at 150 μL final volume) and incubated in a $5\%$ CO2 incubator for 4 h. Cells were harvested, washed, and 7-AAD solution was added. The percentage of dead target cells (CFSE+ 7-AAD+) were measured by a flow cytometer (BD AccuriTM C6). ## 2.9. Intracellular Cytokines Assay PBMCs were cultured at 1 × 106 cells in 300 µL total volume with immobilized anti-CD3 mAb clone OKT3 (25 ng/mL) and soluble anti-CD28 mAb (25 ng/mL) or kept unstimulated. The CB1 agonist, CB2 agonist, or relevant concentration of DMSO was added and incubated in a $5\%$ CO2 incubator for 6 h. During incubation at 1 h, 1 μg/mL brefeldin A and 1 μM monensin were added. Cells were harvested, fixed, permeabilized, and stained with PEcy7-anti-CD3 mAb, and each PE-anti-human cytokine antibody (anti-IFN-γ, -IL-2, or -TNF-α mAb) or PE-isotype-matched control mAb. The intracellular cytokines were determined by a flow cytometer (FACSCelesta). Gating strategy and representative flow cytometric data were exhibited in Supplementary Figure S5. ## 2.10. Statistics All statistical analyses were performed using Prism 9.2.0 (GraphPad Software, San Diego, CA, USA). Data were expressed as mean ± SD. The unpaired t-test or two-way ANOVA were used as indicated in the figure legends. $p \leq 0.05$ was considered significant. ## 3.1. CB1 Agonist ACEA and CB2 Agonist GW833972A Impair T Cell Proliferation We employed the CB1 selective agonist, ACEA [27], and CB2 selective agonist, GW833972A [28], to determine the effect of cannabinoid receptor activation on T cell proliferation. As shown in Figure 1, 20 µM ACEA significantly reduced T cell proliferation compared with the DMSO control, whereas GW833972A impaired T cell proliferation at 5, 10 and 20 µM. To confirm whether T cell proliferation inhibition occurred due to the reaction of the agonists and their specific receptors, the we used CB1 antagonist (SR141716A) [27,29] and CB2 antagonist (SR144528) [28,29] to rescue the inhibitory effect of the cannabinoid receptor agonists. Upon T cell activation, SR141716A rescued the inhibitory effect of the CB1 agonist but not the CB2 agonist (Figure 2A,C). Likewise, SR144528 restored the inhibitory effect of the CB2 agonist but not the CB1 agonist (Figure 2B,D). These results indicated that the impaired T cell proliferation was result of the activation of CB1 and CB2 by the tested agonists. We also investigated the toxicity of the agonists used on T cells. ACEA at 20 µM and GW833972A at 5 µM, which suppressed T cell proliferation, did not show cellular toxicity (Supplementary Figure S6). ## 3.2. CB1 Agonist ACEA and CB2 Agonist GW833972A Differently Altered Activation-Associated Molecules Expressed on T Cells, B Cells, and Monocytes We determined the effect of CB1 and CB2 agonists on activation molecules, CD69, CD25, and PD-1 expressed on CD4+ and CD8+ T cells. As shown in Figure 3, upon anti-CD3 mAb activation, ACEA significantly decreased the number of CD25- and PD-1-expressing CD4+ T cells, but not for CD69 expression. This agonist decreased the number of CD69-, CD25-, and PD-1-expressing CD8+ T cells. For the CB2 agonist, GW833972A significantly diminished the number of CD69-expressing CD4+ T cells. GW833972A reduced the number of CD69- and CD25-expressing CD8+ T cells. These results suggested that the ACEA and GW833972A altered the expression of T cell activation molecules differently. Without anti-CD3 mAb activation, the CB1 or CB2 agonist alone did not affect activation markers (Figure 3 in No OKT3 panels). We investigated the effect of ACEA and GW833972A on co-stimulatory molecules expressed on B cells and monocytes. Upon anti-CD3 mAb activation, the CB1 agonist ACEA significantly reduced the expression level of CD86 on B cells (Figure 4D), whereas the CB2 agonist GW833972A decreased the HLA-ABC expression level on monocytes (Figure 4A). Taken together, these results indicated that the activation of CB1 and CB2 by the tested agonists altered activation-associated molecules and co-stimulatory molecules expressed on T cells, B cells, and monocytes differently. ## 3.3. CB2 Receptor Agonist GW833972A Inhibits Th1 Cytokines Production We determined intracellular cytokine production in T cells upon stimulation of PBMCs using mAb OKT3 and anti-CD28 mAb. The CB2 agonist GW833972A, but not CB1 agonist ACEA, significantly reduced Th1 cytokine (IL-2, TNF-α, and IFN-γ) production (Figure 5). ## 3.4. CB2 Agonist GW833972A Diminishes Cytotoxic Function of CD8+ T Cells but Not NK Cells We determined the effect of ACEA and GW833972A on the cytotoxic function of T cells. We used the anti-CD3-primed P815 cell line as target cells for stimulation of T cell-mediated cytotoxic activity [30,31]. CB2 agonist GW833972A, decreased T cell-mediated cytotoxic activity at E:T 5:1 and 10:1 (Figure 6A). At the concentration of GW833972A used, no cytotoxicity to the effector cells was observed (Supplementary Figure S7). However, we did not observe this effect using the CB1 agonist ACEA (Figure 6A). We further determined the degranulation process of CD8+ and CD4+ T cells upon CB2 activation. As shown in Figure 6B, GW833972A significantly reduced LAMP-1- expressing CD8+ T cells but did not affect CD4+ T cells. Correspondingly, the number of IFN-γ- producing CD8+ T cells were significantly decreased by GW833972A (Figure 6C). These results suggested that the CB2 agonist GW833972A affects the cytotoxic function, degranulation, and IFN-γ production of CD8+ T cells. We also determined the cytotoxic function of NK cells. The tested CB1 and CB2 agonists did not affect NK cell cytotoxicity (Supplementary Figure S8). The results indicated that the attenuation of cytotoxicity by GW833972A occurred on CD8+ cytotoxic T cells rather than NK cells. ## 4. Discussion The endocannabinoid system is involved in immune function [10,13,14,15,32,33,34,35]. Cannabinoid receptors, CB1 and CB2, are expressed on both CD4+ and CD8+ T cells and are upregulated upon T cell activation by anti-CD3/CD28 mAb [13,36,37]. In this study, we investigated the effects of the CB1 agonist ACEA and CB2 agonist GW833972A on T cell regulation. We found that the activation of CB1 and CB2 by either ACEA- or GW833972A-induced T cell hypofunction in different manners. ACEA is a high-affinity agonist of the CB1 (Ki values of 1.4–5.29 nM) with a 2000-fold higher selectivity for the CB1 than for the CB2 [27,38]. The effects of ACEA could be inhibited by the CB1 antagonist SR141716A [27,39,40]. GW833972A is a β-arrestin-biased agonist with a 1000-fold higher selectivity for the CB2 than for the CB1 [28,41]. The effects of GW833972A were blocked by the CB2 antagonist SR144528, but not by the CB1 antagonist rimonabant [28]. In this study, we used the CB1 selective agonist ACEA [27] and CB2 selective agonist GW833972A [28] for activation of their specific receptors and determined T cell responses upon activation. We found that the CB1 agonist ACEA at 20 µM reduced T cell proliferation, this effect was not observed at the lower concentrations. This result, however, is different from the previous finding that ACEA did not affect T cell proliferation [13]. Concentration of ACEA used, however, differed between the studies. The previous study used 1 µM; at this concentration, we also found no effect on T cell proliferation. In our study, ACEA at 20 µM significantly reduced T cell proliferation; however, this concentration was not tested in the previous study [13]. Therefore, the effect of ACEA on T cell proliferation in the previous study might be omitted [13]. Moreover, in our study, we used anti-CD3 activated PBMCs while the previous study used purified T cells [13]. In the present study, we demonstrated that ACEA decreased the co-stimulatory molecule CD86 on B cells. Decreasing the co-stimulatory molecule may involve in the reduction of T cell proliferation. As purified T cells were used in the previous study [13], which lack B cells, the effect of ACEA on T cell proliferation may not be observed. We suggested that activation of CB1 by ACEA is involved in the regulation of the T cell response. To the best of our knowledge, this is the first time this finding has been reported. To investigate the involvement of CB2 on T cell regulation in this study, GW833972A was used for CB2 activation. GW833972A had been used to determine the role of CB2 in airway sensory nerve function for the treatment of chronic cough [28]. However, the effects of GW833972A on T cell responses have not been tested. Our results demonstrated that, as same as the CB1 agonist ACEA, GW833972A impaired T cell proliferation. We nevertheless raised the question whether the observed effect of ACEA and GW833972A on T cell proliferation was actually due to the interaction of cannabinoid receptors with their specific agonists. To address this question, we used CB1 antagonist SR141716A and CB2 antagonist SR144528 [29] to neutralize the effect of the tested agonists. The inhibitory effect of CB1 and CB2 agonists on T cell proliferation was restored by their specific antagonists. According to the different Ki of the CB1 antagonist SR141716A (1.98 nM) and CB2 antagonist SR144528 (0.6 nM), the ratio of agonist to antagonist are at 1:8 for CB1 and 1:500 for CB2. These results confirmed that the inhibitory effects of ACEA and GW833972A occurred through the binding and activation of CB1 and CB2, respectively. We, therefore, suggest that both cannabinoid receptors play a role in T cell responses. The activation of CB1 and CB2 by ACEA and GW833972A, respectively, suppressed T cell proliferation upon stimulation. We elucidated the biological mechanisms of ACEA and GW833972A involving the regulation of T cell responses. We found that the activation of CB1 and CB2 by ACEA and GW833972A attenuated T cell proliferation through different mechanisms. Upon T cell activation, the T cell activation molecules, including CD69, CD25, and PD-1, were upregulated on the T cell surface [42,43,44,45,46]. The CB1 selective agonist ACEA decreased the number of CD4+ T cells expressing CD25 and PD-1, while it decreased the number of CD8+ T cells expressing CD69, CD25, and PD-1. The CB2 selective agonist GW833972A significantly diminished the number of CD4+ T cells expressing CD69 but had no effect on CD25 and PD-1 expression, as well as the number of CD8+ T cells expressing CD69 and CD25. These results indicated that the agonists ACEA and GW833972A affect CD4+ and CD8+ T cells differently. In addition, we determined the effect of ACEA and GW833972A on B cells and monocytes. Monocytes and B cells are antigen presenting cells and express co-stimulatory molecules including HLA-ABC (MHC class I), HLA-DR (MHC class II), CD80, and CD86, which contribute to T cell activation [47,48]. Downregulation of these co-stimulatory molecules was demonstrated to attenuate T cell functions [49,50]. We observed that the CB1 agonist ACEA significantly reduced the expression levels of CD86 on B cells, whereas the CB2 agonist GW833972A decreased HLA-ABC expression levels on monocytes, which might have resulted in reduction of T cell proliferation. These results indicated that ACEA and GW833972A affect different APCs for attenuating T cell responses. However, in this study, the mechanisms regulated by CB1 or CB2 activation in each cell type have not been investigated. It is also likely that the CB1 and CB2 expression levels in each cell type were differentially changed after OKT3 stimulation. There is a report that demonstrated that CB1 was upregulated on macrophages upon PMA stimulation leading to an increasing CB1:CB2 ratio. By changing the CB1:CB2 ratio, the opposing outcomes mediated by CB1 and CB2 were found in which CB2 is a negative regulator of CB1-stimulated ROS production [51]. Furthermore, we determined the effect of ACEA and GW833972A on Th1 cytokine (IL-2, TNF-α, and IFN-γ) production. Th1 cytokine are cytokines that are required for boosting immune cells to eliminate invading pathogens and tumor cells [52,53]. We found that only GW833972A decreased the number of T cell producing Th1 cytokines. This CB2 agonist, however, did not affect the production of IL-17 by T cells and IL-10, TNF-α, and IL-6 by monocytes. The results indicated that the agonists used did not produce cytotoxicity. This result is related to the effects of the CB2 agonist, which suppressed T cell proliferation and inhibited the production of IFN-γ and TNF-α through a CB2-dependent pathway [11,54,55]. CD8+ cytotoxic T cells and NK cells play an important role in eliminating virus-infected cells and tumor cells. Additionally, CD4+ cytotoxic T cells have been demonstrated to play an important role in antitumor immunity [56,57,58]. These cytotoxic cells can recognize tumor cells and induce tumor cell death by releasing cytolytic granules and cytokines, particularly IFN-γ [56,57,58]. The CB1 and CB2 have been reported to be expressed on T cells and NK cells [5,36]. In this study, we investigated the cytotoxic function of T cells and NK cells upon cannabinoid receptor activation by ACEA and GW833972A. The CB2 selective agonist GW833972A, but not CB1 agonist ACEA, suppressed the cytotoxic activity, IFN-γ production, and degranulation process of CD8+ cytotoxic T cells, whereas the cytotoxic functions of CD4+ cytotoxic T cells and NK cells were not affected by the tested CB1 or CB2 agonist. CB2 is expressed in various peripheral tissue; however, its expression is higher in the immune system [5,17,59]. Activation of CB2 modulates various intracellular signal transduction pathways, including inhibition of adenyl cyclase activity to produce cAMP and an increase in the phosphorylation of MAPK [60,61,62]. The CB2 was demonstrated to be involved in the suppression of T cell functions, including suppression of T cell activation [63,64,65,66], reduction of cytokine production [11,55], and migration [67]. In agreement with our results, the CB2 selective agonist GW833972A suppressed the cytotoxic activity of CD8+ T cells. 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--- title: 'Promoting Health Equity: Identifying Parent and Child Reactions to a Culturally-Grounded Obesity Prevention Program Specifically Designed for Black Girls Using Community-Engaged Research' authors: - Haley Allen - Chishinga Callender - Debbe Thompson journal: Children year: 2023 pmcid: PMC10047766 doi: 10.3390/children10030417 license: CC BY 4.0 --- # Promoting Health Equity: Identifying Parent and Child Reactions to a Culturally-Grounded Obesity Prevention Program Specifically Designed for Black Girls Using Community-Engaged Research ## Abstract The Butterfly Girls (BFG) Study is a culturally and developmentally appropriate online obesity prevention program for 8–10-year-old Black girls designed with key stakeholders in the Black community. This multi-methods investigation, conducted with parent–child dyads who participated in an outcome evaluation of the intervention, aimed to understand parent and child reactions to the program. We were particularly interested in understanding perceptions regarding its cultural and developmental appropriateness, relevance and acceptability. Program participation and survey data (demographics, parent and child write-in comments on process evaluation surveys) were analyzed. Participation data demonstrated high adherence in treatment and comparison groups. Descriptive statistics were calculated for survey data and highlighted the socioeconomic diversity of the sample. Post intervention surveys included two fill-in-the-blank questions for parents ($$n = 184$$ for question 1, $$n = 65$$ for question 2) and one for children ($$n = 32$$). Comments were analyzed using structured thematic analysis. The majority of the feedback from child participants was complimentary and many found the program relatable. Among the parent responses, the majority found the program to be beneficial in its educational nature and in promoting behavior change. This multi-methods analysis suggests that the BFG program was perceived as beneficial by parents while being culturally and developmentally appropriate and engaging for young Black girls, highlighting the importance of co-collaboration in program development. ## 1. Introduction Obesity prevalence in the United States is at an all-time high. Between 2017–2020, obesity prevalence among 2–19-year-olds was $19.7\%$ [1]; in 2020, it had increased to $22.4\%$ [2]. Prevalence is not equally distributed. Compared to their non-Hispanic White peers ($14.8\%$), Black girls ($29.1\%$) are more likely to have obesity [3]. This disparity is multi-factorial as Black communities are more likely to be exposed to social determinants of health that increase their risk of developing obesity [4]. For instance, hyper-palatable, low-nutrient foods are more likely to be marketed to Black Americans [5]. In addition, excess weight may be perceived differently across ethnic groups [6]. To effectively address disparities and reduce obesity risk, convenient, accessible and personally relevant interventions are needed. Online interventions offer one possible route of providing convenience and accessibility given that internet access has increased for all racial and ethnic groups [7] and usage among youth is high [8]. Few online interventions promoting healthy diet and physical activity behaviors have been specifically developed for pre-adolescent Black children, or girls, specifically [9,10,11,12,13]. Furthermore, findings from obesity prevention programs for pre-adolescent Black girls highlight the importance of culturally tailored programming [14] as culture influences beliefs and practices around health habits and body image [15]. Finally, given that obese youth are more likely to become obese adults [16], developing effective interventions for youth at the greatest risk of having obesity is of paramount importance. Addressing obesity-related disparities in Black girls is a key aspect of designing programs to achieve health equity. Health equity is evident when everyone is equally positioned to be as healthy as possible [17]. Ensuring that everyone has a fair and just opportunity to achieve optimal health is an ethical imperative, as the health of our nation depends on the most vulnerable populations being adequately protected against disease [18]. To obtain such a state, action must be taken to address historical injustices and eliminate health disparities such as obesity, a preventable disease [19]. To develop effective interventions for under-represented youth, cultural factors need to be considered to help ensure the appropriateness and relevance of program content and structure [14,20]. This includes an awareness of broadly shared cultural values, beliefs and expectations [21]. Community-engaged research, i.e., partnering with the community of interest, to develop program content and structure [22,23], is one way to help ensure a program is culturally aware and reflects important characteristics of the community, including their needs, interests and expectations. Community-engaged research also provides an opportunity for co-learning between the community of focus and researchers, thus increasing the likelihood of more equitable outcomes and stronger, more relatable interventions [24,25]. The Butterfly Girls program (BFG) was designed to address the inequities in obesity prevalence among Black girls. The program was developed using a community-engaged approach with the Black community in the greater Houston, TX area. The purpose of this paper is to report parent and child reactions to the program. ## 2.1. Intervention Although described in detail elsewhere [26], the program will be briefly described here. The BFG program was a three-group randomized controlled trial, consisting of a treatment, comparison and waitlist control group. Data were collected at three timepoints: baseline (prior to receiving the intervention), post 1 (after intervention completion—i.e., approximately 3 months post baseline); and post 2 (approximately 6 months post baseline). The eight-episode online intervention promoted five servings of fruits and vegetables, five glasses of water and 60 min of physical activity per day. Both girls and one parent received an intervention. The centerpiece of the girls’ program was animated stories, populated with six characters designed to serve as role models; the stories were supported by goal setting, goal tracking and feedback. Parents received electronic newsletters corresponding to each episode the girls viewed. Girls could view one episode each week. They received an automatic reminder email when they were eligible to view the next episode and the parents received an email with a link to the episode-specific newsletter. For participants who did not view an episode within three days of eligibility, an email reminder was sent. A reminder call was made to the parent after six days and a follow-up call was made every five days until the child completed the episode (up to five calls); the program was structured so that the girls did not miss an episode (i.e., episodes were available sequentially, regardless of the time lapse between logins). Girls randomized to the treatment group received the full intervention immediately after completing baseline data collection (i.e., animated stories, goal setting, goal tracking and feedback); those in the comparison group (i.e., animated stories only) also received the animated stories after completing baseline; and those randomized to the waitlist control group received the treatment intervention without reminder emails after completing all three data collection timepoints (baseline, post 1, post 2). The BFG program was adapted from an earlier pilot intervention consisting of a summer day camp, followed by an internet component to promote maintenance of change [27]. The internet component was later tested as a stand-alone internet intervention [10]. During adaptation of the BFG intervention [26], community-engaged research was conducted with three groups of stakeholders to provide feedback on the cultural and developmental appropriateness of program content and graphics, as well as to develop a deeper understanding of expectations. The panels consisted of girls ($$n = 20$$), parents ($$n = 20$$) and community representatives ($$n = 10$$). The girls and parents participated in interviews and the community representatives completed online surveys with similar questions to the interviews. Trained staff conducted the interviews following a semi-structured script; each interview lasted about 1.5 h. To help ensure cultural appropriateness and relevance, a Black female playwright authored the scripts for the animated stories. ## 2.2. Eligibility Criteria Black girls, 8–10 years old living in the greater Houston, TX area, were recruited using standard procedures and the volunteer database at the Children’s Nutrition Research Center. Eligibility criteria included 8–10-year-old Black girls with a personal email address, internet access and a parent or legal guardian who was willing to allow them to participate and would themselves participate in the parent component of the program [26]. While girls needed to self-identify as Black to be eligible for participation, parents were not required to identify as Black. ## 2.3. Data Sources This paper reports parent and child reactions to, and child participation in, an intervention designed using community-engaged research. Data for this paper included parent and child quantitative and qualitative data from baseline and post 1 (immediate post-intervention) data collection time points. Surveys were completed online using a private password. As part of baseline data collection, parents completed demographic surveys describing personal and home characteristics. The characteristics included total household income, highest level of household education, parent race and ethnicity, parent gender and the number of adults living in the household including themselves. At post 1 (immediately after their daughter completed the intervention), parents of girls randomized to the treatment or comparison group were eligible to complete a process evaluation survey. The survey included two open-ended questions. The first question required a yes/no response: “Would you recommend this program to other parents?” followed by, “Please tell us your reasons for recommending or not recommending the program to other parents”. At the end of the survey, parents were given an opportunity to provide additional information: “Please use this box for any additional comments you may have”. For the girls, program participation data was automatically collected in each session as girls logged in and interacted with the program. At post 1, girls randomized to the treatment or comparison group were eligible to complete a process evaluation survey with one open-ended question: “If there is anything you would like to tell us about the characters, their voices, the story, or the program, please type it in here”. ## 2.4. Quantitative Analysis Survey responses were analyzed using descriptive statistics (frequencies, percentages). ## 2.5. Qualitative Analysis A structured analytic approach was used to code parent and child data [28]. Prior to analysis, codebooks containing a priori codes and definitions were developed and agreed upon by coders. Separate codebooks were developed for parent and child comments. The parent codebook included six a priori codes: benefits, compliments, suggestions, barriers, data collection and requests. The same codebook was applied to all parent comments reported in this paper. Similarly, the child codebook contained five a priori codes: compliments, relatability, complaints, requests and benefits. Two coders independently applied the codebooks to parent and child comments for each question. Coding was then compared. Differences were discussed and resolved by mutual agreement. After differences were resolved, for both the parent and child codebooks codes were examined and converted to categories. Comments within categories were examined for similarities in underlying topics or ideas, then grouped into subcategories. ## 3.1. Household and Demographic Characteristics Three hundred thirty parents completed the demographic survey (Table 1). Nearly all of the parents identified as Black ($93.0\%$) and female ($98.8\%$). Most were less than 40 years old ($59.4\%$), married ($62.7\%$) and lived in households in which the majority ($70.3\%$) lived with one other adult or by themselves. Household income was nearly evenly split between those making $41,000 or less per year ($43.0\%$) and those making $61,000 or more a year ($38.8\%$). Most reported that the highest level of household education was a college degree or greater ($65.4\%$). ## 3.2. Parent Results Parents ($$n = 205$$) responded to the survey question as to whether or not they would recommend the program to other parents and provided reasons for such a decision. Overall, $98.1\%$ of the parents said that they would recommend the program to other parents. Of these, 184 parents responded to the question, “Please tell us your reasons for recommending or not recommending the program to other parents” and 65 parents responded to “Please use this box for any additional comments you may have”. Responses to both questions were separately coded and grouped into six categories: benefits, compliments, suggestions, barriers, data collection and requests. Results are summarized by question below, supported by representative comments to provide context and insight. Verbatim comments are shown to provide additional insight. To ensure categories accurately captured parent comments, responses could be coded into multiple categories, depending on response content. For the question: “Please tell us your reasons for recommending or not recommending the program to other parents”, 213 comments were grouped into five categories (Figure 1). The category with the most responses was benefits, receiving a total of 149 comments. Comments related to benefits were grouped into 5 subcategories: education-related ($$n = 97$$), behavior change ($$n = 29$$), child agency ($$n = 12$$), family time ($$n = 6$$) and general ($$n = 5$$). The majority of the comments noted the education-related benefits of the program. For instance, parents often stated that the program added “educational value to both parents and kids on living healthily” or generally described it as informational, using words like “informative” and “great information” or made the parents aware of their current dietary habits by using phrases such as “it [the program] helps me and my daughter realize what we ate”. Comments on behavior change indicated that the program was motivational and encouraged healthy eating and physical activity. One parent noted that the program provided “great motivation for healthy changes”. Another parent said, “this program is [a] good way to encourage girls to get and stay active”. Child agency was defined as enhancing self-confidence regarding health choices. For instance, one parent said the program will help “children make better choices for themselves and encourage their friends”. Another said it helps “girls have a positive body image”. Comments related to family time often focused on the program being a bonding opportunity as indicated in this comment: “this program brings mothers and daughters closer”. Another parent said the program helped them “think about the type of foods I was purchasing for my family and some ways we could improve as it relates to eating and just spending quality/active time with the kids”, indicating that the program itself was a bonding experience and helped encourage bonding outside of the time spent on the program. Responses coded under general were not specific enough to be subcategorized. They typically stated something like “great program for young girls” or that “other parents can benefit from this program”. Compliments contained the second largest category of responses ($$n = 58$$). These responses were further sub-categorized as compliments about their overall experience ($$n = 31$$) and program-specific compliments ($$n = 27$$). Responses that were complimentary of the overall experience indicated that parents felt positive about their time in the program. One parent indicated that it was a “great experience for [their) child] and good interaction between parent and child”. Another parent described their overall experience as “relevant to issues in our society with African Americans, engaging and convenient”. Several of the comments described their experience in BFG as being engaging or “fun”. Program-specific comments tended to focus on the information related to health and nutrition provided by the program. For example, a parent said, “I find the program very informative and kid-friendly. The language use is clear and precise and on the level that children understand”. Few comments ($$n = 3$$) were coded as suggestions. One parent indicated that they thought the program would be better if children were asked to watch the videos and repeat the information to the parent in their own words rather than having parents watch the videos with them. They thought the program could be turned into a reading and writing assignment to “sharpen critical thinking skills”. Another parent wished the program had been offered in-person. Similarly, few comments ($$n = 2$$) were coded as barriers. Both were time-related. One parent said, “great program, but time consuming”, while the other said the program took “a lot of time and effort”. Only one comment focused on the data collection experience. This comment described the parent’s awareness of the importance of data collection for healthy lifestyles. There were no comments coded as requests, which were defined as asking for consideration of some type or changes they would like to see in future iterations of the program. Parents were also given an opportunity to respond to the question “Is there anything else about this program that you would like us to know”. Responses from 65 parents were grouped into six categories, listed in descending order: compliments, benefits, suggestions, barriers, data collection and requests (Figure 2). The majority of the responses to the question were coded as compliments ($$n = 39$$). Compliments were further sub-coded as compliments on the overall experience ($$n = 32$$), program ($$n = 4$$), or staff ($$n = 3$$). Compliments on the overall experience tended to be general, e.g., one parent said “it’s a wonderful program”. Compliments on the program focused on how “interactive” parents found it or on the “ideals” exemplified in the episodes. Comments on the staff described their interactions as positive and found it helpful to have reminders from the research team to complete episodes. Parents also viewed the program as beneficial ($$n = 20$$ comments), further grouped into four subcategories: motivation for behavior change ($$n = 11$$), educational ($$n = 5$$), child agency ($$n = 2$$) and family time ($$n = 2$$). The majority of the comments were related to behavior change. For example, many of the comments given by parents were similar to this statement: “the program helped us reinforce what we’re trying to do as a family”. One parent said that they added more fruits and vegetables to their meals and enrolled the children in physical activity programs following program completion. Additionally, parents commented on the educational aspects of the program saying that they learned more about “good food habits” and considered the program to be a resource. Agency was mentioned by two parents. For example, one noted that her daughter is “very proud about her part in the study… It is now great to see how she compares what she has learned from the videos…to life as she sees it and is able to see how either herself or other(s) can make better choices”. Family time was also mentioned by a few parents. As one parent commented: the program “opened up a dialog between the two of us (mother and daughter) about changes we could and should make to our families’ diet.” Fourteen responses were coded as suggestions. Suggestions were further subcategorized as positive ($$n = 8$$) or negative ($$n = 6$$). Several of the positive responses called for expansion of the program in some way: for example, two parents suggested expanding the age range and one suggested targeting the program to low-income families. Negative responses primarily focused on the videos, ranging from parents asking to expand the videos in order to demonstrate more ways to add vegetables into diet, to the need for better technical quality and more realistic character voices. Shortening the time commitment needed to complete the dietary recalls was also suggested. Very few comments ($$n = 3$$) focused on barriers. Two parents indicated that they did not have sufficient time to incorporate more of the lifestyle changes they had wished to incorporate, while another indicated they faced a financial barrier to buying produce. Finally, one parent indicated that they were unable to get the rest of the family interested in making lifestyle changes suggested in the videos. Two responses were coded under data collection. Both were coded as negative. One parent indicated that the diet recall survey “wore out” her daughter due to issues with attention span (i.e., 24-h dietitian-assisted recall). The other parent indicated that the activity tracker was uncomfortable for her daughter to wear (i.e., Actigraph monitor was worn for 7 days at each data collection period). Two comments were coded as requests. One parent requested a focus on promoting independence such as including an activity where the child shops for recipe ingredients. Another parent indicated a desire to have similar programming for a son. ## 3.3. Child Results After completing baseline data collection, 342 girls were randomized to condition: 114 to the treatment (full intervention) condition, 114 to the comparison condition (animated stories only) and 114 to the waitlist control condition. Episode completion for all groups was automatically cataloged as girls navigated the online program. The majority of girls assigned to the treatment ($53.5\%$) and comparison ($68.4\%$) conditions watched all 10 episodes, while few girls in the waitlist control condition did so ($4.4\%$) (Table 2). Girls ($$n = 32$$) provided responses for the question “if there is anything you would like to tell us about the characters, their voices, the story, or the program, please type it in here”). Responses were grouped into five categories: compliments, relatability, complaints, requests and benefits. Results for each category are summarized below and in Figure 3, supported by representative comments to provide additional insight. As with parents, to ensure categories accurately captured child comments, responses could be coded into multiple categories, depending on response content. A total of 53 comments were coded. The category receiving the most comments was compliments ($$n = 32$$). Comments were grouped into three subcategories: storyline ($$n = 13$$ comments), characters ($$n = 13$$ comments) and overall program ($$n = 6$$ comments). In general, girls felt positive toward the storyline. One girl liked how the story was put together and another found the plot “easy to follow along with”. Another girl simply said, “I love everything about the stories”. Compliments regarding characters generally focused on positive attributes they admired in the characters such as strength, confidence, or athleticism. For instance, one girl described the characters as “strong, confident, peaceful and more”. Several girls expressed positive reactions to the overall program, most frequently focusing on the fun they had while participating in the program. For example, one girl commented that the program was “fun, creative and easy to read”. Ten comments from nine girls were coded as relatability, defined as the child feeling positively represented by the story, feeling the overall experience was inclusive, or feeling represented by the characters. Comments were further grouped into reactions to the characters ($$n = 9$$) and the overall program ($$n = 1$$). Findings indicate that girls felt connected to the characters, particularly those who shared a common interest with the girls and looked like them. For example, one girl stated, “I like the characters because they relate to me because I like butterflies and fashion”, while another said, “[the girl characters] remind me of my friends and cousins”. Finally, another indicated that she related to the program saying that participating in the program made her feel part of a “special group”. Five comments were coded as complaints. The comments were further grouped into reactions to the voices ($$n = 2$$), storyline ($$n = 1$$), program ($$n = 1$$) and characters ($$n = 1$$). Findings were varied. Two girls found the voices “weird”. One girl wished the girls had been shown going to school. Another girl had technical difficulties in finishing an episode. Lastly, one girl indicated disliking the character who “berated her friends”. Four comments were coded as requests. These comments related to reactions to the program ($$n = 2$$), characters ($$n = 1$$), or storyline ($$n = 1$$). Requests were varied. One girl wished to taste the recipes she did not get to try during the program. Another girl indicated a desire to repeat the program with a different story stating that she would “love to do it again with a different story”. Regarding the comment related to characters, one girl wished for a change to a character’s role in the storyline requesting that they played for a different soccer team. Similarly, one girl indicated a desire for nicer antagonists (in the storyline, boys were competing with the girls to locate Founder’s Rock). Lastly, two comments were coded as program benefits. One girl said, “it was great how the butterfly girls taught me to stay active”. The other girl stated that the program gave her the opportunity to interact with computers. ## 4. Discussion Parent/child pairs ($$n = 342$$) participated in the Butterfly Girls program promoting healthy diet and physical activity behaviors for 8–10-year-old Black girls. The program was developed using community-engaged research with multiple stakeholder groups, including 8–10-year-old Black girls, their parents and community representatives. Girls randomized to the treatment or comparison groups demonstrated high levels of program participation. This study aimed to understand parent and child reactions to the program following community engagement during program development. Future papers will utilize machine learning to detect patterns in diet, physical activity and body weight change following the BFG program. Parent and child comments indicated high connectedness to the program and that it was viewed as an educational and motivational resource. Results also provide evidence that a program designed with key stakeholders from the community for whom the intervention is intended can result in high program participation. This suggests that co-design approaches, such as community-engaged research, are essential for programs designed to attain health equity. The demographics of the study sample were socioeconomically diverse with $43.0\%$ of parents earning $41,000 a year or less and $38.8\%$ of parents earning over $61,000 a year. However, the majority of the households represented had an individual with at least a college degree. The fact that education status did not necessarily predict the socioeconomic diversity of the sample is consistent with other studies in minority populations that have shown that income disparities can persist despite educational status [29,30]. Program dose was relatively high with $68.4\%$ of the girls in the comparison group and $53.5\%$ in the experimental group watching all of the BFG episodes, suggesting an acceptable program dose. Alternatively, girls in the wait-list control group watched few episodes. A community-based obesity prevention program for parents and children with longitudinal interactions with research staff reported similar attendance to those in our treatment and comparison conditions, with an average attendance of $64.0\%$ of sessions [31]. Striking differences in program dose were observed among the groups, which may partially be explained by participation reminders. While girls in the treatment and comparison groups received reminders to log on, those in the waitlist control group did not. Girls in the treatment and waitlist control groups received the same intervention, with the only difference being participation reminders, suggesting that reminders can enhance participation. The literature supports the importance of participation reminders in program adherence. Shumaker et al. [ 32] and Robiner [33] emphasized the importance of staff in program adherence. The qualitative findings from the girls indicated connectedness to the characters and positive perception of the overall storyline and characters. These findings support the importance of community-engaged research when developing interventions for under-represented children. Collaboration with minority populations in the research process allows for the cultural sensitivity the girls’ comments highlighted [21]. The girls’ positive feedback suggests that collaboration with Black girls in the program development strengthened the program and may have facilitated their high levels of participation. BFG may have been less well-received or adherence may have been lower had cultural factors not been considered in the storyline or character development. Evaluation of youth programming suggests that including their participation throughout program development and implementation can result in greater connection with program messages [21,34]. Other studies with minority populations support the importance of cultural sensitivity in intervention development. Specifically, a study with Black girls requested Black female role models in programming and at-home strategies for physical activity and healthy eating promotion [14]. The qualitative responses from the parents indicated that they found the program to be beneficial, primarily as an educational resource and secondly as a motivator for behavior change, and was an overall positive experience. Parental perception is of particular importance given that parents can be a powerful determinant of children’s health behaviors [35,36]. Parents’ perspectives on strategies for healthy dietary choice promotion for children have been well studied [37,38]. However, less attention has been given to minority parents’ perceptions regarding ways to help children practice healthy lifestyle behaviors [29,39,40]. Furthermore, community-based participatory research, while it can be difficult to evaluate its effect on participation, shows that participant engagement strengthens the program’s ability to equitably distribute the benefits of a program or overcome barriers such as in our socioeconomically diverse parent population [21,41]. Parent perception of other obesity prevention programs is in line with what the parents in the BFG study perceived as beneficial. Specifically, Black parents in a text messaging-based study suggested a need for resources on tips for helping their daughter make healthy dietary choices [42]. The BFG Program had several limitations. Perhaps in part due to the developmental maturity of participants, only 32 children responded to the qualitative prompts in the Post 1 timepoint survey. Furthermore, there is a possibility that responses for parents and children to qualitative prompts were self-selecting in that only those who had significant experiences in the BFG program gave an answer. Further, parental educational status was relatively high, which may limit generalizability. However, the socioeconomic status of parent participants was diverse which may lessen this concern. Finally, it is possible that the Hawthorne effect influenced participation given that participants received phone calls from researchers if they failed to watch an episode [43]. Despite its limitations, the BFG program was strengthened by its three-group design that included a wait-list control group and a comparison group that did not receive the behavioral goal-setting components. In addition, the use of automatic recording of session completion avoided errors due to self-reporting. Finally, the BFG program was strengthened by the socioeconomic diversity of the participant sample. 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--- title: Is Laser Therapy an Adjuvant in the Treatment of Peri-Implant Mucositis? A Randomized Clinical Trial authors: - Luminița Lazăr - Timea Dakó - Izabella-Éva Mureșan - Mircea Suciu - George-Alexandru Maftei - Monica Tatarciuc - Ana-Petra Lazăr journal: Diagnostics year: 2023 pmcid: PMC10047770 doi: 10.3390/diagnostics13061192 license: CC BY 4.0 --- # Is Laser Therapy an Adjuvant in the Treatment of Peri-Implant Mucositis? A Randomized Clinical Trial ## Abstract [1] Background: Early diagnosis and treatment of peri-implant mucositis may reduce inflammatory markers and halt the progression of the condition to peri-implantitis. Adjunctive laser treatment may have therapeutic benefits that are not yet well known. The aim of this study was to determine the advantages and limitations of laser therapy as an adjuvant in the treatment of peri-implant mucositis. [ 2] Methods: A total of 42 patients with at least 2 implants situated in different hemiarches were included in this study and divided into two groups: G1 (received laser therapy) and G2 (no laser therapy). Periodontal health status indices were recorded at the initial moment (T0), and all patients underwent non-surgical debridement therapy accompanied by oral hygiene training. In patients from group G1, one implant site received adjuvant laser therapy (subgroup IL), and the other one did not receive active laser light (IC). The plaque index (PI), probing pocket depth (PPD), and bleeding on probing (BOP) values recorded after 3 months (T1) and 6 months (T2) were analyzed and compared with those at T0. [ 3] Results: PI values considerably reduced at moment T1 and T2 for both G1 and G2 ($$p \leq 0.0031$$). PPD was also reduced, but the difference between the groups and the three recording moments was not statistically significant. Statistically significant differences were found when comparing the BOP values between G1 IL and G1 IC for T0/T1 ($$p \leq 0.0182$$) and T1/T2 ($p \leq 0.0001$), but there was no significant difference between G2 and G1 IL or G1 IC. [ 4] Conclusions: Laser therapy as an adjunct to conventional treatment of peri-implant mucositis leads to a statistically significant reduction in bleeding on probing at 3-month and 6-month re-evaluations. Moreover, it leads to an evident reduction in probing depth but with no statistical significance. These results should be interpreted with caution, and more in-depth research should be performed to create a complete laser therapy protocol for peri-implant mucositis. ## 1. Introduction Implant-assisted edentulous therapy has become a routine treatment in dentistry nowadays as it is increasingly widespread among clinicians worldwide [1]. Although dental implants are a reliable treatment method with a high survival rate ($94.6\%$), the prevalence of peri-implant disease has been reported by several longitudinal and cross-sectional studies [2,3,4,5,6,7,8,9]. Peri-implant mucositis has a prevalence of $29.48\%$ at implant level and $46.83\%$ at patient level, and for peri-implantitis, the prevalences are $9.25\%$ and $19.83\%$, respectively [10]. Over the years, several studies have defined implant success criteria [11,12,13]. The success of dental implants is determined through a comprehensive assessment, reported at different levels: implant, peri-implant soft tissues, prosthetic work, and patient. Judging by the state of the implant, success criteria are absence of mobility, pain, radiolucency, and peri-implant bone loss (more than 2 mm in the first year). For peri-implant soft tissues, the success criteria should be the absence of suppuration and bleeding. Implant success is reached when the prosthetic work has no technical/prosthetic complications and has provided adequate functional and esthetic rehabilitation. For the patient, the success criteria are the satisfaction provided by the esthetics and the ability to perform the masticatory function without any discomfort and/or paresthesia [13]. Most of the complications associated with dental implants are inflammatory conditions of the soft and hard tissues around them, which are induced by the accumulation of bacterial biofilm [14]. Such conditions, which have been called peri-implant mucositis and peri-implantitis, must be clearly defined and differentiated from the peri-implant health status, to establish a proper diagnosis and institute an appropriate treatment. The new classification of periodontal and peri-implant diseases elaborated and published by American and European researchers is intended to simplify and clarify their diagnosis [15,16]. An element of novelty was the inclusion of peri-implant conditions within this classification, starting from the idea that the periodontologist is the clinician to diagnose and treat them. This classification provides specific criteria to accurately define peri-implant status in daily practice: signs of gingival inflammation, bleeding on probing (BOP), probing pocket depth compared to previous visits (PPD), and radiographically detectable bone loss (RBL) [17]. Peri-implant health is defined by the absence of signs of peri-implant soft tissue inflammation, the absence of bleeding and/or suppuration on gentle probing, the absence of increased probing depth (PPD) compared to previous visits, and the absence of radiographic bone loss (RBL) beyond the changes of the crestal bone level that appeared due to initial bone remodeling after implant placement [18]. Peri-implant mucositis is characterized by the presence of bleeding and/or suppuration on gentle probing with or without increased probing depth compared with previous examinations and the absence of additional changes in radiographic bone loss that occurred after initial bone remodeling [19]. The means by which peri-implantitis diagnosis is made depends on the presence or absence of previous records. Using previous records, peri-implantitis is defined by the presence of signs of bleeding and/or suppuration on mild probing, increased PPD compared with previous examinations, and the presence of RBL versus crestal bone level changes after initial bone remodeling that should not be higher than 2 mm. In the absence of previous radiographic records, the signs used to define a case of peri-implantitis are the presence of bleeding and/or suppuration on gentle probing, and PPD ≥ 6 mm and RBL ≥ 3 mm apical to the most coronal part of the intraosseous portion of the implant [20]. The pathological process always begins with peri-implant mucositis, which affects only the soft tissue around the implant. This pathological condition is reversible when detected early and treated properly [21,22,23]. The standard protocol in the treatment of peri-implant mucositis consists of training and monitoring the patient regarding oral hygiene measures and instituting non-surgical therapy [24,25,26]. For the non-surgical treatment of peri-implant mucositis, different methods have been studied such as techniques that improve dental plaque removal, locally applied antiseptics, generally administered antibiotics, probiotics, or the use of mouthwashes containing chlorhexidine [27,28,29,30,31,32,33]. Regarding the use of laser therapy as an adjuvant in the treatment of peri-implant mucositis, the results of the studies conducted are controversial [34,35,36,37]. That is the main reason why we aimed to evaluate the advantages and limitations of using laser therapy in the treatment of peri-implant mucositis in this study. ## 2.1. Study Design This clinical study was conducted as a double-blind, randomized clinical trial. ## 2.2. Selection of Patients Out of 76 adult patients with dental implant restorations who presented at the dental office in Targu Mures (Romania) for periodic check-ups, between 3 January 2021 and 22 December 2022, we selected 42 patients who met the following inclusion criteria:-Presence of at least one implant on two different hemiarches;-Implants must be pillars of fixed prosthetic works;-Presence of bacterial plaque and signs of inflammation of the peri-implant gingival tissue.-The exclusion criteria were the following:-Presence of radiographically detectable bone loss after the initial remodeling of the bone;-Presence of systemic diseases with an impact on the periodontal tissues (diabetes, immunological diseases, acute articular rheumatism, tuberculosis, etc.);-Pregnancy or breastfeeding;-Non-surgical peri-implant treatment performed in the last 6 months;-Antibiotic treatment in the last 6 months; The use of non-steroidal anti-inflammatory drugs (Figure 1). The patients were informed about the procedure and about the fact that they could leave this study at any time, and signed an informed consent. Sample size was determined using the power analysis calculation. A total of 17 patients per group were estimated to provide $90\%$ power for the detection of 1.0 mm of difference in the probing pocket depth (PPD) between the two groups with a standard deviation of 0.8 mm, 0.05 type I error, and 0.1 type II error. Considering the potential withdrawal of patients, we sought to enroll at least 21 patients per group. ## 2.3. The Periodontal Protocol The periodontal status was evaluated by a periodontologist other than the one who performed the laser therapy. Patients who, at the check-ups, after the completion of the fixed prosthetic treatment with implant support, showed accumulation of bacterial plaque and signs of peri-implant gingival inflammation underwent a new periodontal examination. After the radiographic examination proved the absence of bone loss and after the initial physiological remodeling of the bone, the following indices were recorded in a periodontal record:-Plaque index (PI): the presence (+) or absence (−) of bacterial plaque on the buccal, lingual, mesial, and distal surfaces following the application of a plaque disclosing solution. The PI value was calculated by dividing the sum of all surfaces presenting dental plaque by the total number of surfaces examined, multiplied by one hundred;-Probing pocket depth (PPD): the distance from the gingival margin to the apical limit of the peri-implant gingival groove measured in 6 places (mesio-buccal/centro-buccal/disto-buccal/mesio-oral/centro-oral/disto-oral) with a constant force;-Bleeding on probing (BOP): by giving the following scores: 1, minimal punctate bleeding; 2, linear bleeding or in drops; 3, spontaneous or profuse bleeding, with or without suppuration [38]. Patients were divided into two groups:-Group 1: 21 patients who received instructions regarding dental plaque removal and underwent scaling around the implant surface using titanium curettes. Only one out of the two implants each patient had benefited from laser treatment. The peri-implant status was evaluated at the time of the initial examination (T0), three months after (T1), and 6 months after (T2).-Group 2: 21 patients who received instructions regarding dental plaque removal and underwent scaling around the implant surface using titanium curettes. The peri-implant status was evaluated at the time of the initial examination (T0) and at 6 months (T2). ## 2.4. The Laser Protocol Laser therapy was randomly performed for one of the implants (IL) for each patient in group 1. For the second implant, located on another hemiarch, the same protocol was followed but without active light (IC). The patients and the periodontologist who evaluated the periodontal status were informed that only one of the implants benefited from laser therapy without specifying which one. During the irradiation, both the patient and the doctor wore protective glasses. The peri-implant sites were irradiated at moments T0 and T1 by the same clinician for the same implant site. Laser therapy was performed with a dental diode laser (Prime, Litemedics, Lambda SpA, Milano, Italy), with a power of 12 Watt, in pulsed system and operating wave of 980 nm, using the working mode “periodontology”. A 320-micrometer optical fiber was inserted in the gingival sulcus and moved in a mesio-distal direction, both on the buccal surface and on the lingual surface, for 30 s. ## 2.5. Statistical Analysis All data were collected in Microsoft Excel worksheets (Microsoft Corporation, Washington, DC, USA, 2018). Statistical analysis was performed with GraphPad Prism version 8.0.0 for Windows (GraphPad Software, San Diego, CA, USA). For each group of data, descriptive statistics such as mean, standard deviation, median, minimum, and maximum value were determined. Data normality was determined by the Kolmogorov–Smirnov test. The difference between the values of the clinical indices recorded at T0, T1, and T2 was determined using Fischer’s and ANOVA tests. The significance level chosen was set at 0.05. ## 3. Results Patients selected to participate in this study, based on the inclusion and exclusion criteria, were aged between 27 and 58 years. Group 1 consisted of 12 women with mean age of 43 years and 9 men with a mean age of 45 years. Group 2 included 11 women with an average age of 46 years and 10 men with an average age of 42 years. For each patient, the values of the main indicators of peri-implant health status were recorded: plaque index (PI), bleeding on probing (BOP), and probing pocket depth (PPD). The mean values recorded for PI, BOP, and PPD for each group are presented in Table 1. At the initial examination (moment T0), high PI values were recorded for most patients, with an average of $41.07\%$ in the G1 group and $42.85\%$ in the G2 group. In the G1 group at moment T0, 5 patients had PI values >$50\%$, 12 recorded PI values of 30–$50\%$, and 4 patients had PI between 10 and $30\%$. After a rigorous prophylactic cleaning session and patient instruction regarding dental plaque control, the PI values recorded at moments T1 and T2 were lower for all patients in the G1 group. Thus, at moment T1, no patient had a PI > $50\%$, and 3 had a PI value of 30–$50\%$; for 17 patients, we recorded PI values of 10–$30\%$, and 1 patient had a PI < $10\%$ (Figure 2). The values recorded for PI in patients from group G2 at T2 moment, were low in most patients compared with the T0 moment. The values recorded at T2 were PI > $50\%$ for three patients, PI = 30–$50\%$ for four, PI = 10–$30\%$ in eight of the patients, and PI < $10\%$ in six of them (Figure 3). However, three patients had PI values similar to those recorded at moment T0, and two patients showed higher PI values. When comparing the average values of the plaque index between G1 and G2 at moment T2, a statistically significant reduction ($$p \leq 0.0311$$) was observed in patients from group G1. The values recorded for PPD in the peri-implant sites in group G1 IL at moment T0 were 4 mm for 10 patients, 3 mm for 7 patients, and 2 mm for 4 of them. At moment T1, we recorded PPD = 4 mm in four patients, PPD = 3 mm in nine, and PPD = 2 mm in eight of the examined patients. At the 6-month examination (T2), the patients presented the following values: 1 had PPD = 4 mm, 8 had PPD = 3 mm, and 12 of them had PPD = 2 mm (Figure 4). In G2, we recorded at the initial examination (T0) PPD = 4 mm in 10 of the examined patients, PPD = 3 mm in 9, and PPD = 2 mm in 2 of them (Figure 5). The mean probing pocket depth (PPD) in the G1 IL group was 3.28 mm at T0, 2.80 mm at T1, and 2.33 mm at T2. For G1 IC, the values recorded for PPD were 3.33 mm (T0), 2.85 mm (T1), and 2.61 mm (T2). For G2, the average PPD values were 3.38 mm at T0 time and 3.23 mm at T2 time. The difference between the mean values of PPD at T0, T1, and T2 between the G1 IL and IC groups was statistically insignificant ($$p \leq 0.48$$) as well as between G1 IL and G2 at the time of T2 ($$p \leq 0.4003$$). Comparing the mean values of PPD between G1 IL and G1 IC at T1 versus T0, no statistically significant difference was found ($$p \leq 0.48$$). Even when comparing the mean values of PPD for G1 IL and G1 IC, there was no statistically significant difference recorded at T2 compared with T1 ($$p \leq 0.194$$). When recording the bleeding on probing (BOP), we found that all patients included in our study presented a higher score than one at moment T0. In G1 IL, at moment T0, no patient had BOP = 0, 4 had BOP equal to 1, 10 had BOP = 2, and 7 had BOP = 3. At moment T1, 10 patients had BOP = 0 and 11 patients BOP = 1, and at time T2, 14 of them had BOP = 0, and 7 patients BOP = 1 (Figure 6). In the G2 group at the initial examination (T0), 7 patients presented BOP = 3; 12 had BOP = 2; and, in 2 patients, we recorded BOP = 1. At the 6-month examination (T2), the recorded scores for BOP were 2 for 3 patients, 1 for 11 patients, and 0 for 7 of them (Figure 7). The average values of BOP at time T0 were 2.14 for the patients of the G1 IL group, 2.19 for those in the G1 IC group, and 2.23 for patients from group G2. In the G1 IL group, the mean BOP values were 0.52 (T1) and 0.33 (T2), while in the G1 IC they were 0.66 (T1) and 0.47 (T2). In the G2 group at time T2, the mean BOP value was 0.80. The difference between the mean BOP values at moments T0, T1, and T2 between the G1 IL and IC groups was statistically significant ($$p \leq 0.0162$$). When comparing mean BOP values in G1 IL versus G1 IC, a statistically significant reduction was observed ($$p \leq 0.0182$$) in T1 versus T0 and a highly significant difference between T2 and T1 ($p \leq 0.0001$). Comparing the mean BOP values between G2 and G1 IL, the difference was not statistically significant ($$p \leq 0.0743$$) nor between G2 and G1 IC (0.0584). ## 4. Discussion Implant treatments are becoming more and more frequent and so are the potential negative effects that can come with implant-associated pathologies such as peri-implant mucositis and peri-implantitis. A recent review examined the potential risk factors for implant failure and treatments available and stated that pocket depth reduction can be achieved in the short-term with laser, and air powder abrasive could aid in cleaning a contaminated implant surface. The authors also stated that plaque control, surgical pocket elimination, and bone recontouring are other efficient treatments for peri-implantitis [21]. In our study, we tried to create patient groups that were as homogeneous as possible in terms of age and sex, so that these demographics did not influence the study results. To establish the diagnosis of peri-implant mucositis, we examined the patients clinically (PI, PPD, and BOP) and radiographically. In G1, these assessments were made for two implants located at a certain distance from each other to ensure an objective assessment of the peri-implant status. If the patient presented several implants, the two implants that had the highest values of the recorded periodontal indices were included. In G2, the implants that recorded the most advanced signs of peri-implant mucositis were included. In group 1, laser therapy was randomly applied to one implant (IL), so that we could compare the values of the periodontal indices for IL with those obtained from the implant that did not receive active light (IC) in the same patient. Thus, the evaluation of the laser therapy was an objective one without oral hygiene habits, which differ from one patient to another, influencing the results. We used PI, which assesses the plaque accumulation in the entire oral cavity as a percentage, to have an overview of the oral hygiene of each patient. The results of our study showed that there were patients who were not monitored at three months (T1) and had PI values comparable or even higher than those at the time of T0 during the 6-month examination (T2). The findings that the mean values of PI for G2 at the time of T2 were not significantly lower than at T0 and that, at G1, the differences between the PI values at these times were significant prove the importance of repeated controls at intervals of 3 months in patients with implant therapy. The frequency of intervals between training sessions and professional cleaning usually varies between 3 and 6 months, and their frequency should be based on the risk profile of each patient [39]. Monk et al. observed that patients with a history of periodontal disease are more compliant regarding oral hygiene measures and with periodic check-ups [40]. Supportive therapy provides the clinician with the opportunity to monitor peri-implant status, and professional dental care improves peri-implant health and, hence, the success rate of dental implants. The patient’s informed consent form should include the accordance of the patient to comply with personal and professional peri-implant supportive therapy. Rokn et al. observed that after 5 years of implant loading without following a regular maintenance schedule, one in five patients presents with peri-implantitis [41]. In this study we recorded no statistically significant change in PPD recording between G1IL, G1IC, and G2 at moment T2. However, we recorded reductions in PPD values for most patients, which explain the remission of inflammatory phenomena. Al Rifaiy et al. observed a statistically significant decrease ($p \leq 0.001$) in PPD in patients who benefited from laser therapy, both when comparing the values obtained at 12 weeks with the initial one and when comparing those recorded in patients who did not benefit from it [42]. The same results of statistically significant reduction of PPD after using laser therapy were obtained by Lerario et al. [ 43]. The finding that $89\%$ of the implant sites presented at initial levels of PPD higher than 4 mm may explain the difference to the results of our study, in which $71\%$ of patients presented at an initial PPD = 4 mm and no value was higher than 4 mm. For the BOP evaluation, we chose to use the variant of giving a score, proposed by American researchers [38], because it allows the quantification of bleeding on probing at each individual implant site. The percentage evaluation in the entire oral cavity would not have allowed us to evaluate the results of the laser therapy at each implant’s level. In our study, the finding that mean BOP values in G1 IL were significantly reduced at T2 compared with T0 and compared with G1 IC demonstrates that laser therapy can be an adjuvant in the treatment of peri-implant mucositis. Similar results were obtained by Al Rifaiy et al., who concluded that antimicrobial laser therapy, as an adjuvant in the treatment of peri-implant mucositis, is more effective than simple mechanical instrumentation [42]. In a study conducted on 125 implants, the authors observed significantly reduced values of PPD and BOP, with values ≤ $5\%$, in patients treated with laser [43]. Repeated adjunctive application of laser therapy at 0, 7, and 14 days at peri-implant sites produced significant clinical improvements after an observation period of at least 2 years [44]. The results of the study by Sánchez-Martos et al. showed that patients who received laser therapy as an adjunct to conventional treatment of mucositis had less bleeding at the 3-month reassessment than patients who received only conventional therapy ($p \leq 0.001$) [45]. Starting from the observation that in patients who received adjuvant laser therapy, BOP was positive at 44 sites at T0 and t 6 sites at 3 months (T1), while for patients who received only mechanical treatment, BOP was positive at 52 sites at T0 and t 28 of places at 3 months (T1), Tenore et al. considered that laser therapy can be used as an adjunct to mechanical therapy method [37]. Mariani et al. concluded that the additional use of laser showed small additional benefits in the treatment of peri-implant mucositis after a one-year observation period, which was not statistically significant [35]. On the other hand, the results of other clinical studies led the authors to the conclusion that the additional use of laser had no further positive influence on peri-implant healing compared with mechanical instrumentation as monotherapy [34,46]. Atieh et al. concluded that in the management of peri-implant mucositis, the combined use of diode laser and mechanical debridement provided no additional clinical advantage over mechanical debridement alone [47]. Adjunctive therapy such as laser or photodisinfection treatment could provide an auxiliary advantage in peri-implantitis, as was illustrated for periodontitis, especially in patients with other systemic pathologies, such as diabetes, myocardial infarction, or rheumatoid arthritis [48,49,50,51]. Early diagnosis of peri-implant mucositis and the application of effective therapeutic methods are preventive measures in the occurrence of peri-implantitis [52]. The limitations of our study consist of the small group of patients evaluated and the evaluation of only clinical indexes; microbiological or biochemical data could have offered a more complete image of laser treatment efficacy. Another limitation is lack of comparison with other adjuvant methods in order to assess treatment superiority. Given that the data regarding adjuvant laser treatment of peri-implant mucositis are sparse and controversial, future clinical trials are needed to evaluate the potential benefit of this approach. ## 5. Conclusions The peri-implant health status is directly correlated with the maintenance of oral hygiene; therefore, the clinician must give importance to supportive therapy in order to increase the success rate of dental implants. Laser therapy as an adjunct to conventional treatment of peri-implant mucositis led to a statistically significant reduction in probing bleeding at 3-month and 6-month re-evaluations. When PPD ≤ 4 mm, laser therapy leads to an evident reduction in probing depth but not enough to be statistically significant. The conclusions of the present study should be considered preliminary and interpreted with caution. 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--- title: 'Adverse Childhood Experience as a Risk Factor for Developing Type 2 Diabetes among the Jazan Population: A Cross-Sectional Study' authors: - Omar Oraibi - Ali T. Ghalibi - Mohammed O. Shami - Meshal J. Khawaji - Khalid A. Madkhali - Abdulrahman M. Yaseen - Sultan M. Hakami - Nirmin H. Alhazmi - Khulud H. Mahla - Marwah A. Qumayri - Khalid A. Majrashi - Abdulrahman Hummadi - Mohammed A. Madkhali - Abdulaziz H. Alhazmi journal: Children year: 2023 pmcid: PMC10047776 doi: 10.3390/children10030499 license: CC BY 4.0 --- # Adverse Childhood Experience as a Risk Factor for Developing Type 2 Diabetes among the Jazan Population: A Cross-Sectional Study ## Abstract Background: Adverse Childhood Experiences (ACEs), such as childhood abuse, neglect, and family dysfunction, prevent appropriate emotional, behavioral, and physical development. They are also a major public health issue, and have been debatably linked to chronic diseases, including type 2 diabetes mellitus (T2DM). T2DM is highly prevalent in Saudi Arabia, and various theories have been raised to explain the epidemiology of diabetes. However, few studies have discussed the relationship between ACEs and T2DM. Thus, we aimed to evaluate the association between ACEs and T2DM in Jazan Province, Saudi Arabia. Methods: This observational, cross-sectional study was conducted using a validated questionnaire distributed among patients with T2DM in a diabetes center. The t-test and Chi-Square test were used for comparison, and the p-value was set at <0.05 for significance. Results: A total of 579 participants were involved in this study, and 303 ($52.33\%$) were female. Among the included participants, $45.25\%$ were diagnosed with T2DM. About $28.71\%$ of participants with diabetes experienced verbal abuse, $16.09\%$ experienced physical abuse, and $30.91\%$ reported that parents beat them. Additionally, $1.58\%$ of participants with diabetes reported living with a family member who abused substances, $8.83\%$ believed that no one would take them to the doctor even if essential, $12.62\%$ of participants with diabetes felt that no one would protect them, and $23.03\%$ reported that they felt no one in their family loved them. All reported ACEs were significantly associated with a high risk of T2DM ($p \leq 0.05$), and the more frequent the ACEs, the more the risk of T2DM ($$p \leq 0.0001$$). Conclusions: This study indicated that ACEs are significantly associated with the development of T2DM, and the risk increases with the frequency of ACEs, which aligns with other studies. Further national studies are required to understand how ACEs could contribute to T2DM, and preventive interventions in childhood must be considered to reduce the burden of T2DM. ## 1. Introduction Diabetes is a chronic metabolic disease characterized by elevated blood glucose (or blood sugar) levels, leading to serious complications in many organs including the heart, blood vessels, eyes, kidneys, and nerves [1]. Diabetes is classified into many types based on its etiology, and the most common type is type 2 diabetes (T2DM), which is seen in adults, and occurs when the body becomes more resistant to insulin [2]. In the past decades, the prevalence of T2DM has risen dramatically in many countries around the globe and factors contributing to this elevation are obesity, sedentary lifestyles, and unhealthy diets [1,2,3]. Several studies have questioned whether Adverse Childhood Experiences (ACEs), including childhood exposure to physical, sexual, and emotional abuse, neglect, and family dysfunction, could hinder healthy emotional, behavioral, and physical development and could contribute to chronic diseases, including T2DM [3]. ACEs are traumatic events that can be stressful for children and have negative consequences for their neurological, physiological, and social development, especially if repeated. Children cannot access supporting connections or other coping mechanisms [3]. Some studies have found links between early adversity and physical ailments like heart disease, stroke, and chronic respiratory disorders, presumably due to genetic predisposition, induced inflammation, or other processes [4,5,6,7]. It was observed that people with diabetes and ACEs have a greater mortality rate than adults with either diabetes or ACEs alone. In contrast, people with ACEs have greater morbidity rates and are more prone to participate in risky health behaviors, increasing the risk of diabetes [8]. These studies were supported by data from Saudi Arabia, in which Almuneef et al. conducted a national study in 2013 and concluded that ACEs are linked to diabetes mellitus and mental disorders [9]. ACEs bear a significant risk factor for diabetes, and individuals with four or more ACEs have a significantly higher risk (1.6 times) of pre-diabetes and diabetes than those without ACEs [10]. A study conducted in China on middle-aged and old Chinese subjects showed that hunger, unfavored socioeconomic status during childhood, and parental abuse were significantly associated with the development of T2DM and cardiovascular diseases [11]. Another study conducted in Singapore indicated that childhood emotional neglect, parental separation, divorce, the death of a parent, having one or two ACEs, and young age were significantly associated with higher odds of diabetes [12]. The prevalence of diabetes including T2DM in Saudi Arabia is alarming. Factors such as sedentary life, lack of physical activity, high rate of consanguinity, and notional habits have been suggested as important risk factors. However, more studies need to evaluate the influence of ACEs on T2DM in Saudi Arabia. Therefore, this study aimed to evaluate the association between ACEs and T2DM in Jazan Province in southwestern Saudi Arabia. Furthermore, other associated risk factors for T2DM are assessed. ## 2.1. Study Design This observational, cross-sectional survey was conducted in a diabetes center in Jazan, Saudi Arabia, a region hugely populated with about two million people, and records a high number of patients with diabetes. ## 2.2. Study Tool and Data Collection In this study, we used a validated questionnaire distributed among patients with T2DM using a link that directed these patients to the digital version of the questionnaire with the help of a continuous data collector. The questionnaire had two parts, and the first included questions on sociodemographic data, such as gender, age, job status, salary, marital status, education levels of parents, having relatives, body mass index (BMI), and residential place. Questions about T2DM diabetes were included with a confirmational diagnostic method as reported by participants. The second part included the Arabic version of ACEs published by the World Health Organization [13]. The questionnaire comes with questions on childhood events related to physical and emotional abuse, a history of violence against household members, or living with someone who a substance abuser is or with mental or psychological problems [13]. ## 2.3. Sample Size Calculation This study’s sample size was estimated using the Raosoft sample size calculator (Raosoft Inc., Seattle, WA, USA) (http://www.raosoft.com/samplesize.html, accessed on 15 August 2022). Considering the Jazan region population of about two million, a $95\%$ confidence interval, a $5\%$ margin of error, and a $50\%$ response distribution, the minimum sample size was 385. However, we included 579 participants in our study to increase the significant power of this study. ## 2.4. Participants’ Inclusion and Exclusion Criteria We included patients with T2DM and those 18 years or older. Data were collected between September and November 2022, and those diagnosed with diabetes but not T2DM who resided outside of Jazan province, or who refused to participate, were excluded. Patients who visited the diabetes center at the time of data collection and were confirmed not to have T2DM were added for comparison purposes. ## 2.5. Statistical Analysis Statistical analysis was run using the Statistical Package for the Social Sciences (SPSS version 23, IB, Chicago, IL, USA). Data were analyzed using descriptive and comparative statistics. Frequencies and percentages were used for the categorical variables, and continuous variables such as age and BMI were analyzed using mean and standard deviation (SD). T-tests and Chi-Square tests were used to compare variables. A p-value < 0.05 was considered statistically significant. ## 2.6. Ethical Approval The ethical approval was obtained from the Jazan Health Ethics Committee, Jazan, Saudi Arabia (Permission number 2289, dated 1 September 2022). All participants knew the study’s goals to guarantee complete privacy and confidentiality. They were also permitted to express if they wished to discontinue participation at any moment. Further, data collectors did not collect identifiers or personal information; only the study’s investigators had access to the shared document where the study’s data were kept. ## 3.1. General Characteristics of the Participants A total of 579 participants were involved in this study, with a mean age of 40.45 ± 12.93 years. Of all participants, 276 ($47.67\%$) were male, 303 ($52.33\%$) were female, 262 ($45.25\%$) were diagnosed with T2DM, mostly ($$n = 165$$, $62.98\%$) confirmed by HBA1c, and $45.94\%$ had a first-degree relative with T2DM. Most participants ($$n = 378$$, $65.28\%$) were married, employed ($$n = 314$$, $54.23\%$), and with university-level education ($$n = 422$$, $72.88\%$). Regarding the income distribution, $31.61\%$ of participants earned a monthly income of less than 5000 Saudi riyals (SAR), $26.42\%$ of participants earned a monthly income between 5000 and 10,000 SAR, $26.25\%$ of participants earned between 10,000 and 15,000 SAR, and only $15.72\%$ of the participants had a monthly income more than 15,000 SAR. The mean BMI for all participants was 26.97 ± 5.54, 201 ($34.72\%$) were overweight, and 151 ($26.08\%$) were obese. Table 1 shows a detailed description of the general characteristics of the study participants. ## 3.2. Characteristics of the Participants with Type 2 Diabetes Mellitus Compared to Those without Type 2 Diabetes Mellitus There were 262 participants with T2DM, representing $45.25\%$ of all participants. More than half of them were males ($$n = 153$$, $58.40\%$), married ($$n = 193$$, $73.66\%$), employed ($$n = 154$$, $58.78\%$), educated with university-level degrees ($$n = 174$$, $66.41\%$), overweight BMI ($$n = 96$$, $36.64\%$), and with first-degree relatives with T2DM ($$n = 121$$, $46.18\%$). Of participants without T2DM ($$n = 317$$, $54.75\%$), most were females ($$n = 194$$, $61.20\%$), married ($$n = 185$$, $58.36\%$), employed ($$n = 160$$, $50.47\%$), educated with university-level degrees ($$n = 248$$, $78.23\%$), with normal BMI ($$n = 107$$, $33.75\%$), with monthly income less than 5000 SAR ($$n = 117$$, $36.91\%$), and with first-degree relatives with T2DM ($$n = 172$$, $54.26\%$). Table 2 compares the characteristics of the participants with T2DM and without T2DM. All demographic characteristics significantly ($p \leq 0.05$) correlated with T2DM, except for having a first-degree relative relationship. ## 3.3. Adverse Childhood Events among Participants with Type 2 Diabetes Mellitus Compared to Those without Type 2 Diabetes Mellitus Less than a third ($28.71\%$) of participants with T2DM reported experiencing verbal abuse, $16.09\%$ experienced physical abuse, and $30.91\%$ reported being beaten by parents. Additionally, $1.58\%$ of participants with T2DM reported living with a family member who abused substances, $7.89\%$ had a household member with depression or another mental illness, and $2.52\%$ reported having a household member who attempted suicide. Of all participants with T2DM, $8.83\%$ believed that no one would take them to the doctor even if essential, $12.62\%$ felt that no one would protect them, and $23.03\%$ reported that they felt no one in their family loved them. All correlations between ACEs and T2DM were statically significant ($$p \leq 0.001$$). Participants reporting ACEs were at a significantly ($$p \leq 0.001$$) higher risk of T2DM than those without ACEs. Table 3 shows the answers about ACEs by the participants with T2DM compared to those without T2DM. ## 3.4. Adverse Childhood Events of the Participants with Type 2 Diabetes Mellitus Compared to Those without Type 2 Diabetes Mellitus Figure 1 shows the association between ACEs and T2DM; the more ACEs, the greater the odds, and experiencing four or more ACEs was significantly associated with higher odds of diabetes ($$p \leq 0.0001$$). Participants without ACEs had significantly lower odds of T2DM ($$p \leq 0.0001$$). ## 4. Discussion This study evaluated the association between ACEs and T2DM, a relationship that has been debated. Very few studies have discussed this relationship in Saudi Arabia, and therefore we aimed to assess this relationship among adults with T2DM in the Jazan region in Saudi Arabia. This study aligns with another study exploring ACEs, mental health, and risky behaviors among Saudis [14]. We found a significant association between ACEs and T2DM, and the risk of T2DM increased with the frequency of ACEs, which agrees with another study that showed that one or more ACEs were associated with a higher risk of T2DM among individuals living in the central region of Saudi Arabia [8]. Similarly, a systematic literature review and meta-analysis showed that ACEs, particularly childhood neglect, family dysfunction, and two or more ACEs, are associated with a higher risk of T2DM [15]. It was found that childhood economic adversity and physical, sexual, and verbal abuse were associated with T2DM [16,17]. On the other hand, a systematic literature review by Zhu et al. [ 16] reported that emotional abuse, domestic violence, parental divorce, parental death, neglect, and living with a family member with substance abuse were not significantly associated with diabetes. It is noteworthy that the sample size in these studies is different from our study sample, and the explanation for these variations might be related to the heterogeneity of the population and the high risk of recall bias in the previous studies. It was proposed by a 30-year follow-up study using court-recorded data conducted by Widom et al. that childhood physical abuse and neglect are associated with T2DM later in middle adulthood [18]. Physical and sexual abuse in childhood and adolescence were also linked to increased T2DM risk in adult women, supporting Monnat and Chandler’s data [18,19]. Sex has been suggested as another risk factor, and our study found that women with ACEs were at higher risk of T2DM than their male counterparts, which might be due to the higher incidence of ACEs in women or other factors that should be sought. As reported in the current study, studies on sex differences in the profiles of ACEs found that compared to men, women reported a more complex history of ACEs [20,21]. Further, women’s mental health, social functioning, and emotional stability are more negatively impacted by exposure to ACEs compared to men, with family dysfunction as a major poor social outcome [21]. Another study evaluating the relationship between ACEs and T2DM among women showed a strong relationship between excess weight, obesity, and diabetes [22]. The link between ACEs and the development of T2DM and other chronic physical problems has been explained by many theories. ACEs disrupt neurodevelopment, particularly the hypothalamic-pituitary-adrenal axis (HPA) [23]. Dysregulation of HPA has been suggested to be a pre-diabetes condition for people with a high risk of T2DM [23,24]. Moreover, it raises blood triglycerides, free fatty acids, and inflammatory markers, which exposes them to depression and other chronic disease risks, and upregulates gluconeogenesis, causing hyperglycemia [11,23]. Previous studies have suggested that stress and inflammatory markers may explain the link between diabetes and depression [25], and other studies have even suggested that ACEs could be associated with biological risk already present at an early age, which appeared to cause physiological changes that might be associated with later development of diseases [26]. Therefore, ACEs are indirectly linked to a higher risk of T2DM through their effects on mental health, especially their link to depression and low quality of life, which were identified as risk factors of T2DM [11,25,27]. A cohort study showed that ACEs were associated with a high risk of T2DM, with the odds of diabetes increasing by almost $11\%$ for each added ACE [26]. ACEs were also found to be indirectly related to T2DM through cardiometabolic dysregulation in addition to depression [27]. In a pilot study conducted in Riyadh, Saudi Arabia, the authors found that most participants reported experiencing one or more ACEs, and almost a third of the participants had experienced four or more ACEs [28]. Another study conducted to evaluate the prevalence and connection of ACEs with both physical and mental diseases in eastern Saudi Arabia found that the majority of study participants ($81.8\%$) had experienced four or more ACEs, with emotional neglect being the most prevalent ACE type ($82.2\%$) [29]. Additionally, women with four or more ACEs were at higher risk of insomnia, stress, and depression, which aligns with other previous studies [20,21]. Compared to those with only one ACE, those with four or more ACEs are more likely to experience physical diseases [29]. Studies conducted in Saudi Arabia comparing people with ACEs to those without ACEs revealed a two-fold greater risk of physical health problems such as hypertension, T2DM, coronary heart disease, and obesity among people with ACEs [13,30,31], agreeing with another study conducted in Saudi Arabia [19]. This high prevalence of ACEs in Saudi Arabia might contribute to high rates of T2DM in Saudi Arabia, as supported by the WHO ranking, which puts Saudi Arabia in second place among Middle Eastern countries with a high rate of diabetes and seventh highest worldwide [32,33]. Further, a recent study evaluating the prevalence of T2DM in Saudi Arabia from 1999 to 2022 estimated it to be $29.2\%$ in 2011 and $44.1\%$ in 2022 [34]. These reports indicate that diabetes is a huge health burden in Saudi Arabia, and future strategies, programs, and research should consider ACEs as a factor in reducing the burden. It is noteworthy that this study was conducted just a few months after the alleviation of the COVID-19 pandemic restrictions in Saudi Arabia, a period that had social and psychological consequences on most countries around the globe, including Saudi Arabia and the Jazan region [35]. Thus, the results of the current studies should be interpreted carefully as COVID-19 consequences may have resulted in biases related to the reported information. However, the association between ACEs and the development of T2DM in this study cannot be ignored, and this should encourage local health officials to conduct a larger study that may include other chronic diseases. When interpreting and generalizing the results of the current study, some limitations should be considered. The study’s cross-sectional design could not explain the temporal relationship between the study variables. Unlike some previous studies [36], different types of ACEs, such as the correlation of sexual abuse between ACEs, physical health, and diet, were not investigated in our study. However, we studied alcohol and substance abuse, which was not previously studied in Saudi Arabia [13], and our sample was homogeneous, increasing the accuracy of our results. We recommend further longitudinal, prospective clinical studies with an extensive and representative sample to understand the association between ACEs and T2DM [37,38]. ## 5. Conclusions This study confirmed that T2DM is associated with increasing ACEs in Jazan, Saudi Arabia, and more ACEs are significantly associated with a higher risk of T2DM development, highlighting the need to reduce the risk variables linked to ACEs. Since parents and other family members are primarily in charge of raising children, establishing education programs aimed at reducing child abuse and improving child well-being by targeting them might be an effective intervention. Therefore, the findings of this study can be utilized to fine-tune public awareness campaigns that target all families in Jazan and encourage healthy behaviors among youngsters. Further, larger studies based on medical record data and including other chronic diseases could be conducted for a better understanding of the consequences of ACEs at an individual and public level, and how preventive interventions in childhood could be tailored to limit the burden of T2DM. ## References 1. Ahmed A.S., Alotaibi W.S., Aldubayan M.A., Alhowail A.H., Al-Najjar A.H., Chigurupati S., Elgharabawy R.M.. Factors Affecting the Incidence, Progression, and Severity of COVID-19 in Type 1 Diabetes Mellitus. BioMed Res. Int. (2021.0) 2021 1676914. DOI: 10.1155/2021/1676914 2. 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--- title: Why and How Should We Assess the Cardiovascular Risk in Patients with Juvenile Idiopathic Arthritis? A Single-Centre Experience with Carotid Intima-Media Measurements authors: - Marta Gruca - Krzysztof Orczyk - Justyna Zamojska - Katarzyna Niewiadomska-Jarosik - Jerzy Stańczyk - Elżbieta Smolewska journal: Children year: 2023 pmcid: PMC10047782 doi: 10.3390/children10030422 license: CC BY 4.0 --- # Why and How Should We Assess the Cardiovascular Risk in Patients with Juvenile Idiopathic Arthritis? A Single-Centre Experience with Carotid Intima-Media Measurements ## Abstract Background: Children diagnosed with juvenile idiopathic arthritis (JIA) are thought to be more likely to develop cardiovascular disease in adulthood. The factors modulating the cardiovascular risk, involving exposure to secondhand smoking, sedentary lifestyle and abnormal body mass index, might have had a stronger impact during the COVID-19 pandemic. The lack of reliable prognostic markers for a higher probability of cardiovascular events might be solved by carotid intima-media thickness (cIMT) measurement. The paramount goal of the study was to assess its usefulness in JIA patients. Materials and Methods: The results of cIMT measured by a single physician in 45 children diagnosed with JIA were compared to 37 age- and sex-matched healthy counterparts. The analysis also involved anthropometric parameters, laboratory tests, and a survey regarding lifestyle-related factors. Results: Four JIA patients appeared to have cIMT above the 94th percentile. A positive correlation between erythrocytes sedimentation rate (ESR) and right carotid artery percentiles was found. Passive smoking increased the cardiovascular risk regardless of JIA. Doubling the daily screen time during the pandemic led to a significant reduction in children’s physical activity. However, the number of enrolled subjects was not enough to make significant recommendations. Conclusions: cIMT measurements remain an interesting perspective for future cardiovascular screening of children with JIA. It has yet to be determined whether it should be considered in all JIA patients on a reliable basis. ## 1. Background Despite the undeniable progress in therapeutic possibilities for patients diagnosed with the most common arthropathy in childhood, juvenile idiopathic arthritis (JIA), a substantial proportion of patients develop mild to moderate long-term disability with a decreased health-related quality of life [1]. Similarly to other autoimmune juvenile-onset illnesses, including type 1 diabetes and systemic lupus erythematosus, JIA patients are deemed to be at higher risk of developing cardiovascular disease (CVD) during the adulthood [2]. As for adults with rheumatoid arthritis (RA), there was a $48\%$ increased risk of incident CVD involving acute myocardial infarction, cerebrovascular events, and congestive cardiac failure [3]. They were also more likely to develop long QT syndrome when compared to healthy controls [4]. Cardiovascular involvement in JIA may affect all parts of the heart, including peri-, myo- and endocardium, valves, and coronary arteries [5]. The cardiac conduction system may as well be engaged; however, the current findings regarding QT intervention in children with JIA remain ambiguous [6]. Early recognition of subclinical abnormalities in cardiac function and structure is essential for the prevention of apparent cardiovascular symptoms and worse long-term health outcomes [7]. Active inflammation within JIA is combined with the overproduction of proinflammatory cytokines, involving tumor necrosis factor and interleukins 1 and 6, which may subsequently promote endothelial dysfunction, playing a crucial role in atherogenesis [8]. Importantly, the levels of proinflammatory molecules may remain elevated even during the remission of arthritis [9]. Duffy et al. noted that arthritis is very likely to persist in adulthood if the remission does not occur within 10 years of disease onset, which can be achievable only in approximately $30\%$ to $35\%$ of patients [10]. Furthermore, young adults with RA have been reported to develop subclinical signs of atherosclerosis [11]. Given that persistent inflammation combined with negative environmental factors may result in a higher incidence of cardiovascular accidents [12], the promotion of ideal health behavior in JIA patients seems to be underrated. Cardiovascular prevention should be defined as a coordinated set of actions designed for the population or selected individuals to eliminate or minimize the consequences of and disabilities caused by CVD [13]. In 2015, the costs of managing CVD and their sequelae were estimated to be 210 billion euros in the whole European Union [14]. It consumed $16\%$ of healthcare expenditures in Poland, compared to $8\%$ in Germany, $7\%$ in France, and only $3\%$ in Denmark and Sweden [14]. The two main compounds of these costs are hospitalizations and pharmacotherapy [15]. An efficient system of prevention would reduce the treatment expenses related to CVD [16]. The principal objective of CVD prophylaxis in the pediatric population is to maintain an ideal state of cardiovascular health [17]. In 2016, The American Heart Association proposed a set of metrics in children and adolescents to define cardiovascular health [17]. It included: Abstinence from smoking, a normal body mass index (BMI), regular physical activity levels, a proper diet (containing fruits, vegetables, fish, whole grains, and low sodium and low sugar foods and drinks), a low concentration of total cholesterol, proper blood pressure (BP), and a normal fasting glucose level. The assessment of the listed components during the routine visit of a JIA patient in rheumatology outpatient clinic might be a useful screening method for differentiating children with a substantially higher cardiovascular risk [18]. However, meticulous history taking may not be sufficient to evaluate the full impact of the COVID-19 pandemic on the patients’ behavior. The temporary closure of schools and sports facilities along with the sudden transition to online learning must have affected the frequency of physical activity as well as dietary habits and screen exposure time [19,20]. These alterations might have influenced previous cardiovascular risks in JIA patients. Therefore, there is a need to determine a new, easily-accessible tool which can be utilized to identify children who require closer cardiology care. Carotid intima-media thickness (cIMT) measurement is one of the best verified non-invasive methods of prognosing atherogenesis and evaluating cardiovascular risk [21]. The strong correlation between the dimensions obtained with ultrasonography and the vessel thickness in histological samples, followed by the acceptable reproducibility of this method, resulted in its high prognostic value in patients at risk of developing CVD [22]. It has been considered as a reliable outcome measure in clinical trials regarding the effectiveness of lipid-lowering therapy in the reduction of cardiovascular risk assessed with the utilization of cIMT [23]. This assessment is frequently performed in adults diagnosed with RA, who tend to develop atherosclerosis at a younger age than their healthy counterparts and therefore have higher cIMT values [24,25]. Nevertheless, there are conflicting data regarding the utilization of cIMT in the pediatric population. Ilisson et al. [ 26] found increased cIMT in children at the early stage of JIA, whereas Mani et al. [ 27] and Ververs et al. [ 28] did not observe any significant difference between JIA patients and healthy controls. There are no data on cIMT values in healthy children depending on their attitude to the rules of cardiovascular prevention. The principal objective of this study was to further evaluate the relevance of measuring cIMT in the assessment of cardiovascular risk in JIA patients. Furthermore, the authors attempted to distinguish current lifestyle-related factors that are crucial to be eliminated in order to avoid CVD in this population. ## 2. Materials and Methods The study group consisted of 45 patients (33 girls and 12 boys) with a median age of 14.0 (IQR 6.0) years, who were diagnosed with JIA in accordance with ILAR criteria [29]. Eighteen of them were classified as oligoarthritis (which is the most common subtype of the disease), nine as enthesitis-related arthritis (ERA) and the remaining eighteen as other subtypes, including systemic-onset JIA as well as RF negative and RF-positive polyarthritis. The results from the study group were compared to 37 age- and sex-matched healthy controls who were recruited from patients hospitalized for non-arthritic reasons (mainly: functional dysfunction of the cardiovascular system, e.g., syncope). The study database included anthropometric measurements (height, weight and calculated BMI) of all participants. Overweight and obesity were defined as BMI values above the 85th and 95th percentile, respectively. The parents supported by the patients answered a survey regarding the perinatal period, average physical activity (before and during the pandemic), mean screen exposure time (before and during the pandemic), dietary habits (including junk food and soft drinks), exposure to secondhand smoking, and a family history of CVD. The time from JIA diagnosis to study onset and administered treatment were recorded in the database. However, the possible influence of glucocorticoids was not evaluated due to considerable inconsistencies in the data using both intravenous and systemic steroids in a variable period of time prior to the study onset. Approximately 3 mL of blood samples were drawn from all patients after 8 to 12 h of fasting. The serum levels of the following parameters were measured: erythrocytes sedimentation rate (ESR), C-reactive protein (CRP), glucose, total cholesterol, high- and low-density lipoprotein, triglycerides, uric acid, and creatinine and alanine transaminase (ALT). The oscillometric measurement of blood pressure was performed several (at least three) times during the hospitalization of each patient. The examination was executed in the sitting position and was preceded by at least 5 min of rest. A sphygmomanometer with an inflatable cuff adjusted to arm length and circumference was utilized for these measurements. Furthermore, every patient received a 12-lead electrocardiogram (speed paper 50 mm per second) with the assessment of heart-rate corrected QT interval using Bazett’s formula. The assessment of patients’ cardiovascular health was supplemented with an echocardiographic evaluation of heart function and structure performed by pediatric cardiologists (JZ, KNJ, JS) using Philips Epiq Elite. Ultrasound measurements of cIMT were performed using Toshiba Aplio 400 with linear transducer with the frequency 12 MHz by a single trained examiner (MG) who was not blinded to diagnosis. Both right carotid artery (RCA) and left carotid artery (LCA) were assessed and their cIMT values were calculated to percentiles. The procedure was conducted in accordance with Pignoli et al. [ 30] as described in other Polish studies [31,32]. Each patient was asked to lie in the supine position and rotate their head to the left and right to form a 45° angle between the head and the examined artery. B-mode and Color Doppler function were utilized within the procedure. Each artery was measured three times to calculate the mean cIMT for further evaluation. Statistical analysis was conducted with the use of Statistica 13.3 software (Statsoft Polska, Kraków, Poland). Spearman’s rank correlation test was performed for variables that were not normally distributed. Group comparisons were made using the Mann–Whitney U test. Relations between categorical variables were assessed using Pearson’s chi-squared test. Values were presented as median with interquartile range (IQR) in brackets. p values were adjusted using Tukey HSD test. A valid analysis of potential confounders was not possible due to the sample size. p values less than 0.05 were considered significant. The study was approved by the Bioethics Committee of the Medical University of Lodz, Poland (Approval No. RNN/$\frac{101}{19}$/KB issued on 12 February 2019). The patients provided their informed consent to join the study. This was obtained in a written (when a patient turned 16 before the participation in the study) or oral form (before 16 years of age). ## 3. Results The general characteristics of the study group are presented in Table 1. After performing measurements in all 82 patients from the both study and control group, cIMT exceeding the 94th percentile was found in four children with JIA (three boys and one girl). However, the sample size was too small to reach statistical significance for JIA ($$p \leq 0.06433$$) as a potential risk factor. Although the duration of arthritis did not affect cIMT values ($$p \leq 0.74$$), all patients with increased cIMT were diagnosed long enough to be treated with biologic therapy, specifically adalimumab ($$p \leq 0.091$$). Additionally, there was no effect of JIA subtypes on the cIMT results ($$p \leq 0.489$$). The analysis of inflammatory markers shown that 5 out of 18 patients with elevated ESR had cIMT above the 75th percentile. Although it was not yet an absolute abnormality, it did significantly differ from children with ESR within the normal limits, with only 2 out of 27 patients exceeding the 75th percentile ($$p \leq 0.0673$$, see Figure 1). Furthermore, increased ESR values correlated with higher RCA percentiles on the edge of statistical significance ($r = 0.2922$, $$p \leq 0.051443$$, see Figure 2). Interestingly, the parallel effect on LCA had weaker strength and relevance ($r = 0.2603$, $$p \leq 0.084133$$). No other remarkable discrepancies in laboratory test results were found within the study. Although the presence of the human leukocyte antigen (HLA) B27 seemed to influence cIMT values (which were abnormal in 3 out of 15 HLA-B27+ patients comparing to 1 out of 20 HLA-B27- individuals), the postulated effect was not statistically significant ($$p \leq 0.178$$). A routine 12-lead electrocardiogram did not reveal any important abnormalities in both study and control groups (no arrhythmia, hypertrophy, ischemia, or long QT syndrome were detected). Similarly, the echocardiographic assessment did not show any cardiac defect or dysfunction. Blood pressure measurements identified one patient with prehypertension in the control group. Family history of CVD was positive in 20 out of 45 JIA patients compared to 18 out of 37 healthy peers. There was no statistical difference in this parameter between groups. Exposure to secondhand smoking turned out to be a factor modulating the cardiovascular risk regardless of JIA. Abnormal cIMT was found in 2 out of 11 patients whose parents admitted to smoking in the presence of their children, and it considerably differed ($$p \leq 0.0278$$) from the children of non-smoking parents (2 out of 71 patients). JIA patients appeared to be under- ($\frac{4}{45}$ vs. $\frac{0}{37}$), overweight ($\frac{11}{45}$ vs. $\frac{7}{37}$), or obese ($\frac{7}{45}$ vs. 1.37) more frequently than their healthy counterparts ($$p \leq 0.00585$$). Furthermore, overweight or obese patients were more likely to have no physical activity at all. “ Inactive lifestyle” was present in 16 out of 45 JIA patients compared to 7 out 37 healthy peers. Importantly, patients’ parents denied symptoms of JIA as the main factor modulating children’s activity. Nevertheless, the difference did not reach statistical relevance ($$p \leq 0.0976$$). Additionally, an “inactive lifestyle” was also associated with sedentary screen time. Doubling the time of screen exposure during the pandemic markedly resulted ($p \leq 0.001$) in children not receiving the recommended level of regular physical activity (at least 3 times a week). However, JIA patients with an “inactive lifestyle” had screen time exposure exceeding 3 h a day even before the pandemic ($$p \leq 0.04403$$). ## 4. Discussion Persistent active inflammation appears to increase the risk of developing CVD in adults diagnosed with RA [33]. Can one measure the cardiovascular risk in JIA patients too? The current study presents the positive association between elevated ESR and cIMT values, which was concordant with the number of previous findings [34,35]. However, other studies have denied the discrepancies in cIMT values in adults diagnosed with JIA in their childhood when compared to their healthy counterparts [36]. Del Giudice et al. [ 37] proposed that exposure to secondhand smoking, a decreased BMI, and elevated homocysteine levels might be considered as potential risk factors of developing abnormal cIMT in children with JIA. Furthermore, Hussain et al. [ 35] observed a worse lipid profile, left ventricular mass index, and brachial artery flow mediated dilatation. Therefore, they postulated earlier cardiovascular dysfunction in JIA patients. Nonetheless, Breda et al. [ 38] reported an improvement in cIMT values after a year of treatment of JIA. Hence, the aggressive therapy according to treat-to-target approach [39] might provide an additional benefit of maintaining cardiovascular risk along with the inflammatory activity of JIA. Singh et al. postulated that ESR may be considered a sensitive marker for the extensiveness and intensity of atherosclerosis in adults [40]. They reported a significant correlation between ESR and cIMT ($p \leq 0.0001$) and the presence of the atherosclerotic plaque ($$p \leq 0.026$$). Children, however, do not frequently present the apparent manifestations of atherosclerosis and its sequelae [41]. The highest cIMT value in the current study (0.53 mm) was detected in an obese patient treated for four years with adalimumab due to therapy-resistant JIA. Importantly, Satija et al. noted a positive correlation ($r = 0.432$ $$p \leq 0.015$$) between ESR and cIMT in JIA patients [42]. Interestingly, the relationship between ESR and cIMT is more evident in adult RA patients with a risk factor for developing CVD (diabetes, hypertension, hypercholesterolemia, obesity, and smoking cigarettes) [43]. On the other hand, Al-Shehhi et al. did not report a significant correlation between cIMT and ESR in Irish patients with RA or psoriatic arthritis [44]. Although the authors did not confirm the effect of lifestyle-related factors on ESR values, the cardiovascular prevention in children with JIA seems to be legitimate. Despite being more prevalent in boys (3:1), elevated cIMT values were not significantly dependent on gender in this study. Murni et al. [ 45] indicated the positive association of hyperinsulinemia and hypercholesterolemia with cIMT among boys but it was not observed in girls. Superko et al. [ 46] noted that chronic inflammation in adults with RA leads to the structural modification of lipoproteins and an unfavorable lipid profile, leading to a so-called atherogenic phenotype, which manifests as decreased high-density lipoprotein, increased triglycerides, and increased small, low-density lipoprotein. Interestingly, cIMT was found to be correlated with low-density lipoprotein in JIA patients by Breda et al. [ 38]. The authors did not find such a relationship in this study. On the other hand, smoking and drinking alcohol affected cIMT in adult women but not in men who presented higher cIMT values when diagnosed with hypertension [47]. The laboratory test results from the study initially suggested the potential importance of the presence of HLA-B27, but eventually no significant association was found. Patients with ankylosing spondylitis were reported to have higher cIMT values elsewhere [48], but, similarly, there was no correlation with HLA-B27. Long QT syndrome may result in the elevated risk of developing cardiac rhythm disturbances and sudden cardiac death in the general population [49]. Voskuyl et al. [ 4] found RA patients to be at higher risk of these cardiovascular manifestations than healthy controls. However, Koca et al. did not observe a higher incidence of long QT syndrome in JIA patients [6]. Additionally, this study did not reveal any significant abnormalities in the electrocardiogram of JIA patients; however, current state-of-the-art research does not allow for explicit conclusions about cardiac arrhythmias in children with JIA. Blood pressure is another issue to be addressed in the evaluation of the state of cardiovascular health. The authors did not detect any abnormalities in this matter in JIA patients (only one child from the control group was classified as prehypertension). Breda et al. [ 38] reported elevated blood pressure in JIA patients, but with the tendency to decrease after a one-year observation. Such an effect may be caused by the better management of underlying JIA within the time interval between these publications. Accordingly, the general health status of JIA patients has been improved within the last few years thanks to better access to treat-to-target therapy [50]. It may also explain why no significant findings regarding blood pressure were found in this study, which did not include children with a new diagnosis but, rather, patients previously diagnosed with JIA with a median duration of 4.0 years. Can the lifestyle-related factors modulating cardiovascular risk be easily defined? Overall, exposure to secondhand smoking has a deep impact on child development. It is not only considered as a risk factor of developing JIA [51,52] but it essentially increases the probability of CVD developing in adolescents and young adults [53], including those suffering from autoimmune arthritides [54]. Evidence from Young Finns The Cardiovascular Risk revealed that having both parents smoke resulted in vascular age 3.3 years greater in young adulthood when compared to having neither parent smoke [55]. Furthermore, exposure to secondhand smoking was associated with higher cIMT values in the latest meta-analysis [56]. In this study, the effect of passive smoking on cIMT measurements was determined regardless of JIA. The observed influence might have become even stronger during the COVID-19 pandemic, when smokers confessed to having increased their smoking activity, especially during remote working [57]. Moreover, the COVID-19-related restrictions exacerbated the epidemic of overweight and obesity in children and adolescents [58,59]. The excess weight seems to play an important role in developing cardiovascular risk factors in JIA patients, who are more likely to have abnormal BMI [60,61]. However, body composition was not dependent on cIMT in a study of the general pediatric population in Slovenia [62]. The lack of association between cIMT and BMI was also reported by Aggoun et al. [ 63] and reflected in the results of this study as well. Nevertheless, regular physical activity may reduce the risk of developing CVD up to $30\%$ [64]. Beukelman et al. reported that the general level of physical activity was lower in JIA patients than in their healthy peers [65]. Children diagnosed with JIA were also observed to have less leisure activity during the week [66]. Due to psychosocial stress triggered by the diagnosis of JIA, a trend towards the “inactive lifestyle” tended to increase over time from disease onset [67]. Therefore, positive health behaviors, including regular physical activity in childhood and adolescence, need to be further promoted [68]. A reduction in sedentary screen time could also potentially have broad health benefits, as it is associated with, e.g., higher energy intake and poor diet quality [69]. Current approaches regarding the personalized treatment of JIA postulates that complex management should be provided by a multidisciplinary team involving trained physicians, nurses, physiotherapists, psychologists, and other allied health professionals [70]. The current article questions whether a pediatric cardiologist should become a regular member of the team in order to manage cardiovascular risks in JIA patients. ## 5. Limitations The central limitation of this study appears to be its small sample size and its relative heterogeneity. The authors encountered obstacles in recruiting a larger number of patients during the pandemic due to the limitations for non-COVID-19 patients as well as the reluctance of patients’ parents to spend more time in the hospital than necessary. There were also numerous confounders that the authors were unable to measure, which may have influenced the results. The probable effect modifiers include: exposure to glucocorticoids in various ways of administration; age at the onset of treatment; duration of symptoms before diagnosis of JIA; dissimilarities between JIA subtypes; number of older siblings; parents’ marital status; and truthfulness about secondhand smoking exposure or dietary habits. The remaining major restrictions include: selection bias (only consenting patients who were admitted to the department during the recruitment period were included in the study); recall bias (part of the analysis was based on parents’ answers); the subjectivity of ultrasound measurements conducted by a single examiner who was not blinded to diagnosis; and no follow-up assessment after a certain period of time. Future reanalysis would be possible after enlarging the study group and expanding the study team with another physician to cross-check cIMT values. ## 6. 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--- title: 'Motus Vita Est: Fruit Flies Need to Be More Active and Sleep Less to Adapt to Either a Longer or Harder Life' authors: - Lyudmila P. Zakharenko - Dmitrii V. Petrovskii - Margarita A. Bobrovskikh - Nataly E. Gruntenko - Ekaterina Y. Yakovleva - Alexander V. Markov - Arcady A. Putilov journal: Clocks & Sleep year: 2023 pmcid: PMC10047790 doi: 10.3390/clockssleep5010011 license: CC BY 4.0 --- # Motus Vita Est: Fruit Flies Need to Be More Active and Sleep Less to Adapt to Either a Longer or Harder Life ## Abstract Background: Activity plays a very important role in keeping bodies strong and healthy, slowing senescence, and decreasing morbidity and mortality. Drosophila models of evolution under various selective pressures can be used to examine whether increased activity and decreased sleep duration are associated with the adaptation of this nonhuman species to longer or harder lives. Methods: For several years, descendants of wild flies were reared in a laboratory without and with selection pressure. To maintain the “salt” and “starch” strains, flies from the wild population (called “control”) were reared on two adverse food substrates. The “long-lived” strain was maintained through artificial selection for late reproduction. The 24 h patterns of locomotor activity and sleep in flies from the selected and unselected strains (902 flies in total) were studied in constant darkness for at least, 5 days. Results: Compared to the control flies, flies from the selected strains demonstrated enhanced locomotor activity and reduced sleep duration. The most profound increase in locomotor activity was observed in flies from the starch (short-lived) strain. Additionally, the selection changed the 24 h patterns of locomotor activity and sleep. For instance, the morning and evening peaks of locomotor activity were advanced and delayed, respectively, in flies from the long-lived strain. Conclusion: Flies become more active and sleep less in response to various selection pressures. These beneficial changes in trait values might be relevant to trade-offs among fitness-related traits, such as body weight, fecundity, and longevity. ## 1. Introduction From an evolutionary perspective, an organism is designed to extract energy from the environment and use it to produce offspring. In order to increase reproductive success, it is necessary to maintain the balance between energy intake and expenditure, and to optimally allocate energy across the life span whilst growing up to take care of the body, reproduce, and make it easier for the offspring to reproduce [1,2]. Species use very diverse strategies to allocate energy to the essential tasks of growth, maintenance, movement, reproduction, and care of offspring [3]. Natural selection often does not favor the reduction in energy expenditure. The tendency to increase energy expenditure can be exemplified by the evolution of physical activity in its most common form, locomotor activity, which represents an important component of daily energy expenditure in animals and humans [4,5]. Organisms that have evolved with increased levels of locomotor activity would be expected to cope with changes in their environment better than organisms with reduced levels of this activity [4,6]. The increase in locomotor activity has often played a major role in macroevolution of broad taxonomic groups of animals. For example, selection for high levels of aerobically supported locomotor activity can be a key factor causing the evolution of vertebrate endothermy [7,8]. In humans, many pathologies, such as Parkinson’s disease, Huntington’s disease, activity disorders, and depression, are associated with the deficits of locomotion [9,10]. Therefore, understanding the genetic and environmental contributors to locomotor behavior is important from the perspectives of evolutionary biology and human health [11,12,13,14]. Habitual levels of locomotor activity and health-related physical fitness traits appear to be genetically heritable [15,16,17,18]. In human studies, such traits have been associated with various health benefits, while, in conditions of food abundance and limited physical activity, the excessive amounts of fat accumulate and can even cause obesity, mostly due to energy imbalance [19,20,21,22,23]. A low metabolism underlies a slow pace of growth, reproduction, and aging in humans. However, their evolutionary history has deemed that they be more active and sleep less compared to their close relatives, apes [24]. Although higher physical activity requires more energy, humans have not evolved to prevent senescence by its reduction. Instead, they have evolved to remain physically active for the improvement of their health and extension of their lifespans [24,25,26]. Consequently, this integral component of most behaviors plays a very important role in keeping the body strong and healthy, slowing senescence, and decreasing morbidity and mortality. Humans in and after middle age require physical activity to increase their lifespans and reduce the risk of disease and death [27,28,29,30]. In order to manage peoples’ health today, it is necessary to understand [1] why our and many other species evolved to be physically more active and, therefore, to sleep less and [2] how and why the evolutionary changes in longevity and health were accompanied by changes in activity and sleep patterns. Selection for a single trait can often result in correlated changes in other traits [31,32]. The idea that several important traits can evolve together in response to selection is not controversial, but it is difficult to directly test this in human evolutionary studies [33]. Many obvious practical and ethical obstacles limit the scope for experiments using humans in such studies. Therefore, many insights have been derived from experiments on model organisms, and much of what we know about the underlying co-evolution of complex traits had come from studies using these organisms. Laboratory selection has been long used for exploring hypotheses about various adaptations because experimental evolution can quickly and reproducibly shape phenotypes in model species [34,35]. Particularly, some animal species offer unique models for the experimental research of how locomotor activity has co-adapted with other complex traits, such as longevity, fecundity, energy conservation, body weight, resistance to stress and infections, etc. [ 36,37]. Drosophila melanogaster (fruit fly or, more correctly, vinegar fly) is a human commensal of eastern sub-Saharan African origin [38,39]. For more than a century, it has been used as a model organism to study a diverse range of biological processes, including evolution through natural and artificial selection, genetics and inheritance, locomotion and other behaviors, learning and cognitive skills, development and aging, disease vulnerability, etc. Due to its simple and rapid life cycle, cosmopolitan distribution, ease of maintenance in the laboratory, and well-understood evolutionary genetics, Drosophila melanogaster has become one of the most powerful animal models for studying the evolution of complex traits [36,40]. Humans and fruit flies may not look very similar. However, it is well-established that most of the fundamental biological mechanisms and pathways that control the behavior, development, survival, and reproduction of an organism are conserved between these two species across their evolution [41]. Therefore, the genes affecting locomotion and aging in Drosophila often have human orthologs and will elucidate corresponding mechanisms in humans [35,36,42,43,44]. Drosophila melanogaster exhibits a strong response to artificial selection (i.e., selective breeding) for high and low levels of locomotor reactivity [41]. Genes affecting locomotor activity are also likely to be involved in many other forms of behavior, neurogenesis, metabolism, development, major cellular processes, etc. [ 42]. In general, such artificial selection experiments can be used to mimic evolutionary processes to test hypotheses about the correlated evolution of complex traits [45,46,47,48]. Since this experimental approach can help to reveal the underlying genetic correlations between a trait under selection and other traits, Drosophila melanogaster became a favorite model organism for experimental selection in studies of complex traits associated with the aging process [49,50,51,52,53,54,55]. For the elaboration of the mechanisms underlying aging and senescence, artificial selection was employed for Drosophila to create extended longevity strains [56,57,58,59]. It was, in particular, demonstrated that reproduction late in life increases longevity [52,56,60,61,62]. Moreover, the co-evolution of complex traits in Drosophila melanogaster can be investigated using another approach, laboratory (controlled) natural selection (or, more accurately, natural selection in a controlled environment). Instead of selecting flies according to their values for a given trait, they are allowed to evolve for many generations under experimentally imposed environmental treatments, such as changes in nutrient and temperature, different light–dark cycles, etc. This approach allows the experimenter to impose carefully controlled selective conditions in the laboratory and then observe evolutionary responses in real time [63]. One example of evolutionary responses in laboratory settings is the investigation of adaptation of selected strains to different diets [64,65,66,67,68,69,70]. Nutrition is a primary determinant of reproductive capacity, life span, rate of development and aging. The amount and quality of consumed nutrients have a strong impact on life-history (or “fitness components”) traits, such as disease vulnerability, stress resistance, fertility, body weight, reproduction, and longevity [71,72,73,74,75,76,77]. Poor nutrition during development generally results in detrimental fitness effects, which include decreased values of traits such as body weight, fecundity, and lifespan [78,79,80]. Therefore, Drosophila melanogaster has been widely used in experiments on controlled natural selection to address many fundamental questions in evolutionary biology, physiology, and ecology [81,82,83]. Overall, two approaches, artificial selection and controlled natural selection, can be used to test the adaptive changes in various complex traits, including activity, sleep, body weight, fecundity, and longevity. In the present study, these approaches to modeling the evolutionary process in Drosophila melanogaster were applied to examine whether locomotor activity can increase and, consequently, whether sleep duration can decrease in response to artificial selection toward a longer lifespan and in response to controlled natural selection on adverse food substrates. The following major hypothesis was tested: [1] When fruit flies evolve to have a longer lifespan or to live and reproduce on an adverse food substrate, their locomotor activity increases and, consequently, their sleep duration decreases. The alternative hypothesis can be also proposed: [2] Fruit flies are becoming less active and sleep more. ## 2. Results The characteristics of the 24 h cycles of locomotor activity and sleep in flies from the three selected strains (long-lived, salt, and starch) have diverged from the characteristics of the control flies that were left on the control food substrate without any selection imposed on their development and reproduction. Compared to the control flies, flies from each of the three selected strains demonstrated higher locomotor activity and shorter sleep duration (Table 1 and Table 2). The most profound increase in locomotor activity was found in starch flies (Figure 1), which, in fact, were selected for early reproduction due to their reduced lifespan (Figure S1). Consequently, three selected strains (long-lived, salt, and starch) have also evolved a reduced sleep duration (Figure 2 and Table 1 and Table 2). At least two of three selected strains showed significant differences from the control strain not only in mean levels but also in the 24 h patterns of locomotor activity and sleep (Table 1 and Table 2, and Figure 1 and Figure 2). For instance, the selective breeding of the long-lived flies for late reproduction was associated with changes in the positions of morning and evening peaks of locomotor activity. The long-life-selected flies demonstrated a phase advance of the morning peak combined with a phase delay of the evening peak (Figure 1 and Figure 2). Having a higher overall level of locomotor activity, flies from the starch strain were also more active in the morning and evening hours than the control flies. However, flies from this strain did not show a clear difference from the control flies in the positions of morning and evening peaks (Figure 1 and Figure 2). Importantly, all such differences between selected and unselected strains in the 24 h patterns showed stability across substrate (Table 2) and sex (Figure 1 and Figure 2). This prompted us to examine the 24 h patterns in the hybrid flies obtained by the bidirectional breeding of flies from the long-lived and starch strains. As expected, these flies demonstrated intermediate 24 h patterns from their parent strains (Table 2 and Figure 1b,c and Figure 2b,c). The results for the fly weight (Table 3), concentration of two sugars in the fly’s hemolymph (Table 3), survival rate (Supplementary Figure S1 and Tables S1) and fecundity (Supplementary Table S2) provided the possibility to relate changes in the locomotor activity of flies from the selected strains (Table 1 and Table 2) to the changes in several life-history traits. For instance, we found that an adaptation permitting us to allocate a higher amount of energy in locomotion can be, at least partly, explained by adaptive morphophysiological changes (i.e., a decrease in body weight combined with an increase in concentration of trehalose, the major insect sugar; Table 3). Moreover, the results on survival rate (Table S1 and Figure S1) revealed the expected strain-specific differences between flies from the selected and unselected strains that have persisted over several generations in the Novosibirsk institute. For example, the selection of flies through breeding older adults resulted in an evolutionary increase in the survival of flies from the long-lived strain (i.e., a longer lifespan compared to the control flies). In contrast, the strains adapted to living on adverse food substrates had shorter lifespans than the control strain (Table S1 and Figure S1). The lifespan was found to be the shortest in flies from the starch strain reared on the unhealthiest of all diets (Figure S1). Sleep was defined as behavioral quiescence and indexed as the absence of any locomotor activity for five or more consecutive minutes. See also other notes in the legend to Figure 1 and see Table 1 and Table 2 for the results of rANOVAs of data illustrated in Figure 2a,b. Finally, the comparison with the control strain suggested that rearing flies from the salt and starch strains on adverse food substrates also resulted in a significant reduction in the number of offspring (Table S2). ## 3. Discussion Animal models can provide new insights into the evolutionary relationships between locomotor activity and various morphophysiological traits, development, health, and longevity. Here, we used four replicate strains of Drosophila melanogaster to test the following hypotheses: [1] The evolution of flies toward a longer life and life on two adverse food substrates can cause an increase in locomotor activity and a decrease in sleep duration; [2] Alternatively, these flies become less active and sleepier. We found that flies selected to live either longer or on two adverse food substrates demonstrated a significantly enhanced locomotor activity and a significantly decreased sleep duration. In the case of such difference between the long-lived and control strain of Drosophila melanogaster, our results suggested that artificial selection for a long lifespan via delayed reproduction also selects for increased locomotor activity and decreased sleep duration. This association resembles the well-known difference between humans and ape species in terms of longevity and physical activity or sleep duration [24,25,26,84]. Notably, an enhancement of locomotor activity and, consequently, a reduction in sleep duration were consistently observed under rather diverse selection pressures. In particular, such similar responses were found to be caused by selection for two different durations: for imposing either earlier or later reproduction in flies from either the starch or long-lived strain, respectively. A possible reason for the similarity of responses to distinct selection pressures remains to be explored. Our data on these responses of locomotor activity and sleep to various selection pressures are mostly consistent with previously published observations. For instance, the literature suggests that long-life-selected flies have an increased level of locomotor activity (e.g., [85]) and shorter sleep duration (e.g., [86]). Moreover, both short- and long-lived flies can display significantly higher locomotor activity than control flies [85]. However, the interpretation of these selection-induced changes in locomotor activity and sleep are not straightforward because the literature also indicates that different stress selections can cause a decrease rather than an increase in locomotor activity (e.g., [87]) and an increase rather than a decrease in sleep duration ([88,89]). Therefore, further research on experimental selection is required to clarify the effects of diverse stressing factors on the evolution of locomotor activity and sleep. Since genes affecting locomotion are also likely to be involved in neurogenesis, metabolism, development, general cellular processes, etc. [ 42], we tried to answer question such as: can the selection-induced increase in a trait value (i.e., an increase in value in the case of locomotor activity) correlate with the responses of some of life-history traits? Namely, we expected that selection for late reproduction and selection imposed by rearing flies on two adverse food substrates can cause correlated changes in important phenotypic components of fitness such as body weight, the concentration of main insect sugars, longevity, and fecundity. Our results support the assumption that, under selection for either early or late reproduction, these traits can co-evolve with the traits of the 24 h patterns of locomotor activity and sleep. Particularly, we found that, in each of the selected strains, the selection-induced reduction in fly weight was combined with an increase in their locomotor activity and a decrease in their sleep duration. Namely, our results suggest that flies from these selected strains, especially females, had lower body weight compared to the control flies. Such a response was expected in the case of selection on adverse food substrate and is in agreement with the responses reported in the previous studies (e.g., [90]). However, a similar response in the long-lived strain disagrees with the observations of May et al. [ 91], who indicated that selection for late-life reproduction extended lifespan and increased body weight. On the other hand, it has to be noted that our result agrees with the previously reported findings of researchers who have conducted numerous evolutionary experiments on muroid rodents. They demonstrated a decrease in body weight in response to selection for increased voluntary wheel running. This relationship was explained by a negative genetic correlation between body weight and activity [92,93,94]. Moreover, such experiments showed that mice selectively bred for high voluntary wheel running can conserve more fat despite the increased exercise [95], and this spontaneous physical activity can negatively correlate with fat mass gain and obesity later in rodents’ lives [96]. As for the association between physical activity and longevity in these and other mammal species, the comparisons of individual differences in body weight usually suggested that smaller individuals have higher rates of metabolism and live longer than their larger and slower conspecifics [97,98]. From the evolutionary perspective, selection can often favor the increase in the value of trait such as locomotor activity and the decrease in the value of such trait as sleep duration. Flies with these traits’ values appear to have an evolutionary advantage over the unselected flies because they can increase the distance travelled outside of their home area for increasing the chances of encountering more potential sexual partners and food resources. A similar evolutionary advantage has been hypothesized for humans in their ancestral environment. More time spent awake and traveling has provided additional opportunities for a variety of productive activities, such as hunting, fishing, socializing, sex, etc. [ 26,99]. Our results on the effects of adverse food substrates on life-history traits seem to be in line with the previous publications showing that adaptation to a poor-quality diet generally selects for smaller fly weight and decreased fecundity [61,100,101,102]. Notably, flies from the starch strain had the lowest values of lifespan and fecundity among the studied strains, but they consistently demonstrated the highest value of locomotor activity. In natural settings, such an increase in this trait value in flies of this strain would allow, at least partly, the compensation of the disadvantages of other tested traits, such as a smaller body weight, a reduced lifespan, and a lower fecundity than in the unselected flies. Therefore, this result implies that enhanced locomotor activity and reduced sleep duration might be relevant to trade-offs among fitness-related traits (i.e., when an increase in one trait comes at the expense of other). Studies in the laboratory have shown that mating and courtship rhythms are clock-controlled, with mating frequency being highest around the lights-on period [103,104,105]. The results obtained in laboratory settings were corroborated by data on the behavior of groups of flies in seminatural conditions. They showed that morning behaviors mostly comprise chasing, wing expansion, and copulation, which peak around dawn. Therefore, it was concluded that the morning peak of locomotion is mostly linked to courtship-associated activity [106]. Therefore, it is reasonable to suggest that the chance of copulation is higher in flies that are active earlier in the morning. The evening peak was related to general locomotion, to which no specific behavior can be assigned. Despite this, its predominant association with foraging-related behavior was proposed [106]. Therefore, enhanced and delayed evening activity can be beneficial for the purpose of encountering more food resources until the very end of the day. The characteristics of the morning and evening peaks of locomotor activity for flies from the selected strains can be regarded as advantageous for their survival and reproduction in the natural environment. Interestingly, the adaptation of Drosophila melanogaster to a long photoperiod at higher latitudes appears to be limited by the inability of flies from this Drosophila species to delay a position of evening peak of activity relative to the position of the morning peak for more than 16 h interval [107,108,109,110]. Since the significant increase in lifespan imposed by selection for reproduction at an older age was accompanied by a profound change in the chronotype of long-lived flies; the “early to rise and late to bed” chronotype [110] can be a preadaptation to survival and reproduction at somewhat higher latitudes. As we expected, the 24 h patterns of the hybrids from the bidirectional breeding of the long-lived and starch flies demonstrated an intermediate 24 h pattern combining a high level of locomotor activity in one of the parent strains with a longer distance between the morning and evening peaks for another parent strain. Further research can also aim to uncover the genetic basis of the association of the evolutionary changes in longevity or diet with the changes in levels and 24 h patterns of locomotor activity and sleep. Finally, Sujkowski et al. [ 111] reported that $65\%$ of gene expression changes found in flies selectively bred for longevity were also found in flies subjected to three weeks of exercise training and that both selective breeding and endurance training increased endurance, cardiac performance, running speed, flying height, and levels of autophagy in adipose tissue and upregulated stress defense, folate metabolism, and lipase activity and downregulated carbohydrate metabolism and odorant receptor expression. These findings suggest the necessity for future studies to elaborate on the mechanisms underlying the strong response of gene expression to selection for the extension of lifespan under an exercise-provoking environment. Overall, the occurrence of correlated responses to selection may provide important clues for further investigations of the mechanisms responsible for the adaptation of complex traits to various selection pressures [40,112] Artificial selection by age at breeding and controlled natural selection with regard to the adverse food substrates can offer productive experimental approaches to understand the evolution of fitness-related traits. Our results illustrate the usefulness of such evolutionary experiments for testing the adaptive responses of activity, sleep, metabolism, reproduction, and longevity to distinct selection pressures. They particularly revealed that evolutionary changes in locomotor activity and sleep can, at least partly, compensate for the detrimental fitness effects on important life-history traits such as body weight, lifespan, and fecundity. Further studies might be essential for the validation of relevance of Drosophila melanogaster as a model for understanding the potential beneficial effects of the characteristics of the 24 h patterns of activity and sleep for the extension of the healthy lifespan and the success of adaptation to life in suboptimal environmental conditions. ## 4.1. Selection of Three Strains In the evolutionary experiments, which started in September 2014, four strains of Drosophila melanogaster, called here “control”, “long-lived”, “salt”, and “starch”, were maintained [113,114,115,116]. All these strains originate from 30 wild flies caught in south-west Moscow in September 2014. The unselected and selected descendants of these flies were cultured at the Department of Biological Evolution of the Lomonosov Moscow State University (Moscow, Russia). Seven years later, in September 2021, flies from these four strains arrived from Moscow to Novosibirsk, where they were reared without further selection to use them in the experiments reported in this article. After September 2014, the flies were housed in plexiglass boxes (16.5 × 16.5 × 25 cm3). Each such box was supplemented with 12 open cylindrical glass tubes (10 cm height × 2.2 cm diameter) containing 10 mL of food. Each week, 4 of these 12 tubes were renewed. To reproduce flies, females and males were mixed (sex ratio ~ 1:1) to allow them to mate freely and lay eggs under a natural photoperiod and air temperature between 20 °C and 25 °C. Population density was not artificially regulated. The number of flies in a box varied from 200 to 700. Flies of the long-lived strain have been subjected to a sequence of selective breeding for the delayed reproduction, at the ages from 72 to 80 days [114]. Since the beginning of artificial selection in March 2018, as many as 15 selectively bred cohorts have been obtained (approximately four cohorts per year). Flies from this strain were called “long-lived” because they are expected to live longer than the control flies after several rounds of artificial selection imposed on the timing of reproduction. This expectation was confirmed in the present study (Table S1 and Figure S1). Since September 2014 and until recently, flies from the control and long-lived strains have been reproducing in the Moscow university department on the control food substrate (60 g of inactivated yeast, 35 g of semolina, 50 g of sugar, 45 g of crushed raisins, 8 g of agar, and 2 g of propionic acid per liter of food). To maintain the salt and starch strains, the experiment on controlled natural selection started in January 2015. The selection was performed by placing flies either on “salted” food substrate (with additional $4\%$ NaCl) or on starch-based food substrate (60 g of inactivated yeast, 30 g of starch, 8 g of agar, and 2 g of propionic acid per liter of food) [113,115,116]. For more than 6 years, flies from these two strains were forced to adapt to life on such adverse food substrates. The reproduction of these flies, especially the reproduction of flies from the starch strain, shifted at earlier ages due to the shortening of their lifespan due to selection (Table S1 and Figure S1). The origin and maintenance of these four strains are described in detail elsewhere [113,114,115,116]. The samples of flies originating from the control, long-lived, salt, and starch strains were transferred to the Drosophila collection of the Institute of Cytology and Genetics (Novosibirsk, Russia) in September 2021. The same three Moscow food substrates were used for the further cultivation of flies from these 4 strains. Moreover, the strains were kept on two more food substrates, the standard substrate (18 g of dry yeast, 50 g of corn grits, 20 g of sugar, 40 g of raisins, 5.6 g of agar, and 7 mL of nipagin at $10\%$) and the low protein/high carbohydrate substrate (7 g of dry yeast, 50 g of sugar, 12 g of starch, 6.4 g of agar, and 4 mL of nipagin at $10\%$). Flies were fed standard and low protein/high carbohydrate diets in previous experimental studies of the 24 h patterns of locomotor activity and sleep conducted at the Department of Insect Genetics of the Institute of Cytology and Genetics (Novosibirsk, Russia) [110,117]. In order to further reproduce flies and use them in the experiments described in this article, they were kept in groups consisting of 10 males and 10 females in standard breeding vials (10 cm height × 2 cm diameter) supplemented with 10 mL of food per vial. Finally, the same standard substrate was also used to rear the hybrids obtained by breeding flies from the long-lived and starch strains in both cross directions (i.e., by crossing ♂♂ from the long-lived strain with ♀♀ from the starch strain and ♀♀ from the long-lived strain with ♂♂ from the starch strain). ## 4.2. Testing the 24 h Patterns of Locomotor Activity and Sleep Prior to the recording of locomotor activity and sleep, the same-sex groups of 20–25 flies from a strain were kept in standard vials under standard temperature (25 °C) and photoperiod (light between 7:00 and 19:00). We applied the conventional approach [118] to acquire and analyze locomotor activity using the DAMS (Drosophila Activity Monitoring System; “Trikinetics”, Waltham, MA, USA) and the original software package (see the TriKinetics web site (Waltham, MA, USA): www.trikinetics.com, accessed on 12 February 2023). At the age of at least three days after eclosion, each fly was individually placed in a glass locomotor-monitoring tube of the DAMS with three sets of infrared beams for activity detection. To record beam breaks with one-minute intervals, the monitor was connected to a computer. Locomotor activity was recorded in constant darkness for at least 5 days under the same standard temperature (25 °C). The recorded locomotor activity was conventionally expressed as the numbers of beam breaks in 1 min bins. These data on locomotor activity were also used to quantify sleep events. They were defined, in accordance with Donelson et al. ’s [119] criterion, as 5 consecutive minutes of absence of any locomotor activity. On the basis of this approach (www.trikinetics.com, accessed on 12 February 2023), our excel software was developed and used for the conventional analysis of locomotor activity and sleep (i.e., the summation of 1 min data on the 30 min intervals of each record prior to applying statistical analysis, Table 1 and Table 2, and drawing illustrations of its results, Figure 1 and Figure 2). ## 4.3. Testing Fly Weight, Content of Two Sugars, Longevity, and Fecundity We also tested whether the difference in locomotor activity and sleep between four strains might be related to the differences in several important morphological and metabolic indicators of fitness (i.e., fly weight and concentrations of two main insect sugars), and whether the differences between strains in the life-history traits that are known to influence Darwinian fitness (i.e., survival rate and fecundity) persisted after the transferring of flies for their further cultivation from Moscow to Novosibirsk in September 2021. In Novosibirsk, the flies were left without further selection. In the first of such tests, fly weights were measured in each of the 257 groups, including 10 (with further calculating the weight of one fly by the division of obtained weight of a group of 10 flies on 10). In the second of these tests, some of the weighted flies were randomly chosen to also measure the concentrations of two main insect sugars, glucose and trehalose, in the fly’s hemolymph (Table 3). We used the assay that was mainly similar to the assay originally developed by Musselman et al. [ 120] and then slightly modified by Karpova et al. [ 121]. Due to the difference between the strains in terms of fly weight, the concentrations of sugars are expressed as µg/mg fly (Table 3). The methods and results of the tests on survival and fecundity are included in the Supplementary Materials (Figure S1 and Tables S1 and S2). The results of these tests are briefly described in both this Supplementary Materials and Results. ## 4.4. Statistical Analysis The SPSS23.0 statistical software package (IBM, Armonk, NY, USA) was used for all statistical analyses. To illustrate and analyze 24 h patterns of locomotor activity and sleep, the 30 min estimates obtained during the first day were excluded as mainly reflecting the habituation process. The estimates obtained for three consecutive days were averaged over each day to analyze and illustrate the 24 h pattern (i.e., the values of locomotor activity and sleep were calculated for 48 time points, constituting the 24 h cycle of a fly). Figure 1 and Figure 2 illustrate the strain-averaged values obtained in two- and three-way repeated measure ANOVAs (rANOVAs; Table 1 and Table 2, respectively). Degrees of freedom were corrected using Greenhouse–Geisser correction controlling for type 1 errors associated with the violation of the sphericity assumption, but the original degrees of freedom are reported in the upper parts of Table 1 and Table 2. The significance of the difference between strains in the daily mean values of locomotor activity or sleep was examined using post hoc pairwise Bonferroni comparisons (Table 1 and Table 2, lower parts). To analyze the fly weight and concentrations of sugars, three-way ANOVAs were applied (Table 3, upper part). The effect of the factor “Strain” was further examined using a post hoc pairwise Bonferroni comparison (Table 3, lower part). See the Supplementary Materials for methods of testing, statistical analyses, and results on longevity and fecundity (Figure S1 and Tables S1 and S2). ## 5. Conclusions Drosophila models of evolution under different selective pressures allowed use to examine whether increased locomotor activity is associated with the adaptation of this nonhuman species to a longer or harder life. We found that, when flies have been adapted to live longer and to live on adverse substrates, locomotor activity responds to different selection pressures in a similar way. Flies from the selected strains became more active and less sleepy than the control flies. Moreover, we found that selected flies can change their 24 h pattern of locomotor activity by extending the interval of enhanced locomotor activity at both earlier and later hours of the day. 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--- title: 'Development and Validation of a Novel Prognostic Tool to Predict Recurrence of Paroxysmal Atrial Fibrillation after the First-Time Catheter Ablation: A Retrospective Cohort Study' authors: - Junjie Huang - Hao Chen - Quan Zhang - Rukai Yang - Shuai Peng - Zhijian Wu - Na Liu - Liang Tang - Zhenjiang Liu - Shenghua Zhou journal: Diagnostics year: 2023 pmcid: PMC10047797 doi: 10.3390/diagnostics13061207 license: CC BY 4.0 --- # Development and Validation of a Novel Prognostic Tool to Predict Recurrence of Paroxysmal Atrial Fibrillation after the First-Time Catheter Ablation: A Retrospective Cohort Study ## Abstract There is no gold standard to tell frustrating outcomes after the catheter ablation of paroxysmal atrial fibrillation (PAF). The study aims to construct a prognostic tool. We retrospectively analyzed 315 patients with PAF who underwent first-time ablation at the Second Xiangya Hospital of Central South University. The endpoint was identified as any documented relapse of atrial tachyarrhythmia lasting longer than 30 s after the three-month blanking period. Univariate Cox regression analyzed eleven preablation parameters, followed by two supervised machine learning algorithms and stepwise regression to construct a nomogram internally validated. Five factors related to ablation failure were as follows: female sex, left atrial appendage emptying flow velocity ≤31 cm/s, estimated glomerular filtration rate <65.8 mL/(min·1.73 m2), P wave duration in lead aVF ≥ 120 ms, and that in lead V1 ≥ 100 ms, which constructed a nomogram. It was correlated with the CHA2DS2-VASc score but outperformed the latter evidently in discrimination and clinical utility, not to mention its robust performances in goodness-of-fit and calibration. In addition, the nomogram-based risk stratification could effectively separate ablation outcomes. Patients at risk of relapse after PAF ablation can be recognized at baseline using the proposed five-factor nomogram. ## 1. Introduction Atrial fibrillation (AF) is a common arrhythmia posing a severe burden worldwide [1]. Catheter ablation of AF, particularly PAF, is becoming a pivotal therapeutic strategy. In addition, it has demonstrated superiority over medications in maintaining sinus rhythm and improving quality of life [2,3,4]. Notably, the relapse rate after successful ablation is still high [5,6,7], and patients at the stake of relapse cannot be accurately distinguished before ablation for lack of a gold standard. Successful dissociation of the left atrium (LA) with pulmonary veins set the basis for PAF ablation. In addition, left atrial appendage (LAA) closure can offer additional prevention from systemic embolism. However, the enlargement and dysfunction of LA/LAA could strongly undermine the ablation efficacy [8,9,10]. Comorbidities such as hypertension, hyperthyroidism, and obesity, frequently seen in AF patients, have also been connected with LA enlargement and AF occurrences [11,12,13]. Modifiable lifestyles, such as smoking [14] and endurance exercises [15], additionally lift the AF incidence. Channelopathies, well-established to cause ventricular arrhythmias and cardiac arrests, also generate an arrhythmogenic substrate of atria, thus giving rise to AF [16,17]. Moreover, potential risk factors keep emerging, such as the female sex [18] and renal insufficiency [19,20]. Although several predictors have been established, any factor alone meets with limited predictive capacity or inconvenience to generalize. A robust prognostic tool compatible with a bedside setting is needed to promote predictive accuracy and guide reablation initiation. Here, based on the evidence above, we report a five-factor nomogram predictive of AF relapse and its internal validation in 315 patients with PAF undergoing first-time catheter ablation. ## 2.1. Inclusion and Exclusion The research was a retrospective cohort study. Eleven variables were candidates for model construction, followed by the 10 EPV (10 events per variable) criterion to ensure the sample size. Obeying that, we initially included 342 patients who suffered non-valvular PAF and received cryoballoon or radiofrequency ablation from 2017 to 2022 at the Second Xiangya Hospital of Central South University. Patients were excluded if they had any of the following: [1] receiving reablation; [2] with end-stage renal diseases; [3] without attainable electrocardiogram (ECG) recorded in sinus rhythm before ablation; [4] with heart rates less than 50 bpm or greater than 100 bpm on ECG recording; [5] with missing data among the candidate predictors. Overall, 27 out of 342 cases met the exclusion criteria, as specified in Figure 1. ## 2.2. Ablation Procedure PAF ablation was conducted after the exclusion of LAA thrombosis via trans-esophageal echocardiography. For isolating pulmonary veins from the LA substrate, the Carto® or EnsiteTM three-dimensional mapping system navigated the catheter to ablate adjacent areas or the antrum. The bi-directional conduction block was seen as a success. The implementation of additional procedures, including superior vena cava isolation, linear ablation, and cavo-tricuspid isthmus ablation, were at the discretion of operators due to the corresponding findings. Patients were heparinized during operations, with the activated clotting time ranging from 250 to 350 s. If the endeavors above failed to terminate a spontaneous or induced AF state, synchronous direct current or medicative cardioversion would be accomplished selectively. ## 2.3. Observation After ablation, patients were routinely followed up at 3, 6, 12 months, and thereafter, every six months by trained practitioners with limited knowledge of baseline data. Patients received ECG or Holter-ECG reviews every three months after ablation and at any suspected symptomatic episode. Anti-arrhythmia drugs (AAD) would be ceased three months after ablation if there was no evidence of relapse. Any documented episode of atrial tachyarrhythmia (ATa) relapse lasting longer than 30 s after the 3-month blanking period was seen as the endpoint. The shortest and longest observation durations were 182 (0.5 years) and 1831 days (5 years), respectively, with 1098 days (3 years) as the median. ## 2.4. Baseline Characteristics Several variables were recorded as baseline characteristics: the CHA2DS2-VASc score, age at procedure, history of type 2 diabetes mellitus and stroke/transient ischemia attack (TIA), left ventricular end-diastolic volume (LVEDd), left ventricular ejection fraction (LVEF), level of N-terminal prohormone brain natriuretic peptide (NTproBNP), medications at baseline including angiotensin-converting enzyme inhibitors (ACEI)/angiotensin receptor blockers (ARB), beta-blockers, AADs, and novel oral anticoagulants (NOACs). For catheter ablation, we documented its energy type, procedural time, and additional procedures other than pulmonary vein isolations. ## 2.5. Candidate Predictors Eleven variables were treated as candidates for model construction: sex, history of hypertension, body mass index (BMI), estimated glomerular filtration rate (eGFR), LA diameter, LAA emptying flow velocity, P wave duration (PWD) in the lead II, III, aVF, V1, and its terminal negative phase. All data were obtained preablation. The eGFR was calculated through the CKD-EPI equation [21]. LA diameter and LAA emptying flow velocity were measured by trans-thoracic or trans-esophageal echocardiography. On the standard recording, ECG was documented at least five half-lives after AAD discontinuation and then digitized. Independent practitioners, with limited awareness of baseline data and ablation outcomes, analyzed the echo- or electro-cardiographic parameters. MATLAB software (version R2022a) measured the P wave indices via 10-multiple magnification. ## 2.6. Model Construction and Assessment We set different strategies for the continuous variables. First, we used the R package CatPredi to find the optimal cut-points for the continuous variables using the addfor algorithm [22]. BMI, eGFR, LA diameter and LAA emptying flow velocity were then categorized. Second, to ease application, the P wave indices were categorized at the suboptimal cut-points, defined as those values closest to the optimal ones within the range of integral multiples of 20 ms (half of a little block). Categorical data were transferred through dummy coding. For model construction, univariate Cox regression tested the candidates preliminarily. If they were highly correlated, the least absolute shrinkage and selection operator (lasso) [23] algorithm and a random survival forest (RSF) [24] would assist with variable selection. Stepwise regression was finally used, and the variables with a p-value less than 0.05 were selected to build a prediction model. A nomogram was then plotted to visualize it. We assessed the nomogram’s performance among discrimination, goodness-of-fit, calibration, clinical utility, and separative efficacy. Time-dependent receiver operator characteristic (ROC) curves, the areas under the curves (AUC), and the c-index curves described its discriminative power. Akaike information criteria (AIC), Bayesian information criteria (BIC), and Brier-score curves illustrated its goodness-of-fit. Calibration plots depicted the prediction–observation deviations. The assessment above was corrected via bootstrap resampling. Decision curve analyses evaluated its clinical utility. Kaplan–Meier curves and a log-rank test assessed its risk stratification strategy. If there were competing risk scores for prediction, the integrated discrimination improvement index (IDI) and net reclassification index (NRI) would compare their discriminative performances via perturbation resampling. A weighted Kappa test evaluated the agreement between their risk stratification strategies. Mediation effect analysis illustrated the triangular relationship between the risk scores and time-to-events. Model training and internal validation were accomplished by R (version 4.2.2). ## 2.7. Statistical Analyses Descriptive statistics were performed among the relapse and censoring groups. Continuous variables were described with mean ± standard deviance or median (interquartile range). Categorical data were described with frequency (proportion). Student’s t-tests, Mann–Whitney U tests, and chi-square tests determined the groups’ comparability. Spearman’s correlation and a correlogram depicted the inter-predictor relationship. All p-values reported were two-sided, and those less than 0.05 were considered significant. All the statistical analyses were carried out by R (version 4.2.2). ## 3.1. Baseline Characteristics and Categorization of Continuous Variables With 315 patients finally included, 153 relapses occurred, thus the sample size meeting the criterion. We recapitulated baseline characteristics for the relapse and censoring groups (Table 1). Due to the missing values, NTproBNP was reported via categorization. Notably, nearly half of the patients (140, $44.4\%$) were recognized as lone AF. For most characteristics, the two groups remained comparable, whereas the relapse group had a higher CHA2DS2-VASc score, a higher proportion of female sex, slower LAA emptying flow velocity, and a wider PWD than the censoring group. We then randomly split the study population into the training and internal validation cohorts in a 7:3 ratio. For the continuous variables, the optimal cut-points were found as follows: BMI (26 kg/m2), LAA emptying flow velocity (31 cm/s), LA diameter (33 mm), eGFR (65.8 mL/(min·1.73 m2)), PWD in lead II (116.5 ms), III (116.3 ms), aVF (123.9 ms), V1 (103.4 ms), and its terminal negative phase (57.8 ms). Hence, the suboptimal cut-points for P wave indices were ascertained: 120 ms for PWD in the lead II, III, and aVF, 100 ms for PWD in lead V1, and 60 ms for its terminal negative phase. ## 3.2. Variable Selection and Model Construction Univariate Cox regression selected eight risk factors: the female sex, LAA emptying flow velocity ≤31 cm/s, eGFR < 65.8 mL/(min·1.73 m2), PWD in lead II ≥ 120 ms, III ≥ 120 ms, aVF ≥ 120 ms, V1 ≥ 100 ms, and its terminal negative phase ≥60 ms, as specified in Table 2. As anticipated, the candidate predictors were strongly correlated, especially between P wave indices (Figure 2). Therefore, we used a Lasso-Cox model and an RSF for variable selection. A relatively large lambda value of 0.1050327 was chosen to constrain model complexity (Figure 3a), while the RSF estimated the variable importance via subsampled resampling (Figure 3b). Under the L1 normalization, PWD in lead II, III, and the terminal negative phase of PWD in lead V1 were found incapable (Table 2), as reaffirmed by the RSF estimation (Figure 3b). Finally, a bi-directional stepwise regression allowed the remaining factors to construct a prediction model (Table 2). ## 3.3. The Five-Factor Nomogram and Its Application A nomogram was plotted to represent the five-factor model (Figure 4). Female sex, LAA emptying flow velocity ≤ 31 cm/s, eGFR < 65.8 mL/(min·1.73 m2), PWD in lead aVF ≥ 120 ms, and that in lead V1 ≥ 100 ms contributed 93.6, 100, 85.2, 95.5, and 76.1 points, respectively, to the total points of the nomogram. The total points corresponded to specific median relapse-free times and predicted probabilities of relapse across 1, 2, and 3 years after ablation. In addition, they served as evidence for risk stratification. It is worth noting that the transcendence of the predicted probabilities over the preset threshold probabilities led to a reablation in decision curve analyses, which also provided the reference for clinical decision-making. ## 3.4. Discriminative Power We then assessed the discriminative power of the nomogram. It performed evenly at sensitivity and specificity across different thresholds of its total points (Figure 5a,b). The bootstrap-corrected AUC of the training cohort was 0.722, 0.711, 0.777 across 1, 2, and 3 years after ablation, respectively, and that internally validated was 0.755, 0.757, and 0.705. Similar results were yielded from c-index analyses (Figure 5c,d), demonstrating that the nomogram persistently outperformed any predictor alone in discriminative accuracy. The internal validation cohort was prone to having similar AUCs and c-indexes to the training one, setting the potential to generalize. In addition, the nomogram’s total points moderately correlated with the CHA2DS2-VASc score (correlation coefficient: 0.33, $p \leq 0.0001$), and the latter served as a competing risk score (hazard ratio (HR): 1.18, $95\%$ confidence interval (CI) (1.08–1.30), $$p \leq 0.0006$$). However, the nomogram guaranteed remarkable improvement in discrimination, proving its superiority over the CHA2DS2-VASc score (Table 3). ## 3.5. The Goodness-of-Fit, Calibration, and Clinical Utility We then evaluated the model’s goodness-of-fit. The AIC and BIC values were 1013.40 and 1030.37, respectively, which were lower than any predictor alone (Table 4). Parallelly, the nomogram had significantly lower Brier-scores than random guessing (null model) or any predictor alone (Figure 6a), rendering its goodness-of-fit. Moreover, its prediction fitted nicely with the observation across the majority scale of predicted probabilities (Figure 6b). Across the reasonable threshold probabilities, the nomogram guaranteed higher net benefits than default decisions or the CHA2DS2-VASc score (Figure 7a–c). Thus, we advised using the nomogram to decide whether and when to initiate reablation. ## 3.6. Risk Stratification and Its Separative Efficacy The CatPredi package identified two cutoffs for the total points in the training cohort: 103.3 and 173.4, separating patients into the low-, moderate-, and high-risk groups. After converging the two cohorts, the nomogram separated the outcomes distinctively (Figure 8, log-rank test: $p \leq 0.0001$). The stakes of ablation failure were significantly higher in the moderate- and high-risk groups when compared with the low-risk group (HR for the moderate-risk group: 1.90, $95\%$ CI (1.22–2.97), $$p \leq 0.005$$; HR for the high-risk group: 4.06, $95\%$ CI (2.80–5.88), $p \leq 0.0001$). To compare the risk stratification methodologies, we also classified the CHA2DS2-VASc score, with one and two scores being the threshold. Two risk scores were linearly associated in terms of risk stratification (Mantel–Haenszel statistic: 26.04, $p \leq 0.0001$), while no agreement was achieved (weighted Kappa: 0.21, $p \leq 0.0001$). Moreover, the nomogram’s risk stratification mediated 65.1 percent of the CHA2DS2-VASc score’s prediction, with the latter posing no direct impact on the outcomes (Figure 9). Thus, the five-factor nomogram was a reliable prognostic tool to distinguish outcomes after PAF ablation. ## 4. Discussion In the research, we first reported the development of a five-factor nomogram for predicting PAF ablation failure and its internal validation. The nomogram was moderately correlated with the CHA2DS2-VASc score but outperformed the latter significantly in discrimination and clinical utility. It also performed well in goodness-of-fit and calibration. We recommended setting 103.3 and 173.4 points as cutoffs for stratifying patients. The risk of frustrating ablation outcomes mounted hierarchically in line with the risk stratification. With the publication of various predictors for AF recurrence, the development of prediction models becomes an emerging scene. For instance, Zhao et al. reported a four-factor nomogram consisting of AF type, LA diameter, LVEF, and systemic inflammation score [25]. Based on NTproBNP, AF type, LAA volume, and LA volume, Zhou et al. built a deep learning-based model via a convolutional neural network [26]. Obviously, they shared the scheme to include AF type and the LA structural parameters while we focused on PAF patients and abandoned LA diameter. The contrast may be attributed to persistent AF having an overwhelmingly larger LA size than PAF. In our works, it made sense that the exclusion of persistent AF, which restricted the degree of LA dilatation, could at least partly account for the inability of LA diameter. Hypertension and obesity, whose effect on AF is highly mediated by LA dilatation, might be weakened simultaneously [11,27]. Additionally, the unidimensional measurement of LA size might overlook the virtual extent of enlargement, as it may be asymmetric, and the LA is not strictly spheric [28]. In summary, due to the study population and possible underestimation, LA dilatation was not a key predictor for AF recurrence in the research, let alone hypertension and BMI. Despite the absence of LA size, the five-factor nomogram still provided evidence for LA remodeling, malfunction, and arrhythmogenesis, which interpreted its predictive effect. As its component, the prolongation of PWD is a noninvasive indicator of atrial conduction delay, rendering a substrate favoring atrial re-entry. It also unveils electro-anatomical remodeling, which facilitates AF initiation and perpetuation [29]. Unsurprisingly, it elevates the odds of poor ablation outcomes [30,31], as reaffirmed in the current study. Moreover, after adjusting for the other co-variates, PWDs in lead aVF and V1 were both eligible, implying that the electrical heterogeneity among PAF patients might be pivotal to prediction. LAA, the reservoir of LA blood flow, is susceptible to a mild shift in LA status [32]. A decreased LAA emptying flow velocity reveals the LAA flow stasis, as well as its decompensation for a deteriorated LA pressure–volume relationship. As an indirect marker of LA dysfunction, it has surpassed LA diameter or some other structural parameters with limited evidence [33,34]. Likewise, in the current study, a flow velocity slower than 31 cm/s outperformed all the other candidates as the most contributive factor to the nomogram. Renal insufficiency, with co-existing fluid overload, metabolic abnormalities, and activation of the renin-angiotensin-aldosterone system, may lift the incidence of atrial arrhythmias [35]. Its relation with LA dilatation and AF recurrence has been established in a large Asian population [19]. In addition, in PAF patients, a low eGFR may provide additional arrhythmogenesis other than pulmonary veins [20]. In our works, the eGFR decreased in tandem with LA enlargement (Figure 2), redemonstrating its contribution to structural remodeling. In addition, it fitted closer with PWD prolongation than LA diameter, which suggested a potential role in electrical remodeling and explained its eligibility. Evidently, females suffer more AF recurrences than males after catheter ablation [18,36,37]. Some researchers attribute the phenomenon to females having higher LA volume and lower LA voltage, and hence more evidence for LA remodeling [37]. However, our works showed that the female sex had a smaller LA size and lower BMI than its counterpart (Figure 2), similar to that previously reported on a pooled population. Furthermore, it tended to have a narrower PWD. Though disfavoring electro-anatomical remodeling on the external signs, the female sex by itself was still an independent risk factor. Internal mechanisms, such as more ectopic activities, more frequent beating from pulmonary veins, and a higher burden of LA fibrotic remodeling, may help explain why females have lower ablation efficacy than males [38,39]. Regardless of the LA diameter, the five-factor nomogram provided a direct and indirect profile of LA, not to mention the perspectives besides LA, making itself a competitive prognostic tool. LA diameter might not be necessary for developing prediction models, especially among PAF patients. As expected, the CHA2DS2-VASc score competed with the nomogram in prediction. The score contained several AF-related comorbidities and aging, setting the basis for prediction. Its concordance with LA remodeling further enhanced the predictive effect and explained the correlation between the two risk scores [40]. However, a high proportion of lone AF undermined its predictive power, as the incidence of comorbidities was low, and the left ventricular function was quite preserved. With no focus on comorbidities, the superiority of the nomogram might lie in the emphasis on the electrical heterogeneity among PAF patients and the indirect depiction of LA. It is worth noting that the nomogram had space for promoting its performance, as we set the priority of application at a slight sacrifice of discrimination. In addition, it might harm patients when the practitioners set the threshold probabilities at high levels, indicating a relative shortage of specificity [41]. The P wave parameters we neglected may improve the nomogram’s power: P wave amplitude [42], PR interval [43,44], and inter-atrial block [30]. Despite the failure of LA diameter, LA structural parameters, including LA volume and its index, may still be crucial, as they provide an overall assessment of LA enlargement [45]. As a response to the excessive LA load, LAA enlargement is also a candidate. To make the decision wiser, we recommend developing more nomograms based on the predictors above. In the spectrum of AF development, PAF far precedes persistent AF in progressive LA remodeling. The latter manifests unique signs, such as less reliance on pulmonary veins, more ectopic foci, and more complicated LA substrates than the former [7,46]. Resultantly, the ablation strategies between the two vary a lot. Thus, excluding the AF type from model construction seems rational. Since we concentrated on PAF in the study, a prognostic tool targeting persistent AF is then required. To ensure the sample size, we included patients receiving either cryoballoon or radiofrequency ablation. Virtually, the former guarantees a remarkable improvement over the latter, in terms of procedural time, lab efficiency, and even economic costs, with the overall efficacy and safety remaining similar [47]. Additionally, the generation of zero X-ray ablation is approaching, which highly attenuates radiological exposure to patients, thus decreasing the long-term incidence of malignancies [48]. Therefore, besides the model construction to optimize post-ablation decision-making, the evolving technology is believed to provide a brighter future for AF patients. Though well-designed and conducted, the research had several limitations. First, we could not eliminate all the biases due to the retrospective nature. Second, all the conclusions were inferred from a relatively small-scaled population, which might cause data overfitting. 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--- title: Gut Leakage Markers and Cognitive Functions in Patients with Attention-Deficit/Hyperactivity Disorder authors: - Sheng-Yu Lee - Sung-Chou Li - Chia-Yu Yang - Ho-Chang Kuo - Wen-Jiun Chou - Liang-Jen Wang journal: Children year: 2023 pmcid: PMC10047799 doi: 10.3390/children10030513 license: CC BY 4.0 --- # Gut Leakage Markers and Cognitive Functions in Patients with Attention-Deficit/Hyperactivity Disorder ## Abstract Attention-deficit/hyperactivity disorder (ADHD) is a commonly seen mental disorder in children. Intestinal permeability may be associated with the pathogenesis of ADHD. The study herein investigated the role of gut leakage biomarkers in the susceptibility of ADHD. A total of 130 children with ADHD and 73 healthy controls (HC) individuals were recruited. Serum concentrations of zonulin, occludin, and defensin (DEFA1) were determined. Visual attention was assessed with Conners’ continuous performance test (CPT). In order to rate participants’ ADHD core symptoms at home and school, their parents and teachers completed the Swanson, Nolan, and Pelham—Version IV Scale (SNAP-IV), respectively. We found significantly lower DEFA1 levels in the ADHD group compared to that in the HC group ($$p \leq 0.008$$), but not serum levels of zonulin and occludin. The serum levels of DEFA1 showed an inverse correlation with the inattention scores in the SNAP-IV parent form ($$p \leq 0.042$$) and teacher form ($$p \leq 0.010$$), and the hyperactivity/impulsivity scores in the SNAP-IV teacher form ($$p \leq 0.014$$). The serum levels of occludin showed a positive correlation with the subtest of detectability in the CPT ($$p \leq 0.020$$). Our study provides new reference into the relation between gut leakage markers and cognition, which may advance research of the pathophysiology of ADHD. ## 1. Introduction Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder commonly seen in children and may persist into adulthood [1]. Interventions early in life are important to improve quality of life and help achieve effective strategies to deal with the functional impact of ADHD symptoms. However, pharmacological or nonpharmacological interventions may not induce sustained remission of symptoms or cure this disorder. According to recent research, the recognition and diagnosis of ADHD are still relatively limited in most countries, especially in girls [2]. If it persists into adulthood, ADHD may increase the risk for other psychiatric comorbidities and adverse outcomes such as academic underachievement, socio-occupational struggling or even criminality [2]. Increasing numbers of studies have even reported a heightened risk of accidents due to unnatural causes, unintentional injuries, and susceptibility to contracting COVID-19 in patients with ADHD [3,4,5]. Besides clinical symptoms, children with ADHD are also found with emotional dysregulation, worse performance in multiple cognitive domains including alerting and executive attention and executive skills compared to those without ADHD [6,7]. Multiple factors are involved in the etiology of ADHD, including genetic, environmental and psychosocial factors. Recently, the link between intestinal function, gut microbiota and the central nervous system, referred to as dysfunctionality of the “gut–brain axis”, has also been of interest contributing to the underlying pathophysiology of ADHD [8,9,10]. The communication pathway between the gut and the central nervous system was suggested to play a role in the regulation of immune system, mood and cognition. Therefore, increased intestinal permeability, also known as “leaky gut”, has gained attention as a candidate associated with the pathophysiology of mental disorders including ADHD. It was reported that microbiota may use cytotoxins or enterotoxins in the intestine to infiltrate the gut epithelial cells and alter the interaction of cytoskeletal proteins [11]. With increased intestinal permeability, increased intestinal bacteria pass into the blood then to the brain and further trigger neuroinflammation response, contributing to the etiology of ADHD [12]. Epithelial tight junction defensins are important for the intestinal barrier and have, therefore, been proposed as potential markers of mucosal permeability, including zonulin, occludin, and Defensin alpha 1 (DEFA1) [13,14]. Zonulin modulates not only gut–brain barriers but also blood–brain barriers, as it is the principal regulator of endothelial and epithelium tight junctions [15,16,17]. Zonulin is involved in balancing the immune response. The upregulation of zonulin may increase the permeability of macromolecules and thus lead to the uncontrolled inflow of infectious antigens. Zonulin has been proposed as playing a role in the pathogenesis of some chronic inflammatory disorders, such as diabetes type 1 and 2 [18,19] and obesity [20]. It has also been proposed as a peripheral marker for the above metabolic disease. Elevation of zonulin levels have been linked with a worsening in the manifestations of hyperactivity and impairment in social functioning in children with ADHD [12]. Occludin, part of the tetraspan family of integral membrane proteins, is an important element of the tight junction which may cause the weakening of the tight junction barrier and increase the penetrability of the gut wall [21]. Besides the gut wall, occludin has been proposed as a major component of the blood–brain barrier (BBB). Occludin was found to control the cohesion and permeability of the BBB. Furthermore, the level of expression of occludin protein in the endothelial cells of the brain regulate the function of BBB [22]. In this way, occludin has been proposed as a predictor to evaluate BBB damage in patients suffering from acute ischemic stroke [23]. Defensin alpha 1 (DEFA1) belongs to a family of peptides which function as host defenses through the mechanisms of being toxic to cells and bacteria [24]. DEFA1 is abundant in the neutrophils and may assist with phagocyte-mediated host defense. DEFA1 is frequently found in the epithelia of mucosal surfaces including the intestine. DEFA1 may manifest proinflammatory activities in the intestine by triggering the release of interleukin-6 (IL-6) in macrophages and boosting the local inflammatory response in the gut; these activities amplify systemic inflammation and subsequently lead to intestinal leakage [25,26]. The intestinal microbiota may affect the growth and development of the overall body and multiple organs including the brain [27,28]. With immature and still-unstable gut microbiota, children’s brains may be vulnerable to pathological insult [29]. The blood–brain barrier (BBB) and the neurovascular system may be altered by peripheral immune cells released by the microbiome [30]. The low-grade systemic inflammation caused by the disruption of the intestinal barrier may affect the brain–gut axis profoundly and subsequently impair cognitive development [31]. In addition, dysbiosis, which is characterized by an unbalanced composition of microbiota, may cause the growth of inflammatory microbes, further jeopardizing gut permeability and further giving rise to systemic inflammation [9]. Systemic inflammation may affect the absorption of iron in the intestine [32], while iron deficiency is critical to brain development in children [33]. In addition, systemic inflammation may trigger the disruption of the BBB. On the other hand, oxidative stress induced by dysbiosis may further influence neuron cells and neurotransmitters which are associated with the pathogenesis of ADHD [34]. A meta-analysis showed that the certain bacterial taxa that are mostly correlated with ADHD are still inconclusive [9,35]. It may be of interest to explore the association between gut permeability and the bacterial taxa of ADHD. We hypothesized that intestinal permeability may have a role in the pathogenesis of ADHD. Therefore, we plan to explore the serum levels of zonulin, occludin, and DEFA1 in patients with ADHD and controls. In addition, we suggested that increased intestinal permeability is involved in the cognitive dysfunction and clinical symptoms of ADHD. Therefore, our study aimed to investigate the association between the peripheral levels of zonulin, occludin, and DEFA1 and the cognitive function and clinical features of ADHD. ## 2.1. Study Participants All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Kaohsiung Chang Gung Memorial Hospital (IRB No. 201702019A3) on 20 February 2020. Children with ADHD were recruited from the Kaohsiung Chang Gung Children’s Hospital and HCs from the local community. Before taking part in this study, all participants and their parents or guardians fully understood all the terms of the study protocols and wholeheartedly agreed to sign forms to participate in this study. The inclusion criteria for patients with ADHD were as follows: (i) A confirmed diagnosis made by a senior child psychiatrist through structural interviews implementing the Kiddie-Schedule for Affective Disorder and Schizophrenia-Epidemiological Version (K-SADS-E) [36,37]; (ii) children between the age of 6 and 16 years old; and (iii) children who had never received any ADHD-related medications including methylphenidate, atomoxetine, clonidine, or bupropion. The exclusion criteria for children with ADHD were as follows: (i) children who were diagnosed with other major neuropsychiatric diseases, including intellectual disabilities, autism spectrum disorders (ASD), major depressive disorders (MDD), bipolar disorders, psychotic disorders, or substance use disorders (SUD); (ii) children who were diagnosed with any major physical illnesses including neuroendocrine, epilepsy, or gastrointestinal disorders; (iii) children who had taken any antibiotics, anti-inflammatory drugs, or probiotics one month prior to this study or children who were vegetarian. The HC group included children between 6 and 16 years old having no diagnosis of ADHD in the same catchment area. HCs were also interviewed under the structural interview of K-SADS-E to confirm that they had none of the aforementioned major neuropsychiatric diseases (intellectual disabilities, ASD, MDD, bipolar disorders, psychotic disorders, or SUD). Individuals with any major physical illnesses during recruitment who had ever received any antibiotics, anti-inflammatory drugs, or probiotics one month prior to the recruitment, or who were vegetarians, were also excluded. ## 2.2. Intestinal Permeability Markers Assessment It was necessary to store the serum samples, which were obtained between 08:00 and 10:00, under the condition of −80 °C until the laboratory assessment started. Concentrations of zonulin in the serum were measured using commercial ELISA assays according to the manufacturer’s protocols from Immundiagnostik AG (Bensheim, Germany). All study samples and standards were checked in duplicate. The serum occludin levels were measured with the Human Occludin ELISA kit (Biomatik, Wilmington, DE, USA), using the sandwich-enzyme-linked immunosorbent assay method. Serum DEFA1 levels were assessed with the Human DEFA1 PicoKine ELISA Kit. ## 2.3. Clinical and Cognitive Assessments The psychologist adopted Conners’ CPT [38] and Conners’ Continuous Auditory Test of Attention™ (Conners’ CATA™) to examine the visual and auditory attention of each participant [39]. We adopted these two exams because they encompass both visual and auditory attention, which are the two most frequently impaired attentions in ADHD. Previous evidence has indicated that the CPT and CATA neuropsychological tests provide objective information about attention in ADHD cases [40,41]. The measures used in the analyses are omissions, commissions, and detectability (d’). These exams were performed in a room designed to diminish variability in testing conditions. To measure the ADHD core symptoms at home and school, we asked the participants’ parents and teachers to complete the parent and teacher forms of the Swanson, Nolan, and Pelham—Version IV Scale (SNAP-IV), respectively [42,43,44]. ## 2.4. Statistical and Bioinformatics Analysis The sample size was measured with the software package G-Power 3.1, based on the settings of $80\%$ power, $$p \leq 0.05$$, and allocation ratio ADHD/control = $\frac{2}{1.}$ The sample sizes were estimated to be $\frac{39}{19}$ to detect a large effect size (Cohen’s $d = 0.8$); $\frac{96}{48}$ to detect a medium effect size (Cohen’s $d = 0.5$); and $\frac{591}{295}$ to detect a small effect size (Cohen’s $d = 0.2$). In the current study, we planned to detect at least a medium effect size. Data analyses were performed utilizing the Statistical Package for the Social Sciences Version 16.0 (SPSS Inc., Chicago, IL, USA). The average ± standard deviation or frequency was presented in the form of variables. The analysis for the dichotomous variables such as the sex distribution of children with ADHD and HCs was carried out using a chi-square test. As for repeated variables, the differences in demographics, clinical symptoms, and neuropsychological function between patients and controls were decided using an independent t-test. Two-tailed $p \leq 0.05$ was the significance for statistics. Finally, we performed a sensitivity test in which we excluded four healthy control subjects with particularly high levels of DEFA1. A linear regression model was performed to examine the effect of ADHD status on DEFA1 levels (outcome variable), and sex (m/f) and BMI were the explanatory variables. ## 3.1. Demographic Data We recruited a total of 130 children with ADHD (mean age: 9.2 years old, $78.5\%$ male) and 73 HC individuals (mean age: 9.3 years old, $63\%$ male). Table 1 summarizes the demographic characteristics and clinical measures of the ADHD and HC groups. We noticed a higher male-to-female ratio in the ADHD group; however, such a ratio is consistent with the known ADHD prevalence ratio ranging from 2:1 to 10:1 [45]. ## 3.2. Clinical and Cognitive Assessment As shown in Table 1, there were significant differences in gender between the ADHD group and the HC group. Although there were no differences in age, height, or weight between the ADHD and the HC groups, there were significant differences in BMI between the two groups. The ADHD group had a significantly higher BMI compared to the control group. As for clinical manifestations, significant differences in all the clinical measures of SNAP-IV were found between the ADHD group and the HC group. As for cognitive functions, there were significant differences in all the subtests of the CPT and CATA between the ADHD group and the HC group as well. In other words, the ADHD group showed prominent clinical symptoms and worse cognitive functions in all aspects than the HC group. ## 3.3. Gut Leakage Markers Assessment The serum levels of the gut leakage markers are shown in Table 1. We found significantly lower DEFA1 levels in the ADHD group than in the HC group ($$p \leq 0.008$$) (Figure 1C). The serum levels of zonulin and occludin were not significantly different between the two groups (Figure 1A,B). We further analyzed the correlation of the gut leakage markers with clinical measures and cognitive functions in the ADHD group (Table 2). We found that the serum levels of DEFA1 showed an inverse correlation with the inattention scores in the SNAP-IV parent form (r = −0.146 $$p \leq 0.042$$) and that in the SNAP-IV teacher form (r = −0.187 $$p \leq 0.010$$) (Table 2). In addition, the serum levels of DEFA1 also demonstrated an inverse correlation with the hyperactivity/impulsivity scores in the SNAP-IV teacher form (r = −0.179 $$p \leq 0.014$$) (Table 2). However, we did not find a correlation between the serum levels of DEFA1 with the hyperactivity/impulsivity scores in the SNAP-IV parent form. In addition, the serum levels of zonulin and occludin were not significantly correlated with any of the clinical measures. As for cognitive function, we found the serum levels of occludin showed a positive correlation with the subtest of detectability in the CPT ($r = 0.166$ $$p \leq 0.020$$) (Table 2). We did not find correlations between the serum levels of zonulin, occludin, and DEFA1 and other subtests of the CPT or CATA. ## 3.4. Sensitivity Test Finally, we excluded four healthy control subjects with particularly high levels of DEFA1 (≥77.5 ng/mL). A linear regression model (Table 3) revealed that ADHD status still showed and independent effect on DEFA1 levels ($$p \leq 0.034$$), controlling for the potential confounding effects of sex and BMI. ## 4. Discussion In this study, we found significant differences in the gut leakage marker, DEFA1, between the ADHD group and the HC group. This significant difference remained even after we excluded four healthy control subjects with particularly high levels of DEFA1 and controlling for the potential confounding effects of sex and BMI. The sensitivity test in 3.4 further verified that ADHD status did influence the serum DEFA1 levels. However, we found no significant differences in the other two gut leakage markers, zonulin and occludin, between the ADHD and HC groups. In addition, the serum level of DEFA1 showed an inverse correlation with both the inattention and hyperactivity/impulsivity scores in the SNAP-IV teacher form, demonstrating its inverse correlation with symptoms of ADHD. To our knowledge, our study is the first study to evaluate the association between gut leakage markers and vulnerability, clinical symptoms, and cognitive functions of ADHD. Our results were different from our initial hypothesis, for we presumed that higher gut leakage markers were associated with the vulnerability of ADHD, and gut leakage markers may positively correlate with clinical symptoms. Our finding of lower DEFA1 levels in the ADHD group compared to controls may not be unsupported. Previous studies found that decreased levels of DEFA1 were associated with several infectious diseases, while increased levels of DEFA1 may protect against the progression of infection [46,47]. DEFA1 is an antimicrobial peptide of the innate immune system. The DEFA1 gene encodes the human neutrophil peptides. Increased copy numbers of the DEFA1 gene were correlated with increased levels of the DEFA1 protein [48]. Previous study reported that reduced copy numbers of the DEFA1 gene was associated with recurrent urinary tract infections in children [49]. It was also reported that lower copy numbers of the DEFA1 gene contribute to a higher risk for hospital-acquired infections [46]. Another study found that a higher secretion of DEFA1 by immature dendritic cells may protect against the progression of HIV infection [47]. It was proposed that a lower DEFA1 protein level may contribute to impaired innate defenses and a weaker functioning of antimicrobial activity, and consequently leads to infections [46]. Therefore, in the current study, we propose that the significantly decreased DEFA1 level found in the ADHD group may be associated with the fragile functioning of autoimmune and antimicrobial activity. Our result may be another contribution to current findings for we also found an association between lower levels of DEFA1 in ADHD compared to controls, and ADHD is regarded as an inflammatory disease. Our result may be contrary to our previous hypothesis; however, it reminded us that in in vivo studies, levels of certain markers may vary greatly according to clinical condition and a dichotomous hypothesis may not always stand. Previous studies suggested that personal and maternal history of autoimmune disease are associated with a higher risk of ADHD [50]. Although no previous studies ever reported correlations between DEFA1 levels and neither ADHD nor its clinical symptoms (inattention, impulsivity, and hyperactivity), we propose that the inverse correlations we found let us infer that the association between vulnerability to ADHD and the gut leak markers may be connected by infection. Moreover, we did not find an association between ADHD and other gut leakage markers of zonulin and occludin. Our study agrees with [51] which found no significant difference between ADHD and serum zonulin levels. However, our study did not comply with the study by Ozyurt et al. [ 12]; the current study sample was comprised of 130 patients with ADHD and 73 controls, which was almost 3 times more compared to the sample size in the Ozyurt study. The differences in sample size and patient characteristics (such as age and environment) between the two studies may be responsible for the inconsistencies in the findings. Besides zonulin, our study also evaluated the association between occludin and ADHD. Contrary to a recent study which evaluated the levels of zonulin and occludin in a smaller groups of ADHD and controls (around 40 participants in each group) [52], we did not find an association between these two gut leakage markers and ADHD in our enlarged sample. Our negative association may serve as an important reference for further studies regarding the association between occludin and ADHD. Interestingly, we found a positive correlation between occludin levels and detectability in the CPT, indicating that occludin may be associated with inattentiveness and the ability to differentiate targets from nontargets. Occludin serves as an integral membrane (transmembrane) protein of the tight junction barrier and contributes to the blood–brain barrier. Although not correlated with cognitive function, Cakir et al. [ 52] also reported a positive correlation between occludin levels and Conners’ parent rating scale scores, which is related to clinical symptoms. On the other hand, the level of occludin has been found to be elevated corresponding to inflammation in human postmortem brain tissue in schizophrenia [53]. Based on the above information, we propose that increased occludin may be associated with the clinical presentation and attention deficit of ADHD through the mechanism of inflammation. However, future studies are required to clarify how occludin is involved in the pathophysiology of cognitive impairment in ADHD. Although we found a correlation between the CPT and occludin, we did not find any correlation between the gut leakage markers with the other subtest of the CPT besides detectability or any of the subtests in CATA. CPT3 generally detects visual attention while CATA examines auditory attention. A developmental lag in the maturation of the brain may lead to deficits in visual and auditory attention in ADHD [54,55]. The auditory and visual attention systems may have different developmental trajectories [56] and be associated with different networks in the brain area. The frontal and parietal network may be associated with the underlying mechanism of visual attention [57]. The temporal network and frontal network may be responsible for selective auditory attention [58]. We propose that the gut leakage markers may be more influential in the brain areas related to visual hallucination rather than auditory hallucination in patients with ADHD—possibly certain frontal and parietal areas. Previous studies reported that the performance of the CPT may be associated with the value of fractional anisotropy of the genu of the corpus callosum and left forceps minor shown in diffusion tensor imaging [59]. It was also reported that microbiota may pose an important role in enhancing the development of white matter in early life [60]. Although the correlation between gut leakage markers and the development of white matter in certain brain areas is still unknown, it is possible that some gut leakage markers may be involved in the development and maturation of white matter in brain areas associated with the performance of the CPT. However, further study is required to clarify whether gut leakage markers affect different brain areas differently and the mechanism behind it. Our study has the following limitations. First, we may not establish a causal relationship with the current study design. This is a cross-sectional design study analyzing correlations rather than a longitudinal study which may follow temporal changes. In this way, our study was unable to control for many early-life factors that may influence the initial colonization and development of microbiota in the gut system. We did not control for types of birth delivery, breastfeeding or not, dietary patterns, the use of probiotics, and previous antibiotic treatment [61,62] for these factors were not available in this study. To sum up, it will be insufficient to draw a causal relationship between gut leakage markers and ADHD from the current study. Second, the gut leakage markers we measured were from serum samples but not directly from the brain. Whether our positive findings reflect the same association in the brain requires further evidence. On the other hand, we did not measure any immune markers in the current study. To elucidate the underlying mechanism of interaction between gut microbiota and the immune system, a future investigation is needed. Third, although we controlled for sex and BMI in Table 3 to analyze the effect of ADHD status on DEFA1 levels, there may be other demographic characteristics which may confound the gut leakage markers such as diet patterns that were not controlled for in the current study. Fourth, the correlation between DEFA1 and the SNAP-IV parent form (inattentiveness) and that between occludin levels and detectability in the CPT may not survive a correction for multiple comparison. Our result is still preliminary and future investigations with a larger sample size are warranted to confirm our findings. Finally, our study result may not be generalized across different ethnic groups or socioenvironmental circumstances. Our study is the first to evaluate these three gut leakage markers with both ADHD’s clinical symptoms and cognitive functions in a relatively moderate sample size. Whether our findings may be replicated in a larger sample size or applicable in other populations regarding heterogeneity in ethnic, sample size, study design, or drug-naive status still warrants further research. In conclusion, we report significant differences in the gut leakage marker DEFA1 between the children with ADHD and the HC group in the current study. In addition, the serum level of DEFA1 showed an inverse correlation with both the inattention and hyperactivity/impulsivity scores reported by parents and teachers, reflecting its association with the clinical symptoms of ADHD. We also found a positive correlation between occludin levels and detectability in the CPT, indicating that gut leakage markers may not only be associated with vulnerability to ADHD, but also associated with its cognitive deficit, inattentiveness. Our results suggest that the gut leakage mechanism may play a significant role in the clinical presentation, cognitive deficits, and pathophysiology of ADHD. ## References 1. Spencer T.J., Biederman J., Mick E.. Attention-deficit/hyperactivity disorder: Diagnosis, lifespan, comorbidities, and neurobiology. J. Pediatr. Psychol. (2007) 32 631-642. DOI: 10.1093/jpepsy/jsm005 2. Sayal K., Prasad V., Daley D., Ford T., Coghill D.. ADHD in children and young people: Prevalence, care pathways, and service provision. Lancet Psychiatry (2018) 5 175-186. PMID: 29033005 3. Adeyemo B.O., Biederman J., Zafonte R., Kagan E., Spencer T.J., Uchida M., Kenworthy T., Spencer A.E., Faraone S.V.. 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--- title: 'Association between Soccer Participation and Liking or Being Proficient in It: A Survey Study of 38,258 Children and Adolescents in China' authors: - Yibo Gao - Xiang Pan - Huan Wang - Dongming Wu - Pengyu Deng - Lupei Jiang - Aoyu Zhang - Jin He - Yanfeng Zhang journal: Children year: 2023 pmcid: PMC10047813 doi: 10.3390/children10030562 license: CC BY 4.0 --- # Association between Soccer Participation and Liking or Being Proficient in It: A Survey Study of 38,258 Children and Adolescents in China ## Abstract Soccer participation among children and adolescents is low in China. To achieve a coordinated development of soccer in all regions and to promote the physical health of children and adolescents, this study aims to identify the influencing factors regarding the participation of children and adolescents in soccer programs through a cross-sectional analysis of the “soccer population” of children and adolescents. A total of 38,258 children and adolescents aged 7–18 years were included in this study. In addition, the analysis was conducted by dividing the regions where the children and adolescents live into three parts according to socioeconomic status, and by incorporating five dimensions, including environment, family, school, community, and individual levels to find the influencing factors of children and adolescents’ participation in soccer. Chi-square test, Pearson’s correlation, and one-way logistic regression analyses were used. The results showed that the area ($r = 0.487$) and the average annual precipitation (r = −0.367) were associated with the participation of children and adolescents in soccer programs. Moreover, the percentage of children and adolescents who participated in soccer programs ($24.5\%$) was higher than those who liked soccer or were proficient in it ($14.4\%$). Meanwhile, parental encouragement and support (OR = 0.627; $95\%$ CI, 1.102–3.179), as well as the accessibility (OR = 0.558; $95\%$ CI, 1.418–2.155), availability (OR = 1.419; $95\%$ CI, 1.179–1.707), and safety of sports facilities (OR = 0.316; $95\%$ CI, 0.614–0.865), influence children and adolescents’ participation in soccer programs. ## 1. Introduction Competitive sports are one of the best ways to showcase a country’s athletic achievements, and soccer has always been the most internationally popular representative competitive sport [1]. In reality, children and youth aged 7 to 18 years are most exposed to school soccer [2]. Expanding the soccer population, namely, increasing the spread and promotion of the sport among youth, is an issue on which countries are placing more importance [3]. According to the State General Administration of Sports, 6326 schools in China have established school soccer leagues with 191,800 registered players in 2015 [4]. Globally, however, the prevalence of soccer among Chinese children and adolescents is only $2\%$ in European and American countries [5]. Moreover, China has introduced a series of policies to accelerate the popularization of soccer at the children and adolescents level, such as the “Overall Plan for the Reform and Development of Chinese Football”, in order to promote the development of Chinese soccer [6,7,8]. Previous studies have shown that mastery of soccer skills significantly improves metabolic, cardiovascular, and muscular function in children and adolescents, increases bone mineral density, self-confidence, and physical awareness, as well as increases the attractiveness of physical activity to them [9,10,11]. Adolescent soccer players have significantly improved flexibility, coordination, balance, speed, strength, and stamina [12,13]. In addition, participation in soccer helps in reducing the risk of injury and disease [14], increasing knowledge related to health, nutrition, and fitness activities [15], gaining a sense of well-being in life [16], and promoting physical health [17,18]. Studies have shown that the choice of sports to participate in is also influenced by the geographic location and natural environment [19,20]. Snow and ice sports in the Nordic countries and water sports in Australia and other countries have a high proportion of participants; therefore, the choice of sports for children and young people varies from region to region [21,22]. Second, factors from family and social environments, such as parents’ attitudes toward sports and the fitness environment in their neighborhood, may influence children and adolescents’ choice of sports programs [23,24]. Finally, the socioeconomic status of the family in terms of education, income, occupation, fitness equipment, and transportation costs incurred influence the choice of sports programs for children and adolescents [25]. However, current research has not yet conducted a comprehensive survey and analysis of the specific physical activities performed in different regions and by different individuals. China is a vast country with significant differences in the natural and economic environment of each region. The eastern region has a better economy than the other two regions, in addition to a larger population and higher average annual precipitation. The western region covers a wider area, is sparsely populated, and has less average annual precipitation and more mountain ranges. At present, China’s soccer population density is less than $1.5\%$, compared with the soccer population density of 7–$8\%$ in the world’s leading soccer countries, and the soccer level of children and adolescents is at a relatively low level [26,27]. Therefore, the popularization of soccer in China still has a long road ahead. However, there is no research that solves this problem in China, since the reasons for the low participation rate in Chinese soccer are unknown. In summary, many relevant factors influence the choice of sports programs for children and adolescents. Therefore, this study was conducted to find the factors that influence the participation of children and adolescents in soccer programs. By improving these factors, it is possible to encourage children and adolescents to benefit from soccer programs, to promote the development of physical health, to reduce the burden of families on the physical development of children and adolescents, and to provide recommendations to the state and society for the coordinated development of the “soccer population” of children and adolescents. ## 2.1. Participants The study population consisted of children and adolescents aged 7–18 years in 31 provinces (autonomous regions and municipalities directly under the central government) in China. According to the proportional probability sampling method by size, the sample provinces (autonomous regions and municipalities directly under the central government) cover mainland China. A total of 10–20 counties (districts) are randomly selected in each province, 13 villages (neighborhood committees) are randomly selected in each county (districts), and survey respondents are randomly selected in each village (neighborhood committee). In particular, 5760 village (residential) committees in 471 counties (districts) were selected from all over the country, and a total of 38,258 children and adolescents were investigated using household entry and data uploading, with a final effective sample size of 35,653 and a recovery rate of $93.2\%$. Survey participants were divided into three age groups: 7–9, 10–12, and 13–18 years. The number of participants in each age group and the number of people who participated in and liked or were proficient in the soccer program are shown in Table 1. ## 2.2. Division China is a vast country, and the natural environment and socioeconomic status vary greatly from region to region; therefore, the regions of China were divided into three regions according to the needs of national economic development and the degree of economic development, the overall level of economic and technological development, and the geographical differences in natural and socioeconomic status [28] as detailed in Table 2. ## 2.3. Selection of Questions The questionnaire was obtained from the Survey of National Fitness Activities questionnaire and passed the reliability analysis. Then, the questionnaire referred to the family, school, community, and individual levels. At the family level, four factors were selected concerning parents’ attitudes toward their children’s sports, living with parents, and sports opportunities in the family. At the school level, one factor was selected on whether the skills acquired in school will persist. Three factors were selected at the community level concerning the quality of facilities, convenience, and safety of the community environment. Three factors were selected at the individual level, for example, have you played soccer regularly in the past year, and have you been able to find friends or lose weight by playing soccer? The questionnaire was completed by the children and their parents or guardians for the 7–12 age group and by the children themselves for the 13–18 age group. The questions and options corresponding to each stage are listed in Table 3. ## 2.4. Proceduce Prior to conducting the survey, the staff were first trained to familiarize themselves with the specific plan of this study. Information about respondents at each observation site was obtained from the local statistical office, and a random sample of those who met the requirements was selected. Respondents were contacted by telephone and asked for their consent before the survey began. Then, they signed a consent form following the receival of specific information about the process and purpose of the study. In addition, a written informed consent was obtained from the legal guardians of all minor participants. During the survey, respondents’ names were replaced with numbers, and personal information remained confidential throughout the survey process. Questionnaires were completed after face-to-face interviews in specific communities, with each interview lasting approximately 1 h per participant, while quality control was performed through return visits. The quality control system was based on the Internet platform for collecting and managing information on fitness status 2020, and the main body consisted of the national research team (National Center for Physical Fitness Monitoring), the provincial research team (autonomous regions and municipalities directly under the central government), and the county (district) survey team, in which the village (neighborhood committee) of data collection was responsible for both quality control and supervision of the higher-level organizations and quality control of the data obtained from their work. All methods were carried out in accordance with relevant guidelines and regulations. The survey period was 9–11 January 2020, and full ethical approval was obtained from the China Institute of Sport Science, Beijing, China (CISS-2019-10-29). ## 2.5. Statistical Analyses First, the data were obtained from the questionnaire and the questions were dichotomous variables. Second, in the comparative analysis between age groups in each division, the number of participants compared to “like me” or “dominate me” was expressed as a percentage and a Chi-square test was performed. In the process of the Chi-square test, we weighed the cases. In addition, characteristics by the percentage of participants and liking or proficiency were compared using Pearson’s Chi-square test (minimal expected value > 5) and Fisher’s Exact Chi-square test (minimal expected value ≤ 5) [29]. Third, in the process of Pearson correlation analysis, data of 31 provinces were tested for normal distribution, and correlation analysis was carried out by province (city, district) in each region of China and the whole country. In particular, Pearson correlation analysis was conducted between the number of participants and likeability or proficiency in each region and gross domestic product (GDP), provincial area, and average annual precipitation. Finally, a one-way logistic regression analysis was conducted to examine the factors associated with the influence of children and youth soccer. For logistic regression analysis, the dichotomous variables are assigned with “0 = no and 1 = yes”, and the multi-categorical variables are assigned with sequential numbering. The Benjamini-Hochberg method was used to reduce the false discovery rate (FDR) when performing multiple comparisons. After adjusting raw p-values with the Benjamini-Hochberg method to control the FDR level to $5\%$, unadjusted and adjusted logistic regression models were used to identify the four levels (family level, school level, community level, and individual level) associated with soccer participation. A subset of $p \leq 0.05$ was selected in the one-way logistic regression analysis and placed in the adjusted model. Then, the Spearman correlation test was applied to check the relationship between the subitems. If a moderate or high (p ≥ 0.30) correlation was found between two subitems, one of them was selected to be representative. Furthermore, the screened subscripts were jointly included in the regression equation for multifactor logistic regression analysis. The strength of the association was expressed as OR and $95\%$ confidence interval (CI), and the difference was considered as statistically significant at $p \leq 0.05$ [30]. SPSS26.0 (IBM Corp., Armonk, NY, USA) was used for data analysis. ## 3. Results The number of children and adolescents who participated in soccer sports programs accounted for $24.5\%$ of the total number of respondents. Data from the different age groups on the number of people who participated in soccer sports programs showed the following characteristics: Age group 13–18 > age group 10–12 > age group 7–9, and the difference was statistically significant ($p \leq 0.05$). Children and adolescents who liked or were proficient in soccer sports programs accounted for $14.4\%$ of the total number of people surveyed. The percentage was even lower, and it was consistent with the age group of participation in soccer sports programs when viewed in different age groups. Moreover, the difference was statistically significant ($p \leq 0.05$). In each age group, $2.3\%$ ($p \leq 0.05$) more people participated in soccer than those who liked or were proficient in soccer in the 7–9 age group, $2.9\%$ ($p \leq 0.05$) more in the 12–18 age group, and $4.9\%$ ($p \leq 0.05$) more in the 13–18 age group. However, the number of those who participated in the soccer sports program was $10.1\%$ higher in the 7–18 age group than those who liked or mastered the soccer sports program ($p \leq 0.05$). ## 3.1. Overview of the Distribution of the Three Major Divisions In the national division, the percentage of people who participated in soccer was higher than the percentage of people who liked or were proficient in soccer in each region and each age group. In addition, the difference was statistically significant ($p \leq 0.001$). Moreover, the 10–12 age group was higher than the other age groups ($p \leq 0.001$). For the eastern region, in terms of participation in soccer sports program, the percentage of people in the 10–12 age group was $3.7\%$ (χ2 = 12.295, $p \leq 0.001$) and $3.3\%$ (χ2 = 12.096, $$p \leq 0.001$$) higher than the 10–12 and 13–18 age groups, respectively. In terms of liking or proficiency in the sport of soccer, the number of people in the 10–12 age group was $1.7\%$ (χ2 = 4.210, $$p \leq 0.040$$) and $3.7\%$ (χ2 = 17.766, $p \leq 0.001$) higher than the 10–12 and 13–18 age groups, respectively. For the central region, the characteristics of the age groups largely match those of the east. In terms of participation in soccer sports programs, the 10–12 age group has the highest percentage, $25.2\%$. However, there was no significance between all age groups in terms of liking or proficiency in soccer. For the western region, in terms of participation in soccer sports programs, the 10–12 age group has the highest percentage, $27.2\%$. The difference was significant (χ2 = 30.432, $p \leq 0.001$) compared to the 7–9 and 13–18 age groups (χ2 = 11.266, $$p \leq 0.001$$). This is followed by a higher percentage in the 13–18 age group than the 7–9 age group (χ2 = 8.932, $$p \leq 0.003$$). In terms of liking or proficiency in soccer sports programs, the percentage in the 10–12 age group was $14.5\%$, which was $3.8\%$ (χ2 = 18.077, $p \leq 0.001$) and $2.2\%$ (χ2 = 19.042, $$p \leq 0.005$$) higher than the 7–9 and 10–12 age groups, respectively. See Table 4 for more details. The percentage of children and adolescents who participated and liked or were proficient in soccer was calculated by division. In the three major sub-regions, the highest number of soccer participants was $38.2\%$ in the eastern region and the lowest was $27.2\%$ in the central region, while the highest percentage of those who liked or were proficient in soccer was $37.1\%$ in the western region. See Figure 1 for details. ## 3.2. Uneven Distribution across Regions with a Strong Correlation with the Natural Environment of Each Region For the national area, the people correlation coefficient of 0.487 for the region of the percentage of children and adolescents who liked or played soccer well and the provinces showed a positive correlation. The people correlation coefficient for the average annual precipitation is −0.367 ($p \leq 0.05$), which is negative. People correlation coefficients for participation in soccer programs and provincial GDP were 0.430 ($p \leq 0.05$) and 0.403 ($p \leq 0.05$) for the 7–9 and 10–12 age groups, respectively, showing a positive correlation. Participation and liking/skill of children and adolescents aged 13–18 years in soccer sports were positively correlated with provincial land area and negatively correlated with mean annual precipitation. Participation in soccer sports programs was positively correlated with province size, but not as strongly correlated with preference or ability for sports programs. The people correlation coefficients between children and adolescents’ participation in soccer sports programs and liking or proficiency and GDP in the eastern region were 0.652 ($p \leq 0.05$) and 0.591 ($p \leq 0.05$), respectively, and the results showed a positive correlation. The people correlation coefficients for children and adolescents participation in soccer programs with province size and average annual precipitation were 0.804 ($p \leq 0.01$) and −0.717 ($p \leq 0.05$), respectively, while the correlation coefficients for preference or proficiency for soccer programs were 0.882 ($p \leq 0.01$) and −0.620 ($p \leq 0.05$), respectively. Participation and preference or proficiency for soccer programs were positively correlated with province size and negatively correlated with average annual precipitation. In addition, they were negatively correlated with mean annual precipitation. The western region is primarily larger and more economically backward compared to the eastern and central regions. There is no correlation between the individual age groups and the relevant factors. For each age group, the correlation between the 7–9 and 10–12 age groups and GDP in the eastern region remained consistent with the nation. Participation and liking or proficiency in soccer programs for children and adolescents aged 13–18 years in the central region were strongly positively correlated with province size. See Table 5 for details. ## 3.3. Correlation Factors Affecting Children and Adolescents’ Participation in Soccer Due to the large difference between the number of individuals who were proficient in the sport of soccer and the number of individuals who participated in the above analysis, we performed a one-way logistic regression analysis to filter out significant factors ($p \leq 0.05$). The final factors corresponding to the four levels were identified: Family level (four factors), school level (one factor), community level (three factors), and individual level (three factors). The results of the multivariate logistic regression analysis show that the modified decidable coefficient is R2 = 0.297, indicating a good fit of the model. Moreover, a correlation analysis of the participation of children and adolescents aged 7–18 years in soccer sports programs was carried out. Analysis of the relationship between participation in soccer sports programs and variables among children and adolescents aged 7–18 years was carried out through screening of four levels. At the family level, Y1 (OR = 1.756; $95\%$ CI, 1.668–1.849; β = 0.563; $p \leq 0.05$), Y2 (OR = 1.194; $95\%$ CI, 1.084–1.314; β = 0.177; $p \leq 0.05$), Y3 (OR = 0.106; $95\%$ CI, 0.100–0.112; β = 2.247; $p \leq 0.05$), and Y4 (OR = 1.871; $95\%$ CI, 1.102–3.179; β = 0.627; $p \leq 0.05$) all contribute to the children’s participation in soccer sports programs. At the school level, Y5 (OR = 0.675; $95\%$ CI, 0.626–0.727; β = 0.394; $p \leq 0.05$) all contribute to the children’s participation in soccer sports programs. At the community level, Y6-1 (OR = 1.748; $95\%$ CI, 1.418–2.155; β = 0.558; $p \leq 0.05$), Y6-2 (OR = 1.399; $95\%$ CI, 1.136–1.723; β = 0.336; $p \leq 0.05$), Y7-1 (OR = 1.419; $95\%$ CI, 1.179–1.707; β = 0.350; $p \leq 0.05$), Y7-2 (OR = 1.269; $95\%$ CI, 1.057–1.523; β = 0.238; $p \leq 0.05$), and Y8 (OR = 0.729; $95\%$ CI, 0.614–0.865; β = 0.316; $p \leq 0.05$) all contribute to the children’s participation in soccer sports programs. At the individual level, Y9 (OR = 0.017; $95\%$ CI, 0.015–0.018; β = 4.104; $p \leq 0.05$), Y10 (OR = 0.645; $95\%$ CI, 0.532–0.782; β = 0.439; $p \leq 0.05$), and Y11 (OR = 0.634; $95\%$ CI, 0.469–0.858; β = 0.455; $p \leq 0.05$) all contribute to the children’s participation in soccer sports programs. See Table 6 for details. ## 4. Discussion The sports scene of Chinese children and adolescents basically comprises physical education classes in schools. In addition, the top five sports programs mainly include running, skipping rope, badminton, walking, and table tennis [31], and the number of participants in soccer sports programs is very small. However, soccer not only brings about healthy mental and physical development, but also fosters a sense of teamwork, promotes the development of motor skills, etc. [ 12,13,14,15,16,17,18]. In this study, a nationwide sample found that the number of children and adolescents who liked or were proficient in soccer was significantly lower than the number of people who participated in the sport. The larger the region and the higher the GDP, the more children and youth participate in soccer programs. Children and youth are more likely to participate in soccer if they live in a better socioeconomic status, if their parents encourage them to play the sport, and if they live in a better environment [6,7,8]. This study will not only provide guidance to the government and society to increase their “soccer population”, but will also enable all Chinese families to improve the factors that foster their children’s interest in soccer, in order that children and adolescents who are interested in soccer can participate in the sport, and communities in each region of China can improve the factors that promote the physical, psychological, and physiological benefits of soccer for children and adolescents in their communities [12,13,14,15,16,17,18]. Differences in children and adolescents’ participation and liking or proficiency in soccer sports programs and the proficiency of motor skills of children and adolescents both increase gradually with age and are inconsistent across all age groups [32]. In addition, as children and adolescents grow older, their cognitive abilities become more complex, and their fine motor development becomes more refined during subsequent growth and development [33,34]. Generally, the older you are, the easier it is to be proficient in a sport [35]. Therefore, the percentage of children and adolescents who liked or were proficient in soccer gradually increased with age. However, it is worth noting that it is unsuitable for all children and adolescents. Some children and adolescents may show negative emotions during exercise due to their late physical development, poor physical mobility, ability to learn motor skills, and motor perception, which may lead to an aversion to this program [36,37]. As a result, being proficient in a sport is based entirely on participation in the program and having a certain liking and love for the sport, in order to motivate children and adolescents to like or be proficient in the program. In this study, the larger Chi-square values and smaller p-values indicate a more significant variability, which is consistent with the significantly larger proportion of children and adolescents who participated than the percentage of those who liked or were proficient in soccer across all age groups. Conversion from participation to liking or ability in the soccer sports program shows wide variability across all regions and age groups. Children and adolescents’ participation in soccer sports programs is influenced by many factors. Socioeconomic status is a general measure of a certain family class after understanding the relevant factors, such as educational level, income, and employment combined with economics and sociology [38]. Dumuid [39] and Kelishad et al. [ 40] showed a significant effect of family economic status on the physical activity of children and adolescents due to the high economic status in cities, higher family income, high parental education, the increasing availability of sports facilities and soccer clubs in more economically developed areas, and the fact that parents with higher economic status are more willing to raise their children [41]. The eastern regions are mostly coastal cities with more developed economies, and families in cities with higher socioeconomic status can be more supportive than rural families, where children and adolescents can learn more sports and join more sports activities [42], which can be attributed to some extent to participation in soccer sports programs. This is consistent with the result that the distribution of the “soccer population” of children and adolescents in China is higher on the eastern coast. Minuchin [43] elaborated on the dynamic systems view of the family environment, noting that families are composed of complex, multiple interdependent subsystems. In a follow-up study, it was found that children and adolescents’ choice of sports or active participation in physical activity was influenced by their parents’ physical activity behavior [44]. In addition, the study showed that the higher the level of parental sports participation, the higher the children tend to show motivation [45]. In a more in-depth study, Cong found that parental emotional support and encouragement had a significant effect on the increase in physical activity levels [46]. In conclusion, when parents have a positive effect on their children’s participation in sports, they can accelerate the spread of the “soccer population” among children and adolescents. The natural environment is also a very important factor that influences the participation or preference and ability of children and adolescents in soccer. Research has proven that the relationship between participation in sports and the natural environment is two-sided. Specifically, the sport is influenced by the natural environment, while the natural environment is influenced by the sport [47]. Natural factors, in turn, show cross-correlations with the living environment, socioeconomic status, and sociocultural, and all these factors have a correlative effect on children and adolescents’ participation in soccer sports programs; therefore, the natural environment shows a correlation with participation in sports. More importantly, the soccer program requires soccer fields. Since soccer fields occupy an area of land, it is evident that the percentage of children and adolescents in the “soccer population” is higher in the eastern and central regions, both in economic terms and land area. The percentage of children and adolescents in the “soccer population” is negatively correlated with the average annual rainfall of each region due to the outdoor area. Learning is at the core of children and adolescents’ development. Bauman [21] proposed that individuals can be influenced by five levels: Individual level, interpersonal level, environmental level, policy level, and global level. In addition, the socio-ecological model of physical activity is present throughout an individual’s life. At the same time, it should be noted that the interpersonal level and the environmental level play a dominant role in childhood and adolescence. Children and adolescents are exposed to a sports program in the school environment that is entirely teacher-led, and in which their classmates participate. In the larger campus environment, students participate in a sport that can motivate them. In addition, children and adolescents exchange sports tips or sports anecdotes from the school sports program. Therefore, interpersonal communication is an effective way to promote the participation of children and adolescents in this sport. It allows children and adolescents, through the influence of their environment and relationships, to become dominant in the sport [48] over time, from the beginning of their participation in soccer. Sports facilities around the home, accessibility to sports venues, and the type and number of sports facilities that are suitable for children and adolescents influence children and adolescents’ participation in sports programs [49,50,51]. The greater the number of sports facilities and venues and the greater the accessibility to sports venues, the higher the number of sports options for children and adolescents. This suggests that the better the sports facilities in the community, the more children and adolescents can become a “soccer population”. In addition, the safety of the sports environment is an issue of concern to parents, such as sports venues located in local cities with heavy traffic and congested roads. In this case, sports danger substantially increased for children and adolescents; therefore, when participating in sports, the safety of sports venues is important [52]. This is consistent with the study results, and is an influential factor that affects the hindrance of children and adolescents when participating in sports programs. The prerequisite for proficiency in soccer is participation in the sport [53]. Regular contact with sports can have a positive effect on the development of the “soccer population” over time. In addition, motivation in sports is one of the important regulators that motivate individuals to participate in sports [54]. The sport of soccer exhibits a high team-based level. By comparing children and adolescents who participated in soccer with those who did not, Nathan found that participation in the soccer program resulted in closer relationships with peers [55]. This is consistent with the results of this study, since children and adolescents seek more all-around physical development as they get older. The greater the sense of self-efficacy obtained in the sport, the more motivated the individual will be to participate in the sport [56]. By enhancing the self-efficacy of children and adolescents in soccer sports programs, this enabling factor accelerates its popularity. ## 5. Limitations In this study, only four dimensions of the social-ecological theory were selected for comparative analysis, and relevant factors at the policy level were not included. Since the study population involved children and adolescents aged 7–9 years and the questionnaire was completed with the assistance of parents, the accurate measurement of their subjective attitudes was not a simple task. In addition, no comparative analysis (e.g., for gender) was performed. Another limitation was that although this study was a comprehensive survey covering mainland China, many regions in China were not analyzed. Moreover, the sample size of each region made it difficult to ensure the accuracy of the study results. 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--- title: Predictive Value of Inflammatory and Nutritional Indexes in the Pathology of Bladder Cancer Patients Treated with Radical Cystectomy authors: - Nebojsa Prijovic - Miodrag Acimovic - Veljko Santric - Branko Stankovic - Predrag Nikic - Ivan Vukovic - Ivan Soldatovic - Djordje Nale - Luka Kovacevic - Petar Nale - Adrian Marinkovic - Uros Babic journal: Current Oncology year: 2023 pmcid: PMC10047817 doi: 10.3390/curroncol30030197 license: CC BY 4.0 --- # Predictive Value of Inflammatory and Nutritional Indexes in the Pathology of Bladder Cancer Patients Treated with Radical Cystectomy ## Abstract In recent years, the focus of numerous studies has been the predictive value of inflammatory and nutritional parameters in oncology patients. The aim of our study was to examine the relationship between the inflammatory and nutritional parameters and the histopathological characteristics of patients with bladder cancer. A retrospective study included 491 patients who underwent radical cystectomy for bladder cancer between 2017 and 2021. We calculated the preoperative values of the neutrophil-to-lymphocyte ratio (NLR), the derived neutrophil-to-lymphocyte ratio (dNLR), the systemic immune-inflammation index (SII), the systemic inflammatory response index (SIRI), the platelet-to-lymphocyte ratio (PLR), the lymphocyte-to-monocyte ratio (LMR), the prognostic nutritional index (PNI), and the geriatric nutritional risk index (GNRI). Statistically significant positive correlations were observed between NLR, dNLR, SII, SIRI, and PLR and the pathological stage of the tumor. We observed statistically significant inverse correlations for LMR, PNI, and GNRI with the tumor stage. SIRI was identified as an independent predictor of the presence of LVI. dNLR was identified as an independent predictor of positive surgical margins. GNRI was identified as an independent predictor of the presence of metastases in the lymph nodes. We noticed the predictive value of SIRI, dNLR, and GNRI in the pathology of bladder cancer patients. ## 1. Introduction Bladder cancer (BC), with approximately 500,000 newly diagnosed cases worldwide, is the 10th most common cancer in the population. In the male population, bladder cancer occurs more often and ranks 6th in frequency among malignancies, with an incidence of 9.5 per 100,000 inhabitants [1]. In relation to geographic localization, there are variations in morbidity and mortality due to differences in exposure to risk factors as well as differences in diagnosis and the types of treatment that are available, so a higher incidence of bladder cancer has been observed in Western countries [2]. Bladder cancer primarily occurs in the elderly population, and the diagnosis is most often made between the ages of 70 and 84 [3]. It occurs more often in men (up to 3–4 times more), which can be explained by differences in lifestyle as well as by the possible retention of urine containing carcinogens in the field of prostate enlargement and urine retention [4]. Although it is diagnosed more often in men, it has been observed that BC manifests as a more aggressive disease in women [5]. The most significant risk factor for the occurrence of BC is tobacco smoking [6]. The second most common risk factor for the occurrence of BC is professional exposure to chemical agents containing polycyclic aromatic hydrocarbons and aromatic amines, which occurs most often when working with paints [7]. In about $90\%$ of cases, BC is histopathological urothelial carcinoma, where pure urothelial carcinoma is present in about $75\%$ of cases and a “variant” histology is present in $25\%$ of cases [8]. Depending on the depth of the invasion, BC can be non-muscle-invasive BC (NMIBC) or muscle-invasive (MIBC), which is of crucial importance for the treatment method. BC is presented as NMIBC in about $70\%$ of cases, and depending on the level of risk, the possibility of progression to MIBC is up to $60\%$ [9]. In patients who experience disease progression to MIBC, the prognosis is worse than in those with primary MIBC [10]. While the basis of treatment for NMIBC is the transurethral resection of the BC tumor and the prevention of recurrence, the standard of care for localized MIBC and high-risk patients with NMIBC is radical cystectomy [11,12]. The identification of prognostic markers is of great importance in oncology. In BC after radical cystectomy, the most significant histopathological prognostic factors are the tumor stage and lymph node status, and the presence of lymphovascular invasion (LVI) was also recognized as a significant prognostic factor [13,14]. The treatment outcomes and the occurrence of postoperative complications in oncology patients are often related to their nutritional status, which can be impaired by cancer-induced chronic inflammation [15]. In a large number of cancers, it has been shown that biomarkers of the inflammatory response can predict the prognosis and outcomes of these patients [16,17,18,19]. In recent years, the focus of numerous studies with oncology patients has been markers that can be calculated from blood count parameters such as the neutrophil-to-lymphocyte ratio (NLR) [20,21], the derived neutrophil-to-lymphocyte ratio (dNLR), the systemic immuno-inflammatory index (SII), and the systemic inflammatory response index (SIRI) [19,22,23]. Tumor cells can produce colony-stimulating factors, which can subsequently lead to an increase in the number of leukocytes [24]. The roles of blood cells in the proliferation of tumor cells as well as their migration and invasion are also known [25,26]. This influence is explained by the production of cytokines, the suppression of peripheral T lymphocytes, the stimulation of angiogenesis, and the repair of DNA damage [27]. Moreover, studies have shown that there is an association between the immune response and tumor prognosis [28]. Since cancers are chronic debilitating diseases, the course of the disease itself and systemic inflammation can lead to malnutrition. Malnutrition in cancer patients is associated with immune suppression, which contributes to tumor progression and the creation of a favorable environment for tumor recurrence [29,30,31]. Among the indexes that indicate the nutritional and inflammatory statuses of a patient, the prognostic nutritional index (PNI) stands out. Its concept as a potential biomarker in gastrointestinal surgery was introduced by Buzby et al. [ 32]. The calculation of PNI is based on the level of serum albumin and the number of peripheral lymphocytes in the blood, and it is known that hypoalbuminemia and a reduced number of lymphocytes are associated with worse outcomes in patients with cancer [33,34]. One of the parameters that can be used to describe the nutritional status of oncology patients, especially those in whom the disease occurs at a later age, is the geriatric nutritional risk index (GNRI) [35]. GNRI is calculated based on serum albumin levels, body mass, and body height and is considered more reliable for assessing nutritional status than body mass index (BMI) and serum albumin alone in the elderly patient population [36]. Given the relative lack of data on the association of inflammatory and nutritional parameters with the characteristics of invasive BC, the aim of our study was to examine the association of these parameters with the histopathological characteristics of BC in patients treated with radical cystectomy. ## 2.1. Screening Cohort and Baseline Characteristics This retrospective single-center study included patients who underwent radical cystectomy for primary bladder cancer during the period from 1 January 2017 to 31 December 2021 at the Urology Clinic of the University Clinical Center of Serbia in Belgrade. The study included patients undergoing radical cystectomy for previously histopathologically verified MIBC or very high risk NMIBC in clinical stage cT2-T4 without the presence of clinically verified preoperative lymph node metastasis (cN0) or distant metastasis (cM0). The study did not include patients undergoing salvage cystectomy who had previously been clinically verified to have metastases or patients with secondary bladder malignancies. Moreover, the study did not include patients with hematological diseases and immunodeficiency conditions characterized by disturbances in the blood count. Data on demographic and clinical characteristics (gender, age, body weight, body height, body mass index, smoking, and Eastern Cooperative Oncology Group (ECOG) performance status), laboratory results (numbers of leukocytes, neutrophils, lymphocytes, and monocytes; hemoglobin value; platelet count; and albumin level in peripheral blood), and histopathological characteristics (type of cancer, tumor stage, presence of lymphovascular invasion, status of surgical margins, and status of lymph nodes) were taken from the patients’ medical records. Blood samples were taken immediately after admission and were analyzed in the laboratory of the Center for Medical Biochemistry of the University Clinical Center of Serbia in Belgrade. The histopathological diagnoses were determined by an experienced uropathologist. The BC type was determined according to the current World Health Organization classification [37]. The tumor stage was determined according to the current TNM classification of bladder cancer [38]. ## 2.2. Inflammatory and Nutritional Index Calculations We calculated NLR as neutrophil count (×109/L)/lymphocyte count (×109/L). dNLR was calculated as neutrophil count (×109/L) / (leukocyte count (×109/L)—neutrophil count (×109/L)). SII was calculated as platelet count (×109/L) × neutrophil count (×109/L)/lymphocyte count (×109/L). SIRI was calculated as neutrophil count (×109/L) × monocyte count (×109/L)/ lymphocyte count (×109/L). The ratio of lymphocytes to monocytes (LMR) was calculated as lymphocyte count (×109/L) / monocyte count. The platelet-to-lymphocyte ratio (PLR) was calculated as platelet count (×109/L) / lymphocyte count (×109/L). PNI was calculated according to the formula PNI = serum albumin (g/L) + 5 × total lymphocyte count (×109/L). We calculated GNRI as 1.487 × serum albumin (g/L) + 41.7 × body mass (kg)/ideal body mass (kg). We calculated the ideal body mass as 22× body height2 (m2). ## 2.3. Statistical Analysis Descriptive and analytical statistics methods were used for statistical data processing. The Kolmogorov–Smirnov test was used to test the normal distribution of the data. The significance of the difference of variables with normal distributions was analyzed using Student’s t-test for two independent samples, while for variables that did not have normal distributions, the Mann–Whitney U test was used. The existence of correlations between variables was determined using Spearman’s rank correlation. Variables for which a statistically significant difference was observed were analyzed using a binary logistic regression analysis (in a model that included gender, age, and ECOG status) in order to identify independent predictors of certain histopathological properties. A value of $p \leq 0.05$ was considered statistically significant. SPSS version 20 for Windows was used for statistical data processing. ## 3. Results In the period from 1 January 2017 to 31 December 2021, a total of 491 patients who met the criteria for inclusion in this study underwent radical cystectomy for primary localized or locally advanced BC. The demographic, clinical, and histopathological characteristics of the patients are presented in Table 1. Of the total number, 387 ($78.8\%$) patients were men, while 104 ($21.2\%$) were women. The median age was 67 years, with an interquartile range (IQR) of 62–72 years. Observing the ECOG performance status, the largest number (242; $49.3\%$) had a score of 1, 202 ($41.4\%$) of them had a score of 0, 40 ($8.1\%$) had a score of 2, and 7 ($1.4\%$) had a score of 3. In the histopathological findings after radical cystectomy, the pT0 stage was recorded in 13 ($2.6\%$) patients, pTa and pTis were recorded in 12 ($12.4\%$), pT1 was recorded in 37 ($7.5\%$), pT2 was recorded in 160 ($32.6\%$), pT3 was recorded in 154 (31.4 %), and pT4 was recorded in 115 ($23.4\%$) patients. Observing the presence of tumor invasion of the muscle layer, NMIBC was present in 49 ($10.3\%$) patients, while MIBC was present in 429 ($89.7\%$). In $95.4\%$ of cases, urothelial carcinoma was present. Lymphovascular invasion was observed in 334 ($70.6\%$) cases. The surgical margins were positive in 76 ($15.5\%$) patients. The lymph nodes were positive in 81 ($23.2\%$) patients who underwent pelvic lymphadenectomy. Table 2 presents the values of the laboratory, inflammatory, and nutritional indexes. The median NLR was 2.68 (IQR 84–3.80), and the median dNLR was 1.71 (IQR 1.26–2.38). The median SII was 638.08 (IQR 420.00–1032.77), and the median SIRI was 1.53 (IQR 0.97–2.45). The median LMR was 3.08 (IQR 2.26–4.00), while the median PLR was 135.90 (IQR 107.27–190.00). The observed median PNI was 49.00 (IQR 45.00–53.00), and the median GNRI was 107.99 (IQR 100.95–115.33). The results of examining the correlations between the inflammatory and nutritional indexes and the tumor stage are shown in Table 3. Statistically significant positive correlations were observed between NLR ($p \leq 0.001$), dNLR ($p \leq 0.001$), SII ($p \leq 0.001$), SIRI ($p \leq 0.001$), and PLR. ( $p \leq 0.001$) and the pathological stage of the tumor. We observed statistically significant inverse correlations of LMR ($$p \leq 0.001$$), PNI ($p \leq 0.001$), and GNRI ($$p \leq 0.001$$) with the pathological stage of the tumor. The associations of inflammatory and nutritional parameters with muscle invasion are displayed in Table 4. The values of NLR ($$p \leq 0.004$$), dNLR ($$p \leq 0.01$$), SII ($$p \leq 0.007$$) and SIRI ($$p \leq 0.006$$)) were significantly higher in patients with MIBC, while the values of LMR ($$p \leq 0.04$$) and GNRI ($$p \leq 0.033$$) were significantly lower in patients with MIBC. Comparing the values of the examined parameters among patients with urothelial cancer and other types of bladder cancer (Table 5), the SIRI value was significantly higher in patients with other histological types of cancer ($$p \leq 0.042$$). Table 6 presents the results of the comparison of the inflammatory and nutritional parameters with the presence of lymphovascular invasion. The values of NLR ($$p \leq 0.002$$), dNLR ($$p \leq 0.009$$), SII ($$p \leq 0.002$$), and SIRI ($$p \leq 0.001$$) were significantly higher in cases where LVI was present. The values of PNI ($$p \leq 0.003$$) and GNRI ($$p \leq 0.004$$) were significantly lower in patients in whom LVI was present. The associations of inflammatory and nutritional parameters with the status of the surgical margins are shown in Table 7. The values of NLR ($$p \leq 0.02$$), dNLR ($$p \leq 0.007$$), and SII ($$p \leq 0.037$$) were significantly higher in patients with positive surgical margins. Table 8 shows the relationships between the examined parameters and the status of the lymph nodes. The values of NLR ($$p \leq 0.036$$), dNLR ($$p \leq 0.01$$), and SII ($$p \leq 0.022$$) were significantly higher in patients with positive lymph nodes, while the values of GNRI ($$p \leq 0.026$$) were significantly lower in these patients. Table 9 shows the results of the binary logistic regression analysis of predictors of histopathological characteristics of BC. None of the examined parameters were identified as independent predictors of muscle layer invasion. Age was observed as a predictive factor of the histological type of BC ($$p \leq 0.034$$). SIRI was identified as an independent predictor ($$p \leq 0.045$$) of the presence of LVI. Female gender ($$p \leq 0.001$$) and dNLR ($$p \leq 0.032$$) were identified as independent predictors of positive surgical margins among the examined parameters. GNRI was identified as an independent predictor of the presence of metastases in the lymph nodes ($$p \leq 0.026$$). Figure 1 shows the significant associations of NLR with the histopathological characteristics of BC. The significant associations of dNLR with the histopathological characteristics are shown graphically in Figure 2. Figure 3 shows the significant associations of SII with the histopathological characteristics of BC. A graphical presentation of the significant associations of SIRI with the histopathological features of BC is presented in Figure 4. The significant associations of LMR with the histopathological characteristics of bladder cancer are shown in Figure 5. A graphically displayed significant difference in PNI between patients with and without LVI is given in Figure 6. Figure 7 presents the significant associations of GNRI with the histopathological characteristics of BC. ## 4. Discussion Considering that the 5-year survival of patients with MIBC is below $50\%$ [39], there is a need to discover biomarkers that could be linked with the prognosis of the disease. It is widely accepted that a potential biomarker should be easily accessible. Hence, in the last few years, markers that can be calculated based on the complete blood count and routine biochemical analysis parameters, such as albumin, have been the focus of numerous studies in oncology. It was shown that inflammation plays a significant role in the biological behavior of tumors, and therefore it was considered a “seventh hallmark” of cancer [40]. Moreover, there is accumulating evidence connecting inflammation and nutrition with prognosis in various cancer types [41,42]. Considering that white blood cells and albumin reflect the inflammatory response and nutritional status, biomarkers that include these parameters can potentially indicate the characteristics of the tumor and the prognosis of the disease. After radical cystectomy, the estimated pathological tumor (pT) stage is one of the most significant prognostic factors in patients with BC [13]. In our study, we found correlations between all examined inflammatory and nutritional parameters with the pT stage after radical cystectomy. Among the inflammatory parameters, positive correlations with the tumor stage were recorded for all, with the exception of LMR, which showed an inverse correlation. Furthermore, inverse correlations with the tumor stage after radical cystectomy were found for PNI and GNRI. Our results are comparable with the findings of Tang X et al., who showed higher values of the inflammatory parameters NLR, dNLR, PLR, and SII in patients with MIBC compared to patients with NMIBC, while LMR and PNI were lower in patients with MIBC [43]. Moreover, a recent Bi H study from 2020 also showed an association of lower PNI values with a higher pT stage in patients with high-risk NMIBC [44]. Concerning the nutritional parameters, lower values of PNI and GNRI, which indicate malnutrition, correlated with a higher pT stage in our study. To the best of our knowledge, there are no studies published so far examining the association of GNRI with the histopathological characteristics of BC. Moreover, when comparing patients with NMIBC and MIBC, we observed that NLR, dNLR, SII, and SIRI were significantly higher in patients with MIBC, while LMR and GNRI were significantly lower in this group. Nevertheless, we did not find any of the examined inflammatory and nutritional parameters to be independent predictors of muscle layer invasion, which may be explained by the predominantly MIBC patients included in this study ($89.7\%$). Different histologic types of BC and different histologic subtypes of urothelial cancer have different biological behaviors and prognoses [37]. Urothelial carcinoma (UC) is the most common histological type of BC in the developed world, with the pure urothelial cancer histology present in about $75\%$ of cases and a “variant” histology present in $25\%$ of cases [8]. Our results showed that SIRI was significantly higher in patients who had a diverse histology, i.e., non-urothelial BC. The presence of lymphovascular invasion (LVI) is considered as prognostic factor of tumor aggressiveness and the possible existence of occult metastases in various types of cancers [45,46,47]. The presence of LVI in the histology findings after radical cystectomy in patients with MIBC is associated with more aggressive disease and is a predictive factor for survival [14]. In our study, NLR, dNLR, SII, and SIRI were all significantly higher in cases where LVI was present, while LMR, PNI, and GNRI were significantly lower in the same group of patients. Similar findings were observed by Bi et al., emphasizing the association of higher SII and lower PNI with the presence of LVI in a study conducted only in patients with NMIBC [44]. Accordingly, we identified SIRI as an independent predictor of the presence of LVI. The SIRI value is directly proportional to the number of neutrophils and monocytes and inversely proportional to the number of lymphocytes. The interpretation of our results is consistent with the evidence that neutrophils influence tumor progression by releasing elastase, which degrades the extracellular matrix and promotes neovascularization [48]. Moreover, a decrease in the number of lymphocytes leads to a decrease in local immunity, which creates an immunocompromised environment that favors tumor growth [49]. Given that elevated neutrophil counts and decreased lymphocyte counts create a favorable environment for local tumor progression, this could potentially explain the association of higher SIRI values with the presence of LVI observed in our study. The importance of surgical margin status after radical cystectomy is still controversial. Studies that investigated the impact of positive surgical margins after radical cystectomy on survival showed conflicting results [50,51]. However, a meta-analysis by Hong X et al. stressed the association of positive surgical margins with worse overall survival after radical cystectomy [52]. Therefore, the potential preoperative prediction of the status of surgical margins may have significance in surgical practice. In this study, we found higher NLR, dNLR, and SII in patients with positive margins and identified dNLR as an independent predictor of positive surgical margins after radical cystectomy. Findings published in the literature indicate an association of elevated dNLR with more aggressive BC in terms of a higher grade and tumor stage [43]. Nevertheless, in patients with ovarian cancer, Wu et al. showed that dNLR values differ between patients with malignant and benign ovarian pathologies [53]. It has been reported previously that dNLR increases as a consequence of an increased number of neutrophils and a decreased number of other types of white blood cells, including lymphocytes, and such changes in the white blood cell count contribute to local tumor progression [48,49]. The presence of lymph node metastases is one of the most important histopathological prognostic factors after radical cystectomy [13]. Preoperatively, the assessment of lymph nodes using conventional imaging methods is still limited by the fact that metastases cannot be detected in normal-sized or minimally enlarged lymph nodes [12,54,55,56]. Given that the presence of lymph node metastases after radical cystectomy is associated with poor prognosis and worse overall survival [13], the identification of biomarkers that could predict the presence of micrometastases in clinically normal lymph nodes would be of great importance in the selection of patients with BC for radical cystectomy. In our research, we found significantly higher NLR, dNLR, and SII in patients with positive LN, while in the same group the values of GNRI were significantly lower. Moreover, we identified GNRI as an independent predictor of the presence of metastasis in surgically resected lymph nodes after radical cystectomy. According to the literature data, GNRI has been established as an index to predict morbidity and mortality among elderly patients, with lower values indicating malnutrition and being associated with a higher risk of death [35,36]. Hence, we chose GNRI for the purpose of this study because of the age of our patients (median age of 67 years, IQR 62–72). Nevertheless, this index is considered most suitable in the elderly patient population because it is calculated based on albumin values and an ideal body mass, unlike others that include a normal body weight [57,58]. In recent years, this index has been used as a parameter for the prognosis of various chronic diseases and malignancies [59,60]. To the best of our knowledge, no studies examining the association between GNRI and the histopathological features of BC were published so far. Nonetheless, in previously published studies in patients with other genito-urinary cancers, GNRI was recognized as a prognostic predictor in patients with non-metastatic renal cell carcinoma and in metastatic hormone-naive prostate carcinoma [61,62]. Considering that we have identified GNRI as an independent predictor of the presence of metastases in the lymph nodes, there is room for further research into the significance of this index in patients with BC. Our study had several limitations. First, it was a retrospective observational study. In addition, the study was conducted at a single institution. Moreover, a limitation of the study was the relatively small number of patients. ## 5. Conclusions Our study showed multiple associations of inflammatory and nutritional indexes with the histopathological characteristics of urothelial bladder carcinoma. Moreover, we found predictive value of SIRI, dNLR, and GNRI for the presence of lymphovascular invasion, positive surgical margins, and the presence of lymph node metastases after radical cystectomy, respectively. Considering that these easily accessible biomarkers could potentially predict the histopathological features of poor prognosis, they might be used to improve patient selection for radical surgical treatment. In order to further explore the predictive value of inflammatory and nutritional indexes in patients with bladder cancer, it would be necessary to conduct a prospective study with a larger number of patients and investigate the influences of these parameters on patient survival. ## References 1. Globocan. (2020.0) 2. 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--- title: Terpene-Containing Analogues of Glitazars as Potential Therapeutic Agents for Metabolic Syndrome authors: - Mikhail E. Blokhin - Sergey O. Kuranov - Mikhail V. Khvostov - Vladislav V. Fomenko - Olga A. Luzina - Natalia A. Zhukova - Cham Elhajjar - Tatiana G. Tolstikova - Nariman F. Salakhutdinov journal: Current Issues in Molecular Biology year: 2023 pmcid: PMC10047834 doi: 10.3390/cimb45030144 license: CC BY 4.0 --- # Terpene-Containing Analogues of Glitazars as Potential Therapeutic Agents for Metabolic Syndrome ## Abstract Metabolic syndrome is a complex of abnormalities involving impaired glucose and lipid metabolism, which needs effective pharmacotherapy. One way to reduce lipid and glucose levels associated with this pathology is the simultaneous activation of nuclear PPAR-alpha and gamma. For this purpose, we synthesized a number of potential agonists based on the pharmacophore fragment of glitazars with the inclusion of mono- or diterpenic moiety in the molecular structure. The study of their pharmacological activity in mice with obesity and type 2 diabetes mellitus (C57Bl/6Ay) revealed one substance that was capable of reducing the triglyceride levels in the liver and adipose tissue of mice by enhancing their catabolism and expressing a hypoglycemic effect connected with the sensitization of mice tissue to insulin. It has also been shown to have no toxic effects on the liver. ## 1. Introduction Metabolic syndrome (MS) is a complex of pathological changes involving high blood sugar and abnormal cholesterol levels (decreased high-density lipoprotein and/or increased triglyceride levels, leading to hypertension and obesity and increasing the risk of cardiovascular disease, stroke, and heart attack [1]. Until recently, metabolic syndrome was predominantly suffered by the elderly (over 60 years of age), however, the picture has changed considerably over the last 20 years. Trends have shown that the problem is getting younger and more relevant to a younger population. In some countries, the proportion of adults suffering from these symptoms is as high as $25\%$. Over the past 20 years, the number of people worldwide with metabolic syndrome has increased by more than 100 million—a third [2]. There are two main disorders with this syndrome:Type 2 diabetes mellitus. If lifestyle changes are not made and excess weight is not brought under control, insulin resistance can develop, which can cause high blood sugar levels, eventually leading to type 2 diabetes. High cholesterol and high blood pressure contribute to the formation of plaques in the arteries. These plaques narrow the arteries’ openings, which can lead to a heart attack or stroke. Recently, drugs from a new group that target both problems—glitazars—have been successfully developed [3]. Initially, these compounds were classified as glitazones, but a different mechanism of action, the activation of not only peroxisome proliferator-activated receptors gamma (PPAR-gamma) but also PPAR-alpha receptors, and changes in the structure features allowed them to be separated into the glitazar group. The drugs effectively influence the carbohydrate restoration and fat metabolism in patients with diabetes types 1 and 2, and have a favorable effect on the prevention and course of vascular complications [4]. (S)-2-Ethoxy-3-phenylpropanoic acid is considered to be a pharmacophore fragment common to glitazars. The main characteristic feature of this fragment is its binding to both PPAR-alpha and PPAR-gamma receptors, which allows compounds in this class of drugs to effectively regulate not only carbohydrate but also lipid metabolism. Several of the dual PPAR agonists have shown promising results in animal studies and have subsequently been tested in clinical trials. The (S)-2-ethoxy-3-phenylpropanoic acid pharmacophore fragment is common to glitazars, but they all have a different variability. At present, only saroglitazar (Figure 1) has been approved for use, but only in India [5]. Ragaglitazar, tezaglitazar (Figure 1), and several others have failed in clinical trials due to the presence of various side-effects such as hepatotoxicity, cardiotoxicity, and gastrointestinal toxicity [6,7]. Recently, we proposed that the diverse side-effects of pharmacological agents are due to structural differences in the “tail” part of the glitazar molecule. In a recent work, we showed that the use of triterpene acid fragments as the tail of the molecule imbues these compounds with hypoglycemic and hypolipidemic properties [8]. A synthesized compound BM-249 (Figure 2), with a dihydrobetulonic acid fragment coupled to (S)-2-ethoxy-3-phenylpropanoic acid via an amide bond to an aminoethanol spacer, administered orally at a dose of 30 mg/kg for 5 weeks to mice on a high-fat/high-cholesterol diet (HF diet), showed an effect in reducing the blood glucose, total cholesterol (TC), and high-density lipoprotein (HDL) while having a relatively good safety profile. The aim of this work was to synthesize analogues of compound BM-249 containing other terpene fragments, namely, monoterpene and diterpene substituents, and to study their hypoglycemic and hypolipidemic properties. ## 2.1. Chemistry The 1H and 13C NMR spectra for compounds were recorded on a Bruker AV-400 spectrometer (Bruker Corporation, Billerica, MA, USA) at 400.13 and 100.61 MHz, respectively in CDCl3 solution. The signals of the solvent were used as the reference (δH 7.27, δC 77.1 for CDCl3). Chemical shifts were given in ppm and the coupling constants (J) were given in hertz (Hz). The structure of the products was determined by means of 1H and 13C NMR spectra (Figures S1–S24). The mass spectra (15–500 m/z, 70 eV) were recorded on a DFS Thermo Scientific high-resolution mass spectrometer (Waltham, MA, USA). Merck silica gel (63–200 μm, Macherey-Nagel, Düren, Germany) was used for the column chromatography. Thin-layer chromatography was performed on TLC Silica gel 60F254 Merck (Darmstadt, Germany). All reagents were used as described unless otherwise noted. Reagent-grade solvents were redistilled prior to use. Synthetic starting materials, reagents, and solvents were purchased from Sigma-Aldrich, Acros Organics (China) and Alfa Aesar (Germany). All diterpenic acids were donated by colleagues from the Medicinal Chemistry Department. The reference compound tesaglitazar and ethyl (S)-2-ethoxy-3-(4-hydroxyphenyl) propanoate moiety were synthesized according to the methods in the literature [9]. The amine 5 containing ethyl (S)-2-ethoxy-3-(4-hydroxyphenyl) propanoate moiety was synthesized according to the procedure described earlier [8]. The obtained spectral data coincide with the literature data. In a 50 mL flask, 4 mmol of (S)-ethyl 3-(4-(4-(2-(2-aminoethoxy)-phenethoxy) phenyl)-2-ethoxy propanoate 5, 3.9 mmol of the corresponding monoterpene aldehyde in 10 mL of methylene chloride, and then 6.4 mmol NaBH(OAc)3 was added in portions. The reaction mixture was stirred at room temperature for 12 h. Then, the mixture was diluted with 15 mL of water, 4 mL of 1 N NaOH solution, and left to stir for 20 min. Next, the mixture was extracted, the organic layer was washed with saturated NaCl solution, and dried over magnesium sulfate. The purification was conducted by column chromatography (eluent: CHCl3:MeOH-100:1). Yellow oil, $86\%$ yield. 1H-NMR: 0.85 (3 H, s), 1.13–1.20 (3 H, m), 1.20–1.26 (3 H, m), 1.29 (3 H, s), 1.68 (1 H, br.s), 2.11 (2 H, d, $J = 5.5$), 2.18–2.35 (2 H, m), 2.39 (1 H, d.t., $J = 8.6$, 5.6), 2.92–3.00 (4 H, m), 3.03 (2 H, t, $J = 7.1$), 3.19 (2 H, m), 3.35 (1 H, d.q., $J = 9.1$, 7.0), 3.60 (1 H, d.q., $J = 9.1$, 7.0), 3.97 (1 H, dd, $J = 7.3$, 6.0), 4.04–4.21 (6 H, m), 5.38–5.42 (1 H, m), 6.79–6.85 (2 H, m), 6.85–6.89 (2 H, m), 7.15 (2 H, d, $J = 8.6$), 7.19 (2 H, d, $J = 8.6$). 13C-NMR: 14.2, 15.0, 21.0, 26.2, 31.2, 31.6, 34.9, 38.0, 38.4, 40.9, 44.3, 48.2, 54.5, 60.7, 66.1, 67.3, 68.8, 80.4, 114.2 (2C), 114.4 (2C), 117.6, 129.2, 129.9 (2C), 130.3 (2C), 146.3, 157.5, 172.5. Found: m/z 535.3298 [M]+. C33H45NO5. Calculated: M 535.3298. Colorless oil, $75\%$ yield. 1H-NMR: 0.83–0.98 (3 H, m), 1.17 (3 H, t, $J = 7.1$), 1.23 (3 H, t, $J = 7.1$), 1.28–1.44 (2 H, m), 1.45–1.55 (2 H, m), 1.67–1.75 (3 H, m), 1.76–1.8 (3 H, m), 1.86–2.11 (3 H, m), 2.62–2.79 (2 H, m), 2.91–2.98 (2 H, m), 2.98–3.07 (4 H, m), 3.35 (1 H, d.q., $J = 9.0$, 7.0), 3.60 (1 H, d.q., $J = 9.1$, 7.0), 3.96 (1 H, t, $J = 6.6$), 4.04–4.26 (6 H, m), 5.06–5.14 (1 H, m), 6.81 (4 H, m), 7.15 (4 H, m). 13C-NMR: 14.2, 15.0, 17.6, 19.6, 25.4, 25.7, 30.6, 34.9, 37.0, 37.1, 38.4, 47.7, 48.8, 60.7, 66.1, 67.1, 68.8, 80.4, 114.3 (2C), 114.5 (2C), 124.7, 129.2, 129.9 (2C), 130.3 (2C), 130.4, 131.2, 157.4, 157.5, 172.5. Found: m/z 539.3611 [M]+. C33H49NO5. Calculated: M 539.3610. In a 50 mL round bottom flask with 20 mL of N,N-dimethylformamide (DMF), the corresponding diterpene acid (2.5 mmol) and amine 5, 1.1 g (2.75 mmol), were dissolved, then 0.72 g (1.9 mmol) 2-(1H-benzotriazol-1-yl)-1,1,3,3-tetramethyluronium hexafluorophosphate (HBTU) was added. Next, 0.54 g (4.2 mmol) of diisopropylethylamine (DIPEA) was added on cooling in an ice bath. The reaction was carried out in an inert atmosphere under stirring at room temperature for 5 h. The reaction was treated by dilution with water, followed by acidification with $10\%$ hydrochloric acid to pH~2–3 and extraction with EtOAc. The organic phase was washed with saturated NaHCO3 solution and dried over MgSO4. The purification was conducted by column chromatography on silica gel using the system hexane:EtOAc-3:1. Yellow oil, $79\%$ yield. 1H-NMR: 0.78–0.88 (8 H, m), 1.15–1.18 (8 H, m), 1.20–1.25 (3 H, m), 1.25–1.99 (12 H, m), 2.87–2.93 (2 H, m), 3.05 (2 H, m), 3.25–3.36 (1 H, m), 3.51–3.71 (3 H, m), 3.98 (3 H, m), 4.03–4.16 (4 H, m), 4.83 (2 H, m), 5.23 (1 H, m), 5.81 (1 H, m), 6.19–6.28 (1 H, m), 6.79 (4 H, dd, $J = 14.4$, 8.7), 7.07–7.19 (4 H, m). 13C-NMR: 14.2, 15.0, 15.3, 17.2, 17.9, 19.9, 21.4, 24.7, 25.1, 34.8, 36.0, 36.7, 38.7, 39.1, 44.9, 45.6, 44.9, 46.0, 46.3, 51.9, 60.7, 66.1, 66.8, 68.7, 80.3, 109.2, 114.2 (2C), 114.5 (2C), 120.6, 129.2, 130.0 (2C), 130.3 (2C), 130.9, 135.5, 150.3, 157.1, 157.4, 172.5, 178.9. Found: m/z 685.4342 [M]+. C43H59NO6. Calculated: M 685.4343. Orange oil, $76\%$ yield. 1H-NMR: 1.13 (3 H, t, $J = 7.0$), 1.16–1.25 (12 H, m), 1.42 (3 H, s), 1.41–1.75 (7 H, m), 2.12–2.21 (1 H, m), 2.22–2.31 (1 H, m), 2.77 (3 H, s), 2.89–2.94 (2 H, m), 3.10 (2 H, m), 3.26–3.36 (1 H, m), 3.51–3.72 (3 H, m), 3.90–4.03 (3 H, m), 4.04–4.17 (4 H, m), 6.22–6.35 (1 H, m), 6.80 (5 H, dd, $J = 14.2$, 8.7), 6.93–6.99 (1 H, m), 7.08–7.21 (5 H, m). 13C-NMR: 14.1, 15.0, 16.3, 18.6, 21.0, 23.9 (2C), 25.1, 29.8, 33.3, 34.8, 37.0, 37.8, 38.4, 39.2, 45.4, 47.2, 60.7, 66.1, 66.7, 68.7, 80.2, 114.1 (2C), 114.4 (2C), 123.7, 124.0, 126.8, 129.1, 123.0 (2C), 130.3 (2C), 130.8, 134.5, 145.5, 146.8, 157.1, 157.4, 172.5, 178.5. Found: m/z 683.4186 [M]+. C43H57NO6. Calculated: M 683.4186. Yellow oil, $71\%$ yield. 1H-NMR: 0.75–0.8 (3 H, m), 0.97 (5 H, m), 1.17–1.26 (10 H, m), 1.42–1.58 (3 H, m), 1.65–1.98 (7 H, m), 2.10–2.22 (1 H, m), 2.88–2.94 (2 H, m), 3.00 (2 H, t, $J = 7.1$), 3.31 (1 H, dd, $J = 9.1$, 7.1), 3.52–3.69 (3 H, m), 3.93 (1 H, dd, $J = 7.2$, 6.1), 3.99 (2 H, t, $J = 5.1$), 4.05–4.12 (7 H, m), 5.23 (1 H, d, $J = 4.8$), 5.70 (1 H, s), 6.18 (1 H, br. s.), 6.80 (4 H, dd, $J = 15.4$, 8.6), 7.11 (2 H, d, $J = 8.6$), 7.17 (2 H, d, $J = 8.5$). 13C-NMR: 14.1, 14.2 (2C), 15.0, 16.9, 18.2, 20.8, 21.4, 22.4, 23.9, 25.2, 27.3, 29.7, 34.5, 34.8, 37.1, 37.8, 38.4, 39.2, 45.6, 46.3, 50.9, 60.4, 66.1, 66.8, 68.8, 80.4, 114.2 (2C), 114.6 (2C), 120.4, 122.3, 129.2, 130.0 (2C), 130.3 (2C), 130.9, 135.4, 145.1, 157.2, 157.5, 172.5, 178.5. Found: m/z 685.4342 [M]+. C43H59NO6. Calculated: M 685.4342. Yellow oil, $68\%$ yield. 1H-NMR: 0.55 (3 H, s), 1.00 (1 H, td, $J = 13.1$, 3.7), 1.09–1.17 (6 H, m), 1.17–1.30 (5 H, m), 1.47–1.62 (2 H, m), 1.68 (3 H, m), 1.77–1.93 (5 H, m), 1.96–2.09 (2 H, m), 2.14–2.25 (1 H, m), 2.40 (1 H, dd, $J = 8.3$, 2.1), 2.46–2.56 (1 H, m), 2.88–2.95 (2 H, m), 3.00 (2 H, t, $J = 7.1$), 3.32 (1 H, dq, $J = 9.1$, 7.1), 3.51–3.67 (3 H, m), 3.91–4.04 (3 H, m), 4.04–4.19 (4 H, m), 4.50 (1 H, s), 4.82 (1 H, s), 6.06 (1 H, t, $J = 5.4$), 6.22 (1 H, d, $J = 0.7$), 6.72–6.86 (4 H, m), 7.09–7.21 (5 H, m), 7.31 (1 H, t, $J = 1.5$). 13C-NMR: 12.6, 14.1, 15.0, 20.0, 23.4, 24.1, 26.6, 30.0, 34.8, 38.2, 38.4, 38.7, 38.8, 39.2, 40.2, 44.0, 55.0, 56.4, 60.7, 66.1, 66.4, 68.7, 80.3, 106.5, 110.9, 114.2 (2C), 114.4 (2C), 125.3, 129.2, 130.0 (2C), 130.3 (2C), 130.7, 138.6, 142.6, 147.4, 157.0, 157.4, 172.5, 176.7. Found: m/z 699.4135 [M]+. C43H57NO7. Calculated: M 699.4134. Hydrolysis of the ester group of the compounds obtained was performed according to the procedure described in our previous work [8]. Colorless oil, $87\%$ yield. 1H NMR: 0.83 (3 H, s), 1.05–1.21 (3 H, m), 1.22–1.43 (3 H, m), 1.98–2.13 (2 H, m), 2.19–2.37 (2 H, m), 2.39–2.50 (2 H, m), 2.91–3.04 (4 H, m), 3.26 (3 H, m), 3.52–3.68 (4 H, m), 3.96 (1 H, dd, $J = 7.9$, 4.4), 4.00–4.09 (2 H, m), 4.28 (2 H, br.s.), 5.78 (1 H, br. s.), 6.75 (2 H, d, $J = 8.6$), 6.86 (2 H, d, $J = 8.5$), 7.13 (4 H, dd, $J = 13.0$, 8.6). 13C-NMR: 13.8, 15.0, 18.8, 20.7, 21.0, 25.8, 29.6, 31.5, 31.5, 34.6, 34.8, 37.9, 38.0, 40.0, 43.6, 44.8, 51.7, 62.5, 62.9, 66.4, 68.6, 79.7, 114.2 (2C), 114.6 (2C), 127.4, 129.0, 129.9 (2C), 130.5 (2C), 131.3, 138.3, 156.1, 157.4, 175.8, 176.1. Found: m/z 507.2985 [M]+. C31H41NO5. Calculated: M 507.2985. Colorless oil, $91\%$ yield. 1H-NMR: 0.84–0.96 (3 H, m), 1.09–1.20 (3 H, m), 1.24–1.40 (3 H, m), 1.51 (1 H, d, $J = 6.2$), 1.59 (3 H, s), 1.64–1.75 (4 H, m), 1.80–2.07 (3 H, m), 2.81–3.00 (4 H, m), 3.07 (2 H, dtt, $J = 17.4$, 11.7, 11.7, 5.6, 5.6), 3.23 (2 H, m), 3.33–3.46 (1 H, m), 3.50–3.62 (1 H, m), 3.94–4.05 (3 H, m), 4.15–4.26 (2 H, m), 5.04 (1 H, t, $J = 6.9$), 6.71 (2 H, d, $J = 8.6$), 6.78–6.88 (2 H, m), 7.10 (4 H, dd, $J = 18.9$, 8.5). 13C-NMR: 15.0, 17.7, 19.0, 25.2, 25.7, 30.4, 32.6, 34.9, 36.5, 37.8, 46.2, 46.48, 63.0, 66.2, 68.6, 76.6, 77.3, 80.0, 114.2 (2C), 114.6 (2C), 124.1, 128.9 (2C), 130.0 (2C), 130.4, 131.4, 131.6, 156.1, 157.5, 175.5. Found: m/z 511.3298 [M]+. C31H45NO5. Calculated: M 511.3297. Yellow oil, $85\%$ yield. 1H-NMR: 0.78–0.89 (6 H, m), 1.05–1.16 (5 H, m), 1.27–1.38 (2 H, m), 1.39–1.58 (7 H, m), 1.58–2.04 (9 H, m), 2.84–3.05 (4 H, m), 3.37 (1 H, dd, $J = 8.8$, 7.3), 3.52–3.66 (3 H, m), 3.93–4.01 (3 H, m), 4.07 (2 H, t, $J = 7.0$), 4.80–4.93 (2 H, m), 5.14–5.31 (1 H, m), 5.71–5.82 (1 H, m), 6.25 (1 H, t, $J = 5.2$), 6.80 (4 H, dd, $J = 16.5$, 8.4), 7.09–7.21 (4 H, m). 13C-NMR: 15.0, 15.3, 17.3, 17.5, 19.9, 21.4, 21.2, 24.7, 34.8, 36.0, 36.7, 38.4, 38.7, 39.2, 45.6, 46.0, 52.0, 66.1, 66.8, 68.7, 80.3, 109.2, 114.2 (2C), 114.5 (2C), 120.8, 120.9, 129.2, 1230.0 (2C), 130.3 (2C), 130.8, 135.5, 135.6, 150.2, 157.1, 157.5, 172.5, 178.9. Found: m/z 657.4029 [M]+. C41H55NO6. Calculated: M 657.4028. Yellow oil, $88\%$ yield. 1H-NMR: 1.10–1.30 (15 H, m), 1.38–1.58 (3 H, m), 1.63–1.77 (4 H, m), 2.11 (1 H, d, $J = 12.2$), 2.28 (1 H, d, $J = 12.1$), 2.72–3.07 (7 H, m), 3.38 (1 H, dd, $J = 8.1$, 7.8), 3.51–3.70 (3 H, m), 3.95–4.16 (6 H, m), 6.30 (1 H, br. s.), 6.76–6.87 (5 H, m), 6.97 (1 H, d, $J = 8$), 7.09–7.21 (5 H, m). 13C-NMR: 14.7, 16.1, 18.3, 20.7, 23.7 (2C), 24.9, 29.6, 33.0, 34.5, 36.7, 37.6, 38.1, 38.9, 45.1, 46.9, 65.8, 67.0, 68.5, 80.0, 114.0 (4C), 123.5, 123.7, 126.5, 128.8, 129.7 (2C), 130.0 (2C), 130.5, 134.3, 145.3, 146.6, 156.8, 157.2, 172.2, 178.3. Found: m/z 655.3873 [M]+. C41H53NO6. Calculated: M 655.3872. Yellow oil, $83\%$ yield. 1H-NMR: 0.75–0.82 (3 H, m), 0.91–1.02 (5 H, m), 1.08–1.28 (9 H, m), 1.40–1.61 (4 H, m), 1.61–2.09 (9 H, m), 2.90–3.05 (4 H, m), 3.32–3.46 (1 H, m), 3.50–3.71 (3 H, m), 3.94–4.04 (3 H, m), 4.08 (2 H, t, $J = 7.0$), 5.23 (1 H, d, $J = 3.9$), 5.70 (1 H, s), 6.21 (1 H, t, $J = 5.3$), 6.81 (4 H, t, $J = 9.2$), 7.15 (4 H, dd, $J = 15.7$). 13C-NMR: 14.5, 15.3, 17.2, 18.5, 21.1, 21.7, 22.7, 24.3, 25.5, 27.6, 30.0, 34.8, 35.2, 37.6, 38.8, 39.5, 45.9, 46.6, 51.2, 61.0, 66.5, 69.0, 80.7, 114.6 (2C), 114.9 (2C), 120.7, 122.6, 129.6, 130.3 (2C), 130.7 (2C), 131.2, 135.7, 145.5, 157.5, 157.8, 172.8, 178.9. Found: m/z 657.4029 [M]+. C41H55NO6. Calculated: M 657.4028. Yellow oil, $79\%$ yield. 1H-NMR: 0.5 (3 H, s), 0.9–1.3 (11 H, m), 1.4–1.9 (8 H, m), 2.1–2.3 (1 H, m), 2.3–2.6 (2 H, m), 2.8–3.1 (4 H, m), 3.4–3.5 (1 H, m), 3.5–3.7 (3 H, m), 3.9–4.2 (6 H, m), 4.5 (1 H, s), 4.8 (1 H, s), 6.1 (1 H, br. s.), 6.2 (1 H, s), 6.8 (4 H, d, $J = 7.6$), 7.1–7.2 (5 H, m), 7.3 (1 H, s). 13C-NMR: 14.9, 15.8, 20.8, 24.2, 24.9, 27.3, 30.8, 35.4, 38.9, 39.1, 39.5, 39.6, 40.0, 41.0, 44.8, 55.8, 57.2, 66.9, 67.1, 69.4, 81.0, 107.3, 111.6, 114.9 (2C), 115.1 (2C), 126.0, 129.9, 130.7 (2C), 131.0 (2C), 131.5, 139.4, 143.3, 148.2, 157.8, 158.2, 173.2, 177.5. Found: m/z 671.3822 [M]+. C41H53NO7. Calculated: M 671.3822. ## 2.2.1. Animals Male C57BL/6Ay mice weighing 28–32 g were used. Animals were obtained from the Specific Pathogen Free (SPF) vivarium of the ICG SB RAS. The animals were housed in plastic cages with free access to feed and water. In the vivarium, the humidity, temperature, and $\frac{12}{12}$ h light-and-dark cycle were controlled. All animal experiments were performed in accordance with the Russian Federation’s laws, the Ministry of Health of the Russian Federation decree no. 199n of 4 January 2016; the European Parliament and European Union Council Directive $\frac{2010}{63}$/EU of 22 September 2010 on the protection of animals used for scientific purposes. The experimental protocol was approved by the Ethics Committee of NIOCH SB RAS (protocol no. P-01-04.2022-14). ## 2.2.2. The OGTT Mice were fasted for 12 h before the test. Compounds 7a,b, 9a–d were administered orally at a dose of 30 or 20 mg/kg (according to molar mass) in a Tween-80–water suspension. Glucose was given orally at a dose of 2.5 g/kg. Metformin (MF, CAS 1115-70-4 Acros Organics, Geel, Belgium) was used as a positive control at a dose of 250 mg/kg. OGTT was conducted on the 14th and 28th days of the experiment. During the first OGTT, all compounds were introduced by oral gavage 30 min prior to the glucose load. In the second OGTT, the introduction of the last compounds was a day prior to the test. All mice blood samples were collected by tail incision 0 (before dosing), 30, 60, 90, and 120 min after the glucose load. The ONE TOUCH Select blood glucose meter (LIFESCAN Inc., Milpitas, CA, USA) was used for blood glucose concentration measurements. The area under the glycemic curve (AUC) was calculated using Tai’s model [10]. ## 2.2.3. The ITT The test was performed after a 4 h fasting on all AY mice in the experiment. Tested compounds were introduced orally 4 h before the insulin injection. Insulin (soluble human insulin, Medsynthesis Plant, Novouralsk, Russia) at a dose of 5 ED/kg was injected i.p. Blood samples were obtained from tail incision at 0 (before dosing), 15, 30, 45, 60, and 90 min after the insulin injection. Blood glucose concentration was evaluated similar to OGTT. ## 2.2.4. The AY Mice Experiment Design In order to facilitate body weight gain, mice, in addition to standard chow, were fed with lard and cookies for 30 days. Mice with body weight ≥35 g were chosen for further experiments. Animals were divided into groups of six mice each: [1] Vehicle (water + 2 drops of Tween-80); (2–6) 7a,b, 9a–d 30 or 20 mg/kg (according to molar mass); and [7] MF 250 mg/kg. C57BL/6 mice ($$n = 6$$) + vehicle served as an intact control group (N8). The diet stayed the same throughout the experiment. The tested compounds were administered orally once a day. OGTT was performed as described above. ITT was conducted on the 29th day of the experiment. At the end of the experiment (day 31), the animals were decapitated and blood was collected for the biochemical assay. The following tissues were taken for the histological studies: liver, interscapular white and brown fat, pancreas. Gonadal white fat was taken instead of interscapular fat in the C57Bl/6 mice. Food consumption and body weight were evaluated once a week. ## 2.2.5. Biochemical Assays Blood was centrifuged at 1640× g for 15 min for serum separation. A photometer Multiscan Ascent (Thermo Labsystems, Helsinki, Finland) and Standard Kits (Vector-Best, Novosibirsk, Russia) were used to analyze the total cholesterol, triglycerides, alanine aminotransferase, and lactate levels in the serum. ## 2.2.6. Histological Examination Tissue samples were fixed in $10\%$ neutral buffered formalin for 7 days. After that, they were subjected to the standard dehydration in ascending ethanol concentrations and xylene. The samples were then embedded in paraffin on an AP 280 workstation using Histoplast (Thermo Fisher Scientific, Waltham, MA, USA, melting point of 58 °C). Slices were obtained on a rotational microtome NM 335E with a thickness of 4.5 μm. Hematoxylin and eosin, periodic acid–Schiff, and orange G staining were used. Tissue samples were examined with a light microscope at a magnification of ×100–400. ## 2.2.7. Body Temperature Measurement The body temperature was measured using a FLIR C3-X thermal camera (FLIR, Taby, Sweden) at a room temperature of 23 °C. Each animal was placed into the plastic cage and the thermal image was taken. All images were then analyzed using FLIR Thermal Studio Software. The temperature value was taken from a point on the middle of the animal’s back. Results are represented as a group average. ## 2.2.8. Statistical Analysis Statistical analysis was performed by the Mann–Whitney U test. Data are shown as the mean ± SEM. Data with $p \leq 0.05$ were considered statistically significant. ## 3.1. Chemistry For the synthesis of the scaffold, which was common for all the target compounds, we used the synthesis technique described earlier [8], where aminoethanol was chosen as the starting material (Scheme 1). Bromide 1 was prepared in two steps: protection of the amino group by di-tert-butyl bicarbonate followed by the Appel bromination reaction in the presence of CBr4 and PPh3 in methylene chloride. Further interaction of bromide 1 with tyrosol by the nucleophilic substitution reaction (SN2) in the presence of K2CO3 in DMF resulted in the formation of alcohol 2. Ether 4 was obtained by the interaction of alcohol 2 with phenol 3 by the Mitsunobu reaction in the presence of diisopropyl azodicarboxylate (DIAD) and PPh3 in THF. Free amine 5 was obtained in a $92\%$ yield after sequential treatment of trifluoroacetic acid on amine 4 within the methylene chloride and aqueous NaHCO3 solution. It was shown that amine 5 reacts with terpenoids citronellal and (+)-myrtenal by the reductive amination reaction in the presence of triacetoxyborohydride in methylene chloride to form a secondary amine with excellent yields of $75\%$ and $86\%$ after column chromatography. In order to inhibit the possibility of the formation of a tertiary amine in the subsequent addition of aldehyde to the secondary amine formed, we used a $5\%$ amine excess. Condensation of the obtained amine 5 with diterpene acids was performed under solid phase peptide synthesis conditions in the presence of HBTU and DIPEA in N,N-dimethylformamide (DMF). Using this approach, the target amides 8a–d were isolated in high yields of 68–$79\%$ after column chromatography. Hydrolysis of the ester group of compounds 6a,b and 8a–d was carried out under previously selected conditions described earlier [8]. The use of lithium hydroxide in the methanol–tetrahydrofuran–water mixture led to the hydrolysis of the ester group in compounds 8a–d under mild conditions in a short period of time. As a result, after acidic treatment of the reaction mixture, the desired acids were obtained in good yields of 79–$88\%$ with no further purification. The ester group of monoterpene derivatives 6a,b were hydrolyzed under similar conditions; after acidic treatment of the reaction mixture, the target compounds were isolated as acid hydrochlorides 7a,b in yields of 87–$91\%$. ## 3.2. Biology All the synthesized substances were studied in obese mice with impaired glucose tolerance (C57Bl/6Ay line, AY mice). Among them, only compound 9a showed a significant pharmacological effect, and therefore, all the results below will be presented only for this substance. ## 3.2.1. Body Weight and Feed Intake During the first 2 weeks of the experiment, all animals showed a decrease in body weight, which can be attributed to the stress of the daily administration of substances, then in AY mice (negative control), the body weight began to increase and by the end of the experiment, was higher than at the beginning. In the C57Bl/6 mice (intact control), it returned to the baseline by the fourth week of the experiment. Mice receiving metformin (positive control) and 9a showed a steady decrease in body weight by the end of the experiment (Figure 3). It is worth noting that the feed intake of mice in group 9a was at the same level as that of mice in group AY, whereas mice receiving metformin consumed more feed in the first two weeks (Figure 3 and Table 1). ## 3.2.2. OGTT at Weeks 2 and 4 of the Experiment The first study of glucose tolerance in mice was performed after 14 days of 9a administration. A significant hypoglycemic effect was observed, which, however, was inferior to that of metformin (Figure 4). The second OGTT was performed after 28 days of 9a administration, and unlike the previous test, the studied substances were not administered 30 min before glucose load, but the cumulative effect was evaluated for the entire duration of the experiment. As can be seen from Figure 5A, the greatest hypoglycemic effect was detected in mice treated with 9a, while the effect of metformin was not evident until 90 min after glucose administration. ## 3.2.3. ITT at the End of the Experiment At the end of the experiment, an ITT was performed to determine the sensitivity of the mice to insulin. As can be seen from Figure 6, in all animals, insulin administration led to a marked decrease in blood glucose levels, but only in the intact control and in the 9a mice, it fell to 1.1 mmol/L, which was the detection limit of the glucometer. ## 3.2.4. Body Temperature At the end of the experiment, we measured the body temperature of the animals in a non-invasive way. It was found that the administration of 9a and metformin significantly increased the body temperature of the mice, aligning it with the value for the C57Bl/6 mice (Figure 7). ## 3.2.5. Evaluation of Biochemical Parameters of Blood and Tissue Mass At the end of the experiment, the weight of the liver and adipose tissue was assessed in the animals. It was found that the mass of the liver in the 9a group did not differ from that of the AY mice of the other groups. In contrast, the mass of white and brown adipose tissue was significantly lower (Table 2). The biochemical blood analysis demonstrated a decrease in the lactate and ALT levels in mice treated with 9a (Table 3). ## 3.2.6. Histological Analysis Histological examination revealed fatty hepatosis (Figure 8B) and pronounced hyperplasia of islet apparatus in the endocrine part of the pancreas (Figure 9B) in AY mice. Brown adipose tissue examination revealed a marked increase in the adipocyte fat content and fat cysts formed from large fat droplets could be found (Figure 10B). The adipocyte size was also dramatically increased in the white adipose tissue (Figure 11B). These metabolic abnormalities were improved in mice treated with metformin (Figure 8C, Figure 9C, Figure 10C and Figure 11C). In animals treated with 9a, the morphological picture in the studied organs practically corresponds to that in the group of intact animals. Architectonics of the liver and pancreas had typical structure, and no pronounced infiltrative-necrotic, hemodynamic changes were found (Figure 8A and Figure 9A). In brown and white adipose tissue, the fat content in adipocytes was comparable with the intact group (Figure 10A and Figure 11A). ## 4. Discussion In this work, we synthesized several new mono- and diterpenic derivatives with a pharmacophore fragment of (S)-2-ethoxy-3-phenylpropanoic acid, which is common to glitazars. The choice of monoterpenoids for derivatization was also made based on the literature data on the hypoglycemic and hypolipidemic activity of the terpene derivatives. Derivatives of both bicyclic and acyclic monoterpenoids (Figure 12) are known to be partial PPAR agonists and also have hypoglycemic and hepatoprotective activity [11,12,13,14]. Abietic, dehydroabietic, isopimaric, and lambertianic acids (Figure 12) were chosen for the synthesis due to their availability and their own biological properties. Among the diterpene acids, abietane type acids are known to influence glucose levels as well as the lipid profile [15,16]. Lambertianic acid has anti-inflammatory as well as potential antidiabetic activity [17]. Isopimaric acid (IPA) has a wide range of biological activity. It has been found to exhibit marked antitumor [18], antibacterial [19,20], and anti-inflammatory [21,22] activities. It is worth noting that IPA amides were shown to have a weak cytotoxicity against the GepG2 cell lines [23]. IPA amides also exhibit an analgesic effect on male inbred mice [24]. Moreover, IPA was shown to inhibit protein-tyrosine phosphatase 1B (PTP1B) better than abietic and dehydroabietic acid [25]. PTP1B is an enzyme in the protein tyrosine phosphatase (PTP) family that is responsible for the regulation of many processes, particularly metabolism, and often contributes to diseases that occur when these processes are disrupted (diabetes, cancer, autoimmune, and Alzheimer’s diseases) [25]. All synthesized compounds were studied in mice with obesity and impaired glucose tolerance (C57Bl/6Ay). In these mice, agouti gene (Ay/a) mutation resulted in antagonism of melanocortin receptors by the agouti protein and in turn led to the emergence of yellow pigmentation, late-onset obesity, and hyperinsulinemia [26]. Such metabolic abnormalities make AY mice a convenient animal model for examining the effects on glucose and lipid metabolism. Among the studied substances, we found one pharmacologically active compound 9a, which is a derivative of isopimaric acid. Its pharmacological effects in our animal experiments are characteristic of the action of PPAR-alpha and gamma agonists. First, a significant reduction in fatty hepatosis and a decrease in the weight of white and brown adipose tissue were demonstrated. All of this indicates an acceleration of triglyceride catabolism occurring upon the activation of PPAR-alpha [27]. In brown adipose tissue, increased catabolism of fatty acids usually results in increased heat production due to the activation of uncoupling protein 1 (UCP1) [28], which was demonstrated by the increase in the body temperature of the mice at the end of the experiment (Figure 7). However, despite all of these changes, we did not find a decrease in the level of TG in the animals’ blood, perhaps a longer administration of the studied substance could have led to a decrease in this parameter. Second, PPAR-gamma activation is usually associated with increased tissue sensitivity to the action of insulin, and as a consequence, a decrease in the level of glycemia [29]. The administration of compound 9a resulted in a marked decrease in both the fasting glucose levels and significantly improved the glucose tolerance of the mice (Figure 4 and Figure 5). The most pronounced effect was achieved after four weeks of administration. This was due to the increased sensitivity of animal tissues to insulin, which is further confirmed by the results of ITT (Figure 6) and a decrease in the blood lactate levels (Table 3). Lactate in obesity is in large quantities synthesized and excreted into the blood by fat cells and its elevated levels are associated with insulin resistance. The decrease in its blood level can be considered as further evidence of improved tissue sensitivity to insulin [30]. Histological examination data, liver weight, and biochemical blood tests (decreased ALT level, Table 3) demonstrated the absence of toxic liver damage caused by compound 9a, which was the reason for at least the discontinuation of Imiglitazar’s clinical studies [31]. Of course, it is too early to say whether compound 9a is safe because additional experiments are needed, since the most typical side effects of dual PPAR agonists are cardiac and renal damage [31]. It is also interesting to note that the derivatives of these acids, which themselves have hypoglycemic and/or hypolipidemic effects, did not show such an effect in our experiments. ## 5. Conclusions Compounds 7a,b and 9a–d, incorporating various diterpenic acids as well as monoterpenoid fragments in their structure as “tails”, were synthesized. The study of their pharmacological activity in obese and DM2 (C57Bl/6Ay) mice revealed one compound, 9a (isopimaric acid derivative), having effects characteristic for PPAR-alpha and gamma dual agonists. Its administration at a dose of 30 mg/kg for one month led to a decrease in triglyceride levels in the liver and adipose tissue of mice by increasing their catabolism and to a hypoglycemic effect associated with an improvement in the insulin sensitivity of the mouse tissue. In addition, the substance was shown to have no toxic effects on the liver. To the best of our knowledge, compound 9a is the first example among isopimaric acid derivatives with the hypoglycemic and hypolipidemic activities demonstrated in vivo. The synthesis of other isopimaric acid derivatives seems promising to reveal the structure–hypoglycemic/hypolipidemic activity relationship. ## Figures, Scheme and Tables Figure 1: Glitazars–dual PPAR agonists. Figure 2: Dihydrobetulonic acid amide BM-249. Scheme 1: Synthesis of the target compounds. Figure 3: Body weight change during the experiment. $p \leq 0.05$ compared to the AY mice.* Figure 4: OGTT results. The test conducted after 14 days of AY mice treatment by compound 9a. Doses: 9a–30 mg/kg, MF–250 mg/kg. $p \leq 0.05$ compared to the AY mice.* Figure 5: (A) OGTT results. The test conducted after 28 days of AY mice treatment using compound 9a. (B) AUC calculated according to the OGTT data after 28 days of AY mice treatment using compound 9a. Doses: 9a–30 mg/kg, MF–250 mg/kg. $p \leq 0.05$ compared to the AY mice.* Figure 6: Blood glucose levels in ITT performed 29 days after the beginning of the experiment. $p \leq 0.05$ compared to the AY mice.* Figure 7: Body temperature of the mice at the end of the experiment. $p \leq 0.05$ compared to the AY mice.* Figure 8: Histological evaluation of the liver in mice after 4 weeks of experiment. (A) AY mice treated by compound 9a at a dose of 30 mg/kg, (B) AY mice (untreated), (C) AY mice treated by metformin at a dose of 250 mg/kg, (D) C57Bl/6 (healthy control). Hematoxylin and eosin staining, magnification ×200. Figure 9: Histological evaluation of pancreas in mice after 4 weeks of experiment. (A) AY mice treated by compound 9a at a dose of 30 mg/kg, (B) AY mice (untreated), (C) AY mice treated by metformin at a dose of 250 mg/kg, (D) C57Bl/6 (healthy control). Hematoxylin and eosin staining, magnification ×100. Figure 10: Histological evaluation of brown fat in mice after 4 weeks of experiment. (A) AY mice treated by compound 9a at a dose of 30 mg/kg, (B) AY mice (untreated), (C) AY mice treated by metformin at a dose of 250 mg/kg, (D) C57Bl/6 (healthy control). Hematoxylin and eosin staining, magnification ×100. Figure 11: Histological evaluation of brown fat in mice after 4 weeks of experiment. 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--- title: Thai Rat-Tailed Radish Prevents Hepatocarcinogenesis in Rats by Blocking Mutagenicity, Inducing Hepatic Phase II Enzyme, and Decreasing Hepatic Pro-Inflammatory Cytokine Gene Expression authors: - Piman Pocasap - Natthida Weerapreeyakul - Rawiwan Wongpoomchai journal: Cancers year: 2023 pmcid: PMC10047847 doi: 10.3390/cancers15061906 license: CC BY 4.0 --- # Thai Rat-Tailed Radish Prevents Hepatocarcinogenesis in Rats by Blocking Mutagenicity, Inducing Hepatic Phase II Enzyme, and Decreasing Hepatic Pro-Inflammatory Cytokine Gene Expression ## Abstract ### Simple Summary Our previous studies have reported the anticancer activity of Raphanus sativus L. var. caudatus Alef (RS) in many cancer cells, but only in vitro. The anticancer effects of RS were, therefore, investigated in rats with early-stage liver cancer. RS effectively reduced the overexpression of GST-P positive foci and apoptotic cells in the rats injected with DEN (a carcinogen) during the development of early-stage of cancer. The major finding from this study highlights the chemopreventive activity of RS extract given orally in the initial stage of hepatocarcinogenesis in vivo by [1] inhibiting carcinogenic activities, [2] increasing phase II metabolism, and by [3] lower inflammation. The attributed compounds to these activities could be polyphenols and isothiocyanates, mainly sulforaphene. The results confirm sufficient oral bioavailability, with no detected toxicity, and thus support the use of RS as a health-promoting plant and its possible further study and use in humans. ### Abstract Raphanus sativus L. var. caudatus Alef (RS) is an indigenous Thai plant with nutritional and medicinal values such as anticancer activity, but only in vitro. The chemopreventive effects of RS were, therefore, investigated in the initial stage of hepatocarcinogenesis in rats. Diethylnitrosamine (DEN), a carcinogen, was intraperitoneally injected into rats to induce liver cancer. Along with the DEN injection, either aqueous (RS-H2O) or dichloromethane (RS-DCM) extract was administered orally. Immunohistochemistry was used to detect glutathione S-transferase placental (GST-P) positive foci and apoptotic cells in rat livers as indicators of initial-stage carcinogenesis. The underlying mechanisms of chemoprevention were investigated with (a) antimutagenic activity, (b) hepatic phase II enzyme induction, and (c) hepatic pro-inflammatory cytokine gene expression. The results showed that RS-DCM was more potent than RS-H2O in decreasing GST-P positive foci and apoptotic cells induced by DEN. The mechanisms of RS-DCM (phenolics and sulforaphene contents) against liver carcinogenesis [1] block the activity of carcinogens; [2] elevate phase II detoxifying enzymes; and [3] suppress the pro-inflammatory gene expression. RS-H2O (phenolics contents), in contrast, only decreases pro-inflammatory gene expression. In conclusion, the RS extract consisting of phenolics and isothiocyanates exerted significant chemopreventive activity against DEN-induced liver carcinogenesis. ## 1. Introduction Brassicaceae (or Cruciferae) is an economically important family consumed worldwide. In addition to nutritional enrichment, Brassica’s phytochemical profiles make the plant family an ideal natural source for health promotion. The phytochemicals, including phenolics, carotenes, minerals, and isothiocyanates (ITCs), are considered health-benefiting constituents. Many studies have demonstrated the potential of these plants against a wide range of complications such as inflammation, infection, diabetes, and, especially, cancer [1]. Epidemiological studies have indicated that the intake of Brassica vegetables is associated with a lower risk of many types of cancer [2]. The study of plants from the Brassica family is, thus, of interest to elucidate and facilitate its use against cancer. As one of the leading causes of death worldwide, cancer causes tremendous global health problems. In Asia, there is a declining trend in cancer mortality, but the morbidity rate is still increasing, especially among the younger population [3]. In terms of virulence, hepatocellular carcinoma is recognized as one of the most pernicious cancers—ranking as the second cause of cancer death in the Southeast Asia region, where morbidity and mortality incidence is twice the global rate [4]. This is due to the refractory nature of cancer and the lack of awareness due to no specific early signs and symptoms, leading to detection in the late stage. Hence, the countermeasures against liver cancer should not rely only on screening and treatment interventions but also prevention. The chemopreventive properties of Brassica plants have been demonstrated epidemiologically. One of the crops within the Brassica family displaying promising activities and a distinct chemical profile against cancer is Raphanus sativus L. var. caudatus Alef (RS, also known as Thai rat-tailed radish). RS is widely distributed in South and Southeast Asia as an indigenous plant and has been used locally as an ingredient in traditional cuisine and medicines [5]. The anticancer activities of RS have been previously reported. RS aqueous extract consists of several phenolic compounds that contribute to its antiproliferative and antioxidant activity. In contrast, the dichloromethane extract of RS contains ITCs and displays anticancer activities in several types of cancer cell lines [5,6]. The molecular mechanisms of the major ITCs in RS that trigger cancer cell death have also been elucidated [7]. Taken together, RS exhibited anticancer potential and should be further investigated, especially in the in vivo model, to approve its potential against cancer. This study was, therefore, conducted in vivo using an animal model. The objective of the study is to investigate the preventive effect of RS against liver cancer. The ability of both RS aqueous and dichloromethane extracts to suppress cancer formation in carcinogen-induced initial-stage hepatocarcinogenesis in rats was examined, employing immunohistochemistry procedures. The safety profile of RS extracts was also assessed. The corresponding chemopreventive mechanisms were further investigated both in vitro and in vivo. The outcome of this study could explain the chemopreventive efficacy of RS and provide the fundamental knowledge for further study and clinical application. ## 2.1. Chemicals 2-AA (2-aminoanthracene), AF-2 (2-(2-furyl)-3-(5-nitro-2-furyl)-acrylamide), and MeIQ (2-amino-3, 4 dimethylimidazo[4,5-f]quinoline) were obtained from Wako Pure Chemicals (Osaka, Japan). AFB1 (aflatoxin B1), Bovine serum albumin, diethylnitrosamine, ethanol, DMSO, and NaN3 (sodium azide) of analytical grade were purchased from Sigma-Aldrich (St. Louis, MA, USA). Rabbit polyclonal GST placental form (GST-P) antibody was obtained from MBL (Nagoya, Japan). The mouse monoclonal proliferating cell nuclear antigen (PCNA) antibody was from BioLegend (Santiago, CA, USA). EnVision Doublestain system was obtained from Dako (Glostrup, Denmark). The avidin–biotin–horseradish peroxidase complex (ABC) kit was purchased from Vector Laboratories (Burlingame, CA, USA). ApopTaq peroxidase in situ Apoptosis Detection Kit was obtained from Merck (Kenilworth, NJ, USA). PurezolTM Isolation Reagent was from Bio-Rad (Hercules, CA, USA). High-Capacity cDNA Reverse Transcription Kit was purchased from Applied BiosystemTM (Foster City, CA, USA), and SensiFastTM SYBR Lo-ROX Kit was obtained from Bioline (London, UK). Any other chemicals were of analytical grade and were used without any purification. ## 2.2. Plant Materials RS samples were visually authenticated according to the taxonomy [8] before extraction. RS extracts were prepared as described previously [6]. Briefly, RS pods (harvested at 6–7 weeks) were homogenized with ddH2O (1:1, w/v) and left for autolysis at 25 °C for 2 h before filtration. The filtrate was then partitioned with dichloromethane (triplicate). The lower-layer dichloromethane and upper-layer water were collected separately. The dichloromethane layer was then dried using a rotary evaporator to yield a final dichloromethane extract (RS-DCM, $0.06\%$ w/w), while the aqueous layer was dried using a lyophilizer, yielding $2.87\%$ (w/w) of aqueous extract (RS-H2O). ## 2.3. Phytochemical Analysis by HPLC Phytochemical identification in the extracts was performed by comparing the retention time with the standard. The extracts were dissolved in dimethyl sulfoxide. The injection volume was 20 µL. The HPLC analysis was performed with an LC–2030C3D quaternary pump (Shimadzu, Kyoto, Japan), and the stationary phase was a HiQ sil C18W column (4.6 mm × 250 mm, 5 µm) (KYA Technologies Corporation, Tokyo, Japan). The temperature of the column was 38 °C. Mobile phases and elution conditions were performed, as in the previous report [9]. The detection wavelength was 280 nm. The mobile phase comprised solvent A (purified water with acetic acid) and solvent B (acetonitrile) with a flow rate of 0.8 mL/min. The gradient elution was employed from 0 to 5 min with 95–$91\%$ solvent A; 5 to 15 min with 91–$89\%$ solvent A; 15 to 22 min with 89–$85\%$ solvent A; 22 to 30 min with 85–$82\%$ solvent A; 30 to 38 min with 82–$78\%$ solvent A; 38 to 43 min with 78–$20\%$ solvent A; 43 to 46 min with 20–$10\%$ solvent A; 46 to 55 min with 10–$5\%$ solvent A; 55 to 60 min with 5–$95\%$ solvent A; 60 to 65 min with fixed $5\%$ solvent A. Equilibration time was 5 min with $95\%$ solvent A between individual runs. To identify sulforaphene and sulforaphane in the extracts, the mobile phase system was changed to an isocratic $5\%$ THF in ultrapure water (v/v) with a flow rate of 1 mL/min for 30 min. The detection wavelength was 210 nm. ## 2.4. Animals and Exposures Four-week-old male Wistar rats were purchased from the National Laboratory Animal Center, Mahidol University, Nakorn Prathom, Thailand. Rats were acclimatized for 1 week before starting experiments. All rats were housed under controlled conditions (25 ± 1 °C, 50–$60\%$ relative humidity, under 12 h light/dark cycle). A basal diet and water were provided ad libitum. The animal protocol was approved by the Animal Ethics Committee of the Faculty of Medicine, Chiang Mai University, Thailand (Approval number: $\frac{28}{2560}$) and performed according to institutional guidelines. ## 2.5. Acute Toxicity Test The acute toxicity test of RS was determined as per a previous report [10] according to the Organization for Economic Co-operation and Development (OECD) guideline 425. Female Wistar rats were randomly divided into 3 groups (5 rats per group). The control was orally given distilled water, while the latter was fed with a single dose (5000 mg/kg BW) of RS-H2O. Body weight, signs of toxicity, behavior, and mortality were observed during the first 6 h and every 24 h after administration. On day 14 of the experiment, all rats were euthanized with isoflurane. The internal organs were excised for weighing and gross pathological observations. ## 2.6. Experimental Design Male Wistar rats were randomly divided into 8 groups (8 rats per group, 64 total) and treated with the samples, as shown in Figure 1. Groups 1 to 4 were injected with DEN (100 mg/kg BW, intraperitoneal) at weeks 2, 3, and 4 to induce early-stage hepatocarcinogenesis. Groups 5 to 8 were injected with NSS (4 mL/kg BW, intraperitoneal) at weeks 2, 3, and 4. Group 1, as a positive control, received distilled water orally, whereas groups 2, 3, and 4 were given RS-H2O 100 mg/kg BW, RS-H2O 500 mg/kg BW, and RS-DCM 20 mg/kg BW, respectively, from week 0 until the end of the experiment (week 5). Group 5, as a negative control, was given distilled water (with $5\%$ Tween-80) orally, while groups 2, 3, and 4 were fed RS-H2O 100 mg/kg BW, RS-H2O 500 mg/kg BW, and RS-DCM 20 mg/kg BW, respectively, from week 0 until week 5. Body weight and food/water intake were recorded twice a week. At week 5, all rats were euthanized by exsanguination from the abdominal aorta under isoflurane anesthesia. Whole blood was collected from abdominal veins for alanine transaminase (ALT) activity determination using a commercial kit (Olympus Corp., Tokyo, Japan). The internal organs were excised and weighed. The livers were maintained in $10\%$ formalin, and three serial sections (4 µm thick) were prepared from each specimen. The first section was for histological examination with hematoxylin and eosin staining. The second section was used in immunohistochemistry and molecular analysis, as specified below. The remaining portion was kept at −80 °C for further analysis. ## 2.7. Determination of GST-P Positive Foci Immunohistochemical staining for GST-P was performed to determine the preneoplastic lesions in rat liver tissues, as previously described [11]. Briefly, the liver sections were deparaffinized and rehydrated with xylene and ethanol. After that, the slides were soaked in H2O2 ($3\%$) and skimmed milk ($1\%$) to inhibit pseudoperoxidase and inactivate non-specific protein binding, respectively. The samples were incubated with rat anti-GST-P antibody and with secondary antibody (anti-rabbit IgG, ABC kit). Subsequently, the samples were drenched with diaminobenzidine (DAB) and counterstained with hematoxylin. The number and area of GST-P positive foci, with a diameter greater than 0.2 mm, were recorded using the LAS Interactive measurement program (Leica Microsystems CMS GmbH, Mannheim, Germany). ## 2.8. Determination of Apoptotic Cells by TUNEL Assay To identify apoptotic cells in liver sections, a terminal deoxynucleotide transferase-mediated X-dUTP Nick-End Labeling (TUNEL) assay was performed using an ApopTaq peroxidase in situ kit according to a previous report [12]. The samples were deparaffinized, rehydrated, and pretreated with proteinase and H2O2 and incubated with equilibrium buffer (5 min) and working-strength terminal deoxynucleotidyl transferase (TdT) enzyme (1 h, 37 °C). After adding stop/wash buffer, the liver sections were treated with an anti-digoxigenin antibody (30 min). The color of TUNEL-positive cells was developed by soaking samples in DAB solution, and methyl green was used to counterstain the specimens. The number of TUNEL-positive hepatocytes was counted at least under 10 fields per liver section under a light microscope. ## 2.9. In vitro Mutagenicity and Antimutagenicity Assay The mutagenicity of RS was assessed using the Salmonella mutation assay, as previously reported [13]. Briefly, S. Typhimurium strains TA98 and TA100 were incubated with RS extracts in phosphate buffer, with or without a metabolic activation system (S9 mix). Subsequently, the top agar consisted of 0.05 mM L-histidine, and 0.05 mM D-biotin was added and poured onto a minimal glucose agar plate. The mixture plates were then incubated for 48 h (37 °C), and the number of histidine-independent revertant colonies was counted. 2-AA and AF-2 were used as standard mutagens (positive control) in the presence or absence of metabolic activation, respectively, while DMSO or distilled water was used as a negative control. S9 mix was prepared from the liver of a male Wistar rat (8–10-week-old) injected intraperitoneally with phenobarbital and 5,6-naphthoflavone. Mutagenicity was displayed as a mutagenic index (MI) calculated from the number of revertant colonies divided by the number of spontaneous revertant colonies. The mutagenicity was classified as a possible mutagen when the MI value was over 2-fold. Antimutagenicity of RS was performed as per the mutagenicity assay with modification. Without the S9 mix, AF-2 and NaN3 were used as standard mutagens in strains TA98 and TA100, respectively. With the S9 mix, AFB1 and MeIQ were used as standard mutagens in strains TA98 and TA100, respectively. The percentage of inhibition of mutagenicity was then calculated following the formula [14]:%mutagenic inhibition=A−B−(C−B)(A−B)×100 when A = number of revertants in standard mutagen plates, B = number of spontaneous revertants, and C = number of revertants of test plates. ## 2.10. Determination of Phase II Xenobiotic-Metabolizing Enzymes Liver microsomal and cytosolic fractions were prepared using the differential centrifugation method according to a previous report [12]. The protein concentration of the sample fractions was determined by the Lowry method. The activity of phase II metabolizing enzymes was performed as in previous studies [10,15]. UDP-glucuronosyltransferase (UGT) activity was examined by mixing 0.1 M Tris buffer, 4 mM MgCl2, 20 mM UDP-glucuronic acid, and 0.5 mM p-nitrophenol (PNP) in a microsomal fraction (37 °C, 20 min). The reaction was quenched with $10\%$ TCA in an ice bath and was centrifuged at 10,000× g (5 min) prior to alkalinization with 0.5 M NaOH. The absorbance of the mixture was read at 405 nm, and the activity of UGT was calculated using an extinction coefficient of 18 mM−1 cm−1. The calculated UGT activity values were displayed per mg of protein. Cytosolic glutathione-S-transferase (GST) activity was determined by the reaction with its substrate CDNB (1-chloro-2,4-dinitrobenzoic acid). In brief, the cytosolic fraction was incubated with 0.2 M phosphate buffer, 10 mM GSH, and 10 mM CDNB. The reaction mixture was measured for absorbance at 340 nm. The GST activity was calculated using an extinction coefficient of 9.6 mM−1 cm−1 and expressed per mg of protein. The activity of cytosolic NADPH-quinone oxidoreductase (NQO) was examined using DCPIP (2,6-dichlorophenol-indophenol) as an electron acceptor. The rate of DCPIP reduction was measured—in the mixture consisting of 0.025 M Tris-HCl buffer (pH 7.4), 1.0 mg/mL BSA, $1\%$ Tween-20, 150 µM FAD, 30 mM NADPH, and 24 mM DCPIP—an absorbance at 600 nm. The NQO activity was calculated using an extinction coefficient of 2.1 mM−1 cm−1 and displayed as per mg of protein. ## 2.11. Determination of Pro-Inflammatory Cytokine Gene Expression by Real-Time PCR Total RNA from the rat liver section was extracted using PurezolTM Isolation Reagent as per the manufacturer’s instruction. cDNA was synthesized according to the manufacturer’s instruction using a High-Capacity cDNA Reverse Transcription Kit. Quantitative real-time PCR was carried out using specific primers (Integrated DNA Technologies, Inc., Singapore), as listed in Table 1. The PCR amplification was performed using SensiFastTM SYBR Lo-ROX Kit. The PCR conditions included initial denaturation at 95 °C (1 min), 40 cycles of denaturation at 95 °C (15 s), annealing at 56–60 °C (15 s), and extension at 72 °C (10 s). The gene expression was normalized with β-actin and quantified using the 2−ΔΔct method, as previously reported [16]. ## 2.12. Statistical Analysis Data were expressed as means ± SD and were analyzed using SPSS 19.0 for Windows® (SPSS Inc., Chicago, IL, USA). The normality of distribution and homogeneity of variance were analyzed using Shapiro–Wilk tests. The data with a normal distribution, as indicated by $p \leq 0.05$ according to Shapiro–Wilk tests, were further analyzed using one-way ANOVA (post hoc test: least-significant difference (LSD)). The data with non-normal distribution (p ≤ 0.05, Shapiro–Wilk) were analyzed using the non-parametric Mann–Whitney U test. The difference with p ≤ 0.05 (parametric LSD’s post hoc test or non-parametric Mann–Whitney U test) was considered statistically significant. ## 3.1. Phytochemical Identification The phenolics and isothiocyanates were identified and quantified in the RS extracts by our group previously [9], and the results are presented in Table 2. RS-H2O contains several phenolic compounds in a higher amount than RS-DCM. In contrast, RS-DCM comprised a higher isothiocyanate, sulforaphene than RS-H2O. The total phenolics in RS-H2O compared to RS-DCM were 32.68 and 10.97 mg/g extract, respectively. Vanillic acid was the highest phenolic content in RS-H2O extract (26.15 mg/g extract), while p-hydroxybenzoic acid was the highest in RS-DCM. The only isothiocyanate compound presented in both was sulforaphene, not sulforaphane. Sulforaphene was mainly detected in RS-DCM (5.11 mg/g extract) (Table 2). Our data indicate that phenolic compounds are enriched phytochemicals in RS-H2O, while sulforaphene is the major isothiocyanate in RS-DCM. ## 3.2. Toxicity of RS To determine the acute toxicity of RS, a single dose of RS-H2O (5000 mg/kg) was orally fed to female rats in the treatment groups, in contrast with distilled water in the control group. All the rats in the treatment and control groups survived and showed no visible toxicity signs until the end of the experiment (14 days). There was no difference in water/food consumption or average body and relative vital organ weight between the treatments and control group (Table 3 and Table 4). No internal organ damage was visually detected. Our data suggested that RS extract has no acute toxicity at the treatment dose, and the lethal dose (LD50) is supposed to exceed 5000 mg/kg. The toxicity of RS relevant to the experimental regimen was also observed. Rats were fed daily with RS-DCM (20 mg/kg) or RS-H2O (100 and 500 mg/kg) for 5 consecutive days, with or without DEN. The results show no significant effect of RS extracts on average body weight and food/water consumption compared with the positive control (DEN alone, group 1) or negative control (NSS alone, group 5) group (Table 5). For the vital organs, the relative spleen and kidney weight show no significant difference between the groups. In contrast, there is a significant decrease in liver weight between the DEN (group 1–4) and NSS (group 5–8) treatment groups, but no significant difference was observed between groups 1 to 4 and groups 5 to 8 (Figure 2). The data suggested that the liver weight reduction was contributed by DEN, a potent liver carcinogen, not RS. Since carcinogenic liver injury is one of the hepatocarcinogenic processes caused by DEN, the liver inflammatory marker alanine transaminase (ALT) was, therefore, measured to confirm our observations. DEN treatment increased liver inflammation compared with NSS treatment, as indicated by the increasing ALT activity. There was no difference between groups 1 to 4 and groups 5 to 8 (Figure 3). Our results indicated that liver injury was caused by DEN, not by RS. Moreover, the RS extracts have no effect on the observed vital organs during the treatment regimen. ## 3.3. Effect of RS on GST-P Positive Foci The effect of RS on hepatocarcinogenesis was determined from GST-P foci, a characteristic sign to indicate a preneoplastic lesion in the rat liver. Results showed that GST-P positive foci were observed after DEN administration, whereas NSS treatment found no positive trait. Co-administration of RS-H2O (500 mg/kg) or RS-DCM (20 mg/kg) with DEN significantly reduced GST-P positive foci expression (both GST-P number and area) (Figure 4A,B). It was evidence that RS extract potentially attenuated hepatocarcinogenesis. ## 3.4. Effect of RS on Apoptosis Induction The effect of RS on hepatocarcinogenesis was determined from apoptotic cell death, a marker of carcinogenesis, using the TUNEL assay. DEN-treated rats displayed a significantly increased number of TUNEL-positive cells (apoptotic cells) in liver tissues than those with NSS treatment. RS treatment (RS-H2O and RS-DCM) lessened the apoptosis induced by DEN (Figure 5). Our result indicated the efficacy of RS in suppressing the progression of hepatocarcinogenesis. ## 3.5. In Vitro Mutagenic and Antimutagenic Activity of RS The potential of RS on mutagenicity was investigated in the Ames test using S. Typhimurium strain TA98 (frameshift mutation) and TA100 (base-pair substitution) in the presence (+S9) or absence (−S9) of metabolic activation. The mutagenic results showed that both RS-DCM and RS-H2O possess no mutagenic activity. No extract at the used concentrations (0.1–5 mg/plate) could increase the number of revertant colonies with a mutagenic index (MI) higher than 2, in contrast with positive controls (AF-2 and 2-AA). RS-DCM at the highest concentration (5 mg/plate) also displayed a killing effect (or cytotoxic) on S. Typhimurium in both strains. This effect was, however, diminished with metabolic activation (+S9) (Table 6). For the antimutagenic activity, the effect of RS extract to reduce mutagenicity was determined in S. Typhimurium strain TA98 and TA100 by co-treatment with either [1] AF-2 and NaN3, as direct mutagens, without metabolic activation or [2] AFB1 and MeIQ, as indirect mutagens, with metabolic activation. To avoid a killing effect, the concentration range of RS extract was determined between 0.1–1 mg/plate (Table 7). Our results indicated that RS-H2O has no antimutagenic activity. Additionally, the aqueous extract increased the number of revertant colonies when incubated with both direct and indirect mutagens and possibly acts as co-mutagen. Nevertheless, the co-mutagenic potential of RS-H2O is still inconclusive as there is no concentration-dependent correlation observed. In contrast, RS-DCM decreased the revertant colonies (enhancing % mutagenic inhibition) in the co-treatment with both direct and indirect mutagens in a concentration-dependent manner (Figure 6). Therefore, our results indicated that RS-DCM possesses antimutagenic activity. ## 3.6. Effect of RS on Phase II Xenobiotic-Metabolizing Enzymes Phase II metabolizing enzymes play a central role in the detoxification of xenobiotics, carcinogens, and reactive oxygen species, attributed to carcinogenesis. Hepatic phase II enzyme activities, including UGT (UDP-glucuronyltransferase), GST (glutathione-S-transferase), and NQO1 (NADPH-quinone oxidoreductase) were, thus, determined in this study. Results showed no statistical difference between group 1 (DEN alone) and group 5 (NSS alone) based on the inhibition of phase II enzyme activities. RS-H2O treatment did not affect these enzyme activities. On the other hand, UGT and NQO1 activities were increased by RS-DCM co-treatment with DEN. The enhancement of UGT activities after RS-DCM treatment was also observed in the NSS co-treatment group (Figure 7). The data suggested that RS-DCM was more effective than RS-H2O in increasing phase II enzyme activities. ## 3.7. Effect of RS on Pro-Inflammatory Cytokine Gene Expression Inflammation is an essential step in carcinogenesis. Pro-inflammatory cytokines such as NRF-2 (nuclear factor erythroid 2–related factor 2) and TNF-α (tumor necrosis factor-α) were, therefore, investigated in our study. DEN treatment increased rat liver inflammation, as indicated by the increased mRNA level of Nrf-2 (Figure 8A) and Tnf-α (Figure 8B). The overexpression, however, had been downregulated by RS-H2O and RS-DCM treatment. These results indicate the ability of RS extract to reduce liver inflammation, an important carcinogenic process. ## 4. Discussion Genus Raphanus (radish genus) is a rich source of phytochemicals possessing various health-promoting activities. Anthocyanins (i.e., cyanidins and pelargonidins) are commonly found in this genus with health benefits both in vitro and in vivo [17], including cardiovascular protection, anti-inflammatory, and anticancer properties [18]. Raphanus comprises non-flavonoid polyphenols (i.e., phenolic acids, hydroxycinnamates, stilbenes, and tannins) and terpene derivatives (i.e., carotenoids and triterpenoids), which correlated to antioxidant activities in vitro [19,20,21]. Notably, isothiocyanates (ITCs) are the unique constituents in Raphanus and other cruciferous plants, which are responsible for chemoprevention in epidemiological studies [22,23,24]. Our previous studies revealed that RS-DCM consisted of sulforaphene and sulforaphane as the main phytoconstituents [6,8]. The data are in agreement with the present study that isothiocyanate, particularly sulforaphene, is the major constituent. The in vitro data suggested that RS-DCM was capable of inducing HCT116 colon cancer cell death mainly via apoptosis [5]. Moreover, sulforaphene was the predominant active compound [25]. The anticancer mechanisms of sulforaphene, sulforaphane, and other ITCs have been reported to be due to the enhanced intracellular ROS and tubulin depolymerization, as demonstrated using the HepG2 liver cancer cell line [7,26]. In contrast to RS-DCM, RS-H2O contains several non-flavonoid polyphenols, including protocatechuic acid, p-hydroxybenzoic acid, vanillic acid, caffeic acid, and p-coumaric acid (4.2, 1.1, 26.2, 0.6, and 0.7 mg/g extract, respectively) with antioxidant activities. The obtained data suggest that RS extract was a rich source of phytochemicals potentially used for chemotherapeutic purposes. Nonetheless, no previous information indicates the capability of RS against cancer in vivo. The investigation of RS efficacy by employing animal models is, thus, important to support the clinical use of RS. The rat model of a carcinogen-induced initial stage of hepatocarcinogenesis was established in our study to demonstrate the chemopreventive activity of RS. This was the first time that the acute toxicity of RS was assessed. RS extracts (RS-H2O) were safe in rats at a single high dose of 5000 mg/kg orally. The continuous doses for subsequent experiments were the regimen with a reduction ≥10X of the maximum dose (5000 mg/kg) to minimize possible adverse effects. The regimen for RS-DCM was reduced by 250X since our previous study indicated that the RS-DCM extract was more potent than the aqueous extract. During the treatment regimen, RS-DCM (20 mg/kg orally) and RS-H2O (100 and 500 mg/kg orally) showed no toxicity as determined by food/water consumption, weight change, and vital organ inspections. Both extracts caused no liver injury, indicated by no alteration in ALT level. Our data indicated that RS extract was safe to administer in rats. The possible confounding factors rendering the RS extracts toxic in rats were, therefore, ruled out. The initial stage of hepatocarcinogenesis was then initiated by intraperitoneal administration of carcinogen diethylnitrosamine (DEN). In hepatocytes, the carcinogen is subsequently metabolized by cytochrome P450 to form active mutagen and ROS. It was reported that the mutagenic metabolites potentially bind with nucleic acids and trigger DNA mutation, concomitantly with inflammation induced by ROS. Consequently, both DNA mutation and liver inflammation lead to hepatocarcinogenesis [27,28]. In this study, the initial stage of hepatocarcinogenesis was detected by the determination of GST-P positive foci. Since GSTs are a group of hepatic multifunctional proteins participating in the detoxification of toxic and mutagenic agents, the formation of GST-P positive foci depicts the early phase of carcinogenesis due to the enhancement of the detoxification process [29]. Moreover, apoptosis is increased during carcinogenesis as a countermeasure against precancerous cells to prevent excessive cell proliferation [30]. Apoptotic cell death was, therefore, measured to confirm the early stage of cancerous formation. As indicated by the reduction of GST-P-expressing foci, the early stage of cancerous formation was diminished by both RS-DCM and RS-H2O treatments. The data were supported by the TUNEL assay, confirming the efficacy of both RS extracts against the initial stage of hepatocarcinogenesis, as apoptotic cell death was decreased following RS treatments. The results also indicate that the DCM extract is more potent than the aqueous extract. This finding agreed with our previous antiproliferative activity in vitro [6]. ITCs, the predominant active constituents in RS-DCM, might be responsible for the observed chemoprevention in vivo. ITCs prevent carcinogenesis through several mechanisms [31]: [1] blocking the activity of carcinogens, [2] increasing phase II detoxifying enzymes, and [3] inhibiting inflammatory cytokine gene supporting carcinogenesis. The relevant mechanisms regarding ITC activities, therefore, were further investigated. The ability of RS extracts to block the carcinogenic activity was demonstrated in in vitro antimutagenic tests. In the initiation phase of carcinogenesis, carcinogen (or mutagen) causes irreversible damage to genetic material, resulting in DNA mutation before the promotion and subsequent progression phase occur. Our results showed that RS-DCM possesses antimutagenic activity against direct carcinogens (no metabolic activation required) and indirect carcinogens (metabolic activation required). The data imply that RS-DCM could reduce the activity of carcinogens and lessen the chance of DNA mutation in the initiation phase. ITCs, the active constituents in RS-DCM, possibly play a central role in neutralizing carcinogens since several reports displayed their antimutagenic property. Both sulforaphene and sulforaphane exhibited antimutagenic activity against S. Typhimurium strain TA98 and TA100 [32]. A previous study reported a positive correlation between daikon (R. sativus L.) antimutagenic activity and its ITC content [33]. One of the proposed mechanisms of ITCs-neutralizing carcinogenic activity was based on a covalently bound between the isothiocyanates functional group (-N=C=S) and the nitrogen-containing moiety of the heteroaromatic amine of mutagen [34]. In addition, ITCs were reported to compete with procarcinogens to bind with CYP450, interfere with the conversion of procarcinogens to carcinogens, and, finally, decrease carcinogens [31]. Hence, the inhibition of the carcinogens activity of DCM extract could be due to two possible mechanisms, which are [1] direct effect by interacting directly with carcinogens, leading to neutralizing carcinogen reactivity, and [2] indirect effect by reducing carcinogen formation indirectly through competing with procarcinogen transformation via CYP450. For the aqueous extract, there is no antimutagenic activity detected. The concentration of non-flavonoid polyphenol in this extract is possibly insufficient for exerting the observed activity, although the antimutagenicity of these phytochemicals (i.e., vanillic acid) was reported [35]. The ability of RS extract on phase II detoxifying enzymes was further investigated in vivo. Phase II enzymes are important endogenous molecules to prevent carcinogenesis by inactivating carcinogens into a lesser reactive metabolite. In the present study, the detoxifying enzymes (such as UGT and NQO1) were increased in the RS-DCM treatment group. ITCs have been well recognized as potent phase II enzyme inducers [31]. The ITC moiety interacts with thiol residues of KEAP1 (Kelch-like ECH-associated protein 1), leading to the dissociation of KEAP1 from NRF-2. The NRF-2 then translocates freely to the nucleus, where it binds to a transcriptional regulatory element ARE (antioxidant response element) and activates the expression of multiple phase II detoxifying enzymes [36]. The observed enhancement of phase II enzymes after RS-DCM administration is proposed to be due to the presence of ITCs (i.e., sulforaphane and sulforaphane) in the dichloromethane extract. For the RS-H2O, there is no phase II enzyme alteration detected after RS-H2O treatment. The phenolic content employed in this study (i.e., vanillic acid; 13.1 mg/kg orally) might not be high enough to achieve a significant result as previously reported (vanillic acid ≥ 75 mg/kg orally) [16,37]. The effect of RS extract on pro-inflammatory cytokine gene expressions was examined in vivo since inflammation is critical to carcinogenesis, facilitating tumor growth and survival. RS-DCM and RS-H2O treatments decreased Tnf-α gene expression, whereas Nrf-2 displayed no alteration after RS treatments. ITCs reportedly suppressed inflammation mainly by inhibition of NF-κB (nuclear factor kappa B), a transcription factor responsible for several pro-inflammatory gene expressions, including Tnf-α. In normal conditions, NF-κB is retained in an inactive form and sequestered with IκB (inhibitor kappa B). During stress, IκB was phosphorylated and degraded, releasing NF-κB to its active form, which later translocated to the nucleus and acted as a transcriptional activator of many pro-inflammatory genes [38]. ITCs reportedly inhibited IκB phosphorylation, maintaining NF-κB to its inactive form and suppressing the transcriptional activation [39]. Hence, RS-DCM might suppress the expression of Tnf-α, a pro-inflammatory cytokine gene, by inhibiting NF-κB. Additionally, ITC (sulforaphane) displays no in vivo effect on Nrf-2 gene expression [40], which is in agreement with our result. For the aqueous extract, the polyphenols might contribute to the suppression of Tnf-α. For example, vanillic acid (10 mg/kg orally) decreased the expression of Tnf-α and several pro-inflammatory genes in rats [41], whereas intraperitoneal administration of protocatechuic acid downregulated the pro-inflammatory gene, displaying hepatoprotective effect in rats [42]. The proposed mechanism is related to the upstream regulation of NF-κB [43,44]. Nevertheless, the exact molecular mechanism is yet to be elucidated, and other phytochemicals possibly contributed to the observed result. Carcinogenesis requires three stages of development, including initiation, promotion, and progression. Carcinogens play the most important role in the initiation stage, triggering DNA mutation, whereas phase II metabolism nullifies the effects. The inflammation then takes part mainly in the promotion and progression stage, sustaining excessive cell proliferation. The coherence of these factors (carcinogens, phase II enzymes, and pro-inflammation) is manifested in several types of cancer [45]. For example, metabolic activation of procarcinogen is an essential step in initiating carcinogen-induced colon carcinogenesis. Phase II detoxifying enzymes then respond to counteract the cellular stress. Subsequently, inflammatory cytokines up-regulate to facilitate cancer growth [46]. Our results show that by [1] inhibiting carcinogenic activities and [2] increasing phase II metabolism, RS extract may dampen the triggering factors in the initiation stage. Moreover, by [3] lower inflammation, RS extract reduces the supportive factors in the promotion and progression stage. Taken together, RS displays chemopreventive properties in many steps of carcinogenesis. In addition, the protective effect could be reached with oral administration reflecting sufficient oral bioavailability, with no detected toxicity. Furthermore, with the efficacy, safety, and bioavailability, our data support the applications of RS as a dietary supplement for chemoprotective purposes. As of the current knowledge of hepatocarcinogenesis, characteristic inflammation is the crosstalk between carcinogen (i.e., aflatoxin) and non-carcinogen (i.e., viral infection and diabetes) induced hepatocellular carcinoma [47,48,49]. Aberrantly activation of the key signaling pathway Akt/mTOR in liver cancer contributes to the deregulation of the cell cycle, proliferation, cell death, and inflammation [50,51]. Pro-inflammatory cytokine TNF-α is positively associated with overactive Akt/mTOR signaling at both upper and lower downstream levels [47,52]. Our results showed that RS-DCM and RS-H2O suppress Tnf-α expression, implying the potential chemopreventive use of RS not only for the carcinogen but also for non-carcinogen induced tumor formation. Further studies should, therefore, include the investigation of RS on non-carcinogen induced hepatocarcinogenesis in vivo. A pharmacokinetic profile tracking bioactive compounds (i.e., sulforaphene and vanillic acid) in animal models, as well as a more in-depth safety profile, should also be evaluated to extrapolate the use of the RS for a further clinical study ## 5. Conclusions The data demonstrate the chemopreventive efficacy of RS against initial-stage hepatocarcinogenesis induced by a carcinogen in rats. The dichloromethane extract RS-DCM was more potent than its aqueous counterpart. The proposed mechanism for the RS-DCM (containing sulforaphene, p-hydroxybenzoic acid, and other phenolics) was to exert its preventive activity via [1] blocking the activity of carcinogen through both direct and indirect interactions; [2] enhancing phase II detoxifying enzymes; and [3] suppressing the pro-inflammatory cytokine gene expression. In comparison, the water extract RS-H2O (mostly contained vanillic acid and other phenolics) could only downregulate the pro-inflammatory gene, and that possibly explains the difference in potency between the two extracts. Aside from therapeutic activity, the safety profile was also considered. In our animal model, oral administration of RS extracts showed no observable toxicity—both acute (single high dose regimen) and subacute toxicity (treatment regimens)—and had no inflammatory potential against hepatocytes. The two extracts displayed no mutagenicity in vitro. Neither extract can trigger and facilitate carcinogenesis in a rat’s liver. Collectively, RS extracts exhibited a favorable safety profile. Furthermore, regarding the treatment regimen, our data demonstrated that oral administration of RS could achieve sufficient bioavailability and yield therapeutic response efficiently. 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--- title: 'Child Weight Status: The Role of Feeding Styles and Highly Motivated Eating in Children' authors: - Maria A. Papaioannou - Thomas G. Power - Teresia M. O’Connor - Jennifer O. Fisher - Nilda E. Micheli - Sheryl O. Hughes journal: Children year: 2023 pmcid: PMC10047856 doi: 10.3390/children10030507 license: CC BY 4.0 --- # Child Weight Status: The Role of Feeding Styles and Highly Motivated Eating in Children ## Abstract Although parental feeding plays an important role in child eating and weight status, high food motivation among children may also be a factor shaping how feeding impacts child weight. This study explored whether individual differences in preschool children’s food motivation interacted with mothers’ feeding styles in predicting subsequent child weight status. Participants included 129 Hispanic Head Start mother/child dyads. Data were collected at ages 4–5 years (Time 1) and 7–9 (Time 3). Staff measured heights/weights and observed children in an eating in the absence of hunger task. Mothers reported on feeding styles/practices and children’s eating behaviors. A principal components analysis derived a measure of highly motivated eating in children. Multiple regressions predicted Time 3 child BMI z-scores. Time 3 BMI z-scores were positively predicted by authoritative and indulgent feeding styles and negatively predicted by monitoring. Since feeding style interacted with highly motivated eating, separate regressions were run for high and low food motivation in children. Unexpectedly, results showed that authoritative feeding positively predicted Time 3 child BMI z-scores only for children showing low levels of food motivation. Characterizing differential parental feeding and child eating phenotypes may assist in tailoring childhood obesity prevention programs for the target populations. ## 1. Introduction Overweight and obesity remain high among children in the U.S. [1,2], with disproportionately higher rates seen among ethnically diverse populations [2,3]. Disparities and subsequent health burdens among diverse ethnic groups are a major health concern in the U.S. Roughly $30\%$ of Hispanic children have overweight or obesity by preschool age, with the prevalence increasing to $46\%$ at ages six to eleven years [3]. In order to appropriately inform obesity prevention programs for Hispanic children, evidence is needed that represents the cultural experience of the parent/child feeding experience among these racial/ethnic minority families. Parents play a key role in the development of child eating behaviors and subsequent weight status through the various feeding styles [4] and practices [5] used to socialize children during eating episodes. Whereas feeding styles capture the global approach and emotional climate in which feeding takes place [6,7], feeding practices reflect specific, goal-directed behaviors used to direct child eating [5,8]. Feeding styles are believed to be enduring and trait-like, whereas feeding practices are thought to be modifiable [8]. Numerous studies (Hughes and Power [4] provide a review) demonstrate associations between an indulgent feeding style (i.e., low control paired with high responsiveness) and the highest overweight and/or obesity risk among children in Hispanic families with low-incomes. Moreover, general parenting [9] and feeding styles (i.e., a global approach) [7] characterized by high levels of control and low responsiveness (i.e., authoritarian styles) in this population may have protective influences on child weight status. Benefits of high control in feeding among racial/ethnic minority families are consistent with Domenech et al. ’s [10] ‘protective parenting’ concept (i.e., low autonomy granting and high demandingness). These observations suggest that high levels compared to low levels of parental control may be optimal for obesity prevention among some children who may be more responsive to food and its characteristics among racial and ethnic minority families. High food motivation is thought to be a dimension of appetite regulation in children [11,12] that shapes hunger and satiety responses and the quantity/composition of consumption. A wide range of food motivated behaviors have been associated with higher weight status among young children [13,14], including parent report of children’s enjoyment of food and food responsiveness [15,16,17,18,19,20] as well as direct observations of eating in the absence of hunger [21,22,23,24]. Importantly, food motivated behaviors have a strong genetic component [25,26] and show stability over time [23,27]. Multiple studies support the premise that appetitive phenotypes among children confer behavioral susceptibility to obesity [20,28], suggesting that children with higher food motivation exhibit greater susceptibility to obesogenic influences, including snacking [29], consumption of larger portion sizes [30], intake of highly processed foods [31], as well as greater energy intake, in general [23,32,33]. Developmental psychologists have long recognized that different children benefit from different types of parenting [34,35]. However, the feeding literature has been remarkably one-sided in its perspective, focusing only on the effects of what parents do. The need for a more nuanced understanding of child contributions to parental feeding has been recently acknowledged with a call for more research focused on ‘precision approaches to feeding children’ [36]. To date, evidence on the influence of parental feeding on specific child eating behaviors is limited, with only one study showing that children with a higher food approach had higher BMI z-scores when considering parental feeding behaviors [37]. Beyond this one study, little consideration has been given to individual differences in food motivation among children that may increase their obesity risk [13,38]. The aim of the current study was to examine, in a study of Hispanic families with low-income levels, the degree to which individual differences in children’s food motivation in the preschool years interacted with mothers’ feeding styles in predicting subsequent weight status in children. The current paper involves further analyses of data from a longitudinal study showing that indulgent and authoritative feeding in the preschool years was associated with higher child weight status in elementary school [39]. In these analyses, we used both maternal reports [40] and observations of child behavior [41] to assess highly food motivated eating. We hypothesized that parental feeding style would be a stronger predictor of later child weight status for children showing highly motivated eating patterns. Specifically, we expected that low levels of parental control (i.e., indulgent feeding styles) in conjunction with highly motivated eating in children would predict higher child weight status in the elementary school years. ## 2.1. Participants A total of 129 Hispanic parents and their 4–5-year-old children residing in a large urban city in the southern part of the U.S. were included in this study. These families participated in a larger longitudinal study ($$n = 187$$) that examined eating behaviors of children from families with low incomes [39]. Parent/child dyads were eligible to participate if the parent self-identified as Hispanic and spoke either English or Spanish, and the child was attending Head Start. Parent/child dyads were not eligible to participate if either the parent or child had dietary restrictions for any reason, such as diabetes, food allergies, or were following a special diet. Additionally, children that had developmental problems, such as autism or other significant developmental delays, were excluded as it would have limited their ability to perform the study tasks. This study was reviewed and approved by the Institutional Review Board at Baylor College of Medicine (ethics approval number H-26796). Before any study activities took place, study staff explained the purpose of the study to parents in either English or Spanish (their language of choice). Consent was obtained for parents’ participation and verbal assent was obtained for children’s participation. All consenting parents were mothers; therefore, parents will be referred to as ‘mothers’ hereafter. The larger longitudinal study included 187 mother/child dyads (i.e., original sample) [42]. Eighteen months following baseline assessments ($M = 18.39$, SD = 1.58), follow-up assessments (Time 2) were conducted on 144 mother/child dyads. Approximately 24 months after Time 2 ($M = 23.6$, SD = 6.54), Time 3 assessments were conducted on 129 mother/child dyads. Only data from baseline (Time 1) and the Time 3 are included in the present study. Data on all the variables, that were needed for analyses, were available for 129 mother/child dyads. The baseline demographics of the 129 mothers are presented in Table 1. The mean age of the mothers was 31.55 years (SD = 6.6). The majority of the mothers were unemployed ($79.1\%$), born in Mexico ($63.5\%$) or Central America ($17.9\%$), and married ($58.9\%$). The educational status of the mothers ranged from 6th grade to beyond college. The ages of the children at Times 1 and 3 assessments were $M = 4.76$ (SD = 0.46) and $M = 8.34$ (SD = 0.71), respectively. Approximately half of the children were male ($46.5\%$) and had a healthy weight status ($48.8\%$). About $22.5\%$ of the children were classified in the overweight category and $27.1\%$ were classified in the obese category. The percentage of children with overweight and obesity in this study is higher than that of 2- to 5-year-old Hispanic children with overweight or obesity in the U.S. (i.e., $30\%$) [3]. There were no significant differences regarding demographic variables between the initial sample of 187 mother/child dyads and the 129 mother/child dyads with available data at both time points (Times 1 and 3; Table 1). Participants in this study may be representative of Hispanics in this geographical area. ## 2.2. Measures All questionnaires used in this study were translated into Spanish and back translated into English to assure understanding of the wording and concepts. These questionnaires have been used successfully in previous studies with Hispanic participants. All measures were completed at Time 1 (baseline) except for the child anthropometrics, which were completed at both Time 1 (baseline) and Time 3 (approximately 42 months after baseline). ## 2.2.1. Caregiver’s Feeding Styles Questionnaire (CFSQ) The CFSQ is a well-established 19-item questionnaire developed by Hughes and colleagues [7] to measure feeding styles for use with Hispanic parents of young children from families with low-income levels. The CFSQ uses a 5-point Likert scale, ranging from never to always. A cross-classification of scores on dimensions of demandingness and responsiveness identifies four feeding style categories as follows: authoritarian (high demand/low response); authoritative (high demand/high response); indulgent (low demand/high response); and uninvolved (low demand/low response). Evidence of test-retest reliability, internal consistency, convergent, and predictive validity has been obtained with ethnically diverse families, including Hispanic, with low incomes [4,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59]. ## 2.2.2. Child Feeding Questionnaire (CFQ) The CFQ is a validated questionnaire used to assess feeding attitudes and practices [60]. The CFQ measures four attitudes (perceived responsibility, perceived child weight, perceived parent weight, and concern about child weight) and three practices (restriction, pressure to eat, and monitoring). In the current study, only the following subscales were used as they assess feeding practices: restriction (e.g., I intentionally keep some foods out of my child’s reach); pressure to eat (e.g., my child should always eat all the food on her plate); and monitoring (e.g., how much do you keep track of the high fat foods that your child eats?). This questionnaire has been used and validated in low-income samples [7,54,61,62,63]. ## 2.2.3. Children’s Eating Behavior Questionnaire (CEBQ) The CEBQ measures child eating behaviors. It contains 35-items with a 5-point Likert-type response scale, ranging from never to always [40]. Four subscales assess food approach behaviors (Food Responsiveness, Enjoyment of Food, Desire to Drink, Emotional Overeating) and four assess food avoidant behaviors (Satiety Responsiveness, Emotional Undereating, Slowness in Eating, Food Fussiness). Multiple studies with cross- sectional and longitudinal designs support the predictive validity of the measure through robust associations of CEBQ subscales with weight status among young children [18,64,65,66,67,68,69,70,71,72]. The CEBQ scores were used in this study, in part, to measure the highly motivated eating construct in children. ## 2.2.4. Eating in the Absence of Hunger Task (EAH) This task was developed by Fisher and Birch [41] to measure child eating beyond satiation. Higher scores have been associated with higher child weight status across multiple studies ([24,73,74,75,76], also see Lansigan et al. [ 14] for a review). In order to minimize hunger prior to the task, children were provided with a standardized meal of palatable foods accounting for $40\%$ of the estimated daily food energy needs of a four- to five-year-old. After the meal, children were interviewed individually to determine fullness. Each child was then left alone with age-appropriate toys and sweet and savory snacks (i.e., potato chips, Skittles, pretzels, sherbet, ice-cream, Hershey bars, and chocolate chip cookies) for ten minutes while being observed remotely. Scores for each child on this task reflected the total number of kilocalories eaten in the absence of hunger based on weighed food intake. Final scores across the children were highly positively skewed. Thus, data were recoded into three values: 1 = less than 20 kilocalories ($$n = 37$$); 2 = 20 to 125 kilocalories ($$n = 74$$); 3 = greater than 125 kilocalories ($$n = 75$$). High values reflected higher levels of eating in the absence of hunger. The first group was defined as children who ate no food or ate a very minimal amount (the distribution had a natural break at 20 kilocalories); the second and third groups were defined by a median split of the remaining children. EAH scores were used in this study to measure the highly motivated eating construct in children. ## 2.2.5. Anthropometrics Trained research staff took child height and weight measurements following a standard protocol [77]. Children were weighed in duplicate using a digital weight scale (Health-O-Meter model 752 KL, Health O Meter, China) to the nearest 0.1 kg, and height was measured in duplicate using a stadiometer (Seca model 214, Seca, China) to the nearest 0.1 cm. Using the Centers for Disease Control and Prevention Reference Standards, age- and gender-specific Body Mass Index (BMI) standardized scores (BMI z-score) were calculated [78]. The following weight status categories were used for the children: underweight (BMI < 5th percentile), healthy weight (BMI ≥ 5th to < 85th percentile), overweight (BMI ≥ 85th to < 95th percentile), or obese (BMI ≥ 95th percentile). ## 2.3. Data Analyses All analyses were run using the Statistical Package for the Social Sciences (SPSS, Version 28.0, Chicago, IL, USA). First, we conducted a principal components analysis on the eight CEBQ subscale scores and the child EAH score to derive a measure of highly motivated eating in children. To maximize the sample size, all Time 1 data were used in this analysis (n’$s = 187$ for the CEBQ and 186 for EAH). Mean scores were calculated on CEBQ subscales if mothers completed all items. If mothers completed at least $75\%$ of the items on a given subscale, then the score for that subscale was calculated by examining the mean of the non-missing items. If a mother completed less than $75\%$ of the items, then the score was considered missing for that subscale. The main analyses were conducted through multiple regression. For the longitudinal analysis, to allow for comparison with Hughes et al. [ 39], we only analyzed data from the mothers ($$n = 129$$) and children ($$n = 128$$) who completed all relevant assessments at Times 1 and 3. Multiple regressions were conducted to predict the child BMI z-score at Time 3 from those Time 1 measures, showing significant prediction in the previous analysis of this dataset reported in Hughes et al. [ 39]. Based on these previous analyses, predictors were: [1] child BMI z-score; [2] CFQ feeding practices (three scores—restriction, pressure to eat, and monitoring); and [3] CFSQ feeding styles (one dichotomous predictor for each of three feeding styles—authoritarian, authoritative, and indulgent; uninvolved feeding served as the reference group). Additional predictors for these regressions were the highly motivated eating score on the children and the three feeding styles by highly motivated eating interactions. As described below, because of the results of the preliminary analyses, separate regressions were run for highly motivated eating in children derived from the CEBQ and the EAH task. ## 3. Results The principal components analysis on the combined CEBQ and EAH measures for highly motivated eating in children yielded three components—a first component with loadings on six of the eight CEBQ subscales that assessed highly motivated eating, a second component that primarily assessed emotional eating, and a third component assessing EAH. EAH scores loaded a separate component in this analysis and only showed a significant correlation with one of the eight CEBQ subscales, emotional overeating, r[184] = 0.15, $p \leq 0.05.$ Given that the EAH was not highly correlated with the CEBQ scores, we ran separate regressions for the CEBQ-based and the EAH-based assessments of highly motivated eating in children. To derive the CEBQ measure, we reran the principal components analysis specifying only one component. This component accounted for $30.95\%$ of the variance in CEBQ subscale scores. The loadings are presented in Table 2. The highly motivated eating score was calculated by taking the mean of the five CEBQ subscales with loadings > 0.30 (reverse scoring those subscales with negative loadings). Coefficient alpha for this five-item scale was 0.69. Table 3 presents the descriptive statistics and correlations for all variables used in the regression analyses. The Time 1 CEBQ measure of highly motivated eating in children was positively correlated with Time 1 indulgent feeding and Time 1 child BMI z-score and negatively correlated with Time 1 authoritarian feeding. EAH was positively correlated with child BMI z-scores at both Time 1 and Time 3 and not significantly correlated with feeding styles or practices. Finally, Time 1 indulgent feeding style was positively correlated with child BMI z-scores at both time points and Time 1 authoritarian feeding negatively correlated with child BMI z-scores at Time 1. Table 4 presents the results of the multiple regression predicting child BMI z-scores at Time 3 using the CEBQ measure of highly motivated eating in children. For the entire sample, controlling for Time 1 BMI z-score, Time 3 BMI z-scores was positively predicted by authoritative and indulgent feeding styles and negatively predicted by monitoring. As was the case for the bivariate correlations, highly motivated eating at Time 1 did not significantly predict Time 3 child BMI z-scores. However, the authoritative feeding by highly motivated eating interaction was significant. Because authoritative feeding style significantly interacted with highly motivated eating, separate regressions were run on children above and below the median on highly motivated eating. As shown in the two right hand columns of Table 4, in addition to the Time 1 child BMI z-scores, the predictors identified in the sample as a whole were only significant for children below the median on highly motivated eating. The regression replacing the CEBQ highly motivated eating score with the EAH score yielded the same significant predictors as the previous analysis (predicting Time 3 child BMI z-scores from CEBQ highly motivated eating). The regression predicting the Time 3 child BMI z-scores from the EAH motivated eating score showed feeding practices (i.e., monitoring) and feeding style (authoritative and indulgent) as significant predictors. There was no significant main effect for EAH nor any significant EAH by feeding style interactions. ## 4. Discussion The present study further analyzed data from a longitudinal study among Hispanic families that demonstrated associations between authoritative and indulgent feeding styles in mothers of preschoolers and later higher weight status in elementary school-aged children [39]. Specifically, the purpose of this study was to investigate the extent to which children’s individual differences in food motivation, as measured by maternal report and observations, interacted with maternal feeding styles in the prediction of children’s later weight status. Unexpectedly, later child weight status was positively predicted by authoritative feeding (characterized by high levels of parental control and responsiveness) but only for children below the median on highly motivated eating as reported by mothers. Developmental psychologists have posited, and the feeding literature has shown, that children can benefit from tailoring parental feeding to the child’s “genetically influenced behavioral profile” [34,79,80,81]. However, little research has sought to examine the relationship between global feeding styles among parents and children’s eating behaviors. Most studies target specific goal-directed feeding practices such as restriction and pressure to eat [5,54,61,62,63,79,82,83,84,85,86]. To our knowledge, this study provides the first data regarding how individual differences in children’s food motivation in the preschool years may interact with mothers’ global feeding styles in predicting children’s subsequent weight status. Only one interaction was found to be significant, and it was contrary to the hypothesis. Authoritative feeding positively predicted subsequent child weight status but only for children low on food motivation. Based on the feeding literature, we expected that feeding styles would be an important predictor for children who were highly motived eaters, given their greater susceptibility to obesity [12,20,28,37,87,88]. Moreover, the finding, that authoritative feeding showed a positive association with subsequent child weight status, was contrary to the literature that mostly supports the premise that authoritative feeding is associated with healthier child weight and eating outcomes [4,79]. This interaction showed that eating motivation moderated the effects of feeding styles on child outcomes, but also suggests that the relationship may be complex and influenced by other factors. Previous research has found that high levels of control in Hispanic parents may play a protective role against negative child health outcomes [10,89]. Some researchers have proposed that controlling interactions in Hispanic families provide the maternal involvement, care, structure, and guidance that children need to develop later autonomy which, in turn, may facilitate positive health outcomes [10,89,90,91,92,93,94]. This suggests that goal-directed feeding practices may play a greater role in these interactions between feeding styles and eating behaviors in this population. Future research should examine how children’s food motivation, maternal feeding styles, and goal-directed feeding practices may interact to contribute to children’s later obesity risk. Although not the primary focus of this study, monitoring also predicted later child weight. Monitoring during feeding has shown mixed results with child eating [8] and few studies have shown associations with child weight [79,95,96,97]. Similar to Faith and colleagues [98], monitoring in the present study negatively predicted subsequent child weight. Previous studies have also found that parental monitoring has been negatively associated with children eating large amounts of food [99]. However, monitoring was not significantly associated with children’s food motivation in the present study. Several limitations should be considered in the interpretation of these findings. The study sample was comprised of one ethnic group that encompassed Hispanic families from multiple countries and was recruited from Head Start centers in a large urban US city, thus generalizability is limited. Maternal feeding styles were measured using questionnaires, which can be confounded by social desirability that may have biased mothers’ responses based on expectations rather than actual behavior [100]. The study also has several strengths. Data were collected longitudinally during a critical developmental time for approximately 42 months, starting in preschool, which allowed examination of the target behaviors overtime. Anthropometrics were objectively measured and child behaviors were assessed using both maternal report and observations. Furthermore, all questionnaires were well known and widely used in the feeding literature [7,13,40,60]. Furthermore, the feeding styles questionnaire has been validated by home observations [13,101]. In conclusion, this study showed that food motivation in children interacts differentially with parental feeding and this interaction predicted later child weight status in Hispanic families with low incomes. The current findings highlight the importance for research efforts to progress beyond a one-size-fits all approach to parental feeding. This approach is especially needed to address equity in obesity prevention efforts for families with low incomes who are underrepresented in the feeding literature and for whom prevention efforts have shown limited success [79,102,103,104]. Examining parental feeding by both global feeding styles and goal-directed feeding behaviors within those styles, as well as child eating phenotypes may assist in tailoring childhood obesity prevention programs for maximum benefits for the target population. Researchers have identified the need for characterizing and validating the child eating phenotype in order to better understand the parent/child feeding dynamic and its health outcomes [105]. Future research that targets goal-directed feeding practices among child eating phenotypes may shed light on the results from the current study. Additional research is needed to replicate these findings with larger samples that are ethnically diverse, as well as to evaluate the effectiveness of tailoring programs to children’s unique “profiles”. Furthermore, since within group differences may exist among the Hispanic population, future research should investigate subcultural differences in parental feeding styles. ## References 1. 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--- title: Change in Long Non-Coding RNA Expression Profile Related to the Antagonistic Effect of Clostridium perfringens Type C on Piglet Spleen authors: - Zunqiang Yan - Pengfei Wang - Qiaoli Yang - Xiaoli Gao - Shuangbao Gun - Xiaoyu Huang journal: Current Issues in Molecular Biology year: 2023 pmcid: PMC10047886 doi: 10.3390/cimb45030149 license: CC BY 4.0 --- # Change in Long Non-Coding RNA Expression Profile Related to the Antagonistic Effect of Clostridium perfringens Type C on Piglet Spleen ## Abstract ### Simple Summary Clostridium perfringens (C. perfringens) type C is a spore-forming pathogenic bacterium characterized by the secretion of fatal toxins, which are absorbed into the body, causing diarrhea. Diarrhea has already brought about tremendous economic losses in pig farms worldwide. However, the understanding of lncRNAs’ regulatory mechanisms of the spleen in piglets challenged by C. perfringens type C is still limited. This paper aimed to identify antagonistic lncRNAs associated with the spleen in piglets challenged by C. perfringens type C. The study found that four lncRNAs are involved in immune-/inflammation-related pathways to regulate cytokine genes against C. perfringens type C infection. ### Abstract LncRNAs play important roles in resisting bacterial infection via host immune and inflammation responses. Clostridium perfringens (C. perfringens) type C is one of the main bacteria causing piglet diarrhea diseases, leading to major economic losses in the pig industry worldwide. In our previous studies, piglets resistant (SR) and susceptible (SS) to C. perfringens type C were identified based on differences in host immune capacity and total diarrhea scores. In this paper, the RNA-Seq data of the spleen were comprehensively reanalyzed to investigate antagonistic lncRNAs. Thus, 14 lncRNAs and 89 mRNAs were differentially expressed (DE) between the SR and SS groups compared to the control (SC) group. GO term enrichment, KEGG pathway enrichment and lncRNA-mRNA interactions were analyzed to identify four key lncRNA targeted genes via MAPK and NF-κB pathways to regulate cytokine genes (such as TNF-α and IL-6) against C. perfringens type C infection. The RT-qPCR results for six selected DE lncRNAs and mRNAs are consistent with the RNA-Seq data. This study analyzed the expression profiling of lncRNAs in the spleen of antagonistic and sensitive piglets and found four key lncRNAs against C. perfringens type C infection. The identification of antagonistic lncRNAs can facilitate investigations into the molecular mechanisms underlying resistance to diarrhea in piglets. ## 1. Introduction Clostridium perfringens (C. perfringens) type C is one of the most devastating pathogens related to diarrhea, necrotizing enteritis and struck in animals [1]. Recently, C. perfringens type C-induced diarrhea characterized by a high morbidity and mortality has frequently occurred at large-scale pig farms, leading to huge economic losses around the world [2,3]. Pigs, especially piglets, are infected with this bacterium mainly via the oral digestive tract. Then, an increase in the number of C. perfringens type C bacteria in the small intestine can secrete fatal toxins (at least α and β), which impair tight junction integrity and damage the passages of water and solutes, causing diarrhea [4,5]. Additionally, fatal toxins are usually absorbed by the small intestine and then transported to terminal organs (such as the spleen, liver and brain), leading to host tissue injury and organ damage [3,6,7,8,9]. The traditional method of preventing and controlling this bacterium is antibiotic therapy, although this approach has some disadvantages (including bacterial resistance to many antibiotics and antibiotic residues in pork) [2,5]. Under pressure from the public, the United States and the European Union have taken some measures to ban the application of some medical antibiotics for disease prevention and growth promotion [10,11]. Therefore, there is an urgent need to find a new method for preventing and controlling diarrhea caused by C. perfringens type C infection. The difference in host resistance to pathogens (such as bacteria) is associated with host immunity, pathogen exposure and the interaction of host defense and pathogen virulence [12,13]. Pigs with a G to A mutation at locus M307 of FUT1 can resist Enterotoxigenic Escherichia coli F18 infection [14]. Moreover, White Leghorn chicken line 6.3 is resistant to Marek’s disease, and line 7.2 is susceptible to Marek’s disease [15]. Additionally, animals with a low immunity are more sensitive to pathogens than those with a strong immunity [16,17,18]. These results suggest that the identification of candidate molecules with resistant pathogens is beneficial for the breeding of new lines for preventing and controlling infectious diseases. At present, the understanding of the molecular mechanism of piglet resistance to C. perfringens type C infection is still limited. Thus, elucidating the underlying molecular regulation of different levels of resistance in piglets challenged by C. perfringens type C will be an effective approach to artificially selecting resistant piglets for controlling diarrhea prevalence and to find some molecular markers of C. perfringens type C resistance. Long non-coding RNA (lncRNA) is a class of non-coding RNAs with more than 200 nucleotides and present in many species, such as pigs [19], zebrafish [20] and humans [21]. Recently, lncRNAs are receiving more attention because of their important regulatory roles in biological processes, such as normal development, metabolic diseases, cardiovascular diseases and tumor formation [22,23]. Furthermore, lncRNAs also play important roles in autoimmune diseases (such as autoimmune hepatitis [24] and systemic lupus erythematosus [25]) and infectious diseases (including diarrhea [26], malaria [27] and African trypanosomes [28]). However, lncRNAs and their target genes related to C. perfringens type C resistance in piglets are poorly understood. The use of RNA-Seq to identify lncRNAs against C. perfringens type C infection in pigs is the basis for revealing the molecular mechanism of disease resistance and, thus, for discovering candidate genes related to disease resistance traits. The spleen is the main immune organ, and it plays important roles in filtering blood-borne pathogens and antigens to protect the host against various infectious diseases and pathogen infections [29,30,31]. Herein, the spleen is regarded as the ideal organ model for exploring host resistance and susceptibility to pathogenic challenges. Studies of disease resistance have been performed in sheep spleen [32,33], chicken spleen [15,34] and pig spleen [35]. Additionally, differentially expressed lncRNAs and mRNAs in two spleen phenotypes, antagonistic or sensitive to C. perfringens type C, were identified using RNA-Seq in our previous study [36]. However, our previous research only focused on identifying molecules associated with resistance and susceptibility to C. perfringens type C, and we did not further investigate how these molecules function in the process of C. perfringens type C infection. In a continuation of our previous research, the aim of this study was to comprehensively identify and reanalyze the dysregulated lncRNAs and mRNAs in the spleen of resistant and sensitive piglets. Our study provides new insights into piglet antagonism to C. perfringens type C in terms of lncRNAs, which also contributes to formulating a breeding strategy against C. perfringens type C infection. ## 2.1. Experimental Design and Sample Collection The experimental piglets were purchased from the nucleus herd in Dingxi city, Gansu province. We randomly selected a total of 30 seven-day-old piglets displaying normal growth and approximately similar body weight. Additionally, the piglets were not infected with Escherichia coli, Salmonella or C. perfringens, as tested using ELISA kits (Jiancheng Bioengineering Institute, Nanjing, Jiangsu, China). Among these piglets, 5 piglets were selected to form the control group (SC). The other 25 piglets were challenged by a C. perfringens type C strain (CVCC 2032), and the top 5 and bottom 5 piglets according to total diarrhea scores were considered the susceptible group (SS) and the resistant group (SR) by using a previously described method [36,37]. The spleen and other tissues from the piglets in the SC, SR and SS groups were collected. Then, these tissues were frozen in liquid nitrogen and stored at −80 °C until used for RNA isolation. Moreover, the spleen was obtained and stored at −80 °C for detecting cytokine expression levels using RT-qPCR and concentration using ELISA. ## 2.2. Total RNA Isolation The total RNA was extracted from each individual sample using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA). In addition, the purity and quantity of the total RNA were determined using a Nanodrop instrument (Implen, Westlake Village, CA, USA). The integrity of the spleen total RNA was measured using a Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). ## 2.3. Library Preparation for lncRNA Sequencing A total of 3 μg of spleen total RNA was used to construct lncRNA sequencing libraries by utilizing a previous method [36]. Furthermore, the lncRNA sequencing libraries were sequenced on an Illumina Hiseq 4000 platform, and 150 bp paired-end reads were obtained at the Novogene Bioinformatics Institute (Beijing, China). ## 2.4. Quality Control and Mapping The raw reads (raw data) were first processed using in-house Perl scripts. In this step, clean reads (clean data) were generated by removing reads that contained adapters or over $10\%$ of ploy-N, or low-quality reads (>$50\%$ of bases whose Phred scores were <$5\%$) based on the raw data. At the same time, the Phred score (Q20, Q30) and GC content of the clean reads were assessed. The clean reads were mapped to the pig reference genome with Tophat (2.0.9 version) [38]. ## 2.5. Transcriptome Assembly and Expression Level Quantification The mapped reads of each individual sample were assembled using Cufflinks (2.1.1 version) [38] and Scripture (beta2 version) [39]. Cuffdiff (2.1.1 version) [40] was used to evaluate the lncRNA and mRNA expression levels by Fragments Per Kilobase Million (FPKM). For biological replicates, the lncRNAs and mRNAs with a P-adjust < 0.05 were described as differentially expressed (DE) among three group comparisons (SR vs. SC, SR vs. SS and SS vs. SC). ## 2.6. Coding Potential Analysis and Target Gene Prediction To achieve highly reliable novel lncRNAs, previously stringent filtering criteria were used [36]. In addition, protein-coding genes 100 k downstream and upstream of the lncRNAs were considered the cis target genes. The trans target genes of the lncRNAs were obtained by examining the expression levels of the lncRNAs and mRNAs with custom scripts (Pearson correlation coefficient ≥0.95). ## 2.7. Enrichment Analysis of GO and KEGG Gene ontology (GO) enrichment analyses of DE lncRNA target genes and DE mRNAs were implemented using the GOseq R package (1.50.0 version) [41]. KEGG pathway analyses of DE lncRNA target genes and DE mRNAs were performed using KOBAS software (3.0 version) [42]. ## 2.8. Heat Map Construction and lncRNA Secondary Structure Prediction A hierarchical heat map analysis was performed using OmicShare tools, a free online platform for data analyses (http://www.omicshare.com/tools (accessed on 8 November 2022)). The prediction of the secondary structure of the lncRNAs was conducted based on the free energy using the RNAFold web server online tool (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi (accessed on 11 December 2022)). ## 2.9. RT-qPCR Assay and ELISA Detection The total RNA of the spleen tissues used for RNA-Seq were reverse-transcribed into cDNA using a PrimeScript™ RT Reagent kit (Takara, Dalian, China). The primers were designed in Primer3, and primer specificity was assessed using Primer-BLAST (Table S1). An RT-qPCR assay was performed in a reaction system containing 7.5 µL of RNase free ddH2O, 1 µL of cDNA, 1 µL of reverse primer, 1 µL of forward primer and 9.5 µL of the SYBR® Green PCR Master Mix (Takara, Dalian, China) using a LightCycler 480II instrument (Roche, Basel, Switzerland). The thermal cycler conditions included an initial pre-denaturation at 95 °C for 3 min and 40 cycles at 95 °C for 15 s, 58 ± 1 °C for 15 s and 72 °C for 20 s. LncRNA and mRNA expressions were quantified relative to β-actin expression using the 2−∆∆Ct method [43]. All ELISA processes were conducted according to the manufacturer’s instructions. The concentrations of cytokines (including TNF-α, IFN-γ, IFN-α, IL-6 and IL-8) in the spleen tissue were determined using an ELISA kit (Kete Biotech, Yancheng, Jiangsu, China). The RT-qPCR and ELISA data are presented as Mean ± SE. A one-way ANOVA was performed to calculate statistical significance followed by Duncan’s test using SPSS (25.0 version). ## 3.1. Analyses of Differentially Expressed lncRNAs To identify the lncRNAs and mRNAs in the C. perfringens type C-challenged piglets in the SR, SS and SC groups, RNA libraries of the spleen samples were constructed and sequenced. The results indicate that 177 lncRNAs (including 45 down-regulated and 132 up-regulated) and 1707 mRNAs (including 919 down-regulated and 788 up-regulated) were significantly dysregulated between the SS and SC groups (Figure 1A,B and Table S2). A total of 174 lncRNAs and 1542 mRNAs between the SR and SC groups were differentially expressed (Figure 1A,B, and Table S3). A total of 19 lncRNAs (including 10 down-regulated and 9 up-regulated) and 123 mRNAs (including 80 down-regulated and 43 up-regulated) were identified between the SR and SS groups (Figure 1C,D). In addition, 1 lncRNA and 13 mRNAs were significantly expressed among the three SR, SS and SC groups (Figure 1A,B). According to the visible lncRNA and mRNA levels in the SR, SS and SC groups, the DE lncRNAs were distributed across all chromosomes. Chromosomes 1, 2, 3, 7 and 13 displayed more DE lncRNAs (Figure 2A). The distribution densities of the DE mRNAs were different. Most of the DE mRNAs were distributed among chromosomes 1, 2 and 13. However, there were no DE mRNAs in chromosome Y (Figure 2B). Compared to the SC group, a total of 14 lncRNAs and 89 mRNAs were found to be differentially expressed between the SR and SS groups (Figure 1A,B). Table 1 shows the detailed information of these molecules, which were used as potential resources for identifying the lncRNAs related to the antagonistic effects of C. perfringens type C on piglet spleen. To investigate the expression patterns among the three SR, SS and SC groups, we used the 14 DE lncRNAs and 89 DE mRNAs to generate a hierarchical heat map. The heat map of the DE lncRNAs and mRNAs in the spleen of the three groups revealed that the SS and SR groups were clustered together because of their similar expression profiles (Figure 3A,B). ## 3.2. Characterization of lncRNA Subtypes Previous studies have noted major differences in gene structures and expression levels among two subtypes of lncRNAs. Thus, the transcript length, expression level, exon count and ORF length among the different subtypes of lncRNAs were analyzed. The lengths of lincRNAs were greater than those of antisense lncRNAs, with a mean length of 4.778 kb vs. 4.353 kb, respectively. There were no significant differences in length ($$p \leq 0.9118$$, Figure 4A). In particular, the lincRNAs showed a higher expression level than the antisense lncRNAs ($$p \leq 0.0212$$, Figure 4B). Clear differences in the exon count ($$p \leq 0.0003$$, Figure 4C) and ORF length ($$p \leq 0.0051$$, Figure 4D) were observed between the two lncRNA subtypes. ## 3.3. Functional Analyses of C. perfringens Type C-Responsive lncRNAs and mRNAs The secondary structures of the lncRNAs were predicted, which was helpful in recognizing the functions of these lncRNAs. A total of 10 lncRNAs had secondary structures, which mainly included a hairpin loop, a stem loop, an inner ring, a bulge ring and a multi-branch loop (Figure 5). Nevertheless, four lncRNAs (namely, LNC_001987, LNC_001097, LNC_001253 and LNC_001985) had no secondary structures because of their excessive lengths. To further investigate the functions of the lncRNAs, the potential target genes of the 14 identified lncRNAs in cis (co-location) and trans (co-expression) were predicted. The prediction results show that a total of 17 interaction relationships were established in cis between 8 lncRNAs and 17 protein-coding genes in the pig genome (Table S4). In addition, these results indicate that 4 lncRNAs corresponded to 93 protein-coding genes within a range of 100 kb in trans (Table S4). However, the target genes of LNC_001595, LNC_000191, LNC_001065 and LNC_000042 were not predicted because of a possibly incomplete pig genome annotation, which suggests that the pig reference genome annotation should be improved. The target genes of these DE lncRNAs are displayed in Table 2. Next, the predicted target genes of these lncRNAs were examined using GO term and KEGG pathway analyses. A total of 1914 GO terms via cis and trans function analyses were identified in the SR vs. SS group compared to the SC group (Table S5). Among these GO terms, 115 significantly enriched GO terms ($p \leq 0.01$) were obtained from the cis and trans function analyses. The top 30 enriched GO terms are listed in Figure 6. For biological processes, the most enriched GO terms were related to the immune response, including the regulation of the humoral immune response, the positive regulation of T-cell-mediated immunity, the positive regulation of cytokine production and the response to bacteria. For cellular components, the main represented category was the interleukin-12 complex. For molecular functions, the main represented GO terms were interleukin-17 binding, interleukin-12 beta subunit binding and protein tyrosine kinase activity. Moreover, the KEGG pathway analysis showed that a total of 51 pathways were detected (Table S5). Among these KEGG pathways, a total of three KEGG pathways were significantly enriched ($p \leq 0.05$). The top 20 KEGG pathways are shown in Figure 7. Some KEGG pathways (such as inflammatory bowel disease, cytokine–cytokine receptor interaction and Leishmaniasis) were associated with the immune response and infectious diseases. Furthermore, GO term and KEGG pathway analyses of the 89 DE mRNAs were performed. In the SR vs. SS group compared to the SC group, a total of 2336 different GO terms were obtained, and 74 GO terms were significantly enriched (corrected p value < 0.05) (Table S6). For biological processes, the most enriched GO terms were related to the inflammatory response, including the innate immune response and Toll-like receptor 4 binding. For cellular components, the main represented categories were extracellular space and the extracellular region. For molecular functions, the main represented GO terms were monocarboxylic acid binding and Toll-like receptor binding. A total of 10 KEGG pathways were significantly enriched ($p \leq 0.05$), and several immune-related KEGG pathways (such as NF-κB, Jak-STAT and TNF) were identified (Table S6). Several immune-response-related genes participated in these KEGG pathways, such as CD14 and DDX58 in the NF-κB signaling pathway, HSPA1L in antigen processing and presentation and IL1R2 and CD14 in the MAPK signaling pathway. ## 3.4. RT-qPCR Validation and Tissue Expression Profiling of LNC_001595 The expression levels of four DE lncRNAs and two DE mRNAs were detected using RT-qPCR to validate the reliability of our RNA-Seq data. In addition, three genes (PIK3R4, CMPK2 and GADD45G) were selected to detect expression levels. These results show that the trends of these DE lncRNAs and mRNAs determined using RNA-Seq are consistent with those from the RT-qPCR (Figure 8A–C). Lastly, the PIK3R4, CMPK2 and GADD45G genes were differentially expressed between the SR and SS groups (Figure 8D). Previous studies reported that one of the features of lncRNAs is their remarkable tissue specificity. Hence, to verify the tissue specificity of the lncRNAs, we selected one lncRNA (LNC_001595) to detect the expression level in various tissues. Expression profiling across different pig tissues indicated that the transcript LNC_001595 was highly expressed in the spleen, lymph nodes and thymus (Figure 9). ## 3.5. The Identification of lncRNAs Antagonistic to C. perfringens Type C Compared with SC, a total of 14 lncRNAs and 89 mRNAs were identified as being DE in the SR vs. SS group. After C. perfringens type C infection, dysregulated ALDBSSCT0000003048, ALDBSSCT0000009442, LNC_001097, ALDBSSCT0000007366 and ALDBSSCT0000006918 regulated several immune genes (such as PIK3R4, ADGRG3, RSAD2, CMPK2 and GADD45G) via cis and trans. At the same time, these DE target genes in SR vs. SS were mainly enriched in some important immune-related KEGG pathways, including inflammatory bowel disease; cytokine–cytokine receptor interaction; Toll-like receptor; and Jak-STAT, which has been reported to be related to bacterial infection, especially C. perfringens infection. These results suggest that the lncRNAs and their target genes had potential effects on these C. perfringens-related KEGG pathways. To investigate these lncRNAs’ potential roles in C. perfringens type C infection, two criteria were used in this study by following a previous method [37]. Firstly, differentially expressed lncRNA target genes via cis and trans were found to participate in the immune response. Secondly, the downstream immune-related cytokine genes of immune response genes were identified using KEGG pathways and some scientific research papers. Based on the above method, four lncRNAs were identified. These target genes of lncRNAs possibly indirectly or directly regulated cytokine genes in the process of C. perfringens type C infection. Therefore, cytokine gene expression levels and concentrations (TNF-α, IFN-γ, IFN-α, IL-6 and IL-8) were evaluated (Figure 10). To further investigate how lncRNAs function, we constructed a potential diagram among key lncRNAs, target genes and cytokines (Figure 11). For example, ALDBSSCT0000009442 could positively regulate its target gene ADGRG3, and over-expressed ADGRG3 triggered the expression of IL-6 through the NF-κB pathway to improve immunity against C. perfringens type C infection. ## 4. Discussion Recently, due to the rapid development of RNA-Seq technology, lncRNAs have been regarded as new modulators related to infectious diseases, such as malaria [44], diarrhea [32], hepatitis [45] and tuberculosis [46]. Diarrhea caused by C. perfringens type C often leads to major economic losses in pig farms. However, the understanding of the functions of lncRNAs in the spleen of piglets infected with C. perfringens type C is limited. Therefore, we used deep Illumina sequencing and bioinformatics analyses to identify lncRNAs in the spleen of piglets in response to C. perfringens type C infection in our previous study [36]. Nevertheless, we did not comprehensively explore how lncRNAs work during C. perfringens type C infection. Thus, these dysregulated lncRNAs and their target genes in regulating the resistance of piglets infected with C. perfringens type C were investigated in this paper. Tissue specificity is a feature of lncRNAs, unlike protein-coding genes [22]. Thus, LNC_001595 was selected to evaluate the expression levels in different tissues. Indeed, LNC_001595 was highly expressed in the lymph nodes and spleen, which displayed tissue specificity. Additionally, lincRNA showed a greater length, a higher expression level, a lower exon count and a shorter ORF length than those of antisense lncRNAs. These results are consistent with those of other studies [47,48]. Characterizing the structure of lncRNAs is necessary for understanding their mechanism at almost every level of gene function and regulation. The local single-chain structures, local secondary structure motifs and tertiary structure motifs of lncRNAs can interact with target genes to perform biological functions. At present, it is difficult to determine the tertiary structure of lncRNAs. The secondary structure of lncRNA target molecules is beneficial for investigating their function mechanism and exploring the relationship between the structure and function of lncRNAs. In this paper, a total of 10 lncRNAs had secondary structures, including a hairpin loop, a stem loop, an inner ring, a bulge ring and a multi-branch loop. Interestingly, the secondary structures of two lncRNAs (namely, ALDBSSCT0000009442 and LNC_000263) are consistent with two lncRNA structures in another study [37]. In order to identify the key lncRNAs against C. perfringens type C, we referenced two criteria by using a previous method [37]. Based on this method, a potential relationship among four lncRNAs, target genes and cytokines was identified. Four lncRNAs (namely, ALDBSSCT0000003048, ALDBSSCT0000007366, ALDBSSCT0000009442 and ALDBSSCT0000006918) targeted some immune genes (such as PIK3R4, ADGRG3 and GADD45G) via cytokines to exhibit the antagonistic effects of C. perfringens type C on the piglet spleen. A previous study found that the inflammation-related cytokine genes IL-1β, IFN-α, TNF-α and NF-κB in the blood had a higher over-expression after infection with C. perfringens type C [37]. In this study, cytokine gene expression levels and cytokine contents in the spleen were detected. Indeed, cytokine expression levels and concentrations were obviously increased in the spleen of the piglets challenged by C. perfringens type C. Similarly, the levels of TNF-α, IFN-γ, IFN-α, IL-6 and IL-8 were obviously increased in NE chickens caused by C. perfringens [15,34]. Moreover, the concentrations of these cytokines were significantly higher in those in the diarrhea group than in those in the healthy group. These results suggest that the DE lncRNAs in SR vs. SS can affect their target genes through immune-related KEGG pathways to regulate downstream cytokine genes in the piglet inflammatory and immune responses to resist C. perfringens type C. The higher-expressed cytokines were beneficial to the inflammation response in the piglets, which may be related to resistance to C. perfringens type C infection. PIK3R4, also named Vps15 or p150, is one of the immune genes targeted by ALDBSSCT0000003048, and it participates in the regulation of the autophagy pathway. PIK3R4 as a regulatory subunit plays an important role in autophagosome formation and maturation, which is activated and accelerated under the nutrient-limiting status to supply nutrients for cell survival [49,50]. Up-regulated PIK3R4 can enhance the process of autophagy. Additionally, PIK3R4 plays key roles in the immune system to resist disease via the PI3K/AKT pathway [51]. In our study, the expression level of ALDBSSCT0000003048 in the SR group was higher than that in the SS group. Correspondingly, the expression of level of PIK3R4 in the SR group was also higher than that in the SS group. Then, up-regulated ALDBSSCT0000003048 targeted PIK3R4 to stimulate autophagy and secrete massive amounts of TNF-α and IFN-γ against C. perfringens type C infection. CMPK2 and RSAD2 were regulated by ALDBSSCT0000007366. CMPK2 is closely related to monocytic/macrophage terminal differentiation and played important roles in resisting pathogenic microorganisms [52,53]. RSAD2, also called viperin, has been reported to be associated with the innate immune response system and is commonly up-regulated by LPS, type I interferons and microorganism infections [54,55]. In our study, down-regulated ALDBSSCT0000007366 in the SS and SR groups may have depressed the expressions of CMPK2 and RSAD2. CMPK2 via the Remdesivir pathway and RSAD2 via the Influenza A pathway produce IFN-α for resisting C. perfringens type C infection. ADGRG3 was regulated by ALDBSSCT0000009442. GPR97, encoded by ADGRG3, is expressed in immune cells (including neutrophils, mast cells and macrophagocytes), and it is involved in many diseases’ inflammation by regulating the activity of NF-κB and then stimulating cytokine production (such as IL-6) [56,57]. Additionally, WT mice have more severe inflammation caused by obesity than ADGRG3−/− mice [56]. In this study, the expression of ADGRG3 was significantly increased after the piglets were challenged by C. perfringens type C, and the expression of this gene in the SS group was higher than that in the SR group. Therefore, the higher-expressed ALDBSSCT0000009442 targeting ADGRG3 might have some association with the expression of IL-6, and the higher-expressed IL-6 reflected that the piglets in the SR group may have immense inflammatory responses that may be beneficial to resisting C. perfringens type C infection. GADD45G was regulated by ALDBSSCT0000006918. GADD45G plays an important role in the activation of p38 MAP kinase, which promotes IFN-γ cytokine production against pathogenic bacteria [58,59]. Indeed, the deletion of GADD45G genes in mice leads to an obviously reduced number of TH1 cells for resisting Listeria monocytogenes [60]. Compared with the SC group, the GADD45G expression level in the SS and SR groups was drastically increased after the piglets were infected with C. perfringens type C. GADD45G was significantly up-regulated in the SR group compared with that in the SS group. In our previous study, the expression level of ALDBSSCT0000006918 in the SS and SR groups was obviously increased [36]. This shows that up-regulated ALDBSSCT0000006918 targets GADD45G to secrete massive amounts of IFN-γ and IL-8 for resisting C. perfringens type C infection. The above results indicate the relationship between the expressions of lncRNAs and their target genes, suggesting that dysregulated lncRNAs affected their target genes, causing them to participate in the biological process. Thus, these results reflect the resistance of piglets during C. perfringens type C infection. ## 5. Conclusions In this study, the identification of lncRNAs in the spleen of piglets who were antagonistic or sensitive to diarrhea caused by C. perfringens type C was carried out. Our study indicates that DE lncRNAs regulated target genes to adjust the immune system of piglet spleen, which further influenced the difference in the piglet’s resistance to C. perfringens type C infection. This study provides new insights into comprehensively understanding the regulation of lncRNAs in piglet disease resistance. ## References 1. Songer J.G.. Vet. Microbiol. (2010) 140 399-404. DOI: 10.1016/j.vetmic.2009.07.003 2. Songer J.G., Uzal F.A.. J. Vet. Diagn. Investig. (2005) 17 528-536. DOI: 10.1177/104063870501700602 3. Miclard J., Jaggi M., Sutter E., Wyder M., Grabscheid B., Posthaus H.. Vet. Microbiol. (2009) 137 320-325. DOI: 10.1016/j.vetmic.2009.01.025 4. Petit L., Gibert M., Popoff M.R.. Trends Microbiol. (1999) 7 104-110. DOI: 10.1016/S0966-842X(98)01430-9 5. Uzal F.A., McClane B.A.. 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--- title: Healthcare Professionals’ Perceptions, Barriers, and Facilitators towards Adopting Computerised Clinical Decision Support Systems in Antimicrobial Stewardship in Jordanian Hospitals authors: - Fares Albahar - Rana K. Abu-Farha - Osama Y. Alshogran - Hamza Alhamad - Chris E. Curtis - John F. Marriott journal: Healthcare year: 2023 pmcid: PMC10047934 doi: 10.3390/healthcare11060836 license: CC BY 4.0 --- # Healthcare Professionals’ Perceptions, Barriers, and Facilitators towards Adopting Computerised Clinical Decision Support Systems in Antimicrobial Stewardship in Jordanian Hospitals ## Abstract Understanding healthcare professionals’ perceptions towards a computerised decision support system (CDSS) may provide a platform for the determinants of the successful adoption and implementation of CDSS. This cross-sectional study examined healthcare professionals’ perceptions, barriers, and facilitators to adopting a CDSS for antibiotic prescribing in Jordanian hospitals. This study was conducted among healthcare professionals in Jordan’s two tertiary and teaching hospitals over four weeks (June–July 2021). Data were collected in a paper-based format from senior and junior prescribers and non-prescribers ($$n = 254$$) who agreed to complete a questionnaire. The majority ($$n = 184$$, $72.4\%$) were aware that electronic prescribing and electronic health record systems could be used specifically to facilitate antibiotic use and prescribing. The essential facilitator made CDSS available in a portable format ($$n = 224$$, $88.2\%$). While insufficient training to use CDSS was the most significant barrier ($$n = 175$$, $68.9\%$). The female providers showed significantly lower awareness ($$p \leq 0.006$$), and the nurses showed significantly higher awareness ($$p \leq 0.041$$) about using electronic prescribing and electronic health record systems. This study examined healthcare professionals’ perceptions of adopting CDSS in antimicrobial stewardship (AMS) and shed light on the perceived barriers and facilitators to adopting CDSS in AMS, reducing antibiotic resistance, and improving patient safety. Furthermore, results would provide a framework for other hospital settings concerned with implementing CDSS in AMS and inform policy decision-makers to react by implementing the CDSS system in Jordan and globally. Future studies should concentrate on establishing policies and guidelines and a framework to examine the adoption of the CDSS for AMS. ## 1. Introduction The prevalence of multidrug resistance has increased alarmingly globally [1]. Moreover, the inappropriate use of antimicrobial agents has been correlated with antimicrobial resistance (AMR) [2,3]. The World Health Organisation (WHO) released a recent feasible toolkit concerning the use of antimicrobial stewardship (AMS) in healthcare settings in low- and middle-income countries to optimize the use of antibiotics and contain the problem of AMR [4]. The AMS can be referred to as a coordinated intervention designed to enhance the appropriate use of antibiotics by promoting the selection of optimal antimicrobial drug regimens, doses, duration of therapy, and routes of administration to achieve the best desired clinical outcomes with low toxicity and minimum antibiotic resistance [5]. In Jordan, the minister of health launched a four-year action plan aligned with the WHO global action plan to maintain the efficiency of existing antibiotics through AMS [6]. Increasing antibiotic resistance at an alarming rate is further fueled by the widespread misuse of antibiotics [7,8]. In Jordan, people store their unused medication, including antibiotics, for future use [9]. This would enhance the risk of self-medication with antibiotics [3,9,10]. Jordan has a high prevalence of self-medication with antibiotics, recently reported at $40\%$ [3,10]. Additionally, $87.8\%$ of all deaths in Jordan are estimated to be secondary to infectious diseases [3]. The national plan was developed considering the importance of healthcare information technology in combating AMR [6]. Many information technology systems have been developed to aid clinicians in decision-making [11,12]. One such system is the computerised clinical decision support system (CDSS). The CDSS provides real-time, evidence-based decision support at the point of care about the choice of antimicrobial agents in selected infections [12,13]. CDSS is mostly defined as a computer-based system intended to support clinical decision-making in everyday patient care by presenting to the healthcare worker an integrated summary of clinically relevant patient information [14]. The emergence of automated CDSS is facilitated by the introduction of electronic health records (EHRs) and computerized provider order entry systems (CPOE) [14]. However, the CDSS is less commonly used in clinical practice despite its effectiveness in reducing AMR [12,13,15]. The impact of CDSS has been evaluated in many different clinical settings [12,13,16,17,18]. Some studies showed positive impacts, such as improved patient care processes, health care costs, physician workflows, and guideline adherence [12,13,19,20]. In contrast, others showed a negative effect and failed to achieve their intended outcomes [18,21,22]. CDSS studies fail to report consideration of the non-expert, end-user workflow. They have a narrow focus, such as antimicrobial selection, and use proxy outcome measures. Negative perceptions among healthcare professionals towards CDSS could affect the acceptance of such systems. Understanding the perceptions and attitudes of healthcare professionals towards CDSS may provide a platform for the determinants of the successful adoption and implementation of CDSS. Therefore, it is interesting to study healthcare professionals’ perceptions of CDSS [21,23] and their role in CDSS adoption [24,25]. In addition, this study examines potential barriers and facilitators that hinder or enable CDSS use in clinical practice and AMS. Highlighting these factors would provide a framework for successful CDSS implementation and use. The success of the implementation and performance of CDSS depends on the technical characteristics of the software, the clinical aspects of the task at hand, and the physician’s expertise with the CDSS [14]. Next to these, a substantial human factor remains, and acceptance of the CDSS is essential. Many interruptive medication alerts are, e.g., simply ignored by the operator [13,14]. In addition, the problem of alert fatigue is a well-established downside of interruptive messaging in CDSS. However, different aspects of the successful implementation of CDSS devices have been explored, mostly in narrow contexts for well-defined and delineated clinical problems. Little evidence is available on which factors should be taken into account to maximize uptake by clinicians when incorporating CDSS into general EHRs/CPOEs [14,16]. Most EHRs/CPOEs available on the market today are designed from an administrative and informatics perspective. They rarely consider the specific requirements of clinical tasks. Most systems do not take into account local conditions and culture, and most offer general solutions to general problems rather than specific solutions to the actual problems the clinicians and their patients are facing. As a consequence, they produce unrealistic, inapt, or plainly unsuitable advice for the local setting [12,16]. Therefore, there is a huge gap between what healthcare workers have to put into the system to make it work, mainly administrative information, and what they get out in terms of improved care for their patients. After conducting a literature review on health professionals’ perceived facilitators and barriers to CDSS implementation, three main limitations of previous studies were identified. First, the studies primarily focused on technical and usability issues, while they tended to overlook the social, cultural, and contextual factors potentially influencing their implementation [26,27]. Moxey and colleagues [28] suggested that the variability in CDSS uptake may be attributable to the technical aspects of the technology itself. Second, most studies evaluated the perceptions of frontline clinicians but did not address the perceptions of different organizational roles (e.g., hospital administrators, chiefs, or non-physician staff) that are key to establishing the overall mission and vision of the healthcare institution in addition to shaping the expected behavior and standards of its personnel. For example, organizational leadership that supports technological innovation may encourage and reward the use of CDSSs to improve patient care. Third, the studies often addressed contexts in which CDSSs had already been introduced; these studies did not account for the perceived facilitators and barriers existing before CDSS introduction or for the evolution of perceptions throughout the technology’s various stages of uptake. This study aims to identify potential barriers and facilitators to the uptake of CDSS in AMS to curtail the inappropriate use of antibiotics and contain AMR. ## 2.1. Compliance with Ethical Standards Ethical approval to conduct this study was obtained from the ethics committees at Zarqa University ($\frac{3}{3}$/2018–2019), Jordan University Hospital ($\frac{80}{2019}$/23), and King Abdullah University Hospital ($\frac{49}{128}$/2019). In addition, an informed consent form was collected from all participants before participation in the study, ensuring voluntary participation and that the participants could withdraw at any stage with their answers treated confidentially. ## 2.2. Hospital Setting and Participants This prospective cross-sectional study was conducted in two tertiary teaching hospitals, the University of Jordan and King Abdullah University Hospital. The University of Jordan Hospital is a large (600-bed) academic centre located in Amman (Middle), Jordan, and King Abdullah University Hospital (750-bed) is another large academic centre located in Irbid (North), Jordan. Both King Abdullah University Hospital and the University of Jordan Hospital have infectious diseases departments that apply AMS principles, which curtail the use of broad-spectrum antimicrobials within the hospital. Two categories of healthcare professionals were invited to participate: medical and allied healthcare professionals. In addition, senior and junior doctors from different specialties were asked to participate in the questionnaire, as were other healthcare professionals, including pharmacists, microbiology experts, and infection control experts. Therefore, the sample included both prescribers and non-prescribers and was less discretional. ## 2.3. Questionnaire Development and Data Collection The complete questionnaire has been adopted from Zaidi et al. [ 29], as shown in Appendix A. A pool of ten items to measure barriers and nine items to measure facilitators were initially drafted into a questionnaire tool. Two clinical pharmacists and one infectious diseases consultant reviewed the drafted questionnaire items. These items were then reviewed by three research experts familiar with the study design. The research experts commented on the wording, clarity, and comprehensiveness of the questionnaire items, and whether each item was relevant to the study’s aims and objectives. The research experts’ feedback and comments were reviewed by the authors and used to refine the questionnaire. After completing the piloting process, the final questionnaire version was developed. The final version of the questionnaire included 38 items divided into four domains. The first domain collected participants’ demographic data such as age, gender, specialty, and experience in a specialist role. The second domain collected information about the perceptions of healthcare professionals toward AMS. The third domain collected information about the awareness of using CDSS and the perceived benefits. The last domain collected information on the perceived barriers and facilitators towards using CDSS. The questionnaire was piloted in the local region, especially with two clinical pharmacists and one infectious diseases consultant, in March 2021. Two clinical pharmacists distributed the questionnaire in a paper-based format for senior and junior prescribers and non-prescribers at the University of Jordan Hospital and King Abdullah University Hospital. Healthcare professionals were informed that participation would be voluntary, information collected would be anonymous, and the questionnaire would take 10–15 min to finish. The questionnaire delivery lasted four weeks, started on 15 June 2021, and was closed on 14 July 2021. A reminder was sent six weeks later. Before beginning the study, participants were given information outlining the purpose of the study and participant rights and consented to participate. Participants will be notified that their involvement is voluntary, can be withdrawn at any time, and that confidentiality is protected through anonymizing all collected data. ## 2.4. Statistical Analyses The statistical package for social science (SPSS®) version 29 (SPSS® Inc., Chicago, IL, USA) was used for data analysis. The mean ± SD and frequency (percentages) were used for continuous and categorical variables. The chi-square test was used to evaluate the difference between healthcare providers (clinical pharmacists, nurses, and physicians) in their perception of AMS. Univariate and multivariate logistic regression was employed to screen for factors affecting participants’ awareness of electronic prescribing and electronic health record systems in AMS. Variables that were significant on a single predictor level (p-value < 0.25) using univariate logistic regression analysis were contained in the logistic regression analysis model. In the logistic regression analysis, variables independently associated with awareness about the use of electronic prescribing and the electronic health record system in AMS were identified. Statistical significance was considered at p-value < 0.05. ## 3. Results A total of 254 healthcare providers agreed to participate in this study and completed the survey, with a response rate of $45.8\%$ (254 out of 550). The majority of participants ($$n = 193$$, $76.0\%$) were aged between 20 and 30 years old, and $59.1\%$ ($$n = 150$$) were female. About $61\%$ ($$n = 154$$, $60.6\%$) of the participants were physicians, while the remaining were clinical pharmacists ($$n = 60$$, $23.6\%$) and nurses ($$n = 40$$, $15.7\%$). Study Demographics are presented in Table 1. Participants were asked about their awareness of the use of electronic prescribing and electronic health record systems in general and in AMS (Table 2). The majority ($$n = 220$$, $86.6\%$) reported knowing about the presence of electronic prescribing and electronic health record systems at their hospitals. In addition, around three-quarters ($$n = 184$$, $72.4\%$) were aware that such systems could be used to facilitate antibiotic use prescribing. However, a lower percentage of the respondents ($$n = 161$$, $63.4\%$) were aware that those systems could provide a clinical decision-support function to support evidence-based practice. All participating healthcare providers responded to five statements to express their perception of AMS (Table 3). First, healthcare providers showed a positive perception of AMS, where $88.2\%$ of respondents ($$n = 224$$) agreed/strongly agreed that AMS programs might improve patient care. Similarly, $89.7\%$ of respondents ($$n = 228$$) believed those programs might reduce the problem of AMR. In addition, more than $90\%$ of respondents agreed/strongly that stewardship programs should be incorporated at a hospital level and that healthcare providers should be provided with adequate training on antimicrobial use. On the other hand, only $52.4\%$ of the respondents ($$n = 133$$) believed that the antimicrobial prescribing at their hospital is already as good as possible, given many initiatives related to AMS launched at their hospitals. When comparing clinical pharmacists, nurses, and physicians, nurses showed the worst perception towards AMS-related statements, except for the first statement, “Antimicrobial prescribing at your hospital is already as good as it can be,” where they scored the highest percentage of agreement (65.0 for nurses versus 57.1 for physicians and $31.7\%$ for clinical pharmacists). Participants were also asked about their perceived benefits of using electronic prescribing and electronic health record systems in AMS. Results showed that respondents believed that those systems might reduce the expenditure on antibiotics ($$n = 212$$, $83.4\%$), improve the safety of antibiotic use ($$n = 210$$, $82.7\%$), and may improve the ability to deliver AMS ($$n = 205$$, $80.8\%$). More details are included in Figure 1. Regarding the barriers against the use of electronic prescribing and electronic health record systems in AMS (Figure 2), results demonstrate that the most important barrier was the insufficient training to use the systems ($$n = 175$$, $68.9\%$), followed by the lack of access to reliable technical support ($$n = 173$$, $68.1\%$). The least important barrier was the limitation of medical autonomy ($$n = 111$$, $43.7\%$). On the other hand, Figure 3 illustrates the perceived facilitators of AMS electronic prescribing and electronic health record systems. Results showed that the most important facilitator was making the system available in a portable format such as a mobile device or personal digital assistant ($$n = 224$$, $88.2\%$), followed by linking radiology and laboratory results to the system ($$n = 220$$, $86.6\%$ for both). More details are included in Figure 3. Finally, logistic regression (Table 4) showed that female healthcare providers showed significantly lower awareness about using electronic prescribing and electronic health record systems in AMS compared to males ($$p \leq 0.006$$). Moreover, nurses showed significantly higher awareness about using those systems in AMS than clinical pharmacists and physicians ($$p \leq 0.041$$). ## 4. Discussion Healthcare providers should have adequate knowledge, awareness, acceptability, and understanding of AMS to implement the program successfully in their hospital settings. CDSSs are integral to implementing the AMS. Therefore, CDSSs will likely become an integral part of clinical practice to improve patient care and safety continually. Well-established clinical workflows and EHRs are important requisites to successfully introducing and using CDSSs in clinical settings. Users should also be provided with sufficient training, education, and support. In addition to developing technical suggestions on CDSS design and implementation, understanding perceived barriers and facilitators to CDSSs is important to maximize the technology’s usage and its potential to impact patient outcomes. This is a two-centre study (both large tertiary and teaching hospitals) from the middle and northern areas of Jordan in which 254 healthcare professionals provided their insights about their positive perceptions toward AMS, demonstrated by their agreement that its use would improve patient outcomes and curtail AMR. This study aimed to examine the potential barriers and facilitators that hinder or enable CDSS use in clinical practice and AMS based on the healthcare professionals’ views. Therefore, understanding perceived barriers and facilitators to CDSSs is vital to maximise the technology’s adoption and uptake and potentially impact patient outcomes. The study survey adopted was refined after considering various contexts, from two healthcare centres where the healthcare professionals would be unfamiliar with the technology to those in the mature stages of its implementation. Consequently, the results from this study are expected to help guide the development of strategies and recommendations essential to introducing and integrating CDSS into wider national healthcare settings, including the two hospital centres. This study showed that healthcare professionals had positive awareness and perceptions towards electronic prescribing and electronic health record systems, explained by their understanding that AMS use would improve patient outcomes and limit AMR. Moreover, healthcare professionals perceived that electronic prescribing is beneficial and would reduce the high cost of prescribing antibiotics (i.e., reduce the expenditure of antibiotics), improve the efficacy and safety of antibiotic use, and may improve the ability to deliver AMS to optimise the rational use of antibiotics. In addition, many systematic reviews and studies demonstrated the impact of adopting the CDSS on antibiotics management and AMS [18,19,30,31,32,33,34,35,36,37]. The lack of appropriate training to use electronic prescribing and electronic health record systems and the lack of access to reliable technical support were the most perceived barriers to CDSS adoption among healthcare professionals. In addition, more than half of the healthcare professionals had a low level of awareness and were unfamiliar with using electronic prescribing and electronic health record systems. Furthermore, given that healthcare professionals have busy work schedules, they do not have enough time to learn how to use the system. Therefore, adequate training should be encouraged for novice and experienced healthcare professionals to use the CDSS system and improve workflow effectively. Technical support should be provided to the healthcare professionals in each hospital ward to avoid difficulties in using the systems and maximise the effective use of CDSS. Previous studies reported similar results [29,37,38,39,40,41]. For example, one recent cross-sectional study from Australia that evaluated the impact of CDSS adoption on antibiotics management reported that the lack of appropriate training and technical support was an essential barrier to CDSS adoption [37]. In addition, the significance of training and technical support for CDSS adoption was evident in previous studies [29,42,43,44]. Making the system in an easily portable format (i.e., such as a mobile device or a personal digital assistant) was the most perceived facilitator for adopting CDSS by healthcare professionals. This is expected to enable healthcare professionals to make a decision regarding prescribing and monitoring antibiotics from remote areas without the need to be in the ward or hospital to prescribe and monitor antibiotics, thus making work more flexible. Moreover, lab and radiology results linked to the CDSS are facilitators for adopting CDSS. As a result, healthcare professionals will not be required to check different databases to confirm the diagnosis and adjust treatment based on laboratory results. This will make the work schedule more flexible and efficient. Similar studies reported the same results [29,39,41]. For example, one cross-sectional study conducted in a tertiary care university hospital in Melbourne, Australia, reported making the CDSS in an easily portable format and linking lab and radiology results as a facilitator to the adoption of the CDSS [29]. This was also evident in a recent systematic review [41] and a previous study [39]. The logistic regression results about the factors affecting participants’ awareness about the use of electronic prescribing and electronic health record systems in antimicrobial stewardship showed that female healthcare providers had significantly lower awareness about using electronic prescribing and electronic health record systems. In contrast, the nurses showed significantly higher awareness about using those systems in stewardship programs. Gender as a factor affecting participants’ awareness about the use of electronic prescribing and electronic health record systems was studied in the literature [45,46,47,48], with some studies showing no difference [47] and others showing higher awareness among females [46,48], which contrasts with the results from our study. However, our results were consistent with a recent World Health Organisation report, which reported that females are generally less likely to be involved in such activities and skills in developing countries [49]. Males and females are supposed to have equal technological exposure, knowledge, and awareness; however, this will be further explored in future studies. The response rate was less than optimal, similar to other response rates in the literature [37,50]; this may affect the representativeness of the data obtained and may be a limitation of this study. Moreover, the majority of healthcare providers range in age from 20–30 years old, with only seven above 40 years, indicating some bias and a lack of generalizability, as those in the younger category will prefer electronic use; however, the decision-makers are usually older. This is considered another limitation of this study. Using the CDSS was optional to minimise the likelihood that healthcare professionals would be influenced by hospital policy to use the CDSS system. However, this would affect their responses, especially for those who do not routinely use the electronic prescribing system, and thus impact the study findings; this would be another limitation of this study. Nevertheless, it is vital to note that the study attracted healthcare professionals with various degrees of system usage. Therefore, the sample seems adequate to address the study’s aims. This study has several strengths. First, the sample size of 254 healthcare providers (despite a low response rate = $46\%$) from two large tertiary teaching hospitals is considered satisfactory and would add to the representativeness of the results drawn from this study. Second, the CDSS system was designed by developers independent of the end-user healthcare professionals. So, the healthcare professionals were not involved in designing and implementing the CDSS. This is expected to reduce the potential for investigator bias. While the CDSS system is increasingly gaining popularity in implementing hospital guidelines for prescribing antibiotics, significant barriers to its adoption exist. To implement CDSS systems successfully, the developer needs to understand the barriers to their adoption. The present study measures healthcare professionals’ perceptions of using the CDSS system in two tertiary care settings in Jordan’s middle and northern areas. Both the study setting and the study participants represent a metropolitan area. Therefore, the findings from the study could apply to other healthcare settings interested in the launch and implementation of CDSS systems. While the study investigators are independent of the developer and implementer of the CDSS system at the study hospitals, the present study results have been available to them to improve the implementation and deployment strategies. ## 5. Conclusions This study examined healthcare professionals’ perceptions towards adopting CDSS for antibiotic prescribing in Jordan’s two tertiary and teaching hospitals. Findings from this study would help get more insight into the perceived barriers and facilitators to adopting and implementing CDSS, which would help reduce antibiotic resistance and improve patient safety. Moreover, results would help provide a platform for other healthcare settings interested in implementing CDSS in AMS and inform policy decision-makers to react by implementing the CDSS system in Jordan and globally. Future studies should focus on establishing guidelines and a policy framework to examine the adoption of the CDSS for AMS. ## Appendix A.2. Section B. Perception of Participants towards Antimicrobial Stewardship (AMS) Table A1 \| Strongly Disagree–Strongly Agree (1–5) \| Strongly Disagree–Strongly Agree (1–5).1 \| Strongly Disagree–Strongly Agree (1–5).2 \| Strongly Disagree–Strongly Agree (1–5).3 \| Strongly Disagree–Strongly Agree (1–5).4 \| Strongly Disagree–Strongly Agree (1–5).5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Perceptions towards Antimicrobial Stewardship \| Strongly Disagree \| Disagree \| Neither Disagree or Agree \| Agree \| Strongly Agree \| \| Check your level of agreement with the following sentences: \| Check your level of agreement with the following sentences: \| Check your level of agreement with the following sentences: \| Check your level of agreement with the following sentences: \| Check your level of agreement with the following sentences: \| Check your level of agreement with the following sentences: \| \| Antimicrobial prescribing at your hospital is already as good as it can be \| 1 \| 2 \| 3 \| 4 \| 5 \| \| Antimicrobial stewardship programs may improve patient care. \| 1 \| 2 \| 3 \| 4 \| 5 \| \| Antimicrobial stewardship should be incorporated at a hospital level \| 1 \| 2 \| 3 \| 4 \| 5 \| \| Antimicrobial stewardship programs may reduce the problem of antimicrobial resistance. \| 1 \| 2 \| 3 \| 4 \| 5 \| \| Adequate training should be provided to health care professionals on antimicrobial use \| 1 \| 2 \| 3 \| 4 \| 5 \| ## Appendix A.3. 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--- title: Differential Effects of ABCG5/G8 Gene Region Variants on Lipid Profile, Blood Pressure Status, and Gallstone Disease History in Taiwan authors: - Ming-Sheng Teng - Kuan-Hung Yeh - Lung-An Hsu - Hsin-Hua Chou - Leay-Kiaw Er - Semon Wu - Yu-Lin Ko journal: Genes year: 2023 pmcid: PMC10047937 doi: 10.3390/genes14030754 license: CC BY 4.0 --- # Differential Effects of ABCG5/G8 Gene Region Variants on Lipid Profile, Blood Pressure Status, and Gallstone Disease History in Taiwan ## Abstract ABCG5 and ABCG8 are two key adenosine triphosphate-binding cassette (ABC) proteins that regulate whole-body sterol trafficking. This study aimed to elucidate the association between ABCG5/G8 gene region variants and lipid profile, cardiometabolic traits, and gallstone disease history in Taiwan. A total of 1494 Taiwan Biobank participants with whole-genome sequencing data and 117,679 participants with Axiom Genome-Wide CHB Array data were enrolled for analysis. Using genotype–phenotype and stepwise linear regression analyses, we found independent associations of four Asian-specific ABCG5 variants, rs119480069, rs199984328, rs560839317, and rs748096191, with total, low-density lipoprotein (LDL), and non-high-density lipoprotein (HDL) cholesterol levels (all p ≤ 0.0002). Four other variants, which were in nearly complete linkage disequilibrium, exhibited genome-wide significant associations with gallstone disease history, and the ABCG8 rs11887534 variant showed a trend of superiority for gallstone disease history in a nested logistic regression model ($$p \leq 0.074$$). Through regional association analysis of various other cardiometabolic traits, two variants of the PLEKHH2, approximately 50 kb from the ABCG5/G8 region, exhibited significant associations with blood pressure status ($p \leq 10$−6). In conclusion, differential effects of ABCG5/G8 region variants were noted for lipid profile, blood pressure status, and gallstone disease history in Taiwan. These results indicate the crucial role of individualized assessment of ABCG5/G8 variants for different cardiometabolic phenotypes. ## 1. Introduction The adenosine triphosphate (ATP)-binding cassette (ABC) family contains more than 40 ABC transporters in seven subfamilies (ABCA to ABCG) and is one of the largest transporter families. This family couples ATP binding, hydrolysis, and phosphate release to accomplish translocation of diverse substrates across membranes [1,2]. With their involvement in endothelial dysfunction, cholesterol homeostasis, regulation of blood pressure, vascular inflammation, and platelet aggregation, ABC transporters are crucial in the pathogenesis of atherosclerotic vascular diseases [3,4]. ABCG5 and ABCG8 are two functional ABC proteins that mediate the efflux of xenosterols from hepatocytes and enterocytes and prevent xenosterol from accumulating in the body [3,5]. ABCG5 and ABCG8 are half-transporter heterodimers that affect bile cholesterol excretion and intestinal cholesterol absorption rates [6]. The ABCG5 and ABCG8 genes (ABCG5/G8) are highly expressed in the livers and small intestines of both humans and mice [7,8,9,10]. In humans, mutations of ABCG5/G8 may cause autosomal recessive sitosterolemia [11,12,13]. In animals, as determined through quantitative trait locus linkage analysis, Abcg5/g8 has been identified as the mouse gallstone gene, Lith9 [14,15,16,17,18]. In mice, low biliary cholesterol concentrations may develop through the disruption of ABCG5, ABCG8, or both [19,20,21]. Conversely, a more than fivefold increase in biliary cholesterol levels can be caused by ABCG5 and ABCG8 overexpression [22]. In analyses of human and animal models, ABCG5/G8 have also been shown to affect various cardiometabolic traits and disorders, such as lipid and glucose metabolism, blood pressure control, metabolic syndrome, and fatty liver disease [23,24,25,26,27,28,29,30]. ABCG5 and ABCG8 (ABCG5/G8) are coregulated at the transcription level through their sharing of a common bidirectional promoter and their location next to each other on chromosome 2p21 [12]. Exome sequence analysis of 60,706 individuals of diverse ancestries revealed 33 and 36 exome sequences predicted loss-of-function variants for ABCG5 and ABCG8, respectively (https://gnomad.broadinstitute.org/ v3.1.2, accessed on 18 October 2022) [31]. By contrast, to date, 769 and 978 missense variants have been catalogued in the database of SNP according to the PUBMed website (PUBMed.gov) for ABCG5 and ABCG8, respectively. Most of the missense variants are predicted to be benign, whereas the majority of dysfunctional alleles in selected likely pathogenic ABCG5/G8 missense mutants are dysfunctional due to their inability to heterodimerize ABCG5 and ABCG8 and traffic beyond the endoplasmic reticulum [32]. Although familial sitosterolemia is a rare Mendelian recessive disorder, with the affected individuals typically having homozygous loss-of-function variants in the ABCG5/G8 genes, heterozygous ABCG5 gene deficiency has been shown to be associated with increased sitosterol and LDL cholesterol levels and increased risk of coronary artery disease [33]. Elevation of sitosterol serum concentrations due to ABCG5/G8 mutations also showed risk-increasing causal relationships with a detrimental effect on coronary atherosclerosis [34]. These results suggested the critical role of elucidating novel ABCG5/G8 mutations in preventive medicine. Ethnic genetic heterogeneity for ABCG5/G8 variants has been widely reported [11,12,33,34,35,36,37], and the role and differential effects of ABCG5/G8 variants on lipid profile, cardiometabolic traits, and gallstone disease history in Asian populations have not been fully elucidated. The evolution of geographically dispersed populations is affected by factors such as the founder effect and evolutionary selection, resulting in genetic drift and ethnic heterogeneity in genetic architectures [38]. The Taiwan Biobank (TWB) is a population-based cohort study sponsored by the Taiwanese government and has enrolled more than 150,000 individuals aged between 30 and 70 years without a history of cancer [39,40]. By combining both regional association analysis and candidate variant approaches, we have previously shown the crucial role of ethnicity-specific variants on genetic determinants of lipid profiles [41,42,43]. In this study, we investigated the associations of ABCG5/G8 variants with lipid profile, cardiometabolic traits, and gallstone disease history in Taiwanese individuals who were participants in the TWB. ## 2.1. TWB Population-Based Cohort Study The cohort of TWB participants for the current study was composed of 129,542 participants who had Axiom Genome-Wide CHB Array data and were recruited in centers across Taiwan between 2008 and 2020. In total, 11,863 participants were excluded according to the following criteria: quality control (QC) for the array data with identity by descent score >0.187 to remove cryptic relatedness [7216] and fasting for <6 h [4647]. We also performed ultrafast whole-genome secondary analysis of 1478 TWB participants who had whole-genome sequencing (WGS) data, using the Illumina sequencing platform to search for candidate variants within the coding and promoter regions of ABCG5/G8 [44]. The flowchart of participant recruitment is shown in Figure 1. Participants with a history of hyperlipidemia [8799] were excluded from the analyses of lipid profile. In a questionnaire, participants were asked whether they or either of their parents had ever received a clinical diagnosis of gallstone disease. Ethical approval was granted by the institutional review boards of Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation (approval number: 08-XD-005) and the Ethics and Governance Council of the TWB (approval number: TWBR11011-02). All participants provided written informed consent. ## 2.2. Clinical and Laboratory Examinations Data on baseline characteristics, including age, sex, body mass index (BMI), and smoking status, were collected. Total, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol levels and triglyceride levels were measured using colorimetric assays (Hitachi LST008, Automatic Clinical Chemistry Analyzer, Hitachi, Naka, Japan). Non-HDL and remnant cholesterol levels were calculated by subtracting HDL from total cholesterol levels and by subtracting HDL and LDL from total cholesterol levels, respectively [45]. The definition of metabolic syndrome is shown in the Supplementary Materials, and other metabolic traits are shown in Supplementary Table S7 and as previously reported [41,42]. ## 2.3. Regional Association Analysis The genotyping in regional association analysis was performed using the Axiom Genome-Wide CHB Array data, and the data were analyzed after the exclusion criteria were applied (Figure 1). Imputation of the GWAS data was performed using East Asian populations of the 1000 Genomes Project Phase 3 as a reference panel and conducted using SHAPEIT (version 2, Oxford, UK, https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html, accessed on 2 December 2020) and IMPUTE2 (version 2, Oxford, UK, http://mathgen.stats.ox.ac.uk/impute/impute_v2.html, accessed on 2 December 2020). The imputed results were validated through comparison with the WGS data from 137 independent samples [46]. After imputation, SNPs were filtered for QC with IMPUTE2 imputation quality scores of >0.3, and insertion and deletion mutations were removed using VCFtools (version 0.1, https://vcftools.github.io/index.html, accessed on 2 December 2020). For subsequent analyses, all samples were enrolled if the SNP missing call rate was <$3\%$, the minor allele frequency was >0.01, and the Hardy–Weinberg equilibrium showed $p \leq 10$−6. A total of 117,679 participants were finally included for regional association analysis with 311 SNPs within the ABCG5/G8 region ranging at positions between 43.93 and 44.21 Mb. ## 2.4. Statistical Analysis We used Kolmogorov–Smirnov test to test normality of continuous variables in SPSS. Because all the variables we analyzed are skewed, we presented continuous variables as median and interquartile range. Categorical data are presented as percentage and number. In genotype–phenotype association studies, after adjustment for age, sex, BMI, and smoking status, a general linear regression model was used to evaluate the genetic effect of ABCG5/G8 region variants on studied phenotypes. A logistic regression model was used to evaluate the effects of ABCG5/G8 region variants on the risk of categorical phenotypes [expressed as odds ratios (ORs) with $95\%$ confidence intervals ($95\%$ CIs)]. Nested logistic regression was used to cluster ABCG5/G8 region variants to assess their correlations [47]. A stepwise multiple linear regression model was used to evaluate the influence of ABCG5/G8 region variants on lipid profile. The calculations above were conducted using SPSS (version 22; SPSS, Chicago, IL, USA). Genome-wide significance association was defined as a significance level of $p \leq 5$ × 10−8. For Bonferroni correction, according to a total of 311 variants and 31 traits analyzed in regional associational analysis, the significance was indicated by $p \leq 5.0$ × 10−6, calculated as 0.05/(311 × 31); whereas, according to a total of 12 rare variants and 8 traits analyzed in genotype–phenotype association analysis, a more liberal threshold of $p \leq 5.0$ × 10−4, calculated as $\frac{0.05}{12}$ × 8 with rare mutations, was used. The PLINK software package (version 1.07, Shaun Purcell, Cambridge, MA, USA, https://zzz.bwh.harvard.edu/plink/, accessed on 14 August 2021) was used for the regional association analysis and subsequent conditional analysis. LDmatrix (https://analysistools.nci.nih.gov/LDlink/?tab=ldmatrix, assessed on 19 April 2021) was used to calculate linkage disequilibrium (LD). ## 3.1. Baseline Characteristics of TWB Participants According to Gallstone Disease History Baseline characteristics, lipid profile, and family history of gallstone disease of 117,679 participants with whole-genome genotyping array data with and without a history of gallstone disease are summarized in Table 1. In logistic regression analysis, It was found that participants with a history of gallstone disease were older and more likely to be male and have a family history of gallstone disease, higher BMI, waist circumference, waist hip ratio and total, LDL, and non-HDL cholesterol levels and lower HDL cholesterol level. ## 3.2. Selection of Candidate ABCG5/G8 Region Variants To investigate the associations of ABCG5/G8 variants with cardiometabolic traits and the risks of metabolic syndrome and gallstone disease, we first selected candidate variants within the coding and promoter region of ABCG5/G8 among 1478 TWB participants with WGS data. A total of 50 exonic, upstream, and 5′UTR variants were selected, among which 22 were Asian-specific, 30 were nonsynonymous, 12 were synonymous, 3 were nonsense, and 5 were located between ABCG5 and ABCG8 and at the promoter region of ABCG5 and ABCG8 (Supplementary Table S1). Among these selected variants, twelve of them were available on the Axiom Genome-Wide CHB Array and were enrolled for genotype–phenotype association analysis. Eight were nonsynonymous mutations: rs6756629 (p.R50C), rs748096191 (p.E146D), rs148186696 (p.R253H), rs119480069 (p.R389H), rs536081800 (p.R446Q), and rs199984328 (p.H510N) from ABCG5 and rs11887534 (p.D19H) and rs750352877 (p.N160S) from ABCG8. Two were synonymous mutations: ABCG5 rs767751451 (p.A181A) and ABCG8 rs56132765 (p.V151V). Two were located between ABCG5 and ABCG8 and at the promoter region of ABCG5: rs560839317 and rs189132480. The locations and characteristics of the 12 variants are shown in Supplementary Tables S1 and S2. ## 3.3. Genotype–Phenotype Association Analysis of ABCG5/G8 Variants with Lipid Profile and Gallstone Disease History We further investigated the associations of the ABCG5/G8 variants with the lipid profile and gallstone disease history of participants with GWAS Array data (Table 2 and Supplementary Table S3). By linear regression analysis, genome-wide significant associations were noted between rs199984328, rs119480069, and rs560839317 genotypes and total, LDL, and non-HDL cholesterol levels. The rs748096191 genotype also showed significant association with LDL, and non-HDL cholesterol levels and borderline significant association with total cholesterol level after Bonferroni correction ($$p \leq 2.00$$ × 10−4, $$p \leq 2.68$$ × 10−4, and $$p \leq 8.20$$ × 10−4, respectively). By logistic regression analysis, significant associations were also noted between rs6756629, rs11887534, and rs56132765 genotypes and the risk of gallstone disease ($$p \leq 4.18$$ × 10−7, $$p \leq 2.08$$ × 10−7, $$p \leq 6.08$$ × 10−7, respectively) and a trend of association with family history of gallstone disease ($$p \leq 0.0056$$, $$p \leq 0.0061$$, $$p \leq 0.0161$$, respectively). ## 3.4. Regional Association Analysis for the Associations of ABCG5 Region Variants with Lipid Profile and Gallstone Disease History Regional association analyses in participants with GWAS Array data were performed to determine the peak association of genetic variants around the ABCG5/G8 region with lipid profile and gallstone disease history. Our data revealed that the lead SNPs with genome-wide significance were rs75832441 for total, LDL, and non-HDL cholesterol levels and rs115445558 for history of gallstone disease (Figure 2). ## 3.5. Linkage Disequilibrium (LD) between the Selected ABCG5 and ABCG8 Variants and Lead SNPs Because both the selected ABCG5/G8 variants and the lead SNPs were associated to various degrees with lipid profile and gallstone disease history, we further tested the LD between all these variants (Figure 3). In population genetics, LD is the non-random association of alleles at different loci in a given population. Our data revealed nearly complete LD (r2 between 0.9503 and 0.9974) between four variants (rs6756629, rs11887534, rs56132765, and rs115445558) that are significantly associated with history of gallstone disease. Moderate LD was noted between rs75832441 and rs199984328, and both were in moderate-to-weak LD with the four variants associated with the risk of gallstone disease (r2 between 0.1578 and 0.2344). The LDs of all other variant associations were weak (r2 ≤ 0.0001). ## 3.6. Stepwise Linear Regression Analysis for Lipid Profile Because a total of five SNPs were found to be significantly associated with total, LDL, and non-HDL cholesterol levels, we tested the multicollinearity between the variables in the stepwise multiple linear regression model. Since the ABCG5 rs75832441 had the lowest tolerance (0.319, that is <0.4) for all three phenotypes analyzed (Supplementary Table S4), we removed ABCG5 rs75832441 from the model (Supplementary Table S4). Further, principal component analysis was performed according to the method previously reported [46]. Stepwise linear regression analysis for lipid profile with age, sex, BMI, smoking status, and four ABCG5/G8 variants showed independent associations between rs119480069, rs199984328, rs560839317, and rs748096191 and total, LDL, and non-HDL cholesterol levels, which contributed to $0.07\%$, $0.03\%$, $0.02\%$ and $0.01\%$, respectively for total and non-HDL cholesterol levels and $0.07\%$, $0.04\%$, $0.02\%$ and $0.01\%$, respectively for LDL cholesterol levels. Together, these four variants contributed to $0.13\%$, $0.14\%$, and $0.13\%$ for total, LDL, and non-HDL cholesterol levels, respectively (Table 3). ## 3.7. Nested Logistic Regression and Subgroup Analysis for History of Gallstone Disease Among four ABCG5/G8 variants associated with gallstone disease history, only two were nonsynonymous [ABCG5 R50C (rs6756629) and ABCG8 D19H (rs11887534)]. Using nested logistic regression analysis by including ABCG5 R50C as a mandatory explanatory variable to statistically evaluate the causative role of individual variants in the disease association region, we found that the ABCG8 D19H variant showed a trend of significant improvement for model fit ($$p \leq 0.074$$) (Table 4). This result is consistent with that reported by von Kampen, et al. [ 48], who suggested that the ABCG8 D19H variant is the major causative variant in the region. Due to previous reports of a stronger association between the ABCG8 D19H variant and gallstone disease history in female patients and younger individuals [36,49], we further tested the associations in different age and sex subgroups with interaction analysis. Our data showed no evidence of interaction between the age subgroups and sex categories on the associations (Supplementary Table S5). ## 3.8. Regional Association Analysis and Genotype–Phenotype Analysis for the Association between ABCG5/G8 Region Variants and Cardiometabolic Traits Several human and animal studies have linked ABCG5/G8 variants and expression with cardiometabolic traits, such as insulin sensitivity, glycemic control, blood pressure status, and fatty liver disease [23,24,25,26,27,28,29,30]. We tested whether ABCG5/G8 variants in TWB participants were also associated with various cardiometabolic traits and metabolic syndrome (Supplementary Table S6). Our data revealed that, with the exception of total, LDL, and non-HDL cholesterol levels, none of the other study phenotypes reached a genome-wide significant association under either the regional association analysis or candidate genotype–phenotype association analysis (Supplementary Figure S1, Supplementary Tables S8–S14). However, we did identify several SNPs in the intron region of pleckstrin homology, MyTH4, and FERM domain containing the H2 (PLEKHH2) gene that showed significant associations ($p \leq 5$ × 10−7) with mean and diastolic blood pressure; the lead SNPs were rs7596913 and rs2060173, respectively. ## 4. Discussion This study investigated the associations of ABCG5/G8 variants with lipid profile, various cardiometabolic traits, and gallstone disease history in a Taiwanese cohort. Our data revealed that four Asian-specific, low-frequency or rare ABCG5 variants, namely rs119480069, rs199984328, rs748096191, and rs560839317, were independently associated with total, LDL, and non-HDL cholesterol levels. To the best of our knowledge, rs119480069 (p.R389H) is the only variant to have previously been reported to be associated with LDL cholesterol levels [12,33,50,51]; the associations of the other three variants with LDL cholesterol levels are novel discoveries. In addition, all four studied ABCG5/G8 variants that showed a significant association with gallstone disease history were in nearly complete LD with each other, and the most likely causative variant for the development of gallstone disease is ABCG8 D19H (rs11887534), as was previously reported [48]. With the exception of the associations with mean and diastolic blood pressure of two variants at the intron region of PLEKHH2, a gene located very close to ABCG5/G8, associations with other metabolic traits were not found for the ABCG5/G8 variants in our investigation. Further, in contrast to several studies of European populations [34,35,37], we found that the ABCG8 D19H variant was not associated with lipid profile in our study population. Our data indicated differential associations of ABCG5/G8 variants with lipid profile and gallstone disease history. These results also revealed the crucial role of individualized assessment of ABCG5/G8 variants for different phenotypes in populations of different ethnicities. ## 4.1. ABCG5/G8 Variants, Sitosterolemia, and Hypercholesterolemia Although ABCG5/G8 variants may increase total and LDL cholesterol levels, they are generally not considered to be the typical defective genes for familial hypercholesterolemia. Rather, ABCG5/G8 variants can act as a component of an LDL cholesterol genetic risk score [52] and are considered LDL-cholesterol-altering accessary genes that mimic and worsen phenotypes of familial hypercholesterolemia [11,53,54,55]. Our study showed that ABCG5 rs119480069, rs199984328, rs560839317, and rs748096191 were independently and positively associated with total, LDL, and non-HDL cholesterol levels and together contributed to $0.13\%$ to $0.14\%$ for total, LDL, and non-HDL cholesterol levels. All four variants are low frequency or rare in occurrence and are Asian-specific, and the increased LDL cholesterol levels from heterozygous to homozygous variants in rs119480069 and rs199984328 suggest a codominant inheritance model of these two variants. Williams, et al. [ 17] classified experimentally verified sitosterol variants into six classes; the R389H (rs119480069) variant was classified as a class II variant affecting maturation of ABCG5/G8 heterodimers. The mechanism underlying the association between the other variants and cholesterol levels is still unknown; however, Graf, et al. [ 32] analyzed 13 sitosterolemia-causing ABCG5/G8 mutations and found that all the mutations reduced G5/G8 heterodimer trafficking from the endoplasmic reticulum to the Golgi apparatus and that 10 of them prevented stable heterodimer formation between G5 and G8. Thus, further study is necessary to elucidate whether disruption of ABCG5 and ABCG8 heterodimerization or ABCG5 trafficking to the cell surface is the molecular basis for the associations. Serum sitosterol levels have been associated with atherosclerotic cardiovascular disease; however, whether the associations are due to sitosterol levels or are secondary to total cholesterol levels remains controversial [33,34,56]. By analyzing nine sitosterolemia families, Nomura, et al. [ 33] observed that heterozygous carriers of a loss-of-function variant in ABCG5, but not in ABCG8, significantly increased LDL cholesterol and sitosterol levels and increased the risk of CAD twofold. Hypercholesterolemia in individuals with the ABCG5/G8 mutations have also been shown to respond to ezetimibe treatment effectively [57]. Thus, the elucidation of functional ABCG5/G8 mutations is important in determining target drug therapy. Further prospective studies with measurement of serum sitosterol levels of the TWB participants may help to further elucidate the role of the atherogenic effects of sitosterols. ## 4.2. ABCG5/G8 Variants That Increase the Risk of Gallstone Disease Cholesterol gallstone disease, which is secondary to bile supersaturated with cholesterol, is one of the most common digestive diseases in industrialized countries [58]. Gallstone is a disease influenced by genetic factors [59]. A higher prevalence of gallstone disease in identical twins and first-degree relatives in individuals with gallstone disease highlights the importance of searching for genes involved in biliary cholesterol secretion that are critical to gallstone formation, such as ABCG5/G8 [60,61]. Previous studies have shown that the ABCG8 D19H (rs11887534) variant is associated with gallstone disease history, cancer derived from biliary tract, lipid profile, and cardiovascular diseases [34,35,37,48,49,61,62,63,64]. The association may be affected by age and sex, being more prominent in individuals who are young and female and especially in those undergoing hormone treatment [36,49]. Krawczyk, et al. [ 65] further revealed that the ABCG8 D19H variant increases the risk of early-onset gallstone formation in children. Meta-analysis of the association of various ABCG5/G8 variants and gallstone disease showed a strong association of D19H polymorphism with gallstone disease. T400K and Y54C polymorphisms may also be associated with gallstone disease, though to a lesser extent [66]. Meta-analysis of GWAS that involved 8720 cases and 55,152 controls also showed four susceptible regions for gallstone disease, including ABCG8, TM4SF4, SULT2A1, and CYP7A1; the candidate variants for ABCG8 were rs11887534 and rs4245791 [63]. In contrast to the ABCG5 R50C (rs6756629) variant, the ABCG8 D19H variant was shown to be associated with increased transport activity and decreased cholesterol absorption, which may increase the risk of gallstone disease [48]. As in our results, nested logistic regression analysis supported the superiority of the ABCG8 D19H variant as a causative variant, as reported by von Kampen, et al. [ 48]. However, the ABCG8 D19H variant is not responsible for cholesterol synthesis and ileal expression of ABCG5, ABCG8, and NPCIL1 [67]. The ABCG5 Q604E (rs6720173) genotypes have been associated with the risk of gallstone and gallbladder disease [36,68]; however, our data from the TWB participants showed no evidence of such an association (Supplementary Table S9). Furthermore, our data did not find interactions between age and sex on the association between the ABCG8 D19H variant and the risk of gallstone disease. ## 4.3. ABCG5/G8 Variants and Metabolic Traits In this study, we found lead SNPs of suggestive genome-wide significance for mean and diastolic blood pressure at the PLEKHH2 intron region near the ABCG5/G8 region. However, none of the ABCG$\frac{5}{8}$ nonsynonymous mutations showed such strong associations with the blood pressure status. Plekhh2, encoded by the PLEKHH2 gene, is an intracellular protein highly enriched in renal glomerular podocytes. Direct interactions between the FERM domain of the plekhh2 and the focal adhesion protein Hic-5 and actin stabilize the cortical actin cytoskeleton by attenuating actin depolymerization, and are involved in the podocyte foot processes [69]. A high expression of PLEKHH2 also significantly increased the expression of proliferation- and invasion-related proteins and promoted cell proliferation, migration, and invasion [70]. PLEKHH2 variants have been associated with diabetes nephropathy, coronary artery disease and venous thromboembolism, and have interacted with antihypertensive drugs for new-onset diabetes [71,72,73,74]. Further fine mapping may help to elucidate the causative gene/variant of the association. Previous genetic association studies have shown associations between ABCG5/G8 polymorphisms and triglyceride, HDL, and VLDL cholesterol levels, insulin sensitivity, and metabolic syndrome [24,25,26]. Animal studies also showed that mice with ABCG5/G8 deficiencies may cause hypertriglyceridemia via multiple metabolic pathways [27] and that sterol transportation via ABCG5 and ABCG8 opposes the development of fatty liver disease and loss of glycemic control independently of phytosterol accumulation [28]. Acceleration of ABCG5/G8-mediated biliary cholesterol secretion showed restoration of glycemic control and alleviation of hypertriglyceridemia in obese db/db mice [29]. These results suggest that ABCG5/G8 may be involved in the regulation of cardiometabolic traits and metabolic disorders. However, our data revealed that none of the other study phenotypes reached genome-wide significant association under either regional association analysis or candidate genotype–phenotype association analysis. In the future, further larger genetic association studies may be necessary to elucidate whether ABCG5/G8 variants affect cardiometabolic traits in addition to the blood pressure status and total, LDL, and non-HDL cholesterol levels. ## 4.4. Ethnic Heterogeneity on Differential Associations for the Pleiotropic Effects of ABCG5/G8 Variants Ethnic heterogeneity on differential associations for the pleiotropic effects of ABCG5/G8 variants has previously been reported. In an analysis of the genetic causes of sitosterolemia in 33 families from different ethnic populations, all six Japanese probands appeared to have mutations in ABCG5 only [12]. Nomura, et al. [ 2020] [33] also revealed that seven of the nine Japanese sitosterolemia families have mutations on ABCG5. These results are similar to those reported in Chinese patients with sitosterolemia [51] and in 750 index familial hypercholesterolemia patients in a Taiwanese cohort [50]. By contrast, mutations in ABCG8 were more commonly encountered in Caucasian populations with sitosterolemia [12,34,35]. These results indicate differential effects of ABCG5/G8 variants and the crucial role of individualized assessment for ABCG5/G8 variants on different phenotypes in different geographic areas. ## 4.5. Limitations The present study has limitations. First, ABCG5/G8 mutations resulted in autosomal recessive sitosterolemia; however, we did not measure sitosterol levels. Second, previous nutrigenetic studies have identified that dietary intervention may be involved in the association between ABCG5/G8 variants and the interindividual variability of circulating cholesterol levels [75]. Further analysis of dietary effects may help to provide more personalized dietary recommendations. Third, survival bias in this investigation could not be avoided due to the cross-sectional study design. Fourth, Asian-specific variants were the most commonly used in this investigation; thus, our findings may not be applicable to other ethnic groups. Finally, our study lacked a second cohort to determine replicability. Further study with a larger sample size and longitudinal follow-up would strengthen the validity of our findings. ## 5. Conclusions This study, using a Taiwanese population-based genetic approach, confirmed the critical role of ABCG5 variants in the ABCG5/G8 region as the major determinants of LDL cholesterol levels, as has been confirmed in other Asian populations. Associations of PLEKHH2 variants with blood pressure status is a novel finding that requires further confirmation. 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--- title: Hereditary Tyrosinemia Type 1 Mice under Continuous Nitisinone Treatment Display Remnants of an Uncorrected Liver Disease Phenotype authors: - Jessie Neuckermans - Sien Lequeue - Paul Claes - Anja Heymans - Juliette H. Hughes - Haaike Colemonts-Vroninks - Lionel Marcélis - Georges Casimir - Philippe Goyens - Geert A. Martens - James A. Gallagher - Tamara Vanhaecke - George Bou-Gharios - Joery De Kock journal: Genes year: 2023 pmcid: PMC10047938 doi: 10.3390/genes14030693 license: CC BY 4.0 --- # Hereditary Tyrosinemia Type 1 Mice under Continuous Nitisinone Treatment Display Remnants of an Uncorrected Liver Disease Phenotype ## Abstract Hereditary tyrosinemia type 1 (HT1) is a genetic disorder of the tyrosine degradation pathway (TIMD) with unmet therapeutic needs. HT1 patients are unable to fully break down the amino acid tyrosine due to a deficient fumarylacetoacetate hydrolase (FAH) enzyme and, therefore, accumulate toxic tyrosine intermediates. If left untreated, they experience hepatic failure with comorbidities involving the renal and neurological system and the development of hepatocellular carcinoma (HCC). Nitisinone (NTBC), a potent inhibitor of the 4-hydroxyphenylpyruvate dioxygenase (HPD) enzyme, rescues HT1 patients from severe illness and death. However, despite its demonstrated benefits, HT1 patients under continuous NTBC therapy are at risk to develop HCC and adverse reactions in the eye, blood and lymphatic system, the mechanism of which is poorly understood. Moreover, NTBC does not restore the enzymatic defects inflicted by the disease nor does it cure HT1. Here, the changes in molecular pathways associated to the development and progression of HT1-driven liver disease that remains uncorrected under NTBC therapy were investigated using whole transcriptome analyses on the livers of Fah- and Hgd-deficient mice under continuous NTBC therapy and after seven days of NTBC therapy discontinuation. Alkaptonuria (AKU) was used as a tyrosine-inherited metabolic disorder reference disease with non-hepatic manifestations. The differentially expressed genes were enriched in toxicological gene classes related to liver disease, liver damage, liver regeneration and liver cancer, in particular HCC. Most importantly, a set of 25 genes related to liver disease and HCC development was identified that was differentially regulated in HT1 vs. AKU mouse livers under NTBC therapy. Some of those were further modulated upon NTBC therapy discontinuation in HT1 but not in AKU livers. Altogether, our data indicate that NTBC therapy does not completely resolves HT1-driven liver disease and supports the sustained risk to develop HCC over time as different HCC markers, including Moxd1, Saa, Mt, Dbp and Cxcl1, were significantly increased under NTBC. ## 1. Introduction Tyrosine inherited metabolic disorders (TIMD) are a subclass of inborn errors of amino acid catabolism, characterized by an inherited deficiency of a functional enzyme key for the metabolic pathway of tyrosine [1]. Tyrosine is broken down in fumarate and acetoacetate by a five-step enzymatic pathway that is mainly present in the liver and kidney cytosol. Each enzyme of this pathway is associated with one autosomal recessive inborn error [2,3]. Hereditary tyrosinemia type 1 (HT1, OMIM #276700) is the most severe and deadly TIMD with an overall incidence of 1 in 100,000 newborns worldwide [2,4,5]. In this case, the impaired enzyme of the tyrosine degradation pathway is the terminal enzyme fumarylacetoacetate hydrolase (FAH). Loss of FAH activity results in the accumulation of the upstream toxic intermediates fumarylacetoacetate (FAA), maleylacetoacetate (MAA) and succinylacetone (SA) (see Figure 1). These metabolites are responsible for the severe disruption of the intracellular metabolism of the liver and kidney. HT1 has a highly variable clinical presentation characterized by hepatic failure with comorbidities involving the renal and neurological system, which frequently results in death if left untreated [4,6,7]. In HT1, the liver is the most severely affected organ and a major cause of morbidity and mortality. The high risk of developing hepatocellular carcinoma (HCC) in HT1 patients is the most ominous and is the highest among all metabolic disorders [2,8]. Historically, a tyrosine- and phenylalanine-restricted diet has been applied as therapy for HT1 patients but has proven to be inadequate as it does not overcome the chronic complications, the production of the toxic intermediates or the development of HCC [9,10]. Since 1992, nitisinone or 2-(2-nitro-4-trifluoromethylbenzoyl)-1,3-cyclohexanedione (NTBC) has changed the clinical course and management of HT1 [9,11]. NTBC is a potent inhibitor of 4-hydroxyphenylpyruvate dioxygenase (HPD), resulting in an upstream block of FAH in the tyrosine degradation pathway preventing the accumulation of FAA, MAA and SA (see Figure 1) [12,13,14]. Although NTBC rescues HT1 patients from severe illness and early death, late complications can persist [15,16,17,18,19,20,21]. In order to manage the NTBC-induced impairments, a lifelong dietary adjustment by restriction of tyrosine and phenylalanine is applied for HT1 patients [10,22,23]. Despite the multiple benefits of NTBC, it does not restore the enzymatic defects inflicted by the disease nor does it cure HT1 [24]. Furthermore, the occurrence of HCC in some HT1 patients under NTBC therapy is described, meaning that there is still a certain residual activity in the liver even under NTBC treatment [4,25,26]. Although many efforts have already been made in understanding the origin of damages occurring in HT1, many cellular and molecular (oncogenic) mechanisms underlying the progression of HT1 still have to be unraveled [26]. In the present study, we want to identify changes in molecular pathways associated to the development and progression of the liver pathogenesis of HT1 that remain uncorrected under NTBC therapy by using gene expression profiling of Fah-deficient mouse livers under continuous NTBC therapy. As a control of a TIMD without hepatic manifestation, we used a homogentisate 1,2-dioxygenase (Hgd)-deficient mouse model of alkaptonuria (AKU, OMIM #203500) also under continuous NTBC treatment. AKU is a serious, autosomal recessive, multisystem disorder affecting 1 in 250,000 live births [12,13]. It results from a deficiency in the HGD enzyme responsible for the formation of MAA out of homogentisic acid (HGA). Loss of HGD function results in a blockage of the degradation of tyrosine and an accumulation of HGA in body fluids and urine [9,27,28,29]. The accumulated and circulating HGA will oxidize into benzoquinone acetic acid (BQA) and polymerize into a melanin-like pigment (see Figure 1), which preferentially deposits in connective tissues during a process called ochronosis. Ochronosis and thus AKU is characterized by premature arthritis, lithiasis, cardiac valve disease, fractures, muscle and tendon ruptures and osteopenia. Other systemic features include kidney, prostatic, salivary and gall bladder stones, renal damage or failure, respiratory complications and auditory impairment [12,14,24,30,31]. This makes AKU a chronically debilitating disorder with heterogeneous symptoms and although the tyrosine pathway is mainly localized in the liver, AKU is an extrahepatic disease with multifarious systemic symptoms. Since 2020, NTBC therapy is also the standard of care treatment for AKU patients. Our study aimed to acquire fundamental knowledge on the uncorrected liver disease phenotype in continuously NTBC-treated HT1 patients. Our study focused on identifying the molecular mechanisms and pathways that remain modulated in the livers of Fah- and Hgd-deficient mice during continuous NTBC therapy and after NTBC discontinuation for seven days. To accomplish this, we performed microarray-based whole transcriptome profiling. We found that the most affected molecular pathways are those involved in liver-specific metabolic processes (synthesis/degradation), lipid homeostasis and hepatic cholestasis. Most importantly, we identified a set of 25 genes that discriminates HT1 livers from AKU livers, even under continuous NTBC therapy, as such representing remnants of an HT1-driven residual uncorrected liver disease phenotype. ## 2.1. Hereditary Tyrosinemia Type 1 Mouse Model The Fah5981SB strain (referred to as HT1 mice), backcrossed on C57Bl/6J background and kindly provided by Markus Grompe (Oregon Health & Science University, Portland, OR, USA). The mice bear a single N-ethyl-N-nitrosourea-induced point mutation (G>A loss) leading to the splicing out of exon 7 within the Fah gene, resulting in a frameshift and subsequently the introduction of a premature stop codon at amino acid position 303. Consequently, the mice produce a truncated, unstable FAH protein that is degraded, making them a suitable model for HT1. If neonatal HT1 mice are not continuously administered NTBC, they die of acute liver failure [32,33]. Therefore, all mice, except during the withdrawal experiment, received through their drinking water continuous NTBC treatment (8 mg/L). To prevent the formation and accumulation of tyrosine and its toxic metabolites upon NTBC therapy discontinuation, the mice were provided ad libitum with an irradiated diet that was low in tyrosine and phenylalanine (LabDiet® 5LJ5 chow, LabDiet, St. Louis, MO, USA), which resembles the protein-restricted diet of HT1 patients. ## 2.2. Alkaptonuria Mouse Model The homozygous Hgd knockout-first allele mouse (Hgd tm1a−/−), also backcrossed on a C57Bl/6J genetic background, is used as an animal model of human AKU (referred to as AKU mice) [27]. AKU mice are kindly provided by George Bou-Gharios (Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK). Hgd tm1a−/− mice contain an IRES:LacZ gene trap cassette and a promoter-driven neo cassette inserted into the fifth Hgd intron with the sixth exon flanked by loxP sequences. Homozygous Hgd tm1a−/− mice, therefore, show an AKU phenotype based on Hgd gene disruption while heterozygous Hgd tm1a−/+ and homozygous Hgd tm1a+/+ mice show a normal wildtype phenotype. Hgd tm1a−/− mice develop specific symptoms of AKU, including blackening of urine and progressive osteoarthritis when NTBC is not administered [27]. Therefore, similar to the aforementioned experimental conditions of the HT1 mouse model, all mice received through their drinking water continuous NTBC treatment (8 mg/L). An irradiated diet low in tyrosine and phenylalanine (LabDiet® 5LJ5 chow, LabDiet, St. Louis, MO, USA) was provided. ## 2.3. Animal Experiments Animal procedures were conducted at the Vrije Universiteit Brussel and were approved by the Institutional Animal Ethics Committees (grant numbers 16-210-1 and 21-210-2). Mice were housed in group under SPF conditions in individual cages within a regulated environment of 19–23 °C and 30–$70\%$ R.H. with a $\frac{14}{10}$-h light/dark cycle. To maintain their health, continuous NTBC treatment (8 mg/L) was given to both mouse models, and also to the pregnant females up until weaning, through their drinking water. The dosage used to treat both mouse models (HT1 and AKU) was based on the standard NTBC therapy dosage used to treat HT1 mice. As such, AKU mice can be used as a control to identify molecular changes in the liver of HT1 mice associated to the disease itself and not inflicted or masked by NTBC therapy. At eleven weeks of age, both mouse models (referred to as HT1-7dNTBC ($$n = 3$$) and AKU-7dNTBC ($$n = 4$$)) had their NTBC therapy withdrawn for seven consecutive days. ## 2.4. Sample Collection and Preparation Sample collection was carried out according to the previously mentioned protocol [34]. Briefly, at 12 weeks of age, the mice of both experimental groups were anesthetized via intraperitoneal injection of a mixture of ketamine (87.5 mg/kg Ketamidor® (Ecuphar, Catalonia, Spain)) and xylazine (12.5 mg/kg Rompun® (Bayer, Leverkusen, Germany)). After a ventral heart puncture, blood was collected into ethylenediaminetetraacetic acid (EDTA)-coated microtubes (Sarstedt, Nümbrecht, Germany, K3E tube) and onto dried blood spot (DBS) cards (Whatman 903; GE Healthcare, Chicago, IL, USA). The blood samples were centrifuged at 1500× g for 15 min at 4 °C, and the serum was frozen at −80 °C until further use. To prepare liver tissue for transcriptome analysis, cubes with a maximum volume of 1 cm3 of liver tissue were collected in an RNA-protecting solution and stored at −80 °C. ## 2.5. Dried Blood Spot Analysis The DBS cards were analyzed as previously reported [34]. DBS cards were air-dried at room temperature for at least 24 h prior to analysis. An HPLC 1100 Agilent system coupled with AB Sciex API 3200 and API 4000 MS analyzers was used for analysis. In order to quantify Tyr (m/z 238 > 102) and Phe (m/z 222 > 102), small discs were perforated from the DBS cards and placed into a 96-microtiter plate. The DBS were eluted with methanol at room temperature for 20 min, and the resulting eluent was transferred into an additional 96-microtiter plate. Internal standards (IS) were added to separate wells. The dried residues from the original microtiterplate were used for SA quantification. A standard stock solution of labeled amino acids isotopes was added to every well of the additional microtiterplate. The latter was dried under nitrogen at 55 °C, dissolved in a mixture of Butanol-HCl, and incubated at 65 °C under an inert atmosphere. After the last evaporation step using a nitrogen flow, an acetonitrile-water-formic acid mixture was added as an eluting solution and the samples were analyzed by MS/MS via direct flow injection. Plasma Tyr and Phe concentrations were determined using labeled IS (13C6 Phe, m/z 228 > 102 and 13C6 Tyr, m/z 244 > 102). SA (m/z 211 > 137) was quantified by exposing the DBS samples, the dried residues from the first plate, to a hydrazine hydrate solution containing an IS of deuterated SA (m/z 216 > 142). The mixture was incubated for 40 min at 50 °C after which the resulting samples were transferred to a new 96-microtiter plate and analyzed using MS/MS via direct flow injection. SA plasma concentrations were calculated using a standard curve. NTBC (m/z 330 > 218 and 330 > 126) concentrations were determined by eluting DBS discs with pure methanol and internal standard (13C6 nitisinone, m/z 336 > 218 and 336 > 126) for 30 min at room temperature, followed by direct use of the eluents in LC/MS. An isocratic LC method (0.5 mL/min) was used, with a Poroshell 120 EC-C18 column (Agilent, Santa Clara, CA, USA) and acetonitrile-water mixture (85:15) with $0.05\%$ formic acid at 30 °C, to elute the sample solution. The plasma concentrations were calculated using a standard curve. ## 2.6. Extraction of Total RNA RNA samples were collected in a mixture of RNAprotect Tissue Reagent (Qiagen, Hilden, Germany) and phosphate-buffered saline (5:1 v/v) to protect the RNA. The GenElute Mammalian Total RNA Purification Miniprep kit (Sigma-Aldrich, Bornem, Belgium) was used to extract total RNA from all samples in accordance with the manufacturer’s instructions. The extracted RNA was quantified using a Nanodrop spectrophotometer (Thermo Scientific, Wilmington, NC, USA). ## 2.7. Microarray Profiling and Analysis For each experimental group, 100 ng RNA was extracted from liver tissue and amplified and in vitro transcribed using the Genechip Whole Transcriptome PLUS Reagent Kit according to the manufacturer’s instructions (Applied Biosystems, Waltham, MA, USA) as previously described [34]. The amplified RNA and synthetized single-stranded cDNA were purified using magnetic beads, followed by hydrolysis of 15 µg ss-cDNA using RNase H, fragmentation and labelling of, respectively, 5.5 µg and 3.5 µg ss-cDNA using Fragmentation Master Mix and Labelling Master Mix. The labeled cDNA was subsequently hybridized to the Affymetrix Mouse Gene 2.0 arrays and placed in a Genechip® Hybridization Oven-645 (Affymetrix, Santa Clara, CA, USA) rotating at 14 g at 45 °C for 16 h. Post incubation, the arrays were washed on a Genechip® Fluidics Station 450 (Affymetrix) and stained with an Affymetrix HWS kit as was indicated by the manufacturer. An Affymetrix GeneChip® Scanner 3000 7G was used to scan the microarray chips. All chips were further subjected to quality control by using the Affymetrix GCOS software. The datasets were corrected, summarized and normalizes using Robust Multiarray Analysis. Transcriptome Analysis Console (TAC—version 4.0—Applied Biosystems) was used to create the heatmaps. Ingenuity Pathway Analysis software (version 2022-11) was applied to perform transcriptomic pathway analyses and gene set enrichment was determined based on a ≥2-fold difference and Benjamini–Hochberg (B-H) p-value ≤ 0.05. The data discussed in this manuscript have been deposited in the NCBI Gene Expression Omnibus and are accessible through GEO Series accession number GSE225001. ## 3.1. NTBC Treated Fah-Deficient Mice Show Affected Molecular Pathways Involved in Liver-Specific Metabolic Processes, Lipid Homeostasis and Hepatic Cholestasis Compared to NTBC-Treated Hgd-Deficient Mice Under continuous NTBC treatment, HT1 and AKU mice showed comparable blood levels of NTBC (1.00 ± 0.23 µM and 0.91 ± 0.18 µM respectively) and tyrosine (545.4 ± 28.8 µM and 559.6 ± 71.7 µM respectively) (Figure 2A,B). However, a low level of residual SA was detected in the blood of HT1 mice (0.102 ± 0.035 µM) as opposed to AKU mice (not detectable) (Figure 2C). Whole transcriptome profiling of HT1 and AKU liver tissue indicated that 64 genes were more than 2-fold upregulated and 29 genes more than 2-fold downregulated (p-value ≤ 0.05) in NTBC-treated Fah-deficient livers compared to NTBC-treated Hgd-deficient livers. Differentially regulated genes were enriched (2-fold, p-value ≤ 0.05) in toxicological gene classes related to liver disease (Liver Hemorrhaging), liver damage (Degeneration of Liver, Liver Damage, and Liver Necrosis/Cell Death), liver regeneration (Liver Regeneration, and Liver Hyperplasia/Hyperproliferation) and liver cancer (HCC). Liver Hyperplasia/Hyperproliferation (24 genes) and HCC (7 genes) are the most prominent toxicological gene classes with significantly modulated genes, as shown in Figure 3A,B. Canonical pathway analyses showed that pathways involved in liver-specific metabolic processes (FXR/RXR Activation, PXR/RXR Activation, and Aryl Hydrocarbon Receptor Signaling), including the degradation and biosynthesis of amino acids (tyrosine and asparagine), hormones (estrogen and melatonin), neurotransmitters (serotonin and catecholamines), and other substances (ethanol, acetone, nicotine, retinol and bupropion), as well as lipid homeostasis (Adpogenisis Pathway, and Acetate Conversion to Acetyl-CoA), cancer (SPINK1 General Cancer Pathway, and Circadian Rhythm Signaling) and liver disease (Hepatic Cholestasis, and Acute Phase Response Signaling) were modulated (29 genes were downregulated and 64 genes were upregulated) in the livers of HT1 compared to AKU mice despite continuous NTBC therapy (Figure 4A,B). The modulated genes per pathway are represented in Table S1. Analysis of enriched upstream regulator sequences in these differentially regulated genes showed activation of signaling through interleukin (IL) 6 and tumor necrosis factor (TNF) (activation z-score ≥ 2; Figure 5A). Stearoyl-CoA desaturase (SCD) signaling, which regulates the expression of genes involved in lipogenesis and mitochondrial fatty acid oxidation, was inhibited (activation z-score ≤ −2; Figure 5A). ## 3.2. mRNA Marker Profile of the Uncorrected Liver Disease Phenotype in HT1 vs. AKU Liver and Differential Impact of NTBC Discontinuation The genetic signature of the uncorrected remnants of the HT1-driven liver disease phenotype were defined. The gene expression of Hgd (65.3-fold), Moxd1 (8.5-fold), Mt1 (7.3-fold), Mt2 (6.7-fold), Saa2 (6.6-fold), Dbp (5.2-fold), Cxcl1 (4.3-fold), Asns (3.7-fold), Egr1 (3.6-fold), Saa1 (3.1-fold), Nr1d1 (3.0-fold), Nr1d2 (2.5-fold), Saa3 (2.2-fold), Rbp1 (2.2-fold), Lpl (2.0-fold), Nqo1 (2.0-fold) and Abcg8 (2.0-fold) was significantly higher in Fah-deficient livers compared to Hgd-deficient livers under NTBC treatment (Figure 6A). In contrast, the gene expression level of Fah (−9.6-fold), Elovl3 (−2.9-fold), Arntl (−2.6-fold), Cyp3a41b (−2.6-fold), Acss3 (−2.3-fold), Ddc (−2.3-fold), Fitm1 (−2.2-fold), Cyp2c38 (−2.2-fold), Slc22a7 (−2.1-fold) and Nfil3 (−2.1-fold) was significantly lower in HT1 vs. AKU mouse livers under NTBC treatment (Figure 6A, Table S2). Interestingly, when discontinuing NTBC therapy for seven consecutive days in AKU mice, only the gene expression of Saa1 (−2.5-fold) was significantly modulated (Figure 6B). The upregulation of Hgd in Fah-deficient mice, and the downregulation of Fah confirms the gene disruption in both mouse models. As both mouse models are not complete knock-out models, but gene disruption models, some Affymetrix probes still bind truncated Fah and Hgd cDNA resulting in an ‘underestimation’ of the fold changes. In contrast, when depriving HT1 mice from NTBC treatment for seven consecutive days, the gene expression of Nqo1 (13.3-fold), Cyp2c38 (3.5-fold), Ddc (3.1-fold) and Mt2 (2.7-fold) significantly increased more than 2-fold, whereas the expression of the genes Saa1 (−36.4-fold), Elovl3 (−35.3-fold), Saa2 (−22.7-fold), Moxd1 (−6.3-fold), Nr1d1 (−4.1-fold), Cxcl1 (−2.8-fold) and Dbp (−2.7-fold) significantly decreased more than 2-fold (Figure 6C). Finally, when performing hierarchical clustering using these differentially expressed genes (excluding Hgd and Fah), a clear distinction was observed between HT1 and AKU mouse livers, independent of NTBC treatment. Furthermore, AKU livers treated or not with NTBC were clustered independently of the treatment condition, whereas for HT1 mouse livers, a clear difference was observed between continuously treated livers and those livers that underwent seven days of NTBC therapy discontinuation (Figure 6D). ## 4. Discussion Since 1992, NTBC therapy has changed the clinical course and the well-being of HT1 patients. Despite its efficacy in preventing severe illness and early death, some HT1 patients may still experience late complications, such as the development of HCC, suggesting that there persists an uncorrected residual liver disease state even under continuous NTBC treatment. In order to characterize any potential uncorrected remnants of liver disease in HT1 patients under NTBC therapy, we performed whole transcriptome analyses of a preclinical mouse model of HT1 and compared it to a mouse model of AKU, another TIMD that does not harbor any hepatic manifestation. Both mouse models served the same genetic background and were kept under the same therapeutic conditions. The study revealed the upregulation of several genes involved in the development and progress of HCC, which could potentially serve as HCC markers. Biochemical blood analyses of Fah-deficient HT1 mice under continuous NTBC therapy demonstrated residual levels of SA, compared to Hgd-deficient AKU mice in which SA could not be detected. This indicates that, although NTBC is a strong inhibitor of the HPD enzyme, it does not completely block the tyrosine degradation pathway, resulting in a residual enzymatic HPD activity in the liver. Although there are differences in metabolic rate, bioavailability, and the tyrosine catabolic pathway, 8 mg/L NTBC resembles the dose of 1–2 mg/kg/day for HT1 patients assuming the mice drink 3–5 mL water per day in conjunction with the Tyr- and Phe-restricted diet [23]. Nonetheless, higher NTBC concentrations can be used to obtain “complete” suppression of SA; however, mice can still develop HCC [35]. SA is associated with liver damage and with induced oxidative subcellular and tissue damage as SA leads to the accumulation of 5-aminolevulinic acid (ALA) [36]. When comparing the livers of HT1, harboring residual blood SA levels vs. AKU mice under continuous NTBC treatment, we observed that the differentially expressed genes group in toxicological gene classes associated with liver disease, liver damage, liver regeneration and liver cancer, in particular HCC. The most affected molecular pathways were those involved in liver-specific metabolic processes, lipid homeostasis and hepatic cholestasis. These observations point to a residual uncorrected liver disease state of HT1 mice under continuous NTBC therapy. Serum amyloid A (SAA), including SAA1, SAA2 and SAA3, are acute response proteins, mainly produced by hepatocytes and regulated by inflammation-associated cytokines, which promote endothelial dysfunction via a pro-inflammatory and pro-thrombotic effect and were detected to be significantly elevated in Fah-deficient livers under NTBC treatment. Their upregulation is a remnant of residual stress associated with residual SA levels. Its primary function is the regulation of the homeostasis. In chronic inflammation, the driving force in tumor development, SAA levels increase substantially as can be observed in this study. It has been reported that SAA’s augment the toxic effect of acetaminophen in liver tissue by promoting platelet aggregation on the cell membrane of liver sinusoidal endothelial cells [37]. Several genes involved in HCC development and prognosis were found to be significantly increased in liver tissue from NTBC-treated Fah-deficient HT1 mice compared to Hgd-deficient AKU mice. Indeed, Asns, also known as asparagine synthetase is an enzyme involved in the synthesis of asparagine. The expression of Asns has been observed to be elevated in HCC tumor tissues and closely correlates with serum α-fetoprotein (AFP) levels, tumor size, microscopic vascular invasion, as well as tumor encapsulation [38]. As such, Asns upregulation is a first indication that unresolved aspects of HCC development are still ongoing, even under NTBC therapy. This is further supported by the fact that the expression of metallothioneins (MT), Mt1 and Mt2, are modulated in Fah-deficient mouse livers under NTBC treatment. MTs are small cysteine-rich metal-binding proteins that are crucial for metal homeostasis and protection against heavy metal toxicity, oxidative stress and DNA damage [39]. Recent studies have demonstrated that the abnormal expression of MTs, such as Mt1 are able to trigger the process of carcinogenesis in various types of human malignancies, including HCC [40]. Collectively, MTs contribute to tumor metastasis by enhancing the invasion and migration of tumor cells and tumor microenvironment remodeling [40]. In the context of HCC, it was previously reported that the expression of Mt1, Mt2 and metal transcription factor-1 (Mft1) is decreased in human HCC as compared with periportal-HCC and normal tissues [39]. Moreover, MTs have typical circadian rhythms and their expression depends on the differentiation status of the tumor [39]. Consequently, the increase in expression of Mt1 and Mt2, found in Fah-deficient mouse livers under NTBC treatment, could point to an unresolved oxidative stress response against toxic tyrosine metabolites, which might still progress into HCC development. Interestingly, the expression of circadian clock target genes, including nuclear orphan receptor factor protein (Nr1d1) and Nr1d2, as well as D-box-binding protein (Dbp), was found to be upregulated in HT1 mouse livers under NTBC therapy. Dbp encodes a transcription factor that binds to the promoter of genes of albumin and several CYP enzymes. This is in accordance to what is found in HCC livers as compared with periportal-HCC and normal livers [39]. Furthermore, it has been reported that the early growth response protein Egr1 and the copper-dependent monooxygenase Moxd1, which were found to be significantly increased in NTBC-treated HT1 livers, are correlated to the invasiveness of HCC cells and early tumor development, respectively [41,42]. Expression of Moxd1 is also associated with poor survival in glioblastoma, whilst when it is downregulated, it activates ER-stress causing activation of the unfolded protein response. This latter has tumor-promoting functions [43]. NAD(P)H quinone oxidoreductase-1 (Nqo1) is a flavin-adenine dinucleotide (FAD)-dependent flavoprotein that catalyzes the reduction of quinones and their derivatives through the receptor NAD(P)H by loss of two electrons and as such avoid damage to cells [44]. We found that Nqo1 expression is significantly increased in Fah-deficient mouse liver under continuous NTBC treatment. The overexpression of Nqo1 has been observed in HCC and enhances the vulnerability of cells to oxidative stress-induced injury. Nqo1 is also involved in regulating the proliferative and aggressive characteristics of HCC [45,46,47]. In the context of liver disease, we report here several modulated genes. More specifically, we found that Abcg8 is significantly increased in HT1 livers under NTBC treatment. This is a cholesterol transporter in the liver and bile that operates with Abcg5 as a heterodimeric transporter located at the canalicular membrane of hepatocytes and intestinal enterocytes where it actively transports sterols. Loss-of-function mutations in this gene are associated with an increased risk to develop gallstones. On the other hand, gain-of-function variants results in an increased function of the transporter and, as a consequence, increased biliary cholesterol levels [48]. Furthermore, Rbp1, a gene coding for retinol-binding protein 1, was also increased. Rbp1 is involved in vitamin A metabolism and is highly expressed in hepatic stellate cells as well as in hepatic fibroblasts of fibrotic or cirrhotic livers [49]. In addition, Cxcl1 encodes CXCL1 which is a major chemoattractant for neutrophils that binds to its receptor CXCR2 and was found increased in permanently treated HT1 mouse livers [50]. Cxcl1 has an oncogenic role in HCC progression, as it is associated with tumor progression and recurrence in HCC patients [51]. Thereby it leads to the activation of signaling pathways such as PI3K/Akt, MAP kinases or phospholipase- β, resulting in the recruitment of neutrophils to inflamed areas. This neutrophil recruitment is also observed with the activation of the triggering receptor expressed on myeloid cells 1 (TREM-1), which promotes Akt activation [52,53]. Furthermore, it is also implicated in processes such as tissue repair and tumor development [52]. Notably, CXCL1 expression is elevated in the liver of non-alcoholic steatohepatitis (NASH) patients, but not in simple steatotic livers in obese individuals or in high-fat diet (HFD)-fed mice [53,54]. However, in a NASH mouse model induced by a choline-deficient amino acid-defined diet, increased Cxcl1′s hepatic mRNA levels in a toll-like receptor 4-MyD88-dependent manner are observed, causing an accumulated neutrophil infiltration associated with hepatic inflammation and fibrosis [55]. Interestingly, viral overexpression of CXCL1 in the liver is sufficient to trigger progression from steatosis to steatohepatitis in HFD-fed mice by inducing hepatic neutrophil infiltration, oxidative stress and hepatocyte apoptosis [56]. In contrast, several genes involved in lipid metabolism, including Elovl3, Acss3 and Fitm1, were found to be significantly decreased in Fah-deficient mouse liver tissue under NTBC therapy. Importantly, when depriving Fah-deficient mice from NTBC for seven consecutive days, many of the aforementioned signature genes were modulated, which, however, was not the case in Hgd-deficient mice, supporting our conclusion that these genes are remnants from an unresolved HT1-drive liver disease state. The study thus revealed the upregulation of several genes that are involved in the acute phase and cancer development process, and which could serve as potential HCC markers, such as SAA, which is an early stage marker for acute (and chronic) inflammatory disease and CXCL1 is a prognostic indicator for poor outcome. ## 5. Conclusions This study provides the first preclinical data on residual features of a possible unresolved HT1-driven liver disease state under NTBC therapy. Our study revealed numerous genes that are associated with liver disease and HCC that showed a differential expression in HT1 mouse livers vs. AKU mouse livers under continuous NTBC therapy. 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--- title: Relationship between Sports Practice, Physical and Mental Health and Anxiety–Depressive Symptomatology in the Spanish Prison Population authors: - María Penado Abilleira - María-Paula Ríos-de-Deus - David Tomé-Lourido - María-Luisa Rodicio-García - María-José Mosquera-González - Daniel López-López - Juan Gómez-Salgado journal: Healthcare year: 2023 pmcid: PMC10047943 doi: 10.3390/healthcare11060789 license: CC BY 4.0 --- # Relationship between Sports Practice, Physical and Mental Health and Anxiety–Depressive Symptomatology in the Spanish Prison Population ## Abstract The objective of this study was to evaluate, in a group of Galician inmates, if there were variations in the levels of physical and mental health and anxiety–depressive symptomatology, depending on the sports’ practice performed. The relationship between these constructs was also investigated. The sample was composed of 509 people deprived of liberty in prisons in the Autonomous Community of Galicia, Spain. A quantitative methodology was used, with the questionnaire as an information collection instrument, Student’s t-tests, Pearson’s correlation analysis and a stepwise regression analysis were carried out. The results indicated that those who performed physical activity during their stay in prison had higher levels of physical and mental health, as well as lower indicators of anxiety–depressive symptoms. People who did not practice sports showed a decrease in their perceived health levels when compared to those perceived in their pre-prison stage. A negative association was shown between perceived health levels and anxiety–depressive symptomatology. Perceived physical health, alone, explained $35\%$ of the variance in perceived mental health. These results add to knowledge about the relationships between perceived health, anxiety–depressive symptoms and sports practice in the group of people deprived of liberty, highlighting the importance of promoting physical activity in penitentiary institutions. ## 1. Introduction Throughout the last few decades, the scientific literature has collected abundant evidence about the benefits that the practice of physical and sports activities have for people’s health [1]. In terms of physical health, regular physical activity is effective in preventing at least twenty-five chronic medical conditions, with a risk reduction that ranges from 20 to $30\%$ [2,3,4]. The mere fact that people become a little more active in their day to day lives, without reaching the recommendations of one hundred and fifty minutes of physical activity per week from the different international agencies, already implies health benefits [1]. This improvement in health, caused by physical activity, is independent of educational or economic levels [5]. In the same vein, there are many scientific publications that reflect the importance of exercise in adults: not only to improve physical health levels, but also regarding mental health levels [6,7]. Specifically, a wide range of positive results have been identified, regarding sports participation in relation to social, psychological and psychosocial health, such as perceived social support, the sense of belonging or a greater self-esteem [6]. These health benefits that derive from the practice of physical activity and sports have also been found in other age groups, such as in children [8], adolescents [9] or the elderly [10]. Regarding the prison population, inmates usually have worse levels of physical and mental health, tending to suffer from chronic physiological and psychological disorders [11]. In terms of physical health, inmates are disproportionately affected by risk factors for cardiovascular diseases [12] and tend to be obese through rapid weight gain during their stay in prison [13,14]. However, different investigations have shown that structured physical activity can improve cardiovascular risk levels and obesity during hospitalization [14,15]. Similarly, it has also been proven that the practice of physical activity in prison improves the well-being perceived by inmates as well as reducing their levels of depression. The promotion of physical activity is also established as a critical component in older inmates when maintaining a good lifestyle in prison, preventing the onset of the previously mentioned diseases [16]. On the other hand, in relation to mental health, although prison inmates tend to come from socially disadvantaged environments, presenting poor health indicators linked to their situations of social exclusion, detention centers may offer an opportunity to combat these inequalities through health promotion programs [17]. In the international context, there are numerous interventions based on sport and physical activity carried out in prisons with the purpose of improving the levels of inmates’ psychological well-being and reducing mental symptoms [4,18,19,20]. In general, these interventions usually last between six weeks and nine months, and as a general conclusion, it has been established that they provide a positive impact on the psychological well-being of inmates [20]. Physical activity in prison leads to reduced despair among inmates [21], becoming a coping strategy to deal with incarceration and decrease levels of anxiety and depression [22]. As for the prison population of women, the benefits of physical activity are the same as for men in terms of physical and mental health, allowing them to develop a positive social identity and favoring social relationships [3,23,24]. However, the participation of inmates in these activities is very low compared to the levels of physical exercise performed by men, or those recommended for society in general, due to the many obstacles that exist when it comes to participating in physical activities in prison [25]. Among these obstacles, the institutional system itself stands out: the internal functioning, family demands or the need to work in prison, lead to limited time available for sports [26]. In the Spanish context, over the years, legislation has evolved and been adapted to conceive of sports as a habitual activity in prison, aimed at achieving the re-education and social reintegration of inmates [27,28,29]. Therefore, over the last few years, different projects have been carried out implementing proposals to promote physical activities and sports in prisons [30]. The purpose of these programs is to enhance the cardiovascular health of inmates [31], establish a health education system [32], develop maps of assets for health in young inmates [33], learn about the role of sports in prison for social rehabilitation [34], assess the satisfaction and perception of this population with sports programs that seek to promote their reintegration [35,36] or promote the values of responsibility, commitment and enthusiasm [37]. The review of the existing literature reveals the interest in the subject and that more and more there is a tendency to work with specific groups within the prison. It will be necessary to look for references that make it possible to compare what happens in different institutions, since each of them has its own operating dynamics and this can condition moods to the point of wanting to go to prison, as was stated by some inmates, recidivists, in order to have a more orderly, controlled and safe life. A novelty compared with previous studies is having used the sports practice variable prior to entering prison to see to what extent it provided relevant information to the works published to date, in addition to providing data from the Autonomous Community of Galicia. The objective of this research was to analyze whether there are variations in the levels of physical health, mental health and anxiety–depressive symptomatology, in people who were inmates in Galician prisons, depending on the sports practice carried out before and after their entry into prison and above all, we wanted to inquire about the relationships between physical health, mental health and anxiety–depressive symptoms during the duration of the sentence. ## 2.1. Participants The sample was made up of 509 participants ($88.2\%$ men and $11.8\%$ women) interned in penitentiary institutions in the Autonomous Community of Galicia. The ages ranged between 19 and 74 years ($M = 40.85$; SD = 10.55), with women being slightly younger than men, but without these differences being considered significant. Regarding criminal variables, men and women presented differential traits with respect to whether it was the first time they were imprisoned or, on the other hand, whether they were repeat offenders and if the sentence imposed contemplated the commission of a single crime or several (x2 = 343.365, $p \leq 0.01$). A greater recidivism and greater probability of committing multiple crimes were observed in male inmates (see Table 1). For the classification of the crimes committed by the inmates, the International Classification of Crime for Statistical Purpose (UNODC, 2015) provides the following categories of Level 1 crimes (see Table 2). In addition to the previous categories, an extra category (number 12) was established that includes the commission of crimes against a partner, ex-partner or person linked with the same bond or emotional relationship by a man and that constitutes a specific typology of the Spanish judicial environment called gender violence. Regarding the type of crime committed by men and women, the most common in both genders were crimes against property that involved the use of violence (theft and robbery) followed by crimes against public health or that are considered acts with psychoactive substances (drug trafficking). Since women cannot commit acts of gender violence, no data were obtained in this criminal typology that allowed comparisons with men. The high percentage of men who claim to commit crimes that cause harm or intend to cause harm (injuries and threats) is noteworthy, while more women claim to commit acts involving fraud, deceit or corruption (see Table 3). Regarding the criminal variables considered quantitative, results indicated that there were no differences in the sample of men and women studied based on the number of times they were in prison, the age when they were admitted for the first time, sentence time and time served. Significantly longer sentences were observed for men than for women ($t = 2.809$; gl. = 419; $$p \leq 0.005$$ < 0.05). It was not possible to carry out comparative analyses due to the difference in the size of the samples (see Table 4). ## 2.2. Instruments Physical health was assessed through an item formulated as follows “How would you rate your physical health before entering prison?” with 4 options, where 1 = bad; 2 = average; 3 = good; and 4 = very good. Mental health was measured with a similar item. Regarding the measurement of anxiety–depressive symptomatology, anxiety was assessed through the State-Trait Anxiety Inventory (STAI) [38] which assesses state and trait anxiety through 40 items (20 for each subscale). Specifically, it was used in its translation into Spanish [39]. The response scale is a Likert-type scale from 0 to 3. In the state anxiety subscale, the response ranges from 0 = not at all, 1 = somewhat, 2 = moderately so to 3 = very much so; meanwhile, in the trait anxiety subscale, the response range is: 0 = almost never, 1 = sometimes, 2 = often and 3 = almost always Depression symptoms were measured with the Spanish adaptation of the Beck-II Depression Inventory [40], the original instrument being created by Beck, Steer and Brown [1996]. The Spanish version has 21 items with four response alternatives. Finally, the practice of sport in the penitentiary and before entering it, the time spent in prison, if it was your first time in prison, at what age did you enter prison, as well as the sociodemographic data on sex and age, were evaluated by means of an ad hoc questionnaire. The validity of the information given was due to the attention paid by the research team, which in practical terms involved carrying out an interview instead of applying a questionnaire, by standing next to the inmate and explaining if necessary what the question that was asked meant. What, at first, seemed to be an inconvenience became a very relevant source of information that allowed us to support everything that is narrated here with an exact reflection of reality, as indicated in the following section. ## 2.3. Procedure In order to access the population under study, it was necessary to make the corresponding permit application to the General Secretariat of Penitentiary Institutions (Ministry of Interior). To formalize this request, among other data, we had to provide all the information related to the research project that we intended to carry out: objectives, lines of research, tools for information collection and temporary planning of field work. Once the authorization was granted by the Ministry of the Interior, the person in charge of training in the penitentiary was contacted, who was also provided with a presentation letter for its dissemination; this included the objectives of the study, the data to be collected and the informed consent form to be filled in by those people who were willing to collaborate. The field work was carried out throughout the months of July and August, and the data collection instruments were administered once the training activities with the inmates were completed, respecting the hours of access to the penitentiary. This was carried out in small groups, always following the instructions of the person in charge of training, who called on the different modules to allow access and sent the inmates to the room used for the data collection. The initial research design contemplated the application of the questionnaire prepared for this purpose and the use of the scales in a group; however, in practice, it had to be performed in a more individualized way, since many inmates constantly needed help to fill it out, which made completion difficult. The study was approved by the Ethics Committee of the University to which the authors belong and followed the recommendations of the Declaration of Helsinki and the General Data Protection Regulation ($\frac{2016}{679}$), approved by the European Parliament and the Council of the European Union. ## 2.4. Data Analysis Data analyses were carried out with the statistical package IBM SPSS Statistics version 25. They were executed sequentially, beginning with the calculation of the descriptive statistics of the variables under study. Then, comparisons of means were made based on sports practice, by comparing groups with Student’s t-test. Subsequently, correlation analyses were performed between the different variables of the study using the Pearson correlation coefficient. Finally, a stepwise regression analysis was carried out with the objective of determining to what extent mental health in prison was explained by physical health and anxiety–depressive symptomatology. ## 3.1. Descriptive Statistics Table 5 shows the descriptive statistics (mean, standard deviation, minimum and maximum) in the following variables: physical health, mental health, anxiety and depression. The inmates presented moderately high scores in the perception of physical and mental health, both before entering prison and afterwards. The indicators of anxiety and depression were low. Below, we present the comparisons made in the levels of physical and mental health, before and during their internment, indicated by the inmates, depending on whether or not they practiced sports in prison. Table 6 shows the descriptive statistics of these variables for the two groups: those who practiced sports and those who did not. For this, it was considered that those inmates who indicated doing so at least 3 h a week practiced sports. The distribution of the sample was quite balanced with $41\%$ indicating that they met that standard and $59\%$ indicating that they did more hours of sports. -Intergroup comparisons before entering prison There were no significant differences between the group of inmates who practiced sports in prison and the group that did not practice sports prior to imprisonment, both for physical health (t [437] = 0.382; $$p \leq 0.703$$) and for mental health (t [434] = 0.882; $$p \leq 0.378$$). -Intergroup comparisons after entering prison There were significant differences in the levels of physical and mental health after entering prison. The group of inmates who practiced sports in prison had higher levels of physical health (t (248.643) = 7.49; $p \leq 0.001$; $d = 0.81$) and mental health (t [434] = 5.009; $p \leq 0.001$; $d = 0.79$). -Intragroup comparisons Regarding the differences in the levels of physical and mental health within each of the groups, the group of inmates who practiced sports in prison (Figure 1), did not show statistically significant differences between the levels prior to entering prison and the levels shown during imprisonment, both for physical health (t [296] = 1.946; $$p \leq 0.053$$) and for mental health (t [296] = 1.958; $$p \leq 0.051$$). On the other hand, the group of inmates who did not practice sports in prison showed a significant decrease in their levels of physical health (t [138] = 5.419; $p \leq 0.001$) and mental health (t [134] = 4.477; $p \leq 0.001$). Regarding the levels of physical and mental health prior to their stay in prison, the group of inmates who practiced sports had higher levels of physical health ($M = 2.95$; SD = 1) and mental health ($M = 2.89$; SD = 1.01) than the group of inmates who did not perform any sport, both in physical health indicators ($M = 2.45$; SD = 1.01) and in mental health indicators ($M = 2.56$; SD = 1.07). The differences found in relation to physical health (t [432] = 4.973; $p \leq 0.001$) and mental health (t [331,627] = 3.208; $p \leq 0.01$; $d = 0.83$) were statistically significant. ## 3.2. Comparisons in Anxiety and Depression in Those Who Say They Practice Sports and Those Who Do Not Table 7 shown similar findings to the previous section, the levels of anxiety and depression of the inmates shown in Table 1 were also analyzed according to sports practice. These comparisons can be found in Table 3. The group of inmates who practiced sports during imprisonment had lower levels of state anxiety, trait anxiety and depression than the group of inmates who did not play sports. These differences are statistically significant. ## 3.3. Correlation Analysis The correlations among the variables, state anxiety, trait anxiety, depression and physical health in prison and mental health in prison, are shown in Table 4. The dimensions of anxiety and depression correlate with each other positively and significantly. In turn, these variables correlate with physical and mental health in a negative and significant way (see Table 8). ## 3.4. Regression Analysis The initial model of the multiple linear regression analysis (Table 5) only included, as a predictor variable, physical health with a positive direction. Subsequently, trait anxiety and state anxiety, with a negative direction, were included in a second and third model. The changes in adjusted R2 were significant, while the final model explains $49\%$ of the mental health variance. The depression variable was not a significant predictor (see Table 9). ## 4. Discussion In the present investigation, we wanted to know the degree of physical and mental health as well as anxiety–depressive symptoms in a group of inmates in prisons belonging to the Autonomous Community of Galicia, Spain. We also wanted to know if these levels varied depending on the practice of sports carried out in prison and before their entry into it. In addition, the relationships between these variables were analyzed. The results indicate that those inmates who performed physical activity in prison showed higher levels of perceived physical and mental health, as well as lower indicators of anxiety and depression. These results are in line with previous scientific literature that has highlighted the benefits of physical activity to improve physical and mental health indicators [2,3,6,18]. The group of inmates who did not perform physical activity in prison worsened their perceived levels of physical and mental health, which did not occur with inmates who did practice sports. These results favor the thesis, widely reinforced by research, that physical inactivity is a risk factor for both physical and mental health [41,42,43]. Regarding the relationships between the variables: physical health, mental health and anxiety–depressive symptoms, the correlation analysis shows a positive relationship between physical health perception and mental health perception. These results are consistent with the scientific literature previously mentioned, as sports performance is substantially associated with mental health [44], a factor that has special importance in the prison population [4,21,45]. The correlation analysis also reflects the existence of negative relationships between the perception of physical health and the perception of mental health with anxiety–depressive symptomatology, as confirmed in previous studies [3,46,47,48,49]. The establishment of the relationships between perceived physical and mental health with anxiety–depressive symptomatology is essential in those inmates diagnosed with a mental disorder, since investigations show how the practice of physical activity also clearly improves their physical and mental health, both in the general population [7] and in prison inmates [19]. Hence the need for research work, perhaps of a more qualitative nature, that allows for enquiries about subjects in the population affected by inherent situations of deprivation, groups that are very difficult to reach with this type of study. Finally, the results of the regression analysis indicated that the perceived mental health in the inmates is explained to a greater extent by the perceived physical health, which is in line with the existing scientific literature, highlighting the relationship between the attitude towards physical health itself and the perception of mental health [3,50]. In this respect, physical activity is considered as the major factor responsible for the indirect effects between both constructs [51]. Among the limitations of this study, we should mention the impossibility of establishing causal relationships, due to the non-manipulative nature of the investigation, as well as not having measured health through objective indicators, but through questions addressed to inmates about their perceptions of it. However, other previous studies on health perception also evaluate this construct in a similar way [5]. Another limitation has to do with the sample used, which was of an incidental nature, as they were the subjects who voluntarily enrolled in the study. It is considered broad for this type of study; but, even so, it was not balanced by gender because in Galicia there are very few female inmates, and this makes it impossible to establish comparisons based on gender. ## 5. Conclusions The main conclusion we reached with this study is the difficulty involved in working in prison contexts. A feature of this was the bureaucratic procedures that must be faced from the moment the idea arose, until the permit was obtained; we must add that you were always in the hands of the officials who, as they change daily, you then had to explain again what you were doing there at all hours. The fact that the inmates have such great mobility meant that you cannot continue working with the same inmate for two weeks in a row, because either he was on trial, on leave, was transferred to another prison or was in a compulsory activity. You have to constantly adapt to the situation. Each center has its own dynamic of action, which means that different plans had to be used to reach the same end. The difficulty of working there meant that by having access we tried to reach the majority of inmates and collect the greatest quantity of data, and this can turn against the investigation itself. This article is a part of a much more complete and complex investigation. Regarding the subject studied, which seemed very obvious from the outset, it has allowed us to see that there are a multitude of variables that affect it, from the type of center, whether that is more or less crowded, the type of inmates, the prison conditions, the climate that is in it, etc.; all of which may vary the results. Hence, at this point, it can be concluded that a comparative investigation would be necessary to draw conclusions about the variables that are common and, from there, to investigate each case in depth. Future lines of research can focus on confirming the present results in the Galician prison population through studies with stratified samples and according to the object of study, which allow for the establishment of a higher degree of causality, more qualitative methodologies and data triangulation. 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--- title: 'Hot Air Convective Drying of Ginger Slices: Drying Behaviour, Quality Characteristics, Optimisation of Parameters, and Volatile Fingerprints Analysis' authors: - Ruoxi Bai - Jieru Sun - Xuguang Qiao - Zhenjia Zheng - Meng Li - Bin Zhang journal: Foods year: 2023 pmcid: PMC10047944 doi: 10.3390/foods12061283 license: CC BY 4.0 --- # Hot Air Convective Drying of Ginger Slices: Drying Behaviour, Quality Characteristics, Optimisation of Parameters, and Volatile Fingerprints Analysis ## Abstract Ginger is one of the most popular spices and medical herbs with its unique pungent flavour and taste. Although there has been much research into the drying methods of ginger, the effect of drying parameters in hot air convective drying on ginger quality needs to be explored in depth. This study investigated the differences in drying behaviour and quality characteristics of ginger with the variables of temperature, thickness, and loading density. The moisture states and diffusion pattern in the different stages during the drying process were analysed using low-field NMR techniques. The results of quality evaluation showed that the temperature greatly influenced the colour and gingerol content of dried ginger, and the thickness of a ginger slice greatly influenced the rehydration rate. Optimal drying conditions were determined by considering a combination of specific energy consumptions with quality retention based on the response surface methodology: a temperature of 66.41 °C, thickness of 2 mm, and loading density of 5 kg/m2. HS-GC-IMS combined with multivariate chemometrics was used to achieve the characterisation of flavour profiles and fingerprinting of dried ginger. The principal component analysis and correlation analysis revealed that the alterations in ginger quality were intimately related to moisture diffusion during drying. ## 1. Introduction Ginger, the rhizome of Zingiber officinale (Zingiberaceae), is widely used as a seasoning or spice for food and as an herb for medicine [1]. In recent years, it has received extensive attention due to its anti-inflammatory, antipyretic, immunoregulatory, antitumor, antioxidant, hypoglycaemic, and antibacterial properties [2]. The nutritional value of ginger is derived from its complex bioactive components, primarily including gingerols, shogaols, paradols, and zingerone, and its flavour sources are mainly sesquiterpenes and monoterpenes [3]. It is important to note that these components are susceptible to change during storage and processing. Therefore, it is necessary to deeply explore the measures and optimisation strategies to maintain the bioactive compounds of ginger. The high moisture content (85–$95\%$ wet basis, wb) in fresh ginger rhizome makes it susceptible to microbial spoilage and chemical deterioration [4]. Drying (which can remove $90\%$ of water from food) is a crucial way to control the moisture content and extend the shelf life of ginger [5]. With drying, the desirable product quality was protected such as the colour, flavour, nutrients, and texture. In addition, drying reduces the bulk volume and weight of the sample, which is beneficial for packaging and transportation [6,7,8]. Currently, sun drying is a widespread conventional method of ginger drying for many developing countries due to low investment and simple operation. However, its unsecured sanitary environment and uncontrollable drying conditions also limit the quality advancement of ginger. Hot air convective drying (HACD) is a technique of dehydration by diffusion of moisture within the sample through the transfer of heat [9]. It is the most adopted technique in dry product processing plants because of its fast heat transfer and stable temperature. However, it causes an unfavourable thermal degradation of product quality. Therefore, it is essential to choose the proper drying conditions in order to avoid the degradation in flavour, colour, and nutritional components and increase in energy consumption. Low-field nuclear magnetic resonance (LF-NMR) technology has great potential for characterising moisture changes during food drying due to its characteristics of fast speed, high sensitivity, sample retainment, and low cost. It has been successfully used for the real-time detection of water mobility and distribution during the drying of carrot, banana, Pleurotus eryngii, shiitake mushroom, garlic, and burdock [10,11,12]. Moreover, the selection of the optimal drying parameters not only focuses on the independent variables, but also their possible interactions. The response surface methodology (RSM) is considered a widely used, effective statistical method for optimising complex processes [13]. It can describe the comprehensive effects of multiple variables and the interrelationships between variables through a rational experimental design [14]. Due to its advantages of high efficiency, low cost, and convenient experimentation and interpretation, RSM has been applied in chemical component extraction and processing technology optimisation [15,16]. To the best of our knowledge, the combination of the above two practical tools has not been used to study the ginger drying process. Aroma is one of the most sensitive indicators to judge the quality of various foods and condiments, and special changes will occur during the drying process [17]. Therefore, the identification of volatile components (VOCs) in food is conducive to accurately describing the relationship between the changes in VOCs and food quality. In a past report, Pang et al. [ 18] identified components affecting the aroma of ginger using gas chromatography—olfactometry (GC-O). Johnson et al. [ 19] identified 100 volatile components in dried Australian ginger by gas chromatography-mass spectrometry (GC-MS). Yu et al. [ 20] used headspace gas chromatography-mass spectrometry (HS-GC-MS) and fast GC e-nose to distinguish the varieties and geographical origin of ginger. The GC-IMS method, which is an emerging detection technique, can be used to achieve rapid, real-time identification of volatile compounds based on differences in ion mobility rates under ambient pressure in weak electric fields and in combination with gas chromatography [21]. The advantages of GC-IMS for its simplicity, sensitivity, and rapidity are widely used in food origin labelling [22], freshness evaluation [23], food authenticity identification [24], and monitoring of changes in volatile compounds during processing [25]. Li et al. [ 26] successfully established a fingerprint of volatile components in the cured ginger process by GC-IMS and interpreted the influence of the curing process on the flavour characteristics of ginger. Nevertheless, the application of GC-IMS in ginger drying is less reported and correlation analysis with drying and quality parameters has not been reported. The aim of this work was to explore the optimisation and evaluation of the drying process of ginger and to analyse the differences in volatile compounds between fresh and dried ginger. The effects of three variables (drying temperature, thickness, and load density) on the drying rate and quality (colour, brown index value, rehydration rate, and gingerol content) of ginger were evaluated. The transverse relaxation time (T2) combined with the PLSR (partial least squares regression) model was used to analyse the moisture status and water loss pattern during the drying process. In addition, the optimal drying conditions were obtained by considering a combination of specific energy consumptions with total gingerol content based on RSM. Then, HS-GC-IMS combined with multivariate chemometrics was used to achieve the characterisation of flavour profiles and fingerprinting of ginger. ## 2.1. Sample Preparation Fresh ginger was purchased from a local supermarket in Laiwu (Shandong Province, China) and stored in a refrigerator at 4 °C for no more than seven days before drying. In the drying experiments, ginger pieces of the same size (the diameter was about 3.0 cm) were washed and cut into 2, 4, and 6 mm slices. They contained $91\%$ moisture (wet basis, wb) by drying at 105 °C in a hot air convective drying oven (Yiheng Technology Co., Ltd., Shanghai, China) to constant weight. ## 2.2. Drying Experiments Fresh ginger slices were dried in a laboratory-scale HACD (Yiheng Technology Co., Ltd., Shanghai, China) at an air velocity of 0.8 m/s (Figure S1). According to preliminary tests and previous studies, the drying temperatures were set at 60, 70, and 80 °C and the loading densities were set at 3, 4, and 5 kg/m2, respectively [27]. In the drying chamber, the relative humidity of the environment was maintained at 15 to $20\%$. The samples were weighed every 0.5 h under different drying conditions until the final moisture level reached below $10\%$ (wb). Each experiment was performed in triplicate. ## 2.3. Drying Curves Moisture content (MC) (dry basis, db), moisture ratio (MR), and drying rate (DR) were calculated using Equations [1]–[3], respectively [28]. [ 1]MC=Wt−WdWd [2]MR=Mt−MeM0−Me where MC and MR are the moisture content (g/g, db) and the moisture ratio (%), respectively; Wt (g) and Wd (g) are the mass at any time and the constant mass after drying at 105 °C, respectively; Mt (g/g, db), M0 (g/g, db), and Me (g/g, db) are the MC at any time, initially, and at equilibrium, respectively [29]. [ 3]DR=Mt+dt−Mtdt where DR is the drying rate (g/g min, db); Mt, Mt+dt, and t are the moisture contents (g/g, db) at t and t + dt and the drying time (h), respectively. ## 2.4. Water Status An LF-NMR instrument (MesoMR23-060H-I, Niumag Corp., Shanghai, China) was adopted to monitor the water status of the drying process. For T2 determination, the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence was used, with the typical parameters of TW = 2500 ms, TE = 0.2 ms, NECH = 14,000, and NS = 8. ## 2.5. Colour The colours of the ginger slices were measured using a NH310 high-quality portable colorimeter (Shenzhen 3NH Technology Co. Ltd., Shenzhen, China). A D65 light source was used, with a measuring aperture of φ8 mm. Before measuring, a white standard plate was used to calibrate the chroma meter. The CIE LAB colour parameters L* (whiteness), a* (redness), and b* (yellowness) were used to express the colours of the samples. The total colour difference (∆E) and browning index (BI) were calculated by Equations [4]–[6] to describe the colour changes [30]:[4]ΔE=L−L02+a−a02+b−b02 where the subscript 0 refers to the values of the parameters of fresh ginger. [ 5]BI=100(x−0.31)0.17 [6]x=a+1.75L5.645L+a−3.012b* ## 2.6. Rehydration Coefficient The rehydration coefficient (RC) was used to determine the rehydration capacities of dried ginger slices. The dried ginger slices were placed in 250 mL of 95 °C constant-temperature water for 10 min. After removal from the water, the surface moisture was removed by blotting with filter paper, and the rehydrated samples were weighed. Each experiment was performed in triplicate. The RC values were calculated using Equation [7] [5]:[7]RC=W−W0W0 where W (g) and W0 (g) are the weight of the rehydrated sample and the initial weight of the dried sample, respectively. ## 2.7. Specific Energy Consumption Specific energy consumption (Ekg) is the energy consumption necessary for drying 1 kg of raw ginger. To determine the precise power consumption, the electricity meters (DDS7738, Shanghai Li Hua Electric Meter Factory, Shanghai, China) were attached directly to the drying oven. Ekg was calculated using Equation [8] [31]:[8]Ekg=P×tW0 where P is the power (kW), t is the drying time (h), and W0 is the initial weight of the sample (kg). ## 2.8. Gingerol Contents Ginger samples (0.5 ± 0.01 g) were mixed with $75\%$ methanol (25 mL) in a conical flask with a cap. Then, the samples were ultrasonically extracted (KQ-500DE ultrasonic bath, Kunshan Ultrasonic Instrument Co. Ltd., Shanghai, China) at 50 °C and 200 W for 40 min. Furthermore, after centrifugation at 5500× g for 10 min, the filtrate was filtered through a 0.22 μm organic membrane filter to obtain the extract. High-performance liquid chromatography (HPLC) (Shimadzu Corp., Kyoto, Japan) was used to determine the contents of 6-gingerol, 8-gingerol, and 10-gingerol in the ginger extracts. A C18 column (Phenomenex Corp., Torrance, CA, USA; 4.6 mm × 250 mm, i.d., 5 μm) was used for chromatographic separation. The mobile phase was prepared using acetonitrile (A) and water (B). The gradient elution programme was as follows: 0–30 min, 35–$70\%$ A; 30–40 min, $70\%$ A; 40–45 min, 70–$100\%$ A. The flow rate was set at 1 mL/min, the injection volume was 20 μL, and the column temperature was maintained at 30 °C. The peaks for gingerol content were monitored at 280 nm. ## 2.9. Box–Behnken Design (BBD) A Box–Behnken design (BBD) with three factors (X1, drying temperature (°C); X2, thickness (mm); X3, loading density (kg/m2)) at three levels was performed to optimise the conditions of drying ginger in HACD. As shown in Table 1, two dependent variables (specific energy consumption and gingerol content) were taken as the response for acquiring a statistical model. A total of 15 experimental tests (including 3 centre points) are presented in each response surface analysis using a full quadratic equation as follows [32]. [ 9]Y=b0+∑$j = 1$kbjXj+∑$j = 1$kbjjXj2+∑ ∑i<jbijXiXj where Y, Xi, and Xj represent the response variables, and the independent variables b0, bj, bjj, and bij are the constant coefficients. ## 2.10. GC-IMS Analysis Parameters A 0.5 g (d.b.) sample of ginger in the fresh and dry state were placed in a 20 mL headspace vial and incubated at 80 °C for 10 min, respectively. After incubation, the 300 μL headspace extractions were injected into an injector automatically by a syringe with a temperature of 85 °C and separated by a GC column (FS-SE-54-CB-0.5, 15 m × 0.53 mm × 0.5 μm). The carrier gas was high-purity N2 ($99.999\%$) with the flow at 2 mL/min for 2 min, then increasing to 100 mL/min for 23 min, and finally being maintained at 150 mL/min for 10 min. The total GC runtime was 35 min. The pre-separated ions were driven to the 9.8 cm drift tube after a 3H ionisation source (500 V) in positive ion mode with a temperature of 45 °C and the nitrogen flow rate was 150 mL/min. The VOCs were identified by the RIs of standard substances in the GC-IMS library (G.A.S.) and the drift time of experimentally determined measures. ## 2.11. Statistical Analysis The results are expressed as the mean ± standard deviation (SD) and were subjected to analysis of variance (ANOVA) tests in triplicate. Differences were considered significant at the $95\%$ confidence level ($p \leq 0.05$). Identification of volatile compounds and the establishment of fingerprint profiles were carried out using GC-IMS library search software and LAV software (G.A.S., Dortmund, Germany). ## 3.1. Drying Characteristics In this study, three drying variables were selected to evaluate the drying characteristics of ginger during the HACD drying process: temperature (60, 70, and 80 °C), thickness (2, 4, and 6 mm), and loading density (3, 4, and 5 kg/m2). As shown in Figure 1a–f, the drying curves and drying rate curves of ginger revealed that the moisture content under the three variables gradually decreased with the extension of drying time. Compared with that at 60 °C, the drying times at 70 and 80 °C were reduced by $28.2\%$ and $58.8\%$, respectively. In contrast, the effects of thickness and loading density on the drying time were positively correlated. The shortest drying times were observed when the thickness was 2 mm and the loading density was 3 kg/m2. Furthermore, the drying rate increased with increasing drying temperature, while it had negative correlations with the thickness and the loading density. It is worth noting that a constant-rate phase followed by a rate-reducing phase was found during the drying process, which is consistent with the findings of Mahayothee et al. [ 27]. It suggests that the mechanism of the removal of water is internal diffusion. It was also found that the higher temperature led to a shorter duration of the constant-velocity phase, but the thickness and loading had limited effects on the drying rate. Therefore, temperature is the main factor affecting the drying of ginger in hot air, and there were two stages in the drying process: surface vaporisation control and internal diffusion control. ## 3.2. Evaluation and Prediction of Water State by LF-NMR The water state and distribution during the drying process followed a basic mechanism [33]. Figure 2a show a clear display of T2 on the waterfall colour map during the HACD drying process. There were three different water states during the ginger drying process: T21 in the range of 0.01–1 ms indicated a bound state of water (bound water), T22 at approximately 10 ms indicated an immobile state of water (immobile water), and T23 at approximately 100 ms indicated a free state of water (free water). Their ratio in fresh ginger slices was 0.72:12.79:86.49 (T21:T22:T23, %), indicating that the water in ginger was mainly free water. According to the freedom degree (T2) of water and the moisture content (A2, area of each peak) at different temperatures, the mobility of water increased due to heating in the early stages of drying. This result is in agreement with the transient rise in the drying rate shown in Figure 1d–f. In addition, relatively balanced moisture diffusion stages were found at all three temperatures during the drying process, where only free water (A23) was slowly reduced. This phase ended at 270, 180, and 90 min for the drying temperatures of 60, 70, and 80 °C, respectively. Notably, the corresponding drying rate also had a relative equilibrium period at the uniform drying stage. This indicated that the constant rate of ginger drying is dominated by the reduction in free water on the sample surface, which is consistent with the drying rate curve. After reaching a critical point, A23 dropped rapidly, and A22 and A21 also dropped slowly. This was the decelerated drying phase, and the internal diffusion of the material was the dominant factor [34]. Although LF-NMR could be used to analyse and characterise the drying behaviour of ginger, it was necessary to develop a supervised quantitative method to predict the moisture contents of unknown samples. A correction model for predicting the moisture content of ginger was established using PLSR based on the strong collinear properties of NMR signal parameters [35]. According to the NMR calibration model for MR at 60, 70, and 80 °C (Figure 2b–d), the R2c values were 0.919, 0.997, and 0.937; the R2cv values were 0.912, 0.981, and 0.921; the RMSEC values were 0.089, 0.017, and 0.080; the RMSECV values were 0.100, 0.043, and 0.100, respectively. The higher R2 and lower RMSE values indicated that the PLSR model exhibited good performance with regard to calibration and cross-validation. Therefore, LF-NMR was an effective method for analysing and predicting the HACD drying process of ginger. ## 3.3. Colour and BI Colour is an important attribute that determines the acceptability of the sample [9]. As shown in Table 2, the colour parameters and browning index of dried ginger under the three variables changed greatly after drying. Under different conditions, ΔL, Δa, and Δb* values ranged from –9.30 ± 0.03, 1.63 ± 0.03, and –1.50 ± 0.19 to –4.99 ± 0.07, 3.14 ± 0.05, and –0.36 ± 0.18. Therefore, the dried sample showed a darker, redder, and bluer colour than the fresh sample did. The significantly higher variation degree of ΔL* than those of Δa* and Δb* indicated that the brightness played a major role in the colour change during drying. Temperature had a large effect on colour change, and the maximum value of ΔE occurred at 80 °C. Moreover, the temperature and load variables significantly affected the BI value ($p \leq 0.05$). The dried ginger processed at 80 °C had the maximum BI value (49.91 ± 0.50). As reported by Martins et al. [ 36], the changes in the colour parameters and BI values at high temperatures can be mainly attributed to the nonenzymatic browning reactions of food. ## 3.4. Rehydration Coefficients Rehydration determinations were conducted to evaluate the qualities of the dried ginger slices obtained under different drying conditions. Table 2 shows the results of the rehydration coefficients of dried ginger under the three variable conditions of temperature, thickness, and loading. Overall, both the maximum and minimum rehydration coefficients (5.57 ± 0.16 and 3.34 ± 0.36, respectively) were found in the thickness variable group, with a significant difference ($p \leq 0.05$). In contrast, no significant differences were detected in the rehydration rates under the temperature variable ($p \leq 0.05$), and differences produced by loading were not always significant. This suggested that slice thickness was the main factor affecting the rehydration rate of food products, and they exhibited a negative correlation. This phenomenon might be attributed to the structural modifications in the food during drying that affect the rehydration capacity [5]. For large-thickness samples, a longer drying time resulted in sealed surface capillaries [37]. In contrast, severe volume shrinkage and compact internal structures appeared in small-thickness dried samples, which had enhanced water absorption due to the pumping function created by rehydration. Therefore, it is necessary to select an appropriate slice thickness during the drying process. ## 3.5. Gingerol Content 6-Gingerol, 8-gingerol, and 10-gingerol are the main phenolic substances in ginger, and they are also relatively abundant in dried ginger [38]. As shown in Table 2, there was no significant difference in the contents of the three gingerols at different loading densities ($p \leq 0.05$). Under different thickness conditions, the contents of 6-gingerol, 8-gingerol, and 10-gingerol were 8.25 ± 0.11–9.08 ± 0.38 mg/g, 2.05 ± 0.01–2.96 ± 0.90 mg/g, and 4.24 ± 0.18–4.89 ± 4.65 mg/g, respectively. Only the 10-gingerol contents under the thickness variables of 2 and 6 showed a significant difference with that under the thickness variable of 4 ($p \leq 0.05$). Therefore, the loading density and the thickness had relatively small effects on the overall content of gingerol. Furthermore, a positive correlation between the overall gingerol content and the temperature was observed. When ginger was dried at 60 °C, the overall gingerol content of the obtained product was smallest, with a 6-gingerol content of 5.78 ± 0.24 mg/g, 8-gingerol content of 1.27 ± 0.11 mg/g, and 10-gingerol content of 2.48 ± 0.25 mg/g. The reason for this might be that the longer drying time caused a greater decomposition of gingerol. The maximum 6-gingerol content (7.97 ± 0.63 mg/g) of dried ginger processed at 70 °C is consistent with the results of previous findings [4]. Compared with the 6-gingerol content, the contents of 8-gingerol and 10-gingerol were relatively stable. In summary, temperature was the main factor affecting the overall gingerol content in dried ginger. ## 3.6.1. Specific Energy Consumption Drying is a highly energy-intensive process, so energy consumption is a necessary indicator for determining whether a drying method is reasonable [39]. Figure 3a–c show the effects of drying temperature, thickness, and loading density on the Ekg of dried ginger. Ekg was positively correlated with temperature. When the temperature increased from 60 to 80 °C, Ekg increased significantly by $11.66\%$ (Table 3). Furthermore, Ekg was lower than 1.7 kW h/kg for ginger with thicknesses of 2–3 mm, and a further increase in thickness caused Ekg to be as high as 2.15 kW h/kg. Ekg was reduced by $20\%$ with respect to the loading density as the load increased from 3 kg/m2 to 5 kg/m2. The results of the ANOVA for the Ekg of the dried samples obtained by HACD drying are given in Table S1. In the analysis, only the variable X1X3 was not significant ($p \leq 0.05$). This was due to the increase in temperature that shortened the drying time and ultimately decreased Ekg. The higher R2 of Ekg (0.9981) and the nonsignificant lack-of-fit term (0.0718) confirmed that the model fit well and was appropriate for the experimental data. According to the F value representing the effects of the variables on the response values, the variables corresponded to Ekg in the order of X3 > X2 > X1. A p value less than 0.05 indicated that the model term was significant, and a p value less than 0.01 showed that the model term was extremely significant. After the application of the response surface regression process, the predicted polynomial equation was as shown:[10]Ekg=1.74+0.069X1+0.13X2−0.22X3−0.031X1X2−0.021X1X3−0.16X2X3+0.064X12+0.11X22−3.292×10−3X32 ## 3.6.2. Total Gingerol Content The total gingerol content (TGC) was determined by summing each of the three gingerol contents. Figure 3d–f and Table 3 suggest that the temperature exerted a significant effect on the TGC ($p \leq 0.05$). As the temperature increased from 60 to 80 °C, the TGC showed a trend of first increasing and then decreasing. The maximum value (21.37 mg/g) of dried ginger obtained at 70 °C might be attributed to the highest content of 6-gingerol. Compared with that of the dried ginger with a thickness of 2 mm, the TGC of the dried ginger with a thickness of 6 mm was reduced by $44.04\%$ because the longer drying time necessary for the increased slice thickness was detrimental to the stability of gingerol. In contrast, the loading density had little effect on the TGC. In the interaction effect, only the effect of X1X3 on the TGC was significant ($p \leq 0.05$). Table S2 shows the ANOVA results for the TGCs in the dried samples obtained by HACD drying. The R2 value was 0.9501, and the lack of fit was not significant ($p \leq 0.05$). X1 had the largest F value, indicating the significant effect of the temperature on the TGC. The full quadratic model was obtained as:[11] TGC=15.05+0.93X1−0.25X2−0.27X3+0.93X1X2−1.65X1X3−0.68X2X3−2.75X12+0.69X22+0.049X32 ## 3.6.3. Determination and Experimental Validation of Optimal Conditions To meet the requirements of energy savings and quality assurance in actual production, the minimum Ekg and maximum TGC values were used to optimise the drying conditions of the samples. The optimal drying conditions obtained by RSM were as follows: a temperature of 66.41 °C, thickness of 2 mm, and loading density of 5 kg/m2, with an Ekg and a TGC of 1.65 kW h/kg and 16.68 mg/g, respectively. Comparatively, the final experimental values were 1.78 ± 0.04 kW h/kg and 18.54 ± 1.18 mg/g, respectively. They were essentially consistent with no significant differences within a $95\%$ confidence interval, indicating the applicability of RSM in optimising the conditions of the HACD drying process of ginger. ## 3.7. The Volatile Compounds Identified and the Volatile Fingerprints under Fresh and Dried Ginger by HS-GC–MS The VOCs of fresh (FG) and dried (DG) ginger were evaluated by HS-GC-IMS. The topographic plots of the VOCs were obtained after normalising the ion drift time and the position of the reaction ion peaks. In the plots, the X-axis indicates the relative drift time and the Y-axis indicates the retention time [40]. Each dot on the diagram represents a volatile component (Figure 4b). The colour of the dots indicates the signal intensity, with blue meaning low intensity and red meaning high intensity. As shown in Figure 4a, the difference in volatile compounds between fresh and dried ginger can be clearly captured. The signals of some VOCs in dried ginger disappeared or were diminished, which is opposite to the VOCs in the fresh samples. The characterisation of VOCs of ginger was obtained by comparing retention index, retention time, and drift time, while the National Institute of Standard and Technology database (NIST) was also utilised. The results of the analysis are shown in Table 4; the fresh and dried ginger contained a wide range of compounds, with 48 different kinds of VOCs identified and 21 peaks not identified. The VOCs of ginger included aldehydes [11], esters [9], alcohols [7], terpenoids [7], ketones [7], acids [3], and others [4], which is consistent with a previous study of ginger in Yu et al. [ 20]. Further, in order to provide a comprehensive overview of the spots identified in fresh and dried ginger, the fingerprint was established (Figure 4c). In the diagram, one sample was represented with a row and one VOC was represented with a column, and five samples each of fresh and dried ginger were used for evaluation. A noticeable trend was that the concentration of 12 compounds vanished after drying, which were benzothiazole, (E)-2-octenal, α-Phellandrene, hexanoic acid, 2,4-heptadienal, β-Pinene, 2-Heptanone, ethyl 2-methylbutanoate, 1-Hexanol, Nerol, Linalool, 4-hydroxy-2,5-dimethyl-3(2H)-furanone, and 2-Heptanol. In addition, the contents of butan-2,3-dione, pentanal, 3-methylpentane, limonene, 2-phenylethanol, propyl hexanoate, furan, 2-butyl, (2E,4E)-2,4-octadienal, trans-2-hexenal, β-ocimene, methyl acetate, and 1-octanol were dramatically reduced after drying. Nevertheless, the contents of diethyl succinate, hexyl acetate, pyrazine, methyl, 2-methylbutanal, and pentan-2,3-dione were also higher in dried compared to fresh ginger. The above results may be caused by the deoxygenation of compounds produced at high temperatures or the conversion of sesquiterpenoids into monoterpenoids. ## 3.8. Distinction of Fresh and Dried Ginger by PCA Analysis As a well-known stoichiometric dimensionality reduction method, the principal component analysis (PCA) method was used to visualise the large amount of data obtained from HS-GC-IMS results [40]. The results are shown in Figure 5a, and the data basis for the PCA was the peak intensity of each compound in Table 4. The PC1, PC2, and PC3 explained $59.3\%$, $31.1\%$, and $4.1\%$ of the accumulative variance contribution rate, respectively. The PC1 and PC2 accounted for $90.4\%$ of the total variance and they were considered adequate for further discussion. As shown in Figure 5a, the five samples were located in close proximity and the fresh ginger and dried ginger were well separated. The results showed that the aroma characteristics of fresh and dried ginger samples were significantly different, and the GC-IMS technique as a sensitive flavour detection tool could achieve rapid determination of VOCs of the samples before and after processing. ## 3.9. Correlation Analysis of Moisture and Quality Indicators Related to Drying A correlation matrix was used to determine the correlation between moisture and quality parameters of the ginger drying process by the Pearson correlation test. The correlation heat-map in Figure 5b shows that the MR was negatively related to the TGC ($r = 0.95$) and positively related to colour parameters L and a ($r = 0.99$, and 0.89), but negatively related to b ($r = 0.86$). In addition, all VOCs in ginger were significantly correlated ($p \leq 0.05$) with MR except for 2-methylpropanol. Among them, diethyl succinate ($r = 0.31$), hexyl acetate ($r = 0.99$), pyrazine, methyl ($r = 0.24$), pentan-2,3-dione ($r = 0.84$), and 2-methylbutanal ($r = 0.99$) were negative related to the MR, while the other VOCs were positive related to the MR (0.41 < r < 0.99). This is consistent with the results of Figure 4c, indicating that the drying treatment increased the content of these VOCs. These correlations found in this study may indicate that the alterations of ginger quality were intimately related to moisture diffusion during drying. Furthermore, GC-IMS data have the potential to effectively distinguish ginger samples with different dryness levels. ## 4. Conclusions This study integrated several quality attributes to evaluate the three drying variables and optimised the optimal drying process for ginger, and further analysed the differences in volatile compounds of ginger before and after drying. At different variables, the ginger drying process could be divided into three stages: a short-term increasing-rate drying period, a constant-rate drying period, and a rapidly decreasing-rate drying period, where the constant-rate phase is mainly a slow decrease of free water. The optimal drying process was determined by combining multivariate analysis methods: a temperature of 66.41 °C, thickness of 2 mm, and loading density of 5 kg/m2. HS-GC-IMS analysis revealed significant differences in volatile compounds between fresh and dried ginger, with heat treatment leading to a decrease in the levels of most terpenoids and aldehydes. In addition, the correlation analysis yielded a close correlation between the moisture content and the changes in volatile compounds. The method established in this study can be further used for the optimisation of other drying techniques for food products and the exploration of the effect of moisture content on key aroma substances. ## References 1. 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--- title: Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? authors: - Jing Liu - Huibin Lu - Xiuru Zhang - Xiaoli Li - Lei Wang - Shimin Yin - Dong Cui journal: Entropy year: 2023 pmcid: PMC10047945 doi: 10.3390/e25030396 license: CC BY 4.0 --- # Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment? ## Abstract So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale. ## 1. Introduction Epidemiological studies show that diabetes is a high-risk factor for age-related cognitive impairment and dementia. In particular, type 2 diabetes mellitus (T2DM) can lead to impaired cognitive function, increase the risk of mild cognitive impairment (MCI) and Alzheimer’s disease (AD), and seriously affect the survival and quality of life of patients [1]. MCI is the early stage of AD and mainly manifests in the decline of cognitive function, but daily living ability is not affected. However, with the development of the disease course, it not only evolves into dementia but also exists with patients for a long time, further affecting human health. Research data show that the prevalence of MCI in T2DM patients is as high as $45.0\%$ [2]. With the continuous improvement of people’s living standards and the aging population’s deepening, the prevalence of T2DM has increased, and the number of patients with dementia caused by it has also increased. Therefore, early diagnosis, intervention, and treatment of MCI patients have great practical significance. The electroencephalogram (EEG) is a nonlinear, non-stationary, and multi-dimensional complex signal. It has been widely used in researching epilepsy, AD, schizophrenia, MCI, and other brain diseases [3,4,5,6]. With the development of nonlinear dynamics, complexity analysis has become a popular trend in the study of EEG time series, which can reflect the characteristics of the dynamic system. Entropy represents the rate of new information generation and can be used to calculate the irregularity and complexity of nonlinear dynamic signals, which has been widely used in various fields [7,8,9]. Pincus proposed the concept of approximate entropy to measure the regularity and stability of time series [10] and used it in fetal heart rate analysis to detect subtle and potentially important heart rate differences that are not visually obvious [11]. Nie et al. evaluated human health by analyzing the approximate entropy of human pulse signals and found that in subjects with poor health, the individual adaptive ability would decrease, leading to a decrease in the approximate entropy value [12]. Richman and Moorman proposed sample entropy, which overcomes the shortcoming of approximate entropy not self-matching and reduces the dependence on data length [13]. In a study of pressure time series centers, Montesinos demonstrated that sample entropy is less dependent on the data length than approximate entropy, with greater consistency and ability to distinguish different experimental groups [14]. Based on the order pattern of elements in the time series, Bandt and Pompe proposed permutation entropy [15]. Permutation entropy has a simple operation process, fast calculation speed, and widespread use in various fields. Seker et al. analyzed the distribution of permutation entropy in five brain regions to observe the differences between MCI, AD, and the normal control group. They found that the entropy values of AD patients were significantly lower than that of the normal control group, and the MCI group was in the middle level [16]. However, permutation entropy is only a simple sort of multi-dimensional space sequence, ignoring the amplitude changes of elements in the sequence. Chen et al. defined the concept of fuzzy entropy for the first time. As an improvement of the sample entropy algorithm, fuzzy entropy blurs the similarity measurement formula with an exponential function, making entropy values transition smoothly with changing parameters. At the same time, it inherits sample entropy’s characteristics of relative consistency and the ability to handle short data sets [17]. Liu et al. proposed increment entropy, which obtained symbol information based on the fluctuation trend mapping of adjacent elements and amplitude information based on quantitative resolution and combined the two to quantify irregular time series. In the study of real EEG signals of epilepsy, increment entropy can well detect the amplitude and structural changes of time series and has a good performance in detecting epileptic seizures [18]. Dispersion entropy is a new irregularity index proposed by Rostaghi and Azami [19], which extracts signal features by mapping time series into finite integer time series. Rostaghi et al. used dispersion entropy for fault diagnosis and state detection of industrial rotating equipment. They found that dispersion entropy can detect changes in signal amplitude and frequency simultaneously, with better results than approximate entropy and permutation entropy. The calculation speed is faster than the approximate entropy [20]. Most of the actual physiological signals are multivariate signals. The above methods are only suitable for processing univariate time series and cannot express the long-term correlation and complexity of multivariate time series. Researchers extend the concept of entropy and combine it with multi-scale to propose multivariate multi-scale entropy to analyze multi-channel signals. Ahmed and Mandic presented multivariate multi-scale sample entropy (mvMSE) based on multi-scale and sample entropy and analyzed multi-channel signals on different scales [21]. This algorithm can analyze the complexity of multivariate time series and widespread use in physical and physiological signals [22,23,24]. Li et al. substituted the fuzzy membership function for the hard threshold criterion of pattern similarity judgment in mvMSE and obtained multivariate multi-scale fuzzy entropy (mvMFE). Simulation results showed that introducing the fuzzy membership function could effectively improve the statistical stability of the algorithm, and this algorithm could use to guide the research of noninvasive early warning of cardiovascular diseases [25]. Subsequently, Azami and Escudero proposed the refined composite multivariate multi-scale fuzzy entropy (RCmvMFE), which can use to analyze short time series and improve the stability of mvMFE [26]. It has been applied to rolling bearing fault diagnosis [27], horizontal oil-water two-phase flow analysis [28], multi-channel financial data dynamic complexity measurement [29], and other research fields. Morabito et al. proposed multivariate multi-scale permutation entropy (mvMPE) algorithm based on permutation entropy to evaluate the complexity of physiological signals. This algorithm has been used to distinguish the brain states of subjects with AD and MCI patients from those of normal healthy older people. This algorithm has the advantages of fast operation speed, simple concept, and robustness to noise and artifacts. However, the mvMPE has the same defect as permutation entropy in ignoring the difference in amplitude [30]. Azami et al. introduced multivariate multi-scale dispersion entropy (mvMDE) and used this algorithm to analyze 148-channel MEG signals to distinguish AD patients from controls. The results showed that: on the short scale, the mean value of mvMDE in AD patients was lower than the control group; on the long scale, the mean value of mvMDE in AD patients was greater than the control group, which was consistent with the results obtained by mvMFE algorithm [31]. Wang et al. extended the increment entropy to multi-scale increment entropy and multivariate multi-scale increment entropy (mvMIE). They used them to detect flow pattern transition in multi-phase flow. The results showed that mvMIE could effectively detect the evolution behavior of different flow patterns over time as flow conditions change and can reveal the dynamic complexity of varying flow patterns [32]. The entropy algorithm has widespread use in biology, finance, engineering, neuroscience, and other fields, and it has also made some achievements in analyzing EEG signals of patients with neurological diseases. Studies have shown that complexity and functional connectivity coexist in actual EEG signals, especially in MCI EEG signals [33,34]. However, so far, the articles using the multivariate multi-scale entropy algorithm mainly analyze the complexity of multi-channel data without explicitly describing what characteristics of signals these algorithms measure and what factors affect these characteristics. In this article, we analyze six commonly used multivariate multi-scale entropy algorithms from a new perspective and discuss the combined effect of the complexity of each channel signal and the coupling strength between channels on the multivariable entropy value for the first time, which is unprecedented. This paper solves the problem that other reports do not specify what signal characteristics the algorithm measures and which factors affect these features. Moreover, it also studies which algorithm is more suitable for analyzing MCI EEG signals and uses it to analyze the actual EEG signal. Firstly, we simulate and analyze each algorithm using signals with different complexity and correlation to explain explicitly which characteristics each algorithm measures. After that, we jointly analyze the relationships of signal coupling strength, single-channel entropy, and multi-channel entropy to deeply understand the influence of signal complexity and coupling strength on multi-channel entropy. Secondly, we simulate the performance of each algorithm using signals with different noise intensities and data lengths. Consider the above simulation results to search for the algorithm suitable for analyzing MCI EEG signals. Then, we used the RCmvMFE algorithm to analyze the EEG signals of patients with MCI and normal cognitive to explore the nonlinear dynamic characteristics of the EEG signals of MCI. Finally, we analyzed the correlation between the RCmvMFE value of all participants and their neuropsychological scale scores to investigate whether there is a correlation between nonlinear dynamic characteristics of EEG signals and cognitive function. This paper structure is as follows: the second section is the theoretical introduction of six kinds of multivariate multi-scale entropy algorithms and the acquisition of participants’ information and EEG signals; the third section includes the simulation analysis of multivariate multi-scale entropy algorithm and the actual EEG signal analysis; the fourth section is a discussion of this study and other related research results; and the fifth section is the conclusion of this paper. ## 2. Materials and Methods The mvMSE algorithm combines multivariate sample entropy and multi-scale and can analyze multi-channel signals with different scale factors. The mvMFE algorithm uses a fuzzy membership function to replace the hard threshold criterion of pattern similarity judgment in mvMSE, which makes the entropy transition smoothly with parameter changes and effectively improves the statistical stability of the algorithm. The RCmvMFE algorithm improves in two aspects based on mvMFE, including enhancing the coarse-grained algorithm and introducing the adjustment factor to improve the fuzzy membership function to define the similarity. It avoids information loss when the data length of different scale factors is very diverse and can stably detect the dynamic behavior of complex systems. The mvMPE algorithm extends the permutation entropy algorithm to the multivariate time series. It only needs to consider whether the sequence pattern is the same and does not need to consider the threshold value and the distance between the space vectors. It has the advantages of a simple operation process and fast calculation speed. The mvMDE algorithm maps the time series into a finite integer time series to construct the dispersion patterns, which can simultaneously detect the change of signal amplitude and frequency and its effect better than the permutation entropy. The mvMIE algorithm produces incremental time series according to the fluctuation tendency of adjacent elements. It obtains the sign and the magnitude parts to quantize irregular time series in combination patterns, which can effectively detect the dynamic complexity of different time series. ## 2.1. Multivariate Multi-Scale Sample Entropy (mvMSE) From a p-channel time series U={uk,a}$a = 1$,2…Lk=1,2…p with the data length L, we gain a coarse-grained time series X={xk,b}$b = 1$,2…Nk=1,2…p through the following coarse-grained process, as in Equation [1]:[1]xk,b(s)=1s∑a=(b−1)s+1bsuk,a,1≤k≤p,1≤b≤Ls=NHere s stands for the scale factor. The multivariate embedding vector Xm(i) for coarse-grained time series X expresses as:[2]Xm(i)=[x1,i,x1,i+d1,…,x1,i+(m1−1)d1,x2,i,x2,i+d2,…,x2,i+(m2−1)d2,…,xp,i,xp,i+dp,…,xp,i+(mp−1)dp] where each channel’s embedding dimension mk and time delay dk can be different values, for convenience, we set the same embedding dimension m and delay d for each channel and n=m×d, $i = 1$,2,…,N−n. Calculate the Chebyshev distance dijm between the multivariate embedding vector Xm(i) and Xm(j) by using the following formula:[3]dijm=d[Xm(i),Xm(j)]=maxl=1,2…m(\|x(i+l−1)−x(j+l−1)\|),i≠j Given the similarity tolerance r, the probability of dij≤r,i≠j is ϕim(r)=1N−n−1Pi, and then define a global quantity:[4]ϕm(r)=1N−n∑$i = 1$N−nϕim(r) There are p ways to extend the embedding dimension from m to m+1, and the expansion process can express as [m1,m2,…,mp] to [m1,…,mk+1,…,mp]. Thus, we obtained p new multivariate embedding vectors Xm+1(i). Repeat the above steps. Finally, we get the global quantity with embedding dimension m+1 is:[5]ϕm+1(r)=1p×(N−n)∑$i = 1$p×(N−n)ϕim+1(r) The multivariate multi-scale sample entropy defined by Shannon’s theorem is:[6]mvMSE(U,m,d,r)=−ln(ϕm+1(r)/ϕm(r)) ## 2.2. Multivariate Multi-Scale Fuzzy Entropy (mvMFE) According to Equations [1]–[3], we get the Chebyshev distance dijm between Xm(i) and Xm(j). Introduce the fuzzy membership function is to define the similarity degree Dijm,i≠j as follows:[7]Dijm(r)=1, 0≤dijm≤rexp(−ln2(dijm−rr)2),dijm>r Define the global quantity with the embedding dimension m as:[8]ϕm(r)=1N−n ∑$i = 1$N−n(1N−n−1∑$j = 1$,j≠iN−nDijm) We get p new multivariate embedding vectors Xm+1(i) based on the extension method in mvMSE. Similarly, we defined the global quantity with embedding dimension m+1 as:[9]ϕm+1(r)=1p×(N−n)∑$i = 1$p×(N−n)(1p×(N−n)−1∑$j = 1$,j≠ip×(N−n)Dijm+1) The multivariate multi-scale fuzzy entropy defined by Shannon’s theorem is:[10]mvMFE(U,m,d,r)=−ln(ϕm+1(r)/ϕm(r)) ## 2.3. Refined Composite Multivariate Multi-Scale Fuzzy Entropy (RCmvMFE) The algorithm of RCmvMFE has made two improvements based on mvMFE, one of which is to improve the traditional coarse-grained process. We can get s different coarse-grained time series through the improved coarse-grained process. Repeat the process of mvMFE for each coarse-grained time series, and finally calculate the entropy after averaging the obtained s global quantities. The improved coarse-grained process is as follows:[11]xk,b,t(s)=1s∑a=(b−1)s+tbs+t−1uk,a,1≤b≤Ls=N,1≤k≤p,1≤t≤s Based on Equations [2] and [3], we get s Chebyshev distance dij,tm of s multivariate embedding vectors. The second improvement is to introduce the improved fuzzy membership function to define the similar degree Dij,tm,i≠j,1≤t≤s as follows:[12]Dij,tm(r)=1, 0≤dij,tm≤λrexp(−ln2(dij,tm−λrr)2),dij,tm>λr Here the adjustment factor λ ranges from 0.5 to 1.5 typically. When the embedding dimension is m and m+1, the average of s global quantities express as follows:[13]ϕ¯m(r)=1s∑$t = 1$s1N−n−1∑$j = 1$,j≠iN−nDij,tm [14]ϕ¯m+1(r)=1s∑$t = 1$s1p×(N−n)−1∑$j = 1$,j≠ip×(N−n)Dij,tm+1 The refined composite multivariate multi-scale fuzzy entropy defined by Shannon’s theorem is:[15]RCmvMFE(U,m,d,r,λ)=−ln(ϕ¯m+1(r)/ϕ¯m(r)) ## 2.4. Multivariate Multi-Scale Permutation Entropy (mvMPE) Through Equation [1], we get the coarse-grained time series X={xk,b}$b = 1$,2…Nk=1,2…p. By introducing the embedding dimension m and time delay d, we reconstruct the coarse-grained time series:[16]Xm(i)={xk,i,xk,i+d,…,xk,i+(m−1)d},1≤i≤N−(m−1)d Sort the elements in each row of Xm(i) and match them with m! possible patterns πj,$j = 1$,2…m!. Calculate the probability of each pattern as follows:[17]P(πj)=#{i\|i≤N−(m−1)d,type(Xm(i))=πj}(N−(m−1)d)×p The multivariate multi-scale permutation entropy defined by Shannon’s theorem is:[18]mvMPE(U,m,d)=−∑$j = 1$m!P(πj)ln(P(πj)) ## 2.5. Multivariate Multi-Scale Dispersion Entropy (mvMDE) Through Equation [1], we get the coarse-grained time series X={xk,b}$b = 1$,2…Nk=1,2…p, then maps to Y through the normal cumulative distribution function (NCDF):[19]yk,$b = 1$σk2π∫−∞xk,bexp(−(t−μk)22σk2)dt Here μk and σk are the mean and the standard deviation of the time series X. The value of Y ranges from 0 to 1 and then mapping Y to Z, which ranges from 1 to c:[20]Zk,bc=round(c⋅yk,b+0.5) A multivariate embedding vector Zm(i),1≤i≤N−(m−1)d is generated based on Equation [2]. Set the embedding dimension and the time delay to the same value, expressed as mk=m, dk=d. Therefore, the length of each row vector in Zm(i) is ∑$k = 1$pmk=m×p. We are nonredundant to extract m elements from each row vector Zm(j), and there are Cm×pm options. Defined each option as ϕq(j), $q = 1$,2,…,Cm×pm, $j = 1$,2,…,N−(m−1)d. Map the selected m elements into a dispersion pattern πv0v1…vm−1. The following formula obtains the relative frequency:[21]p(πv0v1…vm−1)=#{j\|j≤N−(m−1)d,ϕq(j)has type πv0v1…vm−1}(N−(m−1)d)×Cmpm The multivariate multi-scale dispersion entropy defined by Shannon’s theorem is:[22]mvMDE(U,m,d,c)=−∑π=1cmp(πv0v1…vm−1)log(p(πv0v1…vm−1)) ## 2.6. Multivariate Multi-Scale Increment Entropy (mvMIE) Through Equation [1], we get the coarse-grained time series X={xk,b}$b = 1$,2…Nk=1,2…p, then calculate its increment time series zk,b−1=xk,b−xk,b−1. The increment time series Z={zk,b−1}$b = 1$,2…Nk=1,2…p is reconstructed according to Equation [16] to obtain Zm(i), $i = 1$,2,…,N−(m−1)d−1. Each element of Zm(i) maps into a word contains two parts: the sign and the magnitude. This formula generates the sign part:[23]sk,i+l=+1, zk,i+l>00, zk,i+$l = 0$−1, zk,i+l<0 The following formula generates the magnitude part:[24]qk,i+$l = 0$,std(Zm(i))=0min(R,zk,i+l×Rstd(Zm(i))),std(Zm(i))≠01≤k≤p,1≤i≤N−(m−1)d−1,1≤l≤(m−1)d where R is the quantization resolution generally set R≤4. By formula wk,i=Ul=0(m−1)dsk,i+lqk,i+l,1≤k≤p,1≤i≤N−(m−1)d−1, we combine the sign part and magnitude pate. There are up to (2×(R+1)+1)m possible combination patterns for time series with embedding dimension m and quantization resolution R. Define wn as each unique combination pattern in wk,i, and count its number as Q(wn). The relative frequency is defined as: [25]p(wn)=Q(wn)(N−(m−1)d−1)p The multivariate multi-scale increment entropy defined by Shannon’s theorem is:[26]mvMIE(U,m,d,R)=−∑$$n = 1$$(2(R+1)+1)mp(wn)log(p(wn)) ## 2.7. Participants and Neuropsychological Tests The present study included 20 subjects with T2DM who enrolled in the Special Medical Center of the Chinese People’s Liberation Army Rocket Force, whose medical diagnosis met the World Health Organization requirements. All 20 subjects received a general demographic assessment and a series of standardized neuropsychological assessments. Such as the Mini-mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Auditory Verbal Learning Test (AVLT) to test subjects’ memory and recall, including AVLT immediate recall, AVLT delayed recall and AVLT long-delayed recognition. The Trail Test parts A, B, and the Wechsler Adult Intelligence Scale (WAIS) to test the subjects’ executive ability and attention, the Boston Naming Test and the Semantic Fluency Test to test the subjects’ language ability, and the Frequently Asked Questions (FAQ) to test the subjects’ ability of daily living. Divide all subjects into two groups (8 control subjects and 12 MCI subjects) according to the score of the neuropsychological scale. The grouping criteria met the inclusion and exclusion criteria of MCI [33]. The neuropsychological scale scores were consistent with normality and homogeneity of variance. Statistical analysis of the neuropsychological scale score using an independent sample t-test in the statistical analysis software SPSS (version 25.0). The statistical results are shown in Table 1 and expressed as mean ± standard deviation. ## 2.8. EEG Recording and Preprocessing We collected the EEG signals in a quiet-dark room in the Neurology Department of the Rocket Force Special Medical Center of the Chinese People’s Liberation Army. Use the GES300 (Electrical Geodesics Inc., Eugene, Oregon, USA) 128-channel acquisition device to record the scalp EEG signals of participants. During EEG signal collection, participants sit in a comfortable armchair and stay in a closed-eye resting state. Bilateral mastoid processes served as reference electrodes. The impedance of all electrodes keeps below 5 KΩ, and the sampling frequency is 1000 Hz. Record the EEG signals with a duration of 300 s. We use a band-pass filter (0–200 Hz) and a notch filter (50 Hz) to filter the collected EEG signals. Afterward, we use the wavelet-enhanced independent component analysis method to preprocess the signal and remove EMG, EOG, and ECG artifacts. The wavelet-enhanced independent component analysis method describes in detail in Reference [35]. Then down-sample to 500 Hz and obtain the preprocessed 180 s EEG data. Select 32 electrodes distributed evenly across the scalp of the brain for analysis. These 32 electrode distributions meet the requirements of international standard leads and often use for study by researchers [36]. Divide them into six regions: frontal (F), central (C), parietal (P), occipital (O), left temporal (LT), and right temporal (RT). The schematic diagram of brain regions is shown in Figure 1. ## 3.1. Simulation Analysis In the simulation analysis of the characteristics of the following synthetic signals, the relevant parameters of the six multivariate multi-scale entropy algorithms are set as follows according to some related references [27,31,32]: In mvMSE, mvMFE, and RCmvMFE algorithms, we set each channel’s embedding dimension and time delay to 2 and 1, respectively, and set the similarity tolerance to $r = 0.15$×std(standard deviation). Set the adjustment value in RCmvMFE to λ=0.8. In mvMDE, mvMPE, and mvMIE algorithms, we set each channel’s embedding dimension and time delay to 3 and 1, respectively. Set the number of classes in mvMDE to $c = 3$ and the quantifying resolution in mvMIE to $R = 4.$ ## 3.1.1. Complexity Analysis To evaluate these algorithms’ ability to analyze the complexity of multivariate time series, we generated an uncorrelated three-channel time series consisting of 1/f noise and white Gaussian noise (WGN). The 1/f noise is a signal whose power spectral density is inversely proportional to frequency and has the characteristic of long-range correlation. The irregularity of 1/f noise is lower than that of WGN, but its complexity is higher than that of WGN. WGN is a signal with zero mean and one standard deviation. The total number of generated time series channels is always equal to 3, and as the number of 1/f noise channels decreases, the number of WGN channels increases correspondingly. Set the data length to 6000, and repeat the generation 20 times. Calculate the entropy values of the generated three-channel time series by six algorithms and compute their mean and standard deviation. The results are shown in Figure 2. As seen from the figure, for the four algorithms of mvMSE, mvMDE, mvMFE, and RCmvMFE, the entropy value of the three-channel 1/f noise signal almost remains unchanged or slowly decreases with the scale factor’s increase, while the entropy values of the other three variables time series decrease monotonically. When the scale factor is bigger than or equal to 2, the entropy value increases gradually with the 1/f noise channel number and reaches the highest when the three channels are all 1/f noise. That is consistent with the fact that multivariate 1/f noise is structurally more complex than multivariate WGN. Among them, the mvMSE entropy curves of the first two kinds of signals show an aliasing phenomenon when the scale factor is greater than 15 and the differentiation ability is poor. The entropy curves of mvMDE and mvMFE have a poor ability to distinguish four kinds of signals when the scale factor is 1 and 2, respectively. However, RCmvMFE can distinguish the four signals well at any scale, so RCmvMFE has better signal discrimination ability than other algorithms. For the mvMIE algorithm, the entropy value gradually increases with the increase in the 1/f noise channel number at any scale factor. However, there are some overlaps between the entropy results at a large scale and distinguish the four three-channel signals not well. However, the performance of the mvMPE algorithm is contrary to the above five algorithms: the entropy is unchanged with the increase in the scale factor, and the entropy decreases with the rise of the 1/f noise channel number at any scale factor. Moreover, there are some overlaps between the entropy results at a large scale and distinguish the four three-channel signals not well. From the analysis results, we can conclude that the four algorithms of mvMSE, mvMDE, mvMFE, and RCmvMFE can use to analyze the intra-channel correlation and multivariate signal complexity. Among them, the RCmvMFE algorithm has the best signal discrimination ability. The mvMIE algorithm cannot reflect the intra-channel correlation and only be used to analyze the complexity of multivariate signals, but the signal discrimination is not good. The mvMPE algorithm only measures signal irregularity and cannot reflect intra-channel correlation. ## 3.1.2. Correlation Analysis To evaluate these algorithms’ ability to analyze the inter-channel correlation of time series, we generated the two-channel 1/f noise sequence and the two-channel WGN sequence, respectively, with inter-channel correlation and inter-channel uncorrelation. For both the two-channel sequence with inter-channel correlation, the correlation coefficient between channels is 0.95. The data length is 6000 and repeats the generation 20 times. Calculate the entropy values of the generated data by six algorithms and compute their mean and standard deviation. The results are shown in Figure 3. As seen from the figure, for the three algorithms of mvMSE, mvMFE, and RCmvMFE, the entropy value of the correlated signals is always greater than the uncorrelated signals at any scale factor, whether it is a 1/f noise signal or WGN signal. That is consistent with the fundamental physics that the complexity of correlated signals is higher than the uncorrelated ones. The entropy values of the WGN signal decrease with the increase in the scale factor, while the entropy values of the 1/f noise signal basically remain unchanged. Finally, the correlated 1/f noise has the highest value, followed by the uncorrelated 1/f noise, correlated white noise, and uncorrelated white noise. The RCmvMFE algorithm shows this phenomenon when the scale factor is greater than 5, while the mvMSE and mvMFE algorithms show this phenomenon when the scale factor is greater than 6 and 15, respectively. These three algorithms reflect the intra-channel and the inter-channel correlation, and they can effectively distinguish the white noise and 1/f noise from the inter-channel correlation and uncorrelation on a large scale. Among them, with the increased scale factor, the RCmvMFE algorithm can distinguish the four signals earlier than the mvMSE and mvMFE algorithms and has better signal discrimination ability. For the mvMDE algorithm, the entropy value of the 1/f noise signal remains unchanged with the increase in scale factor. The entropy value with correlated signals is always smaller than the uncorrelated signals and they are well distinguished at any scale factor. The entropy value of the two WGN signals with correlated and uncorrelated decreases with the scale factor’s increase. However, the entropy values of the two signals are too close to well distinguished. For the mvMPE algorithm, regardless of whether there is a channel correlation between 1/f noise and white noise signal, the entropy value basically remains unchanged with the scale factor’s increase, and the entropy value of the 1/f noise signal is always smaller than that of the white noise signal. The entropy values of correlated and uncorrelated signals are too close that it is impossible to distinguish them well, which is the case for both 1/f noise and white noise. For the mvMIE algorithm, regardless of whether there is a channel correlation between 1/f noise and white noise signal, the entropy value decreases with the increase in scale factor, and the entropy value of the 1/f noise signal is always greater than that of the white noise signal. This algorithm can identify the 1/f noise signal and WGN signal, but it cannot distinguish whether there is a correlation between channels. According to the above analysis, mvMSE, mvMFE, and RCmvMFE algorithms reflect the intra-channel correlation and can distinguish whether there is a correlation between signal channels. Among them, the RCmvMFE algorithm can distinguish four kinds of signals at a smaller scale. The MvMDE can only recognize whether there is a correlation between 1/f noise signal channels. However, mvMPE and mvMIE cannot use to analyze the correlation between channels. ## 3.1.3. Joint Analysis of Coupling and Complexity We adopt the coupled MIX model for joint analysis to further study the influence of multi-channel signals’ coupling strength and their single-channel complexity on multi-channel entropy. The MIX model comprises periodic and random noise signals in a particular proportion. [ 27]MIX(p)=(1−p)×x+p×y Here, the periodic signal is x(t)=2sin(2πt/fs),fs=12Hz,$t = 0$,1,…,N−1. The random noise signal y is a sequence of length N uniformly distributed between −3 and 3. Replaced N × p points in periodic signal x with random noise signal y, where p represents the probability parameter range from 0 to 1. The data complexity generated by the MIX model increases with the increase in p. Extended the MIX model to obtain coupling MIX model. The coupling MIX model consists of the same MIX model mixed with two different MIX models to generate two-channel signals A and B. The expression of the coupling MIX model is as follows:[28]A=C×MIX(p0)+(1−C)×MIX(p1)B=C×MIX(p0)+(1−C)×MIX(p2) The value of probability parameters p0,p1,p2 and coupling coefficient C are both from 0 to 1. The complexity of A and B signal increases as the value of (p0,p1) and (p0,p2) increase, respectively. The correlation between A and B is related to the coupling coefficient C. When $C = 0$, the two signals are not correlated, and when $C = 1$, the data of the two channels are the same. The model parameters are as follows: the coupling coefficient C ranges from 0 to 1 with the step size of 0.1, probability parameters p0,p1,p2 are from 0 to 1, and the data length is $$n = 6000$.$ With the increase in C, the correlation between channels increases gradually. With the change of p0,p1,p2, the complexity of model data will change. Figure 4 shows the variation of multi-channel entropy with coupling coefficient C and single-channel entropy (SC-E). The figure shows that the multi-channel entropy of mvMSE, mvMFE, and RCmvMFE increases with the coupling coefficient C and its single-channel entropy. It shows that the entropy obtained by these three algorithms is affected by different signal complexity and coupling strength. For the mvMDE algorithm, the multi-channel entropy increases with the increase in the single-channel entropy when the coupling coefficient C is the same. Moreover, the multi-channel entropy value increases with C when the coupling coefficient C<0.7 and decreases with the rise in C when the coupling coefficient C>0.7 in the case of signal single-channel entropy value is the same. It shows that signal complexity and coupling strength also affect the entropy obtained by the mvMDE algorithm. However, it cannot reflect the complexity and coupling strength of the signal accurately. For mvMPE and mvMIE algorithms, the multi-channel entropy increases with the increase in single-channel entropy when the coupling coefficient C is the same. However, when the single-channel entropy of the signal is the same, the multi-channel entropy is unchanged with the increase in the coupling coefficient C. It indicates that the entropy obtained by these two algorithms cannot use to measure the coupling strength of signals with the same complexity. As can be seen from this simulation analysis: mvMPE and mvMIE algorithms cannot comprehensively describe the coupling strength between signal channels. The mvMDE algorithm is inaccurate when measuring the coupling strength between signal channels. However, the three algorithms of mvMSE, mvMFE, and RCmvMFE can better reflect the influence of the coupling strength between signal channels and the complexity of single-channel. ## 3.1.4. Noise Resistance Analysis To analyze the anti-noise performance of the six algorithms, we superimposed white noise with a different signal-to-noise ratio (SNR) on the coupled MIX model data to generate simulation signals. The model parameters are as follows: $C = 0.4$, p0=0.2, p1=0.5, p2=0.8 [37]. The data length is 6000 and repeats the generation 20 times. Then, add 20 dB, 10 dB, 0 dB, −10 dB, and −20 dB WGN on the model data to generate simulation signals with different SNRs. Calculate the entropy values of the simulation signals by six algorithms and compute their mean and standard deviation. The results are shown in Figure 5. The figure shows that when the additive noise is 20 dB and 10 dB noise to the original signal, the six multivariate multi-scale entropy algorithms show good performance in anti-noise. When the additive noise is 0 dB, except for the mvMDE algorithm, the entropy values change curves obtained by the other five multivariate multi-scale entropy algorithms are between the entropy change curves of 10 dB noise and −10 dB noise. However, when the additive noise is −10 dB and −20 dB, the results of mvMSE, mvMDE, mvMFE, RCmvMFE, and mvMPE algorithms are too different from that of the original signal. In the four algorithms of mvMSE, mvMDE, mvMFE, and RCmvMFE, the entropy values of the four algorithms with additive noise of −10 dB and −20 dB show an overall downward trend at any scale factors, which is very different from the entropy values of the original signal. The entropy result is larger than the original signal entropy value when the scale factor is less than 4. When the scale factor is greater than 6, the entropy result decreases with the increase in noise intensity and is less than the original signal entropy value. The deviation between the entropy value and the original signal entropy value increases with the growth of the scale factor, showing a big difference from the original signal entropy change trend. For the mvMPE algorithm, the entropy value is mainly unchanged with the increase in the scale factor with additive noise of −10 dB and −20 dB. When the scale factor is less than 4, the entropy value is almost equal to the original signal entropy value. When the scaling factor is greater than 5, the entropy value still changes little, and the characteristic that the original signal entropy value varies with the increase in the scale factor is lost. For the mvMIE algorithm, when the additive noise is −10 dB and −20 dB and the scale factor is greater than 2, the entropy value is lower than the original signal entropy value and unchanged with the increase in the scale factor. When the additive noise is −20 dB and −10 dB, the original signal submerges by the noise, and the entropy curve of the original signal is lost. When we analyze the actual signals, we should remove the noise interference as much as possible to avoid a large deviation in the results. ## 3.1.5. Data Length Analysis Use the coupling MIX model for simulation analysis to explore the influence of data length on multivariate multi-scale entropy algorithms. The model parameters are set as follows: $C = 0.4$, p0=0.2, p1=0.5, p2=0.8. The data length N ranges from 100 to 2000 with a step size of 100 and repeats the generation 20 times. Calculate the entropy values of the generated data by six algorithms and compute their mean and standard deviation. The results are shown in Figure 6. As seen from the figure, with the continuous increase in data length N, the values of the six multivariate multi-scale entropy algorithms all show an upward trend and gradually tend to be stable. The entropy values of mvMSE, mvMPE, and mvMIE algorithms tend to be stable when $$n = 700$$, while mvMDE, mvMFE, and RCmvMFE algorithms are basically stable when $$n = 500$.$ Based on all results of the above simulation analysis, the RCmvMFE algorithm has the best comprehensive performance ability. This algorithm is affected by signal complexity and correlation between signals and decreases with the decrease in signal complexity and coupling strength. According to existing research, complexity and functional connectivity coexist in the actual EEG, especially in the MCI EEG signal, which showed a trend of decreased complexity and functional connectivity compared with healthy controls [16,33,34]. After neurofeedback training, MCI patients have improved cognitive function and significantly increased signal complexity and functional connectivity across the brain simultaneously [38,39]. Combined with the characteristics of MCI EEG signals, we believe that the RCmvMFE algorithm is the most suitable algorithm for analyzing MCI EEG data among the six algorithms and is more likely to detect changes in the complexity and functional connectivity of MCI EEG data. ## 3.2.1. The RCmvMFE Analysis of EEG Divide the EEG signals of 180 s into non-overlapping five-second data segments. Use the RCmvMFE algorithm to analyze the data segments of different brain regions. The mean value obtained after removing abnormal values is the multi-channel entropy of each brain region. Set the parameters of the RCmvMFE algorithm as follows: the embedding dimension of each channel is 2; the time delay is 1; the threshold value $r = 0.15$×std; the adjustment factor λ=0.8; and the scale factor ranges from 1 to 20. Each segment of data is 5 s without overlap. The curves of RCmvMFE values of EEG signals in MCI and control groups of each brain region are shown in Figure 7. It can see from the figure that, in the six brain regions, when the scale factor is less than or equal to 4, the entropy of the MCI group is generally less than that of the control group. When the scale factor is greater than or equal to 10, the entropy of the MCI group is greater than that of the control group. The entropy changes of EEG signal in MCI patients under different scale factors may be related to cognitive decline. The decrease in entropy of EEG signals in MCI patients under small-scale factors may be associated with the synaptic inefficiency caused by neuron death, neurotransmitter changes, and loss of local neural network connection. Therefore, a more extensive range of neurons is required to participate in neural activities, increasing entropy under large-scale factors. In addition, we compared the RCmvMFE values in each brain region between the aMCI group and control group at the short scale (1≤s≤4) and the long scale (10≤s≤15), respectively. The RCmvMFE values of each brain region in the MCI and control groups were consistent with normality and homogeneity of variance on the short and long scales and statistically analyzed by independent sample t-test in statistical analysis software SPSS (version 25.0). Adopt the false discovery rate (FDR) and set the correction level FDR≤0.05. The detailed information on RCmvMFE values and statistical analysis results in each brain region between the two groups are shown in Figure 8. From this figure, we can more intuitively observe the differences between MCI and control groups in each brain region on the short and long scales. On the short scale, there are significant differences in frontal, central, parietal, and occipital, and the difference in frontal was significantly more ($p \leq 0.001$); on the long scale, there were significant differences in central, parietal, and left temporal. ## 3.2.2. Correlation between EEG Entropy and Cognitive Function We calculated the Pearson linear correlation coefficient between the RCmvMFE values of EEG signals and the neuropsychological scale scores of all subjects to study the relationship between the entropy values of each brain region and cognitive function. Use the FDR method for strict correction and set the correction level as FDR≤0.05. We obtained scatter diagrams describing the significant correlation between entropy values and neuropsychological scale scores, as shown in Figure 9. On the short scale, the RCmvMFE values in frontal have a significant positive correlation with the test scores of MoCA and AVLT delayed recall. ## 4. Discussion Currently, many EEG analysis algorithms use to study EEG signals in functional brain diseases. Among them, using entropy to process nonlinear signals has attracted extensive attention. In this paper, we analyze six algorithms from a new perspective to solve the problem that other reports do not specify what characteristics of signals the algorithm measures and to explain which algorithm is more suitable for analyzing MCI EEG signals. It is worth noting that we explore the relationship between multi-channel entropy, single-channel entropy, and inter-channel coupling for the first time, which is new and most important in our simulation research and is unprecedented in previous papers. From the simulation analysis results, we found that when the SNR was 20 dB, the entropy curve was close to the original signal curve, and then with the increase in SNR, the entropy curve obtained by the six algorithms all deviated to some extent. Among them, the mvMIE algorithm is the least affected by noise, which may be related to the MIX coupling model data since the algorithm is the entropy calculation of the signal after pairwise difference. The mvMDE algorithm can analyze the intra-channel correlation and multivariable signal complexity and identify whether there is a correlation between 1/f noise signal channels with an inaccuracy measurement. The mvMPE and mvMIE algorithms cannot reflect the correlation within and between channels, so they cannot describe the signal comprehensively. The mvMSE, mvMFE, and RCmvMFE algorithms can not only measure intra-channel correlation and multivariable signal complexity but also distinguish whether there is a correlation between signal channels. They can well reflect the influence of coupling strength between signal channels and single-channel complexity. The RCmvMFE algorithm has the best performance, which may be related to the concept of the algorithm. The mvMSE algorithm combines multivariate sample entropy and multi-scale entropy to make it possible to analyze multi-channel signals with different scale factors. It can analyze multi-channel data’s long-term correlation and complexity well and receives wide attention in extracting multi-channel nonlinear signals. The mvMFE algorithm introduces a fuzzy membership function based on the mvMSE algorithm, which improves the system stability of the algorithm and has good consistency. However, considering the data length of different scale factors varies greatly, information loss will inevitably occur. The RCmvMFE algorithm primely solves this problem by improving the coarse-grained algorithm and can stably detect the dynamic behavior of complex systems. In summary, the RCmvMFE algorithm has the most significant description effect and is more likely to detect changes in MCI EEG signal complexity and functional connectivity. In this paper, we used the RCmvMFE algorithm to calculate the entropy of the EEG signal in each brain region of 20 subjects (8 control subjects and 12 MCI subjects). We observed the changing trend of RCmvMFE entropy values and statistically analyzed whether there was a significant difference between the two groups. On the short scale, the entropy of the MCI group was lower than the control group, and there were significant differences in the frontal, central, parietal, and occipital regions, especially the frontal region. On the long scale, the entropy of the MCI group was higher than that of the control group, with significant differences in the central, parietal, and left temporal regions. These results are consistent with previous studies showing that the short-scale entropy of EEG in cognitive disorder patients (MCI or AD) is lower than that in healthy people, and the long-scale entropy is higher than that in healthy people [40,41,42,43,44]. That may be related to the inefficiency of local neural network connection caused by neuron death and neurotransmitter changes, which requires a broader range of neurons to participate in neural activity. After that, we calculated the Pearson correlation coefficient between the entropy values of all brain regions and neuropsychological scale scores. After FDR strict correction, we found that on the short scale, the frontal entropy has a significant positive correlation with the MoCA score and AVLT delayed recall score, indicating that the damage to cognitive function in patients with MCI may be related to the frontal region. The above studies indicate that the RCmvMFE value is the EEG characteristics related to cognitive impairment, which can be used as EEG markers for the early diagnosis of MCI and provide help for the diagnosis and treatment of MCI. This algorithm is very suitable for analyzing MCI EEG signals. However, this algorithm may have some limitations when applied to EEG analysis of other brain function diseases. It is necessary to know the changing characteristics of EEG signals in this disease and whether the trend affected by signal complexity and functional connectivity is the same. If the effects are opposite, this algorithm is not suitable for analyzing the EEG of this disease. Some deficiencies in our research content still need to be improved, such as a small number of EEG channels analyzed, a small number of research subjects studied, and a lack of sensitivity and specificity analysis of algorithm classification. In the future, other researchers or we can collect EEG signals from more subjects and add AD patients to analyze the differences in EEG characteristics between the control, mild cognitive impairment, and AD groups. The proposed algorithm can combine with various classifier methods to classify the subjects according to the signal features extracted by the algorithm. These will be the content of our further research in the future. ## 5. Conclusions This paper explicitly discusses what signal characteristics algorithms measure and which factors affect them for the first time, explains which algorithm is more suitable for analyzing the MCI EEG signal, then analyzes the actual EEG signal. Among them, the analysis of the relationship between multi-channel entropy, single-channel entropy, and inter-channel coupling is the most important novel point, which has been undiscovered in previous papers. Simulation results show that the mvMSE, mvMFE, and RCmvMFE algorithms can analyze intra-channel correlation and multivariable signal complexity and accurately measure the coupling strength when inter-channel correlation occurs. The mvMSE, mvMFE, and RCmvMFE algorithms can well reflect the influence of coupling strength between signal channels and single-channel complexity, among which the RCmvMFE algorithm is the most prominent. The mvMDE algorithm can analyze the intra-channel correlation and multivariable signal complexity, whereas it cannot accurately measure the coupling strength when the inter-channel correlation. The mvMPE and mvMIE algorithms cannot measure the intra-channel and inter-channel correlation and can only use to analyze the multivariable signal complexity with poor discrimination ability. So, the mvMPE and mvMIE algorithms cannot comprehensively describe the signals’ characteristics. Based on the simulation results, the RCmvMFE algorithm has the best performance in measuring the intra-channel correlation, inter-channel correlation, and multi-channel signal complexity, which is more suitable for analyzing MCI EEG signals. Therefore, we used the RCmvMFE algorithm to analyze the actual EEG signals of the MCI group and the control group. The results showed that on the short scale (1≤s≤4), the entropy values of the MCI group were lower than the control group, and there were significant differences in the frontal, central, parietal, and occipital regions, among which the difference in the frontal was significantly most ($p \leq 0.001$). 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--- title: Effects of Animal Fat Replacement by Emulsified Melon and Pumpkin Seed Oils in Deer Burgers authors: - Elena Martínez - José E. Pardo - Adrián Rabadán - Manuel Álvarez-Ortí journal: Foods year: 2023 pmcid: PMC10047950 doi: 10.3390/foods12061279 license: CC BY 4.0 --- # Effects of Animal Fat Replacement by Emulsified Melon and Pumpkin Seed Oils in Deer Burgers ## Abstract Meat products such as burgers contain large amounts of saturated fat and are considered unhealthy foods by a society that is increasingly aware of the impact of food on their health, as there is a widespread idea that the consumption of large amounts of saturated fats is related to cardiovascular diseases, some types of cancer and obesity. The main goal of this study was to reformulate deer burgers by replacing the saturated fat from its composition with emulsions of oil extracted from melon and pumpkin seeds. Three emulsions were made with these oils (guar gum and inulin, sodium alginate and maltodextrin) to obtain a solid texture. Then, burgers were elaborated, using the vegetable oil emulsions to replace partially ($50\%$) or totally ($100\%$) the animal fat usually used in their elaboration. Physical parameters such as color and texture, consumer evaluation, proximate analysis and the fatty acid composition obtained by gas chromatography were analyzed. The burgers made with emulsified oils showed a higher weight loss, but with a minor loss of caliber and hardness ($p \leq 0.05$). From the sensory point of view, the reformulated burgers were positively valued by consumer judges when external aspect, odor, flavor and texture were evaluated. Furthermore, the addition of oil emulsions results in a lower fat content and in an increment of the proportion of unsaturated fatty acids, especially linoleic acid ($p \leq 0.05$). The inclusion of emulsified melon and pumpkin oil in deer burgers leads to an increase in the content of polyunsaturated fatty acids in burgers that, although they showed small differences in texture attributes (especially hardness and cohesiveness), were well valued by consumer judges in all sensory attributes evaluated. ## 1. Introduction World consumption of meat has doubled in the last 20 years due to income and population growth [1], reaching 320 million tons in 2018 [2]. Growth in global consumption of meat over the next decade is projected to increase by $14\%$ by 2030 compared to data from the 2018–2020 period. Within these data, game meat such as deer meat plays a residual role in developed countries but has a complete nutritional profile; additionally, its environmental impact is lower than farmed meat, so it is growing in popularity among consumers [3,4]. Meat products are generally consumed as a source of proteins with high biological value, minerals such as iron or selenium, and vitamins A, B12 and folic acid [5]. In this sense, deer meat presents a high nutritional value; specifically, it has a high content of total protein and water-soluble nitrogen, as well as a low-fat content compared to other types of meat. In addition, deer meat presents good sensory attributes as taste, aroma or tenderness, so it can be perfectly used for different culinary purposes [6]. Furthermore, the consumption of deer meat has become a multisectoral driver of the rural economy [7]. However, some meat products such as burgers are considered unhealthy due to their high fat content, especially saturated fatty acids and cholesterol, which are associated with cardiovascular diseases, some types of cancer and obesity [8], although recent works have questioned these adverse effects of animal fats [9,10]. Nevertheless, the presence of fat in these meat products is essential, since it plays a crucial role due to its relationship with several sensory attributes, such as flavor or juiciness, very appreciated by consumers. In addition, fat contributes to other processing and technological characteristics of the burgers, as it is related to the rheological properties of the mixture, the cooking loss and the water holding capacity, which make burgers more palatable [11]. Therefore, the current food industry faces the challenge of developing new ways to introduce healthy fats into processed meat products while maintaining their physical and sensory characteristics. The substitution of solid fats for unsaturated oils usually affects the texture of the products, so structuring systems must be developed to provide vegetable oils with similar characteristics to solid fats. Several ingredients have been used to substitute animal fat by oil emulsions, using ingredients such as konjac, whey protein powder or carrageenan [12,13,14]. In addition, promising results have been reported from the use of oleogels due to its heat resistance and their functionality in carrying lipophilic bioactive substances [15]. Moreover, gel emulsions have shown a great potential to be used as a healthy and low-calorie lipid ingredient [16]. Alginate is widely used in meat product processing due to its properties as a viscosifying, gelling agent and stabilizer [17]. Finally, the use of prebiotic fibers such as inulin with vegetable oils in meat products may improve cookability and reduce hardness compared to the traditional ones [18]. Another novel challenge of the agri-food sector is the integral use of food. About $30\%$ of the food that is produced around the world is lost or wasted along the food supply chain, contributing to environmental pollution and the depletion of natural resources. These include waste in the production, handling, processing or consumption process [19]. Due to the economic, environmental and social significance of food waste, the reduction in the residues generated by food industries is also part of the European Union’s Circular Economy Strategy [20]. In this sense, fourth range industries that produce fruits and vegetables that are ready for consumption, raw or cooked discard large amounts of peels and seeds that still contain valuable compounds that can be reintroduced in the food chain. This is the case of melon and pumpkin seeds, which are considered as residues and discarded in the processing of fourth range products. These seeds contain oil-rich unsaturated fatty acids and other bioactive compounds that can be reintroduced in the food chain to produce healthy foods. The oil that is extracted from the melon seed has a high nutritional value due to the elevated proportion of essential fatty acids, so it can be used as an alternative source of fat in the food industry [21]. Many health benefits have been reported from the consumption of this oil, since it presents anti-inflammatory and hypoglycaemic properties, and antimicrobial, antigenic and antioxidant potential [22]. The fatty acid pattern of melon seed oil is mainly characterized by the high content in linoleic acid (51–$69\%$), with a proportion of saturated fatty acids lower than $15\%$ [23]. This, together with the presence of other bioactive compounds such as sterols or tocopherols, make it a promising source of fat extracted from industrial waste that can be included in meat products to help to reduce the levels of cholesterol, as well as facilitate a consequent decrease in the development of atherosclerosis related to the incidence of coronary diseases [24]. On the other hand, pumpkin seed oil is also characterized by a high proportion of polyunsaturated fatty acids, ranging from 52.23 to $57.65\%$ [25], where linoleic acid reaches over $44\%$ and is a good source of other phyto-chemicals and bioactive compounds such as tocopherols, β-carotene, squalene and polyphenolic compounds [26]. The presence of such compounds confers pumpkin seed oil a strong antioxidant capacity. In addition, it has shown beneficial effects against benign prostatic hyperplasia [27] and in reducing levels of colorectal, breast, gastric and lung cancer [28]. The aim of this research was to replace the pork fat usually used in the elaboration of deer burgers by several emulsions of melon and pumpkin oils to obtain products with improved nutritional characteristics, especially related to the inclusion of polyunsaturated fatty acids. Texture analyses were performed to evaluate changes caused by the substitution of saturated fats by unsaturated. In addition, nutritional and fatty acids analyses were carried out to verify the improvement in the nutritional characteristics of the burgers made with melon and pumpkin oils. Finally, sensory tests were made to ensure the consumers acceptability of these burgers. ## 2.1. Ingredients and Preparation of Oil Emulsions Melon (Cucumis melo ‘Santa Claus’) and pumpkin (Cucurbita moschata) seeds were collected as a residue from an agri-food company dedicated to the production, processing and commercialization of fruits and vegetables (Vicente Peris, S.A., Albuixech, Spain). The seeds were washed to remove the adhered pulp and dried in an oven at 50 ± 2 °C until the moisture showed values below $7\%$. Then, 5 kg of dried seed from melon and pumpkin, separately, were subjected to oil extraction with a screw press (Komet Oil Press CA59G, IBG Monforts Oekotec GmbH and Co. KG, Mönchengladbach, Germany) at 49 rpm and 100 °C [23]. The oil yield for melon seed oil was $19.44\%$ and for pumpkin seed oil was $22.44\%$. To obtain stable emulsions, maltodextrin, inulin, guar gum, xanthan gum and sodium alginate were used (Sosa Ingredients, Barcelona, Spain). Maltodextrin is a gelling agent that gives the oil an emulsion-like texture, although the mixture of maltodextrin and oil is not properly an emulsion since an aqueous phase is not involved. Thus, it was considered like an emulsion because the texture was similar to the other two emulsions elaborated. Three types of emulsions were carried out (Table 1). The composition of the emulsions was performed according to the use recommendations provided by the manufacturer of the ingredients. To obtain the Gel and Malto emulsions, the ingredients were mixed at a high speed for 40 s at room temperature, and then stored at 4 °C for 24 h. To obtain the Par emulsion, in addition to homogenizing the mixture of water, oil and alginate at high speed for 20 s, a mixture of water ($95\%$) and calcium chloride ($5\%$) was prepared to help the alginate react and produce the gel. The gel obtained was stored at 4 °C for 24 h. ## 2.2. Elaboration of Burgers Deer meat was obtained in a local industry specialized in game meat (La Catedral de la Caza S.L., Los Yébenes, Toledo, Spain). In addition, pork fat, thickener (corn-starch) and condiments (garlic, salt and pepper) were purchased from local supermarkets. The formulation of the burgers was calculated in order to replace partially ($50\%$) or totally the pork fat used in the traditional recipe of burgers (Table 2). The three oil emulsions (Gel, Par, Malto) were performed for the 2 oils tested (melon and pumpkin), resulting in 12 different batches of burgers (6 of them containing melon oil and 6 containing pumpkin oil). In addition, a control burger was elaborated with pork fat according to the traditional formulation (Table 2, Figure 1). All burgers were made with $78.8\%$ deer meat, $19\%$ fat, $1.4\%$ thickener (maize flour) and $0.8\%$ other condiments (salt, aromatic herbs). The meat was ground in a grinder (Verder Scientific GmbH & Co. KG, Haan, Germany). The ingredients were mixed, and the burgers were formed with a manual molder of 65 mm diameter with approximately 29 g each. Then, the burgers were stored at −18 °C until the analysis. Before cooking the burgers, they were thawed at 4 °C for 24 h and then roasted in a pan until the central temperature measured with a digital probe thermometer reached 70 °C. ## 2.3. Physical Measurements Two physical parameters were measured as follows: color and texture. The color was measured in raw burgers with a Minolta CR-200 colorimeter (Minolta Camera Co. Ltd. Osaka, Japan). The measures were taken in five random zones of the surface of four different burgers of each batch using the illuminant D65. The tristimulus values were used to calculate the CIElab* chromatic coordinates: L* (lightness), a* (red-green component), b* (yellow-blue component) [29]. To evaluate the differences in texture, the burgers were subjected to a texture profile analysis test (TPA), in which five samples of each batch were subjected to two consecutive compression tests simulating the chewing process. The average values of the texture parameters hardness, cohesiveness, springiness and chewiness were annotated. The analysis was carried out with a TA-XT Plus texture analyzer (Stable Micro Systems, Godalming, UK). Texture data were obtained from cooked burgers to evaluate differences in the chewing process. ## 2.4. Consumer Preferences To measure consumers preferences, an affective test with consumer judges was performed. The test was carried out in the sensory analysis laboratory at the Higher Technical School of Agricultural and Forestry Engineering in Albacete (Spain). A nine-point scale ranging from −4 (extremely dislike) to +4 (extremely like) was used. The testing was designed in two different sessions due to the large number of samples to avoid the saturation of the consumer judges [30]. In the first session, the samples corresponding to the burgers formulated with melon seed oil and the control were evaluated by 103 consumers. In the second session, the evaluation was made with the control and the samples that contain pumpkin seed oil. In the second session, the panel was made up of 100 consumers. In both tests, the consumer-judges evaluated the external appearance of raw burgers as well as the smell, flavor and texture of cooked burgers. ## 2.5. Proximate Composition The proximate composition was performed in cooked burgers. To determine the ash content, samples were calcinated at 550 °C until reaching a constant weight [31]. The protein content was calculated by multiplying the total nitrogen content obtained by the Kjeldahl method by a conversion factor of 6.25 [32]. Crude fat was estimated gravimetrically using the filter bag technique after the petroleum ether extraction of the dried sample in an Ankom XT10 extraction system [33]. For the crude fiber content, the Weende technique adapted to the filter bag technique was used. As described in [33], the Weende technique determines the organic residue remaining after digestion with solutions sodium hydroxide and sulfuric acid by using an Ankom 220 fiber analyzer. Total carbohydrate content was calculated by subtracting the sum of the crude protein, total fat, water and ash from the total weight [34]. Total energy was calculated based on 100 g sample using Atwater values for fat (9 kcal/g), protein (4 kcal/g) and carbohydrate (4 kcal/g) [35]. ## 2.6. Fatty Acids With regards to fatty acid profile, first fat was extracted from burgers with a chloroform-methanol mixture (2:1) according to [36]. Then, fatty acid methyl esters (FAME) were obtained by a transmethylation according to ISO 12988-2:2017 [37]. Then, FAMEs were injected in a Shimadzu GC-2010 Plus Gas Chromatograph (Shimadzu, Tokyo, Japan), equipped with a CPSil 88-fused silica capillary column (50 m × 0.25 mm i.d.), 0.20 m film thickness (Varian, Middelburg, Netherlands), using helium as the carrier gas (120 kPa). Each fatty acid methyl ester (FAME) was identified by direct comparison with a standard mixture (FAME 37, Supelco, Bellefonte, PA, USA). Two samples of each batch were analyzed, and the results were expressed as the percentage of each FAME. ## 2.7. Statistical Analysis For the analysis of the data, the values obtained from four samples of burgers for each of the physical, nutritional and fatty acid parameters were used. For the analysis of the results obtained in the sensory evaluation, scores of 100 and 103 consumer judges were used. All data are presented as means ($$n = 4$$, $$n = 100$$, $$n = 103$$) and standard deviation ($$n = 4$$, $$n = 100$$, $$n = 103$$). Statistical differences were estimated from an analysis of variance (ANOVA) test at the $5\%$ level of significance and Duncan Test ($p \leq 0.05$). All statistical analysis were carried out using the SPSS program, release 23.0 for Windows. ## 3.1. Physical Parameters Color is one of the most important physical parameters of food since it can determine the preference of consumers for a given product. The inclusion of new ingredients, or the partial or total substitution of these, can cause changes in color that may be strange to the consumer, resulting in product rejection. Therefore, it is important to determine the changes that occur when substituting ingredients in the traditional recipe. In this sense, an objective way to measure color is the use of the CIELab* color space, in which the coordinates L* (lightness), a* (red-green component) and b* (yellow-blue component) are defined. The inclusion of melon or pumpkin oil in the recipe of burgers resulted in an increase in L* values regardless of the percentage used, with significant differences ($p \leq 0.05$) respect to control burger (Table 3). Lightness in meat and meat products depends on various factors such as water holding capacity and fat content, as well as the type of ingredients used in the reformulation process [38]. The values obtained for the components a* and b* are shown in Table 3. The control sample presents the highest values of component a* since it does not receive any additional ingredients or has fewer added ingredients that alter the color [39]. Differences between melon and pumpkin burgers may be caused by the different characteristics and composition of the oils [40]. It has been reported that burgers with avocado oil have a greater impact on the color due to the chlorophylls present on the oil, which contribute to the typical green color as it happens in the burgers with pumpkin oil due to the green tones shown [41]. Texture changes are one of the main challenges for the development of meat products with reduced fat and a healthier lipid profile. In meat products made from minced meat, the texture depends on the ability of meat proteins to create gels or the emulsifier capabilities of the non-meat ingredients [42]. Texture parameters (hardness, cohesiveness, springiness, chewiness) can be evaluated by the Texture Profile Analysis, in which two consecutive compressions are applied to the food to simulate chewing process. In general, in all the parameters, the addition of emulsified oils has changed the texture compared to the control sample (Table 4) in a similar way to the results reported by [43] in a study carried out on burgers reformulated with oils and flours from nuts and seeds. A reduction in the content of animal fat and its substitution by emulsified oils principally affects hardness, resulting in burgers with lower values in this parameter. Similar results have been obtained by [44,45] in beef burgers when fat is substituted by linseed oil or olive oil, also resulting in a decrease in chewiness values. However, it has been reported that the addition of other vegetable oils may lead to an increase in hardness due to the lower fat globule of vegetable oils compared to animal fat, which result in higher protein–protein and protein–lipid interaction [44]. Hardness is supposed to be related to the protein: lipid ratio of the burgers [46], resulting in harder products when the ratio increases. However, in the burgers elaborated with the emulsions of melon and pumpkin oil with guar gum and inulin, sodium alginate, and maltodextrin, hardness was lower independently of the protein: lipid ratio of the burgers. When the percentage of water is increased, it results in a smoother texture, even though the amount of protein is constant [47]. The dilution effect of non-meat ingredients in meat protein systems is primarily responsible for a softer texture [48]. In the rest of the parameters, differences between the control sample with pork fat and the burgers reformulated with vegetable oils were also significant ($p \leq 0.05$). Due to the high proportion of saturated fatty acids, animal fat that is used in a solid state plays a binding role. Thus, burgers elaborated with vegetables oils usually showed lower values regarding cohesiveness, although when the vegetable oils were textured with calcium alginate (PMB, PPB), the burgers showed similar cohesiveness values to the control. Previous works introducing hydrogelled emulsions of chia or linseed oils elaborated with carrageenan have found no differences in the cohesiveness of burgers compared to the control [46]. Regarding springiness, again the burgers elaborated with vegetable oils showed lower values, probably due to the higher elasticity of the pork fat used in the control burger. ## 3.2. Consumer Preferences Sensory analysis is a useful tool used to identify the changes originated by the substitution of ingredients based in the preferences of consumers [49]. When the affective test was performed with the burgers elaborated with melon and pumpkin oils, in all cases, and for all the parameters evaluated, the mean values by the consumer judges were above 0, which means that the acceptability of the burgers was in all cases in values of ‘I like’. The samples evaluated are shown in Figure 1. The results of the sensory analysis are shown in Figure 2 for burgers formulated with melon oil and in Figure 3 for the ones formulated with pumpkin oil since they were evaluated in different sessions and by different consumer judges. No significant differences were found for external aspect when burgers formulated with pumpkin seed oil were evaluated ($p \leq 0.05$), except PPB burger samples where small green particles could be seen. On the other hand, in the burgers elaborated with melon seed oil, the worst valued sample was GMB $100\%$, as these burgers seemed the least cohesive of all the samples. Regarding texture, when this parameter is evaluated by means of sensory evaluation, the burgers with melon and pumpkin oil obtained better or similar results compared to the control sample, specifically in melon seed oil burgers, since GMB $50\%$, MMB $100\%$, and PMB $100\%$ did not differ significantly from the control sample ($p \leq 0.05$). This can be explained because the reformulated burgers have a softer texture [50]. Another factor could be that there is less connective tissue in burgers with less quantity of animal fat [16]. When burgers made up with pumpkin oil were evaluated, MPB $50\%$ obtained the best results due to the juiciness of the sample, in a similar way as reported by [51], where chicken burgers with pumpkin seeds were examined. The worst evaluated on that case was PPB $100\%$ and GPB $100\%$ because they crumbled in the cooking process. The control sample was, in both cases, among the best valued probably because consumers perceived it as the most traditional. As regards the odor, reformulated burgers with melon seed oil obtained in general better results than the control one. Similar results have been reported by [52] that revealed that fortification of vegetables oils based on ‘emulgels’ improved the odor preferential score of dried fermented deer sausages. In relation to the pumpkin burgers, the two samples reformulated with pumpkin oil and maltodextrin, as well as the sample with a substitution of $100\%$ of animal fat for particles (PPB $100\%$), are the ones with the best scores. A study carried out with functional beef burgers formulated with maltodextrin and collagen showed that these were the best at taste acceptance due to the influence of the degree of polymerization of maltodextrin in the retention of volatile flavors [40]. Instead, in the case of melon seed oil, the control sample and the GMB $50\%$ burgers were the best valued, perhaps for being the ones most like the control [53]. ## 3.3. Proximate Composition Table 5 shows the results of the proximate composition of the burgers. The burgers GMB and GPB, formulated with the GEL emulsion (guar gum and inulin), presented the highest protein content ($p \leq 0.05$). These findings are consistent with those of [54], who stated that there are no differences in raw burgers; however, once cooked, the protein content increases due to the influence of the heat treatments as a result of water loss and the consequent increase in protein content. Burgers GMB and GPB also showed the highest values in crude fiber, with significant differences from the rest ($p \leq 0.05$), which may be due to the presence of inulin in the formula, an oligosaccharide with a dietary fiber function [55]. The functional properties of fibers are related to their good effect on human health, as they are associated with the prevention of some diseases such as colon and rectum cancer, abdominal hernias, diabetes, obesity and coronary hearth diseases [56]. As regards the total carbohydrate content, it was found higher in those samples formulated with maltodextrin as it is a polysaccharide. Related to the fat content, GPB 100 showed the higher values on fats, which was also reported by [57]. In the case of melon seed oil, in GMB 100 the content is lower, and this result can be explained because pumpkin oil presents palmitic oil that is solid at ambient temperature, which can result in a more stable emulsion. During cooking, heat treatment can destabilize emulsions, leading to greater fat loss. In the burgers made with emulsions of melon and pumpkin oils, an excessive loss of fat was not observed during cooking. In this sense, the decrease in fat content can be attributed to the ingredients with which the emulsions were made, which originates a lower amount of fat in the recipe. ## 3.4. Lipid Profile As regards lipid profile (Table 6 and Table 7), a significant increase in the proportion of linoleic acid (C18:2) was observed in all the samples in which animal fat has been partially or totally replaced by melon or pumpkin oil. In addition, a decrease in the content of palmitic acid (C16:0) was observed. The substitution of animal fat by textured vegetable oils resulted in a decrease in saturated fatty acids and the increase in the concentration of polyunsaturated fatty acids, making products healthier in relation to the incidence of cardiovascular diseases, in which it has been seen that these fatty acids may be involved. Oleic acid (C18:1) is lower in all the modified samples and higher in the control sample. These data agree with the composition of melon seed oil provided by [58], which reported that the main fatty acids found on melon seed oil were linoleic acid (52–$69\%$), oleic acid (12–$32\%$), palmitic acid (9–$24\%$) and stearic acid (5–$9\%$). They also coincide with other studies that indicate that burgers with animal fat contain higher percentages of saturated fatty acids and that those reformulated with vegetable oils have higher percentages of linoleic and oleic acid, depending on the type of oil used in the formulation [59,60]. In pumpkin oil burgers, there was also a decrease in saturated fatty acids (especially palmitic acid) and an increase in the concentration of linoleic acid. In both cases, this effect is generally greater when pumpkin oil is completely substituted for pork fat. This is also due to the lipid composition of pumpkin oil, which is also a rich source of linoleic acid, which has beneficial functions in the human body [61]. ## 4. Conclusions This studys suggest that it is possible to reduce the saturated fat content and increase the polyunsaturated fatty acids proportion in traditional burgers by replacing pork fat with melon and pumpkin seed oil using maltodextrin, alginate and guar gum/inulin emulsions. The total substitution of animal fat by texturized vegetable oils increased the proportion of polyunsaturated fatty acids in burgers, especially the content in linoleic acid, independently of the method used to make the oil emulsions. The inclusion of vegetable oils produced small changes in the physical characteristics of burgers, since the burgers elaborated with textured vegetable oils showed, in general, a softer and less cohesive texture. The color of raw burgers was also affected, with a reduction in the component a* (red-green component), especially when melon and pumpkin oil was used to totally replace the animal fat contained in the burgers. From the sensory point of view, all the samples obtained positive evaluations in the affective test performed when flavor, odor, external aspect and texture were evaluated. Even the valuation of some burgers elaborated with melon and pumpkin oils was higher than the control burger elaborated with animal fat in some parameters, showing the good acceptability of these products by consumer judges. Additionally, the feasibility of using residues from the food industry such as melon and pumpkin seed has been shown considering the stability they have shown in the formation of emulsions, the benefits they have on health based on the fatty acid patterns and by the acceptance of consumers due to the pleasant flavor of both oils. Future studies may focus on the behavior of the burgers during storage to verify the stability of the burgers and evaluate possible changes originated by the replacement of ingredients. ## References 1. Ritchie H., Rosado P., Roser M.. Meat and Dairy Production. 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--- title: Droplet Digital PCR Quantification of Selected Intracellular and Extracellular microRNAs Reveals Changes in Their Expression Pattern during Porcine In Vitro Adipogenesis authors: - Adrianna Bilinska - Marcin Pszczola - Monika Stachowiak - Joanna Stachecka - Franciszek Garbacz - Mehmet Onur Aksoy - Izabela Szczerbal journal: Genes year: 2023 pmcid: PMC10047974 doi: 10.3390/genes14030683 license: CC BY 4.0 --- # Droplet Digital PCR Quantification of Selected Intracellular and Extracellular microRNAs Reveals Changes in Their Expression Pattern during Porcine In Vitro Adipogenesis ## Abstract Extracellular miRNAs have attracted considerable interest because of their role in intercellular communication, as well as because of their potential use as diagnostic and prognostic biomarkers for many diseases. It has been shown that miRNAs secreted by adipose tissue can contribute to the pathophysiology of obesity. Detailed knowledge of the expression of intracellular and extracellular microRNAs in adipocytes is thus urgently required. The system of in vitro differentiation of mesenchymal stem cells (MSCs) into adipocytes offers a good model for such an analysis. The aim of this study was to quantify eight intracellular and extracellular miRNAs (miR-21a, miR-26b, miR-30a, miR-92a, miR-146a, miR-148a, miR-199, and miR-383a) during porcine in vitro adipogenesis using droplet digital PCR (ddPCR), a highly sensitive method. It was found that only some miRNAs associated with the inflammatory process (miR-21a, miR-92a) were highly expressed in differentiated adipocytes and were also secreted by cells. All miRNAs associated with adipocyte differentiation were highly abundant in both the studied cells and in the cell culture medium. Those miRNAs showed a characteristic expression profile with upregulation during differentiation. ## 1. Introduction MicroRNAs (miRNAs) are a well-known class of small, noncoding RNAs that regulate post-transcriptional gene expression through mRNA destabilization or inhibition of translation [1]. To date, over 2500 miRNAs have been discovered in the human genome, and it is estimated that they regulate over $60\%$ of protein-coding genes [2]. miRNAs thus play an essential role in all biological processes, including cell differentiation and development [3]. Changes in miRNA expression have been reported in altered physiological conditions and various diseases, so these molecules have been treated as promising therapeutic targets. miRNA-based therapies involve correcting altered miRNA expression levels using mimics or inhibitors [4]. Moreover, miRNAs can be used as biomarkers of pathophysiological conditions [5]. In particular, extracellular miRNAs (ECmiRNAs) can serve as good diagnostic markers due to their stability and ease of sample collection. ECmiRNAs have been detected in cell-free conditions, including cell culture media and biological fluids, such as serum, plasma, saliva, tears, urine, breast milk, etc. [ 6]. The role of miRNAs has been extensively studied in the context of the development of obesity. It has been shown that miRNAs are involved in the control of a range of processes, including adipogenesis, insulin resistance, and inflammation in adipose tissue [7]. Dysregulation of many miRNAs has been identified in the adipose tissue of obese individuals [8,9,10]. The presence of adipocyte-related miRNAs in adipocyte-derived microvesicles indicates their involvement in intercellular communication in both paracrine and endocrine manners [10,11,12]. Studies of miRNA in adipocyte tissue have also been conducted on the domestic pig (Sus scrofa), an important animal model for human obesity and also a major livestock species [13]. There are a number of reports on the functioning of individual miRNAs during the formation of fat tissue in the pig (summarized by Song et al. [ 14]). High-throughput miRNA profiling of porcine adipocyte tissue has also allowed the detection of a complex microRNA–mRNA regulatory network related to fat deposition in pigs [15,16,17,18]. A recent study of the identification of miRNAs in porcine adipose-derived and muscle-derived exosomes showed some miRNAs to be involved in skeletal muscle–adipose crosstalk [19]. Most of the research on porcine miRNAs has been carried out on adipose tissues, while studies on in vitro models of adipogenesis are scarce [20]. Due to the heterogeneous nature of adipose tissue—which is composed of several cell types, including adipocytes, preadipocytes, stem cells, endothelial cells, and various blood cells [21]—cultured adipocytes represent a good system for studying molecular events that occur during adipogenesis, including the secretion of miRNA by adipocytes [22]. The aim of this study was thus to quantify eight miRNAs (miR-21a, miR-26b, miR-30a, miR-92a, miR-146a, miR-148a, miR-199a, and miR-383) during porcine in vitro differentiation of mesenchymal stem cells (MSCs) into adipocytes. These miRNAs were selected on the basis of their role in differentiation and inflammation processes (Table 1). The expression of intracellular and extracellular microRNAs was evaluated using droplet digital PCR (ddPCR), a highly sensitive method. ## 2.1. Mesenchymal Stem Cell Culture Mesenchymal stem cells were derived from the adipose tissue (AD-MSCs) of a three-month-old female Polish Large White pig. Tissue sample collection was approved by the Local Ethical Commission for Experiments on Animals at Poznan University of Life Sciences, Poznan, Poland (approval no. $\frac{57}{2012}$). Following Stachecka et al. [ 39], the AD-MSCs were cultured in Advanced DMEM (Gibco, Life Technologies, Grand Island, NY, USA) supplemented with $10\%$ FBS (v/v) (Sigma-Aldrich, St. Louis, MO, USA), 5 ng/mL FGF-2 (PromoCell GmbH, Heidelberg Germany), 2 mM L-glutamine (Gibco), 1 mM 2-mercaptoethanol (Sigma-Aldrich), 1 × antibiotic antimycotic solution (Sigma-Aldrich), and 1 × MEM NEAA (Gibco) at 37 °C in $5\%$ CO2. To avoid the possible influence of FBS-derived miRNAs on obtained results, the same part of filtered FBS was used during the whole cell culture experiment. The AD-MSCs were propagated by passaging using standard cell culture procedures, and their stemness was confirmed by staining for positive (CD44, CD90, CD105) and negative (CD45) markers (Abcam, Cambridge, UK). ## 2.2. Adipogenic Differentiation Adipogenesis was induced by culturing early-passage MSCs in an adipogenic differentiation medium composed of Advanced DMEM (Gibco), $10\%$ FBS (Sigma-Aldrich), 1 × antibiotic antimycotic solution (Sigma-Aldrich), 1 × MEM NEAA (Gibco), 5 ng/mL FGF-2 (PromoCell GmbH), 1 × linoleic acid albumin, 1 × ITS, 1 µm dexamethasone (Sigma-Aldrich), 100 µm indomethacin (Sigma-Aldrich), and 50 mM IBMX (Sigma-Aldrich). The cells were cultured for ten days. Adipogenic differentiation was monitored using visual examination of lipid droplet formation under a phase-contrast microscope (Nikon TS100 Eclipse, Melville, NY, USA) and BODIPY staining. Cells were fixed with $4\%$ paraformaldehyde in PBS (w/v) for ten minutes at room temperature and washed thrice with PBS. The cells were then incubated with BODIPY $\frac{493}{503}$ (Thermo Fisher, Waltham, MA, USA) in PBS (3 µg/mL) and washed thrice in PBS. The nuclei were counterstained with DAPI in Vectashield medium (Vector Laboratories, Newark, CA, USA) and examined under a fluorescence microscope (Nikon E600 Eclipse, Melville, NY, USA). Each measurement was performed in triplicate. ## 2.3. RNA Extraction from Cells and Culture Medium Total RNA extraction from cells (approximately 2 × 106 in number) and the cell culture medium (200 µL) was performed on days 0, 2, 4, 6, 8, and 10 of adipogenesis using the miRNeasy Micro Kit (Qiagen, Hilden, Germany), following the manufacturer’s protocol. The RNA samples isolated from cell culture medium were enriched in the fraction of miRNAs, both exosomal and non-exosomal ECmiRNAs. All samples were analyzed in duplicate. The RNA concentrations and quality were assessed using a NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and Qubit RNA HS Assay Kit (Thermo Fisher Scientific) on a Qubit 2.0 Fluorometer (Thermo Fisher Scientific). ## 2.4. Real-Time PCR One microgram of RNA was reversely transcribed using a Transcriptor High Fidelity cDNA Synthesis kit (Roche Diagnostic, Mannheim, Germany). Primer sets for quantitative real-time PCR for selected protein-coding marker and reference genes (Table S1) were designed using the PRIMER 3 software (http://simgene.com/Primer3 (accessed on 12 May 2022)). The relative transcript levels were assessed using a LightCycler 480 SYBR Green I Master kit (Roche Diagnostic) with a LightCycler 480 II (Roche Life Science). All samples were analyzed in triplicate. Standard curves were designed as tenfold dilutions of the PCR products. Relative transcript levels of the studied genes were calculated after normalization with the transcript level of a reference gene, ribosomal protein L27 (RPL27), which has shown stability during adipogenic differentiation [40,41]. ## 2.5. miRNA-Specific Reverse Transcription Reverse transcription was performed with 10 ng of total RNA using a TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Reverse transcription reactions were conducted with the use of an RT primer specific to each tested miRNA. The following TaqMan MicroRNA Assays (Applied Biosystems) were employed: miR-21a-5p, (Assay ID: 000397), miR-26b-5p (Assay ID: 000406), miR-30a-5p (Assay ID: 000417), miR-92a-3p (Assay ID: 000431), miR-146a (Assay ID: 005896), miR-148a-3p (Assay ID: 000470), miR-199a-3p (Assay ID: 002304), and miR-383-5p (Assay ID: 000573). RNU6b (Assay ID: 001093) was used as the reference for normalizing the ddPCR results [42]. The reverse transcription reactions were performed following the manufacturers’ recommendations. ## 2.6. Droplet Digital PCR (ddPCR) miRNA quantification was performed using droplet digital PCR (ddPCR). All samples were analyzed in duplicate. Each PCR reaction consisted of 1 µL cDNA, 11 µL of 2 × ddPCR SuperMix for Probes (Bio-Rad, Hercules, CA, USA), 9 µL of H2O, and 1 µL of TaqMan primers and probe from the corresponding TaqMan MicroRNA Assay (Applied Biosystems). The reaction mixtures were divided into approximately 20,000 droplets using a QX200 droplet generator (Bio-Rad) followed by PCR performed on a T100 Thermal Cycler (Bio-Rad) using the following conditions (ramp rate of 2 °C/s): initial denaturation at 95 °C for 10 min, 40 cycles at 94 °C for 30s, followed by 60 °C for 1 min and denaturation at 98 °C for 10 min. A QX200 droplet reader (Bio-Rad) was used to detect fluorescence, and the results were analyzed using QuantaSoft software (Bio-Rad). The fraction of positive droplets was quantified using the Poisson distribution. Since cell culture media may carry miRNAs derived from supplements such as fetal bovine serum (FBS) [43], an experiment on the expression level of the investigated miRNAs in the pure cell culture medium, supplemented with $10\%$ of FBS, was performed. Expression of miR-92a, miR-146a, and miR-26b was not observed, while expression of miR-21a, miR-383, miR-30a, miR-148a, and miR-199a was on very low level (Table S2), which was about $1\%$ of the average expression level of extracellular miRNAs (Table S6). Thus, an additional normalization step was abandoned. ## 2.7. Statistical Analysis Differences between expression levels were assessed separately for each miRNA and for each medium. To give the analyzed variables a normal distribution, the expression levels were transformed by taking the natural logarithm of the original values. The following model was then used to assess the differences between the expression levels on each day:log (Exp) ij = µ + DAY j + sampleID i + error ij, where log (Exp) is the natural logarithm of the expression level recorded on the jth DAY for the ith sampleID. DAY was a categorical variable with six levels [0, 2, 4, 6, 8, 10]. The sampleID and error were random terms. The sampleID was treated as random term to account for repeated observations of the sample on following days. The analyses were performed using the R environment [44]. The effects of the model were estimated using the lme4 package [45] and the significance of the differences between days was assessed using the lmerTest [46] and emmeans packages [47], making use of Satterthwaite’s method [48] for approximating degrees of freedom. The p-values for comparing expression levels on particular days were adjusted for multiple comparisons using Tukey’s method for comparing six estimates. To assess whether there was a relationship between the expression level in the medium and cells, the previously used model was updated to include the log-transformed expression in the medium log (Expmedium). The following model was thus used: log (Exp) ij = µ + log (Expmedium) ij + DAY j + sampleID i + error ij. The regression line was obtained by applying the locally weighted scatterplot smoothing method available from the ggplot2 package [49]. ## 3. Results Eight miRNAs associated with inflammatory processes (miR-21a, miR-92a, miR-146a, miR-383) and adipocyte differentiation processes (miR-26b, miR-30a, miR-148a, miR-199a) were included in this study (Table 1). The abundances of these miRNAs were determined in cells and in cell culture medium over ten days of adipogenic differentiation (Figure 1). The differentiation process was monitored by evaluating the accumulation of lipid droplets using BODIPY staining (Figure 1 and Figure 2A, Table S3). On day 4, individual cells with lipid droplets were seen, while lipid accumulation was highly abundant from day 6. Adipocyte differentiation was also confirmed by the upregulation of expression of three marker genes: CEBPA, FABP4, and PPARG (Figure 2B, Table S3). The expression of all the miRNAs was successfully detected with the ddPCR method (Figure 3). It was found that, of the miRNAs associated with the inflammatory process, miR-21a showed the highest expression in differentiated adipocytes and was also highly secreted by these cells (Figure 4A,B; Tables S4 and S5). Both intracellular and extracellular miR-21a levels were upregulated during adipogenesis. miR-92a was also highly expressed by adipocytes, reaching its highest level on day 10 of differentiation. The abundance of extracellular miR-92a initially decreased on days 2–4, returned to its original level after day 6, and then decreased (Figure 4C,D; Tables S4 and S5). The expression of miR-146a in the studied differentiation system was quite low (Figure 4E,F; Tables S4 and S5). Cellular miR-146a was upregulated during adipogenesis, but was not secreted by the differentiated cells. The lowest expression level was found for miR-383, and this was comparable in the cells and in the cell culture medium (Figure 4G,H; Tables S4 and S5). In terms of miRNAs associated with adipogenesis, all the molecules we examined here were highly expressed during differentiation (Figure 5; Tables S4 and S5). The highest expression in cells was found for miR-26b, next to miR-199a, miR-148a, and miR-30a. Of these, miR-26b, miR-148a, and miR-30a had the highest expression levels at the end of differentiation (day 10), while for miR-199a this occurred on day 4 of adipogenesis. All extracellular miRNAs had similar expression profiles, reaching the highest level on day 6 of differentiation. miR-199a, miR-30a, and miR-148a were secreted at comparable levels, while the amount of miR-26b was lower. Comparing the expression levels of intracellular and extracellular miRNAs showed higher expression in cells than in the medium for all the studied miRNAs except miR-383 (Table S6). No relationship was found between the expression level of intracellular and extracellular miRNAs (Table S7). ## 4. Discussion To better understand the role of miRNA in adipocyte formation, we examined the expression of the selected intracellular and extracellular miRNAs during adipogenesis, using the domestic pig as a model organism. We employed ddPCR, as a robust method for absolute quantification of miRNAs [50]. This method has proven to be especially useful for quantifying extracellular miRNAs [51,52]. Here, we showed the usefulness of ddPCR for detecting less abundant ECmiRNAs in adipogenic spent medium. We found that, of the studied miRNAs, miR-21a showed the highest expression in differentiated cells, and its expression was very high in the cell culture medium. It has been reported that miR-21 is frequently upregulated in many chronic diseases, including obesity [25]. It plays a pivotal role in the functioning of adipose tissue through its regulation of many biological processes, such as thermogenesis, browning of adipose tissue, angiogenesis, apoptosis, and adipogenesis [53]. A previous study of MSCs isolated from human adipose tissue showed that miR-21 expression increased in the early stages of adipogenic differentiation and gradually decreased after day 3 [24]; in our differentiation system, miR-21a was upregulated throughout the entire differentiation period. Studies of miR-21 mimics and inhibitors as therapeutic agents in obesity treatment have also provided varying results [53,54]. miR-21 has also been depicted as secreted by macrophages of adipose tissue [10], while in this study we confirmed its secretion by adipocytes. Further studies, including of both the intracellular and extracellular form of miR-21a, are thus needed. To date, little has been learned about the role of miR-92a in adipogenesis. There are reports of its involvement in brown adipocyte differentiation [55]. Exosomal miR-92a abundance has also been observed in human serum after cold-induced brown adipose tissue activity [56]. In 3T3L1 cells, the miR-17–92 cluster accelerated adipocyte differentiation by negatively regulating the tumor suppressor Rb2/p130 during the early stages of adipogenesis [57]. Here, we provide evidence that miR-92a alone is upregulated during porcine adipogenesis and is secreted by adipocytes. Recently, miR-146a has been recognized as a potential regulator of porcine intramuscular preadipocytes [58]. The authors reporting this observed that miR-146a-5p mimics inhibited preadipocyte proliferation and differentiation, while the miR-146a-5p inhibitors promoted cell proliferation and adipogenic differentiation. Both that study and our present one found a similar expression pattern for these miRNAs, with the expression peaking in the early stages of adipogenesis (on days 2 or 4 of differentiation, respectively). Interestingly, miR-146a was one of the studied miRNAs that was not secreted by differentiated cells (as was miR-383). Of the studied miRNAs, miR-26b, miR-30a, miR-148a, and miR-199a have been reported as involved in adipocyte formation through their promotion or acceleration of adipogenesis, which they achieve by regulating numerous target genes [27,28,34,59]. Their expression was found to gradually increase after the induction of adipocyte differentiation, as in our study. Only miR-199a reached its highest expression at day 4 of adipogenesis, and its expression then decreased, as confirmed by its function in the proliferation and differentiation of porcine preadipocytes [60]. This miRNA was highly expressed in cells and also secreted more. It seems that this molecule has a comprehensive set of functions and plays a role in a range of different processes, such as angiogenesis, aging, apoptosis, proliferation, and myogenic differentiation [61,62]. All the extracellular miR-26b, miR-30a, miR-148a, miR-199a, and miR-92a showed the same expression profiles, with their expression peaking on day 6 of differentiation. It was previously reported that exosomal miRNAs are secreted from hypertrophic adipocytes and transferred to small adipocytes to promote lipogenesis and hypertrophy of emerging adipocytes [63,64]. This may be one reason why high expression of these extracellular miRNAs is observed in the intermediate days of differentiation, when new adipocytes arise at an intense rate. As we found no strong correlation between the expression of intracellular and extracellular miRNAs, it can be anticipated that the secretion of miRNA is an independent process regulated by other mechanisms, such as the formation of extracellular vesicles such as exosomes or transportation via protein–miRNA complexes [65,66]. Our study revealed relatively high intercellular variation of miRNA expression (Tables S4–S6, which may be related to the heterogeneity of cell populations in terms of differentiation timing. It has been shown in previous studies that a high standard deviation is found for low-expressed miRNA [67]. Application of new methods, such as single-cell microRNA–mRNA co-sequencing, revealed that microRNA expression variability might be responsible alone for non-genetic cell-to-cell heterogeneity [68]. The authors found that miRNAs with low expression levels showed inherently large standard deviations, while the variation of high-abundance miRNAs gradually decreased as the expression level increased. It has been shown that the miRNA expression profile can serve as a signature of cell identity, through the expression of a unique miRNA. However, this would seem to be difficult to apply to adipose tissue, as it expresses a wide range of miRNAs [65,69]. Our study of cultured adipocytes allowed us to obtain more detailed knowledge of the relationship between the intracellular and extracellular microRNAs that are expressed during the formation of adipocyte cells. Taking into account the fact that miRNAs from adipose tissue participate in intercellular and interorgan communications, and that their aberrant expression may lead to pathological conditions, further comprehensive studies of extracellular and intracellular miRNAs are needed. ## 5. Conclusions We showed that miRNAs associated with adipogenesis and inflammation processes are expressed by differentiated adipocytes. Both intracellular and extracellular miRNAs have characteristic expression profiles during porcine adipogenesis. We found that there is no relationship between the expression level of intracellular miRNAs and the levels of extracellular miRNAs. ddPCR proved a useful method of quantifying miRNAs during in vitro adipogenesis, especially for less abundant extracellular miRNAs. ## References 1. Bartel D.P.. MicroRNAs: Target recognition and regulatory functions. Cell (2009.0) 136 215-233. DOI: 10.1016/j.cell.2009.01.002 2. Shu J., Silva B.V.R.E., Gao T., Xu Z., Cui J.. 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--- title: 'PDDA/Honey Antibacterial Nanofiber Composites for Diabetic Wound-Healing: Preparation, Characterization, and In Vivo Studies' authors: - Mazeyar Parvinzadeh Gashti - Seyed Ahmad Dehdast - Ali Berenjian - Mohammad Shabani - Ehsan Zarinabadi - Ghazaleh Chiari Fard journal: Gels year: 2023 pmcid: PMC10047982 doi: 10.3390/gels9030173 license: CC BY 4.0 --- # PDDA/Honey Antibacterial Nanofiber Composites for Diabetic Wound-Healing: Preparation, Characterization, and In Vivo Studies ## Abstract In this paper, Poly (diallyldimethylammonium chloride) (PDDA)/honey nanofiber wound dressing composites were prepared and their effects on the diabetic wound-healing was evaluated using in vivo experiments. The release of effective compounds and the solubility of nanofibers were controlled through the crosslinking process by glutaraldehyde. The crosslinked nanofibers (crosslinking time was 3 h) showed an absorption capacity at a maximum value of $989.54\%$. Interestingly, the resultant composites were able to prevent $99.9\%$ of Staphylococcus aureus and Escherichia coli bacteria. Furthermore, effective compounds were continuously released from nanofibers for up to 125 h. In vivo evaluation indicated that the use of PDDA/honey ($\frac{40}{60}$) significantly enhanced wound-healing. On the day 14th, the average healing rate for samples covered by conventional gauze bandage, PDDA, PDDA/honey ($\frac{50}{50}$), and PDDA/honey ($\frac{40}{60}$) were 46.8 ± 0.2, 59.4 ± 0.1, 81.7 ± 0.3, and 94.3 ± 0.2, respectively. The prepared nanofibers accelerated the wound-healing process and reduced the acute and chronic inflammation. Hence, our PDDA/honey wound dressing composites open up new future treatment options for diabetic wound diseases. ## 1. Introduction Diabetes mellitus is one of the most prevalent chronic diseases in the world, which is a consequence of the existence of a malfunction in coagulation, hemostasis, inflammation, proliferation, and the remodeling of wound-healing process [1,2]. Chronic wounds can usually be extended for three main reasons: inflammation, poor perfusion, and the presence of necrotic tissue. On the other hand, wound infection is a major complication in diabetic patients [3]. Micro-organisms can be extensively spread in chronic wounds due to the relative moisture, warmth, and available nutrients. Many studies have been performed on the use of synthetic drug-incorporated nanofibers for treating the diabetic ulcer infections [4]. However, the use of synthetic drugs is not favorable due to their side effects and relatively high costs. As an alternative choice, traditional and herbal medicines have recently gained popularity due to their availability, moderate efficacy, no or fewer side effects, and low costs [5]. In recent years, nanoparticles with different morphologies have extensively been studied owing to their advantages in catalysis reactions, energy storage applications, nanoreactors, biosensors, drug delivery systems, and wound-healing in biomedical industries. It has been proven that nanofibers, nanotubes, and nanospheres exhibit exceptional optical, thermal, electrical, magnetic, and mechanical characteristics in regenerative medicine [6,7]. One-dimensional structures are used in many fields due to their high specific surface area. It has been claimed that electrospun nanofiber platforms have very small pores and can prevent the bacterial penetration [8]. Additionally, the nanofibrous structures resemble the extracellular matrix of the skin, which will lead to an improvement in the healing process. The electrospinning technique is a simple, convenient, low cost, and versatile method for producing one-dimensional nanostructure materials [9,10,11,12]. Moreover, the process parameters can be tailored to produce nanofibers with different diameters, morphologies, and area densities. Across the world, several research studies on the biocompatible and antimicrobial polymers have been carried out due to their biomedical importance in the treatment of infectious diseases [13,14]. Poly (diallyldimethylammonium chloride) (PDDA) is a cationic antimicrobial polymer with remarkable applications in water, paper, mining, and petrochemical as a coagulant, dehazer, or demulsifier reagent [15]. Additionally, it is has become known as a model polymer in recent research on polyelectrolytes, functional nanomaterials, biosensors, fuel cells, capturing cancer cells, and wound dressings [16,17,18,19,20]. In this context, the functionalized graphene nanosheets were synthesized with PDDA and further combined with room temperature-ionic liquids [21]. Recently, PDDA and sodium silicate multilayers had been applied to silica spheres to generate a superhydrophobic layer [22]. PDDA holds the quaternary ammonium moieties as pendant groups in the chemical structure, which displays outstanding antimicrobial activity [23]. In this regard, it could be a good candidate for the production of wound dressings. Sanches et al. stated that cationic bilayer fragment/carboxymethylcellulose (CMC)/PDDA nanoparticles have strong antibacterial properties [24]. Zhou et al. found that PDDA improved the adsorptive capacity of Ag/AgCl/reduced graphene oxide, which in turn enhanced the bactericidal activity of resultant composite particles [25]. Sundarrajan and Ramakrishna optimized the electrospinning of PDDA/poly vinyl alcohol (PVA) nanofibers for tissue engineering and controlled drug delivery [26]. Honey is a natural food substance with several monosaccharides, including glucose, sucrose, and fructose. Honey has several potential applications in wound care due to unique antibacterial properties [27]. We should emphasize that honey is not only able to reduce inflammation, can avert the need for surgical debridement, and neutralize bad smells, it can also accelerate tissue growth and wound-healing [28]. In addition, the fabrication of electrospun honey-based nanofibrous matrices has recently been demonstrated. In a research work, polyethylene terephthalate (PET)/honey/chitosan nanofibers were produced for wound dressing purposes [29]. Additionally, homogenous PVA/honey electrospun nanofibers were recently fabricated [27]. Sarhan et al. postulated the antimicrobial and wound-healing activity of honey/chitosan nanofibers after enrichment with allium sativum and cleome droserifolia [30]. The majority of previous research has been focused on the combination of honey with different biopolymers and no study has been presented which is concerned with the fabrication of PDDA and PDDA/honey nanofiber composites for wound-healing investigation [31]. In this paper, PDDA/honey nanofibers produced using the electrospinning method and were characterized using different analytical techniques. Furthermore, fabricated nanofibers were chemically modified with glutaraldehyde to reduce the solubility and increase the water absorption properties. All in all, we analyzed the resultant nanofiber composites for diabetic wound dressings. ## 2.1. Microscopic Characterization In our previous research, we optimized the fabrication of PDDA and PDDA/honey nanofiber composites with similar molecular weights and properties [31]. In this study, in order to investigate the effect of honey on the healing properties of PDDA/honey composite nanofibers for diabetic wounds, two ratios were produced: ($\frac{50}{50}$) and ($\frac{40}{60}$) (after the confirmation of optimal values by Design Expert Software V 11.0.3). Figure 1A,F showed the FESEM images of PDDA/honey nanofiber composites with the concentration ratio of $\frac{50}{50}$ and 40/$60\%$ w/w at 20 and 17 applied voltages, respectively. As can be seen, nanofibers had a smooth surface and a uniform morphology. They had the average diameter of 91 and 93.5 nm for PDDA/honey ($\frac{50}{50}$) and ($\frac{40}{60}$) nanofiber composites, respectively. Figure 1B–E,G–J illustrated the FESEM images of PDDA/honey ($\frac{50}{50}$) and ($\frac{40}{60}$) [31] nanofiber composites after crosslinking for different time periods, including 2, 3, 4, and 5 h. It was clear that the nanofibers were conglutinated to each other, especially at their touching points, after crosslinking for 2 h. This could be due to the fact that a thin layer of the crosslinking agent was able to cover the nanofiber’s surface, which affected their hydrophilic nature. When the crosslinking duration was increased from 2 to 5 h, the morphology of nanofibers changed from a fibrillary form to a thin film. Furthermore, the visible voids within the nanofiber matrix were filled by glutaraldehyde [31]. Similar to our work, Hixon et al. observed that the addition of Manuka honey in cryogel-hydrogel electrospun nanofibers resulted in thicker tissue with fewer obvious pores than in the untreated samples [32]. ## 2.2. FTIR Analysis The FTIR spectra of PDDA and non-crosslinked and crosslinked PDDA/honey ($\frac{40}{60}$) nanofiber composite samples are shown in Figure 2. For PDDA nanofibers, the bands at 3434 and 1631 cm−1 were attributed to the stretching and bending vibrations of the absorbed water molecules, respectively. Additionally, the band that appeared at 1205 cm−1 was related to the stretching vibration of C-N bond [33]. The peak at 1460 cm−1 was due to the CH2 bending vibrations. The bands at 2931 and 2898 cm−1 was assigned to the methylene C-H asymmetric and symmetric stretching vibrations, respectively. Honey was mainly composed of hydrated glucose, fructose, and carbohydrates. According to Figure 2B, the skeletal vibrations of carbohydrates appeared in the range of 600–1500 cm−1 [29]. Additionally, the band at 3443 cm−1 was related to the stretching vibration of the OH group of carbohydrates [34]. The bands that appeared at 2994 and 2841 cm−1 were assigned to the stretching vibrations of CH bonds. The band at 1513 cm−1 is attributed to the vibration of C=C bond in the aromatic ring of polyphenols in honey [35]. Amino acids that exist in honey generally contain amide groups with several characteristic bands, such as amide A (about 3500 cm−1), amide B (about 3100 cm−1), amide I (1600–1700 cm−1), amide II (1550 cm−1), and amide III (1300–1350 cm−1), depending on the type and number of amino acids. The characteristic peaks of PDDA overlapped with characteristic peaks from honey. By comparing the curves B and C, it was found that the intensity of bands at 1059, 2936, 2848, and 1731 cm−1 increased, which were all related to the acetal band [36]. This change was resulted from the reaction of glutaraldehyde with the hydroxyl containing compounds, the stretching vibrations of CH bonds, and the stretching vibration of C=O groups in honey [37]. Additionally, the intensity of the band at 3446 cm−1 was decreased due to the reaction of the crosslinking agent with OH containing compounds in honey. A new absorption peak at 1690 cm−1 was related to the imine group [38], resulting from the reaction between glutaraldehyde and amine groups of different compounds in honey. Naemi et al. recently observed that addition of honey to a Chitosan/PVA nanocomposite resulted in hydrogen bonds between functional groups of honey with hydroxyl and amine groups from PVA and chitosan [39]. Similarly, Ghorbani et al. observed a shift in the absorption peaks at 1645 and 1522 cm−1 to a high wavenumber after loading honey in ethylcellulose/gum tragacanth nanofibers [40]. ## 2.3. Fluid Absorption and Solubility of Nanofibers It is established that the wound moist is an ideal environment for the bacterial growth. Additionally, bacterial infections can result in impaired healing. In general, antibiotics are largely used for treating wound infections. However, this is not the best treatment methods for burns and chronic wounds. In this regard, using dressing technologies with different biocompatible materials can be among alternative treatments. In comparison with different compounds used for wound-healing, honey-incorporated scaffolds are highly recommended due to their antimicrobial and controlled wound hydration properties [41]. The amounts of fluid uptake of PDDA/honey ($\frac{40}{60}$) nanofiber composites with the crosslinking durations of 2, 3, 4 and 5 h were 314.55, 989.54, 687.29 and $190.43\%$, respectively (Figure 3A). In other words, longer crosslinking duration was able to further change the morphology of nanofibers. Additionally, we noted that crosslinked nanofibers for 4 and 5 h exhibited stiffer tissues with low flexibility, which is a disadvantage for wound dressing applications. In addition, longer crosslinking durations resulted in drastic changes in the hydrophilic nature of the nanofibers, thus hindering the diffusion of water molecules into the nano-fibrous mats. We also considered the solubility of nanofibers as another factor to evaluate the performance of crosslinking in wound dressings. Results revealed that the sample crosslinked for 2 h exhibited a $39.3\%$ weight loss, whereas there was no weight reduction for the other samples crosslinked for longer durations. Furthermore, the solubility of nanofiber composite prepared from PDDA/honey ratio of $\frac{50}{50}$ and crosslinked for different durations was investigated. The nanofiber composite crosslinked for 2 h, showing a $38.58\%$ weight loss, while no weight reduction was observed for longer crosslinking durations. The fluid absorption for the composite samples crosslinked for 2, 3, 4, and 5 h was 300.41, 975.28, 651.11, and $145.43\%$, respectively (Figure 3B). We found no weight loss for the PDDA nanofiber sample crosslinked for 3 h. Additionally, the fluid absorption value for this sample was $665.72\%$. A recent study by Tang et al. expressed the effect of honey on the water absorption capability of honey-loaded alginate/PVA nanofibrous membranes. According to their results, dissolving honey in the nanofiber structure led to the degradation and destruction of the internal structures of membranes, thus decreasing their water absorption ability [42]. ## 2.4. Release Behavior of Nanofibers The release behavior of nanofiber samples produced at different crosslinking duration was investigated, and the results are shown in Figure 4A. When increasing the crosslinking duration, the total amount of released compounds from PDDA/honey nanofiber composite ($\frac{40}{60}$) crosslinked for 2 h was $84.8\%$. However, it decreased to 75.14, 18.06, and $8.72\%$ for those composites crosslinked for 3, 4, and 5 h, respectively. This drastic decrease in the released materials for 4 and 5 h crosslinked samples could be related to the change of morphology of samples and their hydrophobic properties. Our results are also consistent with solubility test measurement of nanofiber composites. The results further showed that the increase of crosslinking duration from 3 to 5 h led to an increase in releasing time from 125 to 150 h. According to Figure 4B, a major increase in the released materials occurred in the first 35 h period, which could be related to the high amount of the entrapped compounds on the surface of nanofibers. The incorporation of effective compounds on the nanofiber surface is attributed to the limited physical interactions between the effective compounds and the polymer matrix in the electrospinning process [43]. After 35 h, the rate of release decreased and an equilibrium point was finally achieved. In this regard, the number of released compounds for the samples crosslinked for 2, 3, 4, and 5 h were 22.5, 20.17, 11.05, 3.74, and $2.04\%$ of the total values. Additionally, the equilibrium values were 65, 125, 155, and 155 h, respectively. Similar behavior was observed for the PDDA/honey nanofiber composite ($\frac{50}{50}$). For this sample, the equilibrium values were 55, 115, 150, and 150 h after crosslinking for 2, 3, 4, and 5 h, respectively. Additionally, the total amounts of the released compounds from these samples were 85.1, 76.51, 19.88 and $9.91\%$, respectively. As can be seen, the total amount of released compounds for the PDDA/honey nanofiber composite ($\frac{50}{50}$) was slightly higher than PDDA/honey nanofiber composite sample ($\frac{40}{60}$). This could possibly be related to the average nanofiber diameter (AND) of samples. The AND of $\frac{50}{50}$ and $\frac{40}{60}$ PDDA/honey nanofiber composites were 91 and 93.5 nm, respectively. We concluded from the results of fluid absorption, solubility, and release behavior of nanofiber composites that the crosslinking duration of 3 h provided the optimum results. ## 2.5. Antibacterial Activity of Nanofibers The antibacterial activity of PDDA and PDDA/honey nanofiber composites were evaluated by using the viable cell-counting process. The effects of fabrication of resultant materials on the Gram-positive (Staphylococcus (S.) aureus) and Gram-negative (Escherichia coli (E.) coli) bacterial culture were investigated, and the results are shown in Figure 5. According to Figure 5A–C, the number of bacterial colonies decreased after treatment with honey incorporated nanofiber composites. For the PDDA/honey nanofiber samples, E. coli and S. aureus bacteria with an initial concentration of 5 × 105 CFU/mL were completely eliminated. On the other hand, certain bacterial colonies were seen on the PDDA nanofiber. Additionally, the antibacterial property of PDDA sample against E. coli was slightly higher than S. aureus. We should emphasize that the antibacterial properties of the PDDA nanofiber were 77.8 and $81.2\%$ for S. aureus and E. coli bacteria, respectively. According to previous studies, the antibacterial characteristics of the PDDA polycation polymer are determined by the permanently charged quaternary ammonium domains in the cyclic structure, which can interplay with the negatively charged bacterial surface, and as a result, bacteria membrane walls can be destroyed, resulting in cell leakage and death [22]. The antibacterial properties of honey are due to the H2O2 and antioxidant polyphenols within its composition. Polyphenols are strong pro-oxidants in the presence of transition metal ions, promoting hydroxyl radicals from H2O2 via the Fenton process [44]. The high sugar content of honey draws fluid from the wound by osmosis, resulting in bacterial death [45]. It was reported that flavonoids, including benzoic and cinnamic acids, contribute to the antibacterial activity of honey. However, the effectiveness of these components in the overall antibacterial properties of honey is low compared to the contribution from hydrogen peroxide [46]. According to the manufacturer, the total phenolic and flavonoid contents of our used honey were 59.65 and mg GAE/100 g honey, respectively. We achieved $99.9\%$ antibacterial efficiency from PDDA/honey ($\frac{40}{60}$ and $\frac{50}{50}$) composite nanofibers against S. aureus and E. coli. Our results demonstrated synergistic antibacterial properties in PDDA/honey composite nanofibers, in comparison with honey or the PDDA nanofiber. In addition, the antibacterial activity of fabricated materials on E. coli and S. aureus did not change after dilution (Figure 5D). ## 2.6. Wound-Healing and Histopathological Studies We conducted wound-healing and histopathological studies on the resultant samples. The images of wounds after treatment with nanofibers for different durations, namely 0, 3, 7, and 14 days, are shown in Figure 6. As can be seen, the wound-healing percentage for diabetic rats after being treated with a gauze bandage, PPDA nanofiber along with PDDA/honey ($\frac{50}{50}$), and PDDA/honey ($\frac{40}{60}$) nanofiber composites was 6.8 ± 0.2, 9.5 ± 0.2, 19.5 ± 0.3, and 21.8 ± 0.1 after 3 days, respectively. It was clear that the rate of wound closure for the wound covered by honey incorporated nanofibers was much higher than that covered with the gauze bandage. This was due to the higher absorption ability and barrier properties of nanofibers against bacteria [8]. On the other hand, the healing percentages for the gauze bandage, PDDA nanofiber, and PDDA/honey ($\frac{50}{50}$) and PDDA/honey ($\frac{40}{60}$) nanofiber composite treated wounds were 11.8 ± 0.4, 25.3 ± 0.2, 46.7 ± 0.1, and 59.8 ± 0.1 after 7 days, respectively. Low healing properties for the gauze-covered sample were attributed to the lack of moisture on the surface of the wound. Although the diameter of wound was diminished after treatment with gauze, the improvement of the wound-healing properties of nanofiber composites could be related to the modulation of the inflammatory responses of the skin dendritic cell line. Notably, PDDA/honey ($\frac{50}{50}$) and PDDA/honey ($\frac{40}{60}$) nanofiber composites are able to influence the fast autolytic debridement and the suppression of inflammation [47]. Previous studies demonstrated that honey enhances the tissue repair process via the stimulation of the leukocytes, which in turn releases cytokines and triggers an immune response against infection [48]. Moreover, honey contains high number of antioxidants, which are important factors in wound-healing applications. Antioxidants have been correlated to the improvement of the wound-healing process by preventing the overexposure of the wound to oxidative stress. Accordingly, delays in the wound-healing process can occur [49]. It was previously revealed that honey’s antioxidants are mainly flavonoids, phenolic acids, catalase, peroxidase, carotenoids, and non-peroxidal [50]. Additionally, it was believed that the antioxidant activity of honey depended on several factors, including the floral source, season, and climatic changes. We should also mention that processing of honey may effect its antioxidant activity [51]. The total antioxidant capacity of the honey we used was 56.61 mg AAE/g honey. The healing percentages for the wounds treated with gauze bandage, PDDA, PDDA/honey ($\frac{50}{50}$), and PDDA/honey ($\frac{40}{60}$) nanofiber composites were 46.8 ± 0.2, 59.4 ± 0.1, 81.7 ± 0.3, and 94.3 ± 0.2 after 14 days, respectively. We also found that the healing rate for the wound covered with the PDDA/honey nanofiber composite ($\frac{40}{60}$) was higher than that covered with the PDDA/honey composite ($\frac{50}{50}$). Therefore, the more honey used in the nanofiber composite, the higher the wound-healing rate. This is also related to the equilibrium point of the effective compounds after release from the nanofiber composites. As mentioned earlier, the equilibrium point of PDDA/honey nanofiber composite ($\frac{50}{50}$) was 100 h, while it was 125 h for the PDDA/honey nanofiber composite ($\frac{40}{60}$). Since the wound dressing was changed every 5 days, the possibility of bacterial growth on the wound surface was increased and the effective compounds were not in direct contact with the generated wound. Therefore, the controlled release of the effective compounds from nanofiber composites were able to promote the wound-healing. More importantly, honey aids in the healing process through the acidification of the alkaline environment in chronic ulcers. Acidification inhibits protease activity, induces fibroblast proliferation, and establishes an aerobic environment [52]. A higher healing percentage for the PDDA nanofiber-covered wound was attributed to the antibacterial activity of the PDDA polymer and the high absorption capability of nanofibers. We also illustrated the histopathological images of the control wound along with the nanofiber-treated wounds at 3, 7, and 14 days (Figure 7). For the wounds covered by nanofiber composites, a large number of inflammatory cells was observed on day 3. On the other hand, they were too low for the gauze bandage-treated wounds. In addition, a massive infiltration of inflammatory cells was observed after 7 days and the matured granulation tissue along with epidermal layer was detected after 14 days. Additionally, the number of inflammatory cells was diminished significantly after 14 days. These anti-inflammatory properties of honey were confirmed by other researchers [53]. A further decrease in the number of inflammatory cells was observed after treatment of the wound with the PDDA/honey nanofiber composite ($\frac{40}{60}$) in comparison with the PDDA/honey nanofiber composite ($\frac{50}{50}$)-treated wound. To put it simply, the formation of fibroblast cells, collagen fibers, and connective fibrils were observed for the PDDA/honey ($\frac{40}{60}$)-treated wound after 14 days. Consistent with other studies, the granulation of tissue components in the honey-incorporated nanofiber composite-treated wounds was improved, which could be due to the increased collagen turnover. For the PDDA nanofiber-treated wound, a large number of inflammatory cells was observed after 7 days. In addition, regenerative responses and tissue granulation were observed after 14 days in these samples. ## 2.7. Histomorphometric Analysis The histomorphometric analysis was performed on nanofiber-treated injured skin after 3, 7, and 14 days, and the results are presented in Table 1. The result indicated better healing for the PDDA/honey nanofiber composite-treated wound. In addition, the re-epithelialization of this wound was higher than other nanofiber-treated wounds after 14 days. Additionally, a significant decrease in the number of inflammatory cells in the wounds covered by honey-incorporated nanofiber composite dressings was confirmed after 14 days. We should note that the PDDA/honey nanofiber composite ($\frac{40}{60}$) demonstrated the best wound-healing properties among all samples. ## 2.8. Identification of the Effect of Variables on Wound-Healing (%) Improvement In this study, we used the analysis of three-dimensional graphs to observe the effect of variables and their effects on the experiments. Moreover, the relationship of different variables and their effects on wound-healing (%) was studied. As can be seen, the combination of the two variables of honey (%) and PDDA (%) was 100 in total. Note that we considered the changes on the chart based on this total value. After consideration of these two variables, the wound-healing rate was changed with increasing the concentration of honey (between 40 to $60\%$) and decreasing the concentration of PDDA from 60 to $40\%$ (the red region in Figure 8A). Figure 8B depicts the effect of crosslinking duration and the percentage of honey on the wound-healing properties of resultant composites. As can be seen, these two variables had a reverse effect: the more honey (%) used in the experiment, the less crosslinking duration (h) required in the final composite. Furthermore, the green region of the chart demonstrates that the honey variable was about $40\%$ when the crosslinking duration was between 3.2 and 4.4 h. Interestingly, the crosslinking duration reached 2.5 h in the red region. There was also a peak in the green region, confirming that honey (at $50\%$ value) had a significant effect on wound-healing at lower crosslinking durations. A 3D diagram of the relationship of honey (%) and static time on wound-healing properties is depicted in Figure 8C. Results showed that application of wound dressing to up to 14 days resulted in an enhancement in wound-healing. It is noteworthy that by using up to $60\%$ honey (%), no significant improvement in wound-healing was observed between 5 to 12 days. On the other hand, wound-healing rate was highly influenced after 14 days. Therefore, it can be concluded that samples with $60\%$ honey content display a rapid improvement in wound-healing after 13 and 14 days. According to the presented diagram in Figure 8E, there was a decrease in wound-healing in the green region. Results revealed that the wound-healing (%) was decreased with any decrease in the amount of PDDA (%) and crosslinking duration. In addition, when we simultaneously changed these two variables (PDDA up to $55\%$ and crosslinking duration for 4.4 h), wound-healing was improved by up to $65\%$. However, longer crosslinking duration had a negative effect on wound-healing. We also evaluated the effect of PDDA content (%) and the duration of static wound dressing on wound-healing (Figure 8F). We found that the wound-healing (%) was improved after treatment for 14 days. We should express that the duration of crosslinking (2–3 h) and static wound dressing had a major effect on wound-healing (%). However, wound-healing was slightly decreased with any increase in crosslinking duration from 3 to 5 (h). We demonstrate the optimum values for wound-healing (%) in terms of different parameters in Table 2C. ## 3. Conclusions In this research, the wound-healing ability of PDDA and PDDA/honey nanofibers was investigated for the first time. To control the release behavior of effective compounds and decrease the solubility of nanofibers, GA was used. The effect of the crosslinking duration on the different properties of nanofiber composites was also studied. We found that honey containing nanofibers exhibited an excellent antibacterial activity ($99.9\%$) against S. aureus and E. coli bacteria. Moreover, releasing the effective compounds from nanofibers was continued up to 125 h. The prepared nanofibers were also evaluated as wound dressings for diabetic wound-healing. The results indicated that honey-containing wound dressings resulted in the best wound-healing properties mainly due to anti-inflammatory, antioxidant, and antibacterial properties of nanofibers. This process also resulted in the formation of high amounts of fibroblast cells, collagen fibers, and connective fibrils in the treated wounds. ## 4.1. Materials Poly (diallyldimethylammonium chloride) (PDDA) with a molecular weight of Mw = 450,000 and 40 wt% in H2O was obtained from Sigma-Aldrich (Germany). The raw multifloral Persian Honey was obtained from a local company at Polour, Iran. Glutaraldehyde (GA) ($50\%$ V/V in water) and aluminum trichloride (AlCl3) were obtained from Merck (Germany). Ethanol, nutrient broth solution, phosphate-buffered saline, Folin–Ciocalteu’s phenol, Sodium carbonate (Na2CO3), methanol, and sodium citrate were of analytical grade and obtained from Merck Millipore (Germany). Sterile streptozotocin (STZ), xylazine, and ketamine were received from Sigma, St Louis, MO, USA. ## 4.2. Preparation of Nanofibers We used the following procedure that was previously reported by our group [31]. Briefly, in order to produce the PDDA/honey nanofiber composites, we prepared two spinning solutions involving 50 and $60\%$ w/w honey in the PDDA solution. Then, 1 mL of ethanol was mixed into the solutions under vigorous stirring to reach the volume of the mixed solution at 5 mL (room temperature for 1 h). Next, we electrospun the prepared solutions under a fixed electrical field of 20 and 17 KV for 50 and $60\%$ w/w honey-containing solutions, respectively. We utilized the electrospinning equipment from Fanavaran nano-meghyas Co. (Iran). Final nanofiber composites were collected onto an aluminum (Al) sheet at the feeding rate of 0.8 mL/h and the distance between tip of needle and collector was set at 16 cm. ## 4.3. Crosslinking of Nanofibers The resultant scaffolds (15 cm × 15 cm) were placed in a polyethylene-framed chamber for exposure to the vapor of 40 mL GA solution. This process was performed at different durations of 2, 3, 4, and 5 h, and the treated nanofibers were finally dried in a vacuum oven at 70 °C for 12 h to remove residual GA. ## 4.4. Assessment of Antibacterial Activity We assessed the antibacterial properties of nanofibers against Escherichia coli (ATCC 25922) as Gram-negative bacteria and S. aureus (ATCC 25923) as Gram-positive bacteria by employing the viable cell counting method. For this purpose, 100 µL from E. coli and S. aureus suspensions were separately cultured in 100 mL of a nutrient broth solution. Next, 1 mL of the bacteria/nutrient solution was mixed into 9 mL of the sterilized nutrient broth solution ($0.8\%$). A concentration of 5 × 105 CFU/mL from testing bacteria was selected for the evaluation. The weight of the disk-shape composite samples was 100 mg with a 2.8 cm diameter. The antibacterial evaluation of nanofibers was conducted by exposing them to 10 mL of the bacteria and nutrient medium. Then, 100 µL of the solution was collected and quickly spread on a plate containing nutrient agar. Finally, plates were incubated at 37 °C for 24 h, and the surviving colonies were subsequently counted. ## 4.5. Assessment of Fluid Absorption Fluid absorption ability (the swelling ratio) was determined via gravimetric analysis. A total of 0.1 g of nanofibers was treated in a phosphate-buffered saline (50 mL of PBS, pH = 7.4) at 35 ± 0.1 °C for 24 h in order to obtain the maximum swelling equilibrium, which was then calculated as Equation [1]. [ 1]Q=ma− mbmb where mb (g) is the weight of scaffolds in the dry state and ma (g) is the weight of the scaffolds after swelling at a specific time period, respectively [54]. ## 4.6. Assessment of Solubility of Nanofibers Nanofibers were evaluated after immersion in a distilled water bath for 360 min (25 °C and pH = 7.4). In the next step, the treated materials were dried at 60 °C for 6 h. The solubility of samples was assessed using the following equation:[2]Solubility %=mX− mymy ×100 where mx and my are the weight of nanofibers before and after immersion in distilled water, respectively. ## 4.7. Evaluation of Phenolic and Flavonoid Compounds The amount of total phenolic compounds was measured using Folin–Ciocalteu reagent [43]. Briefly, 7.5 mL of distilled water was mixed with 0.3 mL of Folin–Ciocalteu’s phenol (diluted 1:10). Then, 1 mg of the nanofiber sample was dipped in the resultant solution for 3 min. Next, 1 mL of Na2CO3 in deionized water ($20\%$ w/v) was added to the mixture. The sample was left in this solution for 1 h at room temperature and in a dark environment, and the absorbance was then assessed at 760 nm using a single beam UV spectrophotometer. Final evaluation was conducted as gallic acid equivalents (mg of gallic acid (GAE)/mg dry weight honey). The total flavonoid compounds were measured using the Dowd method described elsewhere [55]. Briefly, 5 mL of $2\%$ aluminum trichloride (AlCl3) in methanol was mixed with the same volume of a honey solution (0.01 mg/mL). After 10 min, absorption was measured at 415 nm against a blank sample containing of a 5 mL honey solution with 5 mL methanol without AlCl3. The total flavonoid content was determined using a standard curve with quercetin (0–50 mg/L) for comparison. Total flavonoid content was expressed as mg of quercetin equivalents (QE)/100 g of honey. ## 4.8. Evaluation of Total Antioxidant Capacity The antioxidant activities were evaluated as reported by researchers and expressed relative to ascorbic acid [55]. In this test, methanol was used as a blank sample. The reaction material was mixed in a vortex mixer and left in a water bath at 95 °C for 90 min. Absorbance values were measured at the wavelength of 695 nm and the antioxidant activity was calculated as ascorbic acid equivalents (mg AAE/1 g honey). ## 4.9. Conducting Diabetic and Wound-Healing Tests Male Wistar rats with an average weight of 150–200 g were taken for diabetic studies. Animals were first kept at room temperature (23 °C) in a 12 h light/dark cycle, with access to standard rodent chow and water. Testing was carried out according to Islamic Azad University (Rodehen Branch) ethical guidelines, and the ethical permission code was IR.IAU. R.REC.1400.006. Diabetic tests were conducted in rats by a single intraperitoneal injection of 70 mg/kg body weight of sterile streptozotocin (STZ) (Sigma, St Louis, MO, USA) in sodium citrate (0.1 mol/L, pH 4.5). Diabetic rats with a blood glucose level of 300 mg/dL or higher were selected. In the next step, they were anesthetized via the intraperitoneal injection of xylazine (13 mg/kg) and ketamine (66.7 mg/kg). We shaved the dorsal hair of rats, and 1.5 cm diameter full thickness wounds were generated with a biopsy punch. Then, three groups of rats, each containing five rats, were randomly chosen. The wounds of the animals were exposed to PDDA/honey ($\frac{40}{60}$) and PDDA/honey ($\frac{50}{50}$) nanofibers, and the groups were classified as group 1 and 2, respectively. The conventional gauze bandage and PDDA nanofiber composite-treated wounds were considered as controls. The wound-healing of rats was assessed through captured photographs at different time periods. The non-healed spots were measured by a computer analytical system. The healing rate was also evaluated with the following equation:[3]Healing rate $100\%$=primitive area−nonhealing areaprimitive area×100We also monitored the wounds of those rats for 3 days ($$n = 5$$), 7 days ($$n = 5$$), and 14 days ($$n = 5$$) after treatment. ## 4.10. Histopathological Analysis Formalin-fixed paraffin-embedded wound tissue samples were cut into 4 μm tissue sections. Then, they were stained with hematoxylin–eosin, further evaluated under a light microscope, and graded with respect to epidermal regeneration, granulation tissue formation, and the angiogenesis and migration of fibroblast cells. ## 4.11. Histomorphometry Analysis On the 14th day, epithelialization was semi-quantitatively evaluated by using the following scale: 0 (without new epithelialization), 1 ($25\%$), 2 ($50\%$), 3 ($75\%$), and 4 ($100\%$). Sections were also semi-qualitatively considered to be related to angiogenesis. Then, they were assessed according to the number of new vessels within the scar tissue, according to the following scale: 0 (none), 1 (few), 2 (moderate), 3 (many), or 4 (considerably). In these evaluations, results were reviewed by an independent observer blinded to the treatment groups. In our histomorphometric evaluation, neovascularization and collagen density were also monitored and evaluated by using Image-Pro Plus® V.6 software (Media Cybernetics, Inc., Rockville, MD, USA). ## 4.12. Design of Experiments In this study, we utilized experimental design software (Design Expert Software V 11.0.3) and Response Surface Methodology (RSM) with Central Composite design, and results are presented in Table 2A. Some versions were routinely duplicated to create the software reliability which could minimize errors. According to Table 2A, the lowest wound-healing rates were those of Experiment 24, which involved 12 h of wound dressing. However, the highest percentage of improvement was observed in the Experiment 30, which was due to the combination of the desired percentage and the appropriate duration of use. In general, according to the results, it can be concluded that the incorporation of honey in wound dressing had a significant effect and led to improved wound-healing properties. Due to the fact that the test validity of the RSM model was less than 0.05 (in the amount of 0.0001), we found that different experimental variables had significant influence on wound-healing (Table 2B). According to F-statistics, the influence of different variable parameters on wound dressing can be displayed in the following order: wound dressing days, PDDA (%), and honey (%). Therefore, wound dressing days are revealed to have the most effect on wound-healing properties. Furthermore, P-statistics reached less than $0.05\%$ for all variables except the duration of treatment with GA (h). These results from the ANOVA test also illustrated that this parameter did not have a significant effect on wound-healing; however, a significant change was observed in the quadratic equations of the GA executive model. We should further highlight that the effect of the different variables can be observed by using the three-dimensional diagram as well as the executive model, and the mathematical model of the designed experiment can be extracted by using Equation [4]:Wound-Healing: 70.4104 + −9.05634 × [honey] + 2.4282 × [PDDA] + 0.23608 × [GA] + 34.6683 × D + 15.5353 × [honey] [PDDA] + −22.2139 × [honey] [GA] + 5.96673 × [honey] [Day] + −16.7113 × [PDDA] [GA] + 10.4691 × [PDDA] [Day] + −5.06506 × [GA] [Day] + 23.3817 × [honey] 2 + −3.49 × [PDDA] 2 + −9.04543 × [GA] 2 + −14.9567 × [Day] 2 + −7.54687 × [honey] [PDDA] [GA] + 6.34389 × [honey] [PDDA] [Day] + −10.2752 × [honey] [GA] [Day] + −16.9153 × [PDDA] [GA] [Day] + 5.19699 × [honey] 2 [PDDA] + −0.805398 × [honey] 2 [GA] + 0 × [honey] 2 [Day] + 0 × [honey] [PDDA] 2 + 20.1858 × [honey] [GA] 2 + 2.4856 × [honey] [Day] 2 + 0 × [PDDA] 2 [GA] + 0 × [PDDA] 2 [Day] + 0 × [PDDA] [GA] 2 + 0 × [PDDA] [Day] 2 + 0 × [GA] 2 [Day] + 0 × [GA] [Day] 2 + 0 × [honey] 3 + 0 × [PDDA] 3 + 0 × [GA] 3 + −9.02667 × [Day] 3[4] Figure 9A showed the scatter plot for the normal distribution of the conducted experiments. Despite the availability of regular data, some median distributions were expected. We should also mention that specific curved patterns, such as “S-shapes”, were easily recognizable. Therefore, a better analysis could be achieved by performing the transfer function on the dependent variable or model response. In this study, the data followed the central normal line and all experiments were statistically normal and acceptable. A box Cox diagram (Figure 9B) was utilized as a tool to identify the most appropriate power transfer function that could be applied to the response. The lowest point in the box Cox diagram indicated the best Landa value at which the least squares in the converted model were generated. When the ratio of maximum to minimum response value was greater than three, we observed a greater ability to improve the model using the power function. 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--- title: Protective Effect of Hawthorn Fruit Extract against High Fructose-Induced Oxidative Stress and Endoplasmic Reticulum Stress in Pancreatic β-Cells authors: - Hsiu-Man Lien - Hsin-Tang Lin - Shiau-Huei Huang - Yìng-Ru Chen - Chao-Lu Huang - Chia-Chang Chen - Charng-Cherng Chyau journal: Foods year: 2023 pmcid: PMC10047983 doi: 10.3390/foods12061130 license: CC BY 4.0 --- # Protective Effect of Hawthorn Fruit Extract against High Fructose-Induced Oxidative Stress and Endoplasmic Reticulum Stress in Pancreatic β-Cells ## Abstract Hyperglycemia has deleterious effects on pancreatic β-cells, causing dysfunction and insulin resistance that lead to diabetes mellitus (DM). The possible causes of injury can be caused by glucose- or fructose-induced oxidative and endoplasmic reticulum (ER) stress. Hawthorn (Crataegus pinnatifida) fruit has been widely used as a hypolipidemic agent in traditional herbal medicine. The study aimed to investigate whether high fructose-induced pancreatic β-cell dysfunction could be reversed through amelioration of ER stress by the treatment of polyphenol-enriched extract (PEHE) from hawthorn fruit. The extract was partitioned using ethyl acetate as a solvent from crude water extract (WE) of hawthorn fruits, followed by column fractionation. The results showed that the contents of total polyphenols, flavonoids and triterpenoids in PEHE could be enhanced by 2.2-, 7.7- and 1.1-fold, respectively, in comparison to the original obtained WE from hawthorn fruit. In ER stress studies, a sharp increase in the inhibitory activity on the gene expression levels of GRP79, ATF6, IRE1α and CHOP involved in ER stress was evident when dosages of PEHE at 50–100 μg/mL were used against high-fructose (150 mM)-treated cells. HPLC–MS/MS analysis showed that polyphenols and flavonoids collectively accounted for $87.03\%$ of the total content of PEHE. ## 1. Introduction The dried fruit of Hawthorn (Crataegus pinnatifida), a member of the Rosaceae family widely distributed in North America, Europe and Asia [1], is a famous fruit valued for its use with digestion problems. It has been used widely for preventing and treating cardiovascular diseases in China, Europe and the USA [1,2]. Ethnopharmacological studies reported that hawthorn fruit has been used for its anti-inflammatory, anticancer, anti-cardiovascular disease and digestion-enhancing properties, as well as for dissipating blood stasis [2,3,4]. There is also growing evidence indicating an enhancing effect of hawthorn fruit on plasma insulin secretion levels in streptozotocin (STZ)-induced type II diabetes mellitus (DM) rats [5]. The phytochemical composition of hawthorn fruit is rich in polyphenols, including flavonoids, phenolic acids and procyanidins (PC) [6,7]. In addition, hawthorn fruits contain significant amounts of triterpenoids and lignans [8,9]. Compositional investigations of three varieties of C. pinnatifida fruits reported high amounts of total phenolic content in the results of 31.4 to 104.6 mg gallic acid equivalents per g in the dry weight of fruits [10]. In a quantitative analysis of phenolic compounds on C. pinnatifida fruits, chlorogenic acid, hyperoside (quercetin-3-O-galactoside), isoquercitrin (querce-tin-3-O-glucoside), epicatechin (EC) and epicatechin-(4β → 8)-epicatechin (PC-B2) were indicated as the major compounds [11]. Moreover, the two most abundant components in the fruits were indicated from EC and PC-B2, with contents of 348 and 374 mg/100 g, respectively [12]. EC has been found to elevate insulin sensitivity as well as to lower insulin resistance [13]. PC-B2, composed of two molecules of EC linked by a C4-C8 bond, has been reported to prevent reactive oxygen species (ROS) generation and inhibit inflammation [14], and to protect cells from hyperglycemia-induced dysfunction [15]. Therefore, preparing enriched polyphenols extract, especially EC and PC-B2 from hawthorn fruit to exert the pancreatic β-cell function, could be a useful therapeutic strategy for hyperglycemia-induced injury. There are several factors involved in inducing diabetes progression, including hyperglycemia- and hyperlipidemia-induced oxidative stress and inflammation [16,17]. In the physiological environment, pancreatic beta cells are extremely sensitive to oxidative stress due to their low intracellular antioxidant capacity [18,19]; in particular, the expressions of key antioxidant enzymes, catalase and glutathione peroxidase (GPx) are much lower in β-cells than in α-cells [19]. These pathogenic factors are important in leading to apoptosis and a decrease in beta cell mass [20]. Hawthorn fruits have been used to treat symptoms such as cardiovascular disease (CVD) [21], gastrointestinal motility disorder [22] and inflammation [23]. Furthermore, a broad spectrum of pharmacological actions on the neuroprotection, antibacterial, antiviral, anti-diabetes, anti-aging, anti-obesity and other actions have been reported in hawthorn fruits [24], but very little is known about the protective effects on beta cells of hawthorn fruit against high-fructose-induced endoplasmic reticulum (ER) stress and cell apoptosis. Based on previous reports indicated that the antioxidants can be act as ER stress inhibitors in DM, the aim of this study was to investigate the potential inhibition efficacy on ER stress of PEHE. ## 2.1. Materials and Chemicals Bright red and ripe hawthorn (Crataegus pinnatifida) fruits were collected in December 2021 from a local farm in Taichung, Taiwan. The air-dried samples were identified using micro- and macroscopic description in the Taiwan Herbal Pharmacopeia [25], as well as in comparisons with herbarium specimens kept in the Research Center for Biodiversity, Academia Sinica, Taipei, Taiwan. A voucher specimen (No. 110-1) was deposited in the Research Institute of Biotechnology, Hungkuang University, Taichung, Taiwan. (−)-Epicatechin, hyperoside (Quercein-3-O-galactoside), procyanidin B2 and isoquercitrin (Quercein-3-O-glucoside) were purchased from ChemFaces (Wuhan, China). Antibodies against β-Actin (tcba13655), GRP78 (tcea19663) and CHOP (tcba1658) were purchased from Taiclone (Taipei, Taiwan). HPLC-grade methanol, acetonitrile and ultrapure water, trypan blue, 3-(4,5-dimethyl thiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), butylated hydroxytoluene (BHT), Trolox, p-nitrophenyl-α-D-glucopyranoside (pNPG) and 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) andα-glucosidase (from Saccharomyces cerevisiae) were purchased from Sigma-Aldrich Chemical Co., St. Louis, MO, USA. Fetal Bovine Serum (FBS), L-glutamine solution (100 mM) and penicillin–streptomycin (5000 units/mL and 5 mg/mL streptomycin) were purchased from Biological Industries (Beit Haemek, Israel). Dulbecco’s Modified Eagle Medium (DMEM) and trypsin-EDTA solution were provided by Hyclone (Logan, UT, USA). The protein assay kit was a product of Bio-Rad (Hercules, CA, USA). The ESI-L low concentration tuning mix (G1969-85000) was purchased from Agilent Technologies (Santa Clara, CA, USA). ## 2.2. Preparation of Hawthorn Fruit Extract After removing the seeds, the oven-dried and grounded hawthorn fruit powders (10.0 g) were extracted with 200 mL of distilled water under stirring for 1 h at 90 °C. The water extracts were centrifuged at 6000 rpm for 5 min at 4 °C. Collected supernatants were then evaporated and dried in a rotary evaporator under vacuum at 40 °C to afford 2.49 ± 0.65 g of hawthorn fruit extracts. The concentrated water extract (WE) was suspended in water (50 mL), and was partitioned with ethyl acetate (50 mL × 3 times). The obtained ethyl acetate extracts were combined and dried under vacuum at 40 °C to provide the ethyl acetate (EA) fraction. The EA fraction was dissolved in $70\%$ methanol and was subjected to a Sephadex® LH–20 column (id × $L = 1.5$ × 30 cm) for washing with distilled water and eluting with $70\%$ of methanol at a flow rate of 1 mL/min in a total volume of three bed volumes (BV), respectively. The $70\%$ methanol fraction was dried with rotatory evaporation under vacuum at 40 °C to afford 0.31 ± 0.02 g (designated as PEHE). ## 2.3. HPLC and HPLC/ESI–MS/MS Analysis of PEHE The analysis of major polyphenols and flavonoids of PEHE was determined in accordance with a previous report [11], using a HPLC/electrospray ionization (ESI) triple quadruple mass spectrometer. In brief, a Kinetex® EVO C18 column (100 × 2.1 mm, 2.6 µm) with a Security-Guard Ultra C18 guard column (2.1 mm × 2.0 mm, sub-2 µm, Phenomenex, Inc., Torrance, CA, USA) using an HPLC system consisted of a photodiode-array detector (DAD) that was applied to the separation of components in PEHE. The gradient elution system using two solvents was as follows: Solvent A (formic acid/water, 0.1:99.9, v/v) and Solvent B (formic acid/acetonitrile, 0.1:99.9, v/v). The flow rate of the mobile phase was 0.3 mL/min, and the column temperature was 35 °C. The gradient program was conducted as follows: 0–3 min ($2\%$ B), 3–23 min (2–$35\%$ B), 23–30 min (35–$95\%$ B), 30–40 min ($95\%$ B) and 40–45 min (95–$2\%$ B). The absorption spectra of eluted compounds were recorded using the in-line DAD over a wavelength range of 210–600 nm, and the peaks were monitored at 254, 280 and 320 nm, respectively. The compounds were eluted and separated, and further identified with a triple quadruple mass spectrometer (Agilent 6420, Santa Clara, CA, USA). The mass spectrometer was operated in both positive and negative ionization modes with a potential of ±3500 V, respectively. The injection volume was 10 μL using an autosampler. The drying gas was nitrogen (9 L/min), and the pressure of the nebulizing gas was 35 psi. The drying gas temperature was 325 °C. The fragmentor voltage was 125 V, and the in-source collision-induced dissociation (CID) voltage was 15 V. The collision gas was nitrogen. The ions produced by nitrogen collision were detected in the range of 100–1200 amu at a scan time of 200 ms/cycle. The MS instrument was externally calibrated with ESI-L Low Concentration Tuning Mix. The identification of separated compounds was carried out by comparing the retention times, UV-Vis spectra and mass spectra provided by ESI-MS and ESI-MS/MS with those of authentic standards when available, or the data reported from literature. ## 2.4.1. Total Polyphenolic Content (TPC) The previous method [26] was followed for the determination of total phenolics. Briefly, WE or PEHE extract was dissolved into methanol at a concentration of 1 mg/mL. The sample solution (0.1 mL) was added into 2 mL of Na2CO3 ($2\%$). After two min, the Folin–Ciocalteau reagent mixture (0.1 mL, $50\%$) was added and left to stand for 30 min. The absorbance was detected at 750 nm using a microplate reader (ELx800, BioTek Instruments Inc., Winooski, VT, USA). A calibration curve was similarly established using authentic gallic acid. The amount of phenolics was expressed as mg of gallic acid equivalent per g of dried weight of hawthorn fruit (mg GAE/g DW). ## 2.4.2. Total Flavonoid Content (TFC) The assay for total flavonoids was carried out according to the method described [27]. In brief, 250 μL of sample solution (5 mg/mL) and deionized water (1.25 mL) were added into NaNO2 solution (75 μL, $5\%$ w/v) and mixed well, standing for 6 min. In Then, 150 μL of AlCl3·6H2O ($10\%$ w/v) solution was added and left to stand for 5 min. Next, 0.5 mL of 1 M NaOH and 2.0 mL of deionized water were added and mixed well. The absorbance of the solution was detected at 510 nm. A calibration curve was established using authentic quercetin. The amount of flavonoids was expressed as mg quercetin equivalent per g of dried weight of hawthorn fruit (mg QE/g DW). ## 2.4.3. Total Triterpenoid Content (TTC) The content of the total triterpenoids was determined with the vanillin/glacial acetic acid method [28]. The 100 μL of methanolic WE or PEHE solution (1 mg/mL) was added into 150 µL of $5\%$ vanillin/glacial acetic acid (w/v), followed by 500 µL of perchloric acid solution. The sample solution was heated for 45 min at 60 °C. After cooling down to ambient temperature, the solution was detected at 548 nm after being diluted to 2.25 mL with glacial acetic acid. A calibration curve was established using ursolic acid as the reference compound. The amount of total triterpenoids was expressed as mg glycyrrhetinic acid equivalent per g of dried weight of hawthorn fruit (mg GLE/g DW). ## 2.5.1. DPPH Free Radical-Scavenging Activity Determination The stable DPPH was used to determine the free radical scavenging activity of the extracts [29]. A total of 0.1 mL of methanolic solution of DPPH (0.5 mM) was added into 0.1 mL of extract in various concentrations, and was left to stand for 30 min at room temperature. The absorbance of the reacted solution was detected at 517 nm. The experiment was repeated three times. The IC50 value denotes the concentration of the sample (μg/mL) that is required to scavenge $50\%$ of the DPPH free radicals. ## 2.5.2. Trolox Equivalent Antioxidant Capacity (TEAC) A slightly modified method [30] for the TEAC (or the more specific definition as ABTS radical cation scavenging activity) evaluation was followed. The final concentrations of 0.1 mM ABTS•+, 4.4 unit/mL horseradish peroxidase (Sigma-Aldrich) and 50 μM H2O2 in 50 mM phosphate buffer (pH 7.4) were mixed well, and left to stand overnight in the dark at room temperature to form a blue-green color solution of stable ABTS•+ reagent. A total of 50 μL of sample solution was added into the ABTS•+ solution in a moderate proportion for measuring the decrease in absorbance at 734 nm after 10 min. The results were expressed as IC50 values, the concentration (μg/mL) of sample required for $50\%$ inhibition of ABTS•+. ## 2.6. α-Glucosidase Inhibitory Activity Assay The inhibition activity of PEHE or WE on α-glucosidase was determined according to the method of Chu et al. [ 31], with slight modifications. PEHE sample (0–1 mg/mL) was added into aliquots of 20 µL of 100 mM phosphate buffer (pH 6.8) and 20 µL of 2.5 mM pNPG. After incubating at 37 °C for 5 min, 20 µL of α- Glucosidase (0.2 U/mL in 10 mM pH 6.8 phosphate buffer) was added and incubated at 37 °C for 15 min. The reaction was terminated by the addition of 80 μL of Na2CO3 (0.2M), and was determined spectrophotometrically at 405 nm using a microplate reader (Multiskan Spectrum, Thermo Electron Corporation, Waltham, MA, USA). The various concentrations of PEHE or WE were also used for the comparative study in $50\%$ inhibition (IC50 values). All measurements were performed in triplicate. ## 2.7.1. Cell Culture A rat insulinoma cell line RIN-m5F (pancreatic β-cell, ATCC, CRL-11605) was purchased from the Bioresource Collection and Research Center (BCRC, No. 60410) of the Food Industry Research and Development Institute, Taiwan. RINm5F cells were maintained as a monolayer culture in complete medium at 37 °C in a humidified $5\%$ CO2 incubator, with the medium changed every two days. The complete medium contained αMEM with folate (2 µM), thymidint (36 µM), hypoxanthine (36 µM), glycine (600 µM), serine (250 µM) and $10\%$ fetal bovine serum. Penicillin (20 units/mL), streptomycin (0.02 mg/mL) and fungizone (2.5 μg/mL) were also added to the media to eliminate contamination. ## 2.7.2. Cell Viability The previously described MTT method [32] was performed to evaluate the cell toxicity of fructose and extract. In brief, RIN-m5F cells at a density of 5 × 103 cells/well were treated with different concentrations of fructose or extract as indicated. After 24 h of incubation, MTT (final concentration, 0.5 mg/mL) was added to each well and incubated for 2 h. The formed formazan crystal product was dissolved in DMSO, and the absorbance was read at 570 nm with an ELISA Reader (VersaMax, Molecular Devices, Sunnyvale, CA, USA). ## 2.7.3. Intracellular ROS Level RIN-m5F cells were seeded onto a 12-well plate at a density of 1 × 104 cells/mL and incubated for 24 h. After treatment with fructose at a dose of 150 mM for 30 min, the PEHE was added at the indicated concentrations and incubated for an additional 4 h. The used culture medium was sucked off gently with a pipet, replaced with medium containing 10 µM DCFH-DA [33], and further incubated at 37 °C for 30 min. The medium was sucked off by using the pipet. The cultivated cells were then rinsed with cold PBS twice. Finally, the ROS levels were determined with an inverted fluorescence microscope (Olympus IX71, Tokyo, Japan). ## 2.8. Gene Expression All of the following procedures for gene expression studies were achieved, as previously described [34]. ## 2.8.1. Extraction of RNA from RIN-m5F Cells Cells at a density 4 × 105 cells/mL were seeded onto a 6 cm dish and incubated for 24 h. The cells were pre-treated with fructose at 150 mM for 2 h, then treated with PEHE (50 and 100 μg/mL, respectively) or metformin (100 μM), and incubated for 24 h. The cultured medium was sucked off, and the cells were rinsed twice with ice-cooled PBS. A total of 1 mL of TRIzol® Reagent (ThermoFisher Scientific, Waltham, MA, USA) was added and mixed for a while. Then, the culture was transferred into an Eppendorf vial, where 200 µL of chloroform was added and vortexed for 10 min, left to stand for 15 min and centrifuged at 13,000× g for 15 min. The supernatant (400 µL) was transferred into an Eppendorf vial; 600 µL of isopropanol was added, left to stand for 30 min, then centrifuged at 4 °C under 13,000 × g for 15 min, after which the supernatant was discarded. The sediment was rinsed with 500 µL of ethanol ($70\%$), and centrifuged at 4 °C under 13,000 × g for 20 min. The washed sediment was re-dissolved in diethylpyrocarbonate water (Sigma-Aldrich) and stored at −80 °C for further use. ## 2.8.2. Reverse Transcription of RNA to cDNA The operation protocol was carried out by following instructions provided by the Takara PrimeScriptTM RT Reagent Kit (Takara Bio, San Jose, CA, USA). The final total volumes for each sample were all adjusted to 10 µL, incubated at 37 °C for 15 min, then reacted at 85 °C for 5 min and stored at −80 °C for use. A total of five genes involved in the ER stress and a reference gene of β-actin were evaluated in this study. Supplementary Materials Table S1 depicts the sequence of primers used in this study. ## 2.8.3. Quantitative Analysis of mRNA Levels An equal amount of cDNA was used for the subsequent qPCR performed with the SYBR® FAST system (KAPA biosystems). The 20 μL reaction mixture contained 9.2 μL of cDNA, 0.4 μL of 10 uM forward and reverse primers, and 10 μL of KAPA SYBR FAST qPCR Master Mix (2×). Amplification was performed in the StepOnePlus™ Real-Time PCR System (Applied Biosystems). The DNA fragments were amplified for 40 cycles (enzyme activation: 20 sec at 95 °C hold; denaturation: 3 s at 95 °C; annealing: 40 s at 60 °C). The expression of β-actin was determined as the internal control. The relative expression level was calculated using the 2−ΔΔCt method. ## 2.9. Statistical Analysis Means ± standard deviations (SDs) obtained from the studies were used to express the results. Statistical analysis of the results was determined using the GraphPad Prism program (5th edition, GraphPad, San Diego, CA, USA). One-way ANOVA was used for analysis of variations in each group, and Tukey’s post hoc test was applied for analysis of significance of differences among the means. A confidence level of $p \leq 0.05$ was considered to be statistically significant. ## 3.1. The Contents of Total Polyphenols, Flavonoids and Triterpenoids The TPC of the WE and PEHE were determined using the Folin–Ciocalteu method, with gallic acid as a standard. As shown in Table 1, PEHE (122.27 mg/g) had higher total polyphenol content than WE (56.53 mg/g). The TFC in the WE and PEHE were determined with the aluminum chloride (AlCl3) method, using quercetin as a standard. PEHE (192 mg/g) also showed a higher content of TFC than WE (24.98 mg/g) (Table 1). For the determination of TTC, a vanillin/glacial acetic acid solution was applied. Unexpectedly, the contents of total triterpenoid of WE (6.04 mg/g) and PEHE (6.61 mg/g) were fairly close (Table 1). It was supposed that the lower content of triterpenoids in the hawthorn fruit resulted may have resulted from indiscriminate and complete extraction by the water and organic solvent used, respectively. ## 3.2. The Antioxidant Activities of WE and PEHE The free radical 2,2′-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity and ABTS radical cation scavenging activity were widely applied to investigate the antioxidant activity of plant extracts. The disappearance of the DPPH radical at 515 nm detected spectrophotometrically implicates antioxidant activity of the prepared extracts; meanwhile, the ABTS assay was based on the generation of a blue/green ABTS•+ (detected at 734 nm) that can be reduced by antioxidant compounds. Figure 1a,b show the DPPH and the ABTS•+ radical-scavenging activities of WE, PEHE and the positive controls BHT and Trolox, respectively. PEHE exhibited significantly higher free radical scavenging abilities than WE. The results can be found significantly from the comparisons of IC50 values in Table 2, where the values of PEHE were significantly lower than WE for either DPPH or ABTS•+ radical scavenging activities. The PEHE (IC50 = 379.62 ± 23.21 µg/mL) exhibited stronger DPPH radical scavenging activity, and was even better than BHT (IC50 = 547.47 ± 9.69). On the other hand, the ABTS•+ radical scavenging activities also exhibited the same trend (Table 2). These results demonstrated that liquid–liquid partition using ethyl acetate combined with the Sephadex® LH–20 column separation on water extract of hawthorn fruit could effectively enrich polyphenol content in the extract, and lead to a further enhancement in antioxidant activity. ## 3.3. The Main Compounds in PEHE from HPLC–ESI–MS/MS Analysis To investigate the compositions of the prepared extract enriched in polyphenols, two polarity modes in positive and negative ionizations of MS analyses were analyzed. As expected, the total ion chromatogram of the LC–MS measurement in negative ionization provided more significant characteristics of molecular ion than positive ionization mode (data not shown). This is in line with previously described observations that polyphenols are supposed to ionize in negative mode better than in positive mode with presentation of characteristic mass spectra [35]. As shown in Figure 2 (bottom panel), a reversed phase HPLC and mass spectrometry in negative ion mode method was developed, allowing for the separation and identification of ten components in PEHE analyzed with HPLC (Figure 1, top panel). As shown in Table 3, identification of the components existing in PEHE was carried out by comparing the retention times, UV-Vis spectra and mass spectra with those of authentic compounds when available. Results showed that one phenolic acid, two flavonoids and seven epicatechin derivatives were identified in the PEHE. The (epi)catechin derivatives showed characteristic UV spectra with λmax 280 nm and fragment ions at m/z 285, which were also the characteristic basic skeleton of B-type procyanidins [36]. Epicatechin was found to be the highest, followed by epicatechin-4,8′-epicatechin-C-hexoside. These epicatechin derivatives occupied a total of more than $50\%$ of the PEHE content. The second predominant group was found from flavonoid compounds, including quercein-3-O-galactoside (hyperoside) and quercertin-3-O-galactoside (isoquercitrin) (Table 3). The ESI (−)-MS/MS spectra of authentic compounds were identified, and are shown in Supplementary Materials Figure S1. ## 3.4. α-Glucosidase Inhibitory Activities of PEHE and WE The α-glucosidase inhibitory activities can be used as a method to select antidiabetic phytochemicals for in vitro study through the hydrolysis inhibitory ability of α-glucosidase on pNPG. It was found that PEHE exhibited a lower IC50 value than WE (196.37 ± 23 vs. 2633.00 ± 215.32 µg/mL), indicating higher α-glucosidase inhibitory activity. Therefore, in view of the above-mentioned results of antioxidant activity and α-glucosidase inhibitory activity analyses, we choose PEHE for further cell experiments. ## 3.5. Effects of Fructose and PEHE on Cell Viability In order to evaluate the effects of PEHE and fructose on cell proliferation, cells were treated with various concentrations of PEHE and fructose for 24 h, followed by the MTT assay. Cell viability was preserved with up to 0.25 mg/mL of PEHE treatment (Figure 3a); meanwhile, the effect of fructose in cell viability (<$60\%$) was noticed after treatment with 250 mM of fructose (Figure 3b). These reductions were dose-responsive. Considering the dose–effect relationship used in the expression of cell functions, the choices of concentrations of PEHE and fructose for the following experiments were determined at 50 and 100 μg/mL for PEHE (without any toxicity) and 250 mM for fructose (with acceptable cell viability of higher than $80\%$ in inducing cell toxicity), respectively. ## 3.6. PEHE Inhibits the Production of Reactive Oxygen Species Affected by Fructose To investigate whether oxidative stress alters β-cells functions, the cells were compared with those from fructose-treated cells. The invention of fructose (150 mM) significantly increased intracellular ROS levels ($p \leq 0.001$) up to 102.75 ± 7.39-fold compared to the control (Figure 4). PEHE reduced the increase in ROS by 11.31 ± $3.33\%$ and 25.53 ± $2.70\%$ at 50 and 100 μg/mL, respectively, compared to the fructose-induced ROS levels. Metformin reduced the ROS levels by 47.02 ± $3.65\%$ at 100 μM (Figure 4). ## 3.7. Effect of PEHE on Fructose-Induced ER Stress Signaling Pathway Exposure of RINm5F β-cells to a high concentration of fructose (150 mM) for 24 h induced a significant increase in the expression of ER stress-related genes (Figure 5a). Members of ER stress-related genes, including molecular chaperone GRP78 (an indicator of the onset of the unfolded protein response, UPR) and the key regulators of the UPR pathway PERK, IRE1α, ATF6 and CHOP, maintain an inactive state under homeostasis [20]. To evaluate the effect of high fructose treatment and the protection activity of PEHE on the changes of ER stress sensors, the aforementioned genes were detected. In the high fructose condition, the expression genes of GRP78 (Figure 5a) and the genes involved in UPR (i.e., PERK, IRE1α and ATF6, Figure 5b–d) and the apoptosis relative gene CHOP significantly increased in comparison with control cells, and the inhibitory effect on gene expression of PERK had a better effect than that of the anti-diabetic drug metformin. ## 4. Discussion A previous study using the water extract of hawthorn (C. pinnatifida) fruits to treat streptozotocin (STZ)-induced type II diabetes mellitus (T2DM) rats showed remarkable anti-diabetic effects [5]. However, in-depth studies of the functional components in the water extract of hawthorn fruit and its anti-diabetic mechanism have not been conducted. In this study, the composition of PEHE was analyzed with HPLC and LC–ESI–MS/MS, and its mechanism was illustrated by investigating the antioxidant activity, α-glucosidase inhibitory effect, cell protective effect, and inhibition capability on ER stress-related gene expression. The main phenolic compounds such as epicatechin and procyanidin B2, which belong to the flavan-3-ol monomers and dimers in the constituents of hawthorn fruit, have been indicated as the active compounds in reducing the oxidative stress [10,11]. In the purification and identification study, the isomer of epicatechin or catechin was finally confirmed from the 13C NMR spectrum [11]. Thus, the epicatechin relative compounds identified in the study (Table 3) were supposed to be the epicatechin derivatives, not the undecided (epi)catechin by the Karar et al. study [36]. As described above, these phenolic compounds extracted in the study may support the hypoglycemic effect observed in the in vivo study [5]. The major source of fructose is sucrose or table sugar, which is composed of one molecule of glucose and one molecule of fructose. The free fructose is absorbed after the ingestion and digestion of sucrose. The other major source of fructose is high-fructose corn syrup, with the composition of fructose and glucose mostly in the ratio of $45\%$ glucose and $55\%$ fructose. Excess ingestion of fructose has numerous effects on the brain, liver, vasculature, kidney, and adipocyte, resulting in metabolic syndrome and ultimately T2DM [37]. In the physiological response, an excess level of fructose has a deleterious effect on pancreatic β-cell function [37,38]. High fructose triggers oxidative modification and apoptosis in pancreatic β-cells; a study indicated that DNA cleavage, a change characteristic of apoptosis, is observed after 3 days of treatment with 100 mM fructose [39]. An increase in intracellular oxidative stress levels through high fructose treatment may provoke cell apoptosis. As mentioned above, pancreatic β-cells are sensitive to oxidative stress, owing to their lower intracellular antioxidant capacity [18,19]; excess oxidative stress may affect the survival of β-cells. Since β-cells’ functions gradually deteriorated under high fructose conditions, the present study is useful for understanding the protective effects of prepared high antioxidant PEHE (Figure 1 and Table 2) under hyperglycemic conditions. In a study by Lin et al. [ 40], hyperoside, obtained through high-yield extraction from ethanol or acetone extraction on the same identified species of hawthorn fruits used in the study, has been reported to have strong antioxidant and anti-α-glucosidase properties. Moreover, through the results of molecular modeling docking, hyperoside also exhibited a high affinity with α-glucosidase. In addition to the various possible interactions between an inhibitor and enzyme, inhibition kinetics analysis is considered to be one of the main tools to distinguish the inhibition mechanisms involved [41]. As previously reported from the α-glucosidase inhibition study, the inhibition kinetics of polyphenols, which contain the main compounds of epicatechin and procyanidin B2, are a mixed-type of inhibitor with noncompetitive inhibition trends [41]. The significant antioxidant activity of polyphenols, as a crucial health-promoting factor, is one of the issues of concern. The extract PEHE also presented the highest DPPH free radical scavenging. In the study, the PEHE containing hyperoside (Table 3) also significantly showed DPPH free radical scavenging activity. In addition, an analysis of polyphenol antioxidants of hawthorn fruit found that the contents of total flavonoids, including free and bound, were between 5.589 and 6.667 mg GAE/g DW [42]. Flavonoids have been shown to exert a wide range of effects in biological systems, including potent radical scavenging activities [10,43], suggesting that these components also contribute to oxidative stress inhibitory properties. In addition, the antioxidant activity of hawthorn fruit has been demonstrated in a report by Lou et al., resulting from free phenolic compounds which are the main contributors (35.3–$37.8\%$) to antioxidant activity in the fruit, with the most abundant being [-]-epicatechin, followed by procyanidin B2 [44]. A previous report described in detail the close relationships between oxidative stress and ER stress involved in β-cell dysfunction via direct effects on insulin biosynthesis and secretion [45]. Oxidative stress can cause ER stress and provoke multiple deleterious effects on β-cell function, including suppression of insulin transcription and the unfolded protein response (UPR) [45]. Under these conditions, ER stress activates the inactive heat-shock protein/chaperone, glucose-regulated protein GRP78 (also known as BiP), and initiates the activation and disassociation of three effectors, PKR-like ER kinase (PERK), inositol-requiring enzyme 1 (IRE1), and activating transcription factor 6 (ATF6) from chaperone GRP78, and follows by triggering the downstream UPR [45]. In the study, the gene expressions of the three ER stress sensors PERK, IRE1α and AFT6 were significantly activated after high-fructose introduction in beta-cells, while the PEHE treatment at 100 μg/mL ameliorated the damages (Figure 5b–d). Moreover, under non-homeostatic conditions, a driving apoptosis gene, C/EBP-homologous protein (CHOP), can be induced to an active status, and cause cell apoptosis. In Figure 5e, after treatment with fructose, the CHOP gene expression significantly increased 1.6-fold in comparison with the control. Meanwhile, the treatment of PEHE at 100 μg/mL significantly ($p \leq 0.001$) inhibited the gene expression. Therefore, since under hyperglycemic conditions CHOP plays an important role in the induction of β-cell apoptosis, the present study is critical for disclosing the therapeutic effects and understanding the active mechanisms of PEHE. The main constituents of PEHE, combined with epicatechin derivatives, procyanidins and flavonoids, presented significant inhibitory effects on free fatty acid-induced oxidative stress and expression of ER stress-related genes. The study of the relationship between antioxidant activity and the main constituents of PEHE suggest that procyanidin B2 acts as a more effective antioxidant agent than epicatechin, which in accordance with previous results [44,46,47]. However, it is difficult to speculate which compound(s) crucially affect the results of gene expression. This question remains to be resolved further through using purified compounds for conducting in-depth research. ## 5. Conclusions We observed that the treatment of β-cells with a well-characterized PEHE strongly inhibited intracellular free radicals, and markedly influenced the expression of genes (i.e., GRP78, PERK, IRE1α, ATF6 and CHOP) related to ER stress. 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--- title: 'Protein and Lipid Digestibility of Pasture-Raised and Grain-Finished Beef: An In Vitro Comparison' authors: - Lovedeep Kaur - Amrutha Elamurugan - Feng Ming Chian - Xianqian Zhu - Mike Boland journal: Foods year: 2023 pmcid: PMC10047994 doi: 10.3390/foods12061239 license: CC BY 4.0 --- # Protein and Lipid Digestibility of Pasture-Raised and Grain-Finished Beef: An In Vitro Comparison ## Abstract This study compared the digestibility of protein and fat components of pasture-raised and grain-finished beef using an in vitro oral-gastro-small intestinal digestion model. Two commonly consumed beef cuts, tenderloin (Psoas major) and striploin (Longissimus dorsi) were selected for this study. There were no substantial differences between the pasture-raised and grain-finished cuts of meat in terms of protein digestibility, as shown by the protein and peptide breakdown (observed through SDS-PAGE) and the degree of hydrolysis as measured by free amino nitrogen. Tenderloin, however, showed significantly ($p \leq 0.05$) higher overall protein digestibility than striploin. Both striploin and tenderloin digests from pasture-raised beef released significantly ($p \leq 0.05$) higher total amounts of free long-chain n−3 PUFAs and lower amounts of many free saturated fatty acids, notably palmitic and myristic acids, than those from grain-finished animals. The results suggest greater health benefits from consuming pasture-raised beef, particularly tenderloin. ## 1. Introduction Meat is recognised nutritionally as a good source of protein and iron. It is also a source of important long-chain polyunsaturated fatty acids. However, any nutritional value depends on bioavailability. Meat digestion is a complex phenomenon. Generally, red meat contains about 70–$75\%$ water, 5–$20\%$ fat (depending on the cut), 16–$23\%$ protein, and 3.5–$5\%$ non-protein substances and inorganic compounds [1,2]. Years of research have shown the importance of meat as a protein source in the human diet. Although meat protein is known to be highly digestible, it has been argued that residual undigested meat protein in the colon can change the metabolism (and eventually the population structure) of the microbiome, which may lead to adverse health outcomes [3]. Thus, meat with higher digestibility will be seen as healthier. In addition to this, it has been argued that higher rates of release of amino acids during the digestion of meat will cause desirable anabolic effects in muscle, leading to maintenance or gain of muscle mass. This is particularly important for the elderly in managing sarcopenia (muscle wasting) and for athletes or fitness seekers who want to strengthen muscles [4]. The presence of saturated fatty acids and the fat content in meat has been related to health complications in some epidemiological studies conducted in western countries, although more research is being conducted to confirm this [2]. Contrary to popular perception, the fat content in many retail beef cuts has been declining in recent years, due to more effective fat trimming, the production of leaner cattle breeds, and enhanced animal husbandry practices. Hence, it has become crucial to communicate these changes to consumers [1]. The incorporation and moderate consumption of lean red meat in the diet have been shown to positively influence nutrient uptake and overall long-term health [5]. However, the effects of the breed, sex, and diet of animals, especially of pasture- and grain-based diets, need to be studied and reported to understand the breakdown of meat components and their implications for the maintenance of good health. There are studies in the literature that compare the composition, colour, and texture of pasture-raised and grain-finished beef [6,7,8,9,10]. These studies have recognized the differences in the fatty acid composition of the meat, but as yet there is little knowledge in the literature about how red meat is digested in the human digestive tract and how pasture-raised may differ from grain-finished meat, particularly with respect to the kinetics of protein digestion and the digestion and release of lipids, including the fatty acids that are important for health. We have no information yet about the digestion of meat lipids, but it is clear that pasture-raised meat contains significantly enhanced amounts of some of the nutritionally important polyunsaturated fatty acids [9,11,12]. Because the structure of the meat is based around protein, higher rates of digestion of protein are likely to influence the release of fat from within the structures, potentially affecting its digestion and bioaccessibility. The individual amino acid concentrations of meat can potentially be altered by using genetic or feeding strategies, but further research is needed to evaluate their potential to alter the total protein content of meat [13]. Because cattle are ruminants, i.e., foregut fermenters, bioaccessible nutrients comprise the products of fermentation and the remaining undigested or partially digested components of their forage. The composition of the fat in an animal is largely affected by what it has been eating and the results of rumen fermentation. Most of the fat in meat comprises triglycerides. The fatty acid composition of the triglycerides dictates their physical characteristics and the health benefits of consuming the fat. Cattle obtain the precursors for fat synthesis from their diet. Fat that is taken in, for example from plant origin, is broken down to fatty acids in the rumen, and a degree of saturation (removal of double bonds) and breakdown of the fat occurs in the rumen. Anaerobic metabolism in the rumen (notably of carbohydrates) leads to the production of short-chain fatty acids, mostly acetic (C2) and propionic (C3) acids. Starch that is taken in (mostly in grain-fed animals) is metabolized into short-chain fatty acids. The animal can synthesize longer-chain fatty acids from C2, but only up to C16; thus, starch cannot be a precursor for longer-chain fatty acids [14]. Longer-chain fatty acids must be derived from the food source, although they can be modified by the animal through elongation and desaturation reactions. It can be expected that differences in the composition of intramuscular fat will have an effect on muscle structure and hence affect the digestion of meat. The present study was therefore designed to understand the protein and fat digestion of New Zealand beef (striploin and tenderloin) from pasture-raised and grain-finished animals using an internationally recognised in vitro model for human digestion. This study was based around commercially produced pasture-fed and grain-finished beef, so could not be strictly controlled for individual animal diets, but it does reflect normal farming conditions in New Zealand and thus typical New Zealand beef. All the samples were cooked using standard procedures before the digestions and analyses. ## 2.1. Materials All the animals used for the meat in this trial were specifically selected from the wider population as a common type. They were selected to be prime steers of the Aberdeen Angus breed, with a carcass weight of 324–343 kg. Pasture-fed means that the animals have been raised under normal New Zealand farming conditions with year-round access to grass (e.g., hay, silage, Lucerne, feed crops, or other grazed or conserved forages) and other supplementary feeds [15]. In late 2019, five pasture-raised Aberdeen Angus steers, aged between 18 and 36 months, that had been grazed free range on pastures of predominantly perennial ryegrass and white clover were sourced from three farms that supply Silver Fern Farms Ltd. They were slaughtered on the same day in a commercial beef abattoir (Silver Fern Farms Whakatu plant, Hastings, New Zealand) according to humane standard operating procedures. Five additional grain-finished Aberdeen Angus steers, aged 26–28 months, were sourced from the Canterbury feedlot of Five Star Beef where they were finished at an average of 122 days on a ration of maize silage, barley wheat, and straw, producing an average daily gain of 1.4 kg. All animals were pasture-fed as per the definition of pasture-fed by the Animal Status Declaration [15] until the beginning of the grain finishing for the grain-finished animals. They were similarly slaughtered (ANZCO Foods Canterbury Ashburton plant site, New Zealand). The carcasses of the pasture-raised and the grain-finished beef were chilled for 24 h post-slaughter, and their pH was measured in the abattoir. Then the selected beef cuts (striploin—Longissimus dorsi, and tenderloin—Psoas major) were collected from the left side of the carcasses, vacuum packed and aged for 21 days at −1.5 °C (consistent with normal cold ageing, [16]) before sub-dividing into 1-inch-thick steaks. The steaks were individually vacuum-packed and stored at −20 °C until they were used for the digestion experiments. More details on the carcasses are given in Table S1. The striploin and tenderloin steaks were chosen, as they have relatively less connective tissue and hence are the most tender cuts with good marbling and strong beef flavour. The fat on the striploin is easier to trim and there are no large pockets of fat, which favours a faster cooking time and makes it easier to cut. Tenderloin steaks are usually sold defatted with the chain muscle removed. All chemicals and reagents used in this study were analytical grade. ## 2.2.1. Cooking of Striploin and Tenderloin Steaks The meat was cooked using the method of Purchas and Wilkinson [17]. The selected frozen steak was submerged in water at room temperature for 1 min to thaw the surface for easier cutting of the frozen meat. The meat was then cut into approx. 50 g steaks, vacuum packaged and stored at 4 °C to thaw for 16–20 h. The thawed steak was pat-dried, weighed and cooked in an electric skillet (ZIP non-stick electric skillet 26 cm dia.) with a surface temperature of 220–230 °C. The meat was cooked for 2.5 min on each side initially and additionally for 1 min on each side until it reached an internal temperature of 67 ± 2 °C. The cooked meat was rested for 10 min, pat dried and weighed to estimate cook loss. The subcutaneous fat layer of the striploin steak was removed after cooking to reflect normal use when eating the steak. The cooked meat samples were used for analysing physicochemical parameters and in vitro protein and fat digestion as explained in Section 2.2.2 and Section 2.2.3. ## Cook Loss Measurements The cooking loss was estimated as the difference in weight of the samples before and after cooking expressed as a percentage of the weight before cooking [18]: cook loss %=sample wt.before cooking−sample wt.after cookingsample wt.before cooking×100 ## Moisture Analysis and pH The pH of the cooked meat samples was measured using the protocol mentioned by Zhu [19]. Moisture content in cooked samples was analyzed using the AOAC 950.46 method [20]. A conventional electric oven was set to 105 °C with consistent airflow and heat distribution. Two to three grams of cooked samples were placed in individual aluminium dishes and weighed. They were oven-dried for 16 h, cooled in a desiccator and weighed. The percentage loss in weight was reported as moisture content. ## Crude Protein (%) and Fat (%) Analysis The protein (%) in cooked samples was analysed using the Kjeldahl method [21]. The test was performed in triplicate for each of $$n = 5$$ pasture-raised samples and $$n = 5$$ grain-finished samples. The fat content of the samples was determined using the Mojonnier (acid) method (Flour, Baked, extruded products) [22]. ## 2.2.3. In Vitro Digestion Experiments The in vitro digestion of the meat samples was conducted as described by Chian et al. [ 23] using the INFOGEST method [24] with some modifications. Three replicates were performed per sample. For each replicate, two separate in vitro digestion experiments were performed in individual double-jacketed glass reactors at 37 °C with chosen sampling times. For each digestion 8 g of meat was ground using a mortar and pestle for a minute, followed by incubating the samples with 8 mL of simulated salivary fluid containing 1.25 × 10−6 katal/mL bolus of α-amylase (10025, Sigma Aldrich, Saint Louis, MO, USA) at pH 7 ± 0.1 for 2 min to simulate chewing and oral digestion. Subsequent simulated gastric digestion was initiated by the addition of 32 mL of simulated gastric juice containing 1.33 × 10−7 katal/mg meat protein of porcine pepsin (P7000, Sigma Aldrich, Saint Louis, MO, USA) pH 3 ± 0.1. Three glass balls (3–5 mm dia.) were added to mimic meat maceration in the stomach. Sampling was conducted after 0 and 60 min of gastric digestion in the first reactor. Pepstatin A (12 μL, ab141416, Abcam, Cambridge, UK) (0.5 mg/mL in methanol) was immediately added after sampling to every 1 mL of the gastric digests. In a second reactor, in vitro small intestinal digestion was initiated after an hour of gastric digestion as described above. Small intestinal digestion was commenced by adding 48 mL of simulated small intestinal fluid and pancreatin (P1750, Sigma Aldrich, Saint Louis, MO, USA) at 1:100 pancreatin to meat protein ratio into the gastric digests. The pH of the digest was adjusted and maintained at pH 7 ± 0.1 with constant mixing. The sampling was done after 10, 60, and 120 min of small-intestinal digestion. An aliquot of 0.45 mL of SIGMAFAST™ protease inhibitor cocktail solution (S8820, Sigma Aldrich, Saint Louis, MO, USA) (one tablet in 50 mL Milli-Q water) was mixed with every mL of small-intestinal digest after sampling to inactivate the digestive enzymes. The mixtures of digests and enzyme inhibitors were homogenized for 30 s using a homogenizer with a 5 mm diameter disperser element at setting 3 (T10 basic ULTRA-TURRAX® IKA Werke GmbH & Co. KG, Staufen im Breisgau, Germany). Finally, the digests were immersed in an ice bath and then stored at −20 °C until further analysis. ## Ninhydrin Analysis of Digests Digests were thawed and centrifuged at 14,100× g for 3 min (Eppendorf Mini spin plus, Hamburg, Germany) at room temperature. The supernatant was filtered through a 0.45 μm PVDF filter (Millex®, Duluth, MN, USA) before analyzing for ninhydrin reactive amino N [25] using ninhydrin reagent (N7285, Sigma Aldrich, Saint Louis, MO, USA). A standard curve was prepared using a stock solution of 50 μM glycine in $0.05\%$ glacial acetic acid. ## Tricine SDS-PAGE of Digests The homogenized digests were examined for the breakdown of specific proteins using reduced-Tricine-SDS-polyacrylamide gel electrophoresis (SDS-PAGE) as described by Chian et al. [ 23]. The digests were mixed with tricine sample buffer (Bio-Rad Laboratories, Hercules, CA, USA), then 25 µL of each sample was loaded into individual wells ($16.5\%$ gradient Tricine gels, CriterionTM Gel, Bio-Rad Laboratories, Hercules, CA, USA) at a protein concentration of 1 mg/mL. Gels were run using a CriterionTM cell (Bio-Rad Laboratories, Hercules, CA, USA) at 125 V and then stained with Bio-safe Coomassie blue stain (Bio-Rad Laboratories, Hercules, CA, USA). The gel was scanned with a Gel Doc XR + Gel Documentation System (Bio-Rad Laboratories, Hercules, CA, USA), followed by analysis using Image LabTM software (version 6.1.0, Bio-Rad Laboratories, Hercules, CA, USA). ## 2.2.5. Free Fatty Acid Analysis of Digests The free fatty acids were analyzed in the meat digests at the end of digestion (180 min) using the method of Zhu et al. [ 26]. The contribution of lipolysis in the gastric phase was not considered because lipolysis in the stomach has been estimated to be only about $10\%$ of total lipolysis, and gastric lipase contributes nearly as much lipolysis in the intestinal phase as it does in the stomach [27]. Mackie and Rigby [28] also reported that gastric lipolysis is limited in general due to the saturation of relatively low pH lipid interfaces. The unavailability of suitable and affordable lipase is also one of the reasons why gastric lipase is not normally used during in vitro digestion experiments and therefore a reason for not studying gastric lipolysis of meat in this study. ## Methylation of Total Fatty Acids (TFA) The total amount of individual fatty acids (TFA), including free fatty acids (FFA) and esterified fatty acids in monoglyceride, diglyceride, and triglyceride forms, were analyzed in the ground freeze-dried control cooked meat samples and their digests using the method described by Zhu et al. [ 26]. A freeze-dried meat sample or digest (100 mg) was weighed into a glass tube. An internal standard of methyl tricosanoate was prepared in heptane (1 mg/mL). One millilitre was added to the glass tubes. Potassium hydroxide (0.7 mL of 10 M) and 5.3 mL of methanol were then added and the contents vortexed. The tubes were incubated for 90 min at 55 °C and vortexed individually for 5 s every 20 min. The tubes were then cooled to room temperature and 0.58 mL of 12 M H2SO4 was added to form a precipitate of K2SO4. The tubes were then reincubated and similarly vortexed for another 90 min. The tubes were cooled, and the fatty acid methyl esters (FAMEs) were extracted as follows: 3 mL of heptane was added, the tubes vortexed and then centrifuged at 3500 rpm for 10 min. The upper layer was collected in a new glass tube containing a 1 mm bed of anhydrous Na2SO4. Finally, the heptane layers containing methylated total fatty acids were collected in 2 mL GC vials and stored at −20 °C until GC analysis. The procedure was done in triplicate for each sample. ## Methylation of Ester Forms of Fatty Acids Ester forms of the fatty acids (EFA) in the cooked meat samples and their digests that include monoglyceride, diglyceride, and triglyceride forms were determined by the sodium methoxide transesterification method as described by Zhu et al. [ 26]. Ground freeze-dried (100 mg) samples of control meat or its digest were weighed in glass tubes and 1 mL of the internal standard was added. Next 1 mL of sodium methoxide solution was added, the contents were vortexed and then incubated for 60 min at 55 °C. The tubes were cooled to room temperature and 0.1 mL of glacial acetic acid, 5 mL of saturated NaCl solution, and 3 mL of heptane were added, the mixture was vortexed and then centrifuged at 1000× g for 10 min. The upper heptane layer was transferred into tubes containing anhydrous Na2SO4 to remove traces of water. The top layer was finally collected in 2 mL GC vials and stored at −20 °C until GC analysis. The procedure was done in triplicate for each sample. The free fatty acid content in the sample was calculated from the difference between the two results (TFA and EFA) on the same sample. ## GC Analysis of Fatty Acid Methyl Esters (FAMEs) The fatty acid compositions of the samples were determined using a gas chromatograph system (GC-2010 Plus, Shimadzu, Kyoto, Japan) equipped with a flame ionization detector and an AOC-20I auto-injector. The column used was a 60 m RTX®-2330 GC column (Restek, Bellefonte, USA; 0.25 mm internal diameter with a 0.10 µm film thickness). The injection volume was 1 µL and the carrier gas was hydrogen gas at a linear velocity of 40 cm/s. The split ratio was 50:1. The initial oven temperature profile was 125 °C for 3 min, was increased to 220 °C at the rate of 2 °C/min and then maintained for 5 min. The temperatures of the injector and the detector were 260 °C and 265 °C, respectively. The peaks of individual fatty acids were identified and quantified based on the retention time of an internal standard (C23:0) (T9900, Sigma Aldrich, Saint Louis, MO, USA), an external standard mixture (Supelco FAME mix C4-C24) (Sigma Aldrich, Saint Louis, MO, USA), and theoretical flame ionization detector response factors. Relevant algorithms to quantify the FAMEs were standard (AOCS Ce 1f-96, Ce 1h-05 and Ce 1i-07). Unresolved fatty acids were not reported. ## Calculation of Free Fatty Acids (FFA) The amounts of individual free fatty acids (mg fatty acid/g cooked meat) were determined in the digests after 180 min of in vitro gastro-small intestinal digestion as described by Zhu et al. [ 26]: Individual FFA after 180 min of simulated digestion (mg/g cooked meat) = Individual total fatty acid (TFA) at 180 min – individual esterified EFA at 180 min. ## Desaturase Indices and n−6/n−3 Fatty Acid Ratios The stearoyl-CoA desaturase index has been reported as the product/precursor ratio of FA as described by Alarcón et al. [ 29]: DI16 = C16:1/C16:0 and DI18 = C18:1 − cis9/C18:0 The n−6/n−3 FA ratio was calculated as: Linoleic acid (LA) + arachidonic acid (ARA)/linolenic acid (ALA) + eicosapentaenoic acid (EPA) + docosapentaenoic acid (DPA) + docosahexaenoic acid (DHA). ## 2.2.6. Statistical Analysis All the experiments were carried out in triplicate for 2 types of meat cuts, each from $$n = 5$$ pasture-raised and $$n = 5$$ grain-finished animals. Statistical evaluation was performed using a general linear model. The average of three replicates for each animal were taken for performing a two-way analysis of variance (ANOVA) using OriginPro software (OriginLab Corporation, Northampton, MA, USA), with a Tukey’s test for estimating the significance of differences among the production systems and meat cuts ($p \leq 0.05$). Results obtained from the statistical analysis are reported as the means and the standard error of the means. ## 3.1.1. Cook Loss Cook loss is the phenomenon that causes the meat to lose volume and weight through the process of fluid exudation during the cooking process. Cook loss measures the ability of a food matrix to bind water and fat after the denaturation and aggregation of protein molecules. This change in fluid content, along with modifications of texture-forming properties of the proteins and fats in meat, leads to a variation in meat quality attributes [30]. The overall cook loss values for the meat cuts showed similar trends to those reported by Purchas and Wilkinson [17] (Table 1). There were no significant differences ($p \leq 0.05$) in cook loss for meat from the different production systems (pasture-raised and grain-finished). Striploin had lower cook loss than tenderloin, but the difference was only significant ($p \leq 0.05$) for the pasture-raised group. Schönfeldt and Strydom [31] have also reported differences in cook loss between meat cuts, which have been related to the variations in sample dimensions and composition along with the spatial distribution of fat or lean meat areas, and the meat surface properties. These may affect how meat proteins or fat behave when exposed to heat, such as protein denaturation, shrinkage, and fat-melting [32]. Importantly, striploin was cooked with the subcutaneous fat layer on (to reflect normal culinary procedure), which could partly explain the observed differences in cook loss between the two meat cuts. ## 3.1.2. Moisture Content and pH The water in meat is mostly bound to protein molecules and is usually found between and within muscle cells and muscle bundles. As the temperature increases the tertiary and secondary structures unfold, lose or rearrange their disulfide bridges, undergo modifications in their side chains, cross-link with other polypeptides, and lose the surrounding water [30]. The moisture content of the cooked meat depends on factors such as the type of meat cut, cooking temperature, final internal temperature, and portion size. The moisture content remaining in a product after cooking has been reported to be one of the major contributors to the sensation of juiciness [31]. The moisture contents of cooked meat samples were generally linked to the cooking loss, in the present study. Pasture-raised cooked tenderloin samples had more cook loss and significantly ($p \leq 0.05$) lower moisture content than pasture-raised striploin samples (Table 1) while no differences in cook loss or moisture content could be seen among the two meat cuts from grain-finished animals. It is also important to mention that despite having no significant differences in cook loss among pasture-raised and grain-finished meat samples, both striploin and tenderloin samples from pasture-raised animals had higher moisture content than their respective grain-finished counterparts. The higher moisture content of pasture-raised meat could partly be explained by their lower fat contents than grain-finished meat (discussed in Section 3.1.4). No significant effect ($p \leq 0.05$) of the meat production system could be observed on the cooked meat pH (Table 1) whereas the meat cut type significantly ($p \leq 0.05$) influenced the meat pH, possibly due to differences in rates of metabolism of the different muscles post-mortem. ## 3.1.3. Protein Content Slight but significant ($p \leq 0.05$) differences in the total crude protein were observed for striploin samples from the two production systems (Table 1). No such significant ($p \leq 0.05$) differences could, however, be observed among the tenderloin samples. These results were also confirmed by the observed significant interactions ($p \leq 0.05$) between meat cut type and the production system. Among the meat cuts, tenderloin from grain-finished animals had significantly ($p \leq 0.05$) lower protein content than the corresponding striploin samples while the pasture-raised tenderloin and striploin had similar protein contents, which could be explained by the higher fat content of tenderloin samples from the grain-finished animal group. The values of protein content reported in this study are similar to those noted by Purchas and Wilkinson [17] for New Zealand striploin and tenderloin meat cuts. ## 3.1.4. Fat Content The meat cuts used in this study were chosen based on their relatively low fat content. Several studies have shown differences in fat content among retail beef cuts, while in red meat the loin is regarded as the leanest portion of the carcass [33]. Cuts like striploin have been reported to have varying fat content, which could be due to the inclusion of the subcutaneous fat layer [34]. However, the subcutaneous fat layer was removed after cooking in this study to follow the common eating practice, thereby reducing the fat content, which could be the reason for not finding any significant differences ($p \leq 0.05$) among the striploin and tenderloin meat samples within each production system. However, an overall significant effect ($p \leq 0.05$) of the meat cut type was observed on the fat content of the cooked meat. As expected, the meat cuts from the grain-finished animals had a much higher fat content ($p \leq 0.05$) than those from the pasture-raised animals (Table 1). These results agree with long-known facts on the influence of grazing systems on meat fat. Pasture feeding has been reported to lead to a leaner carcass, reduce intramuscular fat deposition, and improve the fatty acid profile of beef lipids [12]. ## 3.2.1. Ninhydrin-Reactive Free Amino Nitrogen The proteins of the food matrix were broken down during digestion into smaller peptides. The rate of protein hydrolysis is represented by the amount of reactive amino nitrogen released during gastro-small intestinal in vitro digestion (Table 2). During the gastric digestion phase, there was a minimal increase in free amino groups. Pepsin cleaves protein molecules into smaller peptides and is generally responsible for about $15\%$ of protein hydrolysis during gastro-small intestinal digestion [35]. The acidic pH of the simulated gastric fluid may have induced the formation of gastric chyme with coagulated meat proteins that were resistant to further protein hydrolysis by pepsin [36]. However, within the first 10 min of the small intestinal digestion, a steep rise in free amino groups was observed. This could be due to greater protein solubilization in a neutral pH environment [36]. Moreover, the pancreatic enzymes present during small intestinal digestion contain peptidases such as trypsin, chymotrypsin, and carboxypeptidase, which cleave the larger polypeptides produced by pepsin hydrolysis into smaller peptides. The wider specificity of the pancreatic proteases for peptide bonds makes small intestinal digestion more efficient, resulting in products with only 6–8 amino acids on average. No significant ($p \leq 0.05$) effects of the animal feeding system for any of the meat cuts on the overall release of free amino N could be seen during digestion after simulated digestion for 180 min. However, tenderloin and striploin samples from pasture-raised animals had slightly lower but statistically significant ($p \leq 0.05$; Table 2) ninhydrin-reactive N values after 60 min of digestion than those from grain-finished animals. Between the two meat cuts, tenderloin showed significantly ($p \leq 0.05$) higher protein hydrolysis than striploin throughout digestion (Table 2). ## 3.2.2. Tricine SDS-PAGE The digestion of soluble proteins and peptides that have a molecular weight > 1 kDa was determined through reduced tricine SDS-PAGE. Figure 1A,B provides information regarding the breakdown of meat and meat alternative proteins by digestive enzymes. The digests from both the meat cuts for pasture-raised and grain-finished meat did not show any noticeable differences in protein breakdown profiles and peptide release patterns. The higher molecular weight (HMW) proteins which correspond to myosin heavy chain (220 kDa) observed at 0 min of gastric digestion were observed to be digested during 60 min of gastric digestion in both striploin and tenderloin. Some of the other meat proteins such as actin (43 kDa), tropomyosin (39 kDa), troponin (35 kDa), and myosin light chain (23 kDa) were identified at 0 min of gastric digestion (Kaur et al., 2014). Small peptides with low molecular weight (<25 kDa) were also formed during 60 min of gastric digestion. Small intestinal digestion was marked by a rapid decrease in band intensity of large meat proteins with molecular weight <100 kDa. By 180 min of gastro-small intestinal digestion, most of the proteins and peptides were digested except for a few bands as shown in Figure 1A,B. Similar results for digested beef muscle were observed by Kaur et al. [ 36]. A greater reduction in intensities of some bands (marked in Figure 1B) was observed for the pasture-raised tenderloin digests when compared to grain-finished tenderloin digests after 60, 120, and 180 min, showing faster protein breakdown. Among tenderloin and striploin, tenderloin appeared to show greater protein breakdown during digestion, which agrees with the two-way ANOVA results that showed significant differences ($p \leq 0.05$) between the free amino N% of tenderloin and striploin meat cuts after 0, 60, and 180 min of gastro-small intestinal digestion (Table 2). ## 3.3. Fatty Acid Profiles of Control Meat Samples and Their Digests The meat samples used in this study were trimmed of all extra-muscular fat before in vitro digestion; thus, the only source of fat was the intramuscular fat. Several studies indicate pasture-raised beef to be leaner in comparison to grain-finished beef in terms of intramuscular fat composition [37,38]. The data presented in Table 1 confirm these reports and shows significantly higher ($p \leq 0.05$) total fat contents for grain-finished meat cuts. ## 3.3.1. Total Fatty Acid (TFA) Profiles of Cooked Meat Fatty acid compositions differed among the cooked meat samples from the two production systems (Table 3). The total fatty acid (TFA) content for oleic acid was observed to be the highest in undigested cooked meat followed by palmitic and stearic acids, for both pasture-raised and grain-finished animals. The concentrations of individual saturated total fatty acids (SFAs) and individual monounsaturated total fatty acids (MUFAs) and n−6 PUFAs were generally higher in the meat from grain-finished animals. No significant differences ($p \leq 0.05$) could be observed in the individual SFAs or MUFAs (as shown in Table 3) among tenderloin and striploin, except for stearic acid. The n−3 PUFAs, particularly EPA, ALA, and DPA, were present in greater amounts in cooked meat from pasture-raised than in grain-finished beef. Tenderloin samples showed significantly ($p \leq 0.05$) higher individual n−6 and n−3 PUFAs than striploin. Meat from animals that are fed grain-based diets have been reported to contain higher concentrations of n−6 PUFAs while pasture-raised animals have greater amounts of n−3 PUFAs [6,9]. The differences in fat composition of the digests observed in this study are broadly in line with the observations of Clancy [39] for pasture-raised beef and milk samples. ## 3.3.2. Free Fatty Acid (FFA) Profiles, Ratios and Desaturase Indices of Cooked Meat Digests The individual free fatty acid profiles of meat digests after 180 min of digestion are shown in Table 4 and Table 5. It is clear from the amounts of free fatty acids released during digestion, in comparison to the total amounts of individual fatty acids present in the undigested meat samples (Table 3), that the digestion of fatty acids was not complete by the end of the small intestinal digestion phase that consisted of one hour of gastric and two hours of small-intestinal digestion. In humans, the normal small-intestinal transit time is 3–4 h [40], which is longer than the duration employed in the present study. The in vitro digestion protocol used in the current study was based on previously published established static digestion protocols [23,24] and included two hours of simulated small intestinal digestion. This is a compromise between the longer time of digestion and the fact that the model becomes less meaningful over time because the digestion products are not being removed, as would be the case in vivo. The total amount of free oleic acid released per gram of meat was the highest for both tenderloin and striploin digests followed by palmitic and stearic acids. This agrees with a study by Smith and Johnson [41]. Digested meat samples from grain-finished animals had significantly ($p \leq 0.05$) higher free palmitic acid content than those from pasture-raised animals. The amounts of almost all the individual free MUFAs released after digestion were significantly ($p \leq 0.05$) higher for grain-finished meats than for their pasture-raised meat counterparts (Table 4 and Table 5). As expected, the amounts of n−3 PUFAs, namely free ALA, EPA, and DPA were mostly higher in the digests from pasture-raised animals. The role of SFAs in increasing the risk of many conditions such as obesity and cardiovascular disease and the role of long-chain n−3 PUFAs in providing health benefits has been reported in the literature, although the former is still under scrutiny [42]. The results in Table 4 and Table 5 point toward the advantages of consuming pasture-raised meat over grain-finished meat, as pasture-raised meat provides higher amounts of free long-chain (LC) n−3 PUFAs (particularly EPA and DPA) after 180 min of digestion. Among the two meat cuts, no significant differences ($p \leq 0.05$) could be observed for the amounts of individual free LCn−3 PUFAs released during digestion. However, tenderloin samples in general released higher amounts of most of the individual SFAs, MUFAs, and n−6 PUFAs along with ALA during digestion than the striploin samples (Table 4) despite showing no significant differences among the total fat contents and the saturated (except for stearic acid) and mono-unsaturated fatty acid profiles among the respective control undigested meat samples (Table 1 and Table 3). This agrees with the higher rates of free amino N release and higher protein breakdown for tenderloin during digestion, showing that the higher rates of digestion of protein may influence the release of fats from within the structures and thus enhance their digestion and bioaccessibility. The free fatty acid ratios and desaturase indices presented in Table 6 serve as health indication markers [29,43]. Our results showed a higher ratio (≥4-fold) for digested grain-finished tenderloin or striploin while the respective digested pasture-raised meat counterparts reported significantly ($p \leq 0.05$) lower n−6/n−3 ratios. Diets with low n−6/n−3 ratios (4:1) have been associated with better neurogenesis, reduced depression, and other cognitive benefits [42,43,44]. The delta-9 desaturase enzymes (also known as stearoyl-CoA desaturases, SCD) play an important role in lipid metabolism by catalyzing the conversion of saturated (SFA) to monounsaturated (MUFA) fatty acids by introducing a cis double bond at the delta-9 position [45]. Saturated fatty acids such as myristic, palmitic, and stearic acids are mainly used as substrates which are converted into myristoleic, palmitoleic, and oleic acids, respectively. Oleic and palmitoleic acids are the major MUFAs in fat depots and membrane phospholipids. Very high activities of estimated SCD-16 (as the ratio of palmitoleic to palmitic acid) and SCD-18 (as the ratio of oleic to stearic acid) have been linked with obesity and other metabolic disorders [29,45,46,47]. Both desaturase indices, DI16 and DI18, were found to be significantly ($p \leq 0.05$) lower for pasture-raised meat digests, particularly for tenderloin than for grain-finished meat digests. ## 4. Conclusions The objectives of this study were to determine and compare the nutritional value, protein digestibility, and free fatty acid release during the digestion of tenderloin and striploin from grain-finished and pasture-raised beef by utilizing an in vitro digestion model. When comparing the meat cuts, tenderloin showed slightly but significantly higher ($p \leq 0.05$) overall protein hydrolysis than striploin, in terms of free amino N release during digestion. However, no significant effect of the animal feeding system was observed on the overall protein hydrolysis of meat during simulated digestion, irrespective of the meat cut. SDS-PAGE results showed that tenderloin from pasture-raised samples had higher and faster protein breakdown during digestion than striploin from pasture-raised animals. The amounts of individual free fatty acids in the digests of the meats from pasture-raised and grain-finished production systems differed and were largely reflective of the composition of the triglycerides that were being digested. The total amounts of free SFAs and MUFAs were higher in the grain-finished meat samples. This suggests that pasture-raised meat was likely to have lower risks of contributing to chronic diseases, such as cardiovascular disease, which are related to levels of SFAs (lauric, myristic, and palmitic acids). The pasture-raised meat digests from both striploin and tenderloin contained slightly but significantly ($p \leq 0.05$) higher amounts of free long-chain n−3 fatty acids (particularly EPA and DPA) which have been extensively studied for their beneficial health effects. Tenderloin samples in general released higher amounts of most of the individual free SFAs, MUFAs, and n−6 PUFAs during digestion than the striploin samples. This agrees with the higher rates of free amino N release and higher protein breakdown for tenderloin during digestion, showing that the higher rates of digestion of protein may influence the release of fats from within the structures and thus enhance their digestion and bioaccessibility. For each meat cut type, significantly lower n−6/n−3 PUFA ratios ($p \leq 0.05$) and desaturase indices ($p \leq 0.001$) were observed for digests from pasture-raised than the counterpart digests from grain-finished animals. The overall effect of the production system on the n−6/n−3 ratio or desaturase indices (except for DI18) for meat digests was, however, not significant ($p \leq 0.05$). For n−6/n−3 PUFA ratio and desaturase indices, significant interactions ($p \leq 0.001$) between the meat cut type and the production system were observed, indicating that the differences observed among the production systems were not consistent for each meat cut. For striploin digests, the differences in the mean values for these parameters between the two production systems were slightly larger than those for tenderloin digests, suggesting that striploin was more influenced by the meat production system than tenderloin. The values for the desaturase indices and n−6/n−3 ratios for tenderloin digests from the pasture-raised animals were the lowest among all the samples, suggesting better health benefits when consuming pasture-raised meat, particularly tenderloin. 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--- title: Competing Endogenous RNA Regulatory Networks of hsa_circ_0126672 in Pathophysiology of Coronary Heart Disease authors: - Muhammad Rafiq - Abdullahi Dandare - Arham Javed - Afrose Liaquat - Afraz Ahmad Raja - Hassaan Mehboob Awan - Muhammad Jawad Khan - Aisha Naeem journal: Genes year: 2023 pmcid: PMC10047999 doi: 10.3390/genes14030550 license: CC BY 4.0 --- # Competing Endogenous RNA Regulatory Networks of hsa_circ_0126672 in Pathophysiology of Coronary Heart Disease ## Abstract Coronary heart disease (CHD) is a global health concern, and its molecular origin is not fully elucidated. Dysregulation of ncRNAs has been linked to many metabolic and infectious diseases. This study aimed to explore the role of circRNAs in the pathogenesis of CHD and predicted a candidate circRNA that could be targeted for therapeutic approaches to the disease. circRNAs associated with CHD were identified and CHD gene expression profiles were obtained, and analyzed with GEO2R. In addition, differentially expressed miRNA target genes (miR-DEGs) were identified and subjected to functional enrichment analysis. Networks of circRNA/miRNA/mRNA and the miRNA/affected pathways were constructed. Furthermore, a miRNA/mRNA homology study was performed. We identified that hsa_circ_0126672 was strongly associated with the CHD pathology by competing for endogenous RNA (ceRNA) mechanisms. hsa_circ_0126672 characteristically sponges miR-145-5p, miR-186-5p, miR-548c-3p, miR-7-5p, miR-495-3p, miR-203a-3p, and miR-21. Up-regulation of has_circ_0126672 affected various CHD-related cellular functions, such as atherosclerosis, JAK/STAT, and Apelin signaling pathways. Our results also revealed a perfect and stable interaction for the hybrid of miR-145-5p with NOS1 and RPS6KB1. Finally, miR-145-5p had the highest degree of interaction with the validated small molecules. Henchashsa_circ_0126672 and target miRNAs, notably miR-145-5p, could be good candidates for the diagnosis and therapeutic approaches to CHD. ## 1. Introduction A total of 17 million annual fatalities worldwide, representing about $30\%$ of all deaths, are caused by cardiovascular diseases (CVDs). The most typical type of CVD is coronary heart disease (CHD) or ischemic heart disease (IHD), which accounts for about $38\%$ of cardiovascular deaths in women and $46\%$ in men [1]. The complex biology of CHD commences with endothelial dysfunction and chronic inflammation in the coronary arteries. The formation and subsequent development of atherosclerotic plaques within the coronary arteries restrict the blood supply to the heart and induce myocardial ischemia [2]. It was generally accepted that CHD is a major global problem that is more widespread in South Asian nations, e.g., Sri Lanka, Bangladesh, India, Nepal, Afghanistan, and Pakistan, compared to other countries [3,4]. This prevalence could be associated with genetics and unhealthy lifestyles [5]. Approximately 40–$60\%$ of the risks for coronary artery disease are caused by genetic predisposition [6]. Sedentary lifestyle, smoking, excessive alcohol consumption, unhealthy diet, oxidative stress, obesity, elevated serum cholesterol, hypertension, and diabetes are also established risk factors for CHD and powerful predictors of the variation in disease rates among populations [7]. It is therefore deduced that CHD is affected by hereditary and environmental factors [8]. Gene expression profiles have reflected pathological states in several disorders, e.g., cancer [9], chronic kidney disease (CKD) [10], metabolic syndrome [11], and CHD [12]. Scientific investigation indicates the gradual changes in gene expression profile during the development of CHD [12]. The use of gene expression signatures to discover CHD biomarkers for diagnosis, treatment, prognosis, and monitoring of the disease has yielded promising results [13,14]. Pro-platelet basic protein (PBP) and α-defensin (DEFA1/DEFA3) were identified as potential biomarkers of CHD in the Thai population [15]. The considerable effects of the pro-protein convertase subtilisin/kexin type 9 (PCSK9) gene on the low-density lipoprotein receptor (LDLR) and ultimately the plasma level of LDL-cholesterol are suggested as a good candidate for the therapy of CHD [16]. In addition, C-reactive protein (CRP) was identified as a powerful inflammatory biomarker in CHD [17]. Additionally, non-coding RNAs, e.g., long-chain non-coding RNAs (lncRNA), micro RNAs (miRNAs), and circular RNAs (circRNAs), play a crucial role in the regulation of more than half of protein-coding transcripts and are implicated in the regulation of almost every biological process within the cellular environment [18]. Thus, they play a pivotal role in the pathology of several diseases, e.g., neurodegenerative diseases [19], metabolic syndrome [20], and cancer [21]. Despite the extensive information regarding CHD, the disease is still prevalent in low- and middle-income countries. Hence, research on the role of ncRNAs as a biomarker of the disease is necessary as it is not fully elucidated. This study helped in the prediction of novel molecular marker(s) and could be utilized as either a therapeutic target and/or diagnostic biomarker of the disease. ## 2.1. Search for circRNAs Associated with CHD and Identification of Their Target miRNAs circRNAs were identified by a comprehensive literature search and the circRNA2Disease database [22]. In five separate experiments, a total of 116 circRNAs associated with CVD were obtained [23,24,25,26,27] (Table S1). These circRNAs were individually submitted to a computational tool known as the “Circular RNA Interactome” that helps in the mapping and prediction of binding positions for particular miRNAs on reported circRNAs [28]. Three circRNAs; has_circ_0092576, has_circ_0078837, and has_circ_0126672, were selected for further study based on the abundance of miRNA binding sites the circRNAs possessed (Table S2). It was speculated that the higher the number of binding sites, the more circRNA could efficiently regulate the target miRNAs. The circRNAs possessing less than 5 binding sites for any miRNA were not captured in either the Supplementary Material or the presented data. ## 2.2. Search for CHD Differentially Expressed Genes (DEGs) Gene expression raw data profiles by array were obtained from the GEO datasets of the NCBI. Only peripheral blood samples with CHD and healthy control data were selected for further analysis (Table 1). These experiments were submitted to GEO2R for statistical analysis by using already reported methodology [29] to acquire CHD differentially expressed genes (DEGs) and their expression pattern. The cutoff value for statistical significance was set at $p \leq 0.05.$ The common differentially expressed genes (cDEGs) across the experiments that satisfied the predefined criteria were sorted by the online tool Bioinformatics and Evolutionary Genomics (http://bioinformatics.psb.ugent.be/webtools/Venn/, accessed on 20 April 2022). The fold changes of each gene obtained from the selected experiments were averaged and assigned to the corresponding gene (Table S3). ## 2.3. Identification of Differentially Expressed miRNA Target Genes (miRNA-DEGs) The miRNA database was queried for a list of predicted target genes for each miRNA [30]. Using the Bioinformatics and Evolutionary Genomics tool, the predicted miRNA target genes were compared to cDEG. The genes present across these two lists were designated as differentially expressed miRNA target genes (miR-DEGs) [20,31] (Table S4). ## 2.4. Functional Enrichment Analysis The database for annotation, visualization, and integrated discovery (DAVID) version 6.8 was employed for the functional enrichment study [32]. This enables a thorough knowledge of the biological significance of a given list of genes and establishes a relationship between the genes and associated illnesses or disorders. The common differentially expressed genes between all five experiments were sorted. Overlapping cDEGs and miRNA target genes (miR-DEGs) were separately identified for each miRNA and individually submitted to DAVID for analysis, setting Homo sapiens as the reference species. The tool first associates each gene identification with a gene ontology (GO) phrase classified into three broad categories: biological processes, molecular activities, and cellular compartments. On the other hand, the Kyoto encyclopedia of genes and genomes (KEGG) was utilized for pathway annotations of miR-DEGs [33]. ## 2.5. Network Illustrations The network interactions of circRNAs/miRNAs, and circRNA/miRNA/pathways, as well as circRNA/miRNA/gene involved in the regulation of biological processes relevant to CHD were generated using Cytoscape (version 3.8.1) [34]. ## 2.6. miRNA and Target Sequence Homology Study FASTA sequences for each miR-DEGs implicated in the control of the JAK/STAT and Apelin signaling pathways were obtained from the NCBI website. The sequences of mature miRNAs were retrieved from the miRDB (http://mirdb.org/ontology.html/, accessed on 20 May 2022). The nucleotide sequence of miRNA and predicted target DEGs were subjected to the BiBiserv RNA hybridization tool for the homology study [35]. The minimum free energy (MFE) value −15 kcal/mol was used as a cutoff value (threshold) [36]. The MFE value of −27 kcal/mol is comparable to the MFE for one of the four best hybridizations of let-7 [35]. Thus, an MFE of ≤−27 kcal/mol was used to predict good hybridization between miRNA and target mRNA. Although, MFE of ≤−30 kcal/mol is considered as perfect hybridization [37]. ## 2.7. miRNA and Small Molecules Interaction In order to investigate the potential of miRNAs as pharmacogenomics biomarkers, small molecule-miRNA network-based inference (SMiR-NBI), assessable at (http://lmmd.ecust.edu.cn/database/smir-nbi/, accessed on 12 June 2022) was employed to identify the experimentally validated small molecule that could interact with the miRNA to inhibit or enhance its expression [38]. The SM-miR network was constructed using the Cytoscape software. The degree of connectivity between the miRNAs and small molecule/drugs was analyzed with the same software [34]. The method employed for the selection and analysis of data was summarized as a flowchart (Figure 1). ## 3.1. Identification of Differentially Expressed Genes and Selected Circular RNAs We have recently published the role of the hsa_circ_0092576 regulatory network in the pathogenesis of CHD [39]. Herein, we emphasize the detailed information about the competing endogenous RNA regulatory network of hsa_circ_0126672 in the pathophysiology of CHD. A total of 13 gene expression array experiments of CHD were identified from NCBI websites (Figure 1). However, only five experiments satisfied the inclusion criteria, thus included in the analysis (Table 1). In order to have a quick visualization of the pattern and degree of expression of genes as well as significantly dysregulated genes, volcanic plots for the GEO datasets of the five included experiments (GSE71226, GSE12288, GSE56885, GSE42148, and GSE20681) were constructed (Figure 2). In addition, a total of five experiments on circRNAs were identified, in which 116 circRNAs were differentially expressed (Table S1). However, only three circRNAs were selected for further analysis, considering their potentials in regulating the target miRNAs due to the high binding sites to the target miRNA (Table S2). The number of dysregulated genes in all five experiments was illustrated by a Venn diagram. Genes present in at least 3 of these experiments were considered common DEGs (cDEGs) (Figure 3A). A total of 566 cDEGs were found and thus considered for further computational analysis. The calculated average fold changes of the cDEGs (Table S3) show that a total of $38\%$ of the genes were down-regulated, compared to $62\%$ that were up-regulated (Figure 3B). ## 3.2. Interaction of Circular RNA with the Selected miRNAs A network of circRNAs and target miRNAs with more than 10 binding sites on at least one of the presented circRNAs was illustrated (Figure 4). It showed that 60 miRNAs were targeted by at least two circRNAs. Conversely, 29, 17, and 7 miRNAs were uniquely regulated by hsa_circ_0126672, hsa_circ_0078837, and hsa_circ_0092576, respectively (Figure 4A). It was observed that a total of 85 miRNAs interact with hsa_circ_0126672, of which 7 of the miRNAs (miR-548c-3p, miR-495-3-p, miR-186, miR-203, miR-21-5p, miR-7-5p, and miR-145-5p) were selected for further analysis (Figure 4B). Several differentially expressed miRNA target genes were presented (Figure 4). It was observed that miR-548c-3p had the highest number of target DEGs, whereas miR-203a-3p had the least number of target genes (Figure S1). The number of up-regulated target genes was higher than the number of down-regulated target genes for all these miRNAs by at least two folds. ## 3.3. Gene Ontology and KEEG Pathway Analysis In order to study the biological function impacted by the dysregulation of selected circRNAs and their corresponding target miRNAs, functional enrichment analysis for miR-DEGs of each of the miR-7-5p, miR-548c-3p, miR-21-5p, miR-186-5p, miR-145-5p, miR-203a-3p, and miR-495-3p was conducted (Table S5A–C). The functional enrichment analysis enabled the discovery of enriched biological themes, notably gene ontology (GO) terms categorized into three primary sections: biological processes, cellular compartments, and molecular functions (Figure 5). All the miRNAs examined in this work were shown to have a substantial role in many essential biological processes linked with CHD diseases, including transcription regulation from the RNA polymerase II promoter and DNA template, cell proliferation, and gene expression (Figure 5A). miR-548c-3p was shown to be solely engaged in circadian rhythm and PI3MAPK regulation. Additionally, the findings demonstrated the essential role of miRNAs in the regulation of genes involved in molecular functions. miR-DEGs were shown to have a significant role in protein binding, poly (A) RNA binding, ATP binding, metal ion binding, and nucleic acid binding (Figure 5B). Each of these miRNAs had a role in the regulation of genes involved in metal ion binding. The transcription factor binding and fibroblast growth factor binding miRNAs controlled the fewest genes. Additionally, miR-548c-3p and miR-203a-3p were shown to be involved in the regulation of genes related to fibroblast growth factor. The third GO term is a cellular compartment that defines the location in which a gene product executes its biological tasks. The findings of this research demonstrated that all tested miRNAs had a substantial impact on the regulation of genes whose products perform biological tasks inside the nucleoplasm, nucleus, and cytoplasm compartments (Figure 5C). ## 3.4. Circular RNA Networks In order to have a clear view of the participation of selected circRNAs involved in CHD-related biological processes as predicted in this research, a regulatory network of circRNA/miRNA/mRNA was constructed (Figure 6A). A total of 124 miR-DEGs were present in the CHD-related biological processes, as shown in Figure 6A. The number of up-regulated miR-DEGs was higher than the number of down-regulated miR-DEGs by three folds, with an approximate percentage composition of $76\%$ and $24\%$, respectively. Similarly, a total of 74 dysregulated genes in biological processes were targets of at least two miRNAs; however, 50 genes were uniquely regulated by different miRNAs. The regulatory role of the has_circ_0126672 targeting miRNAs participating in the CHD-related pathways was further investigated by using KEGG pathway analysis (Table S6). It was revealed that many genes associated with CHD related signaling pathways were regulated via miRNAs (Figure 6B). TGF-β, relaxin, apelin, Hippo, JAK/STAT, MAPK, VEGF, mTOR, and PPAR are examples of these pathways. Insulin resistance and atherosclerosis were among the pathological diseases anticipated to be related to the dysregulation of hsa_circ_0126672 or its target miRNAs. Additionally, cardiac muscle contraction, the complement coagulation cascade, aldosterone synthesis and secretion, platelet activation, cellular senescence, renin secretion, and circadian rhythm were all impacted. The JAK/STAT and Apelin signaling pathways are crucial in CHD pathology and are regulated by a large number of miR-DEGs; thus, it is presented to visualize the genes involved as well as their pattern of expression. All miR-DEGs except JAK2 were up-regulated in JAK/STAT (Figure S2A) and APLNR in Apelin signaling pathways (Figure S2B). ## 3.5. Homology Study and miRNAs Interaction with Small Molecules The results of miRNA and target gene homology studies revealed that all duplexes had an MFE value less than the threshold (−15 kcal/mol) (Table S7). However, only the hybrids with an MFE value of ≤−27 kcal/mol were presented (Figure S3). The hybrid of miR-145-5p and three of its target genes; RPS6KB1, NOS1, and APLNR, had perfect and stable interactions with MFE values of −38.8, −31.4, and −34.9 kcal/mol, respectively. The binding patterns of the aforementioned duplexes are good enough to predict stable interactions between miR-145-5p and its target genes (RPS6KB1, NOS1, and APLNR). ## 3.6. Interaction between miRNAs and Small Molecules A comprehensive network connecting miRNAs and small molecules was constructed (Figure 7). The expression of miRNAs was affected by many drugs. The degree, otherwise called connectivity counts, was analyzed. miR-7-5p had the highest degree with 12 edges, followed by miR-203a-3p with 11 edges, and then miR-145-5p with 9 edges (Table S8), hence they were the most studied miRNAs. miR-7-5p had an equal number of positive and negative regulators. It was noticed that miR-145-5p can be positively regulated by eight different small molecules and negatively regulated by only one small molecule. Vorinostat is presently the only drug deposited in the SMiR-NCBI database that has been validated to regulate the expression of miR-548c-3p. The expression of certain miRNAs was significantly affected by the combination of a few drugs. For instance, the combination of 5-aza-2′-deoxycytidine and trichostatin-A or 4-phenyl butyric acid and 5-aza-2′-deoxycytidine showed an up-regulated effect on miR-495-3p. ## 4. Discussion Non-coding RNAs (ncRNAs) play a vital role in the regulation of physiological processes by regulating gene expression at both transcriptional and translational levels [2]. Aberrant ncRNAs’ expression is one of the underlying mechanisms that leads to the initiation and progression of several diseases, including cancer, CVD, obesity, and diabetes mellitus [40,41]. The present study predicted that ncRNAs are crucial for the regulation of genes involved in cellular processes linked to CHD. hsa_circ_0126672 had played a role in the onset and development of CHD by sponging its target miRNAs, e.g., miR-7-5p, miR-548c-3p, miR-21-5p, miR-186-5p, miR-145-5p, miR-203a-3p, and miR-495-3p. Numerous studies have shown a relationship between the expression of circRNAs and CVDs [27,42,43,44,45]. The number of up-regulated genes in CHD could be the outcome of the overexpression of has_circ_0126672. The miRNA sponge effect eradicates its impact on gene expression, resulting in the overexpression of the target genes [20,46]. Instead of miRNA binding to the target genes and inhibiting their expression, it can preferentially bind to the circRNA, thus favoring the expression of the miRNA target genes. Additionally, the up-regulation of has_circ_0126672 may account for the up-regulation of many cellular functions, including JAK/STAT and Apelin pathways, observed in this study. Based on the functional enrichment study of miR-DEGs, the pathogenesis of CHD is governed by miR-7-5p, miR-548c-3p, miR-21-5p, miR-186-5p, miR-145-5p, and miR-203a-3p, and miR-495-3p. A significant number of differentially expressed genes targeted by these miRNAs participated in many biological processes that are either directly or indirectly linked to CHD, including protein phosphorylation, regulation of gene expression, circadian rhythm, production of vascular endothelial growth factor, and cell proliferation. In addition, many relevant signaling pathways e.g., Apelin, JAK/STAT, MAPK, PI3K, AKT, TGF-β, mTOR, VEGF, FoXO, Relaxin, and PPAR signaling pathways were affected. Other dysregulated signaling pathways associated with CHD included cardiac muscle contraction, insulin resistance, atherosclerosis, vasopressin-regulated water reabsorption, aldosterone synthesis and release, and platelet activation. Protein phosphorylation is an important cellular process necessary for normal homeostasis and development. Dysregulation of protein phosphorylation leads to many types of diseases, including cardiovascular diseases [47,48]. Human heart failure, which could result from CHD, has been related to the limited extent of phosphorylation of thin-filament proteins [48]. The regulation of protein phosphorylation was under the control of miR-145-5p, miR-21-5p, miR-495-3p, and miR-7-5p via their interactions with target genes involved in the process. Except for miR-21-5p, the present study predicted the participation of all these miRNAs in the regulation of cell proliferation. This is in accordance with the previous report of Liu et al., [ 49] who highlighted the regulatory effect of miR-23 in cell proliferation and apoptosis of vascular smooth muscle cells in CHD. Recently, the role of miR-7-5p as a biomarker of CHD was ascertained. However, down-regulation was associated with a high risk of developing CHD, thus miR-7-5p may be involved in the pathophysiology of the disease [50]. Our study showed significant involvement of miR-7-5p in CHD pathology via regulating numerous signaling pathways, including cell proliferation, protein phosphorylation, and vascular and endothelial growth factors production. Contrary to our findings, miR-21 enhanced the cell proliferation of murine cardiac stem cells post-myocardial infarction via the inhibition of PTEN expression and stimulation of the PI3K/Akt pathway [51]. One of the underlying mechanisms of human health is circadian rhythm [52]. It is critically important in cardiovascular physiology. Disturbance in circadian rhythms has been associated with a high risk of acquiring cardiac complications and harmful cardiovascular incidents [53]. Our findings showed miR-548c-3p targets DEGs that are involved in circadian rhythm; thus, it was proposed that miR-548c-3p has a vital role in the physiology of CVD via regulation of NRIP1, NAMPT, CREB1, NAMPT, and ID3. Vascular endothelial growth factor (VEGF) plays an integral part in angiogenesis, vascular pathology, and atherosclerosis and has been associated with CVDs. Thus, VEGF possesses positive and negative effects in CHD [54]. The present study also predicted the involvement of miR-7-5p and miR-548c-3p in the generation of VEGF by targeting the IL6ST, HIF1A, and PTGS2. This may be considered a different fundamental mechanism through which hsa_circ_0126672 and their target miRNAs, miR-7-5p, and miR-548c-3p, have been associated with the pathogenesis of CHD. Previously, miR-7-5p was determined to be an effective molecule for the regulation of VEGF. The inhibition of miR-7-5p resulted in increased angiogenesis via the up-regulation of VEGF by the direct target of Krüppel-like factor 4 (KLF4) [55]. Similar to our findings, the role of miR-203a-3p in the regulation of VEGF secretion was reported [56,57]. Therefore, it was predicted that the down-regulation of miR-7-5p caused by the up-regulation of has_circ_0126672 would promote the production of VEGF in CHD patients. Among predicted miRNAs, the miR-548c-3p exhibited a greater number of target genes and showed strong relation with cellular functions implicated in the pathophysiology of CHD. The function of miR-548c-3p in CHD was not well elaborated in previous studies. However, its strong association with pulmonary hypertension was reported [58]. The KEGG pathway analysis demonstrated the function of non-coding RNA (ncRNA) in the regulation of several signaling pathways and important physiological processes associated with CHD. The present work emphasized the JAK/STAT and Apelin signaling pathways due to their relevance in the pathology of CVDs and a higher degree of interaction with the analyzed miRNAs, as well as a relatively large proportion of miR-DEGs dysregulated in these pathways. It is generally accepted that the JAK/STAT pathway is involved in several cardiac pathologies [59]. It was believed that acute stimulation of the JAK/STAT signaling pathway is protective for cardiac cells, while persistent stimulation of the pathway can result in heart failure [60]. The contribution of a large number of miR-DEGs was depicted in this study. This reflects the regulatory function of the JAK/STAT signaling pathway by the hsa_circ_0126672 and its target miRNAs, notably miR-7-5p, miR-548c-3p, miR-21-5p, miR-186-5p, miR-145-5p, and miR-203a-3p and miR-495-3p. Previous reports have demonstrated the involvement of ncRNAs in the regulation of the JAK/STAT signaling pathway [61,62]. Our findings are supported by Li and Zeng [63], who described the effective regulatory effects of miR-21 on the JAK-STAT signaling pathway via the suppression of STAT3 in juvenile idiopathic arthritis patients. Conversely, in another report, it was suggested that miR-21 and miR-9a may take part in JAK/STAT signaling pathway activation [64]. Our results are also in agreement with the previous report, which stated that the miR-548c-3p regulated genes are potent modulators of pathways associated with tumor development and metastasis, including the JAK-STAT signaling pathway [65]. In addition, it was cited that miR-145 could significantly regulate the expression of genes associated with the JAK/STAT pathway [66]. This study revealed that overexpressed genes were significantly involved in atherosclerosis and the JAK/STAT pathway. Thus, the activation of these pathways was predicted to support the fact that the onset and progression of atherosclerosis and hypertension largely depend on the activation of the JAK/STAT signaling system [67,68]. Our data highlighted that a large number of miR-DEGs were implicated in the control of the Apelin signaling pathway, which is one of the crucial signaling transduction pathways and is considered an important pathway involved in cardiovascular homeostasis [69]. The Apelin signaling pathway provokes several physiological mechanisms and processes, e.g., cardiac contractility, blood pressure regulation, angiogenesis, the endocrine stress response, energy metabolism, and fluid homeostasis. This pathway also contributes to the pathogenesis of several diseases, including obesity, diabetes, heart diseases, and many forms of cancer [69,70,71]. It was previously reported that Apelin signaling may serve as a key atherosclerosis-protective marker to control the onset of CHD [72]. The miR-DEGs involved in the regulation of the Apelin signaling pathway highlighted the implication of hsa_circ_0126672 in CHD pathogenesis. The up-regulated circRNA sponges their target miRNAs, thereby competitively hindering the miRNA’s suppression influence on its target gene, which resulted in the up-regulation of the genes, as observed in the present research. The effect of miRNAs in the regulation of Apelin or the Apelin signaling pathway was previously reported. Zhou et al. [ 73] reported that miR-195 inhibited the development of lung cancer via the regulation of Apelin expression. In another report, miR-503 enhanced angiotensin II-induced cardiac fibrosis via the target of the Apelin 13 gene [74]. Similar to our findings, an increase in the expression of Apelin in the atherosclerotic coronary artery was reported [75]. The higher level of Apelin may have a valuable or adverse impact on the progression of atherosclerosis. The negative consequences may include atherosclerosis and oxidative stress [76]. In contrast, Apelin has a positive effect by decreasing angiotensin II, thereby reducing atherosclerosis [77]. Homology studies between miRNA and mRNA enabled the determination of the pattern of interaction between miRNA and its target gene, as well as the MFE needed for the duplex to form. This is critical for identifying the potential of miRNAs to effectively bind to and control the target expression of the target sequence. Predicting and validating miRNA-target interactions is critical for comprehending miRNA’s involvement in complex networks that regulate cellular activities [78]. In the present study, eight miRNA:mRNA hybrids with good binding affinity and perfect or nearly perfect interaction were presented. These characteristics serve as a predictor of the regulatory potential of miRNA for its target genes. The alignment in Watson and Crick matching is considered perfect if there is no gap observed between the miRNA and target gene sequence [79]. The duplexe miR-186-5p:GNB4, miR-186-5p:IL6ST, miR-145-5p:GNAQ, miR-145-5p:SOCS2, miR-145-5p:RPS6KB1, miR-145-5p:GABARPL1, miR-145-5p:APLNR, and miR-145-5p:NOS1 are relatively good, with MFE results of −27 kcal/mol, which are identical to the MFE results for the hybridization of the let-7 and the 3’UTR of Caenorhabditis elegans. One of the most well-known duplexes of CELF35-1 and let-7 [35]. According to a study, if the MFE value is below −30 kcal/mol, the miRNA:mRNA duplex will execute a stable and efficient interaction [37]. In addition, the lower the MFE value, the better the binding affinity between the miRNA and target sequence [80]. The miRNAs duplexes with their respective genes represent miR-145-5p:APLNR, miR-145-5p:NOS1, and miR-145-5p:RPS6KB1. The MFE result of <−30 Kcal/mol retained the lowest MFE value of <−30 Kcal/mol and was therefore predicted to yield a stable and perfect hybrid. Virtually all the miR-DEGs that exhibit a good hybrid with the predicted regulatory miRNA were up-regulated. However, it is a known fact that miRNA reduces the expression of the target gene by interacting with it [35,36]. The reason for the up-regulation of target genes of miR-186-5p (GNB4 and IL6ST) and miR-145-5p (GNAQ, SOCS2, RPS6KB1, GABARAPL1, and NOS1) could be the up-regulation of hsa_circ_0092576 and hsa_circ_0126672, which suppress the expression of miR-186-5p and miR-145-5p, thereby up-regulating their target genes. Hence the circRNA involved in the disease pathology by ceRNA mechanism. To the best of our knowledge, this is the very first study that mechanistically explains the role of hsa_circ_0126672 in the pathophysiology of CHD. However, microarray data shows up-regulation of the hsa_circ_0126672 in the peripheral blood of CHD patients [25]. Modification of the function of endogenous ncRNA by small molecules could be a promising strategy to achieve efficient treatment for ncRNA-related diseases. As an association between miRNAs and CHD has been predicted in this study, the identification of small molecules that potentially modify the expression of these miRNAs may offer a novel therapeutic approach. In this research, it was revealed that miR-145-5p, miR-203a-3p, and miR-7-5p had a higher degree of interaction with the validated small molecules. It was observed that 5-fluorouracil (5-FU) and goserelin are small molecules that interact with miR-7-5p and miR-203a-3p to inhibit their expression. 5-FU has been used in the treatment of cancer, and its regulatory effect on miRNAs including miR-7-5p and miR-203a-3p has been reported [81]. However, cardio-toxicity of 5-fluorouracil was reported [82,83]. Another small molecule, glucocorticoid, interact with miR-145-5p to enhance their expression and miR-203a-3p to inhibit it. The molecule (glucocorticoid) has been used widely in the treatment of rheumatic diseases and is a potent anti-inflammatory drug [84]. Its anti-inflammatory effect is evident, suggesting that it may have therapeutic benefits for atherosclerosis and CHD [85]. Based on the current data, we presented a summary model to elaborate on the involvement of has_circ _0126672 and their target miRNAs in the pathology of CHD (Figure 8). It was predicted that has_circ_0126672 would successfully sponge their target miRNAs, diminishing their impact on the target genes of the Apelin and JAK/STAT signaling pathways. As a result, the expression of genes was increased, and the pathways were activated. Activation of the JAK/STAT signaling pathway triggers the inflammatory cascade within the vascular environment, which ultimately induces atherosclerosis through the activation of vascular smooth muscle cells (VSMCs) [67,68,86]. Likewise, the activated Apelin signaling pathway facilitated atherosclerosis by promoting oxidative stress in the vascular environment, resulting in the deposition of oxidized lipids and eventually endothelial damage [76,87]. The evolution of atherosclerotic plaque in the coronary artery eventually led to CHD. ## 5. Conclusions In this study, computational analyses were employed to predict the role of circRNAs and their target miRNAs as potential biomarkers of CHD. hsa_circ_0126672 and its target miRNAs (miR-7-5p, miR-548c-3p, miR-21-5p, miR-186-5p, miR-145-5p, miR-203a-3p, and miR-495-3p) were identified as crucial molecules in the pathogenesis of CHD. It was demonstrated that hsa_circ_0126672 and their target miRNAs are implicated in the pathogenesis of CHD via the regulation of CHD-related cellular functions as well as many relevant signaling pathways. The effect of hsa_circ_0126672 on Apelin and the JAK/STAT signaling pathway was emphasized, and the affected genes were IL6ST, STAT3, JAK3, APLNR, GNAQ, GNB4, PRKACB, NOS1, GABARAPL1, and RPS6KB1. Furthermore, we predicted stable and perfect interactions in the duplexes of miR-145-5p:APLNR, miR-145-5p:NOS1, and miR-145-5p:RPS6KB1. Thus, the potential regulatory effect of these miRNAs on their respective target genes was predicted. Small molecules that could interact with the miRNAs to enhance or inhibit their expression were also demonstrated, suggesting a new strategy for the management of the disease through the modification of miRNA expression. Thus, it is suggested that hsa_circ_0126672 along with its target miRNAs could act as potential biomarker panels for the diagnosis and treatment of CHD. However, rigorous wet laboratory experiments are required to further validate these findings. ## References 1. Wong N.D.. 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--- title: Italian Translation and Validation of the Original ABC Taxonomy for Medication Adherence authors: - Sara Mucherino - Marina Maffoni - Clara Cena - Lucrezia Greta Armando - Marta Guastavigna - Valentina Orlando - Giancarlo Orofino - Sara Traina - Anna Giardini - Enrica Menditto journal: Healthcare year: 2023 pmcid: PMC10048001 doi: 10.3390/healthcare11060846 license: CC BY 4.0 --- # Italian Translation and Validation of the Original ABC Taxonomy for Medication Adherence ## Abstract Medication adherence represents a complex and multifaceted process. Standardized terminology is essential to enable a reproducible process in various languages. The study’s aim was to translate and adapt the original Ascertaining Barriers for Compliance (ABC) Taxonomy on medication adherence, first proposed in 2012, into Italian language. The study was carried out according to the Preferred Methods for Translation of the ABC Taxonomy for Medication Adherence adopted by the ESPACOMP. Key steps included: [1] a systematic literature review using PubMed and Embase according to the PRISMA Guidelines to identify published Italian terms and definitions, and Italian adherence experts; [2] a forward translation of terms and definitions; [3] panelists’ selection; [4] a three-round Delphi survey. From the systematic review, 19 studies allowed detection of 4 terms, 4 definitions and 767 Italian experts. To these, Italian ESPACOMP members and experts though snowball sampling were added. The identified Italian adherence experts received the Delphi questionnaire. The Italian ABC Taxonomy was achieved after three rounds of Delphi survey by reaching at least a moderate consensus on unambiguous naming and definition of medication adherence-related terms. The Taxonomy is intended to be used in research, academic, and professional fields in order to harmonize adherence terminology and avoid confusion in comparing research findings. ## 1. Introduction In recent years, there has been a rapid increase in scientific interest in patient medication adherence. The literature is growing, describing the pervasiveness of poor medication adherence, which experts recognize as a significant public health concern mainly related to adverse health care outcomes and increased health care costs [1,2]. For instance, experts estimate that poor adherence is causing €125 billion in avoidable hospitalizations, emergency care, and outpatient visits in Europe and $105 billion in the United States per year, and this expenditure is going to increase in the next few years [3,4,5,6,7,8,9]. Many subjective, relational, and environmental aspects may contribute to non-adherence. On one hand, cognitive impairment, previous negative experiences with medications, poor health literacy, beliefs, and fears of side effects, and drug–drug interactions may threaten medication adherence. On the other hand, a lack of social and family support and a poor alliance between the clinician and the patient may also undermine medication adherence [10,11,12,13,14,15]. Moreover, complex drug characteristics (such as tablet size/dosage unit size, time and method of drug intake, pill burden) and difficulties in accessing healthcare services may also hinder medication adherence [16,17,18]. Thus, medication adherence represents a complex and multifaceted process, and understanding and improving it are an urgent imperative in the present and future health care landscape, considering the increase in multimorbidity and population aging [19]. Standardized terminology is essential to fully understand the medication adherence phenomenon and to enable a reproducible process in various languages, aiming to compare the results obtained from medication adherence studies conducted worldwide [20]. In this scenario, the ABC (Ascertaining Barriers to Compliance) project was created as a European initiative consisting of research groups operating in the field of adherence to medications funded by the European Commission, Seventh Framework Programme. To respond to this need, the ABC Taxonomy was first proposed by ESPACOMP, the International Society for Medication Adherence (https://www.espacomp.eu/project/abc-taxonomy/ (accessed on 7 May 2022)), in 2012 with the aim of promoting consistency and quantification of the terms used to describe [21]. Briefly, this conceptualization describes adherence as a multifaceted process developing through phases over time, which may totally or partially fail because of late initiation or non-initiation (initiation), suboptimal pursuance and perdurance (implementation and persistence, respectively), or early interruption (discontinuation) of a certain drug treatment. Thanks to the growing interest in the ABC Taxonomy in scientific research and to its implications for improving medication adherence in daily practice, the ABC Taxonomy may be considered a promising and useful model to conceptualize and study medication adherence [22,23]. The Taxonomy was first published in English and subsequently translated into French and German with the aim of harmonizing terminology across languages and further increasing comparability in scientific research [24]. Thus, it is necessary to increase the number of languages in which to standardize and validate the terminology related to medication adherence, with the ultimate goal of eradicating ambiguity in adherence research. To do so, a shared document was published by ESPACOMP describing methods to be adopted for the translation of the ABC adherence taxonomy, into other languages, namely Preferred Methods for Translation of the ABC Taxonomy for Medication Adherence [25]. These methods includes several harmonized key steps, such as a literature search, forward translation of terms/definitions, panelists’ selection, and Delphi survey to reach consensus in the target language [25]. Actually, in the Italian setting there is still an unmet need for a unified taxonomy on medication adherence research measures and terminology. This addresses the lack of consistency and clarity in medication adherence national research, which can lead to confusion and difficulty in comparing and synthesizing findings across studies. In this vein, the present study aimed to translate and adapt the original ABC Taxonomy on medication adherence into the Italian language through translation of the related terms and definitions. ## 2. Materials and Methods The present study was carried out according to the Preferred Methods for Translation of the ABC Taxonomy for Medication Adherence adopted by the ESPACOMP [25] for the translation of the ABC Taxonomy, originally described in English by Vrijens et al. [ 21], into other languages. The Delphi method was chosen as the preferred methodology to achieve consensus on the terminology [26]. The key steps included: [1] bibliographic research to identify key papers on medication adherence in the Italian language in order to identify published Taxonomy terms and definitions in Italian, and to identify Italian adherence experts; [2] a preliminary translation of the terms and their definitions; [3] the selection of the panelists; [4] a Delphi survey (design and administration). All the steps described above were divided into an operational phase, carried out by 4 researchers, and the supervisory phase carried out by 5 other researchers. The entire process is graphically shown in Figure 1. ## 2.1. Literature Search A systematic review was carried out according to the PRISMA 2020 (Preferred Reporting Items for Systematic reviews and Meta-Analyses) statement guidelines to identify Italian studies published on medication adherence [27], to describe how the ABC Taxonomy terms and definitions were defined in Italian, and to identify Italian experts in the area (File S2 (Supplementary Materials)). The review was prospectively registered in PROSPERO—the International Prospective Register of Systematic Reviews (registration code: CRD42020212909). Two scientific databases, PubMed (via Medline) and EMBASE (via Ovid), were queried from 2012—the ABC Taxonomy publication year—to July 2020. The reason for the selection of these databases was related to their: (i) Comprehensive coverage in the field of biomedical research indexing thousands of journals in the field of medicine, nursing, pharmacy, and other health-related disciplines, and were likely to contain a substantial portion of the relevant literature on medication adherence in the Italian language; (ii) Language specificity allowed for language-specific searches, which was particularly relevant in this case as the focus was on Italian language research papers; (iii) Established quality standards with rigorous quality standards for the inclusion of articles; (iv) Common practice in the field of medicine and health sciences. This enabled the systematic review study to follow a standardized and widely accepted methodology, which enhances the credibility and replicability of the study. Inclusion criteria were based on the identification of original articles published in peer-reviewed journals and available in the Italian language. An Italian language filter was set. Moreover, the exclusion criteria consisted of books, theses, research protocols, conference proceedings, abstracts, posters, and research studies not available in the Italian language. The search strategy combined principally the 7 terms mainly related to medication adherence research (Medication adherence; Initiation; Implementation; Discontinuation; Persistence; Adherence management; Adherence-related science) [21], as reported from the original ABC Taxonomy on Medication Adherence, with all their pertaining synonyms (Adherence; Compliance; Patient compliance; Treatment adherence; Medication compliance; Medication persistence; Treatment compliance; Adhesion; Interruption) [28]. These terms were searched as MeSH Term or Emtree. The Boolean operators AND/OR were used to combine searches and obtain the two final syntaxes. Entire search strategy is available in File S1 (Supplementary Materials). The screening process was organized in two phases: title and abstract screening and full-text screening. Four researchers (S.M, M.M., S.T., L.G.A.) independently screened titles and abstracts and selected them for the next step. The same four researchers independently screened full-texts included in the analysis for their eligibility according to shared inclusion/exclusion criteria. The other authors participated in the screening process and resolved any disagreements regarding some records to reach a consensus. After making a shared decision, they identified the final number of full-text records to include. Then, they extracted the following information from each study: title, authors’ names, corresponding author’s name, corresponding author’s email, year of publication, journal, and the ABC Taxonomy term and definition used in each study. ## 2.2. Forward Translation A single forward translation from English to Italian of terms and definitions which were not found into the systematic review process was completed by 4 native Italian researchers in the field (S.M, M.M., S.T., L.G.A.) who were also fluent in English. Terms and definitions translated were discussed and confirmed by other native Italian researchers fluent in English (E.M., A.G., C.C., M.G., V.O., G.O.). Country-specific adaptions were performed where needed in view of facilitating the implementation of the terms and definitions into Italian practice [24]. No backward translation was carried out because the experts involved in the three-round Delphi survey in the next phase made implicit backward/forward translations in expressing their views and opinions. ## 2.3. Selection of the Panelists In this stage, we identified Italian adherence experts to participate in the survey using the Delphi method. We enrolled panelists who were Italian natives fluent in English and who had interests in the fields of medication adherence research and education. These panelists were selected as follows:Italian ESPACOMP (International Society for Medication Adherence) members;Corresponding authors of Italian articles selected by systematically reviewing papers identified through PubMed and Embase;“Snowball sampling”: a non-probability sampling technique in which enrolled study subjects recruit other subjects among their local network (personal contacts). Panelists’ occupations were categorized in their professional field, as follows: biology, biostatistics, economics/health management, nursing, medicine (GPs and specialists), psychology, pharmaceutical sciences (community, clinical, hospital pharmacists, academia, etc.), social sciences (rehabilitation, researchers, etc.), and an open field including all other professions not included in the previous ones (patient representative, scientific information, clinical risk, laboratory technicians, etc.). The invitation to participate in the study was sent by e-mail. The consent to participate was properly requested according to the EU General Data Protection Regulation $\frac{2016}{679}$ (GDPR). ## 2.4. Delphi Survey We sent a three-round Delphi survey by email to the identified experts, and we aggregated their responses and shared them with the group after each round. The e-survey consisted of two parts: the first part contained general information, such as consent to data processing, reference email for sending subsequent rounds, and professional field. The second part contained various proposals for the Italian translation of the 7 ABC Taxonomy terms and definitions resulting from the systematic review and/or suitably integrated when missing. The three-round Delphi survey is shown in File S3 (Supplementary Materials). The objective of the Delphi survey was to achieve an unambiguous response through consensus. In line with the previous literature, consensus on the translated items was defined according to the following acceptance rates: Moderate consensus (50–$75\%$ acceptance rate): This level of consensus was achieved when a majority of the participants expressed their agreement on a specific Italian translation of an ABC Taxonomy term/definition. Specifically, at least $50\%$ of the participants were in agreement on the translation; Disagreement (<$50\%$ acceptance rate): An acceptance rate of less than $50\%$ was considered to be a low level of consensus, indicating that there was disagreement among the participants regarding the Italian translation of an ABC Taxonomy term/definition. This meant that less than half of the experts consulted in the Delphi survey agreed with the translation; Consensus (>75–$90\%$ acceptance rate): This level of consensus was achieved when a substantial majority of the participants expressed agreement on a specific Italian translation of an ABC Taxonomy term/definition. Hence, at least $75\%$ of the participants were in agreement on the translation; Strong consensus (>$90\%$ acceptance rate): This level of consensus was achieved when an overwhelming majority of the participants expressed agreement on a specific Italian translation of an ABC Taxonomy term/definition, thus, more than $90\%$ of the participants were in agreement on the translation [24]. Panelists’ responses were iterative in batches, thus eliminating influence. The Delphi survey was carried out by e-mail in three different rounds containing the active link to the survey without a password request. Google forms was used to create the online survey rounds. Three reminders were sent at the frequency of 2–3 weeks for each round. The survey was preceded by a pilot interview among 6 junior researchers in order to re-examine the questions and to check their clarity. Round-1: The items in Round-1 derived from Italian terms and definitions of the ABC Taxonomy resulted from the studies included in the literature review process described above. Questions were sent to the panel of experts with the published definitions (if available); definitions absent in the publications were derived from a native Italian speaker translation and a free text field. Panel members were asked to select 1 preferred item (single choice) or to propose new terms and definitions in a free text field. Items with an acceptance rate <$10\%$ were discarded from the next round. Round-2: A second set of items based on previous answers was sent to the panelists who responded to Round-1. Terms and definitions obtained from Round-1 and the level of agreement were indicated. Definitions were grouped together and similar formulations were reduced to one comprehensive statement. New terms and definitions were allowed to be proposed in a free text field. Items with an acceptance rate <$10\%$ and >$75\%$ were not integrated into the next round. Round-3: The last set of questions based on previous answers was sent to the panelists who responded to Round-2. Terms and definitions obtained from Round-2 and their relative level of consensus were proposed. ## 3.1. Systematic Literature Process During the systematic review process, we identified 79 Italian papers on medication adherence through database searching. After removing duplicates, we selected 72 articles. We included a total of 19 studies that met the inclusion criteria in the analysis [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46] (File S1; Tables S3 and S4 (Supplementary Materials)). Of the included studies, 18 ($94.7\%$) mentioned “adherence to treatment”; 10 ($52.6\%$) mentioned the term “discontinuation”; 7 ($36.8\%$) cited the term “persistence”; 2 ($15.8\%$) named the term “initiation”. We did not detect any other ABC Taxonomy adherence-related terms (File S1; Table S5 (Supplementary Materials)). Regarding the terms’ definitions, five studies ($26.3\%$) included a definition of the term “adherence to treatment”; three studies ($15.8\%$) defined the term “implementation”; ten studies ($21.1\%$) defined the term “discontinuation”; and four studies ($21.1\%$) defined the term “persistence”. In more detail, we detected several different Italian translations for citing and defining each term from the 19 included studies, which are reported in File S1 in Tables S3–S5 (Supplementary Materials). We used the adherence-related Italian studies from the systematic review to identify the Italian translations of terms and definitions to include in Round-1 of the Delphi survey. These detected options were: 9 for the “Adherence to medication” term and 5 for its definition; 4 for the term “Initiation”; 3 for the definition of “Implementation”; 5 for the term “Discontinuation” and 4 for its definition; 3 for the term “Persistence” and 4 for its definition. As for the terms “Implementation”, “Management of adherence”, and “Adherence-related science”, no translations were detected from the studies analyzed, so forward translation was performed by the researchers. ## 3.2. Delphi Survey Overall, 767 Italian adherence experts received the Round-1 online questionnaire. Round-1 reached a response rate of $22\%$, (number of panelists: 165; number of proposed items: 30); Round-2 reached a response rate of $67\%$ (number of panelists: 110; number of proposed items: 29); Round-3 reached a response rate of $80\%$ (number of panelists: 88; number of proposed items: 27) (Figure 2). The most common professional fields of panelists were pharmaceutical sciences (Round-1: $36\%$; Round-2: $38\%$; Round-3: $38\%$) and medicine (Round-1: $32\%$; Round-2: $33\%$; Round-3: $34\%$) (Figure 3). The Italian-speaking panelists reached a moderate consensus for all the terms and definitions reaching at least 50–$75\%$ of agreement. For the term and definition of “Management of adherence”, a higher consensus was reached (75–$90\%$ of agreement). Table 1 and Table 2 show the consensus rate reached for the seven terms and definitions of the translations at each Delphi round, respectively. In Round-2, a moderate consensus was reached for the terms “Inizio della terapia farmacologica” ($58\%$), “Persistenza alla terapia farmacologica” ($61\%$), “Interruzione della terapia farmacologica” ($61\%$), and a consensus was reached for the term “Gestione dell’aderenza terapeutica” ($81\%$). In Round-3, a moderate consensus was reached for the terms “Aderenza alla terapia farmacologica” ($61\%$), “Effettiva assunzione della terapia al dosaggio prescritto” ($64\%$), and “Scienza rivolta allo studio dell’aderenza” ($65\%$) (Figure 4). Proposed definitions of “Inizio della terapia farmacologica”, “Interruzione della terapia farmacologica”, “Gestione dell’aderenza terapeutica”, and “Scienza rivolta allo studio dell’aderenza” reached acceptance rates between 51 and $62\%$ (moderate consensus) in Round-1 and continued to be selected in subsequent rounds despite new proposals. In Round-2, we achieved a final moderate consensus for “Effettiva assunzione della terapia al dosaggio prescritto” ($61\%$), “Interruzione della terapia farmacologica” ($66\%$), and “Scienza rivolta allo studio dell’aderenza” ($57\%$). In the last round (Round-3), definitions of the terms “Aderenza alla terapia farmacologica”, “Inizio della terapia farmacologica”, and “Persistenza alla terapia farmacologica” reached a moderate consensus ($64\%$), while “Gestione dell’aderenza terapeutica” definition translation reached a consensus ($75\%$) (Figure 5). This analysis produced an Italian version of the ABC Taxonomy that includes the following seven Italian terms: [1] “Aderenza alla terapia farmacologica” (Round-3, $61\%$), defined as “Il processo attraverso cui i pazienti assumono i loro farmaci come prescritto” (Round-3, $64\%$); [2] “Inizio della terapia farmacologica” (Round-2, $58\%$), defined as “Il processo inizia con l’inizio del trattamento, quando il paziente assume la prima dose di un farmaco prescritto” (Round-3, $64\%$); [3] “Effettiva assunzione della terapia al dosaggio prescritto” (Round-3, $64\%$), defined as “Il processo continua con il raggiungimento del regime di dosaggio farmacologico prescritto, definito come la misura in cui il dosaggio effettivamente assunto dal paziente corrisponde a quello prescrittogli, dall’inizio della terapia fino all’assunzione dell’ultima dose” (Round-2, $61\%$); [4] “Persistenza alla terapia farmacologica” (Round-2, $61\%$), defined as “La persistenza è il periodo di tempo tra l’inizio della terapia e l’ultima dose assunta immediatamente precedente l’interruzione” (Round-3, $64\%$); [5] “Interruzione della terapia farmacologica” (Round-2, $61\%$), defined as “L’interruzione definisce la fine della terapia, quando la dose successiva da assumere viene omessa e non vengono più assunte altre dosi” (Round-2, $66\%$); [6] “Gestione dell’aderenza terapeutica” (Round-2, $81\%$), defined as “È il processo di monitoraggio e sostegno dell’aderenza alla terapia dei pazienti da parte dei sistemi e degli operatori sanitari, dei pazienti e delle loro reti sociali. L’obiettivo della gestione dell’aderenza è quello di ottenere, da parte dei pazienti, il miglior utilizzo possibile dei farmaci adeguatamente prescritti, al fine di rendere massimo il beneficio e minimo il rischio di danno” (Round-2, $75\%$); [7] “Scienza rivolta allo studio dell’aderenza” (Round-3, $65\%$), defined as “Questo elemento include le discipline che mirano a comprendere le cause o le conseguenze della differenza tra l’esposizione prescritta ai farmaci (cioè prevista dal medico prescrittore) e l’esposizione effettiva. La complessità di questo campo di ricerca, così come la sua ricchezza, derivano dal fatto che esso opera oltre i confini di diverse discipline, tra le quali, ma non solo, la medicina, la farmacia, le scienze infermieristiche, le scienze comportamentali, la sociologia, la farmacometria, la biostatistica e l’economia sanitaria” (Round-2, $57\%$). Table 3 shows the complete Italian translation of all ABC Taxonomy terms and definitions, as compared to the original English Taxonomy that was achieved. ## 4. Discussion We used a systematic review process of the Italian literature and a subsequent Delphi survey to define the Italian ABC Taxonomy and reach a consensus on the unambiguous naming and definition of terms related to the medication adherence process. To the best of our knowledge, this is the first study reporting findings for advancing the harmonization of Italian medication adherence definition by promoting clear and shared terminology to standardize research in the field. This issue is crucial as most of the terms still in use today regarding medication adherence do not have a clear or direct translation in the different European languages [47,48], which can lead to misunderstandings and hinder comparability between studies and implementation in clinical daily practice [49,50]. These considerations support the need to validate the ABC Taxonomy at a local level, as already performed in German and French [24]. In the Italian setting, different terminologies in various fields of action have so far rendered communication difficult, both in research and in the implementation of practical actions. The results of the Delphi among Italian experts confirmed this discrepancy, requiring three rounds to reach a consensus for all terms and definitions related to medication adherence. Specifically, findings indicated that six/seven terms, such as “Adherence to medication”, “Initiation”, “Implementation”, “Persistence”, “Discontinuation”, and “Adherence-related science”, reached at most a moderate consensus, i.e., at least 50–$75\%$ of the experts agreed with the same translation/definition. One of the most sensitive challenges has been to find an Italian term to effectively translate “Adherence”, which differs from “Persistence”. This point can be due to the fact that, for more than two decades, the term adherence has been confused in Italian language with the terms “Compliance”, “Adhesion”, and “Persistence” (translated as “Compliance”, “Adesione”, “Persistenza”, respectively) [28,51]. Therefore, this enabled a greater number of synonyms for a single word to be identified both in the systematic review process and in the questioning of respondents. The term “adherence” was preferred over “compliance” in Italy, as it encompasses the patient’s involvement in the treatment process and willingness to follow the healthcare provider’s advice. While “Persistence” was used to describe the duration of medication use, particularly for chronic conditions, “Concordance” was used to describe a collaborative approach to medication management that emphasizes communication, mutual respect, and shared decision-making to improve adherence and treatment outcomes. This different terminology attitude may help explain why a lower consensus rate (moderate) was identified for most of the terms [24]. In addition to these considerations on the specific characteristics of the Italian versus English language, most of the terms reached a moderate consensus despite the German and French translations, where higher levels of agreement were reached [24]. This could explain how the linguistic contexts may consider the same concepts differently. Hence, it is noteworthy that, following the three Delphi rounds performed, the Italian adherence experts reached fairly high levels of consensus on the choice of the terms “Aderenza alla terapia farmacologica” ($61\%$), “Inizio della terapia farmacologica” ($58\%$), “Persistenza alla terapia farmacologica” ($61\%$), and “Interruzione della terapia farmacologica” ($61\%$). This underlines the fact that the experts agree that the terms relating to the definitions of adherence process phases, e.g., Medication Adherence, Initiation, Persistence, and Discontinuation, are purely drug therapy-related events. In even more detail, the experts reached a consensus of $64\%$ in translating the term “Implementation” with “Effettiva assunzione della terapia al dosaggio prescritto”; in this case, the experts considered it appropriate to specify that implementation is linked to the prescribed dosage, since this process describe the dosing history, so the extent to which a patient’s actual dosing corresponds to the prescribed dosing regimen, from initiation until the last dose is taken. Despite this, a strong consensus ($81\%$) was reached at Delphi Round-2 for the translation of the term “Adherence management” into “Gestione dell’aderenza terapeutica”. For this term alone, “drug therapy” is not specified, but adherence management is understood more broadly, as the management of an entire therapy-related process. Therefore, these findings address that harmonization in this field is an urgent imperative as it will allow adherence researchers to communicate effectively and unambiguously. To sum up, this study could suggest the promotion of a unique adherence Taxonomy which could be applied in real life clinical practice contexts. Overall, providing the clinical and scientific community with a shared terminology on adherence is particularly crucial in the actual and future health care landscape. Indeed, it is widely recognized in the actual literature that success in medication adherence-behavior requires a coordinated intervention by the main actors involved (i.e., patient, general practitioner and specialist, pharmacist, paramedic, psychologist/psychotherapist, family member, health authorities, pharmaceutical industry) [52,53,54], combined with extensive awareness-raising initiatives and dissemination of the basic principles underlying strategies to assess and monitor over time the non-adherence to treatments. To reach this aim, an unambiguous and univocal communication is necessary [55]. Ineffective communication between health care professionals and chronically ill patients could further compromise the patients’ understanding of their disease, also influencing their adherence behavior leading to potential complications [56]. Active patient engagement in all aspects related to the management of their health is crucial for fostering better disease knowledge and effective communication with healthcare professionals. While the accurate terminology on medication adherence disseminated by the review and the Italian translation of the ABC Taxonomy may have a positive impact on chronic patients’ self-efficacy and empowerment, future steps must involve effectively involving patients in the Taxonomy decision-making process. The increasing utilization of patient-reported outcomes (PRO) and health-related quality of life metrics (HR-QOL) in clinical practice and chronic conditions’ management needs a clear and unique vocabulary in questionnaires and/or surveys. Any intervention directed to the improvement of patient health literacy and the capability to communicate about health conditions, disease symptoms and progression, and drug prescriptions, could help the achievement of trust in clinicians and their prescribed therapies. In this way, more “expert” patients could gain self-efficacy, which represents an essential skill to effectively manage their condition, organizing and implementing a set of actions needed to cope effectively with complex therapeutic regimens and, through the activation of cognitive, emotional, relational, and behavioral resources, gain empowerment, acquiring an active and mature role in controlling future events and expectations [57]. As the research on medication adherence has evolved over time, it has encompassed various areas including biomedical, technological, sociological, and behavioral perspectives, each with its own distinct concepts [21]. Given that the ABC Taxonomy is a widely recognized model that views adherence as a process with specific phases, it would be worthwhile to extend this terminology beyond the medical and pharmaceutical fields to the behavioral realm. Reaching one shared and common terminology to foster adherence can also play a pivotal role in case of different types of prescriptions. In this regard, the literature has already unveiled improved medical and functional outcomes when the patient shows a satisfactory adherence to non-pharmacological treatments, such as interventions focusing on rehabilitation (e.g., physical and/or cognitive) or promotion of a positive lifestyle (e.g., no smoking, limited use of alcohol) [58,59,60,61,62]. Thus, as a future recommendation, there is the suggestion to explore the use of a standardized and shared terminology of all facets of adherence, and also in the case of behavioral treatment. Hence, a shared and standardized adherence terminology can influence how medication adherence behavior is understood, measured, and addressed in clinical practice. Choosing the right terminology and understanding the nuances of each term can help healthcare providers and researchers more accurately assess medication adherence and develop effective strategies to improve it. In this regard, ABC Taxonomy could be a promising model that should be validated and further explored to support and foster adherence to psychological/psychotherapeutic prescriptions, as well as to other behavioral recommendations (e.g., lifestyle, rehabilitation interventions). ## 5. Strengths and Limits The present study has several strengths to pinpoint. We adopted a systematic approach to identify eligible experts to include in the survey. Thereafter, the conduct of the systematic review in accordance with the PRISMA Statement also allowed the unveiling of all Italian adherence-related terms present in the literature so far, which were included in the Delphi questionnaire. Thus, a validated methodology was used to perform the survey for reaching consensus, the Delphi approach, already tested elsewhere in order to guarantee the validity and comparability of results [63]. However, certain limitations must be recognized. First, the main point of debate is related to the nature of the Delphi technique, in particular concerning its reliability and validity [25]. An example is that terms and definitions rated with <$10\%$ acceptance were excluded and we cannot theoretically exclude that these discarded voices might have won the consensus process in a later round. Moreover, if the response rate had been higher and more varied, it is not certain that we would have received the same results. This issue was already discussed and could be overcome by considering Lincoln and Guba’s criteria for qualitative studies which are credibility (truthfulness) [64], fittingness (applicability), auditability (consistency), and confirmability. Regarding the validity concern, the involvement of participants who have recognized expertise in the same topic may help with increasing the Delphi content’s validity [65], and the use of consecutive rounds can help to increase the concurrent validity. Nonetheless, it has to be stated that the results’ validity will be ultimately affected by the response rates. ## 6. Conclusions This study provides the Italian-translated ABC Taxonomy on Medication Adherence obtained through a multi-step standardized process involving Italian experts. The Italian Taxonomy is intended to be used in the research, academic, and professional fields in order to harmonize adherence terminology and avoid confusion in comparing research findings. 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--- title: Improvement of Estrogen Deficiency Symptoms by the Intake of Long-Term Fermented Soybeans (Doenjang) Rich in Bacillus Species through Modulating Gut Microbiota in Estrogen-Deficient Rats authors: - Ting Zhang - Yu Yue - Su-Ji Jeong - Myeong-Seon Ryu - Xuangao Wu - Hee-Jong Yang - Chen Li - Do-Youn Jeong - Sunmin Park journal: Foods year: 2023 pmcid: PMC10048008 doi: 10.3390/foods12061143 license: CC BY 4.0 --- # Improvement of Estrogen Deficiency Symptoms by the Intake of Long-Term Fermented Soybeans (Doenjang) Rich in Bacillus Species through Modulating Gut Microbiota in Estrogen-Deficient Rats ## Abstract Traditionally made doenjang (TMD) produced by the long-term fermentation of soybeans with salt may improve symptoms of estrogen deficiency. We aimed to evaluate the effects of four TMD types, containing low and high amounts of Bacillus species and biogenic amines (HBHA, HBLA, LBHA, and LBLA), on energy, glucose, and lipid metabolism, by altering the gut microbiota in estrogen-deficient ovariectomized (OVX) rats. Their mechanisms were also examined. The OVX rats were divided into the control, cooked soybean (CSB), HBHA, LBHA, HBLA, and LBLA groups. Sham-operated rats were the normal control group. Serum 17β-estradiol concentrations were similar among all OVX groups. Tail skin temperatures, which are indicative of hot flashes, were higher in the control than the HBHA and HBLA groups and were similar to the normal control group. Weight gain and visceral fat mass were lower in the TMD and CSB intake groups but not as low as in the normal control group. Lean body mass showed a trend opposite to that of visceral fat in the respective groups. The hepatic triglyceride content decreased with the TMD intake compared to the control and CSB groups. mRNA expressions of the peroxisome proliferator-activated receptor-γ (PPAR-γ) and carnitine palmitoyltransferase-1 in the TMD and CSB groups were as high as in the normal control group, and the PPAR-γ mRNA expression was more elevated in the HBLA group than in the normal control group. The morphology of the intestines improved in the TMD groups compared to the control, and the HBHA and HBLA groups showed an enhanced improvement compared to the CSB group. The HBHA, HBLA, and LBHA groups increased the α-diversity of the cecal microbiota compared to the control. Akkermenia and Lactobacillus were higher in the HBLA and LBLA groups compared to the control. The expression of the estrogen, forkhead box proteins of the class-O subgroup, and insulin-signaling pathways were lower in the control group, and HBHA and HBLA prevented their decrement. In conclusion, long-term treatment with TMD containing high amounts of Bacillus potentially improves estrogen deficiency symptoms more than unfermented soybeans. ## 1. Introduction Globally, life expectancy has increased by more than six years, according to the most current data released by the World Health Organization (WHO) [1]. The mean menopausal age is 52 and 50 years in European and Asian women, respectively, and women live for 30 years after menopause [2]. Although women have much higher levels of body fat than men, they have a lower premenopausal risk of metabolic diseases [3]. However, estrogen deficiency markedly disturbs energy, glucose, lipid, and bone metabolism to induce abdominal obesity, hyperglycemia, hypertension, dyslipidemia, and osteoporosis [3]. Hormone replacement therapy (HRT) offers protection against metabolic diseases but it has some adverse effects [4]. Thus, there is a constant effort to find alternative therapies for managing menopause and its related effects. Soybeans contain high concentrations of isoflavonoids that act as phytoestrogens by mimicking estrogen in the body through their action on the estrogen receptors [5]. Soybean intake has been reported to alleviate menopausal symptoms, but the effects vary individually. A factor influencing the response to soybean intake is the gut microbiota composition of individuals [6,7]. Over $50\%$ of isoflavonoids are glycated forms, such as daidzin, genistin, and glycitin. The glucose moiety is removed during digestion to produce the aglycone forms, daidzein, genistein, and glycitein [8]. Isoflavonoid aglycones have better phytoestrogen activity than glycated isoflavonoids. Equol, which is produced by intestinal bacteria from daidzein, is an isoflavone-derived metabolite with high estrogenic and antioxidant activity [9]. Fermented soybeans contain more isoflavonoid aglycones, thus producing more equol than unfermented soybeans [10]. Traditionally made doenjang (TMD) has been commercialized, and its quality is regulated by the Korean Food and Drug Administration to prevent harmful contamination. However, TMD contains different bacterial compositions and bioactive compounds such as isoflavonoids and biogenic amines. Over 20 TMD types have been collected to measure the bacterial composition and biogenic amines, and they are categorized into four types according to Bacillus and the biogenic amine contents. Each commercialized TMD has consistent bacterial and bioactive compositions, although, different ones have been shown to have different compositions and health benefits. Studying the differential effects of TMD on energy, glucose, and lipid metabolisms is imperative. However, no studies have determined the health benefits of different TMD types. It has been demonstrated earlier that Bacillus spp. in traditionally made kanjang, the salty water component produced after soybean fermentation, can improve estrogen deficiency symptoms in ovariectomized (OVX) rats. The intake of traditionally made kanjang with high Bacillus species (spp.) but not biogenic amine contents affects the gut microbiota composition to improve energy, fatty acid biosynthesis, and bile acid metabolism in estrogen-deficient rats [11]. Therefore, the bacteria and biogenic amines contained in TMD could also affect estrogen deficiency symptoms by altering the isoflavonoid content and gut microbiota composition. However, no studies show how TMD intake may alleviate menopausal symptoms, including hot flashes, obesity, hyperglycemia, and dyslipidemia. The present study aimed to determine the levels of biogenic amines and Bacillus spp. in different TMD samples and reveal how they influence the disturbance of energy, glucose, and lipid metabolism differently by altering gut microbiota in estrogen-deficient rats generated with ovariectomy (OVX). The novel results show that different contents of Bacillus and biogenic amines modulate estrogen deficiency symptoms, including hot flashes and energy, lipid, and glucose disturbance, ands are related to improving insulin and estrogen signaling of the metagenome function in the gut microbiota of OVX rats. Furthermore, TMD with high Bacillus prevented estrogen deficiency symptoms the most, and biogenic amine contents in TMD did not influence the metabolic improvement in OVX rats. ## 2.1. TMD Production Process and Sample Collection Five different batches of TMD samples from different provinces in Korea were collected. Five or more samples from each TMD batch were collected and assayed for bacteria counts and bioactive compounds. TMD in Korea is generally made in a two-stage process: First, meju is prepared by crushing boiled soybeans and then fermenting them in rice straw at about 20–25 °C for about 40–50 days. The second stage is fermentation in 16–18 Brix salt water for 40–60 days, and the solution is separated with the liquid fraction being kanjang and the solid fraction being TMD. A preliminary study measured sodium, beneficial and harmful bacteria, and the biogenic amine contents in TMD. Based on these data, four TMD products were selected for the in vivo trial to evaluate their effectiveness in alleviating menopausal symptoms. The selection criteria were based on the levels of beneficial bacteria and biogenic amines, as follows: [1] high beneficial bacteria plus low biogenic amines (HBLA), [2] high beneficial bacteria plus high biogenic amines (HBHA), [3] low beneficial bacteria plus low biogenic amines (LBLA), and [4] low beneficial bacteria plus high biogenic amines (LBHA). The sodium content in the TMD was measured using inductively coupled plasma atomic emission spectroscopy (ICP-AES; Thermo IRIS Intrepid II XDL, Waltham, MA, USA) after proteins were digested with nitric acid according to the guidelines of the Korean Ministry of Food and Drug Safety (MFDS). The oxygen and acetylene flows were 10.00 L/min and 2.50 L/min, respectively. The flame type was air acetylene, and the wavelength was 589.0 nm. TMD was mixed with methanol and filtered to measure sodium contents in the TMD. The isoflavonoid content in the filtrates was determined by high-performance liquid chromatography (HPLC) (Agilent 1200 series, Agilent Technologies, Santa Clara, CA, USA) equipped with a Shiseido UG 120 (4.6 × 250 mm, 5 μm, Osaka, Japan) column. The mobile phase was a mixture of acetonitrile and water (25:75, v/v; J.T. Baker, Philadelphia, PA, USA), the flow rate was 1.0 mL/min, the injection volume was 10 μL, and a fluorescence detector (FLD) was used (excitation: 360 nm, measurement 450 nm; Agilent Technologies, Santa Clara, CA, USA). The standards of isoflavonoids such as daidzein, daidzin, genistein, genistin, glycetein, and glycetin were purchased from Sigma-Aldrich (St. Louise, MO, USA). The contents of the biogenic amines, such as histamine and tyramine, were determined as described previously [12]. TMD was mixed with an internal standard, 1,7-diaminoheptane (0.1 g/L, Sigma-Aldrich), and it was added into a saturated sodium carbonate (Na2CO3) (Sigma-Aldrich) solution and $1\%$ dansyl chloride (Sigma-Aldrich) to make derivatives. Standards (histidine and tyramine) were prepared with 0.1–100 mg/L concentrations in a 0.01 N HCl solution. Ethyl ether (Samchun, Seoul, Republic of Korea) was mixed with the solution for 3 min, and the supernatants were separated and dissolved in acetonitrile (Duksan, Seoul, Republic of Korea). The biogenic amine contents were then measured by high-performance liquid chromatography (HPLC) analysis with a Cepcell Pak C18 column (2.0 × 250 mm) [12]. As mentioned below, the proportion of bacteria in TMD was calculated after carrying out bacterial measurement by the next-generation sequencing (NGS) method. The beneficial and pathogenic bacteria were designated according to the bacteria characteristics from previous studies [13,14]. ## 2.2. Ovariectomy Procedure Seventy female Sprague Dawley rats (aged 6 weeks, 167 ± 10 g) were purchased from DBL (Yeumsung-Kun, Republic of Korea) and were acclimated for one week in the animal facility at Hoseo University. The rats were housed in individual stainless-steel cages (23 °C, with a 12 h light/dark cycle). This study was conducted according to the Guide for the Care and Use of Laboratory Animals, funded by the National Institutes of Health (NIH) in the USA and with the approval of the Hoseo University Animal Care and Use Committee [2014-03]. The rats were anesthetized with subcutaneous injections of a ketamine/xylazine mixture (100 and 10 mg/kg body weight) during the ovariectomy (OVX) procedure [11]. After a mid-ventral incision, each ovary was removed by ligating the most proximal part of each oviduct, and both ovaries were dissected with scissors. The OVX groups included ten OVX rats per group, and ten rats had a sham operation. ## 2.3. Experimental Design Figure 1 presents the experimental design. OVX rats were orally provided a diet including either 4 different types of lyophilized TMD according to Bacillus and biogenic amine contents to determine the different TMD effects on estrogen deficiency symptoms. Each group was provided with either 4 different TMDs, unfermented soybeans, or a diet containing no soybeans or TMD for the normal control group. TMD or soybeans were incorporated into the diet, and the control diet contained an equivalent nutrient composition to the TMD or soybean diets by adding nutrients to the soybeans. Sixty OVX rats were randomly divided into groups as follows: [1] control, [2] lyophilized TMD with high Bacillus and low biogenic amines (HBLA), [3] high Bacillus and high biogenic amines (HBHA), [4] low Bacillus and low biogenic amines (LBLA), [5] low Bacillus bacteria and high biogenic amines (LBHA), and [6] cooked soybeans (CSB). Sham-operated rats were considered the normal control group and had a nutritionally equivalent diet as the other groups, but without soybean or TMD. During the 12 weeks of dietary intervention, the rats fasted overnight, and their food and water intake and body weight were measured at 10 am every Tuesday. At the end of the 12-week intervention, the rats were anesthetized with ketamine and xylazine, and blood was collected from the portal vein and the vena cava. The liver, abdominal fat, uterine, skeletal muscles in the legs, and feces in the cecum were dissected and stored in a −70 °C freezer. ## 2.4. Diet Preparation All the groups were fed high-fat diets to exacerbate the menopausal symptoms in the OVX rats [15,16,17]. In a semi-purified method, the diet was generated based on an AIN 93 formulation for experimental animals with or without four different types of TMD or CSB. The primary sources of carbohydrates, proteins, and fats were starch plus sugar, casein, and lard (CJ Co., Seoul, Republic of Korea), respectively. The TMD or soybeans dosage was $8.5\%$ in each diet according to the sodium contents designated in AIN 93 formulation. According to the nutrient composition of TMD, the contents of carbohydrates, protein, fat, and sodium were subtracted from diet composition to make their contents equivalent among all diets. When adding corresponding lyophilized TMD in HBHA, HBLA, LBHA, and LBLA, or CSB, the amounts of carbohydrates, protein, fat, and sodium in TMD or CSB were removed from starch, casein, soybean oil, and the mineral mixture from the corresponding group. The carbohydrate, protein, fat, and salt contents in each diet were 39.5, 17.1, 43.4 energy percent (EN%), and 5.9 g salt/kg diet, respectively. Each TMD powder was thoroughly blended into the vitamin and mineral mixture without sodium and sugar and sifted to remove lumps. The vitamin and mineral mixtures were mixed with the designated amounts of starch, casein, and lard and were then resifted. Each group was given a diet with an equivalent primary nutrient composition. ## 2.5. Tail Skin Temperature Rat tail skin temperatures and surrogate measures of hot flashes in menopausal women were measured weekly during the sleep cycle using an infrared thermometer for small rodents (BIO-152-IRB, Bioseb, Chaville, France) three times and 10 min apart [17]. ## 2.6. Fat and Skeletal Muscle Composition After anesthetization, the rats were laid in a prone position with posterior legs with 90° flexion of the knee, hip, and ankle. Abdominal fat and lean body mass (LBM) were determined in the leg, abdomen, and hip upon the completion of scanning of the body using the dual-energy X-ray absorptiometer instrument (DEXA; Norland pDEXA Sabre; Norland Medical Systems Inc., Fort Atkinson, WI, USA) equipped with the appropriate software for the assessment in small animals [18]. ## 2.7. Insulin Resistance and Lipid Profiles The homeostasis model assessment estimate for assessing insulin resistance (HOMA-IR) was used to estimate insulin resistance according to the following equation:HOMA_IR = fasting insulin (µIU/mL) × fasting glucose (mM)/22.5. Serum glucose and insulin concentrations were measured using a Glucose Analyzer II (Beckman-Coulter, Palo Alto, CA, USA) and radioimmunoassay kits (Linco Research, Billerica, MA, USA). Serum 17β-estradiol levels were measured by ELISA (Enzo Life Sciences, Farmingdale, NY, USA). Serum lipid profiles were assayed using colorimetry kits for total cholesterol, HDL cholesterol (HDL-C), and triglycerides (Asan Pharmaceutical, Seoul, Republic of Korea). Serum LDL-C concentrations were calculated from the serum lipid concentrations using the Friedewald equation (LDL-C = total cholesterol − HDL-C − triglycerides/5). We determined serum tumor necrosis factor (TNF)-α and lipid peroxide (malondialdehyde) concentrations using the TNF-α ELISA kit (R & D Systems, Minneapolis, MN, USA) and lipid peroxide ELISA kit (Abcam, Cambridge, UK). ## 2.8. Gene Expression by the Real-Time PCR Method Total RNA was extracted by mixing the liver pieces with phenol/guanidine isothiocyanate monophasic solution (TRIzol reagent; Gibco-BRL, Rockville, MD, USA) according to the manufacturer’s instructions. Equal amounts of the total RNA were used to synthesize cDNA using Superscript III reverse transcriptase. A polymerase chain reaction (PCR) was implemented using high-fidelity Taq DNA polymerase. cDNAs were equally added to the SYBR Green mix (Bio-Rad, Richmond, CA, USA) along with primers for specific genes using a real-time PCR instrument (Bio-Rad) under optimal conditions for thermal cycling. The expressions of the genes of interest were normalized to that of the β-actin gene. The mRNA expressions of peroxisome proliferator-activated receptor gamma (PPAR-γ), sterol regulatory element-binding protein 1 (SREBP-1c), and carnitine palmitoyltransferase I (CPT-1) were determined using corresponding primers, as described previously [19]. A cycle of threshold (CT) for each sample was assessed using a real-time PCR method. Expression levels of the islet genes were quantitated using the comparative CT method (ΔΔCT method). ## 2.9. Histology of the Large Intestines After dissecting the large intestines, the rats were sequentially perfused with saline and a $4\%$ paraformaldehyde solution (pH 7.2). The large intestinal tissues were immediately dissected and post-fixed with $4\%$ paraformaldehyde overnight at room temperature [20]. Two serial 5 μm paraffin-embedded large intestine sections were randomly chosen, and they were stained with hematoxylin–eosin (H-E) and Alcian blue–perchloric acid (PAS). After staining, the area of the intestinal villi in the H-E stained sections was measured using a Zeiss Axiovert microscope (Jena, Germany) with the DIXI Imaging Solution at 10× magnification. The length and width of the villi, the height of the crypt, and the impaired cells were counted in the H-E section. The relative area of the impaired cells was scored 0–3 as 0 (none or minimal), 1 (mild), 2 (moderate), and 3 (severe). The percentage of intestinal goblet cells producing mucin, indicated by blue staining, was calculated using the Alcian blue–PAS-stained sections. ## 2.10. Serum Short-Chain Fatty Acids (SCFA) Concentrations and Gut Microbiome Serum was separated from the portal vein blood and mixed with ethanol (Duksan, Republic of Korea). A total of 1N HCl was blended into the mixture (100:1) and centrifuged at 15,000 rpm, for 15 min, at 4 °C. SCFA concentrations in the supernatants were assayed using a gas chromatograph (Clarus 680 GAS, PerkinElmer, Waltham, MA, USA) equipped with an Elite-FFAP 30 m × 0.25 mm × 0.25 μm capillary column. Helium was used as the carrier gas at a flow rate of 1 mL/min [21]. Exogenous acetate, propionate, and butyrate (1 mM; Sigma Co., St. Louis, MO, USA) were used as the external standards. Metagenome sequencing used the NGS procedures to investigate TMD and fecal microbiome communities from the cecum [16]. According to the manufacturer’s instructions, bacterial DNA was extracted from the cecal feces using a Power Water DNA Isolation Kit (Qiagen, Valencia, CA, USA). DNA was amplified with 16S amplicon primers by PCR, and libraries were prepared for PCR products according to the GS FLX plus library prep guide, as described previously [22]. According to the manufacturer’s instructions, the PCR amplification program was run with 16S universal primers in the FastStart High-Fidelity PCR System (Roche, Basel, Switzerland). The bacterial DNA of the cecal feces was sequenced using the Illumina MiSeq standard operating procedure and a Genome Sequencer FLX plus (454 Life Sciences) (Macrogen, Seoul, Republic of Korea). The 16S amplicon sequences were processed using Mothur v.1.36 package. The Miseq standard operation procedure was used to identify cecal bacterial taxonomy, and bacterial counts were conducted on each fecal sample. Sequences were aligned using the Silva reference alignment v.12350, and bacteria counts and identifications for all taxa were determined as described previously [21,22]. Relative bacteria counts were calculated in the taxonomic assignment order for each sample. PCoA results for gut bacteria were visualized using the R package (Vienna, Austria). ## 2.11. Metabolic Functions of the Gut Microbiomes by PICRUSt2 Pipeline Analysis Metabolic functions of gut microbiota were predicted from the FASTA files and count tables of fecal bacteria using the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUSt2) software [23]. Metabolic functions were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthologues (KO) and mapped by the KEGG mapper (https://www.genome.jp/kegg/tool/map_pathway1.html, 9 September 2022) [22]. The gut microbiome was used to explore the differences in the metabolic functions among the groups. ## 2.12. Statistical Analysis SAS software version 7 (SAS Institute, Cary, NC, USA) was used for statistical analysis. The optimal sample size was evaluated using a G power program (power = 0.90 and effect size = 0.5), and the calculated sample size was 10 per group. Results are expressed as means ± standard deviations (SD) when the results were normally distributed as confirmed using the Proc univariate procedure. Measurements were statistically analyzed using one-way ANOVA. The significant differences among the groups were assessed using Tukey’s test, and differences were considered significant at $p \leq 0.05.$ ## 3.1. Characteristics of TMD according to Bacillus spp. and Biogenic Amine Concentrations TMD was fermented with about 4–$5\%$ salts, and the predominant bacteria in the TMD was the Bacillus spp. ( Table 1). However, the bacterial compositions were disparate among different TMD samples due to the varying average temperatures during the year and the salt content. The amounts of biogenic amines and sodium were different according to the bacterial compositions. We chose four different doenjang varieties containing biogenic amines and Bacillus content. The water content of the four TMD products was about $50\%$ (50–$59\%$). Two TMD products were rich in Bacillus spp. ( HB) and contained high amounts of biogenic amines (histamines and tyramine; HA) (Table 1). The bacterial compositions of the TMD samples measured by NGS are present in Figure 2. The bacterial contents varied among different TMD samples, and they were lower in the order of LBHA, HBHA, HBLA, and LBLA. The differences might be linked to the environmental conditions when making doenjang. Furthermore, HBHA and HBLA contained $95\%$ beneficial bacteria, primarily Bacillus spp. The bacteria contents in the CSB were not included since it was not expected to contain bacteria after boiling soybeans. ## 3.2. Isoflavonoid Contents LBHA, HBHA, and HBLA contained isoflavonoid aglycones such as daidzein, genistein, and glycitein but not glycated isoflavonoids such as daidzin, genistin, and glycitin. The results indicated that glycated isoflavonoids were converted into isoflavonoid aglycones (Table 1). However, LBLA contained fewer total isoflavonoids and isoflavonoid aglycones than the other TMD samples (Table 1). ## 3.3. Uterine Weight, Serum 17β-Estradiol Levels, and Tail Skin Temperature Due to ovariectomy, the uterine weight and serum 17β-estradiol concentrations were much lower in OVX compared to sham rats, and they was unaffected by TMD. The HBHA group showed a marginal increment in uterine weight and a non-significant increase in serum 17β-estradiol concentrations compared to the control (Table 2). Low estrogen in the OVX rats induced a higher tail skin temperature, and intake of TMD lowered it, and HBLA and HBHA intakes lowered the tail skin temperature to that of the normal control group (Figure 3A). Body weight gain during the 12-week intervention was higher in the control than in the normal control group and was lower in the LBHA, HBHA, and HBLA groups than in the control group (Table 2). Food intake tended to be higher, but not significantly, in the control than the normal control group, whereas the TMD and CSB interventions did not alter the food intake. Food efficiency was much lower in the normal control than the control group, and the TMD and CSB interventions did not affect it (Table 2). Uterine and retroperitoneal fat representing visceral fat mass (weight %) was higher in the control than the normal control group and was lower in the TMD groups compared to the control, except in the LBHA group—the visceral fat mass in the three TMD groups viz. HBHA, HBLA, and LBLA were similar to the normal control group (Table 2). DEXA revealed that the lean body mass (LBM) in the hips and legs of the LBHA group was similar to the control. It was lower than those of the HBHA, HBLA, and LBLA groups (Figure 3B). The fat mass in the abdomen and legs showed opposite results to that of the LBM in all the groups (Figure 3C). These results suggested that LBHA decreased the lean body mass (LBM) and increased the fat mass. However, its intake did not elevate weight gain and it could reduce the LBM. ## 3.4. Insulin Resistance and Lipid Profiles Serum glucose concentrations in the fasting state were higher in the control group than in the normal control group. Fasting serum glucose concentrations of all TMD groups were at intermediate concentrations between the control and normal control groups, and all but the LBHA were significantly lower than the control group. At 2 h after food intake, serum glucose concentrations showed a similar trend to fasting serum glucose concentrations in all groups (Table 3). Fasting serum insulin concentrations were much higher in the control than in the normal control group and were lower in the HBHA, HBLA, and CSB groups. The concentrations in the HBLA and CSB groups were similar to the normal control group (Table 3). HOMA-IR, an indicator of insulin resistance, was much higher in the control than the normal control and the four TMD intake groups. The HOMA-IR in the HBLA and CSB groups was similar to the normal control group (Table 3). The total cholesterol, HDL and LDL cholesterol, and triglyceride concentrations were elevated beyond the recommended range in the control compared to the normal control group. The TMD intake improved the lipid profiles compared to the control, and those in the HBHA, HBLA, and CSB groups were similar to the normal control group (Table 3). Serum LDL concentrations were lower in all TMD groups than not only the control but also the normal control group. ## 3.5. Lipid Metabolism in the Liver Estrogen deficiency increased the serum glutamic oxaloacetic transaminase (GOT) and glutamic pyruvic transaminase (GPT) activities compared to the normal control group, and the TMD intake prevented the increase in OVX rats (Table 4). Hepatic glycogen storage decreased in the control group compared to the normal control group, and it was similar in the HBHA and HBLA groups to that seen in the normal control group (Table 4). Liver triglyceride and cholesterol contents were higher in the control than in the normal control group. TMD intake lowered the hepatic triglyceride content to the levels seen in the normal control group. However, the hepatic cholesterol content was lower in LBHA and LBLA groups, but not as low as in the normal control group (Table 4). The hepatic lipid contents were associated with the mRNA expressions of PPAR-γ, SREBP-1c, and CPT-1. PPAR-γ mRNA expressions related to hepatic insulin resistance were lower in the control than in the normal control group. The TMD intake increased its expression to as much as in the normal control group (Table 4). The PPAR-γ mRNA expression in the HBLA group was higher than in the normal control group. Hepatic SREBP-1c mRNA expression involved in cholesterol synthesis was higher in the control than in the normal control group, and its expression decreased in the LBHA, HBHA, HBLA, and CSB groups. The decrease in the expression of the hepatic SREBP-1c mRNA in the HBLA and the CSB groups was similar to that of the normal control group (Table 4). The hepatic CPT-1 mRNA expression was lower in the control than in the normal control group, and an increase in expression was seen in HBHA, which is similar to its expression in the normal control group (Table 4). ## 3.6. Histology of the Large Intestines The height of the villi of the large intestines was shortened in the control group compared to the normal control, and the TMD intake prevented the decrease. However, the increase in the villus height in the LBHA intake group was not as much as that seen in the normal control group (Figure 4A,B). The changes in the villi width were opposite to those in height. The villi width was much higher in the control than in the other groups, with HBHA being the lowest (Figure 4A,B). The crypt of the intestines was also smaller in the control group compared to normal controls, which is similar to the HBHA, HBLA, and LBLA groups (Figure 4A,B). The crypt in the CSB group was not significantly different from the control (Figure 4A). The control group had much fewer mucin-producing goblet cells than the normal control group (Figure 4C,D). Interventions with TMD increased the number of mucin-producing goblet cells similar to that of the normal control group. HBHA and HBLA increased the number more than the normal control group (Figure 4C,D). The cell damage in the large intestinal tissues was also more severe in the control than in the normal controls, and the TMD interventions prevented the damage and were similar to the normal control group (Figure 4D). However, cell damage in the CSB was more severe than that seen in the normal control group. ## 3.7. SCFA in the Portal Vein and Gut Microbiota Portal vein acetate concentrations seemed to be lower in HBLA and CSB than in the control ($$p \leq 0.07$$), but there were no significant differences across all the groups (Figure 5A). Propionate concentrations did not differ significantly across all the groups. However, the butyrate concentrations were much lower in the control than the normal control, and they were highest in the HBLA and CSB groups and lowest in the LBHA and LBLA groups (Figure 5A). The alpha diversity, determined by the Chao1 and Shannon indexes, was lower in the control compared to the normal control group, and the LBHA, HBHA, and HBLA interventions prevented their decrease (Figure 5B,C). However, LBLA and CSB intakes did not improve the α-diversity. A study of the β-diversity showed that the bacteria in the control and normal control groups and those in the TMD groups were distinct and separate (Figure 5D). At the phylum level, the relative abundance of Firmicutes was higher in the control than the normal control and LBLA groups (Figure 5E). The relative abundance of Verrucimicrobia was higher in the LBLA and normal control groups than in the other groups, but that of Proteobacteria was also higher in the LBLA than other groups. At the genus level, the relative abundance of Blutia was higher, but Romboutsia was lower in the control compared to the other groups. The relative abundance of Akkermentia was higher in the LBLA and normal control groups than in the control (Figure 5F). The bacterial composition in the LBLA group was quite different from those of the other groups. The relative abundance of Clostridium and Escherichia was also higher in the LBLA than in the other groups (Figure 5F). At the genus level, the relative abundance of bacteria in the HBLA and HBHA was similar to that in the normal control group. CSB altered the gut microbiota composition as much as TMD (Figure 5F). ## 3.8. Metagenome Analysis of Fecal Bacteria The estrogen-signaling pathway was much more suppressed in the controls than in the normal controls. TMD and CSB increased it, and HBLA elevated it the most in the metagenome analysis of cecal bacteria by Picrust2 (Figure 6A). Steroid biosynthesis was also lower in the control than in the normal control. It increased with the HBLA and LBLA groups (Figure 6A). However, the cAMP-signaling pathway showed the opposite trend as the estrogen-signaling pathways (Figure 6A). The longevity-regulating pathway was lower in the control than the normal control and TMD, especially LBLA, tended to increase (Figure 6A). The insulin-signaling pathway and its related pathway (FoxO signaling) were lower in the control than the normal control. They increased in TMD groups, especially HBLA and LBLA (Figure 6B). However, the glycolysis and gluconeogenesis pathway to increase glucose production was higher in the control than in the normal control and decreased in the LBLA and HBLA groups. A non-alcoholic fatty liver disease involved in hepatic insulin resistance was also higher in the control than the normal control and decreased in the TMD groups, especially LBLA (Figure 6B). These results suggested that gut microbiota in the HBLA and HBHA groups were linked to hepatic lipid metabolism. ## 4. Discussion TMD and traditionally made kanjang comprise the soybean solids and liquid components of fermented soybeans in salty water, respectively. While the effects of TMD supplementation on obesity and blood glucose have been well-researched in previous animal and human studies [24,25,26], the presence of various bacteria and bioactive compounds in TMD may result in varying efficacies with different TMD samples. As seen in earlier studies, TMD primarily contains several beneficial bacteria, including Bacillus, Lactobacillus, Pediococcus, and Weissella spp. [ 14]. However, some varieties of TMD contain small amounts of unhealthy bacteria, such as Enterobacter sakazakii, Acinetobacter baumannii, and Proteus mirabilis, and compounds, such as biogenic amines [14]. Acinetobacter baumannii is an opportunistic nosocomial pathogen with multi-drug resistance, biofilm formation, and motility, and it can infect the host [27,28]. Proteus mirabilis also has similar activities to Acinetobacter baumannii in animals [29]. Enterobacter sakazakii is also an opportunistic foodborne pathogen that can induce necrotizing enterocolitis, bacteremia, and meningitis [30]. However, the amounts of Enterobacter sakazakii, Acinetobacter baumannii, and Proteus mirabilis were small, and the animals did not show infectious disease symptoms in any groups in the present study. Therefore, TMD, regardless of Bacillus and biogenic amine contents, was shown to be a safe food, and TMD with high Bacillus spp. alleviated estrogen-deficient symptoms in OVX rats. However, the results need to be confirmed in a clinical study. Cooked soybeans include isoflavonoid glycones and aglycones, and their fermentation with Bacillus spp. was shown to change the isoflavonoid glycones to isoflavonoid aglycones in a previous study [31]. In the present study, LBHA, HBHA, and HBLA did not contain isoflavonoid glycones and they had increased amounts of isoflavonoid aglycones compared to CSB. However, LBLA contained isoflavonoid glycones, indicating that fermentation was not sufficient. Soybeans fermented for short periods, such as chungkookjang made by traditional methods or by fermentation with Bacillus amyloliquefaciens, have decreased isoflavonoid glycones and increased isoflavonoid aglycones. Isoflavonoid aglycones have a better efficacy than isoflavonoid glycones in human intestines [32], and specifically, daidzein can be potentially converted into equol with potent estrogenic activity [9]. Therefore, the predominant bacillus species and the duration of fermentation determine the conversion of isoflavonoid glycones into isoflavonoid aglycones. Some TMDs contain biogenic amines produced by amino acid decarboxylation by bacteria. The biogenic amines include tryptamine, 2-phenyl–ethylamine, putrescine, cadaverine, agmatine, histamine, tyramine, spermidine, and spermine, ranging within 18–245 mg%. The primary ones are histamine and tyramine in doenjang [33]. Biogenic amines can be toxic, and they need to be controlled. The present study demonstrated that TMD with high biogenic amines contained about 109–165 mg% for tyramine and 50–63 mg% for histamine, and their intake did not show harmful effects on estrogen deficiency symptoms in an animal model. Previous studies have demonstrated the reduction in biogenic amines in fermented soybean foods [14,34], but their contents in TMD might not be detrimental to the metabolism. The biogenic amine contents might be influenced by fermentation conditions such as temperature, sodium contents, bacterium types, fermentation periods, and others. Previous studies have demonstrated that the Bacillus spp., especially Bacillus licheniformis, degrades biogenic amines [35,36]. The present study showed that HBLA mainly contained Bacillus spp., but HBHA included high Bacillus spp. and other bacteria (Leuconostoc mesenteroides, Pediococcus acidilactici, and Weissella confusa) as well. The results suggested that TMD containing mostly Bacillus spp. might be lower in biogenic amines. Estrogen deficiency leads to peripheral vasodilation, which causes hot flashes and excessive sweating in the face, neck, and chest [37]. A hot flash results from elevated central sympathetic activation through the α2-adrenergic, serotonergic, and dopaminergic receptors and is ameliorated by modulating its activation [38,39]. A high-fat diet and obesity may exacerbate menopausal symptoms, including hot flashes [40,41]. The present study used high-fat diets to exacerbate menopausal symptoms. Hot flashes measured by the tail skin temperature were elevated in OVX rats above the temperatures in sham-operated rats, whereas OVX rats fed HBHA and HBLA showed decreased tail skin temperatures, which was similar to the sham rats. However, OVX rats fed CSB and TMD exhibited intermediate tail skin temperatures between the control and normal control groups. Consistent with the present study, soy isoflavonoids have been shown to attenuate hot flashes in menopausal women [42,43]. Furthermore, previous studies have demonstrated that estrogen injections can alleviate hot flashes and reduce the selective serotonin reuptake inhibitor, selective serotonin-norepinephrine reuptake inhibitor, gamma-aminobutyric acid analog, and α-adrenergic receptor agonist [39]. Isoflavonoid aglycones, such as daidzein and genistein, act as partial selective estrogen receptor modulators for improving menopausal symptoms [44]. Estrogen acts as a regulator of energy metabolism, including energy intake and expenditure [45]. Post-menopause decreases energy expenditure and is consistently reported to be linked to decreased skeletal muscle mass [46]. It is related to decreased skeletal muscle mass and muscle dysfunction, which is caused by a decreased proliferation of muscle satellite cells and increased levels of inflammatory markers [47]. The present study showed that the estrogen-deficient (control group) rats exhibited an increased visceral fat and decreased lean body mass compared to those in the normal control group. TMD, especially HBHA and HBLA interventions, decreased the visceral fat mass and increased the lean body mass without increasing the serum 17β-estrogen concentrations. Tang et al. also demonstrated that soy foods prevent obesity and osteosarcopenia [48]. However, in the metagenome analysis of cecal microbiota, the estrogen-signaling pathways and steroid biosynthesis were elevated in the HBHA, HBLA, and LBLA groups compared to the control. Therefore, TMD interventions may improve energy metabolism via the gut metagenome. Menopause disrupts lipid metabolism, which causes serum dyslipidemia and fat deposits in the liver [49]. Consistent with previous research [48,49], our results also show a deterioration of lipid metabolism by elevating cholesterol and triglyceride biosynthesis and decreasing lipid utilization in the liver in OVX rats. HBHA and HBLA prevented the disturbance of hepatic lipid metabolism, which could be linked to increased isoflavonoid aglycones and Bacillus subtitles in HBHA and HBLA. Soybean intake is one of the alternative therapies that is often recommended to improve menopausal symptoms and normalize energy, glucose, and lipid metabolism [48]. The isoflavonoids, oils, and proteins in soybeans are reported to decrease liver fat deposition by promoting adiponectin-mediated AMP-activated protein kinase-α pathways in rats that are fed high-fat and -cholesterol diets [50,51]. The present study also showed that CSB improved hepatic lipid metabolism compared to the control group, and HBHA and HBLA showed slightly better improvement in hepatic lipid metabolism than CSB. Furthermore, the TMD intake, especially HBHA and HBLA, was more effective in reducing the hepatic fat deposition by decreasing the fatty acid synthase activity and increasing the CPT-1 activity in the liver than unfermented soybeans, as shown in a previous study [52]. The gut microbiota influences various metabolic activities in the host. Menopause is associated with lower gut microbiota diversity, and there is a shift of the gut microbiota in post-menopausal women toward that observed in men [53]. It indicates that the action of estrogen could be linked with the gut microbiota, although this involvement remains inconsistent. Since the changes in the bioactive components in soybeans are also associated with the host’s gut microbiota, the effects of the soybean intake on the host metabolism are somewhat varied [7]. Long-term soybean consumption can modulate the gut microbiota to improve its effectiveness. Soybean intake is generally reported to increase the ratio of Firmicutes and Bacteoidetes, Bifidobacterium, and Lactobacilli and to decrease pathogenic bacteria [54]. This study also demonstrates that control rats had a lower α-diversity, and LBHA, HBHA, and HBLA prevented a further decrease in diversity. However, the impact on microbiota diversity with the intake of LBLA and CSB was lower compared to other TMDs. The intake of fermented soybeans has been reported to alter the gut microbiota to shift to Lactobacillus and Bifidobacterium as the predominant genera. However, the TMD intake has been shown to result in a decrease in the Firmicutes to Bacteroidetes ratio in the gut microbiota and a significant decrease in the abundance of Ruminococcaceae and Lachnospiraceae while that of Odoribacter increased [55]. We also showed changes similar to the study mentioned above, and Akkermentia increased in the LBLA and HBLA groups, which was similar to the normal control. Therefore, the improvements in energy and lipid metabolism seen with TMD intake, especially HBHA, and HBLA, might be linked to improvements in the hepatic fat metabolism, stimulation of the estrogen and insulin-signaling pathways by the gut microbiome, and increases in isoflavonoid aglycones. The limitations of the present study were that the [1] TMD was categorized into four types according to the contents of the beneficial bacteria and biogenic amine contents. However, some could not belong to the categories. [ 2] The reproducibility of the TMD could not be high in later studies, although we checked over five different batches, demonstrating the low variability of bacteria and biogenic amines in the same product. Despite the limitations, this study could give insight into alleviating menopausal symptoms by consuming TMD with high Bacillus spp. and Bacillus spp. that are rich in HBLA and HBHA, which could be developed as a starter for standardized doenjang in the future. In summary, HBLA and HBHA, among the TMD varieties, which were produced by the long-term fermentation of soybeans with salt, contained higher levels of isoflavonoid aglycones and Bacillus subtilus, but no opportunistic bacteria. In particular, HBLA contained mostly Bacillus subtilus, which increased isoflavonoid aglycones and low biogenic amines. The intake of HBLA and HBHA, including an abundance of Bacillus spp., ameliorated hot flashes and decreased the visceral fat mass and hepatic lipid deposition via the stimulation of the PPAR-γ and CPT-1 mRNA expressions. The improvement was marginally better than that of CBS but not significantly different. Moreover, the intake of TMD, especially HBLA, enhanced estrogen and insulin signaling, decreased cAMP-signaling pathways in the cecal microbiota, and improved intestinal morphology better than CSB. In conclusion, the long-term intake of HBLA and HBHA reduced hot flashes and restored energy and lipid metabolism homeostasis induced by estrogen deficiency, potentially better than CSB, via the modulating gut microbiota. TMD containing high levels of Bacillus spp. and isoflavonoid aglycones (HBLA and HBHA) can be used daily during cooking as a substitution for salt to prevent and ameliorate estrogen deficiency symptoms in women. 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--- title: How Soon Do Depression and Anxiety Symptoms Improve after Bariatric Surgery? authors: - Laura Aylward - Christa Lilly - Madeline Konsor - Stephanie Cox - Salim Abunnaja - Nova Szoka - Lawrence Tabone journal: Healthcare year: 2023 pmcid: PMC10048012 doi: 10.3390/healthcare11060862 license: CC BY 4.0 --- # How Soon Do Depression and Anxiety Symptoms Improve after Bariatric Surgery? ## Abstract Depression and anxiety are prevalent among bariatric surgery candidates, yet little is known about the course of symptoms after surgery. This study aimed to identify how soon changes in depression and anxiety occur after surgery. A retrospective review of patients treated at a university hospital was conducted. Participants attended a presurgical psychological evaluation, completed surgery, and attended follow-up visits with bariatric medical providers (2 weeks, 6 weeks, 3 months, and 6 months postoperatively). Depression and anxiety symptoms were assessed at all time points by the Patient-Reported Outcomes Measurement Information System (PROMIS) Depression and Anxiety. Generalized estimating equations models with repeated measures by person over time were used to examine change in depression and anxiety symptoms across time. Among 27 patients, anxiety (incident rate ratio (IRR) = 0.81, $$p \leq 0.04$$) and depression (IRR = 0.78, $$p \leq 0.05$$) significantly improved both 6 weeks and 3–6 months after bariatric surgery, after controlling for education, marital status, surgery type, age, and baseline body mass index. This is the first known study to show faster improvement in anxiety compared to depression after bariatric surgery. Understanding reductions in anxiety and depression symptoms may be important for postoperative care and timing of weight maintenance interventions. ## 1. Introduction Depression and anxiety are common comorbid concerns associated with being overweight or obese [1,2]. For those seeking bariatric surgery, it is best practice for affective symptoms to be assessed during the presurgical psychological evaluation [3]. Assessment of depression and anxiety symptoms is important because both uncontrolled and controlled affective symptoms have been associated with negative short- and long-term postsurgical outcomes [3,4,5,6]. Less research has been conducted on psychological factors post-surgically, therefore much less is known about the course of anxiety and depression symptoms after bariatric surgery. Depression has been shown to improve after bariatric surgery. In a sample of low-income patients who completed the Beck Depression Inventory (BDI) before and after surgery, depression significantly improved at 6 months post-surgery compared to baseline, and this effect continued 12 months post-surgery [4]. Similar results have been seen in other studies [5,6,7,8]. Effect size was reported in one study and demonstrated that improvement in depression from pre- to post-surgery had a large effect size [6]. A recent systematic review and meta-analysis revealed that depression improved following bariatric surgery regardless of surgery type, assessment tool for depression, and follow-up status [5]. In this review, follow-up time varied; however, the median was 24 months. When subgroup analyses were conducted at 6, 12, 24, 36, 48, and 60 months after surgery, depression was significantly lower at every time point compared to prior to surgery [5]. This indicated that the course of depressive symptoms appeared to be independent of specific surgery factors and warrants further investigation. Beyond the assessment of depressive symptoms, the prevalence of depressive disorders has also been shown to decrease after bariatric surgery [7]. Compared to depression, anxiety is a more prevalent psychiatric concern among bariatric surgery patients at the time of the psychological evaluation [2,9]. However, less is known about the course of anxiety after bariatric surgery [10]. In a systematic review that examined outcomes at and beyond 24 months after bariatric surgery [11], authors concluded that overall reductions in anxiety occurred ≥24 months afterwards. However, upon review of the results, the course of anxiety as an independent construct was difficult to determine. Of the eight studies the review included, at least four assessed anxiety through the Hospital Anxiety and Depression Scale (HADS) and only reported the total score, which also included a measurement of depression symptoms. The authors acknowledged that anxiety outcomes are less understood due to a lack of studies that examine anxiety separately from depression [11]. It has also been hypothesized that anxiety tends to be less impacted by bariatric surgery, and therefore the course remains similar before and after surgery [7,10]. For example, one study that assessed anxiety disorders (i.e., panic disorder, agoraphobia, social phobia, specific phobia, obsessive compulsive disorder, post-traumatic stress disorder, and generalized anxiety disorder) via the Structured Clinical Interview for DSM Disorders (Diagnostic and Statistical Manual of Mental Disorders) found no significant change in prevalence of anxiety disorders two years after bariatric surgery [7]. They also found that bariatric patients with diagnosed anxiety and depression prior to surgery lost significantly less weight [7]. This study differs from most, as it focused only on diagnosed mental health disorders rather than symptoms. Another study examined the course of anxiety, as measured by the HADS anxiety subscale, prior to surgery and 1, 2, and 3 years postoperatively [12]. Anxiety significantly decreased from before surgery to 1 year afterwards; however, given that the mean difference was one point, it is unclear if this study held clinically relevant meaning. Additionally, anxiety was not significantly different at baseline compared to 2 or 3 years after surgery [12]. Authors proposed that anxiety may be “a more weight-independent trait pattern” [12]. Currently, findings from studies evaluating the prevalence and course of anxiety symptoms after bariatric surgery are mixed. While some studies have looked at the course of depression and anxiety after bariatric surgery, studies have not asked how soon potential changes are observed. Most available data that demonstrates improvements in depression are reported around 6 months postoperatively. For example, one study showed that scores on the BDI significantly decreased from baseline to 20 weeks [13]. Given that depression and anxiety are rarely the primary outcome variables of bariatric studies [10,11], it is even less likely that studies are evaluating the timing of these potential affective changes. Current studies have primarily examined longer-term changes (i.e., >6 months); therefore, it is unknown if improvements are observed sooner. The aim of this study was to identify how soon after bariatric surgery changes in depression and anxiety symptoms occur. Because health behavior engagement can be adversely impacted by depression and anxiety symptoms [14,15,16,17,18,19], changes in affective symptoms may present opportunities to intervene and improve surgical outcomes [3]. More specifically, a reduction in depression symptoms may increase adherence and behavioral activation related to postoperative care. Relatedly, a reduction in anxiety symptoms may also contribute to greater feelings of self-efficacy and positive coping with postoperative body changes [3,14]. Identifying the timing of changes may provide opportunities for impactful change at postoperative visits. ## 2.1. Participants Study participants consisted of patients who underwent bariatric surgery and completed postoperative clinic visits. All data were collected as part of routine clinical care. Patients were eligible for surgery if they aged ≥18 years and presented with body mass index (BMI) ≥ 40 kg/m2 or BMI ≥ 35 kg/m2 with co-morbid medical conditions such as obstructive sleep apnea, type 2 diabetes, and/or hypertension. ## 2.2.1. Demographics Participants’ age, sex, race, educational attainment, employment status, and marital status were collected during the pre-surgical psychological evaluation (baseline). ## 2.2.2. Depression and Anxiety The National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information System (PROMIS) has short forms to assess depression and anxiety. Both are publicly available and written for an average reading level of first grade [20]. The PROMIS Depression Short Form (PROMIS-D; formally called LEVEL 2—Depression—Adult PROMIS Emotional Distress—Depression—Short Form) contains 8 self-report questions. Responses to the 8 items are summed to produce a raw total score. The questions ask respondents about the past seven days and how often the symptoms bothered them (i.e., never, rarely, sometimes, often, or always). Some items are “I felt worthless”, “I felt sad”, and “I felt hopeless.” The PROMIS Anxiety Short Form (PROMIS-A; formally called LEVEL 2—Anxiety—Adult PROMIS Emotional Distress—Anxiety—Short Form) contains 7 self-report questions, and scoring follows the same methods as for the PROMIS D. These questions also ask respondents about the past seven days and how often the symptoms bothered (i.e., never, rarely, sometimes, often, or always). Some items are “I felt anxious” and “I found it hard to focus on anything other than my anxiety”. For both questionnaires, higher scores indicate higher levels of said construct. In this study, depression and anxiety symptoms were assessed at all time points (baseline, 2 weeks, 6 weeks, 3 months, and 6 months postoperatively). Psychometric properties of both the PROMIS-D and PROMIS-A were previously examined among a sample of bariatric surgery candidates, and they showed good reliability, validity, and invariance [21]. In the present study, Cronbach’s alpha ranged from 0.84 to 0.96 for PROMIS-D and 0.88 to 0.93 for PROMIS-A. ## 2.2.3. Body Mass Index Data for objective BMI (participant height and weight) were extracted from the patient’s electronic medical record. ## 2.3. Analysis All analyses were conducted in SAS 9.4 (SAS Institute, Cary, NC, USA). Descriptive statistics included means and standard deviations for continuous variables and frequencies and valid percentages for categorical variables. Continuous variables were generally positively skewed. Generalized estimating equation (GEE) models were run with negative binomial distributions, log links, and repeated measures by person over time for all outcomes; estimates were exponentiated and interpreted as incident rate ratios. Some covariates (i.e., education, surgery type, marital status) were dichotomized to increase cell size count. Time 0 is baseline, Time 1 is 2 weeks post-surgery, Time 2 is 6 weeks post-surgery, and Time 3 is 3–6 months post-surgery. ## 3.1. Descriptive Statistics Demographic and descriptive variables are presented in Table 1. The majority of participants identified as female ($88.9\%$) and average age was 40.4 years. All participants identified as Caucasian, and a majority were married ($55.6\%$). Modal education level was some college and nearly all identified their work status as full-time ($92.6\%$). Most patients underwent laparoscopic sleeve gastrectomy ($76.9\%$). At baseline, average BMI was 48.13 kg/m2 (considered class III or “severe” obesity), PROMIS-D was 13.24, and PROMIS-A was 12.52. See Table 1 for descriptive statistics of outcome variables per time point. ## 3.2.1. BMI BMI steadily decreased from $M = 48.13$ at baseline to $M = 36.07$ at time 3 (Table 1). This was reflected in the GEE model (Table 2), where baseline scores significantly differed from Time 1 (IRR = 0.92, $p \leq 0.0001$), Time 2 (IRR = 0.90, $p \leq 0.0001$), and Time 3 (IRR = 0.79, $p \leq 0.0001$), even after controlling for covariates of education, marital status, surgery type, and age in years. ## 3.2.2. PROMIS-D PROMIS-D decreased from $M = 12.5$ at baseline to $M = 10.7$ at Time 1, increased slightly to $M = 11.3$ at Time 2, and then decreased again to $M = 9.8$ at Time 3 (Table 2). The GEE model (Table 2) showed baseline scores significantly differed from Time 3 (IRR = 0.78, $$p \leq 0.05$$), although the decrease at Time 1 nears statistical significance (IRR = 0.83, $$p \leq 0.08$$), even after controlling for covariates of education, marital status, surgery type, age in years, and baseline BMI. ## 3.2.3. PROMIS-A PROMIS-A decreased from $M = 13.2$ at baseline to $M = 10.8$ at Time 1 (Table 1), and then remained fairly stable at Time 2 ($M = 10.9$) and Time 3 ($M = 11.4$). The GEE model (Table 2) demonstrated that baseline scores significantly differed from Time 2 (IRR = 0.81, $$p \leq 0.04$$), even after controlling for covariates of education, marital status, surgery type, age in years, and baseline BMI. No significant change was observed between baseline scores and Time 1 (IRR = 0.80, $$p \leq 0.07$$). ## 3.2.4. Clinical Interpretation Depression and anxiety were reported as raw scores. To aid in interpretation, raw scores were converted to standardized t scores (with a mean of 50 and standard deviation of 10), averaged, and interpreted according to guidelines of minimal important change [22] (see Table 3). The change in t-scores was calculated and interpreted in the context of the standards for minimally important differences (MID) [23]. There are various guidelines for what is considered MID or minimal important change (MIC). Terwee et al. state that a change of between 2 and 6 t-score points is considered a minimal important change [24]. However, a change of 3 t-score points was considered reasonable to PROMIS leadership [22]. In a study that used the PROMIS emotional well-being depression and anxiety short form specifically, a change of 2.3–3.4 t-score points for anxiety and 3.0–3.1 t-score points for depression were considered minimally important differences [25]. The change in t-score for depression was 4.79. The change in t-score for anxiety was 5.44. Regardless of the guidelines used, the results of the present study are considered clinically meaningful changes for both depression and anxiety symptoms. ## 4. Discussion Anxiety and depression symptoms significantly improved both 6 weeks and 3–6 months after bariatric surgery. This is the first known study to show faster improvement in symptoms of anxiety compared to depression following bariatric surgery. Improvement in symptoms was independent of preoperative BMI. Examining anxiety and depression independently and as outcomes following bariatric surgery is relatively novel. Postoperative follow-up visits with a mental health provider are not standard across bariatric surgery programs, meaning depression and anxiety symptoms are more easily overlooked. Additionally, general postoperative follow-up rates are low, ranging from 3–$50\%$ depending on the study or clinic [26]. Both factors present barriers to research on affective symptoms postoperatively. Findings showed that not only did anxiety symptoms improve after bariatric surgery, but also that this improvement occurred faster than the improvement in depression symptoms following surgery. Given the dearth literature on anxiety symptoms after surgery, there are limited previous results to compare with the present results. The only other study we are aware of that demonstrated a significant change in anxiety after surgery occurred one year after [12]. Some research suggests that anxiety is relatively consistent over time and would be unlikely to change after bariatric surgery [10]. While that may be true for a formally diagnosed anxiety disorder, the present study refutes speculation that individual symptoms of anxiety go unchanged. Due to demonstrated improvement in worry and associated symptoms, 6 weeks after surgery may present a unique opportunity to perpetuate positive health behavior changes at a post-surgical follow-up appointment. Self-efficacy is impacted by the surgical recovery process [14] and has been shown to improve one year after surgery for those who previously had low self-efficacy [27]. Early postoperative appointments are an influential time in patients’ lifelong health changes, and decreased anxiety may enhance patients’ ability to meaningfully engage in follow-up appointments. Another possible explanation for the results is that patients may be anxious about the procedure itself and they experience a reduction in anxiety symptoms after the perioperative period. The bariatric clinic in the current study has standard follow-up visits with patients at 2 weeks, 6 weeks, 3 months, 6 months, and 12 months postoperatively. It is likely that patients who receive a positive report from their medical providers at 6 weeks may experience a reduction in anxiety symptoms, such as worry or fearfulness. Relatedly, many patients are often concerned about the transition from a liquid diet to solid foods. Improvement or remission of potential complications, such as vomiting, pain, dumping syndrome, food intolerances, and nausea may have occurred by 6 weeks postoperatively, therefore reducing their anxiety about the transition. Improvements in depression were also observed in the present study, though at a later point postoperatively. Our result that depression improved after surgery is consistent with previous studies; however, improvements were observed sooner after surgery compared to other studies. The earliest that studies typically found improvements in depression was at 6 months [4,7]. Patients pursuing bariatric surgery are often motivated towards weight loss due to improved management of comorbid health conditions, longevity, and/or increased quality of life [3]. It is plausible that patients who are further out from surgery feel more hopeful about their future, which in turn positively impacts symptoms of depression, including hopelessness and disinterest in activities. Similar to other studied weight-related comorbidities, improvement in affective symptoms may be independent of weight loss after metabolic surgery and have greater response to biochemical changes from surgery. Given that the brain is an organ with high metabolic and nutrient demands, it is probable that bariatric surgery contributes to improvements in affective symptoms independent of weight loss. Nearly 30 years ago, Pories et al. published a pivotal article demonstrating the rapid normalization of blood glucose levels in patients with type 2 diabetes mellitus after bariatric surgery [28]. Several other hormonal changes following bariatric surgery have been found, such as a reduction in obesity-induced corticotropic axis activation and improvements in gonadal profile and insulin resistance. This favorable metabolic profile after surgery is associated with reduction of all-cause mortality [29,30,31] as well as a reduction of comorbidities such as osteoarthritis, respiratory dysfunction, polycystic ovary syndrome, and resolution of cardiovascular risk factors [32,33,34]. One might hypothesize that the dietary education received, vitamin supplementation, and healthy diet following surgery plays an important role in the early observed improvement in mental health. Patients with obesity often deal with maladaptive eating patterns, food insecurity, or inadequate access to healthy food. Nutritional deficiencies, such as vitamin B12, B9 (folate), and zinc, can cause symptoms of depression such as low mood, fatigue, cognitive decline, and irritability [35,36]. In addition to specific nutrient deficiencies, patients with obesity have chronic inflammation. Evidence now strongly suggests the role of neuroinflammation in mental illness [37,38]. Inflammatory dietary patterns found in Western diets (i.e., processed food) are correlated with an increased risk of developing depression, mild cognitive impairment, and attention deficit hyperactivity disorder [39,40,41]. On the other hand, dietary patterns rich in whole foods, such as the Mediterranean diet, have been found to be protective against developing depression and other mental health symptoms [40,41,42]. Our findings should be considered within the context of limitations. A limited sample of postoperative patients completed the questionnaires at multiple time points. This is also confounded by the fact that attendance at multiple follow-up visits may be an indicator of greater overall well-being (e.g., including lower levels of anxiety and depression), compared to those who do not follow-up. The small sample size also exacerbated having a homogeneous sample, with all participants identifying as Caucasian and a majority identifying as female. It is unknown if results would differ with a more heterogeneous sample. Additionally, the prevalence of postoperative complications (e.g., gastrointestinal concerns, chronic pain, acid reflux, etc.) were not considered in this study, which could have impacted outcomes. ## 5. Conclusions The current study evaluated the course of depression and anxiety symptoms after bariatric surgery. Results demonstrated that depression and anxiety symptoms decreased postoperatively and that anxiety symptoms reduced at a faster rate, comparatively, regardless of baseline BMI. The present results encourage additional research to examine anxiety and depression independently as outcomes following bariatric surgery. Since evaluating anxiety and depression symptoms as independent constructs following bariatric surgery has been uncommon, more research is needed to investigate the mechanisms for change. 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--- title: Association of APOE (rs429358 and rs7412) and PON1 (Q192R and L55M) Variants with Myocardial Infarction in the Pashtun Ethnic Population of Khyber Pakhtunkhwa, Pakistan authors: - Naveed Rahman - Zakiullah - Asif Jan - Muhammad Saeed - Muhammad Asghar Khan - Zahida Parveen - Javaid Iqbal - Sajid Ali - Waheed Ali Shah - Rani Akbar - Fazli Khuda journal: Genes year: 2023 pmcid: PMC10048013 doi: 10.3390/genes14030687 license: CC BY 4.0 --- # Association of APOE (rs429358 and rs7412) and PON1 (Q192R and L55M) Variants with Myocardial Infarction in the Pashtun Ethnic Population of Khyber Pakhtunkhwa, Pakistan ## Abstract Coronary Artery Diseases (CAD) remains the top among Non-communicable Diseases (NCDs). Variations in Apolipoprotein E (APOE) and Paroxonase 1 (PON1) have been associated with Myocardial Infarction (MI) in several populations. However, despite the high prevalence of CAD, no such study has been reported in the Pashtun ethnic population of Pakistan. We have conducted a two-stage (i.e., screening and validation) case-control study in which 200 cases and 100 control subjects have been recruited. In the first stage, Whole Exome Sequencing (WES) was used to screen for pathogenic variants of Myocardial Infarction (MI). In the second stage, selected variants of both APOE and PON1 genes (rs7412, rs429358, rs854560, and rs662) were analyzed through MassARRAY genotyping. Risk Allele Frequencies (RAFs) distribution and association of the selected SNPs with MI were determined using the Chi-square test and logistic regression analysis. WES identified a total of 12 sequence variants in APOE and 16 in PON1. Genotyping results revealed that APOE variant rs429358 (ɛ4 allele and ɛ3/ɛ4 genotype) showed significant association in MI patients (OR = 2.11, p value = 0.03; $95\%$ CI = 1.25–2.43); whereas no significant difference (p˃ 0.05) was observed for rs7412. Similarly, the R allele of PON1 Q192R (rs662) was significantly associated with cases (OR = 1.353, p value = 0.048; $95\%$ CI = 0.959–1.91), with particular mention of RR genotype (OR = 1.523, p value = 0.006; $95\%$ CI = 1.087–2.132). Multiple logistic regression analysis showed that rs429358 (C allele) and rs662 (R allele) have a significantly higher risk of MI after adjustment for the conventional risk factors. Our study findings suggested that the rs429358 variant of APOE and PON1 Q192R are associated with MI susceptibility in the Pashtun ethnic population of Pakistan. ## 1. Introduction Non-communicable diseases (NCDs) are becoming the leading cause of mortality, disability, decreased quality of life, and growing healthcare expenses throughout the world [1]. The global mortality rate from these causes is double that of infectious illnesses, and nutritional deficiencies combined [2]. The most frequent among them include cardiovascular diseases (CVDs) are diabetes, malignancies, and chronic respiratory disorders [3]. They are the major cause of mortality in industrialized countries and the second largest cause in developing and underdeveloped nations, accounting for about $74.4\%$ of all fatalities in 2019, which increased by $20.5\%$ from 2009 to 2019 [4]. Coronary Artery Disease (CAD) is a class of CVD which include Myocardial Infarction (MI), hypertension (HTN), and congenital heart disease. Pathology defines MI as myocardial cell loss caused by persistent ischemia [5]. MI is well-known throughout the world for its high mortality and disability rates and is one of the top 10 causes of death in Pakistan [2,6]. There were 4 million MI-related fatalities between 1999 and 2019 [7]. Lifestyle, environmental, and genetic factors have a key role in the development of CVD [8]. Among the former, some major factors to mention are physical activity, diet, smoking habits, obesity, etc. About the latter, information from different studies has recommended a 40–$80\%$ genetic link [9,10]. A person with parental history of premature atherosclerosis has a 1.5- to 2-fold risk of developing the same [11]. Risk can easily be predicted through a better understanding of the genetic components [12,13]. In the last decades, human genetics approaches have recognized genes that have possible contributions towards developing MI [14]. Some of these include variants of different genes such as Apolipoprotein (APOE), Paroxonase 1 (PON1), Cytochrome P4501A1 (CYP1A1), interleukin-6, Cholesteryl Ester Transfer Protein (CETP) and many others [15,16,17,18,19]. One of the major cause of the development of CAD is Atherosclerosis which is a pathological procedure in which lipid is accumulated in the intima and media of the blood vessel and thus leads to the formation of plaques [20]. Both youth and adolescents may develop coronary atherosclerosis [21]. Abnormalities in two proteins, namely APOE and human PON1, play an important role in its development [22,23]. APOE is a serum glycoprotein that plays an important role in the transport and metabolism of lipids and is encoded by the APOE gene, which is located on chromosome 19. Exon 4 of the gene has two common SNPs: rs429358 (388 T > C) and rs7412 (526 C > T). Moreover, three alleles (ɛ2(388 T–526 T), ɛ3(388 T-526C), ɛ4(388C-526C)) and six genotypes (ɛ2/ɛ2, ɛ2/ɛ3, ɛ2/ɛ4, ɛ3/ɛ3, ɛ3/ɛ4 and ɛ4/ɛ4) can be formed by the two SNPs [24,25]. Since allele 3 is the most prevalent in populations, it is referred to as “wild-type. ”The alleles 2 and 4 are considered variants [26]. Various studies have reported their association with MI in different ethnicities such as Chinese and Russian etc. [ 27,28]. Similarly, PON1 is a membrane-bound glycoprotein encoded by the PON1 gene that is located on chromosome 7q21.3-q22.1 [29,30]. It is associated with highly-density lipoprotein (HDL) and is found in a variety of tissues but is predominantly synthesized in the liver [31]. It inhibits the concentration of low-density lipoprotein cholesterol (LDL-C) by hydrolysis of lipid peroxides [31]. PON1 has considerable anti-inflammatory and anti-oxidative actions through its enzymatic Paroxonase, lactonase, and esterase activities [29]. There is some evidence of low serum PON1 activity in patients with lipid disorders such as diabetes mellitus (DM), MI, atherosclerosis, and familial hypercholesterolemia [30]. PON1 gene has two common polymorphisms, namely L55M and Q192R, of which L55L and Q192Q are regarded as wild type, and Q192R, R192R, L55M, and M55M are considered variant genotypes [8,32,33]. To the best of our knowledge, no such study hasreported the association of these variants in the Pashtun population of Khyber Pakhtunkhwa (KP), Pakistan, despite the reports of increasing incidence of CAD in recent years [34,35]. Owing to their unique cultural practices, social values, lifestyle, and behaviors make them suitable for such studies [34,36]. Considering the importance of the above-mentioned gene variants, it seems suitable to know their association with MI in the said population. Therefore, this case-control study has been designed to investigate the possible association of APOE and PON1 variants with the risk of MI in the Pashtun ethnic population of KP, Pakistan. ## 2.1. Ethics Statement Ethical approval was obtained from the Ethical Committee of the Department of Pharmacy, University of Peshawar (No: 906/Pham). Written informed consent was obtained from all the study subjects. The study was conducted in compliance with the ethical guidelines of the 1975 Declaration of Helsinki. ## 2.2. Study Population A total of 300 age and gender-matched individuals ($$n = 200$$ MI cases and $$n = 100$$ healthy controls) of Pashtun ethnicity belonging from different districts such as Peshawar, Mardan, Swabi, Charsadda, Nowshehra, Swat, and others of Khyber Pakhtunkhwa were included in the study. The study period was from July 2018 to July 2019. The mean age of the control subjects was 58.43 ± 12.65 (140 males and 60 females), and the control was 56.63 ± 11.87(63 males and 37 females). The diagnosis of MI was based on the American College of Cardiology/American Heart Association (ACC/AHA) classification. A senior cardiologist diagnosed MI based on medical records that revealed medical indications, abnormal cardiac enzymes, ECG (Electrocardiogram) abnormalities, and angiography/echocardiography results. CAD was defined as stenosis ˃$50\%$ in at least one of the significant segments of the coronary artery. The control subjects had no lumen stenosis (˂$50\%$) on coronary angiography or physical indications of cardiovascular disease. HTN was defined as having a mean blood pressure of ≥$\frac{140}{90}$ mmHg or being currently treated for it. DM was classified as having fasting glucose levels of ≥126 mg/dL or non-fasting glucose levels of ≥200 mg/dL, as well as being on oral hypoglycemic medicines or insulin. Patients were admitted tothe three tertiary care (teaching) hospitals of Khyber Pakhtunkhwa, Lady Reading Hospital (LRH) Peshawar, Hayatabad Medical Complex (HMC) Peshawar, and Khyber Teaching Hospital (KTH) Peshawar, while control samples were collected from different districts. Healthy volunteers had no history of cardiovascular disease, especially MI. Inclusion criteria for cases were (i) confirmed MI patients, (ii) Patients belonging to Pakistani Pashtun origin (iii) age ≥30 years. Exclusion criteria were (i) Age ˃80 and ˂30, (ii) mentally ill patients, (iii) severe liver diseases, (iv) malignant tumor, and (v) renal dysfunction. The consent form and thorough demographic, family, and clinical history of all the participants was taken on a carefully designed Proforma. Demographic information includes age, weight, height, and residence. A family history questionnaire includes information on any CVD, MI, or other cardiac issues in the family. The clinical history section of the Proforma includes details about the current disease, co-morbid disorders, and vital signs. For illiterate participants, who have difficulty understanding English, the consent form for their understanding was read and explained in the local Pashtu language and then signed on his/her behalf by any of his/her relatives/attendants. ## 2.3. Blood Sampling Following an overnight fast, blood samples were collected from each research participant through venipuncture, with 2.5 mL collected in each EDTA (Ethylene diamine tetra acetic acid) tube and plain tube (without anticoagulant). After allowing the blood in the plain tube to clot, it was centrifuged to obtain serum for biochemical examination. Following aseptic procedures, blood samples (properly labeled) were stored at −10 °C. ## 2.4. DNA Extraction and Biochemical Measurements Genomic DNA (Deoxyribonucleic acid) was extracted from peripheral blood leukocytes using the WizPrep DNA extraction kit (WizPrep no. W54100). DNA measurements were carried out with the Qubit ™ dsDNA HS Assay kit (Catalog No. Q32851), and the concentration was adjusted to 10 ng/μL.The serum concentration of Total Cholesterol (TC), Triglycerides (TG), LDL-C, and high-density lipoprotein cholesterol (HDL-C) were measured by standard enzymatic methods using standard reagents on Architect Plus (Ci-4100, Germany) biochemical instrument following strictly manufacturer’s instructions in Hospital clinical laboratory. ## 2.5. DNA Samples Pooling According to the DNA-pooling techniques previously described [37], DNA pools were created from 200 MI patients and 100 control participants in order to cut costs and streamline the sequencing procedure. Each pool contains an equal quantity of genomic DNA (10 ng) from each subject. ## 2.6. Variant Prioritization The annotated data in the Excel file were first manually curated to screen exonic, and missense variants and synonymous variants were eliminated as shown in Figure 1. The functional influence, biological action, and pathogenicity of the selected variants (SNPs) were checked by using prediction algorithms (PolyPhen and SIFT prediction) built within ANNOVAR. ## 2.7. Validation Trial and Genotyping of APOE and PON1 In the research population, Whole Exome Sequencing (WES) discovered a total of 12 variations in the APOE and 16 in the PON1 gene, respectively. The selected SNPs were genotyped to validate WES results and confirm the association with MI. Sequenom MassARRAY (Sequenom Inch., San Diego, CA, USA) platform was employed following the manufacturer’s instructions. ## 2.8. Statistical Analysis The SPSS (Statistical Package for the Social Sciences) software was used to analyze statistical data. Age, gender, weight, smoking, lifestyle, exercise, PON1, and APOE gene variations were the main factors chosen for the study. W Shapiro-Wilk’s test was used to determine the normality of distribution for quantitative data. Categorical data of the cases and control individuals were reported as percentages and frequencies and analyzed with a Chi-square test., whereas continuous variables were displayed as mean standard ± deviation. Odds ratios (OR) of MI cases for each variant using a binary logistic regression model were estimated with a $95\%$ confidence interval (CI). The difference in genotype and allelic prevalence and correlation between cases and control were assessed independently as well as adjusted for conventional risk factors. Age, gender, smoking, and family history of MI, TC, and LDL-C were included as covariates, as well as all the possible genotypes studied. Binary logistic regression was used to determine if the chosen SNP was associated with MI. A p ≤ 0.05 was statistically considered significant. ## 3.1. Population Characteristics Co-morbidities and Sociodemographic characteristics of study subjects are described briefly in Table 1 and Table 2. The prevalence of co-morbidities such as HTN ($55\%$ vs. $36\%$) and DM ($47.5\%$ vs. $32\%$) were higher in cases as compared to the control subjects. The majority of the subject cases hada family history of MI ($55.5\%$). Moreover, $80\%$ of CAD cases were taking anti-hyperlipidemic medicines (statins) to maintain their poor lipid profile, due to which CAD patients might show normal values of lipid parameters. Moreover, the majority ($70\%$) had a sedentary lifestyle. Furthermore, most of the male patients ($58.5\%$) were smokers. Almost half of the patients were totally non-compliant withdiet and medicines. ## 3.2. WES Results WES identified a total of 33,329 exonic SNPs, including 3600 homozygous, 29,729 heterozygous, 31,488 synonymous, 1086 deletion, 68 pathogenic, 3456 missenses, and 460 probably damaging variants. A total of 12 variants were identified in APOE and 16 in PON1, as shown in Table 3 and Table 4. Detailed WES results are shown in Figure 2. ## 3.3. Genotype and Allele Frequencies of APOE (rs429358 and rs7412) and Their Association with MI Both the APOE gene variants (rs429358 and rs7412) were checked for their association with MI by using logistic regression analysis. The genotypic and allelic distributions of both variants are displayed in Table 5. In our study population, the ɛ3 allele is the most common. The results are in broad agreement with data on the frequency ɛ3 allele globally [38]. A significant difference was observed for the variant genotype ɛ3/ɛ4 [OR ($95\%$ CI) = 2.13 (1.32–2.65): $$p \leq 0.031$$)] and ɛ4 allele [OR ($95\%$ CI) = 2.11 (1.25–2.43): $$p \leq 0.03$$)] of APOE in MI patients compared to control; Whereas other genotypes (ɛ2/ɛ2, ɛ2/ɛ3, ɛ2/ɛ4, ɛ2/ɛ2, and ɛ3/ɛ3) and allele (ɛ2) showed no statistically significant difference (all $p \leq 0.05$). ## 3.4. Association of L55M and Q192R Variants of PON1 with MI (SNP×MI) Both the PON1 gene variants (L55M and Q192R) were checked for their association with MI by using logistic regression analysis. The genotypic and allelic distributions of both variants are displayed in Table 6. The allele and genotype distribution of PON1 Q192R was found to be significantly different between the MI cases and control subjects. The frequency of the R allele was found to be significantly higher in the study subjects than in the controls. Moreover, the RR genotype was found more frequently in the MI cases than in the controls ($16\%$ vs. $9\%$). By binary logistic regression analysis, the Q192R genotype of the PON1 gene was found to be significantly associated with MI cases [OR ($95\%$ CI), 1.353 (0.959–1.910): $$p \leq 0.048$$]. There was no significant difference between the MI cases and controls for the L allele and M allele. The results showed no significant association of the PON1 L55M genotypes with MI (p ˃ 0.05). ## 3.5. Logistic Regression Analysis for MI in Pashtun Population Logistic regression analysis was performed to determine independent predictors for MI in the study population. On univariate regression analysis, there was a significantly higher risk of MI in the presence of age, gender, smoking, family history of MI, HTN, DM, rs429358 (e4 allele), and rs662 (R allele). Further multivariable analysis showed that participants with ɛ4 and R alleles of rs429358 and rs662 had a significantly higher risk of MI after adjustment for the established conventional risk factors, as shown in Table 7. ## 4. Discussion The current study investigated the relationship between APOE and PON1 polymorphism and the risk of MI in Pakistan’s Pashtun ethnic population. The genes were selected for genotype validation due to their prominent association with other ethnicities along with data absence in the study population. The selected variants of APOE (rs7412 and rs429358) were genotyped and validated by MassARRAY to confirm the association with MI. The notable variant among the 12 identified variants of APOE was rs429358 (p.Cys130Arg), located on the 4th exon of chromosome 19. SIFT and PolyPhen predicted the variant rs429358 as deleterious and probably damaging, respectively. Likewise, another exonic missense SNP reported was rs7412 (p.Arg176Cys). SIFT and PolyPhen labeled them deleterious and benign, respectively. Furthermore, a significant association between the ɛ4 allele (rs429358) and the risk of MI has been found in the study population, which is in broad agreement with other ethnic populations [27,39,40]. This association remained significant when adjusted for several MI confounding factors. The APOE gene polymorphisms are associated with many diseases such as dementia, Parkinson’s disease, epilepsy, and CAD [41]. Its association with MI or CAD has been extensively studied in the last two decades, and the ϵ4 allele has been found to have a link with it in many studies [42]. Moreover, the same allele was associated with an increased risk of developing HTN [43]. A large-scale genomic study comprising 32,965 controls and 15,492 cases showed that individuals with the ϵ4 allele had a higher risk for coronary heart disease (CHD) compared to individuals with the ϵ3/ϵ3 genotype [44]. However, another study has shown no association of APOE gene polymorphism with the development of CAD in the study on the relationship between APOE gene polymorphism and blood lipid and CAD in African Caribbean people [45]. These inconsistencies may be because of regional and ethnic variability. This study found the ɛ3 allele to be the most common isoform of the APOE gene accounting for $73\%$ of cases and $81\%$ in controls, respectively, which was consistent with most of the previous studies [40,46]. The findings of our study regarding the frequencies of APOE allele are consistent with that of other ethnicities [47,48]. Similarly, this study has also assessed the association of Q192R and L55M variants. Findings suggested the missense SNPQ192R (rs662), located on the short arm of chromosome 7 as significant. The frequency of the RR genotype of Q192R was found to be higher in the MI cases compared to the control. The Q192R (rs662) polymorphism cases with MI revealed a higher frequency of the R allele compared to the control. Both the SIFT and PolyPhen scores predicted it as pathogenic and damaging, respectively. The second missense, exonic SNP, was L55M (rs854560). It was shown tolerable and benign by SIFT and PolyPhen score, respectively, and was found not associated with MI (p ˃ 0.05). This finding is supported by other studies [23]. Studies conducted in different ethnic populations have shown interesting results of the association of Q192R polymorphism of PON1 with MI [49]. Many studies have revealed the RR genotype and R allele of PON Q192R with susceptibility to MI [23]. A study conducted on the Colombian ethnic population proposed Q192R polymorphism of PON1 as a useful biomarker of CAD [50]. Another study also showed an association of the PON Q192R variant with CAD [51]. In line with these findings, a significant association was observed for Q192R with CAD by Liu and colleagues [52]. Similar findings were also found in a Chinese ethnic population, south Indian Tamil, and Asian Indians. [ 53,54,55] Conversely, many other studies have demonstrated conflicting findings and found no association of PON1 Q192R polymorphism with CAD [23]. In particular, a genetic study conducted on 120 CAD and 102 healthy volunteers revealed that PON1 192R allele frequency was the same among the cases and control [56]. Furthermore, no link was found between the Q192R polymorphism and CAD in a Turkish population [57]. Similarly no association was observed in Taiwan ethnic population [58]. Furthermore, Sociodemographic analysis of cases and controls revealed a higher incidence of DM and HTN in cases compared to the control. Moreover, the results showed an increased prevalence of MI in males compared to females ($70\%$ vs. $30\%$). Most of the MI patients were smokers compared to controls ($58.5\%$ vs. $26\%$). Furthermore, a family history of MI and other heart diseases was more prevalent in some cases. Physically activity (exercise) was found to be very poor in cases compared to controls ($30\%$ vs. $70\%$). ## 5. Conclusions The present study has suggested that APOE variant rs429358 and PON1 variant Q192R are associated with MIrisk in the Pashtun population of KP and may be further studied to determine their potential as susceptibility biomarkers for the same. ## Limitations The small sample size is a limitation of our study; similarly, we did not measure the corresponding protein level to know about the expression of the proteins. Moreover, the study was conducted only on patients of Pashtun ethnicity, so it cannot be generalized to the whole of Pakistan or other ethnic populations. ## References 1. Boon M.R., Bakker L.E., van der Linden R.A., van Ouwerkerk A.F., de Goeje P.L., Counotte J., Jazet I.M., Rensen P.C.. High prevalence of cardiovascular disease in South Asians: Central role for brown adipose tissue?. Crit. Rev. Clin. Lab. Sci. (2015) 52 150-157. DOI: 10.3109/10408363.2014.1003634 2. Mokdad A.H.. 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--- title: Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression authors: - Emmanuel Fundisi - Simangele Dlamini - Tholang Mokhele - Gina Weir-Smith - Enathi Motolwana journal: Healthcare year: 2023 pmcid: PMC10048028 doi: 10.3390/healthcare11060881 license: CC BY 4.0 --- # Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression ## Abstract Increased HIV/AIDS testing is of paramount importance in controlling the HIV/AIDS pandemic and subsequently saving lives. Despite progress in HIV/AIDS testing programmes, most people are still reluctant to test and thus are still unaware of their status. Understanding the factors associated with uptake levels of HIV/AIDS self-testing requires knowledge of people’s perceptions and attitudes, thus informing evidence-based decision making. Using the South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey of 2017 (SABSSM V), this study assessed the efficacy of Generalised Linear Poisson Regression (GLPR) and Geographically Weighted Poisson Regression (GWPR) in modelling the spatial dependence and non-stationary relationships of HIV/AIDS self-testing uptake and covariates. The models were calibrated at the district level across South Africa. Results showed a slightly better performance of GWPR (pseudo R2 = 0.91 and AICc = 390) compared to GLPR (pseudo R2 = 0.88 and AICc = 2552). Estimates of local intercepts derived from GWPR exhibited differences in HIV/AIDS self-testing uptake. Overall, the output of this study displays interesting findings on the levels of spatial heterogeneity of factors associated with HIV/AIDS self-testing uptake across South Africa, which calls for district-specific policies to increase awareness of the need for HIV/AIDS self-testing. ## 1. Introduction Sub-Saharan Africa accounts for $70\%$ of Human Immunodeficiency Virus/Acquired Immunity Deficiency Syndrome (HIV/AIDS) infections around the globe [1]. This is despite coordinated efforts from different stakeholders that continue to scale up prevention and treatment programmes. Such programmes have resulted in an increased number of people receiving antiretroviral drugs [2,3,4]. However, poor quality of services, limited access to services, and inadequate resources tailor-made to combat the epidemic continue to undermine responses to HIV/AIDS [4,5]. According to the fifth South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey of 2017 (SABSSM V), approximately 7.9 million people are living with HIV/AIDS in South Africa [6]. One disturbing observation noted from the national level community survey is that HIV/AIDS exceedingly affects women compared to men and it is unequally distributed across different levels of socioeconomic status [3]. To a greater extent, the socioeconomic status of women and unequal gender power structures coerce and lead women into unintended relationships that expose them to elevated risks of HIV/AIDS infection in comparison to men [5]. Similar findings have been observed in the East, West, and Central African regions where most women are mostly affected by HIV/AIDS [1]. Findings from the 2017 SABSSM survey also highlighted a higher prevalence of HIV/AIDS, and this was noted to have been perpetuated by social inequalities in South Africa, with most blacks living in poverty [3]. Similar observations and remarks were also reported by Pengpid et al. and Mabaso et al. [ 7,8] who noted poverty as an overarching factor that increases the disparity of HIV/AIDS prevalence among racial groups in South Africa. Correspondingly, Nehl et al. [ 9] observed higher HIV/AIDS prevalence amongst African–American communities in the United States of America and associated such findings with socioeconomic inequities, high levels of poverty, social exclusion, and above all, limited access to healthcare. As research and various initiatives continue to expand, finding new innovative approaches to help alleviate ills brought about by the HIV/AIDS epidemic, and delivering effective prevention programs across spatial divides, continue to be a challenge. Extant literature investigated the variation in HIV/AIDS prevalence based on human perception, behaviour patterns, attitudes, norms, and stereotypes e.g., [4,5,7,10,11,12,13]. Focusing on people’s disability status using 2012 SABSSM data, Pengpid et al. [ 7] investigated HIV/AIDS prevalence, human behaviours, knowledge, and attitudes. The study utilised multivariable logistic regression and reported high HIV/AIDS stigmatisation of people with disability (Odds Ratio (OR): 0.31, Confidence Interval (CI): 0.25, 0.67) compared to people without disabilities (OR: 0.57, CI: 0.34, 0.96). Furthermore, a higher prevalence of HIV/AIDS infections was recorded from individuals with hearing impairment. Zungu et al. [ 6] associated people’s sexual partners, condom use during the last sexual activity, consistency of condom use and antiretroviral therapy (ART) exposure. Using logistic regression (OR = 0.6 ($95\%$ CI: 0.4–0.8), $$p \leq 0.001$$), the study reported that HIV/AIDS positive respondents on ART were less likely to have numerous sexual partners, thus adopting condom use in sexual encounters compared to HIV/AIDS respondents who were not on ART. HIV/AIDS-infected adults on ART often shifted their sexual behaviours in response to their HIV/AIDS-positive status. Analysing relationships between HIV/AIDS and socioeconomic issues between 2011 and 2016 in Sub-Saharan African countries, Sun et al. [ 1] utilised Poisson regression, and spatiotemporal autoregressive models. Results from the study showed significant spatiotemporal non-stationarity and autocorrelation between HIV/AIDS and socioeconomic factors. A well rounded understanding of human behaviours, and attitudes as well as spatial patterns of HIV/AIDS prevalence across the spatial divide can influence positive public health decisions by the responsible authorities, thus enhancing prioritisation of worst-affected areas [12]. Allocation of resources for HIV/AIDS testing to areas of greatest need is key to controlling the epidemic. Most importantly, it is worth noting that people with HIV infection are generally asymptomatic for several years before the virus progresses to AIDS [14]. The introduction of the 95-95-95 targets initiative by the United Nations, where $95\%$ of HIV-infected people are meant to be cognisant of their HIV/AIDS status by 2030, is an essential global programme. In South Africa, the launch of nationwide HIV/AIDS testing initiatives, including HIV testing and counselling (HTC) in 2010 and the HTC revitalisation strategy in 2013 [11] resulted in the increased uptake of much needed antiviral drugs. Despite such programmes and initiatives (HIV/AIDS testing), millions of people continue to get affected by the HIV/AIDS epidemic and most importantly, are still oblivious to their HIV status. Various studies have explored the association of HIV testing uptake with spatial patterns, socioeconomic status, and demographics, etc., [ 2,3,4,7,11,15,16,17,18,19,20,21,22,23]. For instance, Ntsepe et al. [ 4] analysed people’s perceptions of HIV testing in formal and informal urban communities from different races and age groups in select towns (Cape Town and Durban) across two South African provinces. Findings from the study highlighted that most respondents were afraid of testing HIV positive, and thus have misconceptions about the risks associated with HIV testing. In addition, most respondents were afraid of stigmatisation, with the fear of discrimination by society if found HIV positive. Harichund et al. [ 17] assessed the influence and acceptability of HIV self-testing in KwaZulu-Natal, South Africa. Using a purposive sampling survey, the study results revealed higher HIV/AIDS testing uptake among women compared to a considerably low uptake amongst men who only tested for HIV/AIDS due to convenience. Jooste et al. [ 11] utilised a global, Generalised Linear model to assess variations in HIV/AIDS testing uptake in South Africa, reporting variation across the country. There is a need to examine the strength and weaknesses of covariates in modelling HIV/AIDS testing uptake in different districts since one variable may have a strong influence in one district but exhibit a weaker influence in a different district. It is therefore important to find factors that influence HIV/AIDS self-testing uptake and analyse the interaction between them, by taking into account unaddressed location variables, thus determining districts that require closer attention. Notably, this is the foundation for constructing a spatial dynamics model, as well as the basis for developing location and evidence-based intervention strategies. Although evidence from the literature, [24,25] provides the merits and acceptance of HIV/AIDS self-testing initiatives, there is a lack of evidence on modelling factors associated with HIV/AIDS self-testing uptake in South Africa. Understanding how geographical differences, as well as people’s attitudes and perception, influences HIV/AIDS self-testing uptake, may yield imperative location intelligence interventions, and tailor-made programmes to enhance uptake across the country. This study, therefore, seeks to extend the work of Jooste et al. [ 11] by exploring relationships and scale differences in people’s attitudes and perceptions of HIV/AIDS self-testing uptake using Generalised Linear Poisson Regression (GLPR) and Geographically Weighted Poisson Regression (GWPR) models. Revelations from this study will establish the existence or non-existence of spatial relationships between HIV/AIDS self-testing uptake and its related factors at subnational level. ## 2.1. Data The study utilised the 2017 SABSSM V data [26] which was collected using a multistage stratified, cross-sectional national survey covering all age groups. Household samples were randomly selected from 1457 small area layers, using systematic probability sampling. The selected small area layers were extracted from 84,907 small area layers computed by Statistics South Africa in 2011 [27]. Small area layers were stratified by province using locality type, i.e., urban informal, urban formal, rural formal, and rural informal. Furthermore, a systematic random sample of 15 households was selected from each sampled small area layer. Remarkably, the household response rate from the survey was recorded at $82\%$ and all household members participated in the survey [28]. The outcome variable for this study was the question: If an HIV self-test kit was available to you, would you be willing to use it to test yourself? It should be noted that the question was a categorical outcome variable coded as follows: 1 = yes; 2 = no; 3 = do not know, and for this study, “yes” was selected for the analysis. A set of explanatory variables (Table 1) was selected based on previous studies [4,16,17,24,29,30,31,32,33,34,35] to assess factors associated with HIV/AIDS self-testing uptake or willingness. Notably, explanatory variables used in this study were arrived at after multicollinearity testing, leaving variables with low correlation. The first question used as an explanatory variable for this study is (i) in general, would you say that your health is excellent, good, fair, or poor? The response to the question was a categorical outcome with responses: 1 = excellent, 2 = good, 3 = fair, 4 = poor, and then “excellent” was selected for the analysis. The second explanatory variable was based on the question: when was the last time you went to see a health professional? The responses to this question were coded as follows: 1 = within the past six months; 2 = more than six months but not more than a year ago, 3 = more than one year ago, and 4 = never. For the study codes 2 and 3 responses were combined for further analyses. The third question used as an explanatory variable was: what is the highest education level that you obtained? The responses included 17 different answers, with the study combining at least Grade 7 and up to Grade 12 for the analysis. ## 2.2. Spatial Autocorrelation—Global Moran’s I The global spatial autocorrelation (Global Moran’s I) was computed to assess whether the district level spatial distribution of HIV/AIDS self-testing uptake and the covariates was dispersed, clustered, or random. Moran’s I [36], was utilised in this study to assess the presence or absence of spatial dependence and clustering of residuals (the index ranges between −1 and +1). When Moran’s I is positive, the distribution has a propensity towards clustering of similar values (+1), and 0 is usually indicative of no spatial autocorrelation. However, for a negative Moran’s I (−1), the distribution tends towards a perfect dispersion, with clustering of dissimilar values [36]. A detailed explanation of spatial autocorrelation using Global Moran’s’ I is published in multiple studies [10,37,38,39,40,41]. ## 2.3. Generalised Linear Poisson Regression Modelling of Factors Associated with HIV/AIDS Self-Testing Uptake When predicting discrete, non-negative, and non-continuous variables (count of responses from the SABSSM V, 2017), it is more appropriate to use Generalised Linear Models (GLM) to determine the relationship between outcome and explanatory variables [42]. However, GLM are spatially rigid and assume fixed effects for various locations in space. The models work on the assumption that a single equation can explain the same effect for all spatial units [42]. Given that variables used in this study are count data with discrete and non-negative values, the GLM was computed by implementing the Poisson distribution error [43]. GLPR explains the global relationship (Equation [1]) between HIV/AIDS self-testing uptake and the covariates (excellent health, more than 6 months after the last health professional visit and at least having attained Grade 7–12). [ 1]InEyi=β0+βiInPi+β2x2i+…+βkxki+εi where, Eyi is the natural log of the expected count of HIV/AIDS self-testing uptake per district in the study area, Pi is the offset variable, xki is the k-th explanatory variable, βi is the i-th model parameter, index i refers to the district, and εi is the i-th random error term. ## 2.4. Geographically Weighted Poisson Regression Modelling of Factors Associated with HIV/AIDS Self-Testing Uptake GLPR is not capable of capturing spatial dependence in data, and it ignores the spatial correlation in the estimation of relationships [42,44]. Equally important, it is more unlikely that one single coefficient per explanatory variable can reflect the true spatial relationship between the dependent variable and the explanatory variable since spatial data vary in space [44,45]. This study explored the effectiveness of Geographically Weighted Regression (GWR) [46] that detects spatial heterogeneity in the dataset, relaxing the assumption of spatial stationarity associated with global models. When analysing spatially varied count data, it is plausible to utilise Poisson distribution within the GWR framework [40,47]. GWPR allows for each parameter to vary across the districts capturing important subnational level variations in the association between HIV/AIDS self-testing uptake and explanatory variables [48]. GWPR (Equation [2]) integrates GLM with GWR and extends the concept of the GWR models in the context of GLR [49]. The model was used to establish how relationships in HIV/AIDS self-testing uptake and the covariates differed at district levels in South Africa. More importantly, the model makes a great contribution to the development of South Africa, sub-national level HIV/AIDS self-testing uptake policies by specifying districts in most need of intervention rather than generalising effects of HIV/AIDS self-testing uptake for the entire country. ln(E(Yi)) = β0(s) + β1(s)x1i + β2(s)x2i + … + βk(s)xki[2] where Yi is the observed count data at district locations i. E(Yi) is the HIV/AIDS self-testing uptake at district locations i. β0(s), β1(s), β2(s), …, βk(s) are the spatially varying coefficients, which may differ across different districts. x1i, x2i, …, xki are the predictor variables at district locations i. ln() is the natural logarithm. In this study, spatially varying coefficients are estimated using bi-square, kernel function that assigns weights to nearby observations. Thus, observations further from a particular district location have less influence on the estimation of the coefficients. ## 2.5. Model Diagnostic Indicators The diagnosis of the GLRP model was established by analysing the pseudo R2 (Equation [3]), the Akaike information criterion (AICc) (Equation [4]) and deviance residuals. [ 3]Pseudo R2=1−Dy,ypredDy,ymean where Dy,ypred = deviance of the fitted nonlinear model, Dy,ymean = deviance of the Intercept-only model. The AIC (Equation [4]) that estimates the distance between a model and an ideal but unobservable model [50] was also utilised as the model diagnosis. A lower value for AICc is desired since it implies the most parsimonious model and the amount of variance that the model could not explain. [ 4]AIC=−2log⁡Lθ^+2k where L(θ^) is the maximum likelihood of estimated parameters (θ^) given the data and model. ( θ^) quantifies the effects of explanatory variables on a model and include the intercept, the regression coefficients and the residual variance. Overall model performance between GLPR and GWPR was established using local percent deviance explained. Percent deviance explained is comparable to the local determination coefficient (R2) and allows visualisation of spatial difference of the explanatory power of the model. Higher percent deviance values are more desirable. Furthermore, all analysis and maps for this study were computed and generated in ArcGIS Pro 3.0.2 (ESRI, Redlands, CA, USA). ## 3.1. District Level Spatial Autocorrelation Assessment Moran’s I scatterplots (Figure 1) computed in this study sought to determine the clustering of residuals, modelling HIV/AIDS self-testing uptake, and the covariates using GLPR and GWPR. On the scatter plot, the upper-right quadrant (displays of positive Gi* statistics) and the lower-left quadrant (displays of negative Gi* statistics) correspond with positive spatial autocorrelation and on the contrary, the lower-right and upper-left quadrants correspond with negative spatial autocorrelation. Analysis of deviance residuals derived from GLRP, under the null hypothesis of no spatial autocorrelation, showed the presence of spatial autocorrelation. The output (Figure 1a) exhibited a degree of clustering (Moran’s’ $I = 0.563$; Z-Score = 3.149; p-value = 0.001) in the relationships between HIV/AIDS self-testing uptake with covariates used in the study (excellent health, more than 6 months after the last visit to a health professional and at least having attained Grade 7–12) from GLPR. Furthermore, as indicated from the output of the analysis of deviance residuals derived from GWPR using Moran’s I statistics (Moran’s’ I = −0.105; Z-Score = 0.980; p-value = 0.3268), the output illustrated a lack of spatial autocorrelation indicating spatial randomness (Figure 1b). ## 3.2. Generalised Linear Poisson Regression—Global Model General statistics from the 2017 SABBSSM V survey revealed a high prevalence of HIV/AIDS self-testing uptake (N ranging between 1078 and 1692, total district counts) in Sedibeng, Gert Sibande, Uthukela, eThekwini, iLembe, and King Cetshwayo districts. On the contrary, Central Karoo and Namakwa districts recorded the lowest count of HIV/AIDS self-testing uptake ranging between 37 and 80. Model performance in terms of pseudo R2 and AICc recorded from GLRP resulted in percent deviance explained = 0.88 and AICc = 2552. Comparatively, results from the GWRP model showed better performance with a significantly smaller AICc = 390 and percent deviance explained R2 = 0.91 (Table 2). Statistically significant district level clustering of deviance residual confirms spatial dependency of HIV/AIDS self-testing uptake prevalence using a global model. High negative clustering (deviance residuals < −2.5) was predominantly observed in the districts around Northern Cape, Western Cape, and Eastern Cape including Namakwa, West Coast, Garden Route, and Sarah Baartman districts. Contrary, high positive clustering (deviance residuals > 2.5) was observed in Capricorn Bojanala, City of Tshwane, City of Johannesburg, and Nkangala districts, etc. Theoretically, if the output of the spatial autocorrelation analysis exhibits a lack of spatial dependence, then the GLRP model is sufficient to explain the relationship between HIV/AIDS self-testing and covariates. However, from the output derived from the models, the spatial distribution of deviance residuals (Figure 2) showed evidence of spatial dependence. In light of this, our study further tested a more robust local model i.e., GWPR to account for the unexplained spatial lag. GWPR addresses spatial non-stationarity in the model by removing the limitations of a global model, allowing for local variance to be calculated in different districts. More specifically, GWPR allows regression coefficients to be calculated for different districts in contrast to GLRP which assumes a global fit for multiple geographic units at national level. ## 3.3. District Level Geographically Weighted Poisson Regression—Local Model GWPR was computed to analyse the influence of covariates on modelling HIV/AIDS self-testing uptake and the result showed a slight improvement in local model performance compared to the global model (Table 2). Furthermore, the percent deviance explained by the GWRP model indicates high rates of association between covariates (excellent health, more than 6 months after last visit to the health professional, and at least having attained Grade 7–12) and HIV/AIDS self-testing uptake. The deviance residual analysis of GWPR confirms the superior performance of the local model in comparison to the global model. The deviance residual from the estimated parameters of GWPR shows spatial independence (Figure 3). High negative deviance residuals ranging between −5.66 and −5.45) were observed in Mopani and Fezile Dabi districts around Limpopo and Free State. On the contrary high positive deviance residuals (1.79–3.67) were observed in Vhembe, Sekhukune (Limpopo), City of Johannesburg (Gauteng), Dr Kenneth Kaunda (North West), Ugu (Eastern Cape), and Cape Winelands (Western Cape). Overall, the results displayed in Figure 3 validate and indicate that GWPR explains better the variability in the data and provides favourable results for explaining the relationship between HIV/AIDS self-testing uptake and the covariates in different districts. The local percent deviance explained shows spatial variation in the explanatory power of the GWPR model across different districts in South Africa in terms of predicting the relationship between HIV/AIDS self-testing uptake and the covariates (Figure 4). A significantly lower AICc is indicative of the existence of heterogeneity among different explanatory features and this heterogeneity, however, cannot be captured by the global model. The local percent deviance ranged between 90.2 and 99.7 and all the districts show the best fit (greater than 90 percent deviance explained) which best explains the relationship between HIV/AIDS self-testing uptake. The low predictive power was recorded and well distributed in the districts around Limpopo such as Vhembe, Capricorn, and Mopani; Lejweleputswa in Free State; Central Karoo in the Western Cape; and Chris Hani alongside O.R. Tambo, in the Eastern Cape. In contrast, very high percent deviance values were recorded in ZF Mgcawu, in the Northern Cape, and most districts in KwaZulu Natal including Zululand, Amajuba, King Cetshwayo, Lembe, eThekwini, and Umkhanyakude. GWPR coefficients (Figure 5) present negative and positive strengths of covariates used in the study, thus providing insight into district level targeted interventions. The difference in the associations between HIV/AIDS self-testing uptake and the covariates: at least having attained Grade 7–12, excellent health, and more than 6 months after the last visit to the health professional was observed across different districts (−0.0042–0.0109). Coefficients of the covariate: at least having attained Grade 7–12 showed significantly negative strength (−0.0199–−0.0069) to HIV/AIDS self-testing in the Western Cape (Overberg, Cape Winelands, City of Cape Town, and Eden) and Limpopo (Vhembe and Capricorn). In addition, “having spent more than 6 months after the last visit to the health professional” resulted in negative coefficient values (−0.0042–−0.0041) in Mopani, Pixley ka Seme, Bojanala, and Zululand districts, etc. Excellent health variables exhibited negative strength (−0.0207–−0.0017) in the Western Cape district (Namakwa) which was comparable to the variable “having at least attained Grade 7–12”. Positive strength of covariates (0.0008–0.0200) is more prevalent for “more than 6 months after the last visit to the health professional” which is indicative of greater association to HIV/AIDS self-testing uptake, and this was followed by “having at least Grade 7–12”. Excellent health exhibited a more negative relationship with the HIV/AIDS self-testing, in comparison to the coefficient of the other two variables (more than 6 months after the last visit to the health professional and having at least attained Grade 7–12). ## 4. Discussion This study explored the effectiveness of GLPR and GWPR models in examining the spatial dependence and non-stationary relationships of HIV/AIDS self-testing uptake and the covariates. Output from the study shows that $12\%$ of the relationship between HIV/AIDS self-testing uptake and the covariates used in the study cannot be captured by the global model, compared to $9\%$ by the local model. The results show the inefficiency of the global model due to the non-consideration of the scale of geographical processes associated with HIV/AIDS self-testing uptake. Local regression models capture local spatial disparities in the relationships between outcome and explanatory variables [51]. This is confirmed by the output of our study, modelling spatial differences in HIV/AIDS self-testing uptake with GWPR resulting in deviance variation percentages between 92 and 99, which is indicative of divergence in HIV/AIDS self-testing uptake variation across the country (Figure 4). GLPR showed a high propensity of both overestimation and underestimation in the northern part of the country and more overestimation in the western and eastern parts of the country. On the contrary, GWPR, exhibited a random pattern, with values close to −0.9 to 0.5 across the country. Moreover, the high deviance residual from the global model is indicative of the predicted value of the response variable being significantly different from the observed value, attributed to outliers around Northern Cape, Western Cape, Gauteng, and Limpopo (Figure 2). The variance in the response variable was not constant across the range of covariates used in the study (at least having attained Grade 7–12, excellent health, and more than 6 months after the last visit to the health professional). HIV/AIDS self-testing has been recognised in the literature as having significant advantage of increased privacy and confidentiality, as well as convenience compared to conventional HIV/AIDS testing administered by health professionals [4,17,24,25,28,30,52,53]. Previous studies considered various methodological approaches in documenting, mapping and quantifying HIV/AIDS self-testing uptake [2,4,30,52]. However, such studies have been unable to factor in the presence of spatial autocorrelation and the unobserved heterogeneity that might appear in the area of interest. More specifically, the relationship between the outcome and explanatory variables is assumed to be stationary in space [46]. Results from this study showed GLPR = $88\%$ deviance versus $91\%$ for GWPR, which was a slight increase in the performance of the local model compared to the global model (Table 1). Better performance of GWPR can be attributed to geographical variation in South Africa [54], which can influence access to healthcare facilities as well as access to education. More importantly, differences in access to infrastructure (road, water, electricity, and health facilities) around urban and rural districts widen the disparities, observed from the outcome of the study [8,55]. Furthermore, due to urbanisation and conflict across the globe, migration contributes to diverse cultures which to some extent influence people’s attitudes and knowledge in society towards HIV/AIDS self-testing uptake [56]. Local percent deviance differed across districts illustrating the difference in the combined statistical impact of the local model variables and this could be attributed to unique demographic characteristics. Similar observations were reported by Jooste et al. [ 11] who mapped the uptake of HIV testing at the district level in South Africa. They noted that differences in demographic distribution across the districts in South Africa influenced the observed proportions of people accepting HIV/AIDS self-testing. In a separate study, Sambisa et al. [ 52] reported a higher prevalence of self-reported HIV testing among women in comparison to men in Zimbabwe, and this was due to a different population count of men and women in the various communities. Furthermore, the disparity in acceptance of HIV testing between women and men was attributed to initiatives and programmes targeted at the former, which are in line with the prevention of mother-to-child HIV transmission. Further explanation of the differences in uptake levels reported in this study could be due to HIV/AIDS positive status stigmatisation. Similar conclusions were arrived at by Sonko et al., Berendes and Rimal, and Sambisa et al. [ 29,35,52] who noted that individuals who knew anyone with HIV/AIDS experiencing stigmatisation, would be reluctant to test and subsequently know their status. On the contrary, some people would volunteer to have HIV/AIDS self-testing in order to obtain immediate medical assistance if found positive. Output from this study should set as a benchmark for responsible authorities and various stakeholders to support widespread initiatives of HIV/AIDS self-testing uptake in geographical locations that exhibit low uptake. The campaign should encourage the general population to test, thus enabling individuals to know their status hence reducing undiagnosed HIV/AIDS infections among the general population. Equally important, the creation of more targeted policies to increase HIV/AIDS self-testing uptake can be afforded by location intelligence derived from Geographic Information System based techniques [37]. With notable variance in the strength of GWPR coefficients, the study identified districts with both significant positive and negative relationships between each explanatory variable and HIV/AIDS self-testing uptake. Figure 5 highlights comparisons of covariates (at least having attained Grade 7–12, more than 6 months after having visited a health professional and excellent health) in explaining the model. A negative relationship of “at least having attained Grade 7–12” with HIV/AIDS self-testing uptake was prevalent in districts around Limpopo (Vhembe and Capricorn) and Western Cape (Overberg and City of Cape Town). This reveals the relatively weak prediction power of (having attained at least Grade 7–12) education level in explaining HIV/AIDS self-testing uptake. It is imperative to focus on initiatives targeting educational institutions specifically for Grade 7–12 students in those districts. The authors of [2] associated socioeconomic differences with the uptake of HIV/AIDS testing in South Africa and reported an increased likelihood of testing from respondents with secondary education. Such remarks show low uptake in populations with limited education attributed to possible ignorance in interpreting the self-testing results and potentially making mistakes throughout the whole process of self-testing. Overall, HIV/AIDS self-testing services must be accessible to age groups engaged in sexual activities. Attaining at least Grade 7–12 improves one’s HIV/AIDS knowledge, since Grade 7–12 curriculum covers aspects related to sex education and there would be discussions on the importance of HIV/AIDS status awareness [35]. A significant positive association was established in Namakwa, Pixley ka Seme, Mopani, Sekhukune, and Ehlanzeni districts. This result shows that various individuals feel confident enough to perform self-testing with the confidence of being error-free and having enough knowledge to carefully read and interpret the results. The positive association between more than 6 months since the last healthcare profession visit and HIV/AIDS self-testing highlighted in districts around Northern Cape (Namakwa, ZF Mgcawu and Pixley ka Seme) Eastern Cape (Sarah Baartman, Nelson Mandela Bay, and Amathole, etc.), and Western Cape (Garden Route, City of Cape Town, and West Coast) could be highlighting good health amongst respondents, hence less frequent visits to medical professionals. Further explanation of the positive association between HIV/AIDS self-testing and fewer healthcare professional visits could be associated with lifestyle and access to healthy food items thus boosting their immune system. This is more apparent in urban districts (City of Cape Town and Nelson Mandela Bay), where access to nutritious retail food items is better compared to the rural districts [54]. On the contrary, Jin et al. [ 57] postulated that poor attitudes by some healthcare professionals contribute to hesitancy in frequent visits to healthcare professionals. In addition, the fear of HIV/AIDS-positive test results exerts influence on people to only seek healthcare when they are sick. Results from the GWPR model showed a negative relationship between HIV/AIDS self-testing uptake and respondents with more than 6 months after their last health professional visit in districts around KwaZulu Natal (Ugu, eThekwini, Zululand, and Umkhanyakude, etc.) and Northern Cape (Bojanala, Dr Ruth Segomotsi, and Dr Kenneth Kaunda). Non-metropolitan districts have poor transport facilities, for instance, in Ugu district, the average travel distance to the closest hospital is 4 km and in Umkhanyakude, it is 6.3 km. ZF Mgcawu and Pixley ka Seme are constrained by poor road networks; hence, distance to the closest clinic could also play a role in determining the frequency of visits, because people have to travel very long distances [58]. Geoterra Image data (Geoterra image 2018) show an average travel distance of 10.6 km to the closest hospital in Pixley ka Seme (rural district), while in Cape Town (urban district), the distance is 0.75 km. Another explanation for the negative association of HIV/AIDS self-testing and having more than 6 months after the last healthcare professional visit could be attributed to the higher costs of medical consultation fees, limiting people to fewer healthcare professional visits. Perceptions of excellent health status can convince people to have a negative attitude towards acceptance of HIV/AIDS self-testing. Excellent health status is regarded as an essential indicator of satisfaction with life in relation to any illnesses one can succumb to. This subjective perception of an individual’s sense of excellent well-being, with the absence of feelings of discomfort or pain and psychological consequences of having a health problem, contributes to hesitance in HIV/AIDS self-testing initiatives [29]. Negative association in perception of excellent health with HIV/AIDS self-testing uptake is more prevalent around various districts across the country including West Coast, Namakwa, Vhembe, Capricorn, Waterberg, Mangaung, Gert Sibande, and Thabo Mofutsanyane. The lack of a strong relationship between the perception of excellent health and HIV/AIDS self-testing uptake could be attributed to fear of denunciation and a loss of emotional control because of the mental burden of knowing one’s HIV/AIDS positive status [11,20]. Equally important, the fear of having a positive result without any immediate personal support can be devastating to people, thus the increased hesitancy in self-testing uptake [24,25]. Some of the limitations of this current study include the following. The cross-sectional design of the survey limits the causal inferences between HIV/AIDS self-testing uptake with the analysed covariates. Thus, there may be a high degree of municipal district heterogeneity. The findings of this study should only be explained at the county-scale level and local inferences can be misleading/inaccurate due to ecological fallacy. ## 5. Conclusions This study demonstrated the efficacy of GWPR in modelling HIV/AIDS self-testing uptake and its associated covariates. It has been noted that HIV/AIDS self-testing uptake provides a chance for individual testing to be performed inconspicuously, at one’s convenience. GWPR analysis enables the identification of the specific predictors at each local and specific district municipality. Modelling the uptake of HIV/AIDS self-testing enables targeted interventions on reluctant individuals, by developing evidence-based and location intelligence interventions for areas in most need. The contributing factors analysed in this study, i.e., excellent health, more than 6 months since last health professional visit, and at least having attained Grade 7–12, showed mixed effects in explaining HIV/AIDS self-testing uptake. Several barriers continue to limit the full adoption of HIV/AIDS self-testing with issues raised around the absence of counselling after testing, and challenges in the accurate use of HIV/AIDS kits compromising the reliability of the results. South Africa has a diverse population; thus, the provision of HIV/AIDS self-testing programmes should consider the context and culture of the target populations and that some population groups would prefer the use of self-testing kits to avoid the need for counselling which might be futile to them. 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--- title: Application of Whey Protein-Based Emulsion Coating Treatment in Fresh-Cut Apple Preservation authors: - Ying Xin - Chenhao Yang - Jiahao Zhang - Lei Xiong journal: Foods year: 2023 pmcid: PMC10048030 doi: 10.3390/foods12061140 license: CC BY 4.0 --- # Application of Whey Protein-Based Emulsion Coating Treatment in Fresh-Cut Apple Preservation ## Abstract Fresh-cut fruit requires an edible and water-resistant coating to remain fresh. This article investigated the effects of transglutaminase (TGase) and sunflower oil on the water-resistant characteristics, mechanical properties, and microstructure of a whey protein-based film. The whey protein-based emulsion coating’s preservation effect on fresh-cut apples was confirmed. According to the findings, sunflower oil (added at $1.5\%$ w/w) could interact with β-lactoglobulin, α-lactoglobulin dimer, and β-lactoglobulin dimer to form emulsion droplets that are evenly dispersed throughout the protein film. This effect, combined with the covalent cross-linking of TGase, significantly improves the films’ microstructure, mechanical properties, and water resistance. However, too much and unevenly distributed sunflower oil (add $3\%$ w/w) partially prevented the covalent cross-linking of TGase, reducing the elongation at the break of the composite film. In the fresh-cut apple storage experiment, the whey protein-based emulsion coating treatment significantly reduced the weight loss rate and browning index of fresh-cut apples by $26.55\%$ and $46.39\%$, respectively. This was accomplished by the coating treatment significantly inhibiting the respiration rate increase, PPO and CAT activity enhancement, H2O2 production, and MDA accumulation. This research provides practical, technical, and theoretical guidance for the preservation of fresh-cut fruit. ## 1. Introduction Fresh-cut fruits and vegetables are trimmed, peeled, and cut into fully usable products. Following further packaging, consumers can receive fresh items that are convenient, nutritious, and tasty [1]. Fresh-cut fruits and vegetables, in addition to being clean, hygienic, and ready to eat, have less pesticide residue, which can increasingly meet people’s higher expectations for foods. However, mechanical damage to fresh-cut fruits and vegetables during processing causes fluid loss and the breakdown of the natural tissue structure, reducing their resistance to the environment [2]. When intact fruits and vegetables are destroyed, the exposed and oxidized tissue cells are more exposed to oxygen, which accelerates respiration and causes nutrients to be lost, browning, and microbial infection [3]. Therefore, extending the shelf life of fresh-cut fruits and vegetables while maintaining the quality of the product has become a hot topic in contemporary research. An edible coating can form a selective permeability barrier on the surface of fruits and vegetables, inhibit transpiration and respiration, and delay browning, softening, and microbial infection [4,5]. Natural biomacromolecule-based edible coatings, particularly proteins, have grown in popularity among researchers due to their film-forming abilities and nutritional value [6]. Whey protein, a by-product of the cheese-making process, has good film-forming and oxygen-inhibiting properties; hence, it is frequently employed to prevent oxidation reactions. Apple, potato, and strawberry respiration and oxidative browning have been greatly reduced by whey protein coatings [7,8]. Additionally, whey protein has been utilized to preserve dried peanuts [9], cake [10], Atlantic salmon [11], meat products [12], and foods high in unsaturated fatty acids [13]. However, due to its high hydrophilicity, the use of whey protein on the surface of fresh-cut fruits and vegetables is limited. As a result, it is critical to use specific modification techniques to improve the functionality of the whey protein film, particularly its water resistance. The rather weak water vapor resistance of whey protein films can be improved by enzymatic modification. Transglutaminase (TGase), a cross-linked enzyme, can actively promote protein cross-linking to strengthen the protein network, thereby improving the hydrolytic property and water resistance of protein films [14,15]. The efficacy of a protein film’s water resistance has also been improved by the incorporation of numerous hydrophobic compounds, particularly lipids. Although many researchers have investigated the addition of waxes, fatty acids, or acetylated monoglycerides to whey protein films to increase their water resistance [16], the addition of edible vegetable oils to the coating solution is more advantageous in the application scenarios of fresh-cut fruit and vegetable preservation. Sunflower oil is a type of edible oil that is high in linoleic acid and other unsaturated fatty acids. Carotene and vitamin E are also abundant [3]. An innovative preservation and nutritional application system might be created by incorporating TGase and sunflower oil into whey protein film as a layer that can be eaten on the surface of fresh-cut fruits and vegetables. The purpose of this study was to construct an edible whey protein-based film with good water resistance. The mechanism of whey protein-based complex film formation was studied using particle size distribution, zeta potential, intermolecular force, SDS-PAGE, confocal laser scanning microscopy, and scanning electron microscopy. Furthermore, a suitable whey protein-based emulsion coating was applied in the storage of fresh-cut apples. The preservation effect of the coating treatment was evaluated by measuring the respiration rate, weight loss rate, browning index, enzyme activity, and H2O2 and MDA contents in apples. ## 2.1. Material The whey protein isolate (WPI, $95\%$) was purchased from Fonterra Co-operative Group (Auckland, New Zealand). Sunflower oil was of commercial grade and was produced by Yihai Jiali Golden Arowana Grain, Oil and Food Co., Ltd. (Shanghai, China). Microbial TGase was obtained from Jiangsu Yiming Fine Chemical Industry Co., Ltd. (Qinxing, Jiangsu, China), with an actual activity of 100 U g−1. Glycerol (Gly) was used as a plasticizer of analytical grade and was supplied by Tianjin Tianli Chemical Reagent Co., Ltd. (Tianjin, China). ## 2.2. Preparation of Whey Protein-Based Films The preparation of the film-forming solution was based on Jiang’s method and was appropriately modified [17]. First, a film solution consisting solely of WPI (W) was prepared. WPI was dissolved in distilled water at a concentration of $10\%$ (w/v) for 30 min with constant magnetic stirring at 250 rpm. The solution was then heated in a water bath at 90 °C for 30 min while continuously stirring and rapidly cooling. Following that, glycerol ($60\%$ w/w) was added to the WPI solution as a plasticizer and stirred for 30 min. To prepare WPI with TGase film solution (W+TG), TGase at a concentration of 60 U g−1 was added to the WPI film solution (pH 7.5) at 40 °C. After 70 min of the enzymatic reaction, it was heated to 90 °C to inactivate the enzyme for 5 min. To prepare WP with TGase and sunflower oil film solutions (WPI+TG+$1.5\%$ O/$3.0\%$ O), various amounts of sunflower oil (1.5 or $3.0\%$ w/w based on WPI) were added to the WPI film solution and stirred for 15 min before adding the TGase. Finally, all the film solutions were homogenized for 2 min at 13,500 rpm with Ultra Turrax (IKA Yellowline DI25 basic, IKA, Staufen, Germany). All the degassed film solutions (10 mL) were poured into a 9 cm internal diameter petri dish and dried at 60 °C for 4 h. Before testing, dried films were peeled and conditioned for 24 h at 25 °C and $50\%$ RH. ## 2.3.1. Particle Size Distribution and Zeta Potential The particle size distribution and the zeta potential of the particles in film-forming emulsions were determined using laser light scattering granulometry [18] on a Malvern Mastersizer Hydro 2000 SM instrument (BT-9300ST), a commercial dynamic light scattering and micro-electrophoresis device (Malvern Zeta mNano ZS, Malvern Instruments, Worcestershire, UK). Emulsion samples were diluted in de-ionized water to $10\%$ of the original concentration and analyzed at 25 °C. ## 2.3.2. Sodium Dodecyl Sulfate–Polyacrylamide Gel Electrophoresis (SDS-PAGE) SDS-PAGE was used to evaluate the film-forming solutions under reducing conditions, as described by Zhao [19], with some modifications. A discontinuous system was used, consisting of a $4\%$ (w/v) acrylamide stacking gel and a $12\%$ (w/v) acrylamide separating gel. Furthermore, the TGase treatment combined with sunflower oil film-forming solution was centrifuged for 20 min (25 °C, 5000 rpm) to separate the water phase and oil body emulsion. The lower aqueous phase was analyzed directly through SDS-PAGE. The proteins in the upper oil body emulsion then had to be extracted for electrophoretic analysis. The extraction procedure was as follows: SDS was added to the oil body emulsion, which was shaken for 2 min before being centrifuged for 15 min at 4 °C and 14,000 rpm. ## 2.4.1. Film Thickness The thicknesses of the films were measured using an electronic digital micrometer with a resolution of 0.001 mm [20]. Measurements were taken at 5 points on three films chosen at random for each WPI-based film. ## 2.4.2. Water Vapor Permeability (WVP) A modified test based on Butler [21] was used to assess the water vapor permeability of films. Rubber bands around the rim of the cups were used to seal the films over the cups ($d = 45$ cm). Anhydrous calcium chloride ($0\%$ RH, 20 ± 0.1 g) was placed in the cup. The cups were then placed in a distilled water-filled environmental chamber ($100\%$ RH, 23 °C). A certain difference in vapor pressure between the inside and outside of the cups was maintained. Anhydrous calcium chloride absorbed water vapor through the test films. Over a 12 h period, cups were weighed every 3 h. WVP (g mm m−2 h−1 kPa−1) was calculated using the following formula:[1]WVP=Δm·dA·t·ΔP where Δm is the weight of water vapor permeated (g), d is the film thickness (mm), A is the area of exposed film (mm2), t is the time, and ΔP is the water vapor pressure difference across the film. ## 2.4.3. Moisture Content (MC), Moisture Absorption (MA), and Water Solubility (WS) The moisture content, moisture absorption, and water solubility of films were determined using Oliveira’s method [22]. The moisture content of the composite film was obtained by weighing it before and after drying (at 105 ± 1 °C until a constant weight). The weight gain variations of films transferred from $0\%$ RH to $55\%$ RH were used to determine the moisture absorption of the composite films. Water solubility was calculated as a percentage of the film’s soluble dry matter content after 24 h in water. ## 2.4.4. Tensile Strength (TS) and Elongation at Break (EB) Tensile strength and elongation at break of the films (10 × 50 mm) were measured using an ASTM standard method D882 on a Texture Analyzer TA-XT2i (Stable Microsystems, Haslemere, UK), and each sample was paralleled 5 times. The initial separation distance was 30 mm, and it stretched at a rate of 1.0 mm s−1 until it broke. The TS and EB were calculated according to the following equations:[2]TS=FL·X where F is the maximum force at rupture of the film (N), L is the film width (mm), and X is the film thickness (mm). [ 3]EB=L1−L0L0×$100\%$ where L1 is the film elongation at rupture (mm) and L0 is the film’s initial gage length (mm). ## 2.5. Intermolecular Forces of WPI-Based Films Protein interactions in whey protein films were assessed by using the method described by Chawla [23], which assumes that proteins have varying solubilities in different solvent systems. Tris-HCl buffer, urea, sodium chloride solution, SDS, and β-mercaptoethanol were chosen as reagents. Protein was extracted in its natural state using Tris-HCl buffer, urea and SDS were able to destroy non-covalent interactions, and β-mercaptoethanol was a reducing agent capable of destroying disulfide bonds. Changes in protein interactions are assessed by adding or subtracting several reagents. The reagents are as follows: S1: 0.6 mol L−1 NaCl; S2: 20 mmol L−1 Tris-HCl (pH 8.0); S3: 20 mmol L−1 Tris-HCl containing $1\%$ SDS (w/v) (pH 8.0); S4: 20 mmol L−1 Tris containing $1\%$ SDS and 8 mol L−1 urea (pH 8.0); S5: 20 mmol L−1 Tris containing $1\%$ SDS, 8 mol L−1 urea, and $2\%$ β-mercaptoethanol (v/v) (pH 8.0). An amount of 0.2 g protein film sample was mixed with 4 mL of the above 5 reagents and shaken for 1 min before being bathed in water at 40 °C for 4 h. After 30 min of centrifugation at 12,000× g, 0.5 mL supernatant was added to 0.5 mL of $50\%$ (w/v) TCA and stored at 4 °C for 18 h. The supernatant was poured after centrifugation at room temperature for 15 min at 10,000× g, and 1 mL of $10\%$ (w/v) TCA was added for washing once. After 15 min of centrifugation at 15,000× g, the supernatant was poured, and 3.5 mL of 0.5 mol L−1 NaOH was precipitated to dissolve. The protein content was determined by the biuret method. In addition, as a reference amount of protein, 0.2 g of each sample was added to 4 mL of 0.5 mol L−1 NaOH. ## 2.6.1. Confocal Laser Scanning Microscopy (CLSM) The micro-morphology of emulsion refers to the method of Ma [24]. The film-forming solution was stained with Nile blue and Nile red; then, the stained emulsion sample was placed on the slide and examined with an argon/krypton laser, with an excitation line of 514 nm and a helium–neon laser (HeNe) with excitation at 633 nm. ## 2.6.2. Scanning Electron Microscopy (SEM) According to the method of Galus and Kadzinska [25], the surface and cross-section morphology of the gold-sprayed film sample were analyzed using a field emission scanning electron microscope (JSM-7800F, Japanese Electronics Co., Ltd., Tokyo, Japan). The film sample was placed on the sample table with conductive silica gel and observed at a magnification of ×1000 (surface) or 1500 (cross-section). ## 2.7.1. Fruit Treatment Apples (Yantai Fuji) in the commercial ripening stage were obtained from a local market. Apples of comparable size and color were chosen and chilled for 24 h in a refrigerator at 4 °C. The surface of the apples was sterilized with a sodium hypochlorite solution (200 μL L−1) for 2 min before being washed with distilled water. The apples were then peeled with a pre-sterilized knife, the seeds were removed, and they were divided evenly into 12 portions. The apples were randomly divided into two groups: one was soaked in distilled water for 2 min (CK), and the other was soaked in the coating solution (WPI+TG+$1.5\%$ O) for 2 min (CF). Following the drying of the surface coating solution, the two groups of apples were stored at 8 °C and tested every two days. ## 2.7.2. Weight Loss (WL), Respiration Intensity, and Browning Index (BI) Measurement of Weight Loss Rate The initial mass of the apples was recorded as M0 on the first day of the experiment, and the mass of the fresh-cut apples on the test day after a certain storage period was recorded as M1. Then the weight loss rate can be expressed as:[4]WL=M0−M1M0×$100\%$ Determination of Respiratory Intensity Apples (100 g) were placed in different breathing chambers, and the amount of CO2 mg released per kg of fresh-cut apples per hour was measured and calculated to determine their respiration intensity. [ 5]RI=(V2−V1)×M×44m×t where V1 (mL) is the volume of oxalic acid consumed when titrating the sample; V2 (mL) is the volume of oxalic acid consumed when titrating blank; M (mol L−1) is the molarity of oxalic acid; m (Kg) is the ample quality; t (h) is the determination of the time. Determination of Browning Index (BI) The browning index of fresh-cut apples was calculated using Jiang’s method [26]. A colorimeter (CR-20, Konica Minolta, Tokyo, Japan) was used to measure color parameters. The following is how the BI was calculated:[6]BI=X−0.310.172×100 [7]X=a+1.75L5.645L+a−3.02b ## 2.7.3. Determination of PPO and MDA The PPO activity of fresh-cut apples was determined according to the method of Galeazzi [27]. Five grams of the sample was weighed and ground into a pulp before adding 20 mL of phosphoric acid buffer solution, mixing, and centrifuging (4 °C, 12,000× g). The PPO activities were determined using the supernatant (raw enzyme extract). A 3 mL phosphate buffer solution (500 mmol L−1 catechol) was added to 1 mL crude enzyme extraction and measured at 420 nm. Each sample was taken three times in parallel. The amount of enzyme required to cause a change in absorbance value of 0.01 per minute is expressed as enzyme activity. The MDA concentration was determined using the thiobarbituric acid (TBA) method described by Wang [28]. One gram of pulp sample was weighed and extracted with $5\%$ (m/v) trichloroacetic acid solution before centrifuging at 4 °C at 10,000 rpm for 15 min. The supernatant was then mixed with 2 mL of $0.6\%$ thiobarbituric acid solution and bathed at 100 °C for 30 min. After cooling, the absorbance values at 450 nm, 532 nm, and 600 nm were measured. [ 8]MDA=6.45×(A532−A600)−0.56×A450 ## 2.7.4. Determination of H2O2 and CAT The H2O2 concentration and CAT activity were determined using the method described by Zhao [29]. Five grams of each sample were ground into a pulp before being added to the extract and centrifuged (4 °C, 12,000× g). The supernatant was added to the reaction solution, and the absorbance at the corresponding wavelength was measured. ## 2.8. Statistical Analysis Three independent experiments were carried out, with samples collected at least in triplicate samples in each run. SPSS Statistics 20 was used for statistical analysis, and GraphPad Prism 5 was used for graphing. Duncan’s multiple range tests and one-way analysis of variance (ANOVA) ($p \leq 0.05$) were used to determine the differences in the results among the different samples. ## 3.1. The Effects of TGase and Sunflower Oil Content on the Physical Properties of WPI-Based Films The effects of TGase and sunflower oil content on the water resistance and mechanical properties of WPI-based films are shown in Table 1. The WVP of the WPI film modified with TGase decreased significantly, and it decreased even more after the addition of sunflower oil. A small amount of sunflower oil ($1.5\%$) can be added to the film matrix to improve the composite film’s water vapor barrier capacity and reduce water diffusion by combining the hydrophobic phase. This is consistent with the findings of Valenzuela [30]. When sunflower oil was added to quinoa protein, its WVP decreased significantly. Other lipids, such as olive oil [31], rosemary essential oil [32], and oregano essential oil [22], had the same effect when added to the whey protein film. However, when $3\%$ sunflower oil was added instead of the $1.5\%$ supplemental level of sunflower oil, the WVP rate increased. The WVP of the edible film is heavily influenced by particle size distribution, which is affected by homogenization methods and conditions. The smaller the particle size and the more uniform the distribution, the better the film’s water-blocking performance [33,34]. When too many lipids are added, a portion of the oil accumulates, affecting the uniformity of the grease distribution on the film and decreasing water resistance. The amount of water molecules that occupy the hollow positions in the bio-composite film network structure determines the moisture content of the film, which affects the macroscopic performance of the film [35]. The water content of the whey protein composite film was significantly reduced when sunflower oil was added (Table 1). This is primarily because oil not only takes up the original position of water molecules in the protein network structure, reducing the available space for water molecules, but parts of the protein–water interaction also become replaced by protein–oil interactions, lowering the moisture content of the film. The hydrophilicity of the whey protein, the H-group in the water molecule, and the OH-group in the glycerol in the biopolymer chain are usually attributed to the water solubility and absorption of the whey protein film [13,32]. After the TGase modification treatment, the water solubility and absorption of the WPI-based film gradually decreased. With the addition of sunflower oil, the water solubility of the film decreased gradually as the amount added increased (Table 1). The mechanical strength of the film includes TS and EB. TGase could catalyze the binding reaction between the ε-amino group in lysine and the γ-hydroxyamide group in a glutamic acid to form intramolecular or intermolecular ε-(γ-glutamine) amide lysine covalent bonds, resulting in intramolecular or intermolecular cross-linking of proteins. Protein cross-linking led to enhanced mechanical properties of whey protein membranes [36]. After adding $1.5\%$ sunflower oil, the TS of the whey protein composite film increased significantly. However, as the amount of sunflower oil increased, both the EB and the TS decreased significantly (Table 1). This may be because of the composition of emulsion films. Lipid molecules filled the protein matrix in these structures. The interactions between polar and lipid molecules in those structures appeared to be weaker than those between simply polar molecules in control films [37]. ## 3.2. Particle Size Distribution and Zeta Potential of WPI-Based Film-Forming Solutions Many physical and chemical properties of the edible film are affected by the particle size and disruption of the emulsion. As shown in Figure 1A,B, the single whey protein has a small particle size (3.2 μm) and a unimodal distribution; after TGase cross-linking, the average particle size increases significantly to 6.2 μm and exhibits a bimodal distribution, indicating that TGase has a cross-linking action on whey protein. Cross-linking causes the production of β-lactoglobulin dimers, trimers, and even multimers, resulting in the formation of large-molecule proteins [38,39], as well as an increase in particle size and another peak. When compared to the TGase cross-linking without oil, the average particle size of the whey protein emulsion with $1.5\%$ sunflower oil was significantly reduced to 4.2 μm. Because some proteins interact with the oil, the cross-linking of proteins is hampered, causing the peak to shift to the left and become more concentrated. The oils then accumulate and increase the particle size as the amount of sunflower oil added increases. According to Galus [37], regardless of whether almond or walnut oil is added to whey protein, increasing the amount of oil added increases the average particle size of the coating liquid. Different interactions between the oil and protein may occur in order to form an effective edible film and coating. This interaction can be understood by examining the zeta potential of the emulsion. Figure 1C depicts the effect of various treatment methods on the potential of the film-forming fluid. Except for the positive value of pure whey protein, the other values are negative. This is because $60\%$ of the whey protein is β-lactoglobulin (β-Lg) and $20\%$ is α-lactalbumin (α-La), which have isoelectric points of 5.2 and 4.1, respectively, and a solution environment of pH 7.0. When whey protein molecules are cross-linked by the addition of TGase, the protein structure changes, and the potential becomes negative. When sunflower oil is added, a portion of the protein molecules rise to the oil–phase interface, its structure expands to some extent, and molecular rearrangement occurs, exposing acidic amino acids; these acidic amino acids are negative, so the negative potential increases [40]. ## 3.3. SDS-PAGE Analysis of WPI-Based Film-Forming Solutions Whey protein contains a high concentration of free sulfhydryl groups and disulfide bonds. The degree of cross-linking of TGase to whey protein, as well as the distribution of protein components in the emulsion system, can be determined using polyacrylamide gel electrophoresis. In Figure 2A, it is shown that, compared with band 2, the number of α-lactalbumin dimers and β-lactoglobulin dimers increased significantly in band 3, and large molecular weight polymers also appeared, confirming the cross-linking effect of TGase on whey protein. Bands 4 and 5 showed no significant difference when compared to band 3. Therefore, the proteins in the emulsion’s water phase and oil body emulsion were separated for further study. Bands 4 and 7 in Figure 2B represent the electrophoresis spectrum of the oil body emulsion. The figure shows that a portion of β-lactoglobulin, α-lactalbumin dimer, and β-lactoglobulin exist primarily in the oil body emulsion, indicating that proteins with molecular weights ranging from 18 to 36 kDa tend to become interface proteins and stabilize the emulsion droplets. ## 3.4. Intermolecular Force Analysis of WPI-Based Films Hydrogen bonds, disulfide bonds, electrostatic interactions, hydrophobic interactions, and ionic bonds are the most common interactions between protein molecules. Their formation and distribution can keep the protein’s three-dimensional network structure intact during the film’s formation process [20]. The solubility of these proteins in different solvent systems can express the type and size of their intramolecular or intermolecular interaction forces. Urea tends to break hydrogen bonds, SDS tends to break hydrophobic bonds, and β-mercaptoethanol can reduce disulfide bonds. Table 2 shows that the protein solubility of all TGase-modified films in S5 increased when compared to the single WPI film, indicating increased disulfide bonds in the films. However, after the addition of $3\%$ sunflower oil, the solubility of the composite film in S5 decreased, as confirmed by the electrophoresis results. To some extent, the addition of oil inhibits the cross-linking effect of TGase. ## 3.5. The Effects of TGase and Sunflower Oil Content on the Microstructures of WPI-Based Films We can clearly see from Figure 3A(a,b) that when sunflower oil is not added, there is only a continuous protein phase. After the addition of sunflower oil, the oil interacts with the protein to form emulsion droplets (Figure 3A(c,d)). The size and quantity of emulsion droplets gradually increase as the oil content increases (Figure 3A(e,f)), which is consistent with the particle size analysis results. The untreated whey protein film has a smooth surface with no wrinkles or cracks, and the inside is flat and free of bubbles (Figure 3B(a,b)). After being modified with TGase, the structure of the film becomes denser with obvious protrusions, and the protein network structure becomes more compact (Figure 3B(c,d)). When $1.5\%$ sunflower oil is added to the film, the surface remains smooth, but the inside has wrinkles, and the emulsion droplets are evenly distributed in the protein network (Figure 3B(e,f)). However, as the oil content increased to $3\%$, wrinkles appeared on the film surface and more oil droplet aggregates were formed, which were irregularly separated in the whey protein film (Figure 3B(g,h)). These results suggest that the interaction of oil and protein is insufficient to stabilize the oil droplets in the emulsion. Other studies, such as those on olive oil [31] and rapeseed oil [41], have found a similar situation. ## 3.6. Hypothetical Schematic Images for the Formation Mechanisms of Whey Protein-Based Complex Film Natural globular whey proteins are properly heated to expose the hydrophobic amino acids within their structures, allowing them to form films. Some new disulfide and hydrogen bonds were generated by TGase modification, resulting in a more compact three-dimensional protein network structure, which improves the performance of the whey protein-based complex film (Figure 4B). When sunflower oil is added, some take up the original positions of the water molecules in the protein network, while others interact with the proteins to form emulsion droplets. β-lactoglobulin, α-lactalbumin dimer, and β-lactoglobulin dimer with molecular weights of 18–36 kDa tend to act as interfacial proteins. The sunflower oil embedded in the protein network structure, as well as the dispersed emulsion droplets, improve the water resistance of the whey protein-based complex film (Figure 4C). As a result, this WPI+TG+$1.5\%$ O film can be used in high-moisture preservation situations and demonstrates a good preservation effect. However, when the amount of sunflower oil added was increased, the oil droplets themselves aggregated. The TGase cross-linking effect on the protein was inhibited, which had a negative impact on the performance of the whey protein-based film (Figure 4D). ## 3.7.1. Browning Index (BI), PPO Activity, and MDA Content Enzymatic browning is the main cause of the browning of fresh-cut fruits and vegetables. The fresh-cut apples showed browning during storage regardless of whether they were uncoated or coated, as shown in Figure 5A,B. However, a WPI-based emulsion coating treatment significantly reduced the browning of fresh-cut apples. The browning index of fresh-cut apples after the coating treatment was reduced by $46.39\%$ when compared to the control group. This is related to the whey protein film’s barrier properties. On the other hand, it is possible that the cysteine side chain residues in whey protein can inhibit the polyphenol oxidase-mediated enzymatic reaction, reducing browning [34]. PPO is an enzyme that can hasten the onset of enzymatic browning. It can catalyze the o-hydroxylation of monophenols to o-diphenol, which can then be oxidized to dark quinone [42]. Enzymatic browning is closely related to PPO activity in fresh-cut fruits and vegetables during the post-harvest processing and storage process. As shown in Figure 5C, the emulsion coating treatment significantly reduced the increase in PPO activity over the entire storage period. Lipid peroxidation is an important indicator of a deteriorated membrane system and cell metabolism. MDA is produced in the plant cell membrane as a result of lipid peroxidation. MDA buildup destroys the composition of the cell membrane, promoting the accumulation of brown polymers and causing browning and quality decline in fruits. As a result, MDA content is an important index for determining the degree of lipid peroxidation in the membrane of fresh-cut apples [1]. The effects of the emulsion coating treatment on the content of MDA in fresh-cut apples are shown in Figure 5D. The MDA content in both the coated and uncoated groups increased and then decreased, reaching a maximum on the sixth day of 3.76 μmol/kg and 2.98 μmol/kg, respectively. The MDA content in coated fresh-cut apples decreased by $21.74\%$ when compared to the uncoated group. The content of MDA was significantly reduced by the coating treatment at the early stage of storage (0–4d), but the difference was extremely significant at the late stage (6–8d). These findings indicate that the coating treatment could significantly reduce the MDA content increase, protect the cell membrane of fresh-cut apples from damage, and reduce browning. ## 3.7.2. Weight Loss (WL) and Respiration Intensity Due to mechanical damage and transpiration during processing, water and weight are easily lost during the storage of fresh-cut fruits and vegetables [42]. Although the weight loss in each group increased significantly with storage time, at the end of storage the weight loss in the coated group was significantly ($26.55\%$) lower than that in the uncoated group (Figure 6A). It can be concluded that the modification of TGase and the addition of $1.5\%$ sunflower oil could improve the water resistance performance of the WPI-based coating so that it can effectively prevent the water loss of fresh-cut apples. When the coating material contains lipids, the coating treatment can prevent water loss, and the higher the lipid content, the more noticeable the effect [34]. Due to peeling and cutting operations, the plant tissues become subjected to physiological pressures, which increase the rate of respiration and metabolism for processed fresh-cut apples, resulting in “breath injury”. The respiration intensity of uncoated fresh-cut apples showed a slight upward trend. On the sixth day, there was an obvious respiratory peak of 114 CO2 mg kg−1 h−1, and the respiration intensity showed a downward trend on the last day. Similar findings were reported in the study of Salvia [43]. During the whole storage period, the respiration intensity of fresh-cut apples was significantly inhibited by the WPI-based coating (Figure 6B). ## 3.7.3. H2O2 Content and CAT Activity H2O2 accumulated during the storage of fruits and vegetables can directly or indirectly lead to lipid peroxidation damage of the cell membrane, increase cell membrane permeability, and thus accelerate cell aging and disintegration [29]. As shown in Figure 7A, in both coated and uncoated groups, the H2O2 content for all apples presents a rising trend during the whole storage period. However, in the late storage period (6 d), the uncoated group’s H2O2 content reached 78 µmol/g, and the coating treatment significantly inhibited the rising trend, reducing H2O2 content by $25.32\%$. This may be due to the barrier effect of the WPI-based coating, which reduces the respiration rate of the fruit, inhibiting the oxidative stress response of fresh-cut apples. Catalase is an iron-containing hemoglobin protease found in almost all living organisms that rely on oxygen. It can catalyze the breakdown of accumulated hydrogen peroxide in plants into water and oxygen, reducing the oxidative damage that H2O2 can cause to fruit and vegetable tissues [44]. As shown in Figure 7B, the increasing trend of CAT activity in the uncoated group was roughly consistent with that of the H2O2 content and peaked on the sixth day. After coating the fresh-cut apple, the activity of CAT was significantly reduced. The lower CAT activity could be attributed to the lower H2O2 content of fresh-cut apples due to the WPI-based coating which has an oxygen barrier [45]. ## 4. Conclusions In this study, an edible whey protein-based film with good water resistance was produced. Meanwhile, the film-forming mechanisms and preservation effect of the whey protein-based film on fresh-cut apples were verified. Sunflower oil (add $1.5\%$ w/w) could interact with β-lactoglobulin, α-lactoglobulin dimer, and β-lactoglobulin dimer to form emulsion droplets that are evenly dispersed throughout the protein film. This effect, combined with the covalent cross-linking of TGase, significantly improves the films’ mechanical properties and water resistance. However, too much and unevenly distributed sunflower oil (add $3\%$ w/w) partially prevented the covalent cross-linking of TGase, reducing the elongation at the break of the composite film. The best composite coating treatment significantly reduced the weight loss rate and browning index of coated fresh-cut apples by $26.55\%$ and $46.39\%$, respectively. 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--- title: Does the Disparity Patterning Differ between Diagnosed and Undiagnosed Hypertension among Adults? Evidence from Indonesia authors: - Puput Oktamianti - Dian Kusuma - Vilda Amir - Dwi Hapsari Tjandrarini - Astridya Paramita journal: Healthcare year: 2023 pmcid: PMC10048049 doi: 10.3390/healthcare11060816 license: CC BY 4.0 --- # Does the Disparity Patterning Differ between Diagnosed and Undiagnosed Hypertension among Adults? Evidence from Indonesia ## Abstract Background: Healthcare systems in many low- and middle-income countries (LMICs) are not yet designed to tackle the high and increasing burden of non-communicable diseases (NCDs), including hypertension. As a result, a large proportion of people with disease or risk factors are undiagnosed. Policymakers need to understand the disparity better to act. However, previous analyses on the disparity in undiagnosed hypertension, especially from LMICs, are lacking. Our study assessed the geographic and socioeconomic disparity in undiagnosed hypertension and compared it with diagnosed hypertension. Methods: We used the Basic Health Survey (Riskesdas) 2018 and performed geospatial and quantitative analyses across 514 districts in Indonesia. Dependent variables included diagnosed and undiagnosed hypertension among adults (18+ years) and by gender. Results: A high prevalence of undiagnosed hypertension at $76.3\%$ was found, with different patterns of disparity observed between diagnosed and undiagnosed hypertension. Diagnosed hypertension was 1.87 times higher in females compared with males, while undiagnosed hypertension rates were similar between genders. Urban areas had up to $22.6\%$ higher rates of diagnosed hypertension, while undiagnosed hypertension was $11.4\%$ more prevalent among females in rural areas. Districts with higher education rates had up to $25\%$ higher diagnosed hypertension rates, while districts with lower education rates had $6\%$ higher rates of undiagnosed hypertension among females. The most developed regions had up to $76\%$ and $40\%$ higher prevalence of both diagnosed and undiagnosed hypertension compared with the least developed regions. Conclusion: The disparity patterning differs between diagnosed and undiagnosed hypertension among adults in Indonesia. This highlights the need for effective measures, including healthcare system reforms to tackle NCDs in LMICs. ## 1. Background Hypertension, or high blood pressure, is linked with increased heart, brain, and kidney disease risks [1]. Globally, about 1.3 billion adults aged 30 years and over had hypertension in 2021. Most of those with hypertension (over $60\%$) are in low- and middle-income countries (LMICs) [1]. Moreover, data also showed that less than half ($42\%$) of those with hypertension were diagnosed and treated [1]. In Indonesia, hypertension is also high and increasing. Analyses from the nationally representative survey (RISKESDAS) showed that hypertension prevalence among adults 18 years and over was $34.1\%$ in 2018, which increased considerably from $25.8\%$ in 2013 [2]. Moreover, a study of the Indonesian Family Life Survey 2016 found that the prevalence of hypertension among adults 40 years and over was $47.8\%$, of which almost $70\%$ were undiagnosed [3]. The current literature provides some evidence of social determinants of cardiovascular diseases and risk factors including hypertension [4]. A comprehensive literature review and meta-analysis in 2014 found that income level was positively associated with hypertension, but education level was not. The study also found geographic variation in the association between education and hypertension, showing an inverse association in the East Asian region and a positive one in the South Asian region [5]. A study in 2017 using the South Korean National Health and Nutrition Examination Survey (NHNES) found sexual variation in the association between education and undiagnosed hypertension, showing an inverse association among women but not among men [6]. Recent analyses (2019–2021) of the Demographic & Health Survey data in Peru, Bangladesh, and Nepal also found that adults from lower socioeconomic and educational backgrounds had higher odds of undiagnosed hypertension [7,8,9,10]. Another study in 2016 showed that being in a deprived neighborhood increased the influence of individual socioeconomic status on mortality among newly diagnosed hypertension patients in South Korea [11]. Similarly, a study in Peru found that adult males living in the more remote and deprived areas (e.g., coasts and mountains) had a higher prevalence of undiagnosed hypertension [7]. A study in the United States showed that rural areas were most vulnerable to adverse chronic health outcomes and found a positive association between social vulnerability index and cardiometabolic indicators including hypertension [12]. To achieve the SDG target 3.4.1 to reduce premature mortality from NCDs by one-third by 2030, efforts need to aim at reducing the disparity in diagnosed and undiagnosed hypertension [1]. However, the current literature on such disparity is limited in three ways. First, while most of the current literature used data at the individual level (e.g., national surveys) [3,5,6,7,8,9,10], studies that employed data at the local level (such as districts) are lacking. Such evidence is also crucial, especially in countries with more local decision space, such as Indonesia. Second, because of the better availability of local level data, current geographic analyses are mainly from high-income countries such as the United States and South Korea [11,13,14]. Such analyses from LMICs (e.g., China, Thailand, and Peru) are limited to the urban/rural and provincial levels [15,16,17]. Third, previous studies focused on overall hypertension and lacked disaggregation between diagnosed and undiagnosed hypertension. Effective health system reforms and population-based interventions may be needed to reduce the undiagnosed population [18]. Our study aimed to assess the disparity (geographic and socioeconomic) in diagnosed and undiagnosed adult hypertension across over 500 Indonesian districts. ## 2.1. Study Design This is a cross-sectional study comparing the disparity in diagnosed and undiagnosed hypertension among adults. We analyzed geographic and socioeconomic disparities across 514 districts within 34 provinces in Indonesia. We took advantage of the 2018 Basic Health Survey (Riskesdas) data that were representative at the district level for diagnosed and undiagnosed hypertension. The survey conducted interviews and physical examinations of about 300,000 households from a two-stage sampling procedure. The sampling first randomly selected 30,000 census blocks (out of a total of over 700,000 in Indonesia). Within each block, 10 households were systematically selected, which resulted in 624.563 adults (18+ years). The mean ages (standard deviation) were 41.0 (15.5) years, 40.8 (15.3) years, and 41.3 (15.7) years for all adults, males, and females, respectively [2]. ## 2.2. Independent Variables The main independent variables included geographic and socioeconomic indicators at the district level. The variables used in our analyses were region, urbanicity, income level, and education level. This information was taken from the World Bank. The regional variable includes five regions: Sumatera, Java (including Bali), Kalimantan, Sulawesi, and Papua (including Nusa Tenggara and Maluku). Generally, the eastern parts of the country are the least developed [19,20,21]. Appendix A provides the map reference. The urbanicity variable shows cities as urban and regencies as rural areas. For the income variable, we used the poverty rates information at the district level, which we then grouped into quintiles. For the education variable, we used net enrollment ratios of senior secondary information, which we grouped into quintiles as well [22,23,24]. ## 2.3. Dependent Variables There were six dependent variables used in our analysis, including diagnosed adults, diagnosed males, diagnosed females, undiagnosed adults, undiagnosed males, and undiagnosed females. Diagnosed hypertension was a binary variable with a value of 1 if one reported ever being told by a doctor that they have high blood pressure and 0 if otherwise. We defined undiagnosed hypertension as not diagnosed but meeting the criteria for hypertension based on the blood pressure measurement (i.e., either systolic blood pressure of at least 140 mmHg, diastolic blood pressure of at least 90 mmHg, or both) [25]. ## 2.4. Data Analysis We performed both geospatial analyses and multivariable regression analyses in this paper. In conducting the geospatial analyses, we grouped each dependent variable for 34 provinces and 514 districts by quintile. In conducting the regressions, we employed ordinary least squares and examined the relationship between independent and dependent variables. We compared the regional variations between the western and eastern parts of the country, and the income/education variations between the poorest/least educated and wealthiest/most educated. The geospatial analyses were conducted in ArcMap 10 and the statistical analyses were performed in STATA 15, using $5\%$ as statistically significant. ## 3.1. Analysis at the Provincial Level Figure 1 and Table 1 show results at the provincial level. Figure 1 compares diagnosed hypertension (panels a–c) and undiagnosed hypertension (panels d–f) by quintile. At the provincial level, diagnosed hypertension among all adults ranged from $4.4\%$ to $13.2\%$; males from $3.7\%$ to $9.5\%$; and females from $5.2\%$ to $17.0\%$. At that level, undiagnosed hypertension among all adults ranged from $19.4\%$ to $35.5\%$; males from $18.7\%$ to $35.6\%$; and females from $17.3\%$ to $35.4\%$. Diagnosed hypertension among all adults was highest (quintiles four–five) in many provinces in the Java and Bali region (e.g., Jakarta, Banten, West Java, Yogyakarta, and Bali), several provinces in Kalimantan (e.g., East, North, and South Kalimantan) and Sulawesi (e.g., North Sulawesi, Central Sulawesi, and Gorontalo), and a province in Sumatera (i.e., Aceh). Undiagnosed hypertension among all adults was highest (quintiles four–five) in many provinces in Java (e.g., Jakarta, West Java, Central Java, East Java, and Bali) and Kalimantan (e.g., East, West, Central, and South Kalimantan), many provinces in Sulawesi (e.g., West, South, and Southeast Sulawesi), and two provinces in Sumatera and Papua. By sex, the patterning showed some differences. For instance, diagnosed hypertension among females was higher (quintile four) and that among males was lower (quintile two) in Bangka Belitung. In contrast, diagnosed hypertension among females was lower and that among males was higher in West Kalimantan. Similarly, undiagnosed hypertension among females was higher, and that among males was lower in North Sumatera, South Sumatera, and Lampung. Moreover, Table 1 compares diagnosed hypertension and undiagnosed hypertension by the level of poverty rates at the provincial level. The top box and bottom box compare the ten richest and poorest provinces. The provincial prevalence higher than the national level is shown in grey in each column. Of the ten wealthiest provinces, six provinces (e.g., Jakarta, Bali, South, North, and East Kalimantan) had higher prevalence than average for at least four indicators, while none of the ten poorest provinces did. ## 3.2. Analysis at the District Level Figure 2 and Table 2 and Table 3 show results at the district level. Table 2 shows the characteristics of districts in terms of geographic indicators, socioeconomic indicators, and dependent variables (i.e., diagnosed and undiagnosed hypertension). Of the total of 514 districts in our analysis, 97 ($18.9\%$) and 417 ($81.1\%$) were urban (cities) and rural (regencies). The two regions where urban districts were dominant included Java ($36.1\%$ of 97) and Sumatera ($34.0\%$). For the income variable, most of the urban areas ($78.4\%$) were considered wealthy (quintiles four–five), but fewer than a third of rural areas ($31.2\%$) were. Similarly, for education, $71.1\%$ of urban areas had higher education (quintiles four–five), while only a third ($32.6\%$) of rural areas did. In terms of hypertension, diagnosed prevalence was $7.9\%$, $5.5\%$, and $10.3\%$, while that of undiagnosed hypertension was $25.4\%$, $24.9\%$, and $25.8\%$ among adults, males, and females. Relative to rural areas, diagnosed hypertension was significantly higher, but undiagnosed hypertension among females was significantly lower in urban areas. Diagnosed hypertension among adults, males, and females was $8.9\%$, $6.5\%$, and $11.2\%$ in urban areas and $7.6\%$, $5.3\%$, and $10.1\%$ in rural areas. Undiagnosed hypertension among females was $23.4\%$ and $26.4\%$ in urban and rural areas, respectively. Figure 2 compares the prevalence of diagnosed and undiagnosed hypertension by quintile at the district level. For diagnosed hypertension, many districts in the provinces of Jambi, Riau, Bengkulu, Central Java, East Java, West Kalimantan, Central Kalimantan, South Sulawesi, Papua, and West Papua had higher hypertension among all adults (quintiles four–five). For undiagnosed hypertension, many districts in all provinces in Sumatera and Papua had higher prevalence among adults (quintiles four–five). For socioeconomic disparity analysis at the district level, Appendix C and Appendix D compare districts with the lowest and highest diagnosed and undiagnosed hypertension. For diagnosed hypertension, the prevalence among adults ranged from $0\%$ in Buton Tengah regency (Central Sulawesi province) to $20.8\%$ in Sitaro Kepulauan (North Sulawesi). By sex, diagnosed hypertension among males ranged from $0\%$ in Yahukimo and Pegunungan Bintang (Papua) to $15.8\%$ in Tomohon city (North Sulawesi); that among females ranged from $0\%$ in Buton Tengah (Southeast Sulawesi), Yahukimo, Dogiyai, and Mambramo Raya (Papua) to $27.0\%$ in Sitaro Kepulauan (North Sulawesi). For undiagnosed hypertension, the prevalence among adults ranged from $7\%$ in Puncak Jaya (Papua) to $43.2\%$ in Hulu Sungai Tengah (South Kalimantan). By sex, undiagnosed hypertension among males ranged from $6.8\%$ in Puncak Jaya (Papua) to $44.9\%$ in Madiun city (East Java); that among females ranged from $6.2\%$ in Puncak Jaya (Papua) to $44.6\%$ in Ciamis (West Java). By urbanicity, districts with the lowest prevalence of diagnosed and undiagnosed hypertension for all adults, males, and females were rural. Similarly, most districts with the highest prevalence of diagnosed and undiagnosed were rural. By income, poverty rates among ten districts with the highest diagnosed and undiagnosed hypertension were averaged up to $10\%$, while those with the lowest prevalence were averaged up to $33\%$. Table 3 compares the associations between geographic/socioeconomic variables and diagnosed/undiagnosed hypertension. Districts in the least disadvantaged region had a significantly higher prevalence of both diagnosed and undiagnosed among all adults, males, and females, relative to the most disadvantaged region (e.g., Papua). Compared with Papua, districts in the Java region had $68\%$, $45\%$, and $76\%$ higher diagnosed prevalence among adults, males, and females; they had $40\%$, $39\%$, and $40\%$ higher undiagnosed prevalence (significant at $5\%$ level). Moreover, results showed that districts in the Kalimantan region had the highest diagnosed and undiagnosed prevalence among all adults, males, and females in the country. For the income variable, results show that the richest districts had a higher diagnosed and undiagnosed prevalence among all adults, males, and females than the poorest ones (but not statistically significant in multivariable regressions). For the education variable, the relationships are mixed. Districts with the most education had $23\%$, $18\%$, and $25\%$ significantly higher diagnosed prevalence among adults, males, and females than the least educated ones. However, districts with the least education had a $6\%$ (i.e., $\frac{1}{0.94}$ = 1.06) higher undiagnosed prevalence among females. ## 4. Discussion Using nationally representative survey data of adults, we found the prevalence of overall hypertension was $33.3\%$, of which $76.3\%$ were undiagnosed (i.e., $7.9\%$ diagnosed and $10.3\%$ undiagnosed). Global estimates showed similar hypertension prevalence in adults 30–79 years of age at $32\%$ and $34\%$ among women and men in 2019 [26]. In terms of undiagnosed hypertension, while considerably higher than in high-income countries such as the United States ($19.7\%$ in 2010), South Korea ($33.4\%$ in 2013), and Ireland ($41.2\%$ in 2011) [6,27], the prevalence in Indonesia was relatively similar to that in LMICs such as Nepal ($50.4\%$ in 2016), Bangladesh ($59.9\%$ in 2011 and $50.1\%$ in 2017), and Peru ($67.2\%$ in 2019) [7,9,10]. By sex, diagnosed hypertension among females was 1.87 times higher compared with males (i.e., $5.5\%$ males and $10.3\%$ females), while undiagnosed hypertension was similar between both sexes (i.e., $24.9\%$ and $25.8\%$ among males and females). This finding aligns with evidence from other LMICs, such as Nepal, Bangladesh, and Peru, showing a significantly lower prevalence of undiagnosed hypertension among women [7,9,10]. This might be due to women having more interactions with the health systems (e.g., through antenatal, delivery, and postnatal care) and other population-based interventions more towards women (e.g., conditional cash transfers) [28,29]. We found significant disparities (geographic and socioeconomic) between the prevalence of diagnosed and undiagnosed hypertension across 514 districts. Diagnosed hypertension was higher by up to $22.6\%$ in urban areas, while undiagnosed hypertension among females was higher by $11.4\%$ in rural areas. Previous studies showed a higher prevalence of diagnosed hypertension in urban areas but a higher prevalence of undiagnosed hypertension in rural areas [7,8,9,10]. This is expected, as urban areas tend to have higher access to health facilities and healthcare professionals. However, many rural districts were among the top ten districts with the highest prevalence of diagnosed and undiagnosed hypertension, which may be due to similarities in economic development and mobility between rural and urban areas [30]. For example, the North Sulawesi, Minahasa and Minahasa Selatan regencies, which had similar income levels and were adjacent to Tomohon City, were found to have high rates of hypertension. By region, the patterning is similar for diagnosed and undiagnosed hypertension. Districts in the most developed areas (i.e., Java and Bali) had up to a $76\%$ and $40\%$ higher prevalence of diagnosed and undiagnosed hypertension compared with the least developed areas (i.e., Papua, Nusa Tenggara, and Maluku). This is likely due to a higher burden of hypertension (diagnosed and undiagnosed) among higher socioeconomic levels of the population in more developed regions. By income, the richest districts had a higher prevalence of diagnosed and undiagnosed hypertension among all adults, males, and females than that of the poorest districts (although only statistically significant in bivariate analyses). By education, districts with the most education had up to a $25\%$ higher prevalence of diagnosed hypertension, while those with the least had a $6\%$ higher undiagnosed prevalence among females. While evidence from LMICs are limited in the literature, our findings align with previous studies. Studies using provincial-level data in China showed a higher prevalence of hypertension in the least disadvantaged areas than that in the most disadvantaged ones [15,16]. Similar study at the provincial level in Thailand found a higher prevalence of hypertension in Bangkok and metropolitan areas than in less developed areas in the north and south regions [17]. On the contrary, studies from high-income countries found a higher prevalence of hypertension in the most disadvantaged areas [11,13,14]. Moreover, a higher prevalence of diagnosed hypertension among districts with the most education may be due to better health systems and access to health facilities [31]. In contrast, analyses at the individual level in Peru, Bangladesh, and Nepal found adults with low education had higher odds of undiagnosed hypertension [7,8,9,10]. Studies have also shown strong association between low education and cardiometabolic comorbidities and that education may be considered the best predictor of cardiovascular risk in people with hypertension [32,33]. Effective efforts are needed to reduce undiagnosed hypertension (and other NCD risk factors such as high cholesterol and diabetes) by sex, urbanicity, region, and socioeconomic status [34,35]. Efforts may include health system reforms such as enhanced primary health care in Malaysia or routine assessment national programs such as NHS Health Check in the United Kingdom [18,36]. Healthcare delivery reforms may also include integration with infectious disease platforms [37,38]. Our study is the first analysis from LMICs to compare the disparity (geographic and socioeconomic) in the prevalence of diagnosed and undiagnosed hypertension across over 500 localities. However, our study also has limitations. Because of the lack of information, our analysis could not conduct sub-group analysis by ethnicity [39]. Additionally, because of using cross-sectional data, our analysis could not conduct trend analysis. However, regardless of these limitations, our evidence is crucial for policymaking nationally and globally, especially in low-resource settings. ## 5. Conclusions In Indonesia, a high prevalence of undiagnosed hypertension at $76.3\%$ was found with different patterns of disparity observed between diagnosed and undiagnosed hypertension. Diagnosed hypertension was 1.87 times higher in females compared with males, while undiagnosed hypertension rates were similar between genders. Urban areas had up to $22.6\%$ higher rates of diagnosed hypertension, while undiagnosed hypertension was $11.4\%$ more prevalent among females in rural areas. Districts with higher education rates had $25\%$ higher diagnosed hypertension rates, while districts with lower education rates had $6\%$ higher rates of undiagnosed hypertension among females. The most developed regions had up to a $76\%$ and $40\%$ higher prevalence of both diagnosed and undiagnosed hypertension compared with the least developed regions. This study highlights the need for effective measures, including healthcare system reforms, to tackle NCDs in LMICs. ## References 1. 1. 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--- title: COVID-19′s Psychological Impact on Chronic Disease Patients Seeking Medical Care authors: - Hager Salah - AL Shaimaa Ibrahim Rabie - Amira S. A. Said - Mohammad M. AlAhmad - Ahmed Hassan Shaaban - Doaa Mahmoud Khalil - Raghda R. S. Hussein - Azza Khodary journal: Healthcare year: 2023 pmcid: PMC10048099 doi: 10.3390/healthcare11060888 license: CC BY 4.0 --- # COVID-19′s Psychological Impact on Chronic Disease Patients Seeking Medical Care ## Abstract Background: The outbreak has harmed patients with multiple comorbidities and chronic conditions. The pandemic’s psychological impact is thought to change their routine of seeking medical care. Research Question or Hypothesis: During COVID-19, patients with chronic conditions may experience anxiety, depression, and stress, and their pattern of seeking medical care may change. Materials and Methods: In May 2021, a cross-sectional, web-based study of patients with chronic diseases was conducted. Eligible patients [1036] were assessed for psychological disorders, primarily depression, stress, and anxiety, using the DASS-21 scale, and their pattern of receiving medical care during COVID-19. Results: During the pandemic, $52.5\%$ of the patients with chronic diseases were depressed, $57.9\%$ were anxious, and $35.6\%$ were stressed. Patients with chronic diseases who had moderate to severe depression ($34.9\%$ versus $45.1\%$, $$p \leq 0.001$$), moderate to severe anxiety ($43.6\%$ versus $53.8\%$, $$p \leq 0.001$$), or moderate to severe stress ($14.9\%$ versus $34.8\%$, $$p \leq 0.001$$) were significantly more likely to have no follow-up for their chronic conditions. Conclusions: Patients with chronic conditions experienced significant anxiety, depression, and stress during COVID-19, which changed their pattern of seeking medical care, and the majority of them did not receive follow-up for their chronic conditions. ## 1. Introduction A cluster of pneumonia with an unknown origin was discovered in Wuhan City, Hubei Province, China, in December 2019. A new coronavirus (2019-nCoV) has been discovered as the cause of this illness [1]. The World Health Organization labeled the disease as Coronavirus Disease 2019 (COVID-19) (WHO). The new coronavirus pneumonia (COVID-19) had spread fast throughout China and the world as of 18 February 2020, resulting in thousands of confirmed cases and deaths [2]. Chronic illnesses have a high death rate and are quite costly on healthcare infrastructure [3]. The World Health Organization (WHO) estimates that in 2020, chronic diseases will account for $60\%$ of all disease burden worldwide and $73\%$ of all fatalities. In addition, developing nations will account for $79\%$ of these deaths [4]. Previous studies demonstrate the high rates of stress, anxiety, and depression in chronic disease patients. It is advised that health professionals focus more on preventing and controlling these illnesses [5]. Studies reported more signs of anxiety and more stress in people with chronic disease than in those without any chronic disease during the COVID-19 pandemic [6]. Protecting older people’s mental health is crucial, especially for those who have chronic illnesses. In particular, in these difficult times that we are presently experiencing, it is necessary to provide these vulnerable segments of the population with psychological interventions and instruments aimed at enhancing their emotional and social states [7]. COVID-19 can infect persons of any age; however, older people are more susceptible to infection and have a higher fatality rate [8]. Various public health measures, such as quarantine and social isolation, have emerged in response to the COVID-19 pandemic [9]. Consequently, these measures had a negative impact on mental health, leading to a high prevalence of mental symptoms such as discomfort, anxiety, anger, loneliness, poor mood, sleeplessness, depression, and post-traumatic stress disorder [10]. These mental health side effects were attributed to stressors connected with quarantine, such as the length of the quarantine, the fear of illness or infecting others, a lack of information, and the stigma of discrimination [11]. Mental health symptoms vary from person to person depending on their thinking and sociability [12]. Patients with various comorbidities and chronic conditions such as hypertension, diabetes, renal disease, asthma, or COPD were severely affected by the COVID-19 pandemic [13], with the worst outcomes and mental health consequences [4]. Patients may be avoiding medical attention out of fear of contracting the disease or as a result of quarantine [14]. This delay in obtaining treatment or omitting usual ongoing care can result in increased morbidity and mortality, which have not been considered in the assessment of the pandemic’s harm [15]. Many studies found patients with chronic conditions may be afraid to use their regular health-care services in order to reduce their chance of infection and the consequences that may result from a virus. The pandemic has significantly threatened the general public’s mental and physical health [16]. The limited access to healthcare created a huge mental burden, which results in psychological distress and anxiety disorders [17,18,19,20,21]. Patients with chronic conditions are at higher stress levels because of the higher risk of poorer COVID-19 outcomes [22]. According to the rapid spread of COVID-19 worldwide, combined with compulsory quarantine and widespread lockdowns, it triggered public fear and disseminated rumors and conspiracy theories [23]. During COVID-19, patients with chronic conditions may experience anxiety, depression, and stress, and their pattern of seeking medical care may change. This study intends to assess the effect of COVID-19 on medical care among Egyptian patients with chronic diseases through anxiety, depression, or stress caused by the outbreak. ## 2.1. Study Design A cross-sectional study was conducted in Egypt between March and June 2021. Approved by the Research Ethics Committee, number FMBSUREC/09052021. This study included patients with chronic diseases (diabetes, hypertension, and other chronic diseases) who received medical care in various ambulatory clinics. A total of 2176 participants were invited through text messages to participate as per government recommendations to minimize face-to-face or physical interaction as citizens continue to isolate themselves at home. Potential respondents were invited through a text message, resulting in 1450 total responses; we excluded 379 responses for not having completed data, and 35 participants did not meet the inclusion criteria. The following criteria were used to determine inclusion criteria: [1] informed consent prior to the survey; [2] residence in Egypt; [3] age 18 years or older; and [4] confirmed chronic condition diagnosis. Each participant provided information about their basic demographics as well as chronic diseases such as hypertension, diabetes mellitus, and other comorbidities. Our study aimed to investigate the following hypotheses that were more closely related to psychological impact: A higher level of anxiety, depression, and stress will be significantly associated with less regular medical follow-up for chronic disease patients during the COVID-19 pandemic in Egypt. ## 2.2. Sample Technique An online Google form containing a questionnaire was sent via social media such as WhatsApp, Facebook, emails, and others. Respondent’s target is Egyptian adults above 18 years old with any chronic diseases. We collected data anonymously, without collecting information that could identify the respondents. The first part of the study questionnaire collected socio-demographic information, including age, gender, occupational status, city of residence, marital status, educational level, and comorbidities (diabetes, hypertension, cancer, obesity, cardiac disease, COPD, etc.). ## 2.3. Data Collection Tool The questionnaire was translated from English to Arabic by two professionals and a native Arabic speaker with English as their first language. To evaluate the validity and reliability of the questionnaire, we performed a pilot study on 30 Egyptian participants, who were then excluded from the main study and the subsequent data analysis. A pilot analysis was used to assess the clarity of the DASS and its appropriateness through online interviews with 30 participants. No difficulties were reported in completing it, so no further changes were made. The internal consistency of the questionnaire was assessed using Cronbach’s alpha coefficient. No interclass correlation was detected in the initial pilot study, so no components were deleted from the original version. Cronbach’s alpha for the depression domain was 0.872, that of the anxiety domain was 0.910, and that of the stress domain was 0.891. Part 1: 20-item self-structured questions evaluated the socio-demographic data of study participants, including: age, gender, BMI, academic achievement, employment status, place of residence, and maternal status. In addition, data related to medical status, timing of receiving medications before and during COVID, places of getting medications, and usage of transportation vehicles were collected. The data also included whether safety measures were used while receiving medications during the pandemic or not. The questionnaire contained the status of persons for whom COVID-19 was suspected at any given time and what their response was regarding medical advice or not. Data concerning the seeking of medical advice for their chronic diseases was gathered. Part 2: 21-item self-administered questions; using the DASS-21 to evaluate emotional states of anxiety, stress, and depression [24]. It is measured by the 5-point Likert scale. Final response scores were identified as normal, mild, moderate, severe, and very severe. The depression scale assesses dysphoria, hopelessness, devaluation of life, self-deprecation, lack of interest or involvement, anhedonia, and inertia. The anxiety scale assesses autonomic arousal, skeletal muscle effects, situational anxiety, and the subjective experience of anxious affect. The stress scale is sensitive to levels of chronic non-specific arousal. It assesses difficulty relaxing, nervous arousal, being easily upset or agitated, being irritable or overly reactive, and being impatient. Scores for depression, anxiety, and stress are calculated by summing the scores for the relevant items. The rating score was considered four choices: pick up zero when the participant saw that the choice is not applied to him at all, one when the choice is applied to him to some degree or some of the time, two when the choice is applied to him to a considerable degree or a good part of the time, and three when the choice is applied to him to very much or most of the time. The depression score was considered normal when falling between 0 and 9, mild when falling between 10 and 13, moderate when falling between 14 and 20, severe when falling between 21 and 27, and extremely severe when falling at 28 or above. The anxiety score was considered normal when it was between 0 and 7, mild when it was between 8 and 9, moderate when it was between 10 and 14, severe when it was between 15 and 19, and extremely high when it was 20 or above. The stress score was considered normal when it fell between 0 and 14, mild when it fell between 15 and 18, moderate when it fell between 19 and 25, severe when it fell between 26 and 33, and extreme when it fell between 34 and above. Using Epi Info StatCalc [25], the sample size for a population survey was calculated at a $95\%$ confidence level with a $5\%$ acceptable margin of error, one design effect, and $50\%$ expected frequency (of regular follow-up or a positive DASS). The minimum sample size was found to be at least 384 people, which was tripled to overcome the selection bias. ## 2.4. Statistical Analysis The Statistical Package for Social Science (SPSS) version 25 was used to gather, code, and analyze the data (IBM, USA) IBM Corp. Released 2017. IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp. We estimated the frequency distribution of categorical variables as a percentage and the mean and SD for scale variables. We categorized the scale variables by median (age at less than or equal to 32 and more than 32 years, and BMI at less than or equal to 27.8 and more than 27.8). The Chi-Square Test of Independence was utilized to determine a connection between categorical variables (difference between follow-up and no follow-up and age, sex, residence, working status, occupation, education, chronic disease, degree categories of depression, anxiety, and stress). Binary logistic regression was used to identify the determinants of no follow-up among the hypothesized factors that can affect the probability of its occurrence. The mentioned binary logistic model is the best model that explained the probability of no follow-up occurrence after excluding intercorrelation between variables and redundant variables such as BMI, working status, marital status, and residence. p values of ≤0.05 were considered significant. ## 3. Results The total number of eligible responses was 1036 patients with chronic diseases. They were filling on their behalf and were included. The baseline characteristics of chronic disease patients are shown in Table 1, with a median age of 32, a marriage rate of $52.4\%$, and a majority having more than one chronic disorder (i.e., hypertension plus diabetes) at around $37.5\%$. Diabetes and hypertension were the most common chronic diseases in our sample population, but we also included other comorbidities (cancer, obesity, COPD, cardiac disease, and autoimmune disease); however, they were not significant in our sample population. In addition, Table 2 showed the information about COVID-19 infection status and medical treatment received for it. $59.3\%$ were clinically suspected of having COVID-19, $35.8\%$ were self-isolated, and $32.9\%$ went to the hospital. Moreover, the patients’ follow-up pattern before and during the COVID-19 pandemic was illustrated in Table 3. Our results revealed that $73.6\%$ were regularly collecting their medication before the COVID-19 pandemic and dropped to $43.5\%$ during the COVID-19 pandemic as $63.2\%$ had a fear of COVID-19 infection. Furthermore, the results illustrated that $52\%$, $60\%$, and $35.6\%$ of patients with chronic diseases suffered from depression, anxiety, and stress, ranging from mild to very severe, respectively, as shown in Table 4. The univariate analysis revealed the following statuses: being female, being younger, having a low BMI, being unmarried, having a low educational level, not working, having an urban residency, and not preferring telemedicine were significantly associated with less regular follow-up, as illustrated in Table 5. While having DM plus hypertension was more significantly associated with follow-up. DASS-21 was used to evaluate the emotional states of anxiety, stress, and depression, all of which were significantly associated with regular follow-up. The results illustrated that after adjustment for age, gender, residence, presence of depression, presence of anxiety, and presence of stress caused by the COVID-19 pandemic, it was found that the presence of anxiety caused by the COVID-19 pandemic increased the probability of no follow-up (in other words, the stress caused by the COVID-19 pandemic decreases the follow-up rate) with OR, the $95\%$ CI of OR was 2.693, 1.856 to 3.908 as indicated in Table 6. In addition, being old and male decreased the probability of no follow-up significantly with OR; the $95\%$ CI of OR was 0.318, 0.236 to 0.428, and 0.608, 0.450 to 0.822 for age and sex, respectively. ## 4. Discussion The global healthcare system is being stressed by the coronavirus disease 2019 (COVID-19) pandemic [26]. This study aimed to evaluate the psychological impact of the COVID-19 pandemic on patients with chronic conditions who may have suffered from anxiety, depression, and stress during COVID-19, which may have affected their pattern of seeking medical care among the Egyptian population [27]. Healthcare administrators, emergency responders, and healthcare clinicians must all receive coaching and education on psychological issues from the healthcare system [28]. Identifying, establishing, and allocating evidence-based resources for disaster-related mental health, psychological well-being crises and referral, particular patient needs, and alarm and distress treatment are all tasks that mental health and emergency response systems must collaborate on [29]. Despite health issues, medical treatment professionals eventually have a vital role in identifying psychosocial requirements and providing psychosocial aid to their patients, as well as social efforts that should be incorporated into overall pandemic healthcare. A rise in known risk factors for mental health issues has been attributed to COVID-19. Quarantine and physical isolation are also present, along with oddities and discomfort [30]. This study revealed that $52.2\%$ of patients did not follow-up regularly with their chronic diseases during COVID-19; $63.2\%$ of the patients attributed the absence of follow-up to their fear of COVID-19 infection, $21.3\%$ of the patients attributed the no follow-up status to the cost of medical care with limited resources during COVID-19; and $58\%$ preferred to follow-up with telemedicine. New techniques for providing care through telemedicine to lessen in-person interactions. To enable health care clinicians to keep scheduled appointments, new digital and virtual healthcare practices must be used, in accordance with a previous study [31]. Additionally, the usage of apps can aid in the self-management of chronic illnesses, such as diabetes, where continuous glucose monitoring is possible. However, the bulk of those suffering from non-communicable diseases reside in low- and middle-income nations [32]. Our findings showed that the fear enveloping people’s thoughts about the pandemic and the hazards of becoming infected by stepping outside was the main reason for the absence of medical follow-up in chronic disease patients. About half of individuals with medical illnesses handled their conditions by calling doctors through telemedicine and collecting their own medication from a community pharmacy. Previous studies revealed that around $55\%$ of patients with chronic diseases did not contact their doctors and depended on self-medication [33,34]. In concordance with our findings, previous studies showed that people have generally been practicing—or have been pushed to practice—rational medical practices in the face of the greater concern consuming their minds regarding the pandemic and the risks of contracting it by venturing outside. The majority of participants with medical illnesses controlled their tolerable suffering by following the medications already provided or by calling doctors as necessary. Only a true emergency (fracture) or a perceived emergency (illness) had prompted the travel to a medical facility away from home (suspected COVID-19) [33]. Through timely detection, referral, and care of suspected cases, community pharmacies and pharmacy employees play a critical role in avoiding the “community transmission” stage of COVID-19. Yet, in accordance with government guidelines, our study revealed most patients were aware of self-care to avoid infection transmission, including hand rubs with alcohol for $68.9\%$ of patients and proper use of face masks for $92.5\%$ of patients [35]. Moreover, this study found that a low educational level was significantly associated with no follow-up, as was urban residence, which was more significantly associated with no follow-up. In these times, the socioeconomic division, combined with limited access to high-quality health care, has become even more apparent [36]. On the other hand, many people have limited access to the internet, so teleconsultation would be difficult for them. This may have played a factor due to the reduced study sample size and some target people not receiving the survey, which results in a limitation in our study. Apart from the socioeconomic divide highlighting poor access to health care and advice, the pandemic resulted in the emergence of stress, fear, and anxiety disorders across the population, regardless of social status [37]. As a result, COVID-19 has increased the prevalence of mental health issues, as has been the case in the past following novel disease epidemics and natural disasters. Not just COVID-19, but all significant emergencies surely result in mental health issues. Studies of previous outbreaks revealed that $31.2\%$ of people quarantined due to COVID-19 in Toronto, Canada, and roughly $35\%$ of SARS survivors in Hong Kong both experienced symptoms of anxiety and/or depression [23,38]. Using the DASS-21 tool, we discovered that $45.1\%$ of patients with chronic diseases had moderate to severe depression, $53.8\%$ had moderate to severe anxiety, and $34.8\%$ had moderate to severe stress. A univariate analysis revealed that the more severe the depression, anxiety, and stress, the more severe the disease. We found that the greater the increase in the scores of depression, anxiety, and stress, the more they were significantly associated with the no follow-ups, which matches with previous studies [39,40]. There is a need to raise awareness among chronic disease patients, particularly among the poor, about the significance of sticking to their medications [41]. Patients with chronic conditions, particularly those from poor backgrounds, need to be made aware of the value of taking their prescriptions as prescribed. It would be wise to keep in mind the tremendous patient population of so many other diseases, especially the chronic diseases, which need regular monitoring, advice, and medications. Although the public resources at the moment are primarily focused on overcoming the huge challenge of containing the COVID pandemic and looking for effective therapies. To reduce overall concern and provide the needed incentive for community health promotion, additional proactive steps such as creating consultation facilities or streamlining the prescription refill procedure for such individuals will be helpful [41]. The relevant contribution of this study to the field of literature is the urgency of regular monitoring and providing patients with “counseling for patients,” especially those suffering from chronic diseases, to help them overcome any fear during any pandemic and control their diseases well. ## 5. Limitations The study should be conducted on larger scales in different countries as a multicentered study. Also, the study should be well designed to avoid any bias during the sampling procedure. More comorbidities must be evaluated and compared. ## 6. 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DOI: 10.1001/archinte.167.6.540
--- title: 'Soluble and Insoluble Dietary Fibre in Date Fruit Varieties: An Evaluation of Methods and Their Implications for Human Health' authors: - Lily Stojanovska - Habiba I. Ali - Afaf Kamal-Eldin - Usama Souka - Ayesha S. Al Dhaheri - Leila Cheikh Ismail - Serene Hilary journal: Foods year: 2023 pmcid: PMC10048106 doi: 10.3390/foods12061231 license: CC BY 4.0 --- # Soluble and Insoluble Dietary Fibre in Date Fruit Varieties: An Evaluation of Methods and Their Implications for Human Health ## Abstract Dietary fibre analysis is expensive due to its reliance on enzymes such as α-amylase, protease, and amyloglucosidase. This study investigated whether enzymes are essential in analysing insoluble, soluble, and total dietary fibre (IDF, SDF and TDF) contents in dry fruits with very low starch and protein contents. The IDF, SDF, and TDF were measured in date fruits using the enzymatic gravimetric method AOAC 991.43 in the ANKOM dietary fibre analyser, with and without enzymatic digestion. The study analysed six date fruit varieties with a range of texture profiles. Our results highlighted agreement between both methods in the measured IDF, SDF, and TDF values. TDF values in date fruit varieties varied considerably, from $5.67\%$ g/100 g to $10.33\%$ g/100 g. Results from both methods also indicate that IDF constituted the bulk of dietary fibre content in all date fruit varieties ($77.8\%$ to $91.6\%$), while the proportion of SDF was between $8.4\%$ and $22.2\%$. This study confirms that dates are a rich source of dietary fibre, and can be a valuable functional ingredient in foods that reduce the risk of chronic diseases. The study confirmed that the inexpensive non-enzymatic technique is a viable substitute for the enzymatic method for analysing dietary fibre in dry fruits. ## 1. Introduction Dietary fibre is a diverse group of compounds resistant to digestion by digestive enzymes in the small intestine. These compounds include non-starch polysaccharides and other components such as lignin, cellulose, starch, dextrin, inulin, pectin, beta-glucan, and oligosaccharides, which play a crucial role in maintaining human health. Results from prospective cohort studies on dietary fibre in the previous decades demonstrated the vital role this macronutrient plays in reducing the risk of cardiovascular diseases [1], diabetes [2], and even gastrointestinal tract cancers [1]. Over the years, the definition of dietary fibre has evolved from non-digestible carbohydrates that are naturally present in foods to include non-digestible fibre components, either extracted or synthetic [3]. The bulk of dietary fibre in the diet comes from cereals, fruits, vegetables, legumes, and nuts; therefore, there is compositional variability in dietary fibre depending on the plant species, the part of the plant, and the plant’s maturity, as all of these factors influence the composition of dietary fibre components, such as cellulose, hemicellulose, pectin, and lignin, of the ingested food [4]. Hence, we observe variability in the health outcomes associated with dietary fibre, highlighting the importance of having reliable and cost-effective analytical methods to assess this nutrient’s composition in foods. The importance of having dietary fibre data for foods is also reinforced by the consensus in health advice across the globe that a diet rich in plant-based foods provides the best dietary protection against non-communicable diseases [4]. There are different analytical methods that can be used to determine the dietary fibre content of foods, such as proximate, gravimetric, and enzymatic-gravimetric approaches, which may or may not incorporate colorimetric or GLC/HPLC techniques. These methods allow for the determination of total dietary fibre (TDF) or TDF as separate SDF and IDF proportions, or even the individual structural elements, such as rhamnose, arabinose, xylose, mannose, uronic acid, polysaccharides, or Klason lignin. The Association of Official Agricultural Chemists (AOAC) International publishes validated methods, and some of the commonly used methods include AOAC 985.29, AOAC 991.43, AOAC 2001.03, and AOAC 2009.01. It is important to note that the method used for fibre analysis can affect the results obtained, with each method having its advantages and limitations. AOAC 991.43 is an enzymatic-gravimetric method commonly used for measuring IDF, SDF, and TDF in foods [5]. This method involves the enzymatic hydrolysis of starch and protein, followed by the precipitation of fibrous components by aqueous ethanol. The dietary fibre residues are then weighed, and the total dietary fibre content is calculated, using with the values of residual protein and ash in the sample. ANKOM Technologies (Macedon, NY, USA) developed an automated process for the method using three heat-stable enzymes: α-amylase, protease, and amyloglucosidase [6]. Although traditional methods, such as AOAC 985.29 and AOAC 991.43, are considered gold standards, enzymes increase the cost of these analyses, thereby limiting their use in industry and research due to cost concerns [7]. However, considering all the evidence associated with the importance of including fibre in the diet, these measurements are crucial. It provides consumers with a way to assess their dietary fibre intake and make informed decisions on the nutritive value of the food they are consuming. A key finding by Li and Cardozo suggested that enzymatic hydrolysis is not essential in foods with protein and starch contents of <$2\%$. AOAC adopted this strategy, which was published as AOAC 993.21 [8]. Using the same rationale, a similar strategy can be employed by utilizing the automated AOAC 991.43 IDF/SDF analysis in an ANKOM dietary fibre analyser. Our previous pilot study confirmed that using AOAC 991.43 TDF analysis without enzyme hydrolysis resulted in comparable TDF measurements to those with enzymes [7]. In this study, a selection of ten date fruit varieties and other dry fruits, such as raisins, figs, and apricots, were utilized. Here we investigate whether the same strategy to exclude enzyme hydrolysis affects the accuracy of the TDF measurement as separate IDF and SDF proportions in dry fruits. The main objective of our work is to measure the TDF, IDF and SDF content in different date varieties with and without enzymatic hydrolysis using AOAC 991.43. In addition, we aim to determine the IDF and SDF proportions in the date fruit varieties and highlight their implications for human health. ## 2.1. Materials Six date fruit varieties were obtained from local supermarkets in Al Ain, United Arab Emirates. The date fruit varieties used in the study included Lulu, Barhi, Khalas, Fard, Neghal, and Dabbas. The six varieties were chosen to encompass date fruits with varying texture profiles, ranging from soft to semi-hard to hard varieties, in order to represent date samples with significant variations in dietary fibre content [9]. Three samples of each variety, each originating from different farms, were purchased with the aim of accounting for variability in growing conditions. Chemicals such as sodium hydroxide, boric acid, hydrochloric acid, sulphuric acid, ethanol, acetone, Kjeldahl catalyst tablets, and anti-foam tablets used in the study were purchased from Sigma-Aldrich (St. Louis, MO, USA). Materials required for the ANKOM dietary fibre analyser, such as IDF and SDF filter bags, diatomaceous earth, heat-stable α-amylase, protease, and amyloglucosidase, 2-(N-morpholino)ethanesulfonic acid (MES), and Tris(hydroxymethyl) aminomethane (TRIS), were purchased from ANKOM Technologies (Macedon, NY, USA). ## 2.2. Sample Processing The date fruit samples were deseeded, and the flesh was fine-minced using a bench-top food processor. Subsequently, the samples were desugared with $85\%$ ethanol (ethanol: water, 85:15, vol/vol). In a laboratory shaker, 40 g fruit mince was mixed with 200 mL of the solvent. After centrifuging the mixture at 6000× g rpm for 10 min, the supernatant was discarded, and this process was repeated five times to eliminate the sugar. The desugared date fruit samples were dried to remove all moisture in a hot air oven at 40 °C, and the final weight of the samples was determined to account for bulk loss during the desugaring process. ## 2.3. Dietary Fibre Analysis The dietary fibre analysis in desugared samples was carried out using the automated dietary fibre analyser from ANKOM Technologies (Macedon, NY, USA). We opted to use the AOAC 991.43 IDF/SDF method for our study [6]. The manufacturer’s instructions were adhered to for the enzymatic method, while for the non-enzymatic method, the instrument’s enzymes were substituted with distilled water. Each sample was assessed in triplicate. In summary, 0.5 g of fruit samples were combined with MES–TRIS buffer (0.05 M, pH 8.2) for the enzymatic digestion phase. The enzymatic digestion process comprised three stages, with the first stage involving the α-amylase digestion of samples at 95 °C for 35 min. The second stage involved enzymatic digestion with protease at 60 °C for 30 min. The final phase of the process entailed digesting the samples with amyloglucosidase at 60 °C and a pH ranging between 4.0 and 4.5 for 30 min. Following enzymatic digestion, the samples were filtered via IDF filter bags, which retained the IDF components of the samples. The filtrate moved to SDF bags, where the SDF portions were precipitated with $95\%$ ethanol. After precipitation, the mixture was again filtered by the SDF bags to retain the remaining SDF components in the sample. After the instrument run, the IDF and SDF bags were gathered, washed with acetone, and left to dry in a hot air oven at 105 °C overnight. The dried bags were weighed, and protein and ash contents were determined. The ash content in the IDF and SDF bags was determined by calculating the weight difference of the samples after burning them in a muffle furnace at 600 °C for 3 h. The total protein of the sample IDF and SDF portions was determined by the Kjeldahl method [10], using the general factor (6.25) to convert nitrogen to protein. The samples’ IDF (%) and SDF (%) were calculated using the following formulae. % IDF=R1+R$\frac{2}{2}$−P−A−BM1+M$\frac{2}{2}$×100 R1=fF1−fS1 R2=fF2−fS2 M1 and M2 represent the initial weight, adjusted for sugar loss (g). R1 and R2 indicate the remaining residue after analysis (g). fF and fS correspond to the final and initial weights, respectively, of the IDF filter bag (g). P, A, and B denote the protein value, ash content, and blank value supplied by ANKOM Technologies (Macedon, NY, USA), respectively. % SDF=R1+R$\frac{2}{2}$−P−A−BM1+M$\frac{2}{2}$×100 R1=fF1−fS1−D1 R2=fF2−fS2−D2 M1 and M2 denote the initial weight adjusted for sugar loss (g). R1 and R2 indicate the remaining residue after analysis (g). fF and fS correspond to the final and initial weights, respectively, of the SDF filter bag (g). D represents the original weight of the diatomaceous earth (g). P, A, and B denote the protein value, ash content, and blank value provided by ANKOM Technologies (Macedon, NY, USA), respectively, for both the residue and bag. % TDF=%IDF+%SDF ## 2.4. Statistical Analyses The statistical analysis of the experiments was conducted utilizing GraphPad Prism software version 9.1.0. Data residual were checked by the D’Agostino & Pearson test and the Shapiro-Wilk test for normality. For the comparison of enzymatic and non-enzymatic test results, Bland–Altman’s analysis and correlation plots were constructed. The IDF, SDF, and TDF contents among the different date fruit varieties were compared using ANOVA with the Tukey test. A p-value of ≤0.05 was considered statistically significant. ## 3.1. Comparison of IDF Data between the Two Methods The IDF content in the 18 samples of date fruits measured by AOAC 991.43 with and without enzymatic digestion is provided in Table 1. Overall, the bulk of dietary fibre in date fruits was IDF. The Barhi variety recorded the lowest IDF content across the six date fruit varieties, measuring 4.51 ± $0.05\%$ g/100 g and 4.63 ± $0.18\%$ g/100 g with non-enzymatic and enzymatic methods, respectively. The highest IDF content was recorded in the Neghal variety, measuring 9.47 ± 0.49 and 9.69 ± 0.22 with the non-enzymatic and enzymatic methods, respectively. There was no significant difference between the results of the two methods (p-value 0.9644). In Figure 1, the Bland–Altman plot illustrates a high level of concurrence between the enzymatic and non-enzymatic methods for all of the samples of date fruit varieties. The disparity between the two methods is minimal on average, the limits of agreement were narrow (upper 0.290 and lower −0.344), and all measured values in the study fell within these limits. Additionally, the correlation between the two methods was examined (Figure 1). The calculated Pearson’s correlation coefficient between the enzymatic and non-enzymatic methods was 0.9962, with a $95\%$ confidence interval between 0.9895 and 0.9986. The linear association between the two methods was significant, with a p-value of <0.0001. ## 3.2. Comparison of SDF Data between the Two Methods The results of SDF measured in date fruits using enzymatic and non-enzymatic methods are provided in Table 1. We observed that the content of SDF in date fruits is lower than its measured IDF content. The lowest SDF content was measured in the Neghal variety, measuring 0.79 ± $0.04\%$ g/100 g and 0.88 ± $0.05\%$ g/100 g with the non-enzymatic and enzymatic methods, respectively. At the same time, the highest SDF content was recorded in the Barhi variety, which measured 1.30 ± $0.08\%$ g/100 g and 1.54 ± $0.32\%$ g/100 g with the non-enzymatic and enzymatic methods, respectively. The SDF values measured with the enzymatic method compared to the non-enzymatic method showed no significant difference (p-value 0.1585). The agreement between the two methods was demonstrated using a Bland–Altman plot (Figure 2), which showed a very low average difference and small limits of agreement (upper 0.240 and lower −0.458). Aside from one sample of the Lulu variety, all of the measured values were within these limits of agreement. However, the correlation between the two methods was only moderate (Figure 2). The calculated Pearson’s correlation coefficient between the enzymatic and non-enzymatic methods was 0.7298, with a larger $95\%$ confidence interval between 0.3989 and 0.8929, compared to IDF. Nevertheless, the linear association between the two methods was significant, with a p-value of 0.0006. ## 3.3. Comparison of TDF Data between the Two Methods The TDF content measured using both enzymatic and non-enzymatic methods showed significant variability among different date fruit varieties, as shown in Table 1. Among all the varieties, the Barhi variety recorded the lowest TDF content in the non-enzymatic method, measuring 5.67 ± $0.12\%$ g/100 g. On the other hand, the Lulu variety was found to have the lowest TDF content in the enzymatic method, measuring 5.71 ± $0.08\%$ g/100 g. However, the highest TDF values were observed in the Neghal variety with both non-enzymatic and enzymatic methods (10.33 ± $0.16\%$ g/100 g and 10.63 ± $0.21\%$ g/100 g, respectively). The p-value of 0.8059 indicates that there was no significant difference observed in TDF values between the enzymatic and non-enzymatic methods. The Bland–Altman plot demonstrating the agreement for TDF measurement between both methods is provided in Figure 3. From the figure, we observe that the mean discrepancy between the two methods was minimal, the range of agreement was narrow (upper limit 0.283 and lower limit −0.555), and all measured TDF values of the samples were within this range of agreement. The enzymatic and non-enzymatic methods for TDF data showed a strong correlation (Figure 3). The Pearson’s correlation coefficient between the two methods was 0.9919, with a narrow $95\%$ confidence interval ranging from 0.9778 to 0.9970. In addition, the linear association between the two methods was also highly significant, with a p-value of <0.0001. ## 3.4. IDF, SDF, and TDF in Date Fruit Varieties The IDF, SDF, and TDF contents measured with the non-enzymatic method were used to compare their relative composition across the study’s six varieties of date fruits (Figure 4). The relative proportional average of IDF content measured in the six date fruit varieties in increasing order was: Barhi ($77.8\%$), Lulu ($80.6\%$), Khalas ($80.7\%$), Fard ($82.2\%$), Dabbas ($88.6\%$), and Neghal ($91.6\%$) (Figure 4D). Similarly, the average relative proportion of SDF content in increasing order was: Neghal ($8.4\%$), Dabbas ($11.4\%$), Fard ($17.8\%$), Khalas ($19.3\%$), Lulu ($19.4\%$), and Barhi ($22.1\%$) (Figure 4D). The Neghal variety had the highest IDF content, measuring 9.34 ± $0.25\%$ g/100 g: significantly higher than all other varieties (Figure 4A). The second highest IDF content was measured in the Dabbas variety (7.86 ± $0.92\%$ g/100 g), which was also significantly higher than all others except the Neghal variety. The Lulu, Fard, and Barhi varieties had comparable IDF content (4.62 ± $0.03\%$ g/100 g, 5.66 ± $0.31\%$ g/100 g, and 4.6 ± $0.11\%$ g/100 g, respectively). The Khalas variety (6.05 ± $0.09\%$ g/100 g) measured similar IDF content to that of the Fard variety; however, it was significantly higher than both the Lulu and Barhi varieties. The Barhi, Lulu, Khalas, and Fard varieties all had comparable levels of SDF content, between $1.1\%$ g/100 g and $1.3\%$ g/100 g (Figure 4B). These values were significantly higher than the SDF content measured in the Neghal variety (0.81 ± $0.02\%$ g/100 g). The Dabbas variety also measured comparably low SDF content compared to the Neghal variety, with 1.0 ± $0.15\%$ g/100 g. Consequently, the TDF content in the date fruit varieties also showed large differences, with the Neghal variety, followed by the Dabbas variety, measuring the highest (10.16 ± $0.25\%$ g/100 g and 8.86 ± $0.86\%$ g/100 g, respectively) (Figure 4C). The Fard and Khalas varieties had comparable dietary fibre content, and TDF content in the Khalas variety was significantly higher than that in both Lulu and Barhi varieties, which measured the lowest. ## 4. Discussion Date fruits are a vital agricultural crop in the middle east due to their adaptability to the harsh arid climate, and socio-cultural preferences have made date fruits one of the most popular foods in the region [11]. There are numerous date palm varieties, with around 100 grown in the United Arab Emirates alone [12]. In the Middle East, date fruits are commonly consumed year round [13]. These fruits are distinguished by a significant amount of carbohydrates (ranging from $60\%$ to $80\%$), which comprise soluble sugars and dietary fibre [14]. The starch and protein contents in date fruits varies at different stages of their maturity. Starch degrades to glucose, fructose, and sucrose at the fully mature fruit stage [15]. The variations in the nutritional composition of date fruits are primarily ascribed to the dietary fibre, polyphenols, vitamins, and minerals [11]. This study investigated a new approach to analysing dietary fibre as insoluble and soluble proportions in dry fruits. Here we tested whether the enzymatic hydrolysis step was crucial in measuring IDF, SDF, and TDF in dry fruits with very low protein and starch contents using AOAC 991.43 in an ANKOM dietary fibre analyser. When date fruits are fully ripe, their starch content is negligible, and their protein content is very low (1–$1.5\%$). This makes them a suitable candidate for evaluating the efficacy of the non-enzymatic method in determining the content of both IDF and SDF [16]. Moreover, the dietary fibre in dates varies across the different varieties. The reported dietary fibre can range between 6.5 and $11.5\%$ in these fruits [17]. The dietary fibre of date varieties from the UAE was reported to be between $5.5\%$ and $9.1\%$ [18]. More recent studies have reported TDF between 5.3 and $8.4\%$ [19], and 5.3 and $13.4\%$ [7]. This variability in TDF across different varieties makes date fruits a useful sample with which to measure a different range of IDF SDF, and TDF values. We used six varieties of date fruits with a range of texture profiles already reported in the literature [9]. The Neghal and Dabbas varieties have a hard texture, while the Lulu and Barhi varieties are softer. The Fard and Khalas varieties fall between the two and have a semi-hard texture. IDF, SDF, and TDF results indicate that the non-enzymatic method gave comparable measurements to the enzymatic method in all six date varieties. AOAC 985.29 was the first analytical method accepted as official. This enzymatic-gravimetric method was developed by Prosky et al., and it measures the TDF in dried and defatted samples with enzyme hydrolysis using three enzymes: α-amylase, protease, and amyloglucosidase [20]. AOAC 991.43 was the subsequent official method developed by Lee et al. and modified the previous method, which brought forth the possibility of measuring TDF, IDF, and SDF [21]. The measurement of TDF was similar to AOAC 985.25 and used the same three enzymes. Among the three enzymes used in both methods, α-amylase hydrolyses the α-1,4 glycosidic bonds of α-linked polysaccharides, such as starch-yielding shorter chains, e.g., dextrins. The protease enzyme hydrolyses proteins, and the amyloglucosidase enzyme hydrolyses α-1,4 and α-1,6 glycosidic bonds in starch, resulting in glucose units. Based on the function of each of these enzymes, we understand that they have no effect on the lignin content in the fruit samples during the enzymatic digestion step, as lignin is not a carbohydrate polymer. Lignin is a highly branched phenolic polymer made up of p-hydroxyphenyl, guaiacyl, and syringyl molecules with no regular repeating structures [22]. However, it is a significant component of the insoluble fraction of dietary fibre. Our study data indicate that the majority of dietary fibre found in date fruits is insoluble fibre. Additionally, the data support the notion that these fruits contain a significant amount of lignin. The insoluble phenolic fibres in date fruits accounted for anywhere between 1 and $5\%$, according to Alam et al. [ 23], and George et al. reported that lignin is the major component and determinant of date fruit dietary fibre [19]. Consequently, in the current study, the correlation between the enzymatic and non-enzymatic methods was very high. At the same time, the correlation between the SDF values determined by the enzymatic and non-enzymatic methods was lower than that of the IDF values. This observation may be a direct consequence of the limitation of the original AOAC 991.43, due to its inability to measure low-molecular-weight SDF. A study conducted by Tobaruela et al. compared AOAC 991.43 to the new method, AOAC 2011.25, for measuring dietary fibre content in fruits [24]. The study reported that the IDF content of fruits, quantified by both methods, showed no significant difference. One key difference between the two methods is their ability to measure low-molecular-weight SDF portions. Only AOAC 2011.25 measured the low-molecular-weight SDF, which can be attributed to various factors, such as the type and purity of enzymes, incubation time and temperature, and precipitation conditions. These factors may have affected the SDF quantification, resulting in final values with significant differences. Detailed analysis of date fruit fibres has reported that dates contain SDF sources, such as fructan, pectin, galactomannan, arabinoxylan, and β-glucan in different degrees of variability between the different varieties [19]. Low-molecular-weight SDF components may have been underestimated in the enzymatic and non-enzymatic methods in the study. In the Bland–Altman analysis of SDF data, one sample of the Lulu variety was above the limit of agreement, which could be a direct consequence of the AOAC 991.43’s limitation to account for low molecular weight SDF fractions. It is important to note that the Lulu variety was one of the varieties that reported a higher content of SDF. In general, despite the variations observed, they did not impact the consistency between the two methods or the linear association in the TDF correlation between the enzymatic and non-enzymatic methods. Consequently, the current study’s findings support the conclusions from our prior research, which reported a strong consistency between the enzymatic and non-enzymatic methods for TDF measurement in date fruits and other dried fruits, such as apricots, figs, and raisins [7]. The concept of dietary fibre is not new since its relevance to health, and its extraction from animal feed and forages, was recorded in Germany as early as the 1850s [25]. Various definitions of dietary fibre have been suggested and debated for decades. Recently, a consensus has been forming around the definition, adopted by CODEX in 2009 [26], which broadly describes the types of fibre from naturally occurring food, those obtained from raw food material via extraction, and synthetic carbohydrate polymers. Among these three broad categories, there is significant diversity at the chemical composition and physical structure levels. Classification of dietary fibres based on chemical composition is beneficial from an analytical standpoint, since it increases the analysis’s robustness, accuracy, and repeatability [22]. Other than structural classification, the solubility of the dietary fibre within the gastrointestinal tract during digestion is commonly used to describe dietary fibre types. This classification is helpful because solubility influences its functionality concerning health outcomes [3]. Since the reliability of the non-enzymatic method was established, the dietary fibre content in date fruit varieties analysed by this method was used for comparison. The TDF in date fruits varied between 5.83 ± $0.13\%$ g/100 g and 10.16 ± $0.25\%$ g/100 g, which is comparable to the range reported by Al-Shahib and Marshall (6.4–$11.5\%$) [17], Habib et al. ( 5.52–$9.11\%$) [18], and Ali et al. ( $5.4\%$ to $13.6\%$) [7]. In this study, the calculated proportions of IDF and SDF were 91.6–$77.8\%$ and 8.4–$22.2\%$. An earlier investigation of SDF and IDF proportions in Tunisian date varieties reported comparable data: 84–$94\%$ insoluble and 6–$16\%$ soluble [27]. Cellulose, hemicelluloses, and lignin are the chief components of the IDF proportion in date fruits, while pectin, fructooligosaccharides, inulin, galactomannan, and β-glucan constitute the SDF proportion [19]. It is reported that the pectin content varies anywhere between $0.5\%$ and $3.9\%$ in date fruits [17]. Moreover, the composition of dietary fibre in date fruits changes with the ripening process. The percentages of pectin, hemicellulose, cellulose, and lignin decreases significantly as the fruit reaches the fully ripened stage [17]. George et al. stated that lignin was the major component of dietary fibre in fully ripe date fruits, comprising a significant proportion of TDF [19]. Various nutritional functionalities are associated with date fruits’ IDF and SDF proportions. SDF sources, such as pectin and fructans that are present in date fruits, can increase the viscosity of the food due to its water-holding capacity and consequently decrease the rate of gastric emptying and nutrient absorption, which can help satiety [28] and lower cholesterol levels [29], enhance glucose tolerance [30], and increase insulin sensitivity [31]. Additionally, pectin is also known to improve the serum lipid profile [29,32] and stimulate bile acid secretion [32]. However, the hydration capacity of dietary fibre does not depend on solubility; in fact, IDF sources with large molecular weight can hold water and increase stool volume in order to speed up the rate of faecal passage. Since the significant proportion of dietary fibre in date fruits consist of IDF fractions, their health implications are primarily derived from their interaction with colonic microbiota. IDF sources present in date fruits, such as cellulose, hemicellulose, and lignin, are fermented by gut microbiota, a process which consequently generates short-chain fatty acids, which have numerous associated health benefits. Within the intestinal lumen, these compounds promote the growth of beneficial gut microbes, increase colonic sodium and water absorption, inhibit tumour formation [33], and stimulate mucosal cell proliferation [34]. On a systemic level, SFCA generated from IDF content can inhibit cholesterol synthesis [3] and even positively modulate systemic inflammation [35]. ## 5. Conclusions Our study results indicate that AOAC 991.43 can be used to accurately measure the contents of IDF, SDF, and TDF in dry fruits with low protein and starch contents without the need for enzymatic digestion. This finding makes the non-enzymatic method of AOAC 991.43 an economical alternative for analysing dietary fibre in dry fruits. 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--- title: Replacing Fish Meal with Hydrolyzed Collagen Derived from Fish By-Products Improved Muscle Quality and Glycolipid Metabolism of Triploid Crucian Carp authors: - Fangle Tong - Jinhai Bai - Zhongtian Tang - Chunyan Li - Shaojun Liu - Zehong Wei journal: Foods year: 2023 pmcid: PMC10048121 doi: 10.3390/foods12061235 license: CC BY 4.0 --- # Replacing Fish Meal with Hydrolyzed Collagen Derived from Fish By-Products Improved Muscle Quality and Glycolipid Metabolism of Triploid Crucian Carp ## Abstract Fish by-products are rich in collagen. Hydrolyzed collagen derived from fish by-products was used to replace fish meal to evaluate the effects on muscle quality and glycolipid metabolism of juvenile triploid crucian carp. A total of 240 juvenile fish with body weight of 10.01 ± 0.02 g were divided into four groups and fed four diets for 66 days: fish meal (FM) replaced with hydrolyzed collagen (HC) in $0\%$ (Control), $2\%$ ($2\%$ HC), $4\%$ ($4\%$ HC), and $6\%$ ($6\%$ HC), respectively. The results were as follows: The increased proportion of fish meal replaced with hydrolyzed collagen linearly and quadratically decreased the specific growth rate (SGR) of triploid crucian carp ($p \leq 0.05$). Compared with the control group, the SGR and intestinal α-amylase, trypsin and lipase activities in the $4\%$ and $6\%$ HC groups significantly decreased ($p \leq 0.05$), while there was no significant difference between the control and $2\%$ HC groups ($p \leq 0.05$). Total umami amino acids content, chewiness and myofiber density of muscle in the $4\%$ and $6\%$ HC groups, as well as the essential fatty acids content in all HC groups increased significantly ($p \leq 0.05$). All HC groups significantly increased the serum glutathione peroxidase (GSH-Px) activity and decreased the serum malondialdehyde (MDA) content ($p \leq 0.05$). When the replacement amount reached $4\%$, the serum glucose and liver glycogen content, the liver and serum triglyceride (TG) content, and serum total cholesterol (T-CHO) content were significantly reduced ($p \leq 0.05$). In addition, the expression levels of insulin-like growth factor-1 (IGF-1) of the liver in all HC groups and lipolysis-related genes (lipoprotein lipase (LPL), carnitine O-palmitoyltransferase 1 (CPT 1) and hydroxyacyl-coenzyme A dehydrogenase (HADH)) of the liver in the $6\%$ of HC group increased significantly ($p \leq 0.05$), and the expression levels of lipogenesis-related genes (fatty acid synthase (FAS) and sterol regulatory element-binding protein 1 (SREBP 1)) of the liver in the $4\%$ HC and $6\%$ HC groups decreased significantly ($p \leq 0.05$). In conclusion, the replacement of $2\%$ fish meal with hydrolyzed collagen had no negative effects on the growth of triploid crucian carp, while the replacement of $4\%$ fish meal with hydrolyzed collagen decreased SGR, but improved the muscle quality and decreased glycolipid levels. The maximum proportion of hydrolyzed collagen replacing fish meal should not exceed $4\%$. ## 1. Introduction As a high-quality protein source for fish feed, fish meal is popular for its rich and balanced nutrients, easy digestibility and palatability [1]. With the rapid development of global fisheries, aquaculture has seen an increasing demand for fish meal, which is mainly composed of dried fish carcasses [1,2]. Dependence on fish meal exerts enormous pressure on limited marine resources and finding alternative resources of fish meal remains urgent [2]. The alternative resources of fish meal can be obtained not only from animals but also from plants. However, compared with fish meal, plant protein sources have some defects, such as anti-nutritional factors enrichment and amino acid imbalance [3,4]. Animal protein sources are the ideal component in animal feed formulation, and they are superior to plant protein sources [1]. Some animal protein sources, such as meat and bone meal (MBM), hydrolyzed animal protein and blood meal (BM), are commonly used. It was reported that no significant difference was discovered in the specific growth rate (SGR) of large yellow croaker (Pseudosciaena crocea) fed with MBM replacing $45\%$ fish meal [5]. In juvenile Nile tilapia (Oreochromis niloticus), up to $50\%$ of fish meal could be replaced with hemoglobin powder, which did not affect SGR [6]. Dietary $12.5\%$ hydrolyzed feather meal ($76\%$ fish meal replacement) was possible in European seabass (Dicentrarchus labrax) without growth decline [7]. Notably, there were many studies on fish meal replaced with various animal protein sources in aquatic feed, while few studies were carried out on hydrolyzed collagen. At present, three main sources are used to extract hydrolyzed collagen: terrestrial animal by-products, leather waste and fish by-products [1,8,9]. However, infectious diseases existing in terrestrial animal by-products (such as foot-and-mouth disease) and chromium in leather waste limit the extraction of hydrolyzed collagen from the above two raw materials [1,8], thus fish by-products are gradually welcomed by the public. Collagen is abundant in fish by-products (such as fish skin and bone) [10]. In deep-sea redfish (Sebastes mentella), the collagen content in skin, scale and bone were $47.5\%$, $6.8\%$ and $10.3\%$, respectively [9]. In carp (Cyprinus carpio), the yields of acid-soluble collagens in skin, scale and bone were $41.3\%$, $1.35\%$ and $1.06\%$, respectively [11]. Fish by-products account for about $25\%$ of the global harvest, of which $13\%$ is used for fish meal production, and the rest is discarded [12]. The discarded fish by-products not only pollute the environment but also waste resources. The key to extracting hydrolyzed collagen from fish by-products is to break the peptide bonds of the collagen, which can be achieved using heat, acids, bases, enzymes or a combination of these physical, chemical and biological methods [13]. Hydrolyzed collagen obtained from fish by-products consists of low molecular weight peptides such as Pro-Hyp [14]. The Pro-Hyp is proven to improve the dysfunction of the skin barrier and promote the growth and differentiation of skin fibroblasts [15]. In addition, hydrolyzed collagen helps to lower blood pressure, improve immunity and promote calcium absorption [10]. However, the investigation of the effect of hydrolyzed collagen on fish muscle quality and glycolipid metabolism remains insufficient. Triploid crucian carps (3n = 150) were obtained from crossing the male allotetraploid (4n = 200) (intercrossing between Carassius auratus red var. ( ♀) and Cyprinus carpio L. (♂)) with the female Japanese crucian carp (Carassius auratus cuvieri) (2n = 100) [16]. The triploid crucian carp is sterile, and its sterility makes it possible for reproductive energy to be transferred to growth performance [17]. Therefore, triploid crucian carps are expected to show the advantages of faster growth and longer life span [17]. The sterility of triploid crucian carp also hinders them from mating with other fish in nature, which is of great significance for safeguarding fish genetic resources [16]. In addition, the advantages of strong disease resistance and good quality flesh of triploid crucian carp make it readily accepted by customers and farmers [16]. As far as we know, few studies have been carried out on the feeding and nutrition of triploid crucian carp. In the present study, we aimed to explore the effects of fish meal replaced with hydrolyzed collagen on the growth performance, muscle quality and glycolipid metabolism of triploid crucian carp. This will contribute to the application of fish by-products and the development of an environmentally friendly economy. ## 2.1. Experimental Diets The Animal Ethics Experimental Committee of Hunan Normal University (Changsha, China) ratified all the conduct of this study. The main protein sources of the diets were fish meal, soybean meal, rapeseed meal and wheat flour. The main lipid source of the diets was soybean oil. Four isonitrogenous ($32\%$ crude protein) and isolipidic ($8\%$ crude lipid) diets were designed to replace fish meal with $0\%$ (Control), $2\%$ ($2\%$ HC), $4\%$ ($4\%$ HC), and $6\%$ ($6\%$ HC) hydrolyzed collagen, respectively. The experimental diets of triploid crucian carp are displayed in Table 1. Hydrolyzed collagen (purity: $99\%$) extracted from flesh and by-products of tilapia (Oreochromis niloticus) was from Baichuan Biotechnology Co., Ltd. (Xi’an, China). The ingredients were mixed evenly according to the principle of step-by-step magnification and finally made into floating pellets with a diameter of 1 mm. The dried pellets were stored at −20 °C until use. The comparison of proximate composition between hydrolyzed collagen and fish meal is displayed in Table 2. The crude protein content of hydrolyzed collagen was $88.51\%$, which was about 1.6 times that of fish meal. The crude lipid content of fish meal was $10.6\%$, which was about 1.53 times that of hydrolyzed collagen. The comparison of amino acid content between hydrolyzed collagen and fish meal is displayed in Table 3. The total amino acid content of hydrolyzed collagen was $93.26\%$, which was higher than that of fish meal ($56.10\%$). However, the content of total essential amino acids of hydrolyzed collagen was $23.18\%$, which was lower than that of fish meal ($26.21\%$). It can be calculated from the table that the methionine and isoleucine content of fish meal were about 1.65 and 1.99 times higher than those of hydrolyzed collagen, respectively. Both glycine and proline content in hydrolyzed collagen were about 5 times higher than those of fish meal, while hydroxyproline content was about 18.59 times as much as fish meal. The amino acid composition of diets is displayed in Table 4. The total essential amino acid content in the $4\%$ HC and $6\%$ HC groups were $12.7\%$ and $11.99\%$, respectively, which were lower than those in the control group ($13.25\%$) and $2\%$ HC group ($13.61\%$). The content of eight kinds of essential amino acids (methionine, valine, lysine, isoleucine, phenylalanine, leucine, threonine and histidine) in the $4\%$ and $6\%$ HC groups were lower than those in the control group and the $2\%$ HC group. ## 2.2. Feeding Trial Triploid crucian carp were obtained at the Engineering Center of Polyploidy Fish Breeding of the National Education Ministry, Hunan Normal University, Hunan, China. The experiment was implemented in the circulating aquaculture system of Hunan Agricultural University (Changsha, China). Before the experiment, the juvenile triploid crucian carp were domesticated with the control feed for two weeks. A total of 12 tanks with 20 fish (10.01 ± 0.02 g fish−1) in each tank (volume 400 L tank−1) were set up. The 12 tanks were divided into 4 treatment groups, and every 3 tanks belong to the same treatment group. The water in the tank was changed once a week. Aquariums were equipped with oxygenation pumps and the lamp was used as the source of light (12 h light: 12 h dark). Fish were fed to apparent satiation at 6:00, 12:00 and 17:30, respectively. The feeding trial was conducted for 66 days. The water conditions of the feeding trial were shown as follows: water temperature was 25.3 ± 3.5 °C, pH was 7.4 ± 0.4 and dissolved oxygen was 7.0 ± 0.5 mg/L. ## 2.3. Sample Collection At the end of the feeding trial, all fish were fasted for 24 h and anesthetized with ethyl 3-aminobenzoate methyl sulfonate (MS-222, Sigma, St. Louis, MO, USA) at a concentration of 100 ppm. Fish in each aquarium were counted and weighed to evaluate the growth performance. The length and weight of the body and the weight of the liver and viscera of fish were recorded for analysis of morphometric indexes (three fish per aquarium). Blood was obtained from the tail vein of fish with syringes, and stored in a refrigerator at 4 °C overnight. Subsequently, the blood was centrifuged (4 °C, 3500 r/min, 15 min) to obtain serum for biochemical analysis. Fish were dissected on ice. One side of the dorsal muscle (1 cm × 0.5 cm × 0.5 cm) in the same position of each fish was immersed in $4\%$ paraformaldehyde for histomorphological analysis, and the other side of the dorsal muscle in the same position of each fish was used for texture and pH measurement (three fish per replicate). The rest of the muscle, liver and gut used for biochemical analysis were put into liquid nitrogen irrigation as soon as possible and stored at −80 °C. ## 2.4. Growth Performance and Morphometric Indexes Analysis Weight gain rate (WGR), specific growth rate (SGR), condition factor (CF), hepatosomatic index (HSI), and viscerosomatic index (VSI) were calculated according to the method of the previous studies [5,6]. WGR (%) = (Waf − Wai)/Wai SGR (%/d) = 100 × [Ln (Waf) − Ln (Wai)]/d CF (g/cm3) = 100 × Wf/Lb3 HSI (%) = 100 × Wl/Wf VSI (%) = 100 × Wv/Wf where Waf represents average final body weight, Wai represents average initial body weight, d represents feeding days, Wf represents final body weight of a fish, Lb represents body length of a fish, Wl represents liver wet weight of a fish, and Wv represents viscera wet weight of a fish. ## 2.5.1. Proximate Compositions Analysis The muscle and diets were dried to constant weight using the freeze dryer to determine moisture. Crude protein and crude lipid content of muscle and diets were measured according to the method of the Association of Analytical Communities (AOAC, 2005). Kjeltec auto analyzer (Kjeltec 2300, FOSS, Stockholm, Sweden) and soxhlet extractor (Soxtec 2050, FOSS, Sweden) were used to determine crude protein and crude lipid content, respectively. ## 2.5.2. Hydrolyzed Amino Acid The content of hydrolyzed amino acids in the raw materials and diets was measured according to the method of the previous study [18]. About 0.05 g diet (dry matter) (about 0.025 g fish meal/hydrolyzed collagen) weighed and recorded was put into hydrolysis tubes with 5 mL HCl (6 M) added. Then, the hydrolysis tube filled with nitrogen (N2) was digested at 110 °C for 24 h. After digestion, the solution in the hydrolysis tube was transferred to a 50 mL volumetric flask and ultra-pure water was replenished. Subsequently, 1 mL of solution was removed from the volumetric flask and dried in a nitrogen-filled condition. After the dry matter was dissolved with 1 mL HCl (0.02 M), a 0.22 μm ultrafiltration membrane (Millipore, Billerica, MA, USA) was utilized to filter the solution, and the high-speed amino acid analyzer (L-8900, Hitachi, Tokyo, Japan) was utilized to determine the hydrolyzed amino acids of the solution. ## 2.5.3. Free Amino Acid The content of free amino acids in muscle was determined according to the method of the previous study [19]. Approximately 0.5 g of muscle (wet weight) weighed and recorded was put into a centrifuge tube with 3 mL sulfosalicylic acid ($10\%$) added. After homogenization and centrifugation (13,000 rpm, 4 °C, 15 min), a 0.22 µm ultrafiltration membrane (Millipore, MA, USA) was utilized to filter the supernatant, and the high-speed amino acid analyzer (LA8080, HITACHI, Japan) was utilized to determine the free amino acid. ## 2.5.4. Fatty Acid Composition About 1 g of muscle (dry matter) was added to 10 mL of Folch solution for homogenization. The homogenate filled with nitrogen was shaken by ultrasound for 20 min and centrifuged (2500 r/min) for 10 min. Subsequently, the liquid in the underlayer was dried at 50 °C, filled with N2 and then added with 2 mL of potassium hydroxide (KOH) solution. The solution filled with N2 was incubated in water at 50 °C until the oil droplets disappeared and then added with 2 mL of boron trifluoride (BF3) solution. The mixture solution filled with N2 was incubated in water at 50 °C for 3 min and then added with normal heptane. Saturated saline was added to wash the upper layer. Subsequently, the supernatant was added with a little anhydrous sodium sulfate for gas chromatography analysis. The fatty acids composition of muscle was measured using the Varian column (FAME CP-sil88, 0.25 mm × 50 m × 0.20 μm). The identification of fatty acids was based on the gas chromatogram of fatty acid standard, and the method of peak area normalization was used to calculate the relative percentage content of fatty acids. ## 2.6. Muscle pH, Texture and Histology Analysis The pH meter (Testo-205, Testo AG, Lenzkirch, Germany) was used to analyze the muscle pH value according to the method of the previous study [20]. Three tests were performed in each sample. A texture analyzer (Food Technology Corporation, Sterling, VA, USA), which was equipped with an 8 mm cylinder probe and a 250 N weighing cell, was utilized to measure muscle texture according to the method of the previous study [21]. A double compression experiment with a compression ratio of $60\%$ was carried out. During the experiment, the moving speed of the probe was 1 mm/s, and 2 s after the end of the first compression, the second compression was implemented. Hardness, adhesiveness, cohesiveness, springiness and chewiness of muscle were determined using a texture analyzer. For histological analysis [22], muscle samples soaked in paraformaldehyde were dehydrated step by step in ethanol and xylene. Subsequently, paraffin wax was utilized to embed the dewatered samples. After the samples were sliced, hematoxylin and eosin (HE) were utilized to stain. The optical microscope, which was equipped with a camera system (BX40F4, Olympus, Tokyo, Japan) was utilized to observe the morphology of muscle and photograph. The diameter and density of myofiber were measured and calculated by ImageJ software according to the previous methods [22]. ## 2.7. Serum, Intestinal and Liver Enzymes Activities Measurement The assay kits, which were purchased from Nanjing Jiancheng Bioengineering Institute (Nanjing, China), were utilized to determine the activities of catalase activity (CAT), superoxide dismutase activity (SOD), glutathione peroxidase (GSH-Px), α-amylase, trypsin, and lipase, and the content of malondialdehyde (MDA), glucose, glycogen, triglyceride (TG), total cholesterol (T-CHO), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) following the manufacturer’s specification strictly. ## 2.8. Real-Time Quantitative PCR Analysis The RNA isolator total RNA Extraction Reagent (Vazyme, R401-01) was utilized to extract total RNA. After the determination of quality, concentration and integrity of the total RNA, the reagent of All-in-one RT SuperMix Perfect for qPCR (Vazyme, R33) was utilized to reversely transcribe total RNA into cDNA. Real-time quantitative PCR analysis was implemented with the ChamQ Universal SYBR qPCR Master Mix (Vazyme, Q711-03). Primer Premier 5 was used for primers design, and the specific primer sequences are exhibited in Table 5. The total volume of the reaction was 20 µL and the amplification conditions were as follows: 40 cycles of 95 °C for 15 s and 60 °C for 1 min. At the end of the PCR reaction, dissolution curve analysis was performed, and the data were converted into Ct values. The expression of the target gene relative to β-actin was calculated according to the equation of $R = 2$−ΔΔCt [23]. ## 2.9. Statistical Analysis The software of SPSS 17.0 was utilized to analyze data. All the data were expressed as the mean ± standard error (SE). The method of one-way ANOVA and the test of Tukey were utilized to determine the significance of different research groups. In addition, orthogonal polynomial contrasts were performed to determine whether the effect was linear and/or quadratic. The significant difference limen was set at 0.05. ## 3.1. Growth Performance and Morphometric Indexes The growth performance and morphometric indexes of triploid crucian carp are illustrated in Table 6. The increased proportion of fish meal replaced with hydrolyzed collagen linearly and quadratically decreased the SGR and WGR of triploid crucian carp ($p \leq 0.05$). Compared with the control group, the $2\%$ HC group had not significantly changed SGR and WGR ($p \leq 0.05$), while the $4\%$ HC and $6\%$ HC groups had significantly lower WGR and SGR ($p \leq 0.05$). The influence of replacing fish meal with hydrolyzed collagen on HSI, VSI and CF of triploid crucian carp was not significant ($p \leq 0.05$). ## 3.2. Muscle Nutritional Composition The muscle nutrition components are shown in Table 7. No significant differences were observed in muscle moisture, crude protein and crude lipid content among all the treatments ($p \leq 0.05$). Muscle free amino acid content of fish is listed in Table 8. Compared with the control group, the $4\%$ HC and $6\%$ HC groups had significantly higher content of total umami amino acids ($p \leq 0.05$), and all HC groups had significantly lower content of total bitter amino acids ($p \leq 0.05$). The total sweet amino acids content of muscle in the $4\%$ HC group was significantly lower than that in other groups ($p \leq 0.05$). The muscle fatty acid composition of fish is listed in Table 9. The total saturated fatty acids (SFAs) content of muscle in all HC groups decreased significantly compared with that in the control group ($p \leq 0.05$). No significant difference was detected in total monounsaturated fatty acids (MUFAs) content among all the treatments ($p \leq 0.05$). Compare with the control group, the $4\%$ HC group had significantly higher content of total polyunsaturated fatty acids (PUFAs) ($p \leq 0.05$), and all HC groups had significantly higher content of total n-6 polyunsaturated fatty acids (n-6 PUFAs) ($p \leq 0.05$). The content of α-linolenic acid (ALA, C18:3 n-3) in $2\%$ HC and $4\%$ HC groups and the linoleic acid (LA, C18:2 n-6) in all HC groups increased significantly compared with those in the control group ($p \leq 0.05$). ## 3.3. Muscle Texture, pH Value and Histology Muscle texture and pH value are displayed in Table 10. The proportion of fish meal replaced with hydrolyzed collagen linearly and quadratically affected the muscle hardness, chewiness and adhesiveness of triploid crucian carp ($p \leq 0.05$).Muscle hardness in the $6\%$ HC group and muscle chewiness in the $4\%$ and $6\%$ HC groups were significantly higher than those in the control group ($p \leq 0.05$). Compared with the control group, muscle adhesiveness in all HC groups decreased significantly ($p \leq 0.05$). There were no significant effects in muscle cohesiveness, springiness and pH value of fish with different treatments ($p \leq 0.05$). Muscle morphology is shown in Figure 1, and myofiber density and diameter are listed in Table 10. The increased proportion of fish meal replaced with hydrolyzed collagen linearly and quadratically increased myofiber density of triploid crucian carp ($p \leq 0.05$).The myofiber densities in the $4\%$ HC and $6\%$ HC groups were significantly higher and the myofiber diameters were significantly lower than those in the $2\%$ HC group and the control group ($p \leq 0.05$). ## 3.4. Serum, Intestinal and Liver Enzymes Activities Effects of hydrolyzed collagen replacing fish meal on serum, intestinal and liver enzyme activities of triploid crucian carp are listed in Table 11. The influence of replacing fish meal with hydrolyzed collagen on serum CAT activity was not significant ($p \leq 0.05$). The effects of hydrolyzed collagen replacing fish meal on GSH-Px activity and MDA content in the serum of triploid crucian carp were linear and quadratic, and quadratic on SOD activity ($p \leq 0.05$). Compared with the control group, the $6\%$ HC group had a significantly higher activity of serum SOD ($p \leq 0.05$), and all HC groups had a significantly higher activity of serum GSH-Px ($p \leq 0.05$) and significantly lower content of serum MDA ($p \leq 0.05$). The increased proportion of fish meal replaced with hydrolyzed collagen linearly and quadratically decreased the activities of intestinal α-amylase and lipase of triploid crucian carp ($p \leq 0.05$). The activities of intestinal α-amylase, trypsin and lipase decreased significantly when the proportion of fish meal replaced with hydrolyzed collagen reached $4\%$ ($p \leq 0.05$). The effects of hydrolyzed collagen replacing fish meal on serum glucose and hepatic glycogen content, serum TG, T-CHO and LDL-C content, and liver TG content of triploid crucian carp were linear and quadratic ($p \leq 0.05$). Compared with the control group, the $4\%$ and $6\%$ HC groups had significantly lower content of serum glucose and hepatic glycogen ($p \leq 0.05$), and serum TG and T-CHO content in all HC groups and serum LDL-C content in $6\%$ HC group decreased significantly ($p \leq 0.05$). The liver TG content decreased significantly with the proportion of fish meal replaced with hydrolyzed collagen up to $4\%$ ($p \leq 0.05$). There were no significant differences in liver T-CHO and serum HDL-C content among all the treatments ($p \leq 0.05$). ## 3.5. Expression Levels of Genes Related to Glucose Metabolism Effects of hydrolyzed collagen replacing fish meal on the expression levels of genes related to glucose metabolism in the liver of triploid crucian carp are displayed in Figure 2. Positive linear and quadratic trends were observed between the proportion of hydrolyzed collagen replacing fish meal and expression levels of insulin-like growth factor-1 (IGF-1), glucokinase (GK) or glycogen phosphorylase (GPase) in the liver ($p \leq 0.05$). Compared with the control group, the relative expression level of IGF-1 gene in all HC groups increased significantly ($p \leq 0.05$), and the relative expression levels of GK and GPase genes in all HC groups decreased significantly ($p \leq 0.05$). There were no significant differences in the relative expression levels of pyruvate kinase (PK), glucose-6-phosphatase (G6Pase), hypoxia-inducible factor 1α (HIF 1α), and glycogen [starch] synthase (GSase) genes among all the treatments ($p \leq 0.05$). ## 3.6. Expression Levels of Genes Related to Lipid Metabolism Effects of hydrolyzed collagen replacing fish meal on the expression levels of genes related to lipid metabolism in the liver of triploid crucian carp are shown in Figure 3. The increased proportion of fish meal replaced with hydrolyzed collagen linearly and quadratically increased the expression levels of hydroxyacyl-coenzyme A dehydrogenase (HADH), lipoprotein lipase (LPL) and carnitine O-palmitoyltransferase 1 (CPT 1) in the liver of triploid crucian carp ($p \leq 0.05$).The relative expression levels of LPL and CPT 1 genes in the $6\%$ HC group, and HADH gene in $4\%$ HC and $6\%$ HC groups increased significantly compared with those in the control group ($p \leq 0.05$). The increased proportion of fish meal replaced with hydrolyzed collagen linearly and quadratically decreased the expression levels of fatty acid synthase (FAS) and sterol regulatory element-binding protein 1 (SREBP 1) in the liver of triploid crucian carp ($p \leq 0.05$).The relative expression levels of the FAS gene in all HC groups, and SREBP 1 gene in $4\%$ HC and $6\%$ HC groups decreased significantly compared with those in the control group ($p \leq 0.05$). The impacts of replacing fish meal with hydrolyzed collagen on the expression levels of acyl-CoA oxidase 1 (ACOX1) and acetyl-CoA carboxylase (ACC) genes were not significant ($p \leq 0.05$). ## 4. Discussion Fish by-products, containing some bioactive peptides (such as Hyp-Pro), are one of the main sources of collagen extraction [10,11]. In the present study, the crude protein content of hydrolyzed collagen extracted from by-products of tilapia was as high as $88.51\%$, while that of fish meal was $55.25\%$. The content of glycine, alanine, arginine and proline in hydrolyzed collagen was higher than those in fish meal. Glycine and alanine have the effect of stimulating fish feeding [24]. Arginine, as a precursor, is involved in the synthesis of urea, polyamines, agmatine, proline and glutamate [25]. Proline is proven to be crucial in collagen synthesis and cell differentiation [26]. In the present research, the WGR and SGR of fish decreased significantly when the proportion of fish meal replaced with hydrolyzed collagen reached $4\%$. There might be two reasons for the decline of SGR and WGR. One was the lower content of essential amino acids of hydrolyzed collagen. In the present study, the EAAs content in fish meal and hydrolyzed collagen were $26.21\%$ and $23.18\%$, respectively. With the increase in the proportion of hydrolyzed collagen replacing fish meal, the content of essential amino acids in $4\%$ and $6\%$ HC groups decreased. The content of methionine, valine, lysine, isoleucine, phenylalanine, leucine, threonine and histidine in the $4\%$ and $6\%$ HC groups were lower than those in the control group and $2\%$ HC group, which may cause the nutritional requirements of triploid crucian carp in $4\%$ and $6\%$ HC groups to not be satisfied. Ai et al. [ 5] found that the decline of the growth of large yellow croaker was related to the imbalance of essential amino acids in MBM. The other was the decreased digestion ability of fish to feed. The activities of intestinal amylase, trypsin and lipase decreased significantly when the proportion of fish meal replaced with hydrolyzed collagen reached $4\%$. Starch, protein and fat in food are hydrolyzed under the action of intestinal amylase, trypsin and lipase, respectively [27], which help nutrients in food to be absorbed by the body. In the present experiment, the SGR of triploid crucian carp decreased significantly when the proportion of fish meal replaced with hydrolyzed collagen reached $4\%$, and there were no significant differences in morphometric indexes and nutrition components of muscle among all the treatments. The above results were in conformity with some former research. Zhao et al. [ 28] found that when $4\%$ hydrolyzed collagen was added to the feed, it significantly decreased the SGR of grass carp (Ctenopharyngodon idellus), while affecting muscle nutrition components in a not significant way. There was no significant difference in liver weight between mice fed with a normal diet and oral administration of fish collagen hydrolysates for 14 days, respectively [8]. Umami, sweet, and bitter tastes are closely related to consumers’ acceptance or rejection of food [29]. Glutamates releases a unique taste, which is the original definition of umami taste [30]. The α-glutamyl dipeptides and tripeptides, especially peptides containing aspartic acid, threonine and serine, present a taste of umami, and the umami molecules endow salty taste and increase the intensities of other tastes [29]. The taste of sweetness is largely related to the content of glycine and alanine [30]. The name “glycine” comes from the Greek word “glykys”, which means sweet, and its sweetness is similar to glucose [26]. Bitter food is usually rejected by consumers, however, limited bitterness in food may be desirable [29]. The increased glutamic acid content in $4\%$ HC and $6\%$ HC groups and serine content in all HC groups might be related to the fact that hydrolyzed collagen was rich in proline and glycine, which could be converted into glutamic acid and serine, respectively [26,31]. The free proline content of muscle in all HC groups decreased significantly, which might be related to the conversion of proline into glutamic acid and other amino acids [31]. The arginine content in hydrolyzed collagen was higher than that in fish meal, while the free arginine content of muscle in all HC groups decreased significantly with hydrolyzed collagen replacement, which needed to be further studied. The results that the replacement of fish meal with hydrolyzed collagen significantly increased the umami amino acids content and significantly decreased the bitter amino acids content of muscle showed that hydrolyzed collagen was helpful to improve the taste of the triploid fish. Fatty acids are hydrocarbon chains with 2–36 carbon atoms, one end of which is methyl and the other end is carboxyl [32]. As the substantial components of lipids, fatty acids are important energy substrates, meanwhile, they are the components of phospholipids involved in the formation of cell membranes [33]. Fatty acids can be divided into two categories: saturated fatty acids (SFAs) without double bonds and unsaturated fatty acids with double bonds. Unsaturated fatty acids families include n-3, n-6 and n-9 families, and the position of the first double bond is different among these three fatty acids [32]. Human can synthesize most kinds of fatty acids, except for α-linolenic acid (ALA, C18:3 n-3) and linoleic acid (LA, C18:2 n-6), because human lacks the desaturase enzymes that catalyze the formation of the double bond at the n-3 or n-6 position of the hydrocarbon chain (calculating from the methyl carbon), respectively [34]. The ALA and LA can only be obtained from food, therefore, they are termed essential fatty acids [35]. When the uptake of SFAs increases, the LDL-C content in the body increases, which makes the human body more prone to coronary heart disease [36]. The n-3 PUFAs are vital in decreasing lipogenesis, and the n-6 PUFAs have certain benefits in decreasing total cholesterol, LDL-C and HDL-C content [33]. In the present research, the ALA and LA content increased significantly and the SFAs content decreased significantly with hydrolyzed collagen replacement, indicating that hydrolyzed collagen was beneficial in improving fatty acids composition of fish muscle. Texture profile analysis (TPA) is used to obtain a series of parameters, which include hardness, adhesiveness and so on, to evaluate the texture properties of muscle by simulating human oral movements [37]. Texture characteristics are closely related to the acceptability of consumers [38]. Crispy grass carp (Ctenopharyngodon idellus C. ET V), belonging to freshwater fish, is popular with consumers because of its high crispness, hardness and chewiness of muscle [38]. In the present research, compared with the control group, the $4\%$ HC and $6\%$ HC groups have significantly higher muscle chewiness, and the $6\%$ HC group has significantly higher muscle hardness. The increased hardness in $6\%$ HC group might be linked to the increased myofiber density. The higher the myofiber density, the harder the muscle [22]. In the present experiment, the myofiber density in $6\%$ HC group was highest and significantly higher than that in other HC groups. In addition to this reason, the increase in muscle hardness and chewiness might be related to the increase in muscle collagen cross-linking. The content of hydroxylysine in hydrolyzed collagen ($1.38\%$) was much higher than that in fish meal ($0.39\%$). Hydroxylysine has the function of forming collagen cross-linking, which improves the mechanical, thermic stableness and tractile intensity of collagen fiber [39]. The hardness of Atlantic salmon fillets was positively correlated with cross-linking concentration [40]. Compared with the control group, all HC groups have significantly lower muscle adhesiveness. Adhesiveness is inversely proportional to cell binding force [41]. The decrease in muscle adhesiveness might be related to the increase in intercellular binding force by hydrolyzed collagen. Muscle pH is related to glycolysis, fatty acid composition and some biological reactions, and the decrease in pH will lead to a soft texture [21]. In the present research, no significant difference was detected in muscle pH, and the muscle chewiness and myofiber density increased significantly when the proportion of fish meal replaced with hydrolyzed collagen reached $4\%$, suggesting that hydrolyzed collagen had the function of improving muscle texture of the triploid fish, which will make it more acceptable to customers. Superoxide dismutase (SOD), a kind of metalloenzyme, is capable of catalyzing superoxide radicals (O2•−) into hydrogen peroxide (H2O2) and oxygen (O2), which effectively protects the body from the damage of reactive oxygen species (ROS) [42]. Glutathione peroxidase (GSH-Px) can eliminate organic hydroperoxides [43]. SOD and GSH-Px are crucial in protecting the body from antioxidant damage. Malondialdehyde (MDA) is produced by chemical reactions and enzymatic catalysis of PUFAs, and it is a biomarker of oxidative stress [44]. Compared with the control group, the activities of serum GSH-Px in all HC groups and the serum SOD in the $6\%$ HC group increased significantly, and the content of serum MDA in all HC groups decreased significantly. The increased serum GSH-Px and SOD activities, and the decreased MDA content might be attributed to the antioxidant function of hydrolyzed collagen [10]. Peptides isolated from gelatin hydrolysate of tilapia skin have been proven to be capable of reducing the level of intracellular ROS and increasing the expression of antioxidant factors [45]. In the present study, serum GSH-Px activity increased significantly and serum MDA content decreased significantly with hydrolyzed collagen replacement, which coincided with the former research that the GSH-Px and SOD activities in the skin of mice were significantly increased after oral administration of the diet containing collagen hydrolysate [46]. Excessive ROS would cause the oxidation of subcellular membrane and structural proteins, and ultimately affect muscle texture, water-holding capacity and other quality characteristics, which was harmful to muscle quality [47]. When common carp (Cyprinus carpio) was exposed to oxidative stress, the muscle physicochemical properties decreased significantly [48]. The relationship between oxidative stress and muscle quality indicated that hydrolyzed collagen improved muscle quality probably by improving the antioxidant capacity of triploid crucian carp. Glucose is an important source of energy for physiological activities [49]. Glucose in the blood is absorbed by cells in the liver and muscle under the action of insulin, and stored in the form of glycogen [50]. Hepatic glycogen is crucial for maintaining normal glucose homeostasis in the body, and it is decomposed to keep the blood glucose level in starvation in a normal range, and the content of hepatic glycogen is related to metabolic pathways such as gluconeogenesis, glycogenolysis, glycogen synthesis, glycolysis [51]. The serum glucose content decreased significantly when the proportion of fish meal replaced with hydrolyzed collagen reached $4\%$. The decreased serum glucose content might be attributed to the increased expression level of IGF-1 in the liver. In the present experiment, the expression level of IGF-1 in the liver increased significantly when the proportion of hydrolyzed collagen replacing fish meal reached $2\%$. Insulin-like growth factor-1, a peptide hormone, has similar amino acid sequences and functions to insulin, and it can promote glucose uptake in peripheral tissues [52]. In addition, the decreased serum glucose content in $4\%$ HC and $6\%$ HC groups might also be related to the enhancement of antioxidant capacity. In the present research, the serum GSH-Px activity in all HC groups increased significantly, and the serum MDA content decreased significantly compared with the control group. There is a tight relationship between oxidative stress and insulin resistance, a situation in which cells failed to utilize insulin normally, and increased GSH-Px activity helps the body to resist oxidative stress [50,53]. Glycogen phosphorylase (GPase) is vital in the process of glycogen decomposition, which releases glucose-1-phosphate (G-1-P) by cleaving α-1,4 glycosidic bridges, and G-1-P participates in the glycolytic pathway by conversion to glucose-6-phosphate (G-6-P) [54]. Glucokinase (GK) is the first enzyme in liver glycolysis, and it is also the speed-limiting enzyme, which catalyzes the conversion of glucose into G-6-P [55]. The decreased expression levels of GPase and GK in the liver might be attributed to the decreased hepatic glycogen content, resulting in the decreased activities of glycogen decomposition and glycolysis-related enzymes, but the mechanism of hepatic glycogen decrease needs to be further studied. High glucose level may stimulate vascular cells to produce ROS through the activation of NAD(P)H oxidase, which depends on protein kinase C (PKC) pathway, and ultimately affect the quality of fish [47,56]. In the present experiment, the decrease in serum glucose content may be beneficial in improving the muscle quality of fish to some extent. Lipids are a class of organic molecules, including triglycerides, sterols, and so on, and they are crucial in energy storage and cell signal transmission [49]. When the lipid level in the body is too high, there may be various diseases, such as hypertriglyceridemia [57]. Lipids cannot flow freely in the blood because of their insolubility in water, and lipoproteins (such as HDL-C, LDL-C, very-low-density lipoproteins (VLDLs), and chylomicrons) are the main carriers of lipids transported in the blood [49]. Cholesterol, a kind of sterol, is mainly synthesized in the liver and is the precursor of steroid hormones, bile acids, etc. [ 58]. Low-density lipoprotein (LDL) is the main carrier of cholesterol transportation from the liver to body tissues, and a high level of LDL in serum will greatly increase the risk of arterial disease [58]. High-density lipoprotein (HDL) is the main carrier of cholesterol transportation from organism tissues to the liver, and HDL is decomposed in the liver or excreted as waste, which is essential to prevent the body from suffering from arterial diseases [58,59]. In the present experiment, the content of serum triglyceride and total cholesterol decreased significantly with hydrolyzed collagen replacement. Serum LDL-C content and liver TG content decreased significantly when the proportion of hydrolyzed collagen replacing fish meal reached $4\%$ and $6\%$, respectively. The decrease in lipid content might be attributed to the increased expression levels of genes related to lipolysis and the decreased expression levels of genes related to lipogenesis in the liver. The HADH, LPL and CPT 1 genes are related to lipolysis. Lipoprotein lipase is in the position to hydrolyze triglycerides in chylomicrons and VLDL [60]. Both Hydroxyacyl-coenzyme A dehydrogenase and carnitine O-palmitoyltransferase 1 catalyze the β-oxidation of fatty acids [61,62]. The FAS and SREBP 1 genes are related to fat synthesis. Long-chain fatty acids, the principal component of triglyceride, are synthesized under the catalysis of fatty acid synthase [60]. The synthesis of cholesterol and fatty acids is controlled by the transcription factor of SREBP [63], of which SREBP-1 promotes the expression of adipogenic genes [60]. In the present research, the expression levels of HADH, LPL and CPT 1 in the liver increased significantly, and the expression levels of FAS and SREBP 1 decreased significantly with hydrolyzed collagen replacement, indicating that hydrolyzed collagen decreased the body lipid content probably by promoting fat decomposition and inhibiting fat synthesis. In addition, the decrease in serum lipid content might be related to the decrease in SFAs content and the increase in PUFAs content in muscle. Previous studies have proved that the higher the SFAs content, the higher the LDL-C content in the body [36], and the n-3 PUFAs and n-6 PUFAs are effective in reducing LDL-C and T-CHO content [33]. In the present experiment, the content of serum TG, T-CHO and LDL-C decreased significantly with fish meal replaced with hydrolyzed collagen, and the results coincided with the former research [8], which indicated that the levels of plasma total lipids and triglycerides decreased significantly in mice treated with oral collagen hydrolysates. ## 5. Conclusions It can be concluded that when hydrolyzed collagen replaced $2\%$ fish meal, it did not significantly impact the growth and intestinal digestive enzymes activities of triploid crucian carp. When the ratio of fish meal replaced with hydrolyzed collagen reached $4\%$, SGR decreased, and muscle quality and glycolipid metabolism were improved. The maximum proportion of hydrolyzed collagen replacing fish meal should not exceed $4\%$. Hydrolyzed collagen improved muscle quality by increasing muscle chewiness, myofiber density and the content of total umami amino acids, ALA, LA and n-6 PUFAs in muscle, as well as increasing serum antioxidant capacity. Hydrolyzed collagen decreased serum glucose probably by up-regulating the expression level of IGF-1, and decreased body lipid content probably by up-regulating the expression levels of lipolytic enzyme genes (HAHD, LPL, CPT 1) and down-regulating the expression levels of adipogenic enzyme genes (FAS, SREBP 1). These studies indicated that hydrolyzed collagen had the effect of improving muscle quality and glycolipid metabolism of fish, which proved the feasibility of fish meal replaced with hydrolyzed collagen in fish feed. ## References 1. Ravindran V., Blair R.. Feed resources for poultry production in Asia and the Pacific. III. Animal protein sources. Worlds Poult. Sci. J. (1993) 49 219-235. DOI: 10.1079/WPS19930020 2. 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--- title: Next-Generation Sequencing (NGS) Analysis Illustrates the Phenotypic Variability of Collagen Type IV Nephropathies authors: - Miriam Zacchia - Giovanna Capolongo - Francesca Del Vecchio Blanco - Floriana Secondulfo - Neha Gupta - Giancarlo Blasio - Rosa Maria Pollastro - Angela Cervesato - Giulio Piluso - Giuseppe Gigliotti - Annalaura Torella - Vincenzo Nigro - Alessandra F. Perna - Giovambattista Capasso - Francesco Trepiccione journal: Genes year: 2023 pmcid: PMC10048128 doi: 10.3390/genes14030764 license: CC BY 4.0 --- # Next-Generation Sequencing (NGS) Analysis Illustrates the Phenotypic Variability of Collagen Type IV Nephropathies ## Abstract Mutations in COL4A3-A5 cause a spectrum of glomerular disorders, including thin basement membrane nephropathy (TBMN) and Alport syndrome (AS). The wide application of next-generation sequencing (NGS) in the last few years has revealed that mutations in these genes are not limited to these clinical entities. In this study, 176 individuals with a clinical diagnosis of inherited kidney disorders underwent an NGS-based analysis to address the underlying cause; those who changed or perfected the clinical diagnosis after molecular analysis were selected. In 5 out of 83 individuals reaching a molecular diagnosis, the genetic result was unexpected: three individuals showed mutations in collagen type IV genes. These patients showed the following clinical pictures: [1] familial focal segmental glomerulosclerosis; [2] end-stage renal disease (ESRD) diagnosed incidentally in a 49-year-old man, with diffuse cortical calcifications on renal imaging; and [3] dysmorphic and asymmetric kidneys with multiple cysts and signs of tubule–interstitial defects. Genetic analysis revealed rare heterozygote/compound heterozygote COL4A4-A5 variants. Our study highlights the key role of NGS in the diagnosis of inherited renal disorders and shows the phenotype variability in patients carrying mutations in collagen type IV genes. ## 1. Introduction The glomerular basement membrane (GBM) integrity is critical for glomerular filtration. Many unique structural components allow GMB to contribute significantly to the size selectivity of the glomerular filter. Among these components, type IV collagen is the most abundant. Moreover, collagen type IV chains are major components of basement membranes in the cochlea and eye. In the adult kidney, the GMB is mostly formed by α3, α4, and α5 intertwined subunits. The α5 subunit is encoded by COL4A5, located on the X-chromosome, and is associated with the classic X-linked Alport syndrome (AS) in male patients. The α3 or α4 subunits are encoded by COL4A3 and COL4A4, respectively; their mutations cause either autosomal dominant/recessive Alport syndrome and thin basement membrane nephropathy (TBMN) [1,2]. Typically, AS patients’ phenotype is characterized by hematuria from infancy, with subsequent onset of glomerular filtration rate (GFR) decline up to end-stage renal disease (ESRD) in young-adults; extra-renal features include sensorineural hearing loss and sometimes lenticonus or retinal/corneal dystrophy [3]. Conversely, thin basement membrane nephropathy (TBMN) is characterized by a milder renal phenotype, with hematuria and sometimes proteinuria [4]. Affected individuals may have a uniformly thinned GMB, normal GFR, and a family history of hematuria. Their clinical course is usually benign. Sometimes, TBMN represents the carrier state for autosomal recessive AS. Genetic analysis to detect COL4A3 and COL4A4 mutations is not routinely performed in the diagnosis of TBMN, and technical difficulties may occur in variants’ interpretations. Recent advances in the genetic field have demonstrated that the spectrum of kidney dysfunction caused by collagen type IV mutations is not limited to the described clinical entities [5]. The present study shows the variability in the kidney phenotypes of patients carrying COL4 mutations, ranging from cystic disorders to glomerular barrier structural anomalies leading to proteinuria and progressive renal dysfunction. This study was conducted on 176 patients that underwent genetic analysis; in most patients, genetics confirmed the clinical suspicion, especially in patients with polycystic kidney disease or syndromic ciliopathies, as Bardet–Biedl syndrome. Interestingly, 5 out of 83 patients who had a molecular diagnosis showed unexpected results. Among these, three patients showed mutations in collagen type IV (COL4) genes. The study suggests that COL4 variants may underlie variable structural and functional kidney abnormalities, including both glomerular and tubulointerstitial disorders. ## 2.1. Clinical Information Adult patients referred to our Center of Rare Kidney diseases that underwent genetic analysis in the Unit of Genetics of the University of Campania, Luigi Vanvitelli, were recruited. All procedures performed in this study were in accordance with the ethical standards of the University of Campania “Luigi Vanvitelli” research committee (# 0017030/i-$\frac{13}{07}$/2020) and with the 1964 Helsinki declaration and its later amendments and informed consent was obtained from all individual participants included in the study. We collected data from the genetic analysis of 176 patients suffering from genetic kidney disorders that showed a broad spectrum of phenotypes divided into the subgroups shown in Figure 1. Clinical information was obtained from medical records of our Unit of Nephrology; in addition, two individuals were screened in the “Maria SS. Addolorata” hospital’s Unit of Nephrology (Eboli, SA, Italy). Experienced nephrologists performed physical examinations and assessments. Information including age, family history, and history of gestation was collected. Patients with a probable acquired pathogenesis (diabetes, probable immune disorders, and so on) were excluded from genetical analysis. The suspicion of inherited kidney disorders was based on the criteria described below. Autosomal dominant polycystic kidney disease (ADPKD) was diagnosed evaluating the number of kidney cysts at ultrasound and/or abdominal CT and familial history of kidney cysts, according to PEI criteria [6]. If no family history was present, patients were included only in the presence of imaging tests for enlarged multicystic kidneys suggestive of ADPKD. Among cystic patients, 40 patients fulfilled the clinical criteria of Bardet–Biedl syndrome (according to Beales). Patients with glomerulonephritis underwent genetic analysis in case of familial forms and steroid resistant nephrotic syndrome. The latter was defined by the presence of oedema, massive proteinuria (>3.5 g/24 h) and hypoalbuminemia (<3 g/dL). Alport syndrome (also included in the glomerulonephritis’ group in Figure 1), was diagnosed following current guidelines [7]. Concerning tubulopathies, hypokalemic tubulopathies were diagnosed in case of metabolic alkalosis and hypokalemia (after excluding a gastrointestinal or endocrine etiology), distal renal tubular acidosis (dRTA) was diagnosed with the urine acidification test, and Fanconi syndrome was diagnosed when metabolic acidosis, aminoaciduria and low molecular weight proteinuria were present. Monogenic forms of urolithiasis were indagated in the case of family history, childhood onset, frequent recurrences, and nephrocalcinosis. In total, 104 patients with cystic diseases (64 with polycystic kidney and 40 with Bardet–Biedl syndrome), 29 with glomerulonephritis, 5 with kidney stones and metabolic diseases, and 38 with tubulopathies were selected for genetic analysis. Their ages ranged from 18 to 62 years old. Ninety-four patients were female and 84 were male. ## 2.2. Molecular Analysis Patients’ DNA was extracted from peripheral whole blood obtained using the QIAamp DNA Blood Kit by Qiagen. DNA quality and quantity were evaluated with spectrophotometry (Nanodrop ND 1000, Thermo Scientific Inc., Rockford, IL, USA) and fluorometry-based (Qubit 2.0 Fluorometer, Life Technologies, Carlsbad, CA, USA) methods. Patients were analyzed through NGS, 107 using a kidney-focused genetic panel (Nephroplex) containing 115 genes causing kidney diseases and the other 69 using Clinical Exome Sequencing, containing all disease-causing genes (approximately 5000). All analyzed genes have a clinical association with disease. Analysis was conducted using as a strategy the HaloPlex TM Target Enrichment System (Agilent)—using SureSelect Custom DNA Target Enrichment Probes, UNSPSC Code 41116134—followed by sequencing—using the HiSeq1000 system (Illumina inc., San Diego, CA, USA), as described elsewhere [8]. ## 2.3. Variants Interpretation After filtering variants for quality and number of reads (at least five reads for each variant), population databases (ExAC, gnomAD, and an internal database) [9,10] were used to filter variants according to population frequency and only rare alleles (minor allele frequency <$1\%$) were included. In silico tools including SIFT, FATHMM, MutationAssessor, Polyphen-2, MutationTaster and Provean, MuPRO, PANTHER, PhD-SNP, and SNP n GO [11,12,13,14,15,16,17,18] were used to predict mutation pathogenicity; detected mutations were searched in the Clinvar database and familial segregation was performed when possible. Interpretation of variant pathogenicity followed the guidelines of The American College of Medical Genetics and Genomics (ACMG) [19]. The supposed causative mutations were confirmed by Sanger sequencing, as described elsewhere [20]. ## 2.4. Patient Selection Patients’ NGS reports were carefully analyzed by a multidisciplinary team including nephrologists and geneticists. All patients with a disease-causing mutation were analyzed to assess the correspondence between clinical diagnosis and molecular diagnosis. Patients with a discrepancy between clinical and molecular diagnosis were selected for a deeper analysis. ## 3.1. Patient Cohort A total of 176 subjects underwent genetic analysis with NGS technology using a kidney-focused genetic panel (Nephroplex) [2] (see additional data given in Online Resource 1) or exome sequencing. Disease-causing variants were found in 83 individuals ($47.2\%$). As indicated in Figure 1, in 78 ($94\%$) cases the genetic analysis confirmed the clinical diagnosis, whereas in 5 cases the genetics resulted in unexpected results. Three of these individuals showed a collagen type IV nephropathy. The composition of the cohort is briefly described in Table 1, and the diagnostic rate of the entire cohort is depicted in Figure 2. As shown, the patient cohort included kidney cystic individuals, syndromic patients, metabolic disorders, tubulopathies, and familial glomerulonephritis. The diagnostic rate was higher in patients with cystic and metabolic disorders (Figure 2). ## 3.2. Clinical Features of COL4 Patients The clinical information and the genealogical tree of the five cases are shown in Table 2 and Figure 3, respectively. Case 1 was a 43-year-old male patient with personal history of microhematuria during childhood that disappeared later, referring to the Nephrology Unit for the onset of proteinuria; a familiar history of chronic kidney disease was ascertained. His mother was diagnosed as having nephrotic syndrome due to focal and segmental glomerulosclerosis (FSGS). Moreover, three uncles were under dialysis treatment from a young age, suggesting the presence of an inherited nephropathy. At presentation, the patients showed an estimated glomerular filtration rate (eGFR) of 60 mL/min/1.73 m2. Proteinuria was 2 g/24 h. The screening for secondary forms of glomerulonephritis was negative. The patient underwent kidney biopsy, which revealed a classic picture of FSGS, with IgM and C3 mesangial deposits (Figure 4). Considering his personal and familiar story of kidney disease, the case and his mother underwent genetic analysis. Case 2 was a 49-year-old man that referred to the hospital in emergency for epistaxis who had unremarkable personal and familial history of disease. He denied suffering from any significant morbidity; he was not under chronic medication and did not exhibit previous medical reports. On admission, high blood pressure and advanced renal dysfunction (estimated GFR of 13 mL/min/1.73 m2) were revealed. Sensorineural deafness was referred. Renal ultrasound showed a decreased size of the right kidney, with parenchymal calcifications and fetal lobulation bilaterally. The abdomen computed tomography (CT) scan confirmed cortical calcification (Figure 5). Subsequent analysis revealed that circulating parathyroid hormone (70 pg/mL), calcium (9 mg/dL, 2.25 mmol/L), and phosphate (4.1 mg/dL) levels were unremarkable considering the level of chronic renal disease. No vitamin D and/or calcium supplementation had been taken in the past. Few months later, the patient started chronic hemodialysis and, after two years, he underwent kidney transplantation. Because of the relatively young age at ESRD and the unknown cause of renal dysfunction, the patient underwent genetic analysis. Case 3 was a 59-year-old male admitted to the Nephrology Unit for chronic kidney disease of unknown cause. The patient had suffered from polyuria and microhematuria since childhood; proteinuria appeared when he was thirty-one years old. In his familial history, the mother had intermittent hematuria, with normal overall renal function. His son showed hyperuricemia and a slight decline of eGFR. The proband underwent abdomen ultrasound and CT scan: the right kidney showed a reduced longitudinal diameter (10 cm) and subcentimetric cysts and four large cysts were detected on the left kidney (Figure 5B). Increased echogenicity with reduced corticomedullary differentiation were evident on ultrasound. The representation of the vascular tree was reduced. Because of the familiar history of renal disorders, the genetic analysis of the patient and his son was performed. ## 3.3. Genetics Analysis of Patients Genetic variants found in patients are shown in Figure 6 and in Table 3. For missense mutations we also calculated and reported the effect on protein structure according to SIFT. SIFT is a software that predicts the effect of aminoacidic changes on the protein structure using sequence homology and aminoacidic properties. For this reason, it is only applicable to non-synonymous variants. MuPro is a vector machine-based tool that predicts changes in protein stability for a single amino acid mutation. Panther measures PSAP (position-specific evolutionary preservation) to calculate the probability of the deleterious effect for missense mutations. PhD-SNP generates a comparative conservation score of multiple sequence alignment to identify the effect of the SNP as disease-related or neutral. SNP n GO is a support vector machine (SVM)-based method that uses information from multiple sequence alignment and gene ontology to predict if a given mutation is classified as disease-related or not. The effect of splicing of the first variant was predicted through SpliceAI [21]. Case 1 was studied with his mother. Both carried the rare heterozygote c.693+2T>C variant in COL4A4(NM_000092). The phenotype segregated with the genotype, as both had a history of nephrotic syndrome along with the histological diagnosis of FSGS. The mutation is predicted to affect protein splicing and is considered damaging by in silico programs. After genetic analysis, the proband and his mother were screened for ocular and auditory function; both were negative. Molecular screening of Case 2 revealed the presence of the heterozygote c.991G>A COL4A5 variant, resulting in a p.Gly331Ser change. The variant is predicted as pathogenic by in silico programs such as SIFT, MuPRO, PANTHER, PhD-SNP, and SNP n GO. Case 3 showed the c.1589G>A (p.Gly530Glu) mutation in COL4A4, predicted as likely to be pathogenic; the same mutation was also reported in his son, showing a similar renal phenotype. ## 4. Discussion This paper shows unexpected clinical presentations of patients carrying COL4A4-A5 mutations, suggesting that the spectrum of renal phenotypes associated with these genetic loci is wider than has been believed until now [7,22]. Collagens constitute a superfamily of structural proteins; 28 different subtypes have been described to date in vertebrates [23]. All of them, including collagen type IV, have a triple-helical structure composed of three α-chains. There are six different types of α-chains with similar structural domains. These include a short non-collagenous NH2-terminal domain (known as 7S domain), a long central collagenous domain composed of repeats of Gly-X-Y aminoacidic triplets, and a COOH-terminal domain (NC1 terminus). Collagen type IV chains participate in the composition of basal membranes, whose defects are known to cause kidney dysfunction. In the human kidney, the α1 and α2 chains of collagen type IV are detected during embryonic development and their expression gradually decreases over time. Conversely, the α3(IV), α4(IV), and α5(IV) chains form the adult kidney basal membranes [24]. Mutations of type IV collagen are associated with the following human disorders: [1] AS, a progressive renal disorder characterized by structural abnormalities of the glomerular basement membrane (GBM) leading to hematuria and eGFR decline that is often associated with hearing loss and ocular symptoms and [2] TBMN, characterized by thinned GBM leading to hematuria and (sometimes) proteinuria. Moreover, an autoimmune disorder due to anti-α3(IV) antibodies (binding to alveolar and renal GBM) causes Goodpasture syndrome [4,25]. Recent studies have suggested that mutations in COL4A3-5 may also cause additional renal abnormalities [22]. However, little information is available on the relationship between cortical calcifications and COL4 variants and very few data are available on the reasons for tubulointerstitial defects, including cysts. Alport syndrome is the best-known nephropathy caused by collagen type IV mutations. It is caused by mutations in COL4A5, leading to X-linked Alport syndrome (XLAS) for $85\%$ of cases [22,26]. Whereas the phenotype of female individuals is generally mild, male XLAS patients show progressive renal failure, especially when carrying truncating mutations. Extra-renal manifestations include hearing loss and ocular lesions, uncommon findings in hemizygote female carriers [27]. Biallelic mutations in COL4A3 and COL4A4 genes cause the autosomal recessive forms of AS, accounting for $15\%$ of all AS patients, with a phenotype resembling male XLAS patients. Heterozygote mutations in COL4A3 and COL4A4 have been reported in autosomal-dominant AS and in TBMN. Recently, NGS studies have demonstrated that mutations in these three genes may also result in atypical clinical presentations. Malone et al. performed whole-exome sequencing of a cohort of 70 families with a diagnosis of familial FSGS of unknown cause. In their study, seven families ($10\%$) showed rare or novel COL4A3 and COL4A4 variants [28]. Additional studies have shown that familial FSGS is associated with mutations in COL4 genes, suggesting that GBM abnormalities due to defects in collagen IV chains may result in secondary FSGS [29]. Moreover, some reports have shown that COL4A3 and COL4A4 mutations have been detected in patients with multiple renal cysts [30,31,32]. The present study shows a series of patients carrying pathogenic variants in COL4A4-A5 that show intriguing clinical presentations, expanding our understanding of the role of abnormalities in collagen type IV into the pathogenesis/predisposition to develop kidney disease. In this study, NGS analysis has been applied to 176 individuals to elucidate the molecular landscape underlying several classes of inherited renal disorders, including cystic, tubular, and glomerular disorders. We selected patients whose diagnosis was unexpected. A high prevalence of these patients showed COL4A4-5 mutations. The patients showed the following clinical pictures: familial FSGS (Case 1), cortical calcification with ESRD of unknown origin (Case 2), and familial dysmorphic kidneys with multiple cysts and renal function decline in adulthood (Case 3). Case 1 underwent an analysis with his mother. Both had a biopsy-proven diagnosis of FSGS. They both showed the c.693+2T>C (NM_000092) COL4A4 variant. The detected mutation is predicted to affect protein splicing and is considered damaging according to in silico programs. The latter has never been reported in the literature nor in the public database ClinVar [33]. Even though no confirmation of functional abnormalities has been ever provided, the position of the mutation suggests that it could interfere with normal splicing, affecting a donor splice site. Multiple in silico tools predicted that Case 2 and Case 3 harbor pathogenic missense variants as pathogenic; similar tools have also been utilized to predict pathogenic variants of other genes related to kidney disorders [34]. Case 2 showed the hemizygous missense c.991G>A p.Gly331Ser COL4A5 (NM_000495) variant. Public databases do not report this variant. It has been largely demonstrated that the conserved Gly residues are crucial components of collagen’s primary sequence. Given their small size and conformational flexibility, they are thought to play a role in the stability of the triple-helical structure [35]. Accordingly, glycine mutations are considered hot spots in the collagenous genes. Plant KE et al. reported the c.992G>T p.Gly331Val mutation classified as pathogenic. Moreover, in 2014 Hashimura Y et al. reported the c.991-1G>A mutation, which is considered pathogenic in public databases. As these mutations are located in the same region of Case 2, we assumed that it could affect protein function and the patient’s phenotype [36,37]. Thus, it is not excluded that the mutation in our patient perturbs normal splicing. Functional evidence regarding mRNA is needed to confirm this hypothesis. The patient’s renal phenotype is of high interest. Few reports in the literature have shown whether COL4 mutations cause cortical calcification. The term nephrocalcinosis has been introduced to define generalized calcium deposition in renal parenchyma [38,39]. Cortical calcification is about 20 times less common than medullary calcification. According to the literature, it is the consequence of acute cortical necrosis [38]; however, chronic glomerulonephritis may sometimes cause this uncommon finding on imaging. On ultrasound, the patient showed increased echogenicity of the renal cortex. The CT scan confirmed this finding: atrophy of right kidney and bilateral thin circumferential cortical calcification. The last patient (Case 3) showed multiple kidney cysts. Focal renal cysts are common in older subjects. The prevalence, size, and number increase with age. The exact pathogenesis is unknown. However, it is not excluded that weakening of the basement membrane along the nephron for different causes may induce or favor cysts formation [40]. The patient under examination showed centimetric cysts in the right kidney and large cysts on the left. The analysis of PKD1 and PKD2, the causative genes of autosomal dominant polycystic kidney disease [41], failed to show any pathogenic variant. This was not surprising as the imaging was atypical. Even if the number of bilateral cysts was quite high, kidneys were asymmetric, with a hypomorphic kidney with subcentimetric cysts and a contralateral enlarged kidney with multiple large cysts. This patient and his son showed the hemizygous c.1589G>A p.Gly530Glu variant in COL4A4, not reported in ClinVar but affecting glycine and therefore potentially impairing protein structure. In patients with COL4A3-5, the pathogenesis of tubulointerstitial nephritis and cyst formation is unclear [32,42]; some studies support the hypothesis that proteinuria may lead to progressive tubulointerstitial fibrosis. Whereas most investigations are focused on the alterations of the GBM, little is known about the pathogenesis and changes in the tubular basal membrane (TBM). The α3(IV), α4(IV), and α5(IV) chains are detectable in GBM and the TBM of distal and collecting tubules [43]. Whether these defects predispose people to tubulointerstitial fibrosis and cyst formation is unknown. Our data suggest that mutations of COL4A4-A5 are associated with a wide spectrum of kidney abnormalities, providing a dataset of patients showing different renal phenotypes. Disturbed collagen type 4 chains may affect kidney structure and predispose the patients to developing several kidney structural abnormalities, including glomerular sclerosis, cortical calcification in the absence of abnormal calcium–phosphate homeostasis, and fibrocystic kidneys. Thus, similar genetic landscapes may underlie different renal entities, whose clinical onset is variable and includes proteinuria and declined renal function of unknown cause. 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--- title: Cellular Transcriptomics of Carboplatin Resistance in a Metastatic Canine Osteosarcoma Cell Line authors: - McKaela A. Hodge - Tasha Miller - Marcus A. Weinman - Brandan Wustefeld-Janssens - Shay Bracha - Brian W. Davis journal: Genes year: 2023 pmcid: PMC10048144 doi: 10.3390/genes14030558 license: CC BY 4.0 --- # Cellular Transcriptomics of Carboplatin Resistance in a Metastatic Canine Osteosarcoma Cell Line ## Abstract Osteosarcoma prognosis has remained unchanged for the past three decades. In both humans and canines, treatment is limited to excision, radiation, and chemotherapy. Chemoresistance is the primary cause of treatment failure, and the trajectory of tumor evolution while under selective pressure from treatment is thought to be the major contributing factor in both species. We sought to understand the nature of platinum-based chemotherapy resistance by investigating cells that were subjected to repeated treatment and recovery cycles with increased carboplatin concentrations. Three HMPOS-derived cell lines, two resistant and one naïve, underwent single-cell RNA sequencing to examine transcriptomic perturbation and identify pathways leading to resistance and phenotypic changes. We identified the mechanisms of acquired chemoresistance and inferred the induced cellular trajectory that evolved with repeated exposure. The gene expression patterns indicated that acquired chemoresistance was strongly associated with a process similar to epithelial–mesenchymal transition (EMT), a phenomenon associated with the acquisition of migratory and invasive properties associated with metastatic disease. We conclude that the observed trajectory of tumor adaptability is directly correlated with chemoresistance and the phase of the EMT-like phenotype is directly affected by the level of chemoresistance. We infer that the EMT-like phenotype is a critical component of tumor evolution under treatment pressure and is vital to understanding the mechanisms of chemoresistance and to improving osteosarcoma prognosis. ## 1. Introduction Osteosarcoma (OSA) treatment and prognosis have remained unchanged over the past thirty years [1]. It continues to be the most common human primary bone malignancy and comprises under $1\%$ of adult cancers and 3–$5\%$ of pediatric cancers [2]. The current standard of care, first implemented in the 1980s, entails surgical excision, radiation, and an aggressive chemotherapy regimen [1,3]. This offers a ten-year survival rate of $60\%$ for patients with nonmetastatic disease and $30\%$ for patients with metastatic disease [1,3]. Canine OSA is a naturally occurring cancer and the predominant bone malignancy that affects dogs. Most dogs impacted by OSA are large or/giant breeds that experience increased cell proliferation and rapid growth in the long bones of the appendicular skeleton [4]. In addition, the rate of incidence is 10-fold greater in dogs than that in humans, and a canine’s shorter lifespan allows for more rapid data collection [4]. The canine model is particularly relevant due to mutations in the TP53, RB1, MTAP/CDKN2A, and MDM2 genes that are commonly associated with human and canine OSA [5,6,7]. However, with the exception of TP53, the specific mutations within these shared genes that infrequently occur in both species and mutations in SETD2 and DMD are unique to canine OSA [6,8,9,10]. The primary cause of OSA treatment failure is chemoresistance, which has been attributed to tumor adaptability and the trajectory of tumor cell evolution [11,12]. Tumor adaptability is driven by the key hallmarks of tumorigenesis, genomic instability, and compromised regulation of DNA replication and the cell cycle [11]. Genomic instability results in high levels of copy number variation and genotypic, transcriptomic, and phenotypic heterogeneity within cancerous tissues that can promote adaptive mechanisms for resistance to chemotherapy drugs and the production of highly resistant cancer cells [11,12]. In addition, chemotherapy-induced mutations may drive the tumor’s trajectory by selecting for drug-resistant cell populations [11,12,13]. The gold-standard chemotherapies for OSA in both humans and canines are platinum-based drugs [14]. The mechanisms of resistance are similar among platinum chemotherapeutic agents and include reduced drug accumulation, inactivation by glutathione (GSH) and metallothionein (MT), increased DNA repair, and suppression of apoptosis [13,14,15,16]. A preliminary study characterized the proteomes of canine osteosarcoma HMPOS-derived carboplatin-resistant cell lines and their exosomes, and evaluated exosome-mediated chemotherapy resistance [17]. There were differences between each cell line in the response to chemotherapeutics and in the proteins expressed [17]. Proteomic analysis indicated an association of chemotherapy resistance with the glutathione biosynthesis, conjugation, and recycling pathways, and the γ-glutamyl biosynthesis pathway [17]. These pathways minimize the effectiveness of chemotherapeutic drugs by promoting glutathione S transferase (GST) enzyme activation, which hydrolyzes the platinum agents’ active group [17,18]. Carboplatin-resistant cell lines, particularly HMPOS-10R, have high expression of β-catenin, an oncogene [17]. β-catenin plays a key role in the Wnt/β-catenin signaling pathway and promotes chemoresistance through upregulating MDR1 and inducing epithelial–mesenchymal transition (EMT) [19]. Exposure to HMPOS-2.5R exosomes was found to induce chemotherapy resistance in naïve HMPOS-S cells, while exposure to HMPOS-10R exosomes only induced β-catenin expression in naïve HMPOS-S cells [17]. The results of this study indicated the complexity of chemotherapy resistance induction and conveyance, which we will continue to investigate by evaluating the transcriptomes of these cell lines [17]. We hypothesized that OSA cancer cells develop their drug resistance by adapting to the evolutionary selective pressure resulting from increased chemotherapy concentration, either along a singular or a diversified path. The goal of this study was to examine the evolutionary trajectory, assess the results of adaptation due to selective pressure, and determine the nature of resistance mechanisms in previously induced OSA carboplatin-resistant cell lines. This in vitro approach mitigates the cellular complexity of the bone marrow niche to address the diverse tumor microenvironment, which aids in tracking the evolutionary progress of each cell population as they acquire carboplatin resistance [20]. ## 2.1. Carboplatin Chemoresistant Cell Lines For our experiment, we used a highly aggressive and malignant cell line, Highly Metastasizing POS (HMPOS). The HMPOS cells were a kind donation from the Barroga and Fujina Lab [21]. This cell line was previously derived from the canine OSA cell line POS, which was generated by harvesting cells from canine metastatic lesions passaged in mice. Cells were incubated at 37 °C with $5\%$ CO2 in growth media (RPMI 1640 and $10\%$ FBS supplemented with 100 units/mL penicillin and 100 μg/mL streptomycin) [21]. All cells were tested for mycoplasma prior to incubation. The cells used for this study were passaged fewer than five times before arriving in our lab. Expansion in-house entailed fewer than 3 passages prior to introducing carboplatin. Carboplatin resistance was induced at 0, 2.5 μM, and 10 μM dosages in a previous study, and clones were validated to be resistant and were used in this study (Figure 1) [21]. The HMPOS 0, 2.5 μM, and 10 μM-carboplatin-resistant cells will be referred to as HMPOS, HMPOS-2.5, and HMPOS-10, respectively. ## 2.2. Carboplatin Sensitivity Assay HMPOS, HMPOS-2.5, and HMPOS-10 cell lines were grown in T-25 tissue culture flasks containing RPMI 1640 cell culture media with $10\%$ fetal bovine serum. The flasks were incubated at 37 °C with $5\%$ CO2 until $50\%$ confluent. The tissue culture flasks were washed with 1× PBS and replenished with serum-free RPMI 1640 cell culture media. The cells were incubated at 37 °C and serum-starved for 24 h. After incubation, tissue culture flasks were washed with 1× PBS, and varying concentrations of carboplatin diluted in cell culture media were added to the flasks. All three cell lines were treated with carboplatin doses ranging from 0–480 µM. Conditions were set up in triplicate and cells containing drug-free RMPI 1640 with $10\%$ fetal bovine serum were used as a negative control. All flasks were incubated for 72 h at 37 °C. After incubation, the cells were trypsinized, harvested, and centrifuged at 500× g for 5 min. The supernatant was decanted and the remaining cell pellet was resuspended in 100 µL of 1× PBS. The cell suspension was mixed 1:1 with ViaStain AOPI Staining Solution (Nexcelom Biosciences, Lawrence, MA, USA) and cell viability was determined using a Cellometer Auto 2000 (Nexcelom Biosciences, Figure S1). Three technical replicates of three biological replicates were performed. IC50 values were determined using non-linear regression and curve fit analysis. To determine the difference between curves, pairwise extra sum-of-squares F tests were performed, with the null hypothesis as IC50 being the same and the alternative hypothesis as IC50 being different. We also tested this as IC50 being different in at least one of the three replicates. For all tests, $p \leq 0.001.$ ## 2.3. Cell Invasion and Migration Assay The coating buffer was produced from $0.7\%$ NaCl and 0.1 M tris in distilled water and filtered using a 0.2 µm sterile syringe filter. The Matrigel Matrix (Corning, Tewksbury, MA, USA) was diluted using chilled coating buffer to a final concentration of 250 µg/mL. Thincerts were placed in a 24-well plate (Greiner Bio-one, Kremsmünster, Austria) and coated with 100 µL of Matrigel Matrix. The plates were placed in an incubator at 37 °C for 2 h. A cell suspension was prepared using serum-free cell culture media at a concentration of 1 × 106 cells/mL. An amount of 600 uL of cell culture media containing serum was pipetted into the bottom of each test well and a coated Thincert was placed on top of the media; 200 µL of the cell suspension was pipetted into each Thincert and the plates were returned to the incubator. Positive control wells to measure migration were set up in the same manner as the test wells, but included uncoated Thincerts. Negative control wells contained uncoated Thincerts to measure invasion, but only contained serum-free media. All conditions were run in triplicate. Plates were removed after 20 h and the Thincerts were gently removed and placed in a new 24-well plate containing serum-free media and 8 µM Calcein-AM. The plates were incubated for 45 min at 37 °C. After incubation, the cell suspension was removed from the inside of the Thincert, and the Thincerts were placed in a new 24-well plate containing 500 µL of pre-warmed trypsin–EDTA. The plates were incubated for 15 min at 37 °C with occasional tapping to encourage detachment. After 15 min, the thincerts were discarded and the trypsinized cells were mixed via a pipette. A total of 200 µL of the trypsinized cells was pipetted into a 96-well plate in triplicate and the plates were read by a fluorescent plate reader (excitation 485 nm/emission 520 nm) (BioTek Synergy 2, Winooski, VT, USA). The invasion index percentage was calculated using the following formula: Invasion index % = (experimental average–negative control average)/(positive control average–negative control average) × $100\%$. The migration index was calculated using the following formula: Migration index% = (positive control average − negative control average)/200,000 (total number of cells that were originally plated). Analysis was performed using ANOVA single-factor analysis to evaluate the p values for the invasion and migration assays, and Tukey’s HSD test was performed for the invasion assay data (Table S1). ## 2.4. Single Cell RNA Sequencing Cell suspensions were prepared using Next Gel Bead-in-Emulsion (GEM) technology using the Chromium Controller (10× Genomics). The Chromium Single Cell 3′ Library, Gel Bead & Multiplex Kit v3 (10× Genomics) was used to construct the scRNA-seq libraries following the manufacturer’s protocols. Sequencing libraries were constructed using the Nextera XT DNA sample Pre-Kit (Illumina, San Diego, CA, USA). The final libraries were analyzed using the Agilent Bioanalyzer by running a High Sensitivity DNA Kit (Agilent Technologies, Santa Clara, CA, USA). The pooling of individual libraries was performed using 75-cycle run kits on an Illumina HiSeq X platform with 150 bp paired-end reads. The Texas A&M Institute for Genome Sciences and Society performed the scRNA sequencing (Figure 1). ## 2.5. Analysis and Visualization All following computational methods were performed on a private 96-core server running Scientific Linux v7. Raw base call files (BCL) were demultiplexed using the cellranger mkfastq command to generate FASTQ files [22]. These FASTQ files were filtered and the cell barcodes and unique molecular identifiers (UMIs) were extracted using Cell Ranger v6.0 and then aligned to the canFam4 reference [23]. The “count” command then was used to group reads with the same cell barcodes, UMIs, and genes to calculate the number of UMIs per gene per cell. The Seurat package (version 4.0.0) was used to process the raw output data in R Studio software (version 3.3) for each individual tissue sampled [24,25]. Cells were filtered for bioinformatic analysis by parameters to exclude cells with the percentage of mitochondrial genes expressed over $5\%$ and those that fell outside of the 250–2500 number of genes. The filtered cells underwent cell cycle regression to minimize cell cycle heterogeneity effects. The filtered cells were then scaled and underwent principal component analysis (PCA), nearest neighbors were computed, and clusters were found using a resolution of 0.5. The data were dimensionally reduced using both t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP). Differential expression was determined using the R package Seurat between each cell line, and genes with a p-value less than 2 × 105 were further evaluated in gene enrichment and network analyses. Pseudotime analysis was conducted using Monocle and Slingshot [24,26,27]. Each cell line was mostly homogenous, but scRNAseq was performed in order to capture small heterogeneous populations that illustrated the evolutionary trajectory that bulk RNA sequencing would not be able to detect. Differential expression testing was performed by pairwise comparison of each cell line (Tables S2–S4), between each line and all other cells (Tables S5–S7), between the three Seurat clusters within HMPOS-10 (Tables S8–S10), and between clusters 8, 10, and 11 with their respective primary cell line clusters HMPOS, HMPOS-10, and HMPOS-10, respectively (Tables S11–S13). Gene set enrichment analysis was performed on the upregulated and downregulated gene lists, comparing HMPOS with HMPOS-2.5, HMPOS with HMPOS-10, and HMPOS-2.5 with HMPOS-10 in gProfiler [28]. Network and pathway analyses were performed on all differential expression comparisons with the core analysis algorithm in Ingenuity Pathway Analysis (IPA) to compare the log fold-change (LogFC) variation for significant genes and the Bonferroni-corrected p-values within each gene list [29]. A visual overview of this experiment illustrates the pipeline from cell culture to sequence data to cell analysis (Figure 1). Figure 1: Visual overview of experimental design. HMPOS cell lines were previously challenged with increased concentrations of carboplatin to generate the HMPOS-2.5 μM and HMPOS-10 μM drug-resistant cell lines [23]. Single-cell RNA sequencing was performed on these two chemoresistant cell lines and the HMPOS cells. Created with BioRender.com, (accessed on 22 October, 2022) [30]. ## 3.1. Characterization Confirmation of the HMPOS Cell Lines The resistance of HMPOS, HMPOS-2.5, and HMPOS-10 confirmed the results of previous carboplatin sensitivity assay characterization (Figure 2A) [17]. The HMPOS-10 cell line exhibited the highest resistance to carboplatin, followed by the HMPOS-2.5 cell line, and the HMPOS cell line was found to be the least resistant (Figure 2A). The IC50-values calculated for HMPOS, HMPOS-2.5, and HMPOS-10 were 88.10 μM, 151.99 μM, and 248.85 μM, respectively. There was no statistical difference between each group using the extra sum-of-squares F test with p-values of 0.653, 0.368, and 0.377 for HMPOS and HMPOS 2.5, HMPOS and HMPOS-10, and HMPOS-2.5 and HMPOS-10, respectively. Cell morphology was visually different between the HMPOS cells, which exhibited a more cuboidal shape than the HMPOS-2.5 and 10 cells, which were comprised of a spindled morphology with a more severe shape in HMPOS-10 (Figure 2D–F). The Invasion Index Assay was statistically significant according to ANOVA analysis, but Tukey’s range test showed no statistical significance between samples, except for HMPOS-2.5 (Figure 2B). The Invasion Index assay indicated that HMPOS-10 was more invasive than HMPOS, but HMPOS-2.5 had the lowest invasion index scores of the three cell lines (Figure 2B). There was no statistically significant difference in the rate of migration between all three cell lines (Figure 2C). ## 3.2. Distinction in Transcriptomes between Chemoresistance Levels Post-filtration, there were 12,644, 10,883, and 8668 cells for HMPOS, HMPOS-2.5, and HMPOS-10 respectively. The distribution across cell phases for each cell type was similar, with the exception of a lower proportion of cells in G1 in HMPOS-10 (Figure S2). Each cell line clustered distinctly using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP), resulting in fourteen subclone groups (Figure 3A). The inferred pseudotime values showed a trajectory in evolution from HMPOS to HMPOS-2.5, then HMPOS-10 (Figure 3C and Figure S3). A clear distinction between these three cell lines due to the differential expression was evident. In total, 688 genes were upregulated and 400 genes were downregulated in the HMPOS line in comparison with the HMPOS-2.5 cells; 1147 genes were upregulated and 1164 genes were downregulated in the HMPOS cells compared with the HMPOS-10 cells; and 976 genes were upregulated and 1465 genes were downregulated in the HMPOS-2.5 cells compared with the HMPOS-10 cells. ## 3.3. Differential Gene Expression in Chemoresistant Cell Lines Genes that were differentially upregulated in HMPOS compared with the induced carboplatin-resistant cell lines were found to be related to biological pathways that involved biosynthesis, metabolism, and proliferation (Figure 4A). The biological pathways upregulated in HMPOS-2.5 cells from HMPOS were in response to chemicals and morphogenesis. Catalytic activity and protein binding were the upregulated biological pathways in HMPOS-2.5 compared with HMPOS-10 cells. Protein, enzyme, and transcription factor binding were upregulated in HMPOS-10 cells compared with the naïve and HMPOS-2.5 cell lines. Further analysis of pathway differentiation using IPA implicated the EIF2 signaling pathway, the sirtuin signaling pathway, and the mTOR signaling pathway. The EIF2 signaling pathway was activated in HMPOS-2.5 compared with HMPOS, while the sirtuin and mTOR pathways were downregulated in HMPOS-2.5 compared with HMPOS (Figure 5B). In HMPOS-10, the EIF2 signaling pathway was also activated, and the mTOR pathway was downregulated when compared with HMPOS and HMPOS-2.5 (Figure 5C). In contrast, the sirtuin pathway was activated in HMPOS-10 (Figure 5C). TMSB4X, LGALS3, COL3A1, and HMGA1 increased and THBS2, BAG2, BASP1, PEG10, and NPR3 decreased in correlation with the increase in the carboplatin dosage (Figure S4). The HMPOS-10 cell line was particularly distinct and exhibited high expression of S100A6, TPM2, CCND2, TFPI2, HMGA2, and GSTT2B (Figure S5). CDKN2A, TMEM126A, TMEM126B, MTAP, COX7A1, and LTBR were downregulated in the HMPOS-10 cell line (Figure S5). For the HMPOS-2.5 cell line, PIEZO2, IGFBP2, and ACVR2A were distinctly downregulated and THBS1, SFRP2, PTN, FSIP1, DPT, OMD, OGN, COL5A2, and ENO1 were distinctly upregulated (Figure S6). Network and pathway analyses comparing HMPOS with HMPOS-2.5, HMPOS with HMPOS-10, and HMPOS-2.5 with HMPOS-10 are presented in Figures S7–S9 respectively. ## 3.4. Subclonal Cell Populations For each cell line, there was a cluster distinct from the main cell population, only detectable using the single cell approach. Cluster 8 and cluster 13 were secondary clusters for HMPOS and were labeled as HMPOS-Var-1 and HMPOS-Var-2, respectively. HMPOS-Var-2 was not investigated further as it consisted of only 249 cells (Figure S10). Cluster 10 was a secondary cluster for HMPOS-2.5 and was labeled HMPOS-2.5-Var (Figure S11). Cluster 11 was a secondary cluster for HMPOS-10 and was labeled HMPOS-10-Var (Figure S12). Additionally, clusters 1 and 5 within the HMPOS-10 group were compared due to their divergent expression patterns. A comparison of HMPOS with HMPOS-Var-1 revealed the downregulation of THBS1 and LGALS3. In HMPOS-2.5-Var, FSIP1 and OMD were upregulated in comparison with HMPOS-2.5. SFRP2 and COL12A1 were downregulated and FSIP1 was upregulated in HMPOS-10-Var in comparison with HMPOS-10. Clusters 1, 5, and 9 within the main HMPOS-10 cell population were also compared using IPA and revealed the inhibition of the EIF2 signaling pathway and promotion of the sirtuin signaling pathway and the mTOR signaling pathway (Figure 5D and Figure S13). ## 4. Discussion One of the main causes of treatment failure in OSA and many other cancers is chemotherapy drug resistance [11,12]. Tumor adaptability and the trajectory of cancer cell evolution play a major role in treatment evasion and the development of evasion mechanisms. It is essential to understand the trajectory of tumor adaptation in order to tailor treatment strategies to fit the genetic profile of patients in order to improve prognosis. The cell viability assay confirmed the induction of carboplatin drug resistance, with HMPOS being the most sensitive, HMPOS-2.5 having improved survival, and HMPOS-10 exhibiting the highest resistance to carboplatin exposure. The observed morphology changed from cuboidal to spindled, correlating with the morphological changes seen in the epithelial-to-mesenchymal transition (EMT), where spindled tumor cells are more aggressive and chemoresistant [30]. The invasion and migration assays, which assessed the metastatic potential of cells via movement, were not significant between the three conditions. This was expected, as the metastatic nature of HMPOS was well established, and was not likely to change given exposure to carboplatin. However, the gene expression patterns between HMPOS, HMPOS-2.5, and HMPOS-10 were distinct from one another, with HMPOS-10 being the most phenotypically and transcriptionally divergent. ## 4.1. Epithelial-to-Mesenchymal Transition in Correlation with Chemotherapy Resistance The resistance to carboplatin and change to a spindled morphology from HMPOS to HMPOS-2.5 to HMPOS-10 may indicate the induction of EMT or an EMT-like process as a result of adaptive resistance. Several lines of evidence for the resistant cell lines in this work demonstrate an evolutionary progression toward this transition. As chemoresistance increased, TMSB4X, LGALS3, COL3A1, and HMGA1 were consistently upregulated, while THBS2, BASP1, NPR3, BAG2, and PEG10 were downregulated. The upregulation of TMSB4X (thymosin-β4 X-linked), LGALS3 (galectin 3), COL3A1 (collagen type III α 1), and HMGA1 (high-mobility group AT-hook 1) is associated with chemoresistance and poor prognosis [31,32,33,34,35,36,37,38,39,40]. thymosin-β4 is an actin-binding protein and plays a major role in the development of tissue and wound repair [32,33]. The upregulation of TMSB4X is correlated with tumor progression and induces the activation of myocardin-related transcription factors (MRTF) that regulate EMT transition and downregulate E-cadherin [31,32,33]. LGALS3 plays a role in apoptosis and cell adhesion, and stimulates bone marrow mesenchymal stem cells to express interleukin-6 (IL-6) to promote tumorigenesis, inflammation of the tumor microenvironment, and metastasis [34,35,36,37,38]. TMSB4X suppresses and LGALS3 interacts with E-cadherin, a tumor-suppressor protein that maintains cell adhesion and epithelial structural integrity, and is a key gene that is downregulated to allow EMT transition [31,35]. Collagen type III α 1 is a component of the extracellular matrix [39,40]. COL3A1 is a marker for EMT and is shown to be associated with POSTN (periostin), which activates the ERK and p38 pathways and downregulates miR-381 expression to regulate EMT [39,40]. HMGA1 is an architectural transcription factor, and its overexpression activates Akt signaling to promote survival and proliferation [41,42,43]. The HMGA1–TRIP13 axis has been shown to induce EMT when HMGA1 is overexpressed [44]. THBS2 (thrombospondin 2), BASP1 (brain acid soluble protein 1), and NPR3 (natriuretic peptide receptor 3) are tumor-suppressor genes, for which downregulation is associated with poor prognosis [45,46,47,48,49]. Deficiency of THBS2 is associated with the degradation of collagen and the extracellular matrix to allow metastasis [45]. Upregulated miR-191 expression promotes EMT and activates the Wnt pathway for tumor promotion through the inhibition of BASP1 [47,48]. POU2F1 regulates NPR3 expression to block the PI3K/Akt pathway to inhibit OSA cell proliferation and EMT [49]. BAG2 (BAG cochaperone 2) plays a role in the Akt/mTOR and ERK pathways to promote tumorigenesis [50,51,52]. PEG10 (paternally expressed gene 10) promotes tumor invasion and metastasis, and is a major regulator in TGFB1-induced EMT [53,54,55]. The increased chemoresistance, the cell morphology change, and the gene expression suggest that the gain of chemoresistance is associated with the transition from epithelial to mesenchymal. The downregulation of BAG2 and PEG10 with the increase in chemoresistance may indicate the effectiveness of other mechanisms in promoting tumorigenesis and resistance. ## 4.2. Epithelial-to-Mesenchymal Transition in HMPOS-10 HMPOS-10 exhibited high expression of genes associated with tumorigenesis and directly correlated with chemotherapy resistance, including S100A6, TPM2, CCND2, TFPI2, HMGA2, and GSTT2B. S100A6 (calcium-binding protein A6) is upregulated in breast cancer through mesenchymal stem cell-secreted exosomes to promote chemotherapy resistance [56,57]. S100A6 is involved in the Wnt/β-catenin signaling pathway and induces EMT by downregulating E-cadherin [56,57]. TPM2 (tropomyosin 2) is mainly expressed in muscle fibers and, when upregulated, decreases E-cadherin and β-catenin expression [58,59]. CCND2 (cyclin D2) is a driver of cell cycle progression and can be suppressed by miR-646 to prevent tumorigenesis and EMT [60,61,62]. TFPI2 (tissue factor pathway inhibitor-2) is a tumor-suppressor gene that induces apoptosis, but hypermethylated TFPI2 is associated with several human cancers and dysregulated TFPI2 overexpression promotes EMT through the TGF-β pathway [63,64]. HMGA2 (high-motility group AT-hook 2) overexpression activates the Dvl2/Wnt pathway to increase chemoresistance and promote EMT through the MAPK pathway [65,66]. GSTT2B (glutathione S-transferase theta 2) is a pseudogene of GSTT2, and glutathione S-transferases are associated with chemotherapy, such as platinum agents and detoxification [67]. The genes downregulated in HMPOS-10 were CDKN2A, TMEM126A, TMEM126B, MTAP, COX7A1, and LTBR. CDKN2A (cyclin-dependent kinase inhibitor 2A) is an inhibitor of cellular proliferation through the Akt/mTOR pathway, and loss-of-function correlates with chemotherapy resistance [68,69]. TMEM126A and TMEM126B (transmembrane protein 126A and transmembrane protein 126B, respectively) downregulation promotes mitochondrial and extracellular matrix dysregulation, attributed to poor prognosis, EMT, and chemoresistance [70]. EMT is also promoted by the purine metabolic enzyme MTAP (methylthioadenosine phosphorylase), which is downregulated in lung adenocarcinoma and predicts prognosis [71,72]. Knockout of MTAP was found to downregulate E-cadherin and p-GSK3β and lead to EMT progression [73]. COX7A1 (cytochrome c oxidase subunit 7A1) is involved in the mitochondrial respiratory chain and its overexpression inhibits cell proliferation and promotes apoptosis [74]. LTBR (lymphotoxin β receptor) mediates apoptosis in tumor cells and activates tumorigenesis by promoting the NF-Kβ pathway [75,76]. Analysis of HMPOS-10 showed that there were many different genes at play targeting various pathways, including the Wnt/β-catenin, TGF-β, Dvl2/Wnt, MAPK, Akt/mTOR, and NF-Kβ pathways, which are related to tumorigenesis and the induction of an EMT-like phenotype. The mechanisms of chemotherapy resistance linked to EMT include improved proliferation and maintenance, resistance to apoptosis, the overexpression of ABC transporters that remove chemotherapeutics, and the induction of a hypoxic tumor microenvironment. ## 4.3. Epithelial-to-Mesenchymal Transition in HMPOS-2.5 HMPOS-2.5 exhibited upregulation of THBS1, SFRP2, PTN, FSIP1, DPT, OMD, OGN, COL5A2, and ENO1 and downregulation of PIEZO2, IGFBP2, and ACVR2A. THBS1 (thrombospondin 1) upregulation activates the TGF-β pathway to promote tumorigenesis and EMT [77,78]. SFRP2 (secreted frizzled-related protein 2) modulates the Wnt/β-catenin pathway and controls WNT16B to promote acquired resistance [79,80]. In vitro and in vivo studies show that the downregulation of SFRP2 expression can reverse the EMT process [81]. PTN (pleiotrophin) is a growth factor involved in proliferation and in osteosarcoma; its overexpression promotes EMT and doxorubicin resistance [82,83,84]. The upregulation of FSIP1 (fibrous sheath interacting protein 1) correlates with poor prognosis in breast cancer and FSIP1 knockout in a mouse model has been found to improve docetaxel sensitivity [85]. DPT (dermatopontin) promotes cellular adhesion and enhances TGFB1 during the process of wound repair [86,87]. OMD (osteomodulin) is involved in osteoblast differentiation and OGN (osteoglycin) regulates bone and glucose homeostasis and has been indicated as a tumor suppressor [88,89]. In colorectal cancer, OGN upregulation has been found to induce EGFR endocytosis and inhibit EMT through the EGFR/Akt pathway [90]. EMT is accelerated and metastasis is promoted by the upregulation of COL5A2 (collagen type V α 2 chain), while its downregulation inhibits the TGF-β and Wnt/β-catenin signaling pathways in OSA [91,92]. ENO1 (enolase 1) is a glycolytic enzyme that suppresses ERK$\frac{1}{2}$ phosphorylation to inhibit EMT in vivo [93,94,95]. PIEZO2, IGFBP2, and ACVR2A upregulation is associated with the promotion of EMT [96,97,98,99,100,101]. PIEZO2 (piezo-type mechanosensitive ion channel component 2) regulates the actin cytoskeleton, and actin remodeling can alter drug response [96,97]. IGFBP2 (insulin-like growth factor-binding protein 2) promotes cellular proliferation, and its upregulation correlates with chemotherapy resistance [98,99,100]. ACVR2A (activin A receptor type 2A) mediates members of the TGF-β family and its loss-of-function results in an increase in tumorigenesis and metastasis [101]. The difference in gene expression between HMPOS-2.5 and HMPOS-10 may be due to HMPOS-2.5 being in an earlier state of EMT or an EMT-like process than HMPOS-10. There are fewer pathways associated with the gene expression patterns in HMPOS-2.5 compared with HMPOS-10. Many of the upregulated genes in HMPOS-2.5 are associated with EMT, but only implicate only the role of TGF-β, Wnt/β-catenin, and EGFR/Akt pathways. The downregulation of PIEZO2, IGFBP2, and ACVR2A may be due to HMPOS-2.5 being at an early stage of an EMT-like process or may reflect the effectiveness of other mechanisms of chemotherapy resistance. ## 4.4. Cell Populations Variating from the Main Cell Lines There were small populations of cells from each identity that clustered separately from the main cell lines HMPOS, HMPOS-2.5, and HMPOS-10. These clusters were 8, 10, 11, and 13, which were renamed HMPOS-Var-1, HMPOS-2.5-Var, HMPOS-10-Var, and HMPOS-Var-2, respectively, to distinguish the cell lines that comprised these clusters. These clusters were compared with the main cell lines that they were split from, though HMPOS-Var-2 was excluded from further analysis because the cell count of 249 was too low to obtain meaningful results. Clusters 1 and 5 were also compared because of the distinction of these clusters within the HMPOS-10 cell line in comparison with the homogeneity of the HMPOS and HMPOS-2.5 cell lines. LGALS3 and THBS1 were downregulated in HMPOS-Var-1 when compared with HMPOS. The upregulation of these two genes was shown to be correlated with chemotherapy resistance when comparing the expression of HMPOS, HMPOS-2.5, and HMPOS-10. LGALS3 and THBS1 are both associated with the promotion of tumorigenesis, metastasis, and EMT [34,35,36,77,78]. FSIP1 and OMD were upregulated in HMPOS-2.5-Var compared with HMPOS-2.5, which aligns with the pattern already seen for the upregulation of FSIP1 and OMD in HMPOS-2.5 in comparison with HMPOS. These two genes are related to docetaxel resistance and the regulation of osteoblast differentiation, respectively [85,88]. In HMPOS-10-Var, SFRP2 and COL12A1 were downregulated, while FSIP1 was upregulated in comparison with HMPOS-10. SFRP2 and another collagen, COL5A2, were previously seen to be upregulated in HMPOS-2.5 in comparison with HMPOS. SFRP2 controls the Wnt/β-catenin pathway and its upregulation promotes EMT, the upregulation of COL12A1 and other collagen genes is associated with chemotherapy resistance, and the upregulation of FSIP1 promotes chemotherapy resistance [79,80,81,85,102]. The existence of these small variant clusters from the large, homogenous main clusters for each cell line may have been the result of the divergence of each cell line as chemotherapy resistance evolved. Clusters 1 and 5 within the main HMPOS-10 cluster were also compared using IPA and revealed the inhibition of the EIF2 signaling pathway alongside the promotion of the mTOR signaling pathway and the sirtuin signaling pathway (Figure 5D). EIF2 signaling pathway upregulation promotes tumorigenesis, metastasis, and tumor hypoxia, which results in chemotherapy resistance [103]. Oxygen is necessary for DNA damage to occur with radiation and activate chemotherapeutic agents [103]. The mTOR signaling pathway regulates cell proliferation and apoptosis, and its upregulation is associated with tumorigenesis and chemotherapy resistance [104]. DNA repair, apoptosis, and drug metastasis are controlled by the sirtuin signaling pathway and its upregulation is associated with tumorigenesis, metastasis, and drug resistance [105]. The differences in the promotion and inhibition of these pathways between these two clusters suggest that there are competing strategies for chemotherapy resistance. This is particularly because the pattern observed in cluster 5 of the downregulation of the EIF2 signaling pathway, the upregulation of the mTOR signaling pathway, and the increased upregulation of the sirtuin signaling pathway in contrast to cluster 1 was distinctively different to the pattern seen in HMPOS-2.5 compared with HMPOS. ## 4.5. Overlap in Proteomics and Transcriptomics In our previous paper investigating the proteomics of these HMPOS-derived cell lines and their exosomes, there were several genes that were expressed in similar patterns to the transcriptomic work performed [17]. FSTL1 (follistatin-related protein 1) was upregulated in both HMPOS-2.5 and HMPOS-10 in the previous proteomic data and the scRNAseq results [17]. GLUL (glutamine synthetase) and ENO2 (γ-enolase) were only upregulated in HMPOS-10 in parallel to the proteomics results [17]. CDH2 (N-Cadherin) was upregulated in both HMPOS-2.5 and HMPOS-10 in the proteomics study, but only in the HMPOS-10 transcriptomics [17]. FSTL1 is a marker of EMT and invasion, GLUL plays a role in glutamine metabolism, ENO2 increases glycolysis, and CDH2 is a marker of EMT [17,106,107,108,109]. CTNNB1 (β-catenin) was seen to be upregulated in the previous proteomic analysis in HMPOS-10 and in the naïve HMPOS cell line when treated with exosomes derived from the chemoresistant cell lines [17]. These results and the difference in the expression of dephosphorylated and phosphorylated β-catenin between cell lines indicated the importance of CTNNB1 in chemotherapy resistance [17]. The scRNAseq results showed a downregulation of CTNNB1 in HMPOS-10 in comparison with HMPOS and HMPOS-2.5, which is not in line with the proteomic results [17]. As has been recurrently documented in proteome–transcriptome integration approaches, these data do not always align. The transient nature of the expressed genes tends to be more dynamic, and the proteome more static [110,111]. Future multiomic approaches provide promise for better understanding the covariation in these mechanisms [112]. However, the observed upregulation of SFRP2 and COL5A2 in HMPOS-2.5 and S100A6 and TPM2 in HMPOS-10 indicates the importance of the Wnt/β-catenin signaling pathway in chemoresistance [17,56,58,79,91]. ## 5. Conclusions There are clear distinctions in the stage of the EMT-like phenotype and the mechanisms of tumorigenesis and chemotherapy resistance between HMPOS, HMPOS-2.5, and HMPOS-10. The differentiation of HMPOS-2.5 and HMPOS-10 from HMPOS demonstrates the ability of cancer cells to acquire resistance when under selection pressure from exposure to chemotherapy drugs. The contrast between the mechanisms of the HMPOS-2.5 and HMPOS-10 cell lines shows the complexity of chemotherapy resistance and the evolution of the adaptative mechanisms of cancer cells. The investigation of subclonal populations of each cell line helps to explore to evolution and acquisition of chemotherapy resistance. The EIF2 signaling pathway, the mTOR pathway, and the sirtuin pathway particularly seem to play important roles in chemotherapy resistance and the differentiation of HMPOS-10. The sirtuin pathway is known to play an essential role in maintaining malignancy, affecting cell longevity [113]. The role of sirtuins is complex and varies between cancer types, though evidence suggests that sirtuins have an inhibitory effect on cell viability. Interestingly, the member SIRT1 induces EMT and enhances prostate cancer cell migration and metastasis [114]. The gene expression patterns of each cell line indicate the correlation of the EMT-like phenotype with chemotherapy resistance. Fifteen genes that were upregulated and five genes that were downregulated were strongly associated with EMT in the previous literature. Nine of these genes, TMSB4X, LGALS3, COL3A1, HMGA1, S100A6, TPM2, CCND2, TFPI2, and HMGA2, were either upregulated or only expressed in HMPOS-10. The previous proteomic analysis performed using these HMPOS-derived cell lines showed that the EMT marker FSTL1 was upregulated in both HMPOS-2.5 and HMPOS-10, as previously observed, but CDH2 was only upregulated in HMPOS-10 in this experiment. This indicates that HMPOS-10 is further involved in the EMT-like process in comparison with HMPOS-2.5. This study was only able to evaluate the transcriptome of each cell and does not provide a full picture of the genetic landscape between these cell lines, nor the cell surface protein complement. This experiment also does not encapsulate the effects of the tumor microenvironment or transmissible chemotherapy resistance. A lack of correlation in expression between the previous proteomics and the present transcriptomics data may be due to annotation differences. The genetic divergence of these three cell lines will be explored in the future, along with the potential causative mutations that are affecting the regulation of genes associated with these varying levels of chemotherapy resistance. Another limitation of this study is the use of a long-established cell line, which may mean that the results do not fully reflect the mechanisms involved in vivo, though it allowed us to clearly see the genetic divergence from a very homogenous cell line. This can be rectified in future investigations by developing primary cell lines from tumor samples that have and have not been treated with chemotherapy to compare the expression of the tumor pre- and post-treatment. Additionally, comparisons of the tumor microenvironment, bone marrow distal to the tumor, and healthy bone marrow could be utilized for expression analysis using the methods in this work. Examining transcriptomics at the time of excision between cell populations of the heterogeneous tumor tissue and homogeneous cell lines would add to future experimental designs in the context of conveying chemotherapy drug resistance. These results indicate the importance of the EMT-like process in the evolution of chemotherapy resistance. 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--- title: Variability in ‘Capri’ Everbearing Strawberry Quality during a Harvest Season authors: - Kristyna Simkova - Robert Veberic - Metka Hudina - Mariana Cecilia Grohar - Tea Ivancic - Tina Smrke - Massimiliano Pelacci - Jerneja Jakopic journal: Foods year: 2023 pmcid: PMC10048161 doi: 10.3390/foods12061349 license: CC BY 4.0 --- # Variability in ‘Capri’ Everbearing Strawberry Quality during a Harvest Season ## Abstract Strawberries are appreciated by consumers for their characteristic taste and health benefits, which enhance their demand throughout the year. Everbearing strawberries can produce fruits for a longer period and could thus meet this demand, but the fruit quality depends on environmental factors and the cultivar. This study focused on the effect of environmental conditions on the physical attributes and the composition of everbearing Capri cultivar fruit harvested from the end of June to the end of October. A positive correlation was observed between temperature and organic acid content ($r = 0.87$), and a positive correlation was observed between sunshine duration, anthocyanin ($r = 0.87$) and phenolic compound contents ($r = 0.89$). Additionally, the composition of sugars was affected by the environmental conditions. While strawberries harvested towards the end of October, when lower temperatures predominated, were larger in size and had a higher sugar/acid ratio, fruit harvested in the middle of August, when there were longer periods of sunshine, had higher anthocyanin and phenolic compound contents. In conclusion, strawberries with higher sugar/acid ratios are obtained when temperatures are lower, while strawberries exposed to longer periods of sunshine are richer in health-promoting compounds. ## 1. Introduction Strawberry is one of the most popular fruits, with its worldwide production having reached approximately 12.2 million tons in 2020 [1]. Strawberries are appreciated by consumers for their characteristic flavour, which is attributed to the ratio of sugar to acid content combined with the volatile compound profile [2]. Strawberries are also valued due to an extremely high amount of vitamin C, which increases their nutritional importance [3,4]. Together with vitamin C, strawberries are also a source of other important micronutrients, such as folate, and, in lesser amounts, thiamin, riboflavin, niacin, and vitamins B6, K, A and E [4]. Apart from vitamins, strawberries contain minerals such as manganese, potassium, magnesium, copper, iron, and phosphorus [4]. Moreover, strawberries accumulate a variety of phenolic compounds, such as flavonoids (mainly anthocyanins) and phenolic acids (hydroxybenzoic and hydroxycinnamic acids) [5]. Additionally, according to epidemiological studies, phenolic compounds are responsible for lowering the risk of chronic diseases such as cancer and cardiovascular diseases [6,7,8]. These health benefits make strawberries an important source of nutrients and can further enhance the consumer demand. The consumer demand in Europe has increased all year round, and there is a growing interest in everbearing cultivars, since they can produce fruits for a longer period, from late spring to autumn [9,10]. While June-bearing strawberry genotypes have been extensively studied, environmental regulations and the impact of environmental conditions on fruit quality and composition have been less studied in everbearing genotypes. Previous studies on everbearing strawberries [10,11] have mainly focused on flowering and yield performance under different environmental conditions, but there are few studies focusing on the changes in the fruit composition of everbearing strawberries [12,13]. The quality of strawberry fruit depends on many factors, including the cultivar, growing location, climatic conditions during growth and harvest, time and method of harvest, ripening stage and postharvest handling [14]. According to previous works [15,16,17], the genotype largely determines the organoleptic and functional fruit quality parameters (acidity, sugar content and phenolic content), but within the same cultivar, the contents vary at different harvest timepoints. Among the environmental factors that influence strawberry plant performance are temperature and sunshine duration [18,19]. Based on previous studies, strawberry total sugar content increases [20], but sugar content tends to decrease, towards the end of the season [13], since the photosynthetic capacity of these plants is lower due to the decrease in sunshine [13]. These changes depend on the photosynthetic rate, which, in general, increases as the temperature increases, but once it exceeds the optimal value for plant growth, photosynthesis is inhibited, and photorespiration may occur [21]. Higher temperatures could result in increased respiration but can also cause lower contents of organic acids [13]. However, the opposite trend was also previously reported [22,23]. Additionally, the synthesis of phenolic compounds is affected by different environmental factors [24]. Both light and temperature are important factors affecting the flavonoid pathways in fruits [25,26], which are responsible for the biosynthesis of the main pigments of strawberries, i.e., anthocyanins and other flavonoids. It was previously reported that anthocyanin accumulation is higher in strawberries grown under higher temperatures and with a longer sunshine duration [12,27,28]. However, some anthocyanins can be less affected by environmental conditions [27]. Overall, the combined effect of genotype and environmental interactions determines fruit quality and, consequently, consumer acceptance. Therefore, understanding these interactions is necessary to find the optimal conditions for each selected cultivar in order to obtain fruit of good organoleptic and nutritional quality. The objective of this study was to determine the optimal harvest conditions for the common everbearing Capri strawberry cultivar, commercially grown in Slovenia. Our aim was to study the impact of environmental conditions during the harvest season on the physical and chemical attributes of strawberry fruit, with a focus on sugars, organic acids and phenolic compounds. Additionally, ascorbic acid was analysed as the major micronutrient in strawberries. The environmental conditions (temperature, relative humidity and sunshine duration) were closely monitored throughout the season in the field so that their effect on fruit quality could be evaluated. ## 2.1. Plant Material The experiment was carried out in Pesje, southeast Slovenia (latitude 45°56′26″ N, longitude 15°33′11″ E), in an open field with an integrated production system equipped with a drip irrigation system. The experimental design had five blocks with sixty plants per block. The cultivar selected for this study was the everbearing Capri cultivar, planted in autumn 2020. This strawberry cultivar was chosen because it is commonly grown in Slovenia, provides a high yield and has strong disease resistance [29]. Strawberry fruit was collected bi-weekly during the period between the end of June and the end of October 2021. A total of 10 harvests were evaluated. With each sampling, commercial strawberries were randomly picked from the whole plot, and only fruit that appeared technologically ripe was selected for this experiment. Technologically ripe fruit was fully red, including the area around the calyx, and was fit to be sold for fresh consumption based on the producer’s experience. With each harvest, 1 kg of strawberries was obtained for analysis. Additionally, fruit was sorted based on colour to eliminate fruits that may have been unripe or overripe (Supplementary Figure S1). For the measurement of physical parameters, 15 representative fruits were chosen and individually measured. For chemical analysis, 5 biological repetitions were prepared by pooling at least 15 strawberry fruits from the whole plot. Temperature and relative humidity (RH) data were collected hourly with sensors (Voltcraft DL-121TH; Hirschau, Germany) in the fields. Data on sunshine duration were obtained from Slovenian Environment Agency (ARSO) at the meteorological station in Novo Mesto. ## 2.2. Physical Measurements Strawberries were sorted by colour and freedom from defects. Upon each collection, fifteen fruits of the same colour and size were randomly picked for the measurement of colour, size, weight, total soluble solids (TSSs), firmness and index of ripeness. The weight of each fruit was noted. For size determination, the length and diameter of each fruit were measured with a digital ruler. The length was measured from the calyx to the apex of the fruit, and the diameter was measured at the widest part of the fruit. The colour of the fruits was measured with a colourimeter (CR-10 Chroma; Minolta, Osaka, Japan). The colour parameters were measured in the CIELAB colour space, where the L* value corresponds to lightness (0 is black, and 100 is white); the h° value corresponds to colour expressed in degrees (0° is red; 90° is yellow; 180° is green; and 270° is blue); and the C* value corresponds to chroma (a higher value means more intense colour). TSSs were measured with a refractometer (MA885 Wine Refractometer; Milwaukee, WI, USA). Firmness was measured with a digital penetrometer (TR Turoni, Turin, Italy) with a 3 mm plunger. The index of ripeness of fruit was measured with a DA meter FRM01F (Sintéleia, Bologna, Italy), which measures the amount of chlorophyll inside the fruit using its absorbance. ## 2.3. Dry Matter For each harvest, strawberry dry matter was determined in an amount of approximately 10 g, pooled from at least 15 fruits, which was placed in paper bags in 5 repetitions and dried in an oven at 110 °C for 3 days. Subsequently, the percentage of dry weight was calculated, and these results were used for the calculation of chemical composition per dry weight. ## 2.4. Sample Preparation Fruits were cut in quarters, and the pieces were randomly separated. Five replicates (approx. 10 g each) were prepared for each extraction procedure. ## 2.5. Ascorbic Acid Extraction and Determination Extraction was performed using fresh samples in five repetitions following the procedure described by Mikulic-Petkovsek et al. [ 30]. The sample (2.5 g) was extracted with 5 mL of $3\%$ metaphosphoric acid and shaken for 30 min at room temperature. After extraction, the samples were centrifuged for 10 min at 7000× g at 4 °C (Eppendorf Centrifuge 5810 R; Hamburg, Germany). The supernatant was then filtered using 0.20 µm cellulose filters (Macherey-Nagel, Düren, Germany). Samples were stored at −20 °C until analysis. Samples were analysed using Vanquish HPLC (ThermoScientific, Waltham, MA, USA). The conditions of analysis were as follows: column (Rezex ROA-Organic acid H+ $8\%$ (150 mm × 7.8 mm); Phenomenex, Torans, CA, USA) at a temperature of 20 °C and a flow rate of 0.6 mL min−1, with 4 mM sulphuric acid in bi-distilled water as mobile phase. The injection volume was 20 µL. The response of samples was measured with a UV detector at 245 nm. Ascorbic acid was identified using an external standard from Sigma-Aldrich (Steinheim, Germany). ## 2.6. Sugar and Organic Acid Extraction and Determination The extraction and identification of sugars and organic acids followed the method previously described by Mikulic-Petkovsek et al. [ 31]. The samples were chopped, and 1 g was extracted with 1 mL of bi-distilled water and shaken for 30 min. After extraction, the samples were centrifuged for 10 min at 10,000× g at 4 °C (Eppendorf Centrifuge 5810 R; Hamburg, Germany). The supernatant was then filtered using 0.20 µm cellulose filters (Macherey-Nagel, Düren, Germany). Samples were stored at −20 °C until HPLC analysis. Organic acids were analysed using Vanquish HPLC (ThermoScientific, Waltham, MA, USA). The conditions of analysis were as follows: column (Rezex ROA-Organic acid H+ $8\%$ (150 mm × 7.8 mm); Phenomenex, Torans, CA, USA) at 65 °C and a flow rate of 0.6 mL min−1, with 4 mM sulfuric acid in bi-distilled water as mobile phase. The injection volume was 20 µL. The response of samples was detected with a UV detector at 210 nm. Organic acids were identified using external standards for citric, malic and fumaric acids from Fluka Chemie (Buchs, Switzerland) and shikimic acid from Sigma-Aldrich (Steinheim, Germany). The results were expressed as mg g−1 dry weight (DW). Individual sugars were analysed using Vanquish HPLC (ThermoScientific, Waltham, MA, USA). The conditions of analysis were as follows: column (Rezex RCM-monosaccharide Ca+ $2\%$ (300 mm × 7.8 mm); Phenomenex, Torans, CA, USA) at 65 °C and a flow rate of 0.6 mL min−1, with bi-distilled water as mobile phase. The injection volume was 20 µL. Individual sugars were identified using external standards for fructose, glucose and sucrose (Fluka Chemie GmBH, Buchs, Switzerland). The results were expressed as mg g−1 dry weight (DW). ## 2.7. Phenolic Extraction and Determination Phenolic extraction was performed according to Mikulic-Petkovsek et al. [ 32] with some minor modifications. The sample (3 g) was extracted with 6 mL of $80\%$ methanol acidified with formic acid ($3\%$), put in a cooled ultrasonic bath (0 °C) for 1 h and then centrifuged for 10 min at 10,000× g at 4 °C (Eppendorf Centrifuge 5810 R; Hamburg, Germany). The supernatant was then filtered using 0.20 µm polyamide filters (Macherey-Nagel, Düren, Germany). Samples were stored at −20 °C until HPLC analysis. Phenolic compound composition was analysed using a Dionex UltiMate 3000 HPLC (Thermo Scientific, Waltham, MA, USA) system. Spectra were measured at 280, 350 and 530 nm. The flow rate of the system was 0.6 mL min−1, and the injection volume of the samples was 20 μL. The mobile phases used were $3\%$ acetonitrile and $0.1\%$ formic acid in bi-distilled water (v/v/v), as mobile phase A, and $3\%$ bi-distilled water and $0.1\%$ formic acid in acetonitrile (v/v/v), as mobile phase B. The linear gradient used was $5\%$ of solvent B from 0 to 15 min, 5–$20\%$ of solvent B from 15 to 20 min, 20–$30\%$ of solvent B from 20 to 30 min, 30–$90\%$ of solvent B from 30 to 35 min, 90–$100\%$ of solvent B from 35 to 45 min and then 100–$5\%$ of solvent B from 45 to 50 min. Phenolic compounds were identified using the comparison with the standard retention times and using an LTQ XL mass spectrometer (Thermo Scientific, Waltham, MA, USA) based on their fragmentation pattern. The sample injection volume was 10 μL, and other chromatographic conditions were the same as those described for HPLC analyses. The mass spectrometer was operated in both negative and positive ion modes with electrospray ionisation (ESI). The capillary temperature was 250 °C, with sheath gas at 20 units and auxiliary gas at 8 units. The source voltage used was 4 kV, with m/z scanning from 115 to 1600. The quantification of phenolic compounds was performed according to a corresponding external standard or chemically similar compounds and expressed as mg 100 g−1 DW. ## 2.8. Enzyme Activity Measurements Enzyme extraction and assays followed the procedure by Cebulj et al. [ 33] with minor modifications, as specified below. ## 2.8.1. Extraction of Enzymes For this extraction procedure, fruits were cut in quarters, shock-frozen with liquid nitrogen and stored at −80 °C. The fruits were ground with an IKA A11 basic grinder (IKA-Werke, Staufen, Germany) at a low temperature using liquid nitrogen. The sample (1 g) was mixed with 0.5 g of Polyclar and 4 mL of extraction buffer (0.01 M TRIS, 0.007 M EDTA and 0.01 M Borax). The samples were vortexed for 30 s and then centrifuged for 10 min at 10,000 rpm at 4 °C (Eppendorf Centrifuge 5810 R, Hamburg, Germany). The supernatant (400 µL) was then passed through a Sephadex G-25 gel column to remove low-molecular-weight compounds before measurement. ## 2.8.2. POD and PPO Assays For polyphenol oxidase (PPO) activity, the sample (130 μL) was mixed with 300 µL of McIlvaine buffer (0.1 M Na2HPO4) and 170 μL of 0.2 M pyrocatechol solution; then, absorbance was measured for 20 min at 410 nm. For peroxidase (POD) activity, the sample (100 μL) was mixed with 1000 µL of H2O2 -KPi buffer and 10 μL of 0.04 M o-dianisidine solution in methanol; then, absorbance was measured for 20 min at 460 nm. The measurements were performed with Genesys 10S UV-Vis Spectrometer (Thermo-Scientific, Waltham, MA, USA), and data were collected with VISIONlite software. Enzyme activity was expressed as U (units) per mg protein. One unit (U) is defined as the change in absorbance in one minute. The protein content was determined with the Bradford method with minor modifications in accordance with Kruger [34]. ## 2.9. Statistical Analysis The data were statistically analysed in R, version x64 4.1.2, using the Rcmdr graphical interface package, version 2.8-0. The data were expressed as means ± standard error. In order to determine significant differences among the data, one-way analysis of variance (ANOVA) was used with Tukey’s tests. Significant differences were considered at $p \leq 0.05.$ To determine the correlations of physical and chemical parameters with environmental conditions (temperature and relative humidity), the Pearson correlation test was applied. In correlation tests, the average daily values of temperature and relative humidity, as well as sunshine duration, of the 7 days before the collection of fruit were considered. ## 3.1. Environmental Conditions The daily average temperature and RH values are shown in Figure 1. During the experiment, the maximum average daily temperature was 27.9 °C (8 July). At this time, the lowest average RH was also reached, which was $59.8\%$ (9 July). On the other hand, the highest RH was $99.5\%$ (10 October), and the minimum average daily temperature was 6.1 °C (25 October). Figure 2 shows the daily and weekly average sunshine duration from the middle of June to the end of October. The highest weekly average sunshine duration was 12.6 h, during the week from 21 June to 27 June, and the lowest weekly average was 3 h, during the week from 4 October to 10 October. ## 3.2. Physical Parameters All parameters measured after each harvest are presented in Table 1. TSSs (total soluble solids) and the ripening index were constant during the harvest season, apart from a few exceptions. The TSS values were not lower than 7 °Bx, which is the recommended limit for acceptable flavour [35]. Small differences in the ripening index confirmed that all the fruits picked for measurement were in the same ripening stage. Physical characteristics such as size and firmness are also important quality attributes for consumers and could affect the marketability of fruit. The size of fruits can be affected by the temperature during the growth period [36]. For the Capri cultivar, the collected fruits were smaller in size and weight when the temperature was higher. However, the average diameter did not decrease under 18 mm, which is considered the minimum, as defined by the UNECE standard [37]. The length of fruit showed a positive correlation with RH and a negative correlation with temperature. The weight of the strawberries significantly changed during the collection season and showed correlations similar to those relative to the length, reaching the highest weight at the beginning and the end of the season, i.e., in June and October, respectively. This is in agreement with previous studies where fruit weight was lower at higher temperatures when grown in a controlled environment in greenhouses [20,38]. Water stress was excluded as a factor from our study, since there was a stable irrigation system. On the other hand, there seemed to be no correlations between these parameters and sunshine duration. The firmness of the fruit changed during the harvest season, with the firmest fruit having been picked at the end of the season (28 October) and the least firm fruit having been picked at the beginning of the season (23 June and 7 July). However, we did not find any correlation between firmness and environmental factors, which contradicts previous findings [12,16,39] that indicated that high temperatures had a negative impact on fruit firmness. However, firmness can also be influenced by other factors, such as the activity of degrading enzymes and cell wall composition [23,39]. Regarding colour, all the parameters showed a negative correlation with temperature. Additionally, C* (chroma) showed a positive correlation with RH and a negative correlation with sunshine duration, meaning that the colour intensity was higher at higher RHs and lower temperatures, and with shorter sunshine durations. A similar effect was also observed for the L* (lightness) parameter, meaning that strawberries were darker at lower temperatures and with shorter sunshine durations, but the differences were very small between the lowest (28.0) and the highest obtained values (32.4). ## 3.3. Ascorbic Acid Content In strawberries, ascorbic acid is recognised as an essential hydrophilic micronutrient; therefore, its content is an important quality parameter. As seen in Figure 3, ascorbic acid content reached the maximum at the beginning of the season (23 June) and reached the minimum at the end of the season (27 October). Ascorbic acid content was positively correlated with both the average temperature and sunshine duration ($84.8\%$ and $83.2\%$, respectively; Table 2). Additionally, ascorbic acid content was negatively correlated with RH (−$89.6\%$). Although light is not necessary for the synthesis of ascorbic acid in plants, its amount and intensity during fruit development can influence the amount of ascorbic acid formed [40]. In our study, the strawberries harvested after days with longer sunshine durations showed higher levels of ascorbic acid. Significant differences in ascorbic acid content among different harvests were previously reported in different cultivars [12] and were attributed to the difference in light intensity. Moreover, it was reported that ascorbic acid content is enhanced by light intensity in tomatoes [41]. Its increased accumulation under high light intensity could reflect its use in H2O2 detoxification [42]. In our study, we observed a higher accumulation of ascorbic acid at higher temperatures, even though it was reported that high temperatures during the day (30–35 °C) have a negative effect on ascorbic acid content [28,40]. In our case, the highest average temperature did not exceed 28 °C, so it is suggested that temperature can have a positive effect on ascorbic acid content within a specific range. The effect of environmental factors on ascorbic acid synthesis is also dependent on the cultivar according to a previous study involving short-day strawberry cultivars that reported that ascorbic acid content did not show any correlation with the environmental conditions in most of the studied cultivars [16]. ## 3.4. Organic Acid Contents Total organic acid content is represented in Table 3. Total organic acid content reached the maximum in August and the minimum at the end of October. Moreover, a similar variation in content was observed in individual organic acids. Based on previous studies [13], higher temperatures could result in increased respiration and, consequently, in lower contents of organic acids. However, in our study, the results show that the environmental factors had the opposite effect on organic acid contents. Total organic acid content, as well as the individual contents of all detected organic acids, were correlated with the average temperature and RH (Table 2). On the other hand, no effects of sunshine duration were observed. A positive correlation between titratable acidity and air temperature was previously reported by Agüero et al. [ 22] for strawberries grown under subtropical conditions, and a similar effect of temperature on acidity was also reported by Kannaujia and Asrey [23], whose study showed that the titratable acidity decreased as the temperature decreased, which was attributed to the lower accumulation and slower depletion of organic acids with respiration. In contrast, other studies indicated that organic acid content either was stable throughout the harvest season [43] or decreased at higher temperatures [20], which suggests that the effect of temperature on the contents of organic acids is also probably dependent on the cultivar. ## 3.5. Sugar Contents Together with organic acids, sugar content is one of the most important quality attributes of strawberries, as they both greatly contribute to fruit flavour. There were no significant changes in total sugar content during the season (Table 4), a finding which is consistent with the values of total soluble sugars (Table 1). It was suggested that TSSs and total sugar content decrease towards the end of the season, since the photosynthetic capacity of plants is lower due to the decrease in sunshine [13]. In our study, this effect was not observed, as no correlations with the environmental conditions were detected, which was also previously reported for other cultivars [16,22]. Moreover, it is also important to look at the individual contents of different sugars, since the perception of sweetness of individual sugars can be different [44]. There were significant changes in individual sugar contents in our study (Table 4). The contents of glucose and fructose reached their maximum at the beginning of the season in June and their minimum at the end of October, whereas sucrose content reached the maximum value in October and the minimum value at the beginning of the harvest season, between the end of June and the beginning of August. Regarding the influence of environmental factors, a lower accumulation of sucrose was observed at higher temperatures and under a longer sunshine duration (Table 2). Additionally, the content of sucrose was positively correlated with RH. The synthesis of glucose and fructose contents increased under higher average temperatures and longer sunshine durations. Significant changes in the individual contents of different sugars were also observed by Ruan et al. [ 13] for different day-neutral and everbearing cultivars. These changes could be explained by the effect of environmental factors on the activity of enzymes involved in the sucrose/hexose interchange, such as sucrose-phosphate synthase, invertase and sucrose synthase [21]. ## 3.6. Sugar/Acid Ratio Among consumers, a high sugar/acid ratio is desired, which results from high sugar content or low acid content. As seen in Table 4, the sugar/acid ratio was the highest at the end of the harvest season (end of October) and the lowest in August. As mentioned above, total sugar content was stable during the season, so the ratio values were affected by the changes in organic acid content. The sugar/acid ratio was negatively correlated with the average temperature and was positively correlated with RH (Table 2), which corresponds with previous findings [43]. In the mentioned study, the changes were mainly caused by the changes in total sugar content, as sugar content decreased due to a lower photosynthetic rate. In our study, the changes were mainly caused by the changes in total organic acid content, not by those in sugar content. ## 3.7. Phenolic Contents Strawberries contain various phenolic compounds, and their synthesis is affected by different environmental factors [24]. Phenolic compound content enhances the nutritional quality of fruit, but it also affects the colour of fruit, and it is an important quality parameter for juice processing [4,45,46,47]. The identified phenolic compounds are listed in Supplementary Material Table S1. Total phenolic content (including anthocyanin content) was positively correlated with RH (−$77.2\%$) and sunshine duration ($89.2\%$), but no correlations with temperature were observed (Table 5), which is in contrast to previous studies [12,28] where a higher accumulation of phenolic compounds was observed in strawberries grown at higher temperatures in greenhouses. However, the highest non-anthocyanin phenolic content values were reached in August and at the end of September, and the total content of non-anthocyanin phenolic compounds showed a positive correlation with temperature ($69.4\%$). This suggests that it is the synthesis of non-anthocyanin phenolic compounds that is affected by temperature, rather than the content of anthocyanins. Most of the separate groups of phenolic compounds, except for flavonols, also showed increased synthesis at higher temperatures. The highest correlation with the average temperature was observed in flavanols, which were, in this case, only represented by propelargonidin dimers. Secondary metabolite biosynthesis can also be affected by light conditions [48]. This was also confirmed by our results, as total phenolic content ($89.2\%$), as well as the contents of hydroxycinnamic acid derivatives ($77.5\%$) and flavanols ($76.1\%$), was correlated with sunshine duration. ## Individual and Total Anthocyanin Contents The content of anthocyanins determines the colour of fruit, and they are the main group of phenolic compounds present in strawberries. The colour of fruit is one of the most important quality parameters in fresh fruit but also in processed strawberry products [49]. The colour can be affected by the content but also by the composition of anthocyanins [47]. Additionally, the initial content of anthocyanins is an important quality attribute for the further processing of fruit, as higher contents are desired in order to achieve a product with good colour stability [46,50]. Six different anthocyanins were identified in the strawberry samples (Supplementary Material Table S1). Anthocyanin content differed among the harvests and reached the maximum values in August and September (Table 6). Among the identified anthocyanins, pelargonidin-3-O-glucoside showed the highest content. Both light and temperature are important factors that affect the flavonoid pathways in fruits [25,26]. This was partly confirmed by our results, as the average values of total anthocyanin content were negatively correlated with RH (−$74.7\%$) and were positively correlated with sunshine duration ($87.1\%$). However, no significant effects of temperature were observed, which is in contrast to previous studies on strawberries [12,27,28] where higher anthocyanin content was measured in strawberries grown under higher temperatures. Pelargonidin-3-O-glucoside, as the major anthocyanin, seemed to be less affected by environmental factors than other anthocyanins did, which is in accordance with a previous study [27] where six short-day strawberry genotypes were studied at different locations. This could explain why the correlation with temperature was not significant. The synthesis of pelargonidin-3-o-glucoside was not affected by the average temperature recorded during the season, while longer periods of sunshine contributed to a higher concentration of this anthocyanin in the fruit. On the other hand, higher RH during the season contributed to lower levels of pelargonidin-3-o-glucoside. Among the other individual anthocyanins, the highest correlation percentage with the average RH was observed in pelargonidin-3-O-rutinoside (−$96.4\%$). Moreover, this anthocyanin also showed a positive correlation with temperature ($86.1\%$). Additionally, pelargonidin-3-(6″ malonyl) glucoside content was also correlated with temperature ($72.1\%$); its content was the highest at the beginning of collection, i.e., at the end of June, and then decreased throughout the season, until it reached its minimum at the end of October. However, not all individual anthocyanins followed the same trend. Pelargonidin-3-O-acetylglucoside content showed lower accumulation at higher temperatures and longer sunshine durations. It reached the lowest value at the beginning of the season (at the end of June) and kept increasing, with some variations, until it reached the maximum value at the end of October. These results show that some anthocyanins, such as pelargonidin-3-O-rutinoside or pelargonidin-3-O-acetylglucoside, can be affected by environmental factors more than others. The synthesis of cyanidin-3-O-glucoside and 5-pyranopelargonidin-3-glucoside was not affected by environmental factors. These results suggest that the influence of environmental conditions depends on the composition of anthocyanins in strawberry. In contrast to these results, the colour parameters (L* and C) showed a negative correlation with sunshine duration, and the hue angle did not show any correlation with sunshine duration. Previous work [51] found correlations between the colour parameters and the content of anthocyanins, which suggests that there are other factors that influence the colouration of fruit. ## 3.8. Enzyme Activity Polyphenol oxidase and peroxidase are enzymes responsible for enzymatic browning in fruits, including strawberries [52]. This process can affect the marketability of strawberries. The detected enzyme activity is presented in Figure 4. Peroxidase activity reached its maximum on 15 September, and the lowest values were detected on 2 September and 27 October, when the average temperature also dropped. Regarding polyphenol oxidase activity, the maximum value was observed on 15 September and the lowest was observed on 2 September and 27 October, when the average temperatures dropped. Although there were significant differences among the different collection dates, no significant effects of environmental conditions on enzyme activity were found. Additionally, no correlations between enzyme activity and the contents of anthocyanins and other phenolic compounds were observed (data not shown). ## 4. Conclusions Strawberry fruit quality is a complex concept that depends on both the physical and chemical attributes of this fruit and that can be affected by environmental factors and the cultivar. Since the importance of everbearing strawberries is growing, it is necessary to study the impact of environmental factors on the fruit quality of these strawberries. Our study shows that everbearing strawberries can provide fruit fulfilling the quality parameters throughout the season, but it also shows that the composition of strawberries, and the contents of nutrients and bioactive compounds, can vary during the harvest season. While the strawberries largest in size and with a high sugar/acid ratio were harvested under lower temperatures towards the end of the harvest season, in October, strawberries with the highest content of anthocyanins, phenolic compounds and ascorbic acid were collected following longer sunshine durations in August. These changes influenced the nutritional value and taste of the fruit. All the collected strawberries fulfilled the recommended limits regarding diameter and TSSs. 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--- title: Injectable Chitosan-Based Hydrogels for Trans-Cinnamaldehyde Delivery in the Treatment of Diabetic Foot Ulcer Infections authors: - Henry Chijcheapaza-Flores - Nicolas Tabary - Feng Chai - Mickaël Maton - Jean-Noel Staelens - Frédéric Cazaux - Christel Neut - Bernard Martel - Nicolas Blanchemain - Maria José Garcia-Fernandez journal: Gels year: 2023 pmcid: PMC10048173 doi: 10.3390/gels9030262 license: CC BY 4.0 --- # Injectable Chitosan-Based Hydrogels for Trans-Cinnamaldehyde Delivery in the Treatment of Diabetic Foot Ulcer Infections ## Abstract Diabetic foot ulcers (DFU) are among the most common complications in diabetic patients and affect $6.8\%$ of people worldwide. Challenges in the management of this disease are decreased blood diffusion, sclerotic tissues, infection, and antibiotic resistance. Hydrogels are now being used as a new treatment option since they can be used for drug delivery and to improve wound healing. This project aims to combine the properties of hydrogels based on chitosan (CHT) and the polymer of β cyclodextrin (PCD) for local delivery of cinnamaldehyde (CN) in diabetic foot ulcers. This work consisted of the development and characterisation of the hydrogel, the evaluation of the CN release kinetics and cell viability (on a MC3T3 pre-osteoblast cell line), and the evaluation of the antimicrobial and antibiofilm activity (S. aureus and P. aeruginosa). The results demonstrated the successful development of a cytocompatible (ISO 10993-5) injectable hydrogel with antibacterial ($99.99\%$ bacterial reduction) and antibiofilm activity. Furthermore, a partial active molecule release and an increase in hydrogel elasticity were observed in the presence of CN. This leads us to hypothesise that a reaction between CHT and CN (a Schiff base) can occur and that CN could act as a physical crosslinker, thus improving the viscoelastic properties of the hydrogel and limiting CN release. ## 1. Introduction Diabetic foot ulcers (DFU) are a complication of diabetes mellitus that affect around $6.8\%$ of the world population [1]. Infection results in numerous complications such as poor vascularisation and biofilm formation, which can trigger gangrene or lead to death in around $5\%$ of patients in the first month and $42\%$ after the fifth year [2]. First-line treatment consists of debridement, wound dressing, offloading, revascularisation, and intravenous/oral antibiotic therapy [2]. In the last decades, research has been focused on treatment using functionalised wound dressing [3] or drug delivery systems for local treatment [4]. More recently, hydrogels have been deemed a promising strategy since they mimic the extracellular structure and can also be used as a drug delivery system, which are both essential properties to improve wound healing management [5,6]. Furthermore, hydrogels have been shown to preserve the internal moist environment, to improve debridement, and to prevent or impede the spread of infection by delivering local antibacterial treatment [7]. Hydrogels can be formed by physical and chemical interactions. Chemical hydrogels are known for their high mechanical properties and toxicity, whereas physical hydrogels have low mechanical properties but high biocompatibility [8]. Some of the most studied hydrogel-forming natural polymers are chitosan, collagen, and cellulose [9,10,11]. Chitosan (CHT) is a cationic polysaccharide composed of D-glucosamine and N-acetyl glucosamine units. It is obtained by chitin deacetylation (a compound extracted from crustacean shells or mushrooms). As a biocompatible, bioresorbable, and bioactive polymer, it has been widely used for the development of biomaterials [12], such as wound dressings [13], nano- or microparticles [14], and scaffolds [9]. Several combinations have been made to improve the antimicrobial activity of CHT, such as quaternisation [15], the use of drugs such as doxycycline [16], metallic particles [17], and plant-derived extracts [17]. The combination of CHT with other polymers via the formation of polyelectrolyte complexes (PEC) is a promising strategy to improve its intrinsic viscoelastic properties, resulting in physical hydrogels. Anionic polymers used include alginate [14], collagen [18], hyaluronic acid [19], or synthetic polymers [5,20]. On the other hand, cyclodextrins (CD) are a cyclic-form family of oligosaccharides structured by 6, 7, or 8 glucose units joined by α-1,4 glycosidic bonds [21]. CDs have been widely used for the formation of “host-guest” complexes, trapping pharmacological molecules and targeting local treatments. The nature of complex formation is achieved by reversible bonds in the internal cavity of the CD, thus avoiding drug retention [22]. Furthermore, CDs have the property to improve the physicochemical properties, the solubility of non-soluble molecules, bioavailability, and stability [21,22]. Some new approaches to improve CD properties are modification with hydroxypropyl, sulfate, or methyl groups or CD polymerisation like the epichlorohydrin β-Cyclodextrin polymer proposed by Rohner et al. [ 23] for drug delivery in wound healing or the citric acid polymer of cyclodextrin proposed by Martel et al. [ 24]. The latter presented poly(cyclodextrin citrate), an anionic carboxylate polymer, and reported successful viscoelastic hydrogel formation with CHT through PEC interactions [25]. Recently, the potential of new drug derivates or plant-derived molecules like phenolic or polyphenolic compounds with high activity against bacteria proliferation and biofilm formation has attracted attention as a novel treatment option. The compounds are characterised by at least one phenolic group in their molecular structure. Some examples are flavonoids, tannins, stilbenes, lignin, and cinnamaldehyde. Cinnamaldehyde (CN) is a natural phenolic compound obtained from the essential oil of cinnamon (about $98\%$ of the total oil volume) with a high activity against Gram (+) and Gram (−) bacteria. The CN mechanism of action has still not been completely clarified, but the most coherent hypothesis proposes that the lipophilic nature of CN enables the disruption of lipid membranes in bacterial cell walls, which can destabilise bacteria and lead to their death [26]. Our research is based on the previous results reported by Flores C et al. [ 27] and Palomino-Durand et al. [ 25] on the development of CHT/PCD hydrogels and sponges. Based on all these aspects, the aim of this project was to develop an injectable hydrogel composed of chitosan/citric acid, beta-cyclodextrin polymer (PCD)/cinnamaldehyde (CN) for the treatment of diabetic foot ulcers. The challenge was to incorporate CN in the formation of hydrogel to obtain antibiofilm activity without losing their intrinsic properties (injectability, stability, and hydrogel formation). We first performed a formulation step and then cohesion and stability steps in the physiological medium of each hydrogel and the control. Afterwards, we characterised the samples and studied the activity against bacteria and biofilm eradication. ## 2.1.1. Phase Solubility Diagram Cyclodextrin’s properties as a host-guest molecule have been widely studied as a means of improving the physicochemical properties of drugs [28]. Poly(cyclodextrin citrate) (PCD) has shown enhanced solubilising properties compared to native CD [27,29]. Table 1 presents the Kf and CE values obtained in the presence of βCD and PCD. CN has a low intrinsic solubility in aqueous solutions (15.89 ± 0.47 mM). PCD has a Kf constant value of 119 M−1 while βCD was only measured at 21 M−1. A similar gap was obtained in the CE calculation (0.38 ± 0.1 and 1.67 ± 0.2 for βCD and PCD, respectively). Thus, we can conclude there is significant ($p \leq 0.05$) PCD activity on the CN solubility. Similar results have previously been observed by Hill L. et al. [ 2012] [30], who reported a Kf constant value of 28.47 M−1 for the complexation with the βCD. Furthermore, Yildiz Z. et al. [ 2019] [31] did a comparison between the Kf value of CN with hydroxypropyl βCD and hydroxypropyl α CD. Their research demonstrated the higher affinity of βCD cavity for complexation with CN. In fact, Kf values of 140 M−1 for HP βCD and 110 M−1 for HP αCD were found. Therefore, we can conclude that the complexation of βCD cavity with CN will be higher compared to αCD cavity. ## 2.1.2. Nuclear Magnetic Resonance (NMR) Study The equimolar solution of PCD:CN was compared to a PCD and CN solution in order to identify the proton shift in the spectra of the complex. Results of the 1H NMR and ROESY spectra are shown in Figure 1 and Figure 2, respectively. First, the 1H NMR spectra corresponding to the PCD show the signal of the glucopyranose unit of cyclodextrins (βCD): H1, H2, H3, H4, and H5 are found at 4.83, 2.93, 3.89, 3.78, and 3.59 ppm, respectively. Concerning the protons corresponding to the CN, the H1, H2, H3, and H4 are found at 9.54, 7.51, 7.45, and 6.79 ppm, respectively [32]. The PCD:CN equimolar solution showed an H3 proton shift in the internal cavity of βCD. Indeed, the H3 proton shifted from 3.89 to 3.85, and no shift for the H5 proton was observed. On the other hand, the CN spectrum shows a shift of the protons corresponding to the aromatic group (H2) and H3 from 7.51 to 7.67 and from 7.45 to 7.49, respectively. Furthermore, a ROESY NMR study was performed in order to understand the geometries of the inclusion complexes. This test was performed using an equimolar solution of CD cavities in PCD and CN. Figure 2 shows the two-dimensional ROESY spectrum of the complexation. A correlational signal is observed between the H3 and H5 cavities (3–4 ppm) of the cyclodextrin cavities (as in 1H NMR) and the aromatic protons of the CN (7–8 ppm region of the CN spectra). Finally, the shift and signal correlation confirm the PCD:CN complexation through the interaction between the CN aromatic group and H3 proton with the internal cavity (H3 proton) of βCD in PCD molecules. ## 2.1.3. Hydrogel Formation: Vial Turnover Test and Hydrogel Stability Figure 3A shows the results obtained for the vial turnover test of CHT/PCD/CN hydrogel gelation immediately after extrusion of the hydrogel into the vial (0 h) and after incubation of the vial for 1 h and 24 h. The control sample (without PCD and containing only CHT) displayed fast flow from 1 h and complete flow at 24 h. A lower flow rate is observed for the other control containing only CHT and CN (3:0:1), with slight flow at 24 h. In addition, the samples containing PCD (formulations 3:2:0 and 3:2:1) did not display flow within 24 h. Indeed, no visual difference after 24 h was observed. As shown in the Figure 3B, hydrogels were injected into PBS, in order to evaluate the extruded cord’s stability and cohesion. First, control hydrogel 3:0:0 was dispersed in PBS medium after 1 h, whereas cord made of control 3:0:1 presented better stability and cohesion. Concerning the hydrogel cords 3:2:0 and 3:2:1, both presented good stability after 24 h. Nevertheless, we observed a shrink pattern after 24 h for both samples. This effect is more noticeable in the 3:2:1 formulation. ## 2.1.4. Rheological Analysis A shear rate sweep was performed to study the flow curve since it is an important feature for the development of injectable hydrogel, and the results are shown in Figure 4A. In this context, all samples showed shear-thinning behaviour (viscosity decreases as shear rate increases). Figure 4A shows the viscosity curves obtained for formulations 3:2:0 and 3:2:1 only, since the trends in the curves were comparable with the other two samples. Furthermore, in order to compare the viscosity values obtained at the initial and final shear rates applied, all data values were recorded in Table 2. An increase of initial viscosity from 280 ± 11 to 397 ± 5 Pa.s at a 0.01 shear rate was observed for controls 3:0:0 and 3:0:1, respectively (a $41\%$ increase of viscosity after CN addition). Concerning the 3:2:0 and 3:2:1 formulations, values of 783 ± 72 and 1200 ± 56 Pa.s, respectively, were obtained ($53\%$ increase in viscosity after CN addition). Concerning the viscosity values at a high shear rate (1000 s−1), values of 1.32 ± 0.07, 0.97 ± 0.22, 1.20 ± 0.16, and 1.42 ± 0.18 Pa.s were obtained for controls 3:0:0 and 3:0:1 and hydrogels 3:2:0 and 3:2:1, respectively. Statistical analysis indicates that CN addition significantly increases ($p \leq 0.05$) the initial viscosity of the control and hydrogel. However, non-significant differences were found between all samples ($p \leq 0.05$) at a high rate. Thus, a similar shear-thinning behaviour can be concluded for all samples. Another important parameter for the development of injectable hydrogels is the recovery of hydrogel elasticity after a high shear strain (also known as the self-healing property, Figure 4B). Samples were tested by applying cycles alternating between a low shear strain of $1\%$ and a high shear strain of $500\%$ in oscillation mode. A substantial decrease of G′ is observed at a high shear strain, and immediate recovery is observed once back to a low shear strain. Afterwards, hydrogel evaluation of G′ and G″ was assessed at the linear viscoelastic region (LVR) at 37 °C (shear strain of $1\%$ and shear rate of 10 rad/s) in order to differentiate the viscoelastic properties of all the samples (Figure 4C). All samples presented a higher G′ modulus than G″, thus proving the formation of a viscoelastic solid. Despite the low cohesion of formulation 3:0:0, the values of elastic and viscous modulus for this formulation can be explained by the intermolecular hydrogel bonds between acetylated and amino units present in CHT, as previously stated in the literature [25,26]. The damping factor tan δ values were compared, and a significantly higher elasticity was observed for hydrogels once CN was added. A significantly higher elasticity was also observed for the hydrogel 3:2:1 ($p \leq 0.05$) compared to all samples. A reaction between CHT and CN in the formulation could explain the decrease in the damping factor and the increase in the elastic behaviour of the hydrogel. As a matter of fact, CN could improve the viscoelastic properties by the formation of a Schiff base, as suggested by Rieger, K.A. and Schiffman, J.D. [2014] [33]. Recently, Zhou et al. [ 34] reported that this type of amphiphilic polymer obtained from Schiff base formation between CHT and CN is able to self-assemble to form nanoparticles. Therefore, we hypothesise that part of the CN in the hydrogel is linked to chitosan by imine bonds (Schiff base). The phenylpropylene groups of the covalently reacted CN–CHT could interact together through π–π stacking, provoking the self-assembly of the chitosan chains in addition to the polyelectrolyte complex interactions between PCD and CHT [34]. In addition, phenylpropylene groups linked to CHT could also interact with CD cavities of PCD by the formation of inclusion complexes (as presented in Section 2.1.2). Thus, the involvement of phenylpropylene groups linked in both types of interactions mentioned above could increase the elasticity of the hydrogel. Furthermore, in order to evaluate the ratio of bound/free CN in our hydrogels, we calculated the molar ratio of amino groups versus cinnamaldehyde in the starting solid powder formulations (Table 3). The concentrations of reactive groups of CHT and CN are shown in Table 3. The concentration of amino groups in the formulation is much higher compared to CN, which increases the probability of the formation of a covalent bond, CHT-CN. Further studies need to be undertaken in order to understand the interaction between CHT and CN in the presence of a CD. ## 2.1.5. CN Release Study and Modelling A release study was performed under dynamic conditions and in physiological conditions ($$n = 3$$, PBS pH 7.4 at 37 °C) in order to evaluate the release kinetics of CN and the impact of PCD. Figure 5 shows the percentage of CN released. A partial drug release is observed for both samples. The release kinetics were characterised by a burst release in the first 8 h and a maximal but partial release after 24 h (release of $61\%$ ± 4 and $52\%$ ± 3 of active molecule release for hydrogel 3:2:1 and control 3:0:1, respectively). The percentage of release was compared, and a significantly higher release for hydrogel 3:2:1 was observed ($p \leq 0.05$). Therefore, in order to explain the increase in CN availability in solutions, a calculation of the concentration of βCD cavity available per gramme of hydrogel was undertaken. A 10 µmol of βCD/g of hydrogel (corresponding to $57\%$ of βCD in PCD as described in Section 4.1) in the formulation was obtained, which explains the $9\%$ increase in active molecule availability in solution (formation of complex 1:1 CN:CD). The release kinetics data were subsequently fitted into four mechanism models: Zero Order, First Order, Higuchi, and Korsmeyer-Peppas. Linear equation values obtained for both formulations are shown in Table 4. As evidenced, data modelling fitted the model proposed by Korsmeyer-Peppas for polymeric systems (r2 values of 0.96 ± 0.02 and 0.95 ± 0.01 for formulations 3:0:1 and 3:2:1, respectively). As previously mentioned, several drug diffusion mechanisms can be found in a drug delivery system. Based on the results obtained, the concentration gradient (Fickian diffusion) is the main drug transport mechanism involved in the release mechanism of CN from the 3:2:1 hydrogel since the n value obtained is below 0.45 ($$n = 0$.29$ ± 0.06). On the other hand, an n value of 0.50 ± 0.02 for control 3:0:1 describes a non-Fickian or anomalous drug transport (0.45 < n < 1). The anomalous transport involves the polymeric chain relaxation (or swelling) in the release profile mechanism. This behaviour can be explained by the reaction between the CHT and CN and the high swelling property of the CHT. Additionally, release constant (kkp) values (also described as the drug release velocity) of 18.5 ± 4 and 38.0 ± 6 were obtained for the 3:0:1 and 3:2:1 hydrogels, respectively, which explains the faster release of hydrogel 3:2:1. ## 2.1.6. Cytotoxicity Test Finally, the cytotoxicity of hydrogels was assessed using an extraction method. The formulation without CN was tested and proved the formation of a cytocompatible hydrogel. A pre-osteoblast cell line was selected as it is a clinically relevant cell type involved in diabetic foot osteomyelitis. According to ISO 10993-5, a sample with a minimal cell survival rate of $70\%$ can be considered a cytocompatible sample. In this respect, $99.9\%$ of cell viability observed after incubation over 24 h showed excellent cytocompatibility of the hydrogel (Figure 6). ## 2.1.7. Antimicrobial Assessment: Kill Time The bacterial kinetic reduction was evaluated to determine the bacterial concentration reduction over time. Samples of formulation 3:2:0 as control and formulation 3:2:1 were studied ($$n = 3$$). As observed in Figure 7, no antibacterial activity was observed against S. aureus for the hydrogel 3:2:0 whereas formulation 3:2:1 significantly reduced (5 log10 reduction) the bacterial concentration between 6 h and 24 h ($p \leq 0.05$). On the other hand, a similar and intrinsic activity is observed against a Gram (−) strain. P. aeruginosa is a common standard with a high resistance to antimicrobial drug activity. The study carried out on the P. aeruginosa strain confirmed the activity of the 3:2:1 hydrogel from the second hour and the intrinsic activity of the 3:2:0 hydrogel (6 log10 reduction and 5 log10 reduction after 24 h, respectively). This activity has previously been reported by several authors, who proposed that the ammonium group of CHT interacts with the anionic charge of the bacterial cell membrane, therefore, altering the membrane properties (permeability and osmosis) and leading to cell death [35]. Furthermore, some authors have attributed the high bactericidal property of CN to its affinity for the Ftsz protein. Indeed, the Ftsz protein is a 40 KDa protein responsible for the formation of the bacterial cell cytoskeleton in binary fission. CN has an affinity to react with the Ftsz protein by a Schiff base and Michael reactions with amino acids [36]. The results obtained prove the intrinsic activity of hydrogel 3:2:0 and the improvement of the antimicrobial activity of hydrogel 3:2:1. Lastly, a significant difference can be concluded between both formulations after comparing the results using a Student’s t-test ($p \leq 0.05$). To conclude, these tests confirmed the significant antimicrobial activity of formulation 3:2:1 by inducing a $99.99\%$ bacterial reduction after 24 h for both strains. ## 2.1.8. Antibiofilm Activity The antibiofilm evaluation was performed on the same strains used in the previous test (S. aureus CIP224 and P. aeruginosa ATCC 9027). Formulations of 3:2:1 and 3:2:0 were tested ($$n = 5$$), and a brain-heart (BH) broth was used as a control in order to conserve the biofilm structure formed on a hydroxyapatite-coated surface. Samples were compared statistically by using the ANOVA test and Dunnett’s tests. Figure 8 presents the OD values obtained for the samples and the control. On the positive control, the biofilm formation was higher for a P. aeruginosa strain than for an S. aureus strain, which is consistent with references found in the literature [37]. Indeed, P. aeruginosa is the principal species found in complicated DFU infections and the principal cause of DFU aggravation due to its quick proliferation [37]. The activity on an S. aureus strain showed non-intrinsic activity of the 3:2:0 hydrogel and significant antibiofilm activity of formulation 3:2:1 ($p \leq 0.05$). Indeed, a $58\%$ ± 18 bacterial reduction was observed after 24 h. Concerning the P. aeruginosa strain, comparable antibiofilm activity ($p \leq 0.05$) of the hydrogel 3:2:1 ($60\%$ ± 14 bacterial reduction) and slight but not significant ($p \leq 0.05$) intrinsic activity of the formulation 3:2:0 was observed. The results confirmed the antimicrobial activity of CN against bacteria. Even when the sample is not tested by direct contact with biofilm, intrinsic activity for sample 3:2:0 is observed. This activity can be explained by the dissolution of CHT particles in the extraction medium, which has an antimicrobial activity as already shown in the kill-time test. Some other authors have already reported the enhancement of CHT antimicrobial properties in the presence of CN. Wang X et al. [ 38] attempted to develop cinnamaldehyde-loaded liposomes decorated with chitosan. This study demonstrated a synergic activity in bacterial wall destabilisation and leakage of the internal cell components in the presence of CHT and CN. ## 3. Conclusions Injectable hydrogels are an innovative and promising approach for treating DFU. They can be used not only as a drug carrier but also to achieve a sustained release and improve treatments. This study presented a new approach for the use of a naturally derived antimicrobial in a hydrogel by merging the properties of CHT/PCD hydrogel and the antimicrobial properties of CN. Indeed, we have demonstrated the intrinsic properties of a CHT/PCD hydrogel and the improvement of a hydrogel containing CN. Furthermore, an interaction between CHT-CN has been evidenced, which leads us to the hypothesis that CN can act as a “cross-linker” and improve the viscoelastic and antimicrobial properties of CHT hydrogels. The results obtained in this research are the first study for the development of CHT/PCD hydrogels with natural phenolic compounds. Further studies in order to study the interaction of CHT and CN and to achieve the optimal controlled release will be performed in a future study. ## 4.1. Materials CHT (batch STBJ0437, Sigma Aldrich, Saint-Quentin-Fallavier, France) has a molecular weight (MW) of 256 kD (determined by size exclusion chromatography (SEC)) and a desacetylation degree (DD) of $76\%$ (determined by 1H Nuclear Magnetic Resonance (NMR, Bruker, MA, USA)). Poly(cyclodextrin citrate) (PCD) was prepared as described by Martel et al. [ 30]. Briefly, this method consists of polyesterification between β cyclodextrin (βCD) and citric acid (CTR, Saint-Quentin-Fallavier, France) in the presence of sodium hypophosphite (NaH2PO4). βCD was provided by Roquette (Kleptose, Lestrem, France). CTR was used as a cross-linking agent, and sodium hypophosphite (NaH2PO4, Saint-Quentin Fallavier, France) was used as a catalyst. After the reaction, the water was removed in a Rotavapor (Büchi, Flawil, Switzerland), and the solid mixture was treated at 140 °C for 90 min under vacuum. The mixture was then dispersed in water and filtered using a sintered glass funnel. The insoluble fraction (PCDi) was finally obtained after drying at 90 °C overnight, and the soluble fraction (PCD) in the filtrate was further concentrated, purified by dialysis (Spectra/Por®, MWCO 20 kDa, Sigma Aldrich, Lesquin, France), and freeze-dried (Alpha 1–2 LDplus, Christ, Germany) at 0.06 mbar and −53 °C. The molar mass of the PCD used in this study was 21 kDa, determined by size exclusion chromatography (SEC) using multiangle light scattering and a differential refractometer. The percentage of cyclodextrin weight was determined at $57\%$ by 1H NMR. Lastly, the CN used in this study was obtained from Alfa Aesar (Kandel, Germany) at a purity of $98\%$. ## Phase Solubility Diagram A phase solubility diagram was carried out according to the method first proposed by Higuchi and Connors [1965] [39]. This method has also been used for the study of complexes with drugs or essential oils [21,40,41]. Vials containing 5 mL of an aqueous solution (ultrapure water) of βCD (from 2 mM to 15 mM) and PCD (from 2 mM to 40 mM) were prepared, and an excess amount of CN was added in order to have a final concentration of 151 mM (concentration above CN intrinsic solubility). Then, the solutions were incubated at room temperature for 24 h with constant stirring at 320 rpm. CN solubility was quantified from supernatants by UV-visible spectroscopy (UV-1800 spectrophotometer, Shimadzu, Columbia, Portland, OR, USA) at 285 nm. All samples were prepared in triplicate. The formation constant (Kf) and complexation efficiency (CE) were calculated from the slope of phase solubility diagrams in the initial linear range for CN/βCD and CN/PCD complexes. The equation used is as follows:Kf = Slope/S0 (1 − Slope)[1] CE = Slope/1 − Slope[2] where S0 is the intrinsic solubility of CN after 24 h under stirring. ## Nuclear Magnetic Resonance (NMR) Study The complex of PCD/CN was studied by 1H NMR, and two-dimensional ROESY was performed to characterise the geometry of the interaction between CN and PCD. 1H NMR was performed using a solution of CN and PCD in deuterated water (D2O), and an equimolar mixture of 10 mM of CN and 10 mM of CD in PCD (calculated from the % of CD in the PCD) was prepared to evaluate the complexation. ROESY was performed using a higher concentration of CN and PCD (25 mM:25 mM) in a deuterated methanol:deuterated water solution (MeOD:D2O 1:9). The solution was stirred for 24 h at 37 °C. First, 1H NMR was tested in an AVANCE III 300 MHz spectrometer (Bruker, Billerica, MA, USA) with 16 scans. The ROESY study was performed on an AVANCE III 400 MHz spectrometer (Bruker, Billerica, MA, USA) in order to study the geometry of complexation. Data was acquired in the phase-sensitive mode, and the probe temperature was regulated to 300 K. The data obtained was processed by TopSpin software version 4.0.6. ## Mixed CHT/PCD Powder Preparation The first step consisted of grinding CHT and PCD in order to obtain a particle size of less than 125 µM. PCD was milled in a mortar and pestle and then sieved using a 125 µm sieve. The CHT powder was milled using a Fritsch Pulverisette 14 (Idar-Oberstein, Germany) and sieved as previously performed for PCD. Finally, both polymers were co-milled using a Mixer Mill MM400 (Restch, Steinbach, Germany) at a frequency of 10 Hz for 3 min. ## Hydrogel Preparation The formulation was prepared and compared to two controls to assess the impact of each compound on hydrogel formation and viscoelastic properties (Table 5). Two syringes connected to each other through a female-female Luer lock connector (Vygon, Ecouen, France) were used to prepare the hydrogel. The CHT/PCD powder was collected in one syringe, and ultrapure water (W) and cinnamaldehyde (CN) were added to the other. Both syringes were connected, and both plungers were pulled alternately for one minute to mix the contents of both syringes. Lactic acid (LA) (volume adjusted for a final concentration of $1\%$ w/w) was then introduced in one syringe, and the mixing step was repeated for a further minute. Finally, a viscoelastic injectable hydrogel was formed in the syringe [42]. The hydrogel compositions are shown in Table 5, and the hydrogel preparation graphic description is presented in Figure 9. ## Hydrogel Formation: Vial Turnover Test and Hydrogel Injection in Phosphate-Buffered Saline (PBS) To evaluate the hydrogel formation, 1 mL of hydrogel was injected into an empty vial (diameter: 2 cm × height: 3 cm). This test is based on the flow resistance property of hydrogels since they have a yield stress, whereas a viscous liquid flows quickly [22]. Based on this, the recipients were inversed and stored at 37 °C. The hydrogel morphology and structural stability at a physiological pH (7.4) were evaluated by injection into PBS at 37 °C. Both tests were visually evaluated after 1 h and 24 h ($$n = 1$$). ## Rheological Analysis An MCR 301 rheometer (Anton Paar, Les Ulis, France) with a parallel plate geometry of 25 mm (PP25) was used to characterise the viscoelastic properties of hydrogels. A first study of the amplitude and frequency sweep was performed previously to determine the linear viscoelastic range (LVR). Then, the dynamic viscosity (η) of hydrogels was studied in a rotational mode by applying a shear rate from 0 to 1000 s−1. Afterwards, the elasticity recovery of hydrogels was evaluated by applying a high and low shear strain of $1\%$ and $500\%$, respectively, in oscillatory mode. Finally, the hydrogel formation at 37 °C was assessed by measuring the storage and loss moduli (G′ and G″, respectively) in the linear viscoelastic range (LVR). ## CN Release Kinetics Study and Modelling CN release was performed using a flow-through dissolution method using a SOTAX® CE7 Smart—USP IV (SOTAX®, Aesch, Switzerland). A quantity of 0.2 g of hydrogel was injected directly into each cell container. PBS was used as a dissolution medium (pH 7.4 at 37 °C) at a flow velocity of 5 mL/min. The dissolution medium was coupled to an automatic sampler, Sotax® C615 (SOTAX®, Aesch, Switzerland), and a sample volume of 1 mL was taken at each hour up to 24 h. Finally, CN release was quantified using a Nexera Ultra performance liquid chromatograph (LC2040C, Shimadzu®, Noisiel, France) at 285 nm by using a C18 reverse phased column (Gemini®, 5 µm NX-C18, 250 × 4.6 mm) and a mobile phase composed of acetonitrile/methanol/acetic acid ($\frac{50}{20}$/30) at a flow rate of 1 mL/min. Subsequently, four release models were used to compare and understand the drug release mechanism: Zero-order release: where Q is the amount of drug released or dissolved, Q0 is the initial amount of drug in solution, and K0 is the zero-order release constant [6]:Q = Q0 + K0 t[3] First-order release: where K is the first-order rate constant expressed in units of time −1. C0 is the initial concentration of drug, and Ct is the concentration of drug in solution at time t. This equation can be expressed as [6]:dC/dt = −Kt[4] Higuchi model: where Q is the amount of drug released in time t per unit area A, C is the drug initial concentration, Cs is the drug solubility in the medium, and D is the diffusivity of the drug molecules (diffusion coefficient) in the matrix [43]:Q = A [D (2C − Cs) Cs t] $\frac{1}{2}$[5] Korsmeyer-Peppas model: where Mt is the cumulative amount of drug released at time t and Mα is the amount of drug released after infinite time. KKP is the constant incorporating structural and geometric characteristics of the drug delivery system [42]:Mt/M∞ = kKP.tn[6] According to the Korsmeyer-Peppas model, exponent “n” can describe four mechanisms of molecule transport in a polymeric system: Fickian diffusion (n < 0.45), anomalous transport (0.45–0.85), case II transport (0.89), and super case II transport (>1). ## Cytotoxicity Assay The cytotoxicity of formulation 3:2:0 (hydrogel without CN) was evaluated by the extraction method (indirect contact) according to the ISO 10993-5 standard on a pre-osteoblast MC3T3-E1 cell line (ATCC® CRL-2594™, Manassas, VA, USA). The hydrogels were preconditioned in the Minimum Essential Medium (MEM–α, Gibco®, Thermo Fisher Scientific, Illkirch-Graffenstaden, France) at 37 °C and 80 rpm for 2 h. Then, MEM–α was removed, and a concentration of 200 mg of hydrogel/1 mL MEM–α was added. MC3T3–E1 cells were seeded at 4 × 103 cells/well in a 96-well tissue culture polystyrene plate containing 100 µL/well MEM–α medium supplemented with $10\%$ foetal bovine serum (FBS; Gibco®, Thermo Fisher Scientific, Illkirch-Graffenstaden, France) and incubated at 37 °C and $5\%$ CO2 for 24 h. After incubation, each extraction medium ($$n = 3$$) was sterilised using a 0.22 μm filter. Then, the culture medium on the cell layer was replaced by 100 μL of the sterile extraction medium. The cells were incubated for an additional 24 h at 37 °C in an atmosphere of $5\%$ CO2. Finally, cell viability was evaluated by fluorometry with the AlamarBlue® reagent (Uptima, Interchim, France). The fluorescence reading was measured at 530 nm as an excitation wavelength and 590 nm as an emission wavelength by a fluorometer (CLARIOstar®, BMG Labtech, Ortenberg, Germany). ## Antimicrobial Assessment: Kill Time The kill-time test was studied to assess the bacterial reduction over time against Staphylococcus aureus CIP224 and Pseudomonas aeruginosa ATCC 9027. One mL of hydrogel was injected into a tube containing 9 mL of around 1 × 107 CFU/mL of bacteria suspension in cysteinated Ringer solution (CR). The tubes were incubated at 37 °C, and samples were taken after 2 h, 4 h, 6 h, and 24 h. At each interval, 100 µL were seeded directly on a Mueller Hinton Agar (MHA), and the others with 100 µL were successively diluted $\frac{1}{10}$ up to 10−5 in a CR solution and seeded as for the non-diluted sample. Finally, the number of viable bacteria were counted after incubation at 37 °C for 24 h and the results were expressed in log CFU/mL. All tests have been performed in triplicate ($$n = 3$$) ## Antibiofilm Study The same strains as in the previous tests were used at a concentration of about 1 × 105 CFU/mL in brain-heart (BH) broth. An Innovotech® biofilm (Edmonton, AB, Canada) 96-well plate with a hydroxyapatite-coated peg cover was used as a support for the fixation of bacteria biofilm. Therefore, 200 µL of bacterial suspension was added in each well to sink the hydroxyapatite pegs for 48 h at a stirring speed of 60 rpm and at 37 °C. Afterwards, the hydroxyapatite-coated cover was rinsed twice with PBS (pH 7.4) for 3 min each time in order to eliminate the excess bacterial suspension on the peg. Meanwhile, an extraction medium was prepared by sinking 1 mL of hydrogel in 5 mL of BH broth for 24 h at 37 °C. The extraction medium was filtered through a 0.2 µm sterile syringe filter, and the previously prepared biofilm pegs were immersed in 200 µL of extraction medium in a 96-well plate. 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--- title: Assessing Gluten-Free Soy Bread Quality and Amino Acid Content authors: - Teruyo Nakatani - Manami Tanaka journal: Foods year: 2023 pmcid: PMC10048178 doi: 10.3390/foods12061195 license: CC BY 4.0 --- # Assessing Gluten-Free Soy Bread Quality and Amino Acid Content ## Abstract The nutritional and palatability relevance of bread prepared with soy flour was examined. There are a few effective nutritional measures that combine palatability, convenience, and functionality in the suppression of muscle loss (contributing to the improvement and prevention of sarcopenia). Therefore, in the present study, we attempted to produce bread using soybeans, which are rich in amino acids involved in the synthesis and degradation of skeletal muscle proteins. Rice flour was also used to avoid gluten intolerance. The bread was baked in an automatic bread maker, and the rheological properties of its breadcrumbs were determined using a creep meter. We found that a 70 g slice of soy bread satisfied approximately one-fifth of the daily nutritional requirement for leucine. Although soy decreased the specific volume of bread by preventing starch construction, the use of preprocessed rice flour recovered the volume, and corn starch improved the taste. We propose that the addition of soy bread to the daily diet may be an effective protein source. ## 1. Introduction The 2022 annual report of the World Health Organization (WHO) shows that both healthy life expectancy (HALE) and overall life expectancy (LE) have increased in developed countries [1]. HALE was defined by the WHO in 2000 as the average period during which a person experiences no impediment to everyday life. The time difference between LE and HALE represents the “unhealthy period”, during which there are limitations to everyday life (such as being bedridden), thereby leading to a deterioration of an individual’s quality of life and a reduction in the social security burden. Therefore, an increasing gap between HALE and LE has raised concerns. For instance, in Japan, an approximately 10-year gap exists; thus, strategies are needed to extend HALE. Sarcopenia is defined as an age-related decline in skeletal muscle mass and physical function (strength) and is associated with an increased risk of falling and disability [2,3,4,5,6]. To acquire a super-aged society, the need to establish preventive measures against sarcopenia increases. This involves maintaining or increasing skeletal muscle mass, requiring the stimulation of protein synthesis and the suppression of proteolysis in skeletal muscle tissue. However, efficient prophylaxes to minimize the sarcopenia burden through diet regulation have not yet been sufficiently developed. Particularly for the elderly, nutrition is important to prevent the development and progression of skeletal muscle loss. Notably, aged muscles require more amino acids to stimulate their anabolism due to a reduction in muscle protein synthesis [7], known as anabolic resistance. However, the inability of skeletal muscles to respond to low doses of essential amino acids has been reported with aging, whereas higher doses are capable of stimulating muscle protein synthesis to a level that is equal to that of young individuals [8]. However, appropriate protein intake remains essential for preventing the development of sarcopenia, and, with the tendency that elderly people decrease their food intake, it is difficult to ensure adequate protein consumption. Therefore, efficient protein ingestion is a key factor contributing to the stimulation of protein synthesis and the suppression of proteolysis in the skeletal muscles of elderly people. Certain amino acids play a more prominent role in protein metabolism, rendering them ideal targets for inclusion in a protein-rich food source. The mechanistic/mammalian target of rapamycin (mTOR) is an evolutionarily conserved serine/threonine kinase that is known to be a master regulator of cellular metabolism. mTOR stimulates anabolic processes, such as protein synthesis while simultaneously inhibiting autophagy, which includes protein degradation. Leucine, a branched-chain amino acid (BCAA), activates mTOR signaling more efficiently than other amino acids [9]. In addition, lysine suppresses protein degradation through autophagy inhibition [10,11]. Soy protein has high-protein digestibility-corrected amino acid and digestible indispensable amino acid scores, which consider digestibility and absorption capacity [12]. Nutritionally, soy possesses complete, high-quality proteins containing essential amino acids rich in BCAAs (comprising approximately $20\%$ of amino acids in boiled soybean) and approximately $7\%$ lysine. Additionally, the predominant soy protein, glycinin, contains a sequence similar to that of the ubiquitin ligase CbI-b inhibitor peptide, whereby it produces an inhibitory effect on CbI-b-mediated skeletal muscle atrophy in vitro and in vivo [13,14]. Indeed, dietary soy protein upregulates skeletal muscle volume and strength in humans with low physical activity or those that are bedridden [15]. Another important factor affecting efficient protein synthesis is the timing of its intake, which can influence muscle hypertrophy. In particular, a high-protein (BCAA) breakfast is more efficient in establishing muscular hypertrophy than an equal distribution of protein intake across the day or a higher intake at dinner. This time-dependent effect is affected by clock genes, such as Clock and Bmal1, and by BCAA ingestion [16]. However, there is a tendency for people to consume less protein during breakfast [17]. The current study seeks to evaluate formulations for producing protein-rich bread that can assist in efficient protein synthesis, which in turn can prevent the loss of skeletal muscle mass. Although wheat-based bread is commonly consumed for breakfast, it contains gluten that can induce gastrointestinal discomfort. Zonulin is a physiological modulator of intestinal tight junctions that leads to a leaky gut. Gliadin, a protein component of wheat gluten, activates zonulin signaling, which increases intestinal permeability to macromolecules [18,19,20,21,22]. Therefore, taking gluten intolerance into account, we used rice flour as a grain product that can be consumed safely over a long period. To achieve a positive nutritional effect, consistent consumption is important, and we evaluated the quality of the bread produced. The specific aims of the study were to investigate the effects of sub-ingredients on the quality of bread baked with rice flour and soy flour supplements as an initiative to increase protein consumption by humans and thus contribute to the prevention of sarcopenia. Our results showed that although soy flour supplementation to rice bread reduced the specific volume (bread quality indicator), the volume was recovered using preprocessed rice flour and cornstarch, which participate in the fermentation process. ## 2.1. Ingredients The ingredients of the bread comprised rice flour (Mizuhochikara; Kumamoto Flour Milling Co., Ltd. Kumamoto, Japan), soy flour (Perican Co., Ltd., Saitama, Japan), wheat flour (Nisshin Flour Milling Inc., Tokyo, Japan), cornstarch (Tokan Co., Ltd., Aichi, Japan), starch (Imazu Co., Ltd., Osaka, Japan), sugar (Mitsui Sugar Co., Ltd., Tokyo, Japan), salt (Naikai Salt Industries Co., Ltd., Okayama, Japan), preprocessed rice flour (Musubi Co., Wakayama, Japan), dry yeast (Nisshin Flour Milling Inc., Tokyo, Japan), and canola oil (The Nisshin OilliO Group Ltd., Tokyo, Japan). ## 2.2. Formulations and Baking The bread samples were prepared using an original method designed by us. The components of each base formulation (baker’s %) were rice flour [100], water [82], sugar [5], salt (2.1), dry yeast (1.5), and canola oil (5.7). For wheat bread, wheat flour was used instead of rice flour. The total amount of flour was adjusted when soy flour, preprocessed rice, and cornstarch were added to the formula. The bread was baked using the rice bread program or wheat bread program of an automatic bread machine (ST-MT3; Panasonic Corp., Osaka, Japan). The baked bread loaves were cooled at room temperature (25 ± 2 °C) for 1 h before performing various measurements. ## 2.3. Specific Volume Measurements After cooling, the weight and volume of the bread loaves were measured. The volume was determined based on the rapeseed displacement method (AACC Method 10-05.01; AACC, 2000). The mass was measured using a digital scale (TANITA CORPORATION). The specific volume was calculated as the ratio of volume to weight (mL/g). ## 2.4. Analysis of Rheological Properties A creep meter (Rheoner RE2-33005S; Yamaden Co., Ltd., Tokyo, Japan) was used to determine the rheological properties of the breadcrumbs. The parameters for measuring rupture characteristics included sample size, 30 × 30 × 20 mm; plunger, wedge form (No. 49, Yamaden, with a base width of 1 mm and length of 30 mm); measurement strain rate, $100\%$; and compression speed, 1.0 mm/s. Conditions for the measurement of bread texture included sample size, 30 × 30 × 20 mm; plunger, circular form (12 mm diameter); measurement strain rate, $50\%$; and compression speed, 5 mm/s. Each experiment was performed at least in triplicate, and averages were obtained for the data. ## 2.5. Analysis of Protein and Amino Acid Content Protein and amino acid contents of bread types (wheat, rice, and soy bread) were analyzed using the combustion method and HPLC (JLC-500/v2; JEOL. Ltd., Tokyo, Japan), respectively, by the Japan Food Research Laboratories. For each bread sample, 0.31–0.39 g and 0.2–0.3 g were used for the protein and amino acid analyses, respectively. The samples were dissolved in 20 mL of $20\%$ hydrochloric acid containing $0.04\%$ 2-mercaptoethanol. After hydrolysis (110 °C, 24 h), the samples were quantitated and prepared for HPLC measurement. Ninhydrin was used as the reaction reagent, and tryptophan was measured by HPLC (fluorescence detection), whereas other amino acids were measured by post-column derivatization. Each sample was analyzed twice. ## 2.6. Measurement of Water Absorption The water absorption of each flour sample was determined using the technique developed by Matsuki et al. [ 23]. Seven holes (1.5 mm diameter) were created in the bottom of a column-shaped plastic container (4.7 cm × 9 cm × 1.5 mm), 12 mm apart. One piece of glass fiber filter paper (GA-55, 4.7 cm diameter, Advantec Tokyo Roshi, Tokyo, Japan) was placed inside the container. The flour sample (10 g) was weighted in the container. A weight (25 g) was placed onto the container to ensure that the sample did not float during the experiment. The container was placed in a tray filled with 1 cm-deep water to let water in through the holes at the bottom. A piece of filter paper (#2, 7.0 cm diameter, Advantec Tokyo Roshi, Tokyo, Japan) was placed under the container in the tray to avoid tight contact between the bottom of the container and the tray. The container was taken out of the water for weighing and was quickly placed back in the water. The amount of water in the flour sample was calculated from the moisture content of the flour. ## 2.7. Sensory Analysis Sensory analysis of the bread was carried out according to Japanese Industrial Standard (JIS; 9080:2004) [24] by a panel of 30 females, aged 21–22 years. Sensory attributes included appearance, aroma, taste, texture, sponginess, stickiness, chewiness, and firmness. A ten-point hedonic scale was applied to evaluate each sensory attribute. Panelists scored on a scale from 1 (disliked extremely) to 10 (liked extremely). All sessions were performed in single booths in an air-conditioned room at 20–22 °C. Before the first and between each sensory sample, the panelists rinsed their mouths (≥ 10 s) with water. Approximately 10 g of each sample was provided (3 × 3 × 3 cm) at the same time to each panelist, 2 h after baking. The sensory profiles of the optimized soy rice bread with preprocessed rice flour and/or cornstarch were assessed, and comparisons were made with reference to the soy rice bread. The test was performed with prior approval from the Ethics Committee for Research with human beings (No.22-05). ## 2.8. Statistical Analysis All results were obtained from at least three separate experiments. Statistical differences were analyzed via one-way analysis of variance (ANOVA) and Tukey’s range test for multiple comparisons using IBM SPSS (version 26; IBM Japan, Ltd., Tokyo, Japan). The results are expressed as mean ± standard deviation (SD), and $p \leq 0.05$ was considered statistically significant. ## 3.1. Replacement Rate of Soy Powder To determine the ideal amount of soy flour for baking rice bread, we investigated 15, 25, 35, 50, and $75\%$ replacements of soy flour with rice flour. The specific volume is an important indicator of the technological quality of bread and is used to express the technological aptitude of a formulation for bread production [25]. With soy, the specific volume significantly decreased when compared with that of the control ($0\%$ soy). Although the specific volume of loaves did not differ significantly among the soy groups (Figure 1), a $35\%$ soy replacement bread was selected as a strong beany flavor was noted at soy contents > $50\%$. ## 3.2. Changes in Bread-Specific Volume after Soy Addition Saito et al. [ 26] reported that the addition of hot water (at approximately 70 °C) to the bread batter led to the swelling of the starch grains, which began to string together, resulting in superior bread quality (highest specific volume and soft texture) when compared with those obtained at other temperatures. We examined how auxiliary ingredients could improve the reduction in the specific volume via the addition of soy to rice flour bread as a preliminary experiment and found that the addition of hot water (70 °C) or pregelatinized rice flour was the most efficient (data not shown). This was likely due to the pregelatinization effect of starch; therefore, we used pregelatinized rice flour. Next, the most appropriate quantity of pregelatinized rice flour was determined. Figure 2 shows that the specific volume of the bread significantly increased with increased ratios of pregelatinized rice flour. Although there were no statistical differences between 5, 10, and $20\%$ (ratio to total flour) pregelatinized rice flour additions, a $10\%$ pregelatinized rice flour addition produced the greatest bread volume increase; however, the bread acquired a sticky texture. To investigate potential improvement, we examined the effect of using cornstarch. Figure 3 shows that a $30\%$ (ratio to total flour) cornstarch addition tended to improve the specific volume of the bread; however, this difference was not statistically significant. As shown in Figure 4, although the addition of soy decreased the specific volume of bread to approximately $70\%$ in comparison to that of the control (only rice flour), the reduction was significantly recovered to approximately $90\%$ by adding pregelatinized rice flour and to $92\%$ by adding a combination of pregelatinized rice flour and cornstarch. There was no significant difference between the results of these two conditions; however, the sliced end of the bread gained a smoother texture (Figure 4). ## 3.3. Amino Acid Content of Bread The amino acid content of the different bread types is shown in Figure 5. Soy bread exhibited higher levels of BCAAs and lysine, whereas wheat bread contained an abundance of non-essential amino acids. BCAA levels in soy bread were approximately 1.2 and 2.0 times higher than those of wheat and rice bread, respectively (Table 1). One 70 g slice of soy bread provided approximately $19\%$ of the BCAA, $19\%$ of the leucine, and $18\%$ of the lysine daily requirements (for 60 kg of body weight, according to a report from the WHO/the Food and Agriculture Organization (FAO)/United) [27,28]. Although the protein contents did not differ between wheat bread and soy bread, wheat bread contained higher non-essential amino acid contents, such as glutamic acid or tyrosine (Figure 5, Table 1). A nutritional evaluation of soy was performed using wheat flour bread [29], but rice flour breads were investigated for bread quality [30,31,32]. We analyzed not only proteins but also amino acids (which may be mainly involved in skeletal muscle metabolism). The BCAA content of a slice of soybean bread presented here was $19\%$ of the daily recommended intake, which may not be excessive, but we highlight that it is important to consume a good balance of foods to prevent nutritional imbalance. ## 3.4. Bread Texture Properties The effects of pregelatinized rice flour and cornstarch on the baking properties of soy bread are shown in Figure 6. Although the breadcrumb fracture strain was significantly lower in the bread containing soy flour than in the control, this reduction was reversed by adding pregelatinized rice flour. Cohesiveness significantly decreased in the bread containing soy flour when compared with the control, and no significant difference was observed following the addition of pregelatinized rice flour. The fracture stress tended to decrease in soy bread. Although pregelatinized rice flour significantly increased crumb adhesiveness, cornstarch returned it to the levels observed for the rice flour control. These results indicate that, although the bread attained a brittle structure when soy was used as an ingredient, the addition of pregelatinized rice flour reversed this effect by providing viscoelasticity. Cornstarch improved the excess viscosity of pregelatinized rice flour further to retain elasticity. ## 3.5. Effect of Pregelatinized Rice and Cornstarch on Soy Bread Quality The volume and specific volume of bread depend on the retention of gas by the matrix during fermentation and affect bread quality [33]. Next, we examined whether the effect of specific volume recovery by pregelatinized rice and cornstarch was due to participation in the fermentation process. After 30 min of fermentation, the batter volume was lower than that of the control when using soy, but this volume was significantly recovered after adding pregelatinized rice. Cornstarch had the same effect as pregelatinized rice. In the absence of soy, pregelatinized rice did not have this effect on the batter volume (Figure 7). These results suggest that the specific volume of bread recovery observed following the addition of pregelatinized rice was due to its involvement in the fermentation process. Aoki et al. [ 34] assessed rice bread using different rice flour samples containing amylose contents ranging from 9.6 to $22.3\%$ and found that the amylose content was positively correlated with the dough volume and the specific volume during leavening, which indicates that amylose plays an important role in making bread with high loaf volume. They also found that there was no correlation between the protein content and the specific volume. In this study, we used a rice flour cultivar, Mizuhochikara, which has been reported to have a high amylose content ($22.3\%$) and low damaged starch (about $3\%$) when compared with other rice flour cultivars [34]. Yano et al. [ 35] reported that gluten-free rice bread has a high specific volume without additives, whereas rice flour bread exhibits low starch damage (<5 g/100 g). Damaged starch granules had higher water absorption than intact starch granules [36], which negatively affected bread quality, such as specific volume [37,38,39,40,41,42]. Pregelatinized rice can improve dough properties through increased cohesion, elasticity, and viscosity, which increases CO2 gas retention [43]. Consistent with other studies, the addition of soy flour to wheat flour [29] and rice flour [30,31,32] reduced the specific volume. Islam et al. [ 29] reported that this may be due to the baking suitability of soy flour. Although Sciarini et al. [ 44] observed an increase in the specific volume after the addition of $10\%$ soybean flour to rice flour, when $20\%$ was added, a negative effect was observed. Soybean protein could form a structure capable of incorporating more air bubbles and thus retain CO2 during mixing and proofing. Furthermore, soy proteins may interact with amylose and starch granules through non-covalent bonds, thereby reducing viscosity and interfering with the association of hydrogen bonds between the starch molecules [44]. Mizuhochikara alone, without any other ingredients, has a sufficient ability to produce bread of good quality; therefore, pregelatinized rice may not have any effect (Figure 7, second from the left). However, in the presence of soy, pregelatinized rice showed an improvement (Figure 7, second from the right). These results suggested that soy decreased the batter volume due to the disruption of the rice starch structure. Bread quality is also affected by the water absorption of its constituent components. Accordingly, we examined whether there was a difference in water absorption between each bread type. In comparison with the control (rice flour alone), water absorption significantly increased in the soy bread and increased further by the use of pregelatinized rice. In contrast, the addition of cornstarch returned water absorption levels to those of the control (Figure 8). Next, we examined whether water absorption influenced batter adhesion and whether pregelatinized rice flour increases the absorption of water, resulting in the thickening of the batter. Figure 9 illustrates how batter adhesiveness significantly decreased while using soy. Pregelatinized rice flour increased batter adhesiveness. These results indicate that pregelatinized rice flour allows the easy absorption of water, which influences batter viscosity and may lead to a retention of CO2 gas emitted during the fermentation process, thereby improving bread quality in terms of specific volume. Soy absorbs water but does not influence batter viscosity and only increases its weight; this may be due to the prevention of the pregelatinizing action of rice flour, thereby leading to a reduction in bread-specific volume. Cornstarch returned the increase in adhesiveness observed for pregelatinized rice flour to that of the control. Cornstarch suppressed water absorption and attenuated excess batter adhesiveness, resulting in an appropriate viscosity of the bread. In summary, soy disrupted the cross-linking of rice starch (which determines adhesiveness) and interfered with the retention of CO2 gas in the batter, thus leading to a loss of specific volume in the bread. The results of the descriptive sensory analysis performed on the three breads are shown in Figure 10. Although no significant ($p \leq 0.05$) differences were observed between the $35\%$ soy flour replacement bread (soy rice bread; F1), the soy rice bread with preprocessed rice flour (F2), and the soy rice bread with preprocessed rice flour and cornstarch (F3), the chewiness and stickiness of F3 tended to be lower than those of F1 or F2. Moreover, F3 tended to have sponginess and firmness qualities similar to those of F1, which were superior to those of F2. In the sensory evaluation, the following comments were made on the soy rice bread with preprocessed rice flour: “I felt that F2 is like a mochi, F3 has no bad taste and aroma, and F3 is dry as compared with F2”. It is important to note that we chose to include a panel of young people for this preliminary study to ensure safety, as textures similar to that of rice cake can readily cause dysphagia in elderly individuals. Thus, further sensory analysis is needed with a panel comprised of elderly people, and additional studies are required to assess the inhibitory effect of these breads on skeletal muscle declension. An increasing number of people exhibit symptoms of gluten intolerance. The most common wheat flour substitutes for gluten-free bread are rice flour, which is mainly composed of carbohydrates and has a low protein content. Numerous studies have attempted to improve not only the quality but also the nutritive values of gluten-free bread by increasing its protein contents using plant- and animal-derived components. In this study, we evaluated the formulations for producing protein-rich bread. We used soy flour owing to its efficient protein synthesis activation and proteolysis inhibition effects. Although several studies have added soy flour to wheat or rice flour bread, the bread quality has remained uninvestigated. We observed a deterioration in the specific volume of soy bread; however, we attempted to improve the soy bread quality. Although there have been several studies on the addition of soybean flour to wheat and rice flour breads, improvements in bread quality have not been explored. We found that soybean inhibits the cross-linking of rice starch, which determines adhesion, and inhibits carbon dioxide retention in the batter, thereby reducing the specific volume of the bread. This can be applied to the production of gluten-free soy bread, for example, by preparing bread with improved cross-linking of rice starch, i.e., by either inhibiting or not inhibiting cross-linking. In this study, we used preprocessed rice flour to improve the cross-linking of rice starch, but we believe that this is possible with other materials as well, which are currently considering. Although we developed soy bread that may increase protein supply, it is necessary to continue investigating other formulations to improve nutritional aspects, quality, taste, and costs, including the inclusion of additional ingredients or changes to the recipe, while also confirming whether soy bread has beneficial effects on skeletal muscle in elderly people. ## 4. Conclusions This study considers a nutritional approach to contribute to the prevention and improvement of skeletal muscle decline in the future. We attempted to produce quality soy bread as a breakfast meal option for the elderly, considering its provision of amino acids at an appropriate time to allow efficient muscle protein synthesis. Since the sustainability of use is a key factor to success, we considered bread taste and avoided gluten intolerance issues by using rice flour in analyzing bread quality. Nutritionally, one 70 g slice of soy bread delivered approximately $19\%$ of the total BCAA and daily leucine requirements (60 kg body weight). In terms of baking quality, soy significantly decreased the specific volume of bread by inhibiting starch cross-linking that retains CO2 in the dough. However, the lost volume was recovered using preprocessed rice flour, and the taste was improved using cornstarch (which inhibited the excess of adhesiveness produced by preprocessed rice flour). We propose that soy bread has the potential to become an effective countermeasure against skeletal muscle declension. 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--- title: Fatty Acid Composition of Selected Street Foods Commonly Available in Malaysia authors: - Zainorain Natasha Zainal Arifen - Mohd Razif Shahril - Suzana Shahar - Hamdan Mohamad - Siti Farrah Zaidah Mohd Yazid - Viola Michael - Tanaka Taketo - Kathy Trieu - Sakinah Harith - Nor Hayati Ibrahim - Shariza Abdul Razak - Hanapi Mat Jusoh - Chua Hun Pin - Jau-Shya Lee - Risyawati Mohamed Ismail - Lee Lai Kuan - Hasnah Haron journal: Foods year: 2023 pmcid: PMC10048182 doi: 10.3390/foods12061234 license: CC BY 4.0 --- # Fatty Acid Composition of Selected Street Foods Commonly Available in Malaysia ## Abstract Despite growing evidence of increased saturated and trans fat contents in street foods, little is known about their fatty acid (FA) compositions. This study aimed to analyse the saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), and trans fatty acids (TFAs) content of 70 selected and most commonly available street foods in Malaysia. The street foods were categorised into main meals, snacks, and desserts. TFAs were not detected in any of the street foods. Descriptively, all three categories mainly contained SFAs, followed by MUFAs, and PUFAs. However, the one-way ANOVA testing showed that the differences between each category were insignificant ($p \leq 0.05$), and each FA was not significantly different ($p \leq 0.05$) from one to another. Nearly half of the deep-fried street foods contained medium to high SFAs content (1.7 g/100 g–24.3 g/100 g), while the MUFAs were also high (32.0–$44.4\%$). The Chi-square test of association showed that the type of preparation methods (low or high fat) used was significantly associated ($p \leq 0.05$) with the number of SFAs. These findings provide valuable information about fat composition in local street foods for the Malaysian Food Composition Database and highlight the urgency to improve nutritional composition. ## 1. Introduction Globalisation has resulted in a nutrition transition towards high-fat, sugar, and salty foods in low- and middle-income countries (LMICs), including Malaysia, an upper-middle-income country [1]. Street foods are easily accessible [2], widely available, and inexpensive compared to other formal food premises [3]; hence, they are a major contributor to dietary intake and nutrition for most of the population. Fats in food exist in varying proportions of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs) and trans fatty acids (TFAs) [4]. Several studies from other countries (Eastern Europe, Central Asia, Southeast Asia and Africa) found the presence of TFAs [5,6,7,8,9,10,11] and high amounts of SFAs [6,7,8] in their local street food. The presence of TFAs and SFAs may be influenced by the use of TFAs- or SFAs-rich ingredients, such as butter [7] and partially hydrogenated vegetable oils [9,12], and the preparation methods used, such as deep-frying [9,11,13]. Practices related to the frying process, such as the reuse of frying oil [6], the frying temperature [14], and the duration of frying using different types of oils [10,13,15,16,17], also affect the levels of SFAs and TFAs in foods. As most Malaysian foods incorporate ingredients that are rich in fats, such as coconut milk and peanuts, and approximately $30\%$ of Malaysian street foods are deep-fried [18], local street foods may contain high amounts of SFAs and TFAs, as well as MUFAs and PUFAs [13]. Knowledge of the fatty acids (FAs) composition in foods is crucial because the high consumption of SFAs [19,20] and TFAs [21] has been associated with adverse health effects, including hyperlipidaemia and hypercholesterolemia. Meanwhile, the consumption of unsaturated fats is associated with many health benefits. A meta-analysis [22] concluded that unsaturated fatty-rich oils were more effective in lowering low-density lipoprotein-cholesterols (LDL-C) than highly saturated solid fats. Although there was inconsistent evidence to associate SFAs intake with cardiovascular diseases (CVDs), healthy dietary patterns that reduce CVD risk are typically not high in SFAs content [23]. In fact, replacing SFAs with PUFAs is associated with CVD risk reduction [24]. Given that CVD deaths are the leading cause of death worldwide and are responsible for one in three deaths in Malaysia [25], knowledge about fat levels in foods could inform food and nutrition policies for preventing CVD. Documentation of FA composition in Malaysian street foods is still lacking compared to the well-documented FA composition in street foods from other countries [5,6,7,8,9,10,11]. Previous studies in Malaysia have measured the FA composition in homemade meals [26] and the presence of TFAs in foods ranging from bakery products, snacks, breakfast cereals, dairy products, fast foods, Malaysian fast foods [27] to supermarket foods [28], but did not assess the FAs levels in street foods. Currently, only 23 ready-to-eat foods have their FA composition available in the Malaysian Food Composition Database (MyFCD) [29]. Therefore, this study aimed to analyse the FA composition of the commonly available street foods in Malaysia. Data from this study would expand the FA composition database in the MyFCD, which is used to provide consumers with public information on FA levels in foods and offer better quality nutrient data, especially for those involved in food preparation [30]. The detailed abbreviations and definitions used in the paper are listed in Table 1. ## 2. Materials and Methods In this cross-sectional study, data collection was conducted in two phases: [1] a survey of street foods and [2] sampling and analysis of FA composition in selected street foods. In Phase 1 of the study, a survey was conducted in all states to identify the top 15 most frequently available street foods for each food category in every state. In Phase 2 of the study, food sampling of 70 street foods (5 foods × 14 states) was carried out to analyse the FA composition. However, among the 70 analysed street foods, 14 similar foods were sampled from more than one state, and 25 different foods were sampled from only one state. In order to achieve the aim of this study, the FA contents of the 14 similar foods were presented as average values from the respective states. Thus, this study presented the FA composition for 39 street foods. This study is an extension of previous research undertaken to determine the most frequently available street food in all states of Malaysia while assessing the sodium levels [18]. Furthermore, the current study applied an additional sampling criterion for FAs composition analysis in Phase 2. This study was carried out from October 2021–May 2022. The methods of conducting this study have been approved by the Research Ethics Committee of the National University of Malaysia with reference number UKMPPI/$\frac{111}{8}$/JEP-2020-433. ## 2.1. Phase 1: Survey of Street Food in All States of Malaysia Phase 1 involved a field survey of locally available street foods in 13 states and one Federal Territory of Kuala Lumpur in Malaysia. The definition of street food was adopted from the Food and Agriculture Organization (FAO) [31]: “Ready-to-eat foods consumed without further processing or preparation, and sold by roadside hawkers such as trolleys, bicycles, markets, trucks or stalls that do not have fixed building or four walls.” Therefore, the eligible street food vending sites included in the survey were food establishments selling ready-to-eat food and not contained within a fixed building or four walls, i.e., mobile and stationary vending sites, individual stalls, and stalls at the morning and night markets. The surveyed markets were identified through the city council websites of each state. The study had a total of 380 locations surveyed across 68 districts in Malaysia. A survey form was used to record information such as the name of street food, state, district, category of street food (e.g., main meal, snack, or dessert), and preparation method of the street food. The criteria for main meals, snacks, and desserts in this study are based on the description by the International Scientific Committee [32] as follows:Main meal: food commonly eaten during main mealtimes, i.e., breakfast, lunch, and dinner;Snack: savoury food eaten between the main mealtimes, i.e., morning tea and afternoon tea;Dessert: sweet food eaten at the end of a main meal or as part of the main meal. A total of 10,520 street foods were surveyed, in which $40\%$ ($$n = 4234$$) of the street foods were snacks, while $37\%$ ($$n = 3887$$) and $23\%$ ($$n = 2399$$) were the main meals and dessert categories, respectively. The top 15 most frequently available street foods for each category in all states were subsequently identified and recorded. ## 2.2.1. Sampling of Street Food from Each State The selection of food for sampling in each state was conducted based on the top 15 most frequently available street foods for each category previously identified in Phase 1 (in which several types of street foods varied between states) as well as the following criteria: [1] use of high-fat preparation method, [2] use of high-fat ingredients, and [3] availability of the street food during the sampling (food sampling was conducted during the COVID-19 pandemic; hence the operating hours of stalls were hugely affected). Based on the selection, five street foods (regardless of the food categories) from every state were selected for food sampling, according to a method used by Tee et al. [ 33]. Thus, 70 street food samples from 14 states (including the same type of food from a different state) were analysed for the FA composition. While sampling each selected food, samples were purchased from two stalls within the respective states. This also applied to foods that were sampled from only one state. For instance, kerepek (only found in one state) was analysed based on two kerepek purchases from stall 1 and stall 2 within the respective state. The purchased food samples were transported in an ice box and stored in the freezer at −20 °C to delay food spoilage before further analysis. ## 2.2.2. Preparation of Street Food Samples for FA Composition Analysis The preparation of street food samples was conducted in the food analysis laboratory. Each purchased food sample was weighed with the packaging using a top pan balance (Mettler Toledo, OH, USA, Dragon 3002). Then, the food was removed from the packaging, weighed, and placed on a plate or bowl. The inedible portions of the food samples, such as bones, were removed, and the sample was reweighed. The second sample of the same food purchased from a different location was also prepared using the same method. The two samples were then homogenised in a food processor, scooped into an airtight container, and subsequently stored in the freezer at −20 °C for further analysis. Prior to the FA composition analysis, the determination of fat content using the homogenised samples was first carried out through the Soxhlet method [34]. The extraction, esterification, and analysis of FAs conducted in duplicates of the homogenised samples were performed within the same day. ## 2.2.3. Chemicals and Solvents Methanol ($99.9\%$), chloroform, potassium chloride ($0.88\%$), and sodium sulphate were used during the extraction of FAs. Meanwhile, toluene, $2\%$ sulphuric acid in methanol, sodium chloride ($5\%$), heptane, and sodium bicarbonate ($2\%$) were used during the esterification of FAs. The Reference Standard 37 Component FAME Mix (Supelco, St. Louis, MO, USA, 47885-U, 10 mg/mL) was used to analyse FAs using the Gas chromatography flame ionization detection (GC-FID) method. All reagents and standards were prepared with deionised water, except for the $2\%$ sulphuric acid in methanol (2 mL in 100 mL methanol). ## 2.2.4. Extraction of FAs Approximately 1.5 g (±0.1 g) of the homogenised sample was placed into a glass bottle. Next, 10 mL of methanol and 20 mL of chloroform were added to the glass bottle. The glass bottle was then vortexed and left to sediment for a few minutes. The solvent extracts were then filtered into a Schott bottle by leaving a small portion of the sample in the glass bottle to retain any food debris. The extraction process was repeated an additional two times. After the extraction, 20 mL of $0.88\%$ potassium chloride was added to the Schott bottle. The bottle was then shaken to allow the separation into two layers, and the upper layer was discarded. Similarly, 10 mL of $0.88\%$ potassium chloride and 10 mL methanol mixture (ratio 1:1) were added into the same Schott bottle and shaken to allow separation, and the upper layer was discarded. The solvent extract was filtered through sodium sulphate and poured into a new glass bottle to remove moisture. The extracts were then concentrated under nitrogen gas below 60 °C. ## 2.2.5. Esterification of FAs The extract containing the FAs underwent an esterification process to form fatty acid methyl esters (FAMEs). During this procedure, the sample dried with nitrogen gas was mixed with 1 mL of toluene and 2 mL of $2\%$ sulphuric acid in methanol in the same glass bottle. The glass bottle was incubated overnight at 50–55 °C. After incubation, the following steps were conducted and repeated twice: [1] 5 mL of $5\%$ sodium chloride in water was added to the glass bottle, and [2] 5 mL of heptane was added, shaken slightly, and left for a few minutes. The upper layer (heptane) was then transferred into a new glass bottle and combined with the subsequent upper layers (heptane) from the repeated process. Then, 4 mL of $2\%$ sodium bicarbonate in water was added to the extract. The sample mixture was shaken and left to separate. The upper layer was filtered through sodium sulphate and placed in a new glass bottle. The 1 mL of the filtered n-heptane layer was pipetted from the glass bottle directly into the vials. Then, a 10× dilution using n-heptane was performed by mixing 100 μL of the sample and 900 μL of n-heptane in a 2 mL vial. ## 2.2.6. Detection and Quantitation of FAs by GC-FID Blanks for each batch of samples were run through the entire procedure to measure any contributions to residue from reagents. GC-FID analysis was carried out using a gas chromatograph (Agilent 6890, Santa Clara, CA, USA) equipped with a ZB-FAME GC Column (60 m × 0.25 mm × 0.2 µm) and a flame ionisation detector (FID). The chromatographic conditions were as follows: initial column temperature 100 °C; injection temperature 250 °C; detector temperature 280 °C; and run time 64 min. The flow rate of the carrier gas (nitrogen) was set at 20 mL/min. In this method, 1 μL of the methyl esters (extract) solution was injected with the reference standard mixture into the GC-FID. The reference standard mixture was analysed under the same operating conditions as those employed for the sample, and the retention times were measured, along with the eluted methyl ester from the reference standard mixture were identified. The limit of detection (LOD) for all FAs was 0.01 mg/100 g. The quantification and identification of FAs present in the food sample were conducted using the peak area normalization method. Graphs showing the logarithm of the retention distance of each peak were constructed. All peaks in the sample chromatogram (including those that were not within the 37 FAME retention time) were integrated. The entire sample component was assumed to be represented on the chromatogram. Hence, the total area under the peaks represents $100\%$ of the constituents. As for the result, only 37 components were reported based on the reference standards. The analytical results of SFAs, MUFAs, PUFAs, and TFAs in each sample were expressed as a percentage (%) based on total fats in the respective samples, similar to the MyFCD. For the purpose of classifying the street food samples based on the low, medium, or high content of SFAs, the amount of SFAs was also expressed as g/100 g of food. The percentage of SFAs based on total FAs was converted into grams of SFAs based on 100 g of food using the following calculation:[1]SFA(g/100 g food)=Fat g100g food×SFA % total fat$100\%$ ## 2.2.7. Classification of SFAs Content There is no standardised classification for high SFAs, MUFAs, PUFAs, and TFAs in ready-to-eat dishes like street foods in Malaysia. However, the United Kingdom’s Traffic Light Labelling Scheme [35] has a classification for labelling low (≤1.5 g/100 g food), medium (>1.5 g to ≤5 g/100 g food), and high (>5 g/100 g food) SFAs in foods. Thus, this study adopted this classification for the SFAs content of street foods. ## 2.2.8. Classification of Preparation Method Based on the latest Malaysian Dietary Guidelines (MDG) [36], the preparation methods identified in the survey were further classified as either healthier (e.g., pan-frying, steaming, stir-frying, grilling, stewing, and baking) or less healthy (e.g., deep-frying), to identify whether the preparation methods affected the SFAs content of street foods. ## 2.3. Statistical Analysis A descriptive test was used to determine the total frequency of every street food surveyed for each state, the frequency of the street foods based on food categories, the preparation methods, and the type of street foods. Following the analysis of FA composition, a descriptive test was also used to determine the average and standard deviation of each FA for the selected street foods. Inferential test such as the one-way ANOVA was used to compare the average SFAs, MUFAs, and PUFAs between food categories. If the food categories had unequal sample sizes and both one-way ANOVA and homogeneity of variance were significant, a Games-Howell posthoc test was performed to identify the specific differences between the three food categories. The one-way ANOVA was also used to compare each FAs within each food category. The Chi-square test of association was used to determine the association between the SFAs content (g/100 g food) and the preparation method used for each street food. All the descriptive and statistical tests were conducted using IBM (Armonk, NY, USA) Statistical Package for Social Sciences (SPSS) version 25.0. The significance level for all conducted statistical analyses was set at $p \leq 0.05.$ ## 3. Results A total of 70 street foods (32 snacks, 26 main meals, and 12 desserts) were analysed for their FA composition, as displayed in Table S1. This paper presents the SFAs, MUFAs, PUFAs, and TFAs contents of similar street foods from different states as average values. Since 25 street foods were obtained from only one state, and 14 similar street foods were obtained from more than one state, this study reported the average SFAs, MUFAs, PUFAs, and TFAs contents in 39 selected street foods (16 snacks, 14 main meals, and 9 desserts) that are frequently available in Malaysia. The description of each street food is shown in Table S2. None of the analysed street foods contained any amount of TFAs. Figure 1 shows the average content of SFAs, MUFAs, and PUFAs based on the food category (16 snacks, 14 main meals, and 9 desserts, respectively). The dessert category had the highest SFAs content (52.8 ± $18.2\%$), followed by the main meals (48.4 ± $11.2\%$) and snacks (43.1 ± $5.1\%$). MUFAs content was the highest in snacks (36.6 ± $7.2\%$), followed by main meals (36.0 ± $8.1\%$), and desserts (29.7 ± $12.0\%$). PUFAs content was the highest in snacks (20.0 ± $8.3\%$), followed by desserts (17.6 ± $12.4\%$) and main meals (15.6 ± $5.9\%$). The FAs content between the food categories was not significantly different ($p \leq 0.05$) from one to another. Within each food category, all three food categories were composed mainly of SFAs, followed by MUFAs, and the least was in PUFAs, although the findings were not significantly different ($p \leq 0.05$). Table 2 shows the preparation method and the average FA composition of the 39 street foods arranged according to the classification of high, medium, and low SFAs content per 100 g of food. In general, the SFAs, MUFAs, and PUFAs content of street foods ranged from 29.3–$88.5\%$, 7.0–$44.4\%$, and 4.5–$38.8\%$, respectively. Based on the classification of SFAs content in 100 g of food sample, cakoi (24.3 ± 3.6 g/100 g) contained the highest SFAs content, while chicken rice (1.2 ± 0.3 g/100 g) had the lowest SFAs content. Most of the analysed main meals ($85.7\%$) and all the snacks and desserts contained medium to high SFAs content, ranging from 1.7–24.3 g/100 g. According to the classification [35], $56.4\%$ of the street foods had medium SFAs content (1.7–4.5 g/100 g), while the other $38.5\%$ had high SFAs content (5.1–24.3 g/100 g). Among the foods with medium to high SFAs contents, $43.2\%$ were deep-fried, which included cakoi, kerepek, french fries, chicken nuggets, and donuts. This was followed by other preparation methods, such as pan-frying ($21.6\%$), steaming ($13.5\%$), stir-frying ($8.1\%$), grilling ($8.1\%$), stewing ($2.7\%$), and baking ($2.7\%$). Meanwhile, only $5.1\%$ of the 39 street foods contained low SFAs content (1.2–1.4 g/100 g). The foods with low SFAs content were those prepared by stir-frying (fried vermicelli) and steaming (chicken rice). There were 32 street foods that contained the highest MUFAs content (32.0–$44.4\%$), mostly found in snacks ($43.8\%$), main meals ($40.6\%$), and desserts ($15.6\%$). Compared to the other street foods, fried vermicelli contained the most MUFAs, while kuih jelurut had the least. The majority ($46.9\%$) of the street foods were prepared using deep-fried methods such as curry puff, fried chicken with cheese, banana fritters, kuih keria, kerepek, fried popiah, chicken nuggets, fried fish ball and donuts. This was followed by street foods that were pan-fried ($21.9\%$), stir-fried ($12.5\%$), steamed ($9.4\%$), grilled ($6.3\%$), and stewed ($3.1\%$). The remaining 7 street foods contained MUFAs ranging from $7.0\%$ to $27.5\%$. The MUFAs content for all 39 street foods, from highest to lowest amount, is shown in Table S3. Surprisingly, only 7 street foods had the highest amount of PUFAs (30.0–$38.8\%$), mainly found in snacks ($57.1\%$), desserts ($28.6\%$), and main meals ($14.3\%$). Approximately $42.9\%$ were pan-fried, $28.6\%$ were grilled ($28.6\%$), and $14.3\%$ were deep-fried and baked, respectively. The remaining 32 street foods contained PUFAs ranging from $4.5\%$ to $24.6\%$. Apam balik was reported to contain the highest amount of PUFAs, while kuih jelurut had the least amount of PUFAs. The PUFAs content for all 39 street foods, from highest to lowest amount, is shown in Table S4. Figure 2 displays the percentage distribution of all 70 street foods based on the classifications of the preparation methods and SFAs content. Most ($48.6\%$) of the street foods that were prepared using healthier preparation methods, such as steaming, baking, stewing, grilling, pan-frying, and stir-frying, contained low to medium SFAs content. This was followed by street foods that used a less healthy preparation method (i.e., deep-frying) and contained high ($31.4\%$) and low to medium ($12.9\%$) SFAs content, respectively. Lastly, only $7.1\%$ of street foods that were prepared in a healthier manner reported a high SFAs content of more than 5 g/100 g of food. An association test (shown in Table 3) reported that the SFAs content in the 70 street foods was significant ($p \leq 0.001$) and strongly associated (Φ = 0.59) with the classification of the preparation methods used. This means that foods prepared using a healthier method were more likely to contain low to medium SFAs contents than foods prepared less healthily. ## 4. Discussion This study found that the gas chromatography did not detect TFAs in any of the local street foods, which is not in agreement with the findings from other countries in the Asian [5,6,8,12], European [7,9,37], and African [10] regions. This is possibly due to varying ingredients used in the local street foods in other countries. For instance, street foods in other Asian countries, such as India [12], are mainly fried in or used vanaspati, a vegetable ghee rich in TFAs [38] that is not commonly used in Malaysian street food. Despite the fact that a large proportion of street foods in the current study are also deep-fried, it is possible that palm oil was used by the local vendors, as it is a common cooking or frying oil in Malaysia [39]. According to the Malaysian Palm Oil Council [40], palm oil is popularly used in the local food scene as it is naturally stable against oxidation when used for high-temperature cooking methods, such as deep-frying, due to the high composition of SFAs in the oil. Moreover, the semi-solid form enables palm oil to be used without the need to be further solidified through the process of hydrogenation, making palm oil free from TFAs [41]. Besides vanaspati, other sources of TFAs, such as partially hydrogenated margarine and shortenings, are commonly used in street foods, such as roti canai, curry puff, chicken burgers, and donuts [36]. However, the findings in this study may suggest that the use of partially hydrogenated ingredients among vendors in Malaysia is low and that they might use tub-style margarine products which do not contain TFAs [42]. Notably, the substitution of stick margarine with tub margarine was found to be associated with a lower risk of CVD [43]. Other than the type of fat-based ingredients used in the local street foods, the absence of TFAs may also be explained by the cooking temperature used to prepare the foods. Most of the studied street foods are deep-fried using a cooking temperature below 200 °C [44]. A recent review [14] found that heating edible oils to below 200 °C, a common cooking temperature, does not affect the TFAs levels in the oil. Yi Chen et al. [ 17] also found that there was only a small increase in TFAs levels in foods deep-fried below 200 °C using palm oil. In this study, desserts contained the most SFAs, whereas snacks had the most MUFAs and PUFAs. These findings were in line with street food studies in Kazakhstan [5] and Moldova [7], in which SFAs content was significantly the highest in street foods that are sweet, such as chocolates, cakes, and wafers. Albuquerque et al. [ 7] also reported that PUFAs were highest in savoury street foods, such as pateuri (a fried pastry). In this study, all street foods under the dessert category were sweet; meanwhile, all street foods under the snack category were of the savoury type. Despite SFAs being the highest in desserts and MUFAs and PUFAs being the highest in snacks, SFAs were the most dominant FA present in all three food categories. This finding supports the evidence from a study [45] reporting that local Asian foods are, indeed, high in saturated fats and more saturated than westernised-styled foods. Although there is an ongoing debate on the link between saturated fats and CVD risk [46,47,48], with growing evidence [49] of contrasting associations between different dietary sources of SFAs and the risk of CVD (rather than the amount of SFAs), it is still imperative for Malaysians to control their consumption of local street foods, as more than half ($64.4\%$) of the population consumes at least one out-of-home meal daily [50], as well as to be conscious of the sources of SFAs consumed. Nevertheless, the FA profile of each food category presented a similar proportion of SFAs, MUFAs, and PUFAs. This was also previously observed in a study conducted among local street foods in Mozambique [10]. As street foods are relatively less expensive than regular brick-and-mortar food establishments [3,51,52], this may indicate that local street foods could be a good source of MUFAs and PUFAs, especially among the consumers in developing countries [53,54]. The SFAs, MUFAs, and PUFAs content between the street foods varied considerably, indicating possible differences in the preparation methods or ingredients used [55]. Cakoi ranked first among the 39 street foods in terms of SFAs content and is classified as a high-SFA food. Cakoi is a long, golden-brown deep-fried strip of dough that originated in China but is now a popular snack in Malaysia. On the contrary, chicken rice contains the least SFAs and is classified as a low-SFA food. This is most likely due to the difference in preparation methods, as cakoi is deep-fried, whereas steaming and roasting methods are mainly used to prepare the rice and chicken in the chicken rice. Cakoi, in the present study, is deep-fried in palm oil, which explains the high SFAs content, considering that palm oil has approximately $50\%$ SFAs [56,57,58]. On the other hand, the SFAs content of the chicken in chicken rice may have been reduced after undergoing the roasting process. Alina et al. [ 59] found that the roasting method slightly reduces the SFAs content in chicken meat. Chicken rice, in this study, had marginally higher SFAs ($43.6\%$) than the current reports in MyFCD ($39.5\%$). Additionally, nearly half of the street foods with medium to high SFAs content that were deep-fried (such as cakoi, kerepek, french fries, chicken nuggets, and donuts) may also be linked to the use of palm oil. Thus, street food vendors should be encouraged to strain the excess oil off the fried food, and consumers should be reminded to control their consumption of deep-fried street foods, which is also in accordance with the MDG 2020 [36]. It is important to note that street foods not prepared by deep-frying also contain medium to high SFAs, possibly due to the usage of vegetable- and animal-based fats. In terms of ingredients, the main meals, such as net crepes (roti jala), glutinous rice with rendang, noodles with gravy, nasi lemak, and nasi lemak with fried chicken and desserts, such as kuih seri muka and kuih jelurut, are all coconut milk-based dishes. Traditionally, coconut milk is used extensively in Malaysian main dishes and desserts (except for kuih jelurut) [60], such as in the curry gravy served with net crepes (roti jala), the rendang and glutinous rice, and the pandan custard layer of kuih seri muka [61]. The rice in nasi lemak is also cooked with coconut milk [62]. Kuih jelurut, a local steamed delicacy among the Brunei ethnicities [63] in Sabah, incorporates coconut milk as the main ingredient [64]. As the SFAs in coconut milk are present in high proportions ($92.0\%$) [36], this may corroborate the current findings. Vendors could opt for substituting coconut milk with fresh milk in coconut milk-based desserts and main meals. Marina and NurulAzizah [65] found that custard pudding and green curry prepared using fresh milk had significantly lower fat content than those prepared using coconut milk and fresh coconut milk. Besides vegetable-based fat, such as coconut milk, animal-based fats, such as butter, processed meats, animal fats, and cheese, are used in these street foods. Popcorn was the second street food with the highest SFAs content. This may be because the popcorn samples were coated with a caramel butter sauce, and the latter is one of the primary sources of saturated fats in the diet [66]. Chicken patties, fried sausage, chicken nuggets, and pizza also contain processed meats high in saturated fats. In addition, the meat in satay, also known as meat skewers, is often skewered with meat fats to add more flavour. As expected, street foods with added cheese, such as fried chicken, banana fritters, and apam balik with cheese, had more SFAs than the regular versions. The majority ($82.1\%$) of the street foods in this study contained high MUFAs content. The MUFAs content is likely from the use of palm oil used to deep-fry most of the foods and other MUFA-rich ingredients. Although palm oil contains a high percentage of SFAs, it is also rich in MUFAs, with a nearly equal amount of SFAs [67,68]. Hence, this might have accounted for the high SFAs content in deep-fried snacks, such as cakoi, kerepek, french fries, chicken nuggets, fried popiah, curry puff, fried chicken, fried chicken with cheese, keropok lekor, fried sausage and fried fish ball, and deep-fried desserts, such as donuts, kuih keria, banana fritters with cheese, and banana fritters. Besides palm oil, nuts and seeds are also good sources of MUFAs, contributing almost $50\%$ to the FA profile [39,67,68]. The MUFAs content in main meals, such as roti canai, might be due to the lentils gravy or dhal, as these two are commonly served together. In contrast, nasi lemak and apam balik might be good sources of MUFAs due to the use of peanuts that predominantly contain MUFAs [69]. Likewise, the kerepek or deep-fried chips in this study also contained peanuts, which contribute additional MUFAs to this deep-fried snack. Chicken burgers also contain high MUFAs content, which may be linked to the chicken patty used. Laskowski et al. [ 70] reported that meat products, including processed poultry products, contribute as much as $18\%$ of MUFAs to the average Polish diet. Interestingly, noodle-based and rice-based main meals, such as fried kuey teow, fried noodles, noodles with gravy, fried rice, and chicken rice, were also among the high-MUFA-containing foods. The low MUFA ($7\%$) content in kuih jelurut was possibly due to the lack of MUFA-rich ingredients used in this sweet dessert, as the main ingredients [71] (p. 148) needed are rice flour, gula apong, and coconut milk. The rich source of MUFAs reported in fried vermicelli should be made known to the consumers, as this main meal is one of the most commonly consumed ready-to-eat dishes among adults in Malaysia [72]. Apam balik and apam balik with cheese were the two street foods with the most amount of PUFAs. In a normal set of apam balik, this sweet pancake-like dessert is filled with nuts, sugar, and corn. The PUFAs content may be due to the use of PUFA-rich ingredients such as nuts. Therefore, apam balik could be considered a good source of PUFAs and may be beneficial to health, as frequent consumption of nuts has been demonstrated to have an association with lowering the risk of developing diet-related chronic diseases such as CVD [73,74]. However, the intake should also be controlled due to the sugar content. This study showed that deep-fried street foods tended to contain higher SFAs content than foods prepared using healthier methods, such as steaming, baking, stewing, grilling, pan-frying, and stir-frying. Deep-frying is the process of immersing food in hot oil between 130 °C and 190 °C [44]. Several studies compared the SFAs content of foods from different cooking methods. Choo et al. [ 75] found that deep-frying significantly increased the SFAs in a Japanese threadfin beam fillet (a type of fish) in comparison to grilling, baking, and steaming methods. Asmaa and Tajul [76] found that the SFAs content in chicken sausage significantly increased after deep-frying, whereas steaming in an oven did not affect the SFAs content in chicken sausage. These might explain the association between the deep-frying method and the SFAs content of street foods in this study. Given that Malaysia is one of the main producers of palm oil [77], and the high smoke point makes it suitable for deep-frying purposes, it is also worth noting that palm oil may account for the SFAs content in the studied deep-fried street foods. Notwithstanding little evidence that has managed to link the intake of saturated fats with health risks, deep-frying is a popular cooking method due to its fast preparation and ability to produce sensorily acceptable foods [78]. It is also the most preferred cooking method chosen by Malaysians [79]. In light of this, street food vendors in Malaysia should be advised to use as little oil as possible when preparing food. As suggested by the MDG 2020 [36], vendors could opt for using cooking methods that use less fat, such as grilling, pan-frying, and stir-frying instead of deep-frying, and they should also remove the excess oil after cooking. Besides that, they should also be aware not to reuse cooking oil more than twice to avoid imposing unwanted health effects on the consumers, as local food operators still have moderate awareness regarding the practice of reusing oil [39]. Given the limited amount of data currently available in the MyFCD on the FA composition in local, ready-to-eat dishes, the findings from this study could be used to update the database. Since the MyFCD is publicly available, the updated data could assist consumers in selecting street foods that are low in saturated fats and high in unsaturated fats. ## 5. Conclusions This study demonstrated that SFAs content predominated in all three categories of street foods—main meals, snacks, and desserts, followed by MUFAs and PUFAs. Desserts contained the most SFAs, whereas snacks contained the most MUFAs and PUFAs. The majority of street foods that contained medium to high SFAs were deep-fried dishes, coconut milk-based dishes, and those that contained butter, processed meats, animal fats, or cheese. Meanwhile, street foods with low amounts of SFAs were prepared by steaming and stir-frying. Street foods that contained nuts had the highest content of MUFAs and PUFAs. Similarly, deep-fried street foods were also high in MUFAs. The local street foods contained no TFAs, possibly due to the limited use of partially hydrogenated fats such as ghee. The street foods that were prepared by deep-frying mostly contained high SFAs content, whereas, among the street foods that were prepared using healthier methods, most contained low SFAs content. Apart from updating the MyFCD database, these findings may encourage consumers to limit their consumption of coconut milk-based foods, deep-fried foods, and processed foods, as well as recognise several local street foods that may potentially increase their intake of unsaturated fats. Improving the nutritional composition of local street foods is imperative to creating a healthier food environment and reducing the development of diet-related chronic diseases. Educating street food vendors could be one of the ways to achieve this. Given that no amount of TFAs was detected in any of the street foods, especially those that are deep-fried, future studies could focus on the factors that may influence the TFAs content in foods, such as the frying temperature, duration of frying, usage of TFA-rich ingredients, and the practice of reusing oils. ## References 1. Ronto R., Wu J.H., Singh G.M.. The Global Nutrition Transition: Trends, Disease Burdens and Policy Interventions. Public Health Nutr. 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--- title: Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes authors: - Rashid M. Ansari - Mark F. Harris - Hassan Hosseinzadeh - Nicholas Zwar journal: Healthcare year: 2023 pmcid: PMC10048183 doi: 10.3390/healthcare11060903 license: CC BY 4.0 --- # Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes ## Abstract The use of Artificial intelligence in healthcare has evolved substantially in recent years. In medical diagnosis, Artificial intelligence algorithms are used to forecast or diagnose a variety of life-threatening illnesses, including breast cancer, diabetes, heart disease, etc. The main objective of this study is to assess self-management practices among patients with type 2 diabetes in rural areas of Pakistan using Artificial intelligence and machine learning algorithms. Of particular note is the assessment of the factors associated with poor self-management activities, such as non-adhering to medications, poor eating habits, lack of physical activities, and poor glycemic control (HbA1c %). The sample of 200 participants was purposefully recruited from the medical clinics in rural areas of Pakistan. The artificial neural network algorithm and logistic regression classification algorithms were used to assess diabetes self-management activities. The diabetes dataset was split 80:20 between training and testing; $80\%$ [160] instances were used for training purposes and $20\%$ [40] instances were used for testing purposes, while the algorithms’ overall performance was measured using a confusion matrix. The current study found that self-management efforts and glycemic control were poor among diabetes patients in rural areas of Pakistan. The logistic regression model performance was evaluated based on the confusion matrix. The accuracy of the training set was $98\%$, while the test set’s accuracy was $97.5\%$; each set had a recall rate of $79\%$ and $75\%$, respectively. The output of the confusion matrix showed that only 11 out of 200 patients were correctly assessed/classified as meeting diabetes self-management targets based on the values of HbA1c < $7\%$. We added a wide range of neurons (32 to 128) in the hidden layers to train the artificial neural network models. The results showed that the model with three hidden layers and Adam’s optimisation function achieved $98\%$ accuracy on the validation set. This study has assessed the factors associated with poor self-management activities among patients with type 2 diabetes in rural areas of Pakistan. The use of a wide range of neurons in the hidden layers to train the artificial neural network models improved outcomes, confirming the model’s effectiveness and efficiency in assessing diabetes self-management activities from the required data attributes. ## 1. Introduction Diabetes mellitus is one of the leading causes of chronic health problems globally [1]. The International Diabetes Federation (IDF) estimated a worldwide population of 463 million diabetics in 2019 [2], with type 2 diabetes being more prevalent in adults aged 40–59 [2,3]. With a diabetes incidence rate of $19.9\%$ among people aged 20–79, Pakistan is among the world’s top-10 countries for diabetes cases [2]. Type 2 diabetes is a significant public health issue in Pakistan, particularly among the population aged 40–60. This demographic group is at high risk for type 2 diabetes because its members are more likely to be overweight or obese, physically inactive, and have unhealthy eating habits [1,2]. In addition, the social and health disparities of the region’s population [3,4,5,6] contribute to the high prevalence of diabetes and obesity. The literature considers self-management to be the cornerstone of diabetes care [7]. Several studies [8,9,10] have highlighted the importance of diabetes self-management and its association with improved diabetes knowledge, responsible behaviour of patients towards their disease, and improved clinical outcomes. Diabetes self-management (DSM) plays an essential role in controlling and preventing the disease’s complications. Nonetheless, patients often do not adhere to self-management recommendations [1,2,3], which is extremely concerning. In the middle-aged population of Pakistan, adherence to recommendations and barriers poses significant challenges [4,5,6] due to unhealthy eating patterns and lack of physical activity [6]. In diabetes self-management, AI-based techniques are incorporated into patient self-management tools, clinician tools, and healthcare systems. It has been demonstrated that AI-powered solutions have a significant impact on patient comorbidities, lifestyle choices, and healthcare center visits, both in terms of frequency and duration [11]. In the case of diabetes self-management, m-health devices already enabled patients to track and collect information regarding their blood glucose, diet, and exercise. Machine learning can now be applied to patient data to generate data-driven, patient-specific interventions [12]. As a result, machine learning has the potential to empower patients by providing them with otherwise-unavailable information, assisting them in making data-driven decisions about their health, and nudging them towards adopting healthier lifestyle habits [13]. Data-driven AI applications, especially in healthcare, have revolutionised medical research in several areas, such as disease diagnosis, image processing, and disease prediction. Deep learning models, recurrent neural networks, and genetic algorithms play an important role in Artificial Intelligence applications. AI is ideal for detecting, analyzing, and predicting heart disease [14], diabetes complications [15], breast cancer [16], hepatitis B [17], and COVID-19 severity [18]. In this article, the prediction paradigm for successfully assessing diabetes self-management is considered using a dataset of 200 middle-aged type 2 diabetes patients in rural Pakistan. Typical regression models may provide a solution but assume statistical independence and interdependencies of input and output variables, homogeneity of continuation, and external factors. However, intricate physiological traits usually offend or discredit these assumptions. Models are being designed to improve diabetes self-management. Various models and programmes have been established to improve medical research, but their testing is incomplete and inadequate, making them challenging to perform and manage. Since AI has been used in medical research and analytical investigations, this research presents a well-organised and developed artificial neural network model that assesses type 2 diabetes self-management activities and behaviours. This work applies AI models in the diagnostic strategy to measure diabetes self-management behaviours by selecting criteria related to self-management. AI can create and apply such models, which are more useful, efficient, and effective in numerous medical disciplines, such as analysis, diagnosis, and prediction; this development could help professionals and patients alike [19]. ANNs mathematically represent the human brain system, showing the power of training and generalisation. Most ANN approaches use nonlinear functions with complicated or unknown input feature links [19]. A neural network is composed of neuron layers. Each neuron in the ANN model is directly linked to neurons in the other layers via weighted values [19,20]. The weighted permutation of multiple input signals that may contain different computations influences the input and output of each neuron. These neurons determine the threshold value by applying the transfer function to inputs with weights. If the threshold is exceeded, the activation function transmits the signal to the following neuron. Important ANN functions include prediction, perception classification, pattern recognition, and training [20]. However, ANN development for medical applications, such as classifications, clustering, data optimisation, and input-based prediction, is ongoing; therefore, it is important to understand that, when they present predictions, perception classifications, and pattern recognitions along with training [20,21], ANN models include input layers, hidden layers, output layers, neurons, and their interactions. Few features hinder training, but several reduce network processing power [21]. This article used ANN and machine learning algorithms such as logistic regression to choose attributes, evaluate data, and assess diabetes self-management activities. ## 2.1. Dataset Description Al-Rehman Hospital’s primary healthcare and diabetes management clinic in Abbottabad, Pakistan, recruited 200 patients with diabetes (250 patients were approached). This group comprised poorly managed type 2 diabetes patients aged 40–60 years old (Table 1). This study included patients with Hemoglobin (HbA1c) > $7\%$ (Hemoglobin is a simple blood test that measures average blood sugar levels over the past three months) and excluded those with liver, renal, or thyroid issues. All 200 patients gave informed consent and completed a questionnaire thereafter. ## 2.2. Data Visualization Figure 1 shows distribution of the variable “Age_year”. The minimum value is 40 years and maximum is 65 years. The verticle line divides the surface area between Q2 quartile (52 years) and Q3 quartile (56 years). Figure 2 provides dustribution of the variable “Body mass index” with minimum value of 16.9 kg/m2 and maximum value of 56.5 kg/m2. Figure 3 shows dustribution of the variable “DiabetesTime” with minimum and maximum values bewteen 2 years to 13 years. Figure 4 shows the distribution of the variable HbA1c (%) with minimum and maximum values between $6.8\%$ to $13.2\%$. Figure 5 displays the distribution of the variable “Execise” with minimumand maximum values between 3 to 5 dyas/week. Figure 6 shows the distribution of variable “income”. ## 2.3. Aims and Objective The main objective of this work was to prepare and carry out diabetes self-management (DSM) assessments for patients with type 2 diabetes in rural areas of Pakistan. The simplified proposed modelling approach is shown in Figure 7. The raw dataset includes the input variables used in the main algorithm, such as age (years), BMI (kg/m2), exercise, diet, blood glucose testing, medication, formal education, diabetes duration (time), and HbA1c levels (%). The outcome variable is diabetes self-management (DSM). DSM is a function of HbA1c % in the analysis. The lower the levels of HbA1c%, the better the DSM activities. We excluded HbA1c levels (%) as an input variable in our analysis to avoid collinearity since DSM is a function of HbA1c. ## 2.4. Data Pre-Processing Data pre-processing is an important part of the data analysis; before model evaluation, many strategies could pre-process the dataset [23,24]. In this study, the preprocessed output matched expectations, and pre-processing responses measured learning rate, momentum, and time, fulfilling the acceptance requirement of the transformed data. The dataset was imbalanced but had no missing values; therefore, we used SMOTE (Synthetic Minority Oversampling Technique) to fix it [25]. In the proposed methodology or modelling approach shown in Figure 7, we normalised the diabetes dataset, and used $80\%$ for validation and training and $20\%$ for testing. Python programming was used to develop the model. We have applied the selected ANN algorithms and optimisation techniques to obtain the best prediction model to assess diabetes self-management activities. ## 3.1. Algorithms Used for Classification We used AI algorithms to classify the dataset. The logistic regression algorithm was used as a baseline classification algorithm. Logistic regression is the most suitable method for the analysis of binary classification tasks with the high diagnostic ability [14]; ANN was used as the main algorithm and accommodated several features, such as age, exercise, diet, blood glucose testing, formal education, diabetes duration, and HbA1c levels. Logistic regression models have been employed to solve this type of problem and enhance patients’ diabetes self-management assessment [26]. These models’ basis usually includes inference to statistical independence, the interdependencies of their input and output variables, uniformity of continuity, and presence of external variables. ## Logistic Regression Analysis The logistic regression model used is represented as follows:[1]log(π/(1−π))=α+β1x1+β2x2……….+βkxk where Logistic regression analysis was carried out using Python Programming, splitting the data into the training set ($80\%$) and test set ($20\%$). The logistic regression model’s performance was evaluated based on the confusion matrix. The accuracy of the training set was $98\%$, while the test set’s accuracy was $97.5\%$; each set had a recall rate of $79\%$ and $75\%$, respectively. The confusion matrices for the training and test sets are displayed in Figure 8 and Figure 9, respectively. Figure 10 shows the results of the Receiver Operating Characteristics (ROC) curves for training and test data. The area under the curve (AUC) is 0.96 on training and test sets. For the training set, we correctly assessed/classified that the patients do not follow DSM. TP = True positive = 146—we correctly predicted/assessed/classified that these patients do not meet Diabetes Self-management (DSM) targets. TN = True negative = 11—we correctly predicted/assessed/classified that these patients meet DSM targets based on the values of HbA1c < $7\%$. FP = False positive = 03—we incorrectly predicted/assessed that these patients do not meet DSM targets. FN = False negative = 0—we incorrectly predicted/assessed that these patients meet DSM targets. The confusion matrix for the test set output can also be elaborated. ## 3.2. Artificial Neural Network Establishing a neural network model that accurately assesses patient self-management activities was the main objective of this study. In this study, the network was trained using ANN algorithms. We categorised the 200-patient dataset according to the requirements. Training, validation, and test data that were needed for 200 diabetes patients were divided into a ratio of 80:20 between training and testing; $80\%$ [160] of cases were utilised for training, while 40 instances were selected for testing purposes. The performance of algorithms was evaluated using the confusion matrix. The other AI-based algorithms, such as support vector machine (SVM) and Naïve Bayes, are the algorithms used most frequently in previous studies to predict and evaluate diabetes management practices [25,26,27]. These algorithms find hidden data by balancing processing time and accuracy [26,27]. In this study, the process of using ANN algorithms to forecast, validate, and test the network to improve diabetes patients’ self-management is displayed in Figure 11. The framework requires the network to collect 200 diabetes patients’ self-management data. Due to noise or null data, various features (diet, exercise, glucose testing, age, formal education, diabetes duration, HbA1c levels, etc.) in the dataset may confuse the results. To minimise errors, we carefully selected these features using the data-pre-processing. ANN architecture varies between classifiers, exhibiting underlying algorithm parameters that are dependent on the classifier that is required to train the network. The ANN structure contained an input layer and three hidden layers. Each hidden layer was equipped with an activation function and neurons. Similarly, the second and third hidden layers were applied with different neurons. Finally, we have the output layer, which had only one neuron. Specific applications were also applied to incorporate the optimisation techniques when developing the model within the framework of ANN. ## 3.3.1. ANN Model_1 with SGD Optimiser ANN Model_1 was developed using the SGD (Stochastic Gradient Descent) optimiser technique [29,30]. Gradient descent is a well-known optimisation strategy in the field of machine learning and deep learning; the ANN model_1 optimiser follows this data optimisation approach [29]. Instead of consuming the entire dataset in each iteration, the SGD optimiser randomly selects a small subset of samples [30]. We employed the ReLU activation function and 128 neurons in the first hidden layer. With the addition of non-linearity provided by a rectified linear unit (ReLU), we used a deep learning model to avoid the problem of vanishing gradients, ensuring that the positive half of the argument was properly interpreted. In the second layer, we added 64 neurons with the ReLU activation function. The output layer had one neuron as well as sigmoid as an activation function. Bu having the activation function of a neuron as a sigmoid function, we ensured that the output of this unit consistently falls within the range of 0 and 1, regardless of the state of the neuron. In addition, because the sigmoid is a non-linear function, the output of this unit was be a non-linear function of the weighted sum of the inputs. The accuracy was $90\%$ on the model evaluation of the test data. Though the model was overfitting, the training loss was smooth; overall, it decreased in correlation with an increase in the epochs (Figure 12). The confusion matrix (Figure 13) of the model shows that only 36 patients met the DSM targets. The confusion matrix in Figure 10 shows that 36 patients with type 2 diabetes were correctly classified by the neural network classifier as following DSM targets. None of the type 2 diabetes patients was misclassified by the neural network classifier. ## 3.3.2. ANN Model_2 with Adam Optimiser The Adam optimiser technique was used to create and use ANN model_2 (Adaptive Moment Estimation). This is an efficient method for stochastic optimisation that only needs first-order gradients and does not need much memory. Estimates of the first and second moments of the gradients are used to calculate the individual adaptive learning rates for each parameter [31]. We added 128 neurons in the first hidden layer and used the ReLU activation function. In the second layer, we added 64 neurons with the ReLU activation function; in the third hidden layer, we added 32 neurons. The output layer contained only one neuron and used sigmoid as an activation function. The accuracy was $100\%$ on the model evaluation of the training and test data. The model was overfitting (as may be observed from Figure 14), but the model accuracy was promising (as shown in Figure 15). Table 2 provides a performance comparison for different classification techniques. The ANN model using Adams’ optimiser outperformed all other classification and optimisation techniques. The main reason for this outcome is that, similar to the RMSpropr optimiser, the Adam optimiser uses squared gradients to scale the learning rate; it also takes advantage of momentum by moving the average of the gradient in the same way as the SGD optimiser [32]. The criteria of comparison were set to obtain the high score of Recall of the model. The higher the score of Recall, the lower the probability of false negatives. Other scores, such as accuracy and F1, were also considered for comparison purposes. ## 4. Discussions We used the SGD optimiser technique in this study, which has proved itself as an efficient and effective optimisation method central to many machine learning solutions, such as recent advances in deep learning [33,34]. The accuracy of the ANN model using SGD optimiser was $90\%$ on the model evaluation of the test data. The model predicted/assessed by the use of a confusion matrix that only 36 patients met the diabetes self-management targets. In this study, we also employed the Adam optimiser, a method for efficient stochastic optimisation that requires first-order gradients and minimal memory. Using estimations for the first and second moments of the gradients, the approach computes individual adaptive learning rates for various parameters. Our method combined the benefits of two prominent methods within Adam optimisation in online and non-stationary environments [35,36]., The ANN model utilizing the Adam optimiser to examine training and test data with $100\%$ accuracy. In this study, AI evaluated type 2 diabetes patients against a four-part diabetes self-management criteria: the practice of diet control, regular physical activity, medication adherence, and glucose monitoring (keeping HbA1c < $7\%$) [10]. DSM is key to ensuring effective control of serum glucose, which reduces the development of diabetes-related comorbidities [37]. AI analyses revealed the majority of the study’s participants complied with taking medication prescribed by their physician. This high rate of medication adherence was also observed in other studies carried out in Pakistan by Khattab et al. [ 37] and Ahmad et al. [ 38]. The study conducted on the US population revealed that just $64\%$ of patients complied with medication adherence [39]. While medication adherence is associated with effective diabetes self-management [40], this study’s high medication compliance rate relative to other DSM behaviours suggested that the majority of patients with type 2 diabetes chose to take medicines rather than adjust their behaviour. This behaviour is a major obstacle to satisfying the diabetes self-management requirements and maintaining a healthy lifestyle. The application of AI revealed that diet control was another important feature. This assessment was in agreement with a qualitative study carried out by Ansari et al. [ 41], which showed that a very low percentage of participants practiced diet control [42]. Other previous research [43,44] identified lack of motivation, the frequency of social meetings, and the time and energy required for meal preparation as factors that impeded diet control. A previous study also demonstrated that counselling on diet control would improve respondents’ comprehension of its significance, resulting in a significant decrease in total HbA1c levels and BMI [29]. Few participants engaged in 30 min of physical activities for at least five days per week, as indicated by Jafar et al. [ 5] and Ansari et al. [ 6]. However, research conducted in the United States recorded a somewhat greater prevalence of physical activity [45]. The obstacles preventing patients with type 2 diabetes from engaging in physical exercise may include inclement weather, such as hot or rainy days, and staying at home due to a lack of available walking space. As nearly half of the respondents were over the 60 years old, many type 2 diabetes patients may not be able to conduct the suggested regular exercise due to poor health; age may also contribute to this low performance. The ANN model used a confusion matrix to predict/assess that only 36 patients met the DSM targets. This shows that blood glucose monitoring was not practiced regularly by the participants. The patients with uncontrolled glucose levels (HbA1c > $7\%$) were less likely to undertake appropriate diabetes care activities than those with controlled glucose levels ($$p \leq 0.050$$). The main reason may be the high cost of glucose testing strips, which is beyond the reach of participants with low incomes. Longer duration of diabetes was associated with poor glycemic control, though this result was not statistically significant ($$p \leq 0.422$$). This result is in agreement with other studies that reported a similar association between long-duration diabetes and poor glycemic control [46]. This relationship may stem from the progressive impairment of insulin secretion with time due to β-cell failure, which does not respond to diet or other oral agents [46]. The other independent variables related to patients’ characteristics, such as age, formal education, and body mass index, had little impact on their diabetes self-management activities ($p \leq 0.05$). However, the lack of a correlation between glycemic control and age is not consistent with the findings in other studies, which reported that younger age was associated with poor glycemic control [45,46]. ## 5. Conclusions This study assessed the factors associated with poor diabetes self-management activities among type 2 diabetes patients in rural areas of Pakistan. The use of a wide range of neurons in the hidden layers to train the artificial neural network models improved outcomes, confirming the model’s effectiveness and efficiency in assessing diabetes self-management activities. The optimization techniques used in ANN models identified four important features related to patients’ self-management activities. AI application revealed that the majority of diabetes patients heavily relied on medication adherence to manage their disease, rather than adjusting their self-management behaviour. Resultantly, it will remain challenging for healthcare professionals to encourage rural patients to adopt healthy lifestyles. In future studies, AI may be extended to develop specific web-based applications to facilitate patients’ self-management activities; these applications’ features map include advice on diet control, planning physical activity routines, and glucose monitoring and control. ## References 1. Narayan K.. 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--- title: Vine Foliar Treatments at Veraison and Post-Veraison with Methyl Jasmonate Enhanced Aromatic, Phenolic and Nitrogen Composition of Tempranillo Blanco Grapes authors: - Itziar Sáenz de Urturi - Freud M. Ribeiro-Gomes - Sandra Marín-San Román - Rebeca Murillo-Peña - Lesly Torres-Díaz - Miriam González-Lázaro - Eva P. Pérez-Álvarez - Teresa Garde-Cerdán journal: Foods year: 2023 pmcid: PMC10048190 doi: 10.3390/foods12061142 license: CC BY 4.0 --- # Vine Foliar Treatments at Veraison and Post-Veraison with Methyl Jasmonate Enhanced Aromatic, Phenolic and Nitrogen Composition of Tempranillo Blanco Grapes ## Abstract Methyl jasmonate (MeJ) is an elicitor that, when applied in the vineyard, can improve grape quality. There are several studies about the MeJ influence on red grape varieties; however, to our knowledge, there is little information about white grape varieties, specifically Tempranillo Blanco. Therefore, the aim of this work is to evaluate the effect of MeJ foliar treatments, carried out at veraison and post-veraison, on the aromatic, phenolic and nitrogen composition of Tempranillo Blanco grapes. The results showed that grape volatile compounds content increased after MeJ application, especially terpenoids, C13 norisoprenoids, benzenoids and alcohols, and, in general, mainly at post-veraison. Regarding phenolic and nitrogen compounds, their concentrations were enhanced after MeJ treatments, regardless of application time. Consequently, MeJ treatment improved grape volatile, phenolic and nitrogen composition, particularly when this elicitor was applied post-veraison. Therefore, this is a good and easy tool to modulate white grape quality. ## 1. Introduction Tempranillo Blanco is a mutation of the Tempranillo grape variety that was discovered in Murillo de Río Leza (La Rioja, Spain) in 1988 [1]. This variety was authorised in the D.O.Ca. Rioja in 2008 and is currently one of the white grape varieties with a larger area under cultivation. This variety has similarities with Tempranillo, such as morphological characteristics and adaptation behaviour to growing conditions in La Rioja or response to diseases and pests [2]. Tempranillo Blanco, compared to other white grape varieties, has a high content of organic acids and a high concentration of phenolic compounds [3]. Due to the effects of climate change, vine physiology and phenology are changing, resulting in a mismatch between phenolic and technological maturities [4,5], which is conditioning grape and wine quality, increasing the concentration of sugars in the berry and causing a decrease in acidity without reaching optimum phenolic maturity. Currently, to mitigate these effects, some techniques are being used to improve the phenolic content in grapes, such as cluster thinning or deficit irrigation [6]. In this regard, there is a growing interest in the use of elicitors since they are molecules capable of activating plant defence mechanisms, contributing to their resistance against external attacks. Preliminary studies have shown that foliar application of methyl jasmonate (MeJ) can affect grape composition, mainly phenolic compounds [7,8,9], nitrogen compounds [10,11,12] and volatile compounds [13,14,15]. All of these studies have been carried out with red grape varieties. There are only two works where foliar treatment with MeJ has been performed in white varieties, focused on the effect on the terpene content in grapes [16] and in a white table grape variety, in which the influence of MeJ on phenolic composition was studied [17]. MeJ foliar application is an interesting and easy viticultural practice to improve the grape’s aromatic, phenolic and nitrogen composition. In addition, this work allows us to evaluate the behaviour of an important white grape variety in La Rioja and in one so little studied as Tempranillo Blanco. Grapes are rich in many secondary metabolites, such as phenolic compounds, which are classified into two main groups: flavonoids (anthocyanins, flavonols and flavanols) and non-flavonoids (hydroxybenzoic acids, hydroxycinnamic acids and stilbenes). The concentration of phenolic compounds is conditioned, among other factors, by soil characteristics, climatic conditions, environmental stress and grape variety. Phenolic compounds play a fundamental role in grape and wine quality as they are responsible for sensory attributes such as colour, astringency or bitterness [8]. In addition, they play an important role as plant protectors against biotic and abiotic stress factors. Phenolic compounds stand out for their health-promoting properties due to their antioxidant activity [18]. Preliminary studies showed an increase in polyphenol concentration after foliar treatment with MeJ [7]. The nitrogen composition of the grape largely determines the quality of the wine, as amino acids are precursors of important fermentative volatile compounds [19]. In addition, the amount of nitrogen influences the growth and development of yeasts; hence, to ensure proper vinification and avoid stuck fermentation, the minimum assimilable nitrogen content is approximately 140 mg N/L [20]. However, an excess of nitrogen can have negative consequences, with the formation of undesirable compounds [21]. Nitrogen composition depends on multiple factors such as growing conditions, terroir, fertilisation and grape variety [22]. It should be noted that the application of chemical fungicides reduces the concentration of amino acids in grapes [23]; nevertheless, Garde-Cerdán et al. [ 10,24] reported that the foliar application of nitrogen compounds can improve the concentration of amino acids in the musts. Therefore, the quality of must and wines is determined by several parameters. One of the most important is the grape’s aromatic composition [25]. Within the volatile compounds of the grape, varietal and pre-fermentative aromas can differentiate [26]. Previous studies show an increase in volatile compounds after MeJ application in grape varieties, such as Garnacha [27], especially terpenoids and C13 norisoprenoids. There are many studies in reference to Tempranillo but not to Tempranillo Blanco; a study of the phenolic, nitrogen and aromatic profiles of the grapes of this white variety is needed. In view of all the foregoing, the aim of this work is to study the effect of the foliar application of an elicitor, such as MeJ, to improve the phenolic, aromatic and nitrogen composition of grapes. For this purpose, two partial objectives were established: to analyse the impact of the foliar application of methyl jasmonate on the phenolic, nitrogen and volatile composition of the Tempranillo Blanco variety and to determine the most appropriate time to apply the treatment (veraison or post-veraison). ## 2.1. Vineyard, Treatments and Grape Samples The Tempranillo Blanco (Vitis vinifera L.) variety grown in the experimental vineyard located in Finca La Grajera, Logroño, La Rioja (Spain) (42°26′26″ North Latitude; 2°30′51″ West Longitude, at 447 m above sea level) in the 2020 season was used. Climatic data were obtained from the Agroclimatic Information Service of La Rioja (SIAR); the weather station was located near the plot. Annual precipitation was 498 L/m2, with the accumulated precipitation from bud breaking to harvest (April to August) of 190 L/m2 ($38\%$ of annual precipitation). Over the growing season (April to August), the average maximum temperature was 25.1 °C, and the average minimum temperature was 13.3 °C. The vineyard was planted in 2002 with a spacing between rows of 3.00 m and within the rows of 1.10 m. Grapevines were grafted onto 110-Richter rootstock, and a training system was used in the vertical shoot position. The soil was classified as Typic haploxerepts. The texture is loamy in the two most superficial horizons (55 and 70 cm) and sandy-loam in the subsurface horizon (13 cm). The soil had no physical–chemical or nutritional limitations. Methyl jasmonate (MeJ) foliar applications to the vineyard were studied at two phenological stages: veraison (MeJ-Ver) (EL: 34–37; BBCH: 83–85) and post-veraison (MeJ-Post), i.e., seven days after veraison. To carry out the foliar applications, aqueous solutions were prepared with a concentration of 10 mM of methyl jasmonate (MeJ) (Sigma-Aldrich, Madrid, Spain), using Tween 80 (Sigma-Aldrich) as the wetting agent (1 mL/L), according to previous works [15,28]. Control plants were treated with Tween 80 water solution. All treatments were applied to the grapevine twice at veraison or post-veraison and one week later (DOY MeJ-Ver, first application: 215; DOY MeJ-Ver, second application and MeJ-Post first application: 222; DOY MeJ-Post second application: 229). For each application, 200 mL/plant was sprayed over leaves. The treatments were performed in triplicate and were arranged in a complete randomised block design, with three vines for each treatment and replication. Grapes from all grapevines and treatments were picked at their optimum technological maturity when the potential alcoholic strength of the grapes reached $13\%$ (v/v). A random set of 140 berries per replicate and treatment was collected and frozen at −20 °C until the analyses of volatile (50 berries), phenolic (50 berries) and nitrogen (40 berries) composition were carried out. Another set of 100 berries was separated and weighed to obtain the weight of 100 berries. Then, grape berries were crushed, and general parameters were determined in the different musts. ## 2.2. Determination of General Parameters in Musts The must enological parameters were analysed by OIV [29] official methods: ºBrix, probable alcohol, pH and total acidity. In addition, glucose, fructose, tartaric and malic acids, total phenols, amino nitrogen, ammonium nitrogen and yeast assimilable nitrogen (YAN) were determined using a Miura One enzymatic instrument (TDI, Barcelona, Spain). As the treatments were performed in triplicate, the results of these enological parameters are shown as the average of 3 analyses ($$n = 3$$). ## 2.3. Analysis of Must Volatile Composition by HS-SPME-GC-MS The determination of volatile compounds in the musts was carried out by headspace solid-phase micro-extraction (HS-SPME) and subsequent analysis by gas chromatography (GC) coupled to mass spectrometry (MS), according to the method described by Garde-Cerdán et al. [ 30]. The SPME fibre used was divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, $\frac{50}{30}$ µm) (Supelco, Bellenfonte, PA, USA). In 20 mL vials (Supelco), 9 mL of the sample, 2.5 g NaCl and 10 µL of 2-octanol (internal standard) were added. After adding a stir bar, the vial was closed and placed in the GC-MS (Agilent, Palo Alto, CA, USA). Sample conditioning was done at 60 °C for 15 min and with stirring (500 rpm). After this step, the fibre was automatically inserted into the headspace for the extraction of the must volatile compounds for 105 min with agitation (500 rpm). After completion of the extraction process, the fibre was immediately inserted into the GC injection port at 250 °C and held for 15 min for desorption of the aromatic compounds. The capillary column used was SPB™-20 (30 m × 0.25 mm I.D. × 0.25 μm film thickness) (Supelco). Helium was used as the carrier gas at a flow of 1.2 mL/min. The chromatographic conditions used were: initial temperature, 40 °C for 5 min, a temperature gradient of 2 °C/min, up to a final temperature of 220 °C, to be maintained for 20 min (total time = 115 min). The ionisation of the volatile compounds was performed at 70 eV. The detector worked at full scan mode (35–300 m/z). Identification was carried out using the NIST library and compared with the mass spectra and retention times of chromatographic standards (Sigma-Aldrich), when available, as well as with data found in the literature. Semi-quantification was performed, relating the areas of each compound to the area and known concentration of the internal standard (2-octanol). Since the treatments were performed in triplicate, the results of volatile compounds in musts are expressed as the mean of 3 replicates ($$n = 3$$). ## 2.4.1. Extraction of Grape Phenolic Compounds Phenolic compounds were extracted from 50 g of grape samples with 50 mL of an extractant solution of methanol/water/formic acid (50:48.5:1.5, v/v/v) according to the method reported by Portu et al. [ 9]. The grapes were homogenised using an Ultra-Turrax T-18 (IKA, Staufen, Germany) at 18,000 rpm for 1 min, producing a homogeneous paste. Then, the extraction of phenolic compounds was carried out for 10 min in an ultrasonic bath (JP Selecta, Barcelona, Spain). After 10 min, the samples were centrifuged at 5000 rpm for 10 min at 10 °C (Centrifuge 5810-REppendorf, Hamburg, Germany). The supernatant was collected, and the resulting pellet was second-extracted. Finally, the two supernatants were mixed, and the volume obtained was recorded. Samples were frozen at −20 °C in 250 mL amber plastic bottles for the subsequent determination of phenolic compounds by HPLC-DAD. ## 2.4.2. Analysis of Grape Phenolic Compounds by HPLC-DAD Phenolic compounds were separated, identified and quantified from grape extracts by high-performance liquid chromatography (HPLC) using an Agilent 1260 Infinity chromatograph coupled to a diode array detector (DAD). The chromatographic conditions were based on the Castillo-Muñoz et al. [ 31] method. Samples were filtered with a 0.45 μm filter (OlimPeak, Teknokroma, Barcelona, Spain), and the separation was performed on a reverse-phase column (LiCrospher 100 RP-18; 250 × 4.0 mm I.D.; 5 μm particle diameter; Agilent), with a LiCrospher 100 RP-18 precolumn (4 × 4 mm; 5 μm particle size, Agilent) thermostated at 40 °C. For the analysis of phenolic compounds, the injection volume was 20 μL, and the flow rate was 0.630 mL/min. Phenolic compounds were identified according to the retention times of pure compound standards (Sigma-Aldrich). For the quantification of phenolic compounds, DAD chromatograms were extracted at the following wavelengths for each chemical family: 360 nm (flavonols), 320 nm (hydroxybenzoic and hydroxycinnamic acids and stilbenes) and 280 nm (flavanols). Moreover, the calibration graphs of the respective standards (R2 > 0.988) were done for each family of phenolic compounds. Quercetin-3-O-glucoside was used for flavonols; trans-caftaric acid was used for hydroxycinnamic acids; gallic acid was used for hydroxybenzoic acids; catechin was used for flavanols; trans-piceid and trans-resveratrol were used for stilbenes. As the foliar applications were performed in triplicate, the results for grape phenolic compounds correspond to the average of 3 analyses ($$n = 3$$). ## 2.5. Analysis of Must Nitrogen Composition by HPLC-DAD-FLD The separation, identification and quantification of amino acids were carried out by HPLC using an Agilent 1260 Infinity Series coupled to a DAD and a fluorescence detector (FLD). Amino acid analysis was performed by the method described by Garde-Cerdán et al. [ 10]. Sample preparation was made by homogenising 40 berries in a Masticator homogenisator (IUL Basic, Barcelona, Spain). Then, the samples were centrifuged at 4000 rpm for 10 min and 20 °C. To 5 mL of each must sample, 100 μL of sarcosine (internal standard to quantify proline) and 100 μL de norvalina (internal standard to quantify primary amino acids) were added. The mixture was filtered through a 0.45 μm filter (OlimPeak) and submitted to automatic derivatisation with o-phthaldialdehyde (OPA Reagent, Agilent) for primary amino acids, with 9-fluorenylmethylchloroformate (FMOC Reagent, Agilent) for proline, the secondary amino acid. The injected volume was 10 μL, and a constant column temperature of 40 °C was maintained. All separations were performed on a column, Hypersil ODS (250 × 4.0 mm, I.D. 5 μm, Agilent). The eluents that were used as mobile phases were: A: 75 mM sodium acetate and $0.018\%$ triethylamine (pH 6.9) + $0.3\%$ tetrahydrofuran; B: water, methanol and acetonitrile (10:45:45, v/v/v). The identification of the amino acids was made by comparison with the retention times of the standards of each amino acid (Sigma-Aldrich) as well as the UV–vis spectral characteristics. Their quantification was carried out using the calibration graphs of each respective standard (R2 > 0.96). DAD at two wavelengths (λ = 338 nm for primary amino acids; λ = 262 nm for the secondary amino acid, proline) and FLD (λ excitation = 340 nm, λ emission = 450 nm, for primary amino acids; λ excitation = 266 nm, λ emission = 305 nm, for the secondary amino acid, proline) were used for the detection. Since the treatments were performed in triplicate, the results of the must nitrogen compounds are expressed as the mean of the 3 replicates ($$n = 3$$). ## 2.6. Statistical Analyses The statistical study was performed using the SPSS statistical package (Chicago, IL, USA). General enological parameters and the volatile, phenolic and nitrogen compounds data were processed using the variance analysis (ANOVA) (p ≤ 0.05). ## 3.1. General Parameters in the Musts Table 1 shows the parameters in control grapes and in samples from MeJ-treated vines at veraison (MeJ-Ver) and post-veraison (MeJ-Post). The weight of 100 berries increased with MeJ application at veraison, with respect to post-veraison. Although neither of the two treatments showed significant differences with the control for this parameter, it seems that the application of MeJ at veraison may favour vineyard production. Moreover, glucose concentration was higher in the control samples compared to MeJ post-veraison samples, with intermediate values for MeJ-Ver grapes. This may be related to the ripening delay found by D’Onofrio et al. [ 14] in their Sangiovese grapes after MeJ application. On the other hand, ammonium nitrogen and YAN increased after foliar application of MeJ-Ver, with intermediate values for MeJ-Post grapes. In addition, amino nitrogen increased after foliar application of MeJ, regardless of the time of elicitor application. These nitrogen content increases in the treated grapes could be related to the elicitor effect of MeJ since it activates the plant’s enzymatic metabolism. It was also observed that all samples reached the minimum recommended YAN content of approximately 140 mg N/L to achieve a correct development of alcoholic fermentation [20]. For the rest of the general parameters, no significant differences were observed due to the foliar application of MeJ (Table 1). ## 3.2. Influence of the Foliar MeJ Treatments on Must Volatile Compounds Figure 1 and Figure 2 and Table 2 show the results of the must volatile primary aroma content in the control and in the samples from the treated grapevines with MeJ at veraison (MeJ-Ver) and post-veraison (MeJ-Post). Within the group of terpenoids, linalool, citronellol, geraniol, p-cymene and geranyl acetone were identified (Figure 1a–e); however, citronellol was not found in the control samples (Figure 1b). For all compounds, an increase in their concentration was clearly observed in the samples treated foliarly with MeJ, with the exception of linalool for MeJ-Ver (Figure 1a). In the case of citronellol and p-cymene, this increase was significant regardless of the time of application (Figure 1b,d). In this regard, for geraniol and geranyl acetone (Figure 1c,e), as well as for total terpenoids (Figure 2a), the highest concentration was observed in the samples treated after veraison (MeJ-Post). Yue et al. [ 32] also observed an increase in this group of compounds with the application of this elicitor in the vineyard since it regulates their synthesis and activates their de novo biosynthesis. This increase, described in the terpene content when applying MeJ, both individually (Figure 1a–e) and totally (Figure 2a), is very important from the organoleptic quality point of view since these compounds have low thresholds of olfactory perception and contribute decisively to the fruity and floral aroma [33]. In the group of C13 norisoprenoids, (E)-β-damascenone, (Z)-β-damascenone, β-ionone, β-cyclocitral and methyl jasmonate were identified (Figure 1f–j); (Z)-β-damascenone was not found in the control samples (Figure 1g). For (Z)-β-damascenone, β-ionone, β-cyclocitral and total C13 norisoprenoids, an increase in their concentration was observed with the foliar application of MeJ, regardless of the time of application (Figure 1g–i and 2b). However, for (E)-β-damascenone and methyl jasmonate (Figure 1f,j), the effect of the elicitor application was different, depending on the application time; in the case of methyl jasmonate (Figure 1j), the highest concentration was found at veraison (MeJ-Ver), whereas, for (E)-β-damascenone, the highest concentration was observed in the MeJ-Post samples (Figure 1f). Marín-San Román et al. [ 27] also found an increase in this family of volatile compounds with the application of MeJ, probably due to the fact that MeJ increases the activity of the enzymes involved in the synthesis of these compounds [34]. As mentioned for terpenes, C13 norisoprenoids are also of great importance for aroma, likewise due to their low perception thresholds, which make their contribution to aroma relevant and important as they confer floral notes [35]. Therefore, the treatment with MeJ, either at veraison or at post-veraison, is an effective technique in order to improve the aromatic quality of Tempranillo Blanco. Three benzenoid compounds, 2-phenylethanol, 2-phenylethanal and benzyl alcohol, were identified in the control and MeJ-treated samples (Table 2). For all of them, the treatment with MeJ increased their concentration. It should be noted that the samples treated foliarly with MeJ at post-veraison reached the highest concentration for each of the benzenoid compounds identified (Table 2) as well as for the total content of these compounds (Figure 2c). It should be highlighted that these compounds are positive for aroma, so applying MeJ at post-veraison could be a good tool to increase their content in grapes. A positive effect of the application of MeJ on the content of these compounds was also observed in the Tempranillo variety, a variety from which Tempranillo Blanco comes by spontaneous mutation, as has been mentioned. However, a foliar treatment with this elicitor in the Garnacha variety produced a decrease in the concentration of the benzenoid compounds in the grapes and did not show any effect in Graciano [36]. These compounds are also involved in the aroma, highlighting 2-phenylethanol, with notes of rose, although it should be noted that its synthesis occurs mainly during fermentation [37]. Regarding alcohols, the following five compounds were identified in the musts: 1-heptanol, 1-octanol, 1-nonanol, 1-octen-3-ol and 2-ethyl-1-hexanol (Table 2). As for the previous group, treatment with MeJ increased the concentration of these compounds, this effect being greater when applying the elicitor at post-veraison, except for 2-ethyl-1-hexanol, for which there were no differences between control and MeJ-treated samples. The results for total alcohols are shown in Figure 2d, where it is observed that the foliar application of MeJ enhanced their concentration and the moment of application has an influence in the same way that it has been described for the individual alcohols. Table 2 shows the ten carbonyl compounds that were identified in the samples analysed. These compounds were: heptanal, (E)-2-heptenal, octanal, (E)-2-octenal, nonanal, (E)-2-nonenal, decanal, (E,E)-2,4-hexadienal, (E,E)-2,4-nonadienal and γ-decalactone. The highest concentration of (E)-2-heptenal and (E)-2-octenal was found in the post-veraison (MeJ-Post)-treated samples (Table 2). On the other hand, for octanal, decanal and γ-decalactone, an increase in their concentration was appreciated for samples after MeJ application, regardless of the time of application. In the case of (E)-2-nonenal, only the MeJ application at veraison increased its content. Furthermore, for heptanal, nonanal, (E,E)-2,4-hexadienal and (E,E)-2,4-nonadienal, no effect of MeJ application was observed (Table 2). Regarding total carbonyl compounds, their highest concentration was observed in the MeJ-Post samples; when applying MeJ at veraison, there was no effect on their content (Figure 2e). Regarding C6 compounds, five were identified: hexanal, n-hexanol, (E)-2-hexenal, (E)-2-hexen-1-ol and (Z)-3-hexen-1-ol (Table 2). For this group of compounds, MeJ application at veraison (MeJ-Ver) enhanced the content of n-hexanol, (E)-2-hexen-1-ol and (E)-2-hexenal, whereas a decrease on (Z)-3-hexen-1-ol was observed when compared with control samples. For (E)-2-hexen-1-ol, a higher concentration was observed after the application of MeJ, being higher for MeJ-Ver; for (E)-2-hexenal, an increase in its content was appreciated after MeJ application, regardless of the time of application. In the case of (Z)-3-hexen-1-ol, its concentration decreased with the application of the elicitor, both at veraison and at post-veraison (Table 2). The total concentration of C6 compounds is shown in Figure 2f, which shows that the application of MeJ at veraison increased the content, while the post-veraison treatment did not affect the concentration of total C6 compounds. AS to the concentration of C6 compounds in general, it is highest during the pre-veraison and veraison stages but declines after veraison. It is possible that as long as the concentration of these compounds remains at a limit, the plant is able to synthesise them as a defensive response to the effect of the elicitor; however, once the concentration drops below this limit, the effect of the elicitor (post-veraison application) is diluted. This group of compounds in high concentrations can attribute negative aromas to the wines [38]; therefore, the treatment carried out at post-veraison would be more respectful since the content of these compounds in grapes did not increase compared to the control content, unlike the MeJ application at veraison (Figure 2f). ## 3.3. Effect of the Foliar MeJ Treatments on Grape Phenolic Compounds Table 3 and Figure 2 show the results of grape phenolic composition in the control and in samples from treated grapevines with MeJ at veraison (MeJ-Ver) and post-veraison (MeJ-Post). The phenolic compounds identified and quantified in the Tempranillo Blanco grapes were five flavanols: quercetin-3-glucuronide (3-glcU), quercetin-3-glucoside (3-glc), kaempferol-3-galactoside (3-gal), kaempferol-3-glucoside (3-glc) and isorhamnetin-3-glucoside (3-glc); two flavanols: catechin and epicatechin; one hydroxybenzoic acid: gallic acid; five hydroxycinnamic acids: trans-caftaric acid, trans+cis-coutaric acids, caffeic acid, p-coumaric acid and trans-fertaric acid; and two stilbenes: trans-piceid and trans-resveratrol (Table 3). Within the flavonols group, only the concentration of kaempferol-3-gal and isorhamnetin-3-glc in the grapes increased after the foliar application of MeJ, regardless of the time of application (Table 3). For the remaining compounds, quercetin-3-glc, quercetin-3-glcU and kaempferol-3-glc, there was no influence of the treatments in their content. Regarding the content of total flavonols (Figure 2g), only the application of MeJ at post-veraison increased their concentration in grapes. In general, flavonol synthesis occurs primarily during the early stages of fruit development and ends at around veraison; it is possible that at MeJ-Ver application, the plant had a sufficient concentration and did not need to synthesise more flavonols. However, at post-veraison time, the application of MeJ will activate its synthesis mechanism by increasing the concentration of these compounds. Quercetin-3-glc and quercetin-3-glcU were the most abundant phenolic compounds in Tempranillo Blanco grapes, as reported by other authors [39,40] for most white grape varieties. Flavonoid synthesis is conditioned by the expression of the enzyme flavanoid 3’5’-hydroxylase in white grape varieties, which limits the synthesis to quercetin, kaempferol and isorhamnetin compared to red grape varieties, where myricetin, laricitrin and syringetin are also synthesised [41]. Flavonols are yellow pigments that contribute to the colour of white varieties and provide several positive effects on human health due to their antioxidant activity [39,42]. With respect to flavanols, only catechin and epicatechin were found in the Tempranillo Blanco samples (Table 3). A significant increase in their concentration was observed in the treated samples (MeJ-Ver and MeJ-Post), regardless of the time of application. The results for total flavanols (Figure 2h) confirm that foliar treatment with MeJ produced an increase in the concentration of flavanols, this effect being independent of the time of application. These results contrast with those obtained by Portu et al. [ 8], where there was no effect of MeJ application in total flavanol content for the Tempranillo variety. This fact could be justified by the study by Ruiz-García et al. [ 43], where it was shown that flavanol accumulation after MeJ treatment depends on the grape variety. These phenolic compounds play a role in the grape and wine quality since they are responsible for sensory attributes, such as astringency, due to their ability to precipitate salivary proteins in the oral cavity. Moreover, flavanols are also involved in colour stability through co-pigmentation reactions [44]. In the group of hydroxybenzoic acids, only gallic acid was identified (Table 3), obtaining a higher concentration of this phenolic compound in MeJ grape samples, both at veraison and post-veraison, when compared with the content of control grapes. This result, as well as that of flavanols, contrasts with those obtained by Portu et al. [ 8] for the Tempranillo variety. Gallic acid has antioxidant and antifungal activities [45]. As for hydroxycinnamic acids, six compounds were identified: trans-caftaric acid, trans+cis-cutaric acids, caffeic acid, p-coumaric acid and trans-fertaric acid (Table 3). trans-caftaric and trans+cis-coutaric acids were the most abundant hydroxycinnamic acids in the grape samples; these compounds increased their concentration after MeJ application at post-veraison. However, the caffeic acid content was not affected by either of the two MeJ treatments; p-coumaric acid increased its concentration after MeJ application with respect to the control, regardless of the time of application. Finally, trans-fertaric acid content in grapes increased significantly after the foliar application of MeJ, with the highest concentration of this hydroxycinnamic acid being obtained for MeJ-Post. Figure 2i shows that the total concentration of hydroxycinnamic acids in the samples increased with both MeJ treatments, in agreement with Moro et al. [ 46], who observed higher concentrations of these compounds in juices after MeJ application. Hydroxycinnamic acids can act as precursors of vinylphenols during wine ageing in oak barrels [47] and, therefore, can be responsible for a depreciating wine sensory characteristic. Finally, two stilbenes, trans-piceid and trans-resveratrol, were found in Tempranillo Blanco grapes. For these compounds, no significant differences in their concentration were obtained after MeJ foliar application (Table 3). However, it is observed that the total concentration of stilbenes was higher after the foliar application of MeJ (Figure 2j), regardless of the time of application. This result agrees with previous studies, such as those reported by Portu et al. [ 8,9], who observed that MeJ foliar application in Tempranillo plants improved stilbene synthesis. Hence, these results highlight the elicitor effect of MeJ since, although stilbene content varies among varieties, it is known that grapevine increases its synthesis in response to abiotic stresses [41], such as MeJ application or pathogen infections. Grapes and wines are among the major dietary sources of stilbenes for human nutrition [48], especially red varieties. These phenolic compounds have been demonstrated to possess a great range of biological activities potentially beneficial for human health, such as neuroprotective, antioxidant and antitumor effects, among others [49]. ## 3.4. Influence of the Foliar MeJ Treatments on Must Nitrogen Compounds Table 4 and Figure 2 show the results of must amino acid content in control and treated grapevines with MeJ at veraison (MeJ-Ver) and post-veraison (MeJ-Post). The most abundant amino acid in Tempranillo Blanco must was arginine (Arg), which, together with proline (Pro) and Gaba, represent approximately $76\%$ of the total amino acids present in the must. This result coincides with that reported by Garde-Cerdán et al. [ 10], who observed that the most abundant amino acids in Tempranillo must were Arg, Pro and Gaba. In contrast, Gutiérrez-Gamboa et al. [ 50] reported that the most abundant amino acids in Tempranillo Blanco were arginine, glutamic acid, aspartic acid, citrulline and alanine. The Tempranillo Blanco grape variety is an arginine-accumulating variety because the Pro/Arg ratio is less than 1. The data coincide with other white or red varieties [19,51]. However, Tempranillo Blanco differs from Tempranillo as Tempranillo tends to accumulate more proline than arginine [52]. It was observed that the foliar application of MeJ favoured the synthesis of aspartic acid, glutamic acid, valine, tryptophan, phenylalanine, isoleucine and leucine in the must, increasing their concentration in the samples, regardless of the time of application (Table 4). It should be noted that these amino acids are some of the main precursors of higher alcohols and esters [20]; therefore, they are the nitrogen sources that most influence the wine’s fermentative aroma [53]. The asparagine and histidine content also increased with the application of MeJ, but to a greater extent when it was applied at veraison or post-veraison, respectively, while the alanine concentration was only affected by the MeJ treatment carried out at veraison (Table 4). The content of the rest of the amino acids in the musts was not affected by either of the two treatments. In addition, foliar application of MeJ significantly increased the total amino acid content, with and without proline, with respect to the control, regardless of the time of application (Figure 2k,l), which could be of special interest for musts poor in nitrogen. Garde-Cerdán et al. [ 10] observed that the foliar application of MeJ in Tempranillo increased the content of histidine, serine, tryptophan, phenylalanine, tyrosine, asparagine and methionine, although without affecting the total content of amino acids in the musts. However, Gil-Muñoz et al. [ 11] found that the application of MeJ increased the total amino acid content in the musts, affecting practically all the amino acids studied in grapes from the Monastrell variety. These results evidence the influence of the grape variety in response to the application of elicitors in vineyards [54]. ## 4. Conclusions In this work, the effect of the foliar application of MeJ to the vineyard, carried out at two phenological stages, veraison and post-veraison, on the phenolic, aromatic and nitrogen composition of Tempranillo Blanco grapes was studied for the first time. The results showed that, in general, the content of volatile compounds increased after MeJ treatments compared to control, mainly at post-veraison, such as total terpenoids, benzenoids, alcohols and carbonyl compounds. Generally, the increase in concentration for each of the groups of volatile compounds after treatment with MeJ was notable. Regarding phenolic compounds, their content increased in grape samples from MeJ foliar treatments. Furthermore, it was observed that the content of only hydroxybenzoic acid (gallic acid), flavanols and total stilbenes increased after treatment with MeJ, regardless of the time of application of this elicitor. Similarly, hydroxycinnamic acids improved their concentrations because of MeJ treatment, mainly after veraison. However, the influence of MeJ was less evident on flavonols. In terms of nitrogen composition, Tempranillo Blanco behaved as an arginine-accumulating variety, unlike its parent Tempranillo. In addition, foliar application of MeJ increased the content of different amino acids in the must in general, regardless of the time of application. Likewise, MeJ treatments significantly increased the content of total amino acids, with and without proline, with respect to the control, regardless of the time of application. Consequently, this study shows that both foliar applications of MeJ at veraison and post-veraison improved the content of different aromatic, phenolic and nitrogen compounds in Tempranillo Blanco grapes, with the best results being achieved with the post-veraison treatment. 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--- title: Comparison of Hemodynamic and Cerebral Oxygenation Responses during Exercise between Normal-Weight and Overweight Men authors: - Szu-Hui Wang - Hui-Ling Lin - Chung-Chi Huang - Yen-Huey Chen journal: Healthcare year: 2023 pmcid: PMC10048205 doi: 10.3390/healthcare11060923 license: CC BY 4.0 --- # Comparison of Hemodynamic and Cerebral Oxygenation Responses during Exercise between Normal-Weight and Overweight Men ## Abstract Obesity has negative impacts on cardiovascular function and may increase cerebrovascular complications during exercise. We compared hemodynamic and cerebral oxygen changes during high-intensity exercise between overweight (OW) and normal-weight (NW) individuals. Eighteen NW and fourteen OW male individuals performed high-intensity ($70\%$ of peak oxygen uptake, VO2peak) cycling exercises for 30 min. Hemodynamics were measured using a bioelectrical impedance device, and cerebral oxygenation status was measured using a near-infrared spectrophotometer during and after exercise. The VO2peak of NW individuals was significantly higher than that of OW individuals (41.3 ± 5.7 vs. 30.0 ± 5.0 mL/min/kg, respectively; $p \leq 0.05$). During the 30 min exercise, both groups exhibited an increase in oxygenated hemoglobin (O2Hb) ($p \leq 0.001$), deoxygenated hemoglobin ($p \leq 0.001$), and cardiac output with increasing time. Post-exercise, cardiac output and systemic vascular resistance were significantly higher in the OW group than in the NW group ($p \leq 0.05$). The O2Hb in the NW group was significantly higher at post-exercise times of 20 min (13.9 ± 7.0 μmol/L) and 30 min (12.3 ± 8.7 μmol/L) than that in the OW group (1.0 ± 13.1 μmol/L and 0.6 ± 10.0 μmol/L, respectively; $$p \leq 0.024$$ vs. 0.023, respectively). OW participants demonstrated lower cerebral oxygenation and higher vascular resistance in the post-exercise phase than non-OW subjects. These physiological responses should be considered while engaging OW and obese individuals in vigorous exercise. ## 1. Introduction Currently, obesity is a global problem, with $60\%$ of adults in the US and Europe being overweight or obese [1]. According to the World Health Organization (WHO), a body mass index (BMI) of over 25 kg/m2 is defined as overweight, and individuals with a BMI of over 30 kg/m2 are considered obese [2]. However, the WHO definition is based primarily on criteria derived from studies involving populations of European origin. It has been suggested that the BMI cut-off point (≥30 kg/m2) might be too high for Asians, thereby underestimating the associated health risks [2,3]. In some Asian countries, obesity is defined as a BMI ≥ 27 kg/m2, overweight as a BMI of 24–27 kg/m2, and a normal BMI as 18.5 to <24 kg/m2 [4]. Obesity is an important risk factor for many diseases, including metabolic syndrome, hypertension, diabetes, cardiovascular disease, and cancer [4,5]. It leads to excessive fat accumulation in blood vessels, resulting in atherosclerosis, along with an increase in blood flow resistance and cardiac afterload. This results in left ventricular hypertrophy and cardiovascular dysfunction. Obesity also affects physical fitness in individuals who are not sick or asymptomatic [5]. Vella et al. compared exercise performance in normal-weight and obese participants and found that obese participants had lower left ventricular ejection fraction (LVEF) and peak oxygen consumption during exercise tests [6]. In humans, the brain represents only 2–$3\%$ of the total body mass, requires $15\%$ of the cardiac output, and consumes $20\%$ of the available O2 under normal conditions. The high metabolic rate of the brain, combined with limited energy stores, highlights the importance of cerebral blood flow for nutrient and O2 delivery, as well as for the removal of cellular, metabolic, or toxic by-products [7]. In addition to cardiovascular dysfunction, obesity also has profound effects on brain structure and vasculature owing to metabolic disturbances and blood flow dysregulation [8]. Studies have shown that obesity increases the stiffness of the carotid artery, which may affect blood supply to the brain at rest [9,10]. However, most of these studies were performed in the resting state. Thus, the mechanisms underlying the interaction between cerebral oxygen and the central hemodynamic system remain unclear. Cerebral oxygenation can serve as an index of cerebrovascular function, as it depends on blood flow and endothelial integrity [11]. Near-infrared spectroscopy (NIRS) assesses cerebral oxygen delivery and uptake by cerebral tissues through the measurement of oxy- (O2Hb) and deoxygenated hemoglobin (HHb), which provides information on cerebrovascular function [12]. During exercise, cerebral oxygenation increases in healthy humans due to an increase in regional total hemoglobin (tHb) and O2Hb [13]. This increase in oxygen delivery is important because brain function can deteriorate when oxygenation is reduced. Subjects with obesity are at risk of cerebrovascular dysfunction; however, few studies have examined their cerebral oxygenation response to exercise. In order to improve cardiopulmonary function and physical fitness, the American College of Sports Medicine recommends that adults engage in at least 30 min of moderate-intensity exercise at least five times a week or at least 25 min of vigorous-intensity exercise at least three times a week [14]. However, owing to the negative effects of obesity on the cardiovascular system, obese people are at a higher risk of cardio- and cerebrovascular accidents when exercising than non-obese people, especially during moderate or vigorous exercise. In addition, cardiovascular function and cerebral oxygenation status are strongly related to recovery from exercise. A previous study reported that compared with a control group, obese individuals have higher sympathetic activity, as evaluated by an exercise recovery index following volitional exhaustion during an incremental exercise test [15]. Obesity-related slowing in recovery of oxygen uptake and cardiovascular response may serve as secondary measures of cardiovascular fitness and disease risk [16]. Although many studies have examined cardiovascular responses following exercise, the influence of submaximal work on subsequent recovery of cerebral oxygenation status has not been extensively evaluated. The primary purpose of this study was to compare the cardiovascular and cerebral hemodynamic responses to submaximal work in young male participants with normal weight and overweight. The secondary objective was to investigate the role of overweight in recovery from submaximal work and the association between cerebral oxygenation and hemodynamic indices. ## 2.1. Participants This was a cross-sectional, interventional, controlled study. Thirty-two healthy university students were recruited from a university campus between June and December 2016. Subjects were divided into two groups based on their BMI. Subjects with a BMI which was in the normal range (18.5 ≤ BMI < 24 kg/m2) were in the non-overweight (NW) group ($$n = 18$$), and those with a BMI ≥ 24 kg/m2 were in the overweight (OW) group ($$n = 14$$). The inclusion criteria for all participants were as follows: [1] age 20–40 years and [2] male sex. The exclusion criteria were as follows: [1] engaged in regular exercise during the past 6 months (i.e., moderate or vigorous exercise for ≥20–30 min/time, ≥3 times/week); [2] any cardiovascular, metabolic, neuromuscular, respiratory, renal, or other systemic diseases; and [3] any musculoskeletal or joint problems in the upper or lower extremities. All participants completed a self-reported physical activity questionnaire (IPAQ) [17], which was administered to exclude potential volunteers who engaged in regular physical activities. This study was approved by the Institutional Review Board of Chang Gung Hospital (201600418B0). This study was performed in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all of the participants prior to inclusion. ## 2.2. Procedures All participants reported to the laboratory for two visits which were separated by at least 1 week. At the first visit, participants underwent anthropometric assessments and incremental exercise tests in that order. On the second visit, the subjects performed 30 min of cycling exercise. Participants were instructed to refrain from alcohol and caffeine for 12 h before each visit. Participants were also required to maintain normal hydration, to refrain from strenuous exercise for at least 24 h prior to each visit, and to report to the laboratory at least 3 h after their last meal. At the first visit, anthropometric assessments were performed using bioelectrical impedance analysis (Inbody 720, Inbody Co., Seoul, Republic of Korea). The subjects were required to stand barefoot on the metal sole plates of the device while holding electrodes, one in each hand, and a safe electrical signal (50 kHz, 800 µA) was sent through the body. The circumferences of the waist and hip were measured using an unstretchable metric tape. Waist circumference was measured by passing the measuring tape through the midpoint between the superior iliac crest and lowest rib. Hip circumference was measured at maximum protrusion. An incremental exercise test was performed using a cycle ergometer (VIAspritn 150P; Carefusion Corp., San Diego, CA, USA). An individualized protocol began with a 3 min rest to familiarize the participants with the equipment, followed by a 3 min warm-up at a workload of 20 W. Power was gradually increased at a rate of 20–30 W/min until the participant was exhausted. Cycling cadence was maintained at 60–80 rpm. Gas exchange was measured continuously during the exercise test session using a metabolic system (Vmax Encore Metabolic Care; Carefusion Corp., USA). The following data were recorded every 15 s: oxygen uptake (V˙O2 mL/min), product of carbon dioxide (VCO2), respiratory exchange ratio (RER), minute volume V˙E (L/min), and respiratory rate (breaths/min). Electrocardiography (ECG) was used to monitor the heart rate, utilizing a 12-lead system before and during the test for safety. The incremental exercise test was terminated when subjects met three of the following four criteria: [1] a perceived exertion score of ≥17 on the Borg scale (scale 6–20); [2] respiratory exchange ratio ≥1.1; [3] an increase in heart rate to >$90\%$ of age-predicted maximal heart rate (220−age); and [4] volitional exhaustion in accordance with the American Thoracic Society standard test termination criteria [18]. The anaerobic threshold (AT) was also defined by the following criteria: [1] steeper increase in VCO2 as compared to VO2; [2] a respiratory exchange ratio ≥0.95–1.0; [3] PETO2 increase; and [4] VE/VO2 increase [18]. In the second visit, the participants performed 30 min of continuous cycling exercise at $70\%$ of the maximum workload, which was obtained from the incremental exercise test during the first visit. Exercise of this intensity and duration is recommended in order to reduce body weight in adults [19]. Briefly, the subjects began with a 5-min warm-up at a pedaling cadence of 60 rpm, after which the workload was increased to $70\%$ of the maximum workload. The subjects were asked to exercise at this intensity for 30 min. After task termination, the subjects pedaled at a free load for a 5 min cool down period. The subjects then rested on a chair for 30 min during the recovery phase. During the incremental exercise test (first visit) and 30 min cycling exercise (second visit), central hemodynamic and cerebral oxygenation responses were continuously measured. ## 2.3.1. Central Hemodynamic Measurements The central hemodynamic responses of the subjects were continuously monitored using the bioimpedance method (PhysioFlow, Manatec Biomedical, Paris, France), following the procedure described in a previous study [20]. This method has been validated during maximal incremental exercises [21] and has been used in obese subjects [6]. PhysioFlow values were synchronized and mediated every 10 s. The transthoracic bioimpedance system (PhysioFlow) measures the variation in impedance (Z) to high-frequency, low-amperage alternating electrical current using two electrodes in the thoracic (xiphoid process) and two electrodes in the neck area. The physiological principle is based on the change in impedance, which is related to systolic and diastolic fluid variations in the thorax [22]. The derivative of the waveform (dZ) is related to the contractility and systolic volume. The waveform was also related to the atrial and ventricular systole and diastolic function. Therefore, the Physioflow device provided the following hemodynamic parameters: heart rate (beats/min), stroke volume (ml), cardiac output (L/min), heart rate (HR), stroke volume (SV), stroke volume index (SVI), cardiac output (CO), cardiac index (CI), systemic vascular resistance (SVR), and systemic vascular resistance index (SVRI). SV was obtained using the following equation: SV = k · [(dZ/dtmax)/(Zmax − Zmin)] · W (thoracic flow inversion timecal), where k is a constant, W is a proprietary correction algorithm, and “cal” indicates that the value was obtained during auto-calibration [22]. Some parameters were calculated according to the following equation: CO (L/min) = SV · HR; CI (L/min/m2) = CO/body surface area; SVR (dynes∗sec/cm5) = 80 ∗ (mean blood pressure-central venous pressure)/CO (the central venous pressure was set to 7 mmHg); systemic vascular resistance index (SVRi) (dynes∗sec/cm5∗m2) = SVR/body surface area. Hemodynamic parameters were monitored at rest, during exercise, and during the recovery phase. ## 2.3.2. Cerebral Oxygenation Measurements Cerebral hemodynamic responses were obtained by measuring changes in the relative concentration (Δμmol) of oxygenated hemoglobin (O2Hb) and deoxygenated hemoglobin (HHb) using a continuous-wave NIRS system (PortaMon, Artinis, The Netherlands) during the rest, exercise, and recovery phases. NIRS technology emits near-infrared light into tissues, where it is either scattered within the tissue or absorbed by chromophores such as oxygenated hemoglobin (O2Hb) and deoxygenated hemoglobin (HHb). The levels of HHb and O2H within tissues can be assessed by measuring the returned light at specific wavelengths. O2Hb represents the status of tissue oxygenation and HHb represents the status of local tissue deoxygenation from O2 extraction. The total hemoglobin (tHb) is the sum of O2H and HHb and is an indicator of regional blood volume. A probe was placed on the left forehead to measure cerebral oxygenation. Elastic bandages were used to shield the probe from ambient light. The relative concentration changes (Δ) in resting baseline oxyhemoglobin (ΔO2Hb), deoxyhemoglobin (ΔHHb), and total hemoglobin (ΔtHb) were measured. Data were sampled at 10 Hz during the rest, exercise, and recovery periods [23]. ## 2.4. Statistical Analysis Sample size estimates were generated based on the detection of a $10\%$ difference between NW and obese subjects for the stroke volume index [24] and submaximal cardiac index [25]. At least 15 subjects per group were required, assuming a statistical power of 0.8 and an α of 0.05. Considering the possibility of 10–$20\%$ of individuals dropping out, we set the sample size to 18 subjects. Statistical analyses were performed using the IBM SPSS for Windows (version 22.0; IBM Corp., Armonk, NY, USA). Normality was confirmed using the Shapiro–Wilk test. The results are expressed as the mean ± standard deviation for nominal distributions and the median (interquartile range) for nonparametric distributions. The differences in physical characteristics between the NW and OW groups were compared using Student’s t-test, and the $95\%$ confidence intervals (CIs) for the comparisons were reported as outcomes. Within-group differences were evaluated using repeated-measures analysis of variance for the variables at different time points (rest, exercise, and post-exercise period). A two-way ANOVA with repeated measures (group × time) was performed on hemodynamic and cerebral oxygenation variables in order to compare group differences in response to exercise. When a significant group-by-time interaction was present, post hoc comparisons were conducted between groups at each time point (Bonferroni correction and independent t-test). The effect size (partial η2) from the repeated ANOVA test was reported as the outcome. The magnitude of this effect size was interpreted as follows: small (η2 = 0.01–0.08), medium (η2 = 0.09–0.24), and large effect (η2 = 0.25 and over). The relationships between VO2 peak and maximal cerebral oxygenation variables during the exercise test were assessed using the Pearson’s coefficient of correlation. All p values were two-sided, and significance was set at $p \leq 0.05.$ ## 3.1. Clinical and Anthropometric Characteristics A total of 32 participants (18 in the NW group and 14 in the OW group) completed the study (Figure 1). The participants’ basic information is presented in Table 1. There was no significant difference in mean age between the NW and OW groups (21.6 ± 0.7 and 21.8 ± 1.0 years, respectively; $$p \leq 0.543$$). Regarding body composition, there was no significant difference in muscle mass between the NW and OW groups (31.1 ± 1.9 vs. 32.0 ± 4.4 kg, respectively; $$p \leq 0.475$$). The average body fat rate of the OW group was 30.0 ± 7.8; this was significantly higher than that of the NW group (19.3 ± $4.8\%$) ($p \leq 0.001$) (Table 1). Table 2 lists the variables used in the incremental exercise test. There was no significant difference in resting heart rate between the two groups (76.0 ± 12.1 vs. 82.6 ± 9.8 bpm for the NW and OW groups, respectively; $$p \leq 0.174$$). The resting diastolic blood pressure in the OW group was significantly higher than that in the NW group (88.4 ± 9.1 vs. 76.2 ± 8.3 mmHg, $$p \leq 0.004$$). The anaerobic threshold load of the OW group (64.7 ± 20.1 watts) was significantly lower than that of the NW group (87.2 ± 26.8 watts, $$p \leq 0.032$$). The peak oxygen uptake (normalized to body weight) of the NW group was significantly greater than that of the OW group (41.3 ± 5.7 and 30.0 ± 5.0 mL/min/kg, respectively; $p \leq 0.001$). Furthermore, the peak oxygen uptake/predicted value (%) of the NW group was also significantly greater than that of the OW group (80.1 ± 14.3 and 66.0 ± $7.5\%$, respectively; $$p \leq 0.008$$). When pooling the data from the two groups, peak oxygen uptake was positively correlated with cerebral ΔO2Hb ($r = 0.39$, $$p \leq 0.044$$), ΔHHb ($r = 0.469$, $$p \leq 0.014$$), and ΔtHb ($r = 0.442$, $$p \leq 0.021$$) (Table 3). ## 3.2. Cardiovascular and Cerebral Hemodynamics Response during 30 min Continuous Exercise The trends in cerebral hemodynamics during and after 30 min of continuous exercise are presented in Figure 2a,b, respectively. The main effect of time was significantly different for ΔO2Hb ($F = 12.2$, $p \leq 0.001$, η2 = 0.526) (Figure 2a) and ΔHHb ($F = 4.0$, $p \leq 0.001$, η2 = 0.267) (Figure 2b). In both the NW and OW groups, the ΔO2Hb significantly increased from resting to 30 min of cycling exercise (from 6.4 ± 6.4 to 27.6 ± 10.8 μmol/L in NW, $p \leq 0.001$; from 7.7 ± 0.45 to 17.6 ± 1.0 L/min in OW, $p \leq 0.001$). The main effects of group were significantly different for ΔO2Hb ($F = 5.2$, $$p \leq 0.044$$, η2 = 0.321) (Figure 2a) and ΔHHb ($F = 5.1$, $$p \leq 0.046$$, η2 = 0.315) (Figure 2b). At 30 min post-exercise, the ΔO2Hb in the NW group (13.9 ± 7.0 μmol/L) was significantly higher than that in the OW group (0.6 ± 10.0 μmol/L, respectively) ($$p \leq 0.049$$) (Figure 2a). Additionally, at 30 min post-exercise, the ΔHHb in the NW group (2.1 ± 2.8 μmol/L) was significantly higher than that in the OW group (−3.0 ± 5.4 μmol/L)($$p \leq 0.046$$) (Figure 2b) (Supplementary Table S1). The cardiac output, stroke volume, and SVRi responses during and after continuous exercise are shown in Figure 3a–c. The main effect of time was significantly different in the measurement of cardiac output ($F = 265$, $p \leq 0.001$, η2 = 0.903), stroke volume ($F = 92$, $p \leq 0.001$, η2 = 0.807), and SVRi ($F = 151$, $p \leq 0.001$, η2 = 0.873). In both the NW and OW groups, the CO significantly increased from resting to 30 min of cycling exercise (from 5.4 ± 0.37 to 16.9 ± 0.85 L/min in NW, $p \leq 0.001$; from 7.7 ± 0.45 to 17.6 ± 1.0 L/min in OW, $p \leq 0.001$) (Figure 3a). The SVRi at 30 min of exercise was significantly lower than that in their resting status in the NW (from 2395 ± 485 to 752 ± 125 dynes ∗sec/cm5 ∗ m2, $$p \leq 0.001$$) and OW groups (from 2140 ± 604 to 922 ± 222 dynes ∗ sec/cm5 ∗ m2, $$p \leq 0.001$$) (Figure 3c). In the OW group, the resting CO (7.7 ± 0.45 vs. 5.4 ± 0.37L for the OW and NW groups, respectively; $$p \leq 0.009$$) and SV (84.8 ± 4.2 vs. 72.8 ± 3.6 mL/beat for the OW and NW groups, respectively; $$p \leq 0.000$$) were significantly higher compared with those in the NW group. The CO and SV during the exercise and post-exercise phases tended to be higher in the OW group than in the NW group. However, no significant differences were found between the NW and OW groups. Moreover, no significant difference was found between the groups in the measurement of SVRi during and after the cycling exercise (Supplementary Table S2). ## 4. Discussion This study examined the effects of acute exercise on hemodynamic and cerebral oxygen status. Our study showed that OW individuals had lower cardiorespiratory fitness than non-OW individuals. In addition, overweight individuals experienced cerebral and hemodynamic dysfunction during and after a 30 min continuous exercise session. Cardiometabolic risk factors are associated with increased risks of cerebrovascular disease and cardiopulmonary dysfunction [4,5]. Participants in the NW group had significantly higher cardiorespiratory fitness than those in the OW group. Myung et al. reported that body fat percentage, but not skeletal muscle mass, was inversely correlated to VO2peak in obesity individuals [26]. In our study, although muscle mass was not significantly different between the NW and OW groups, the proportion of skeletal muscles to the total body weight was lower, whereas the body fat percentage was significantly higher in the OW group. Excessive body fat increases cardiovascular workload during exercise, resulting in a decrease in exercise capacity. We also observed that the waist circumference (WC), waist/hip ratio (WHR), and body fat percentage (BF) were significantly higher in the OW group than in the NW group. WC and WHR are indicators of abdominal body fat, which are associated with cardiometabolic disease and are predictors of mortality [4]. Adipose tissues in the abdomen have been reported to limit movement of the diaphragm, which may lead to decreased lung volume, early carbon dioxide retention, and hypoxia during exercise, thus resulting in exercise intolerance [4]. In our study, CO was significantly higher at rest in the OW group than in the NW group. In obese individuals, the increased metabolic demand imposed by excessive adipose tissue results in hyperdynamic circulation and increased blood volume [27]. This results in an increased stroke volume and cardiac output. The increased cardiac output in obese patients meets the metabolic demand for excess adipose tissue. Although the heart rate is higher in obese individuals owing to increased sympathetic activation, increased cardiac output is related to an increase in stroke volume [27,28]. We also observed elevated hemodynamic responses during and after exercise. However, no statistical difference in hemodynamic variables was found between the OW and NW groups. As mentioned above, the elevated hemodynamic status at rest in obese individuals may remain consistently high in order to meet the increasing oxygen demand during exercise. Zeiler et al. examined hemodynamic responses after exercise and reported that stroke volume and cardiac output were higher in the obese group than in the non-obese group at rest and throughout the 60 min post-exercise period [29]. Cavuoto et al. reported a higher cardiac output a submaximal exercise in obese individuals when compared to those in the non-obese group [30]. The discrepancies among studies might be explained by different factors, including the level of obesity, exercise intensity, and/or other factors. Collectively, these reports suggest that adaptations to cardiac output and stroke volume during exercise mirror those observed during rest in overweight adults. Hiura et al. reported a significant increase in cerebral ΔO2Hb and cerebral blood flow in healthy subjects during 15 min of low-intensity exercise [31]. In a review study, it was summarized that ΔO2Hb increases from low-, moderate-, and vigorous-intensity endurance exercise, and reaches a steady state in vigorous-intensity exercise. In our study, we also observed a significant increase in cerebral ΔO2Hb levels from rest to exercise for 30 min. The magnitude of the effect of time was 0.526, which is considered large. Exercise can be regarded as an ultimate integrative stimulus for the brain to regulate cerebral blood flow and oxygen uptake. During exercise, oxygen uptake, cardiac output, sympathetic nerve activity, and brain uptake increase constantly challenge the sufficient delivery of cerebral blood flow to meet metabolic and oxygen demands [7]. An increase in cardiac output in our study from baseline to 30 min of exercise suggests that increased cerebral oxygenation may be related to an increase in systemic circulation during exercise. During the exercise and recovery phases, the NW group showed higher ΔO2Hb and ΔHHb values than the OW group. The significances of the group effects were large (0.321 and 0.315, respectively). Cavuoto et al. observed a higher ΔO2Hb and ΔHHb in post-repetitive incremental lifting exercises in non-obese participants than in obese participants [32]. Hallmark et al. observed that blood vessel dilation was maintained in normal-weight subjects until 4 weeks after exercise, whereas the obese group showed non-significant vessel dilation through the post-exercise phase [33]. Mechanisms that influence cerebral oxygenation include arterial blood gases, central hemodynamics, cerebral metabolism, and neural mechanisms, including extrinsic autonomic and sensory nerves and intrinsic neurons, which are closely associated with the vasculature within the brain parenchyma [7]. Cerebral oxygen delivery is dependent on cerebral perfusion pressure (CPP) and is inversely proportional to cerebrovascular resistance (CVR) [34]. The fatty tissue releases adipocytokines which induce insulin resistance, endothelial dysfunction, hypercoagulability, and systemic inflammation in obese individual [34,35]. These pathological processes in obesity result in increased arterial stiffness, impaired cerebral endothelial function, and reduced cerebral blood flow and oxygenation [35]. We also observed higher SVRi, an indicator of systemic vascular resistance, in the OW group during the post-exercise period when compared to the NW group. High vascular resistance decreases cerebral blood flow, perfusion, and, consequently, O2Hb [35]. A decreased cerebral oxygenation status may increase the risk of cognitive dysfunction at a later age [35,36]. Our study showed that OW subjects had a reduced ability to maintain cerebral oxygenation in the post-exercise phase. This should be considered when prescribing exercise programs to overweight populations. This study has several limitations. First, this study had a relatively small sample size, which may have limited our ability to interpret the results. However, the trends in both cardio- and cerebral hemodynamics were consistent with those reported in previous studies. Second, cerebral oxygenation was assessed noninvasively using NIRS at the left prefrontal area level, which implies a very limited spatial resolution and relatively superficial brain tissue measurement. Therefore, our measurements may differ from other gold-standard measurements of cerebral oxygenation (e.g., transcranial Doppler ultrasonography) or measurements from other brain regions. In addition, hematological factors and inter-individual variability in NIRS could also influence our results during the exercise and recovery phases. Future studies with gold-standard measurements of cerebral oxygenation and larger sample sizes are required in order to validate the role of overweight and obesity on cerebral hemodynamic response during and after exercise. Third, the grouping of subjects was based on their BMI status, as suggested by the WHO. However, BMI cannot distinguish between body fat and lean body mass. Studies have reported that waist circumference (WC), waist–hip ratio (WHR), and body fat percentage (BF) measures should be assessed along with BMI, because increasing evidence supports visceral adiposity and/or central obesity as markers of cardiovascular risk [4]. 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--- title: Genome-Wide Associations and Confirmatory Meta-Analyses in Diabetic Retinopathy authors: - Xinting Yu - Shisong Rong journal: Genes year: 2023 pmcid: PMC10048213 doi: 10.3390/genes14030653 license: CC BY 4.0 --- # Genome-Wide Associations and Confirmatory Meta-Analyses in Diabetic Retinopathy ## Abstract The present study aimed to summarize and validate the genomic association signals for diabetic retinopathy (DR), proliferative DR, and diabetic macular edema/diabetic maculopathy. A systematic search of the genome-wide association study (GWAS) catalog and PubMed/MELINE databases was conducted to curate a comprehensive list of significant GWAS discoveries. The top signals were then subjected to meta-analysis using established protocols. The results indicate the need for improved consensus among DR GWASs, highlighting the importance of validation efforts. A subsequent meta-analysis confirmed the association of two SNPs, rs4462262 (ZWINT-MRPS35P3) (odds ratio = 1.38, $$p \leq 0.001$$) and rs7903146 (TCF7L2) (odd ratio = 1.30, $p \leq 0.001$), with DR in independent populations, strengthening the evidence of their true association. We also compiled a list of candidate SNPs for further validation. This study highlights the importance of consistent validation and replication efforts in the field of DR genetics. The two identified gene loci warrant further functional investigation to understand their role in DR pathogenesis. ## 1. Introduction Diabetic retinopathy (DR) is a leading cause of visual impairment. It leads to blinding complications and affects more than one-third of global diabetic patients, posing blindness to one in three DR patients [1]. DR is a progressive retinal microvascular complication of diabetes mellitus. Its pathology involves microaneurysms, hemostatic changes, and capillary occlusion in the retina. The vessel permeability increases, resulting in retinal nonperfusion, neovascularization, and vessel wall thickening. Further progress into macular edema, vitreous hemorrhage, or retina distortion can lead to severe visual loss and blindness [2]. Effective interventions, such as tight glycemic and blood pressure control, lipid-lowering, and laser therapies, could delay its progression and preserve vision. Early diagnosis and appropriate treatment can help prevent visual damage and the blinding consequences [3,4,5,6,7,8]. Genetic studies play an important role in uncovering DR-related genes and shedding light on the underlying pathophysiology and potential diagnostic and therapeutic markers. Over 300 DR candidate gene association studies have been published [9]. Common variants within genes involved in various processes, such as angiogenesis, glucose metabolism, lipid metabolism, inflammation, vascular regulation, cell communication, and extracellular matrix, have been evaluated for their associations with DR and its subtypes, with noticeable inconsistent findings [10,11]. In an effort to address inconsistencies and uncover a broader range of genetic factors, the field has shifted towards genome-wide association studies (GWASs) for investigating DR. Genome-wide association studies exam the entire genome in populations to identify single nucleotide polymorphisms (SNPs) that are associated with a particular trait or disease. GWASs have been conducted in different populations to investigate the genetic architecture of DR [12,13,14,15,16,17,18,19,20] and its subtypes [21,22,23,24,25,26,27,28], such as proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME)/diabetic maculopathy. These GWASs have reported more than 100 gene loci and SNPs that significantly or suggestively alter the risk of DR in European, Asian, and African populations. However, the reported SNPs varied among the DR GWASs [10,11,12,13,29]. Therefore, a systematic summary and evaluation of existing DR GWAS results is needed to prioritize the efforts in replicating these GWAS signals in independent populations, ultimately advancing our understanding about the DR genetics. In this study, we first summarized all reported SNPs from published GWASs on DR and its subtypes. Second, we sifted through SNPs of genome-wide or suggestive significance to identify overlapping signals between independent GWASs. Finally, we calculated the summary outcomes for the top GWAS SNPs using a meta-analysis and all available genetic data from independent replication studies. Our study provides a comprehensive summary and update, emphasizing the urgent need for improved consensus among DR GWASs. Through a meta-analysis of the reported GWAS SNPs, we confirmed the association of two SNPs, rs4462262 (ZWINT-MRPS35P3) and rs7903146 (TCF7L2), with DR. However, to ensure the robustness and generalizability of GWAS findings, further replication of the candidate SNPs summarized in our study is necessary. ## 2.1. Identification of DR GWASs and Data Extraction Genome-wide association studies on DR were identified via searching the GWAS catalog [30], literature databases, and citation lists of relevant publications. The search strategies used for the identification of DR GWASs in PubMed/MEDLINE are detailed in Table S1. The search terms used in the GWAS catalog were DR (trait ID in the Experimental Factor Ontology (EFO): EFO_0003770), PDR (EFO_0009322), DME (EFO_0009321), and diabetic maculopathy (EFO_0010133). We summarized all records that met the following criteria: [1] studies tested the associations of genetic variants with DR and its subtypes (i.e., nonproliferative DR (also known as NPDR), PDR, sight-threatening DR, DME, or diabetic maculopathy) at a genomic scale; [2] study population was clearly defined; [3] background disease was type 1 diabetes (T1D), type 2 diabetes (T2D), or both T1D and T2D; [4] diagnosis of DR was based on clinical data; and [5] publicly reported genome-wide significant variants. The data extraction process for the SNPs associated with DR involved screening and extracting data from both GWAS catalog datasets and identified GWASs from a literature search. Two authors, X.Y.T. and S.S.R., reviewed the records, performed the data extraction, and resolved any discrepancies through consensus. The extracted data were then organized into summary tables for further analysis. ## 2.2. Identification of Overlapping Signals between GWASs To identify overlapping SNPs between documented DR GWASs, we took a multistep approach. Three DR phenotypes were analyzed separately, including DR, PDR/sight-threatening DR, and DME/diabetic maculopathy. Within each phenotype group, the first step was to search for identical SNPs present in two or more independent DR GWASs. Secondly, for each DR phenotype, we searched for SNPs that were near each other within a ±100 K base pair window. This step aimed to identify SNPs that may be in linkage disequilibrium and may have a similar effect on the DR phenotypes. This method of searching for identical and nearby SNPs between different DR GWASs allowed for the identification of SNPs with the best available genetic evidence supporting their effects on the risk of DR and provided a list of SNPs for the subsequent meta-analysis. ## 2.3. Genetic Meta-Analysis A genetic meta-analysis was conducted for any SNPs or gene loci repeatedly identified by independent DR GWASs and SNPs with a genome-wide significant p-value (<1 × 10−7) following previously published protocols [31,32,33,34,35]. Briefly, we performed the literature search using Boolean logic and search terms with controlled vocabularies (i.e., Medical Subject Heading terms) in the PubMed/MEDLINE databases. The search terms were constructed as follows: (Identified genes and genetic loci OR SNP IDs) AND (“diabetic retinopathy” (MeSH Terms) OR (“diabetic” (All Fields) AND “retinopathy” (All Fields)) OR “diabetic retinopathy” (All Fields)) (Table S2). Additionally, we scanned the reference lists of relevant research articles, reviews, and meta-analyses identified during the screening process to include all relevant genetic data. The last search was conducted on 28 January 2023. We included SNPs and studies that met the following criteria in the genetic meta-analysis: [1] original genetic case-control studies that enrolled unrelated individuals from explicitly defined populations; [2] studies that used T1D, T2D, or both as the background conditions; [3] odds ratio and $95\%$ confidence intervals (CIs)/standard error (SE) were reported or calculable based on the reported data; and [4] SNPs with genome-wide significant p-values (<1 × 10−7) in the documented GWAS discovery cohorts, or the gene loci contained genome-wide significant SNPs. We excluded animal studies, case reports, reviews, abstracts, conference proceedings, and editorials. Two investigators (X.T.Y. and S.S.R.) screened, reviewed, and extracted all the data independently. Disagreements were resolved through consensus. If the allele counts were not provided, we calculated them from the genotype data. If only the OR and $95\%$ CI were reported, we estimated the SE using the equation SE = (β−ln(lower limit of $95\%$ CI))/1.96, where β = ln (OR) [36]. Fully adjusted outcomes were used in the meta-analysis when available. If duplicated cohorts were identified, we used the larger and more recent cohort in the meta-analysis. We also adopted the Newcastle Ottawa Scale (NOS) (accessed via http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp, accessed on 1 January 2023) to evaluate the overall quality of the case-control studies (Appendix A) [37,38,39]. A study with ≤6 stars was considered high risk for introducing biases; therefore, it was subject to removal in the sensitivity analysis [40]. We performed a meta-analysis for each SNP if it was reported in two or more independent cohorts. The genetic association was evaluated using an allelic model, i.e., effect allele frequency vs. reference allele frequency. We calculated the summary OR and $95\%$ CI for each polymorphism using the random-effects model. The heterogeneity was tested using the I2 statistics [41]. An I2 value lower than $50\%$ indicated low heterogeneity. We plotted the funnel plots to assess the publication bias [42,43,44]. We also conducted a sensitivity analysis to confirm the associations by sequentially omitting each of the studies one at a time if the studies deviated from the HWE or were of suboptimal quality [31,45]. We performed the meta-analysis using RevMan 5 (https://training.cochrane.org/online-learning/core-software/revman/, accessed on 1 January 2023). Summary p-values of <0.05 were considered statistically significant. ## 2.4. Functional Annotation of SNPs and Gene Loci To understand the functional relevance of the identified SNPs related to DR, we employed in silico functional prediction scores, such as SIFT [46], PolyPhen [47], CADD [48], and RegulomeDB [49]. Moreover, we also assessed the SNPs in high (D-prime ≥ 0.8 and r2 ≥ 0.8) linkage disequilibrium (LD) with the identified SNPs. The LD data for European populations from the 1000 Genome project were used [50]. Additionally, we analyzed the expression quantitative trait loci (eQTL) databases through the Genotype-Tissue Expression (GTEx) portal to gain direct knowledge of the effects of risk alleles on nearby gene expression [51]. ## 3. Results We identified 14 GWASs conducted in diverse populations, including European [12,21,23,24,25,27], Asian [12,21,23,24,25,27], African [26,28], and Arabian [15], through searching the GWAS catalog and literature databases (Figure S1 and Table 1). With the exception of one GWAS that focused on T1D [21] and another GWAS that included both T1D and T2D [15], the majority of the GWASs enrolled individuals with DR in a T2D context. Only half of the GWASs tested significant SNPs discovered in the initial stage in at least one replication cohort [14,17,22,23,25,26,28]. Although the majority of GWASs had small sample sizes, with less than 1000 participants, no power analysis results were reported (Table 1). Additionally, we found three pertinent GWASs that used samples from the UK Biobank [18,19,20]. However, we excluded them from the summary table, as they primarily used nondiabetic population controls. It is worth noting that there have been exciting developments in the field of DR genetics in recent years. These advancements include exome sequencing for rare and functional variants [15], and a more in-depth analysis of DR GWAS datasets has provided further understanding of the underlying pathological pathways [52], causal factors for DR [53,54,55], ability to predict DR using polygenic risk scores [56], and pharmacogenetic responses to treatment [57]. ## 3.1. Genome-Wide Associations of DR A total of six GWASs identified 76 SNPs located in 61 gene loci, with p-values that suggest a potential or confirmed genome-wide significance for DR ($p \leq 5$ × 10−5) (Table S3) [13,14,16,17,21,26]. Our subsequent analysis revealed a lack of consensus among the existing DR GWASs, as we found no overlap in SNPs from different studies within a ±100 K base pair window. Seven out of the seventy-six SNPs exhibited genome-wide significance ($p \leq 1.0$ × 10−7), with the top seven being rs17376456 (KIAA0825/C5orf36, $$p \leq 3.0$$ × 10−15), rs2038823 (HS6ST3, $$p \leq 5.0$$ × 10−11), rs12630354 (THRAP3P1-STT3B, $$p \leq 7.0$$ × 10−10), rs4838605 (ARHGAP22, $$p \leq 2.0$$ × 10−9), rs12219125 (AMD1P1-PLXDC2, $$p \leq 9.0$$ × 10−9), rs202069793 (OR13D3P-OR13D1, $$p \leq 6.0$$ × 10−8), and rs4462262 (ZWINT-MRPS35P3, $$p \leq 9.0$$ × 10−8) (Table S3). Notably, two of these seven SNPs, rs12630354 and rs202069793, were validated in separate populations in the original GWASs [17,26]. All seven SNPs were included in subsequent meta-analysis. ## 3.2. Genome-Wide Associations of PDR and DME In our investigation of PDR, we identified six GWASs that tested PDR against a T2D background (Table S4) [22,23,24,25,26,28], with all but one study including a validation cohort [24] (Table 1). A total of 29 SNPs located near 27 gene loci demonstrated suggestive or lower p values. Among them, eight SNPs showed genomic significance, including rs3081219 (WDR72, $$p \leq 1.0$$×10−9), rs3913535 (NOX4, $$p \leq 4.0$$ × 10−9), rs11018670 (FOLH1B, $$p \leq 1.0$$ × 10−8), rs72740408 (HNRNPA1P46, $$p \leq 2.0$$ × 10−8), rs184340784 (LINC01646, $$p \leq 4.0$$ × 10−8), rs1065386 (HLA-B, $$p \leq 5.0$$ × 10−8), rs4726066 (PRKAG2, $$p \leq 5.0$$ × 10−8), and rs200295620 (GOLIM4-EGFEM1P, $$p \leq 7.0$$ × 10−8). Two GWASs were identified for DME, which collectively found 12 SNPs located near 10 gene loci (Table S5) [24,27]. Notably, only one of the SNPs, rs9966620, was genome-wide significant ($$p \leq 7.0$$ × 10−8). Neither of the two GWASs included a validation cohort (Table 1). Furthermore, our analysis revealed a lack of overlapping SNPs or gene loci among different GWASs, and neither PDR nor DME had overlapping SNPs or gene loci among the different GWASs within a ±100 K base pair window, which was comparable to the results obtained from the DR GWASs. ## 3.3.1. Meta-Analysis of Top DR-Associated SNPs Replication studies and meta-analyses are crucial for validating findings and strengthening evidence for a true association, especially given the lack of consensus among existing GWASs. In our meta-analysis, we identified 37 records of published studies from the literature search and extracted data from 20 replication studies (Figure S2). Four of the seven top DR GWAS SNPs (Table S3) were replicated in two or more independent studies, including rs4462262 (ZWINT-MRPS35P3) [58,59,60], rs12219125 (PLXDC2-NEBL) [59,60,61], rs4838605 (ARHGAP22) [58,59,60], and rs17376456 (C5orf36) [59,60]. Notably, all four of these SNPs were reported without an internal validation cohort in the initial GWAS [13]. The remaining three of the seven GWAS SNPs, rs2038823 [13], rs12630354 [17], and rs202069793 [26], have not been replicated in independent studies. Additionally, we found five replication studies [62,63,64,65,66] that tested the DR-association of rs7903146 in the TCF7L2 gene locus, which was identified by rs34872471 ($$p \leq 4$$ × 10−15) in a UK Biobank GWAS using population controls [20]. These two SNPs were in high linkage disequilibrium in European populations (r2 ≥ 0.99) [50]. Therefore, we conducted a meta-analysis for rs7903146. Our meta-analysis confirmed the significant association of rs4462262 (OR = 1.38, $$p \leq 0.001$$), rs12219125 (OR = 1.24, $$p \leq 0.03$$), and rs7903146 (OR = 1.30, $p \leq 0.001$) with DR in replication cohorts (Figure 1 and Figure S4). The heterogeneity measurements (I2) were low for all of these combined outcomes. However, we found no evidence of association between rs4838605 and rs17376456 with DR in our analysis ($p \leq 0.05$) (Figure 1). All studies included in the meta-analysis scored six or higher on the NOS quality assessments (Table S6). Funnel plots showed no significant deviations (Figures S3 and S4), indicating a lower risk of introducing potential biases by pooling results from these studies. The sensitivity analysis confirmed the stability of most of the meta-analytical results by omitting one study at a time and recalculating the combined outcomes. Only rs12219125 became insignificant after removing Hosseini S. M.’s study [61], highlighting the need for further replication efforts. ## 3.3.2. Meta-Analysis of Top PDR- and DME-Associated SNPs Our literature search did not identify replication efforts for the top GWAS hits of PDR and DME, preventing us from performing meta-analyses for these DR phenotypes. ## 3.4. Biological Relevance of rs4462262 and rs7903146 to DR The SNP rs4462262 is an intergenic variant, located approximately 750 kbp upstream to the nearest gene, IPMK. A microRNA, MIR3924, was situated approximately 110 kbp upstream of the SNP. However, the functional annotations for rs4462262 and 54 other SNPs in high LD with rs4462262 were unremarkable (Table 2), providing limited evidence for the biological relevance of these variants. In contrast, the SNP rs7903146 is located in intron 3 of TCF7L2 and is significantly associated with the expression of TCF7L2 ($$p \leq 2.9$$ × 10−7) (Table 2). Another SNP, rs7074440, in high LD with rs7903146 (D’ = 0.95 and r2 = 0.91) has a CADD score of 10.1 and is also significantly associated with the expression of TCF7L2 ($$p \leq 2.1$$ × 10−6) (Table 2), indicating a potentially functional role in TCF7L2 regulation. ## 4. Discussion In this study, we comprehensively summarized the genomic association signals of DR, PDR, and DME/diabetic maculopathy from published GWASs. Our results emphasized the urgent need for improved consensus among DR GWASs, underlining the importance of validation and replication efforts. We confirmed the association of two SNPs, rs4462262 (ZWINT-MRPS35P3) and rs7903146 (TCF7L2), with DR through meta-analysis in independent populations, strengthening the evidence of their true association. Moreover, we provided a list of candidate SNPs for further validation studies. The lack of consensus among DR GWASs is a complex issue that arises from several potential factors. One such factor is the clinical heterogeneity of DR populations, which can lead to conflicting results between studies. A possible solution is to carefully stratify patients based on specific clinical characteristics and to conduct studies in homogeneous patient populations. Second, ethnic differences in SNP allele frequencies can impact the strength and direction of genetic associations. To address this issue, large-scale studies with diverse populations are needed to increase the generalizability of results. Additionally, the standardization of SNP definitions and frequency databases can improve the accuracy of genetic association analyses [67]. Third, the study definition of DR and its subtypes could be another factor contributing to the lack of consensus. To address this, a standardized definition and diagnostic criteria, such as the ETDRS grading system [68,69], should be universally adopted by the DR genetic research community to ensure consistency between studies. Fourth, whether to include a validation design within a GWAS is another potential factor. Including a validation design within a GWAS can increase the confidence in the findings and help to reduce false positive results. This is particularly important in a complex disease such as DR, where multiple genetic and environmental factors are likely to be involved [11,70]. Finally, differences in the statistical power between studies can also contribute to the lack of consensus among DR GWASs. To overcome this issue, larger sample sizes and more powerful study designs are needed to increase the chances of detecting true associations, especially in rare variant studies. Addressing these potential factors through careful study design and increased collaboration among DR researchers can help to improve the consistency of DR GWAS results and eventually lead to a better understanding of the underlying mechanisms of DR. In our meta-analysis, we noticed a lack of replication studies for DR GWASs findings. Replication is a critical step to confirm the validity of a discovery and strengthens the evidence for a true association. Without replication, the results of a study may be considered unreliable or of limited generalizability, especially when the study is based on a small sample size or has limited statistical power. However, replication studies are often challenging and resource intensive, which can limit the number of independent validation studies performed for DR GAWS discoveries. This is particularly true in the case of DR genetics, which are often complex due to the heterogeneity of DR populations, differences in SNP allele frequencies across ethnic groups, and variations in the definition of DR and its subtypes. Therefore, it is essential to prioritize replication efforts in DR genetics research, which directly pointed us towards conducting this study. The association between rs7903146 and DR is significant, and the regulatory effects of this SNP and other SNPs in LD provide evidence supporting the functional role of TCF7L2 in DR pathogenesis. As a transcriptional regulator of the canonical Wnt signaling pathway, TCF7L2 plays a vital role in cell proliferation and fate specification. The involvement of TCF7L2 in DR pathogenesis can be two-fold. First, TCF7L2 may promote pathological retinal neovascularization via the ER stress-dependent upregulation of VEGFA, making retinal cells from rs7903146-TT patients more sensitive to VEGF upregulation and at greater risk of developing PDR [63]. Second, TCF7L2 may also increase the risk of DR development and progression by influencing the responses to glycemic control drugs, such as glipizide and metformin, which are two commonly used glycemic control drugs [71]. In addition, good glycemic control is closely associated with short- and long-term beneficial effects on DR prevention and intervention [72,73]. However, the functional relevance of rare mutations in the TCF7L2 gene and their impact on DR pathophysiology in diverse ethnic groups remain unknown, and further sequencing studies of large cohorts are needed to understand their role. In addition, understanding the functional regions of the TCF7L2 gene and their impact on specific phenotypes will also be important for comprehending their role in diverse human diseases, including DR [74,75]. On the other hand, the lack of functional annotations for rs4462262 and 54 other SNPs in high LD with rs4462262 limits our understanding of their functional relevance in DR pathogenesis. To reveal their role in DR, the identification of rare and common variants that truly alter the risk of DR and functional investigations are crucial. The confirmation and functional annotation of these variants could be a critical step in discovering new genetic markers for DR, facilitating better prediction of disease susceptibility and tailoring individualized treatments for patients. This study has several potential limitations, including the limited number of independent replication cohorts and subphenotypes, limited diversity of population ancestries, and the use of population controls instead of DR-free diabetic controls in the DR GWASs using the UK Biobank datasets. These limitations affected our ability to confirm more GWAS discoveries. ## 5. Conclusions Our study underlines the urgent need for consistent replication efforts in the field of DR genetics. Two SNPs, rs4462262 (ZWINT-MRPS35P3) and rs7903146 (TCF7L2), showed consistent association with DR. Confirmation and functional investigations of reported and new variants are needed for a better understanding of the underlying mechanisms of DR development. ## References 1. 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--- title: Exome-Wide Association Study of Competitive Performance in Elite Athletes authors: - Celal Bulgay - Anıl Kasakolu - Hasan Hüseyin Kazan - Raluca Mijaica - Erdal Zorba - Onur Akman - Isık Bayraktar - Rıdvan Ekmekci - Seyrani Koncagul - Korkut Ulucan - Ekaterina A. Semenova - Andrey K. Larin - Nikolay A. Kulemin - Edward V. Generozov - Lorand Balint - Georgian Badicu - Ildus I. Ahmetov - Mehmet Ali Ergun journal: Genes year: 2023 pmcid: PMC10048216 doi: 10.3390/genes14030660 license: CC BY 4.0 --- # Exome-Wide Association Study of Competitive Performance in Elite Athletes ## Abstract The aim of the study was to identify genetic variants associated with personal best scores in Turkish track and field athletes and to compare allelic frequencies between sprint/power and endurance athletes and controls using a whole-exome sequencing (WES) approach, followed by replication studies in independent cohorts. The discovery phase involved 60 elite Turkish athletes (31 sprint/power and 29 endurance) and 20 ethnically matched controls. The replication phase involved 1132 individuals (115 elite Russian sprinters, 373 elite Russian endurance athletes (of which 75 athletes were with VO2max measurements), 209 controls, 148 Russian and 287 Finnish individuals with muscle fiber composition and cross-sectional area (CSA) data). None of the single nucleotide polymorphisms (SNPs) reached an exome-wide significance level ($p \leq 2.3$ × 10−7) in genotype–phenotype and case–control studies of Turkish athletes. However, of the 53 nominally ($p \leq 0.05$) associated SNPs, four functional variants were replicated. The SIRT1 rs41299232 G allele was significantly over-represented in Turkish ($$p \leq 0.047$$) and Russian ($$p \leq 0.018$$) endurance athletes compared to sprint/power athletes and was associated with increased VO2max ($$p \leq 0.037$$) and a greater proportion of slow-twitch muscle fibers ($$p \leq 0.035$$). The NUP210 rs2280084 A allele was significantly over-represented in Turkish ($$p \leq 0.044$$) and Russian ($$p \leq 0.012$$) endurance athletes compared to sprint/power athletes. The TRPM2 rs1785440 G allele was significantly over-represented in Turkish endurance athletes compared to sprint/power athletes ($$p \leq 0.034$$) and was associated with increased VO2max ($$p \leq 0.008$$). The AGRN rs4074992 C allele was significantly over-represented in Turkish sprint/power athletes compared to endurance athletes ($$p \leq 0.037$$) and was associated with a greater CSA of fast-twitch muscle fibers ($$p \leq 0.024$$). In conclusion, we present the first WES study of athletes showing that this approach can be used to identify novel genetic markers associated with exercise- and sport-related phenotypes. ## 1. Introduction Whether pure talent or long-term experiences promotes athletic performance is one of the questionable issues [1]. Progression in the sport sciences has underlined that athletic performance was a phenomenon affected by lots of factors including physiology and environment [2]. Recent studies have also figured out the possible association of the genetic background of the athletes in their high personal performances, resulting in the rise of a novel scientific branch, called sport genetics [3,4]. Sport genetics could be defined as the investigation of the genes and their molecular mechanisms affecting athletic performance and the determination of the possible association of the variants, especially single nucleotide polymorphisms (SNPs), with diverse athletic parameters including branch or personal performances [5]. According to the studies on sport genetics, $66\%$ of athletic performance has been linked to the genetic background [6]. Moreover, physical parameters were also associated with the genetic background. For instance, 44–$68\%$ of endurance and 49–$56\%$ of muscular force were shown to be affected by genetic variations [7,8]. Thus, both genetics and the environment, which would influence each other, have key roles in athletic performance [9,10]. For example, training periods to reach a performance level were proved to be linked to the genetic background of the athletes [9]. Recently, identification of candidate genes and/or variants associated with sports parameters has greatly attracted scientists. Until now, more than 235 genetic variants have been linked to the athletic parameters [10,11]. However, the results of the single-gene and/or variant approach may mislead, owing to the ignorance of the other related genes and/or variants. Consequently, it was realized that the results for the associations of each gene and/or variant were controversial [12]. Hence, multigenetic factors should be targeted to totally explore the possible associations. In parallel, several genome-wide association studies (GWAS) have been conducted on sports genetics. GWAS is a powerful technique to cover all known or unknown SNPs [13,14,15]. GWAS has proposed novel associated genes and/or SNPs for the athletic parameters such as endurance, aerobic capacity, metabolism, and muscle fiber composition [16,17]. However, the complexity and cost of GWAS limit such studies, and pilot experiments are suggested [18]. Exome-wide association studies (EWAS) could be an alternative to overcome the problems with GWAS. EWAS has also been previously chosen as a strategy to find the possible associations in the sports genetics [19]. The aim of the present study was to identify genetic variants associated with personal best scores in Turkish track and field athletes and to compare allelic frequencies between sprint/power and endurance athletes and controls using a whole-exome sequencing approach, followed by replication studies in independent cohorts of athletes and controls. ## 2.1. Ethical Approval The study was carried out in accordance with the Declaration of Helsinki, and approval was obtained from the Gazi University Non-Interventional Clinical Research Ethics Committee (with the decision dated 5 April 2021 and numbered 09) and from the Ethics Committee of the Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency of Russia (Approval number $\frac{2017}{04}$). ## 2.2.1. The Turkish Cohorts The Turkish study involved 60 elite athletes (sprint/power: 11 females ($35.5\%$) and 20 males ($64.5\%$); endurance: 10 females ($34.5\%$) and 19 males ($65.5\%$); mean age ± SD: 25.1 ± 4.8; height (cm): 174.97 ± 7.9; body weight (kg) 72.5 ± 22.4; sport experience (year) = 9.4 ± 4.8; personal best (PB) = 1005.63 ± 94.55) licensed in different clubs and affiliated with the Turkish Athletics Federation. The number of controls (non-athletes) was 20 (6 females ($30.0\%$) and 14 males ($70.0\%$); mean age ± SD: 23.5 ± 7.1), and they were healthy unrelated citizens of Turkish descent without any competitive sports experience. The athletes were categorized as either sprint/power or endurance athletes as determined by the distance, duration, and energy requirements of their events. All athletes were nationally ranked in the top ten in their sports discipline and had participated in international competitions such as the Olympic Games, European Championships, Universiade, Mediterranean Games, and Balkan Championship. The sprint/power group included sprint and power athletes whose events demand predominantly anaerobic energy production. The athletes in this group ($$n = 31$$) were 100–400 m runners ($$n = 9$$), jumpers ($$n = 3$$), and throwers ($$n = 19$$). The endurance athlete group ($$n = 29$$) included athletes competing in long-distance events demanding predominantly aerobic energy production. This group included 3000 m ($$n = 12$$), 5000 m ($$n = 5$$), 10,000 m ($$n = 4$$), and marathon ($$n = 8$$) runners. The informed voluntary consent and demographic information forms were obtained from the participants before the measurements. The International Association of Athletics Federations (IAAF) score scale was used to determine the performance levels of the athletes, depending on their personal best/competitive performance [20]. For instance, the IAAF score scale of a male athlete who runs 100 m in 10.05 sec is 1189, while that of a marathon runner who completes the race in 2 h 20 min 11 sec is 997. Thus, the performance scale of the marathon runner is less than that of the 100 m runner. The IAAF scales are useful for the determination of performances of athletes from diverse athletics events and genders. ## 2.2.2. The Russian Cohorts The Russian case–control study involved 488 elite athletes (293 males and 195 females), of whom 115 were elite sprint/power athletes (29 100–400 m runners, 38 500–1000 m speed skaters, 22 sprint cyclists, 26 50 m swimmers), and 373 were elite endurance athletes (52 rowers, 32 biathletes, 7 long-distance cyclists, 30 kayakers and canoers, 37 middle- and long-distance speed skaters, 92 cross-country skiers, 63 middle- and long-distance runners, 31 middle- and long-distance swimmers, 8 race walkers, and 21 triathletes). The athletes were Russian national team members (participants and prize winners in international competitions) who had never tested positive for doping. Of 373 endurance athletes, 46 male endurance athletes (rowers, kayakers, speed skaters, biathletes, and cross-country skiers) and 29 female endurance athletes (rowers, kayakers, speed skaters, biathletes, and cross-country skiers) participated in the study of aerobic performance. Controls were 209 healthy and unrelated citizens of Russia without any competitive sport experience. The Russian muscle biopsy study involved 148 physically active participants of Russian origin (99 males: mean age ± SD: 30.4 ± 7.9 years; 49 females: mean age ± SD: 27.1 ± 7.3 years). ## 2.2.3. The Finnish Cohort The Finnish muscle biopsy study (replication phase) involved 287 individuals (167 males, age 59.5 ± 8.1 years; 120 females, age 60.7 ± 7.4 years) from the FUSION study as previously described [21]. ## 2.3.1. Russian Study Vastus lateralis samples were obtained from the left legs of the participants using the modified Bergström needle procedure with aspiration under local anesthesia using $2\%$ lidocaine solution. Serial cross-sections (7 μm) were obtained from frozen samples. The sections were then incubated at RT in primary antibodies against slow or fast isoforms of the myosin heavy chains, as previously described [17,22]. ## 2.3.2. Finnish Study Muscle fiber composition in 287 Finnish individuals was estimated based on the expression of the myosin heavy chain 1 (MYH1), myosin heavy chain 2 (MYH2), myosin heavy chain 7 (MYH7), Ca2+ ATPase A1, and Ca2+ ATPase A2 genes, as previously described [21]. ## 2.4. VO2max Measurement Maximal oxygen consumption rate (VO2max) in rowers, kayakers, speed skaters and biathletes was determined using an incremental test to exhaustion on specific ergometers. VO2max was determined breath-by-breath using a MetaLyzer II (Cortex Bio-physik, Leipzig, Germany), MetaMax 3B (Cortex Biophysik, Leipzig, Germany) or MetaMax 3B-R2 gas analysis systems (Cortex Biophysik, Leipzig, Germany), as previously described [23]. ## 2.5. Whole-Exome Sequencing (WES) The peripheral blood obtained from the participants was processed to isolate total DNA by DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Next, qualities of isolated DNA were checked by $1\%$ agarose gel, and the concentrations were determined by a NanoDrop (NanoDrop 1000 Spectrophotometer V3.8; Thermo Scientific, Waltham, MA, USA). WES was performed after library preparation by the Twist Human Comprehensive Exome Panel (Twist Biosciences, San Francisco, CA, USA) according to the supplier’s instructions. Briefly, enzymatic DNA fragmentation was performed, and Twist Hybridization probes and Dynabeads™ MyOne™ Streptavidin T1 (Invitrogen, Carlsbad, CA, USA) were used for the hybridization. After the steps of library enrichment and determination of the library sizes, the samples were uploaded to the flow cells and the run was performed by Illumina NextSeq500 (Illumina Inc., San Diego, CA, USA). Average read depth was aimed as minimum 200×. Raw data were processed to by the Genome Analysis Toolkit (GATK)’s [24]. The HaplotypeCaller program was used to obtain Binary Alignment Map (BAM) files and subsequently produce an output Variant Call Format (VCF) file via the GRCh38/hg38 reference genome. Finally, variants were annotated by ANNOVAR [25]. ## 2.6. Data Extraction As the primary evaluation of the data, the VCF files were combined, and 511,061 variants were detected. Only SNPs were analyzed in the context of the present study. The variants with a minor allele frequency (MAF) < 0.01, incorrectly annotated, and non-autosomal were eliminated, and 219,232 SNPs were further evaluated. ## 2.7. Genotyping DNA samples from Russian individuals were obtained from leukocytes (venous blood). DNA extraction and purification from blood samples were performed using commercial kits (Techno-sorb), according to the manufacturer’s instructions (Techno-clon, Moscow, Russia). Genotyping of the candidate SNPs from the discovery phase was performed using microarray technology [26]. DNA samples from Finnish individuals were extracted from the blood, and the polymorphisms were genotyped using the HumanOmni2.5–4v1_H BeadChip array (Illumina, San Diego, CA, USA), as previously described [21]. ## 2.8. Statistical Analyses Association analyses of Turkish data were performed by a Chi-square test using thet R program [27]. During the EWAS, the unified mixed-model method [27] was used. y = Xβ + Sτ + e[1] where y is the phenotypic observation; Xβ is the fixed effect; and Sτ is the SNP effect [27]. The statistical significance probabilities of the SNP effects were converted to −log10p. The results of EWAS analyses were presented as a Manhattan Plot. The exome-wide significance level was set at $p \leq 2.3$ × 10−7 (i.e., $\frac{0.05}{219}$,232 SNPs). Statistical analyses of Russian and Finnish data were conducted using GraphPad InStat Version 3.05 (GraphPad Software, Inc., San Diego, CA, USA) software. The PLINK 1.9 program (National Institutes of Health, Bethesda, MD, USA) was used to perform genetic data quality control, and PLINK 2.0 was used to perform principal component analysis and association testing via generalized linear models. Bcftools was used for vcf file conversion. The phasing and imputation of genotypes were completed using the shapeit2 and impute2 programs. Differences in phenotypes between groups were analyzed using regression analysis adjusted for covariates. The chi-square test (χ2) was used to test for the presence of the Hardy–Weinberg equilibrium (HWE). Thereafter, the frequencies of genotypes or alleles were compared between sprint/power and endurance athletes and controls using Fisher’s exact test. All data are presented as means (SD). The p-values < 0.05 were considered statistically significant. ## 3.1. Discovery Phase None of the SNPs reached an exome-wide significance level ($p \leq 2.3$ × 10−7) in genotype–phenotype and case–control studies of Turkish athletes (Figure 1). The only SNP that was close to the threshold ($$p \leq 1.0$$ × 10−5) was rs8037843 in the Pyroglutamyl-Peptidase I Like (PGPEP1L) gene (Figure 1). Although rs8037843 correlated with personal bests in athletes, there were no allelic differences between the Turkish and Russian endurance and sprint/power athletes and controls with respect to this SNP ($p \leq 0.05$). The genotypic differences between the groups were evaluated by principal component analysis on an SNP matrix (PCA). PCA of the genotyping data pointed out no significant influence of sport disciplines (Figure 2) on genotype distributions. Comparisons of allelic frequencies between three groups (endurance vs. sprint/power athletes; endurance athletes vs. controls; sprint/power athletes vs. controls) showed 53 SNPs whose frequencies were significantly differentiated between the sprint/power and endurance group (but not in the separate sub-groups of female and male athletes due to low sample sizes) (Supplementary Table S1). The genes in which these SNPs were located were further analyzed by the String database (v.11.5; https://string-db.org/, accessed on 10 December 2022) for the functional interaction and pathway analyses. The results showed minimal interactions between the proteins, and the Markov Cluster Algorithm (MCL) option in the database demonstrated five clusters (Figure 3). ## 3.2. Replication Studies Of the 53 nominally ($p \leq 0.05$) associated SNPs, four variants were replicated in the following studies involving Russian and Finnish individuals. More specifically, the SIRT1 rs41299232 G allele was significantly over-represented in Turkish (44.0 vs. $4.0\%$; $$p \leq 0.047$$) and Russian (63.5 vs. $55.4\%$; $$p \leq 0.018$$) endurance athletes compared to sprint/power ones and was associated with increased relative VO2max (C/C ($$n = 9$$): 63.2 (8.0) mL/min/kg, C/G ($$n = 35$$): 64.3 (7.0) mL/min/kg, G/G ($$n = 31$$): 66.2 (6.4) mL/min/kg; $$p \leq 0.037$$ adjusted for sex), and a greater proportion of slow-twitch muscle fibers in Finnish subjects (C/C ($$n = 45$$): 42.8 (12.7)%, C/G ($$n = 147$$): 44.4 (16.0)%, G/G ($$n = 95$$): 47.0 (13.7)%; $$p \leq 0.035$$ adjusted for age and sex). The NUP210 rs2280084 A allele was significantly predominant in Turkish (68.0 vs. $34.0\%$, $$p \leq 0.044$$) and Russian (59.2 vs. $50.0\%$, $$p \leq 0.012$$) endurance athletes compared to the sprint/power group. In addition, the rs2280084 A allele was over-represented in highly elite Russian endurance athletes ($$n = 119$$) compared to controls (64.1 vs. $52.9\%$, $$p \leq 0.003$$). The frequency of the TRPM2 rs1785440 G allele was significantly higher in Turkish endurance athletes compared to sprint/power athletes (57.0 vs. $18.0\%$, $$p \leq 0.034$$) and was associated with increased VO2max (A/A ($$n = 1$$): 58.3 [0] mL/min/kg, A/G ($$n = 16$$): 63.2 (6.9) mL/min/kg, G/G ($$n = 58$$): 65.6 (6.8) mL/min/kg; $$p \leq 0.008$$). The AGRN rs4074992 C allele was significantly over-represented in Turkish sprint/power athletes compared to endurance athletes (83.0 vs. $44.0\%$, $$p \leq 0.037$$) and was associated with a greater CSA of fast-twitch muscle fibers in physically active Russian individuals ($$p \leq 0.024$$ adjusted for sex, age, type, and level of physical activity). ## 4. Discussion Athletic performance and branches have widely been proved to be a result of the combination of environmental and genetic factors [1,28]. The latter, named as sports genetics, has attracted sports scientists since it was relatively a new branch [10]. The studies on sport genetics have focused on single-gene and/or SNP alteration between the sport branches, which may mislead [12]. Hence, studies aiming at the involvement of multigenetic factors are needed. Limited studies, but not on the Turkish population, have reported GWAS results in sports genetics [10,13,14,16,29]. Thus, the present study focused on the assessment of the multigenetic factors in the elite sprint/power and endurance athletes using the WES approach. Although WES is not a cumulative approach compared to whole-genome sequencing (WGS), it may be advantageous for a pilot study such as the presented one to eliminate the analysis efforts and cost problems. In our present study, we could not detect any SNPs whose frequencies reached an exome-wide significance. The primary problem with such studies would be the limitations with the number of participants [28]. Sport genetics has been established on a population- and sport-branch-specific manner. However, the restricted number of elite athletes would be a challenge to conclude exact findings [5]. Still, by the fact that the number of participants in such studies could affect the results according to the literature [30], such studies are still needed as a pilot comprehensive report to guide both the geneticists and sport scientists. By the lack of associations with a threshold of $p \leq 2.3$ × 10−7, we further compared the frequencies of the SNPs between the sprint/power and endurance groups with $p \leq 0.05$ using the Chi square test. The results pointed out 53 SNPs whose frequencies significantly differentiated between the sport groups ($p \leq 0.05$; Supplementary Table S1). Of the 53 SNPs, four functional (i.e., affecting gene expression) variants located on the (or near) SIRT1, NUP210, TRPM2, and AGRN genes were replicated in Russian and Finnish individuals with consistent effects. The SIRT1 gene encodes the sirtuin 1 protein which is considered as a functional regulator (through the deacetylation and activation) of peroxisome proliferator-activated receptor-γ coactivator (PGC-1α) that induces a metabolic gene transcription program of mitochondrial fatty acid oxidation (one of the positive factors of aerobic capacity) [31]. In our study, we found that the SIRT1 rs41299232 G allele was significantly over-represented in Turkish and Russian endurance athletes compared to sprint/power ones and was associated with increased VO2max and a greater proportion of slow-twitch muscle fibers. Both phenotypes are considered advantageous for endurance athletes. According to the GTEx portal [32], the SIRT1 rs41299232 (intronic variant) is significantly ($$p \leq 4.2$$ × 10−33) associated with the altered expression of the SIRT1 gene in the whole blood. Previously, the rs41299232 G allele was reported to be associated with an increased red blood cell count ($$p \leq 0.0000015$$), higher hemoglobin concentration ($$p \leq 0.0032$$), and higher physical activity ($$p \leq 0.0031$$) in the UK Biobank cohort [33], which is in line with our findings. The NUP210 gene encodes nucleoporin 210 (a membrane-spanning glycoprotein), which is a major component of the nuclear pore complex. Previously, the NUP210 has been shown as a critical regulator of muscular and neuronal differentiation [34]. Muscle function experiments in mice have shown that Nup210 is required for muscle endurance during voluntary running and muscle repair after injury [35]. In our study, we found that the frequency of the NUP210 rs2280084 A allele was significantly higher in Turkish and Russian endurance athletes compared to sprint/power athletes, as well as in highly elite Russian endurance athletes compared to controls. According to the GTEx portal [32], the NUP210 rs2280084 (missense variant) is significantly associated with changed expression of the NUP210 gene in the brain ($$p \leq 3.9$$ × 10−11) and the whole blood ($$p \leq 0.000032$$). The TRPM2 gene encodes the transient receptor potential cation channel subfamily M member 2 protein. TRPM2 plays an important role in a variety of cellular functions, including cell proliferation, insulin release, cell motility, and cell death [36,37]. Recently, it has been shown that TRPM2-mediated Ca2+ signaling is required for training-induced improvement in skeletal muscle mitochondrial functions and fiber-type transition in mice [38]. In our study, we found that the TRPM2 rs1785440 G allele was significantly over-represented in Turkish endurance athletes compared to sprint/power ones and was associated with increased VO2max in Russian athletes. According to the GTEx portal [32], the TRPM2 rs1785440 (intronic variant) is significantly ($$p \leq 2.5$$ × 10−12) associated with an altered expression of the TRPM2 gene in the skeletal muscle. The AGRN gene encodes the agrin protein, which regulates the maintenance of the neuromuscular junction [39]. Previous studies have linked the AGRN gene variants with sarcopenia-related traits (muscle mass and strength) and congenital myasthenia [39,40]. Furthermore, Agrn gene expression has been shown to be upregulated after progressive weighted wheel running in mice [41]. In our study, we found that the AGRN rs4074992 C allele was significantly over-represented in Turkish sprint/power athletes compared to endurance athletes and was associated with a greater CSA of fast-twitch muscle fibers in physically active Russian individuals. Muscle fiber size is a surrogate indicator of muscle mass and is positively associated with power and strength [42,43,44]. According to the GTEx portal [32], the AGRN rs4074992 (intergenic variant) is significantly (p ≥ 3.8 × 10−8) associated with altered expression of the AGRN gene in multiple tissues. Previously, the rs4074992 C allele has been reported to be associated with increased appendicular lean mass ($$p \leq 0.0031$$) in the UK Biobank cohort [33], which is in line with our findings. Like the present study, a study in diverse populations conducted in the literature reported that none of the SNPs reached genome-wide significance with the endurance athlete status [14]. Nonetheless, others reported the associations of the specific SNPs with different exercise-related parameters [15,16,17,45,46,47,48]. However, the number of participants in those studies was increased by the involvement of the athletes from close countries. Importantly, only one study was able to present a clear association between Tatar wrestlers and a specific SNP in an athletic group with limited participants [5]. Therefore, we can also underline that such studies are critically influenced by the populations, number of the participants, and sport branches. The present study had some limitations that may be common in other sport genetics studies. These limitations could be the restricted number of participants in the discovery phase ($$n = 80$$), heterogeneity in the branches, diverse ethnicity in the Turkish population, lack of controllability of environmental factors, and ignorance of the epigenetic mechanisms. On the other hand, we regard that we were able to reduce the probability of obtaining false-positive results by replicating our initial findings—a widely used approach in sports genetics—in the larger cohorts of Russian ($$n = 845$$) and Finnish individuals ($$n = 287$$) [14,23,49]. Still, the present study figured out four important SNPs that would further be analyzed in the next studies. ## 5. Conclusions In conclusion, by conducting the first comprehensive WES study on elite athletes, we showed that the SIRT1 rs41299232 G, NUP210 rs2280084 A, and TRPM2 rs1785440 G alleles are associated with endurance athlete status, whereas the AGRN rs4074992 C allele is linked with sprint/power athlete status and muscle fiber hypertrophy. Our data indicate that the WES approach followed by replication studies can be used to identify novel genetic markers associated with exercise- and sport-related phenotypes. ## References 1. 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--- title: Network-Based and Machine-Learning Approaches Identify Diagnostic and Prognostic Models for EMT-Type Gastric Tumors authors: - Mehdi Sadeghi - Mohammad Reza Karimi - Amir Hossein Karimi - Nafiseh Ghorbanpour Farshbaf - Abolfazl Barzegar - Ulf Schmitz journal: Genes year: 2023 pmcid: PMC10048224 doi: 10.3390/genes14030750 license: CC BY 4.0 --- # Network-Based and Machine-Learning Approaches Identify Diagnostic and Prognostic Models for EMT-Type Gastric Tumors ## Abstract The microsatellite stable/epithelial-mesenchymal transition (MSS/EMT) subtype of gastric cancer represents a highly aggressive class of tumors associated with low rates of survival and considerably high probabilities of recurrence. In the era of precision medicine, the accurate and prompt diagnosis of tumors of this subtype is of vital importance. In this study, we used Weighted Gene Co-expression Network Analysis (WGCNA) to identify a differentially expressed co-expression module of mRNAs in EMT-type gastric tumors. Using network analysis and linear discriminant analysis, we identified mRNA motifs and microRNA-based models with strong prognostic and diagnostic relevance: three models comprised of (i) the microRNAs miR-199a-5p and miR-141-3p, (ii) EVC/EVC2/GLI3, and (iii) PDE2A/GUCY1A1/GUCY1B1 gene expression profiles distinguish EMT-type tumors from other gastric tumors with high accuracy (Area Under the Receiver Operating Characteristic Curve (AUC) = 0.995, AUC = 0.9742, and AUC = 0.9717; respectively). Additionally, the DMD/ITGA1/CAV1 motif was identified as the top motif with consistent relevance to prognosis (hazard ratio > 3). Molecular functions of the members of the identified models highlight the central roles of MAPK, Hh, and cGMP/cAMP signaling in the pathology of the EMT subtype of gastric cancer and underscore their potential utility in precision therapeutic approaches. ## 1. Introduction Gastric cancer (GC) is one of the most common malignancies with extreme inter- and intra-tumoral heterogeneity [1,2]. With more than a million new cases each year and approximately 769,000 deaths in 2020, it comprises one of the leading causes of cancer-related deaths worldwide [3]. Despite its substantial burden, little progress has been made regarding the development of effective therapeutic interventions for GC patients [4]. This reflects the inability of the conventional one-size-fits-all diagnostic/therapeutic approaches for combatting such a heterogeneous disease. Fortunately, in recent decades, various classifications with either histologic [5] or molecular [6] bases have been developed for this malignancy. These classification systems guide the development of disease management strategies that are tailor-made for specific subtypes of GC. In comparison with histologic classifications, molecular classifications display a wider association with tumor heterogeneity and patient prognosis, suggesting their broader utility in the clinical setting [7]. One of the major molecular classifications of stomach cancer was developed based on the mRNA expression data of gastric tumors almost a decade ago by the Asian Cancer Research Group (ACRG) [8]. This classification stratifies gastric tumors into four subtypes, namely (i) microsatellite instability (MSI), (ii) microsatellite stable/epithelial-mesenchymal transition (MSS/EMT; EMT for short), (iii) microsatellite stable/TP53+, and (iv) microsatellite stable/TP53−. Among these, the EMT subtype is associated with significantly poorer overall survival and a higher chance of recurrence, possibly demanding a more aggressive treatment approach [8,9,10]. Despite the obvious benefits of tumor classifications, the substantial costs of the current experimental approaches required for patient stratification impede the clinical translation of these subtypes, underscoring the necessity of the development of practical biomarkers for disease/patient management [7]. Specifically, considering the aggressive nature of the EMT-type tumors, exploration of the molecular landscape of these tumors and the development of practical means for the stratification of patients into EMT and non-EMT cases is of substantial interest. In this line, Lee at el. [ 9] developed a NanoString-based 71-gene signature assay that can potentially be used for diagnostic/prognostic purposes in the clinical setting. Nevertheless, there is still room for reductions in the costs and availability of patient stratification approaches, and the underlying biology of the phenotypes observed in patients with EMT-type tumors remains elusive. In this study, we established the EMT GC subtype, proposed by the ACRG, as the subtype with the most distinct transcriptomic landscape and moved on to identify some of the core elements involved in the pathology of this subtype through the combination of co-expression module discovery and motif extraction approaches. These elements were further explored in terms of their clinical utility, and the most potent candidates with diagnostic and prognostic relevance were identified and discussed. The pipeline designed for this study appears to be robust for the identification of central regulators of biological phenomena and can readily be employed in other similar contexts. Moreover, the top motifs identified represent potent candidates for further validation to be used as affordable means for the stratification of GC patients in the clinical setting. ## 2.1. Datasets We retrieved RNA-seq and miRNA-seq raw counts from treatment-naïve adenocarcinomas of The Cancer Genome Atlas-STomach ADenocarcinoma (TCGA-STAD) cohort ($$n = 316$$; only the samples that were not flagged as low quality were retrieved) using the Genomic Data Commons (GDC) data portal [11] and microarray data from the ACRG cohort ($$n = 300$$) and the Singapore cohort ($$n = 192$$) via the Gene Expression Omnibus (accession numbers GSE62254 and GSE15459). The clinical information for the analyzed samples is available in the Supplementary Table S1. The distribution of the clinical information within each subtype for all three cohorts is also presented in Table 1. Since not all of the 316 TCGA samples possessed all the required data categories for the different steps of the analysis (e.g., survival data, ACRG classification, etc.), for each specific step of the study, only the subset of the original cohort that included all data modalities relevant to that step was utilized. Tumors from all three cohorts have been previously classified into the four ACRG-based molecular subtypes [8]. The same classification was used in this study. This reduced the samples with classifications for the TCGA to a total of 167 samples (MSI = 37; EMT = 47; TP53+ = 42; TP53− = 41). In the ACRG cohort, three samples (#369, #533, and #542) were removed since they were identified as outliers based on the Principal Component Analysis (PCA) of the log2 transformed intensities (total: 297; EMT = 46; MSI = 68; TP53+ = 77; TP53− = 106). The RNA-seq data for gastric tumors and paired normal gastric tissues were also retrieved from GSE184336 for tumor vs normal comparisons. ## 2.2. Data Analysis and Visualization R version 4.1.1 and Cytoscape version 3.9.0 were used for the statistical and network-based analysis of the data and visualization of the results. Differential gene expression analysis was carried out using the DESeq2 R package [12], which uses negative binomial generalized linear models for the identification of the differentially expressed genes. Venn diagrams were constructed using the VennDiagram package and PCA was carried out using the prcomp function in R. ## 2.3. Evaluation of ACRG Subtypes Enrichment analysis of the TCGA tumor samples classified into the four distinct subtypes in comparison to the normal samples was carried out using the Hallmark gene sets of the Gene Set Enrichment Analysis (GSEA) desktop application version 4.1.0 [13]. GSEA is one of the most popular methods from the second generation of enrichment analysis techniques. This method ranks genes based on the correlation of their expression levels with the phenotype under investigation and calculates an enrichment score for each predefined gene set (in this case, the gene sets in the Hallmark collection of the GSEA) based on the aggregation of the members of these sets at the top or the bottom of the ranked list of genes. Identification of the top modules of the differentially expressed genes for each subtype was conducted using the greedy search algorithm of the jActiveModules plug-in in the Cytoscape [14]. ## 2.4. Weighted Gene Co-Expression Network Analysis and Motif Identification Co-expression modules are, in essence, clusters of genes that present a coordinated variation in their expression levels across samples, and they potentially represent groups of genes with related functions regulated by the same transcriptional program. The interpretation of these modules within specific biological contexts can reveal novel insights regarding how specific functions/phenotypes are regulated [15]. Here, the identification of co-expression modules was performed using the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm [16]. WGCNA first constructs an adjacency matrix by applying a hard or soft thresholding procedure on the co-expression similarity measurements between each pair of genes and then utilizes a clustering approach for the identification of the co-expression modules. In this study, the co-expression module discovery was carried out with the following parameters: a signed topological overlap matrix was used, the minimum module size was set to 20, the optimum soft threshold was identified as 20 using the scale independence and mean connectivity plots, and the dendrogram cut height for module merging was set to 0.25. The significance of the modules was determined by taking the average of the −log10(adj. p-value) of the differential expression of their members in the EMT samples compared to the pooled samples of the other subtypes (Wald test; corrected for multiple hypothesis testing by the Benjamini–Hochberg method). Motifs in protein–protein interaction (PPI) networks are small subgraphs that occur much more often than is expected by chance. Alterations in the activity and expression levels of these regulatory units are a common observation in pathological states such as cancer [17]. In this context, the identified top module was further queried for biologically relevant regulatory subunits through the utilization of motif identification approaches. The PPI data were retrieved from the STRING database version 11.5 [18], and the NetMatchStar plug-in in the Cytoscape [19] was used to identify triangle motifs with three nodes and three edges. The choice of the triangle motifs was based on the high frequency with which they are observed in the biological systems and the fact that many larger motifs are comprised of multiple triangle motifs [20]. A modified version of the multi-objective scoring function used in [21,22] was used for motif scoring:Sij=W1j2×(ND)imax(ND)+W1j2×(BC)imax(BC)+W2j×(DP)imax(DP)+W3j×(AUC)imax(AUC)+W4j×(\|LFC\|)imax(\|LFC\|), where W stands for the weight, i is any given motif, j is any one of the weighting scenarios (all of the 13 utilized weighting scenarios are available in the Supplementary Table S2), ND is the mean of the node degree of each of the motif members, BC is the mean betweenness centrality, DP is the number of the nodes in a given motif that are members of the pathways in the cancer KEGG pathway (hsa05200), AUC is the mean area under the ROC curve, and the LFC is the mean absolute log2 fold change of the expression of the nodes in a motif in the EMT subtype in comparison to the pooled samples of the other subtypes. The ‘max (parameter)’ denotes the maximum value of each parameter achieved by a motif. ## 2.5. Assessment of Diagnostic and Prognostic Values of the RNAs Survival analysis was performed using the survival and survminer packages in R. The TCGA RNA-seq data for 288 solid tumor samples with appropriate clinical information based on the criteria used by Anaya [23] were subjected to Variance Stabilizing Transformation (VST), and the ACRG microarray data were Robust Multichip Average (RMA)-normalized prior to the survival analysis. The top and bottom $40\%$ of the samples (based on the expression of the gene under investigation) were used as the high-expression and low-expression groups, respectively. As for the motifs, the intersection of the samples in the top/bottom $40\%$ based on the expression of each motif member was used to form the high-expression and low-expression groups. The age and sex of the patients were used as covariates in the Cox regression analysis in order to account for their possible confounding effects. Due to the inclusion of samples that exhibited concordant high/low expression of all of the motif members in each analysis, a varying number of samples were analyzed for each motif. Considering this, only motifs with at least 30 samples in each group (high- and low-expression groups) and a total of at least 100 samples were selected for further examination. Among these, we specifically looked for motifs that were consistently present among the top five motifs of both cohorts (based on their Hazard Ratio [HR]). The glm built-in function in R was used for the logistic regression analysis. Since quantile normalization was found to be an excellent method for making the microarray and RNA-seq data comparable for machine learning applications [24], the raw counts and intensities for TCGA and ACRG samples were pooled, log2 transformed, and quantile normalized prior to logistic regression analysis. After normalization, the TCGA and ACRG samples were again separated, and the regression models for discrimination between tumor subtypes were first fitted to the TCGA data and then validated on the ACRG data. To assess the robustness of the models, their performance on the independently quantile normalized data of the samples from the Singapore cohort was also evaluated. The ability of the motifs to distinguish tumors from normal samples was also assessed by fitting a model to the TCGA RNA-seq data for both STAD solid tumors ($$n = 316$$) and the available adjacent normal tissue samples from the gastric cancer patients in the TCGA-STAD cohort ($$n = 30$$; cases for which adjacent normal tissue samples were available are distinguished with bold script in the Supplementary Table S1) after VST normalization. The same method was also applied to the GSE184336 dataset (with $70\%$ of the samples as the training set and the remaining samples as the validation set) for independent validation of the capacity of the motifs for discrimination between normal and tumor samples. Multi-candidate miRNA combinations capable of discriminating EMT-type tumors from other subtypes were identified using the linear discriminant analysis (LDA) with leave-one-out cross-validation, using the method described in [25]. Eighty percent of the samples were allocated to the training set for this analysis and the remaining samples were used for validation. The validated mRNA targets of the differentially expressed miRNAs were obtained using the multiMiR library in R [26]. ## 2.6. MiRNA-mRNA Network Construction The miRNA-mRNA network was constructed in R using the PPI interaction information from STRING and the validated miRNA-target interactions obtained from multiMiR. Twenty-three centrality measures were calculated for the network using the igraph and centiserve [27,28] packages in R. PCA was used to identify the most suitable centrality measure among these 23 centrality measures based on the structure of the network, using the method described in Ashtiani et al. [ 29]. The final network was visualized using Cytoscape. ## 3.1. EMT-Type Gastric Cancer Displays a Distinct Transcriptional Profile In order to assess the transcriptional rewiring of the tumors in different ACRG subtypes, we performed a set of exploratory analyses on 167 TCGA samples classified into four distinct subtypes (MSI, EMT, TP53+, and TP53−) [8]. GSEA has shown that EMT-type tumors did indeed exhibit hallmarks of epithelial–mesenchymal transition (False Discovery Rate (FDR) = 0.038) and angiogenesis (FDR = 0.047) as their top enrichment signals. Other subtypes, however, have consistently shown G2M checkpoint and E2F/MYC targets as their top enrichment results (FDR < 0.05) (Supplementary Figure S1). This suggests a more profound difference in the transcriptional rewiring of EMT-type tumors compared to other subtypes. Next, we reconstructed PPI networks, highlighting interactions among the differentially expressed genes in each subtype compared to normal samples (adjusted p-value ≤ 0.05, absolute LFC ≥ 3). We then identified and compared the top-scoring modules of the different subtypes based on the greedy algorithm of the jActiveModules Cytoscape plug-in. Considerable overlap between the top modules of MSI, TP53+, and TP53− subtypes was observed, yet the top module of the EMT subtype did not share any genes with the other subtypes (Supplementary Figure S2). Finally, the results of the PCA on the complete expression matrices of TCGA tumors revealed that the samples belonging to the EMT subtype are roughly distinguished in PC1; this is while no tangible difference can be observed between the other three subtypes (Supplementary Figure S3). In accordance with our observations in the TCGA samples, similar results were also observed in the PCA of the ACRG samples (Supplementary Figure S3). Overall, these results indicated that the samples belonging to the EMT subtype display the most distinct transcriptional profile among all the ACRG subtypes. ## 3.2. WGCNA and Motif Ranking Identify 39 Core mRNA Motifs In order to find robust prognostic/diagnostic RNA markers, we sought to take advantage of co-expression module and motif identification approaches to identify core RNA regulators of EMT-type tumors. The workflow implemented for the identification of these RNAs is shown in Figure 1A. Fourteen co-expression modules with varying numbers of genes were identified by applying WGCNA on the expression data of the 47 EMT-type tumors in the TCGA cohort. A list of members of each module is provided in Supplementary Table S3. We used the negative logarithm of each gene’s adjusted p-value, after differential expression analysis between EMT-type samples and other subtypes, as the criterion for gene significance. Using this criterion, the module with the most significant average differential expression was designated as the “EMT” module and the members of this module were selected for further investigation (Figure 1B). Since a high level of module membership indicates that the expression level of a gene is an adequate proxy for the general behavior of a module, the label for the rest of the modules was based on the gene with the highest level of module membership in that module. The association of the eigengenes of each module with clinical parameters (gender, age at diagnosis, pathological stage, TNM stages, and the tissue of origin) was also assessed (Figure 1C). There is a significant negative correlation between the eigengene of the EMT module and the age at diagnosis, suggesting the potential role of the members of this module in the earlier onset of the disease. Triangle motifs (with three nodes and three edges) are the most common type of motifs and are known to largely regulate the higher network structures and serve as the core building blocks of complex biological networks [20,30]. To identify core regulatory elements of the EMT module by taking advantage of the biological relevance of triangle motifs, the PPI network of the members of this module was reconstructed in Cytoscape. A total of 920 triangle motifs were identified. Each one of these motifs was scored based on 13 different weighting scenarios (Supplementary Table S2) using the multi-objective scoring function (see Section 2). Supplementary Table S4 contains all 920 motifs with their corresponding scores in each of the weighting scenarios. The top 10 motifs based on each of the weighting scenarios were selected. After removing the redundant motifs, a total of 39 top motifs remained and were used for further evaluation (Table 2). These motifs represent potent candidates for playing central roles in GC, specifically the EMT subtype. This is due to the fact that the utilized scoring function was designed to designate the best scores to the motifs with the most profound topological significance, diagnostic value, and differential expression in the EMT subtype in comparison to the other subtypes. ## 3.3. Expression of the DMD/ITGA1/CAV1 Motif Is a Strong Predictor of Patient Survival Next, we set out to characterize the 39 top motifs and identify the most potent candidates in terms of their prognostic capability. To this end, we conducted a survival analysis on the motifs based on the expression levels of the members of the motifs. For each member of the motifs, and for each motif considered a single entity, samples were divided into high expression and low expression groups both for the TCGA and ACRG cohorts, Kaplan–Meier curves were constructed (Figure 2A), and multivariate cox regression results (to account for the effects of age and sex) were extracted (Table 2). Considering our stringent criteria (Section 2), the DMD/ITGA1/CAV1 motif was identified as the top motif with consistent relevance to prognosis (HR > 3 in both TCGA and ACRG cohorts). ## 3.4. EVC/EVC2/GLI3 and PDE2A/GUCY1A1/GUCY1B1 Are Robust Diagnostic Motifs In order to assess the diagnostic capacity of the motifs and identify the most significant motifs with diagnostic relevance, we conducted a logistic regression analysis. Members of the motifs were used as predictors and the subtype of the samples (EMT versus non-EMT) as the response variable. We used the TCGA cohort as the training set and the ACRG cohort as the validation set. Additionally, the independently normalized data from the samples of the Singapore cohort were used to assess the robustness of the models. The top two motifs based on their Area Under the Receiver Operating Characteristic Curve (AUC) in the validation set were EVC/EVC2/GLI3 (AUC = 0.97) and PDE2A/GUCY1A1/GUCY1B1 (AUC = 0.97) (Figure 2B; Table 3). We also assessed the diagnostic capacity of the motifs for distinguishing tumors from normal samples using the data from TCGA-STAD normal and tumor tissues and the GSE184336 dataset as an independent test set. Interestingly, PDE2A/GUCY1A1/GUCY1B1 achieved the highest AUC in the TCGA cohort (AUC = 0.95) and an AUC of 0.85 in the test set of the GSE184336 dataset, reinforcing its diagnostic importance (Table 4). ## 3.5. A Two-Membered miRNA Model Accurately Distinguishes EMT-Type Tumors from Other Gastric Tumors The candidate miRNAs regulating the expression of the identified motifs were determined through the identification of differentially expressed miRNAs (EMT vs other subtypes; $$n = 220$$) that targeted one or more genes among the members of the top 39 motifs (109 miRNAs). The top multi-candidate miRNA combination was identified using LDA with leave-one-out cross-validation. The top two-membered miRNA combination consisting of hsa-miR-199a-5p and hsa-miR-141-3p with an AUC of 0.963 in the training set and an AUC of 0.995 in the test set was identified as the best discriminant multi-candidate miRNA combination (index: (0.597167 × hsa-miR-199a-5p) + (−0.798247 × hsa-miR-141-3p) + 2.02755). The results of the survival analysis for these miRNAs and their combination are demonstrated in Figure 3. Finally, the integrated interaction network of the members of the top 39 motifs and the 109 differentially expressed miRNAs targeting them was visualized (Figure 4). ## 4. Discussion Among the molecular classifications of gastric tumors by ACRG, tumors of the EMT subtype are associated with significantly worse patient prognosis and likely demand more drastic therapeutic interventions [9]. Coupling this with the vastly unknown nature of the tumors of this subtype, further investigation of the molecular landscape of these tumors and the development of diagnostic and predictive biomarkers are of utmost importance. Here, we have identified a differentially expressed co-expression network in the tumors of the EMT subtype using WGCNA. The negative correlation of this module with the age of the patients at the time of diagnosis (Figure 1C) is in line with the characterization of this subtype by ACRG [8] and indicates the relevance of this module to the EMT subtype. We have further explored this co-expression module in order to extract its central motifs and regulatory miRNAs with relevance to diagnosis and prognosis. ## 4.1. Poor Outcomes for Patients with High Expressions of DMD/ITGA1/CAV1 Motif Our results are able to characterize the signaling circuits involved in the aggressive phenotypes often observed in the gastric tumors of the EMT subtype (e.g., invasion, chemoresistance, etc.). We have identified the DMD/ITGA1/CAV1 motif as the top motif with consistent relevance to prognosis (HR > 3 in both TCGA and ACRG cohorts). The ITGA1 gene encodes the α-1 subunit of the integrin superfamily of glycoproteins. These transmembrane receptors are responsible for a variety of cellular functions including cell adhesion, migration, and intracellular signaling in response to the extracellular environment (ECM) [32]. ITGA1 is extensively associated with cancer invasiveness and poor patient prognosis in various tumor types. It promotes EMT, proliferation, and drug resistance in response to dysregulations in the tumor extracellular matrix. This is in part realized through upregulation of the Ras/MEK/ERK (MAPK) pathway [33,34,35,36]. Additionally, a wealth of studies indicate that the EMT-promoting effects of dysregulation in various molecules in GC converge on ITGA1, highlighting its potential as a therapeutic target [37,38]. Upon stimulation, the integrin receptors activate Ras through the recruitment of the Grb2/SOS complex. This is a process in which Caveolin-1 (Cav-1), a protein encoded by another member of the identified motif (CAV1), has been shown to play a pivotal role [39]. Cav-1 is best known for its crucial roles as a component of the caveolae—invaginations in the cell membrane involved, among other functions, in cell surface receptor localization and signal transduction [40]. Similar to ITGA1, Cav-1 is strongly associated with poor treatment outcomes, poor prognosis, and EMT [41,42]. Importantly, MAPK is not the only pathway through which Cav-1 has been associated with EMT. It has been shown that Cav-1 stimulates the dephosphorylation of β-Catenin, culminating in the activation of the WNT pathway and upregulation of Met receptor tyrosine kinase. Met (also known as HGFR), through its positive crosstalk with HER2, contributes to tumor aggressiveness, migration, proliferation, and chemoresistance by upregulating MAPK, WNT, and PI3K/AKT pathways [40]. Studies investigating the role of DMD, the last member of the identified motif, are sparse and contradictory [43], warranting a need for further investigation of the role of the DMD in the GC EMT subtype and its functional association with ITGA1 and Cav-1. ## 4.2. The EVC/EVC2/GLI3 Motif Performs Well Both as a Diagnostic and a Prognostic Marker Our analysis pipeline resulted in the identification of two motifs with superior relevance to the diagnosis of gastric tumors of the EMT subtype. The top identified motif consists of EVC, EVC2, and GLI3; genes coding for essential members of the Hedgehog (Hh) signaling pathway [44]. The Hh pathway is firmly associated with the exhibition of stem-like phenotypes in cancer, cancer cell migration, EMT, and drug resistance in various cancer types including GC [45,46,47]. GLI3 is a transcription factor central to the regulation of the Hh pathway and plays dual roles both as an activator and a repressor of the genes downstream of this pathway [44]. In the absence of the Hh pathway ligands, GLI3 is bound to SUFU, which mediates its proteolytic cleavage, resulting in the abundance of cleaved GLI3 proteins, which act as suppressors of the Hh pathway. In the presence of the Hh ligands, SUFU dissociates from the GLI3 in a process in which both EVC and EVC2 have been shown to be of vital importance [48]. The dissociated full-length GLI3 promotes upregulation of the Hh pathway. The activity of GLI3 is strongly associated with various malignancies. For example, it promotes proliferation and EMT in multiple cancer types [49,50] and plays a role as a cancer driver gene in GC [51]. Importantly, multiple lines of evidence associate the overexpression of GLI3 with poor prognosis in various tumor types [50,52]. In line with these reports, our results indicate considerably worse outcomes for patients with higher expression of the EVC/EVC2/GLI3 motif in both TCGA (HR = 2) and ACRG (HR = 2.7) cohorts, suggesting the possible utility of this motif as a prognostic indicator as well as a diagnostic marker. ## 4.3. PDE2A/GUCY1A1/GUCY1B1—A Strong Diagnostic Marker The other identified top motif with potential diagnostic capacity for the EMT subtype of GC is comprised of PDE2A (a member of the phosphodiesterase superfamily), GUCY1A1, and GUCY1B1 (also known as GUCY1A3 and GUCY1B3, respectively). These molecules are central regulators of the metabolism of cyclic guanosine monophosphate (cGMP) and cyclic adenosine monophosphate (cAMP), secondary messengers involved in many cellular functions including cell proliferation, differentiation, and apoptosis [53]. Interestingly, in addition to its exceptional performance in discriminating the samples of the EMT subtype from other gastric tumors, this motif presented a capacity for distinguishing gastric tumors from normal samples (AUC = 0.95; highest AUC among the assessed motifs), demonstrating its potential use as a diagnostic marker of GC in general. Notably, the presence of other proteins of the phosphodiesterase superfamily (PDE1A and PDE3A) and adenylate cyclase 5 (ADCY5) in addition to guanylate cyclase (GUCY) proteins among the identified top motifs (Table 3; Figure 4) points to a likely central role of cAMP and cGMP metabolism in the EMT subtype of GC. In line with this, there are a plethora of studies indicating the viability of phosphodiesterase inhibition as a treatment approach for the suppression of proliferation and reduction of the invasion capacity of tumors in various cancers [54]. However, the exact role of these molecules in tumorigenesis and cancer progression is ambiguous, and specifically, the interplay between the cyclase and phosphodiesterase proteins in cancer remains largely unexplored. ## 4.4. MiR-199a-5p and miR-141-3p Dysregulations Are Associated with Tumor Invasiveness Another important result of this study is the identification of a candidate two-membered miRNA diagnostic biomarker (AUC = 0.995; Figure 3) consisting of hsa-miR-199a-5p (upregulated in the samples of the EMT subtype; LFC = 1.4) and hsa-miR-141-3p (downregulated in the samples of the EMT subtype; LFC = −1.9). In contrast to its downregulation in various tumor types, the expression of hsa-miR-199a is shown to be increased in the case of GC and has been associated with increased tumor invasiveness and metastasis in multiple studies [55,56]. These reports are in accordance with the observations of the current study and support the positive coefficient of this molecule in the identified diagnostic model. The other member of our two-membered diagnostic model, hsa-miR-141-3p, is a member of the miR-200 family of miRNAs, the downregulation of the members of which is tightly associated with increased proliferation, EMT, and invasiveness of gastric tumors among other tumor types [57,58,59]. Altogether, these results highly support the relevance of the identified two-membered miRNA-based diagnostic model in distinguishing gastric tumors of the EMT subtype. Additionally, the expression of both of these miRNAs was associated with patient outcomes in GC in previous studies [55,59]. However, our results only indicate a positive association between the high expression of hsa-miR-199a-5p and poor survival (p-value = 0.034). No association between the expression of hsa-miR-141-3p and patient prognosis could be observed (p-value = 0.34; Figure 3). ## 5. Conclusions A few points regarding the implemented methods for motif identification and their limitations in this study should be noted. Considering the effects of multi-collinearity, the coefficients in the logistic regression modeling of the motifs should be utilized with caution when inferring the behavior of the mRNAs in these motifs since they are all extracted downstream of WGCNA. Nevertheless, this does not affect the precision of the prediction of the disease status by the motifs, and thus the top motifs with diagnostic capacity represent viable candidates. One should also take note that, based on the design of this study, the identified motifs are inclined to be more important in the EMT subtype, but their importance is not necessarily restricted to it; especially due to the inclusion of weighting factors such as the topological significance and previous association with cancer pathways in the motif ranking procedure. Additionally, while the top motifs in terms of prognostic and diagnostic capacity were the main focus of this discussion, all of the other high-scoring motifs in different weighting scenarios (Supplementary Table S4) represent potential candidates for playing significant roles in the pathology of GC and are encouraged to be further explored. Finally, this investigation was carried out entirely in silico, and subsequent wet-lab experiments are necessary for further validation of the results. Overall, the current study took advantage of the biological relevance of both co-expression modules and network motifs through the combination of their identification methods in an end-to-end analysis workflow. Exploiting the abilities of WGCNA, a multi-objective motif scoring function, and machine learning approaches, we identified combinations of mRNAs and regulatory miRNAs with considerable prognostic and diagnostic capability. These results highlight the central roles of MAPK, Hh, and cGMP/cAMP signaling in the pathology of the EMT subtype of GC and provide an unprecedented picture of rewired signaling circuits that possibly contribute to the phenotypes observed in tumors of this subtype. Additionally, the identified co-expression modules and the large number of characterized motifs provide an opportunity for further exploration of this subtype of gastric tumors through various study designs. ## References 1. Ho S.W.T., Tan P.. 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--- title: Interplay between microRNAs, Serum Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9), and Lipid Parameters in Patients with Very High Lipoprotein(a) Treated with PCSK9 Inhibitors authors: - Tina Levstek - Tina Karun - Andreja Rehberger Likozar - Miran Šebeštjen - Katarina Trebušak Podkrajšek journal: Genes year: 2023 pmcid: PMC10048228 doi: 10.3390/genes14030632 license: CC BY 4.0 --- # Interplay between microRNAs, Serum Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9), and Lipid Parameters in Patients with Very High Lipoprotein(a) Treated with PCSK9 Inhibitors ## Abstract Proprotein convertase subtilisin/kexin type 9 (PCSK9) has an important function in the regulation of lipid metabolism. PCSK9 reduces hepatic low-density lipoprotein receptors, thereby increasing low-density lipoprotein cholesterol levels. However, its regulation remains to be elucidated, including post-transcriptional regulation by microRNAs (miRNAs). We aimed to explore the interplay between miRNAs, total serum PCSK9, and lipids during treatment with PCSK9 inhibitors. A total of 64 patients with stable coronary artery disease and very high lipoprotein(a) levels and 16 sex- and age-matched control subjects were enrolled. Patients received a PCSK9 inhibitor (evolocumab or alirocumab). Total serum PCSK9 levels were measured by immunoassay. RNA was isolated from plasma using magnetic beads, and expression of selected miRNAs was analyzed by quantitative PCR. Total serum PCSK9 levels were significantly higher in control subjects compared with patients. After 6 months of treatment with PCSK9 inhibitors, total serum PCSK9 levels increased significantly. The expression of miR-191-5p was significantly lower, and the expression of miR-224-5p and miR-483-5p was significantly higher in patients compared with control subjects. Using linear regression, the expression of miR-483-5p significantly predicted the serum PCSK9 level at baseline. After the 6-month period of therapy, the expression of miR-191-5p and miR-483-5p significantly increased. Our results support a role for miR-483-5p in regulating circulating PCSK9 in vivo. The difference in expression of miR-191-5p, miR-224-5p, and miR-337-3p between patients and control subjects suggests their possible role in the pathogenesis of coronary artery disease. ## 1. Introduction The World Health Organization estimates that about one-third of all deaths worldwide are accounted to cardiovascular disease (CVD). The leading cause of CVD is atherosclerosis [1]. Elevated levels of low-density lipoprotein cholesterol (LDL-C) are casually associated with atherosclerotic CVDs and are the most important modifiable factor in the prevention of atherosclerosis and cardiovascular events [2]. Lipoprotein (a) (Lp(a)) is an independent risk factor for coronary artery disease (CAD), regardless of LDL-C levels before and also after its reduction, especially with statins [3]. The structure of Lp(a) resembles LDL-C in the composition of the lipid core and presence of apolipoprotein B (apoB). However, it also consists of the unique glycoprotein apolipoprotein(a) (apo(a)), which is bound to the apolipoprotein B100 moiety of an LDL-like particle via a disulfide bond [4,5]. Apo(a) is encoded by the LPA gene, and variants in this gene are responsible for as much as $90\%$ of the variability in circulating Lp(a) levels [6]. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a soluble serine protease expressed predominantly in the liver and is considered a fundamental regulator of lipid metabolism [7]. When PCSK9 bound to LDL-C interacts with the LDL receptor (LDLR), the catalytic domain of PCSK9 interacts with LDLR. After endocytosis, the affinity of the interaction between PCSK9 and LDLR increases under acidic conditions, preventing LDLR from recycling. Instead, the complex is directed to the lysosome, where both are degraded. A lower abundance of LDLR on the surface of hepatocytes results in higher levels of LDL-C in plasma [8,9,10]. PCSK9 may also promote the degradation of LDLR through an intracellular pathways reported in cultured cells [11]. Beyond its role in cholesterol homeostasis, PCSK9 mediates inflammatory responses by binding to Toll-like receptors (TLRs) and promotes platelet activation and thrombosis by binding to scavenger receptor class B (SR-B) receptors. In addition, binding to low-density lipoprotein receptor-related protein 1 (LRP1), apolipoprotein E receptor-2 (ApoER2), very-low-density lipoprotein receptor (VLDLR), and other receptors promotes vascular endothelial hyperplasia and increases lipoprotein levels [12]. PCSK9 loss-of-function variants have been associated with a reduction in circulating LDL-C levels and risk of coronary heart disease [13]. In the last decade, several monoclonal antibodies specifically targeting PCSK9 have been introduced in the clinical practice [14]. PCSK9 inhibitors bind circulating PCSK9, preventing its binding to LDLR and thus allowing its recycling. PCSK9 inhibitors reduce LDL-C levels by approximately 40–$65\%$ when used in conjunction with statins. Moreover, unlike statins, PCSK9 also reduces Lp(a) levels by 25–$30\%$ [15,16]. Nevertheless, the understanding of the pathways regulating PCSK9 availability is insufficient. Post-transcriptional regulation by microRNAs (miRNAs) is of particular interest because they are small, single-stranded, non-coding RNAs known to regulate gene expression by degrading messenger RNAs or inhibiting their translation [17]. Accumulating evidence suggests that miRNAs play a crucial role in cholesterol metabolism and in the development and progression of atherosclerosis through their involvement in endothelial integrity, monocyte/macrophage recruitment, and vascular smooth muscle and inflammatory cell function [18,19,20]. One of the most intensively studied miRNA implicated in the regulation of lipid homeostasis is miR-33. miR-33 is a family of sterol-sensitive miRNAs. They are involved in the regulation of very-low-density lipoprotein cholesterol (VLDL-C), LDL-C, and high-density lipoprotein cholesterol (HDL-C) metabolism, HDL-C uptake, cholesterol storage, transport, excretion, and efflux [21]. Understanding the regulation of PCSK9 by miRNAs could lead to the discovery of new biomarkers and provide new insights into the pathophysiology of atherosclerosis. The aim of the present study was to investigate miRNA expressions and circulating PCSK9 levels in patients with stable phase of CAD at least 6 months after acute myocardial infarction and very high Lp(a) levels and in control subjects. We also aimed to elucidate the relationship between serum PCSK9, miRNAs expression and lipid parameters after the patients were treated with PCSK9 inhibitors. ## 2.1. Study Participants We included 64 patients and additional 16 control subjects matched for sex and age. Patients were in the stable phase of CAD at least 6 months after acute myocardial infarction. The inclusion criteria for patients were premature myocardial infarction before 50 years of age and an Lp(a) level of more than 1000 mg/L or an Lp(a) level of more than 600 mg/L and an LDL-C level of more than 2.6 mmol/L. Patients were receiving state-of-the-art therapy that had not been changed for at least 2 months before enrollment in the study. They received a statin at a maximally tolerated dose plus ezetimibe, if needed. They also received β-blockers, antiplatelet agents, and angiotensin-converting enzyme inhibitors. The following exclusion criteria applied: elevated liver transaminases enzyme activity (more than three times above reference values) and severe renal dysfunction (serum creatinine more than 200 mmol/L). In addition, patients were excluded if they had acute illness in the last 6 weeks. Control subjects had no history of CVD, no hypercholesterolemia, and an Lp(a) level less than 300 mg/L. After enrollment in the study, blood samples were collected, and patients underwent clinical and laboratory examinations [22]. They were divided into two groups regarding treatment with PCSK9 inhibitors. The first group received placebo for 6 months. This was followed by subcutaneous treatment with a PCSK9 inhibitor, namely alirocumab at a dose of 150 mg or evolocumab at a dose of 140 mg, every two weeks. The second group, however, received the PCSK9 inhibitor immediately after enrollment in the study. Laboratory parameters were determined at baseline and later after the placebo phase, and after treatment with PCSK9 inhibitors. The Slovenian National Ethics Committee endorsed the protocol of the study (No. 0120-$\frac{357}{2018}$/8 and No. 0120-$\frac{317}{2021}$/3). All participants were informed about the study protocol and signed an informed consent prior to the enrollment, as recommended by the Declaration of Helsinki. ## 2.2. Biochemical Analysis Blood for laboratory analysis was drawn in the morning after 12 hours of fasting. We collected samples from the antecubital vein into 5 mL vacuum-sealed tubes containing clot activator (Vacutube; LT Burnik, Skaručna, Slovenia). We centrifuged the blood at 2000× g for 15 min to separate the serum. In fresh serum samples, we measured total cholesterol, triglycerides, HDL-C, apolipoprotein A1 (apoA1), and apoB by standard colorimetric or immunologic assays. We used an automated biochemical analyzer (Fusion 5.1; Ortho-Clinical Diagnostics, Raritan, NJ, USA). The same biochemical analyzer was used to determine Lp(a) with the Denka reagent (Randox, Crumlin, UK). Because of the apo(a)-isoform-insensitive antibodies used in the reagent, the bias associated with apo(a) size is minimal. The Friedewald formula [23] was used to calculate LDL-C. ## 2.3. Measurement of Serum PCSK9 Levels Total PCSK9 was measured in serum samples using a sandwich enzyme-linked immunosorbent assay (ELISA) (Human Proprotein Convertase 9/PCSK9 Quantikine® ELISA Kit, R&D Systems, Minneapolis, MN, USA) on a Sunrise microplate reader and Magellan software (Tecan, Männedorf, Switzerland) as instructed by the manufacturer. Sensitivity and assay range provided by the manufacturer were 0.219 ng/mL and 0.6–40 ng/mL, respectively. Samples were diluted 1:20 with calibrator diluent. Samples exceeding the upper limit were diluted 1:40 and reanalyzed. The assay used measured total PCSK9 in serum, so it was not possible to distinguish between bound and unbound PCSK9. ## 2.4. Extraction of Circulating RNAs from Plasma Samples and Reverse Transcription Circulating RNAs were extracted from 600 µL plasma samples. We used the NextPrep™ Magnazol™ cfRNA Isolation Kit (PerkinElmer, Waltham, MA, USA) as instructed by manufacturer. We eluted the circulating RNAs in 18 µL of RNA Elution Solution (0.1 mM EDTA). To avoid RNA decay, we added RiboLock RNase Inhibitor (Thermo Fisher Scientific, Waltham, MA, USA). We stored isolated circulating RNA samples at −80 °C in LoBind DNA tubes (Eppendorf, Hamburg, Germany). RNAs were transcribed into cDNA with the miRCURY Locked Nucleic Acid (LNA) Universal RT Kit (Qiagen, Hilden, Germany). The amount of input RNA was optimized using three different volumes of RNA sample, as recommended by the manufacturer. The volume 0.24 µL was selected as optimal for cDNA synthesis. The reaction mixture contained the following: 5× miRCURY RT SYBR® Green RT Reaction Buffer (2 µL), RNase-free water (6.6 µL), 10× miRCURY RT Enzyme Mix (1 µL), and previously isolated template RNA (0.4 µL). The reverse transcription was carried out using a GeneAmp® PCR System 9700 thermocycler (Applied Biosystems, Waltham, MA, USA). cDNA was stored in a LoBind DNA plate (Eppendorf, Hamburg, Germany) at 4 °C and analyzed within 4 days. ## 2.5. Quantification of miRNA Expression by Quantitative PCR A literature search was performed to identify miRNAs previously associated with regulation of PCSK9 levels. Five miRNAs were selected for analysis. Expression of the selected miRNAs was measured by quantitative PCR (qPCR). We used specific miRCURY LNA miRNA PCR Assays (Qiagen, Hilden, Germany) (Table 1). Each sample was analyzed in triplicate. cDNA was diluted 1:30 with RNase-free water, except for miR-483-5p, which was diluted 1:10. For each reaction, we prepared a reaction mixture consisting of 2x miRCURY SYBR® Green Master Mix with added low ROX Reference Dye (5 µL), PCR primer mix (1 µL), RNase-free water (1 µL), and cDNA template (3 µL). Each plate also contained a no-template control in triplicate. The qPCR reaction was carried out on a QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems, Waltham, MA, USA) using the following protocol: 2 min at 95 °C; 40 cycles of 10 s at 95 °C, 1 min at 56 °C. The mean cycle of quantification (Cq) values and melting curves for each miRNA were obtained from the instrument software. miR-16-5p and miR-4516 were used as reference miRNAs [24,25]. Relative expression was calculated as 2−ΔCq, where ΔCq = average Cq (target miRNA) − average Cq (reference miRNAs) [26]. ## 2.6. Statistical Analysis We used IBM SPSS Statistics version 27.0 (IBM Corporation, New York, NY, USA) to perform the statistical analyses. For descriptive statistical analysis, we analyzed the normality of the distribution of continuous variables with the Shapiro–Wilk test. We used the median with interquartile range (25–$75\%$) or the mean with standard deviation to describe the central tendency and variability of the continuous variables. We used frequencies to describe the distribution of the categorical variables. The Chi-square test was used to compare the distribution of the categorical variables between different groups, whereas the t-test or nonparametric the Mann–Whitney U test was used for the continuous variables. The Wilcoxon signed rank test was used to assess changes during the placebo and treatment periods. Linear regression was used to test the association between PCSK9 levels and expression of miRNAs at enrollment. Correlations between continuous variables were calculated using Spearman’s Rho coefficient. All statistical tests were two-sided. We used 0.05 as the level of statistical significance. Figures were generated using GraphPad Prism 9 (San Diego, CA, USA). ## 3.1. Baseline Characteristics of Study Subjects Patients and control subjects were age- and sex-matched and did not differ significantly in systolic and diastolic blood pressure. However, patients had significantly lower total cholesterol, non-HDL-C, LDL-C, apoB (all $p \leq 0.001$), HDL-C ($$p \leq 0.005$$) and apoA1 ($$p \leq 0.021$$). Triglycerides did not differ significantly between patients and control subjects. Lp(a) was significantly higher in patients ($p \leq 0.001$). Details are shown in Table 2. ## 3.2. Characteristics of Patients after Treatment with PCSK9 Inhibitors Total cholesterol, non-HDL-C, LDL-C, triglycerides, apoB, and Lp(a) decreased significantly (all $p \leq 0.001$) after the 6-month period of treatment. Total cholesterol decreased by 40.8 (28.1–45.1)%, LDL-C by 69.7 (59.1–78.3)%, triglycerides by 21.1 ((−1.59)–34.4)%, and Lp(a) by 24.0 (12.2–33.0)%. On the other hand, HDL-C and apoA1 levels increased significantly ($$p \leq 0.004$$ and $$p \leq 0.002$$, respectively). HDL-C increased by 4.78 ((−3.54)–13.3)% and apoA1 by 3.39 ((−2.34)–9.32)% (Table 3). ## 3.3. Total Serum PCSK9 Level Patients and control subjects differed significantly in PCSK9 levels ($$p \leq 0.005$$). In patients, PCSK9 levels increased significantly after the placebo period ($$p \leq 0.007$$). Total serum PCSK9 levels also increased significantly after the 6-month period of treatment. Serum PCSK9 levels are shown in Figure 1. ## 3.4. miRNA Expressions in Patients and Control Subjects before Treatment As shown in Figure 2, the expression of miR-191-5p was significantly downregulated in patients compared with control subjects ($p \leq 0.001$). Contrary, the expression of miR-224-5p and miR-337-3p was significantly upregulated in patients compared with control subjects ($p \leq 0.001$ and $$p \leq 0.001$$, respectively). We found no significant difference in miR-483-5p expression between patients and control subjects ($$p \leq 0.700$$). miR-552-3p could not be determined due to its low expression in plasma. ## 3.5. miRNAs Expression after Placebo and Treatment Period A cohort of 28 patients received placebo for 6 months. Expression of miR-191-5p decreased significantly during the placebo period ($p \leq 0.001$), whereas expression of other miRNAs did not change significantly (Figure 3). The expression of miR-191-5p and miR-483-5p was significantly higher after 6 months of treatment with PCSK9 inhibitors ($$p \leq 0.028$$ and $$p \leq 0.020$$, respectively). Additionally, the expression of miR-224-5p decreased, but the difference was not statistically significant ($$p \leq 0.094$$) (Figure 4). ## 3.6. Prediction of Serum PCSK9 Level by Circulating miRNAs A multiple linear regression was performed to predict serum PCSK9 levels based on the expression of the included miRNAs in patients at the time of enrollment. All variables were log transformed because of the skewed distribution. A significant regression equation was found (F [4, 58] = 2.982, $$p \leq 0.026$$), with an R2 of 0.171. miR-483-5p significantly predicted PCSK9 (β = 0.201, $$p \leq 0.003$$), whereas miR-191-5p, miR-224-5p and miR-337-3p did not significantly predict PCSK9 level. Details are shown in Table S1. Next, a simple linear regression was calculated to predict PCSK9 based on the expression of miR-483-5p. A significant regression equation was found (F [1, 61] = 8.904, $$p \leq 0.004$$), with an R2 of 0.127. A one percent increase in miR-483-5p expression was associated with about a 0.19 percent increase in serum PCSK9 level (Table S2). ## 3.7. Correlation between the Change of miRNAs Expression and Lipids after Treatment with PCSK9 Inhibitors Spearman’s Rho correlation analyses were performed to investigate possible associations between the change in miRNAs expression, serum PCSK9 levels, and lipid parameters after a 6-month period of therapy with PCSK9 inhibitors. Significant correlations are shown in Figure 5. We found a negative correlation between the change in expression of miR-224-5p and miR-191-5p (Rho = −0.359, $$p \leq 0.005$$) and a positive correlation between the change of miR-224-5p and miR-337-3p (Rho = 0.776, $p \leq 0.001$) and miR-483-5p expression (Rho = 0.427, $$p \leq 0.001$$). There was also a positive correlation between the change in miR-337-3p and miR-483-5p expression (Rho = 0.555, $p \leq 0.001$). The change in serum PCSK9 was not significantly associated with the change in miRNAs expression after treatment with PCSK9 inhibitors. Details are provided in Table S3. When we evaluated the correlations between the change in miRNAs expression and lipid parameters, including total cholesterol, HDL-C, non-HDL-C, LDL-C, triglycerides and Lp(a), we found a negative correlation between the change in miR-191-5p expression and HDL-C (Rho = −0.299, $$p \leq 0.020$$). All results are shown in Table S4. ## 4. Discussion Several studies have demonstrated the involvement of miRNAs in regulating cholesterol homeostasis [27], but the regulation of PCSK9 by miRNAs remains poorly understood. Most studies used in vitro or animal models, whereas studies in human samples are rare. In the present study, we aimed to investigate the regulation of total serum PCSK9 levels by circulating miRNAs in a well-defined cohort of patients with stable CAD and very high Lp(a) levels. In addition, the expression of miRNAs and lipid parameters were measured after a 6-month period of therapy with PCSK9 inhibitors. Because all patients received statin in maximum tolerated dose to reduce the risk of recurrent cardiovascular events, total cholesterol, LDL-C, and apoB were significantly lower in patients compared with control subjects. However, HDL-C and apoA1 were significantly higher in control subjects. We selected only patients with highly elevated Lp(a) levels; therefore, patients had significantly higher Lp(a) levels. Treatment with PCSK9 inhibitors significantly lowered Lp(a) levels, total cholesterol, LDL-C, triglycerides, and apoB, whereas levels of HDL-C and apoA1 increased significantly. Serum PCSK9 levels were higher in control subjects than in patients, which is surprising at first glance because they have been associated with the severity of CAD and vascular inflammation [28,29]. Moreover, statin treatment activates the transcriptional activity of sterol regulatory element binding protein-2 (SREBP2), increasing the expression of LDLR and PCSK9 [30]. Indeed, all included patients received statin therapy in maximum tolerated dose. Nevertheless, the variability of PCSK9 levels was markedly higher in patients, whereas PCSK9 levels were more homogeneous in control subjects. In addition, the cohort of control subjects in our study was relatively small because of the strict inclusion criteria. PCSK9 levels also increased significantly during the placebo period although the therapy patients received did not change. The immunoassay used in our study measured total circulating PCSK9, i.e., unbound PCSK9 and PCSK9 bound to therapeutic antibodies. As shown previously, total PCSK9 levels increase after treatment with PCSK9 inhibitors, possibly due to a large increase in PCSK9 bound to therapeutic antibody. Indeed, circulating PCSK9 remains bound to the therapeutic antibody for 2 to 3 weeks [31]. Nevertheless, total serum PCSK9 levels increased approximately 10-fold after 6 months of treatment with PCSK9 inhibitors, similar to previous studies [31,32]. Studies measuring only free circulating PCSK9 (without inactive PCSK9 bound to the therapeutic antibody) showed a decrease in PCSK9 levels after treatment with PCSK9 inhibitors [33]. Recently, preliminary results from the ALIROCKS study reported that total plasma PCSK9 levels could serve as a biomarker for adherence to treatment with PCSK9 antibodies. They suggested that in non-adherent patients, LDL-C levels should decrease by no more than $25\%$ and total PCSK9 levels should increase by no more than threefold after treatment with PCSK9 inhibitors [32]. Based on these criteria, we identified three non-adherent patients in our cohort. Identification of patients with reduced or even absent lipid-lowering response is important because of the costliness of treatment with PCSK9 inhibitors. In this study, we focused on the expression of five miRNAs that have previously been reported to interact with and regulate the expression of PCSK9 mRNA. In addition, miR-224-5p, miR-337-5p, miR-483-5p, and miR-552-3p were found to decrease LDL-C levels in murine models [34,35,36,37,38,39]. miR-552-3p was reported to regulate PCSK9 levels in the human hepatocellular carcinoma cell line (HepG2) [34]. However, we could not clearly determine the level of miR-552-3p in plasma because its expression was very low. Therefore, its contribution to the regulation of serum PCSK9 in humans is questionable and the role of other miRNAs may be more prominent. Moreover, HepG2 cells are not involved in the atherosclerotic process in vivo, which could also be a reason for the discrepancies. None of the above studies examined the difference between patients and control subjects. In our study, we showed decreased expression of miR-191-5p and increased expression of miR-224-5p and miR-337-5p in patients compared with control subjects, indicating their possible involvement in the pathophysiological processes of CAD. The decreased expression of miR-191-5p in patients may be due to the fact that all patients were treated with acetylsalicylic acid and more than half also received another antiplatelet drug (clopidogrel, ticagrelor, or prasugrel). miR-191 is a platelet-derived miRNA, and antiplatelet drugs have been shown to reduce miR-191 expression. In addition, more aggressive antiplatelet treatment decreased miR-191-5p expression more than acetylsalicylic acid treatment alone [40]. Decreased expression of miR-191 was found in patients with acute myocardial infarction compared with control subjects, which returned to the value of controls within 48 h [41] and in patients who experienced major adverse cardiovascular events compared with matched subjects who had no adverse cardiovascular events during the 2-year follow-up period after ST segment elevation myocardial infarction treated with endovascular revascularization [42]. Although miR-483-5p expression was not significantly different between patients and control subjects, miR-483-5p expression was significantly associated with serum PCSK9 levels in patients at baseline. This finding suggests that miR-483-5p is involved in the post-transcriptional regulation of PCSK9 expression in vivo. However, in contrast to the results in HepG2 cells [36], our results showed a positive association between serum PCSK9 level and miR-483-5p expression. Nevertheless, miR-483-5p was associated with cardiometabolic risk factors at baseline and during the 3.7-year follow-up period with new-onset diabetes and CAD [43,44]. Increased serum miR-483-5p expression was also reported in patients with asymptomatic carotid artery stenosis compared with controls. At the same time, serum miR-483-5p expression was an independent predictive factor for the occurrence of an acute cerebrovascular event in patients with asymptomatic carotid artery stenosis [45]. During the placebo period, miR-191-5p expression decreased significantly, which is rather unexpected. To our knowledge, the effect of treatment with PCSK9 inhibitors on the expression of the included miRNAs has not been investigated. In our study, the expression of miR-191-5p and miR-483-5p increased significantly after 6 months of treatment with PCSK9 inhibitors. Decreased expression of miR-191-5p implies a higher risk of both acute coronary and cerebrovascular events, whereas treatment with PCSK9 inhibitors increased miR-191-5p expression. Based on this, we could propose that one of the pleotropic mechanisms of PCSK9 inhibitors contributing to the reduction of cardiovascular events is the increased expression of miR-191-5p. An inhibitory role was attributed to all of included miRNAs in murine models or in vitro studies. These results can be partially justified by the fact that each miRNA targets many genes and is therefore involved in different biological processes [17]. For example, miR-224 also targets inducible degrader of the low-density lipoprotein receptor (IDOL), the chaperone protein that promotes lysosomal LDLR degradation, 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the rate-limiting enzyme of cholesterol biosynthesis [35,39], and acyl-CoA synthetase long-chain family member 4 (ACSL4), an essential enzyme in fatty acid metabolism. miR-224 is also a negative regulator of adipocyte differentiation [46]. In agreement with previous studies, treatment with PCSK9 inhibitors significantly lowered total cholesterol, LDL-C, triglyceride, and Lp(a) levels and increased HDL-C levels [47,48]. Non-adherent patients were excluded from the correlation analysis. The change in miR-224-5p expression was negatively correlated with the change in miR-191-5p expression and positively correlated with the change in miR-337-3p and miR-483-5p expression. Additionally, there was a positive correlation between the change in miR-337-3p and miR-483-5p expression. This suggests intertwined relationship between these miRNAs. For lipid parameters, we found only a significant correlation between the change in miR-191-5p and HDL-C level after PCSK9 inhibition. Although miR-224-5p, miR-337-5p, and miR-483-5p have previously been shown to lower LDL-C levels in murine models [34,35,36,37,38,39], the change in their expression was not associated with the change in LDL-C level after treatment with PCSK9 inhibitors. The following limitations should be considered when interpreting our results. First, we included a relatively small cohort of patients, mainly because of the strict exclusion criteria. On the other hand, this resulted in a homogeneous cohort. Second, because of the limited specificity of the ELISA assay, it was not possible to determine the free PCSK9 levels after PCSK9 inhibition, which would be interesting to evaluate possible correlations between the change in free PCSK9 and the miRNAs studied. In this study, we focused only on five previously identified miRNAs that regulate PCSK9 levels. Further studies are needed to comprehensively evaluate the expression of different miRNAs in relation to circulating PCSK9 levels. We must also consider that we do not know to what extent the effects of PCSK9 inhibitors on lipids, especially LDL-C and Lp(a), are responsible for the effects on various miRNAs and how much of the effect can be attributed to so-called pleotropic effects. In conclusion, our findings demonstrate that miR-483-5p is involved in the regulation of serum PCSK9 levels and therefore may participate in the pathogenesis of CVD. Given the importance of PCSK9 regulation in CVD, miR-483-5p should be considered as a potential biomarker and therapeutic target. Expression of miR-191-5p, miR-224-5p, and miR-337-3p was not associated with serum PCSK9 levels but differed between patients and control subjects and, therefore, may reflect other biological processes in CVD. Treatment with PCSK9 inhibitors increases miR-191-5p expression, which may contribute to the effect of PCSK9 inhibitors on reducing the incidence of cardiovascular events. To our knowledge, our study is the first to investigate the relationship between PCSK9, miRNAs, and lipid parameters. Therefore, this area is clearly understudied, although results from in vitro studies and studies in animal models are promising. Further studies are needed to translate this knowledge into the clinic. ## References 1. Libby P., Buring J.E., Badimon L., Hansson G.K., Deanfield J., Bittencourt M.S., Tokgozoglu L., Lewis E.F.. Atherosclerosis. Nat. Rev. Dis. Prim. (2019) 5 56. DOI: 10.1038/s41572-019-0106-z 2. Ference B.A., Ginsberg H.N., Graham I., Ray K.K., Packard C.J., Bruckert E., Hegele R.A., Krauss R.M., Raal F.J., Schunkert H.. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. 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--- title: The Role of Tableware Size in Healthy Eating—Effects on Downstream Food Intake authors: - Sashie Abeywickrema - Mei Peng journal: Foods year: 2023 pmcid: PMC10048240 doi: 10.3390/foods12061230 license: CC BY 4.0 --- # The Role of Tableware Size in Healthy Eating—Effects on Downstream Food Intake ## Abstract Recent studies show that visual exposure to different portion sizes can lead to portion alterations in subsequent meals, suggesting that manipulations of tableware sizes may also modify portion size perception and downstream eating behaviour. The present study aims to address this novel question by testing 61 male participants (20–40 years; 19.7–41.5 kg·m−2) over three breakfast sessions in a controlled laboratory. In each session, the participant was served a pre-determined breakfast portion in either medium (control; CT), small (SC), or large (LC) jars. Participants were asked to rate post-meal satiety, and then recorded food intake for the rest of the day using Food Records. Our results indicated significant changes in post-meal satiety following the SC or LC condition, compared to CT (SC: 55.3 ± 10.8, LC: 31.0 ± 8.4, CT: 42.1 ± 9.6, F[2, 108] = 25.22, $p \leq 0.001$). SC led to a reduction in post-breakfast energy intake (F[2, 108] = 61.28, $p \leq 0.001$), but was counteracted by a substantial increase in downstream intake at the following meal (F[2, 108] = 47.79, $p \leq 0.001$), resulting in an overall increase in total daily energy intake (F[2, 108] = 11.45, $p \leq 0.001$). This study provides the first evidence that small tableware may not be a long-term solution for addressing overeating and related health issues (e.g., obesity), reinforcing the importance of considering downstream intake in eating-related intervention. ## 1. Introduction The modern food environment, providing easy access to abundant supply, is thought to be one of the key contributors to a wide range of health issues, including obesity [1,2]. Over the past few decades, researchers from diverse disciplines have endeavoured to devise interventions to rectify the common phenomenon of overeating [3,4,5]. One promising approach is to manipulate environmental factors, such as lighting [6,7], sound [8,9], and tableware dimensions [10,11], to alter food perception and correspondingly facilitate healthier eating. However, to date, most studies have solely focused on assessing these effects for within-meal behaviour, and rarely consider food intake subsequent to the test meal—termed ‘downstream food consumption’ [12] in this paper. In the fields of sensory nutrition and food psychology, there is increasing evidence for the importance of physical eating environments on food perception and consumption behaviour. Of the various factors that have been tested, tableware size is thought to play an important and direct role in activating implicit consumption norms, as many consumers use this visual cue to calibrate their food intake [10,11]. In keeping with the Delboeuf illusion, a small spatial ratio between food and tableware is hypothesised to give a perception of a more-than-actual portion, and thus lead to increased expected satiety [13,14,15]. This effect is particularly relevant in the context of pre-meal planning, when consumers determine their portion sizes, especially in cultures using individual-serving models, see Peng et al. [ 16]. While the approach based on tableware sizes is supported for its promise to reduce energy intake and food waste, and is being recommended by public health sectors [17], its efficacy has constantly been a subject of debate [10,18]. Indeed, studies using ad libitum serving models have found a mixture of results, with some detecting significant differences in intake [13,14,19] and others not [20,21,22]. In a systematic review of tableware effects [23], several possible explanations were put forward for the observed inconsistencies across previous studies. Specifically, these authors argued that the effects of tableware sizes are subject to distraction factors, types of containers, serving models, and types of food in some cases. A more recent review proposed that insufficient power was also likely an issue, pointing out that most existing studies had included too few participants [24]. In addition to methodological factors, studies of tableware sizes with different sub-populations appear to suggest that personal factors can substantially affect results. For instance, Peng, et al. [ 16] found that Asian consumers (e.g., Chinese and South Koreans) were affected by tableware sizes to a lesser degree than Western consumers (e.g., Canadians and New Zealanders), highlighting that cultural background appears to be a moderating factor of the tableware size effect. Shimpo and Akamatsu [25] later confirmed similar cultural differences using a Japanese cohort. More intriguingly, people from different weight groups possibly have different levels of susceptibility to tableware size manipulations [26]. Specifically, Peng [27] compared healthy-weight individuals versus overweight for their estimated intake of food presented on large versus small plates. Their results showed that overweight individuals were more likely to change their intake estimates in response to variations in tableware sizes. By contrast, Shah et al. [ 21], in a preliminary study, found no such differences among individuals in different weight groups. Broader research with regards to food-related visual biases has also produced inconsistent results regarding weight-specific differentiations, with some observing differences across weight groups in responding to food cues [28,29,30], while others did not [31,32,33]. Given the potential application of the effects of tableware size to improve healthy portion selection, particularly among overweight populations, more research is warranted to enhance our understanding of these effects and their interactions with weight groups. Previous studies of tableware size effects have predominantly focused on consumption within a meal episode [13,34]. However, it remains unknown whether tableware size has effects on intake behaviour subsequent to the meal of interest. Studies of portion size effects have consistently suggested an accordance between portion size and energy intake within a meal—with larger portions almost always leading to increased intake [31,35,36]. Recently, Robinson et al. [ 37] found that small portions do not only have an impact on the intake within a meal episode, but also can lead to substantial reductions in energy intake on the subsequent day. Robinson et al. [ 36,37] further demonstrated that such long-lasting effects of small portions were not a mere learning outcome of post-ingestive behaviour, but rather the consequence of shifting portion size normality. According to these authors, simple visual exposure to small portions can lead the participants to reduce their choice of portion size for the subsequent meals. These recent findings point to the intriguing possibility that tableware size manipulations may also modify subsequent eating behaviour. The present study aims to test for tableware size effects on downstream food consumption subsequent to the testing meal. Building upon previous research exploring the portion size effect, we hypothesise that changes in tableware size may impact downstream food intake. More specifically, we predict that eating from smaller or larger tableware can lead to a shift of portion norm and thus increase or decrease downstream food intake. Overall, this study contributes to the longstanding debate regarding the efficacy of changing visual perception of food portion sizes via tableware size manipulation. The findings from this study can give important and timely implications for achieving more sustainable and healthy dietary behaviours. ## 2.1. Subjects Males aged 20–40 years from the general community of Dunedin (New Zealand) were invited to participate in this study, initially advertised as a consumer food study. Only male participants were included to eliminate sex-related differences in visual perception. Individuals with chronic sensory dysfunction, neurological disease, or dietary restrictions (due to, e.g., allergies, religious practices, medications) or having body-mass-index (BMI) under 18.5 kg·m−2 were excluded from the study. Eligible participants were asked for their height (cm) and weight (kg) to guide representativeness of different weight groups [38]. A total of 61 participants participated in the study. Sample size was determined based on previously reported effect sizes of satiety and energy intake measures for tableware or portion size manipulations [27] using the GPower 3.1.9.7 software [39]. Analyses suggested that a minimum of 45 participants would be sufficient to detect a medium-sized effect of jar size on satiety measures with a $90\%$ power and an α-level of 0.05 using ANOVA: repeated measures, within between interactions (effect size $f = 0.25$). Correlation among repeated measures was 0.5, and non-sphericity correction was 1. We recruited an additional 16 participants to adjust for attrition or missing data (e.g., physiologically implausible food records) [40,41]. Informed written consent was obtained from each participant prior to the study. Participants were given monetary compensation upon completion. The study was approved by the University of Otago Human Ethics Committee (Reference: $\frac{20}{108}$). ## 2.2. Testing Food Models Oat is one of the common breakfast choices among New Zealand consumers [42]. Notably, data from dietary records collected for our previous studies (N > 400) showed that oat pudding is the most familiar breakfast to local consumers. A formulation of water-based overnight oat pudding was, therefore, specially developed for this study, using rolled oats (Harraway and Sons Ltd., Dunedin, New Zealand), chia seeds (Alison’s Pantry, Hamilton, New Zealand), and carbon-filtered water (rolled oat: chia seeds: filtered water ratio = 1 g:0.07 g:2.28 mL). It derives approximately 464 kJ per 100 g, consisting of 16.8 g carbohydrate, 3.6 g protein, and 2.6 g fat. ## 2.3. Serving Jars Three types of glass jars with the same height (11.5 cm) but varying diameters were used in the small container (SC; $d = 5.7$ cm; 9.9 oz), large container (LC; $d = 8.9$ cm; 24.2 oz), and control (CT; $d = 7.3$ cm; 16.3 oz) conditions (Figure 1). All the glass jars were from the same supplier (Arthur Holmes, Petone, New Zealand). Figure 1 shows three glass jars in one frame for comparison; however, no study participants were shown them displayed together. ## 2.4. Experimental Procedure Each participant of the study attended four morning sessions, following a >10 h overnight fasting (either at 0700–0730 h, 0745–0815 h, 0830–0900 h, or 0915–0945 h), at the Sensory Neuroscience Laboratory, University of Otago. Any two sessions were at least two days apart, with each participant’s starting weekday being randomised to mitigate behavioural biases. The four sessions included an initial session and three testing sessions. Orders of the three testing sessions were randomised across the participants following a William Latin Square design [43]. Participants maintained their exercise levels across testing days. Upon arrival at each session, the participants were asked to rate the level of hunger and fullness on a Satiety Labeled Intensity Magnitude (SLIM) scale (Anchors of the SLIM rating include; greatest imaginable hunger = −100.0; extremely hungry = −67.4; very hungry = −56.2; moderately hungry = −38.2; slightly hungry = −18.6; neither hungry nor full = 0; slightly full = 31.9; moderately full = 46.7; very full = 74.3; extremely full = 79.4; greatest imaginable fullness = 100): [44,45]. In the initial session, the participant was presented with five glass jars (all in medium jar), which contained 159 g to 388 g oat pudding following a logarithmic scale with a step of 0.1. The participants were asked to rate each sample for expected satiety on a 100-point Visual Analogue Scale (VAS; 0 = not full at all; 100 = extremely full): [46], and then to select a sample as ideal portion size for breakfast. This portion was then used as their self-selected portion size in the subsequent sessions. In each of the next three testing sessions, the participant was served oat pudding of their self-selected portion sizes in medium (CT), small (SC), or large jars (LC). Notably, 7 out of the 61 participants selected portions that exceeded the volume of the SC jar size, which was not anticipated. These participants were served a full SC jar (i.e., 287 g) and an additional portion in a plastic portion cup. After the consumption task in each session, the participant was asked to report post-meal satiety on a SLIM scale and hedonic response on a 100-point VAS (anchors; 0 = very unpleasant; 100 = very pleasant). After each breakfast session, the participants were required to record all foods and beverages consumed within a day, following a standard 24 h weighed Food Record [12,47]. This Food Record was proofed by a NZ registered dietitian and nutritionist. An electronic food scale (Model No. 1023, Salter, Manchester, UK) was provided, along with a food portion catalogue containing imagery measures of portion sizes (e.g., for dining out). Additionally, the participants reported alcohol, supplements, medicines intake, and any event that might influence their eating behaviour. The same experimenter delivered instructions to all participants. All the participants were then requested to complete a Dutch Eating Behaviour Questionnaire (DEBQ); [48] and a demographic questionnaire, including questions regarding their physical activity (i.e., bed rest to very heavy/vigorous activity; Capra) [49]. At the end of the last session, each participant’s height and weight were measured in light clothing without shoes. Finally, each participant was asked to write down their thoughts on the study aim and differences of the oat pudding across sessions (Q1: Please use the space below to write down what you think the study was about; Q2: Please write down if you have noticed any sensorial difference about the oat puddings across the three sessions). The purpose of the study was not disclosed to the participants until the completion of their participation. The participant was then given the opportunity to withdraw data. ## 2.5.1. Data Pre-Processing Individual weighed Food Records of each experimental session were separately entered into FoodWorks (Brisbane, Australia: Xyris Pty Ltd., 2019), which translated individual food consumption into energy intake (in kJ). We applied the method of Huang et al. [ 50] to identify physiologically implausible dietary reports, which prescribes calculations of individual predicted energy requirement for the reporting period (pER; via Harris–Benedict equation; Roza and Shizgal [51], and self-reported physical activity level). Using ±1.5 SD cut-offs, individuals whose reported energy intakes (rEI) outside of 67–$133\%$ over pER were considered implausible. With this method, 19 reports were identified to be under-reported and 2 were over-reported. This misreporting rate (i.e., $35\%$) was in line with previous studies [40,41]. Averaged daily food intake (in kJ) from participants with physiologically plausible Food Records ($$n = 40$$) was extracted for analyses. In addition, the participant’s food intake within five-time intervals, including post-breakfast, lunch, post-lunch, dinner, and post-dinner, were extracted (in kJ). Time gaps between two adjacent meals (e.g., breakfast–lunch) were approximately 4-to−6 h. In addition, individual BMI was calculated as kg/m2, where kg is the participant’s weight in kilograms and m2 is their height in metres squared. ## 2.5.2. Statistical Analyses The main statistical analyses of the study were pre-specified and shared at https://osf.io/rdb4p/ (uploaded to OSF storage on 30 November 2021). However, some modifications were made during the publication process to give more insights. Specifically, originally proposed univariate comparisons across BMI groups were changed to using BMI and self-selected Portion Size as continuous covariates in the main analyses. Additionally, energy intakes for separate time intervals were separately evaluated with additional repeated-measures analysis of covariance (ANCOVA). Lastly, an additional analysis of order effects was performed. Participant characteristics were summarised with descriptive statistics (e.g., age, BMI), with additional Cronbach’s alpha coefficients calculated for each DEBQ subscale to indicate internal consistency. ANCOVAs were employed to test differences for baseline SLIM and hedonic ratings across the Conditions, with BMI being treated as a covariate. Generalised Linear Mixed Models ANOVA was used to test whether the order of the experimental session had any effect on the primary outcome variables. For the main analyses, repeated-measures ANCOVAs were separately applied to analyse the two primary testing outcomes—i.e., post-meal SLIM ratings (for initial and post-processing data; $$n = 61$$ or 40) and total daily energy intake obtained from the Food Records (for post-processing data only; $$n = 40$$). In each analysis, the Condition (i.e., CT, SC, LC) was treated as the within-subject variable, with BMI and the self-selected Portion Size of the test meal as continuous covariates. Any significant effect was explained by post-hoc tests with Bonferroni multiple comparisons [52]. In order to give more insights into the observed effects against time, energy intake within five-time intervals was extracted from the Food Records, including post-breakfast, lunch, post-lunch, dinner, and post-dinner. Separate repeated-measures ANCOVAs were used to assess the differences across Conditions, while controlling for BMI and self-selected Portion Size. Significance was indicated by $p \leq 0.05.$ All the statistical analyses were performed using R-software (version 1.1.463, RStudio, Boston, MA, USA). ## 3.1. Participant Characteristics Table 1 summarises participant characteristics and baseline SLIM/hedonic measures for all participants ($$n = 61$$) and for the post-processing dataset ($$n = 40$$). Overall, the BMIs of the participants were 28.3 ± 6.3 kg∙m−2, comparable to national health reports [53,54]. Cronbach’s alpha coefficients for DEBQ restrained, emotional, and external subscales were 0.79, 0.93, and 0.80, respectively, all exceeding the criterion of 0.70 for internal consistency [55]. The summary of 40 participants did not vary substantially from the overall statistics. The main datasets can be found at https://osf.io/3uecx/ (data has been deposited on 6 April 2022). No significant differences were found across Conditions in terms of baseline SLIM rating (F[2, 177] = 1.34, $$p \leq 0.541$$, ηp2 = 0.01) or hedonic ratings (F[2, 177] = 1.74, $$p \leq 0.243$$, ηp2 < 0.01). BMI was not a significant covariate in either model ($p \leq 0.05$). ## 3.2. Effects of the Session Order The Generalised Linear Mixed Model ANOVAs on post-meal SLIM ratings and total daily energy intake did not detect a significant interaction between Condition and Session Order (post-meal SLIM ratings: F[4, 108] = 1.17, $$p \leq 0.270$$, ηp2 = 0.06; total daily energy intake: F[4, 108] = 0.12, $$p \leq 0.902$$, ηp2 = 0.04). ## 3.3. Comparisons of Within-Meal Measures across Three Jar Size Conditions With regard to post-meal SLIM ratings, analysis based on 61 participants showed a significant main effect due to Condition (F[2, 171] = 9.01, $$p \leq 0.010$$, ηp2 = 0.22, Figure 2A). Post-hoc tests with Bonferroni corrections revealed that the SC condition yielded significantly higher SLIM ratings than CT (t[114] = −3.40, $$p \leq 0.010$$, d = −0.44) or LC (t[114] = −5.33, $$p \leq 0.009$$, d = −0.76), with the latter being the lowest (t[114] = 3.97, $$p \leq 0.005$$, $d = 0.66$). Neither BMI nor Portion Size was a significant covariate (BMI: F[1, 171] = 1.09, $$p \leq 0.330$$, ηp2 = 0.02, Portion Size: F[1, 171] = 2.60, $$p \leq 0.104$$, ηp2 = 0.04). The analyses based on post-processing data ($$n = 40$$) revealed similar results, with Condition having a significant effect on post-meal SLIM ratings (F[2, 108] = 25.22, $p \leq 0.001$, ηp2 = 0.45; Figure 2B). SC yielded a significantly higher SLIM rating than LC (t[72] = −7.52, $p \leq 0.001$, d =−1.47) or CT conditions (t[72] = −3.35, $$p \leq 0.006$$, d = −0.58), with LC being lower than CT (t[72] = 4.17, $p \leq 0.001$, $d = 0.75$). Moreover, neither BMI nor Portion Size was a significant covariate (BMI: F[1, 108] = 2.04, $$p \leq 0.156$$, ηp2 = 0.04, Portion Size: F[1, 108] = 3.27, $$p \leq 0.075$$, ηp2 = 0.07). ## 3.4. Effects of Jar Size on Subsequent Energy Intake ANCOVA on the total daily energy intake of 40 participants revealed a significant main effect due to Condition (F[2, 108] = 11.45, $p \leq 0.001$, ηp2 = 0.42). Post-hoc tests indicated that SC (10,181 ± 2777 kJ) led to a significantly higher total daily energy intake than LC (9346 ± 2391 kJ; t[72] = −5.15, $p \leq 0.001$, d = −0.67) and CT conditions (8674 ± 1715 kJ; t[72] = −4.10, $p \leq 0.001$, d = −0.54), with no significant difference between the latter pair (t[72] = 1.99, $$p \leq 0.404$$, d = −0.08). Neither BMI nor Portion Size was a significant covariate (BMI: F[1, 108] = 2.58, $$p \leq 0.174$$, ηp2 = 0.06, Portion Size: F[1, 108] = 2.80, $$p \leq 0.139$$, ηp2 = 0.05). Furthermore, individual differences in total daily energy intake across Condition were assessed against pER. Results indicated that, on average, the difference in total daily energy intake between SC and CT was 2407 ± 404 kJ, representing approximately $23\%$ of pER. The energy intake difference between SC and LC was 1734 ± 320 kJ, counting for $20\%$ of pER. Separate repeated-measures ANCOVAs were performed on energy intake within different time intervals of the day (i.e., post-breakfast, lunch, post-lunch, dinner, post-dinner). The results revealed significant differences in post-breakfast (F[2, 108] = 61.28, $p \leq 0.001$, ηp2 = 0.51) and lunch (F[2, 108] = 47.79, $p \leq 0.001$, ηp2 = 0.45; Figure 3). Specifically, the SC and LC conditions significantly reduced and increased post-breakfast snack intake compared to CT (SC: t[72] = 3.10, $$p \leq 0.007$$, d = −0.93; LC: t[72] = −4.20, $p \leq 0.001$, $d = 1.56$; SC-LC: t[72] = 4.13, $p \leq 0.001$, d = −2.17). Assessment of the lunch data suggested that the SC condition was associated with a significantly higher energy intake than the LC (t[72] = −3.56, $p \leq 0.001$, $d = 1.58$) and CT conditions (t[72] = −3.56, $p \leq 0.001$, $d = 1.87$). No significant difference was present at other eating episodes (post-lunch: F[2, 108] = 0.77, $$p \leq 0.464$$, ηp2 = 0.01; dinner: F[2, 108] = 1.01, $$p \leq 0.365$$, ηp2 = 0.02; post-dinner: F[2, 108] = 1.90, $$p \leq 0.153$$, ηp2 = 0.03). BMI and Portion Size were not significant covariates (all $p \leq 0.05$). ## 3.5. Debrief from the Participants Thirty-four out of forty participants believed that this study was to investigate consumer acceptance of different products of oat puddings (as advertised during the participant recruitment). Four other participants thought the study was to assess the health benefits of oats, while the remaining two participants did not provide an answer. With regards to differences across sessions, 24 participants mentioned that they noticed variable sizes of the breakfasts, along with other sensory differences (e.g., sweetness and texture). Notably, none of the participants mentioned differences in serving utensils (e.g., jars, spoons) in their answers. ## 4. Discussion Effects of tableware size on food consumption have represented a controversial research topic, characterised by conflicting findings [18,56]. The present study tested the effects of tableware (i.e., jar) size on satiety and downstream food consumption subsequent to the test meal. Our results showed that using small tableware can effectively lead to increased satiety. Furthermore, this study provides the first demonstration that tableware size can potentially alter downstream effects on energy consumption in subsequent meals. The present analysis of satiety measures indicated that serving food using large tableware significantly diminishes the feeling of satiety at the end of the meal. This finding is in line with the original hypothesis of the tableware size effect, and consistent with many previous studies that also measured satiety as a primary variable for detecting this effect [27,57]. Notably, many other studies of tableware size effects employ a double-serving, ad libitum method, which requires participants to self-serve from a large tableware to their immediate serving tableware. With this approach, tableware size effects on satiety are often offset, or even counteracted by participants having multiple servings [21]. Additionally, the present study did not find BMI to be a significant moderator of tableware size effects, with individuals of all BMI groups showing similar levels of responses to glass jar variations. These results were in line with Shah et al. [ 21]. Our study indicated that using small tableware led to increased overall energy intake. Moreover, temporal analyses indicated that small tableware initially led to reductions in energy intake but was followed by substantial increases in energy intake at the following meal. The immediate reduction in intake was a likely outcome of increased post-meal satiety when eating from small jars, in line with some previous reports [58], but see [22] for contradictory findings. The subsequently increased energy intake at lunchtime may be interpreted in two ways. First, the increased lunch intake can be seen as compensatory eating in response to the preceding low intake [36]. An alternative explanation relates to the theory put forward by Robinson et al. [ 37]—exposure to an altered portion size (either larger or smaller than the original norm) causes sustained shifts in perception of “normal” food portions, and thus affects the amount of food eaten in subsequent meal(s). Given that a constant portion size (for each participant) was used across our study, the observed differences in downstream energy intake reiterate the point made by Robinson et al. [ 36,37] that shifting portion norms do not necessarily involve post-ingestive behaviour, but rather may stem from visual exposure. Findings from the present study have some important new implications with regard to eating interventions via tableware sizes. Despite highly controversial results, some health organisations have promoted the use of small tableware to help with portion reduction within a meal. The present findings cautioned against using tableware manipulations for eating interventions. Even though using smaller tableware appeared to have positive effects on reducing initial intake, it led to a substantial increase in daily energy intake (i.e., 2407 kJ, $23\%$ of pER). From a broader perspective, downstream food consumption should be considered in assessments of food-related interventions. The present study had a few limitations to consider. First, we used glass jars which are more prone to biases due to individual factors and task instructions, as opposed to plates/bowls [59,60]. Additionally, data from seven participants whose selected portion exceeded the small jar capacity led to additional uncountable variabilities. Another potential limitation of the current study relates to the use of self-reported Food Records. Despite representing the ‘gold standard’ in nutritional science for recording habitual energy intake [40], the self-reporting nature of this measure is prone to biases [41,61,62]. Another caveat of the current study is that it comprised a highly homogenous testing population (males aged between 20–40 years), which may limit the generalisability of our results. Future research is required to repeat our findings with a wide heterogeneous population. ## 5. 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--- title: 'NaRnEA: An Information Theoretic Framework for Gene Set Analysis' authors: - Aaron T. Griffin - Lukas J. Vlahos - Codruta Chiuzan - Andrea Califano journal: Entropy year: 2023 pmcid: PMC10048242 doi: 10.3390/e25030542 license: CC BY 4.0 --- # NaRnEA: An Information Theoretic Framework for Gene Set Analysis ## Abstract Gene sets are being increasingly leveraged to make high-level biological inferences from transcriptomic data; however, existing gene set analysis methods rely on overly conservative, heuristic approaches for quantifying the statistical significance of gene set enrichment. We created Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA) to facilitate accurate and robust gene set analysis with an optimal null model derived using the information theoretic Principle of Maximum Entropy. By measuring the differential activity of ~2500 transcriptional regulatory proteins based on the differential expression of each protein’s transcriptional targets between primary tumors and normal tissue samples in three cohorts from The Cancer Genome Atlas (TCGA), we demonstrate that NaRnEA critically improves in two widely used gene set analysis methods: Gene Set Enrichment Analysis (GSEA) and analytical-Rank-based Enrichment Analysis (aREA). We show that the NaRnEA-inferred differential protein activity is significantly correlated with differential protein abundance inferred from independent, phenotype-matched mass spectrometry data in the Clinical Proteomic Tumor Analysis Consortium (CPTAC), confirming the statistical and biological accuracy of our approach. Additionally, our analysis crucially demonstrates that the sample-shuffling empirical null models leveraged by GSEA and aREA for gene set analysis are overly conservative, a shortcoming that is avoided by the newly developed Maximum Entropy analytical null model employed by NaRnEA. ## 1. Introduction Next-generation sequencing technologies and highly accurate annotations for prokaryotic and eukaryotic genomes have transformed biology into a data-rich scientific discipline [1]. Consequently, the field of computational biology has prioritized the development of algorithms that enable researchers to accurately leverage large-scale, gene-level biochemical measurements to make mechanistic inferences involving biological and cellular processes [2,3], as well as to measure the activity of molecular pathways and proteins [4,5]. It is not surprising that gene set analysis methods, which were developed to integrate statistical information from groups of genes belonging to a common ontology (e.g., biological process, metabolic pathway, regulatory network), have rapidly emerged as some of the most widely utilized tools in biomedical research. Most frequently, the differential expression of genes between two cellular states or phenotypes is used as the ranking criterion, though various other procedures may be employed. See (Maleki et al., 2020 Frontiers in Genetics) [6] and (Das et al., 2020 Entropy) [7] for recent reviews discussing the wide variety of published gene set analysis methods as well as the statistical assumptions implicit to each one. While existing gene set analysis methods employ distinct mathematical approaches for calculating the test statistic associated with a gene set’s enrichment, the field of gene set analysis is uniquely dominated by the question of how to accurately evaluate the statistical significance of gene set enrichment. The origins of this debate are alluded to by Mootha et al. [ 8] in their analysis of DNA microarrays profiling the expression of genes in skeletal muscle biopsy samples from patients with normal glucose tolerance (NGT) or type 2 diabetes mellitus (DM2). Mootha et al. computed the differential expression of each gene in the test phenotype samples (i.e., the DM2 patients) with respect to the reference phenotype samples (i.e., the NGT patients) using the Signal-to-Noise Ratio (SNR). When this analysis failed to identify any single gene with statistically significant differential expression, Mootha et al. created a procedure they referred to as Gene Set Enrichment Analysis (GSEA); this method was designed to test the null hypothesis that the rank ordering of genes from a gene set in the differential gene expression signature (i.e., the vector of SNR values computed between the test phenotype and the reference phenotype) is random with regard to the diagnostic categorization of the samples. Mootha et al. used a two-sample Kolmogorov–Smirnov test to compare the SNR values for genes associated with oxidative phosphorylation (OXPHOS) with the SNR values for all other genes. Rather than calculating the statistical significance of this enrichment score using the existing analytical null model for the two-sample Kolmogorov–Smirnov test, Mootha et al. chose to approximate the null model for their gene set enrichment score using an empirical phenotype-based permutation test procedure. In this procedure, Mootha et al. shuffled the phenotype label of each DM2 sample and NGT sample to produce two new groups: a null test phenotype and a null reference phenotype. Each of these null phenotypes consisted of samples from both the original test phenotype (i.e., patients with DM2) and the original reference phenotype (i.e., patients without DM2). They recomputed a null SNR for each gene based on its expression in the null test samples and the null reference samples, producing a null differential gene expression signature; the same two-sample Kolmogorov–Smirnov test statistic was then calculated as the null enrichment score for the OXPHOS gene set in this null differential gene expression signature. Mootha et al. repeated this procedure of permuting phenotype labels, calculating a null differential gene expression signature and computing a null enrichment score 1000 times, allowing them to estimate the statistical significance (i.e., two-sided p-value) of the OXPHOS gene set enrichment. The logic of this empirical phenotype-based permutation null model for GSEA was more clearly described by Subramanian et al. [ 9] in their follow-up manuscript, in which GSEA was modified to use a weighted two-sample Kolmogorov–Smirnov test statistic, as follows: “We estimate the statistical significance (nominal P value) of the [GSEA enrichment score] by using an empirical phenotype-based permutation test procedure that preserves the complex correlation structure of the gene expression data. Specifically, we permute the phenotype labels and recompute the [GSEA enrichment score] of the gene set for the permuted data, which generates a null distribution for the [GSEA enrichment score]. The empirical, nominal P value of the observed [GSEA enrichment score] is then calculated relative to this null distribution. Importantly, the permutation of class labels preserves gene-gene correlations and, thus, provides a more biologically reasonable assessment of significance than would be obtained by permuting genes”. This justification for the empirical phenotype-based permutation null model of GSEA was defended by Tamayo et al. [ 10] when they compared GSEA with an alternative gene set analysis procedure, referred to by Tamayo et al. as Simple Enrichment Analysis (SEA), which relied on a null model for gene set analysis that ignores correlations between genes rather than the empirical phenotype-based permutation null model of GSEA. Tamayo et al. made the following claim about their comparison of GSEA and SEA: “We show, in agreement with earlier observations, that the gene independence assumption is not realistic because gene correlations are non-trivial and produce a substantial amount of variance inflation in the global statistic that in turn produces a large number of false positive results”. We can more clearly state the central claim underlying this procedure with symbolic logic as follows: A → B Tamayo et al. sought to prove their primary claim (i.e., statement A) by showing that SEA produced a large number of false positives in their benchmark analyses (i.e., statement B). Unfortunately, such a verification would only be valid if the converse of the statement (i.e., B → A) were true in general, and the converse of a statement and the statement itself are not logically equivalent in general. However, the statement (A → B) does prove useful through its contrapositive, which may be stated as follows: ¬B → ¬A Since the contrapositive of a statement and the statement itself are always logically equivalent, we find that the primary claim underlying the validity of the empirical phenotype-based permutation null model for GSEA can be falsified if we are able to create a gene set analysis method that assumes that genes in a gene set are independent when the gene set is not enriched in a gene expression signature and subsequently show that this method adequately controls the Type I error rate of gene set analysis. Additionally, we note that the approach undertaken by Tamayo et al. as an attempt to defend GSEA and falsify SEA is fundamentally flawed because the method that Tamayo et al. used to evaluate the specificity of GSEA and SEA is the same empirical phenotype-based permutation procedure that GSEA relies on to estimate the null model for gene set enrichment. Tamayo et al. describe their benchmarking procedure as follows: “For each dataset in the benchmark, we randomized the phenotype labels 1000 times and ran both algorithms … The p values are computed using the areas under the empirical null histograms from GSEA and areas under the normal distribution for SEA”. We see this is the same description provided by Subramanian et al. [ 9] for the empirical phenotype-based permutation procedure used to construct the null model for GSEA; thus, Tamayo et al. have attempted to defend the sample-shuffling null model of GSEA by way of tautology, rendering their argument invalid. This discussion also serves to highlight the challenges intrinsic to accurately benchmarking gene set analysis methods using experimental data; in particular, gene sets that are frequently analyzed are often derived from the literature for biological processes or other gene ontologies that are not amenable to systematic, experimental validation. However, recent work in the field of cancer systems biology has shown that gene set analysis methods can be used to measure the differential activity of transcriptional regulatory proteins [4,11]; in the same way that the Michaelis–Menten equation measures enzymatic activity based on the conversion of biochemical substrates to metabolic products, we may interpret the differential expression of a transcriptional regulator’s targets as an estimate of that regulator’s differential activity. More formally, we define the differential activity of a transcriptional regulatory protein as the contribution of the regulator to the implementation of a specific differential gene expression signature. Consistent with this definition, and akin to using a highly multiplexed gene reporter assay, we previously introduced the Virtual Inference of Protein Activity by Enriched Regulon Analysis (VIPER) algorithm to measure differential protein activity based on the enrichment of each regulator’s transcriptional targets (i.e., regulon gene set) in a differential gene expression signature [4]. The tissue-specific regulon gene sets required for these analyses can be effectively reverse-engineered using a variety of methods [12]; in the context of this study, we use the ARACNe3 algorithm, which is the newest implementation of the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) [13,14]. Previous versions of this algorithm have been experimentally validated, and the regulon gene sets created by ARACNe have been used extensively to measure the differential activity of transcriptional regulatory proteins in combination with VIPER, effectively identifying Master Regulator proteins representing mechanistic determinants of tumor transcriptional states [11,15,16]. In this manuscript, we derive and benchmark Nonparametric analytical-Rank-based Enrichment Analysis (NaRnEA), a novel gene set analysis method that leverages a fully analytical null model for gene set enrichment created using the information theoretic Principle of Maximum Entropy [17]. By virtue of its derivation, the null model for NaRnEA assumes that genes in a gene set are independent when the gene set is not enriched in a gene expression signature. We show that NaRnEA adequately controls the Type I error rate of gene set analysis using gene expression data from the lung adenocarcinoma (LUAD), colon adenocarcinoma (COAD), and head and neck squamous cell carcinoma (HNSC) cohorts in The Cancer Genome Atlas (TCGA). Our finding that NaRnEA adequately controls the Type I error rate of gene set analysis effectively falsifies the primary claim underlying the empirical phenotype-based permutation null model of GSEA. Furthermore, we demonstrate that NaRnEA is highly sensitive, identifying far more statistically significantly enriched regulon gene sets in these TCGA cohorts than either GSEA or analytical-Rank-based Enrichment Analysis (aREA), the gene set analysis method originally developed as the computational engine of VIPER that also employs an empirical phenotype-based permutation null model. Using independent proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) for LUAD, COAD, and HNSC cancer types, we demonstrate that the differential activity of transcriptional regulatory proteins measured by NaRnEA in TCGA is significantly correlated with the differential abundance of the same transcriptional regulatory proteins in CPTAC. Given that the abundance and the activity of transcriptional regulatory proteins differ due to a number of biochemical processes (e.g., post-translational modification, subcellular localization, cofactor binding, chromatin accessibility), this agreement provides substantial biological support for NaRnEA-inferred differential protein activity. Crucially, this comparative analysis is not possible in a large-scale, systematic fashion for literature-curated gene sets since the corresponding gene ontologies are infrequently amenable to independent, experimental validation. These findings demonstrate that NaRnEA is statistically robust, having greater sensitivity than either GSEA or aREA without loss of specificity, and produces biologically meaningful inferences. We also interrogated the statistical properties of the empirical phenotype-based permutation procedure leveraged by GSEA and aREA and determined that the resulting null gene expression signatures exhibit substantial correlation with the true gene expression signature, thus providing a rigorous explanation for the reduced sensitivity of any gene set analysis method that relies on this procedure to approximate the null model for gene set enrichment. Finally, we identify systematic flaws in both GSEA and aREA when these methods are applied using alternative null models for gene set analysis while further highlighting the excellent performance of NaRnEA. NaRnEA and ARACNe3, along with all the code necessary to reproduce these analyses, are freely available for research use on GitHub (https://github.com/califano-lab/NaRnEA (accessed on 3 March 2023)). ## 2.1. Nonparametric Analytical-Rank-Based Enrichment Analysis (NaRnEA) We begin our derivation of NaRnEA by first considering two phenotypes, which we will refer to as the test phenotype (A) and the reference phenotype (B). Let us assume that there is some gene (g) and that we may represent the expression of this gene using the discrete random variable (Xg); biochemically speaking, this is a compositional discrete random variable that represents the relative molar concentration of transcripts originating from the gth genomic locus. If we would like to be more specific, we can say that we are representing the expression of the gene in the test phenotype (A) with the discrete random variable (XgA) and we are representing the expression of the gene in the reference phenotype (B) with the discrete random variable (XgB). To perform gene set analysis, we must first determine whether the gth gene is more highly expressed in the test phenotype (A) or the reference phenotype (B); this calculation forms the basis of our differential gene expression signature. To remain as general as possible, let us assume that we may represent the differential gene expression signature value for the gth gene between the test phenotype (A) and the reference phenotype (B) with the value (zgAB). We will assume in the following derivation that (zgAB) has the following properties:zgAB ϵ ℝWe assume that the differential gene expression signature value for the gth gene between the test phenotype (A) and the reference phenotype (B) is a real number that may be positive or negative.zgAB>0 iff Pr(XgA>XgB)>12We assume that the differential gene expression signature value for the gth gene between the test phenotype (A) and the reference phenotype (B) is greater than zero if and only if the expression of the gth gene in the test phenotype (A) is greater than the expression of the gth gene in the reference phenotype (B). More formally, this may be expressed by stating that the discrete random variable that represents the expression of the gth gene in the test phenotype (A) stochastically dominates the discrete random variable that represents the expression of the gth gene in the reference phenotype (B).zgAB<0 iff Pr(XgA>XgB)<12We assume that the differential gene expression signature value for the gth gene between the test phenotype (A) and the reference phenotype (B) is less than zero if and only if the expression of the gth gene in the test phenotype (A) is less than the expression of the gth gene in the reference phenotype (B). More formally, this may be expressed by stating that the discrete random variable that represents the expression of the gth gene in the test phenotype (A) is stochastically dominated by the discrete random variable that represents the expression of the gth gene in the reference phenotype (B).zgAB=−zgBAWe assume that the differential gene expression signature value for the gth gene between the test phenotype (A) and the reference phenotype (B) will be equal in magnitude and opposite in sign if the ordering of the phenotypes were reversed; this follows naturally from the aforementioned definitions of positive differential gene expression (i.e., upregulation) and negative differential gene expression (i.e., downregulation).\|zgAB\|>\|zkAB\| iff \|Pr(XgA>XgB)−12\|>\|Pr(XkA>XkB)−12\| for g≠kWe assume that the magnitude of the differential gene expression signature value for the gth gene between the test phenotype (A) and the reference phenotype (B) should be greater than the magnitude of the differential gene expression signature value for the kth gene between the test phenotype (A) and the reference phenotype (B) if and only if the extent of differential expression for the gth gene is greater than the extent of differential expression for the kth gene between the two phenotypes; we formalize this notion here using the language of stochastic dominance as mentioned previously. Having considered the differential gene expression signature, we may now turn our attention to the gene set itself. So long as the gene set members are determined a priori, the biological rationale underlying the gene set’s construction is irrelevant for the statistical analysis of the gene set’s enrichment in the differential gene expression signature; however, it plays a crucial role in the biological interpretation of the enrichment, if it is indeed present. To formalize the notion of gene set analysis from first principles, we may select a gene set for which the expression of each member exhibits a statistical dependency on a common biochemical species; from this we may construct a conceptual rationale for gene set analysis and derive an appropriate mathematical framework that will facilitate accurate statistical inference. We assume, for the sake of this derivation, that a transcriptional regulatory protein (r) is responsible for regulating the expression of the gene (g). To formalize this relationship, let us assume that we may represent the activity of the regulator (r) using the discrete random variable (Yr); biochemically speaking, this is a compositional discrete random variable that represents the relative molar concentration of the transcriptional regulatory holoenzyme. Then, due to the statistical dependence induced by this biochemical relationship, the random variables (Yr) and (Xg) form the following Markov Chain (Equation [1]):[1]Yr→Xg If the rth regulatory protein regulates many genes, it would be helpful to distinguish between these different targets based on the strength of each regulatory relationship. We can quantify the degree to which the expression of the gth gene depends on the activity of the rth regulator using a parameter to which we refer as the Association Weight (AWrg). We require the Association Weight to have the following properties:AWrg≥0The Association Weight between the rth regulator and the gth gene is strictly non-negative. AWrg>0 iff I[Yr;Xg]>0The Association Weight between the rth regulator and the gth gene is greater than zero if and only if the expression of the gth gene exhibits a statistical dependency on the activity of the rth regulator; this may be formalized by stating that the mutual information between the discrete random variable representing the expression of the gth gene and the discrete random variable representing the activity of the rth regulator is nonzero. AWrg>AWrk iff I[Yr;Xg]>I[Yr;Xk] for g≠kThe Association Weight between the rth regulator and the gth gene is greater than the Association Weight between the rth regulator and the kth gene if and only if the expression of the gth gene exhibits a greater statistical dependency on the activity of the rth regulator than the expression of the kth gene as measured using the mutual information between the corresponding discrete random variables. In addition to characterizing the regulatory relationships between the rth regulatory protein and its targets based on their strength, we can describe them based on their directionality. In some cases, as the activity of a regulator increases, it may cause the expression of some targets to increase; in other cases, an increase in the activity of the regulator may cause the expression of some targets to decrease. We quantify the degree to which the expression of the gth gene increases or decreases monotonically based on an increase in the activity of the rth regulator using a parameter to which we refer as the Association Mode (AMrg). We require the Association Mode to have the following properties:AMrg ∈[−1, 1]The Association Mode between the rth regulator and the gth gene is a real number less than or equal to one and greater than or equal to negative one. AMrg>0 iff SCC[Yr,Xg]>0The Association Mode between the rth regulator and the gth gene is greater than zero if and only if there is a positive monotonic relationship between the activity of the rth regulator and the expression of the gth gene; this may be formalized by stating that the Spearman correlation coefficient between the discrete random variable representing the expression of the gth gene and the discrete random variable representing the activity of the rth regulator is positive. AMrg<0 iff SCC[Yr,Xg]<0The Association Mode between the rth regulator and the gth gene is less than zero if and only if there is a negative monotonic relationship between the activity of the rth regulator and the expression of the gth gene; this may be formalized by stating that the Spearman correlation coefficient between the discrete random variable representing the expression of the gth gene and the discrete random variable representing the activity of the rth regulator is negative.\|AMrg\|>\|AMrk\| iff \|SCC[Yr,Xg]\|>\|SCC[Yr,Xk]\| for g≠kThe magnitude of the Association Mode between the rth regulator and the gth gene is greater than the magnitude of the Association Mode between the rth regulator and the kth gene if and only if the Spearman correlation coefficient between the expression of the gth gene and the activity of the rth regulator is greater in magnitude than the Spearman correlation coefficient between the expression of the kth gene and the activity of the rth regulator quantified from the corresponding discrete random variables. Given that we have now parameterized the relationship between the rth regulator and its transcriptional targets using the Association Weight and Association Mode, we can formalize the notion of regulon gene set enrichment in a differential gene expression signature resulting from a change in the activity of the regulator. More specifically, let (YrA) be the discrete random variable that represents the activity of the rth regulator in the test phenotype (A), and let (YrB) be the discrete random variable that represents the activity of the rth regulator in the reference phenotype (B). These discrete random variables may be related in one of three ways:Pr(YrA>YrB)>12In the first scenario, the activity of the rth regulator in the test phenotype (A) is greater than the activity of the rth regulator in the reference phenotype (B). More formally, this may be expressed by stating that the discrete random variable that represents the activity of the rth regulator in the test phenotype (A) stochastically dominates the discrete random variable that represents the activity of the rth regulator in the reference phenotype (B).Pr(YrA>YrB)<12In the second scenario, the activity of the rth regulator in the test phenotype (A) is less than the activity of the rth regulator in the reference phenotype (B). More formally, this may be expressed by stating that the discrete random variable that represents the activity of the rth regulator in the test phenotype (A) is stochastically dominated by the discrete random variable that represents the activity of the rth regulator in the reference phenotype (B).Pr(YrA>YrB)=12In the third scenario, the activity of the rth regulator in the test phenotype (A) is equal to the activity of the rth regulator in the reference phenotype (B). More formally, this may be expressed by stating that the discrete random variable that represents the activity of the rth regulator in the test phenotype (A) neither stochastically dominates nor is stochastically dominated by the discrete random variable that represents the activity of the rth regulator in the reference phenotype (B). We refer to scenario 1 as our positive alternative hypothesis (Ha+), scenario 2 as our negative alternative hypothesis (Ha−), and scenario 3 as our null hypothesis (Ho). As we have stated previously, the primary statistical concern in the field of gene set analysis has been correctly defining the joint sampling distribution of gene set members in the differential gene expression signature when the null hypothesis is true. In order to construct the null model for NaRnEA, we begin by considering the joint sampling distribution of the gene set members in the differential gene expression signature when each form of the alternative hypothesis is true. Under the positive alternative hypothesis (Ha+), the activity of the rth regulator in the test phenotype (A) is greater than the activity of the rth regulator in the reference phenotype (B). It follows from our earlier discussion about the gene set parameters that the genes with a nonzero Association Weight (AWrg) will exhibit differential expression between the test phenotype (A) and the reference phenotype (B), such that zgAB will be nonzero. Furthermore, a gene with a positive Association Mode (AMrg>0) will have a positive differential gene expression signature value (zgAB>0), and a gene with a negative Association Mode (AMrg<0) will have a negative differential gene expression signature value (zgAB<0). Under the negative alternative hypothesis (Ha−), the activity of the rth regulator in the test phenotype (A) is less than the activity of the rth regulator in the reference phenotype (B). It follows from our earlier discussion about the gene set parameters that the genes with a nonzero Association Weight (AWrg) will exhibit differential expression between the test phenotype (A) and the reference phenotype (B), such that zgAB will be nonzero. Furthermore, a gene with a positive Association Mode (AMrg>0) will have a negative differential gene expression signature value (zgAB<0), and a gene with a negative Association Mode (AMrg<0) will have a positive differential gene expression signature value (zgAB>0). We find that either form of the alternative hypothesis implies that the joint sampling distribution of the gene set members has greater probability density at the extremes of the differential gene expression signature than near the center of the differential gene expression signature; whether that increase occurs at the positive extreme or negative extreme for a given gene depends on the version of the alternative hypothesis for its regulator and the Association Mode for that gene. Additionally, the degree to which the probability mass increases at the extremes for a particular gene depends on the Association Weight for the gene as well as the magnitude of differential activity for its regulator. Our analysis of the joint sampling distribution for the gene set members can be greatly simplified if we apply a nonparametric transformation to the differential gene expression signature, as follows (Equation [2]):[2]{zg}AB⟼{rgsg}ABrgAB=rank(\|zgAB\|)sgAB=sign(zgAB) As a result of the nonparametric transformation in Equation [2], we can instead consider the discrete joint sampling distribution for the gene set members where the domain of each marginal is {r1ABs1AB, …, rGABsGAB}. If we let [3]Nr=∑$g = 1$GI{AWrg>0} be the number of genes in the regulon gene set of the rth regulator, where I{·} is the indicator function, then the discrete joint sampling distribution has the dimensionality (Nr×G). Subsequently, we recognize that from an information theoretic perspective, both versions of the alternative hypothesis reduce the Shannon entropy [18] of the discrete joint sampling distribution for the gene set members due to an increase in the probability mass at extremes of the nonparametric differential gene expression signature. Furthermore, if we consider the magnitude of the difference in the protein activity between the test phenotype (A) and the reference phenotype (B), we conclude that a greater difference in the activity of the rth regulator corresponds with a greater increase in probability mass at the extremes of the nonparametric differential gene expression signature and therefore a more substantial reduction in the joint Shannon entropy of the discrete joint probability distribution. It follows from this line of reasoning that when the magnitude of the difference in the activity of the rth regulator between the test phenotype (A) and the reference phenotype (B) tends to zero, the joint Shannon entropy of the discrete joint probability distribution for the members of the corresponding regulon gene set in the nonparametric differential gene expression signature will tend to its largest possible value. Therefore, we invoke the Principle of Maximum Entropy to motivate our selection of the discrete joint sampling distribution with the greatest Shannon entropy in the nonparametric differential gene expression signature as the null distribution for the gene set members. To derive the Maximum Entropy null model for the gene set members in the nonparametric differential gene expression signature, we first consider the gene expression marginals, each of which constitutes a discrete probability distribution over (G) elements. Under the null hypothesis, the only information available to us is that each gene set member is present somewhere in the nonparametric differential gene expression signature; beyond this, we have no additional information about the expected value or higher moments (e.g., variance, skewness) of the marginal distribution under the null hypothesis. Thus, it follows from the log-sum inequality [19] that the entropy of each gene expression marginal is maximized when we assign equal probability mass to all possible elements of the nonparametric differential gene expression signature for each gene under the null hypothesis. This is equivalent to assuming that each member of the gene set is uniformly distributed in the nonparametrically transformed differential gene expression signature under the null hypothesis of gene set analysis. Furthermore, it follows from the log-sum inequality that for an ensemble of discrete random variables, the entropy of the joint probability distribution is always less than or equal to the sum of the univariate entropies with equality if and only if the discrete random variables that compose the ensemble are statistically independent. Thus, to maximize the joint entropy of the null model, we assume that the gene set members are independent and uniformly distributed in the nonparametric differential gene expression signature under the null hypothesis of gene set analysis. We note that this Maximum Entropy null model for NaRnEA contradicts the primary claim underlying the validity of the empirical phenotype-based permutation null model for GSEA (i.e., that genes in a gene set are correlated when the gene set is not enriched in the differential gene expression signature). Thus, to falsify this claim, it will be sufficient to demonstrate that NaRnEA is capable of adequately controlling the Type I error rate of gene set analysis. To quantify the extent to which the regulon gene set for the rth regulator is enriched in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B), NaRnEA leverages two complementary test statistics—the Directed Enrichment Score (DESrAB) and the Undirected Enrichment Score (UESrAB)—which are defined as follows (Equation [4]):[4]DESrAB=∑gDESrgABDESrgAB=(AWrg)(AMrg)(rgABsgAB)UESrAB=∑gUESrgABUESrgAB=(AWrg)(1−\|AMrg\|)(rgAB) Both the Directed Enrichment Score and Undirected Enrichment Score weight the contribution of each gene toward the enrichment of the gene set based on the Association Weight parameter since the differential expression of a gene whose expression depends more strongly on the activity of the regulator is a better indicator of the change in the regulator’s activity. However, these complementary test statistics differ in how each incorporates the Association Mode. The Directed Enrichment Score considers both the magnitude and sign of the differential expression for each gene set member, whereas the Undirected Enrichment Score considers only the magnitude of differential expression. It follows that gene set members that are monotonically regulated by the rth regulator should contribute more to the Directed Enrichment Score since the sign of their differential gene expression signature values will be important for determining whether the null hypothesis of gene set analysis ought to be rejected in favor of the positive alternative hypothesis or the negative alternative hypothesis. However, gene set members that are non-monotonically regulated by the rth regulator should not contribute substantially to the Directed Enrichment Score since the sign of their differential expression does not clearly support one version of the alternative hypothesis over another. To that end, the formulation of the Undirected Enrichment Score provides a mechanism by which the non-monotonically regulated members of the regulon gene set may contribute to the enrichment. Since the Directed Enrichment Score and Undirected Enrichment score for the regulon gene set of the rth regulator are computed from the same gene set members, they form a bivariate vector {DESrAB,UESrAB}. We recognize that each of these test statistics is equal to the sum of independent random variables under the null hypothesis of gene set analysis, allowing us to invoke the multivariate version of the Lindeberg Central Limit Theorem [20] to derive the asymptotic null distribution of this bivariate vector. More formally, we define the Normalized Directed Enrichment Score (NDESrAB) for the regulon gene set of the rth regulator in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B) as follows (Equations [5]–[7]):[5]NDESrAB=DESrAB−E[DESrAB\|Ho]V𝕒𝕣[DESrAB\|Ho] [6]E[DESrAB\|Ho]=∑$g = 1$GE[DESrgAB\|Ho] E[DESrgAB\|Ho]=(AWrg)(AMrg)E[rgABsgAB\|Ho]E[rgABsgAB\|Ho]=∑$k = 1$G(rkABskAB)×Pr(rgABsgAB=rkABskAB\|Ho)=1G∑$k = 1$GrkABskAB [7]V𝕒𝕣[DESrAB\|Ho]=∑$g = 1$GV𝕒𝕣[DESrgAB\|Ho]V𝕒𝕣[DESrgAB\|Ho]=E[DESrgAB2\|Ho]−E[DESrgAB\|Ho]2E[DESrgAB2\|Ho]=(AWrg)2(AMrg)2E[rgAB2\|Ho]E[rgAB2\|Ho]=∑$k = 1$G(rkAB2)×Pr(rgAB2=rkAB2\|Ho)=1G∑$k = 1$GrkAB2=[16](2G2+3G+1) Then, if the condition [8]limG→∞∑$g = 1$GE[(DESrgAB−E[DESrgAB\|Ho])2×I{\|DESrgAB−E[DESrgAB\|Ho]\|>εV𝕒𝕣[DESrAB\|Ho]} \| Ho]V𝕒𝕣[DESrAB\|Ho] is satisfied for all (ε>0) where I{·} is the indicator function, the Central Limit Theorem holds such that [9]p(NDESrAB\|Ho)→D N[0,1] and the Normalized Directed Enrichment Score for the regulon gene set of the rth regulator in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B) converges in distribution to a standard normal random variable under the null hypothesis of gene set analysis. Similarly, we define the Normalized Undirected Enrichment Score (NUESrAB) for the regulon gene set of the rth regulator in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B) as follows (Equations [10]–[12]):[10]NUESrAB=UESrAB−E[UESrAB\|Ho]V𝕒𝕣[UESrAB\|Ho] [11]E[UESrAB\|Ho]=∑$g = 1$GE[UESrgAB\|Ho]E[UESrgAB\|Ho]=(AWrg)(1−\|AMrg\|)E[rgAB\|Ho]E[rgAB\|Ho]=∑$k = 1$G(rkAB)×Pr(rgAB=rkAB\|Ho)=1G∑$k = 1$GrkAB=[12](G+1) [12]V𝕒𝕣[UESrAB\|Ho]=∑$g = 1$GV𝕒𝕣[UESrgAB\|Ho]V𝕒𝕣[UESrgAB\|Ho]=E[UESrgAB2\|Ho]−E[UESrgAB\|Ho]2E[UESrgAB2\|Ho]=(AWrg)2(1−\|AMrg\|)2E[rgAB2\|Ho]E[rgAB2\|Ho]=∑$k = 1$G(rkAB2)×Pr(rgAB2=rkAB2\|Ho)=1G∑$k = 1$GrkAB2=[16](2G2+3G+1) Then, if the condition [13]limG→∞∑$g = 1$GE[(UESrgAB−E[UESrgAB\|Ho])2×I{\|UESrgAB−E[UESrgAB\|Ho]\|>εV𝕒𝕣[UESrAB\|Ho]} \| Ho]V𝕒𝕣[UESrAB\|Ho] is satisfied for all (ε>0) where I{·} is the indicator function, the Central Limit Theorem holds and p(NUESrAB\|Ho)→D N[0,1] such that the Normalized Undirected Enrichment Score for the regulon gene set of the rth regulator in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B) converges in distribution to a standard normal random variable under the null hypothesis of gene set analysis. We note that these sufficiency conditions, derived by Lindeberg, will be satisfied by any gene set with enough members (i.e., at least 30 targets) for which the Association Weight and Association Mode parameters are sufficiently well balanced; this ensures that the variance of the summand is not dominated by the variance of any element of the summand under the null hypothesis. Thus, the vector that consists of the Normalized Directed Enrichment Score and the Normalized Undirected Enrichment Score is a bivariate normal random vector under the null hypothesis of gene set analysis; furthermore, the mean of each marginal is equal to zero, and the variance of equal marginal is equal to one. We can also compute the covariance of this bivariate normal random vector under the null hypothesis of gene set analysis as follows (Equation [14]):[14]C𝕠𝕧[NDESrAB,NUESrAB\|Ho]=ρ[NDESrAB,NUESrAB\|Ho]ρ[NDESrAB,NUESrAB\|Ho]=ρ[DESrAB,UESrAB\|Ho]ρ[DESrAB,UESrAB\|Ho]=C𝕠𝕧[DESrAB,UESrAB\|Ho]V𝕒𝕣[DESrAB\|Ho] V𝕒𝕣[UESrAB\|Ho]C𝕠𝕧[DESrAB,UESrAB\|Ho]=∑$g = 1$GC𝕠𝕧[DESrgAB,UESrgAB\|Ho]C𝕠𝕧[DESrgAB,UESrgAB\|Ho]=E[DESrgABUESrgAB\|Ho]−E[DESrgAB\|Ho] E[UESrgAB\|Ho]E[DESrgABUESrgAB\|Ho]=(AWrg)2(AMrg)(1−\|AMrg\|)E[rgAB2sgAB\|Ho]E[rgAB2sgAB\|Ho]=∑$k = 1$G(rkAB2skAB)×Pr(rgAB2sgAB=rkAB2skAB\|Ho)=1G∑$k = 1$GrkAB2skAB where ρ[X,Y] is the Pearson product moment correlation between the random variables (X) and (Y). To determine how we should interpret the bivariate vector consisting of the Normalized Directed Enrichment Score and the Normalized Undirected Enrichment Score as providing evidence for either positive or negative gene set enrichment in the nonparametric differential gene expression signature, we return to our previous discussion regarding the differential gene expression signature values for the members of the regulon gene set for the rth regulator under each version of the alternative hypothesis. Since each gene in the regulon gene set for the rth regulator with a positive Association Mode will have a positive differential gene expression signature value under the positive alternative hypothesis, and each gene in the regulon gene set for the rth regulator with a negative Association Mode will have a negative differential gene expression signature value under the positive alternative hypothesis, it follows that both the Normalized Directed Enrichment Score and the Normalized Undirected Enrichment Score will be positive under the positive alternative hypothesis. Based on this rationale, we can combine the Normalized Directed Enrichment Score with the Normalized Undirected Enrichment Score to produce a single test statistic that will be strongly positive under the positive alternative hypothesis, which we refer to as the positive Normalized Enrichment Score (Equation [15]):[15]NESrAB+=NDESrAB+NUESrAB2+2C𝕠𝕧[NDESrAB,NUESrAB\|Ho] Since each gene in the regulon gene set for the rth regulator with a positive Association Mode will have a negative differential gene expression signature value under the negative alternative hypothesis, and each gene in the regulon gene set for the rth regulator with a negative Association Mode will have a positive differential gene expression signature value under the negative alternative hypothesis, it follows that the Normalized Directed Enrichment Score will be negative under the negative alternative hypothesis while the Normalized Undirected Enrichment Score will be positive under the negative alternative hypothesis. Based on this rationale, we can combine the Normalized Directed Enrichment Score with the Normalized Undirected Enrichment Score to produce a single test statistic that will be strongly negative under the negative alternative hypothesis, which we refer to as the negative Normalized Enrichment Score (Equation [16]):[16]NESrAB−=NDESrAB−NUESrAB2−2C𝕠𝕧[NDESrAB,NUESrAB\|Ho] This biologically motivated change of variables is mathematically equivalent to an affine transformation of the original bivariate vector {NDESrAB,NUESrAB}↦{NESrAB+,NESrAB−}. It is well established that an affine transformation of a multivariate normal random vector produces a new multivariate normal random vector whose mean vector and covariance matrix can be immediately calculated [21]. More formally, if (Y=BX+c) is an affine transformation of the multivariate normal random vector (X) with a mean vector equal to (μX) and a covariance matrix equal to (ΣX), then the random vector (Y) is a multivariate normal random vector with a mean vector equal to (μY=Bμx+c) and a covariance matrix equal to (ΣY=BΣXBT). Letting (φ=C𝕠𝕧[NDESrAB,NUESrAB\|Ho]), we can calculate the mean vector and covariance matrix of our new bivariate vector under the null hypothesis of gene set analysis as follows (Equations [17]–[19]):[17](E[NESrAB+\|Ho]E[NESrAB−\|Ho])=(12+2φ12+2φ12−2φ−12−2φ)(E[NDESrAB\|Ho]E[NUESrAB\|Ho])+[00](E[NESrAB+\|Ho]E[NESrAB−\|Ho])=(12+2φ12+2φ12−2φ−12−2φ)[00]+[00](E[NESrAB+\|Ho]E[NESrAB−\|Ho])=[00] [18](V𝕒𝕣[NESrAB+\|Ho]C𝕠𝕧[NESrAB+,NESrAB−\|Ho]C𝕠𝕧[NESrAB+,NESrAB−\|Ho]V𝕒𝕣[NESrAB−\|Ho])=…=(12+2φ12+2φ12−2φ−12−2φ)(1φφ1)(12+2φ12−2φ12+2φ−12−2φ)(V𝕒𝕣[NESrAB+\|Ho]C𝕠𝕧[NESrAB+,NESrAB−\|Ho]C𝕠𝕧[NESrAB+,NESrAB−\|Ho]V𝕒𝕣[NESrAB−\|Ho])=…=(12+2φ+φ2+2φφ2+2φ+12+2φ12−2φ−φ2−2φφ2−2φ−12−2φ)(12+2φ12−2φ12+2φ−12−2φ)V𝕒𝕣[NESrAB+\|Ho]=(12+2φ)(12+2φ+φ2+2φ)+…+(12+2φ)(φ2+2φ+12+2φ)V𝕒𝕣[NESrAB+\|Ho]=(12+2φ)+(φ2+2φ)+(φ2+2φ)+(12+2φ)V𝕒𝕣[NESrAB+\|Ho]=(2+2φ2+2φ)=1V𝕒𝕣[NESrAB−\|Ho]=(12−2φ)(12−2φ−φ2−2φ)−…−(12−2φ)(φ2−2φ−12−2φ)V𝕒𝕣[NESrAB−\|Ho]=(12−2φ)−(φ2−2φ)−(φ2−2φ)+(12−2φ)V𝕒𝕣[NESrAB−\|Ho]=(2−2φ2−2φ)=1 [19]C𝕠𝕧[NESrAB+,NESrAB−\|Ho]=…=(12+2φ)(12−2φ−φ2−2φ)+(12+2φ)(φ2−2φ−12−2φ)C𝕠𝕧[NESrAB+,NESrAB−\|Ho]=…=(12+2φ2−2φ)−(φ2+2φ2−2φ)+(φ2+2φ2−2φ)−…−(12+2φ2−2φ)C𝕠𝕧[NESrAB+,NESrAB−\|Ho]=(1−1+φ−φ2+2φ2−2φ)=0 We find that {NESrAB+,NESrAB−} is a bivariate standard normal random vector under the null hypothesis of gene set analysis where the mean of each marginal is equal to zero, the variance of equal marginal is equal to one, and the covariance is equal to zero. Motivated by the previous discussion about the behavior of the positive Normalized Enrichment Score under the positive alternative hypothesis and the behavior of the negative Normalized Enrichment Score under the negative alternative hypothesis, we can calculate the statistical significance of each element of this vector using the standard normal cumulative distribution function as follows (Equation [20]):[20]prAB+=1−Φ(NESrAB+)prAB−=Φ(NESrAB−) These p-values can be interpreted in a one-tailed manner as providing evidence against the null hypothesis in favor of the positive or negative version of the alternative hypothesis, respectively. Under the frequentist paradigm of null hypothesis significance testing, a sufficiently small (prAB+) would lead us to reject the null hypothesis in favor of the positive alternative hypothesis, whereas a sufficiently small (prAB−) would lead us to reject the null hypothesis in favor of the negative alternative hypothesis. Furthermore, through an appeal to the Neyman–Pearson lemma, we can motivate the use of the ratio between these two one-sided p-values, which is equivalent to a likelihood ratio, to decide on the most likely form of the alternative hypothesis in a manner that is uniformly most powerful. In order to control the overall Type I error rate of NaRnEA, we recognize that selecting the most likely form of the alternative hypothesis based on the minimum of these one-tailed p-values constitutes a form of multiple hypothesis testing that must be corrected for in a manner that accounts for the dependence between these one-tailed p-values under the null hypothesis of gene set analysis. Since these one-tailed p-values are calculated from the positive Normalized Enrichment Score and the negative Normalized Enrichment Score, their statistical dependence under the null hypothesis of gene set analysis can be established using the following Markov Chain (Equation [21]):[21]prAB+↔NESrAB+↔NESrAB−↔prAB− It follows from the Data Processing Inequality Theorem [19] that (I[prAB+;prAB−\|Ho]≤I[NESrAB+;NESrAB−\|Ho]). Since the positive Normalized Enrichment Score and the negative Normalized Enrichment Score are jointly normally distributed under the null hypothesis of gene set analysis, we can calculate the mutual information between them using the following formula:I[NESrAB+;NESrAB−\|Ho]=(−12)log(1−ρ[NESrAB+,NESrAB−\|Ho]2) We have already shown that, as a result of the affine transformation that produced the positive Normalized Enrichment Score and the negative Normalized Enrichment Score, the Pearson product moment correlation between these test statistics is equal to zero under the null hypothesis of gene set analysis. Thus, it immediately follows that the mutual information between the positive Normalized Enrichment Score and the negative Normalized Enrichment Score is equal to zero under the null hypothesis of gene set analysis. As a result, (prAB+) and (prAB−) are independent under the null hypothesis of gene set analysis. Thus, we can correct for our multiple hypothesis testing to obtain the final p-value for NaRnEA as follows (Equation [22]):[22]prAB=1−(1−min(prAB+,prAB−))2 We recognize that this is a two-sided p-value since it may be statistically significant under either the positive alternative hypothesis or the negative alternative hypothesis. We can use the magnitude of this final two-sided p-value and our knowledge of whether (prAB+) or (prAB−) is smaller to calculate the final Normalized Enrichment Score for the regulon gene set of the rth regulator in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B) for NaRnEA as follows (Equation [23]):[23]NESrAB={Φ−1(1−prAB2) if prAB+<prAB− Φ−1(prAB2) if prAB+>prAB− By virtue of its construction, the NaRnEA Normalized Enrichment Score has the following properties:p(NESrAB\|Ho)→DN[0,1]The final Normalized Enrichment Score for the rth regulator is a standard normal random variable when the regulon gene set for the rth regulator is not enriched in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B). Formally, the rate of this asymptotic convergence depends on the Association Weight and Association Mode values for the gene set members in accordance with the Berry–Esseen Theorem for non-identically distributed summands. E[NESrAB\|Ha+]>0The expected value of the final Normalized Enrichment Score for the rth regulator is positive when the regulon gene set for the rth regulator is positively enriched in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B).E[NESrAB\|Ha−]<0The expected value of the final Normalized Enrichment Score for the rth regulator is negative when the regulon gene set for the rth regulator is negatively enriched in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B). The asymptotic normality of the NaRnEA Normalized Enrichment Score under the null hypothesis of gene set analysis follows from the Lindeberg Central Limit Theorem, for which Lindeberg’s condition is sufficient. Since the Association Weight and Association Mode parameters for the members of the gene set are the reason that the summands are not necessarily identical, we require that these parameters do not exhibit extreme imbalance, which would violate Lindeberg’s condition; we show that a simple nonparametric procedure for parameterizing regulon gene sets that have been inferred from context-specific transcriptional regulatory networks produces sufficiently well-balanced gene sets that fulfill Lindeberg’s condition, thus allowing NaRnEA to maintain adequate control of the Type I error rate for gene set analysis. We also note that if NaRnEA is applied to gene sets in which all members have equal Association Weight and Association Mode values, such as literature-derived gene sets, the asymptotic normality of the NaRnEA Normalized Enrichment Score under the null hypothesis of gene set analysis is guaranteed by the classical Lindeberg–Lévy Central Limit Theorem [21]. The NaRnEA Normalized Enrichment Score is an optimal and robust test statistic for gene set analysis due its nonparametric integration of differential gene expression signature values, its nuanced ability to apply differential weighting to gene set members, its flexibility regarding uncertainty in the monotonicity of transcriptional regulation, and the derivation of its null model using the information theoretic Principle of Maximum Entropy. However, since the NaRnEA two-sided p-value, which may be analytically calculated from the NaRnEA Normalized Enrichment Score using the standard normal cumulative distribution function, does not measure the magnitude of gene set enrichment, we also provide an effect size for NaRnEA. To derive an effect size for NaRnEA, we first consider the Wilcoxon signed-rank test, another nonparametric null hypothesis significance test that returns the T-statistic. Like the NaRnEA Normalized Enrichment Score, the T-statistic has a mean of [0] and is approximately normally distributed under the null hypothesis of the Wilcoxon signed-rank test. If the T-statistic is divided by its maximum possible value (i.e., the total sum of ranks), the resulting effect size is known as the rank–biserial correlation; it has a maximum value of [1] and minimum value of (−1). We can leverage a similar approach to calculate the NaRnEA Proportional Enrichment Score (PES), which serves as the effect size for the enrichment of the rth regulon in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B), as follows (Equation [24]):[24]PESrAB={NESrAB\|max(NESrAB)\| if NESrAB>0NESrAB\|min(NESrAB)\| if NESrAB<0 By virtue of its construction, the NaRnEA Proportional Enrichment Score has the following properties:PESrAB∈[−1, 1]The Proportional Enrichment Score for the regulon gene set of the rth regulator is less than or equal to [1] and greater than or equal to (−1).E[PESrAB\|Ho]=0The expected value of the Proportional Enrichment Score for the regulon gene set of the rth regulator is equal to [0] when the regulon gene set of the rth regulator is not enriched in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B).E[PESrAB\|Ha+]>0The expected value of the Proportional Enrichment Score for the regulon gene set of the rth regulator is positive when the regulon gene set of the rth regulator is positively enriched in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B).E[PESrAB\|Ha−]<0The expected value of the Proportional Enrichment Score for the regulon gene set of the rth regulator is negative when the regulon gene set of the rth regulator is negatively enriched in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B). The effect size for NaRnEA (i.e., Proportional Enrichment Score) is calculated from the test statistic for NaRnEA (i.e., Normalized Enrichment Score) in the same way that the effect size for the Wilcoxon signed-rank test (i.e., rank–biserial correlation) is calculated from the test statistic for the Wilcoxon signed-rank test (i.e., the T-statistic). Thus, the Proportional Enrichment Score can be interpreted as type of nonparametric correlation coefficient. A confidence interval for the NaRnEA Proportional Enrichment Score can be estimated by applying the Fisher z-transformation to achieve approximate normality of this effect size under the alternative hypothesis; the associated standard error of the Fisher z-transformed NaRnEA Proportional Enrichment Score can be estimated by applying a suitable resampling procedure such as bootstrapping members of the gene set [22]. If the two-sided p-value computed from the NaRnEA Normalized Enrichment Score is statistically significant, we can reject the null hypothesis of gene set analysis in favor of either the positive alternative hypothesis or the negative alternative hypothesis based on the sign of the Normalized Enrichment Score. In a manner inspired by Subramanian et al. [ 9], we can subsequently identify the members of the gene set that contribute most significantly to the enrichment by calculating a Leading Edge Score for each gene set member as follows (Equation [25]):[25]LESrgAB={ (1−\|AMrg\|)(rgAB)+(AMrg)(rgABsgAB) if NESrAB>0 (1−\|AMrg\|)(rgAB)−(AMrg)(rgABsgAB) if NESrAB<0 If either the positive alternative hypothesis or the negative alternative hypothesis is true, we would expect the gth gene to have a strongly positive Leading Edge Score with respect to the rth regulator if the gth gene is contributing to the enrichment of the regulon gene set of the rth regulator in the nonparametric differential gene expression signature computed between the test phenotype (A) and the reference phenotype (B). Thus, we can calculate the statistical significance of this Leading Edge Score for the gth gene with respect to the rth regulator using the Maximum Entropy null model for gene set analysis as follows (Equation [26]):[26]prgAB={1G∑$k = 1$GI{(1−\|AMrg\|)(rkAB)+(AMrg)(rkABskAB)≥LESrgAB} if NESrAB>01G∑$k = 1$GI{(1−\|AMrg\|)(rkAB)−(AMrg)(rkABskAB)≥LESrgAB} if NESrAB<0 where I{·} is the indicator function. These post hoc, one-tailed Leading Edge p-values may be adjusted for multiple hypothesis testing to identify those gene set members that contribute most significantly to the gene set enrichment. Importantly, the Leading Edge Score does not depend on the Association Weight of the gth gene with respect to the rth regulator; this ensures that the selection of genes that belong to the leading edge of the gene set a posteriori is not biased by any measure of gene set member importance that has been determined a priori. ## 2.2. The Algorithm for the Reconstruction of Accurate Cellular Networks 3 (ARACNe3) ARACNe3 is an updated implementation of the Algorithm for the Reconstruction of Accurate Cellular Networks. The goal of ARACNe3 is to reverse-engineer a context-specific transcriptional regulatory network that consists of bivariate interactions between a set of predefined, putative transcriptional regulators and potential transcriptional targets. ARACNe3 accepts properly normalized gene expression profiles that correspond to independent samples from a single biological phenotype. Like previous versions of the algorithm, ARACNe3 recommends that users reverse-engineer multiple estimates of the transcriptional regulatory network topology and integrate these to form a consensus network. Previously, ARACNe-AP recommended that the estimates of the transcriptional regulatory network topology should be reverse-engineered in a decorrelated manner by sampling from the original set of gene expression profiles with replacement (i.e., bootstrapping). While this approach is commonly employed in the field of ensemble machine learning (e.g., random forest bagging [23]), we find that sampling gene expression profiles with replacement increases the bias and variance of the adaptive partitioning mutual information (APMI) estimator; the increase in bias occurs when sampling with replacement produces regions in the joint probability distribution with higher density due to replicated data points, while the increase in variance occurs because these fluctuations in the joint probability distribution occur stochastically between different iterations of the bootstrapping procedure. To avoid these pitfalls, ARACNe3 generates decorrelated individual networks by sampling (1−1e≈$63.21\%$) of the gene expression profiles without replacement each time; this is equal to the probability of a unique sample appearing in a single bootstrap and thus achieves the same level of decorrelation between individual estimates of the context-specific transcriptional regulatory network as bootstrapping without unduly increasing the bias or variance of the APMI estimator. ARACNe3 estimates the null distribution for mutual information by applying the APMI estimator to ~1,000,000 pairs of shuffled, copula-transformed gene expression marginals (i.e., gene expression marginals are rank-transformed and divided by the number of gene expression profiles plus one to ensure the marginals are uniform). ARACNe3 then fits a piecewise null model to these null mutual information estimates where an empirical cumulative distribution function is used for the body of the null model up to the 95th percentile of the data and the tail of the null model is fit analytically using robust linear regression applied to logarithmically transformed tail probabilities past the 95th percentile with the mblm R package from CRAN [24]. The ARACNe3 piecewise null model controls the Type I error rate for the APMI estimator more accurately than the null model implemented in ARACNe-AP and allows ARACNe3 to perform the first round of individual network pruning based on the control of the False Discovery Rate (FDR), resulting in a substantial gain in power over previous versions of the algorithm that performed the first round of individual network pruning based on the control of the Family-Wise Error Rate (FWER). ARACNe3 performs the second round of individual network pruning in a manner nearly identical to previous versions of the algorithm; briefly, all three-gene cliques that remain after the first round of individual network pruning are identified, and the weakest edge of each three-gene clique is removed from the network. The edges that remain after both rounds of pruning constitute an ARACNe3-inferred individual network. This procedure is carried out until one of two stopping criteria is met: [1] a prespecified maximum number of individual networks have been reverse-engineered, or [2] each putative transcriptional regulator has been assigned a prespecified minimum number of unique targets. The individual networks are then integrated to form an ARACNe3-inferred consensus transcriptional regulatory network. The mutual information and Spearman correlation for each putative transcriptional regulatory interaction in the ensemble network are estimated a final time using all gene expression profiles for greater accuracy. The ARACNe3-inferred regulon gene set for the rth transcriptional regulator is constructed by extracting all putative transcriptional regulatory interactions for the rth transcriptional regulator from the ARACNe3-inferred consensus transcriptional regulatory network. The Association Weight values are calculated by sorting all putative target genes based on [1] the number of individual networks in which they appeared as targets of the rth transcriptional regulator and [2] the final estimated mutual information between the rth transcriptional regulator and target gene. A copula transformation is then applied to the Association Weight values to ensure that the ARACNe3-inferred regulon gene sets are sufficiently well balanced to meet Lindeberg’s condition and guarantee the asymptotic standard normality of the NaRnEA Normalized Enrichment Score under the null hypothesis of gene set analysis. The Association Mode values are taken to be the Spearman correlation coefficient between the rth transcriptional regulator and each regulon gene set member as estimated from all gene expression profiles. The lung adenocarcinoma (LUAD) context-specific transcriptional regulatory network was reverse-engineered with ARACNe3 from 476 unpaired primary tumor gene expression profiles from TCGA using 2491 putative transcriptional regulators. Gene expression profiles were downloaded using the TCGAbiolinks R package from Bioconductor [25] and normalized for sequencing depth prior to network reverse engineering (i.e., counts per million). The first round of individual network pruning was carried out with a threshold for mutual information calculated to control the FDR at $5\%$. Individual networks were reverse-engineered until each putative transcriptional regulator had at least 50 unique transcriptional targets; this was achieved after seven iterations. The final consensus ARACNe3-inferred transcriptional regulatory network for LUAD consists of 2491 regulators, 19,350 targets, and 790,200 regulatory interactions. The colon adenocarcinoma (COAD) context-specific transcriptional regulatory network was reverse-engineered with ARACNe3 from 437 unpaired primary tumor gene expression profiles from TCGA using 2491 putative transcriptional regulators. Gene expression profiles were downloaded using the TCGAbiolinks R package from Bioconductor and normalized for sequencing depth prior to network reverse engineering (i.e., counts per million). The first round of individual network pruning was carried out with a threshold for mutual information calculated to control the FDR at $5\%$. Individual networks were reverse-engineered until each putative transcriptional regulator had at least 50 unique transcriptional targets; this was achieved after seven iterations. The final consensus ARACNe3-inferred transcriptional regulatory network for COAD consists of 2491 regulators, 19,350 targets, and 675,373 regulatory interactions. The head and neck squamous cell carcinoma (HNSC) context-specific transcriptional regulatory network was reverse-engineered with ARACNe3 from 457 unpaired primary tumor gene expression profiles from TCGA using 2491 putative transcriptional regulators. Gene expression profiles were downloaded using the TCGAbiolinks R package from Bioconductor and normalized for sequencing depth prior to network reverse engineering (i.e., counts per million). The first round of individual network pruning was carried out with a threshold for mutual information calculated to control the FDR at $5\%$. Individual networks were reverse-engineered until each putative transcriptional regulator had at least 50 unique putative transcriptional targets; this was achieved after 12 iterations. The final consensus ARACNe3-inferred transcriptional regulatory network for HNSC consists of 2491 regulators, 19,350 targets, and 812,199 regulatory interactions. ## 2.3. Gene Set Enrichment Analysis (GSEA) GSEA accepts properly normalized gene expression profiles from samples representing a test phenotype and a reference phenotype; the differential expression of each gene is then estimated using the Signal-to-Noise Ratio (SNR). The enrichment of a gene set in this gene expression signature is calculated with a weighted Kolmogorov–Smirnov-like statistic (i.e., the GSEA enrichment score). Subramanian et al. [ 9] recommended that the null distribution of the GSEA enrichment score for a particular gene set should be approximated using an empirical phenotype-based permutation procedure. Alternatively, if there are not enough samples to generate the number of desired phenotype-based permutations, an empirical gene-based permutation procedure may be used to approximate the null distribution of the GSEA enrichment score. Paired gene expression profiles from primary tumors and phenotype-matched normal tissue samples from TCGA were normalized using a blinded DESeq2 [26] variance-stabilizing transformation prior to analysis with GSEA, which was performed as described previously by Subramanian et al. [ 9] using the Java command line implementation of GSEA from the Molecular Signatures Database (http://www.gsea-msigdb.org/gsea/downloads.jsp (accessed on 1 October 2020)). The GSEA null model was estimated using 1000 sample-shuffling permutations. Empirical two-sided p-values returned by GSEA were corrected to a minimum of ($\frac{1}{1001}$), the smallest possible two-sided p-value for an empirical null model constructed from 1000 sample-shuffling permutations [27]. ## 2.4. Analytical-Rank-Based Enrichment Analysis (aREA) aREA accepts properly normalized gene expression profiles from samples representing a test phenotype and a reference phenotype; the differential expression of each gene is then estimated using Welch’s unpaired t-test [28]. The enrichment of a gene set in the resulting differential gene expression signature is calculated using a three-tailed approach, returning the aREA enrichment score test statistic. Alvarez et al. [ 4] recommended that the null distribution of the aREA enrichment score for a particular gene set should also be approximated using an empirical phenotype-based permutation procedure. Alternatively, if there are not enough samples to generate the number of desired phenotype-based permutations, an analytical approach may be used to approximate the null distribution of the aREA enrichment score. Paired gene expression profiles from primary tumors and phenotype-matched normal tissue samples from TCGA were normalized using a blinded DESeq2 variance-stabilizing transformation prior to analysis with aREA. The VIPER R package from Bioconductor was used to run aREA, as described previously by Alvarez et al. [ 4]. The aREA empirical null model was estimated using 1000 sample-shuffling permutations. Empirical two-sided p-values returned by aREA were corrected to a minimum of ($\frac{1}{1001}$), the smallest possible two-sided p-value for an empirical null model constructed from 1000 sample-shuffling permutations. ## 2.5. Clinical Proteomic Tumor Analysis Consortium (CPTAC) Differential Protein Abundance Log-ratio normalized protein abundance data for primary tumors and phenotype-matched normal tissue samples were downloaded from CPTAC for the LUAD, COAD, and HNSC cancer types (http://linkedomics.org (accessed on 1 October 2020)) [29]. Data were loaded into R, and the differential abundance of each protein between primary tumors and phenotype-matched normal tissue was estimated with a two-tailed Mann–Whitney U test [30]. Gene name conversion was performed using the biomaRt R package from Bioconductor [31]. ## 2.6. Plotting and Visualization All figures were created in R using the ggplot2 R package from CRAN [32]. ## 2.7. Statistical Analysis p-values were corrected for multiple hypothesis testing to control the FDR according to the methodology of Benjamini and Hochberg or to control the FWER according to the methodology of Bonferroni [33]. The $95\%$ confidence intervals for the binomial test of proportions were computed using the procedure of Clopper and Pearson [34]. ## 3.1. Evaluating the Sensitivity and Specificity of NaRnEA for Gene Set Analysis We evaluated NaRnEA by performing gene set analysis using gene expression data from The Cancer Genome Atlas (TCGA) for lung adenocarcinoma (LUAD), colon adenocarcinoma (COAD), and head and neck squamous cell carcinoma (HNSC); these cancer types were selected because of [1] the availability of phenotype-matched primary tumor and normal tissue RNA-Seq gene expression profiles in TCGA and [2] the availability of phenotype-matched primary tumor and normal tissue mass spectrometry protein abundance profiles in the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Crucially, the differential protein abundance inferred from mass spectrometry data in CPTAC provides orthogonal validation for the differential protein activity inferred from gene expression data in TCGA. For each TCGA cohort, we separated the primary tumor gene expression profiles into two groups based on whether each primary tumor was submitted with or without an associated adjacent normal tissue sample; we refer to these as paired and unpaired primary tumor gene expression profiles, respectively. From the unpaired primary tumor gene expression profiles, we reverse-engineered a context-specific transcriptional regulatory network for each cancer type with ARACNe3 for 2491 putative transcriptional regulatory proteins (i.e., transcription factors, co-transcription factors, epigenetic modifying enzymes) [4]. From each context-specific ARACNe3-inferred transcriptional regulatory network, we extracted all edges associated with each regulator, producing the tumor-specific (TS) regulon gene sets. Subsequently, we created an identical number of null model (NM) regulons by swapping out the members of each TS regulon with an equal number of genes selected at random from the complement of the corresponding TS regulon gene set. By virtue of their construction, the NM regulons are biologically meaningless and therefore will not be enriched in any differential gene expression signature. We used the gene expression profiles from 57 LUAD, 41 COAD, and 43 HNSC patient-matched primary tumor (i.e., test phenotype) and adjacent normal tissue (i.e., reference phenotype) samples from TCGA to estimate cohort-specific differential gene expression signatures. We then used NaRnEA to test for the enrichment of the NM regulons in the corresponding differential gene expression signatures. The number of statistically significantly enriched NM regulons was determined from the NaRnEA two-sided p-values based on the control of the False-Positive Rate (FPR < 0.05), False Discovery Rate (FDR < 0.05), or Family-Wise Error Rate (FWER < 0.05). This analysis demonstrates that after correcting for multiple hypothesis testing, NaRnEA did not find any of the NM regulons to be statistically significantly enriched in the differential gene expression signatures computed between primary tumor and adjacent normal tissue samples from TCGA (Table 1). Crucially, this finding is demonstrated using biologically meaningless gene sets (i.e., NM regulons) and biologically meaningful differential gene expression signatures that should not alter the complex higher-order dependencies that Mootha et al. [ 8], Subramanian et al. [ 9], and Tamayo et al. [ 10] claim exist between gene set members under the null hypothesis of gene set analysis. This finding that NaRnEA maintains specificity while using a null model for gene set analysis that explicitly assumes that gene set members are independent when the gene set is not enriched in the differential gene expression signature effectively falsifies the primary claim underlying the validity of the empirical phenotype-based permutation null model used by both GSEA and aREA. Having established the specificity of NaRnEA using the NM regulons, we subsequently evaluated the sensitivity of NaRnEA using the TS regulons. Given the substantial differences in gene expression between malignant and benign phenotypes, we expected that at least some fraction of the 2491 transcriptional regulatory proteins to which these TS regulons correspond would exhibit differential activity between the primary tumor and adjacent normal tissue samples from TCGA. However, we did not know a priori which subset of transcriptional regulatory proteins would exhibit differential activity, since no experimental methodology exists to measure the activity of transcriptional regulatory proteins in a systematic, high-throughput manner in vivo. Instead, we used NaRnEA to test for the enrichment of the TS regulons in the corresponding differential gene expression signatures computed between the primary tumor and adjacent normal tissue samples from TCGA; then, we used independent mass spectrometry data from CPTAC to determine whether the differential activity of the transcriptional regulatory proteins, inferred from TCGA gene expression data using NaRnEA, was correlated with the differential abundance of the transcriptional regulatory proteins, inferred from CPTAC proteomic data using a Mann–Whitney U (MWU) test. The statistical dependence between differential protein activity inferred by NaRnEA from gene expression data in TCGA and differential protein abundance inferred by an MWU test from mass spectrometry data in CPTAC can be expressed by the following Markov Chain (Equation [27]):[27]NESrAB←DRArAB←DPArAB→MWUrAB where (NESrAB) is the NaRnEA Normalized Enrichment Score of the rth regulator between the test phenotype (A) and the reference phenotype (B), (DRArAB) is the differential regulatory activity of the rth regulator between the test phenotype (A) and the reference phenotype (B), (DPArAB) is the differential protein abundance of the rth regulator between the test phenotype (A) and the reference phenotype (B), and (MWUrAB) is the result of the MWU test inferring the differential protein abundance for the rth regulator between the test phenotype (A) and the reference phenotype (B). It follows from the Data Processing Inequality Theorem [19] that the mutual information between the NaRnEA-inferred differential protein activity from TCGA and the MWU-inferred differential protein abundance from CPTAC is a lower bound on the mutual information between the NaRnEA-inferred differential protein activity and the true differential protein activity; we expect this mutual information to be reduced by several factors, both technical and biological. Since the activity of a transcriptional regulatory protein depends on numerous post-translational events (e.g., nuclear localization, post-translational modification, cofactor binding, chromatin accessibility), the activity of the regulator and the abundance of the regulator will differ, thereby weakening the statistical dependency between random variables in the Markov Chain. From a technical perspective, the extent to which the MWU-inferred differential protein abundance from CPTAC agrees with the true differential protein abundance will depend strongly on the accuracy of the mass spectrometry experimental analysis and the accuracy of the MWU test. Similarly, the extent to which the NaRnEA-inferred differential protein activity from TCGA agrees with the true differential protein activity will depend on the accuracy of the gene expression profiling, the accuracy of the ARACNe3-inferred TS regulons, and the accuracy of NaRnEA as a statistical method. Taking into consideration this myriad of confounders, any statistically significant correlation between NaRnEA-inferred differential protein activity from TCGA- and MWU-inferred differential protein abundance from CPTAC offers strong support for the sensitivity and biological validity of NaRnEA as a gene set analysis method. The number of TS regulons that were statistically significantly enriched in the differential gene expression signatures computed between primary tumor and adjacent normal tissue samples in each of the cancer types from TCGA was determined from the NaRnEA two-sided p-values based on the control of the False-Positive Rate (FPR < 0.05), False Discovery Rate (FDR < 0.05), or Family-Wise Error Rate (FWER < 0.05). This analysis demonstrates that after correcting for multiple hypothesis testing, many of the TS regulons were inferred by NaRnEA to be statistically significantly enriched in these differential gene expression signatures from TCGA (Table 2). To determine whether this NaRnEA-inferred differential protein activity from TCGA agreed with MWU-inferred differential protein abundance from CPTAC, we compared primary tumor (ntumor = 110, 97, 109) and phenotype-matched normal tissue (ntissue = 101, 100, 64) samples in the LUAD, COAD, and HNSC cohorts from CPTAC, respectively. For each cancer type, we restricted our analysis to the transcriptional regulatory proteins for which a TS regulon and mass spectrometry data were available. We corrected the MWU test two-sided p-values from CPTAC for multiple hypothesis testing and classified each transcriptional regulatory protein as upregulated (UP, MWU test rank–biserial correlation > 0, FDR < 0.05), downregulated (DOWN, MWU test rank–biserial correlation < 0, FDR < 0.05), or not statistically significantly differentially abundant (NS, FDR ≥ 0.05). Similarly, we also corrected the NaRnEA two-sided p-values from TCGA for multiple hypothesis testing and classified each transcriptional regulatory protein as activated (UP, NaRnEA PES > 0, FDR < 0.05), deactivated (DOWN, NaRnEA PES < 0, FDR < 0.05), or not statistically significantly differentially activated (NS, FDR ≥ 0.05). We tested for an association between NaRnEA-inferred differential protein activity from TCGA- and MWU-inferred differential protein abundance from CPTAC using a three-by-three contingency table for LUAD (Table 3), COAD (Table 4), and HNSC (Table 5). The agreement between the rows and columns was evaluated using Kendall’s Tau-B correlation coefficient, which adjusts for tied values within each of the three marginal categories; the statistical significance of this dependence was calculated with a Chi-Squared Test. This analysis revealed that the NaRnEA-inferred differential protein activity from TCGA was statistically significantly positively correlated with the MWU-inferred differential protein abundance from CPTAC for the LUAD (Kendall’s Tau-$B = 0.3832$, $$p \leq 7.481$$ × 10−51), COAD (Kendall’s Tau-$B = 0.2913$, $$p \leq 1.456$$ × 10−18), and HNSC (Kendall’s Tau-$B = 0.3455$, $$p \leq 5.237$$ × 10−38) cancer types. These results offer biological validity for the NaRnEA-inferred differential protein activity, further reinforcing that NaRnEA is a highly sensitive gene set analysis method. The NaRnEA-inferred differential protein activity for all putative transcriptional regulatory proteins can be visualized en masse by plotting the absolute value of the Normalized Enrichment Score vs. the Proportional Enrichment Score for each TS regulon using a modified version of a volcano plot [35] (Figure 1, Figure 2 and Figure 3). Alternatively, one can directly visualize the distribution of ARACNe3-inferred transcriptional regulatory targets in the nonparametric differential gene expression signature computed between primary tumor and adjacent normal tissue samples for a subset of the most differentially activated proteins using a Master Regulator Analysis plot (Figure 4, Figure 5 and Figure 6). To demonstrate the utility of the NaRnEA Leading Edge analysis, we calculated the post hoc Leading Edge p-values for each of the TS regulons that were statistically significantly enriched in the differential gene expression signatures computed between the primary tumor and adjacent normal tissue samples from TCGA (FWER < 0.05). Since there was no overlap between the gene expression profiles that were used by ARACNe3 to reverse-engineer the context-specific transcriptional regulatory networks and the gene expression profiles that were used to estimate the differential gene expression signatures for these cancer types, the NaRnEA Leading Edge p-values and the ARACNe3-inferred Association Weight values would be independent under the null hypothesis that the NaRnEA Leading Edge analysis cannot identify the gene set members that contribute most significantly to the gene set enrichment. In support of the NaRnEA Leading Edge analysis, this analysis revealed that the ARACNe3-inferred Association Weight values and the NaRnEA-inferred post hoc Leading Edge p-values exhibited a statistically significantly negative Spearman correlation (FWER < 0.05) for the vast majority of TS regulons in TCGA LUAD ($91.75\%$), TCGA COAD ($90.56\%$), and TCGA HNSC ($88.33\%$). This analysis was restricted to those TS regulons that NaRnEA inferred were statistically significantly enriched in the differential gene expression signatures computed between the corresponding primary tumor and adjacent normal tissue samples from TCGA (FWER < 0.05) since post hoc Leading Edge analysis is only interpretable for these gene sets. ## 3.2. Identifying Systematic Biases in Phenotype-Based Permutation Null Models for Gene Set Enrichment To compare NaRnEA with GSEA and aREA, we first applied these alternative gene set analysis methods to test for the enrichment of the NM regulons in the gene expression data from TCGA for LUAD, COAD, and HNSC. The number of statistically significantly enriched NM regulons was determined for GSEA (Table 6) and aREA (Table 7) from the resulting two-sided p-values; statistical significance was established based on the control of the False-Positive Rate (FPR < 0.05), False Discovery Rate (FDR < 0.05), or Family-Wise Error Rate (FWER < 0.05). This analysis demonstrates that, after correcting for multiple hypothesis testing, neither GSEA nor aREA found any of the NM regulons to be statistically significantly enriched in the differential gene expression signatures computed between primary tumor and adjacent normal tissue samples from TCGA; thus, we conclude that both methods adequately control the Type I error rate of gene set analysis. We subsequently applied GSEA and aREA to test for the enrichment of the TS regulons in the gene expression data from TCGA for LUAD, COAD, and HNSC. The number of statistically significantly enriched TS regulons was determined for GSEA (Table 8) and aREA (Table 9) from the resulting two-sided p-values; statistical significance was established based on the control of the False-Positive Rate (FPR < 0.05), False Discovery Rate (FDR < 0.05), or Family-Wise Error Rate (FWER < 0.05). This analysis demonstrates that, after correcting for multiple hypothesis testing, neither GSEA nor aREA found any of the TS regulons to be statistically significantly enriched in the differential gene expression signatures computed between primary tumor and adjacent normal tissue samples from TCGA. Given that the NaRnEA-inferred differential protein activity from TCGA was significantly correlated with MWU-inferred differential protein abundance from CPTAC, we conclude that NaRnEA is significantly more sensitive than both GSEA and aREA; furthermore, the fact that NaRnEA did not identify any statistically significantly enriched NM regulons after correcting for multiple hypothesis testing demonstrates that this gain in sensitivity is achieved without loss of specificity. The low sensitivity of GSEA and aREA, as evidenced by their inability to identify statistically significantly enriched TS regulons after correcting for multiple hypothesis testing, can be attributed directly to their reliance on the empirical phenotype-based permutation null model, which we have shown to be unnecessary for achieving adequate control of the gene set analysis Type I error rate. In order to determine why the use of the empirical phenotype-based permutation null model decreases the sensitivity of these methods, we applied this procedure to the gene expression data from TCGA while determining how many primary tumor and adjacent normal tissue samples were distributed to the null test phenotype and null reference phenotype during each permutation. Then, for each of the sample-shuffling permutations, we estimated a null differential gene expression signature between the null test phenotype samples and the null reference phenotype samples using an MWU test; we repeated this process 1000 times for each cancer type. We then tested for the enrichment of the ARACNe3-inferred TS regulons in each of these null differential gene expression signatures with NaRnEA. After correcting the resulting NaRnEA two-sided p-values for multiple hypothesis testing (FWER < 0.05), we found that some of the TS regulons were statistically significantly enriched in each of the null differential gene expression signatures computed from TCGA LUAD (minimum = 112, median = 568, maximum = 1053), TCGA COAD (minimum = 65, median = 532, maximum = 1041), and TCGA HNSC (minimum = 78, median = 489, maximum = 916). Since we established that NaRnEA adequately controls the Type I error rate of gene set analysis, we can conclude that the enrichment of the TS regulons was not an artifact; rather, this finding suggests that each of the null differential gene expression signatures exhibited some degree of correlation with the original differential gene expression signature. To test for this, we calculated the Spearman correlation between each null differential gene expression signature and the original differential gene expression signature. After correcting the two-sided p-values for multiple hypothesis testing (FWER < 0.05), we found that nearly all of the null differential gene expression signatures from TCGA LUAD ($94.7\%$), TCGA COAD ($95\%$), and TCGA HNSC ($93.2\%$) were statistically significantly correlated with the original differential gene expression signature from the same cancer type. Furthermore, we found that this correlation between each null differential gene expression signature and the original differential gene expression signature was strongly associated with the degree of imbalance between the corresponding null phenotypes; for example, when the null test phenotype contained a greater number of primary tumor samples than adjacent normal tissue samples, the resulting null differential gene expression signature was more likely to be positively correlated with the original differential gene expression signature (Figure 7, Figure 8 and Figure 9). This analysis provides an immediate explanation for the reduced sensitivity of GSEA and aREA: the empirical phenotype-based permutation null models leveraged by each of these methods are contaminated with enrichment test statistics that do not strictly follow the null distribution due to the enrichment of the gene sets in a portion of the null differential gene expression signatures. Thus, in addition to demonstrating that NaRnEA adequately controls the Type I error rate of gene set analysis with an analytical null model derived using the information theoretic Principle of Maximum Entropy, we have also shown that systematic biases in the empirical phenotype-based permutation null model leveraged by GSEA and aREA can fully explain the substantial difference in sensitivity between these methods. ## 3.3. Examining the Alternative Null Model for GSEA Having established that the empirical phenotype-based permutation null model for gene set analysis is both systematically flawed and unnecessary to maintain adequate control of the Type I error rate, we next examined the alternative null model for GSEA that Subramanian et al. [ 9] described as follows: “… [GSEA] can be applied to ranked gene lists arising in other settings. Genes may be ranked based on the differences seen in a small data set, with too few samples to allow rigorous evaluation of significance levels by permuting the class labels. In these cases, a P value can be estimated by permuting the genes, with the result that genes are randomly assigned to the sets while maintaining their size. This approach is not strictly accurate: because it ignores gene-gene correlations, it will overestimate the significance levels and may lead to false positives. Nonetheless, it can be useful for hypothesis generation”. Tamayo et al. [ 10] formally describe the GSEA enrichment score as the following weighted variation of the two-sample Kolmogorov–Smirnov statistic (Equation [28]):[28]SkGSEA=supi=1,…,N(Figk−Fig¯k)Figk=∑$h = 1$i\|sh\|αIh∑$h = 1$N\|sh\|αIhFig¯k=∑$h = 1$i(1−Ih)(N−nk)Ih={1 if h∈gk0 if h∈g¯k where (SkGSEA) is the GSEA enrichment score test statistic for the kth gene set, (sup) is the supremum operator, (Figk) is the component of the running sum statistic computed at the ith ranked gene in the gene expression signature that corresponds with the gene set, (Fig¯k) is the component of the running sum statistic computed at the ith ranked gene in the gene expression signature that corresponds with the gene set’s complement, (Ih) is an indicator variable that identifies whether the hth ranked gene belongs to the gene set or the gene set’s complement, (sh) is the differential gene expression signature value of the hth ranked gene, (N) is the total number of genes in the differential gene expression signature, and (nk) is the number of genes in the kth gene set. The exponential (α) determines the extent to which GSEA is sensitive to the magnitude of differential gene expression for each gene in the gene set when computing the enrichment score test statistic. Mootha et al. [ 8] originally recommended setting (α) to zero, which renders the GSEA enrichment score equivalent to the two-sample Kolmogorov–Smirnov test statistic, while Subramanian et al. [ 9] recommended setting (α) to one; they set (α) to one for GSEA based on the following observation: “In the original implementation, the running-sum statistic used equal weights at every step, which yielded high scores for sets clustered near the middle of the ranked list … These sets do not represent biologically relevant correlation with the phenotype. We addressed this issue by weighting the steps according to each gene’s correlation with a phenotype”. The behavior of GSEA, as implemented by Mootha et al. [ 8] with (α) set to zero, follows directly from the statistical formulation of the two-sample Kolmogorov–Smirnov test, which was created to test the null hypothesis that two sets of observations are independently and identically distributed from the same sampling distribution. By virtue of its design, the two-sample Kolmogorov–Smirnov test is sensitive to any sufficiently large deviation between the two sample-specific empirical cumulative distribution functions regardless of where in the set of observations that deviation occurs. Thus, the alternative hypothesis for the two-sample Kolmogorov–Smirnov test is far too broad for gene set analysis if one is interested in rejecting the null hypothesis only when the gene set members are enriched at the extremes of the differential gene expression signature. Subramanian et al. [ 9] attempted to modify GSEA by setting (α) equal to one; however, we can demonstrate that this pathological behavior is still present when the alternative empirical gene-based permutation null model for GSEA is used. We constructed a new group of gene sets, which we refer to as totally null (TN) regulons, by replacing the members of each TS regulon with genes drawn at random from the set of genes for which the MWU differential gene expression two-sided p-value was greater than 0.50. Thus, these TN regulons consisted solely of genes that were not enriched at the extremes of the corresponding differential gene expression signature; therefore, an accurate gene set analysis method should not identify any statistically significant enrichment for these gene sets. We used NaRnEA (Table 10) and GSEA (Table 11) to test for the enrichment of the TN regulons in the corresponding differential gene expression signatures computed between the primary tumor and adjacent normal tissue samples from TCGA; here, we used the alternative empirical gene-based permutation null model for GSEA. The number of statistically significantly enriched TN regulons was determined from the resulting two-sided p-values; statistical significance was established based on the control of the False-Positive Rate (FPR < 0.05), False Discovery Rate (FDR < 0.05), or Family-Wise Error Rate (FWER < 0.05). We determined that, after correcting for multiple hypothesis testing to control the False Discovery Rate, GSEA found $100\%$ of the TN regulons to be statistically significantly enriched in the differential gene expression signatures computed between primary tumor and adjacent normal tissue samples from TCGA. In contrast, even without correcting for multiple hypothesis testing, NaRnEA did not find any of the TN regulons to be statistically significantly enriched in the differential gene expression signatures. These results falsify the secondary claim made by Subramanian et al. [ 9] and Tamayo et al. [ 10] that the weighted two-sample Kolmogorov–Smirnov test statistic prevents GSEA from detecting statistically significant enrichment for gene sets whose members exhibit biologically meaningless non-uniform distribution in the differential gene expression signature. Taken together, these findings demonstrate that GSEA exhibits significant and irreparable flaws that render its use as a gene set analysis method inappropriate regardless of whether the original empirical phenotype-based permutation null model or the alternative empirical gene-based permutation null model is employed. ## 3.4. Examining the Alternative Null Model for aREA We also examined the alternative null model for aREA, which is described by Alvarez et al. [ 4] as follows: “… the statistical significance for the enrichment score is estimated by comparison to a null model generated by permuting the samples uniformly at random or by an analytic approach equivalent to shuffle the genes in the signatures uniformly at random … Gene shuffling can be approximated analytically as follows: according to the central limit theorem, the mean of a sufficiently large number of independent random variables will be approximately normally distributed. The enrichment score of our null hypothesis fulfill this condition, and we ensure a mean of zero and variance equal to one for the enrichment score under the null hypothesis by applying a quantile transformation based on the normal distribution to the rank-transformed gene expression signature before computing the enrichment score”. Alvarez et al. [ 4] invoked the Central Limit Theorem to claim that the aREA test statistic would be normally distributed with a mean of zero and a variance of one under the null hypothesis of gene set analysis. We directly tested this claim by permuting the values of the original differential gene expression signature to create 1000 shuffled differential gene expression signatures for each cancer type from TCGA. By virtue of this shuffling procedure, none of the TS regulons would be enriched in these shuffled differential gene expression signatures. In order to test the claim made by Alvarez et al. [ 4] that the aREA test statistics would be normally distributed with a mean of zero and a variance of one under the null hypothesis of gene set analysis, we used aREA to test for the enrichment of each TS regulon in each shuffled differential gene expression signature, producing 1000 aREA test statistics for each TS regulon in each cancer type from TCGA. We then tested the null hypothesis that the aREA test statistics for each TS regulon followed a standard normal distribution as Alvarez et al. [ 4] claim using a one-sample Kolmogorov–Smirnov test. We found that, after correcting the resulting p-values for multiple hypothesis testing (FWER < 0.05), we rejected the null hypothesis that the aREA test statistics were normally distributed with a mean of zero and a variance of one under the null hypothesis of gene set analysis for $100\%$ of the TS regulons in TCGA LUAD, TCGA COAD, and TCGA HNSC. This analysis demonstrates that the alternative null model provided for aREA does not behave in the manner described by Alvarez et al. [ 4]; unfortunately, no formal analysis could be conducted to identify the source of this discrepancy as the alternative null model for aREA was published without accompanying proof to provide its mathematical justification. Thus, we conclude that aREA also exhibits significant flaws as a gene set analysis method regardless of whether the original empirical phenotype-based permutation null model or the alternative analytical null model is employed. When we repeat this analysis with NaRnEA instead of aREA, we do not reject the null hypothesis for any of the TS regulons after correcting for multiple hypothesis testing (FWER < 0.05). Thus, while these results effectively falsify the alternative analytical null model for aREA, they demonstrate that the NaRnEA analytical null model behaves precisely as expected. ## 4. Discussion It is widely appreciated that the rigor and reproducibility of scientific research depends on the use of computational and experimental methods that are sufficiently sensitive to make meaningful inferences while maintaining adequate control of the Type I error rate to reduce spurious findings [36]. Gene set analysis methods are being increasingly applied for hypothesis generation [37], precision oncology [15], systems pharmacology [38], analysis of single-cell transcriptomic data [39,40], and biomarker development [41]. Here, we demonstrate that NaRnEA significantly outperforms both GSEA and aREA for the purpose of gene set analysis in three independent cancer types; despite the widespread use of both competing methods, NaRnEA is the only method capable of consistently distinguishing between biologically coherent gene sets and gene sets constructed at random in these cohorts. Furthermore, the substantial agreement between NaRnEA-inferred differential protein activity in TCGA cohorts and MWU-inferred differential protein abundance in phenotype-matched CPTAC cohorts confirms that gene set enrichment inferred by NaRnEA cannot be explained away as erroneous false positives. Indeed, the specificity of NaRnEA is established by the fact that NaRnEA did not infer statistically significant enrichment for any of the NM regulons between primary tumor and adjacent normal tissue samples in TCGA after correcting for multiple hypothesis testing. We find that the low sensitivity of both GSEA and aREA can be attributed to their reliance on an empirical phenotype-based permutation null model that we show to be overwhelmingly confounded by genuine gene set enrichment due to persistent associations between null gene expression signatures and the original gene expression signatures. Finally, we show that the alternative null models for GSEA and aREA are statistically invalid due to similarity with the two-sample Kolmogorov–Smirnov test and an inaccurate mathematical framework, respectively. 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--- title: 'Shiver Me Tinders and Ring a Ding for a Fling—Sex Tech Use during COVID-19: Findings from a UK Study' authors: - Hannah R. Marston - Deborah J. Morgan - Sarah Earle - Robin A. Hadley journal: Healthcare year: 2023 pmcid: PMC10048256 doi: 10.3390/healthcare11060897 license: CC BY 4.0 --- # Shiver Me Tinders and Ring a Ding for a Fling—Sex Tech Use during COVID-19: Findings from a UK Study ## Abstract Existing research surrounding dating apps has primarily focused on younger people with few studies exploring usage of such apps by middle aged and older adults. The worldwide COVID-19 pandemic challenged social behaviours and forced people to adapt intimacy and wider relationship conduct. The objective of this study was to examine how older adults utilized dating apps during the lockdowns of the UK pandemic (December 2020–May 2021). Findings presented here focus on qualitative data collected from an online survey and eight online, one-to-one interviews with adults aged 40–54 years. The online survey targeted adults across the UK while interviewees were located across England. Employing interpretative phenomenological analysis, findings identified three key themes: 1. Morality, health, and law breaking and COVID-19; 2. Self-surveillance and moral signalling; 3. Loneliness and social isolation. Qualitative findings show engaging with apps was a proxy which alleviated feelings of loneliness and social isolation. Some users used the premise of their social bubble as a way of meeting other people. Using the same premise, others justified breaking the law to engage in physical and sexual intimacy to mitigate their loneliness. The work presented here contributes to the fields of social sciences, gerontology, and human computer interaction. The inter- and multi-disciplinary impact of this study intersects across those fields and offers a cross-sectional insight into behaviours and engagement with technology during one of the most extraordinary global events. ## 1. Introduction The 2020 COVID-19 pandemic brought society to a standstill on a global scale [1]. Industries and routines within the United Kingdom (UK) had to adapt by adopting an agile approach to respective (devolved) government (England, Northern Ireland, Scotland, and Wales) lockdown-related directives which were deployed [2] for public safety. The use and accessibility of dating apps has increased over the last decade, facilitating the ease of meeting a wide range of people online and in person—for example, those from different regions and cultures, or with similar interests including sexual preference sub-cultures (e.g., BDSM, swinging, polyamory). During the 2020 pandemic the UK government implemented two lockdowns which resulted in a restriction of movement. Pre-pandemic, dating apps offered users a variety of options to meet others ranging from texts, voice and video calls to meeting in person for a date, intimacy, and/or sex [3]. The latter three would be severely constrained during the lockdowns of 2020. Previous studies show that many people typically seek out companionship, hook-ups, relationships, and intimacy via dating apps [4]. However, what is missing from current research is the dating app experiences of people during the pandemic and of people in mid- and later life. Although much of society is transitioning into living with COVID-19 as an endemic virus [5,6], for some people in society their use of facemasks and shielding are still ongoing. This is especially the case for people who are clinically vulnerable or have life-limiting or life-threatening health conditions [5,7]. There is a growing body of work surrounding the pandemic and its impact on people, and communities, from the perspectives of health, wellbeing, technology, and social connections [2,8,9,10]. Technology use intensified during the pandemic across many sectors, industries, and households, enabling necessary tasks to be undertaken such as online grocery shopping, attending church services [11], connecting friends and family via social media platforms [10], or managing their health, and wellbeing [12]. In addition, social distancing and the government’s stay-at-home messaging significantly increased the levels of both social isolation and loneliness for people of all ages [13,14,15]. Furthermore, the different lockdown orders impacted dating apps users who used them specifically to find long-term companionship or an intimate relationship or to engage in sexual activity such as ‘hook-ups’ or to seek out ‘friends with benefits’. Therefore, the aims of this study included gathering insights and understanding of the pandemic lockdowns on people’s use of dating apps and the impact dating apps had on their health and wellbeing, intimate relationships, and sexual activity. In this paper we present qualitative data from eight online interviews in conjunction with descriptive data from an online survey alongside textual responses to open survey questions from 138 respondents. The findings illustrate how the participants and respondents were using dating apps during the 2020 UK lockdowns. This research contributes to the fields of gerontechnology, gerontology, health, ‘sex tech’, and social sciences. The inter- and multi-disciplinary approach of this work intersects with (sexual) health, wellbeing, and intimacy via the use of dating apps. ## Background Literature Dating apps have been accessible since 2006 following the creation of the first dating app—MeetMoi [16], followed by Grindr [2009] [17] and Scruff [2010] [18], while Tinder and Hinge have been around for a decade [2012] [19,20]. Nikiforova [21] notes $27\%$ of adults aged between 18 and 24 years and who are users of dating apps have reported feeling lonely [22,23]. Dating apps offer users access to a wide range of intimacy, relationships, one-night stands/hook-ups, sexual preferences, and sub-culture sexual interests [4,10,23,24,25,26]. In 2020, $32\%$ of men and $28\%$ of women in America were reported to use dating apps/sites, with most users aged between 18 and 29 years ($48\%$) and usage being lowest in those aged 65+ years ($13\%$). The numbers of non-heterosexual users—$55\%$ of lesbian, gay or bisexual (LGB) people—far outweighed the $28\%$ people who identified as heterosexual [23]. From a UK perspective, the majority of dating app users ($31.4\%$) are aged between 25 and 34 years, followed by those aged between 35 and 44 years ($25.7\%$), 18 and 24 years ($20.0\%$), with the least number of adult users ($11.4\%$) aged between 45 and 54- and between 55 and 64-years [3]. Dating app use in the UK is primarily conducted by men ($57.1\%$), with only $42.9\%$ of women using dating apps. The majority of UK dating app users ($40.6\%$) were categorized as low income, followed by $37.5\%$ and $21.9\%$ of people reporting to have high and medium incomes, respectively [3]. Research exploring dating apps shows a growing and wider area of interests and characteristics including loneliness [25,26,27,28], associated risks (e.g., scamming, sexual violence) [29,30,31,32]), infidelity [33,34,35], wellbeing [36], and motivations [37,38,39,40]. Yet, the majority of this work encompassing dating apps focused on younger users rather than adults in mid- and later life. Nonetheless, scholars in the social sciences have explored sex and intimacy in later life (50+ years) including issues such as erectile dysfunction, medication, and/or reduced libido [41,42,43,44,45,46,47,48]. Loneliness is a key factor impacting the wellbeing of both older and younger people [49]. Nevertheless, there is limited understanding of the impact of loneliness on those in mid- and later-life [50]. Pikhartova, Bowling, and Victor [51] note how loneliness can be defined as ‘discrepancy between one’s desired and achieved levels of social interaction’ [52]. Furthermore, scholars note the differences between loneliness and social isolation [53], where loneliness is a negative subjective feeling experienced by individuals and social isolation refers to not having connections with other people [54]. Gorczynski and Fasoli [55] note how $7\%$ (~30 million) of Europeans feel lonely. The European Social Survey found there was a greater feeling of loneliness experienced by those people with poor health, who live alone, who are widowed, who earn a low income or who were unemployed, and who either live in Eastern or Southern Europe [56,57,58]. A qualitative study conducted by Jiménez, Conde-Caballero, and Juárez [59] aimed to understand how technology can facilitate greater social interactions and connections with older adults living in the border regions of Spain and Portugal. The conclusions from this study ascertained how older participants rejected the notion of technology, and many of the participants had limited digital skills and competence. ## 2.1. Aims and Objectives The goal of the study was to examine the dating app activity of users aged 18 years and over in the UK during the COVID-19 pandemic. The aim was to understand the relationship between dating app usage and health, wellbeing, sex, and intimacy. The qualitative data presented are based on an online survey conducted between December 2020 and May 2021 and semi-structured interviews conducted between February and April 2021. ## 2.2. Ethical Approval Ethical approval was granted by the lead institution (The Open University) [HREC/3441/Marston] and subsequently was submitted for secondary approval by the partner institution—Swansea University. ## 2.3.1. Survey Participants A total of 138 respondents completed the survey. The characteristics of the survey respondents are given in Table 1. We acknowledge that this is a small sample and does not fulfil the criteria for quantitative significance. Consequently, only the descriptive and qualitative data from the open survey questions are presented here. Descriptive data (Table 2, Table 3 and Table 4) shows a change in dating app use by users during the first and second UK lockdowns. ## 2.3.2. Interviewees Participant recruitment for qualitative data collection employed different approaches, including emails to existing mailing lists, social media profiles (e.g., Twitter and Facebook), and utilized purposive and snowball sampling strategies. A total of eight participants were recruited for online, face-to-face interviews, and these included three females and five males aged between 40–54 years. One interviewee self-identified as a trans woman in a polyamorous relationship, one interviewee identified as a gay married man, and one interviewee identified as a lesbian. The remaining sample consisted of four single, heterosexual men and one single heterosexual woman. All interviewees lived in England, UK, and were regular users of dating apps pre-pandemic. All had continued to use dating apps during the pandemic. ## 2.4.1. Survey The online survey comprised of 64 items across four sections. Some of the survey sections comprised of existing validated surveys [60,61,62,63], developed to measure dating app use, in addition to survey questions being used based on previous iterations of the survey [64,65,66,67,68,69,70,71]. A link to the online survey was included in all social media posts and emails to mailing lists. Respondents’ anonymity was built into the survey which was hosted via Qualtrics. Data analysis was conducted using SPSS version 27 (IBM Corp, Armonk, NY, USA). Given the small sample size ($$n = 138$$), we are unable to present meaningful and significant quantitative findings. Therefore, we present the descriptions pertaining to dating app use and reasons for uninstalling and changing dating apps during the pandemic (Table 2, Table 3 and Table 4). Consequently, we are specifically focusing on the open-ended question presented in the online survey: ‘Please describe your experience of using dating apps during the 1st COVID-19 lockdown and the 2nd COVID-19 lockdown’. Can you describe your reason(s) for breaking or for considering breaking the rules during the two UK different lockdown periods. Can you describe why you did not break UK lockdown rules during these periods. Is there anything else you would like to describe about your experiences during the pandemic/lockdown? The online qualitative data provide an insight into how dating apps were utilized during this period across the UK. ## 2.4.2. Procedures—Interviews Online interviews were conducted by three members of the research team (two women and one man) between February and April 2021. The qualitative data were generated through online, one-to-one, semi-structured video interviews, following a semi-structured protocol (see Supplementary file) created by the research team. The semi-structured interview schedule was flexible to allow interviewees’ stories and experiences to organically unfold. The project investigator (PI) was the point of contact (POC) for the study and all prospective interviewees emailed the POC. At this, point key interviewee details were recorded such as gender, region of the UK, preference of interviewer (male or female), preference of date/times, type of data apps used, and an agreed pseudonym. Having met the study criteria, the next stage involved the PI introducing the interviewer and interviewee via Zoom, and then they exited the interview. All interviews were audio-recorded and once completed, were automatically saved to a secure folder. The PI downloaded and securely transferred the file to the external transcription company. The external transcription company transcribed each interview verbatim and returned them via email to the PI. ## 2.4.3. Qualitative Analysis Employing Interpretative Phenomenological Analysis (IPA) facilitated the research team to acquire greater insight and knowledge of the interviewee’s experiences of dating apps during the pandemic. There are six stages to IPA data analysis [72]:Reading each transcript several times. Notation of important descriptive, linguistic, and conceptual qualities. Development of emerging themes. Condense emerging themes to form super-ordinate (main) themes including sub-themes. Consideration of convergences and divergences. Main (super-ordinate) themes formed. IPA is an idiographic approach (a method that focuses on the human and unique experience of the individual) which requires the in-depth analysis of each participant’s data [73]. Each transcription was uploaded to NVivo version 12 by the PI. Each team member independently coded the transcripts following the first four stages of IPA. During the process of analysis, the team met regularly to develop the emerging themes and agree to a consensus of the main (super-ordinate) themes. ## 3. Results We are presenting two types of data: data from the open-ended online survey questions and from the one-to-one interviewees. Quotes from the online survey are identified by the respondent number. Quotes from the individual interviews are characterised by the pseudonym of the interviewee, their age, and the region in England. ## 3.1.1. Morality, Health, and Law Breaking and COVID-19 Some of the qualitative survey respondents and face-to-face interviewees reported either having broken or considered breaking the law to meet up with someone. Others perceived that a perspective suitor had shown their poor judgement because they had suggested ‘stretching the rules’. This was seen as a ‘betrayal’ or a ‘difference in our values’. Some felt that, at the time, their circumstances of being single and alone justified breaking the rules: The quote above is important because it demonstrates the mindset of two people who had met via an app, who were living alone and single, and they believed to be low-risk because they had not been mingling with other people, coupled with the low prevalence of COVID-19 in their local area; this resulted in these two people making a decision to break the law after weighing the risks to each other. One person noted their mindset toward meeting up with people, because of the ‘lack of opportunity,’ as well as acknowledging that ‘the risk was too severe,’ but also they noted their thinking towards users who were interested in meeting up and breaking the law, ‘if they were that cavalier with meeting me they may have been the same with others, increasing my chances of exposure’ [P8]. This notion of a ‘cavalier’ approach, while this respondent may have considered breaking the rules given the opportunity, left them cautious of the fact their health could be put at risk by the other person who may have met many other people and been exposed to the Coronavirus. Breaking the law was incomprehensible for some survey respondents, and the potential of meeting up with someone was viewed by one respondent as ‘potentially killing someone’ [P6], while another respondent noted the fear or risk of ‘meeting a stranger and potentially catching the virus or getting caught meeting someone’ [P54]. This insight highlighted a double jeopardy—fear of catching the virus and fear of been caught. The latter resulting in possible punishment (e.g., a fine). Some app users perceived those engaged in ‘rule breaking’ as not worthy ‘because […] the people I chatted with—they wanted sex and nothing else—I am looking for a relationship’ [P55]. Yet, breaking the rules may have occurred when users were ‘keen enough’ [P56], while one respondent notes ‘during the second lockdown there was more of this, and many people were trying to meet while the lockdown was in force’ [P6]. Breaking the rules set out by the government shows that, for many people, their mindset was not necessarily about keeping safe or keeping those around them safe, but instead, it was their emotional or sexual needs that superseded their rationale for rule breaking, with little consideration for the health implications of exposure to the Coronavirus and spreading it. The physical restriction imposed by the regulations meant that alternative approaches toward engagement with others were utilized. Some app users accessed sex tech [74] in order to conduct alternative ways of engagement—‘I would facetime people—sometimes they just wanted to engage in some sort of sexual activities on camera though’ [P6]. While some virtual engagements were not positive, other respondents had a different outcome. [ P35] reported that their experiences of dating apps and virtual dating led to a relationship: ‘once the rules relaxed in June, each got COVID tested in order to meet up. Met up. Now dating’. The virtual dates during lockdown facilitated an alternative approach to dating, and because they were using sex tech to connect, while following the rules, this facilitated the two people to decide whether to commit or not in the future. Pandemic directives were instilled to protect people. Nonetheless, for some people the notion(s) of rule breaking were worth the effort. Yet, for others conducting alternative approaches to dating in person, the risks were deemed more appropriate and safer. Diane [50 years, London] describes how she broke the law ‘a couple of times’, and her reasoning and thought process was ‘because I think you just crave human contact, intimacy.’ Diane acknowledged breaking the law to hug a friend; she noted, ‘I did break the rules a couple of times. But I was really law abiding $90\%$ of the time’. Diane describes how she ‘didn’t feel guilty’ rather, she accounts that ’I was actually glad I did it to be honest, even though things didn’t work out. I just thought actually at least I had a bit of fun’. Diane shows no remorse for her rule breaking because, for her, physical and emotional intimacy superseded the regulations. Although she acknowledges that she is a law-abiding person for the majority of the time, on this occasion her needs were more important. Harry, a 40-year-old man from the Southeast of England describes how he ‘kept to the rules’ but also notes how he ‘would have been OK with seeing people, and it would have been nice to have a support bubble’. Upon further questioning, Harry describes what if a television celebrity appeared at his door, requesting to come in he ‘probably wouldn’t have said no’. Yet, Harry continues noting how ‘intention and reality are two different things. So, I just don’t know what I’d have done if I’d had the opportunity’. Conversely, Noah, a 40-year-old man from the East of England, describes how rule breaking was not a consideration for him. He explained, ‘if they want to break the rules, that’s down to them’ because ‘at the end of the day my goal with this lockdown is to get to the end of it so I can see my children on a regular basis’. Breaking the rules for Noah was not a priority. For example, he chose not to be with his children ‘because children can’t keep to that two-metre distance. My boy doesn’t even know what two metres is, not even my daughter doesn’t even know what two metres is. So, it was best just to keep to the rules’. This insight is important because it is an altruistic element to observing the lockdown regulations. Furthermore, Noah describes the impact of this self-imposed isolation that he chose for himself and his children—‘It would have been too hard’ because he would not be able to hug them. Racheal, a 44-year-old woman from the Southeast, describes how she considered breaking the rules with her ‘friend with benefits’ whom she usually sees ‘once every two to three weeks’ and ‘That feels, if I can see other friends outside, it feels worth having that as my bubble essentially’. This decision by Racheal was more of a social responsibility and worked within the respective government directives to see each other. Support bubbles, whether it was with a friend with benefits or others, were important for Brett (54 years old, London). He also broke the rules. He was in a relationship with a woman, categorized as vulnerable. Brett described how their relationship started prior to lockdown, but their sexual intimacy did not occur until the lockdown measures were rolled out: Brett and his partner utilized the notion of ‘support bubbles’ as a means of spending time together, while also ensuring their extended engagement with other people was limited within the household numbers. For Racheal and Brett, they chose to replace support bubbles consisting of friends for their lovers or partners. Their actions demonstrate the balancing of personal needs against societal strictures. Indeed, ambiguity and misunderstanding, based on the directives changing weekly or monthly, led Brett to describe how ‘lots of things fall down between the cracks’. The uncertainty generated by government ambiguity led to further questioning of the policy, ‘[…] if I were to decide to go for a really long walk tomorrow and somebody gave me a ticket for being 10 miles from home, you know, I’d fight it, because I would be walking for exercise not for going to see somebody or whatever’. The point highlighted above by Brett shows both how people could misunderstand what was expected of them and/or use the system/the laws to their own advantage. However, Brett stated that if he were to receive a fine for something that he believed was legitimate, he would feel compelled to act: ‘if I saw a rave going on, I’d call the police about it if that makes sense’. During the pandemic and across the UK, each devolved region had different directives, set out by their respective governments. In England, during the time this study was conducted, different areas/counties had different regulations, and crossing a county line could have resulted in breaking the law. Brett reiterates how he does not see himself as a person who would report other people for breaking the law by noting: The participants’ and respondents’ narratives demonstrate the confused messaging by national and local government(s), while also expecting people to be responsible in their own decision-making. Tracey, a 52-year-old woman from the Northeast, describes how she ‘technically’ broke the law, and had it not been for her daughter living with her, she could have said ‘this chap was in my bubble’. Similar to Racheal and Brett, the rationale of a support bubble was used as a justification for being physically intimate with someone else. Previously, Tracey and this person would meet ‘for walks, and it was raining one day, and I said, “Oh you can come in but sit over there” kind of thing and then yeah, one thing led to another’. Reflecting on this occasion, Tracey believed there were no risks posed because she had conducted a ‘risk assessment’ of the environment (her home): ‘[I] just thought he’s working from home, he’s not seeing anyone, I’m at home, I’m really careful at work and most of the time I’m working from …. So, I did a risk assessment. Yes, I did break the rules I suppose’. This notion of a risk assessment is used as a validation for her actions and decision in including her male friend in her support bubble: ‘you know I can be really honest about this, but I haven’t broken, it’s technically I suppose breaking’. However, Tracey acknowledges that her choices and behaviour have broken the COVID-19 restrictions. She goes on to note how she usually has a set of rules in place before she engages in sexual activity: Although Tracey declares how she is very careful about her sexual health, she believes ‘the COVID risk I felt was worse than the sexual health screening. There was a COVID risk, and then there was an STI risk’. This is an interesting insight into Tracey’s behaviour, and, while she generally takes a cautious approach to her sexual health, she acknowledges feeling ‘really pressurised’ during the lockdown to relax her typical sexual mores. The narratives of the participants highlight how emotional and sexual intimacy could take precedence over COVID-19 restrictions. Furthermore, the concept of a support bubble was used as a means of rationalizing sexual engagement/intimacy The respondents to the online survey perceived the rules were set in place for a reason and should not be broken for their own health risks and that of the greater society. ## 3.1.2. Self-Surveillance and Moral Signalling Survey respondents described their motivations for not breaking the rules, but, in some instances, they instead displayed behaviours pertaining to self-surveillance and moral signalling: The quote above is interesting because the respondent notes how they would consider breaking the rules with someone who they had met before in a pre-pandemic society, but not necessarily a stranger. Yet, they continue to describe how they ‘did meet up with someone at one point, but it wasn’t breaking the rules to meet up. Although, I did then break the rules and kiss them—so maybe if I had liked someone enough, I might have met up and broken the rules.’ [ P16]. This insight is using the premise of social bubbles again to be able to meet up with someone whom they may have met on a dating app and also implying that had there been a greater attraction, further rule breaking would have occurred. Being alone or separated from someone led one person to describe how they broke the rules during the first lockdown because of how they were feeling after ‘eight weeks of lockdown’, [P20] and they ‘mutually decided that as we were both living on our own’, so the risk was potentially not severe. Yet, we can see from this respondent, there was a conscious decision to break the rules because their feeling of loneliness superseded the national directives of not mixing. Additionally, this insight shows the impact that government directives and lockdowns placed on people, whether it was the need for sexual and physical intimacy or just the company of someone to whom you are attracted and exploring whether it is going to go somewhere. Moral signalling was portrayed by online survey responses, coupled in the rise of reported daily deaths through the media outlets, as heightening respondents’ reluctance to engage with people that they were talking to on dating apps. Respondents describe how daily mortality rates associated to the directives is evident: ’[…] they are important for both the people to follow looking at mortality’ [P4] and ‘Because potentially killing someone in any way is not really part of my personality type!’ [ P6]. While for one respondent, seeing their children in the future was important to them: ‘I wanted the lockdowns to be over and life back to normal so I can see my kids.’ [ P5]. The quotes above relating to moral signalling show how respondents were seeing the bigger picture of lockdowns, and the unfolding consequences of the Coronavirus were more important than having an elicit meeting with someone to whom they had little attraction or had just started talking to on a dating app. ## 3.1.3. Loneliness and Social Isolation This theme intersects across mental health, lack of physical and sexual intimacy, and the living environment. There is little understanding of the impact on people who were living on their own during the pandemic. One respondent describes how their routine of childcare did not take away the feeling of loneliness and the desire for companionship: For this person, the void of their children leaving enhanced their feelings of loneliness and lack of purpose during these specific moments. Continuing support bubbles also exacerbated the feeling of loneliness for [P3] because ‘All of my friends are in their own support bubbles; all of my family too. I have nobody and have had to watch as friends and family have gone on and on about how difficult it is and how lonely they are whilst having spouses to lean on’. Support bubbles set out by the government seemed to be, for this person, a hinderance because they did not have anyone to be with and to share the emotional turmoil and to alleviate their feeling of loneliness. Moreover, it could be argued that ‘Even having kids’, for this person, did not help because ‘I’m the adult—having a grownup around makes such a difference. Plus, they go to bed, and I’m back on the sofa,’ [P3]. Yet, what this person was craving was adult company, conversation, and even sexual/physical intimacy because, as they note, once their children are in bed, they are alone again, and they perceive the impact of the pandemic as a year taken away from them, by not being able to date and meet new people because ‘I’ve missed out on a year of dating, and, thanks to the vaccine being so slow […] I’ll likely miss out on another before I get to go out and meet people again’. Although there is acknowledgement and positivity around the vaccine, [P3] is reconciling themselves to living in a different society with new social norms because ‘Even then, it won’t be as it was. I fear that I may have missed the boat when it comes to meeting someone and am trying to readjust my expectations accordingly’ [P3]. Conversely, one respondent describes how the UK directives did not cater to or consider people who are in polyamorous relationships: ‘It’s been difficult when the rules don’t cater for, e.g., polyamorous families, and assume nuclear monogamous families. It has been useful as a space to experiment with being more open about gender identity and expression.’ [ P7]. Across the directives, there was no consideration for people who do not lead a conventional life and, instead, choose to have and conduct a polyamorous relationship, which may involve the individuals living in different homes rather than under one roof. This too may have had greater consequences for individuals practicing polyamory because they may have been forced to decide to change their living circumstances, by moving in together or choosing to be alone or choosing one partner over another. The survey was deployed across the UK during the second lockdown, and the various directives set out by the devolved government (England, Northern Ireland, Scotland, Wales) varied considerably [2]. At the time of survey deployment, it was unclear how this second lockdown would impact the health and wellbeing of people, and two respondents describe their feelings towards the second lockdown. For example, [P9] states, ‘It’s as bad as it was before, if not worse’ [P9], while [P11] notes that they ‘[…] felt more sad and lonely than I have ever felt before because I find it hard to admit to friends and family how sad and lonely I am’. Although, for these people, the apprehension and fear of another lockdown impacts their feeling of loneliness, and, for [P11], they describe how they cannot be truthful to their friends or family; another person describes how lockdown has been a learning process for them: ‘Both lonely but a lesson in liking my own company. Lack of physical contact has been the hardest’ [P17]. While [P9 and P11] have not described the lack of physical or sexual contact as the driver for their feelings of loneliness, [P17] does acknowledge this, and, for many people, we can assume that it is the implementation of lockdowns—taking away any opportunities of physical/sexual contact that impacts the most, and it could be described as the invisible barrier within our society at that moment in time. Interviewees described the impact of lockdown relating to their feelings of loneliness and social isolation but also were seeking alternative ways to continue meeting people when permitted with the lifting of restrictions. For example, Racheal reported that during ‘mid-lockdown where I was probably—oh, I did few speed dating events. So there probably was a time mid-pandemic where I was sort of quite feverous in my approach to it’. Racheal notes how ‘it’s very unusual for a person to spend that much time on their own’, and she was ‘just seeing if there was anyone out there’ by attending some dating events. Racheal acknowledges her loneliness, and she continues to describe how the occurrence of a second lockdown being implemented ‘without having pinned someone down to at least something’ was something she did not want to experience again, having experienced the first lockdown ‘entirely on my own’. Yet, building a connection on a dating app can be difficult, and Racheal notes how dating app users were not ‘seriously looking’ for dates because of the impending winter lockdown. The goal of using apps is to find a relationship, but instead, Racheal describes how the character flaws of a person can be overlooked; instead of building the foundations and seeking out a positive connection, there are instead ulterior motives as Racheal describes: This insight into dating apps and user behaviour may have been exacerbated during the pandemic because individuals did not want to experience the feeling of loneliness and social isolation again. Therefore, chasing a person or settling for second best was (for some people/users) better than nothing, better than being alone. Noah discussed how he categorises friendships, and, although he may have cyberfriends, in his opinion ‘a [real] friend is go to the pub, go shopping, go to the cinema. That’s what I class as a friend. You do social activities’. During the pandemic, Noah describes himself as a ‘lonely person’ who keeps to himself and does not ‘want to burden’ others. However, although he experienced intense loneliness in lockdown, it also afforded him the opportunity for growth; he says: ‘I learnt so much about myself being in lockdown, which I never thought possible’. This section has presented insights into the feelings of loneliness and isolation experienced in lockdown and the need for adult company. In some instances, this meant participants settled for ‘second best’ when it came to seeking out a partner. For others, it was an opportunity for growth and self-development. ## 4. Discussion This paper primarily focuses on and presents two types of qualitative findings: 1. qualitative findings from an online survey deployed between December 2020 and May 2021 and 2. qualitative data collected via online, one-to-one interviews conducted between February and April 2021. Data analysis identified three key themes across the narratives: 1. Morality, health, and law breaking and COVID-19, 2. Self-surveillance and moral signalling, and 3. Loneliness and social isolation. The analysis explored how the participants perceived the use of dating apps to moderate their existential emotions and behaviours experienced during the pandemic. Although the participants described their behaviour(s) and communication with app users, overall, the notion and execution of conducting physical contact was limited. Narratives indicated that consideration had been given by some participants to break the law by meeting up with someone. For others, this action was viewed negatively because they perceived this type of behaviour as not being a good citizen as well as risking the possibility of extending lockdowns. It was evident that using apps was perceived as a mode of engaging and communicating with people to relieve boredom and alleviate the feeling of loneliness. At the heart of either consciously breaking the law or even considering it was the feeling of loneliness and the need for physical contact—whether it is a hug or sexual intimacy—and demonstrates the importance of social relationships to wellbeing. However, the findings of this study highlight how many of the participants were not necessarily using dating apps during this period to engage with sexual activity but more for companionship and online communication because they were feeling lonely and isolated. The current literature surrounding dating apps, including [4,21,22,23,24,25,26,31,32,33,34,35,36,37,38,39], aims to understand various characteristics, and, indeed, this work aligns and contributes to the existing scholarly work in the disciplines of sex research, gerontology, health and wellbeing, and social sciences. This work contributes to the fields of social sciences, gerontology, human computer interaction because of the inter- and multi-disciplinary nature of this study, coupled with the qualitative findings (via online, one-to-one interviews), primarily focusing on adults aged 40–55 years. To date, there is a paucity of published work in this area focusing on middle aged and older adults and their dating app use. However, the narratives around rule breaking show how individuals attempted to justify and rationalize their behaviour. Further, it was noted in the survey responses that different self-imposed rules that were being applied depended on the circumstance—for example, if there was a rave going on, one participant noted they would have informed the police while smaller groups were seen in a more accepting light. Moreover, we speculate how loneliness was highlighted as a key theme in this work, and we provide narratives from interviewees and survey respondents alike who describe their sense of loneliness and of feeling lonely during this period. For many people they chose not to break the rules even though they may have been feeling lonely because they wanted the pandemic to end, and, with the cases of COVID-19 increasing, they probably felt that it was never going to end. The interconnections associated to the themes identified are connected to government directives that were reducing freedom of movement and change in social interactions, work, and our pre-pandemic routines. We have shown how dating apps during this period were used for different reasons, primarily enabling users to connect with people. Previous habits conducted via apps were not possible, as Stuart who, with his husband, used Grindr to invite a third person into their marriage for a ‘hook-up’ describes. Although some users were using apps as a way of continuing to find their ideal partner, respondents noted how they used the apps as a way of reliving boredom, and, as noted, there was a lack of enthusiasm because of limitations placed on society which limited freedom. Finally, dating app use did change, as shown in our findings (Table 2, Table 3 and Table 4), and this is supported by the qualitative findings presented, whereby many of the quotes presented signalled greater morality, the (un)consciousness of rule breaking, and the experience of loneliness by users which, in turn, led to a change in dating app behaviour and use during these two periods. ## Strengths and Limitations A strength of this work is that this is the first piece of work, to the best of the authors knowledge, to be conducted on user’s experiences of dating app use during COVID-19. There is an increasingly strong scholarly interest in dating apps and their usage. However, it is mainly focused on the changes made within apps at the start of the pandemic [72] or taking an exploration through a student lens [73] or the incentives and business model of Tinder [74]. Although the American Association of Retired Persons (AARP) has provided information for older users interested in using dating apps or dating websites [75], there still remains a gap in the literature and in the field of gerontology surrounding dating apps, and sex tech use by adults in mid- and later life. Moreover, existing scholarly articles [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39] have primarily focused on younger users of dating apps, with the exception of Marston and colleagues [4] who conducted a review of existing dating apps and how this type of sex tech could be utilized by older adults and people with life-limiting and life-shorting conditions. However, the qualitative data shows how loneliness and social and sexual intimacy were drivers for using dating apps during this period. This has implications for the future from a public health perspective, especially how digital transformation was exacerbated throughout society, and the authors believe this work can feed into future public health narratives surrounding interventions to combat loneliness and social isolation. The interviewees are categorized as Generation X [76], and with this, many existing initiatives are targeting adults aged 60+ years. Therefore, if people are presenting loneliness and social isolation in mid-life, there is the possibility that existing interventions and initiatives may not be successful. This study presents insights from users categorized as Gen X [76], who are rarely considered when investigations are exploring dating apps or technology in general [6,14,77,78,79]. Therefore, this work is unique from the standpoint of gerontology, sex tech, and social sciences. In this manuscript we present qualitative findings from online interviews pertaining to discourse through the lens of Gen X’ers who will be the next ageing cohort in our society to follow the Baby Boomers, and both cohorts have very different experiences and understandings surrounding the use of sex tech and technology in general. Yet, as we look to the future, scholars, industry professionals (e.g., sex tech), and policy makers need to start to understand the differences between different cohorts because, if not, when Gen X reach later life, there will not be the appropriate initiatives in place to combat social issues. A significant constraint to the findings of this project is the small sample size of the online survey. Consequently, the statistical analysis of the relationships and associations between loneliness, health, wellbeing and dating app use was limited. Nonetheless, we have presented some descriptions pertaining to specific dating app use during the pandemic, coupled with the qualitative data from the online interviews to show how Generation X uses dating apps. However, one solution that could have been considered was to keep the online survey open for longer (past May 2021) although that was not possible for practical reasons. The research team utilized existing networks and social media specifically for the online survey which included Age NI. However, greater onboarding of national organisations such as Age UK and other franchises such as Age Cymru, the Campaign to End Loneliness, in conjunction with grass-root networks, may have provided greater survey responses to allow a larger sample size. Regarding the interviewees, they were all recruited from different regions of England. Future work should consider and recruit interviewees from across the devolved nations of the UK. ## 5. Conclusions The findings presented here have shown three themes derived from qualitative analysis and highlights how dating app use during this time was used for alleviating loneliness and played a role in social, sexual, and physical connections and intimacy. This project was specifically a UK-wide study, and although many of the online survey respondents were located outside of the UK, we believe given the popularity of dating apps, coupled with the importance of loneliness, health, and wellbeing, there is a need to extend this work to afford greater understanding of adults, specifically Generation X, who were the core interviewees and who described their experiences of feeling lonely and loneliness in mid-life. Extending and scaling this work as we transition into a post-pandemic society would afford greater understanding of people’s behaviour in challenging socio-political circumstances. For example, this study captured people’s responses to specific and diverse government directives while tackling COVID-19. Future investigations are needed to understand the use and motivations of dating apps by people in mid- to later life because, from a gerontological standpoint, people who are categorized as Generation X are going to be the next ageing cohort. To date, the field of gerontology and gerontological research does not focus its efforts on this cohort, and, instead, it is packaged and narrated in the way of exploring intergenerational behaviours, employing a life course perspective. Given how the interviewees and most survey respondents were over the age of 40 years, there is a clear need to understand dating app use in mid- and later life. ## References 1. Director-General’s Opening Remarks at the Media Briefing on COVID19-March 2020 2. Marston H.R., Shore L., White P.. How does a (Smart) Age-Friendly Ecosystem Look in a Post-Pandemic Society?. Int. J. Environ. Res. 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--- title: Potato Chips Byproducts as Feedstocks for Developing Active Starch-Based Films with Potential for Cheese Packaging authors: - Ana M. Peixoto - Sílvia Petronilho - M. Rosário Domingues - Fernando M. Nunes - Joana Lopes - Marit Kvalvåg Pettersen - Magnhild S. Grøvlen - Elin M. Wetterhus - Idalina Gonçalves - Manuel A. Coimbra journal: Foods year: 2023 pmcid: PMC10048258 doi: 10.3390/foods12061167 license: CC BY 4.0 --- # Potato Chips Byproducts as Feedstocks for Developing Active Starch-Based Films with Potential for Cheese Packaging ## Abstract The potato chip industry generates brownish frying residues, which are usually landfilled. While spent frying oil has value as biodiesel, the defatted brownish water-soluble extract (BrE) does not yet have an application. In this work, it was hypothesized that BrE can be a source of compounds for active packaging. BrE is composed of carbohydrates ($66.9\%$), protein ($5.7\%$), and a small amount of phenolics and esterified fatty acids. When incorporated into starch-based formulations and casted, BrE at $5\%$, $10\%$, and $15\%$ w/w (dry starch weight) conferred a yellowish coloration while maintaining the transparency of neat films. The BrE increased the films’ traction resistance, elasticity, and antioxidant activity while decreasing their hydrophilicity. Furthermore, starch/$15\%$ BrE-based films showed diminished water vapor and good UV-light barrier properties. Their contact with sliced cheese did not change the products’ hardness during storage (14 days). Weight loss of the cheese was observed after 7 days of storage, stabilizing at $6.52\%$, contrary to the cheese packed in polyamide (PA)/polyethylene (PE), already used in food packaging. The cheese packed in the starch/$15\%$ BrE-based films showed a significant yellowish darkening and lower content of volatile oxidation products compared to the PA/PE. Therefore, BrE revealed to have compounds with the potential to tune the performance of starch-based films for food packaging. ## 1. Introduction Potato (Solanum tuberosum L.) is one of the most widely cultivated and consumed carbohydrate-rich food crop worldwide, with a global production of ca. 359 million tons in 2020 [1]. Although they can be eaten in different ways, potato chips are one of the most widely appreciated and consumed potato product due to the fact of their characteristic salty flavor and crispy texture [2,3]. Their industrial frying process involves multiple steps, including washing, peeling, slicing, and frying in fat or vegetable oil at high temperatures [2]. Several tons of potato byproducts are generated, including washing slurries, peels, spent frying oil, and a brownish frying residue present in the frying oil. This residue is composed of burned potatoes accumulated at the bottom of noncontinuous friers, usually used in small and medium industries and household and restaurant kitchens. The valorization of these byproducts as raw materials to create added-value products is under research, promoting a more natural, low-cost, and sustainable production while encouraging a circular economy [4]. Potato washing slurries have been used to recover starch to develop bioplastics with physicochemical and barrier properties competitive with the ones provided by commercial potato starch [5]. To improve the elastic and hydrophobic properties of starch-based films, spent frying oil [6] and waxes recovered from potato peels [5] have been used. In addition, potato peel phenolic extracts [7] have demonstrated the potential to improve the antioxidant activity and UV-light barrier properties of starch-based films. While the frying oil is channeled for biodiesel production [8], at present, the defatted brownish water-soluble extract (BrE), a putative source of high molecular weight nitrogenous, brown-colored compounds (i.e., melanoidins), is a disposable industrial byproduct that originates environmental issues. Due to the frying conditions of potatoes (high-temperature and anhydrous environment), melanoidins can be formed during nonenzymatic browning reactions (i.e., Maillard reaction) involving compounds with amino and carbonyl groups [2,9]. Moreover, the fatty acids of triacylglycerides present in the frying oil may be incorporated into the potato chips’ melanoidins’ structure, similar to what has been observed for other organic acids in coffee melanoidins [10]. Despite these assumptions, the chemical composition of BrE remains unknown. Since melanoidins from thermally processed food have shown bioactive potential [11,12], they should be exploited for different applications, such as in the development of active bio-based materials with potential for food packaging, similar to what has been conducted with potato peels and starch [7]. In this work, it was hypothesized that BrE, when gelatinized with starch recovered from potato washing slurries and casted, can give rise to bioplastics that can be used as active packaging to preserve sliced cheese characteristics, a foodstuff susceptible to lipid oxidation. For this, a hot-water extraction was applied to the defatted brownish frying residue. The influence of BrE on the optical, mechanical, wettability, water solubility, water vapor transmission rate, and active (UV-protective and antioxidant) properties of potato starch-based films was evaluated. The suitability of using starch/BrE-based films as active packaging was assessed in sliced cheese. ## 2.1. Materials The potato washing slurries and potato chip frying residue were supplied by the A Saloinha Lda. company (Mafra, Portugal). Starch ($25\%$ amylose, 59–71 °C gelatinization temperature, and 12.5 J/g enthalpy) was recovered from the lyophilized slurries [5]. The brownish water-soluble extract (BrE, Figure S1) was obtained after defatting (spent frying oil recovered by Soxhlet extraction with a chloroform/methanol (2:1, v/v) mixture), hot-water extraction (80 °C, 1 h, constant stirring), centrifugation (24,000× g, 4 °C, 20 min), and freeze-drying. The BrE was stored in a desiccator containing phosphorus pentoxide until further characterization and usage in the films’ production. Glycerol was purchased from Scharlab S.L. (Barcelona, Spain), and the sodium azide, chloroform and methanol were acquired from Sigma-Aldrich (Lisbon, Portugal). All reagents were of analytical grade and used without purification. ## 2.2. Fractionation of Potato Chip Brownish Water-Soluble Extract (BrE) The BrE was submitted to a fractionation procedure by ultrafiltration (UF) using the Millipore Labscale™ TFF System equipped with a 500 mL reservoir [13]. Five cut-off membranes were used (100, 50, 30, 10, and 5 kDa from Merck Millipore Pellicon XL 50 cassettes with a flat plate format) at room temperature (20 °C ± 2 °C) and working between 10 to 30 psi transmembrane pressures. The separation efficiency was controlled by measuring the permeate conductivity (until it presented a value below 10 μS/cm) and determining the UV–Vis absorption (420 nm and 405 nm to monitor the presence of brown-colored compounds and 325 nm and 280 nm for phenolics and proteins). The ultrafiltration process was repeated for 3 cycles for each membrane (Figure S2). At the end of the procedure, 6 fractions corresponding to the >100 kDa, 50–100 kDa, 30–50 kDa, 10–30 kDa, and 5–10 kDa retentates, and the <5 kDa permeate were recovered. Figure 1 illustrates the flowchart to obtain each fraction according to the relative molecular weight. All UF fractions were frozen, freeze-dried, and stored under an anhydrous atmosphere until further characterization. ## 2.3. Characterization of Potato Chip Brownish Water-Soluble Extract (BrE) The sugar composition and content of the BrE and correspondent UF fractions were determined, in triplicate, by gas chromatography with flame-ionization detection (GC-FID) as alditol acetates. The 2-deoxyglucose was used as the internal standard [14]. The protein content of the BrE and UF fractions was estimated, in triplicate, by determining the total nitrogen in a Truspec 630-200-200 elemental analyzer (St. Joseph, Berrien, MI, USA) with a thermal conductivity detector (TDC). The nitrogen content was converted into protein content (%, w/w extract) using the 6.25 factor [15]. The total phenolic content of the BrE and UF fractions was determined, in triplicate, using the Folin–Ciocalteu method, where the absorbance of the samples was measured at 750 nm. A calibration curve of gallic acid (50–250 μg/mL) was used, and the results were expressed as μg gallic acid equivalents/mg of extract (μg GAE/mg) [12]. The esterified fatty acids (EFAs) content was determined by GC-FID, in triplicate, as fatty acid methyl esters (FAMEs) after the alkaline-catalyzed transesterification of the esterified fatty acids of the sample. For this, heptadecanoate methyl ester (0.03 mg/mL prepared in n-hexane) and KOH methanolic solution (2M) were used [6]. A DB-FFAP column (30 m × 0.32 mm and 0.25 μm of film thickness, J&W Scientific Inc., Folsom, CA, USA) was used. The compounds were identified by comparing their retention times with those of a commercial FAME mixture (C8–C24). The presence of melanoidins was assessed by determining the specific extinction coefficient (Kmix) and the melanoidin browning index (MBI). The Kmix was determined at 405 nm (Kmix 405nm) to determine the relative content of the brown-colored compounds in each UF fraction [14,16]. Briefly, for each sample, different solutions were prepared in distilled water (0.1 to 1 mg/mL), and the absorbance was measured, in triplicate, at room temperature (20 °C ± 2 °C). The MBI was calculated by dividing the Kmix 405nm value by the relative content of the unknown material estimated by the difference in the known one (sugars and protein) [12,17]. The antioxidant activity of each sample was determined, in triplicate, using the ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)) assay [18] and expressed as IC50 (minimal concentration of extract required for $50\%$ inhibition of ABTS●+). Briefly, 50 μL of each sample was added to 250 μL of 7 mM ABTS solution. The absorbance was read after 20 min at 734 nm using a microplate spectrophotometer (BioTech™, Eon™, Richmond Scientific, Lancashire, Great Britain). Water was used as a blank, ABTS solution as the control, and ascorbic acid as the standard [12]. ## 2.4. Production of Starch/BrE-Based Films by Solvent Casting The starch/BrE-based films were produced by solvent casting following a previously described method [6], with the addition of the BrE at different proportions ($5\%$, $10\%$, and $15\%$ w/w, starch dry weight). Briefly, a water dispersion of freeze-dried potato starch (40 g/L) was prepared, and 12 g/L of glycerol was added as well as BrE in different proportions (2 g/L, 4 g/L, and 6 g/L. Then, each dispersion was gelatinized at 95 °C ± 0.1 for 30 min with constant stirring (ca. 200 rpm). The potato starch-based films without BrE (neat films) were used as a control. ## 2.5.1. Chromatic Properties The chromatic properties of the films were assessed by tristimulus colorimetry (CIELab). The CIELab coordinates L* (luminosity), b* (yellow/blue), and a* (red/green) were determined using a CR-400 Chroma Meter. The total color difference (ΔE) was also assessed [6]. ## 2.5.2. Thickness and Mechanical Properties The mechanical properties of the films were determined using a texture analyzer (model TA. Hdi, Stable Micro Systems, Surrey, UK). Each film formulation was cut (12 strips for each film sample, each one a size of 1 cm × 9 cm), and the thickness was measured in 3 different strip points with a digital micrometer (ca. 0.001 mm accuracy; Mitutoyo Corporation, Kanagawa, Japan). During the uniaxial tensile tests till film failure, the tensile strength (MPa), elongation at break (%), and Young’s modulus (Mpa) values were determined from the stress–strain curves provided by Exponent software [6]. ## 2.5.3. Wettability The wettability of each film was determined at room temperature (20 °C ± 2 °C) through static water contact angles (WCAs) using a tensiometer (Attention Theta by Biolin Scientific, Madrid, Spain) fitted with an automatic image capture system (One Attension). Each formulation was cut in strips (1 cm × 6 cm) and tested in triplicate for both film surfaces: one exposed to air (film upper surface) and one in contact with a plexiglass plate (film’s lower surface). Briefly, 3 μL of ultrapure water were dispensed on the film strips’ surfaces, and the corresponding WCAs (at least 6 droplet images along each strip) were measured using One Attention software following the Young–Laplace method [6]. ## 2.5.4. Water Solubility To determine the films’ solubility in water, squares (4 cm2) of each film formulation were cut, weighed, and immersed into distilled water containing sodium azide ($0.02\%$ v/v) at room temperature (20 °C ± 2 °C) at 80 rpm for 14 days. The non-solubilized films were dried (105 °C, overnight), cooled down to room temperature, and weighed. The solubility of each sample was evaluated as a weight loss percentage, in triplicate [6]. The possible migration of the BrE components to the water during the solubility assays was evaluated by UV–Vis (200 to 700 nm), in triplicate, at the end of the assay. ## 2.5.5. Water Vapor Transmission Rate The water vapor transmission rate (WVTR) of the films (28 mm diameter per sample) was assessed, in triplicate, using test dishes and a humidity chamber ($53\%$ relative humidity) maintained at 23 ± 2 °C and with ca. 160 m/min of air velocity [6]. ## 2.5.6. UV-Protective and Antioxidant Activity The UV-protective capacity of the films was monitored through a UV–visible spectrophotometer (Shimadzu UV 1280) from 200 to 500 nm. For each spectrum, a representative average of 3 scans was obtained using a data pitch of 0.5 nm, a bandwidth of 2.0 nm, and a scanning speed of 100 nm/min [19]. The antioxidant activity of the films was determined by an adaptation of the ABTS method, as already described [19]. Briefly, each film square (4 cm2) was placed in 1.5 mL ABTS•+ solution (7 mM ABTS in 2.45 mM potassium persulfate) and left to react under dark conditions (20 °C ± 2 °C, 80 rpm). The absorbance at 734 nm was measured for 4 h. An ABTS•+ solution without film was used as a blank. The antioxidant activity was determined, in triplicate, as the percentage of ABTS•+ inhibition. ## 2.6. Sliced Cheese Package with Starch/15% BrE-Based Films A commercial sliced cheese (Norvegia® Original, semi-hard matured cheese, $27\%$ fat, valid until 22 November 2022, storage 0–4 °C, L6 17.27, TINE SA, Norway) was packed using the starch/$15\%$ BrE-based films. A total of 4 cheese slices were sealed with the films (31 cm × 21 cm) using a sealing machine (Packer®, Packer Poly Sealer, King’s Lynn, Norfolk, UK). The packed samples were stored at 4 °C at $80\%$ relative humidity for 14 days. A commercially available plastic of 90 µm polyamide/polyethylene (70 µm PA/20 µm PE, Allfo Vakuumverpackungen, Waltenhofen, Germany) was used as a control. After 7 and 14 days, the cheese samples were evaluated in terms of weight loss, the color CIELab coordinates L, a, and b* were determined using a CR-400 Chroma Meter (Konica Minolta, Inc, NJ, USA), as well as the texture (texture analyzer model TA. Hdi, Stable Micro Systems equipped with a 6 mm cylinder stainless probe for puncture tests [20]). The volatile profile of the cheese samples was analyzed by headspace-gas chromatography-mass spectrometry (HS-GC-MS) [21], where ethyl heptanoate (~$98\%$ purity, Fluka, Steinheim, Germany) was used as the internal standard, and the results are expressed as μg ethyl heptanoate equivalents (eq.)/g of the sample. The results are reported as the average of three independent replicates per each sampling moment. ## 2.7. Statistical Analysis The results were statistically evaluated using the Student’s t-test with a significance level of $95\%$ and $p \leq 0.05$, using the “t-test” tool in Excel 2016. Moreover, for multiple comparison analysis, one-way ANOVA with $95\%$ probability level was used using GraphPad Prism version 8 for Windows (trial version GraphPad software, San Diego, CA, USA). ## 3. Results and Discussion The defatted potato chip sample used in this work corresponded to $58\%$ w/w of the potato chips frying residue supplied by the industry (Figure S1) [6]. From this, the brownish hot water-soluble extract (BrE) corresponded to $57.2\%$ w/w concerning the defatted residue (Table 1). The BrE was fractionated by ultrafiltration (UF) and characterized in terms of carbohydrates, protein, lipids, and phenolic content. Moreover, the presence of melanoidins in the UF fractions was estimated, as well as their antioxidant activity. ## 3.1. Characterization of the Brownish Water-Soluble Extract (BrE) The BrE was mainly composed of carbohydrates ($66.9\%$ w/w), with glucose, the structural unit of potato starch, as the major constituent (93.9 mol%). Galactose and arabinose, probably from arabinogalactan proteins [22], were also determined in this brownish extract, but in smaller amounts (5.20 mol% and 0.95 mol%, respectively). The BrE also presented protein ($5.70\%$ w/w) and a small amount of phenolics and lipids (ca. $0.40\%$ and ca. $0.10\%$ w/w, respectively). The BrE was fractionated by ultrafiltration using five cut-off membranes, followed by freeze-drying. A total of six fractions, corresponding to the >100 kDa, 50–100 kDa, 30–50 kDa, 10–30 kDa, and 5–10 kDa retentates and to the <5 kDa permeate, were obtained. Since the yield of the fractions 30–50 kDa, 10–30 kDa, and 5–10 kDa only represented a total of ca. $3\%$ w/w of BrE (Figure S3), only the fractions with a higher yield (>100 kDa, 50–100 kDa, and <5 kDa) were chemically characterized (Table 1). The major fraction (>100 kDa, $49.3\%$ of BrE weight) was very rich in carbohydrates ($89.4\%$), mainly glucose from starch, with a small portion of galactose and arabinose (Table 1). Protein accounted for $7.6\%$. The second largest fraction (<5 kDa, $39.9\%$ of BrE weight) consisted of $37.1\%$ sugars, with $10.0\%$ accounting for free sugars and $16.4\%$ protein. The presence of minerals can explain the remaining material [23]. Although not relevant in terms of yield ($3.30\%$ of BrE weight), the 50–100 kDa fraction was also different from the others due to the fact of its intense brown color, measured at 405 nm (Kmix,405 nm = 0.69 L/g/cm). Carbohydrates accounted for $29.4\%$, the protein was $17.4\%$, and the phenolics were $7.20\%$. Esterified fatty acids accounted for only $0.16\%$, mainly constituted by oleic ($53.7\%$) and palmitic ($36.3\%$) acid residues derived from spent frying oils due to the fact of their similar proportions [6]. These values allowed to estimate the amount of unknown material of $53.20\%$, corresponding to a melanoidin browning index (MBI) of 1.48. This value is not as high as the MBI value of 2.44 determined for the 30–100 kDa fraction of instant coffee [17]. In instant coffee, the >100 kDa fraction presented the highest MBI (3.31), not verified for BrE fraction > 100 kDa. This can be explained by the high content of starch in the potato present in the >100 kDa fraction and the high content of protein in the 50–100 kDa fraction, which should have promoted the Maillard reactions with carbohydrates [10,12]. In addition, a significantly higher antioxidant activity was determined in 50–100 kDa fraction (IC50 = 0.28 mg/mL, Figure 2), which can have a contribution of melanoidins [11], although the most abundant fractions also present a relevant antioxidant activity. This value was consistent with the IC50 value determined for chicory melanoidins (IC50 = 0.28 mg/mL) and higher than the one for instant coffee melanoidins (IC50 = 0.08 mg/mL) [12]. Due to the low yield of the 50–100 kDa fraction, the unfractionated BrE was used for the preparation of potato starch-based films. ## 3.2.1. Chromatic Properties The neat films exhibited L, a, and b* values of 93.1, 1.73, and −1.20, respectively (Table S1), following the trend reported for films produced from starch recovered from the potato chip industry [6]. The L* values significantly decreased from 93.1 (neat films) to a minimum of 89.7 for films containing $15\%$ BrE (Figure 3, Table S1), indicating that the incorporation of the extract decreased the films’ lightness. In addition, the b* (yellow–blue) values significantly increased from −1.20 (neat films) to 1.39 ($5\%$ BrE), 4.00 ($10\%$ BrE), and 6.31 ($15\%$ BrE), thus revealing that the incorporation of the BrE conferred a yellowish coloration to the neat films, this being even more evident for higher BrE dosages. The calculated total color difference (ΔE) values corroborated these chromatic changes, varying from 3.21, 5.94, and 8.26 in films with $5\%$, 10, and $15\%$ BrE, respectively (Table S1). Thus, the high the BrE amount, the higher the color difference of the materials. These differences can be related to the presence of melanoidins in the 50–100 kDa fraction (Table 1). This trend observed for BrE was found in potato starch-based films containing coffee silverskin, a coffee roasting industry byproduct [19]. Nevertheless, as revealed in the images, the typical transparency of the neat films was preserved for all of the BrE dosages (Figure 3). Moreover, similar a* values were observed among all of the films (Table S1), meaning that the red–green coordinate was kept constant. ## 3.2.2. Thickness and Mechanical Properties The neat films had a thickness of ca. 58 μm and a low elongation at break (ca. $6\%$) and tensile strength (ca. 11 MPa), although showing a high Young’s modulus (ca. 1078 MPa) (Figure 4). These values are consistent with the brittle and rigid character of potato starch-based films [5,6,19]. Compared to the neat films, although no significant differences were observed in thickness (Figure 4a), the incorporation of BrE influenced the mechanical performance of the films. The BrE significantly increased the films’ tensile strength from ca. 11 MPa (neat films) to ca. 16 MPa, 23 MPa, and 21 MPa in films with $5\%$, $10\%$, and $15\%$ of BrE, respectively (Figure 4b), improving the traction resistance of the materials. Additionally, BrE significantly decreased the Young’s modulus from ca. 1078 MPa (neat film) to values lower than ca. 615 MPa (Figure 4c), revealing that their elasticity increased with the incorporation of BrE, independently of its concentration. This effect can be related to the presence of BrE compounds that decrease the cohesion forces within the starch molecules, promoting a discontinuity in the polymeric matrix, similar to the trend observed with starch-based films containing spent frying oil-derived fatty acids [6]. In addition, the incorporation of $5\%$ BrE decreased the elongation at break of the films from ca. $6\%$ (neat film) to ca. $4\%$, thus decreasing their plasticity. However, for $15\%$ BrE, the elongation at break increased to ca. $8\%$ (Figure 4d). This increase may be justified by the presence of a higher amount of low molecular weight compounds ($39.9\%$ of <5 kDa materials, Figure S3), such as phenolic compounds and small peptides (Table 1), which may promote a plasticizing effect due to the decrease in the hydrogen bonding within the starch network. This effect follows the trend already observed for starch-based films containing phenolic-rich extracts of potato peels [7] and hibiscus [24], as well as films with coffee silverskin [19]. Hence, according to the dosage, the BrE increased starch-based films’ resistance to break and elasticity, promoting their flexibility. ## 3.2.3. Wettability The neat films exhibited water contact angles (WCAs) below ca. 45° at both surfaces, which highlights their hydrophilic character and homogeneous composition, following the trend that has been reported for potato starch-based materials [6,19]. When compared to the neat films, the incorporation of 5, 10, and $15\%$ BrE increased the WCAs on both film surfaces, from ca. 45° (neat films) to ca. 58°, 59°, and 62° at the upper side and from ca. 38° (neat films) to ca. 68°, 58°, and 66° at the lower side, respectively. However, these values were neither statistically different among the three BrE dosages used nor among the two films’ surfaces (Figure 5). Therefore, BrE allowed for increasing the water tolerance of potato starch/BrE-based films. Since BrE was mainly composed of carbohydrates $66.9\%$ w/w (Table 1), their hydroxyl groups may justify all of the WCA values below the hydrophobicity benchmark of 90°. Moreover, the presence of the 50–100 kDa brown compounds and phenolics (Table 1), may have contributed to the increase in the WCA of films containing BrE, similar to the trend observed for starch-based films containing coffee silverskin [19] and potato peel phenolic-rich extract [7]. In addition, it can be suggested that BrE can contain frying oil-derived fatty acids incorporated into the melanoidins structure, similar to what was observed for other organic acids in coffee melanoidins [10], although in insufficient amounts to promote hydrophobicity to the materials. ## 3.2.4. Solubility and Water Vapor Permeability When immersed in distilled water, the neat films ($0\%$ BrE, Figure 6a) lost their native weight in ca. $21\%$, which is in line with the literature on potato starch-based films [6]. This weight loss has been related to the high-water solubility of glycerol [19], the plasticizing agent. However, as a peak at 260 nm was observed in the aqueous medium of the neat films (Table S2), their weight loss can also have the contribution of leaching of the protein present in the recovered potato starch [25]. With the incorporation of BrE, the films’ weight loss significantly decreased for all BrE dosages studied (Figure 6a). These results revealed that BrE hinders the film hydration and leaching of water-soluble compounds. When compared to the aqueous medium of the neat films, a significant increase in the absorbance values at 260 nm was determined for all formulations, although not significant among the tested BrE dosages (Table S2). This indicates that the films’ weight loss can be related to the solubilization of the BrE components, possibly protein, as observed for the neat films. This suggests that the crescent incorporation of other BrE components, such as melanoidins (Table 1), avoids the glycerol bonding with water molecules and its release from the starch matrix, the efficiency of which varies linearly with the BrE concentration. The WVTR of the potato starch-based films was ca. 97 g/m2·day, which is in line with the literature [19], as amylose and amylopectin from potato starch and glycerol are responsible for the passage of water vapor through the films. The incorporation of $15\%$ BrE allowed to significantly decrease the WVTR values of the neat films to 68 g/m2·day. Although the other BrE dosages also tended to decrease the WVTR of the films, the values were not statistically different when compared to the neat films (Figure 6b). The highest WVTR decrease observed in films containing $15\%$ BrE reinforces the hypothesis that BrE melanoidins (50–100 kDa, Table 1) had hydrophobic features promoted by the incorporation of frying oil-derived fatty acids, although in an insufficient amount to promote the full water vapor barrier of the materials. A similar trend was observed in starch-based films containing spent frying oil-derived fatty acids [6]. ## 3.2.5. UV-Protective and Antioxidant Properties From the UV–visible absorption range of 200 nm to 500 nm, two major bands at 250 nm and 340 nm were determined for films containing BrE, while no absorption was observed for the neat films (Figure 7a). This allowed for the conclusion that the BrE brown-colored components conferred a UV-protective ability, especially to the films containing $15\%$ BrE, which had a higher melanoidins proportion (Kmix, 405 nm = 0.69 L/g/cm and MBI = 1.48, Table 1). In addition, the major band was registered in the UV-C region at 250 nm, the intensity of which significantly increased with the BrE dosage. A less intense band in the UV-A region, at 340 nm, was also determined, being similar for all tested BrE dosages. The protection against UV radiation may be explained by the presence of melanoidins and phenolic compounds in the 50–100 kDa BrE fraction (Table 1). Phenolic compounds from coffee silverskin have been related to the UV absorption properties of potato starch-based films [19]. This property is relevant for the application of starch/BrE-based films in food packaging for preventing food oxidation promoted by UV radiation [26]. The neat films exhibited a percentage of ABTS•+ inhibition of ca. $15\%$ and $26\%$ after 20 min and 240 min (Figure 7b). This minimal ABTS•+ inhibition can be explained by the presence of the phenolic compounds in the starch used to produce the films [7,27]. However, this inhibition greatly increased for films containing BrE, being similar among all tested concentrations: after 20 min and 240 min of incubation, ca. $77\%$ and $93\%$ of ABTS•+ inhibition, respectively, was achieved (Figure 7b), which may be related to the melanoidins and phenolic compounds present in the BrE (Table 1). ## 3.3. Effect of Potato Starch/BrE-Based Films on Packed Sliced Cheese Quality The films containing $15\%$ BrE were chosen to pack sliced cheese due to the fact of their higher water tolerance, gas barrier, UV-protective, and antioxidant properties (Figure 6 and Figure 7), while the thickness, mechanical properties, and wettability were similar to the other BrE dosages studied (Figure 4 and Figure 5). After packaging the sliced cheese with the starch/$15\%$ BrE-based films (day 0), the appearance of wrinkles in the films promoted by direct contact with the cheese was observed. This was not observed when commercial PA/PE plastic was used (Table 2). The absorption of water molecules by the starch/$15\%$ BrE-based films was corroborated by the significant cheese weight loss: cheese packed in PA/PE material resulted in only $0.12\%$ weight loss, while a $6.52\%$ weight loss was observed for the cheese packed in the material containing BrE after 7 days of storage (Table 2). Accordingly, an increase in the weight of $3\%$ was observed in the starch/$15\%$ BrE-based films. No significant differences were observed from day 7 until the end of storage, suggesting that an equilibrium in the weight lost and gain was achieved. During the storage period (14 days), the starch/$15\%$ BrE-based films wrinkles were always present. In addition, the films did not lose their adhesiveness to the cheese surface. Similar behavior was observed in starch-based films with potato peel phenolic extracts when in contact with smoked fish fillets [7]. Regarding the chromatic parameters (Table 2), on the day of packaging (day 0), the cheese samples had L, a, and b* values of 81.44, −2.86, and 31.04, respectively. Over time, significant changes were observed in the cheese packed in PA/PE, mainly regarding the lightness (L) and redness (a) coordinates (79.68 and −3.57 (day 7) and 81.74 and −2.44 (day 14), respectively), which was related to the cheese darkening. When compared to the cheese packed in the PA/PE plastics, a significant decrease in the L* values of the cheese packed with starch/$15\%$ BrE-based films was observed over the 14 days of storage (76.10 and 77.12 at day 7 and 14, respectively), indicating that the lightness of the cheese tended to decrease. In addition, a significant increase in the a* and b* values of the cheese packed with starch/$15\%$ BrE at day 14 of storage (−1.81 and 34.17, respectively) was observed, reflecting a significant decrease in the green color and an increase in the cheese yellow color. The total color difference (ΔE) values confirm the chromatic changes observed, varying from 0.52 and 2.09 in cheese packed in PA/PE and from 5.34 and 5.44 in cheese in starch/$15\%$ BrE-based films. All these chromatic changes resulted in the yellow-colored darkening of the cheese (Figure S4). The removal of water from the cheese’s surface decreases the water activity, which is a factor reported to promote Maillard reactions between lactose and lysine residues of protein when their concentration increases [28]. Nevertheless, the migration of the brown-colored compounds from the starch/BrE packaging to the cheese surface cannot be excluded, possibly due to the fact of the hydrophobic interactions with the cheese fatty acids. However, at day 7, the a* and b* coordinates of the cheese packed with the materials with BrE did not differ from the cheese at day 0; thus, the original color of the cheese was maintained over this period, evidence not observed for the cheese packed in PA/PE (Table 2). The cheese texture properties are expressed in terms of the maximum in puncture (N) (i.e., the maximum force required to produce deformation) and puncture work (N.mm) (i.e., the mechanical work needed to reach the rupture point) (Figure 8). These parameters allowed to evaluate the surface hardness (cheese surface in direct contact with the packaging) and the inner hardness (cheese internal layers/paste not in contact with the packaging material) of the cheese samples, respectively [20]. Compared to the cheese before packaging (day 0) or even to cheese packed in the PA/PE materials, no significant changes were observed in terms of the maximum puncture and puncture work values for the cheese packed with starch/$15\%$ BrE-based films (Figure 8). These results revealed that both the surface and inner hardness of the cheese slices were preserved over the 14 days of storage. Considering the volatile compounds present in the cheese samples packed with PA/PE and starch/$15\%$ BrE materials, a total of 31 compounds were determined during the 14 days of storage, belonging to the chemical families of aldehydes [7], ketones [10], alcohols [12], and acids [2] (Table S3). These chemical families were already referred in semi-hard cheese packed in poly(lactic acid) material (PLA) [29]. At day 0, a total of 46.72, 27.26, and 7.49 μg ethyl heptanoate eq./g of cheese of ketones, alcohols, and aldehydes, respectively, was determined in the cheese. During storage, a significant increase in the content of aldehydes was observed in cheese packed in PA/PE, mainly due to the higher increase in 3-methyl-butanal, nonanal, benzaldehyde, and benzeneacetaldehyde. A similar trend was observed for ketones, namely, 2-butanone, 2,3-butanedione, and 3-hydroxybutanone. These compounds, if present in amounts higher than their odor threshold (i.e., a lower concentration of a compound in the vapory phase that can be detected by smell [30]), can contribute with acrid, waxy, almond-like, burnt sugars, and grassy notes in the case of aldehydes and with acetone-like, butter, and woody aromas in the case of ketones [31]. Furthermore, the presence of acetic and butanoic acids was determined, which can contribute with vinegar and rancid odors [30,31]. On the other hand, a significant decrease was observed, mainly on day 7 of storage, in the amount and number of alcohols. This can be related to their oxidation, forming aldehydes, ketones, and acids, contributing to their increase in the cheese during storage (Figure 9, Table S3). In the cheese packed in starch/$15\%$ BrE-based films, when compared to PA/PE, no significant differences were observed in the content of aldehydes and acids. However, a significantly lower concentration of ketones and a higher concentration of alcohols in the cheese were observed when stored in starch/$15\%$ BrE-based films. When compared to day 0, an increase in the aldehydes, ketones, and acids was observed, while the content of the alcohols remained similar (Figure 9, Table S3). These results indicate that BrE films can act as antioxidants, mitigating the oxidation of cheese components. In addition, over the entire storage time, no visual growth in the molds and yeasts was observed in the cheese samples, neither when the bioplastics nor when the plastics were used (images in Table 2 and Figure S4), accordingly with the absence of volatiles emitted by microbial growth. ## 4. Conclusions The starch/$15\%$ BrE-based films revealed to be suitable for packaging semi-hard sliced cheese. They were able to preserve both the inner and surface cheese hardness over the 14 days of storage at 4 °C and $80\%$ of relative humidity, similar to the cheese packed in PA/PE plastic. Nevertheless, a cheese weight loss and a yellow-colored darkening were observed, related to the migration of water from the cheese to the film. A similar volatile profile was also determined in the cheese packed in both materials, although the BrE constituents allowed for decreasing the content of ketones, a chemical family of compounds responsible for oxidation aromas. The BrE, mainly constituted by carbohydrates, protein, and small amounts of phenolics, esterified fatty acids, and melanoidins, enhanced the traction resistance and promoted a plasticizing effect on the starch-based films, allowing for the development of flexible films. Additionally, the BrE decreased the films’ surface hydrophilicity and water solubility, thus enhancing their tolerance to water conditions. Moreover, the potato starch/BrE-based films possessed UV-protective and antioxidant properties, which were even more relevant for the higher BrE amount ($15\%$, w/w in relation to starch dry weight). Therefore, the BrE was revealed to contain molecules, including melanoidins, with interest to tune the mechanical and physicochemical performances of the potato starch-based films, allowing for the development of active bio-based materials suitable for use in active food packaging applications, including cheese coating. ## References 1. Potatoes Production in the World. (2020) 2. 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--- title: Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk authors: - Zhiqiu Yao - Pei Nie - Xinxin Zhang - Chao Chen - Zhigao An - Ke Wei - Junwei Zhao - Haimiao Lv - Kaifeng Niu - Ying Yang - Wenna Zou - Liguo Yang journal: Foods year: 2023 pmcid: PMC10048274 doi: 10.3390/foods12061199 license: CC BY 4.0 --- # Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk ## Abstract Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow’s milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis. ## 1. Introduction Milk is an important route in humans for nutrient intake, and the fatty acids in milk are associated with many biological functions in humans [1]. The dietary intake of fatty acids has an important influence on coronary disease; specifically, saturated fatty acids (SFA) increase serum cholesterol levels, whereas polyunsaturated fatty acids (PUFAs) reduce the risk of coronary disease [2]. In addition, studies have shown that the fatty acids in milk are also related to the technological properties of milk and the processing of dairy products [3]. The composition of milk fat and fatty acid content reflects to a certain extent the health status of the cow [4]. Linoleic acid (LA) is a type of PUFA that has been shown to have various health benefits, including reducing the risk of chronic diseases and improving insulin sensitivity [5,6]. Milk is an important source of LA, which is considered a potential anticarcinogen and can be manipulated through dietary management [7]. As the economy continues to develop, there is a growing demand for milk that is nutritionally valuable. The dairy industry, therefore, faces two major challenges: [1] aligning the fatty acid composition of milk with consumer preferences, and [2] finding reliable and precise methods to quantify the FA composition of milk [8]. The traditional methods for determining LA content in milk products are gas chromatography (GC) [9] or gas chromatography–mass spectrometry [10], which are time-consuming and labor-intensive and often involve the use of harmful chemicals. Fourier transform mid-infrared spectrometry (FT-MIR) is a widely utilized analytical technique that has been demonstrated to be effective in a range of applications within the dairy industry. Specifically, FT-MIR has been demonstrated to be valuable in the analysis of antibiotics present in milk [11,12], the quantification of fat and protein content in milk [13], the prediction of methane emissions [14], and the prevention of early lactation diseases in cattle [15,16]. FT-MIR has been increasingly used for the analysis of fatty acids in milk due to its advantages of high throughput in real-time, sensitivity, and low sample preparation requirements [17]. In recent years, there has been a growing interest in using FT-MIR in combination with multivariate analysis techniques, such as partial least squares regression (PLSR), to quantify the PUFA content in milk products. Mid-infrared spectroscopy has been widely used in the rapid prediction of fatty acids. PLSR is probably the most widely used technique in spectral analysis. Many researchers have successfully measured the fat, protein, solid non-fat, and fatty acid content in milk using PLSR regression [18,19,20,21]. With the development of computational power and machine learning methods, more and more multivariate models are used to calibrate the concentration of components in milk. The principal component regression (PCR) algorithm downscales the original features using principal components analysis (PCA) and performs linear regression on the reduced predictor variables, which are the principal components, to predict the target variable. By utilizing a smaller number of principal components that explain the majority of the variance in the data with respect to the target variable, PCR is more effective in mitigating overfitting than linear regression on all original features, particularly for high-dimensional data such as spectra [22]. In recent studies, artificial neural networks (ANNs) have recently been investigated in FT-MIR spectroscopic analysis [23,24]. Random forests (RF) employ an evaluation of the relevance of variables to selectively choose informative variables, thereby facilitating the construction of models that are both parsimonious and robust, and ultimately enhancing the predictive power [25,26]. Some recent endeavors employing FT-MIR spectroscopy have explored quantifying linoleic acid. Beriain et al. [ 27] predicted the α-linolenic acid and LA in intramuscular fat by using the ANN algorithm and achieved good forecasted results. In the field of dairy analysis, Bonfatti et al. [ 28] successfully developed a milk fatty acid prediction model for Italian Simmental cattle using MIR with PLSR algorithm on 1040 milk samples. Similarly, Coppa et al. [ 21] used GC combined with FT-MIR to develop a fatty acid prediction model for 250 Holstein milk samples. However, although GC is a useful technique for analyzing monounsaturated fatty acids, its accuracy may be reduced when analyzing complex mixtures of polyunsaturated fatty acid methyl esters containing trans double bonds, such as LA and alpha-linolenic acid [29]. In addition, the complexity and cost associated with GC analysis for large numbers of samples and the need for expert operators are important factors to consider. Notably, there have been no previous investigations on the prediction of linoleic acid content in buffalo milk using FT-MIR, and only a limited number of studies have assessed the accuracy and precision of FT-MIR-based predictions of PUFA [30]. Direct or spectral interference is a common issue in chemical analysis based on spectroscopic methods, where the sensor is not perfectly specific for the analyte [31]. Unintended interference can occur, especially when utilizing PLSR for the compositional analysis of highly complex samples [32]. It is important to carefully evaluate and control potential sources of interference in spectroscopic methods to ensure accurate and reliable chemical analysis. Therefore, the objectives of this study were twofold: [1] to modify the FT-MIR method by incorporating the standard addition technique and establish a more streamlined machine learning prediction model for linoleic acid in milk, and [2] to employ a novel validation strategy to evaluate the accuracy and precision of FT-MIR for the determination of linoleic acid in milk. ## 2.1. Sampling Over a 3-month period from April to June 2022, milk samples were collected from 31 buffaloes in Hubei, China. For each sampling day, 50 mL of milk was collected in the morning and another 50 mL in the afternoon, and then mixed into a single sample to reflect changes in milk composition throughout the day. In total, 12 L milk samples were collected and stored at −20 °C for further analysis. ## 2.2. FT-MIR and Preprocessing Method To process the samples, they were first rapidly thawed in a 40 °C water bath and then centrifuged at 2 °C, using a refrigerated centrifuge, at 3000 rpm for 15 min to eliminate fat [33]. The composition of skimmed milk was analyzed using the MilkoScan FT-6000 (FOSS Analytical A/S, Hillerød, Denmark), which revealed that the fat content of whey was less than $0.05\%$. Linoleic acid (LA) was randomly added to skimmed milk samples in seven different concentrations (1, 5, 10, 20, 50, 70, 100 mg/100 mL milk). Isopropanol was utilized as the diluent for the LA [34]. There were 15 samples for each concentration, and a total of 105 samples were used for FT-MIR analysis. MIR spectra were obtained for each sample using the Milkoscan FT 6000. The acquisition was performed twice, and the results were subsequently averaged. The MIR spectra were recorded in the region between 926 and 5012 cm−1 and omitted the O–H bending region (1600–1710 cm−1) and the O–H stretching region (3020–5012 cm−1) due to the high water content in milk [35]. The remaining region (926 to 1618 cm−1 and 1705 to 3025 cm−1; 524 data points) was selected for analysis [36]. To further process the raw spectra, 7 different preprocessing methods were applied, including standard normal variate (SNV), 11-point Savitzky–Golay algorithm (SG), first derivative + Savitzky–Golay algorithm (SG-1), second derivative + Savitzky–Golay algorithm (SG-2), SNV + Savitzky–Golay algorithm (S-SG), SNV + Savitzky–Golay algorithm + first derivative (S-SG-1), and SNV + Savitzky–Golay algorithm + second derivative (S-SG-2) (Figure 1). The R packages “prospect” (version 0.26) and “baseline” (version 1.3-4) were utilized for the preprocessing steps. ## 2.3. Machine Learning Algorithms In this study, we aimed to determine the optimal quantitative model for the estimation of linoleic acid in milk using various machine learning techniques. All the machine learning algorithms utilized the CARET package version 6.0–93 in R program (version 4.2.2 https://www.r-project.org/ (accessed on 8 September 2022)) [35]. The FT-MIR data ($$n = 105$$) was randomly divided into a training set ($80\%$) and a test set ($20\%$) for building and validating the models, respectively. The numerical parameters for each model were entered using the “expand.grid” function and optimized using cross-validation (CV) statistics. We selected the model with the lowest root mean square error of cross-validation (RMSECV) from all preprocessing methods. PLSR is a widely utilized chemometric method in the analysis of spectroscopic data, utilizing latent variables (LV) to decompose the spectral data into systematic variations that account for the observed variance [37]. In comparison, the latent variable of PCR is the number of principal components and the minimum number of principal components required to explain $95\%$ of the variance [38]. ANNs represent a nonlinear extension of traditional linear regression models [39]. While linear regression is limited to modeling linear relationships between features and targets, ANNs have the capability to model complex nonlinear relationships through the utilization of hidden layers [40]. Regularization techniques play a crucial role in preventing overfitting in ANN models, thus improving their accuracy on novel data sets [41]. In the context of the CARET package, the parameter “size” refers to the number of units in a hidden layer, and the parameter “decay” represents the regularization strength. For PLSR and PCR, the maximum number of latent variables was set to 25. The number of the hidden layer for the ANN was varied from 1 to 5, and the decay values were tested for 0, 0.0001, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, and 0.5. The performance of each model was evaluated using internal 10-fold cross-validation statistics, including RMSECV and coefficients of determination (R2 cv). The models were then validated by estimating RMSE of prediction (RMSEP) on the external test set. Random forests (RF) have been demonstrated to hold promise in the realm of feature selection [42]. This procedure involves creating a random forest model and then performing 1000 iterations. Through the creation of a random forest model and subsequent iterations, the importance scores of features were evaluated based on the accuracy of model predictions of the target variable (LA) after replacing the response variable (spectral bands). Spectral bands that are more predictive of the outcome will have relatively high importance scores in each run, while other spectral bands with lower predictivity will only have randomly importance scores. This process enables the significance of features to be calculated [43]. In this study, we employed the rfPermute package (version 2.5.1) in R to perform variable selection using RF. The number of trees utilized in the RF model was set at 500 [44]. The PLS, PCR, and ANN models were again constructed by selecting spectral regions with significance levels less than 0.05. These models underwent variable optimization and performance evaluation in a manner consistent with the methodology previously described. ## 2.4. Quality Control for the Method The developed method underwent validation in accordance with the International Conference on Harmonization (ICH) Q2 (R1) guidelines. The Limit of Detection (LOD) was determined by utilizing 10 skimmed milk samples, and calculating the standard deviation of the matrices. The LOD was determined as three times the standard deviation of the ten sample [45]. [ 1]LOD=3×S0 S0 is the estimated standard deviation of single results at zero concentration. In terms of relative bias, recovery, repeatability, intermediate precision, lower limit of quantification (LLOQ) and upper limit of quantification (ULOQ), the validation protocol employed a 3 × 5 × 3 (i × k × j) full factorial experiment design [46]. Five different concentration levels (k) of linoleic acid (5 mg/100 mL, 10 mg/100 mL, 20 mg/100 mL, 50 mg/100 mL and 100 mg/100 mL) were investigated, with each level being conducted in three replicates (i) on three different days (j), resulting in a total of 45 samples [46]. The trueness of the method was evaluated through the expression of Bias and Recovery. [ 2]Bias(%)=Y¯−YrYr×100 [3]Recovery(%)=Y¯Yr×100 where Yr is the theoretical value, Y¯ is the average value of a series of measurements. Precision is evaluated at two levels: repeatability and intermediate precision. This requires the calculation of the mean square of inter-series (MSB) and intra-series (MSE) [47]. If MSE < MSB, then:[4]Repeatability:σRe2=MSE [5]Intermediate precision:σIn2=MSB−MSEn Otherwise:[6]Intermediate precision=Repeatability=1mn−1∑$i = 1$m∑$j = 1$n(Yij−Y¯)2 where Yij is the average of the calculated concentration of the j-th concentration level of the i-th series; m is the number of days; n is the number of replicates per series. The current assay acceptance criteria in practice require that at least four out of six samples have an observed mean at the lower limit of quantitation (LLOQ) within $20\%$ of the theoretical value (β = $\frac{4}{6}$ ≈ $66.7\%$), and the observed precision to be ≤$20\%$ coefficient of variation [48]. The accuracy profile based on β-content tolerance intervals is a powerful tool for method validation and quality control [49]. This ideal acceptance criterion would ensure that a high proportion (β = $66.7\%$) of future observations lie within acceptance limits (±$20\%$), with a high level of confidence (confidence level = 0.9). By calculating the lower limit (L) and upper limit (U) of the tolerance at a particular concentration, the tolerance of the measured value of a specified proportion (β) of all samples will be within the interval [L,U] with the specified confidence level. It can be considered that if the number of observed values Yn+1 within the tolerance interval [L,U] accounts for more than 0.667 of the total Yn, and the confidence level is 0.9, then the detection method is valid, and the formula is shown as follows. [ 7]Confidence level=PP[L≤Yn+1≥U/Yn]≥β For instance, for β = 0.667 and confidence level is 0.9, the β- content tolerance interval represents a $90\%$ probability (p) that $66.7\%$ of the individual observations of the population are included in the interval [L,U] [50]. The determination is accepted if the resulting tolerance limits L and U are completely within acceptance limits (±$20\%$) of the theoretical value; Otherwise, it’s not. According to Kulkarni’s approach [51], the tolerance interval [L,U] can be rewritten into the following form:[8]L%,U%=bias%−χk×RSD(%),bias%+χk×RSD(%) Where:[9]RSD(%)=σInYr×100 [10]χk=k×χ1;0.6672(λ)χk;0.92(0.1)2 χ1;0.6672τ is the 66.7th quantile of a noncentral chi-square distribution with the degree of freedom 1. λ is the noncentrality parameter. χf′;0.92(0.1) is the 90th quantile of a noncentral chi-square distribution with the degree of freedom k. χk denotes the chi-square distribution associated with the variable k [50,52]. [ 11]k=R′+12R′+1n2/m−1+1−1n/mn [12]λ=nR′+1mnR′+1 [13]R′=MAX0,1nMSB/MSEF0.85m(n−1);m−1−1 F0.85m(n−1);m−1 is the 85th percentile value of F distribution with the degree of freedom m(n−1) and m−1. The concentration at which the tolerance interval is less than acceptance limits is the limit of quantification. The acceptance limits is typically set at ±$20\%$ [52,53]. The procedure for building an accuracy file can be outlined as follows:[1]Calculate the β-content tolerance interval at a confidence level of 0.9 for each concentration level using Equations [13] or [8], resulting in a lower and upper limit for the interval, denoted as [L,U].[2]Graphically represent the results in a 2D plot, with the concentration level plotted on the horizontal axis and the tolerance interval limits (L,U) plotted on the vertical axis.[3]Compare the tolerance interval limits (L,U) with the acceptance limits of −$20\%$ to +$20\%$ around the theoretical value. If the tolerance interval falls entirely within this acceptance range, the analytical method is deemed valid for the corresponding concentration level. However, if the tolerance interval exceeds these limits, the method is not accepted for use at that concentration level. ## 3.1. Set Up of the Prediction Models First, we implemented a 10-fold cross-validation process to avoid overfitting. Cross-validation has proven to be a good method for model resampling and is widely used for the mid-infrared prediction of milk composition [54,55]. The performances of the various methods (PLSR, PCR, and ANN) are summarized in Table 1, with the RMSE and coefficient of determination (R2). The best model was determined by the smallest RMSE and highest R2. The RMSECV values for the PLSR, PCR, and ANN models were all found to be below ten. In our study, the RMSECV values of the training set were always lower than the one observed for test set, as mentioned by Soyeurt and Grelet [35]. Results showed that RMSECV values for PLSR, PCR, and ANN were similar, ranging from 5.1 mg/100 mL–7.3 mg/100 mL, with R2CV values also globally similar and ranging from 0.96–0.98. This indicates that the predictive performance of the three models is similar. There were also some differences in correlation values between predictions on the test set. Higher correlation was observed between the predictions given by PLSR and PCR(0.98) compared to those given by ANNs. Our analysis revealed that the PCR method outperformed the PLSR method, with slightly higher predictive accuracy. This difference in performance may be attributed to the distinct component extraction processes employed by PCR and PLSR. Specifically, PLSR identifies regressors from predictors that maximize the covariance with the response variable, while PCR employs principal component analysis (PCA) to identify the direction of greatest variability in the predictor variables and project them into a low-dimensional space to form principal components, which are subsequently used to explain the response variable. The component extraction step in PCR is capable of identifying superior candidate regression components by meticulously scrutinizing the covariance structure among the predictor variables, which may be overlooked by PLSR. Such phenomena have been observed in previous studies as well [56,57]. It is worth noting that the performance of different methods depends on the nature of the analyzed data and the data processing methods used. This is one of the reasons why it is not recommended to use the same milk fatty acid prediction model across different species. ## 3.2. Models Built with the Spectral Regions Selected by RF Our research on importance measures in random forests has focused on finding data points where the predictor variables are highly correlated. The application of RF results in a significant reduction in the number of variables in each model. The number of data points dropped from 524 before the selection to 135. RF is widely used to assess the importance of features. Wang et al. [ 58] employed RF feature selection to investigate the relative contributions of soil factors, microbial parameters, and climatic factors in altering soil organic carbon levels. Chen et al. [ 59] used RF to evaluate the most significant drivers of soil fungal diversity, including plant communities and soil physicochemical properties. Similarly, Andreas et al. [ 60] utilized RF to identify tillage type as the most important factor affecting dairy cows with Fasciola hepatica, with higher-ranking variables yielding more accurate predictions than those with lower importance scores. The variable importance measure can be used by RF to select and order the spectral regions that are most predictive. Usually, MIR data points are ranked according to decreasing asymptotic p-values and importance value. The process of changing random number seeds will result in slightly different results for random forests [61]. Therefore, the response variable was permuted 1000 times to generate new RF models, and the data points that were most correlated with linoleic acid and significant at a p-value of less than 0.05 were selected for modeling [62,63]. Most of the selected data points were included in the spectral subsets 940–1215 cm−1, 1342–1489 cm−1, 2364–2399 cm−1, 2823–2935 cm−1, and 3715–3846 cm−1 (Figure 2). These regions are highly correlated with fatty acids. The first region (940–1215 cm−1) is related to the asymmetric vibrations of the C-O-C group in esters; the second region (1342–1489 cm−1) is characteristic of the C = O ester Fermi resonance; the third region (1720–1766 cm−1) is characteristic of the stretching vibrations of the carbonyl group in esters; the fourth region (2350–2357 cm−1) is a synergistic region associated with fatty acids and has been shown to assist in the prediction of fatty acids in milk to some extent [30]. The fourth region (2823–2935 cm−1) is characteristic of C-H stretching absorption [64]. The validation results for the prediction models built using the wavelengths selected by RF are presented in Table 2, along with the number of latent variables. The results indicate that compared to the full spectrum model, the RMSECV value of the RF model is generally lower. The application of RF resulted in a reduction of the R2P value by $0.2\%$ in the ANN mode, while the R2P value increased by $0.3\%$ in the PCR model. No differences were observed in the R2P value of the PLSR model, but the R2cv value of PLSR increased by $2.1\%$ after RF feature selection. Thus, the application of RF has produced simpler models, and the predictive power of these simplified models is comparable to that of full spectrum models. As mentioned above, the performance of a method is closely related to the characteristics of the data set, the preprocessing methods, and the relationship between the predictor and response variables. After using RF to extract the original features, the performance of the PLS method was slightly better than that of the PCR method. In detail, lower RMSEP values were observed between the predictions given by the PLSR and PCR (4.1) compared with ANNs (6.5). This suggests that nonlinear methods, such as ANN models, were not suitable, but linear PLSR showed good performance. Previous research has suggested that FT-MIR predictions with partial least square models are promising approaches [65,66]. This is in agreement with Soyeurt et al. [ 35], showing that PLSR has better predictive performance than ANNs in orange variety classification. Improving the performance of ANNs requires a large training data set to learn complex data interactions by tuning its hyperparameters, such as size and decay, in this study [67]. In addition, epochs, activation function, and learning rate all affect the predicting capabilities of the ANN. From our results, the ANN does not seem to perform well when the training population is small. The RMSE values for the other four linear models are smaller than those with the ANN, which also suggests that the complex nonlinear relationship between the predictors and target traits is limited [68,69]. It was evident from the values of R2 and RMSE that, even though all three models (PLSR, PCR, and SVR) fitted well to the experimental design, PLSR offered better predictive and approximation accuracy. The best predictive performance was achieved by the PLSR with the mean R2P value of 0.984 and a RMSEp value of 4.113 mg/100 mL. We observed a higher predictive ability for linoleic acid content compared with previous studies on FT-MIR predictions, which obtained R2 values ranging from 0.43–0.89 [30]. This improvement could be attributed to the expression of fatty acid content estimated in g/100 mL milk, which is more accurate than g/100 g FA [21,70]. Additionally, the utilization of the standard addition method, which is commonly used in the method validation of other analytical methods such as GC, was instrumental in avoiding interference from other fatty acids [34]. To the best of our knowledge, it was the first time that the RF method was used on FT-MIR data to select salient features. Although this method appears promising, further studies will be needed to fully understand its limitations. ## 3.3. MIR Method Validation The method detection limit for LA was determined using FT-MIR spectroscopy according to the ICH Q2 (R1). The LOD was found to be 3.42 mg/100 mL based on the standard deviation of the blank sample signals ($$n = 10$$). The FT-MIR method was validated towards recovery, repeatability, intermediate precision, range, and accuracy for the quantification of LA according to the ICH Q2 (R1). The acceptable limits were set at ±$20\%$ for the IR method [53,71]. The trueness represents the closeness of the average to the true value, and precision is the closeness among a series of measurements [72]. The uncertainty is a dispersion of measured values from the expected value [73]. The total uncertainty includes the random error and the systematic error. Table 3 illustrates that the results for LA at 20, 50, and 100 mg/100 mL concentration levels have good relative bias and recovery, with relative bias ranging from −$0.56\%$ to $2.15\%$ and recovery ranging from $99.44\%$ to $102.47\%$. The repeatability and intermediate precision of LA at 10 mg/100 mL and 20 mg/100 mL concentrations were $1.89\%$ and $2.07\%$ respectively. The repeatability and inter-assay precision of LA at 50 mg/100 mL were $8.34\%$ and $9.46\%$ respectively, while the repeatability and inter-assay precision of LA at 100 mg/100 mL were $11.76\%$ and $13.61\%$, respectively. In our results, the intermediate precision is worse than the repeatability, which means that there is an effect of day-to-day variability on the spectral data at these concentration levels [74]. The accuracy of LA at 20 mg/100 mL (−9.11, 13.41), 50 mg/100 mL (−10.17, 9.07), and 100 mg/100 mL (−10.24, 14.96) concentration were found to be within the acceptable range of −20 to $20\%$. The accuracy of 5 mg and 10 mg/100 mL level were outside the acceptance limits. This suggests that systematic and random errors increase as the concentration level decreases [46]. As shown in Figure 3a, the relationship between the predicted concentrations and the true concentrations was evaluated by the linear equation: $y = 1.013$x + 0.4959 with R2 of 0.9948. The slope and R2 values of the linear equation demonstrate the good agreement between the MIR predictions and the theoretical values. The accuracy profile is a pictorial tool that is widely used for the quality control of medicines [75]. LLOQ and ULOQ are the lowest and highest concentration levels where the β-tolerance expectation limits are included within the acceptable limits. In our study, the LLOQ value was 15.54 mg/100 mL, and the ULOQ value was 100 mg/100 mL. In this study the acceptable limit was set at ±$20\%$, and in the other literature the acceptable limit has been set at ±$5\%$ to ±$30\%$ [50,75,76]. It is a widely recognized standard in the field of bioanalytical methods that pre-study acceptance criteria mandate that the observed mean should be within ±$15\%$ of the theoretical value, and the precision’s coefficient of variation should not exceed $15\%$ [50]. The levels of linoleic acid in buffalo milk measured using gas chromatography ranged from 51 mg/100 mL to 85.4 mg/100 mL, which is in between our quantitative ranges [8,77]. Our results show that the MIR method within the quantitative interval fully meets the above criteria. ## 4. Conclusions The objective of this study was to assess the efficacy of three machine learning models in quantifying the levels of linoleic acid (LA) in raw milk and to theoretically determine the upper and lower bounds of LA quantification. These models included partial least squares (PLSR), principal component regression (PCR), and artificial neural networks (ANNs). The study applied random forest feature selection to the models in order to improve the model performance and reduce complexity. The results of calibration and cross-validation analyses showed that the random forest partial least squares (RF-PLSR) model had the best performance among the three models, with low error values and high regression coefficients. The accuracy profile of the model was further validated using accuracy files, and it was demonstrated that Fourier transform mid-infrared (FT-MIR) could reliably quantify LA levels in the range of 15.54 mg/100 mL to 100 mg/100 mL. 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--- title: 'Novel Plant-Protein (Quinoa) Derived Bioactive Peptides with Potential Anti-Hypercholesterolemic Activities: Identification, Characterization and Molecular Docking of Bioactive Peptides' authors: - Feyisola Fisayo Ajayi - Priti Mudgil - Amie Jobe - Priya Antony - Ranjit Vijayan - Chee-Yuen Gan - Sajid Maqsood journal: Foods year: 2023 pmcid: PMC10048307 doi: 10.3390/foods12061327 license: CC BY 4.0 --- # Novel Plant-Protein (Quinoa) Derived Bioactive Peptides with Potential Anti-Hypercholesterolemic Activities: Identification, Characterization and Molecular Docking of Bioactive Peptides ## Abstract Hypercholesterolemia remains a serious global public health concern. Previously, synthetic anti-hypercholesterolemic drugs were used for ameliorating this condition; however, long-term usage presented several side-effects. In this regard, natural products as an adjunct therapy has emerged in recent times. This study aimed to produce novel bioactive peptides with anti-hypercholesterolemic activity (cholesterol esterase (CEase) and pancreatic lipase (PL)) from quinoa protein hydrolysates (QPHs) using three enzymatic hydrolysis methods (chymotrypsin, protease and bromelain) at 2-h hydrolysis intervals (2, 4, and 6 h). Chymotrypsin-generated hydrolysates showed higher CEase (IC50: 0.51 mg/mL at 2 h) and PL (IC50: 0.78 mg/mL at 6 h) inhibitory potential in comparison to other derived hydrolysates and intact quinoa proteins. Peptide profiling by LC-MS QTOF and in silico interaction with target enzymes showed that only four derived bioactive peptides from QPHs could bind in the active site of CEase, whereas twelve peptides could bind in the active site of PL. Peptides QHPHGLGALCAAPPST, HVQGHPALPGVPAHW, and ASNLDNPSPEGTVM were identified to be potential CEase inhibitors, and FSAGGLP, QHPHGLGALCAAPPST, KIVLDSDDPLFGGF, MFVPVPH, and HVQGHPALPGVPAHW were identified as potential PL inhibitors on the basis of the maximum number of reactive residues in these bioactive peptides. In conclusion, QPHs can be considered as an alternative therapy for the treatment of hypercholesterolemia. ## 1. Introduction The rise in diet-related metabolic disorders such as hyperlipidemic or hypercholesterolemia, cardiovascular diseases, hypertension, and other related metabolic diseases has been identified as the major cause of mortality globally, and thus has become a serious public health issue. The consumption of processed food and animal products has been associated with the occurrences of these health issues. Cholesterol esters are normally found in the human diet, which the human intestine cannot sufficiently absorb, thereby producing free fatty acids and cholesterol that elevate the level of blood cholesterol. It is a known fact that rising levels of cholesterol and triglycerides in the blood serum results in hyperlipidemia or hypercholesterolemia [1]. According to [2], the molecular mechanism for understanding the physiological development of hyperlipidemia is quite difficult. However, two enzymes (cholesterol esterase (CEase) and pancreatic lipase (PL) have been associated with hyperlipidemia and hypercholesterolemia complications, and their inhibitory potentials have been targeted by scientists in order to develop anti-hypercholesteremic drugs [1,3]. For many years, synthetic drugs have been used to lower blood cholesterol production and absorption. However, synthetic drugs (mainly the CEase or PL inhibitors) are often accompanied with harmful consequences on human health and metabolism due to long-term usage. Therefore, there is a high demand for natural products that can prevent these malignant diseases. Nowadays, generating bioactive peptides from plant-based proteins have gained more attention owing to their biological and functional activities, which is highly significant in alleviating chronic diseases, as well as in promoting human health. Moreover, the effectiveness of the derived peptides depends on their sequence, structure, and inherent amino acid composition. Quinoa proteins are a very good source of essential amino acids [4]. Apart from providing significant nutrients, they also possess several peptide sequences with specific physiological benefits [5]. Protein hydrolysates from quinoa have been reported to be a potential ingredient in designing functional foods and nutraceuticals because of their multifunctional bioactivity, including antioxidant, antihypertensive, antidiabetic, dipeptidyl peptidase IV (DPP-IV) inhibitory, and angiotensin-converting enzyme (ACE) inhibitory properties [6,7]. Similarly, several bioactive peptides from protein hydrolysates have demonstrated the capacity to inhibit the digestion of dietary lipids in in-vitro and cultured cell assays [8]. Peptides that possess anti-hyperlipidemic activity operate in the gastrointestinal tract, hepatocytes, and adipocytes by binding cholesterol micelles and bile acids, thereby hindering their absorption. According to Boachie, Yao and Udenigwe [8], their operation affects the activity of some regulatory enzymes, particularly the 3-hydroxy-3-methylglutaryl CoA reductase (HMGCoAR), which is well-known to be primarily responsible for cholesterol biosynthesis, and this enzyme inhibition is targeted to decrease the rate of cholesterol biosynthesis. Several scientific investigations have shown that natural peptides found in plant proteins can inhibit cholesterol absorption or HMGCoAR activity [9]. However, protein hydrolysates and bioactive peptides derived from quinoa have not been studied for their anti-hypercholesterolemic effect via the inhibition of CEase and PL enzymes to date. Therefore, this study investigated the role and binding mechanism of quinoa derived bioactive peptides for inhibiting enzymatic markers i.e., CEase and PL that are responsible for hypercholesterolemia. For effective inhibition of the cholesterol metabolizing enzymes, the potential of generated bioactive peptides to bind to their specific hotspots on the active site of the enzyme depends on the cleavage sites where the proteolytic enzymes will act on the protein [10]. As such, different enzymes can result in generating varying amino acids sequences in peptides, which is vital in demonstrating their bioactivity. Moreover, numerous studies have demonstrated the potential of anti-hypercholesterolemic peptides derived using specific enzymes (alcalase, pepsin, trypsin, flavourzyme, chymotrypsin, etc.) in inhibiting CEase and PL active sites [11,12,13]. Accordingly, quinoa protein isolates were hydrolyzed using three different enzymes (chymotrypsin, protease, and bromelain) in order to get a diverse sequence of peptides which might be potential inhibitors of CEase and PL. The generated quinoa hydrolysates were characterized biochemically via the degree of hydrolysis, and their CEase and PL inhibitory potentials were determined. Furthermore, peptides generated from quinoa protein isolates were characterized and identified using LC-MS-QTOF. A molecular docking approach was carried out to elucidate the molecular binding mechanism between potent anti-hypercholesterolemia peptides with the target enzymes (CE and PL) through the in silico structural activity relationship. ## 2.1. Materials (Quinoa Seeds) and Chemicals Quinoa seeds used in this study were purchased from the local market (Al-Ain, United Arab Emirates). Enzymes such as protease (Type XIV; ≥3.5 units/mg solid derived from Streptomyces griseus), α-chymotrypsin (EC 3.4.21.1 from bovine pancreas; C4129 40 units/mg), and bromelain (B4882: 3–7 units/mg solid; EC 3.4.22.33 from pineapple stem), HPLC grade reagents (acetonitrile and methanol), and other chemicals such as formic acid p-nitrophenyl butyrate, o-phthaldialdehyde, trizma base, β-mercaptoethanol, SDS, and sodium tetra-borate were procured from Sigma Aldrich (St. Louis, MO, USA). Other analytical grade reagents used were purchased from the UAE (BDH Middle East, Dubai, United Arab Emirates). ## 2.2. Preparation of Quinoa Protein Isolate Firstly, quinoa seeds were oven dried (40 °C for 3 h), dry-blended (IKA A11, Guangzhou, China), and passed through a screen (3 μm pore size) to obtain a fine powder. The methodology described by [6] as adopted from [14] was used to obtain quinoa protein isolate (QPI). In sum, defatted quinoa flour was dispersed in 0.015 mol L−1 NaOH (1:10 ratio) maintained at a pH of 10–11 and was stirred for 2 h on an electric shaker (24 ± 2 °C), and the resulting slurry was stored at 4 °C overnight. The slurry was homogenized for consistency and centrifuged twice at 4 °C at a speed of 10,000× g for 20 min after overnight storage. Whatman No. 1 paper was used to filter the obtained supernatant, and the pH of the filtrate reduced to 4.5 using the pH drop method with 1.0 mol L−1 HCl solution to precipitate quinoa protein. Afterwards, precipitated proteins were separated by centrifuging at 4 °C for 10 min at the speed of 10,000× g, and were subsequently washed twice with deionized water. The pH of the resultant pellets was adjusted to pH 7.0 (with 1.0 mol L−1 NaOH) and stored at −20 °C until further use. The Kjeldahl method was adopted to determine the protein content of quinoa protein isolate (QPI) slurries, which was found to be $44\%$. ## 2.3. Quinoa Protein Hydrolysate Production The methodology as described by [6] was used in the production of quinoa protein hydrolysates (QPHs). The obtained QPI slurry was thoroughly homogenized (T 25 digital ULTRA-TURRAX® homogenizer IKA®-Werke GmbH, Staufen, Germany). The protein content of the slurry was set to $4\%$ with deionized water, and the content was divided into four batches for treatment with bromelain, chymotrypsin, and protease, respectively, while the last part was kept as control. Then, 1 mol/L NaOH and 5N HCl were used to adjust the pH of the treated protein slurries for the bromelain enzyme to 7.0, the chymotrypsin enzyme to 7.8, and the protease enzyme to 8.0, respectively. Furthermore, enzyme concentration was pre-calculated, suspended in 1 mL of distilled water resulting in a $1\%$ enzyme/substrate ratio, and subsequently added to the QPI slurry. The samples were then distributed into 50 mL falcon tubes in triplicate for each period of hydrolysis and incubated (water bath) for up to 6 h at the speed of 100 ramps/min at a controlled temperature of 50 °C. Samples were taken every 2 h from all enzymatic incubations, and the reaction was inactivated at 95 °C for 600 s. Generated QPHs were centrifuged at 4 °C at a speed of 10,000× g for 10 min, and supernatants were collected and stored at −20 °C for further analysis. All of the analyses were performed within two weeks of the production of hydrolysates. ## 2.4. Degree of Hydrolysis (DH%) The previously published o-phthaldialdehyde (OPA) methodology of [15] was used for the determination of DH values. [ 1]Degree of hydrolysisDH%=hhtot×100 Herein, htot = total number of peptide bonds per protein equivalent; and h = number of hydrolyzed bonds, and calculated using h = (SerineNH2 − β)/α. Where htot, β, and α, β and htot values for quinoa proteins were used as 8.0, 0.40, and 1.00 mEq/g protein, respectively. ## 2.5.1. Determination of Pancreatic Lipase (PL) Inhibitory Activity of QPHs The PL-inhibitory activity of generated hydrolysates was measured using the methodology of [13]. Here, PL (20 μL) and p-nitrophenyl butyrate (25 μL) were mixed with each sample (50 μL) in a sodium chloride: sodium phosphate buffer (pH 7.2, 100 mM), and then incubated in a 96-well microplate reader. The quantity of the resultant reaction was modified with the above buffer to 150 μL and incubated (37 °C for 30 min). After the incubation period, the released p-nitrophenyl for each sample was measured on a plate reader (Epoch 2, BioTek, Winooski, VT, USA) at 405 nm. The PL-inhibitory activity was calculated using the equation below [2]% Enzyme inhibition=1−C−DA−B×100 Here: A = the absorbance of control; B = the absorbance of control reaction blank; C = the absorbance of sample, and D = the absorbance of the sample blank. The IC50 values were determined by plotting the percentage inhibition against the concentration of the test compound and expressed in mg (eqv protein)/mL. ## 2.5.2. Determination of Cholesterol Esterase Inhibitory Activity of QPHs The CEase-inhibitory activity was determined as per the procedure documented by [13]. Briefly, QPHs (25 μL), substrate (50 μL) containing 5 mM p-nitrophenyl butyrate in sodium phosphate (100 mM): NaCl buffer (pH 7.2, 100 mM) was placed in a 96-well microtiter plate. Subsequently, the mixtures were incubated with 50 μL of porcine pancreatic CEase (5 μg/mL) at 37 °C for 180 min. After that, the p-nitrophenol released from the enzymatic hydrolysis of p-nitrophenyl butyrate was read at 405 nm. The percentage CEase inhibition was determined using Equation [2] above. The IC50 values were determined by plotting the percentage inhibition against the concentration of the test compound and expressed as mg (eqv protein)/mL. ## 2.6.1. Sequencing of Peptides Implied in PL and CEase-Inhibitory Activities Using Liquid Chromatography-Mass Spectrometry Quadrupole Time-of-Flight (LC-MS Q T-O-F) For the identification of peptides in selected hydrolysates, QC-6 (chymotrypsin-generated at 6 h hydrolysis), LC-MS Q T-O-F was conducted as described previously by [16]. The identification of peptides was performed as follows: Advance Bio Peptide Map, C18 column (2.1 × 100 mm, 2.7 µm particles; Agilent, Santa Clara, CA, USA) was used for the peptide separation whereas the LC-MS Q T-O-F (model 6520, Agilent, Santa Clara, CA, USA) was used for the analysis. The mobile phases used were: (A) deionized water containing $0.1\%$ formic acid, and (B) acetonitrile containing $0.1\%$ (v/v) formic acid with a flow rate of 15 µL/min. The mobile phase gradient of the HPLC system was as follows: (a) 0–5 min, $10\%$ B; (b) 5–115 min, 10–$95\%$ B, (c) 115–120 min, $95\%$ B, (d) 120–135 min, 95–$10\%$ B, and (e) 140–150 min, $10\%$ B. The electrospray ionization-quadrupole-time-of flight system (ESI-QTOF) condition was: (a) mass range: 70–2000 m/z; (b) collision energy: 6V/100 Da (offset-2); (c) flow rate: 15 µL/min; (d) ion spray sources: 3.5 kV; (e) drying gas: Nitrogen at a temperature of 350 °C with a flow rate of 10 L/min; (f) nebulizer pressure: 3 psig; (g) fragmentor voltage: 110 V; and (h) fragmentation mode: collision induced dissociation (CID). Data generated from the mass spectrometry approach were analyzed using PEAKS studio version 6.0 [17]. The precursor was selected on the basis of a minimum charge of 2 and a maximum charge of 10. From the generated peptides, only peptides with average local confidence (ALC) > $70\%$ were chosen for further analysis. ## 2.6.2. Potential Biologically Active Peptides Selection Using PeptideRanker Potential bioactive peptides were screened using the PeptideRanker web server (http://bioware.ucd.ie/, accessed on 26 July 2022) [18]. Peptides showing a score of more than 0.5 were regarded as potentially biologically active and subjected to further in silico analysis. ## 2.6.3. Selection of Identified PL and CEase Inhibitory Peptides Using Peptide Ranker The preliminary insight into the molecular mechanism of peptides for their PL and CEase inhibitory activities were explored through an in silico docking investigation using the online program Pepsite2, accessible at http://pepsite2.russellab.org (accessed on 16–19 August 2022) [19]. Herein, 3D structures of CEase (PDB code: 1AQL) and PL (PDB code: 1ETH) were introduced from the Protein Data Bank through http://www.rcsb.org/pdb/ (accessed on 16–19 August 2022). Peptide inputs were then entered jointly with a protein receptor in PDB format. The best interaction of peptides with enzymes showing p-values < 0.05 and the interaction with key hotspots on enzymes playing a significant role in enzyme inhibition were taken into consideration for further analysis. ## 2.7.1. Preparation of Protein Structure The 3D structures of human and bovine CEase, PDB IDs 1F6W and 1AQL respectively, and PL (PDB IDs: 1LPB and 1ETH) were obtained from the Protein Data Bank (PDB) [20]. These proteins were processed and optimized prior to docking using the Schrödinger Suite’s Protein Preparation Wizard (Schrödinger Suite 2021-1: Protein Preparation Wizard; Schrödinger LLC, New York, NY, USA). This workflow prepares a PDB structure for docking by adding and optimizing hydrogen bonds, assigning bond orders, simplifying multimeric complexes, creating disulfide bonds, deleting unwanted water molecules, adjusting ionization states, fixing disoriented groups, filling missing loops and sidechains and finally, energy minimizing and optimizing to produce a geometrically stable structure [21]. ## 2.7.2. Active Site Identification and Grid Generation The active sites of CEase (PDB IDs: 1F6 W and 1AQL) and PL (PDB IDs: 1LPB and 1ETH) that are directly involved in ligand-binding were identified from the literature The active site of lipase is located in the N-terminal domain and their catalytic activity is mediated by the catalytic triad Ser152, Asp176 and His263 Similarly, CEase also shares a comparable catalytic triad composed of Ser194, Asp320 and His435. For the docking analysis, a receptor grid suitable for peptide docking was generated for the minimized protein structures enveloping the active site residues. No constraints were applied when default parameters for van der Waals scaling factor (1.00) and charge cut-off (0.25) were used. The OPLS 2005 force field was used for structural representation [22]. ## 2.7.3. Peptide Docking and Binding Free Energy Calculation The Peptide Docking panel of Schrödinger Maestro [23] was employed to perform the docking of peptides to the protein structures. Using this tool, small peptides lower than 16 amino acids can be docked and scored with either Glide Score or molecular mechanics-generalized Born surface area (MM-GBSA) approach. Each peptide was docked to the protein receptor using Glide with increased sampling in several docking runs. Subsequently, OPLS molecular mechanics force field was used for pose optimization. After carrying out conformer clustering, ten representative peptide poses were chosen. Lastly, selected poses were re-scored and ranked based on GlideScore empirical scoring function [23,24]. The binding free energy of the best docked poses obtained after peptide docking were evaluated using the MM-GBSA approach. Binding free energy calculations were performed using Schrödinger Prime with OPLS 2005 force field combined with VSGB 2.0 implicit solvent model. The peptide was minimized, and the receptor was treated as rigid for the MM-GBSA calculations. ## 2.8. Statistical Analysis All hydrolysates were produced in three batches representing three replicates. All analyses were carried out in triplicate. Data analysis was performed with SPSS 24.0 statistical software (SPSS INC., Chicago, IL, USA) using one-way analysis of variance. Means separation was conducted using Tukey’s multiple range test, and a p-value of $5\%$ was defined as statistically significant ($p \leq 0.05$). ## 3.1. Degree of Hydrolysis (DH%) The DH and enzyme specificity are among the measurable factors to be controlled because they define the biological activities of the resulting peptides. Studying the hydrolysis of protein reveals the capability of an enzyme to degrade proteinaceous substrate, and this serves as a pointer for their proteolytic reaction [25]. Noteworthy, the protein profile obtained from intact quinoa (QPI), and quinoa protein hydrolysates (QPHs) from the three enzymes understudied after 2, 4, and 6 h of hydrolysis is depicted in Supplementary Figure S1. In this work, the effects of different enzymes (chymotrypsin, protease, and bromelain) at 2 h-interval reaction periods on the DH of quinoa protein hydrolysates (QPHs) were reported (Table 1). The results obtained showed a characteristic similar hydrolysis pattern among all the enzymes, where the DH values significantly increased with the progression of hydrolysis time up to 6 h ($p \leq 0.05$). These results are comparable with those obtained by [26], where they reported increased DH values of quinoa protein hydrolysates as a function of increased hydrolysis time. Moreover, varying DH values were demonstrated among the QPHs generated using the three enzymes of study. The DH levels in the range of 36.01 to $66.05\%$, and 45.05 to $63.3\%$, and 34.99 to $51.91\%$ were obtained for chymotrypsin, protease, and bromelain generated QPHs, respectively. Based on the enhanced DH values with progressive hydrolysis time, chymotrypsin-produced hydrolysates after 6 h (QC-6) displayed the highest value of $66.05\%$. However, it was not noted that the DH value obtained in protease-produced hydrolysates after 6 h (QP-6) was not significantly different from the DH value of QC-6h ($p \leq 0.05$). Bromelain-produced hydrolysates after 6 h (QB-6) showed an intermediate DH value of $51.91\%$, and the lowest protein hydrolysis was demonstrated in both bromelain-produced hydrolysates (QB-2) and chymotrypsin-produced (QC-2) after 2 h of bio-catalysis ($p \leq 0.05$). In general, chymotrypsin was found to be the most effective enzyme in hydrolyzing quinoa proteins as opposed to the other two proteolytic enzymes (bromelain and protease). The hydrolytic pattern of chymotrypsin indicates greater proteolytic efficiency of the enzyme, which may be attributed to the size and action of the released peptides. As a result, the abundance of small-sized peptides with enhanced effectiveness against metabolic markers could be produced from chymotrypsin at prolonged hydrolysis periods. This is consistent with previous studies that suggested that higher DH due to disintegration of more peptide bonds could be attributed to the higher activity of the enzyme [27]. The enzymatic hydrolysis of quinoa proteins using bromelain and chymotrypsin exhibited a rapid and steady increase in the DH values over a period of 6 h, an indication that the larger peptides that were released at a lower hydrolysis time were further disintegrated into smaller peptides as the hydrolysis time progressed. We hypothesize that a higher degree of hydrolysis due to the increased time of hydrolysis is often related to further enzyme activity on the reaction substrates. In contrast, the progression of hydrolysis-reaction from 2 h to 4 h ($p \leq 0.05$) of protease-generated hydrolysates did not show a very rapid increase in the displayed DH. However, after progressing the bio-catalysis reaction to 6 h, a significantly higher DH ($p \leq 0.05$) was recorded, as presented in Table 1. A similar hydrolysis reaction where increasing hydrolysis up to 4 h was found to be slower than between 4 to 6 h of hydrolysis [6]. The level of DH values obtained in this study remains comparable to those of other studies. For instance, the hydrolysis of quinoa proteins after 2 h of hydrolysis by pepsin pancreatin and papain was noticed to be in the range of 15–$35\%$ [7]. Similarly, 4 h of hydrolysis by Alcalase reported a DH value of $48\%$ [14]. The variations noticed between DH produced by different enzymes after a similar time of hydrolysis could be ascribed to the specificity and substrate affinity of each enzyme towards the quinoa protein substrate [16]. ## 3.2. Cholesterol Esterase Inhibitory Activity QPHs were analyzed for their ability to inhibit CEase. CEase is a known polymeric enzyme existing in the bile that initiates dietary cholesterol esters’ hydrolytic reaction, thereby releasing cholesterol and free fatty acids [28]. Inhibiting CEase can indirectly prevent cholesterol absorption by the human body by diminishing cholesterol’s release from dietary lipids. The CEase inhibitory potential of generated QPHs and QPI were measured in terms of IC50 (half maximal inhibitory concentrations), and the results are illustrated in Table 1. The low IC50 value of hydrolysates connotes high CEase inhibitory activity. Upon hydrolysis, the QPHs derived from bromelain showed an increased CEase inhibition, with progression of the bio-catalysis reaction from 2 to 6 h (IC50 values = 0.67 mg/mL to 0.64 mg/mL to 0.63 mg/mL, respectively). Therefore, bromelain-generated hydrolysates with high CEase inhibitory activity can be developed at longer hydrolysis times. On the other hand, the CEase inhibitory potential of chymotrypsin and protease derived hydrolysates decreased with the increased bio-catalysis reaction time from 2 to 6 h. From this study, it was evident that chymotrypsin-generated QPHs showed the highest CEase inhibitory activity with IC50 values of 0.51 mg/mL, 0.55 mg/mL, and 0.55 mg/mL after 2, 4, and 6 h of hydrolysis, respectively. However, there was no significant difference ($p \leq 0.05$) observed between hydrolysates generated at 4 h and 6 h hydrolysis time. Similar results were obtained by [6], where chymotrypsin exhibited higher activity compared to other enzymes used, and this implies that it is more effective in hydrolyzing quinoa protein. This was attributed to the broad specificity of chymotrypsin enzymes to generate small-sized peptide and free amino acids. In addition, the variation exhibited in the IC50 values of hydrolysates with respect to different time periods could be explained with the ability of specific enzymes to either degrade or generate CEase inhibitory peptides during hydrolysis [29]. The protease-generated hydrolysates showed a lower CEase inhibitory activity in comparison to bromelain and chymotrypsin generated hydrolysates. A possible explanation could be that the cleavage sites that the enzyme binds to was not fully exposed, which consequently reduced the inhibition effect. However, the protease hydrolyzed under hydrostatic pressure processing showed an increased inhibition rate of up to $49.1\%$ [30]. Unhydrolyzed quinoa protein, i.e., intact quinoa protein (QPI), showed the least CEase inhibitory activity (IC50 value of 1.01 mg/mL). A study of the CEase-inhibitory activity of amaranth protein recently reported in our lab also showed a significant low CEase-inhibitory effect of amaranth protein isolate in comparison to IC50 values recorded for hydrolysates [13]. The hydrolysate with the utmost CEase inhibitory IC50 value of 0.51 mg/mL (chymotrypsin-derived for 2 h) was approximately two times lower than the IC50 value (1.01 mg/mL) of QPI, indicating that derived hydrolysates can therefore inhibit CEase two-fold more than their intact protein counterpart. Furthermore, the results suggested an overall high CEase inhibitory activity of QPHs compared to intact quinoa protein, which is evident from the lower IC50 values observed. This is similar to the study of Jafar, et al. [ 31] that reported the higher CEase inhibitory activity of protein hydrolysates derived from camel whey compared to intact protein from Camel whey. ## 3.3. Pancreatic Lipase (PL) Inhibitory Activity Pancreatic lipase is the primary enzyme that contributes largely to the digestion of dietary lipids, and the only effective way to adjust lipid absorption is by inhibiting the enzyme [32]. The pancreatic lipase inhibitory activity IC50 values for QPHs (bromelain, chymotrypsin, protease) and un-hydrolyzed quinoa protein are depicted in Table 1. Results from this study showed that the PL-inhibitory IC50 values varied from 0.90 to 10.4 mg/mL, implying a substantial ability to inhibit PL activity. The PL-inhibitory activity of all the hydrolysates (0.90–3.32 mg/mL) exhibited significantly higher inhibition than the QPI, indicating that hydrolysis efficiently increased the inhibitory activity of quinoa protein. Hydrolysate QC-6 demonstrated the highest PL-inhibitory activity (0.78 mg/mL), followed by QB-6, recording IC50 values of 0.90 mg/mL, respectively. All chymotrypsin and bromelain generated hydrolysates showed higher PL-inhibitory activities than the protease generated hydrolysates. The different inhibitory activity observed among hydrolysates generated from different enzymes (bromelain, chymotrypsin and proteases) could be explained with the specificity and varying degree of hydrolysis among other factors of the individual enzyme [5]. On the other hand, a significant effect ($p \leq 0.05$) was observed in the pancreatic lipase inhibitory activity of QPHs upon hydrolysis with different enzymes at different hydrolysis times. A significant increase in the PL inhibitory activity of QPHs was observed with the increase in hydrolysis time progression, which is shown with lower IC50 values. For instance, chymotrypsin derived hydrolysate with the most potent PL-inhibitory activity IC50 values decreased from 2.90 to 0.78 mg/mL as the hydrolysis time increased. In the past, various studies have demonstrated an increase in the enzyme inhibitory potential of protein hydrolysates with the increase in the duration of hydrolysis. This implies that the progressive time of hydrolysis would sufficiently release peptides that might bind the active site of pancreatic lipase, thus inhibiting lipase enzymes. In addition, hydrolysates generated from chymotrypsin showed the maximum inhibitory activity in comparison to bromelain and proteases. This result upholds the potent characteristic of chymotrypsin generated hydrolysates, as reported by [6,16]. Furthermore, overall, QPHs showed the highest pancreatic lipase inhibitory activity in comparison to unhydrolyzed samples, and this accounted for an approximately $110\%$ reduction in the IC50 value. This result agrees with the findings of Mudgil, et al. [ 33], who reported an increased PL-inhibitory activity of milk proteins after a bio-catalysis reaction using bromelain, chymotrypsin, alcalase and papain. Similarly, [34] had previously shown the potential of quinoa as a coadjutant therapeutic agent of cardiovascular diseases. The authors also reported quinoa’s ability to minimize lipid profile and glucose levels that are initiated by fructose which often leads to most of the well-known detrimental effects in humans. The use of derived hydrolysates from quinoa seeds comprising grains greater than $10\%$ was reported to significantly lessen high levels of plasma and liver total cholesterol in nourished mice [35]. Consequently, QPHs can substitute synthetic drugs as anti-hyperlipidemia therapeutic agents, having shown excellent potential towards PL-inhibition. ## 3.4. Selection and Identification of Cholesterol Esterase Inhibitory Peptides from Selected QPHs Based on the overall higher CEase and PL inhibitory activities of chymotrypsin generated hydrolysates obtained at the highest hydrolysis time of 6 h (QC-6), it was further chosen for peptide identification by LC-MS Q T-O-F. Additionally, the ABTS radical scavenging and anti-hemolytic activities shown by this specific hydrolysate reported in a previously published work by [6] made it an exciting hydrolysate to characterize its existing bioactive peptides. The identified peptides were classified as bioactive peptides by their scoring of >0.80 on a web server known as Peptide Ranker and further exploration into their in silico interaction with target enzymes using the Pepsite 2 web server [19]. In total, 136 peptides were identified (Supplementary Table S1), however, only 35 peptides were shortlisted to be biologically active based on a Peptide Ranker score greater than 0.8 (Table 2), and these peptides were subjected to an in-silico mode for further structure-activity analysis. The potential active binding sites of the identified peptides on the binding sites of CEase enzyme were accessed based on a statistical significance of $5\%$ (Table 2). The significant variation ($p \leq 0.05$) implies that peptides derived from QC-6 significantly bound to the hotspot sites of CEase, resulting in non-competitive inhibition due to the modification in protein interaction networks or loss of enzyme activity that blocked its catalytic binding sites and substrate [19]. The results obtained from PepSite 2 showed that the peptides FFE, DFTF, DFLM, ML, CDCP, CYTF, QHPHGLGALCAAPPST, LR, RR, HVQGHPALPGVPAHW, AGLR, FTVM, LLPYH, ASNLDNPSPEGTVM, and HMLH had a significant ($p \leq 0.05$) binding effect on CEase (Table 2). However, QHPHGLGALCAAPPST, HVQGHPALPGVPAHW, and ASNLDNPSPEGTVM among the listed peptides were observed to be capable of binding the active sites of CEase. The most potent identified peptides HVQGHPALPGVPAHW and QHPHGLGALCAAPPST that could bind up to 13 and 14 bound residues were selected for mass spectrometry analysis. Two to sixteen amino acid residues were observed in the thirty-five peptides recorded (Table 2). This number is within the range of anti-hypercholesterolemic peptides reported by [36] for β-lactoglobulin peptides. Previous studies have suggested that peptides that can bind to more than eight residues have the ability to effectively inhibit the activity of antidiabetic and antihypertensive properties [37]. This connotes that the identified peptides (QHPHGLGALCAAPPST, HVQGHPALPGVPAHW, ASNLDNPSPEGTVM) from this study have the potential of inhibiting the activity of CEase. According to [38], the active binding sites of CEase, which is vital for its catalytic function, contain an oxyanion hole (glycine and alanine), the catalytic triad (histidine and serine), and esteratic site. Some generated bioactive peptides from QC-6 derived hydrolysates could bind to part of the active site containing the catalytic triad, specifically His435 and Ser194. For instance, QHPHGLGALCAAPPST, HVQGHPALPGVPAHW, ASNLDNPSPEGTVM, HVASGAGPW, and KPGGTAGSALPRPAHW could bind up to fourteen, thirteen, thirteen, nine, and six bound residues of CEase, respectively. This result is consistent with previous studies where peptides that have shown higher inhibition potential to His435 and Phe324 residues are categorized as CEase inhibitors because they play a major role in the binding of cholesterol [29]. Similarly, [32], stated that peptides derived from Camel milk whey hydrolysates that could bind to residues His435 and Phe325 are important CEase inhibitors. Likewise, peptide HVASGAGPW could only bind to residue Phe324, which also suggests a potential CEase inhibitor. Furthermore, [29] had earlier predicted the binding site residues of nine inhibitors of CEase using molecular docking to human and bovine CEase. The observation from this peptide sequencing indicated that the identified peptide sequence was able to form a hydrogen bonding with Gly107 in human CEase and formed a hydrophobic interaction with Ala108 in both human and bovine CEase. In addition, the Trp227 and Phe 324 of bovine CEase was observed to form cation-π interactions and π-stacking with the Phe1 residue of the peptide. Similarly, here Ala108, Phe324, and Trp227 were the major residues with active hotspots, except for Gly107 residues with no active site. This suggests that the most potent peptides QHPHGLGALCAAPPST, HVQGHPALPGVPAHW and ASNLDNPSPEGTVM could bind Ala108, Ser194, Ala195, Trp227, Phe324 and His435 in the active site of CEase, which could suggest an excellent bioactive peptide with prospects of inhibiting CEase, thus limiting dietary cholesterol absorption, which is anticipated for a CEase inhibitor. Moreover, the CEase inhibitory capacity can further be explained with existing hydrophobic amino acids, and residue near the C-terminal and N-terminal end in the sequenced peptides. A previous study reported the major role the hydrophobicity of peptides plays in the anti-hypercholesterolemic activity of peptides, which is mainly the binding of bile acids. Identified peptides with anti-hypercholesterolemic ability inhibit bile acids absorption in the ileum; as a result, they reduce the level of blood cholesterol concentration [30]. Hydrophobic amino acids, namely alanine, leucine, phenylalanine, proline, etc. have been directly associated with the hypercholesterolemic-lowering activity of peptides. Similarly, peptides with specific hydrophobic amino acids at their terminal ends have demonstrated antidiabetic inhibitory potential. In the present study, aliphatic (Leu and Ala) and aromatic (Phe, Tyr and Trp) hydrophobic amino acids were identified, which are desirable at the C-terminal of the peptide. A previous study identified peptide FDGEVK derived from β-lactoglobulin tryptic hydrolysate as a potential anti-hypercholesterolemic peptide due to the presence of two such C-terminal amino acids [36]. Therefore, bioactive peptides derived from quinoa proteins could potentially inhibit CEase enzymes. ## 3.5. Identification of PL Inhibitory Peptides Peptides with PL inhibitory potentials are shown in Table 3. From this study, results show that the peptides ML, MLLL, QHPHGLGALCAAPPST, LPLLR, MFVPVPH, HVQGHPALPGVPAHW, FTVM, LLPYH, MVLP, and HMLH derived from QC-6 showed a significant ($p \leq 0.05$) binding effect to PL. In this study, the number of peptides derived from QC-6 was 35, and the number of amino acids ranged from 2 to 16. This value is comparable to the range reported by [33] for PL-inhibiting peptides derived from Camel milk protein hydrolysates and the range reported by [39] for pinto beans. However, this was lower than the amino acid value of twenty-three derived from Cumin seeds [40]. The results indicated two peptides (QHPHGLGALCAAPPST and HVQGHPALPGVPAHW) with the most significant ($p \leq 0.05$) potent PL-inhibitory potential, binding up to 14 and 13 important amino acids, suggesting superior linkages to the target enzyme (Table 3). Furthermore, these peptides have also demonstrated CEase inhibitory potential, as explained above, implying that these peptides have valuable anti-hypercholesterolemic properties. Other peptides (FSAGGLP, MFVPVPH, and ASNLDNPSPEGTVM) have shown a similar potent inhibitory potential binding up to 11, 11, and 12 important amino acids of PL, respectively. These peptides could be considered as potential PL inhibitors due to their maximum interaction with the binding sites of PL enzyme. Similarly, some peptides showed a high number of binding sites, thus possessing promising PL-inhibitory activity. Peptide HMCH could interact with nine bound residues, four peptides (LPLLR, FTVM, LLPYH, and MVLP) with eight bound residues, and peptide DFLM interacting with six bound residues. Among all the peptides that significantly ($p \leq 0.05$) bound to the active sites, only peptide ML interacted with a lower number of hotspots (four sites), and can be regarded as a poor PL inhibitor. Furthermore, all six of the most potent peptides that interacted with maximum bound residues could bind Phe78, Ser153, Phe216, His264, and His152 from the hotspot sites of PL. The other peptides (HMCH, LPLLR, FTVM, LLPYH, and MVLP, DFLM) were capable of binding to similar active sites. Similar binding spots for PL inhibitory activity from cumin seeds have been reported by [40], and camel whey protein hydrolysates by [29]. Moreover, the peptides DFLM, KIVLDSDDPLFGGF, LPLLR, FTVM, LLPYH, MVLP, ASNLDNPSPEGTVM, and HMLH showed the ability to bind to similar hotspots (Phe78, Ser153, His152, Phe216, and His264). Furthermore, peptides showed the potential to indirectly inhibit PL by binding Try115, Leu154, Gly155, Ala179, and Pro181, which do not belong to the characteristic catalytic triad of pancreatic lipase. It was observed that peptides that have the potential to bind a higher number of active sites of lipase were the hydrophobic amino acids. A majority of the peptides that exhibited the potential of binding a high number of binding sites of lipase consisted of hydrophobic amino acid residues such as proline and leucine. This is comparable to a previous study that showed proline and leucine to be the most active residue bound of lipase inhibiting peptides derived from pinto beans [39]. In addition, hydrogen bonding can also influence hydrophilic amino acid to bind generated bioactive peptides inhibitors to PL [40]. The PL inhibitory method of derived peptides from seed proteins has been reportedly measured against the mechanism of a synthetic lipase inhibitor (Orlistat), and a similar PL inhibitory mechanism observed between them [39,40]. Thus, novel QPHs derived from chymotrypsin at 6 h have PL-inhibitory peptides with the potential to bind active sites of lipase enzyme. This study provides novel PL inhibitory peptdies derived from quinoa that could be health-promoting in combating hypercholesterolemia. ## 3.6. Molecular Docking of Shortlisted Peptides in the Active Site of Human and Bovine CEase To elucidate the binding pose and to identify the underlying intermolecular interactions of potential quinoa derived peptides in the active site of human and bovine CEase, molecular docking was performed. Table 4 details the GlideScore, MM-GBSA binding free energy, and the observed intermolecular interactions of the top ranked peptides. It was observed that the peptide HVASGAGPW docked to human CEase with a GlideScore of −9.07 kcal/mol and an MM-GBSA binding free energy of −64.32 kcal/mol, while the peptides AHCGGLPY, LYNDWDLR, MFVPVPH and FSAGGLP exhibited GlideScores of −8.95, −8.9, 8.3 and 7.4 kcal/mol, respectively. CEase is a member of the alpha/beta hydrolase fold family. In the human CEase protein, the active site region includes an oxyanion hole comprising Gly107-Ala108 and Ala195 residues, and a catalytic triad containing Ser194-His435-Asp320 residues; both regions are essential for the functional activity of the protein. The hydroxyl group of Ser194 functions as a nucleophile that facilitates the hydrolytic action [41]. Apart from this, two acidic amino acids, Asp434 and Glu437, are positioned close to the active site region which renders a negative charge to the whole catalytic domain of the protein. Among the docked peptides, the top docked peptide HVASGAGPW fits perfectly in the active site and is stabilized by hydrogen and hydrophobic bonds. Interestingly, Gly107, Ala108, and Ala195 residues, of the oxyanion hole, and His435, from the catalytic triad, were involved in the strong binding of this peptide with human CEase. The peptide AHCGGLPY showed interactions with several residues surrounding the active site including Ala108. Similarly, other top-docked quinoa peptides such as LYNDWDLR, MFVPVPH and FSAGGLP have shown interactions with the residues involved in the active site (Figure 1). These interactions can further block the catalytic activity of human CEase, supporting their potential as human CEase inhibitors. Like human CEase, bovine CEase shares similar residues (Ser194-His435-Asp320) in the catalytic triad and oxyanion hole of the molecule [42]. Sequence analysis revealed that the residue identity of human CEase and bovine CEase is $80\%$, and the overall three-dimensional structure of the active site region closely resembles human CEase [43]. The docked peptide AHCGGLPY exhibited a GlideScore of −10.05 kcal/mol and an MM-GBSA-based binding free energy of −72.14 kcal/mol. The active site residues Ala108 and Ser194 formed hydrogen and hydrophobic interactions with this peptide. Following this, the peptides MFVPVPH, FSAGGLP, HVASGAGPW and LPLLR also exhibited good GlideScores and MM-GBSA values (Table 4; Figure 1). The top peptides identified were observed to interact with the critical residues involved in the active site of the enzyme, which directly contributes to the inhibitory activity of the peptides and ligand interaction diagrams, which are represented in Figure 2. ## 3.7. Molecular Docking of Identified Peptides in the Active Site of Human and Porcine PL The binding pose and intermolecular interaction of quinoa peptides with PL was elucidated using molecular docking simulations. GlideScore and calculated binding energy, based on MM-GBSA, for the best-docked poses as well as intermolecular interactions observed in the docked protein-peptide complex are provided in Table 5. Similar to several esterases and lipases, the PL active site is positioned in the N-terminal domain, which has a catalytic triad (Ser152, Asp176, and His263), and access to this site is controlled by a surface loop. Movement of the lid induces conformational changes in the protein structure that eventually leads to oxyanion hole unmasking that is involved in the interfacial binding region [44]. The binding mode of the best-docked peptide, HVASGAGPW, within the active site of PL was analyzed. Results indicated that the peptide docked to the enzyme recorded a GlideScore of −11.74 kcal/mol and an MM-GBSA binding energy of −46.02 kcal/mol. The other top docked peptides AHCGGLPY, FSAGGLP, LLPYH and MFVPVPH also exhibited good GlideScores and binding energies (Table 5). It was observed that peptide AHCGGLPY recorded a GlideScore of −11.73 kcal/mol, and −61.96 kcal/mol for the calculated binding energy. FSAGGLP recorded −9.72 kcal/mol and −79.39 kcal/mol for the GlideScore and MM-GBSA binding energy, respectively. The GlideScores for peptides LLPYH and MFVPVPH were −9.72 kcal/mol and −8.82 kcal/mol, respectively, and the MM-GBSA binding energies were −77.18 kcal/mol and −70.12 kcal/mol, respectively. All of the reported peptides exhibited interactions with critical amino acids in the catalytic binding pockets of the enzyme by forming several hydrogen and hydrophobic interactions (Figure 3). Similar to human PL, the active site of porcine PL comprises the catalytic domain, consisting of Ser153, Asp177, and His264. Similar to human PL, the peptides HVASGAGPW, AHCGGLPY, MFVPVPH and FSAGGLP exhibited good GlideScores and binding energies. Apart from these peptides, LPLLR and CYTF also docked well in the active site of the protein. It was observed that the peptide HVASGAGPW was well-docked in the catalytic region by exhibiting −10.19 kcal/mol and −80.44 kcal/mol for the GlideScore and binding energy, respectively. Followed by this, the peptides AHCGGLPY and MFVPVPH had GlideScores of −10.95 and −8.58 kcal/mol and a binding energy of −53.03 and −67.89 kcal/mol. The peptides were stabilized primarily by electrostatic and hydrophobic interactions, with residues in the active site of the enzyme (Figure 3 and Figure 4). ## 4. Conclusions This study investigated the CEase- and PL-inhibitory potential of QPHs as a functional ingredient for producing nutraceuticals that can be used to manage hypercholesterolemia. This current study explored the generation of hydrolysates from quinoa proteins using three enzymatic approaches (chymotrypsin, proteases, and bromelain). The three enzymes explored produced hydrolysates with higher inhibitory activities towards CEase and PL compared to unhydrolyzed quinoa proteins. Nonetheless, chymotrypsin-generated hydrolysates at 6 h hydrolysis time exhibited higher CEase and PL inhibitory activity as confirmed by lower IC50 values. Findings showed that QPHs effectively inhibited the two enzymatic markers responsible for hypercholesterolemia by disrupting the enzyme-substrate interactions at the enzyme hotspot site. Novel peptide ASNLDNPSPEGTVM was found to have the best potential to be a CEase inhibitor, while FSAGGLP, KIVLDSDDPLFGGF, and MFVPVPH were found to be the potential PL inhibitors. The peptides QHPHGLGALCAAPPST and HVQGHPALPGVPAHW we observed to be potential inhibitors against both CEase and PL. In silico data showed that Gly107, Ala108, Ala195, and His435 were involved in the strong binding of these peptide with human CEase. Thus, peptides generated from quinoa seed protein may have a valuable application in ameliorating hypercholesterolemia based on predictions using software tools and molecular docking studies. 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--- title: Effect of Dietetic Obesity on Testicular Transcriptome in Cynomolgus Monkeys authors: - Yanru Zhang - Jia Qi - Juan Zhao - Miaojing Li - Yulin Zhang - Huizhong Hu - Liangliang Wei - Kai Zhou - Hongyu Qin - Pengxiang Qu - Wenbin Cao - Enqi Liu journal: Genes year: 2023 pmcid: PMC10048326 doi: 10.3390/genes14030557 license: CC BY 4.0 --- # Effect of Dietetic Obesity on Testicular Transcriptome in Cynomolgus Monkeys ## Abstract Obesity is a metabolic disorder resulting from behavioral, environmental and heritable causes, and can have a negative impact on male reproduction. There have been few experiments in mice, rats, and rabbits on the effects of obesity on reproduction, which has inhibited the development of better treatments for male subfertility caused by obesity. Nonhuman primates are most similar to human beings in anatomy, physiology, metabolism, and biochemistry and are appropriate subjects for obesity studies. In this investigation, we conducted a transcriptome analysis of the testes of cynomolgus monkeys on high-fat, high-fructose, and cholesterol-rich diets to determine the effect of obesity on gene expression in testes. The results showed that the testes of obese monkeys had abnormal morphology, and their testes transcriptome was significantly different from that of non-obese animals. We identified 507 differentially abundant genes (adjusted p value < 0.01, log2 [FC] > 2) including 163 up-regulated and 344 down-regulated genes. Among the differentially abundant genes were ten regulatory genes, including IRF1, IRF6, HERC5, HERC6, IFIH1, IFIT2, IFIT5, IFI35, RSAD2, and UBQLNL. Gene ontology (GO) and KEGG pathway analysis was conducted, and we found that processes and pathways associated with the blood testes barrier (BTB), immunity, inflammation, and DNA methylation in gametes were preferentially enriched. We also found abnormal expression of genes related to infertility (TDRD5, CLCN2, MORC1, RFX8, SOHLH1, IL2RB, MCIDAS, ZPBP, NFIA, PTPN11, TSC22D3, MAPK6, PLCB1, DCUN1D1, LPIN1, and GATM) and down-regulation of testosterone in monkeys with dietetic obesity. This work not only provides an important reference for research and treatment on male infertility caused by obesity, but also valuable insights into the effects of diet on gene expression in testes. ## 1. Introduction Obesity is marked by an excessive accumulation of body fat that presents a risk to health and has reached a pandemic level in the world [1]. According to a recent report from the World Health Organization, more than 1.9 billion adults aged 18 and above were overweight (body mass index (BMI) ≥ 25 kg/m2), of which more than 650 million were obese (BMI ≥ 30 kg/m2), and among them, $39\%$ of men are overweight and $11\%$ are obese [1,2]. Currently, the increase in the number of obese individuals coincides with the increase in male infertility, which threatens the reproduction ability of thousands of couples and the health of their offspring [3,4]. Obese men are more likely to have decreased sperm concentration, and to have sperm with abnormal morphology, damaged chromatin, and reduced motility [5,6]. BMI is closely and negatively correlated with sperm quality parameters, and obese men are three times as likely to have abnormal sperm parameters as normal men [7]. Sperm from obese men show higher levels of reactive oxygen species (ROS) and DNA damage [8]. Paternal obesity can result in embryos with abnormal cleavage, abnormal gene expression, and lower developmental competence [9]. The offspring of obese fathers also have an increased risk of metabolic disease, and a high-fat diet can change the DNA methylation, histone modification, transcriptome, proteome, etc., of gametes, and some obesity factors can be inherited through the sperm [10,11,12]. At present, a consensus has been reached on the adverse effects of obesity from a high-fat or high-sugar diet on male reproduction. Non-human primates, as the animals closest to humans, can be used as important disease models to study human metabolic diseases [13]. Up to now, there have been few reports on the testicular transcriptome in non-human primates. In this study, we conducted a transcriptome analysis of the testes of cynomolgus monkeys on a diet high in fat, fructose, and cholesterol, which is similar to the modern Western pattern diet likely to lead to obesity. Our goal was to reveal differentially abundant genes in testis related to obesity to provide a reference for the prevention and cure of male sterility. ## 2.1. Animals All experimental protocols involving nonhuman primates were approved by the Laboratory Animal Care Committee of Xi’an Jiaotong University. Cynomolgus monkeys (Macaca fascicularis) were provided by Spring Biological Technology Development Co., Ltd. (Fangchenggang, China). Ten male monkeys (age ≥ 9 years) were used in this study, five in the control group, which were fed with normal chow diet, and five in the obesity group, which were fed a diet high in fat, fructose, and cholesterol. The normal chow diet was made according to the Nutrient Requirements of Nonhuman Primates (https://www.nature.com/articles/laban1103-26, accessed on 4 June 2019). The high-fat diet was purchased from Beijing Keao Xieli Feed Co., Ltd. (www.keaoxieli.com, accessed on 4 June 2019) with $24\%$ fat, $15\%$ fructose, and $1\%$ cholesterol. Both groups of monkeys were fed for 15 months. Afterward, monkeys with BMI > 42 were assessed as overweight. The temperature of the animal room was 22–25 °C, the humidity was 48–$65\%$, and the light time was 12 h a day. All monkeys were housed in individual cages with access to water and food ad libitum. In the end, the monkeys were anesthetized and then treated with bloodletting. The testes were quickly collected, and both sides were placed in liquid nitrogen or $4\%$ paraformaldehyde. ## 2.2. Hematoxylin and Eosin (H & E) Staining of Testes Testes were fixed in $4\%$ paraformaldehyde for 24 h, and then placed in a dehydrator for dehydration, cleared, immersed in wax, and embedded in paraffin blocks. The tissues were cut into 5 µm-thick sections and dried. The sections were dewaxed in xylene, then in $100\%$, $95\%$, and $90\%$ alcohol, successively, and rinsed in Deionized (DI) water for 3 min. They were stained with H & E then immersed in $90\%$, $95\%$, and $100\%$ alcohol for dehydration, and lastly in two changes of xylene, and were mounted using neutral resin. ## 2.3. Assay for Testicular Testosterone About 200 mg of testicular tissue, the middle portion of the left testis, was weighed and rinsed with pre-chilled phosphate-buffered saline (PBS) to remove residual blood. PBS was added at a weight-to-volume ratio of 1:9; the tissue samples were treated on ice for 5 min with TissueMaster™ Handheld Homogenizer (Beyotime Biotechnology, Shanghai, China), and the homogenates were centrifuged at 5000× g for 10 min to collect the supernatant. The monkey testosterone enzyme-linked immunosorbent assay kit (Enzyme Linked Biotechnology, Shanghai, China) was used to assay testosterone. The coated plate was removed after equilibrating at room temperature for 1 h. Standard wells were added with 50 μL different concentrations of standards, and blank and sample wells were added with 50 μL sample diluents and samples, respectively. Then, 100 μL horseradish peroxidase (HRP)-labeled antibody was added to each well, and the plate was covered and incubated in the 37 °C incubator for 1 h. After that, the plate cover was removed, and the contents of the microtiter wells were thoroughly aspirated after washing each well five times with 350 μL washing buffer. Subsequently, the plate was incubated at 37 °C for 15 min (without direct exposure to intense light) after the addition of 50 μL substrates A and B to each well. Finally, the absorbance (OD) of each well was measured at 450 nm after adding 50 μL stop solution to each well. ## 2.4. RNA-Sequencing Total RNA was extracted from tissues using TRIzol (Invitrogen, Carlsbad, CA, USA) following the company’s instructions. After weighing out 60 mg of tissue, it was flash-frozen in liquid nitrogen and ground to powder. The powder was transferred to a 2 mL centrifuge tube, dissolved in 1.5 mL TRIzol, and centrifuged at 12,000× g for 5 min at 4 °C. The supernatants were transferred to another 2 mL centrifuge tube and mixed with 0.3 mL of chloroform/isoamyl alcohol. After shaking for a few seconds, the extracts were centrifuged at 12,000× g for 10 min at 4 °C, and the supernatants were transferred to clean 1.5 mL centrifuge tubes. Equal volumes of isopropanol were then added to the transferred liquid, mixed, and centrifuged at 12,000× g for 20 min at 4 °C. The supernatants were removed and discarded, and the precipitated RNA was rinsed with 1 mL of $75\%$ ethanol. Samples were placed in a biosafety cabinet for air-drying, and then dissolved in 25 µL to 100 µL of diethyl pyrocarbonate (DEPC)-treated water. RNA quality was checked by A$\frac{260}{280}$ using a NanoDrop spectrometer and concentration was determined with an Agilent 2100 bioanalyzer (Thermo Fisher Scientific Waltham, MA, USA). Ribosomal RNA (rRNA) was isolated using target-specific oligos. After solid-phase reversible immobilization (SPRI) selection, the RNA was separated into multiple small fragments under the treatment of divalent cations. The separated RNA pieces were copied into first-strand cDNA with reverse transcriptase and random primers, followed by second-strand cDNA synthesis using DNA polymerase and Ribonuclease H (RNase H). The RNA template was digested by this method; a fresh substitute strand was synthesized, and double-stranded cDNA was generated by replacing dTTP with dUTP. These cDNA pieces possess a sole ‘A’ base and afterwards, sequencing adapters were ligated to the ends. After treatment with uracil DNA glycosylase (UDG), the merger of dUTP quenches the second strand during amplification. Products were enriched by PCR to establish the final cDNA library. Fragment size distribution was measured with an Agilent 2100 Bioanalyzer, and libraries were quantified and evaluated for quality with real-time quantitative PCR (RT-qPCR) using TaqMan probes. Valid libraries were sequenced end-to-end on the BGISEQ-500/MGISEQ-2000 system (BGI-Shenzhen, Shenzhen, China). ## 2.5. Statistical Analysis Analysis of RNA-sequencing data was conducted using the ‘Dr. Tom’ system (https://biosys.bgi.com, accessed on day 1 January 2022). The expression level of the gene was calculated by RSEM (v1.3.1) [14]. The heatmap was drawn by pheatmap (v1.0.8) [Raivo Kolde. Package ‘pheatmap’. 4 January 2019 13:50:12 UTC.] according to the gene expression difference [15]. GO (http://www.geneontology.org/, accessed on 1 May 2022) and KEGG (https://www.kegg.jp/, accessed on 1 May 2022) enrichment analysis was performed by Phyper (https://en.wikipedia.org/wiki/Hypergeometric_distribution, accessed on 1 May 2022) based on a hypergeometric test. The significant levels of terms and pathways were corrected by Q value with a rigorous threshold (Q value ≤ 0.05). Results of the testosterone assay were compared with Student’s t test using SPSS ver20 statistics software (IBM, Armonk, NY, USA). A $p \leq 0.05$ was considered statistically significant. Moreover, the inter-assay is less than $15\%$; the intra-assay is less than $10\%$; and the minimum detection concentration is less than 0.1 ng/mL. ## 3.1. Dietetic Obese Monkeys Showed Abnormal Testes Morphology The results of morphological analysis showed that the seminiferous tubules in the control group were normal. The seminiferous cells were closely arranged, and the structure was clear. The spermatogonia, primary spermatocytes, spermatids, and other cells at various developmental stages could be observed in the seminiferous epithelium. In the obesity group, however, the structure of the spermatogenic epithelium was severely disordered and atrophic. The number of spermatogenic cells was reduced compared to the control, and the thickness of the spermatogenic epithelium was significantly thinner. The connection between Sertoli cells and spermatogenic cells was disrupted and loosely arranged. Only a small number of spermatozoa were present in the lumen of the seminiferous tubules (Figure 1). ## 3.2. The Testes Transcriptome of Obese Monkeys Differed Significantly from That of the Control Group RNAs from the two groups of monkey testes were sequenced by RNA seq. With adjusted p values set to <0.05 and log2 [FC] > 1, we identified 2527 differentially abundant genes, including 1034 up-regulated genes and 1493 down-regulated genes (Figure 2A). When adjusted p values were set to <0.01 and log2 [FC] > 2, we extracted 507 differentially abundant genes, including 163 up-regulated genes and 344 down-regulated genes (Figure 2B). Using the transcripts per million (TPM) of 507 differentially abundant genes to draw a volcano map (Figure 2C) and heat map (Figure 2D), the results showed clear differences between obese and control animals. Key driver analysis (KDA) was utilized to identify the most important genes between the two groups, and ten driving genes, including interferon regulatory factor 1 (IRF1), interferon regulatory factor 6 (IRF6), homologous to the E6-AP carboxyl terminus (HECT) and regulator of chromosome condensation 1 (RCC1)-like domain (RLD) containing E3 ubiquitin protein ligase family member 5 (HERC5), HECT and RLD containing E3 ubiquitin protein ligase family member 6 (HERC6), interferon induced with helicase C domain 1 (IFIH1), interferon induced protein with tetratricopeptide repeats 2 (IFIT2), interferon induced protein with tetratricopeptide repeats 5 (IFIT5), interferon induced protein 35 (IFI35), radical S-adenosyl methionine domain containing 2 (RSAD2), Ubiquilin like (UBQLNL), six initial genes and fifty-two associated genes were found (Figure 2E). ## 3.3. Gene Ontology and KEGG Pathway Analysis of the Differentially Abundant Genes between the Two Groups GO and KEGG pathway analysis of the 163 up-regulated genes and 344 down-regulated genes was conducted. Among the 163 up-regulated genes, the top ten cellular components (CCs) included anchored components of membranes, extracellular regions, extracellular spaces, anchored components of the plasma membrane, eukaryotic 80 S initiation complex, integral component of the membrane, membranes, Coding Region Determinant (CRD)-mediated mRNA stability complex, cell surface, and pi-body (Figure 3A). Among the 344 down-regulated genes, the top ten CCs included stress fibers, contractile fibers, perinuclear region of cytoplasm, myosin complex, cell–cell contact zone, actin cytoskeleton, cytoplasm, heterotrimeric G-protein complex, Actin-related proteins-$\frac{2}{3}$ (Arp$\frac{2}{3}$) protein complex, and septin cytoskeleton (Figure 3B). Among the 163 up-regulated genes, the top ten biological processes (BPs) were enriched in basement membrane organization, DNA methylation involved in gamete generation, cation transport, inhibition of neuroepithelial cell differentiation, methionine catabolic process, deoxyribonucleoside monophosphate catabolic process, polarity specification of proximal/distal axes, regulation of mRNA stability involved in response to stress, 4-nitrophenol metabolic process, pallium cell proliferation in forebrain, and P granule organization (Figure 4A). Among the 344 down-regulated genes, the top ten BPs were enriched in the defense response to the virus, interleukin-27-mediated signaling pathway, neutrophil aggregation, positive regulation of protein autophosphorylation, muscle filament sliding, positive regulation of smooth muscle cell differentiation, regulation of protein tyrosine kinase activity, regulation of hematopoietic stem cell proliferation, neutrophil chemotaxis, and leukocyte migration involved in the inflammatory response (Figure 4B). Among the 163 up-regulated genes, the top ten molecular functions (MFs) were enriched in small molecule binding, copper ion binding, retinal isomerase activity, endochitinase activity, 4-nitrophenol 2-monooxygenase activity, voltage-gated proton channel activity, all-trans-retinyl-palmitate hydrolase, 11-cis retinol forming activity, all-trans-retinyl-ester hydrolase, 11-cis retinol forming activity, deoxyribonucleoside 5′-monophosphate N-glycosidase activity, and type I activin receptor binding (Figure 5A). Among the 344 down-regulated genes, the top ten MFs were α-1,3-mannosylglycoprotein 4-β-N-acetylglucosaminyltransferase activity, GTP binding, microfilament motor activity, GTPase activity, GDP binding, 2′-5′-oligoadenylate synthetase activity, C-X-C motif chemokine receptor 3 (CXCR3) chemokine receptor binding, actin binding, phospholipid scramblase activity, and actin filament binding (Figure 5B). Among the 163 up-regulated genes, the top 20 KEGG pathways were enriched in glycosylphosphatidylinositol (GPI)-anchor biosynthesis, various types of N-glycan biosynthesis, Th1 and Th2 cell differentiation, viral protein interaction with cytokine and cytokine receptor, cysteine and methionine metabolism, graft-versus-host disease, steroid hormone biosynthesis, type I diabetes mellitus, chemical carcinogenesis-DNA adducts, signaling pathways regulating pluripotency of stem cells, arachidonic acid metabolism, and others (Figure 6A). Among the 344 down-regulated genes, the top 20 KEGG pathways included coronavirus disease-COVID-19, chemokine signaling pathway, thyroid hormone signaling pathway, long-term depression, ribosome, Interleukin (IL)-17 signaling pathway, circadian entrainment, advanced glycation end products- receptor for advanced glycation end products (AGE-RAGE) signaling pathway in diabetic complications, tyrosine metabolism, RIG-I-like receptor signaling pathway, estrogen signaling pathway, and others (Figure 6B). ## 3.4. Abnormal Expression of Infertility-Related Genes and Down-Regulation of Testosterone in Obese Monkeys The 507 differentially abundant genes showed an intersection with genes related to the male sterility phenotype as deduced from the mouse genome informatics (MGI) database (Figure 7A). In the MGI database, 1014 genes were related to the male sterility phenotype, and among those genes, tudor domain-containing 5 (TDRD5), chloride voltage-gated channel 2 (CLCN2), mcrorchidia1 (MORC1), regulatory factor X8 (RFX8), spermatogenesis and oogenesis-specific basic helix-loop-helix 1 (SOHLH1), interleukin 2 receptor subunit beta (IL2RB), multi-ciliate differentiation and DNA synthesis-associated cell cycle protein (MCIDAS), and zona pellucida binding protein (ZPBP), were abnormally highly expressed in obese monkeys (Figure 7B), while eight genes, nuclear factor I A (NFIA), protein-tyrosine phosphatase non-receptor type 11 (PTPN11), TSC22 domain family member 3 (TSC22D3), mitogen-activated protein kinase 6 (MAPK6), phospholipase C beta 1 (PLCB1), defective in cullin neddylation 1 domain containing 1 (DCUN1D1), lipin 1 (LPIN1), and glycine amidinotransferase (GATM), were abnormally under-expressed (Figure 7C). Testosterone in the testes was measured in the two groups and was significantly lower in obese monkeys than controls (Figure 7D). ## 4. Discussion Many important findings about reproduction, fertility, and development were based on experiments with mice, rats, and rabbits. However, human physiology and metabolism differ in many ways from that in these animals, which has limited the development of better treatments for sterility, infertility, and other problems caused by obesity [16]. Nonhuman primates are more similar to human beings in anatomy, physiology, and biochemistry than rodents are, and they have been used to study human reproductive physiology, especially in the composition, proliferation, and differentiation of gamete cells, and are considered as an ideal model for studying human reproductive physiology [17,18,19,20]. Important achievements have been made in male contraception based on monkey experiments [21,22]. Azoospermia, which has been described in nonhuman primates, is also more similar to the human condition [23,24]. Monkeys have an important value and incomparable advantages over other species, and there have not been enough studies on obesity-related infertility or reproductive decline in monkeys, which has limited the development of drugs for controlling those diseases. In this study, we found that the pathological morphology of the testes of cynomolgus monkeys was very similar to the testicular pathology of obese and infertile humans. The structure of the seminiferous epithelium in testes from obese individuals showed significant disorder, and the walls of the seminiferous tubules were noticeably thinner. The number of spermatogenic cells was reduced; the protective connection between the supporting Sertoli cells and the spermatogenic cells was broken; the arrangement was loose; and the integrity of the BTB was also damaged. Spermatogenesis and the BTB integrity depend on a high level of testosterone in the testes [25,26]. Previous studies reported that obesity led to hormone dysfunction. The hypothalamic pituitary gonadal axis (HPG) negative feedback pathway was disrupted, which resulted in decreased testosterone levels [27]. Our results showed that the testosterone in the testes of obese monkeys was significantly lower than that of the normal weight control group. This is similar to the disorders of the testosterone regulatory axis seen in obese humans. The results of transcriptome analysis showed a significant change in gene expression in the testes of obese monkeys. Key driver analysis showed that ten genes were closely related to immunity or inflammatory processes, which suggested that dietetic obesity could lead to chronic inflammation of the testes. This condition could be one pathogenic mechanism leading to infertility in obese people. Using relatively strict threshold screening, 163 up-regulated genes and 344 down-regulated genes were identified in testes from obese monkeys. The biological process with the highest score among the 163 up-regulated genes is basement membrane organization, which is closely related to the BTB, suggesting that obesity may cause damage to the BTB and the dysfunction of spermatogenesis by negatively affecting the expression of regulatory genes related to basement membrane organization. Enterogenic endotoxin production by intestinal microbiota, which can be induced by a high-fat or high-sugar diet, can activate toll-like receptors (TLRs) related to inflammation on testicular microvascular endothelial cells and release inflammatory factors [28]. Inflammatory cytokines infiltrate the testicular parenchyma through the damaged microvascular endothelial cells, and inflammatory cells gather in the testis stroma, causing inflammation, damaging the spermatogenic cells, and resulting in male reproductive dysfunction [29]. In this study, the biological processes of differentially abundant genes were mostly enriched in immune and inflammatory responses, and also in related KEGG pathways and key driver genes, suggesting that the infertility associated with male dietetic obesity may have close links to the inflammatory process. The DNA methylation level of spermatozoa showed a dynamic change different from that of somatic cells during spermatogenesis [30]. The methylation of male DNA starts before maturation of the fetal gonads and ends in the process of sperm maturation [31]. Demethylation takes place twice in one’s life. The first time is when re-establishing the imprint in the period of forming mature gametes, and the second wave is when the imprint is replaced by a new imprint pattern, which occurs during embryonic development [31]. Abnormal DNA methylation is closely related to male infertility [32]. Abnormal methylation of sperm DNA may be an important reason for early spontaneous abortion or fetal termination [33]. Metabolic abnormalities and decreased fertility in offspring of dietetic obese male mice are considered to be related to changes in sperm DNA methylation [34,35]. Paternal obesity could change DNA methylation patterns in sperm, and the offspring of an obese father had an abnormal methylation pattern in the differentially methylated region (DMR) of imprinted genes [11,35,36]. This study revealed that the DNA methylation process involved in gamete generation was significantly enriched, suggesting that dietetic obesity might be related to abnormal spermatogenesis through alterations in DNA methylation. TDRD5 protein is essential for pachytene piRNA biogenesis and may play an important role in male infertility. Previous studies found that TDRD5 was abnormally expressed in the testes of individuals with azoospermia [37,38]. SOHLH1 is a testis-specific basic helix-loop-helix transcription factor which is essential for spermatogonial differentiation. Mutations in the SOHLH1 protein resulted in non-obstructive azoospermia [39,40]. MCIDAS is necessary for the generation of functional multi-ciliated cells in the efferent ducts that are required for spermatozoa to enter the epididymis [41]. Mutations in MCIDAS reduced the generation of multiple motile cilia and caused male infertility [41]. ZPBP is localized to the acrosome in human spermatozoa, and males lacking ZPBP1 were sterile, with abnormal round-headed sperm lacking forward motility [42]. TSC22D3 is a widely expressed dexamethasone-induced transcript that has been proposed to be important in immunity and adipogenesis. TSC22D3-deficient males were infertile and exhibited severe testicular dysplasia and a high number of apoptotic cells within the seminiferous tubules [43]. MORC1 is expressed in germ cells (spermatogonia and spermatocytes) of the testis [44]. Besides spermatogenesis, MORC1 is also essential for the facilitation of DNA methylation and transposon repression in the male embryonic germ cells. The finding showed that mutations in MORC1 caused the loss of male-specific germ cells and infertility in mice [45]. The NFI (nuclear factor I) family is mainly involved in regulating the development of stem cells. NFIX, one of the members, is expressed in spermatocytes and plays an important role in spermatogenesis. The deficiency of NFIX resulted in multinucleation in spermatocytes, structural defects in the synaptonemal complex, etc., which ultimately led to the inhibition of spermatogenesis [46]. PTPN11, also known as SHP2 (SH2-containing tyrosine phosphatase 2), is essential for the self-renewal of spermatogonial stem cells and the production of other male germ cells [47]. It is also associated with the integrity of the BTB [48]. Mutations in PTPN11 resulted in Noonan and LEOPARD syndromes, both of which manifested reproductive defects in the reproductive system such as male infertility [49,50]. PLCB1 is a vital regulator of the acrosome reaction in spermatozoa. Mutations in PLCB1 reduced the rate of acrosome reaction, fertilization rate, and the probability of embryo development to mulberry or blastocyst [51], resulting in a reduction in male fertility. DCUN1D1, also known as SCCRO (squamous cell carcinoma-related oncogene), is an important regulator of neddylation in mammals. An animal study found various abnormal morphologies that appeared in the spermatozoa of DCUN1D1-/- mice, such as macrocephaly and multiple flagella. In addition, these aberrant spermatozoa indicated the presence of abnormal intercellular bridges, which were probably responsible for specific infertility and inability to release mature spermatozoa of DCUN1D1-/- mice [52]. Here, we found that the genes TDRD5, SOHLH1, MCIDAS, and ZPBP were abnormally expressed in the testes of obese monkeys compared with normal-weight controls, suggesting that the dysfunction of those genes and their related biological functions should be given prominence in studies of human obesity-induced male infertility. Despite the rapid development of multiomics, research on obesity or the effects of a high-calorie diet on the testicular transcriptome have mainly been confined to rats and mice with few studies focused on humans or non-human primates. In our research, we investigated the influence of a typical modern diet, high in fat and fructose, and cholesterol-rich, on gene expression in the testes of cynomolgus monkeys, which afforded valuable insights into the effects of diet and obesity on male infertility. ## 5. Conclusions Dietetic obese monkeys showed abnormal testicular morphology, and their testes transcriptome was significantly altered. The differentially abundant genes were predominantly associated with blood–testes barrier function, immunity, inflammation, DNA methylation involved in gametogenesis, decreased testosterone level, and abnormal expression of infertility-related genes. 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--- title: 'Koala Genome Survey: An Open Data Resource to Improve Conservation Planning' authors: - Carolyn J. Hogg - Luke Silver - Elspeth A. McLennan - Katherine Belov journal: Genes year: 2023 pmcid: PMC10048327 doi: 10.3390/genes14030546 license: CC BY 4.0 --- # Koala Genome Survey: An Open Data Resource to Improve Conservation Planning ## Abstract Genome sequencing is a powerful tool that can inform the management of threatened species. Koalas (Phascolarctos cinereus) are a globally recognized species that captured the hearts and minds of the world during the $\frac{2019}{2020}$ Australian megafires. In 2022, koalas were listed as ‘Endangered’ in Queensland, New South Wales, and the Australian Capital Territory. Populations have declined because of various threats such as land clearing, habitat fragmentation, and disease, all of which are exacerbated by climate change. Here, we present the Koala Genome Survey, an open data resource that was developed after the Australian megafires. A systematic review conducted in 2020 demonstrated that our understanding of genomic diversity within koala populations was scant, with only a handful of SNP studies conducted. Interrogating data showed that only 6 of 49 New South Wales areas of regional koala significance had meaningful genome-wide data, with only 7 locations in Queensland with SNP data and 4 locations in Victoria. In 2021, we launched the Koala Genome Survey to generate resequenced genomes across the Australian east coast. We have publicly released 430 koala genomes (average coverage: 32.25X, range: 11.3–66.8X) on the Amazon Web Services Open Data platform to accelerate research that can inform current and future conservation planning. ## 1. Introduction The field of conservation genomics is rapidly developing (Box 1), with genomics touted as a technological tool that can assist in the conservation of many species. In response to the ever-growing biodiversity crisis, a range of genome initiatives have been established to sequence all life on earth [1,2]. The global objective is to create reference genomes for either a group of taxa or all taxa within a country/landscape/ecosystem. On their own, these reference genomes may appear to have little use for conservation, yet they are a foundational tool needed to interpret variation within genes and improve our understanding of species’ adaptive potential. In a more practical sense, reference genomes and population genomics can be used to inform both policy and conservation actions [3,4]. While still in its infancy, conservation genomics has been applied to infer historical and contemporary inbreeding [5], assess mutational loads [6], and understand disease associations [7], with the aim of integrating such genetic knowledge to better protect species. The year 2020 saw a massive change in the way many view the natural world and how science can assist in our understanding of it. From catastrophic megafires in Australia that saw 12.6 million acres burned [8] and an estimated 3 billion individual specimens lost [9] to a global pandemic that has seen over 6.5 million people die and more than 630 million people infected across 195 countries [10]. Using genome sequencing, scientists showed how COVID-19 originated in wildlife species [11] and was then transported around the globe [10]. Genome sequencing can also assist with threatened species recovery efforts, such as identifying individual relatedness to inform captive breeding programs, e.g., [12,13], population differentiation that is valuable for translocation decision making, e.g., [14,15,16,17], and maintenance of genetic diversity in threatened remnant populations, e.g., [18,19]. Genomics provides the capacity to identify genetically distinct populations where the exchange of migrants occurs so infrequently that these populations become ecologically isolated [20]. Broad-scale high-throughput sequencing also permits the assessment of functional diversity and differentiation within and between populations [21]. However, genomic data can only be a valuable resource for species conservation if samples for sequencing exist across populations of interest and the resource is made publicly available for the research and management communities. The koala (Phascolarctos cinereus) is an iconic Australian species. This specialist folivore only eats certain types of feed trees, making their ongoing management problematic in a changing world [32], with increasing CO2 levels directly impacting the nutritional quality of leaves [33] and warmer climates affecting the moisture content of trees [34]. As koalas range from northern Queensland down the Australian east coast through to South Australia (Figure 1), they reside in a diverse range of habitats and feed on an array of eucalypt species, e.g., [35,36,37]. The cultural and economic value of koalas is well known, and the species has been subject to conservation planning since 1998 with the release of the first National Koala Conservation Strategy [38]. Many areas of koala habitat are in high demand for human land use through urban development and agriculture [39,40]. As a result of a changing landscape and a range of threats, including habitat loss, vehicle collisions, dog attacks, and disease, koalas were listed as ‘Endangered’ in Queensland, New South Wales, and the Australian Capital Territory in February 2022 [41]. Populations in Victoria and South Australia also suffer from these threats, and although they occur in their thousands in these states, these populations are suffering from significant historic genetic bottlenecks resulting in reduced genetic diversity [42]. The number of koalas in Australia is currently unknown. Although an expert elicitation on koala numbers, published in 2016, estimated there to be 329,000 (range: 144,000–605,000) koalas in Australia, with numbers declining by $53\%$, $26\%$, $14\%$, and $3\%$, in Queensland, New South Wales, Victoria, and South Australia, respectively, over the past three generations (i.e., 18 years; generation time ≈ 6 years) [43]. One of the primary causes for the ongoing decline in Queensland and NSW is wholesale land clearing and habitat loss [44]. A 2017 Australian government report found that more than half of NSW (400,000 km2) had experienced significant losses in ecological communities, with losses between $26\%$ and $50\%$ of their original extent [45]. In addition to direct impacts on koala numbers, habitat clearing increases the fragmentation of koala populations, impeding historical gene flow. This was further compounded by the significant areas of habitat that were burned during the $\frac{2019}{2020}$ megafires [8], many of which overlay with known koala habitats (Figure 1B). Combined with other threats, it is now estimated that koalas in eastern Australia could face extinction by 2050 [46]. Long-term management of koalas requires an understanding of the demographic and genetic status of different populations, gene flow between populations, and current threats. Although there are many factors contributing to the management of these fragmented populations, an understanding of genetic diversity is needed to determine the capacity of a population to be able to survive long-term in this fragmented landscape or if active management interventions, such as translocation and/or captive breeding, are required. During previous periods of climate instability, koalas retracted into refugia [42,47], but it remains unclear the extent to which genetic variation is lost during short-term catastrophic events such as bushfires. After the $\frac{2019}{20}$ megafires, we sought to understand the current genetic information that was available for the species and identify gaps in our knowledge. Although an iconic species that has been studied since the 1800s [48,49], genetic studies on koalas have only been undertaken since the 1990s, primarily using microsatellites (Table 1), with limited literature using next-generation sequencing (Box 1). Despite reduced representation sequencing being commonly used in wildlife species since 2012 [26], and the koala reference genome published in 2018 [42], there are limited examples of reduced representation sequencing in the koala literature. To better understand the potential knowledge gaps in koala genomics, we undertook a systematic review of the current literature. The aim was to understand [1] how much genetic data existed for koala populations across eastern Australia, [2] what timescale these data covered, and [3] how this may contribute to the genetic management of koalas post $\frac{2019}{20}$ megafires. We found there to be limited population genetic studies published between 1996 and 2020 that used SNP data (3 of 24 studies; SNP range: 3060–4606 SNPs), and many studies were undertaken on the same limited number of populations. Both the NSW and Australian governments were seeking guidance in late 2020/early 2021 on how to best protect the remaining genetic diversity. To accelerate research in population genomics, functional diversity, and disease, as well as inform conservation planning for this iconic species, we established an open data resource, the Koala Genome Survey. ## 2. Systematic Review A systemic review was undertaken in August 2020 by querying Web of Science (WoS) and Scopus using the “Topic” (WoS) and “Title” (Scopus) functions. The keywords used were ‘koala’ AND ‘genet’ and ‘koala’ AND ‘genom’ to obtain all genetic and genomic papers for koalas. This resulted in 551 papers that included 191 duplicates between the two search engines. Removing the duplicates left 360 individual papers. Each paper was attributed to 15 groupings (Table S1), of which 41 papers were some form of population genetic/genomic study. A full assessment of the 41 population genetic papers resulted in a further five groupings: population genetics ($$n = 24$$; Table 1); development of methods for using scat DNA ($$n = 4$$; [73,74,75,76]); DNA profiling ($$n = 3$$; [77,78,79]); differentiation of populations using mitochondrial DNA ($$n = 8$$; [52,80,81,82,83,84,85,86], and phylogenetics ($$n = 2$$; [87,88]. Of the 24 population genetics studies, two were reviews (Table 1). Several studies encompassed the whole of the koala range from Queensland to South Australia ($$n = 5$$), while others were restricted to only two states ($$n = 5$$), and 12 studies were of koalas in one specific state (Table 1). Population genetics papers (1996–2020) predominantly used microsatellites ($$n = 17$$; Table 1). Unfortunately, there was minimal consistency with the microsatellite markers used, with 14 studies using those developed by Houlden et al. [ 50] while others developed their own ($$n = 4$$), making a comparison between studies difficult [72]. Only three studies used single nucleotide polymorphisms (SNPs) from two different reduced representation sequencing methods, Diversity Arrays Technology (DArTseq; $$n = 2$$) and double digest Restriction-site Associated DNA (ddRAD; $$n = 1$$), and the reference genome paper used SNPs generated through exon capture [42]. ## 3. Genomics and Conservation Planning Future koala conservation management needs to address issues surrounding habitat loss and fragmentation, disease, climate change, dog attacks, and vehicle collisions via expanding and restoring habitat, vaccine development, and protecting climate refugia. If koala populations continue to decline, they will become subject to small population pressures of genetic drift, inbreeding, and loss of adaptive potential to respond to emerging threats to survival, which can ultimately lead to local extinction. Unfortunately, there is limited genetic knowledge for many koala populations across the species’ range. To make conservation planning decisions, we need to understand if populations have unique genetic variants, their level of inbreeding and relatedness, their disease status, their adaptive potential, and their effective population size. Genomics is a useful tool to address these questions and for informing conservation planning [4,89]. Looking at the number of population genetic studies in NSW, as the epicenter of the most significant megafires (Figure 1B) [8], 5 of the 10 studies published in the past six years (2014–2020; 1 koala generation) were on populations in NSW. Of these, only one study included the dates and locations of sample collection and used microsatellites to investigate koala populations in northeastern NSW [70]. Nine of 10 NSW studies have occurred within the northeastern region and within similar habitats. Expert elicitation estimated that NSW koala populations have declined over the past three generations (18 years; [43]), but we are unable to link this to changes in genetic diversity with the current data. It is similar in Queensland, where 10 of 12 studies have occurred in southeastern Queensland (Table 1). There is a wholesale lack of contemporary (i.e., within 1–2 koala generations) genetic data across the species’ range. Unfortunately, data that do exist do not have the power to provide information on genome-wide diversity (when using microsatellites) nor adaptive potential (when using ddRAD or dArTseq, see Box 1). Recently (published in 2022), two publications used target capture methods (immune genes [90] and exon capture [47]) to begin to characterize functional diversity for this species. A suitable conservation policy can only be implemented in the presence of sound science [91]. As is the case with many species, the lack of published and, therefore, accessible genetic data for conservation planning are limited for koalas. Less than half of published population genetic studies include the year when the samples were collected, and $45\%$ of published studies include independent locations. Most studies include one or more sites that have previously been subject to study. For example, in NSW, this means that only six ($12\%$) of the 49 areas of regional koala significance have contemporary (1–2 generations) genetic data that can be used to inform conservation management. Management of genetic diversity within and across populations has been linked to a species’ adaptive potential [92]. As a high-quality koala reference genome exists [42], we have the capacity to investigate genetic differences between populations, as well as functional adaptations to certain habitats and environments. Many biological functions associated with survival are the result of an interplay between a variety of genes. Understanding potential drivers associated with koala survival and gene flow can inform conservation planning in relation to habitat restoration, translocations, habitat connectivity, etc. For instance, western koala populations are experiencing more drying and significant droughts [93]. Comparative analysis of these western populations with coastal eastern populations can inform our understanding of potential genetic variants associated with ‘heat tolerance’, such as those that have been noted in Arctic charr (Salvelinus alpinus; [94]) and loggerhead sea turtles (Caretta caretta; [95]). In a similar vein, preliminary work suggests that the strength of an immune response to a Chlamydia infection is influenced by genetic variation within MHC Class II DMA and DMB genes and CD8-a genes [42], while MHC Class II variants are associated with infection status, serologic response, and age of presentation of Chlamydia disease in koala populations [96]. A recent study investigating 1209 immune genes showed that 25 SNPs across 17 genes are associated with the resolution of Chlamydia infection [90]. This level of information has the power to transform the way that we manage koalas in the future. For instance, as the climate changes, understanding variants involved in ‘heat tolerance’ will inform the assessment of extinction risk for populations that lack these variants. Similarly, we could reduce disease prevalence in populations by boosting immunogenetic diversity in vulnerable populations. We do not advocate management actions to promote specific variants within a population but rather promote increased genetic variation at genomic regions with low diversity, particularly regions associated with known threatening processes, such as disease [21]. ## 4. Koala Genome Survey Our solution to the current conundrum of genetic data deficiencies for koala conservation planning was to generate a publicly available resource of whole-genome resequencing data (to at least 30X coverage; [24]). We aimed to sequence up to 20 individuals per population/area to accelerate research into population differences and adaptive potential across the species’ range. Commencing in March 2021, we contacted all known koala researchers and museum collections to obtain as many samples as possible from northern Queensland to Victoria (Figure 1C). A total of 802 samples collected between 2004 and 2022 (representing the past 1–3 koala generations) were submitted for the survey. There was one sample from 1997 from an NSW location (Pilliga) that was also used due to the small sample sizes from that location. A total of 672 ear biopsies and 128 whole blood samples in EDTA were extracted using either a MagAttract HMW DNA kit (Qiagen, Hilden, Germany; cat: 67563) or a high salt method (following a modified protocol from Aljanabi and Martinez [97]). DNA concentration and quality were assessed using a Nanodrop 2000 Spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA), $0.8\%$ agarose gel electrophoresis for 30 min at 90 V, and quantified using a Qubit 2.0 Fluorometer (ThermoFisher Scientific, Waltham, MA, USA). Initial sequencing yielded poor results, likely due to DNA quality and library pooling. As a result, DNA repair using an FFPE DNA repair protocol (New England Biosciences, Ipswich, MA, USA) improved DNA quality. Sequencing was undertaken at the Ramaciotti Centre for Genomics (University of New South Wales, Sydney, Australia) on an Illumina NovaSeq 6000, using a TruSeq DNA PCR free library prep kit (Illumina, San Diego, CA, USA). Forty-eight samples were pooled across one lane of an S4 200 cycle flowcell. Coverage was assessed after the first sequencing run, and pooling was adjusted accordingly to meet the 30X coverage goal. A total of 430 (413 wild and 17 individuals from captive trios) samples were sequenced across 48 wild locations (Figure 1C) and two zoological institutions with an average sequencing depth of coverage 32.25X (range: 11.3–66.8X). Only $3\%$ of the samples sequenced were collected prior to 2011. Fastq files were aligned to the koala reference genome (GCA_002099425.1_phaCin_unsw_v4.1 [42] using the Dragen Platform (v 3.8.4, Illumina San Diego, USA). After each sequencing run of 48–96 samples, data files (fastq and BAM; 58.9 TB for 430 genomes) were publicly released on Amazon Web Services Open Data program (https://awgg-lab.github.io/australasiangenomes/species/Phascolarctos_cinereus.html). A total of 430 genomes were released in September 2021 ($$n = 116$$), March 2022 ($$n = 144$$), and October 2022 ($$n = 170$$), under an open-access licensing agreement (see webpage). Metadata for each sample sequenced and released, includes sampling location, date, sex, estimated age (if known), name and contact details of sample provider, and permits that samples were collected. Other researchers who are interested in using the data and require more metadata are encouraged to contact the sample providers to facilitate research engagement and potential collaboration. This open data resource will now be used by teams of researchers across the globe to investigate key genetic questions pertaining to koala management, including population differentiation, signatures of selection, populations at extinction risk, genetic basis of diseases such as koala retrovirus and Chlamydia, genetic variants associated with climate conditions and habitat types, taste receptor variation and feed tree preferences, and more. ## 5. Conclusions Koalas are one of the most iconic globally recognized species. Even before the catastrophic megafires, many koala populations across the northern part of their range were declining due to a range of threats that will continue to be exacerbated by a warming climate. It has been predicted that Australia will continue to experience more drying and significant droughts. By creating an open data genomic resource across eastern Australian koala populations, we have generated an asset that will inform current conservation planning and be a future resource to assess whether conservation actions improve/maintain/lose genetic variation across the species’ range over time. The power of genomic data is fully realized, and with ever-declining sequencing costs, the opportunity to apply this technology to threatened species is increasing, as seen by whole-genome resources for kākāpō (Strigops habroptilus; [6]), hihi (Notiomystis cincta; [5]), killer whale (Orcinus orca; [98]), and Pyrenean desman (Galemys pyrenaicus; [99]). By generating the Koala Genome Survey, we have provided a foundational tool to protect this iconic species for future generations and provide a pathway for others to follow in generating open genomic data solutions for biodiversity conservation. ## References 1. 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--- title: The Development of a Polysaccharide-Based Hydrogel Encapsulating Tobramycin-Loaded Gelatine Microspheres as an Antibacterial System authors: - Mingsheng Shi - Yongmeng Xu - Shuai Li - Lifeng Wang - Junyao Gu - Yi-Xuan Zhang journal: Gels year: 2023 pmcid: PMC10048335 doi: 10.3390/gels9030219 license: CC BY 4.0 --- # The Development of a Polysaccharide-Based Hydrogel Encapsulating Tobramycin-Loaded Gelatine Microspheres as an Antibacterial System ## Abstract Bacterial infection contributes to the bioburden of wounds, which is an essential factor in determining whether a wound can heal. Wound dressings with antibacterial properties that can promote wound-healing are highly desired for the treatment of chronic wound infections. Herein, we fabricated a simple polysaccharide-based hydrogel dressing encapsulating tobramycin-loaded gelatine microspheres with good antibacterial activity and biocompatibility. We first synthesised long-chain quaternary ammonium salts (QAS) by the reaction of tertiary amines with epichlorohydrin. The amino groups of carboxymethyl chitosan were then conjugated with QAS through the ring-opening reaction and QAS-modified chitosan (CMCS) was obtained. The antibacterial analysis showed that both QAS and CMCS could kill E. coli and S. aureus at relatively low concentrations. QAS with 16 carbon atoms has a MIC of 16 μg/mL for E. coli and 2 μg/mL for S. aureus. A series of formulations of tobramycin-loaded gelatine microspheres (TOB-G) were generated and the best formulation was selected by comparing the characters of the microspheres. The microsphere fabricated by 0.1 mL GTA was selected as the optimal candidate. We then used CMCS, TOB-G, and sodium alginate (SA) to prepare physically crosslinking hydrogels using CaCl2 and investigated the mechanical properties, antibacterial activity, and biocompatibility of the hydrogels. In summary, the hydrogel dressing we produced can be used as an ideal alternative for the management of bacteria-infected wounds. ## 1. Introduction Microbial infection has become increasingly recognized as one of the major causes of chronic wounds. It has been reported that the medical costs of wound management are approaching 96.8 billion each year, and recent consensus reports call for the early and aggressive treatment of microbes in chronic wounds [1]. Wound infections are common, serious, and expensive complications after injuries and can progress from colonization and local infection to systemic infection, sepsis, and multiple organ dysfunction syndrome, even death [2]. Infection results in nonhealing and it is appropriate to treat the infected wounds with a combination of topical antibacterial and systemic antibiotics, especially in the presence of invasive infections [3,4,5]. The most commonly isolated organisms of microbes vary and include common pathogens such as Staphylococcus aureus (S. aureus), Pseudomonas aeruginosa (P. aeruginosa), and Escherichia coli (E. coli) [6,7]. Systemic or topical antibiotics application has proven efficacy against wound infection. However, the inappropriate use of systemic antibiotic places the patient at risk for the acquisition of drug resistance, and the topical application of antibiotics has many disadvantages, including contact toxicity and safety issues, such as dermatitis reactions, and difficulties in accurately determining the dose. Concerns over the use of antibiotics and the search for new antimicrobial agents have led to the emergence of advanced tissue engineering technology. Tobramycin (TOB), is a polycationic aminoglycoside antibiotic with abundant amine groups that can treat various types of infections caused by Gram-negative and -positive bacteria [8]. TOB works mainly by inhibiting bacterial protein synthesis and binding to bacterial outer membranes [9]. Although TOB exhibits excellent antibacterial activity, such small molecule drug may diffuse quickly on the surface of the infected wounds and lead to burst release [10]. On the other hand, the misuse or overuse of TOB may also develop drug-resistance. Therefore, the development of a TOB release system is promising for bacterially infected wounds and the correct topical use of TOB. Due to the high solubility and small molecular character of TOB, a particle release system is needed to effectively control TOB release. Gelatine, a denatured protein, is one of the most natural biopolymers widely used in pharmaceutical industries due to its biodegradability, biocompatibility, and ease of chemical modification and crosslinking [11]. These properties encourage gelatine’s use as particle/nanoparticle delivery systems for drugs [11,12]. Glutaraldehyde (GTA) is a relatively widely used reagent for gelatine crosslinking. By optimising the degree of GTA crosslinking, improved properties of TOB-loaded gelatine particles can be obtained at the desired release rate, suitable particle size, proper porosity, appropriate mechanical strength, and less toxicity. Hydrogel dressing is claimed as one of the most effective alternatives for wound treatment [13]. Hydrogels are three-dimensional, have high-water content (over $90\%$), and are crosslinked networks with a certain porosity. Hydrogels are fabricated by the physical or chemical crosslinking of natural and/or synthetic polymers and are able to delivery bioactive [14,15,16]. All of these factors make hydrogels interesting candidates for future smart wound-dressing platform. Hydrogel dressing provides a biocompatible platform, cooling effect, adequate moisture, and the ability to isolate bacteria, which play significant roles in promoting wound-healing [13,17]. Hydrogel dressing can also function as a delivery platform, allowing it to be a promising alternative for hard-to-treat wounds, such as infected chronic wounds [18,19]. Therefore, the design and development of a multifunctional hydrogel dressing is emerging as a promising strategy for antibacterial treatment. The advantages of chemical crosslinking are highly stable network structures ensured by covalent bonds between different polymer chains, high mechanical properties, and thermal resistance. However, toxic potentials caused by the residual chemical and organic solvents during chemical crosslinking may release toxicity. Physically crosslinked hydrogels are produced without the use of chemical modification or the addition of crosslinking agents. Compared with chemically crosslinked hydrogels, such networks exhibit weak mechanical properties, as they are more cell-compatible and more environmentally friendly. Among the most used natural materials, polysaccharides, which are biocompatible and biodegradable materials, have attracted much attention in the application of wound dressings, drug release, and tissue engineering [20]. Polysaccharides are abundant in nature and reproducible. Chitosan, alginate, cellulose, hyaluronic acid, chondroitin sulphate, and starch are commonly used polysaccharides for hydrogel fabrication. Additionally, each of these materials has its own benefits and drawbacks, which are critical features when deciding its biomedical applications. For example, alginate is an anionic polymer extracted from brown algae, such as Macrocystis pyrifera, laminaria digitate by treatment with aqueous alkali solutions [21]. Alginate has been extensively investigated and used for may biomedical applications, due to its biocompatibility, low cost, and mild gelation conditions. Alginate hydrogel-based dressing can be formed via a simple and effective approach, namely an ionic crosslinking method using calcium or other multivalent ions [22]. Alginate dressing facilitates cell migration, provides a moist environment, absorbs exudate, and does not adhere to the wound tissue [23]. However, one critical drawback of ionically crosslinked alginate hydrogel is its relatively short-term stability in physiological conditions due to the release of divalent ions into the surrounding environment. Moreover, alginate hydrogel alone as wound dressing is insufficient for solving bacterial infection, especially in chronic wounds. Chitosan is derived from chitin via an alkaline deacetylation technique and has randomly distributed D-glucosamine and N-Acetyl-D-glucosamine units in its backbone [24]. Chitosan is a promising biomaterial for the fabrication of wound dressings due to a number of advantages, including its low cost, good biocompatibility, basic structural unit that mimics a mammalian extracellular matrix (ECM), and ease of processing [25,26]. More importantly, chitosan has abundant positive charges on the main chain, which interact with the bacterial membrane, suggesting its potential benefits as an antibacterial material against bacteria-infected wounds [27,28]. Films, hydrogels, sponges, and membrane powders have been made from chitosan and extensively studied. The major drawbacks of chitosan are its insolubility in water, mechanical insufficiency, and non-resistance to acid, which limit its biomedical applications. Chemical modification has been used to produce chitosan derivatives with controlled solubility, ionic characteristics, and enhanced antibacterial performance. Among the modification approaches, the quaternary ammonium salts (QAS) of chitosan are the most promising for biomedical applications. Quaternisation is an efficient form of modification due to its desirable solubility, its retention of most of the properties of the modified materials (mainly polysaccharide), and its ability to reduce infections [29,30]. QAS as antibacterial constituents can be introduced into the abundant reactive amino and hydroxyl groups of the chitosan skeleton [31]. Moreover, QAS-modified chitosan improves the solubility of chitosan, thereby expanding the application range of chitosan into different biomedical fields [29]. Therefore, a TOB release hydrogel system based on QAS-modified chitosan and alginate have significant potential in the treatment of infected wounds. The objective of this study is to develop a simple polysaccharide-based hydrogel system with outstanding antibacterial ability through QAS-modified chitosan and the release of TOB-loaded gelatine particles. Firstly, QAS with different carbon chains were synthesised and characterised to confirm the antibacterial activity. QAS-modified chitosan was then synthesised by the ring opening method, where amino groups of chitosan become quaternary amines. GTA-crosslinked gelatine particles as a TOB drug delivery system were formed and optimised GAT formulation and particle profiles were studied. Lastly, alginate, QAS-modified chitosan, and TOB-loaded gelatine particles were mixed together with calcium to form a homogenous hydrogel-based drug release system. The morphology and the antibacterial performance of the combined hydrogel dressings were investigated. Key benefits of TOB-loaded particles include the avoidance of rapid release from hydrogel, prolonged retention at the target site, and reduced risk of serious dose-related side effects. This study will provide a strategy for treating bacteria-infected wounds. ## 2.1. Preparation and Characterisation of QAS In this study, we aimed to develop a polysaccharide-based antibiotic release hydrogel to manage wounds susceptible to bacterial infection (Figure 1). First, a series of quaternary ammonium salts with various carbon chains were synthesised and their structure and antibacterial effects were identified. The chemical shifts δ = 1.24 ppm and δ = 0.87 ppm of 1H-NMR are the peaks of quaternary ammonium N; H on the carbon chain and methyl H at the end of the carbon chain (Figure 2A–D). The chemical shift δ = 3.48 ppm is two methyl H, associated with quaternary ammonium N, and the appearance of this peak indicates the successful synthesis of QAS. The carbon chain length can be obtained according to the ratio of integrated peak area. With terminal methyl H as the basic unit, the peak area ratios of carbon chain H to methyl H are 3.83:1, 5.01:1, 5.59:1, and 6.08:1, respectively, which are close to the theoretical values of 4.00:1, 5.33:1, 6.67:1, and 8.00:1. These data indicate that the QAS materials obtained exhibited different carbon chain lengths and can be distinguished by 1H-NMR. The peak area ratios of the two methyl H and terminal methyl H associated with quaternised N were 1.91:1, 1.89:1, 1.97:1, and 1.61:1, which were also close to the theoretical value of 2:1. The epoxy values of C10, C12, C14, and C16 QAS were $13.3254\%$, $15.5526\%$, $22.2877\%$, and $15.9615\%$, respectively (Table 1). The epoxide values were not high, indicating that most of the epoxides of the synthesised QAS had been ring-opened. In addition to further improving the reaction conditions to increase the epoxide value, the cyclic process should be considered in the subsequent reaction. ## 2.2. Antibacterial Activity of the QAS The antibacterial activity of the quaternary ammonium salts was determined through the testing of different concentrations of the polymers against Gram-positive bacteria (S. aureus) and Gram-negative bacteria (E. coli) by MIC values. As shown in Figure 2E,F, all of the quaternary ammonium salts with different carbon chain lengths exhibited antimicrobial activity against both bacteria strains and showed increased activity with an increase in the length of the carbon chain. The MIC data presented in Table 2 show that quaternary ammonium salt with 16 carbon atoms (Eq-N+-C16) exhibited the strongest activity again both E. coli (16 μg/mL) and S. aureus (2 μg/mL). ## 2.3. Synthesis and Characterisation of Quaternary Ammonium Salt-Modified Chitosan (CMCS) From the above study, EP-N+-C10 was found to have the worst antibacterial effect. Therefore, the other three quaternary ammonium salt-modified chitosan derivatives were synthesised and denoted by CMCS-N+-C12, CMCS-N+-C14, and CMCS-N+-C16. By comparing the 1H-NMR results of carboxymethyl chitosan (Figure 3A) with CMCS, it can be seen that the chemical shift δ = 1.3 ppm appears on the spectra after the addition of quaternary ammonium salts, this signal is assigned to the CH2 of the alkyl chains. ( Figure 3B–D). The results showed that the quaternary ammonium salt modified carboxymethyl chitosan was synthesised successfully. ## 2.4. Antibacterial Effect of CMCS The influence of the CMCS on the antimicrobial properties was evaluated. As shown in Figure 3E,F, all of the modified chitosan exhibited antibacterial effect while no significant differences between each modified chitosan was observed in either bacterium strain. Moreover, the antibacterial efficacy was weaker than QAS. The possible reason for the significant decrease in antibacterial efficiency could be caused by the partial neutralisation of the positive charge moiety in QAS by chitosan. In addition, free QAS are mainly used to alter cell permeability by aggregating on the surface of the bacterium, but when QAS are conjugated to polymer chains, their ability to alter cell permeability is greatly reduced. Thus, the MIC of modified chitosan was not discussed in this study. Modified chitosan with 16 carbon atoms (Eq-N+-C16) was selected for the further experiments. ## 2.5. TOB-Loaded Gelatine Microspheres (TOB-G) Glutaraldehyde (GTA) is one of the commonly used crosslinking agents for collagen- and gelatine-based materials. To optimise the crosslinked gelatine microspheres, a series of GTA amounts were used to crosslink gelatine while encapsulating tobramycin. Figure 4A–E shows the morphology of the prepared gelatine microspheres through different GTA amounts. From the SEM images, microspheres formed by 0.1 mL and 0.15 mL GTA exhibited the best morphology. With a decrease in the GTA amount used for gelatine crosslinking, the microspheres lost their spherical shape and stuck to one another. Higher amounts of GTA resulted in heterogeneous microspheres and adhesion due to excess crosslinking reagents. Microspheres prepared by 0.1 mL GTA exhibited uniform particle sizes and good dispersion in comparison with other groups. The sizes of the microspheres were distributed in the range of 20 ± 70 μm, with an average diameter of approximately 57 μm. Thus, the formulation of 0.1 mL GTA was selected for the fabrication of TOB-loaded gelatine microspheres. Gelatine microspheres without TOB were formed using 0.1 mL GTA as the control group for further study. The results in Figure 4F show that in the slightly acidic microenvironment, TOB is rapidly released from microspheres and $30\%$ of the total drug is released in 12 h, while only $10\%$ TOB was released in the physiological microenvironment at this time. TOB release reached over $50\%$ at day 6 for the acidic environment and $30\%$ for the physiological microenvironment. These results indicate that the TOB release profile can be adjusted by changing the pH environment, which is due to the pH responsive structure of the crosslink formed by the Schiff base reaction of glutaraldehyde with amino and aldehyde groups in gelatine [15]. The pH sensitivity of hydrogel dressings show potential as a promising material for the treatment of infected wounds. Excessive microbial growth affects the pH of the wound environmental conditions. Dressings with pH sensitivity allows the release of loaded drugs in a more controlled and on-demand manner, providing effective ideas to develop antimicrobial dressings. ## 2.6. Preparation and Characterisation of the TOB-Loaded Polysaccharide Hydrogels (CMCS/SA/TOB-G) Sodium alginate (SA) and QSA modified chitosan (CMCS) have been proven to form a physically crosslinked polysaccharide hydrogel network through the electrostatic interaction between polyanion and polycation and hydrogen bonding [32]. In this work, we used CaCl2 to induce alginate physical crosslinking and balance the charge of alginate and QSA-modified chitosan. TOB-loaded gelatine microspheres were added to QSA-modified chitosan before mixing with alginate solution. QSA-modified chitosan with different carbon chains were used to crosslink with alginate to compare the influences of carbon chains of QSA on the mechanical properties of the hydrogels (Figure 5A,B). The rheological behaviour of the CMCS/SA/TOB-G hydrogels was investigated by an oscillatory rheology and was shown in Figure 5C–E The storage (G′) and loss (G″) modulus represent the mechanical properties. All four hydrogels formed exhibited a similar storage modulus with a range of 700–1070 Pa without any relation in the length of carbon chain on CMCS. The strain sweep curve in the figure confirms the gel-like behaviour, and the hydrogels maintained their strength well under a shear force between $1\%$ and $100\%$. Based on the abovementioned results, we selected CMCS-N+-C16 and 0.1 mL GTA-produced TOB-G as the optimised components for generating the biocompatible and antibacterial hydrogel dressing and further experiments. ## 2.7. Antibacterial Effect of the CMCS/SA/TOB-G Hydrogel The representative images of bacteria treated with CMCS/SA/TOB-G, TOB-G, and CMCS-N+-C16 were shown in Figure 6. As expected, CMCS/SA/TOB-G exhibited the strongest antibacterial activity against both E. coli and S. aureus. The hydrogel killed over $95\%$ of S. aureus at a concentration of 128 μg/mL and E. coli at 512 μg/mL (Figure 6C–E). TOB-G demonstrated antibacterial activity against both bacteria strains, which is caused by the TOB released from the microspheres. The CMCS group did not show satisfactory antibacterial activity, which is due to the immobilisation of CMCS in the agar plates and the limited contact with bacteria that occurred during the experiment. ## 2.8. Morphology of the CMCS/SA/TOB-G Hydrogel SEM was performed to assess the hydrogel’s microstructure. The SEM images in Figure 7A show that the scaffold has a highly porous structure and the pore size is about 110 ± 20 μm. This advantage reveals that it is possible to load a high number of drug-loaded microspheres (Figure 7B). ## 2.9. Cytocompatibility Good biocompatibility is one of the basic requirements of biomedical devices. To investigate the cytocompatibility of the hydrogel combination, we used MTT assay and live/dead staining. The hydrogel extract showed over $90\%$ cell viability after 24 and 48 h of contact (Figure 7C). As shown in Figure 7D, live/dead staining investigations revealed that a majority of cells were alive and remained healthy after co-culturing with the hydrogel extract. The result of live/dead staining was consistent with the MTT result, indicating that the hydrogel has good biocompatibility and can be used as a medical wound dressing. ## 3. Conclusions In this report, we prepared a simple polysaccharide-based hydrogel system encapsulating TOB-loaded gelatine microspheres as an antibacterial wound dressing. The findings demonstrated that the antibacterial activity of QAS-modified chitosan depended on the length of carbon chain and the best antibacterial performance was shown to be the 16-carbon chain QAS-modified chitosan. Furthermore, the morphology investigation of the TOB-loaded gelatine microspheres indicated the crosslinking degree, and the spherical shape could be effectively controlled by adjusting the amount of GTA. The relative long-term release of TOB from gelatine microspheres indicated its ability to cope with the slow healing of infected wounds. These data suggest that the polysaccharide-based encapsulating TOB-loaded gelatine microspheres could be used for bacteria-infected wounds. The in vitro and in vivo degradation of the polysaccharide-based hydrogel will be further studied and reported on in order to verify the possibility of its use in wound treatment. ## 4.1. Materials Chitosan, Type B gelatine, Sodium Alginate (SA), and Calcium chloride (CaCl2) were all purchased from Aladdin Co. Ltd., Shanghai, China. DMEM (Gibco, Thermo Fisher Scientific, Inc., Waltham, MA, USA), MTT, and live/dead staining was purchased from Thermo Fisher. ## 4.2. Synthesis of Quaternary Ammonium Salts (QAS) with Different Carbon Chains A series of quaternary ammonium salts with different carbon chains were synthesised. Briefly, 4.732 g epoxy chloropropane was added in a 50 mL round-bottom flask (4 replicates) and heated in an oil bath at 60 °C. Then, 2.811 mL of N,N-dimethyldecamine (Sigma, China); 2.732 mL of dodecyl dimethyl tertiary amine; 3.091 mL of tetacylethyl dimethyl tertiary amine; and 3.450 mL (molar ratio 4:1) of cetyldimethyl tertiary amine were added into the above four flasks, respectively. The flasks were sealed, and the reaction mixtures were stirred for 4 h. Subsequently, the reaction solutions were precipitated using hexane and centrifuged at 2000 r/min for 5 min. The precipitate was washed with hexane and ethanol under the same centrifugal conditions before vacuum-drying for 24 h. Finally, quaternary ammonium salts with different carbon chains were obtained and named as Ep-N +-C10, Ep-N +-C12, Ep-N +-C14, and Ep-N +-C16. ## 4.3. Characterisation of QAS The structure of the quaternary ammonium salts was detected by nuclear magnetic resonance spectroscopy (1H-NMR). A total of 5 mg of a quaternary ammonium salt sample was dissolved into 600 µL deuterated chloroform reagent, and then added into the NMR tube for testing. The epoxy value of QAS was determined by titration calorimetry. The titration process requires $20\%$ tetraethylamine bromide glacial acetic acid solution and 0.1 mol/L perchloric acid glacial acetic acid solution (the standard solution concentration is recorded as C mol/L). The calibrated concentration of perchloric acid solution was 0.0947 mol/L. A total of 0.2 g of the quaternary ammonium salt dissolved in 10 mL of acetic acid was weighed and transferred to a 50 mL round-bottomed flask at room temperature on a magnetic stirring table. An additional 5 mL of acetic acid was used to transfer the quaternary ammonium salts as completely as possible. The solution changed from transparent to purple by adding 5 mL tetraethylamine bromide solution and dropping $0.1\%$ crystal violet in volume fraction for 4–5 d. The perchloric acid solution was slowly added, and the amount of perchloric acid dropped V mL was recorded. The titration process changed from violet to blue and then to green, and the titration endpoint was considered when it turned to a stable green and never returned to blue. The epoxy value was calculated using the following equation:E=V×C×M/1000m×$100\%$ where E is the % epoxy value of quaternary ammonium salt sample; V is the volume of perchloric acid standard solution consumed in the titration process; C is the concentration of perchloric acid standard solution mol/L; m is the mass of quaternary ammonium salt sample; and M is the molecular weight of quaternary ammonium salt sample. ## 4.4. Microorganisms and Culture Media Gram-negative E. coli (CMCC44103) and Gram-positive S. aureus (CMCC26003) were chosen to evaluate the antibacterial activity of the materials obtained in this study. E. coli was grown in LB Broth and S. aureus in TSB media. ## 4.5. Antibacterial Activity Test of the QAS The antibacterial ability of the quaternary ammonium salts was tested via a minimal inhibitory concentration (MIC) assay. A pure single E. coli colony from the agar plate was inoculated into the LB broth and grown overnight in an incubator (37 °C). A stock solution of 1 mg/mL of tested compound (e.g., Eq-N+-C10) was prepared by dissolving the quaternary ammonium salts in the bacterial media. Compound stock solutions were serially diluted from 1 mg/mL to 1 μg/mL using bacterial media. Bacterial suspension was added to the diluted and tested compound to obtain a final concentration of 3 × 105 CFU/mL. Bacteria suspension without quaternary ammonium salts served as the negative control, bacteria culture media without bacteria suspension as the control group. The samples were incubated at 37 °C for 12 h in a shaker and the optical density (OD) at 600 nm was measured using a microplate spectrophotometer. All tests were repeated three times. The antibacterial rate was calculated using the following equation:Antibacterialratio%=(ODexperimental-ODnegative)(ODcontrol-ODnegative)×$100\%$ where ODexperimental: OD value of the experimental group; ODpositive: OD value of the control group; ODnegative: OD value of the negative group. The MIC of the compounds was determined by monitoring the pure growth of bacteria in nutrient solution with different polymer concentrations. MIC value is the concentration of the compound at which no bacteria growth was detected. The same method was used to analyse the MIC of Eq-N+-C12, Eq-N+-C14, and Eq-N+-C16 and the MIC of all the quaternary ammonium salts against S. aureus. ## 4.6. Preparation of QSA-Modified Chitosan (CMCS) The synthesis of QSA-modified chitosan followed a previously reported method [29]. Briefly, 1 g of carboxymethyl chitosan (CMC) was added to 0.01 M NaOH solution in a 200 mL two-sided round-bottomed flask at room temperature and heated at 60 °C in an oil bath after it had completely dissolved. A certain amount of QAS with a carbon chain length of C10 was weighed and dissolved in 0.01 M NaOH solution before it was added to the round-bottom flask and stirred at 200 r/min for 24 h. The reaction solution was adjusted to slight acidity by adding 200 μL of acidic solution to obtain the crude product of QAS-modified chitosan (denoted as CMCS-N+). Then, the reaction solution was cooled at 4 °C and precipitated with 20 mL ethanol. The precipitation was centrifuged at 2200 r/min for 5 min and washed 2–3 times with ethanol. Finally, pure QAS-modified carboxymethyl chitosan with carbon chain length of C10 (recorded as CMCS-N+-C10) was obtained after vacuum drying in an oven for 12 h. QAS-modified carboxymethyl chitosan with carbon chain lengths C12 (CMCS-N+-C12), C14 (CMCS-N+-C14), and C16 (CMCS-N+-C16) were synthesized using the same method. The structures of carboxymethyl chitosan and carboxymethyl chitosan quaternary ammonium salt were detected by 1H-NMR. The method of sample preparation is to add 5–8 mg of sample into 600 µL D2O reagent to fully dissolve for detection. ## 4.7. Antibacterial Activity Test of the CMCS A similar method of antibacterial activity testing was used to determine the antibacterial performance of the chitosan quaternary ammonium salts. The concentration range of the tested samples was between 0.25 mg/mL and 16 mg/mL. ## 4.8. Preparation of Gelatine Drug Delivery Particles Gelatine microspheres were prepared using the emulsification crosslinking method. Briefly, gelatine (0.5 g) was dissolved in 4 mL of ultra-pure water at 50 °C. TOB (0.05 g) was dissolved in 1 mL of ultra-pure water at room temperature and added dropwise to the dissolved gelatine solution. The mixed solution was then added to a 100 mL corn oil and preheated to 60 °C. A total of 0.5 mL of Span-80 was added into heated oil. The biphasic system was thoroughly mixed to form a without emulsion using a magnetic stirrer for 30 min. Then, the emulsion solution was chilled at 4 °C and 0.5 mL $25\%$ glutaraldehyde was added to the cooling mixture. The mixture was stirred for 1 h before demulsification was carried out using isopropanol. Then, ethanol was used to wash the demulsified solution. The gelatine microspheres were obtained after being vacuum-dried at room temperature overnight. The same method was used to prepare TOB-loaded gelatine microspheres (TOB-G) using GTA of 0.3 mL, 0.15 mL, 0.10 mL, and 0.05 mL. ## 4.9. Characterisation of TOB-Loaded Gelatine Particles The gelatine microspheres were spread onto a double-sided adhesive tape fixed to an aluminium platform. The microspheres were sprayed with gold. The morphology of the gelatine microspheres was observed using a scanning electron microscope (SEM) (AMETEK® Quanta 3D FEG machine). The TOB release profile was determined by placing gelatine microspheres in Transwell inserts and immersing the inserts in PBS buffer in a 24-well plate without shaking. A total of 2 mL of solution in the wells was collected at pre-determined times and 2 mL of fresh PBS buffer was added to maintain a constant volume. The content of TOB was determined by the o-phthalaldehyde (OPA) method. The concentration of the released TOB was measured with a UV–vis spectrophotometer at 333 nm. ## 4.10. Hydrogel Formation A total of 20 mg/mL sodium alginate (SA) solution and 20 mg/mL CaCl2 solution was prepared using ultra-pure water, respectively. A total of 50 mg of TOB-loaded gelatine microspheres (0.10 mL GTA was used for the microspheres’ preparation) were weighed and dispersed in 4 mL ultra-pure water. Subsequently, the microsphere solution was mixed with 200 mg of CMCS and stirred until the mixture was completely dissolved. CMCS fabricated from different carbon chains were used to prepare a series of hydrogels (CMCS/SA/TOB-G). Then, 0.5 mL sodium alginate solution was added in a dropwise manner to the liquid obtained above and stirred to obtain a homogenous hydrogel precursor. Lastly, hydrogels were formed by adding 50 μL CaCl2 solution to the stirring mixture. ## 4.11. Rheological Testing of the Hydrogels Rheological testing was performed on a Discovery HR-2 Rheometer (TA Instruments) with steel parallel-plate geometry (20 mm diameter). The storage (G′) and loss (G″) modulus were measured under a constant strain of 0.05 and frequency, ranging from 0.1 to 103 rad/s at 25 °C. The tested volume of hydrogel was 350 μL. ## 4.12. Antibacterial Analysis of the Hydrogels Then, the antibacterial activity of the CMCS-N+-C16/SA/TOB-G (0.1 mL GTA) hydrogel was evaluated using E. coli and S. aureus, as reported previously [33]. First, the bacteria were incubated overnight at 37 °C with shaking. The bacteria suspension was then diluted to a concentration of a 106 colony-forming unit (CFU)/mL. Then, the selected hydrogel formulation, the TOB-G, and CMCS-N+-C16 were diluted to a series of concentrations through the agar media and added to the peri dishes. Subsequently, 100 μL diluted bacterial suspension were seeded onto the solid agar surface and incubated for 4 h at 37 °C. Then, the plates were washed by 2 mL PBS to resuspend the viable bacteria. Suspensions from each dish were cultured in new agar plates at 37 °C for 24 h and the number of bacterial colonies were counted and recorded. Bacteria with no treated material were used as the control group. The antibacterial ratio (%) was calculated by the following equation:Antibacterial ratio (%)=Con-MCon×$100\%$ where Con represents the bacterial colony counts of control group and M represents the experimental groups. ## 4.13. Hydrogel Micromorphology The freeze-dried hydrogel scaffold was mounted on stubs and coated with gold. The structure of the scaffold was observed using a SEM (AMETEK® Quanta 3D FEG machine) at the required magnification. ## 4.14. Cytocompatibility of the Hydrogel The in vitro cytotoxicity of the hydrogel was tested following the instruction of ISO 10,993 by MTT assay. Hydrogel extract was prepared by immersing 1 g hydrogel in 5 mL of full cell culture media (DMEM, $10\%$ FBS, $1\%$ P/S) for 24 h at 37 °C. L929 with a concentration of 8000 per well were seeded in a 96-well plate and allowed to attach overnight. The extraction was co-cultured with seeded fibroblasts for 24 h and 48 h at 37 °C. Cells cultured with no extraction served as a negative control and cells cultured with polyurethane were used as the positive control. An MTT assay was carried out following the manufacturer’s instructions. Briefly, MTT stock solution was prepared by diluting MTT in DMEM at a concentration of 1 mg/mL. The cell culture media prepared as mentioned above were carefully removed from the plates at the examination time. Then, 50 μL of 1 mg/mL MTT stock solution was added to each test well and the plate was further incubated for 2 h in the incubator. The MTT solution was gently discarded and 100 μL isopropanol was added to each well to dissolve the precipitated formazan. Then, absorbance was read using a microplate reader at a wavelength of 570 nm. 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--- title: 'Causal Association between Iritis or Uveitis and Glaucoma: A Two-Sample Mendelian Randomisation Study' authors: - Je Hyun Seo - Young Lee journal: Genes year: 2023 pmcid: PMC10048342 doi: 10.3390/genes14030642 license: CC BY 4.0 --- # Causal Association between Iritis or Uveitis and Glaucoma: A Two-Sample Mendelian Randomisation Study ## Abstract Recent studies have suggested an association between iritis or uveitis and glaucoma. This study investigated the causal relationship between glaucoma and iritis and uveitis as exposures in a multi-ethnic population. Single-nucleotide polymorphisms associated with exposures to iritis and uveitis from the genome-wide association study (GWAS) data of Biobank Japan (BBJ) and the meta-analysis data from BBJ and UK Biobank (UKB) were used as instrumental variables (IVs). The GWAS dataset for glaucoma was extracted from the meta-analysis data ($$n = 240$$,302) of Genetic Epidemiology Research in Adult Health and Aging and UKB. The casual estimates were assessed with a two-sample Mendelian randomisation (MR) test using the inverse-variance-weighted (IVW) method, weighted median method, MR–Egger method, and MR-Pleiotropy Residual Sum and Outlier test. The IVW method revealed a significant causal association between iritis and glaucoma using IVs ($p \leq 5.0$ × 10−8) from the East Asian population ($$n = 2$$) (odds ratio [OR] = 1.01, $$p \leq 0.017$$), a significant association between iritis exposures ($p \leq 5.0$ × 10−8) in the multi-ethnic population ($$n = 11$$) (OR = 1.04, $$p \leq 0.001$$), and a significant causal association between uveitis exposures ($$n = 10$$ with $p \leq 5.0$ × 10−8) and glaucoma in the multi-ethnic population (OR = 1.04, $$p \leq 0.001$$). Iritis and uveitis had causal effects on glaucoma risk based on IVs from the multi-ethnic population. These findings imply that the current classifications of uveitic glaucoma and open-angle glaucoma overlap, indicating the need for further investigating these complex relationships. ## 1. Introduction Uveitis is an inflammatory condition that affects the middle layer of the eye wall [1,2]. Anterior uveitis, also known as iritis, is the most prevalent form of uveitis. A consequence of uveitis is glaucoma, a leading cause of irreversible vision loss characterized by the degeneration of the retinal ganglion cells and their axons [3]. The classification of glaucoma is based on anatomical and known-causal factors, with open-angle glaucoma (OAG) being the most common form according to the anatomical mechanism [4]. Uveitic glaucoma (UG) is a complex range of disorders characterized by the coexistence of iritis and glaucomatous optic neuropathy, which encompasses several diverse clinical entities with varying prognoses [5,6,7]. Nevertheless, not all patients with uveitis develop glaucoma, which can be caused by uveitis itself or occur as a side effect of treating inflammation. Previous studies have reported that the prevalence of glaucoma in uveitis ranges from 11–$20\%$ after 5 years [2,8,9,10]. Numerous studies have shown that multiple factors are involved in the pathophysiology of glaucoma; however, its pathogenesis is not fully understood [2,11]. Alterations in the intraocular pressure (IOP) are correlated with the development of glaucoma [11]. IOP elevation in UG occurs due to the obstruction of aqueous outflow due to changes in the trabecular meshwork tissue architecture or open-angle mechanisms [12] and angle closure mechanisms, such as pupillary block. Despite the complexity of the categorization and mechanism of UG, it is possible to discern whether the mechanism is open- or closed-angle based on the structure. Early stages of UG may present with open-angle and shared features of OAG. As uveitis and glaucoma are complex diseases with genetic predisposition and predisposing environmental factors [5,6,7,13,14,15,16,17,18,19], further research using genetic data is required to gain additional information regarding the association between uveitis and OAG. Recent investigations on iritis and uveitis have indicated that uveitis is characterised by a strong relationship with the human leucocyte antigens (HLA) gene. Moreover, acute anterior uveitis (AAU), Behcet’s disease (BD), and Vogt–Koyanagi–Harada disease were found to be highly correlated with HLA-B27, HLA-B51, and HLA-DR4/DQA1, respectively [5,13]. The most recent genetic research data and information regarding single-nucleotide polymorphisms (SNPs) for glaucoma in a multi-ethnic population were obtained from a meta-analysis of the Genetic Epidemiology Research in Adult Health and Aging (GERA) cohort and UK Biobank (UKB) [20]. The disease pathophysiology and epidemiology can be studied by applying bioinformatics and statistical techniques to these data, and our study group recently evaluated a genetic risk score model based on the genome-wide association study results of OAG [21] and various conditions [22,23,24]. The causal effect of iritis and uveitis on glaucoma can be analysed by calculating the ratio of iritis-related SNPs and uveitis-related SNPs in the genetic data of patients with glaucoma. Mendelian randomisation (MR) analysis is a methodology used in genetic epidemiology that employs genetic variants linked with putative risk factors (exposures) as instrumental variables (IVs) to analyse the causal impact of these factors on clinical outcomes [25,26]. These techniques have been used in numerous studies that investigated the impact of risk factors, such as type 2 diabetes and the effect of coffee on glaucoma onset [27,28]. Thus, it may be possible to confirm whether uveitis and iritis are causal factors of glaucoma using MR analysis based on the calculated risk ratio or odds ratio (OR). In addition, it is anticipated that the selection of exposure factors from a large cohort of Biobank Japan (BBJ) and UKB data will lead to more significant results, which would provide a basis for recognizing iritis and uveitis as causal factors of glaucoma based on genetic data and aid in understanding UG pathology, as the pre-existing classification of glaucoma is a combination of anatomy and aetiology. For this purpose, we performed a two-sample MR test using summary data from BBJ for an East Asian population, a meta-analysis of BBJ and UKB for a multi-ethnic population [29], and glaucoma genetic data from a meta-analysis of GERA and UKB [20]. ## 2.1. Study Design Overview The institutional review board of the Veterans Health Service Medical Center approved this study protocol (IRB No. 2022-12-007) and waived the need for informed consent due to its retrospective nature. The study was conducted in compliance with the Declaration of Helsinki. ## 2.2. Data Source A schematic plot of the design of this analytical study is shown in Figure 1. To explore the causal effect of exposure to iritis and uveitis on the risk of glaucoma, we selected datasets for SNP-related iritis and SNP-related uveitis as the exposures (Table 1) from the summary statistics of the genome-wide association study (GWAS) for [1] the East Asian population from BBJ ($$n = 175$$,653 for iritis, $$n = 174$$,725 for uveitis), and [2] the meta-analysis of the multi-ethnic population ($$n = 656$$,395 for iritis, $$n = 655$$,467 for uveitis) from BBJ and UKB [29]. The OAG summary statistics of the GWAS data were adopted from the meta-analysis ($$n = 240$$,302; 12,315 cases and 227,987 control) of GERA and UKB [30]. The OAG dataset was used in this study as most of the genetic data related to glaucoma have been investigated for OAG. Since most early stage UG cases are OAG, it was assumed that UG and OAG are classified on the basis of aetiology and angle shape, respectively [2]. Moreover, since this study was genome-based, this assumption is reasonable as the angle structure of elderly individuals with the angle-closure form is open-angle at birth, with the exception of congenital glaucoma, which presents with abnormal angle structure congenitally. The datasets for the summary statistics are described in detail in Table 1 and Figure 1. ## 2.3. Selection of the Genetic Instrumental Variables SNPs associated with iritis and uveitis at the genome-wide significance threshold ($p \leq 5.0$ × 10−8) were used as the IVs. However, SNPs with $p \leq 5.0$ × 10−4 were chosen as the IVs when there were no noteworthy thresholds, as the number of participants was limited in comparison to the rarity of the disease. SNPs were clumped using linkage disequilibrium (LD) with R2 < 0.001 within 10,000 kb to ensure the independence of the IVs. The 1000 Genomes phase III dataset (East Asian) was used as the reference panel for computing LD for the clumping procedure. F-values were used to evaluate the strengths of the genetic IVs. The F-value was determined using the formula F = R2(n − 2)/(1 − R2), where n is the sample size and R2 is the proportion of variances of exposure by the genetic variances [31]. F-values larger than 10 are regarded as no evidence of weak instrument bias [32]. ## 2.4. Mendelian Randomisation The MR analysis was performed under the following assumption: [1] IVs should have a significant relationship with the exposure; [2] IVs should not be linked to confounders of the exposure–outcome association; and [3] IVs should affect the outcome solely through exposure. We used the inverse-variance-weighted (IVW) method as our primary analysis method [32,33,34]. A fixed-effects model with three or fewer IVs was applied; a multiplicative random-effects model was used otherwise. Weighted median [35] and MR–Egger (with or without adjustment using the Simulation Extrapolation [SIMEX] method) regression [36,37] were considered the sensitivity analyses. The IVW approach has maximum efficiency when all genetic variations satisfy the three IV assumptions [38]. The estimate of IVW might be biased if one or more of the variants are invalid [35]. However, the weighted median technique generates consistent estimations of causality even when up to $50\%$ of the IVs are invalid [35]. The MR–Egger method provides estimates of suitable causal effects even in the presence of pleiotropic effects by considering a non-zero intercept, which represents the average horizontal pleiotropic effects [36]. Bias can be corrected using MR–Egger regression with SIMEX when the assumption of no measurement error is violated (I2 value < $90\%$) [37]. The Cochran’s Q statistic and Rücker’s Q′ statistic tests were used to assess the heterogeneity of the IVW and MR–Egger methods, respectively [33,39]. Pleiotropy in the genetic variant may be observed if the Cochran’s Q statistic and Rücker’s Q′ statistic tests have p-values less than 0.05. Directional horizontal pleiotropy was evaluated using the MR Polyhedral sum of residuals and outliers (MR-PRESSO) global test, and the presence of pleiotropic outliers was evaluated using the MR-PRESSO outlier test [40]. p-values of less than 0.05 for the MR-PRESSO global test and outlier test indicated possible pleiotropy in the genetic variations and the presence of pleiotropic outliers, respectively. All analyses were performed using the TwoSampleMR and simex packages in R version 3.6.3 (R Core Team, Vienna, Austria). ## 3.1. Genetic Instrumental Variables Two and eleven IVs at the significance level ($p \leq 5.0$ × 10−8) were identified for iritis in the East Asian and multi-ethnic populations, respectively. Sixty and ten IVs at the significance level ($p \leq 5.0$ × 10−4) were identified for uveitis in the East Asian and multi-ethnic populations, respectively (Table 2). The mean F statistics for iritis (41,588 for the East Asian population and 7704 for the multi-ethnic population) and uveitis (6433 for the East Asian population and 8396 for the multi-ethnic population) that were used for MR were greater than 10, demonstrating that there was a low chance of weak instrument bias (Table 2). Detailed information regarding the IVs utilized in this study is provided in Supplementary Table S1. ## 3.2. Heterogeneity and Horizontal Pleiotropy of Instrumental Variables The IVW approach was employed as the IVs for iritis and uveitis were not heterogeneous in the Cochran’s Q test (all $p \leq 0.05$) (Table 2). In addition, the Rücker’s Q test from the MR–Egger test revealed no heterogeneity between IVs, and the MR–Egger regression intercepts showed that there was no horizontal pleiotropic effect before (all $p \leq 0.05$) and after SIMEX adjustment (all $p \leq 0.05$). These results indicated that there was no pleiotropic effect (Table 2). In addition, the MR-PRESSO global and outlier tests did not demonstrate horizontal pleiotropy (all $p \leq 0.05$; Table 2). ## 3.3. Mendelian Randomisation for the Casual Association between Iritis and Glaucoma The IVW method showed a significant causal association between iritis and glaucoma in the East Asian population (MR OR = 1.01, $95\%$ confidence interval (CI): 1.00–1.01, $$p \leq 0.017$$, Figure 2). Similarly, the IVW method also showed a significant causal association between iritis and glaucoma in the multi-ethnic population (IVW MR OR = 1.04, $95\%$ CI: 1.01–1.06, $$p \leq 0.001$$, weighted median MR OR = 1.05, $95\%$ CI: 1.02–1.08, $$p \leq 0.003$$, MR–Egger MR OR = 1.05, $95\%$ CI: 1.01–1.09, $$p \leq 0.028$$, and MR–Egger [SIMEX] MR OR = 1.05, $95\%$ CI: 1.02–1.09, $$p \leq 0.020$$). Scatter plots show the genetic association with iritis against the genetic association with glaucoma for each SNP (Figure 3). ## 3.4. Mendelian Randomisation for the Casual Association between Uveitis and Glaucoma The IVW method showed a non-significant causal association between uveitis and glaucoma in the East Asian population (MR OR = 0.9995, $95\%$ CI: 0.9990–1.0000, $$p \leq 0.49$$, Figure 4). Since significant SNPs were not available ($p \leq 5.0$ × 10−8), a less significant SNP ($p \leq 5.0$ × 10−4) for uveitis in the East Asian population was chosen for analysis; therefore, the dependability of the analysis was low, as anticipated. In contrast, the IVW method showed a significant causal association between uveitis and glaucoma in the multi-ethnic population (IVW MR OR = 1.04, $95\%$ CI: 1.02–1.06, $$p \leq 0.001$$, weighted median MR OR = 1.05, $95\%$ CI: 1.02–1.08, $$p \leq 0.003$$, MR–Egger MR OR = 1.05, $95\%$ CI: 1.01–1.08, $$p \leq 0.039$$, and MR–Egger [SIMEX] MR OR = 1.05, $95\%$ CI: 1.01–1.08, $$p \leq 0.034$$). Scatter plots show the genetic association with uveitis against the genetic association with glaucoma for each SNP (Figure 5); a negative slope was observed for the East Asian population, whereas a significant positive slope was observed for the multi-ethnic population. ## 4. Discussion Our study demonstrates a causal association between iritis and glaucoma in an East Asian population and a multi-ethnic population. In addition, uveitis showed a causal association with glaucoma in the multi-ethnic population, although power constraints impeded the detection of this in the East Asian population. Research on the epidemiology of uveitis have shown results that are highly variable, primarily due to substantial methodological discrepancies. A recent systematic study on the frequency of uveitis showed prevalence numbers ranging from 9 to 730 occurrences per 100,000 and an overall incidence of 50.45 instances per 100,000, suggesting that geographical location is a significant driver of heterogeneity [41]. According to estimates, uveitis affects at least 2 million people worldwide and is a leading cause of blindness [42]. The inflammatory process that occurs within uveitis triggers the development of complications such as cataracts, macular edema, glaucoma, and retinal detachment [43]. According to one study, posterior synechiae ($19.0\%$) was the most prevalent consequence of uveitis, followed by ocular hypertension ($14.0\%$), macular edema ($7.5\%$), and glaucoma ($6.6\%$) [43]. Causal experimental studies on the induction of glaucoma by such severe uveitis have rarely been conducted. In this respect, this study is expected to be meaningful. Causal interference plays a major role in genetic epidemiology and clinical investigation and understanding the aetiology is implicit for identifying disease prevention and treatment opportunities. Existing epidemiological studies have limitations in the case of rare diseases; therefore, a prospective cohort study would be ideal. The MR is advantageous in this respect. Several MR studies have been conducted on ocular disease as a powerful approach to identify causal interference using human genetic data [44]. MR analysis has been used to investigate the impact of education levels, vitamin D, or medication on myopia; and lipid levels, refractive errors, or c-reactive protein levels on age-related macular degeneration [44]. In addition, lipid levels, central corneal thickness, type 2 diabetes, and refractive errors have been evaluated as risk factors for glaucoma in MR analyses [45,46,47]. From this perspective, the impact of iritis or uveitis exposure on the development of glaucoma can be investigated through a MR research approach. The association between uveitis and glaucoma was first reported in 1813 as arthritic iritis followed glaucoma [2]. UG is one of the most common intraocular complications, and glaucoma after uveitis has an incidence of $11\%$ after 5 years [10]. According to a Turkish epidemiologic study on 4604 eyes with glaucoma [48], UG accounted for $4.1\%$ of all cases, and $92.4\%$ of UG cases were OAG. In addition, glaucoma developed in $6.6\%$ of patients with uveitis 1 year later [43]. While there have been numerous studies on the relationship between glaucoma and uveitis, the present study was performed as there was no data to support a causal relationship based on genetic determinants. Since uveitis is a rare disease and highly likely to be related to the HLA region, it is expected that fine-mapping analysis or next-generation sequencing will yield more desirable data. From this perspective, the exposure of SNPs associated with iritis and uveitis was confirmed to be involved in the occurrence of glaucoma, and a clear conclusion could be reached. SNPs, such as rs146683910, had a high positive beta value (beta = 14.87, Table S1), which was related to the HLA-B locus associated with ankylosing spondylitis [49] (https://pheweb.org/UKB-TOPMed/pheno/715.2). An empirical study reported that HLA-B may be associated with OAG [50,51]. The causative analysis of uveitis and glaucoma also included immune-related genes in additional to HLA. The major histocompatability complex class I polypeptide-related sequence A (MICA) gene-related rs115681000 (beta = 1.732, located MICA;LINC01149) and rs146683910 (beta = 1.835, located HLA-B;MICA-AS1) also had high beta values (Table S1). The LINC01149 variation modifies the expression of MICA, making it easier for it to function as a key gene in the susceptibility to uveitis development. Nevertheless, despite the fact that the MR analysis used SNPs were primarily concentrated on chromosome 6, significant results were still seen despite the analysis’s usage of a relatively small number of SNPs due to the LD with R2 < 0.001 within 10,000 kb to ensure the independence of the IVs. AAU is the most common type of uveitis, which is related to synechiae formation. Glaucoma, which is frequently associated with AS, psoriatic arthritis, and inflammatory bowel disease [52,53], had characteristics of autoimmune and inflammatory illnesses. In addition, a recent study using whole-exome sequencing identified rare variants and genes associated with IOP and glaucoma, including BOD1L1, ACAD10, and HLA-B, demonstrating the power of including and aggregating rare variants [54]. HLA-associated regions, such as HLA-DRB1/DQA1, HLA-C, and HLA-DOA/HLA-DPA1, were adopted while evaluating the SNPs utilized in our analysis (Table S1). These loci showed significant association with uveitis, such as AAU, AS, and BD [5,13,49,52,53]. Moreover, significant SNPs were identified on comparing East Asian genetic data with the meta-analysis data, despite the ethnic differences. Nevertheless, our study demonstrated the value of using rare variants to enhance our understanding of the biological mechanisms regulating IOP and uncovered potential therapeutic targets for glaucoma [54], which we believe is consistent with our findings. Moreover, in our study, an association with glaucoma was found without intraocular pressure phenotype analysis, which further supports the findings. It is anticipated that future research will separate the description of disease into causal linkages rather than currently recognized categories. The main strength of this study is the utilization of a relatively large Asian and European cohort dataset to reveal the causal association between iritis or uveitis and glaucoma. However, there are a few limitations of this study. First, we were unable to explain the presence of numerous confounding factors using summary statistics based on two-sample MR since we did not have access to individual-level data. Second, since iritis and uveitis are very infrequent disorders associated with immune-related (such as HLA) SNPs, they were not ideal study topics for MR analysis. However, the significant causality that emerged despite these limitations suggests that there is a very strong association. Third, there are test procedures to validate the MR hypotheses, but they do not provide complete validation. Therefore, as violation of MR assumptions can lead to invalid conclusions, the results should be interpreted with caution. ## 5. 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--- title: 'Association between Shift Work and Metabolic Syndrome: A 4-Year Retrospective Cohort Study' authors: - Byeong-Jin Ye journal: Healthcare year: 2023 pmcid: PMC10048347 doi: 10.3390/healthcare11060802 license: CC BY 4.0 --- # Association between Shift Work and Metabolic Syndrome: A 4-Year Retrospective Cohort Study ## Abstract [1] Background: Previous studies on the association between shift work and metabolic syndrome have had inconsistent results. This may be due to the cross-sectional study design and non-objective data used in those studies. Hence, this study aimed to identify risk factors for Metabolic syndrome using objective information provided by the relevant companies and longitudinal data provided in health examinations. [ 2] Methods: In total, 1211 male workers of three manufacturing companies, including shift workers, were surveyed annually for 4 years. Data on age, smoking, drinking, physical activity, length of shift work, type of shift, past history, waist circumference, blood pressure, blood sugar, triglyceride, and high-density cholesterol (HDL) were collected and analyzed using generalized estimating equations (GEE) to identify the risk factors for Metabolic syndrome. [ 3] Results: In the multivariate analysis of Metabolic syndrome risk factors, age (OR = 1.078, $95\%$ CI: 1.045–1.112), current smoking (OR = 1.428, $95\%$ CI: 1.815–5.325), and BMI (OR = 1.498, $95\%$ CI: 1.338–1.676) were statistically significant for day workers ($$n = 510$$). Additionally, for shift workers ($$n = 701$$), age (OR = 1.064, $95\%$ CI: 1.008–1.174), current smoking (OR = 2.092, $95\%$ CI: 1.854–8.439), BMI (OR = 1.471, $95\%$ CI: 1.253–1.727) and length of shift work (OR = 1.115, $95\%$ CI: 1.010-1.320) were statistically significant. Shift work was associated with a higher risk of Metabolic syndrome (OR = 1.093, $95\%$ CI: 1.137–2.233) compared to day workers. For shift workers, shift work for more than 20 years was associated with Metabolic syndrome (OR = 2.080, $95\%$ CI: 1.911–9.103), but the dose–response relationship was not statistically significant. [ 4] Conclusions: This study revealed that age, current smoking, BMI, and shift work are potential risk factors for Metabolic syndrome. In particular, the length of shift work (>20 years) is a potential risk factor for Metabolic syndrome in shift workers. To prevent metabolic syndrome in shift workers, health managers need to actively accommodate shift workers (especially those who have worked for more than 20 years), current smokers, and obese people. A long-term cohort study based on objective data is needed to identify the chronic health impact and the risk factors of shift work. ## 1. Introduction Metabolic syndrome (MetS) is a state in which several conditions, including hyperglycemia, hypertension, hyperlipidemia, and obesity, occur concurrently [1], and it is defined as a cluster of risk factors that can directly facilitate the onset of arteriosclerotic cardiovascular disease [2]. The prevalence of Metabolic syndrome is rising steadily in developed and developing countries [3,4,5]. A meta-analysis revealed that Metabolic syndrome is associated with an elevated risk of coronary artery disease, myocardial infarction, and stroke [6,7]. Risk factors for Metabolic syndrome include aging [8], biological sex [9], body weight [10], low level of physical activity [11], alcohol consumption [12], smoking [13], and high-calorie intake [14]. The International Labour Organization defines shift work as a “method of organization of working time in which workers succeed one another at the workplace” during normal hours (9:00–18:00) or other hours and night hours [15]. The percentage of shift workers in the European Union rose from $17\%$ in 2010 to $21\%$ in 2015 [16], and in Korea, $9.7\%$ of the working population were shift workers in 2017, with a continuous increase in the percentage over time [17]. Other studies on the association between shift work and Metabolic syndrome do not always show a positive association between the two factors [18,19,20,21,22,23]. The discrepancy in these results is likely due to cross-sectional design and non-objective data. Most previous studies have adopted cross-sectional designs. In a recent systematic literature review, the number of cross-sectional studies among articles meeting the researcher’s criteria was found to be much higher than cohort studies [24,25,26]. Previous studies have used subjective data from questionnaires to categorize work schedules crudely into shift and non-shift workers [27,28,29]; for example, a recent retrospective cohort study analyzed the association between Metabolic syndrome and shift work types using time series data [30] and another study investigated the association between the number of shifts and Metabolic syndrome components by calculating the number of shifts using daily work hours data. In light of the results [31], it is important to address the shortcomings of cross-sectional studies and non-objective data. Therefore, this study aimed to identify the relationship between shift work and Metabolic syndrome using four-year health examination data of field workers, including shift workers working in manufacturing companies, and information related to shift work provided by the companies. ## 2.1. Study Design and Participants This study was conducted using the health examination data of male workers (including shift workers) in three manufacturing companies. A total of 1438 workers who completed an annual health examination for 4 years from 2015–2018 were selected. From this population, workers with missing data on some essential tests required for the diagnosis of Metabolic syndrome ($$n = 28$$), workers diagnosed with Metabolic syndrome in 2015 ($$n = 158$$), and workers who experienced a change in their shift work schedule between 2015 and 2018 ($$n = 41$$) were excluded, resulting in a total of 1211 participants being selected, of whom 701 were shift workers, and 510 were day workers (Figure 1). At each health examination, a physician questioned workers about their work department, length of employment, shift work status, and changes in shift schedules. Shift workers were required to complete a night-shift questionnaire during their health checkups. This questionnaire contained questions about the type of shift work and the length of shifts. ## 2.2. Shift Work Status In this study, shift work and shift workers were defined by the regulations of the Korean Ministry of Employment and Labor [30]. Shift work was categorized into rotating 8 h shifts and rotating 12 h shifts. Rotating 8 h shifts were divided into morning, evening, and night shifts. Rotating 12 h shifts were divided into day shifts and night shifts. The companies participating in the study consisted of rotating 8 h and rotating 12 h shifts. The worker was divided into daily work and shift work according to their department and job. The shift work consisted of two shifts and three shifts. However, changes in the type of shift work were often experienced according to the circumstances of the company. The duration of the shift was verified by the company’s health manager through the employee’s work history based on the employee’s personnel information data. Therefore, the duration of the shift is measured from the time of joining the company to the period of study. The type of shift work was confirmed through health checkup questionnaires and doctor-led interviews but was limited to the study period due to complex shift work pattern changes and memory dependence. ## 2.3. Assessment of Metabolic Syndrome In this study, Metabolic syndrome was diagnosed based on the modified National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III criteria [32]. Metabolic syndrome were diagnosed if at least three of the following five criteria were met:Blood pressure (BP) ≥ $\frac{130}{85}$ mmHg or currently undergoing treatment for hypertension;Fasting glucose ≥ 100 mg/dL or currently undergoing treatment for elevated blood sugar;High serum triglyceride (≥150 mg/dL) or currently undergoing treatment for dyslipidemia;Low serum high-density lipoprotein (HDL): <40 mg/dL for males, <50 mg/dL for females, or currently undergoing treatment for dyslipidemia;Waist circumference (WC) ≥90 cm for males, ≥80 cm for females. ## 2.4. Data Collection and Measures Past history, smoking, drinking, physical activity, length of shift work, and type of shift work were recorded for the period from 2015 to 2018 using annual history-taking and self-reported questionnaires. Waist circumstance, obesity, blood pressure, blood glucose, triglyceride, and HDL were determined with physical examination and blood tests. Smoking status was categorized into current smoker and non-smoker. Drinking was divided into drinker (drinking alcohol at least once a week) and non-drinker (drinking alcohol less than once a week). Physical activity was defined as aerobic activity and measured using the Korean version of the short form of the International Physical Activity Questionnaire [33]. Physical activity was divided into adequate physical activity and inadequate physical activity, based on the criteria of moderate-intensity exercise for ≥2.5 h per week or high-intensity exercise for ≥1.25 h per week (one minute of high-intensity exercise = two minutes of moderate-intensity exercise). The duration of the shift was verified by the company’s health manager through the employee’s work history based on the employee’s personnel information data. Thus, the length of shift work was defined as the number of years of shift work since hiring, and the type of shift work was confirmed through health checkup questionnaires and doctor interviews. However, the type of shift was determined based on the type of shift work performed during the study period due to complex shift work pattern changes and memory dependence. ## 2.5. Statistical Analysis First, we compared age, drinking status, smoking status, and physical activity in 2015 and differences in the prevalence of Metabolic syndrome in 2015 and 2018 between day workers and shift workers using t-tests and chi-square tests. The length and type of shift among shift workers were analyzed according to the mean (standard deviation) and frequencies. We used generalized estimating equations (GEE) to analyze the risk factors for Metabolic syndrome based on the four years of annual health examination data for each individual worker. The risk factors of Metabolic syndrome in the day workers and shift workers were analyzed by performing multivariate analyses. The factors used in the analyses are the well-known risk factors for Metabolic syndrome, namely age, smoking, drinking, physical activity obesity, and shift work. Length and type of shift work were also used in the analysis related to shift workers. Then, we analyzed the effects of shift work and shift hours, which are statistically significant factors in multivariate analysis, on Metabolic syndrome. The data were analyzed using SPSS software (Version 25.0; IBM Corp., Armonk, NY, USA). ## 3. Results In 2015, the first year of the study, age ($$p \leq 0.135$$), drinking ($$p \leq 0.588$$), BMI ($$p \leq 0.784$$), and physical activity ($$p \leq 0.319$$) did not differ between day workers and shift workers. Current smoking ($p \leq 0.001$) differed significantly between day workers and shift workers. Among shift workers, the mean length of shift work was 16.27 + 9.11 years and there were more workers in three shifts ($87.75\%$) than in two shifts ($12.25\%$). The prevalence of Metabolic syndrome in 2015 and 2018 was statistically different between day workers and shift workers ($p \leq 0.001$, $$p \leq 0.028$$), respectively (Table 1). The incidence rates of metabolic syndrome in day workers and night-shift workers were 5.3 and 10.5 cases per 1000 person-years, respectively. As a result of the multivariate analysis of Metabolic syndrome risk factors, age (OR = 1.069, $95\%$ CI: 1.049–1.090), current smoking (OR = 2.006, $95\%$ CI: 1.428–2.817) and BMI (OR = 1.378, $95\%$ CI: 1.294–1.467) were found to be statistically significant for all workers. Age (OR = 1.078, $95\%$ CI: 1.045–1.112), current smoking (OR = 1.428, $95\%$ CI: 1.815–5.325), and BMI (OR = 1.498, $95\%$ CI: 1.338–1.676) were statistically significant for day workers. Furthermore, for shift workers, age (OR = 1.064, $95\%$ CI: 1.008–1.174), current smoking (OR = 2.092, $95\%$ CI: 1.854–8.439), BMI (OR = 1.471, $95\%$ CI: 1.253–1.727), and length of shift work (OR = 1.155, $95\%$ CI: 1.010–1.320) were statistically significant (Table 2). In the multivariate analysis of the effect of shift work on Metabolic syndrome, shift work was positively associated with Metabolic syndrome in all models (Model 1 (OR = 1.204, $95\%$ CI: 1.080–1.479), Model 2 (OR = 1.444, $95\%$ CI: 1.164–1.791), Model 3 (OR = 1.093, $95\%$ CI: 1.137–2.233) (Table 3)). In the multivariate analysis performed by dividing the three groups by length of shift work, there was a positive correlation with Metabolic syndrome in the group that worked shifts for more than 20 years in all models (model 1 (OR = 2.227, $95\%$ CI: 1.469–3.376), Model 2 (OR = 2.847, $95\%$ CI: 1.500–5.403), Model 3 (OR = 2.080, $95\%$ CI: 1.911–9.103)). The dose–response relationship was statistically significant in Model 2, but not significant in Model 1 and Model 3 (Table 4). ## 4. Discussion This study retrospectively reviewed the 4-year annual health examination data for the field workers of manufacturing companies. In addition, the work history information obtained through the company’s personnel data was used to confirm the shift work status and shift work period and results of recurrent annual tests for the same workers; we also ensured that data were adjusted for intra- and inter-personal changes, as was the case for the panel data. The incidence rates of metabolic syndrome in day workers and night shift workers were 5.3 and 10.5 cases per 1000 person-years, respectively, in this study. This is a considerable difference compared to the prevalence ($25.6\%$ in men and $12.4\%$ in women) in the analyzed data obtained from the National Health and Nutrition Examination Survey conducted from 2016 to 2018 in Korea [34]. A possible explanation for this is the difference in age distribution and activity level. The proportion of people aged 60 and older with a high prevalence of metabolic syndrome is very low in this study ($0.5\%$ vs $16.4\%$). Additionally, although there was a difference in terms of the measurement method (physical activity VS sitting time), the normal sitting time rate was found to be $42.9\%$. However, on the other hand, the appropriate physical activity rate ($91.77\%$ for day workers and $88.24\%$ for shift workers) in this study was found to be very high. In this study, shift work was a significant risk factor for Metabolic syndrome, which is similar to previous findings [35,36]. Previous studies have suggested that circadian rhythm disturbance may induce Metabolic syndrome [37,38,39]. A number of previous studies demonstrated that shift work does not significantly increase metabolic syndrome risk. A study of 3008 shift workers and 8015 day workers, specifically male railway workers, who worked for more than 10 years found no link between shift work and metabolic syndrome [40]. However, since the age was limited to 40 years of age or older, it is possible that the development of metabolic syndrome in young workers who work shifts was overlooked. In addition, in a study of 3007 male employees (1700 day and 1307 shift workers) at a car-manufacturing company, two-shift work was associated with a lower risk of metabolic syndrome than day work [18]. However, this study had limitations in that it was a cross-sectional study in which the status of shift work in the past was unknown and that it did not consider differences in work according to department. The present study included young workers as well as older workers, and objectively identified the past shift work status. Additionally, since field workers were included in the study, the difference in work by the department may not be large. The difference between these study participants and methods might help to explain the difference in results. Furthermore, our findings showed elevated ORs for Metabolic syndrome after 20 years of shift work. Similar findings have been reported previously. A meta-analysis of 13 observational studies published between 1971 and 2013 [41] confirmed a dose–response relationship between the length of shift and Metabolic syndrome. A cross-sectional study of 134 male blue-collar workers also reported that the risk for Metabolic syndrome increased with >30 years of shift work [42]. Considering that examining the onset of chronic disease, such as Metabolic syndrome, as an outcome in relation to shift work requires the study of a specified time period, it appears that the length of shift work [43] and the frequency of shift work [23] are more meaningful determinants than working shifts. However, in this study, the dose–response relationship could not be confirmed. Further research using longitudinal data should be carried out in the future. Obesity is a known risk factor for Metabolic syndrome, and this was also observed in this study. The results of this study were consistent with previous reports that BMI is related to WC, an Metabolic syndrome component, and shift work is positively correlated with obesity [44,45]. Smoking was significantly associated with Metabolic syndrome. It has been reported that the risk of Metabolic syndrome increases with increasing cigarette consumption [12,13]. Although drinking is also a lifestyle factor linked to the onset of Metabolic syndrome [46,47], no significant differences were observed in this study. Changes in drinking frequency and drinking status should be further examined in a larger study population. Age is a known risk factor for Metabolic syndrome [48,49], as confirmed in this study; this was consistent with the findings of previous studies. In addition, several studies also report no difference in physical activity between day and shift workers [50,51], which is consistent with our findings. In this study, a significant association between the type of shift work and Metabolic syndrome was not observed. Previous study findings on Metabolic syndrome and rotating 12 h shifts or 8 h shifts are inconsistent [52,53]. It is possible that the observation period of shift work patterns in this study was too short (4 years) and the changes in shift work patterns were too complex, which led to a scarcity of data in the analysis. Considering that two-shift work is more likely to cause circadian rhythm disturbances than three-shift work, it is necessary to extend the observation period in future studies. ## 4.1. Limitations This study has a few limitations. First, the study period was rather short (4 years). The Korean government launched a worker’s health examination program for shift workers in 2014 [2], and the program is still in effect. However, prior to this program, collecting data on factors such as length of shift work and type of shift work was difficult. Longer follow-up studies of shift work should be conducted to investigate the health impacts. Second, we did not have information on workers’ education level and dietary patterns, so we were unable to adjust for these variables as potential confounders in the multivariate analyses. Future studies should include these variables to clarify the metabolic risk mechanism affecting shift workers. Third, molecular-based studies are important for identifying any contributing mechanisms. However, this study was based on health screening, meaning that adiponectin, leptin, other inflammatory markers, or oxidative stress markers were not included in this study. Fourth, the use of alcohol units for drinking and pack-years for smoking are ways to further increase the objectivity of the study. However, due to a lack of data, alcohol units, and pack-years were not used in this study. Since the lack of objectivity of the variables can cause confusion in the interpretation of the results and limit comparisons with other studies, future study designs should aim to further increase the objectivity of the variables. Finally, we could not collect data on sleep conditions due to missing data. Sleep deprivation is linked to circadian misalignment in shift workers [54], and circadian misalignment has been identified as a fundamental cause of poor cardiovascular health [55]. Future studies should include sleep status to clarify mechanisms affecting cardiovascular diseases. ## 4.2. Strengths Despite these limitations, this study used a 4-year longitudinal dataset obtained via annual health examinations for the same population, similar to panel data, and a time series analysis was performed using GEE. Moreover, the classification of shift workers and day workers and shift work period was performed according to the work histories available in the company’s personnel records. Hence, there is little risk of errors in participant classification and shift work duration. ## 5. Conclusions In conclusion, this study investigated the same workers over a 4-year period using annual data. In particular, by using objective data, the risk of errors in shift work classification and shift work period was lowered. The study revealed that shift work, the length of shift work, age, obesity, and smoking are potential risk factors for Metabolic syndrome. 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--- title: Hydrogen Sulfide Enhances Browning Repression and Quality Maintenance in Fresh-Cut Peaches via Modulating Phenolic and Amino Acids Metabolisms authors: - Li Wang - Chen Zhang - Kaili Shi - Shouchao Chen - Jiawei Shao - Xingli Huang - Mingliang Wang - Yanyan Wang - Qingyuan Song journal: Foods year: 2023 pmcid: PMC10048349 doi: 10.3390/foods12061158 license: CC BY 4.0 --- # Hydrogen Sulfide Enhances Browning Repression and Quality Maintenance in Fresh-Cut Peaches via Modulating Phenolic and Amino Acids Metabolisms ## Abstract Effects of hydrogen sulfide (H2S) on the browning and quality maintenance of fresh-cut peach fruit were studied. The results showed that H2S treatment repressed the development of surface browning, suppressed the increase in respiration rate and weight loss, and delayed the decline of firmness while soluble solids content (SSC) and microbial growth were unaffected during storage. H2S treatment maintained higher contents of phenolic compounds, especially neo-chlorogenic acid, catechin, and quercetin, and delayed the degradation of phenolic compounds by enhancing the activities of phenolic biosynthesis-related enzymes and inhibiting the oxidative activities of polyphenol oxidase (PPO) in comparison with control. Moreover, H2S stimulated the accumulation of amino acids and their derivatives including proline, γ-aminobutyric acid (GABA), and polyamines (PAs) via enhancing biosynthesis and repressing degradation compared to control. These results suggested that H2S treatment enhanced the accumulation of phenolic, amino acids, and their derivatives by modulating phenolic and amino acids metabolisms, which contributed to the higher antioxidant activity and membrane integrity maintenance, ultimately repressing browning development and maintaining the quality. Therefore, the current study speculated that H2S might be a promising approach for browning inhibition and quality maintenance in fresh-cut peach fruit. ## 1. Introduction Fresh-cut products have become an important part of the fruits and vegetables industry and family consumption owing to their convenience and freshness in recent years [1]. Fresh-cut peach fruit is extremely popular among consumers and the processing industry due to its distinct flavor, and special nutritional and functional ingredients such as phenolics, vitamins, minerals, and amino acids [2]. However, cutting as a kind of mechanical damage destroys the integrity of fruit cells and accelerates the loss of nutrients [3]. The cut caused by cutting in peach fruit is easy to brown, and certainly susceptible to microbial infections and quality deterioration, which has a negative impact on market value. At present, various physical and chemical treatments containing nitric oxide (NO), ascorbic acid, and vacuum packaging have been performed to alleviate the browning of fresh-cut peaches [4,5], but it is still necessary to explore an effective technique that can not only inhibit browning but also maintain fruit quality. Hydrogen sulfide (H2S) is traditionally known as a poisonous gas, which is harmful to human health. However, 34–65 μmol kg−1 of H2S concentration are found in various types of mammalian cells and play a positive role in physiological regulation [6]. Similarly, plants can also produce H2S with the content of about 100 μmol kg−1 [7,8]. H2S in low concentrations acts as a gaseous regulator like carbon monoxide and NO, which can freely pass through the cell membrane and directly combine with the corresponding target cell or molecule to regulate the process of growth and development in plants [9]. Meanwhile, increasing studies have implicated that low concentrations of H2S play crucial roles in regulating physiological metabolic processes, retarding senescence, and resisting abiotic stresses of horticultural products [9,10]. A previous study elucidated that H2S treatment eliminated the excess reactive oxygen species (ROS) by promoting enhanced antioxidant enzymes activities, thus delaying the maturation, and expanding the shelf life of kiwifruit [9]. Moreover, recent research has unveiled that H2S treatment has positive effects on the browning suppression by regulating the antioxidant and phenolic metabolisms of fresh-cut fruits and vegetables, such as apples and lotus roots [11,12,13]. A quite low concentration of H2S is used in these studies, which is within the physiological concentration range of plants and the human body, suggesting that H2S could be a safe and effective strategy for post-harvest treatment of fruits and vegetables. However, there is limited evidence on the effect of H2S treatment on browning inhibition and quality maintenance of fresh-cut peaches. As a kind of unique nutrient in horticultural crops, the function of bioactive substances in human health has attracted scientists to study how to maintain or even enhance their content via post-harvest treatments, processing techniques, and storage conditions [14]. Cutting, as an abiotic stress, has been proven to be a convenient and innovative technique to enhance bioactive substance contents in fruits and vegetables [15]. Phenolic compounds and amino acids are considered to be two important types of bioactive substances in fruits and vegetables with a high proficiency to defend against chronic diseases such as cardiovascular disease and cancer [16]. Numerous studies have shown that the accumulation of phenolic compounds induced by cutting improves total antioxidant capacity by providing hydrogen and decomposing peroxides to prevent cells from oxidative injury in fruits and vegetables such as carrots, strawberries, and pitaya fruit [17,18,19]. Huang et al. [ 4] reported that peaches also responded to cutting stress and promoted a higher content of total phenolic at earlier storage, whereas NO treatment enhanced the accumulation of total phenolic. Moreover, the content of phenolic compounds in fruits and vegetables is linked to the activities of phenolic anabolism enzymes, comprising L-phenylalanin ammonia-lyase (PAL), cinnamate-4-hydroxylase (C4H), and 4-coumarate: coenzyme A ligase (4CL). Prior research demonstrated that high activities of phenolic anabolism enzymes along with enhanced contents of phenolic compounds play effective roles in diminishing ROS and protecting membranes from oxidative damage in fresh-cut pitaya [17] and carrots [18]. Amino acids and their derivatives containing proline, GABA, and PAs, are not only bioactive compounds, but also important substrate for abiotic stresses resistance [20]. Proline, as a protein-genic secondary amino acid, could protect proteins and eliminate ROS, which helps to stabilize membrane structure and keeps cells from oxidative stress [21]. Liu et al. [ 22] demonstrated that proline-treated potatoes induced the higher concentration of intrinsic proline and antioxidant activity, contributing to the suppression of browning occurrence. GABA, a non-protein amino acid, is a vital signal molecule to regulate the adverse stresses of post-harvest storage [23]. In carrots, a large amount of GABA was found in shredded tissue, which indicated that GABA responded to cutting stress [24]. Meanwhile, Gao et al. [ 25] reported that GABA treatment had a beneficial effect on browning suppression in fresh-cut potatoes, indicating that GABA was involved in browning development. PAs, comprising putrescine (Put), spermidine (Spd), and spermine (Spm), play a positive role in regulating cellular osmolarity due to their polycationic nature in physiological pH values, and their accumulation contributed to the senescence resistance in fresh-cut products [26]. However, there is little to report on how H2S treatment regulates phenolic and amino acids metabolisms in fresh-cut peaches. This study aims to study [1] the changes of quality attributes, including firmness, SSC, weight loss, browning index (BI), and total aerobic bacterial count (TABC), [2] the changes of phenolic metabolism, including phenolic compounds and related enzyme activities, [3] the changes of amino acid metabolism, including proline, GABA, and PAs, and related enzyme activities, which could provide a new perspective into the effect of H2S on browning suppression and quality maintenance. ## 2.1. Fruit Material and Treatment Peach fruit (Prunus persica Batsch cv. ‘ Jinhuangjin’) used for the experiment were harvested at a maturity of 57 N firmness and $16\%$ SSC in Hefei, Anhui, China. All work surfaces and knives were wiped by $75\%$ ethanol solution. Two hundred and sixteen fruits with similar shapes, uniform sizes, and without damage were chosen and used for three replicates, then disinfected with 200 µL L−1 sodium hypochlorite for 2 min for further treatment. The air-dried peaches were cut into uniform cubes (1 × 1 × 1 cm) and randomly divided into the following two groups: (i) one group was fumigated with H2S gas released by 1.6 mmol L−1 sodium hydrosulfide (NaHS) solution in 85 L closed containers for 24 h, while (ii) the other group was fumigated with purified water in 85 L closed containers for 24 h as control. After fumigation, the fruits were placed in the colorless transparent plastic boxes (170 mm × 120 mm × 60 mm) and stored at 4 ± 1 °C for 12 h. The time when the control and H2S treatment ended was defined as 0 h. Mesocarp samples were collected every three hours for the determination of quality attribute indexes, including firmness, SSC, weight loss, respiration rate, BI, and TABC, and frozen in liquid nitrogen, then stored at −20 °C for biochemical analysis. ## 2.2. Quality Attributes Firmness was evaluated by a firmness tester (GY-4-J, TOP Instruments Co., Ltd., Hangzhou, China) with an 8 mm diameter probe, and the result was expressed as N. SSC was determined with a portable refractometer (PAL-1, ATAGO, Tokyo, Japan) and the result was presented as %. Weight loss was expressed by evaluating the weight of fruits at each sampling point, and the following formula was used to calculate: [(M0 − M1)/M0] × $100\%$, where M0 and M1 represented the initial fruit weight and the fruit weight at each sampling point, respectively. The respiration rate was determined by a fruit and vegetable breathing tester 3051H (Technology-Research Institute, Nanjing, China), and the result was expressed as mg kg−1 h−1. A chroma meter (CR-400, Konica Minolta Sensing, Inc, Tokyo, Japan) was used to assess the lightness (L), and the chromaticity of green (a) and yellow (b) on two opposite sides of fresh-cut peach cubes. Browning index (BI) was calculated by following the formula: [100 × (p − 0.31)]/0.172, where p = (a + 1.75 × L)/(5.645 × L + a* − 3.012 × b), BI increase = [(BIp − BIo)/BIo] × $100\%$, where BIp and BIo represented BI on day p and zero, respectively [27]. TABC was assayed by the method of Adiani et al. [ 28]. ## 2.3. Contents of Total Phenolic, Total Flavonoid, and Individual Phenolic Compounds The contents of total phenolic and total flavonoid were measured with the method described by Wang et al. [ 29]. Folin-Colorimetric reagent was applied to measure total phenolic content, and the absorbance was recorded at 765 nm. 5 mL of methanol containing $1\%$ HCl were used to extract the flavonoid from two grams of mesocarp tissue. 1 mL of the supernatant was blended with 1 mL of AlCl3 ($3\%$) and 0.5 mL of ethanol ($30\%$) for 20 min, and the absorbance was recorded at 430 nm. The contents of total phenolic and total flavonoid were presented as g kg−1 based on the fresh weight (FW) of gallic acid equivalent and rutin equivalent, respectively. Individual phenolic compounds were measured according to the description of Wang et al. [ 30] with high performance liquid chromatography (HPLC, Waters 2695, Milford, MA, USA) to identify and quantify by analyzing the retention times and standard curve, respectively, and the results were represented as mg kg−1 FW. ## 2.4. Activities of Key Enzymes Related to Phenolic Metabolism The activities of PAL, C4H, and 4CL were assayed using the description of Wang et al. [ 29], while PPO activity was examined following the method of Liu et al. [ 22]. One unit of these enzyme activities was described as the enzyme capacity causing a 0.01 absorbance variation at 290, 340, 333, and 420 nm per min, respectively, and results were represented as U kg−1 FW. ## 2.5. GABA Content and Glutamate Decarboxylase (GAD) Activity Two grams of peach tissue were ground with 5 mL of 50 mmol L−1 lanthanum chloride and centrifuged at 12,000× g for 10 min, then 2 mol L−1 KOH was added to the supernatant. After re-centrifugation, the supernatant was prepared for GABA content determination according to the description of Wang et al. [ 31]. The absorbance was measured at 645 nm, and the result was assessed using the GABA standard curve and represented as g kg−1 FW. GAD activity was estimated as GABA formation following the method of Wang et al. [ 31] from two grams of peach tissue. One unit of GAD activity was described as the enzyme capacity that generated 1 g of GABA per min. The result was represented as U kg−1 FW. ## 2.6. Proline Content and Related Metabolic Enzymes Activities The content of proline and the activities of proline-5-carboxylate synthetase (P5CS), ornithine δ-amino-transferase (OAT), and proline dehydrogenase (PDH) were measured from two grams of tissue sample based on the method of Wang et al. [ 30]. Proline content was assayed with the calibration curve and represented as g kg−1 FW. The enzyme capacity causing a 0.01 absorbance variation at 340 nm per min was described as one unit of P5CS and PDH activity. One unit of OAT activity was described as the enzyme capacity of generating 1 mol P5C per min at 510 nm. The activities of these enzymes were represented as U kg−1 FW. ## 2.7. PAs Contents The identify and quantify of PAs was referred to the modified method of Wang et al. [ 30] by using HPLC with a UV detector at 254 nm from one gram of peach tissue. Samples were ground with 3 mL of perchloric acid ($5\%$) for 1 h. After centrifugation, 2 mL of 2 mmol L −1 NaOH, 10 μL benzoyl chloride, and 2 mL of supernatant composed of the reaction system, which was then reacted for 30 min at 37 °C. Then 3 mL of saturated NaCl and 2.5 mL of precooled ether were blended with the reaction mixture. After re-centrifugation, the ether phase was dried and diluted in 0.5 mL of methanol, then passed through a 0.22 μm filter for PAs measurement. The mobile phase was methanol at a concentration of $65.5\%$ (v/v), injected sample volume was 20 μL, and the column operating temperature and flow rate were maintained at 30 °C and 0.8 mL min−1, respectively. PAs quantification was based on a calibration curve and the result was presented as mol kg−1 FW. ## 2.8. Activities of Key Enzymes Related to PAs Metabolism Arginase activity was assayed based on the modified description of Bokhary et al. [ 32], and one unit of arginine activity was described as the capacity of 1 mmol urea formation per min. The activities of ADC, ODC, PAO, and DAO were determined following the method of Wang et al. [ 26], while one unit of ADC or ODC activity was described as the capacity of 1 mol of Agm or Put production per hour at 254 nm, and one unit of PAO or DAO activity was described as the enzyme capacity causing a 0.01 absorbance variation at 550 nm per min. Results of enzyme activities were presented as U kg−1 FW. ## 2.9. Data Analysis All experiments were assayed with a completely random design and measured three replicates. Results were represented as mean ± standard error. The difference between treatments was analyzed using a one-way analysis of variance (ANOVA) and SPSS (version 9.1, Chicago, IL, USA), and a p-value less than 0.05 was considered as significant. ## 3.1. Firmness, SSC, Weight Loss, Respiration Rate, BI, and TABC of Fresh-Cut Peaches As shown in Table 1, the firmness showed a slightly slow downward trend during storage time while H2S treatment maintained a slightly higher firmness of fresh-cut peaches in the middle stage of storage, but there was no significant difference between the control and H2S treatments. SSC showed a similar tendency with a small range of change in control and H2S treatment during storage time. The respiration rate of fresh-cut peaches rose considerably during storage. And the respiration rate of fresh-cut peaches in control was always higher than that in H2S treatment. The weight loss rate of fresh-cut peaches increased dramatically within 3 h of storage and then slowed down due to the increased contact area with air during cutting. Compared with the control, the weight loss rate of H2S treatment was lower as a whole, and the growth range of weight loss rate was also smaller. A steady rise in BI of fresh-cut peaches was discovered throughout the storage period, and the significant lower BI was found in H2S treatment ($p \leq 0.05$). Moreover, H2S treatment showed a lower TABC compared to control in fresh-cut peaches during storage, but there was no significant difference with the control. ## 3.2. Contents of Total Phenolic, Total Flavonoid, and Individual Phenolic Compounds of Fresh-Cut Peaches Total phenolic and total flavonoid contents in both control and H2S treatments exhibited an increasing tendency during storage (Figure 1). H2S treatment induced the accumulation of total phenolic and total flavonoid in fresh-cut peaches and maintained them at the higher levels in comparison with control during storage. After 12 h of storage, the contents of total phenolic and total flavonoid in H2S-treated fresh-cut peaches were $3.0\%$ and $8.7\%$ higher than those in control, respectively (Figure 1A,B). The changes of individual phenolic compounds were displayed in Table 2. Eight phenolic compounds contained cyanidin-3-glucoside, catechin, chlorogenic/neo-chlorogenic acid, quercetin-3-rutinoside/glucoside/galactoside, and kaempferol-3-rutinoside were identified and quantified in ‘Jinhuangjin’ fresh-cut peaches, with neo-chlorogenic acid being the most abundant followed by chlorogenic acid. The contents of these compounds in both treatments showed a trend of first increasing and then decreasing, in which neo-chlorogenic acid reached the peak value at 9 h, while other individual phenolic compounds peaked at 6 h. The contents of neo-chlorogenic acid, cyanidin-3-glucoside, catechin, and chlorogenic acid in H2S-treated fresh-cut peaches ranged from 48.37–61.59, 0.77–1.95, 3.14–5.56, and 15.56–21.10 mg kg−1 during storage, respectively. The increase of quercetin-3-rutinoside/glucoside/galactoside and kaempferol-3-rutinoside was remarkably promoted by H2S treatment in comparison with control. ## 3.3. Activities of Key Enzymes Related to Phenolic Metabolism PAL activity of fresh-cut peaches increased continuously during the whole storage time, and the dynamic change of PAL activity was similar to that of total phenolic content. Meanwhile, PAL activity was significantly ($p \leq 0.05$) different between control and treatment in fresh-cut peaches (Figure 2A). Moreover, C4H and 4CL activities increased gradually and then decreased after 9 h (Figure 2B,C). H2S treatment promoted the higher activities of 4CL and C4H, which brought a $5.1\%$ and $16.7\%$ increase in 4CL and C4H activity at 9 h in comparison with the control, respectively. PPO activity dramatically declined at 3 h and then rose until the end of storage in control, while it continued to decline in H2S treatment. PPO activity in H2S treatment was $66.7\%$ lower than that of the control after 12 h of storage (Figure 2D). ## 3.4. GABA Content and GAD Activity GABA content of fresh-cut peaches in both treatments rose rapidly with a storage period (Figure 3A). H2S treatment induced and maintained a higher level of GABA content compared to the control. At the end of storage, GABA content in H2S treatment increased by $10.7\%$ compared with the control. GAD activity increased gradually and decreased thereafter, which peaked at 9 h (Figure 3B). H2S treatment maintained higher GAD activity, with increases of $13.5\%$ and $11.9\%$ compared to control at 3 h and 9 h, respectively. ## 3.5. Proline Content and Related Metabolic Enzymes Activities Proline content generally increased first and decreased afterwards, which peaked at 3 h in H2S treatment and 6 h in the control, respectively (Figure 4A). H2S treatment induced the proline accumulation and retained a higher level than control during storage. The level of proline in H2S-treated fresh-cut peaches was three times higher than in control at 3 h. The change of P5CS activity was consistent with that of proline content. H2S treatment induced higher levels of P5CS activity than control during 3 h of storage (Figure 4B). OAT activity in H2S treatment peaked at 9 h, whereas OAT activity in control increased steadily (Figure 4C). PDH activity increased in control while decreasing in H2S treatment at 3 h, then gradually increased in both treatments. H2S treatment up-regulated OAT activity but inhibited PDH activity, which promoted proline accumulation (Figure 4C,D). ## 3.6. PAs Contents and Related Key Enzymes Activities Put was the main amine in fresh-cut peaches, followed by Spd and Spm (Figure 5A–C). The contents of Put and Spm increased first and then decreased during storage, which reached the peak at 9 h and 6 h, respectively. The contents of Put and Spm in H2S treatment were significantly ($p \leq 0.05$) higher than those in control (Figure 5A,C). The Spd content of the two treatments exhibited an increasing trend, and the Spd content of H2S treatment was always significantly ($p \leq 0.05$) higher than that of control (Figure 5B). After 12 h of storage, the Put and Spd contents of fresh-cut peaches treated with H2S were $19.8\%$ and $6.7\%$ higher compared with control, respectively. Therefore, H2S treatment may promote the accumulation of amines in fresh-cut peaches. The activities of arginase, ADC, ODC, PAO, and DAO of fresh-cut peach in H2S treatment and control showed a trend of first increased and then decreased (Figure 5D–H). Arginase activity in H2S-treated peaches was $14.4\%$ higher than control during 3 h of storage (Figure 5D). The activity of ADC and ODC enzymes peaked at 9 h except for DAO and PAO activities, which reached the peak at 6 h after storage. Compared with the control, H2S treatment significantly ($p \leq 0.05$) enhanced the activities of ADC and ODC, while inhibiting PAO and DAO activities (Figure 5E–H). ## 4. Discussion H2S, as the third primary gaseous transmitter, has been confirmed to be a positive tactic on inhibiting the browning of lotus roots, Chinese water chestnuts, and apples during the fresh-cut procedure [11,12,13]. However, few studies have been conducted on its physiological functions in regulating browning and quality deterioration, the main commercial and industrial problems of fresh-cut peach fruit. Therefore, the current study investigated some quality and physiological indexes to explore the effect of H2S treatment on fresh-cut peach fruit, which could provide valuable data and insights for the endogenous regulation of H2S in post-harvest physiology of horticultural products. Browning and quality deterioration are the principal limited factors on quality maintenance of fresh-cut apples, which have a strong and negative impact on the purchase desire of consumers [13]. In this study, BI, the intensity of the brown color, increased continuously throughout the storage time, whereas the increase of BI in H2S-treated fruit was significantly suppressed. Firmness, SSC, weight loss, respiration rate, and TABC were used to appraise the effect of H2S on the sensory quality and security of fresh-cut peaches, respectively. Results illustrated that the changes of firmness and SSC were relatively stable, which may be related to the shorter storage time. H2S treatment maintained slightly higher firmness and SSC from 3 to 9 h of storage. The lower weight loss in H2S treatment was attributed to more water retention, which led to higher turgor pressure in cells; thereby contributing to the maintenance of firmness. Respiration rate in fresh-cut peaches increased immediately in both control and H2S treatments, which was related to physical injury. Whereas, H2S treatment inhibited the rise of respiration rate, leading to lower weight loss, ultimately contributing to the quality maintenance of fresh-cut peaches. TABC was a significant factor in determining the shelf life of fresh-cut fruit [23], which typically required 6 to 9 log CFU g−1. TABC in control and H2S treatments were all less than 6 log CFU g−1 at the end of storage, revealing the security of fruit during storage time. Moreover, Morteza et al. [ 8] reported that peach fruit contained about 20 μmol kg−1 of H2S, and exogenous H2S treatment increased the H2S content by about 4 times, which was still within the physiological concentration range. The concentration of H2S applied in the present study was also quite low and far below the safe critical concentration of 20 mg L−1, thus indicating that it should not had adverse effects on human health. Therefore, H2S might be a safe and beneficial strategy to inhibit browning and maintain the quality of fresh-cut peaches. Phenolic compounds are commonly acknowledged to be essential antioxidants and important nutrients for human health. Current study showed that cutting-stress promoted the continuous increase of total phenolic and total flavonoid within 12 h in peaches, which coincided with previous results that significant increases in total phenolic content after cutting in pitaya and strawberries [17,19]. Meanwhile, H2S treatment enhanced the cutting-induced accumulation of total phenolic and total flavonoid, which were consistent with H2S-treated fresh-cut lotus roots and apples [11,13], unveiling that the application of H2S combined with cutting stress could synergistically induce the accumulation of total phenolic and total flavonoid during a shorter storage time. Moreover, Li et al. [ 33] reported that phenolic compounds were regarded as the crucial secondary metabolites that contributed to the antioxidant capacity in fruits. In the current study, the higher contents of total phenolic and total flavonoid in H2S-treated fresh-cut peaches were directly associated with the accumulation of individual phenolic profiles, including cyanidin-3-glucoside, catechin, chlorogenic/neo-chlorogenic acid, quercetin-3-rutinoside/glucoside/galactoside, and kaempferol-3-rutinoside. Among these individual phenolic compounds, H2S treatment remarkably enhanced the contents of neo-chlorogenic/chlorogenic acid, and maintained the most obvious neo-chlorogenic acid accumulation in fresh-cut peaches, which indicated neo-chlorogenic acid might act as the main individual phenolic compounds to provide cell antioxidant protection to resist cutting stress. This positive effect of H2S on improving the levels of individual phenolic compounds was also found in glycine betaine-treated intact peach fruit [29], which suggested H2S could induce the phenolic metabolism, thus contributing to the accumulation of phenolic compounds. Furthermore, PAL, 4CL, and C4H are vital enzymes in phenolic metabolism that collaborate to regulate the synthesis and utilization of phenolic compounds [23]. PAL is the principal enzyme that catalyzes phenylalanine into cinnamic acid. 4CL and C4H synthesize the precursors of phenolic profiles 4-hydroxycinnamic acid and p-coumaroyl CoA, respectively. Apart from the enzyme involved in phenolic compounds synthesis, PPO acts as the phenolic compound oxidase enzyme, which promotes the browning process of fruits [29]. Li et al. [ 17] claimed that higher activities of PAL, C4H, and 4CL were accompanied with higher levels of phenolic compounds accounting for membrane stability and enhanced cutting-stress tolerance in methyl jasmonate-treated fresh-cut pitaya fruit. A similar result was also reported in UV-C-treated strawberries [33]. In the current study, the increased activities of PAL, C4H, and 4CL were related to the accumulation of phenolic compounds and antioxidant activity in cutting-stressed peaches after 12 h of storage, while H2S-treated fresh-cut peaches maintained the significantly higher activities, contributing to the enhanced ROS scavenging and membrane protection. In addition, this study showed that the suppressed PPO activity along with the enhanced contents of the phenolic compounds in H2S treatment might play vital roles in the inhibition of surface browning in fresh-cut peaches, which was consistent with H2S-treated apples [13] and lotus roots [11]. Therefore, it could be postulated that H2S treatment has a positive effect on regulating phenolic metabolism during storage, which not only contributed to the browning inhibition, but also to the enhancement of phenolic compounds, thus improving sensory quality and nutritional value. Accumulating studies point out that proline, GABA, and PAs are not only known as vital bioactive components, but also stress regulator molecules that play important roles in coping with browning development and adverse conditions in fruits [21,34]. Proline acts as an osmotic regulator to stabilize the structure of membranes and proteins and maintain cellular functions by scavenging hydroxyl radicals [2]. P5CS, OAT, and PDH are closely related to the accumulation or degradation of proline content, among which P5CS and OAT mediate the biosynthesis of proline via the glutamate and ornithine pathways, while PDH regulates the degradation of proline through the oxidation of proline to pyrroline-5-carboxylate, thus regulating its accumulation and metabolism [35]. Positive correlations between the accumulation of proline and the inhibition of cutting stress induced browning in fresh-cut fruits [36]. Liu et al. [ 22] showed that enhanced proline content contributed to the browning alleviation by regulating browning-related enzymes and substrates in proline pretreated fresh-cut potatoes. The current study showed that the accumulation of proline was associated with improved activities of P5CS and OAT and reduced PDH activity, while H2S treatment remarkably enhanced the biosynthesis enzymes activities and suppressed the degradation enzyme activity, which contributed to retarding the browning of fresh-cut peaches during storage time. Similar results were found in glycine betaine-treated intact peaches, the higher activities of P5CS and OAT promoted proline accumulation to induce the antioxidant capacity, resulting in increased stress resistance [37]. According to these results, H2S treatment-induced higher proline content might have a positive effect on eliminating ROS and restricting lipid peroxidation, thereby contributing to cellular membrane protection and quality maintenance in fresh-cut peaches during storage time. GABA, as a signal molecule, is essential for membrane protection, sub-cellular structures stabilization, and stress regulation, and its accumulation has been frequently discovered under adverse stresses in plants [38]. Prior studies pointed out that enhanced CO2 content and exogenous GABA application were effective in alleviating the browning in pears [2,39]. Here, a dramatic rise in GABA content was recorded in fresh-cut peach fruit, which speculated GABA accumulation was involved in cutting stress. Meanwhile, H2S enhanced the beneficial effect on GABA accumulation of peaches during storage, which was in accordance with a prior study that calcium might act as signal molecular to induce the GABA accumulation under cutting stress [32], indicating that H2S might also play a similar role as a signal molecule. Moreover, the present study found that the accumulation of GABA was attributed to higher GAD activity, which catalyzed glutamate to GABA [40]. H2S treatment maintained higher GAD activity leading to the higher GABA content compared with control, which played beneficial roles in resisting cutting stress. These results were in accordance with CO2-treated pears, in which higher GAD activity was correlated to the accumulation of GABA, which was partly responsible for the browning repression [2]. Furthermore, prior research has demonstrated that GABA could be converted from PAs degradation by DAO and PAO under abiotic stresses [41]. In this study, H2S treatment suppressed the activities of DAO and PAO, speculating that H2S induced the accumulation of GABA was primarily synthesized through GABA shunt rather than PAs degradation pathway. Consistent with CaCl2-treated fresh-cut pear, Chi et al. [ 42] pointed out a low contribution proportion of PAs degradation pathway for GABA generation. PAs also serve as free-radical scavengers and antioxidants to eliminate the cytosolic ROS and maintain cell redox homeostasis [43]. Accumulating evidence suggested that PAs accumulation had positive effects on abiotic stresses in fruits and vegetables [32,44]. Cao et al. [ 45] speculated that the alleviation of fruit softening and browning in peaches might be attributed to the accumulation of Spd and Spm induced by cold stress during early storage. Similar results were also reported in melatonin-treated peaches, in which the reduction of browning was related to up-regulated contents of PAs during cold storage [46]. In current study, Put was the principal PAs, followed by Spm in ‘Jinhuangjin’ peaches, which was consistent with ‘Yuhua’ peaches [30]. Cutting stress, as do cold stress, also generally promoted the increased contents of Put, Spd, and Spm during early storage. Meanwhile, higher PAs contents in H2S-treated peaches were accompanied by lower browning in comparison with control, which might be conducive to maintaining cell redox homeostasis and alleviating browning during storage. Moreover, evidence demonstrated that H2S treatment promoted the enhancement of PAs contents in Trigonella foenum-graecum under cadmium stress [47] and Spinacia oleracea* under drought stress [48], which played a vital role in improving abiotic stress tolerance. Therefore, it could be deduced that H2S induced the PAs accumulation and could account for the browning mitigation, which provided the capacity to retard oxidative stress and maintain membrane integrity. In plants, PAs are generated from arginine or ornithine by ADC or ODC, respectively, and degraded by DAO and PAO [49]. The current study showed that enhanced activities of ADC and ODC and suppressed activities of PAO and DAO, which promoted PAs synthesis and inhibited PAs degradation, coincided with PAs accumulation in H2S treatment, thus contributing to suppressing the browning development. Similar results reported that the inhibition of melatonin on browning in peaches was associated with the higher enzyme transcription in PAs synthesis pathway and the lower enzyme transcription in PAs degradation pathway during cold storage [46]. Furthermore, it has been generally reported that the generation of PAs, especially Put, mainly depends on ADC pathway [50]. Interestingly, this study observed that the ODC pathway also participated in Put production in H2S-treated peaches, which was consistent with H2S-treated Spinacia oleracea seedlings [48], where H2S mediated Put accumulation via both the ADC and ODC pathways to resist drought stress. Thus, it was worth noting that H2S treatment had beneficial effect on PAs accumulation, contributing to membrane stability and signal pathway regulation. Furthermore, H2S treatment could be deduced to play a beneficial role in modulating amino acids metabolism, which contributed to the browning inhibition and the enhancement of proline, GABA, and PAs, thereby maintaining fruit quality during storage. ## 5. Conclusions To sum up, the present study suggested that H2S treatment could be a safe and useful tactic for fresh-cut peach fruit quality maintenance and browning suppression. Meanwhile, phenolic and amino acids metabolisms might be implicated in the browning of fresh-cut peach fruit. H2S treatment stimulated the accumulation of phenolic compounds by enhancing the activities of phenolic biosynthesis-related enzymes (PAL, C4H, 4CL), and delayed the degradation of phenolic compounds by inhibiting the oxidative activities of PPO. Moreover, H2S maintained the higher levels of proline, GABA, and PAs via enhancing biosynthesis and repressing degradation. 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--- title: Anti-Inflammatory Effect and Toxicological Profile of Pulp Residue from the Caryocar Brasiliense, a Sustainable Raw Material authors: - Julia Amanda Rodrigues Fracasso - Mariana Bittencourt Ibe - Luísa Taynara Silvério da Costa - Lucas Pires Guarnier - Amanda Martins Viel - Gustavo Reis de Brito - Mariana Conti Parron - Anderson Espírito do Santo Pereira - Giovana Sant’Ana Pegorin Brasil - Valdecir Farias Ximenes - Leonardo Fernandes Fraceto - Cassia Roberta Malacrida Mayer - João Tadeu Ribeiro-Paes - Fernando Yutaka de Ferreira - Natália Alves Zoppe - Lucinéia dos Santos journal: Gels year: 2023 pmcid: PMC10048353 doi: 10.3390/gels9030234 license: CC BY 4.0 --- # Anti-Inflammatory Effect and Toxicological Profile of Pulp Residue from the Caryocar Brasiliense, a Sustainable Raw Material ## Abstract Caryocar brasiliense Cambess is a plant species typical of the Cerrado, a Brazilian biome. The fruit of this species is popularly known as pequi, and its oil is used in traditional medicine. However, an important factor hindering the use of pequi oil is its low yield when extracted from the pulp of this fruit. Therefore, in this study, with aim of developing a new herbal medicine, we an-alyzed the toxicity and anti-inflammatory activity of an extract of pequi pulp residue (EPPR), fol-lowing the mechanical extraction of the oil from its pulp. For this purpose, EPPR was prepared and encapsulated in chitosan. The nanoparticles were analyzed, and the cytotoxicity of the encapsu-lated EPPR was evaluated in vitro. After confirming the cytotoxicity of the encapsulated EPPR, the following evaluations were performed with non-encapsulated EPPR: in vitro anti-inflammatory activity, quantification of cytokines, and acute toxicity in vivo. Once the anti-inflammatory activity and absence of toxicity of EPPR were verified, a gel formulation of EPPR was developed for topical use and analyzed for its in vivo anti-inflammatory potential, ocular toxicity, and previous stability assessment. EPPR and the gel containing EPPR showed effective anti-inflammatory activity and lack of toxicity. The formulation was stable. Thus, a new herbal medicine with anti-inflammatory activity can be developed from discarded pequi residue. ## 1. Introduction The skin is the largest organ of the human body, and it acts as a physical barrier against the external environment. The protective function of the skin and its annexes may be damaged by aggressive factors, which may lead to various types of injuries, such as physical injuries caused by cuts and sunburn, chemical burns from organic solvents, or viral or fungal infections [1]. In response to these different types of traumas, an inflammatory process is induced in the skin as a defense mechanism to repair damaged tissues [2]. This process is characterized by tissue and functional changes such as vasodilation, increased permeability, and recruitment and activation of leukocytes [3]. These changes are clinically manifested in five main signs that characterize an inflammatory process: heat, pain, redness, edema, and loss of function. When these signals are expressed in an exaggerated manner, the use of anti-inflammatory drugs is necessary [2,3]. In clinical medicine, non-steroidal anti-inflammatory drugs (NSAIDs) are the first choice of treatment for reducing the exaggerated and inappropriate inflammatory response, followed by glucocorticoid anti-inflammatory drugs. However, NSAIDs and glucocorticoids are not effective in many chronic inflammatory processes, and anti-inflammatory drugs such as ketoprofen and ibuprofen generally generate many side effects. Thus, the search for new options in traditional medicine is important for the treatment of inflammation. In particular, medicinal plants can be used as a source of new active ingredients for the development of anti-inflammatory drugs [3]. A striking example of such a medicinal plant is the pequi tree, Caryocar brasiliense Cambess. It is the main tree of the Cerrado, the second-largest Brazilian biome. The economic and cultural importance of the pequi fruit for the Cerrado population is well known, especially for those who work in family farming [4]. The pequi tree is the subject of industrial agriculture in Brazil, and the fruit of the pequi is extensively used in food and oil production; therefore, the raw materials are widely available [5]. Additionally, in traditional medicine, pequi pulp oil from the pequi fruit is used in the treatment of various conditions resulting from the inflammatory response [6]. The therapeutic applications of pequi oil are based on the chemical composition of the pequi pulp and almond [7]. The phenolic compounds present in the pequi fruit, mainly flavonoids, exhibit the antioxidant and anti-inflammatory properties observed for its oil, and preclinical studies have confirmed that pequi oil shows anti-inflammatory activity [8,9]. However, an important factor that hinders the use of pequi oil as an anti-inflammatory drug is the low yield of oil extracted from the pulp of this fruit [10]. Nevertheless, pequi pulp residue, a solid residue which results from the oil extraction process through pressing and is usually discarded, contains secondary metabolites that may have anti-inflammatory potential. Spectrophotometric analysis showed the presence of a high concentration of total phenolic compounds in a hydroethanolic extract prepared from this residue [11]. New technologies for drug delivery systems based on plant extracts have been established, including polymeric nanoparticles, which have been widely explored in the development of formulations for topical use. The advantages of nanocarriers compared to conventional topical preparations have been proven; these advantages include improved solubility, pharmacological activity, skin absorption, and formulation stability, in addition to decreased toxicity [12,13,14]. Based on these considerations, with the aim of developing a new herbal medicine which presents good efficacy and low adverse effects, EPPR was initially developed. EPPR was encapsuled in chitosan (CTS), and the cytotoxicity of encapsulated EPPR was evaluated. Because of the cytotoxicity of encapsulated EPPR, analyses of its anti-inflammatory and toxicological potential were performed using non-encapsulated EPPR as well as a gel containing EPPR, which did not present cytotoxicity in a previous study [11]. ## 2.1. Flavonoid Content of EPPR After oil extraction from pequi pulp, the residue generated is normally discarded. The composition of the flavonoids remaining in the EPPR was verified (Table 1). Barreto et al. [ 15] showed that the content of flavonoids in the hydroalcoholic extract of pequi pulp is 7.41 mg catechin equivalent (CE)/g. Thus, this result demonstrated the high added value for this residue because the flavonoids, which are phenolic compounds, represent a main group of substances with pharmacological activities in plants [16]. Similarly, in our previous study, Frasao et al. [ 17] evaluated the ethanolic extract of pequi residue (epicarp and external mesocarp) and verified a low flavonoid content of only 1.64 mg of quercetin equivalent per gram dry weight of the sample (mg QE/g). In our case, the EPPR had a higher value, showing remarkable results. Other phenolic compounds of EPPR were identified by Pegorin Brasil et al. [ 11] via ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). These compounds included chlorogenic acid, p-coumaric acid, coumaroylquinic acid, caffeic acid glycoside, and sugars (sucrose, methyl-rhamnose-glucose, and rhamnose-galactose-fucose). Additionally, a comparison was made between the total phenolic compounds in EPPR (21.56 mg gallic acid equivalent (GAE)/g) determined using spectrophotometry by Pegorin Brasil et al. [ 11] and data already published in the literature for extracts obtained from the pequi residue. The result of the comparison indicated that the content of the total phenolic compounds in EPPR is higher than that reported by Frasão et al. [ 17], who showed that the ethanolic extract of epicarp and external mesocarp of pequi presented 3.77 mg GAE/g, and by Roesler et al. [ 18], who revealed total phenolic values of 15.03 mg GAE/g for the ethanolic residue of pequi pulp/seed. The difference in the content of phenolic compounds and flavonoids in the pequi residue can probably be attributed to the processes used to remove the oil from the pulp, the solvents used in the preparation of the extracts, and the use of different parts of the fruit in the extraction process. Furthermore, the total phenolic compound content of different extracts of pequi pulp was lower than that reported by Pegorin Brasil et al. [ 11], Magalhães et al. [ 19] (1.09 mg GAE/g, aqueous extract), Nascimento-Silva et al. [ 20] (0.78 mg GAE/g, hydroethanolic extract), and Ribeiro et al. [ 21] (1.78–3.34 mg GAE/g, ethanolic extract). These results suggest that after the extraction of the oil from the pequi pulp, which corresponds to approximately $35\%$ of the weight of its pulp, the phenolic compounds present in the pulp residue appear proportionately more concentrated because they are water-soluble [22]. These results revealed the high added value of the pequi pulp residue, because the phenolic compounds were preserved even after oil processing. ## 2.2. Characterization of CTS Nanoparticles Containing EPPR CTS/tripolyphosphate (TPP)-hydroethanolic EPPR was characterized using three different techniques: dynamic light scattering (DLS), nano tracking analysis (NTA), and atomic force microscopy (AFM) (Figure 1). According to the DLS results (Figure 1a), the hydrodynamic size of the nanoparticles was 189 ± 8 nm; the polydispersity index (PDI) was 0.45 ± 0.03; and the zeta potential was +26 ± 1 mV. According to NTA analysis (Figure 1b), the nanoparticles showed a hydrodynamic size of 171 ± 3.6 nm and a concentration of 2 × 1010 ± 4 × 109 nanoparticles/mL, which is consistent with the DLS results. AFM images (Figure 1c) showed nanoparticles with a spherical morphology and a mean size of 158 ± 25 nm. CTS nanoparticles are used for the encapsulation of bioactive compounds [23]. CTS polymers offer great advantages, such as biocompatibility, biodegradability, and nontoxicity for nanoencapsulation [23]. Furthermore, CTS has mucoadhesive properties that enhance the paracellular transport of bioactive compounds, opening the space between epithelial cells and improving bio-compound delivery and bioavailability [24]. Nanoparticle formation occurs via the ionic-gelation method, an intramolecular interaction between TPP and the positive charge of amino groups from CTS. This type of interaction results in pH-stimuli-responsive nanocarrier systems, followed by a sustained release of active compounds [25]. The results obtained in this study showed nanoparticles with the same characteristics of size, surface charge, and morphology as that described in the literature for bio-compounds such as essential oils or plant extracts [26]. According to Mondéjar-López et al. [ 27], CTS nanoparticles loaded with garlic essential oil have sizes of approximately 172–352 nm. The authors additionally obtained nanoparticles with a positive surface charge (+19 to +48 mV). Mahmoudi et al. [ 26] used CTS nanoparticles to encapsulate Physalis alkekengi L. extract. The nanoparticles had a size of approximately 167 nm and a spherical morphology. In this study, the nanoparticles had a surface charge near neutral (7.69), which the authors attributed to the presence of polyphenol from the extract on the nanoparticle surface. Owing to the amino groups in the CTS structure, CTS nanoparticles present a positive charge, which can increase or decrease according to the pH. The nanoparticle surface is an important characteristic of nanoformulation stability. A high zeta potential value increases repulsion between the nanoparticles, preventing aggregation and precipitation over time [28]. The nanoparticles prepared in this study showed good colloidal characteristics, and they could be used as a nanocarrier system for EPPR. ## 2.3. Determination of Toxicity of the Encapsulated EPPR by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium Bromide (MTT) Assay The encapsulated EPPR, at all concentrations used in this assay, produced a significantly lower cell viability than the negative control (NC) after 24, 48, and 72 h (Figure 2). Analysis of the cellular cytotoxicity of a plant extract by measuring its viability guarantees the safety of the final product [11]. In the MTT assay, cell viability was evaluated on the basis of the metabolic activity of the mitochondria. This is because microsomal enzymes can reduce MTT, break down its substrate, and transform it into insoluble blue-violet formazan crystals. The color intensity of formazan crystals measured by spectrophotometry is proportional to the cell viability [21]. Mouse fibroblasts (NIH/3T3) were used for investigating the cytotoxicity of encapsulated EPPR because these cell lines are considered suitable in vitro models for investigating the toxicity potential of substances or products for cosmetic purposes [11]. The results of the MTT test showed that the encapsulated EPPR was cytotoxic to fibroblast cells (Figure 3), even at the lowest analyzed concentrations, and the addition of this extract in a formulation proved to be unfeasible. Consistent with the results obtained in this study, Kaisar et al. [ 29] found that nanoformulations with CTS at a concentration of 500 μM significantly reduced cell viability. In contrast, Pegorin Brasil et al. [ 11] reported that mouse fibroblasts (NIH/3T3) exposed to 15.6 to 250 µg/mL EPPR (non-encapsulated extract) for 24 h showed cell viabilities of 132, 132, 138, 134, and $119\%$, respectively. The cell viability for only the group treated with the highest concentration was statistically equal to that of NC, whereas the cell viabilities for the other groups were higher. After 48 h, the cell viabilities increased slightly to 129, 134, 140, 138, and $136\%$, respectively, except for that in the group treated with 15.6 µg/mL. All treatments were statistically superior to the NC. However, at 72 h, the viability reduced, reaching 105, 116, 123, 112, and $102\%$, respectively. Again, all treatments were statistically superior to the NC. Therefore, EPPR did not demonstrate cytotoxic effects, and the cell viability was not dose-dependent. In another independent experiment, Pegorin Brasil et al. [ 11], using higher concentrations of EPPR (from 625 to 20,000 µg/mL), obtained an IC50 value >2500 µg/mL, that is, the extract inhibited cell proliferation above this concentration. To understand the cytotoxic effect of the encapsulated EPPR, acetic acid was analyzed by MTT assay at the same concentrations that were used for EPPR encapsulated with CTS. In this analysis, pure acetic acid showed the same cytotoxicity profile as the encapsulated EPPR (Figure 3). The results obtained in this study suggest that acetic acid may be responsible for the toxicity of the extract in fibroblast cells. Doughty et al. [ 30] showed that acetic acid is non-toxic to human fibroblast cultures only at concentrations below $0.0025\%$. As Pegorin Brasil et al. [ 11] did not observe cytotoxicity for the non-encapsulated extract (EPPR), all analyses presented below were performed with this extract. ## 2.4.1. Phagocytosis The effects of all treatments were significantly different from that of the NC, saline (Figure 4). EPPR inhibited phagocytosis by 50.61 ± $0.96\%$ at 200 µg/mL; 60.49 ± $1.53\%$ at 400 µg/mL; and 69.13 ± $1.00\%$ at 600 µg/mL. The positive control (PC), dexamethasone, inhibited phagocytosis by 55.77 ± $5.42\%$. Macrophages exert immunomodulatory effects on skin wound repair, which is a critical process for restoring skin integrity [23]. Tissue repair comprises three sequential and overlapping healing phases: inflammation, proliferation, and remodeling [31,32]. The inflammatory phase involves the formation of clots by platelets and recruitment of phagocytes [33]. Thus, an uncontrolled process of phagocytosis, rather than repair of the injured tissue, can promote chronic damage [33]. The results obtained in this study indicate that EPPR inhibited macrophage phagocytosis in a similar manner to the commercial anti-inflammatory drug dexamethasone 100 μg/mL (PC). EPPR promoted greater inhibition of phagocytosis than PC, even at concentrations of 400 and 600 μg/mL. The literature has no reports on the anti-inflammatory activity of pequi plant extracts. Athira and Keerthi [34] observed that Sigmadocia extract produced a low level of phagocytosis. ## 2.4.2. Spreading Spreading is generally defined as an unsuccessful attempt at phagocytosis. However, as no substance or microorganism is present to be phagocytosed, spreading is the action of a responsive cell with the ability to adhere to the slide and emit microvilli [16]. All treatments used in this assay produced significantly different effects compared with the NC ($p \leq 0.05$) (Figure 5). EPPR promoted the inhibition of spreading in the following proportions: 39.95 ± $1.17\%$ at 200 µg/mL; 64.36 ± $0.99\%$ at 400 µg/mL; and 72.84 ± $1.07\%$ at 600 µg/mL. This assay corroborates the results of the phagocytosis assay, demonstrating that EPPR can reduce inflammation symptoms in macrophages. This capacity is similar to that of 100 µg/mL dexamethasone, which reduced spreading by 86.74 ± $0.94\%$. ## 2.4.3. Membrane Stabilization The human red blood cell stabilization method was used to analyze the anti-inflammatory activity of the plant extracts [19]. According to Kumar et al. [ 35], during the inflammatory response, anti-inflammatory drugs interfere with different biochemical processes, promoting several effects, including the stabilization of lysosome membranes. Thus, because the structure of the erythrocyte membrane is analogous to that of the lysosomal membrane, the ability of a substance to promote its stabilization may be a predictive factor for its anti-inflammatory activity [35]. Figure 6 shows that according to the erythrocyte membrane stability test, all treatments used in this assay produced significantly different effects compared with the NC ($p \leq 0.05$). EPPR promoted the protection of the red blood cell membrane in the following proportions: 18.71 ± $2.01\%$ at 200 μg/mL; 22.85 ± $0.20\%$ at 400 μg/mL; and 76.67 ± $0.22\%$ at 600 μg/mL. Protection from hemolysis promoted by the PC, dexamethasone, was 99.11 ± $0.32\%$. Kumar et al. [ 35], using the membrane stabilization test, evaluated the anti-inflammatory activity of the extract of leaves of the plant Skimmia anquetilia, originally from India; the extract exhibited a protection of $68.40\%$ at a concentration of 400 mg/mL. Dias [36] analyzed the microencapsulated essential oil of Lippia pedunculosa and observed $100\%$ inhibition of hemolysis at concentrations of 10, 50, 100, and 250 µg/mL. Sousa et al. [ 37] evaluated the extract of Pavonia glazioviana Gürke (Malvaceae) at 500 and 1000 µg/mL and reported a protective effect of less than $60\%$. Other analyses have shown that pequi oil protects against inflammation [9]; however, no information is available regarding the residue from pequi oil extraction. The three in vitro tests performed in this study show that EPPR exhibits effective anti-inflammatory activity, and that the best results were obtained at a concentration of 600 µg/mL. ## 2.5. Quantification of the Levels of Cytokines IL-6 and IL-10 Induced by the Non-Encapsulated Extract (EPPR) Studies have established that lipopolysaccharides (LPS) induce the secretion of pro-inflammatory cytokines such as TNF-α and interleukins [38]. Cytokines are signaling polypeptides used in cell communication by the immune system during the inflammatory response; they act on virtually all cell types and in mRNA synthesis [38]. Interleukin-6 (IL-6) is the most important mediator of the acute inflammatory response and is the main procoagulant cytokine [3,38]. IL-10 has anti-inflammatory and suppressive effects on most hematopoietic cells and indirectly suppresses the production of other cytokines. However, IL-10 has also been shown to produce stimulatory effects on CD8+ effector T cells, increasing their cytotoxic and proliferative capacities [3]. Thus, IL-10 is increased in certain pathologies, such as HIV (human immunodeficiency virus) and Burkitt’s lymphoma [39]. In addition, dysregulation of IL-10 is associated with enhanced hyperinflammation in response to infection, as well as an increased risk for the development of many autoimmune diseases. Thus, an understanding of IL-10 participation in the progression and resolution of certain inflammatory-response-related diseases is critical [40]. The results of the present analysis, as expected, showed that the group treated with LPS as the PC showed a significant increase ($p \leq 0.05$) in the production of IL-6 (Figure 7a) and IL-10 (Figure 7b) when compared with the NC group of untreated cells. Conversely, treatment with EPPR (400 µg/mL) significantly ($p \leq 0.05$) prevented LPS-induced increase in interleukin concentrations. To date, no study has reported on extracts obtained from pequi residue. However, Torres et al. [ 41] reported that pequi oil reduced IL-6 expression. The EPPR-induced decrease in the levels of the pro-inflammatory mediator IL-6 complements the results of the in vitro tests on the anti-inflammatory effect of EPPR. ## 2.6. Acute Toxicity In Vivo of the Non-Encapsulated Extract (EPPR) EPPR administered in a single dose of 2000 mg/kg did not promote the death of animals. The results obtained during 14 days of analysis of the manifestation of toxic signs showed that among all the parameters analyzed concerning motor control and consciousness, only slight signs of CNS hyperexcitability and hypnosis were manifested by the animals in the first 4 h after EPPR administration (Table 2). These aspects were no longer observed after this period. Considering that on the scale, acute toxicity “0” indicated absence of the effect analyzed and “4” indicated the maximum effect, we verified that EPPR promoted subtle signs of toxicity in the first 4 h of evaluation. Thus, in the acute toxicity test in animals, EPPR did not cause mortality, morbidity, unusual behavior, or severe and permanent adverse clinical signs. In addition, EPPR did not kill the animals at a dose of 2000 mg/kg after a single oral administration. Thus, this extract can be classified as a Class 4 drug, according to the acute toxicity classification criteria for chemicals [17,19,42]. In addition, on the 14th day of EPPR administration, the EPPR-treated animals exhibited a significant increase in weight compared with the control animals ($p \leq 0.05$) (Table 3). According to Di Santo et al. [ 43], when the plant extract stimulates appetite and, thus, increases the body weight of animals, it tends to behave as a non-toxic substance, as it does not harm the physiological functions of the organism and allows the regular execution of metabolism. ## 2.7. Determination of the In Vivo Anti-Inflammatory Activity of the Gel Containing the Non-Encapsulated Extract (EPPR gel) on Carrageenan-Induced Paw Edema The paw edema test in rodents has been well established for the evaluation of new drugs. In this test, the development of carrageenan-induced paw edema occurs in two phases. In the initial phase (0–1 h), serotonin, histamine, bradykinin, and substance P are released. The second phase (after 1 h) involves invasion of mainly neutrophils at the site of inflammation and production of large amounts of pro-inflammatory mediators, such as PGE2, and various cytokines, such as IL-6 and IL-10 [44]. EPPR gel (5 mg/g) and dexamethasone gel (1 mg/g, PC) reduced carrageenan-induced edema, which was determined by measuring the paw volume (mL) of the animals. The reduction of edema was considered an anti-inflammatory activity. Gels containing dexamethasone and EPPR showed a significant ($p \leq 0.05$) reduction in carrageenan-induced paw edema when compared with the NC. This reduction was observed from the second hour onwards and remained until the sixth hour after the application of carrageenan. More specifically, in the second hour, the NC, PC, and EPPR gels showed, respectively, the following edema values: 0.08 ± 0.005 mL, 0.04 ± 0.002 mL, and 0.04 ± 0.006 mL. In addition, the gels containing EPPR and dexamethasone inhibited edema by $49.95\%$ and $53.33\%$, respectively (Table 4). Figure 8 shows that from the fourth to the sixth hour of carrageenan administration, dexamethasone gel (PC) and EPPR gel exhibited anti-inflammatory activity. The edema values with these treatments differed significantly from those with the base gel (NC) treatment (p ˂ 0.05) and remained constant. The NC, PC, and EPPR gel treatments resulted in the following edema volumes per hour: fourth hour (0.14 ± 0.006, 0.04 ± 0.004, and 0.05 ± 0.005, respectively) and sixth hour (0.14 ± 0.01, 0.04 ± 0.01, and 0.05 ± 0.01, respectively). The PC and EPPR gel treatments, respectively, inhibited edema by similar extents at the fourth and sixth hours: $70.27\%$ and $67.79\%$. Anti-inflammatory in vivo tests using formulations containing pequi oil are well established in the literature. However, nothing is known regarding the anti-inflammatory effects of topical products containing EPPR. Diniz [45] found a significant reduction in paw edema 1 h after administration of a microemulsion containing pequi oil. Bezerra [6] verified the anti-inflammatory effect of an emulsion with pequi oil and observed that the application of the emulsion in the paws of mice significantly reduced the release of myeloperoxidase (MPO) in neutrophils by $64.8\%$. Santos et al. [ 46] reported that the application of pequi oil (700 and 1000 mg/kg) resulted in a significant decrease in paw edema ($64\%$ and $79\%$), reaching its maximum inhibition peak. Thus, this study verified that the gel containing the pequi residue showed equal activity to that of its oil form. These results are probably owing to the large number of phenolic compounds present in the EPPR, particularly catechin. ## 2.8. In Vitro Ocular Irritability Test of the Gel Containing the Non-Encapsulated Extract (EPPR gel) in the Chorioallantoic Membrane of Chicken Eggs The final classification of the degree of irritation of the gels was based on the average of the sum of the values obtained by the three samples per group. The results of the in vitro irritability test (Table 5) showed a score of zero (non-irritant) for saline solution (SS) and a score of 21 (severe irritant) for NaOH (PC). All other treatments were classified as non-irritants or mild irritants (MI), suggesting that they can be safely applied to the skin. Mansur et al. [ 47] reported that antioxidant extracts did not cause irritation, corroborating our findings. ## 2.9. Preliminary Stability Evaluation of the Gel Containing the Non-Encapsulated Extract (EPPR Gel) Finally, with considerations for the possibility of developing an herbal medicine according to the demands of the international consumer market, the stability of the formulations was evaluated. In the centrifugation test, the base gel and EPPR gel did not show any changes. The thermal stress test verified that none of the samples of the gel presented separation into phases upon reaching a temperature of 80 °C. The pH values of the base gel (the gel without the extract) and the EPPR gel did not present any statistically significant differences. The EPPR gel showed a mean pH of 5.91 ± 0.05, and the base gel showed a mean pH of 5.54 ± 0.06. The values were within the threshold range of compatibility with the physiological pH of the skin, which ranges from 5.5 to 7.3. ## 3. Conclusions In conclusion, the chromatographic quantification of EPPR showed a significant presence of flavonoids, suggesting a relationship between the pharmacological activity of this extract and its phytochemical composition. The nanoparticles prepared in this study with CTS showed good colloidal characteristics and could be used as a nanocarrier system for EPPR. However, in the MTT assay, the encapsulated EPPR showed cytotoxicity due to acetic acid. EPPR, a non-encapsulated extract, showed anti-inflammatory activity in vitro. In addition, it significantly reduced the concentration of the inflammatory cytokines IL-6 and IL-10 and showed no toxicity in vivo. Furthermore, in vivo testing of the EPPR gel showed anti-inflammatory activity similar to that of the PC, dexamethasone, an anti-inflammatory agent widely used in clinical medicine. Lastly, the formulation of the EPPR gel showed stability and a lack of toxicity in the ocular test. In conclusion, it is possible to develop a new herbal medicine from pequi residue that is normally discarded for the treatment of inflammatory skin diseases. ## 4.1. Material of Vegetable Origin Pequi fruits were obtained from the city of Mirabela, state of Minas Gerais, in the southeast region of Brazil (latitude: 16°15′46″ S; longitude: 44°09′52″ W, altitude: 800 m). The fruits were first washed and peeled and subsequently packed in plastic bags and stored under refrigeration (−18 °C; Figure 9). The species under study was identified as Caryocar brasiliense Cambess in the Herbarium of the Universidade Estadual Paulista (UNESP), where a voucher specimen was deposited under the number 1998. This study was registered in the National System for the Management of Genetic Heritage and Associated Traditional Knowledge (SisGen) under license no. A23C398. Fruits were washed, peeled, manually cut into small pieces, and subjected to the oil extraction process by pulp cold pressing. The residue resulting from the pulp pressing was dried in an oven (36 °C). ## 4.2. Preparation of the Hydroethanolic EPPR The hydroethanolic EPPR was prepared at a proportion of 1 g of dry pequi pulp residue per 10 mL of ethanol solution ($70\%$, v/v), as shown in Figure 9. Subsequently, it was subjected to vigorous agitation for 30 min and kept in static maceration for 3 days in the dark. The resulting extract was filtered to obtain a liquid fraction. This fraction was placed in a rotary evaporator (Fisatom®, São Paulo, SP, Brazil) under reduced pressure at 70 °C to eliminate the alcohol and placed in an oven (Solab®, Piracicaba, SP, Brazil) to dry at 36 °C for the complete elimination of water and to obtain a constant weight of the dried EPPR [48]. ## 4.3. Flavonoid Content of EPPR The flavonoid content of EPPR was determined using the method described by Serdar et al. [ 49]. An analytical curve was constructed using solutions of 10–100 μg/mL quercetin (y = −0.0039 + 0.0082x and R2 = 0.9999). The absorbance of the samples (solubilized in ethanol) was measured at 425 nm using a UV–Vis spectrophotometer (800× XI, Femto®, Brazil). The experiments were performed in triplicates. The results were expressed in mg QE/g. ## 4.4. Encapsulation of EPPR in CTS via Ionic Gelatinization CTS used in this study was a commercial product (CAS 9012-76-4, Sigma Aldrich®, St. Louis, MO, USA). Encapsulation was performed according to the protocol proposed by Calvo et al. with modifications [50]. The CTS nanoparticles were obtained by gelation of a CTS solution with polyanion sodium tripolyphosphate. For this purpose, initially, CTS was dissolved in aqueous solutions of acetic acid at various concentrations ($0.015\%$, $0.030\%$, $0.060\%$, $0.120\%$, and $0.240\%$). The concentration of acetic acid was, in all cases, 1.2 times higher than that of CTS. The dry EPPR was incorporated into the QTS solution at the following concentrations: 31.25, 62.5, 125, 250, and 500 μg/mL ## 4.5.1. DLS The nanoparticles were characterized by DLS to obtain their hydrodynamic size (nm) and PDI. The analyses were performed using ZetaSizer (model ZS90, Malvern Instruments®, Malvern, UK) with a scattering detection angle of 90°. The analysis was performed in triplicates at 25 °C. ## 4.5.2. Surface Charge The nanoparticle surface charge (zeta potential) was determined using electrophoresis. Analyses were performed using ZetaSizer equipment (model ZS90, Malvern Instruments®, UK). The analysis was performed in triplicates at 25 °C. ## 4.5.3. NTA The size distribution and the nanoparticle concentration were determined by NTA (model LM-10, Malvern Instruments®, UK). The samples were diluted 100× in ultrapure water, the analyses were performed five times, and 400 nanoparticles were counted for each measurement. The samples were stored at 25 °C. ## 4.5.4. AFM Images were obtained using the easyScan 2 basic AFM system (Nanosurf®, Liestal, Switzerland). The samples were scanned in contact mode using TapAl-G cantilevers (BudgetSensors®, Sofia, Bulgaria). The nanoparticles were diluted 1000× in ultrapure water. The size of the nanoparticles was determined by analyzing the images using the ImageJ® software (Bethesda, MD, USA). ## 4.6. Determination of Toxicity of the Encapsulated EPPR by the MTT Assay The MTT cytotoxicity assay was performed as described previously by Tsuboy et al. [ 51]. For this assay, mouse fibroblasts of dermal origin (NIH/3T3, ATCC® CRL-1658™) were inoculated into 96-well microtiter plates and incubated in culture medium for 24 h at 37 °C under $5\%$ carbon dioxide (CO2). After reaching approximately $75\%$ confluence (24 h), these cells were exposed to five different concentrations of the encapsulated EPPR (31.25, 62.5, 125, 250, and 500 μg/mL). In addition, to understand the cytotoxicity of the encapsulated EPPR, acetic acid was evaluated at the same proportions used in the encapsulation process ($0.015\%$, $0.030\%$, $0.060\%$, $0.120\%$, and $0.240\%$). For the NC, the extract was replaced with a physiological solution, and for the PC, it was replaced with $2\%$ (v/v) Tween 80. The treatment times were 24, 48, and 72 h. EPPR was not included in this screening because Pegorin Brasil et al. [ 10] analyzed the same extract and showed that it did not present significant cytotoxicity. ## 4.7.1. Treatments In all anti-inflammatory tests, the samples analyzed were EPPR. The dry EPPR was dissolved in distilled water at the following concentrations: 200, 400, and 600 μg/mL. For the PC, the extract doses were replaced with dexamethasone (100 μg/mL), and the NC was replaced with saline solution. In the hemolysis stabilization test, the saline solution was replaced with a hyposaline solution ($0.18\%$) to induce hemolysis. ## 4.7.2. Cell Culture Murine macrophages of the Raw 264.7 (ATCC TIB-71) strain were thawed and cultured in a cell culture flask with Dulbecco’s Modified Eagle Medium (DMEM) Ham’s F-12 culture medium at 37 °C under $5\%$ CO2. Cells were grown to 70–$80\%$ confluence. ## 4.7.3. Selection of Macrophages Cells were harvested using a cell scraper, counted in a Neubauer chamber, and centrifuged at 1500 rpm for 5 min. The supernatant was discarded, and the cells were resuspended in culture medium to reach the desired concentration for each experiment. ## 4.7.4. Phagocytosis The method described by Azedo et al. [ 52] was used in this assay. The prepared slides were examined under an optical microscope at 400× magnification, with a total count of 100 cells. This test was performed in triplicates. Inhibition of phagocytosis (IP) was calculated using the following formula:IP (%) = E0 − ET/E0 × 100,[1] where E0 represents the mean number of cells in the NC group that phagocytosed the zymosan particles, and ET represents the mean number of cells in the treated groups that phagocytosed the zymosan particles. ## 4.7.5. Macrophage Spreading The method described by Bastos et al. [ 53] was used in this study. The prepared slides were examined under an optical microscope at 400× magnification, with a total count of 100 cells. This test was performed in triplicates. The inhibition of spreading was calculated using the following formula:Inhibition of spreading (%) = E0 − ET/E0 × 100,[2] where E0 represents the mean number of spread cells in the NC group, and ET represents the mean number of cells spread in the treated groups. ## 4.7.6. Membrane Stabilization The human red blood cell membrane stabilization (HRBC) test was performed according to the method proposed by Ananthi and Chitra [54]. The test reaction was performed by adding 2 mL of hyposaline solution ($0.18\%$), 1 mL of sodium phosphate buffer (0.1 M, pH 7.4), 1 mL of the analyzed samples, and 0.5 mL of HRBC solution. The hemoglobin content in the suspension was estimated using a spectrophotometer at 560 nm. The percentage of protection can hence be calculated from the equation given below:Protection (%) = E0 − ET/E0 × 100,[3] where E0 represents the mean absorbance value of the NC group and ET represents the mean absorbance value in the treated groups. ## 4.8. Quantification of the Levels of the Cytokines IL-6 and IL-10 Induced by the Non-Encapsulated Extract (EPPR) For cytokine determination, bone-marrow-derived macrophages (BMDMs) from C57BL/6 mice were prepared as previously described by the Organization for Economic Co-operation and Development (OECD) [53]. BMDMs were conditioned in a 96-well bottom plate (Nunc, Thermo Fisher Scientific, Waltham, MA, USA) (2 × 105 cells/well) and stimulated with LPS from Escherichia coli (Sigma-Aldrich) at a concentration of 500 mg/mL. After 3 h, the cells were washed with 1× phosphate-buffered saline and treated with EPPR (600 µg/mL) for 18 h. The supernatant was collected, and the cytokines were measured by enzyme-linked immunosorbent assay (ELISA) using a mouse IL-6 and IL-10 kit (R&D Quantikine ELISA) according to the manufacturer’s instructions. ## 4.9. Animals Twelve-week-old male Swiss mice weighing 31–40 g were housed in polypropylene cages (five animals per box). Food (Nuvilab CR-1 kibble) and water were provided ad libitum. The vivarium was maintained at a controlled temperature (23 ± 2 °C) and humidity ($55\%$ ± $10\%$) with an artificial lighting program that corresponded to 50 lx (lights on at 7:00 a.m. and off at 7:00 p.m.). The study was approved by the local Ethics Committee on the Use of Animals of Universidade Estadual Paulista “Júlio Mesquita Filho”, Assis campus, São Paulo, Brazil (protocol no. $\frac{13}{2018}$; approved on 22 March 2018) and conducted according to the Brazilian Legal Framework for the Scientific Use of Animals. ## 4.10. Determination of In Vivo Toxicity of the Non-Encapsulated Extract (EPPR) This analysis was performed in accordance with the OECD Guideline No. 423 [55]. Male Swiss mice ($$n = 8$$) were used for the control and experimental groups. The control group was treated with distilled water (5 mL/kg of body weight). Treatment with dry EPPR dissolved in distilled water was administered by gavage in a single dose to one animal at a time from an initial concentration of 2000 mg/kg of body weight. This dose was determined on the basis of the classification of the OCDE, which defines a harmful substance as being capable of promoting the death of $50\%$ of a test population (LD50) after acute administration of a dose of 200–2000 mg/kg. Physiological aspects were analyzed for 14 days. The analysis scale ranged from 0 to 4, where 0 indicated the absence of the effect analyzed and 4 indicated the total observation. Each group contained eight male animals. The initial and final weights of the animals were measured, and the average values for the group were determined. ## 4.11. Preparation of the Gel Formulation Containing the Non-Encapsulated Extract (EPPR Gel) To prepare the formulation, $1\%$ (w/w) carbopol and $0.1\%$ (w/w) methylparaben, a preservative, were dissolved in water, and the mixture was allowed to rest for 24 h. Subsequently, the mixture was stirred, and dry EPPR was added at a concentration of 5 mg/g. The pH of the solution was adjusted to 5.5. ## 4.12. Determination of the In Vivo Anti-Inflammatory Effect of the Gel Containing the Non-Encapsulated Extract (EPPR Gel) on Carrageenan-Induced Paw Edema According to Winter, Risley, and Nuss [56], male mice ($$n = 8$$/group) were subjected to a subplantar injection in the animal’s right hind paw with 0.1 mL of $1\%$ carrageenan. The treatments were EPPR gel (5 mg/g), base gel as the NC, and dexamethasone gel (1 mg/g) as the PC. To assess the acute anti-inflammatory effect, the volume of the right hind paw was measured using a plethysmometer (Ugo Basile®, Gemonio, Italy) before the first carrageenan administration and at 0, 2, 4, and 6 h after administration (Figure 10). The inhibition percentage was calculated using the following formula: % inhibition = (E0 − ET)/E0 × 100,[4] where E0 represents the mean volume of paw edema observed in the control group and ET represents the mean volume of paw edema observed in the treated groups. ## 4.13. Ex Vivo Ocular Irritability Test of the Gel Containing the Non-Encapsulated Extract (EPPR Gel) in the Chorioallantoic Membrane of Chicken Eggs (MCA) This test was performed according to the methodology described in the Journal Officiel de la République Française [57]. Four fertilized eggs from White Leghorn chickens were used per treatment group: NC (saline $0.9\%$, w/v), PC (sodium hydroxide 0.1 N), base gel (gel without the extract), and EPPR gel 5 mg/g. On the tenth day of incubation, the treatments were applied to the MCA, and the presence or absence of irritating effects was observed. After visual analysis, a thiopental solution was injected into the fertilized eggs. The graduation of each phenomenon was determined in 5 min and graduated in numerical values (1, 3, 5, 7, and 9) depending on time (Table 6). Visual analysis of the MCA was performed using a magnifying glass. The analyzed samples were classified according to the mean value of the sum of the scores of three independent tests ($$n = 3$$), and the degree of irritation was divided into four categories: between 0.0 to 0.99, non-irritating (NI); 1.0 to 4.99, mild irritant (MI); 5.0 to 8.99, moderate irritant (MI); and 9.0 to 21, severe irritant (SI) [44]. ## 4.14. Preliminary Stability Evaluation of the Gel Containing the Non-Encapsulated Extract (EPPR Gel) The formulations with non-encapsulated EPPR were evaluated at a concentration of 5 mg/g. The gel without the extract, the denominated base gel, was used as the negative control. These tests were performed in triplicates. ## 4.14.1. Accelerated Stability Test or Centrifugation Test Five grams of each formulation was weighed into centrifuge tubes. The centrifugation test (MEGAFUGE 16R centrifuge, Thermo Scientific®, New York, NY, USA) was performed at room temperature with a rotational speed of 210× g for 30 min. Subsequently, the formulation was macroscopically analyzed by its appearance. ## 4.14.2. Thermal Stress Test Tubes containing 5 g of each formulation were subjected to thermal stress in a thermostat water bath (Solab®, São Paulo, SP, Brazil) at temperatures ranging from 40 to 80 °C, with an increase of 10 °C every 30 min up to 80 °C. The formulation was analyzed macroscopically by its appearance after reaching room temperature (25 °C). ## 4.14.3. pH evaluation The pH of the formulation was evaluated weekly for 30 days using a calibrated pH meter (mPA210MS; TECNOPON®, Piracicaba, SP, Brazil). ## 4.15. Statistical Analysis The data are expressed as the mean ± standard deviation. Statistical analyses were performed using the Prism 8. To verify the statistical differences between the groups, a one-way analysis of variance (ANOVA) was performed according to the experimental protocol, followed by Tukey’s multiple comparison test. For all analyses, a p-value < 0.05 was considered to indicate statistical significance. ## 5. Patents The patent for the EPPR gel was granted by Instituto Nacional da Propriedade Industrial (INPI, Brasília, Brazil) on 25 November 2020 with the process number BR 10 2020 024093 5. ## References 1. Sabat R., Wolk K., Loyal L., Döcke W.-D., Ghoreschi K.. T Cell Pathology in Skin Inflammation. 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--- title: 'Obesity during Adolescence and Feeding Practices during Infancy: Cross-Sectional Study' authors: - Reem Sharaf-Alddin - Radhia Almathkoori - Hara Kostakis - Ahmed N. Albatineh - Abdullah Al-Taiar - Muge Akpinar-Elci journal: Epidemiologia year: 2023 pmcid: PMC10048365 doi: 10.3390/epidemiologia4010011 license: CC BY 4.0 --- # Obesity during Adolescence and Feeding Practices during Infancy: Cross-Sectional Study ## Abstract Background: Breastfeeding is proposed to play a role in reducing the risk of obesity throughout life. Kuwait has an extremely high prevalence of childhood obesity ($45\%$ of adolescents are overweight/obese) and extremely low breastfeeding indicators, particularly exclusive breastfeeding. In fact, little is known about the association between breastfeeding and obesity from Kuwait and the broader Middle East. Aims: To estimate the prevalence of overweight/obesity in female adolescents in Kuwait and assess its association with breastfeeding during infancy. Methods: This is a cross-sectional study that included 775 girls randomly selected from public and private high schools in Kuwait. The primary exposure was breastfeeding in the first four months of life, and the outcome was overweight/obesity during adolescence. Multivariable logistic regression was used to assess the association between breastfeeding and overweight/obesity while adjusting for potential confounders. Results: Approximately $45\%$ of adolescent girls were either overweight/obese. We found no significant association between breastfeeding (exclusive/mixed breastfeeding and formula feeding/no breastfeeding) and overweight/obesity neither in univariable analysis (Crude Prevalence Ratio: 1.14, $95\%$CI [0.92–1.36] & Crude Prevalence Ratio: 1.29, $95\%$CI [0.86–1.68]; $$p \leq 0.293$$) for mixed feeding and no breastfeeding respectively, nor in multivariable analysis (Adjusted Prevalence Ratio: 1.14, $95\%$CI [0.85–1.42] & Adjusted Prevalence Ratio: 1.20, $95\%$CI [0.68–1.68]; $$p \leq 0.589$$) for mixed feeding and no breastfeeding respectively. Conclusion: Breastfeeding during infancy was not significantly associated with overweight/obesity during adolescence. However, breastfeeding should be encouraged for its indisputable benefits for infants and their mothers alike. Further prospective studies are needed to assess the association. ## 1. Background The benefit of breastfeeding for both mothers and their infants are now well-recognized beyond any doubt. Breastfed infants, compared to formula-fed infants, have better neurological development [1], stronger immune systems, and a lower rate of hospitalization [2]. Despite these obvious short-term benefits, breastfeeding practices remained not optimal worldwide. Globally, only $41\%$ of infants are exclusively breastfed during the first six months of life [3]. Kuwait has lower breastfeeding indicators compared to many low, middle, and high-income countries [4]. Only $10.15\%$ and $8.41\%$ of infants aged ≤3 and ≤6 months are exclusively breastfed, respectively, and around 8 out of 10 Kuwaiti infants aged ≤6 months are fed formula milk [5]. Childhood obesity and its consequences represent a major public health problem worldwide. Globally, about $18\%$ of those aged 5–19 years are overweight/obese [6]. In Kuwait, almost half of school children aged 5–19 years are overweight/obese [5]. As a result, Kuwait is among the top ten countries in terms of obesity [7], and type II diabetes is the number one health problem in Kuwait ($14.6\%$ of Kuwaiti adults are diabetic) [8]. According to Barker hypothesis, later known as Developmental Origin of Health and Disease (DOHaD), exposure at early life (during pregnancy or early childhood) is assumed to have a long-term impact on health [9]. Breastfeeding, as an early life exposure, has been proposed to play a role in reducing the risk of overweight/obesity throughout life. One of the explanations is that breastmilk contains leptin, a hormone that works to ensure energy balance and control metabolic processes [10]. Leptin stimulates lipid catabolism and inhibits lipogenesis, hence controlling fat accumulation from an early stage of life, and consequently lowers the risk of obesity-related health problems in adulthood [11]. Several epidemiological studies have attempted to demonstrate the link between breastfeeding during infancy and the risk of overweight/obesity in adolescence or adulthood, but the findings remained inconclusive [12]. Several studies have shown a negative association, with some demonstrated a dose-response relationship [13]. On the contrary, other studies reported little or no association between breastfeeding during infancy and overweight/obesity throughout life [14]. As mentioned above, Kuwait has an extremely high prevalence of childhood obesity [5] and extremely low breastfeeding indicators, particularly exclusive breastfeeding [5]. Therefore, investigating the association between breastfeeding in infancy and obesity or overweight in adolescence is of utmost importance in our setting. If this association is demonstrated in our setting, it will revive the effort to improve breastfeeding as an early intervention to combat childhood obesity. In fact, little is known about this issue from Kuwait and the broader Middle East. ## Objectives This study aims to investigate the association between breastfeeding during infancy and overweight/obesity during adolescence. ## 2.1. Study Design and Study Participants Kuwait has a population of 4.5 million, of whom two-thirds are non-Kuwaiti. Education is compulsory until secondary school for Kuwaiti, and school enrolment is very high for both genders. Only less than $1\%$ of Kuwaiti females aged 15–18 years, which is the study’s target population, are illiterate [15]. The government partially subsidizes formula milk, and there is only one private and one public baby-friendly hospital in Kuwait. This is a cross-sectional study in which data were collected on schoolgirls attending public and private high schools in Kuwait (age range: 14–22 years). The data for this study is part of a project, and details of the project have been published previously [16,17]. The project profile is depicted in Figure 1. ## 2.2. Data Collection Data collection has been described in detail previously [16,17]. In brief, data were collected from schoolgirls by a self-administered questionnaire, while data from mothers were collected through telephone interviews using a structured questionnaire. Mothers were the only recognized source of information about the history of breastfeeding. The telephone interview with the mothers were conducted independently from the time of measuring the schoolgirls height and weight. Which means the researcher, during the phone interview with the mother was not aware about the adolescent girl (the daughter) BMI status. This is to avoid observer bias that might arise when the data collector is aware about the study hypothesis, hence overestimate the association. In order to improve the response rate, those mothers who did not respond at the first time were approached three times at different timing through their cell phones and landline. Breastfeeding was defined as infant was fed with breastmilk either directly from the mother’s breast or by a cup or a bottle [18]. Questions on breastfeeding were rephrased to aid recall. Questions were focused on the feeding practices during the first four months of life. This included whether the schoolgirl was breastfed at all, and whether it was exclusive (Breastmilk only) or mixed feeding (Breastmilk with formula milk/solid food). Further questions aimed to ascertain the exact duration of breastfeeding among mothers who breastfed. Those who were not able to recall the information in any question were given a code and considered as a category during the analysis. Height and weight were measured in the school theatre or school clinic for privacy. Weight was measured by the school nurse using a digital weight scale (Beurer GS 19 digital scale, Ulm, Germany) and recorded to the nearest 0.1 kg after removing heavy clothes and shoes. Height was measured using a stadiometer (Seca 217 height rod, Hamburg, Germany) and recorded to the nearest 0.1 cm after the participant was correctly positioned and her position was verified by the researcher from the front and left sides. A set of potential confounders was selected based on previous knowledge about the factors that might be associated with obesity including sociodemographic factors, dietary factors, and based on Barker hypothesis (including complication during pregnancy, birth weight and prematurity). Data on potential confounders were collected by self-administered questionnaires from the schoolgirls and telephone interviews from the mothers. The questionnaires included data on sociodemographic factors, index girls’ (age, nationality, birth order, birth weight, whether born premature or not, exposed to passive smoking at home, age of menarche), age of the mother at index girls’ birth and whether she experienced complications (e.g., eclampsia or gestational diabetes) during that pregnancy (Table 1). Data on lifestyle factors (dietary habits, physical activity, and physical inactivity) were collected by questions that have been used previously among adolescents in Arab settings. Dietary habits including frequency per week of (eating vegetables, fresh fruits, fast food, fries/chips, cake/biscuit/donuts, sweats/chocolate, having soft drinks). Physical activity factors including, the frequency and duration per week of (attending PE in school, walk to school, biking, walking, jogging, running, swimming, play football, play basketball, play volleyball, practice self-defense sports, do weight training and body building). Physical inactivity including, the frequency and duration per week of (watching TV, using internet, playing video games, reading book, doing homework). The questionnaires for mothers and daughters were developed in English and translated into Arabic, then independently back-translated into English. The Arabic versions of the questionnaires were pilot tested on 30 mothers and schoolgirls whose data were not included in the study. ## 2.3. Data Management and Data Analysis Body mass index (BMI) was calculated by dividing the weight in Kg by the square of height in meter (Kg/m2). BMI-for-age z-scores were calculated using WHO growth charts. Overweight was defined as BMI-for-age greater than one standard deviation (SD) to two SD; while obesity as greater than 2 SD using the WHO growth reference median [19]. We found few schoolgirls older than 18 years; therefore, we used BMI cut-off points for adults for those girls (<18.5 kg/m2 underweight, 18.5–24.9 kg/m2 normal weight, 25.0–29.9 kg/m2 overweight, >30.0 kg/m2 obese) [19]. Continuous variables were summarized by mean (SD) (if were found normally distributed) and categorical variable were summarized by percentages and frequencies. In order to investigate the association between breastfeeding and overweight/obesity, we created a binary outcome (underweight/normal weight vs. overweight/obese). Only 12 participants were underweight; hence they were included in the normal weight. Chi-square test for independence was used to assess the differences in overweight/obesity prevalence between those who were exclusively breastfed, mixed feeding (breastmilk and formula-milk), or never breastfed. Since our outcome is binary variable (Overweight/obesity vs. Normal weight), We used binary logistic regression to investigate the association between breastfeeding and overweight/obesity. Logistic Regression calculates the Odds Ratio (OR) with its $95\%$ CI of the association while adjusting for multiple covariates. The goodness of Model fit was assessed by Hosmer-Lemeshow test. Because overweight/obesity was common (rare disease assumption was not met), we calculated prevalence ratio (PR) instead of odds ratio (OR) using Stata command “oddsrisk,” as described by Zhang et al. [ 20]. Separate analyses were used for the following infants’ feeding practices: breastfeeding (ever breastfed/never breastfed), breastfeeding type (exclusive/mixed/no breastfeeding), duration of breastfeeding (as ≤4 vs. >4 months & or ≤6 vs. >6 months), formula-milk (yes/no), age of introducing formula-milk (≤4 vs. >4 months), and age of introducing solid food (as ≤4 months vs. >4 months & ≤6 vs. >6 months). First, crude PR was calculated for breastfeeding and each of the infants’ feeding practices as well as other covariates. Then, adjusted PR was calculated by introducing covariates that showed association at $20\%$ level of significance in univariable analysis. The impact of this on the association was noted by comparing the crude and adjusted PR. The steps of the univariable and multivariable analyses were repeated for each of the infants’ feeding practices. ## 3. Results Of the 907 students selected, 800 ($88.2\%$) responded, and 775 ($85.4\%$) were included in this analysis (Figure 1). The mean (SD) age of the study participants was 16.7 (1.1) years. About three-quarters of the schoolgirls were Kuwaiti ($76.8\%$), and most of them were from public schools ($77.8\%$). The prevalence of overweight or obesity was ($23.6\%$) and ($22.2\%$) respectively, Table 2. This was not significantly different between public and private schools ($$p \leq 0.926$$) nor between Kuwaiti and non-Kuwaiti nationals ($$p \leq 0.853$$). Table 3 shows factors that were significantly associated with overweight/obesity in univariable analysis. These factors were age of the schoolgirls, their living arrangement (whether living with both parents or not), mother’s level of education, number of offspring’s the mother has, age of menarche of the schoolgirls, and the frequency of weekly consumption of each (soft drinks, fruits, dairy products and fries). Of 800 mothers approached by telephone calls, 496 responded; hence the association between overweight/obesity and infants’ feeding practices before and after adjusting for the potential confounders shown in Table 4 is for 496 schoolgirls. Whether the participant was ever breastfed or not showed no association with overweight/obesity; crude PR = 1.32 [$95\%$CI: 0.81–1.74], ($$p \leq 0.214$$) and adjusted PR = 1.07 [$95\%$CI: 0.52–1.65], ($$p \leq 0.813$$). Similarly, type of breastfeeding (exclusive, mixed, no breastfeeding) during the first four months of life was not significantly associated with overweight/obesity in univariable ($$p \leq 0.293$$) or multivariable analyses ($$p \leq 0.589$$). There was no significant association between breastfeeding duration and overweight/obesity, whether it was fitted as a continuous or a categorical variable. We categorized breastfeeding duration as (≤4 months and >4 months) of birth and as (≤6 months and >6 months) and conducted separate analyses. In both analyses, no significant association was found between duration of breastfeeding and overweight/obesity. Whether the participant was formula fed or not was not significantly associated with overweight/obesity in both univariable ($$p \leq 0.410$$), or multivariable analysis ($$p \leq 0.758$$). There was no significant association between age at which solid food was introduced and overweight/obesity whether age was fitted as a continuous variable ($$p \leq 0.354$$), or categorized as (≤4 vs. >4 months) of life ($$p \leq 0.643$$). However, when we re-categorized this variable as (≤6 months and >6 months) of life, there was a significant association with overweight/obesity in both univariable and multivariable analysis; crude PR: 1.42 [$95\%$CI: 1.13–1.68], ($$p \leq 0.019$$) and adjusted PR:1.77 [$95\%$CI: 1.39–2.03], ($p \leq 0.001$). The other factors that showed significant association with overweight/obesity in multivariable analysis were age of menarche ($$p \leq 0.039$$), weekly consumption of dairy products ($$p \leq 0.008$$), and weekly consumption of fries ($$p \leq 0.040$$). We also conducted separate analyses while re-categorizing BMI as overweight, normal weight in one category, and obese in the other category. The analysis revealed the same conclusion- i.e., no association between breastfeeding or breastfeeding duration during infancy and obesity during adolescents. Additionally, the analysis was repeated while excluding the 12 participants who were underweight and the result remained unchanged. Finally, we used stepwise selection to identify factors associated with overweight/obesity using stepwise logistic regression and none of the variables related to breastfeeding or infants’ feeding was selected in this method; hence our conclusion from the main analysis above remained practically unchanged. ## 4. Discussion This study aimed to examine the association between overweight/obesity and feeding practices during infancy. We estimated the prevalence of overweight and obesity to be ($23.61\%$) and ($22.19\%$) respectively and found no significant association between breastfeeding during infancy and overweight/obesity during adolescence. The estimated prevalence of overweight and obesity is similar to that reported by Kuwait Nutritional Surveillance System ($47.13\%$ of schoolgirls aged 15–19 years were overweight/obese) [5]. It is also similar to that reported in many other studies in Kuwait [21] and neighboring countries. For example, the prevalence of overweight or obesity among schoolgirls was ($40.4\%$) in Qatar [22], and ($41.4\%$) in United Arab Emirates [23]. Efforts should be made to combat overweight and obesity in adolescents in Kuwait in order to reduce its short-term and long-term impact on the health of the population. The prevalence of overweight/obesity among the 496 participants who have their breastfeeding status reported by their mothers is about $46\%$ which is the same as that of the total number of participants including those who we did not have their breastfeeding status reported. Among the 496 participants who have their breastfeeding status reported by their mothers, we found no association between breastfeeding (exclusive or mixed feeding) in the first four months of life and overweight/obesity during adolescence in female schoolgirls in Kuwait. Also, we found no association between breastfeeding duration in infancy and overweight/obesity during adolescence in female schoolgirls (Table 4). Our findings are consistent with other studies that showed no association between breastfeeding during infancy and obesity during childhood or adolescence, including longitudinal studies [14,24]. However, other studies suggested a protective effect or an inverse association between breastfeeding during infancy and overweight/obesity later in life. For instance, A meta-analysis included 25 studies published between 1997 to 2014 revealed that there is a protective effect of breastfeeding against obesity during childhood, with 17 out of 25 studies found a dose-response relationship, although the authors acknowledged the role of publication bias in their findings [13]. Although it is possible that there is no association between breastfeeding during infancy and overweight/obesity and that our findings truly describe the reality, there are other possible explanations for the lack of association in our study. Collecting data on breastfeeding and breastfeeding duration with a long recall period may have resulted in non-differential misclassification that attenuated the association towards the null. The other possible explanation for the lack of association between overweight/obesity and breastfeeding in our study could be the confounding by diet. Although we collected data on the current diet, diet may have changed over time from the time our study participants were infants to the time they became adolescents. Furthermore, collecting data on diet using food frequency questionnaire always results in a substantial non-differential misclassification, which in our case would results in residual confounding, another explanation for our negative findings. Another explanation could be that we have only 18 participant who never been breastfed during infancy, which would impact the findings since we will not have enough power to detect the association. However, we conducted the analysis based on the duration of breastfeeding with numbers of participants in the categories being large enough to detect meaningful association and our findings remain the same in both analyses. Second, the data about breastfeeding was available for 496 ($64\%$ of the total number of participants). If those we do not have their breastfeeding status are different from the 496 with reported breastfeeding this might impact the association. However, we can see that the prevalence of obesity/overweight among the 496 is ($46\%$) which is similar to the prevalence in the whole study group ($45.8\%$). This is reassuring as those who completed the interview are not different from those who did not in terms of overweight/obesity. Finally, it is possible that our negative findings could be due to confounding by genetic or epigenetic factors, which we did not measure in our study. We also found no association between whether schoolgirls were formula-fed during infancy and overweight/obesity during adolescence. Our findings are different from that reported in other studies that suggest a positive association between formula-milk intake and weight gain or overweight/obesity [25]. We also found no association between the age at which formula milk was introduced and overweight/obesity, which is different from other studies that reported negative association (i.e., early initiation of formula milk associated with a higher risk of overweight/obesity) [26]. The absence of association in our analysis could be due to the fact that most of the participants consumed formula milk. Similar to the above, the lack of association could be due to non-differential misclassification due to a long recall period or confounding by other factors such as diet, genetic or epigenetic factors. We found no association between the age at which solid food was introduced and overweight/obesity (≤4 moths vs. >4 months of life). This is consistent with studies from other settings among breastfed children there was no association between early introduction of solid food (<4 months of life) and prevalence of obesity [27,28]. In contrast, in a systematic review, the early introduction of solid food at ≤4 months was associated with an increased risk of childhood overweight [28]. Another study found that [27]. Although we found no association between overweight/obesity and age of introducing solid food when categorized as (≤4 moths vs. >4 months) which similar to previous studies [28], we found a significant positive association between age of introducing solid food when categorized into (at >6 months). Such unexpected finding has been reported previously from other studies that found late introduction of solid food (at ≥9 months) was positively associated with obesity during childhood [28,29]. In our analysis, there are several explanations for this association. First, 67 mothers ($15.69\%$ of the participants) did not remember the age at which they introduced solid food. Our findings may change if these mothers remembered the age at which they introduced solid food for their daughters. Second, studies suggested that it is the quality of solid food that is more influential on BMI or overweight/obesity rather than the time of introducing solid food [30,31]. No data were collected on the type of solid food introduced in our study; and such data would be unreliable with this long recall period. Third, some studies have suggested that the interaction between breastfeeding and the age at which solid foods were introduced, is related to obesity later at life [27]. Therefore, we assessed for interaction in our analysis and found no interaction. This is the first study that assessed the association between breastfeeding during infancy and overweight/obesity during adolescence among females in Kuwait and Gulf region, where obesity is a major health problem in this age group. This study was conducted among nationally representative sample of adolescent females. The participants were recruited from both public and private schools and in Kuwait almost all girls in this age group are enrolled in schools. However, there are several limitations in our study that require consideration while interpreting the data. First, data on breastfeeding (the exposure) were dependent on mother’s ability to recall their practices over a long time period. Although the questions were designed to aid accurate recall, there is possibility for non-differential misclassification in breastfeeding, which attenuates the association between breastfeeding during infancy and overweight/obesity during adolescence. Second, the association could be confounded by factors that we did not address in this analysis (e.g., genetic or epigenetic factors). Third, using BMI as a proxy for obesity (fat accumulation) is not accurate to determine adiposity since it might reflect greater bone density or muscle bulk but not necessarily excess body fat. This might lead to non-differential misclassification of the outcome, which attenuates the association towards the null. ## 5. Conclusions In conclusion, we found no significant association between breastfeeding or breastfeeding duration during infancy and overweight/obesity during adolescence. Breastfeeding has other indisputable benefits for mothers and children and should be encouraged whether or not it is associated with obesity later at life. Further longitudinal studies that collect data on breastfeeding and other feeding practices prospectively from birth until adolescence are needed to elucidate the long-term benefits of breastfeeding in terms of obesity during adolescence. Such studies should collect data on potential confounders such as genetic and epigenetic factors in addition to repeatedly monitor diet over the whole study period. ## References 1. Jedrychowski W., Perera F., Jankowski J., Butscher M., Mroz E., Flak E., Kaim I., Lisowska-Miszczyk I., Skarupa A., Sowa A.. 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--- title: Carotenoid and Tocopherol Profiling in 18 Korean Traditional Green Leafy Vegetables by LC-SIM-MS authors: - Eun-Young Ko - Ji-Ho Lee - Iyyakkannu Sivanesan - Mi-Jung Choi - Young-Soo Keum - Ramesh Kumar Saini journal: Foods year: 2023 pmcid: PMC10048374 doi: 10.3390/foods12061312 license: CC BY 4.0 --- # Carotenoid and Tocopherol Profiling in 18 Korean Traditional Green Leafy Vegetables by LC-SIM-MS ## Abstract Fruits and vegetables are a vital source of redox-active phytochemicals in the diet. Traditional green leafy vegetables (GLVs) are a rich source of carotenoids, dietary fiber, minerals, phenols, vitamins, and tocopherols and are commonly consumed in rural areas worldwide. In traditional Korean medicine, many GLVs are used to treat various ailments. However, data on the carotenoid and tocopherol content of many traditional GLVs consumed in the Republic of Korea are insufficient. The current work aims to compare the carotenoid and tocopherol profiles of 18 traditional GLVs by utilizing a single ion monitoring LC-MS approach to identify the potential GLVs for commercial cultivation and healthy diet formulations. Among the traditional GLVs investigated, (all-E)-lutein was the most abundant carotenoid, ranging from $44.4\%$ in Glehnia littoralis to $52.1\%$ in Heracleum moellendorffii. It was followed by (all-E)-violaxanthin and (all-E)-β-carotene. The highest contents of (all-E)-violaxanthin (75.6 µg/g FW), 9-Z-neoxanthin (48.4 µg/g FW), (all-E)-luteoxanthin (10.8 µg/g FW), (all-E)-lutein (174.1 µg/g FW), total xanthophylls (310.5 µg/g FW), (all-E)-β-carotene (69.6 µg/g FW), and total carotenoids (380.1 µg/g FW) were recorded in Pimpinella brachycarpa. Surprisingly, Taraxacum mongolicum also showed the highest contents of (all-E)-violaxanthin, (all-E)-lutein, and total carotenoids, which were statistically non-significant ($p \leq 0.05$, Tukey HSD) with P. brachycarpa. The highest concentration of (all-E)-zeaxanthin (14.4 µg/g FW) was recorded in Solidago virga-aurea. Among the studied herbs, 13.9 (H. moellendorffii)–133.6 µg/g FW (Toona sinensis) of α-tocopherol was recorded. Overall, the results suggest that P. brachycarpa and T. mongolicum are rich sources of carotenoids. On the other hand, T. sinensis is a rich source of α-tocopherol. These GLVs can be utilized in the diet to enhance the intake of health-beneficial carotenoids and α-tocopherol. ## 1. Introduction The World Health Organization recommends adequate intake (400–500 g per day) of fruits and vegetables (including green leafy and cruciferous vegetables) to minimize the risk of high blood pressure, coronary heart disease, and stroke [1]. Green leafy vegetables, or GLVs, are an important part of a healthy diet as they are rich in essential nutrients and phytochemicals with health benefits. These include dietary fiber, vitamins, minerals, carotenoids, and polyphenolic compounds. Clinical trials have also demonstrated the advantages of the enhanced intake of vegetables and fruits in reducing the risk of developing chronic and metabolic disorders, including cancer, type 2 diabetes, obesity, and cardiovascular and neurological diseases [2,3,4]. The redox-active phytochemicals involving carotenoids and tocopherols in fruits and vegetables help prevent these disorders by minimizing free radical-mediated oxidative damage to proteins, cellular lipids, DNA, and other protein biomolecules [5,6,7,8]. Vitamin E, also known as tocols, which includes four tocotrienols (α-, β-, γ- and δ) and four tocopherols (α-, β-, γ- and δ), differs by the position of methyl groups on the chromanol ring [9]. Tocols serve as critical components of cellular lipids. They neutralize free radicals, thus preventing the free radical-mediated oxidative damage of lipids and minimizing the incidence of diseases associated with oxidative stress [10,11,12,13]. Carotenoids are mainly tetraterpenoid (C40) pigments commonly synthesized de-novo by photoautotrophs, including higher plants. Animals rely on provitamin A carotenoids (converted by the body into vitamin A, e.g., β-cryptoxanthin and α- and β-carotene) as a dietary source to carry out vital functions. Additionally, carotenoids without pro-vitamin A activity (e.g., xanthophylls) have antioxidant abilities that shield against chronic and metabolic ailments, as well as photooxidative harm to the skin and eyes in animals [7,14]. The Republic of Korea is well known for its traditional high-vegetable diet, which is probably responsible for the significantly lower rates of chronic diseases than other industrialized countries with similar economic development [1]. Several traditional GLVs such as Amaranthus lividus L., Angelica gigas Nakai, Glehnia littoralis F. Schmidt ex Miq., Heracleum moellendorffii Hance, Peucedanum japonicum Thunb., Pimpinella brachycarpa (Kom.) Nakai, Aralia continentalis Kitag., Kalopanax septemlobus (Thunb. ex A.Murr.) Koidz., Artemisia princeps Pamp., Cirsium setidens Nakai, Ligularia fischeri (Ledeb.) Turcz., Petasites japonicus (Siebold & Zucc.) Maxim., Rudbeckia laciniata L., Solidago virga-aurea L. var. asictica Nakai, Taraxacum mongolicum Hand.-Mazz., Adenophora triphylla (Thunb.) A.DC. var. japonica (Regel) H. Hara, Allium victorialis var. platyphyllum Makino, and Toona sinensis (A.Juss.) M.Roem. are sold at local markets in Korea (Table 1). The extracts and compounds obtained from traditional GLVs (1–18) have been shown to possess antioxidant [15,16,17,18,19,20,21,22], anticancer [18,23,24,25,26], antiinflammation [20,25,27,28], anti-melanogenic [29,30], anti-fatigue [31], anti-obesity [32,33], antidiabetic [34], and immunostimulatory [35] activities. GLVs have abundant phytopigments. Several studies have confirmed the content of α-carotene, β-carotene, lutein, violaxanthin, zeaxanthin, and α-tocopherol in a few traditional GLVs [15,16,36,37,38,39,40]. However, data on carotenoid content and compositions of several traditional GLVs consumed in Korea are still unavailable. Moreover, GLVs are not widely investigated for tocopherol content. Thus, quantifying bioactive phytochemicals in these species can help identify potential GLVs for healthy food formulations. A report by Yoon et al. [ 10] revealed that GLVs consumed in the Republic of Korea are good sources of carotenoids (β-carotene and lutein), and their contents are higher than other commonly consumed plant foods. The authors [10] investigated the contents of β-carotene, lutein, and total phenolic in several vegetables consumed in Korea. Given the information presented above, this study aimed to determine the levels and composition of carotenoids and tocols (Vitamin E) in 18 different types of traditional GLVs using liquid chromatography (LC)–mass spectrometry (MS) with a single ion monitoring (SIM) approach. ## 2.1. Reagents, Standards, and Plant Materials An authentic standard of tocols mix (α-, β-, γ-, and δ-tocotrienols and α-, β-, γ-, and δ-tocopherols) was obtained from ChromaDex (ChromaDex Inc., Irvine, CA, USA). ( all-E)-β-carotene was procured from Merck Ltd., Seoul, Republic of Korea. ( all-E)-lutein, 9-Z-neoxanthin, (all-E)-violaxanthin used in this investigation were isolated from lettuce, while (all-E)-zeaxanthin was prepared from corn seeds using our established protocol [41]. An acid-catalyzed reaction was used to transform (all-E)-luteoxanthin from (all-E)-violaxanthin [42]. The solvents used in the study were of LC grade and sourced from J.T. Baker® located in Suwon-Si, Republic of Korea. The 18 traditional green leafy vegetables were collected from natural habitats and the traditional market, as detailed in Table 1. The vegetables were brought to the lab, cleaned, individually packed in Ziplock polythene bags, and stored at −90 °C in an ultra-low temperature deep freezer (CLN-2300CW, Nihon Freezer Co., Ltd., Yushima, Japan) until analysis. ## 2.2. Extraction of Carotenoids and Tocols The lipophilic bioactive carotenoids and tocols were simultaneously extracted from fresh foliage using our recently optimized method [43]. In sum, a 2 g fresh sample was placed into a Falcon 50 mL conical centrifuge tube and homogenized with 25 mL of a solvent mixture (acetone/ethanol/cyclohexane, 1:1:2, v/v) containing $0.1\%$ butylated hydroxytoluene (BHT) as an antioxidant [44]. The mixture was then subjected to bath sonication (JAC-2010; 300 w, 60 Hz, for 10 min) and ultra-shaking for 2 min in collomix viba x.30 (Tinting Solutions B.V., Nederland) to ensure complete extraction. The sample was vacuum filtered and pellets were extracted again until obtaining the colorless pellets. The filtrate containing lipophilic compounds were pooled, transferred to a 300 mL Short Neck Boiling flask (round bottom), and dried in a vacuum rotary evaporator at 35 °C. The extract containing carotenoids and other lipophilic compounds were recovered in 4 mL of acetone containing $0.1\%$ BHT and transferred to a 5 mL glass vial fitted vial with a PTFE-lined screw cap closure. A small portion of the extract was filtered using a Nylon syringe filter (pore size 0.45 μm; Whatman) and transferred to an amber HPLC vial for the analysis of tocols and carotenoids. The carotenoids and tocols were analyzed in their non-hydrolyzed form, as the hydrolysis process can lead to the degradation of these compounds [45]. ## 2.3. LC-MS Analysis To analyze the tocols and carotenoids, a liquid chromatography (LC)–mass spectrometry (MS) with a single ion monitoring (SIM) approach was employed. The LC-MS/SIM analysis was carried out using an LCMS-9030 quadrupole time-of-flight (Q-TOF) mass spectrometer manufactured by Shimadzu in Tokyo, Japan. The analysis was performed in an atmospheric pressure chemical ionization (APCI; Positive mode), following the LC separation in a YMC C30 carotenoid column (150 mm × 4.6 mm, 3 μm; YMC, Wilmington, NC) maintained at 20 °C. The solvent system was methanol/water (95:5; v/v) containing 5 mM of ammonium formate (Mobile Phase A) and methyl tertiary butyl ether/methanol/water (90:7:3, v/v/v) containing 5 mM of ammonium formate (Mobile Phase B). Ammonium formate was added as an ionization enhancer in the mass spectrometer. The gradient elution program involved starting at $0\%$ B at 0 min and reaching $100\%$ B at 45 min, followed by a 5 min post-run at $0\%$ B. The flow rate was maintained at 0.5 mL/min. The source and compound parameters were optimized as follows: drying gas flow, 10 L/min; nebulizing gas flow, 3 L/min; corona needle voltage, 4.0 kv; interface temperature, 400 °C; DL temperature, 300 °C; heat block temperature, 300 °C; Q1 resolution, ±20 ppm; and data acquisition (sampling), 1.85625 Hz [43]. Quantitative analysis was performed using the selected ion monitoring (SIM) mode. Table 2 lists the optimized SIM transitions (m/z). To quantify each carotenoid and tocol compound, external standards were used. The linearity range for each standard compound can be found in Table A1. ## 2.4. Calculation of Vitamin A Activity The vitamin A activity, as retinol activity equivalents (RAEs), was calculated based on the in vivo conversion factor of 1 µg RAE = 12 µg of β-carotene proposed by the Food and Nutrition Board, Institute of Medicine (IOM), USA [46]. ## 2.5. Statistical Evaluation and Quality Assurance Three separate replicates of extraction and analysis were performed for each green leafy vegetable (GLV). The statistical analysis was conducted using IBM SPSS statistics version 25, including a one-way analysis of variance (ANOVA) with a significance level of 0.05 and post hoc testing with Tukey B HSD. The lower limits for detection (LOD) and quantitation (LOQ) of utilized LC-MS methods were determined based on a signal-to-noise (S/N) ratio of more than 3 and more than 10, respectively [47]. Moreover, the employed LC-MS/SIM method was tested for precision (ability to produce consistent and reproducible results), linearity (relationship between the concentration of the analyte and its response), and accuracy (closeness of the measured value to the true value of the analyte) [48,49]. To calculate the precision of the instrument (both inter-day and intra-day) for chromatographic retention time and peak area measurement, multiple injections of the same concentration within the working range were performed, and the coefficient of variation (% CV) was calculated. The intra-day precision was determined by performing six replicate injections of the same concentration in a single day. On the other hand, to establish the inter-day precision, the standard compounds were analyzed six times over two separate days that were not consecutive. ## 3.1. Validation of LC-MS/SIM Methodology The LC-MS/SIM method used to quantify carotenoids and tocols underwent validation to assess its accuracy, precision, and linearity [48,49]. The coefficient of variation (CV; a ratio of the standard deviation (SD) to the mean of the peak area counts) or relative standard deviation (RSD) was measured and found to be <$0.35\%$ and $9.23\%$ (inter-day and intra-day) for chromatographic retention times and peak area counts, respectively, for carotenoids and tocopherols (Table A1). The calibration curves demonstrated a high coefficient of correlation (r2; >0.999–1.000) between standard concentrations and corresponding peak area counts. These findings provide evidence that the employed LC-MS/SIM method is reliable and can be used with confidence. ## 3.2. Carotenoid Composition Carotenoids are crucial bioactive substances that greatly influence the nutritional quality and appealing color of food [50]. In the present investigation, six major carotenoids, including five xanthophylls ((all-E)-zeaxanthin), (all-E)-lutein, (all-E)-luteoxanthin, 9-Z-neoxanthin, and (all-E)-violaxanthin) and a provitamin A carotenoid (all-E)-β-carotene were quantified (Figure 1; Table 3 and Table 4). The quantified levels of all identified carotenoids were significantly higher than the limit of quantification (LOQ) (Table A1). Among the traditional GLVs investigated in the present investigation, the (all-E)-lutein (β,ε-carotene-3,3′-diol) was the most prominent carotenoid ranging between 44.4 (Glehnia littoralis)–$52.1\%$ (Heracleum moellendorffii) of total carotenoids, followed by (all-E)-violaxanthin (5,6:5′,6′-diepoxy-5,5′,6,6′-tetrahydro-β,β-carotene-3,3′-diol) and (all-E)-β-carotene (Table 3 and Table 4). The highest contents (µg/g FW) of (all-E)-lutein (174.1), (all-E)-luteoxanthin (10.8), 9-Z-neoxanthin (48.4), (all-E)-violaxanthin (75.6), total xanthophylls (310.5), (all-E)-β-carotene (69.6), and total carotenoids (380.1) were recorded in Pimpinella brachycarpa. Surprisingly, Taraxacum mongolicum also showed the highest contents of (all-E)-violaxanthin, (all-E)-lutein, and total carotenoids, which were statistically non-significant with Pimpinella brachycarpa. In contrast, Solidago virga-aurea exhibited the highest contents (14.4 µg/g FW) of (all-E)-zeaxanthin among all of the traditional GLVs investigated. Only a few GLVs investigated in the present study were previously explored for carotenoid composition and content. Sathasivam et al. [ 40] also recorded the dominance of lutein and β-carotene in Heracleum moellendorffii leaves, with a total carotenoid content of 1668 µg/g dry weight (DW). In Pimpinella brachycarpa, Yoon et al. [ 51] recorded 54.5 and 32.3 µg/g FW of lutein and β-carotene, respectively. In contrast, we recorded 174.1 and 69.9 µg/g FW of lutein and β-carotene, respectively. Similarly, in Toona sinensis, 223 µg/g FW of lutein and 186 µg/g FW β-carotene are reported by Cheng et al. [ 36], which is substantially greater than the contents documented in the present investigation. Kao et al. [ 52] recorded the prominence of (all-E)-β-carotene, followed by (all-E)-violaxanthin, 9-Z-neoxanthin, and (all-E)-lutein in Taraxacum officinale, a close relative of T. mongolicum investigated in the present study. It is commonly known as dandelion and is traditionally used for heat relieving, detoxification, diuretic, and hepatoprotective activities [53,54]. The carotenoid compositions and contents varied significantly among the different plants. Moreover, a significant variation has been documented among the species of the same genus. In a study of the carotenoid composition of medicinally important GLVs consumed in India, a near 3-fold variation was recorded for the total carotenoid content among the leaves of three species of the genus Amaranthus, with the highest total carotenoid content in A. viridis L. (2538 µg/g DW), followed by A. gangeticus L. (789 µg/g DW), and A. tristis L. (675 µg/g DW) [39]. The (all-E)-β-carotene is the provitamin A carotenoid predominantly found in herbs. The recommended dietary reference intake (DRI) of vitamin A for adult men is 900 retinol activity equivalents (RAEs) according to the dietary guidelines [46]. The vitamin A content calculated as the RAE, using the conversion of 1 RAE = 12 μg of β-carotene, revealed that the consumption of 100 g of herbs investigated in the present study can supply the 24.8 (Kalopanax septemlobus)–64.4 % (Pimpinella brachycarpa) DRI of vitamin A (Table 4). Along with the provitamin A carotenoids, the traditional GLVs investigated in the present investigation are found to be rich in (all-E)-lutein. Lutein and zeaxanthin are pigments in the macula that act as filters for blue light, thus protecting the retina and maintaining vision [55]. Research has demonstrated that a higher intake of these compounds can support eye health [55]. Thus, among the traditional GLVs studied in the present investigation, Pimpinella brachycarpa and Taraxacum mongolicum are the richest sources of (all-E)-β-lutein; thus, their enhanced intake may help to improve ocular health. We have previously explored the carotenoid contents of several herbs, including baby leaf vegetables [56], green and green/red perilla (Perilla frutescens Britt.) [ 57], and 23 diverse lettuce cultivars [58]. In baby leaf vegetables, the (all-E)-β-carotene content ranged from 19.3 to 60.2 µg/g FW, with the total carotenoid content ranging from 57.1 to 195.2 µg/g FW [56]. In green/red and green perilla foliage, the (all-E)-β-carotene content was 51.2–52.1 µg/g FW, with a total carotenoid content of 196.1–209.4 µg/g FW [57]. Among the 23 different lettuce cultivars, the (all-E)-β-carotene content w ranged from 4.2 to 13.6 g/g FW, with the total carotenoid content ranging from 54.4 to 129.8 g/g FW [58]. In this investigation, the contents of (all-E)-β-carotene were 26.8–69.6 µg/g FW, with a total carotenoid content of 193.3–380.1 µg/g FW, indicating that traditional GLVs investigated in the present study are more concentrated sources of carotenoids than commonly consumed GLVs. These results are also supported by a recent study by Lee et al. [ 59], who observed much higher contents of β-carotene in underutilized GLVs, such as Moringa foliage (108 µg/g FW), sweet leaf bush (125 µg/g FW), and sweet potato foliage (110 µg/g FW), compared to iceberg lettuce (4 µg/g FW). ## 3.3. Tocols Composition The term “tocols” encompasses four forms of tocopherols (α-, β-, γ-, and δ-) and four forms of tocotrienols (α-, β-, γ-, and δ-) [60]. In the present study, the tocol content and composition were analyzed using an LC-SIM-MS-based method. Among the studied herbs, 13.9 (Heracleum moellendorffii)–133.6 µg/g FW (Toona sinensis) of α-tocopherol was recorded, whereas other types of tocopherols and tocotrienols were not detected in a significant amount (Figure 2). Limited studies exist on the alpha-tocopherol content of green leafy vegetables (GLVs), as most research has been concentrated on seed oil. Previous studies on α-tocopherol levels in GLVs have revealed significant variation. Among the foliage of several edible tropical plants, α-tocopherol contents ranged between 6.9 (Brassica oleracea)–426.8 (Sauropus androgynus) µg/g FW [61]. Among the several GLVs commonly consumed in Southeast Asia, 1.9 (green amaranth)–183 µg/g FW (foliage of Moringa oleifera) of α-tocopherols was documented by Lee et al. [ 59]. Our recent investigation found that α-tocopherol levels in leaf mustard varied among the four cultivars studied, with recorded amounts ranging from 67.2 (cv. Asia Curled) to 83.4 µg/g FW (cv. Cheong) [62]. In another recent study on GLVs, α-tocopherol levels ranging from 22.0 µg/g FW in spinach to 87.7 µg/g FW in Moringa were recorded [43]. Considering these previous reports, Toona sinensis foliage investigated in the present study is a rich source of α-tocopherol. α-tocopherol plays a key role as a chain-breaking antioxidant, thus preventing the free radical-mediated oxidative damage of lipids and minimizing the incidence of diseases associated with oxidative stress, such as heart disease, certain types of cancer, and age-related cognitive decline [10,11,12,13]. Additionally, α-tocopherol may help improve skin health and immune function [63]. The DRI of α-tocopherol for both women and men is 15.0 mg per day [13]. Among the various forms of tocols, α-tocopherol has the maximum vitamin E activity, with 1 mg equaling 1 α-TE [13]. Vegetable oils, mainly wheat germ oil, are the richest source of tocols in the diet [13]. Nevertheless, taking into account the highest α-tocopherol content (133.6 µg/g FW), Toona sinensis foliage can provide $90\%$ of the DRI of vitamin E. ## 4. Conclusions In this study, 18 traditional green leafy vegetables (GLVs) were analyzed for their carotenoid and tocol content using LC-MS/SIM. Among the studied GLVs, the most abundant carotenoid was (all-E)-lutein, followed by (all-E)-violaxanthin and (all-E)-β-carotene. The highest content of carotenoids was found in Pimpinella brachycarpa and Taraxacum mongolicum, while the highest content of (all-E)-zeaxanthin was recorded in Solidago virga-aurea. 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--- title: Physiological Response of Stored Pomegranate Fruit Affected by Simulated Impact authors: - Pankaj B. Pathare - Mai Al-Dairi - Rashid Al-Yahyai - Adil Al-Mahdouri journal: Foods year: 2023 pmcid: PMC10048388 doi: 10.3390/foods12061122 license: CC BY 4.0 --- # Physiological Response of Stored Pomegranate Fruit Affected by Simulated Impact ## Abstract Mechanical damage resulting from excessive impact force during handling and other postharvest operations from harvesting to consumption is a critical quality problem in fresh produce marketing. The study investigates the impact of bruise damage, storage temperature, and storage period on the physiological responses of Omani pomegranate fruit cultivar ‘Helow’. Fruits were subjected to low (45°; 1.18 J) and high (65°; 2.29 J) impact levels using a pendulum test by hitting the fruit on the cheek side. Bruised and non-bruised fruit were stored at 5 and 22 °C for 28 days. Bruise measurements, water loss per unit mass, water loss per surface area, firmness, fruit size measurements, geometric mean diameter, surface area, fruit volume, color parameters, respiration rate, and ethylene production rate were evaluated. Bruise area, bruise volume, and bruise susceptibility of damaged pomegranate fruit were increased as impact level, storage duration, and storage temperature increased. Pomegranates damaged at a high impact level and conditioned at 22 °C showed $20.39\%$ weight loss on the last day of storage compared to the control and low-impact-bruised fruit. Firmness and geometric mean diameter were significantly ($p \leq 0.05$) reduced by bruising at a high impact level. Impact bruising level and storage temperature decreased lightness, yellowness, browning index, and increased redness over time. Furthermore, the respiration rate was five times higher in the non-bruised and low- and high-impact-injured fruit stored at 22 °C than that stored at 5 °C. The ethylene production rate recorded its highest value on day 21 in high-level-impact-bruised pomegranate fruit. The bruise susceptibility was strongly correlated with the majority of the studied parameters. This study can confirm that bruising can affect not only the visual quality characteristics but also the physiological attributes of pomegranate fruit; therefore, much care is required to preserve fresh produce and avoid any mechanical damage and losses during postharvest handling. ## 1. Introduction Pomegranates have become a popular commercially grown fruit in many regions of the world. A high number of commercial pomegranate trees orchards are grown in different parts of the world [1]. The consumption of pomegranates has shown a remarkable increase due to their exceptional nutritional and sensory attributes linked with various medicinal advantages attributed to the fruit’s healthy substance helping to provide high antioxidant and phytonutrient capacity [2]. However, using inappropriate handling equipment in the supply chain can make pomegranate fruit prone to external mechanical forces that finally lead to bruising [3]. Mechanical damage in fresh produce is the leading cause of postharvest quality damage and losses during handling and other postharvest operations [4]. Mechanical damage, including bruising in fresh produce during handling and transportation, is attributed to different types of forces such as compression, abrasion, impact, and cutting [5]. Bruising results from action due to severe external forces on the fresh produce surface when fruit hits other fruit or another rigid surface/body during handling [6]. Bruising is known as a failure observed in the subcutaneous tissue of the impacted fruit without rupturing the skin. Discoloration can be observed in the damaged tissues, which indicates the injured spot [7]. Previous research has shown that the occurrence of bruising can affect the exterior fruit attributes, interior quality deterioration, physiological process alterations, and increase postharvest decay [8]. Besides, bruising can reduce the weight of different fruit and vegetables, thus reducing their market value [9]. Moreover, it modifies the metabolic and physiological processes, resulting in faster ripening, browning, and other quality and economic losses [10]. Bruising causes softening and accelerates the changes in color attributes of bruised fruit. The negative consequences of bruising can reduce the shelf life of produce. Bruising is considered a risk for fungal and bacterial infection [11]. Studies in bruising have shown that the respiration rate increased with increasing impact levels. In addition, it can reduce firmness and cause different changes in the lycopene of tomatoes [12].The intensity of a bruise can be expressed as bruise diameter, area, and susceptibility. The diameter and depth of the bruise are the primary direct measurements utilized to define the bruise size [10]. Several methods are used to replicate fruit bruising in the laboratory and test the effect of impact on different agricultural products. The most common methods used are the drop test [13,14] and the pendulum method [15]. These methods are mainly structured to simulate various types of dynamic loading that occur during the time of harvesting operations. The present study concentrated on the applied impact force, which is the most common cause of loading [3], particularly in pomegranate fruit. Therefore, an impact test involving dropping a pendulum arm with a particular weight from the desired angle into the fruit is one of the commonly known techniques and has been applied to study bruising measurements of different fruit such as apples [16], pears [15], nectarines [17], and grain (pea pods) [18]. There is no study evaluating the influence of impact damage using a pendulum impact test during storage on the physiological attributes of Omani pomegranates. Therefore, this study aims to assess the effect of two different impact levels on the bruising magnitude and the quality characteristics of the pomegranate cultivar ‘Helow’ stored at two storage conditions (5 and 22 °C) for 28 days (water loss, firmness, size, and color) and 56 d (respiration and ethylene production rate) of storage. ## 2.1. Fruit Selection and Preparation Pomegranates (Punica granatum L.) of the cultivar ‘Helow’ (sweet) were harvested manually from farms located in Al-Jabal Al-Akhdar, Ad-Dakhliyah Governate, Oman. Pomegranates were packaged in cardboard boxes and delivered to Postharvest Technology Laboratory, Sultan Qaboos University, Oman. The duration of transportation was about two hours. On arrival, pomegranate fruits were sorted to ensure color (lightness (L) = 55.90 ± 3.10, redness (a) = −3.63 ± 0.95, and yellowness (b) = 53.17 ± 2.78), weight (456 ± 0.033 g), and size (length (L) = 90.555 ± 0.162 mm, width (W) = 91.47 ± 0.240 mm, and thickness (T) = 94.265 ± 0.187 mm) uniformity and that the samples were free from surface blemishes, cracks, and defects. A total of 75 pomegranate samples were used for the present study. ## 2.2. Fruit Impact Bruising (Pendulum Test) and Storage A pendulum system was designed to produce bruises in pomegranate fruit (Figure 1A). Unlike the free drop of mass (weight) through a hollow-guided tube, the pendulum concept permits better observation and control of the falling weight during testing [19]. The size of the steel pendulum arm (mass = 609.25 g, length = 68 cm) was carefully selected to trigger the tested fruit. A half-spherical weight (56.10 g) is connected to the pendulum arm to damage the pomegranate fruit. This test was conducted by raising the pendulum arm from a specific angle and then dropping it once to hit/damage the tested fruit with the half-spherical weight. To* generate various energy levels, the pomegranates were divided into three groups. The first and second groups included pomegranate fruit bruised (cheek-side) from angles of 65° and 45° that represent the high and low impact levels, respectively. The third group was considered a control (without damage). Each group includes 24 ($$n = 24$$) pomegranate fruit. After the initial rebound, the arm was caught by hand to avoid repeated impacts. Under these conditions, impact energy (Ei-J) (Equation [1]) was determined assuming that the fruit absorbed all the energy of the dropped mass [19]:[1]Ei=mgh1 To calculate the actual energy absorbed (Ea) by the pomegranate fruit that results in the measured bruise damage, it is essential to estimate the equivalent rebound height (h2-cm) at the maximum rebound. The equivalent rebound height (h2-cm) was used to calculate the rebound energy (Er-J) as follows (Equation [2]) [19]:[2]Er=mgh2 The graduated scale on the whiteboard was used to record the actual rebound angle. In addition, a camera (Model: EOS FF0D, Canon Inc., Tokyo, Japan) was utilized to record the accurate readings of the rebound angles (equivalent rebound heights). The absorbed energy (Ea-J) was determined from the difference between energy at first impact and rebound, as shown in Equation [3] [17]:[3]Ea=mg(h1−h2) where m is the mass of the steel ball (kg), g is the gravitational constant (9.81 ms−2), h1 is the equivalent drop height (cm), h2 is the equivalent rebound height (cm), Ei is the impact energy (J), Er is the rebound energy (J), and Ea is the absorbed energy (J). The bruise size was measured by slicing the center of the damaged area (marked) of each pomegranate fruit. The bruise damage of the sliced fruit was estimated by the presence of apparently damaged tissues, which were visibly distinguishable from other unbruised (undamaged) parts of the same pomegranate fruit. Major (w1) and minor (w2) diameters (Figure 1B) and bruise depth (d) (Figure 1C) of the elliptical bruise shape were identified using a digital caliper (Model: Mitutoyo, Mitutoyo Corp., Kawasaki, Japan). Results of bruise damage size on pomegranate fruit were expressed as bruise area (BA-mm2) and bruise volume (BV-mm3) (Equations [4] and [5]). The bruise susceptibility (BS-mm3/J), which is the ratio of the BV to the energy absorbed (Ea) during the pendulum experiment impact, was also measured using Equation [6]. For possible reduction of further implications of fruit mass on resulting BS, specific bruise susceptibility (SBS-mm3 J−1 g−1), also known as bruise sensitivity index, was identified by using Equation [7] below [14]. [ 4]BA=π4w1 w2 [5]BV=πd243w1w2+4d2 [6]BS=BVEa [7]SBS=BSmf where mf is the pomegranate fruit mass. After the impact test, each group (high- and low-impact-bruised fruit groups and the control group) was divided equally into two sets and stored at cold temperature (5 ± 1 °C; 95 ± $5\%$ RH) and ambient temperature (22 ± 1 °C; 80 ± $5\%$ RH) to investigate the effect of bruising and storage temperature on pomegranate fruit, and different physiological attributes (fruit size, weight/water loss, firmness, color, respiration rate, and ethylene production rate) for 28 days, as described in Section 2.3. All measurements were taken after 3, 7, 14, 21, and 28 days. However, two more measurements (readings) were taken for both the respiration rate and ethylene production rate. A total of 3 pomegranate fruits were analyzed for day-0 analysis. ## 2.3.1. Water Loss Water loss was determined with respect to the pomegranate fruit weight (WL%) (Equation [8]) and in terms of the unit surface area (WLA-g cm2) (Equation [9]) [20]. Pomegranate fruit mass was determined using an electric weight balance (Model: GX-4000, Japan) with an accuracy of ±0.01 g. [8]WL %=Wi−WtWi×100 [9]WLA=Wi−WtAs where Wi is the initial weight on day 0 and Wt is the recorded fruit weight after storage day. ## 2.3.2. Firmness Two opposite sides of each pomegranate fruit peel (non-bruised parts) were used to record firmness (N) by using a digital fruit firmness tester (Model: FHP-803, L.L.C., USA) with an 8 mm diameter probe. A total of 4 readings were taken from two fruit of each group (high- and low-impact-bruised fruit groups and the control group) per storage condition, per day. ## 2.3.3. Geometric Mean Diameter, Surface Area, and Fruit Volume Length (L-mm), width (W-mm), and thickness (T-mm) of pomegranate fruit were determined by three linear dimensions. Each pomegranate fruit’s length (L) was determined at the longitudinal perimeter (without calyx). The width (W) and thickness (T) were taken at the equatorial perimeter [20]. The measurements were recorded using a digital caliper and the geometric mean diameter (Dg-mm) was calculated using Equation [10]. The surface area (As-mm2) was calculated using the Dg and pi presented in Equation [11]. In addition, the volume (V-mm3) of pomegranate fruit was calculated from the LWT parameters and pi number using Equation [12] [21]. [ 10]Dg=LWT0.3333 [11]As=πDg2 [12]V=πLWT/6 ## 2.3.4. Color Pomegranate fruit peel color evaluation was conducted using a computer vision system (CVS) explained by Al-Dairi et al. [ 22]. In addition, the ImageJ software (v. 1.53, National Institute of Health, Bethesda, MD, USA) was applied to process and analyze all acquired red, green, and blue values (RGB) from the system. Later, RGB values were transferred to CIELab* color space. L, a, and b* for lightness, redness/greenness, and yellowness/blueness, respectively, were measured on bruised and non-bruised fruit (excluding the bruised/marked area) for each sample per group stored at two different conditions. A total of 60 readings were taken per day (10 per group). Chroma (C) and hue° (h), which describe the color intensity and purity, respectively, were calculated (Equations [13] and [14]). The total color difference (TCD) and browning index (BI) given in Equations [15] and [16] were also measured. [ 13]C=a2+b2 [14]H=tan−1 ba [15]TCD=Δa2+Δb2+ΔL2 [16]BI=100×(X−$\frac{0.31}{0.17}$)Where X=a+1.75La5.645L+a−3.012b* ## 2.3.5. Respiration Rate and Ethylene Production Rate Respiration rate and ethylene production rate of bruised and non-bruised (control) pomegranate fruit were measured after 3, 7, 14, 21, 28, 48, and 56 d of the impact experiment using the method of the closed system described earlier by Hussein et al. [ 9] and Pathare and Al-Dairi [23]. For each group, two plastic boxes (2.3 L) containing one pomegranate fruit were used to measure the respiration (CO2) rate and ethylene (C2H4) production rate. O2/CO2 analyzer (Model: 90 2D, Quantek Instruments, Inc., Grafton, Australia) and ethylene detector (Model: SCS 56, Fricaval89, Valencia, Spain) were used to measure respiration rate and ethylene production rate, respectively. Respiration and ethylene production rates were calculated according to Castellanos et al. [ 24]. ## 2.4. Statistical Analysis The results were statistically analyzed using SPSS 20.0 (International Business Machine Crop., Armonk, NY, USA) software. Analysis of variance (ANOVA) was implemented to assess the influence of three investigated factors (A: impact damage level; B: storage temperature; and C: storage duration) and the factors’ interaction with the physiological attributes of pomegranate fruit at a $5\%$ level of significance. GraphPad Prism software 9.3.1 (GraphPad Software, Inc., San Diego, CA, USA) was applied for graph construction. In addition, the Pearson correlation coefficient was calculated to evaluate the relationship between pomegranate fruit quality parameters. Minitab statistical software 21.2 (State College, PA, USA) was also used to perform principal component analysis. ## 3.1. Measurements Related to Bruising Table 1 shows the results of the impact, rebound, and absorbed energies generated from the pendulum system during the dropping of a 56.10 g weight on pomegranate fruit from different angles (45° and 65°). Increasing the drop angle from 45° to 65° increased the energy absorbed by the fruit during impact. Figure 2 presents the overall results of bruising measurements. Impact damage level, storage temperature, and duration statistically ($p \leq 0.05$) affected both BA and BV of damaged pomegranate fruit (Figure 2A,B). The findings showed that BA and BV increased significantly from the lower impact level (1.18 J) to the higher (2.29 J) impact level during the storage period. Pomegranate fruit stored at 22 °C and bruised at the highest impact level showed the highest BA (226.61 mm2) and BV (2447.51 mm3) values on the last day of storage. The lowest BA and BV values were recorded in the pomegranate fruit bruised at the low-impact level and stored at 5 °C with 88.10 mm2 and 663.90 mm3, respectively. These results are in accordance with the findings recorded by Shafie et al. [ 3], where the impact energy level was the main parameter identifying the BV in bruised pomegranate fruit. Later, Shafie et al. [ 25] confirmed that the BV of pomegranate fruit was primarily proportional to the impact energy and the drop height using different impact surfaces. Tabatabaekoloor [26] stated that as the fruit dropped from the highest level of damage, more potential energy was generated, potentially expediting the content intensity, and resulting in increased BA. Similarly, Hussein et al. [ 14] reported a significant increment in BA across all pomegranate cultivars when the impact level increased. They recorded a $39.1\%$ and $18.6\%$ increase in BA after doubling the drop height from 20 to 40 cm and 40 to 60 cm, respectively. Moreover, Pathare and Al-Dairi [7] revealed a significant relationship between the main factors (drop height level and storage condition) and the resulting values of BV and BA of pears during 14 days of the storage period. The BS was influenced by damage level ($$p \leq 0.0118$$), storage temperature ($$p \leq 0.0233$$), and storage duration ($$p \leq 0.0486$$) (Figure 2C). Alterations in BS with increasing impact levels were consistent across all storage conditions. Increasing the level of impact from 1.18 J (low) to 2.29 J (high) intensified the BS of the bruised pomegranate by $10.14\%$. The highest BS values were observed on pomegranate stored at 22 °C and damaged at the highest impact level (1254.93 mm3J−1), followed by those impacted at the lowest impact level (1139.34 mm3J−1) after 28 days of storage. BS values of pomegranate fruit bruised at low and high impact levels were 549.27 mm3J−1 and 1116.11 mm3J−1, respectively, on day 28 of storage at 5 °C. Ahmadi [27] emphasized that storage temperature influences cell wall strength and viscosity. Ahmadi et al. [ 28] suggested that storage at high-temperature conditions can boost the incidence of bruising in fresh fruit due to the active status of enzymes, hence resulting in stiffness and cell wall degradation. Similarly, Pathare and Al-Dairi [12] found a high bruise occurrence in tomatoes stored at 22 °C compared to those stored at 10 °C. Bugaud et al. [ 29] observed lower BS on bruised bananas stored in low-temperature conditions. In addition, this study revealed that increasing storage duration increased bruising. Azadbakht et al. [ 15] found that bruising increased by $0.21\%$ and $47.36\%$ on days 5 and 15, respectively. Figure 2D shows the results of bruise sensitivity tests which are presented as specific bruise susceptibility (SBS). There was a significant effect of the level of impact ($$p \leq 0.0127$$), temperature ($$p \leq 0.0393$$), and duration ($$p \leq 0.0137$$) on SBS values of the bruised pomegranates. The SBS gradually increased with impact level at both storage conditions during 28 days of storage. Overall, the SBS value was the highest for pomegranates bruised following the highest impact (dropped from an angle of 65°) under ambient (22 °C) temperature conditions with 3.49 mm3J−1g−1, followed by pomegranate fruit bruised at the lowest impact level (dropped from an angle of 45°) with 2.89 mm3J−1g−1 after 28 days of storage. Pomegranate fruit stored at 5 °C for 28 days showed the lowest value of SBS with 1.19 mm3J−1g−1. The study can suggest that pomegranate fruit stored at ambient (22 °C) temperature conditions after being damaged at a high impact level could be the most sensitive to bruising after a prolonged storage duration. ## 3.2.1. Water Loss and Firmness The water loss per unit fruit mass (WL%) and per unit surface area (WLA) profiles of bruised and non-bruised pomegranate fruit stored at 5 and 22 °C for 28 days are presented in Figure 3A,B. The analysis of variance showed that the impact level, storage temperature, and storage duration significantly ($p \leq 0.05$) influenced WL% (Figure 3A). After 28 days of storage at 22 °C, the highest WL% was observed in pomegranate fruit bruised at the highest impact level ($20.39\%$), followed by those impacted at the lowest impact level ($19.29\%$) and the non-bruised (control) fruit ($17.74\%$). At the end of 28 days of storage at a cold temperature (5 °C), the average WL% measured in pomegranate fruit impacted at high and low levels was 7.14 and $6.28\%$, respectively. A lower % of WL was observed in the non-bruised control fruit with $5.70\%$ under low-temperature storage conditions on day 28. The results of water loss per unit surface area (WLA) did not follow the trend of measured WL% (Figure 3B). The effects of storage duration and temperature on WLA were significant ($p \leq 0.05$). However, no pronounced effect of impact level on WLA ($p \leq 0.05$) could be observed. The WLA values increased with temperature and storage time, in non-bruised and bruised pomegranate fruit. The expectation was confirmed in ambient-conditioned pomegranate fruit. For instance, the non-bruised fruit showed the highest WLA (0.37 g cm2) followed by low- (0.36 g cm2) and high-level (0.34 g cm2) impacted pomegranate fruit. The WLA of pomegranate fruit stored at 5 °C was lower than that of fruit stored at ambient conditions. Generally, the findings from the current study have confirmed that bruise damage could expedite the physiological WL and pomegranate fruit senescence during storage. In terms of storage conditions, Hussein et al. [ 14] and Fawole and Opara [1] suggested that storage at 5 °C reduced the moisture content loss of bruised and non-bruised fruit resulting in a low increment in WL% of the fruit during the storage. This might be because of the metabolic activity reduction rate at low temperatures. Ambaw et al. [ 30] revealed that WL% in pomegranates during storage increased due to the fruit peel’s high porosity, which enables the movement of free vapor. Hussein et al. [ 14] found that the WL% on bruised pomegranate fruit stored at low storage was eight-fold lower than that of fruit stored under ambient conditions. As revealed in the present study, the weight loss increased as bruising increased. Tissue damage caused by bruising can permit the interchange of atmospheric gases, resulting in increasing respiration and transpiration rates [13], which showed in some symptoms of wilting and shriveling in all fruit samples stored at 22 °C starting from day 21. The results from this study showed that the firmness of the bruised and the non-bruised pomegranate fruit was significantly (p ≤ 0.0001) dependent on impact level, storage duration, and storage temperature (Figure 3C). Pomegranate fruit firmness values were reduced by increasing all investigated factors. Both temperature and impact were highly pronounced in the bruised and the control pomegranate fruit. On the last d of storage, the firmness was reduced by 5.1, 8.13, and $9.51\%$ in the control, low-, and high-impact-bruised fruit stored at 5 °C. However, the reduction was higher in the pomegranate stored at 22 °C, where the reduction % reached 10.09, 10.65, and $13.18\%$ for the control, low-, and high-impact-bruised fruit, respectively, on the last day of the experiment. Generally, bruising decreased the firmness status of pomegranate fruit, particularly under ambient storage conditions during the prolonged storage period. Similar findings were observed by Azadbakht et al. [ 15] and Pathare and Al-Dairi [7]. Regarding storage conditions, Arendayse et al. [ 31] revealed that increasing storage temperature and storage duration could reduce the firmness of pomegranate fruit, probably due to the decline in the cell wall integrity of the pomegranate fruit peel. In addition, the chilling injuries produced on fruit at low temperatures could be the main reason for firmness and cell wall strength reduction in bruised and non-bruised pomegranate fruit at 5 °C. The study of Pathare et al. [ 13] recorded a significant decrease in the firmness of pear fruit when the impact energy increased from 0.129 J (low) to 0.38 J (high). ## 3.2.2. Size Measurements, Geometric Mean Diameters (Dg), Surface Area (AS), and Fruit Volume (V) Table S1 shows the size dimension measurement reduction % of the bruised and the non-bruised pomegranates stored at two storage conditions for 28 days. The analysis of variance showed that all the studied factors (impact level, storage duration, and storage temperature) significantly influenced ($p \leq 0.05$) the length (L) and thickness (T) of the investigated fruit. The widths (W) of the bruised and the non-bruised pomegranate fruit were affected statistically ($p \leq 0.05$) by storage temperature and duration but not by the impact level ($p \leq 0.05$). The % of loss in LWT gradually increased as all investigated factors increased. For instance, pomegranates bruised at a higher level and stored at 22 °C showed a higher % of loss on LWT with 2.57, 2.36, and $2.38\%$, respectively. Generally, the fruit dimension (LWT) is the main factor influencing the overall geometric mean diameter (Dg), surface area (AS), and fruit volume (V) [32]. There was a significant effect of impact damage ($$p \leq 0.0334$$), storage condition ($$p \leq 0.0259$$), storage time ($$p \leq 0.0134$$), and their interaction on the Dg values of control and damaged pomegranate fruit (Figure 4A). The values of AS and V were highly influenced ($p \leq 0.05$) by storage temperature and duration with no pronounced effect with impact level ($p \leq 0.05$) (Figure 4B,C). At ambient (22 °C) storage conditions, pomegranate fruit bruised at higher (Ea = 2.28 J) and lower (Ea = 1.18 J) impact levels exhibited Dg values of 89.55 and 89.90 mm after 28 days of storage compared to the non-bruised fruit with 90.14. By contrast, the lowest value of Dg at the 5 °C storage condition was detected in the non-bruised (control) (90.24 mm) pomegranate fruit, followed by the bruised pomegranates at lower (90.75 mm) and higher (90.86 mm) impact levels, respectively, after 28 days of storage. A similar scenario was observed for the AS and V values of both bruised and non-bruised fruit at both storage conditions. The reduction observed in LWT, Dg, AS, and V during storage for all samples is attributed to water loss [33]. In addition, the bruising caused by external factors such as impact can cause damage to the internal cells, which leads to increased respiration rate and enzymatic activity due to vibration, as observed by Dagdelen and Aday [21]. ## 3.2.3. Peel Color Figure 5 presents the overall changes in color attributes among all pomegranate fruit. There was a significant effect of damage level ($$p \leq 0.0007$$), storage temperature ($$p \leq 0.0260$$), and storage duration ($$p \leq 0.0057$$) on the lightness (L) of pomegranates (Figure 6A). The L value reduced gradually across all pomegranate samples at both storage conditions during 28 days of storage. At ambient (22 °C) temperature, the L* value was reduced by $48.72\%$ and $44.54\%$, on high- and low-impact-bruised pomegranate fruit, respectively, and by $36.20\%$ on the non-bruised fruit (control) on the last d of storage. However, the L* reduction % was $28.94\%$ and $29.28\%$ on low- and high-impact-bruised fruit stored at cold storage conditions (5 °C) after 28 days of storage. The non-bruised pomegranates stored at low temperatures showed the lowest reduction % in the L* value, by $19.32\%$. The influence of impact bruising, storage temperature, and duration on the redness (a) of pomegranate fruit was statistically significant ($p \leq 0.05$), as given in Figure 6B. All fruit samples became redder at the end of the storage period, regardless of the treatments. At the end of 28 days of storage, the a value increased from −3.63 to 12.5, 17.05, and 18.55 for control, low-, and high-impact-bruised fruit, respectively stored at 22 °C. The changes in a* values were more minor on bruised and non-bruised pomegranate fruit stored at 5 °C compared to those stored at ambient (22 °C) conditions. In addition, the changes in yellowness (b) were influenced by impact damage ($$p \leq 0.004345$$), storage temperature ($$p \leq 0.0066$$), and duration ($$p \leq 0.0001$$) (Figure 6C). The lowest b value was observed after 28 days of storage at ambient (22 °C) storage conditions, with a value of 28.04 for low- and high-impact-bruised fruit. The non-bruised fruit stored at 5 °C recorded fewer changes in yellow color with a value of 34.15 compared to other bruised fruit after 28 days of storage. The differences observed in color values could be attributed to the accumulation and biosynthesis of anthocyanin pigments in the peel of pomegranate fruit, resulting in increasing red coloration, as suggested by Arendayse et al. [ 31]. They indicated similar results obtained by the current study, where storage at a cold temperature (5 °C) can maintain the peel color of the pomegranate fruit. Chroma (C) and hue (H) values are presented in Table 2. There were significant differences ($p \leq 0.05$) in C* values and between all investigated factors (impact bruising, storage temperature, and storage duration). The effect of all investigated factors was more pronounced in high- (65°; 2.29 J) impact-bruised fruit which was reduced by $36.75\%$. Hue (H), which represents color purity, showed a significant effect with storage duration ($$p \leq 0.0438$$) with no further significance of impact damage ($$p \leq 0.2776$$) and temperature conditions ($$p \leq 0.2721$$). As a result of bruising, the total color change (TCD) of pomegranate fruit peel was significantly affected by the level of impact ($$p \leq 0.0003$$), storage temperature ($$p \leq 0.0144$$), and storage duration ($$p \leq 0.0013$$). The TCD increased with bruising impact level and storage temperature, reaching the highest values of 34.06, 39.63, and 43.36 after 28 days of storage time for control, low-, and high-impact-damaged pomegranates, stored at 22 °C, respectively. By comparison, pomegranates stored at lower temperature conditions recorded lower values of TCD on the last day of storage, which was 24.00 obtained for non-damaged pomegranates and increased with impact levels to 26.84 and 26.95 for low- and high-impact-bruised fruit, respectively. Table 2 shows the mean ± sd values of the browning index (BI). Changes in the peel browning index were significantly influenced by bruising and the combination of both storage duration and temperature ($p \leq 0.05$). After 28 days, fruit stored at 22 °C exhibited an increase in BI from the initial of −228.58 (day-0) to 796.52, 1220.67, and 1333.73 for the non-bruised, low- (45°; 1.18 J), and high- (65°; 2.29 J) impact-bruised fruit, respectively. The BI increment was slightly lower than that measured after 28 days at a cold temperature (5 °C) for the non-bruised, low-, and high-impact-bruised fruit, with values of 302.07, 353.32, and 378.27, respectively. Generally, the browning reaction is assumed to be an immediate consequence of the polyphenol oxidase (PPO) and peroxidase (POD) action on polyphenols, which form quinones that produce the browning appearance [34]. These findings revealed that storage temperature is highly linked with the browning associated with the bruise-damaged fruit due to its impact on the polyphenol enzyme activity produced by the fruit [35]. Despite the importance of storage at 5 °C, the slow increase in BI observed in fruit at this condition is mainly attributed to the enhancement of some physiological disorders such as chilling and oxidative injuries [36]. Overall, the results presented an excellent relationship between BI and TCD, therefore suggesting its appropriateness as an essential index for evaluating browning in pomegranates and distinguishing between the non-bruised and bruised fruit. Moreover, Mitsuhashi-Gonzalez et al. [ 37] found that enzymatic browning in mechanically injured fruit begins by producing phenolic compounds (intra-membrane cell content) in the intercellular space because of cell rupture, which mostly depends on the damage intensity. ## 3.2.4. Respiration Rate (RR) and Ethylene Production Rate (EPR) The findings from this study showed that both the respiration rate and ethylene production rate of the examined pomegranates were significantly ($p \leq 0.05$) dependent on bruising impact level, storage temperature, and storage duration (Figure 7A,B). The RR of all samples increased during storage at both storage conditions and was highly enriched in pomegranate fruit stored at ambient (22 °C) storage conditions. The RR values reached their peak on day 21 for high-impact-bruised pomegranate fruit (13.49 CO2 mL kg−1h−1) stored at 22 °C and then stopped compared to low-impact bruised fruit and non-bruised fruit. Fawole et al. [ 1] stated that the anaerobic respiration and metabolic activity that are a consequence of microbial infestation of the fruit could lead to the discontinuation of the RR experiment on high-impact-bruised pomegranate fruit. The non-bruised fruit and low- and high-impact-bruised pomegranates stored at 5 °C showed a slow increment in RR followed by a reduction after day 28. Impact level had a considerable influence on the cellular respiration for bruised pomegranate fruit compared to the non-bruised (control) fruit at both storage conditions. This means that at the same storage condition, the damaged fruit respired faster than the control fruit. In addition, the storage temperature of 22 °C increased and showed a five-fold increment in the rate of CO2 production in pomegranates compared to those stored at 5 °C. Mechanical damage such as bruising can influence the RR of different fresh produce [8]. The present study’s findings support the results recorded by Hussein et al. [ 9], which stated that pomegranate fruit bruised at higher impact levels showed a two- to three-fold higher RR than non-bruised fruit. As observed, the respiration rate reduced with prolonged bruising duration. These results agree with those recorded for citrus [8] and olive [38]. A similar scenario was observed for EPR across all pomegranate samples at both storage conditions. Mechanical damage expedited ethylene production mainly at ambient (22 °C) storage conditions. This was also observed in a study conducted on banana fruit by Maia et al. [ 39]. ## 3.3.1. Pearson Correlation The Pearson correlation analysis was conducted to determine the relationships among the physical characteristics of non-bruised (control), low- (45°; 1.18 J), and high- (65°; 2.29 J) impact-damaged fruit during 28 days at 5 °C and 22 °C storage conditions (Table S2). A significant correlation (, $p \leq 0.05$; *, $p \leq 0.001$) was recorded between the majority of the investigated quality attributes (variables) of the pomegranate fruit. In the low- and high-impact bruised fruit at both storage conditions, the BS showed a strong positive correlation with WL% (r ≥ 0.950), a (r ≥ 0.920), RR (r ≥ 0.887), and EPR (r ≥ 0.905). While it exhibited a strong negative correlation with values of firmness (r ≥ −0.817), AS (r ≥ −0.940), L, and b (r ≥ −0.905). A similar scenario was observed with BA and BV and their correlation with other quality attributes. In all tested fruit across all examined conditions, fruit WL% had a significant positive correlation with a* (r ≥ 0.967), TCD (r ≥ 0.981), BI (r ≥ 0.967), RR, and EPR (r ≥ 0.943). WL% was negatively correlated with firmness (r ≥ −0.920), Dg (r ≥ −0.988), L* (r ≥ −0.960), and b* (r ≥ −0.984). As observed in Table S2, a* strongly correlated with all studied parameters ($p \leq 0.001$), mainly with BI (r ≥ −0.999), except in the case of some values with H. This indicated that increasing the redness of pomegranate fruit due to bruising and storage conditions, particularly at 22 °C, can result from the increment in BI over time. ## 3.3.2. Principal Component Analysis (PCA) Principal component analysis (PCA) was implemented to reveal the correlation between the studied quality attributes and bruise parameters with treatment (impact height and storage temperature) of pomegranates during the storage period. Thus, the variability of physical attributes of non-bruised and bruised pomegranates is summarized in principal component analysis (PCA). The location of the impact height and storage temperature after 28 days of storage is demonstrated in Figure 8A, while Figure 8B defines the distribution of quality and bruise parameters by first and second principal component analysis (PCA1 and 2) dimensions. The total variability at the different impact levels and storage conditions was described by 16 principal components (PC1 to PC16), and the first two factors were considered and retained to summarize the pattern of variance among the measured physical variables of the present study. The sum of the two first principal components (PC1 and PC2) explained $89.8\%$ of the variations, with PC1 and PC2 characterizing $73.8\%$ and $16.0\%$, respectively. Generally, Figure 8A,B shows that the fruit exhibited both distinct and similar variability in features at different impact levels and storage periods. Figure 8A shows that pomegranate fruit was affected by both studied factors (impact level and storage temperature) after 28 days of storage which recorded a strong correlation with the resulting parameters, respectively. Both storage temperature conditions and impact levels showed comparable characteristics with pomegranate fruit, respectively. Along with PC1, Figure 8B reveals that non-bruised and bruised pomegranate fruit (65°; 2.29 J and 45°; 1.18 J) stored at ambient temperature (22 °C) were more distinctly characterized by higher bruise volume (BV), bruise area (BA), and bruise susceptibility (BS); by respiration rate (RR), ethylene production rate (EPR), the total color difference (TCD), browning index (BI), redness (a), and weight loss (WL%); and by lower firmness (Fir), lightness (L), hue (H), yellowness (b), surface area (As), and geometric mean diameter (Dg) which were in contrast to those of fruit stored at 5 °C. This could be attributed to the role of low temperature in slowing down the quality changes of pomegranate fruits during storage. Bruise parameters were strongly positively correlated with weight loss%, redness, browning index, and respiration rate, and significantly negatively correlated with firmness, lightness, hue, yellowness, and surface area. This analysis can help to assume changes in quality attributes under prolonged shelf life. The study can finally confirm the importance of storage management to maintain the quality and increase the shelf life of pomegranate fruits during prolonged storage. ## 4. Conclusions This study has investigated the magnitude of bruise size and the physiological alterations coupled with impact damage and storage in pomegranates. The results showed that bruising measurements (bruise area, bruise volume, and bruise susceptibility) increased as impact level, storage temperature, and storage duration increased. This study showed that storage temperature, storage duration, and impact level had an essential and direct effect on different quality characteristics, mainly fruit color (lightness, redness, yellowness, chroma, total color difference, and browning index), water loss per unit surface area, water loss per unit mass, firmness, geometric mean diameter, respiration rate, and ethylene production rate. An excessive weight loss percentage ($20.39\%$) was detected in high-level impact pomegranate fruit stored at ambient storage conditions on the last d of storage. Additionally, high and low impact levels expedited the peel color changes, mainly at 22 °C in mechanically injured fruit. In addition, there was a significant reduction in the firmness values of pomegranates impacted at high and low levels. The respiration rate and ethylene production rate increased gradually among all studied factors, particularly in bruised and non-bruised fruit stored under ambient conditions. 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--- title: Characterizing Meat- and Milk/Dairy-like Vegetarian Foods and Their Counterparts Based on Nutrient Profiling and Food Labels authors: - Noelia María Rodríguez-Martín - Patricia Córdoba - Beatriz Sarriá - Vito Verardo - Justo Pedroche - Ángela Alcalá-Santiago - Belén García-Villanova - Esther Molina-Montes journal: Foods year: 2023 pmcid: PMC10048389 doi: 10.3390/foods12061151 license: CC BY 4.0 --- # Characterizing Meat- and Milk/Dairy-like Vegetarian Foods and Their Counterparts Based on Nutrient Profiling and Food Labels ## Abstract Vegetarian foods are plant-based (PB) foods, often perceived as healthier foods than animal-based (AB) foods. The objective of this study was to analyze the nutritional quality of a set of PB foods (meat, milk and dairy products) marketed in Spain, and to compare their nutrient profiles with respect to some AB counterparts. Nutritional information per 100 g or mL, ingredients, and nutritional declarations, as well as the Nutri-Score, NOVA, and Eco-Score of each food were collected from Open Food Facts. Differences in the nutrient compositions between PB foods and their counterparts, and between the different groups of PB foods, were assessed at a $5\%$ significance level. A total of 544 PB foods and 373 AB foods were identified. Overall, PB foods had a higher median content of fiber and carbohydrates, but a lower amount of proteins (except PB “meat” analogues: 14 g) and saturated fats (except PB “cheese alternatives”: 12.5 g), than the AB counterparts ($p \leq 0.05$). PB “milk alternatives”, particularly oat “milk”, showed a higher median content of total carbohydrates (8 g) and sugars (5.5 g) compared to cow milks (4.7 g carbohydrates/sugars, on average; $p \leq 0.001$). PB “meat alternatives” also had a significantly higher value of carbohydrates (9 g) than AB meats (2 g, on average; $p \leq 0.001$). PB foods were mostly classified as Nutri-Score A and B ($86\%$). However, more than half of them were of NOVA groups 3 and 4. Thus, there is a great diversity of PB meat and milk/dairy product alternatives on the Spanish market. Despite being products of good nutritional quality compared to AB foods, they also carry drawbacks that could have an impact on nutritional health. ## 1. Introduction Plant-based (PB) foods have undergone a creeping trend in consumption over the last few years. Indeed, the increasing demand for PB foods, fueled by the need to find new alternatives for health-conscious consumers, has led the food industry to spread the production of the so-called vegetarian or veggie foods [1]. Consumers have nowadays far more choices of vegetarian-like foods than ever before. For instance, milk-free dairy products, or meatless burgers, among others, are readily available in the market. Several facts illustrate this phenomenon: the European project SMART PROTEIN reported that PB food sales increased by $49\%$ from 2018 to 2020 in Europe [2]. In Spain, those sales entailed $20\%$ of the value in the market. This report also showed that the PB sector in Spain was dominated by vegetable drinks, with oat beverages being the most important ones, followed by PB “meat alternatives” and vegetable yoghurt, of which sales increased by $55\%$. In addition, during a survey carried out between 2021 and 2022 among Spanish people, approximately seven percent of people aged between 20 and 29 reported to follow a PB diet [3]. There are three well-established reasons why people may decide to adhere to a vegetarian-like or PB diet: health, animal welfare, and environmental sustainability [4]. Indeed, PB diets have been considered as more sustainable than an omnivorous diet, wherein both plant and animal food sources are eaten [5,6]. Conversely, meat, and animal foods in general, account for nearly $60\%$ of all greenhouse gases from food production [4,7]. The fact that a PB diet is considered a healthier diet alternative is supported by some nutritional studies that have shown that vegetarian dietary patterns have an adequate nutrient supply, even during pregnancy, lactation, or in sport nutrition [8,9], except regarding nutrients provided exclusively or overwhelmingly by animal-based (AB) foods [10,11,12]. The healthiness of PB diets is also supported by many studies that have depicted the favorable impact that this dietary pattern has on different health outcomes including cardiovascular diseases and cancer, amongst others [13,14,15,16,17,18,19]. PB diets also seem to promote a healthier microbiota [20,21,22]. However, as aforementioned, some nutrient deficiencies are also likely, most notably in vegan diets, regarding vitamin B12 and iron, but also for omega-3 fatty acids, calcium, vitamin D, and zinc. These potential deficiencies, however, can be alleviated by taking nutritional supplements [10,11,12]. The health benefits of plant-source foods in PB diets (legumes, nuts, whole grains, fruits, and vegetables, as well as vegetable oils) can be attributed to the presence of bioactive compounds in these foods [23,24,25]. More precisely, plant bioactives including fiber, sulfur compounds, carotenoids, and polyphenols, present in cruciferous and allium vegetables, tomatoes, green tea, and whole grain cereals, have a well-known antioxidant and anti-inflammatory potential [23,26], while AB foods are poor in these compounds. For the above reasons, PB foods can be conceived as healthier than their AB counterparts by the consumer, but this need not necessarily always be the case [27]. Some of these foods can be regarded as ultra-processed since they can be obtained through different combinations of ingredients (soy-protein isolates, vegetable fats, modified starches, malto-dextrins, cosmetic additives such as flavour and colouring enhancers, sweeteners, emulsifiers, and others) and by using several processing methods (for example, extrusion/texturization, hydrolysis, and fermentation) [28,29,30]. In fact, some studies have revealed that vegetarians eat more ultra-processed foods than meat eaters, and that consumption of these foods leads to more detrimental health effects [28]. Regarding the nutritional characteristics of foods of animal and vegetable origin, there are also some key differences to note. For instance, in relation to protein quality, the protein digestibility, the biological value, the net protein utilization, and the amount of essential amino acids are lower in vegetable proteins compared to those of animal origin. Other downsides of vegetable proteins are the presence of anti-nutritional factors that interfere in their absorption, and of potential allergenic proteins. On the other hand, PB foods feature a higher content of carbohydrates, fiber and sugars (in cereal-based foods mostly) than AB foods, whereas the latter add more fats (saturated fats) to the diet than vegetable food sources [23,24]. Thus, PB foods can be of different nature, with varied nutrient composition and effects on health [11]. Indeed, PB ultra-processed foods can be high-energy-dense foods, rich in sugars and fats, making their habitual consumption unhealthy. The reverse could also be possible if PB foods involve healthy and natural foods. It is therefore highly relevant to characterize their composition and nutrient content since not all PB foods might be equally healthy from a nutritional point of view. There is also a need to provide information on their nutritional value with respect to AB foods, but there are only a few studies that have compared the nutritional profiles of some PB and AB foods [31,32,33]. We aimed to conduct a comprehensive analysis of the nutritional composition of vegetarian foods available on the Spanish food market, including vegetable beverages and dairy products and PB “meat alternatives”, by collecting the nutritional information, ingredients, and facts reported on their front-of-pack and pack labelling. To complement this analysis, we also considered other information available for the consumer with regard to nutrient profiling models used to classify the nutritional quality, processing degree, and ecological fingerprint: the Nutri-Score, the NOVA index, and the ECO score [34,35,36]. In addition, the nutrient profiles and composition of these foods, as well as the index scores, were compared with that of some of their counterparts (AB meat products, cow milk, and dairy products). ## 2.1. Sample Selection Nutritional data of the food products were obtained from websites of supermarket and food companies, and from Open Food Facts [37]. The latter was the main information source used to retrieve the food products. Open Food Facts is an open data base of front-of-pack nutrition and pack labelling information, which contains complete information on ingredients including additives, allergens, and nutrition facts of food products sold on the market of several countries, including Spain. The website and app offer easy to access information to the consumers, helping them to make better food choices. It is also widely used by scientists and food producers to explore the food system and the nutritional quality of food products [38,39]. However, the information is made up of voluntary contributions of scanned barcodes, pictures, and labels. While this information is checked and regularly updated [37], we used the aforementioned market websites to complement and verify the data. For the present study, the search was restricted to foods sold on the Spanish market following the inclusion criteria: [1] vegetarian-like foods marketed in Spain, but restricted to meat and milk/dairy product substitutes; [2] food items with complete information in Open Food Facts, or easy to complete with information posted in this and other sources (market websites). The exclusion criteria were: [1] repeated products, e.g., similar foodstuffs but with a different flavour (because their nutritional value is similar); [2] products with incongruent labelling information with respect to the product’s characteristics. Additionally, in order to gather some homologous AB foods for the comparative study on nutritional differences between PB and AB foods, we looked for food products of pate and commercial meat products, cheese, yoghurt, and cow milk. For AB yoghurt, cheese, and pate, a restricted sample of items was selected due to product variability on the market. In particular, AB that were similar to the PB ones were selected in an attempt to match food items of both groups. For example, yoghurts with fruits as ingredients were chosen for both sets of PB and of AB foods; i.e., to make PB and AB yoghurts comparable, we selected PB “yoghurt alternatives” with fruits available on the market and an equivalent number of AB yoghurts with fruits. Since the PB “cheese alternatives” were quite heterogeneous, for the AB cheese group we selected items of various types of cheese on the Spanish market (fresh, cured, and semi-cured cheeses), resembling those selected for the PB group. The same was applied for AB pate. To account for high- and low-fat milks, we considered whole milk, as well as semi-skimmed and skimmed cow milk. Similarly, for meat products, we covered red (beef and pork) and white (chicken/poultry) meat. A total of 1652 food products, of which 1201 were PB foods classified as veggies, and the remaining potential AB foods to be considered as analogues, were available in Open Food Facts. After the removal of PB foods other than PB “meat”, “milk”, and “dairy product” alternatives, repeated products, products with missing information, or those not complying with the inclusion criteria, a total of 917 food items remained available for the analysis: 544 PB and 373 AB food items. ## 2.2. Data Collection The search in the aforementioned online resources was performed between March and May of 2022; additional data on PB beverages was collected in November 2022. Key words used to search in Open Food Facts were: veggie, oat “milk”, soy “milk”, almond “milk”, vegetable “yoghurt”, vegetarian “spreadable”, plant-based “cheese alternatives”, vegetable “paste”, and “tempeh”. For the AB food homologues, the key terms were: cow milk (skimmed, semi-skimmed, and whole milk), pate, cheese, yoghurt, and meat products (chicken, pork and beef hamburgers and sausages). Information was extracted from pictures of the labels provided in Open Food Facts and market websites when needed; thus, the information collected corresponded to that reported by the manufacturer. The variables collected for every food item were: the commercial name, denomination, portion size, brand of the product, the ingredients (from high to low content), and the nutrition and health claim declarations according to Council Regulation (CE)$\frac{1924}{2006}$ [40]. For the latter, we collected information on the nutrition claim (high in, source of, etc.) and the amount of the specified nutrient. In addition, we collected information on the presence of indications for gluten and lactose-free products, among others. Secondly, the nutritional information present in the pack labelling was collected according to Council Regulation (UE)$\frac{1169}{2011}$ [41]: energy (kcal per 100 g/mL and kJ per 100 g/mL), and grams per 100 g or mL of total fat, saturated fat (part of total fat), carbohydrates, sugars (part of carbs, and accounting for natural and added sugars), fiber, proteins, and salt, as well as the list of ingredients. This nutritional information was also collected in terms of Reference Intake values for a healthy adult (daily nutritional requirement of 2000 kcal), according to the aforementioned European labelling rules. In addition, the rating of the product according to the nutrient profiles of Nutri-Score, NOVA, and Eco-Score were collected [34,35,36]. All variables were collected through an online form via Google Forms to facilitate data retrieval, sharing, and processing. Three researchers, who were trained to collect all the data under the same standards and protocols, retrieved and reviewed the data (NRM, PC, and EMM). ## 2.3. Data Processing Several quality controls were applied on the data. For instance, as described before, food items with missing information on nutritional values taken from the front-of-pack nutrition and pack labelling, were completed by re-consulting Open Food Facts and the online market websites. Potentially incorrect values, such as extreme values that may have arisen from data entry or data inconsistencies, were detected by describing the distribution of each variable via box-plots and by sorting values from the lowest to the highest (minimum and maximum values). The values were revised and corrected, where necessary, after verifying the accuracy of the value in all the information sources. In addition, the nutritional data of some food products was checked with published data in Spanish Food Composition Tables to detect potential errors or inconsistencies [42]. Likewise, for the PB foods, the nutritional values of a set of products ($$n = 100$$) were checked with those reported in the Spanish Veggiebase [43]. PB food items were grouped in the dataset according to their nutritional characteristics: (a) PB beverages (considered as PB “milk alternatives”), (b) PB “meat alternatives” (“hamburgers” and “sausages”, both sharing similar ingredients), (c) PB spreadable or paste (considered as PB “pate alternatives”), (d) PB “yoghurt”, (e) PB “cheese alternatives”. As for the AB products, the following categories were established: [1] cow milk (distinguishing between whole, semi-skimmed and skimmed milk), [2] pate, [3] cheese, [4] yoghurt, [5] and meats (distinguishing by type of meat—beef, pork and chicken, and product—hamburgers and sausages). A description of the food products included in each group is provided in Appendix A. ## 2.4. Nutri-Score, NOVA, and Eco-Score Assessment The values of Nutri-Score of each food item were firstly obtained from the Open Food Facts database, which implements an algorithm to calculate it upon the nutritional data of the front-of-pack labels [44]. Nutri-Score is based on the following classification scheme: A (dark green), B (light green), C (yellow), D (orange), and E (dark orange). To calculate the value, different nutrient profiles are used. The unfavorable are: energy (kJ/100 g), saturated fat (g/100 g), sugars (g/100 g), and salt (g/100 g); whereas the favorable are: the % of fruits, vegetables, legumes, dried fruits and olives, walnut and rapeseed oils, fiber (g/100 g), and protein (g/100 g) [34]. Nutri-Score has been proposed to become mandatory in the near future. Therefore, for food products with missing information on this score ($2.5\%$ of all products), we considered the Excel sheet provided by the Spanish Agency of Food Security and Nutrition (AECOSAN) [34]. To apply it, the percentage of fruits, vegetables, legumes, nuts and rapeseed, walnut, and olive oil was taken from the ingredients list. The fiber content was assigned to zero in those food items ($$n = 92$$, of which 62 were AB food items) without indication for this component on the label after revising the list of ingredients to verify the absence of fiber. Moreover, we double-checked Nutri-Score labelling from Open Food Facts by calculating this score with the AECOSAN tool in the above described set of food products. Open Food Facts also served to retrieve the NOVA and Eco-Score values of all items [45]. However, in the case of lack of information for either NOVA or Eco-Score in the pack labeling, these scores were not recalculated by us in order to report the missingness rate of these values. Both NOVA and Eco-Score are not mandatory labels in food products. The NOVA score is related to the degree of processing of a food product [35]. Foods are classified into four categories according to the nature and industrial process: Group 1 (unprocessed foods or minimally processed), Group 2 (processed by culinary ingredients), Group 3 (processed food), and Group 4 (ultra-processed food, by using cosmetic additives, fractioning of whole foods into substances, and sophisticated technologies). The Eco-Score is an indicator that classifies the products into 5 categories (A, B, C, D, E) according to their environmental impact. The factors taken into account are the pollution of air, water, oceans, and soils. In addition, the product life cycle (LCA) was considered [36]. Based on the total score (maximum value = 100), products were classified from A (low impact t) to E (high impact) [46]. ## 2.5. Description of Ingredients, Additives, and Claims As for the other variables, pack labels from Open Food Facts and other websites were used to retrieve information on all ingredients and claims. A complete count was carried out regarding the number of ingredients, the additives, and the nutritional and/or health claims of every food item. For this purpose, keywords (i.e., nutrients) were searched in the dataset. These searches were done in an automated manner since there were specific fields for every variable in the data collection form and dataset. Variables were created to further account in the data analyses for the presence/absence of nutrition claims and of health claims, the presence/absence of gluten or lactose-free indications, the number of ingredients, and the number of additives. The frequency of a certain claim or nutritional indication among all foods, or by food group, was expressed as a percentage rate. ## 2.6. Statistical Analysis The statistical analysis was carried out using the statistical software R version 4.1.2 [47]; p-values less than 0.05 were considered statistically significant. According to the Kolmogorov–Smirnov normality test, all nutritional variables followed a non-normal distribution (all p-values < 0.05). Therefore, non-parametric tests were used to perform statistical tests. Continuous variables relative to nutritional information were expressed as median (p50 values) and interquartile ranges (IQR), i.e., p25 and p75 values. For categorical variables (considering food items classified into Nutri-Score, NOVA, and Eco-Score), relative and absolute frequencies were used. Differences in the median content of nutrients of the pack labelling across all PB food groups (for example, PB “milk alternatives” vs. PB “meat alternatives” vs. PB “cheese alternatives”, etc.) or by PB and AB food groups (for example, PB vs. AB food groups: PB “pate alternatives” vs. pate, PB “cheese alternatives” vs. cheese, PB “milk” vs. cow milk of different types, PB “meat alternatives” vs. AB meats of different types) were analyzed by the Kruskal–Wallis test (non-parametric ANOVA test for more than 2 independent sample groups) and the Wilcoxon rank sum test (for two independent sample groups). Post hoc Tuckey test was applied for pair-wise comparisons of PB foods within a PB food group (milks, meat and meat products, dairy products, and others) by their nutrient composition (corrected by the family-wise error rate) to detect significant differences by groups. Chi-Square tests were carried out to evaluate differences among Nutri-Score, NOVA, and Eco-Score from PB or AB food groups. Boxplots and pie charts were used for graphical illustrations of the results using ggplot2 package in R. ## 3. Results Figure 1 shows the amount of beverage and food items collected, overall and by food group for PB and AB foods. We included PB “meat alternatives” (134 items); PB “pate alternatives” (64 items), PB “milk alternatives” (313 items), including oat, almond, and soy “milk”, PB “yoghurt” (12 items), and PB “cheese alternatives” (21 items). AB foods were classified as whole milk (79 items), skimmed milk (50 items), semi-skimmed milk (75 items), pate (16 items), cheese (16 items), yoghurt (16 items), and meats (121 items from hamburgers and sausages). ## 3.1. Nutrient Composition and Characteristics of PB and Animal-Based Foods The nutritional features of the food products included in this study are shown in Figure 2. Regarding vegetable milks (Figure 2a), i.e., PB “milk alternatives”, all contained fiber and a relatively high amount of carbs and sugar per 100 mL. Interestingly, these milks were poor in proteins, except soy “milk”, and in fats, mostly in the case of oat “milk”. Similarly, the other PB “dairy product alternatives” (Figure 2c), cheese and yoghurt, were also characterized by containing fiber, a low amount of proteins and fats (except in cheese), and a high amount of carbs (mostly in yoghurt). PB “cheese alternatives” had outstanding amounts of fats and saturated fats. As for other PB foods (Figure 2b), a similar pattern was observed: presence of fiber, but absence of saturated fats (except for PB “pate alternatives”, and to some extent, PB “milk alternatives”), and low content of proteins. With respect to AB foods, the content in proteins appeared to be high in all types of cow milk (Figure 2a). For other AB foods (Figure 2b,c), varying nutrient compositions were observed. These foods shared the common feature of a very high protein content. Salt and saturated fats were also over-represented in these foods. Overall, AB foods lacked fiber, except in yoghurt (some brands contained fruits) and in pate (some brands contained vegetables or pulses). ## 3.2.1. PB “Milk Alternatives” vs. AB Milks Table 1 shows the nutrient composition of PB ”milk alternatives” and AB milks and their differences. Figures S1–S3 show the same for oat, almond, and soy “milk”, with respect to AB milks. The highest energy value per 100 mL was observed for whole milk, driven by the higher fat content of this milk (3.6 g) compared to all PB “milk alternatives” (0.9 g to 1.8 g fat, $p \leq 0.001$). While some PB “milk alternatives” contained fats due to the constituent of these milks (soy or almonds), the presence of saturated fats was relatively low and similar to that of skimmed cow milk. On the contrary, the total carbs and sugar content was higher in PB “milk alternatives” compared to cow milks. Particularly, oat “milk” showed the highest content of these nutrients (8.3 g and 5.5 g, respectively), with respect to almond “milk” (3 g carbs; $p \leq 0.001$), soy “milk” (3.5 g carbs; $p \leq 0.001$), and also with respect to all types of cow milks (4.6 g to 4.8 g carbs; $p \leq 0.001$). Sugars were notably higher in oat “milk” with respect to the other PB “milk alternatives” (2.5 g to 2.9 g; $p \leq 0.001$). Other findings to note are that fiber was only present in PB “milk alternatives”, and the protein content was alike in the tree types of cow milks, as well as in soy “milk” (3.1 g). However, the amount of proteins was much lower in almond “milk” (0.7 g) and oat “milk” (1.1 g) when compared with all other milks ($p \leq 0.001$). In relation to the adequacy of nutrients provided by 100 mL PB “milk alternatives” and AB milks according to the recommended intakes (Table S1 for an adult with average energy intake of 2000 kcal), soy “milk” provides $6\%$ of the requirements for proteins, similar to that provided by cow milk. However, other types of PB “milk alternatives” provide only 1.5 to $2\%$ of the recommendations for protein intake. ## 3.2.2. Nutrient Composition of PB Foods Table 2 shows the nutrient composition of PB and AB foods. Within both groups, the following results were observed: The median energy content of all PB “meat” analogues was 177 kcal per 100 g. Among PB foods, “cheese alternatives”, and “pate alternatives”, were even more energy dense. PB “cheese alternatives” and PB “pate alternatives” had by far the highest fat content, whereas PB “yoghurt alternatives” seemed to have the lowest. Interestingly, PB “cheese alternatives” had a remarkably high content of saturated fats (14 g/100 g). Saturated fats were present in other PB foods, albeit in smaller amounts. Sugars were relatively high in PB “yoghurt alternatives”. Regarding proteins, the amount of this nutrient was not outstanding in PB foods, with an overall content of less than 5 g proteins, except in PB “meat alternatives” (14 g proteins). All PB foods were a source of fiber (1 to 9 g fiber), with PB “meat alternatives” presenting the highest amount of this component. Finally, all PB foods, except “yoghurt alternatives”, contained salt (~1 g). Among AB foods, cheese and pate were also foods with a notably high value of calories (>250 kcal per 100 g), fats (>20 g), and saturated fats (9 to 12.5 g). The median protein content was 14 per 100 g in meats, and 7 g per 100 g cheese, while 3.8 g per 100 g yoghurt. A certain amount of fiber was present in pate and yoghurt, likely provided by some of their ingredients (vegetables/legumes and fruits, respectively). As in PB foods, all AB foods, except yoghurts, contained salt (~1 g). ## 3.2.3. PB Foods vs. AB Foods In Figure 3, comparisons between the PB “meat alternatives” and the AB counterparts (burgers and sausages) are shown. Figure S4 accounts for the same, but for the groups of PB “pate alternatives”, “cheese alternatives”, “yoghurt alternatives”, and “meat alternatives” compared with their AB counterparts. Table 2 also shows differences in the median nutrient content between PB and the AB analogues:[1]PB “pate alternatives” vs. AB pate (Figure S4, Table 2): PB “pate alternatives” was significantly richer in carbs ($p \leq 0.001$), but not in sugars or fiber, compared to AB pate. While both types of products had a similar median content of fats, the median content of saturated fats was significantly higher in the AB pate ($p \leq 0.001$). The median amount of proteins also differed significantly between PB and AB pate, with the latter one showing a higher content of this nutrient ($p \leq 0.001$). The quantity of total salt also seemed to be higher in the AB pate compared to the PB alternative ($$p \leq 0.006$$). Both products had a similar caloric contribution.[2]PB “cheese alternatives” vs. AB cheese (Figure S4, Table 2): The energy median value did not differ significantly between PB “cheese alternatives” and AB cheese. However, there were significant differences in the median content of proteins (higher in AB cheese, $p \leq 0.001$), fiber (higher in PB “cheese alternatives”, $$p \leq 0.03$$), carbs (higher in PB “cheese alternatives”, $p \leq 0.001$), and salt (higher in PB” cheese”, $$p \leq 0.03$$). As aforementioned, an unexpected finding was the relatively high amounts of fats and saturated facts in PB “cheese alternatives”, which were similar to those found in AB cheese.[3]PB “yoghurt” vs. AB yoghurt (Figure S4, Table 2): Both groups showed few differences regarding the nutrient composition. Indeed, significant differences were only found regarding saturated fat, with AB yoghurts having a higher median value than PB “yoghurt alternatives” ($$p \leq 0.03$$).[4]PB “meat” vs. AB meat (Figure 3 and Figure S4, Table 2): PB “meat” appeared to provide a similar quantity of energy than the other AB meats, and nearly the same amount of proteins. However, the nutrient composition differed significantly from each other. Compared to AB meats (either beef, pork, or chicken meats), the PB “meat” alternatives were richer in total carbs and sugars ($p \leq 0.001$). In addition, PB “meat alternatives” had a lower median content of fats compared to pork burgers or sausages ($p \leq 0.001$), and a notably lower median content of saturated facts compared to all other meats, most outstanding when compared to pork meats ($p \leq 0.001$) and chicken sausage ($p \leq 0.05$). AB meats also lacked fiber. Regarding the adequacy of nutrients provided by 100 g of PB foods compared to their AB counterparts, and with reference to the recommended intakes for adults (average energy intake of 2000 kcal), as shown in Tables S2–S4, PB foods are furthest from achieving the recommended intakes for proteins. Some exceptions are in PB “meat alternatives”, which account for a similar amount of the recommended intakes than AB meats (around $25\%$), and in PB “yoghurt alternatives” and AB yoghurt, accounting similarly to the daily recommended protein requirements (around $7\%$). The highest difference in the protein supply was seen for PB “cheese alternatives” and AB cheese ($2\%$ and $17\%$ of protein requirements, respectively), followed by PB “pate alternatives” and AB pate ($9\%$ and $23\%$ of protein requirements, respectively). ## 3.3.1. Nutri-Score, NOVA, and Eco-Score Classifications in PB “Milk Alternatives” and PB Foods Figure 4 shows the categories of the different index scores of the pack labelling for PB “milk alternatives” and foods. The majority of PB “milk alternatives” ($93\%$) were classified as A and B according to Nutri-Score, and fewer products were worse rated ($7\%$) (Figure 4(a1)). In contrast, more than half of the other PB foods were rated as A or B, around one third of these products were assigned to C, and the remaining to D (Figure 4(a2)). With regard to the NOVA index (Figure 4(b1,b2)), it was found that a large proportion (>$35\%$) of both PB “milk alternatives” and other PB foods (>$60\%$) did not have this information on the labelling. Among those products with NOVA information available, there were more than $40\%$ of the PB “milk alternatives” classified as ultra-processed foods, compared to $22\%$ of PB foods that were classified in this category. Information regarding the ECO-score was given for $85\%$ of the PB “milk alternatives” to reinforce the sustainable value of these products, but rarely provided in the other PB foods. Around $60\%$ of the PB “milk alternatives” that had this information were indicated to have a low ecological impact, compared to less than $5\%$ of the other PB foods. Differences in the proportions of Nutri-Score, NOVA, and Eco-Score categories between PB “milk alternatives” and foods were all statistically significant (data not shown). ## 3.3.2. Nutri-Score, NOVA, and Eco-Score Comparison by PB Foods and Milks, and by AB Food Analogues As shown in greater detail in Table 3 for PB foods and Table S5 for AB foods, the distribution of the different index scores of the pack labelling also differed by food groups. Among PB foods (Table 3), “dairy product alternatives” (“milk” and “yoghurt”) had the most complete information on the index scores. Both were more frequently categorized as Nutri-Score B (over $55\%$ of the products), and as Eco-Score B. Nevertheless, about $25\%$ of the PB “milk alternatives” received an Eco-Score rating of D or E (high impact). It is important to point out that around $95\%$ of PB “meat alternatives” and “pate alternatives” had no information on this score. Nutri-Score A and B accounted for more than $50\%$ of all PB “meat alternatives”, while Nutri-Score C, D, or E were more often present in PB “cheese alternatives” and “pate alternatives”. Missing information in NOVA was also prominent in some food groups. Nonetheless, the category 4 was more commonly assigned to PB “meat alternatives” and PB “cheese alternatives”, as well as to PB “milk” and PB “yoghurt”. For instance, 146 of the PB “dairy product alternatives” ($43\%$), including almost all yoghurts, were rated as NOVA 4. Thus, the nutritional quality assessed by Nutri-Score and NOVA was opposite for some food groups, such as for PB “meat alternatives”: $33\%$ PB “meat alternatives” were ranked as A in Nutri-Score, while $22\%$ were of NOVA 4. In relation to AB foods (Table S5), milk and yoghurt were more commonly of Nutri-Score A and B (80 and $50\%$, respectively), whereas cheese, meat, and pate were of Nutri-Score C, D, and E. In NOVA, for which more than half of meats and milks lacked this information, the majority of pates and yoghurts were of category 4. Remarkably, $25\%$ the cheese products also fell into this category. The nutritional quality for AB meats assessed by Nutri-Score ($70\%$ ranked as C or D) was in line with the processing degree assessed by NOVA ($27\%$ ranked as 4). Information on the Eco-score was quite complete, the majority (except yoghurt) being classified as D and E (high ecological impact). ## 3.4. Ingredients and Additives Used in PB “Milk Alternatives” and Foods Common additives (% of use) among PB “milk alternatives” were emulsifiers and stabilizers including lecithin from soybeans or sunflower seeds ($11\%$), citric acid esters of mono- and diglycerides of fatty acids ($2\%$), gellant gum ($33\%$), guar gum ($3\%$), and locust vegan gum ($4\%$). Of note, $7\%$ of the PB “milk alternatives” contained red algae extracts as a source of calcium. In addition, PB “milk alternatives” had added calcium or potassium through calcium orthophosphates ($13\%$), tricalcium phosphate ($12\%$), sodium or potassium citrate ($1\%$), mono or dipotassium phosphate ($2\%$), and calcium carbonate ($9\%$). In PB “meat alternatives” and PB “pate alternatives”, the most frequently used ingredient was soy (in $55\%$ of these food products), followed by wheat or wheat gluten ($14\%$) and eggs ($6\%$). Additives used in these products were thickeners and stabilizers such as methyl cellulose ($13\%$), xanthan gum ($14\%$), and other gums. In PB “cheese alternatives”, it was found that $52\%$ of this foodstuff contained coconut oil as a main ingredient. Starch was another important ingredient in $67\%$ of the products. Modified starches were also used as a main additive in this food product. Focusing on PB “meat alternatives”, the common ingredients were soy (in $72\%$ of these products), wheat ($22\%$), pea and chickpea flour ($20\%$), and eggs ($8\%$). Regarding PB “pate alternatives”, the most frequently used fats as ingredients were sunflower oil ($61\%$), one of which was high oleic, olive oil ($28\%$), and extra virgin oil ($20\%$). There were few PB “pates” ($$n = 3$$) that contained palm or canola oil. ## 3.5. Nutrition and Health Claims Used in PB “Milk Alternatives” and PB Foods Nutrition claims are summarized as counts and percentages in Table 4. The following nutrient fortifications (% of foods with a claim by food groups) regarding minerals and vitamins were found in PB “milk alternatives” and PB foods: calcium ($15\%$) and vitamins ($13\%$)—mainly E, B2, B12, A, and D vitamins in almond “milk”; B2, B12, and D vitamins in oat “milk”; and B2, B12, A, and D in soy “milk”. Concerning nutrition claims (% of foods with a claim by food groups) according to Regulation (EU) $\frac{1169}{2011}$, some PB foods and PB “milk alternatives” were indicated to be sources of calcium ($34\%$ of “milk alternatives”), sources of proteins ($9\%$ of “milk alternatives” and $21\%$ of other PB foods), sources of vitamins ($24\%$ in “milk alternatives” and $0.4\%$ in other PB foods), sources of fiber ($2\%$ of “milk alternatives” and $16\%$ of other PB foods), sources of iron ($1\%$ of all PB foods), low sugar ($7\%$ of “milk alternatives”), low fat ($9\%$ of “milk alternatives” and $5\%$ of other PB foods), and low sodium ($3\%$ of “milk alternatives”). Apart from nutrition claims, other statements in PB foods were related to the non-presence of ingredients or allergens such as additives ($0.7\%$), gluten ($15\%$), lactose ($11\%$), or soy ($1\%$). Additionally, $21\%$ of these foods had an organic declaration, and some ($0.7\%$) were labelled as GMO free foods. There were no health claims reported in these products. ## 4. Discussion This study presents a comprehensive overview of the nutritional characteristics and nutrient profiling models provided in the pack labelling of 544 vegetarian foods (meat and dairy alternatives) marketed in Spain. Several differences in the nutritional composition of these foods have been encountered. In general, these food products were mostly ranked in Nutri-Score categories A and B; at the same time, some of them belonged to NOVA categories 3 and 4. In comparison to some homologous AB food groups, notable variations in the nutritional quality and in the index scores classifications were observed. The largest gaps were, in essence, that PB foods seemed to feature a healthier nutrient composition in terms of fats, saturated fats, and fiber, but PB alternatives could also be inappropriate to provide proteins at the same levels as meat and dairy products. The high content of carbs and sugars in some PB food compared to the AB counterparts was also noteworthy. Some exceptions were observed regarding PB “cheese alternatives” and PB “pate alternatives” (high-fat products), as well as PB “meat alternatives” (high-protein products). PB food products have emerged in recent years to suit the needs of health-conscious consumers or vegetarians, who are demanding more sustainable and healthier foods [2]. This is why many new PB products, such as meat and dairy product alternatives, that mimic the organoleptic characteristics of AB foods, have come into the market [48]. The current study included a study sample of 544 PB foods, including 134 PB “meat” analogues, 313 PB “milk alternatives” and 97 other PB foods (“pate”, “cheese”, and “yoghurt”), as well as 373 AB foods for comparison purposes. Two previous European studies have retrieved information on PB “meat products” marketed in Italy ($$n = 269$$ PB “meat” analogues) [31], and the UK ($$n = 207$$ PB “meat” analogues, including burgers and meatballs) [32]. A German study also evaluated the nutritional characteristics of 316 PB “meat alternatives” and 159 PB “cheese alternatives” alternatives [33]. Beyond Europe, other studies have also attempted to evaluate the nutritional features of PB “meat alternatives” (e.g., 137 PB “meat alternatives” in Australia, and 37 PB beef alternative products in the US) [49,50], but comparisons with the AB counterparts were not undertaken in these studies. PB beverages as milk substitutes and PB “dairy products” have also received attention, as evidenced in some studies carried out in the UK ($$n = 136$$ PB “milk alternatives”, 55 PB “yoghurt alternatives”, and 109 PB “cheese alternatives”) [51], and in other countries [52,53]. However, other types of PB foods were not included in these previous studies. A summary of the data reported in these studies is provided in Table S6. The protein content of PB “meat alternatives” in our study (14 g/100 g, on average) was close to that of the corresponding AB meat products, which is in agreement with results reported in other studies (~16 g/100 g for burgers and meatballs) [31,32]. We could not confirm previous findings regarding differences by type of meat [32], where plant-based steaks were found to have the best nutritional characteristics. Indeed, in our study, the majority of PB “meat alternatives” were “burgers” and “sausages” made of similar ingredients. By contrast, we considered different types of AB meat products (sausages, burgers, and red and white meats) for the comparative study. Overall, PB “meat alternatives” showed a more favorable nutrient composition than AB meats. Regarding essential amino acids, as has been reported before [54,55], their amount should be lower in PB “meat alternatives” (for example, soy protein isolates reach $27\%$ of the recommended content) compared to AB meats, where all essential amino acids are provided. However, we did not examine the food´s content of these amino acids. In addition, PB “meat alternatives” are of a meaty texture, appearance, and meat-like flavour. To achieve this goal, specific additives, processes of fractioning of whole foods into substances, and chemical modifications of these substances are required [29,56]. The supplement of protein in these foods can be also fulfilled by incorporating protein-rich vegetable materials and by adopting a proper technological process [54,57]. The vegetable base of these PB “meat alternatives” are often cereals and pulses, as they can easily enhance the nutritional and functional features of these products [58]. Our study reveals that traditional protein sources are being used in Spanish PB foods. PB “milk alternatives” that were included in our study also had a varied nutritional composition. For instance, the protein content of almond “milk” was the lowest (<1 g/100 mL), whereas that of soy “milk” was the highest (>3 g/100 mL). Protein levels of soy “milk” were even comparable to that of cow milk. Similar results have been obtained in previous studies on this issue [51,52,59]. Nevertheless, the protein quality and content of essential amino acids in PB “milk alternatives” is lower than that of cow milk [54]. A key finding that has also been underscored in other studies is related to oat “milk” [53,59]. Oat “milk” presented a nutrient composition characterized by a low protein content compared to the other PB “milk alternatives” and the AB counterparts, as has been also reported by others [53]. While it was not possible to analyze further the nutritional composition of the PB “milk alternatives”, other studies have reported that calcium content of oat “milk” is similar to that of cow milk [59]. However, its bioavailability should be also taken into account, since PB foods contain compounds that could limit calcium absorption. In general, cow milk contains more energy, saturated fat, protein, and also vitamin B2, vitamin B12 and iodine, than PB “milk alternatives” [52]. For the other PB foods, we also found some striking differences when comparing the nutrient composition with the AB counterparts. PB “pate alternatives”, for example, showed a lower nutritional quality given its high fat content and low protein supply. The same was observed for PB “cheese alternatives”, which showed a high value of both fats and saturated fats. PB “cheese alternatives” are made of coconut oil and other eatable oils of industrial use, which might explain these findings. PB “pate alternatives” contained other vegetables oils (mostly olive oil) in our study samples. The same has been observed for PB “cheese alternatives” in the study on PB foods on the German market [33]. The nutrient composition of PB “yoghurt alternatives” resembled that of the AB counterparts regarding proteins and other nutrients, although providing less saturated fats and more fiber, as has been also observed in other studies [51]. Regarding food classification systems on the food labels, it is important to note that half of the PB foods in Open Food Facts did not have information on the NOVA score available, and only PB “milk alternatives” provided the ECO-Score label. Nutri-Score only accounts for the nutritional quality of food products, but does not consider the degree of the processing, as has been shown in a study comparing both labelling systems on Open Food Facts data [38]. Likewise, in our study, while most of the PB foods included were ranked as Nutri-Score A and B (good nutritional quality), some were further classified as NOVA 3 and 4 (processed and ultra-processed, respectively). This was the case of PB “meat”, for example. A call to pay attention to the need to incorporate this information should be made to guide consumer’s choices of the healthier and less processed PB foods on the market. Several studies have even requested that Nutri-Score should be accompanied by the NOVA category to provide this information [38,60]. At the same time, efforts should be made to enhance people´s understanding of these food label systems, since this knowledge is limited in some sectors of the population [61]. While PB dietary patterns are characterized by avoiding the intake of meat and meat products, different PB dietary patterns exist. The strictest PB dietary pattern is the vegan diet, which is characterized by excluding all kinds of foods of animal origin (meat, fish, seafood, dairy, and eggs). The ovo-lacto-vegetarian diet allows the consumption of eggs and dairy products; the pesco-vegetarian diet includes fish, seafood, dairy products, and eggs; and a flexitarian diet is featured by consuming mainly PB foods, but allows the consumption of meat and fish occasionally [28]. All, but particularly vegans, are prone to nutritional deficiencies (iron, vitamin B12, and others) [10,11,12]. In fact, meat and dairy products are the major providers of proteins, vitamins, and minerals in the diet, but PB “meat alternatives” and “milk alternatives” lack the nutrients contained in these foods [51]. As our study shows, in general, proteins contained in PB foods cover a lower percentage of the reference intakes for adults than those provided by AB foods. However, as a recent systematic review of 141 observational and intervention studies showed, the protein intake in a vegan or vegetarian diet still meets the recommended intake levels [62]. Therefore, PB diets are being promoted as a healthy dietary pattern, considering that food fortification and dietary supplements can alleviate the nutritional shortcomings of this dietary pattern. Our study also shows that a number of PB foods, mostly PB “milk alternatives”, were fortified with some of these nutrients. However, nutrition claims were given only in some cases (in less than $15\%$ of all PB foods). As mentioned before, current public health nutrition policies are promoting the consumption of nutrient-dense PB foods in the population due to the healthiness of PB diets [63]. Indeed, PB foods are considered a protective factor against the development of cancer [19,64], and of cardiovascular diseases (CVD) [63]. For instance, the intake of vegetable-source monounsaturated and polyunsaturated fatty acids (mostly linoleic and a-linoleic acids) are associated with an improved CVD risk profile [13]. However, PB foods can also contain refined starches and simple sugars, which have been associated with the opposite effects [18]. A distinction is therefore made between the healthy and the unhealthy PB dietary patterns [65,66]. In this regard, there is also a need to evaluate the nutritional composition and quality of PB foods on the market. The results of our study show that the nutritional composition varies greatly between the PB foods, with PB “cheese alternatives” being the most energy-dense food and PB “milk alternatives” being the lowest. As aforementioned, PB “cheese alternatives” also contained an unexpectedly high amount of saturated fats, whereas PB “yoghurt alternatives” were low in this nutrient. The nutritional information on AB foods was taken from Open Food Facts and other online resources to follow the same procedure applied to retrieve information of PB foods, and to get the information on the nutrient profile index scores and ingredients of the food labels. In addition, given that some food products were varied (for example, sliced cheese or cream cheese, Greek-style yoghurt, or fruit yoghurt), we aimed to include a sample of each. While it is possible that this issue could pose a problem when comparing these features with regard to the PB foods, we took into consideration those AB products that appeared to be more similar to the PB ones. Regarding PB “meat alternatives” and PB “milk/dairy alternatives”, special care was taken to collect AB products of different nutritional compositions, for example, by accounting for whole and skimmed milks, as well as red and white meats (from high to low fat content, etc.). In addition to the exhaustiveness of our study, another strength to highlight is the fact that we evaluated the nutrient composition of different PB foods (five food groups), with each other and in comparison with some of their AB counterparts, as well as the nutritional quality, the processing degree, and the ecological impact, together with the ingredients, and the nutritional and health claims reported on the food labels. A limitation to note is that we could not consider nutrients different to those reported on these labels, such as vitamins and minerals, except when these nutrients were part of the nutritional claim or when they were added. Moreover, the NOVA and ECO-Score were available for only some of the food products in Open Food Facts [37]. Furthermore, we cannot guarantee the reliability of the nutritional information of the labels since no chemical analyses were undertaken in this study. In addition, we selected PB foods (meat and dairy alternatives) available on the Spanish market, but different PB food formulations with other nutrient profiles and compositions may exist in other countries; this fact could jeopardize the extrapolation of our results. ## 5. Conclusions PB alternative products of meat, milk, and dairy products marketed in Spain are numerous and diverse, and seem to provide varied amounts of nutrients, including proteins, carbs, sugars, fats, and saturated fats. While all are rich in dietary fiber and vegetable proteins, compared to their AB counterparts, these foods do not usually provide higher protein rates than AB foods. The nutritional composition of PB “milk alternatives” also varies greatly depending upon the base vegetable that they are made from. Even if most of these PB foods typically have a low Nutri-Score, some can be regarded as ultra-processed due to the high degree of processing of these foods. However, it should also be noted that PB foods often do not provide information on the processing or ecological impact on the food label, despite their relevance. Thus, since PB foods may not always represent a healthier alternative to an AB product, it is important to advise consumers to pay attention to the nutrition labelling of these products. Commercial PB foods should also have specific denominations since they are quite different to the AB foods that they try to mimic regarding ingredients and technological processes used in their formulation. Furthermore, it is highly necessary to conduct more studies on vegetarian-like foods that are becoming available on the market in order to gain a deeper understanding about the differences between PB and AB foods regarding nutritional quality, ingredients, and processing. 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--- title: Application of Rapeseed Meal Protein Isolate as a Supplement to Texture-Modified Food for the Elderly authors: - Gabriella Di Lena - Ann-Kristin Schwarze - Massimo Lucarini - Paolo Gabrielli - Altero Aguzzi - Roberto Caproni - Irene Casini - Stefano Ferrari Nicoli - Darleen Genuttis - Petra Ondrejíčková - Mahmoud Hamzaoui - Camille Malterre - Valentína Kafková - Alexandru Rusu journal: Foods year: 2023 pmcid: PMC10048395 doi: 10.3390/foods12061326 license: CC BY 4.0 --- # Application of Rapeseed Meal Protein Isolate as a Supplement to Texture-Modified Food for the Elderly ## Abstract Rapeseed meal (RSM), a by-product of rapeseed oil extraction, is currently used for low-value purposes. With a biorefinery approach, rapeseed proteins may be extracted and recovered for high-end uses to fully exploit their nutritional and functional properties. This study reports the application of RSM protein isolate, the main output of a biorefining process aimed at recovering high-value molecules from rapeseed meal, as a supplement to texture-modified (TM) food designed for elderly people with mastication and dysphagia problems. The compositional (macronutrients by Official Methods of Analyses, and mineral and trace element profiles using Inductively Coupled Plasma Optical Emission Spectrometry ICP-OES), nutritional and sensory evaluations of TM chicken breast, carrots and bread formulated without and with RSM protein supplementation ($5\%$ w/w) are hereby reported. The results show that the texture modification of food combined with rapeseed protein isolate supplementation has a positive impact on the nutritional and sensory profile of food, meeting the special requirements of seniors. TM chicken breast and bread supplemented with RSM protein isolate showed unaltered or even improved sensory properties and a higher nutrient density, with particular regard to proteins (+20–$40\%$) and minerals (+10–$16\%$). Supplemented TM carrots, in spite of the high nutrient density, showed a limited acceptability, due to poor sensory properties that could be overcome with an adjustment to the formulation. This study highlights the potentialities of RSM as a sustainable novel protein source in the food sector. The application of RSM protein proposed here is in line with the major current challenges of food systems such as the responsible management of natural resources, the valorization of agri-food by-products, and healthy nutrition with focus on elderly people. ## 1. Introduction Protein availability in the future will not be sufficient to meet the increased demands of a growing world population, expected to reach 10 billion people by 2050 [1]. Therefore, the exploitation of new sustainable sources of proteins has become an emergent issue in the agri-food and human nutrition sectors [2]. With the need for sustainable and nutritionally valid protein sources, there is increasing interest in plant-based proteins, characterized by a lower environmental impact in terms of greenhouse gas emissions, water requirements, and land use compared to animal proteins [3]. The adoption of plant-rich dietary patterns not only benefits the planet by lowering the environmental impact of food production, but has also a positive impact on public health by reducing the risk of chronic diseases associated with animal-based dietary patterns [4,5,6,7,8]. For the total or partial replacement of animal proteins in the diet, plant protein choices with good performance, both in techno-functional and nutritional terms, are the preferred ones. In some cases, traditional and novel technologies (i.e., fermentation, enzyme technology, and nanotechnology) may aid in improving the physico-chemical, sensory, and nutritional properties of plant food [9,10,11]. Agri-food by-products represent a highly sustainable source of nutrients and bioactive compounds that, with a circular economy approach, may be upgraded to fully exploit their inherent value. Oilseed processing by-products are a class of agrifood by-products attracting growing interest because of their high content in proteins with excellent nutritive properties and for the opportunities they offer to develop healthy foods or food supplements [12]. Rapeseed meal (RSM), a by-product of rapeseed oil extraction connected to the agrifood and biofuel sectors, is produced in large quantities worldwide (40 million tons in 2020) and in the EU (12.5 million tons in $\frac{2021}{2022}$ and 13.0 million tons expected in $\frac{2022}{2023}$) [13]. Future projections indicate a constant growth of rapeseed oil production, with proportional increments of this by-product, currently used as animal feed and for other low-value purposes [13,14]. In a previous study, we provided a chemical characterization of RSM produced by an oilseed-producing factory serving a biofuel plant in view of its exploitation and valorization. The study highlighted the potentialities of this rapeseed oil co-product to establish new cross-sector interconnections between the biofuel and food value chains [15]. RSM is an interesting source of sustainable proteins with a complete amino acid profile and peculiar functional properties. It is recognized as a promising ingredient for food product formulation when protein fortification, modulation of the rheological properties, or replacement of animal proteins are desired [16,17,18]. Other advantages of using rapeseed proteins in food formulations are connected with their techno-functional properties, i.e., water-holding, gelling, emulsioning, and foaming properties, reported to be comparable or even higher than those of conventional proteins such as casein, soybean, or egg proteins [19,20]. With a biorefinery approach, RSM could be valorized as a sustainable protein source for high-value applications, opening perspectives to the full and sustainable exploitation of this residual biomass. One of the challenges connected with the utilization of rapeseed proteins is their extraction in an economically and environmentally sustainable way and also in mild conditions so as to retain as much the native properties of proteins as possible. Studies reporting the uses of rapeseed proteins in food formulation move from the assumption that they are a suitable and promising ingredient for vegetarians, vegans, and consumers concerned with a reduction in the environmental impact of their diet. Several studies have been carried out in recent years on the applications of rapeseed/canola proteins in food products as a partial or total replacement of animal proteins. Depending on the food application tested, rapeseed proteins have been proposed as a thickener ingredient or as an emulsifier, binder, foaming, or gelling agent able to modify texture or simply to fortify the protein content of a product. The range of possible food applications for rapeseed/canola proteins include bakery and dairy products, meat, confectionery and beverages, as well as dressings, sauces, snacks or flavorings [21,22,23,24,25,26,27]. With the increased life expectancy registered worldwide, it is assumed that, by 2050, elderly people over 80 years old will account for more than 400 million of the world population [28]. One of the challenges for the future is to assure that, despite such a demographic change, mankind can address the health and nutrition security issues posed by an ageing population in a targeted manner. Personalized dietary programs for seniors and technological advances to produce nutritious, palatable, innovative, and affordable food products tailored to the special needs of seniors are among the expected progresses of healthcare systems and food industries for the upcoming years. In this context, we propose an innovative application of rapeseed proteins that adds new perspectives for the valorization of currently underutilized sustainable protein sources. RSM protein isolate obtained through sustainable and green processes was applied as a supplement for texture-modified (TM) food suitable for elderly people with mastication and swallowing difficulties. The compositional, nutritional, and sensory evaluations of the food products formulated are hereby described. This is a first-of-its-kind application of rapeseed proteins, coupling green and sustainable technologies for the recovery of proteins from agri-food by-products with advanced food formulations delivering personalized nutrition solutions for an ageing population. ## 2.1. RSM Protein Isolate RSM protein isolate was obtained from the extraction and purification of RSM, a by-product of an oil-pressing factory (Poľnoservis a.s., Leopoldov, Slovakia) serving an adjacent biofuel biorefinery plant. RSM met the legal requirements of the Slovak Government Regulation no. $\frac{438}{2006}$, Act $\frac{271}{2005}$ Coll., the European Parliament and Council Regulation (EC) no. $\frac{767}{2009}$, and the Commission Regulation no. $\frac{68}{2013}$ of the Fodders Catalogue. The original feedstock was a nongenetically modified rapeseed (Brassica napus L. var. Napus) with low erucic acid and low glucosinolate contents grown during the $\frac{2020}{2021}$ crop season in Central and East European regions, namely Slovakia, Poland, Hungary, Czech Republic, Romania, and Ukraine. Seed quality met the STN 462300-1 and 2 and the Codex Alimentarius requirement of the Slovak Republic, Government Regulation no. $\frac{439}{2006}$, and the requirements set out in the list of permitted varieties. The protein extraction and purification from RSM was performed at the semi-pilot scale on 2–5 kg pre-batch at Celabor (Herve, Belgium). The RSM was first ground using a MASUKO® supercolloider (Masuko Sangyo Co., Ltd., Kawaguchi, Japan). The ground material was extracted using an aqueous alkaline solution in mild conditions, in a 65 L maceration tank (Ferrari srl, Ghislenghien, Belgium). The solid–liquid separation was performed with a vertical centrifuge (RC30, Rousselet-Robatel, Annonay, France). An extract with a yield of 19.0 ± $3.5\%$ was obtained, containing 23.3 ± $4.5\%$ of proteins, determined with the Kjeldahl method. Protein concentrate was obtained with a purity of 76.0 ± $5.1\%$ with isoelectric precipitation, and the following purification step was performed using membrane microfiltration, using 10 L pilot equipment (Evonik Industries AG, Essen, Germany) with a ceramic membrane of 50 kDa. The obtained protein isolate was freeze-dried before analysis with a final yield of 7.2 ± $0.7\%$ and a high purity (batch 1, Figure S1). After validation at the semi-pilot scale, the process was up-scaled on a 50–100 kg full-pilot-scale batch at ENVIRAL a.s. ( Leopoldov, Slovak Republic), delivering the final rapeseed meal protein isolate product by means of spray drying (batch 2, Figure S2). The main steps of the RSM protein production process are detailed in Figure 1. The compositional data of the RSM protein isolates from semi-pilot- and full-pilot-scale trials are reported in Table 1. The two protein isolates were used for food product formulation by Biozoon GmbH (Bremerhaven, Germany) as described below. ## 2.2. Formulation and Preparation of Texture-Modified Food Texture-modified (TM) food was formulated and prepared at Biozoon GmbH according to internally established protocols, as described below. In order to evaluate the suitability of RSM protein isolate as a supplement to TM products, different types of food were preliminary tested. Chicken breast, carrot, and bread were selected for this study. Food matrices were pureed, texture-modified, and tested for their sensory quality and nutritional value without and with supplementation with RSM protein isolate from semi-pilot- and full-pilot-scale tests. All ingredients, except texturizers and RSM protein isolate, were purchased from a local supermarket in Bremerhaven, Germany. Steam-cooked and spiced chicken breast and carrots were chopped, added with water (1:1 w/v), and pureed in a food blender (Blixer® 3, Robot Coupe, France). RSM protein isolate ($5\%$ w/w) and GELEAhot instant® ($4\%$ w/w), a texturizing system owned by Biozoon, were added to the pureed food and homogenized manually with a whisk. GELEAhot instant® is composed of maltodextrin, agar-agar, and xanthan, requiring an activation temperature of approximately 87 °C before forming a gel while cooling. The pureed food with added RSM protein isolate and GELEAhot instant® was brought to boil and then molded into silicone molds resembling the shape of the original food in order to increase the appeal and sensory acceptability of the product. The main preparation steps of the TM chicken breast and carrots are detailed in Figure 2. The extent of RSM protein supplementation ($5\%$ w/w) was the one adopted in Biozoon’s internal protocols based on previous experience, indicating this ratio as the one with the best performance, delivering an additional amount of useful protein without significantly affecting the technological and sensory characteristics of the products. The control samples were prepared following the same procedures described above, except the addition of RSM protein. RSM protein isolate was also tested as a supplement for TM bread, prepared by using Biozoon’s SMOOTHBROT® mix, based on gluten, maltodextrin, whey protein, oil powder, agar-agar, and xanthan gum. TM bread (1.2 kg loaf) was prepared as follows. Stale wheat bread, roughly chopped into small pieces, was added to tap water (about 1:2.3 w/v) and left to soak for approx. 20 min in a flat bowl. The content of the bowl was then transferred into a blender (Blixer® 3, Robot Coupe, France), mixed to a smooth dough-like mass, added to SMOOTHBROT® mix powder ($16.7\%$ w/w) and RSM protein isolate ($5\%$ w/w), and gently stirred manually. The mixture was further transferred into a baking pan and steamed for about 90 min until a kernel temperature of nearly 90 °C was reached. Afterwards, the bread was cooled down to room temperature while resting in the pan to allow the gel structure to form while cooling. The main steps of TM bread preparation are detailed in Figure 3. ## 2.3. Sensory Evaluations A descriptive sensory analysis was conducted at Biozoon’s laboratories according to the DIN 10964:2014-11 standard method [29]. The evaluations were performed by a group of 5 staff experts trained in sensory analyses. The evaluation group analyzed all three RSM-protein-enriched products in order to identify individual product aspects in terms of descriptive attributes (appearance, odor, taste, and mouthfeel/texture). The attributes were collected correspondingly for further interpretation. Control samples of each product (meaning without any RSM protein supplementation) were presented to the group as well, in order to describe possible differences between the two products. For the sensory evaluation, RSM-protein-enriched TM chicken breast and TM carrots were reshaped in gel blocks and served as such. TM bread was cut into slices of approx. 0.8 cm (the thickness of a standard bread slice). Each participant in the sensory evaluation was allowed to take as much as needed in order to describe the products. The evaluation group was informed about the purpose of the sensory analyses. Each sample was served separately, and the selection of attributes was free and unbounded to a list. A list of product-specific attributes was further developed in order to identify relevant differences between the supplemented products and their respective controls. ## 2.4. Chemical Analyses The freshly prepared TM food samples (chicken breast, carrots, and bread) were packed under vacuum and shipped in a refrigerated state to CREA laboratories in Rome (Italy). Upon arrival, the food was immediately frozen and freeze-dried (Scanvac Coolsafe 55-4 Pro, Labogene, Allerød, Denmark) for further chemical analyses. The results were further normalized and expressed on a wet mass basis. The semi-pilot-scale and full-pilot-scale batches of RSM protein isolate were analyzed as received. The moisture, crude protein, crude fat, and ash contents were determined separately in individual RSM protein isolate batches and in the formulated foods with and without supplementation with RSM protein following the methods of the Association of Official Analytical Chemists [30]. The crude protein content was evaluated using the Kjeldahl procedure, using 6.25 as a nitrogen-to-protein conversion factor. Nonprotein nitrogen (NPN) was determined using the Kjeldahl method after protein precipitation with $10\%$ (w/v) trichloroacetic acid and filtration. The crude fat content was determined using Soxhlet extraction. The ash content was determined gravimetrically after incineration in a muffle furnace at 550 °C. Total dietary fiber was determined according to the method of Prosky et al. [ 31]. Carbohydrates were calculated by difference. All macronutrients’ analyses were performed in triplicate. The energy content was calculated by using the conversion factors indicated by the EU Regulation $\frac{1169}{2011}$ [32]. The conversion factor from kcal to kJ was 4.184. Macrominerals and trace elements were quantified using inductively coupled plasma optical emission spectrometry (Optima 8000™ ICP-OES, Perkin-Elmer, Waltham, MA, USA) after liquid ashing in a microwave digestion system (1200 Mega, Milestone srl, Sorisole (BG), Italy). Mineral analyses were performed in quadruplicate. ## 2.5. Quality Assurance For the validation of the applied methods and quality control of the proximate and dietary fiber data, the standard reference materials peanut butter (NIST 2387, National Institute of Standards and Technology, Gaithersburg, MD, USA) and dried haricot beans (BC514, European Reference Material ERM®, Geel, Belgium) were analyzed. For the validation of the method and the quality control of minerals and trace element data, three Certified Reference Materials, cabbage (IAEA-359, International Atomic Energy Agency Reference Materials Group, Vienna, Austria), peanut butter (NIST 2387), and haricots verts (BCR 383, Community Bureau of Reference, Brussels, Belgium), were analyzed. All analyses were performed at least in triplicate. ## 3.1. Food Formulation The TM chicken breast, carrots, and bread were formulated without and with supplementation with the RSM protein isolate. The formulations of TM chicken breast, carrots, and bread are reported in Table 2, Table 3 and Table 4. The TM food without and with RSM protein supplementation (Figure 4) underwent a sensory evaluation at first and then chemical and nutritional evaluations. ## 3.2. Sensory Assessment The RSM-protein-supplemented TM chicken breast samples were darker in color compared to the control and slightly brownish. No off smell was reported, so the product kept the original smell of chicken. The collected taste attributes of the samples were described as slightly bitter and off-tastes also occurred (strawy), but, still, the product was reported as acceptable with a recognizable original taste. The supplemented TM chicken breast samples were described as softer, with the results being acceptable within TM food applications. Rapeseed protein supplementation ($2\%$) in beef and pork sausages has been also reported in the literature [22,33] with acceptable results regarding product quality maintenance. In the case of TM carrots, supplementation with $5\%$ RSM protein isolate showed limited applicability, especially with regard to odor and taste. Besides the darker color, which was expected, an off smell as well as seedy notes were reported when the supplementation with RSM protein isolate was used. The characteristic carrot flavor was also masked by the RSM protein addition. These results suggest that, in the case of carrots, an adjustment to the formulation, consisting of a lower supplementation with RSM protein, is advisable. The texture was described as softer than the control, but still with good uses for texture-modified purposes. To the best of our knowledge, studies on the incorporation of rapeseed protein into vegetable matrices have not been reported in the literature; thus, a comparison on this matter was not possible. As already reported for chicken and carrots, RSM protein supplementation in TM bread also resulted in a darker color compared to the control sample, which was, in this special case, recognized positively due to associations with whole-grain bread. Similar results were also described by Korus et al. [ 34], who incorporated different amounts of rapeseed protein (6–$15\%$) as a starch replacer in gluten-free breads and registered improved color characteristics. Furthermore, our study showed that the attribute “whole-grain characteristics” was additionally mentioned in the odor and taste description. The odor was described as bread-like and slightly roasted, which is positive. In terms of the described taste, besides the bread-like characteristics, malty notes and seedy notes were also reported. Nevertheless, the positive attributes with regard to taste must not be disregarded. The reported texture attributes were summarized as slightly drier. This could be due to the increased dry matter of the product resulting from the rapeseed protein supplementation, as described in the next section. ## 3.3. Nutritional Evaluations The macronutrient composition and energy value of the TM chicken breast, carrots, and bread without and with supplementation with RSM protein are reported in Table 5, Table 6 and Table 7. The results highlight a higher nutrient density in RSM-protein-supplemented food compared to the control samples. In particular, the supplemented products showed higher dry matter (chicken breast: +14–$16\%$; carrots: +45–$108\%$; and bread: +8–$9\%$) and protein (chicken breast: +19–$24\%$; carrots: +1035–$1120\%$; and bread: +38–$41\%$) contents compared to the control samples. With the addition of the RSM protein isolate, the chicken breast and bread showed a slight increment in the energy value (chicken: +10–$15\%$, depending on the protein batch used; bread: +7–$10\%$) and no relevant changes in the other macronutrients (Table 5 and Table 7). The TM carrots (Table 6) were the product that received more nutritional advantage from supplementation with rapeseed meal protein isolate, not only in terms of protein content (with an over 10-fold increment) but also of total minerals (ashes +72–$87\%$). Similar trends as regards the macronutrient profile were observed in the two independent trials carried out to test the semi-pilot and full-pilot batches of RSM protein isolates. This is an indication of the robustness and reproducibility of the protein extraction process and the reliability of the formulations used. The mineral profile of the TM food without and with the addition of RSM protein is reported in Table 8, Table 9 and Table 10. With the addition of RSM protein isolate, the TM chicken breast showed an increment of minerals (Table 8), in particular phosphorus and copper, to different extents depending on the protein batch used. The TM carrots added with RSM protein (Table 9) showed a higher content of most minerals and trace elements, in particular phosphorus, zinc, and copper. The RSM-protein-fortified bread (Table 10) also showed a nutritionally favorable increment in minerals in the two trials, in particular phosphorus and copper, reflecting the peculiar composition of the semi-pilot and full-pilot RSM protein isolate batches. The higher nutrient density of RSM-protein-supplemented food is a nutritionally favorable attribute, considering that the texture modification of food implies the addition of a high amount of water and that, as a consequence, TM products have a low nutrient density compared to the original food matrix [35]. The increased protein and mineral contents of TM products with added RSM protein isolate reported here are nutritionally correct and adequate since, while a lower energy intake is needed at an advanced age, the micronutrient and protein requirements are not diminished [36]. In fact, an adequate protein intake is necessary at an advanced age to prevent the loss of muscle mass, a frequent negative health concern of ageing [37]. Rapeseed proteins napin and cruciferin, the major storage proteins of rapeseed, have a balanced amino acid composition, and a protein efficiency ratio comparable to that of other proteins commonly used in food preparations such as egg and milk proteins [16]. Therefore, supplementation with rapeseed protein isolate has a positive impact on the nutritional properties of the TM food. In addition, besides providing all essential amino acids, supplementation with RSM protein gives products an added value in functional terms because, once digested, rapeseed proteins are potentially cleaved into bioactive peptides with beneficial health properties such as antihypertensive, antioxidant, bile-acid-binding, and antithrombotic [33,38,39,40]. An adequate intake of dietary fiber is important at any age to increase bowel motility in order to prevent constipation and chronic diseases typical of older people [41]. Thus, targeting adequate protein and fiber intakes is of pivotal importance at an advanced age. As regards minerals, the contribution given by rapeseed protein isolate added to food is also favorable. In chicken breast and bread, RSM protein supplementation positively affected the content of phosphorus, calcium, and copper, minerals essential in bone mineralization, oxygen transport, energy metabolism, and enzyme activities. Adequate levels of these minerals in the diet have beneficial implications for the elderly. The copper values in the RSM-protein-supplemented samples (about 0.2 mg 100 g−1) were largely within the nutritionally recommended levels (corresponding to 0.9 mg/day for adults) and comparable to those present in several foods of animal and plant origin (i.e., contents per 100 g: pork meat, 0.15 mg; beans, 0.7 mg; barley, 0.29 mg; and carrots, 0.19 mg) [42]. The increment in sodium observed in the carrots and bread supplemented with batch 1 of the RSM protein isolate (by $50\%$ and $16\%$, respectively) does not represent a serious health concern, as the detected levels (200–240 mg per 100 g product), in the frame of a daily diet, are very far from the maximum advisable levels of sodium for hypertension prevention established at 2 g/day. Meat, vegetable, and bread consumption may be limited in old age because of mastication and swallowing difficulties, especially in patients with dysphagia problems. This may lead to protein, energy, dietary fiber, vitamin, and mineral deficits. Texture modification combined with the rapeseed protein fortification of food has a positive impact on the nutritional profile of the food and increases the palatability, acceptability, and nutrient density of the diet for patients affected by dysphagia or with mastication difficulties. Furthermore, the texture modification steps imply a modification of the initial food matrices from solid to fluidic and, finally, to special texture (e.g., gel), with such a procedure being highly advantageous as the fluidic stage offers the perfect condition for allowing fast, high, and specific ingredient supplementation. These are unique features with potential in future applications of tailor-made food with a high nutritional profile. The enhanced protein and mineral contents of food reported here are in line with the current dietary recommendations for elderly people [43,44,45]. Clinical studies demonstrate that elderly people are at risk of malnutrition. Physiological changes, a decline in physical activity, and a loss of appetite and taste sensitivity are only some of the factors that expose seniors at an increased risk of nutritional inadequacy in advanced age [46]. Elderly people with mastication and swallowing difficulties are a category at increased risk of malnutrition. The use of pureed food as a nutritional solution is very limited as it is not adequately formulated and does not address the key requirements of a food (e.g., sensorially pleasant). The careful design and formulation of TM food are, therefore, needed in order to give it the desired nutritional and sensory properties [47] as well as provide the motivation to eat it [48]. Furthermore, protein supplementation based on the specific needs of seniors can be applied to counteract the prevalence of malnutrition in this population group. Here, the use of rapeseed protein has potential. Furthermore, malnourished elderly people are most likely not able to consume an entire meal; thus, smaller portions supplemented with key ingredients as proteins are necessary. Moreover, there is a connection between suffering from eating difficulties (e.g., dysphagia) and a decrease in overall food intake, which underlines the need for supplemented texture-modified foods for such groups of elderly people [48,49]. ## 4. Conclusions Food consumption may be limited at old age because of mastication and swallowing difficulties. This may lead to protein, energy, dietary fiber, vitamin, and mineral deficiencies. Texture modification increases the palatability, acceptability, and nutrient density of the diet of aged people affected by dysphagia or with mastication difficulties. Protein supplementation based on the specific needs of seniors can be combined with texture modification in order to design highly nutrient-dense food and counteract the prevalence of malnutrition in this population group. This study highlights the potentialities of RSM protein isolate as a food supplement for TM food for elderly people with mastication and dysphagia problems. The obtained results show that the texture modification of food combined with rapeseed protein isolate supplementation may have a positive impact on the nutritional and sensory profile of food. The consistency of the obtained results in terms of protein enrichment of TM food in the two independent trials, testing RSM protein isolates obtained from semi-pilot and full-pilot-scale extractions and purifications, is an indication of the robustness of the processes and the reliability of the formulations. Within the tested food applications, TM chicken breast and bread were the products giving the best results, showing unaltered or even improved sensory properties and a richer nutritional profile with special regard to the protein and mineral contents. On the contrary, supplemented TM carrots, in spite of the higher nutrient density, showed limited acceptability due to poor sensory properties that could be overcome with an adjustment to the formulation. 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